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Adipose tissue grows by two mechanisms: hyperplasia ( cell number increase ) and hypertrophy ( cell size increase ) . Genetics and diet affect the relative contributions of these two mechanisms to the growth of adipose tissue in obesity . In this study , the size distributions of epididymal adipose cells from two mouse strains , obesity-resistant FVB/N and obesity-prone C57BL/6 , were measured after 2 , 4 , and 12 weeks under regular and high-fat feeding conditions . The total cell number in the epididymal fat pad was estimated from the fat pad mass and the normalized cell-size distribution . The cell number and volume-weighted mean cell size increase as a function of fat pad mass . To address adipose tissue growth precisely , we developed a mathematical model describing the evolution of the adipose cell-size distributions as a function of the increasing fat pad mass , instead of the increasing chronological time . Our model describes the recruitment of new adipose cells and their subsequent development in different strains , and with different diet regimens , with common mechanisms , but with diet- and genetics-dependent model parameters . Compared to the FVB/N strain , the C57BL/6 strain has greater recruitment of small adipose cells . Hyperplasia is enhanced by high-fat diet in a strain-dependent way , suggesting a synergistic interaction between genetics and diet . Moreover , high-fat feeding increases the rate of adipose cell size growth , independent of strain , reflecting the increase in calories requiring storage . Additionally , high-fat diet leads to a dramatic spreading of the size distribution of adipose cells in both strains; this implies an increase in size fluctuations of adipose cells through lipid turnover .
Obesity is an enlargement of adipose tissue to store excess energy intake . Hyperplasia ( cell number increase ) and hypertrophy ( cell size increase ) are two possible growth mechanisms . Adipose tissue obesity phenotypes are influenced by diet and genetics , as well as by their interaction [1]–[4] . Starting from Johnson and Hirsch's studies [5] , there is an extensive literature on adipose tissue growth in normal and abnormal development , characterizing the state of the tissue in terms of the mean cell size and cell number . Hyperplastic growth appears only at early stages in adipose tissue development [6] , [7] . Hypertrophy occurs prior to hyperplasia to meet the need for additional fat storage capacity in the progression of obesity [8] . However , it has proven difficult to understand how diet and genetics specifically affect hyperplasia and/or hypertrophy of adipose cells , because of limited longitudinal data about adipose tissue growth . Beyond these studies , it has recently become possible to measure cell-size distributions precisely . This detailed information , compared with the mean cell size and total cell number , can be used to compute many size-related quantities that permit a finer characterization of the adipose tissue growth process . Cumulants of the cell-size distribution can be used to compute other physiological quantities such as the volume-weighted mean cell size . The cell-size distribution can be used to estimate total cell number within a fat pad from its mass . Furthermore , it is believed that some specific metabolic properties , e . g . , insulin resistance [9] and adipokine secretion [10] , depend on the precise cell-size distribution rather than the mean cell size . Indeed , several studies have addressed the change of the size distribution of adipose cells under various conditions in chick embryo development [11] , lean and obese Zucker rats [12] , [13] , partially lipectomized Wistar rats [14] , rabbit biopsy [15] , and human adipose tissue [16] , [17] . These studies focused only on the static differences between cell-size distributions under different conditions . However , cross-sectional static cell-size distributions for a range of snapshots of animal development can be used to deduce the dynamics of adipose tissue growth , if we can appropriately analyze the snapshots with the help of mathematical modeling . Given present technical limitations , this may be the best available approach to a microscopic and longitudinal understanding of in vivo adipose tissue growth , although a recent experiment has obtained microscopic observations of lipid accumulation in lipid droplets of adipose cells [18] . To address genetic and dietary effects on the dynamic process of adipose tissue growth , we obtained cell-size distributions of epididymal fat of obesity-resistant FVB/N ( hereafter FVB ) and obesity-prone C57BL/6 ( C57 ) mouse strains under standard chow and high-fat diets . The C57 mouse is the best characterized model of diet-induced obesity [19] , and the FVB mouse is a preferable model for generating transgenic mice [20] . These two commonly-used inbred mouse strains are genetically quite distant [21] , [22] , and they have distinct metabolic phenotypes: Compared with FVB mice , C57 mice have low circulating triglyceride levels [21] and increased triglyceride clearance [23] , [24]; FVB mice are characterized by relatively higher hepatic insulin resistance , counter-regulatory response to hypoglycemia , and reduced glucose-stimulated insulin secretion [25]; FVB mice are also known to be spontaneously hyperactive [26] and relatively lean since they appear to be less responsive to high-fat diet than C57 mice [27] . However , the development of diet-induced obesity in these two strains has not been formally compared . In this study , we developed a mathematical model predicting the change of the cell-size distribution as a function of the epididymal fat pad mass to analyze quantitatively the dynamic characteristics that depend on genetics and/or diet . The model of adipose tissue growth describes how many new cells are formed , how each cell grows depending on its size , and how lipid turnover leads to size fluctuations that cause a spreading in the cell-size distribution . As the epididymal fat pad mass increases , the cell-size distribution changes in a systematic manner depending on both genetics and diet . Comparing experimental results with the theoretical growth model , we found that hypertrophy is strongly correlated with diet . Hyperplasia , on the other hand , is dependent on genetics . Diet-induced changes in hyperplasia are strain-dependent , suggesting an interaction between diet and genetics .
At the beginning of the experiment ( 5 weeks of age ) , C57 mice were significantly lighter than FVB mice ( Fig . 1A ) due to a difference in lean mass , although total fat mass was not different ( Fig . 1B ) . When mice were maintained on regular chow diet , the difference in body weight disappeared by the age of 11 weeks ( week 6 of experiment , Fig . 1A ) . Under regular diet conditions , FVB and C57 mice maintained comparable fat mass throughout the whole course of the experiment ( Fig . 1B ) . High-fat diet caused significant increase in body weight and fat mass in both strains; however , changes in body weight and fat mass were more dramatic in C57 mice . The C57 mice had twice as much fat after 12 weeks of high-fat feeding ( Fig . 1B ) . The overall difference in total fat mass between FVB and C57 mice correlated with proportional differences in the amounts of epididymal ( intra-abdominal ) , inguinal ( subcutaneous ) , and brown fat ( Table 1 ) . Caloric intake and activity were comparable in FVB HF and C57 HF mice; however , FVB HF mice had higher resting and total oxygen consumption , and higher rectal temperature , suggesting that increased energy expenditure rather than reduced caloric intake was the reason for relative resistance to high-fat diet-induced obesity in the FVB mice . Interestingly , during the first 2 weeks of high-fat feeding , FVB and C57 mice showed comparable increase in total fat mass ( Fig . 1B ) . C57 HF mice continued to increase fat mass rapidly until week 10 of the experiment , whereas FVB HF mice slowed down accumulation of fat around week 3 . In C57 mice , high-fat feeding caused a gradual increase of both epididymal and inguinal fat pads; in contrast , in FVB mice , epididymal fat mass increased only slightly after 4 weeks on high-fat feeding , while inguinal fat pad continued to increase in size throughout the course of experiment ( Fig . S1 ) . High-fat feeding caused significant increase in blood glucose and insulin levels in both FVB and C57 mice ( Table 1 ) . Insulin levels and glucose intolerance were higher in C57 HF mice than in FVB HF mice , suggesting more severe insulin resistance ( Fig . 2A ) . Consistent with previous reports [23] , [24] , C57 REG mice showed reduced serum triglyceride levels , compared with FVB REG mice with no difference in FFA ( Table 1 ) . This was not due to higher fat utilization , since respiratory exchange ratio ( Table 1 ) and the rates of fatty acid oxidation measured in vivo ( Fig . 2B ) and in isolated skeletal muscle ( Fig . 2C ) were comparable in FVB REG and C57 REG mice . More likely , lower serum triglycerides in C57 REG mice were caused by much more efficient clearance of circulating triglycerides as suggested by triglyceride clearance test ( Fig . 2D ) . High-fat feeding reduced circulating triglyceride levels in both FVB and C57 mice and improved triglyceride clearance in the latter strain ( Table 1 and Fig . 2D ) . Both strains showed comparable reduction of respiratory exchange ratio , suggesting comparable increase of fatty acid utilization under high-fat diet condition ( Table 1 ) . Taken together , these data suggest that the ability to efficiently clear triglyceride from circulation may contribute to the high capacity of fat accumulation in C57 mice . To test the underlying mechanism of different rate of fat accumulation in epididymal fat of FVB and C57 mice , we measured mass and cell-size distribution in tissue samples of epididymal fat collected at 0 , 2 , 4 , and 12 weeks of controlled feeding ( Fig . 3 ) . Since histological analysis does not allow accurate determination of adipocyte cell size , which will be discussed later , we measured cell size distribution using a Coulter Counter and estimated volume-weighted mean cell size and total cell number of epididymal fat pad from these measurements , which had similar values in other mouse study [5] . Strong correlations were observed between fat pad mass and volume-weighted mean cell size , and between fat pad mass and total cell number , regardless of strain and diet difference ( Fig . 4 ) . The first correlation gave a scaling relation , , between fat pad mass , , and volume-weighted mean cell size , ( Figs . 4A and 4B ) . In addition , an exponential relation was found between fat pad mass , , and total cell number , : where the initial fat pad mass , , was obtained from control mice; and the initial cell number , , and the rate of increase of cell number in fat pad mass , , were estimated from data ( Figs . 4C and 4D; Table 2 ) . The initial cell number , , in C57 mice was larger than the initial cell number in FVB mice ( Table 2; Figs . S2C and S2D ) . As the fat pad mass increases , the total cell number increases . The rate of increase of cell number , , was larger under regular diet than under high-fat diet , a tendency more evident in C57 mice ( Figs . 4C and 4D; Table 2 ) , suggesting a genetic difference . The ratios of between the results of regular and high-fat diets are 1 . 42 and 3 . 22 for FVB and C57 mice , respectively ( Table 2 ) . This may indicate an interaction between genetics and diet on the increase of cell number . Note that we also observed similar results with body weight and fat mass , since the three quantities ( epididymal fat pad mass , fat mass , and body weight ) are correlated with each other . However , the results with epididymal fat pad mass were the best fits: The mean square deviation between data and fit in Figs . 4A–D was 9 . 73 , 7 . 94 , 3 . 56×105 , and 5 . 54×106 , respectively; the result with body weight was 11 . 82 , 15 . 35 , 3 . 65×105 , and 4 . 82×106; the result with fat mass was 8 . 58 , 8 . 61 , 4 . 28×105 , and 6 . 11×106 . These two strong correlations , between fat pad mass and hypertrophy , and between fat pad mass and hyperplasia , suggest that the increase in adipose tissue can be described as a systematic growth process with respect to fat pad mass increase . We arranged cell-size distributions sorted with respect to epididymal fat pad mass ( Fig . 5 ) . Remarkably , the adipose tissue growth model in Eq . ( 1 ) describes the evolving pattern of cell-size distributions with respect to the fat pad mass increase . The model fitted experimental cell-size distributions quantitatively , despite the fact that all distributions are cross-sectional data obtained from individual animals . The different parameter values in the model , which fit each individual cell-size distribution from both strains and both diet regimens , gave quantitative differences in the epididymal adipose tissue growth process between strains and between diets ( Table 2 ) . First , the maximal size-dependent growth rate , , and the rate of cell-size fluctuations due to lipid turn over , , demonstrated a diet-induced difference , and a smaller strain-induced difference . Size-dependent growth and size fluctuations , which resulted in hypertrophy , appear to be regulated mainly by diet . Specifically , the size-dependent growth moved the large cell mode of the cell-size distributions in Fig . 5 to larger sizes , and the lipid turnover fluctuations increased the spread of the distribution around the large cell mode . It is important to note that the results must be carefully interpreted because every rate is a rate per unit fat pad mass increase , not per unit time increase . Second , the geometrical parameters ( , and ) , which determine the shape of the size-dependent growth rates , had essentially the same values regardless of the diet and strain difference , except for . Therefore , the lower critical size , which gives the size initializing cell size-dependent growth , and two scale parameters could be fixed at sl = 37 µm , ηl = 12 µm , and ηu = 63 µm , respectively . On the other hand , the upper critical size limiting cell size-dependent growth of big cells depended on diet; under the high-fat feeding , this cutoff size shifted to a larger size ( Fig . 6 ) . Under high-fat diet , the changes of parameters ( , , and ) enlarge the lipid-storage capacity of fat tissues through both hyperplasia and hypertrophy . Lower serum triglycerides in the high-fat diet condition ( Table 2 and Fig . 2D ) may be correlated with the increasing lipid storage in enlarged fat cells because no significant difference in fatty acid oxidation was found as suggested by no difference in respiratory exchange ratio ( Table 1 ) .
Our central finding is that hyperplasia and hypertrophy of adipose cells in the epididymal fat pad is a function of the fat pad mass , even though it may take individual animals different time periods to reach a given fat pad mass . Therefore , adipose tissue growth , represented as changes of the cell-size distribution , can be systematically modeled as a growth process with respect to fat pad mass increase; this may reflect a correlation between fat pad mass and the secretion of adipokines and other signaling molecules controlling adipose tissue growth . Accordingly , it should be noted that the rates in our model are not the usual rates per unit time increase but the rates per unit mass increase . Thus , several rates ( , , and ) in the model had larger values for animals on a chow diet than for those on a high-fat diet . However , if these rates were converted to the usual rates per unit time increase , they had larger values for the high-fat diet , because it takes less time for a unit increase in the fat pad mass from larger , and more numerous , cells on a high-fat diet than for an increase of the same magnitude from smaller , and fewer , cells on a chow diet ( Fig . S1 ) . It has been suggested that when obesity progresses , hypertrophy of adipose cells occurs early , and then triggers hyperplasia [8] . Our study showed that new cell recruitment increases exponentially as fat pad mass increases . Hypertrophy of adipose cells is the main contributor to fat pad mass increase , whereas hyperplasia does not contribute much to this increase because it occurs in small cells that have a much smaller volume of fat stored . Therefore , our model naturally embodies the concept that hyperplasia is affected by the hypertrophic growth of cells . On the other hand , it has been reported that hyperplasia of adipose cells occurs only at early development stages; hence , no new cell recruitment would be expected at late stages even under obesogenic conditions [6] , [7] . It may be the case that the age of the animals in our study ( 6 weeks old ) allows the occurrence of hyperplasia . The model developed here may give microscopic insights into the size-dependent growth of adipose cells that cannot be addressed by static cross-sectional studies . For example , we found the following specific properties of size-dependent cell growth: the lower critical size , , initializing lipid accumulation , did not depend on diet in the two mouse strains , whereas the upper critical size , , limiting cell growth from reaching an extraordinary size , was elevated on a high-fat diet . This size-dependence of cell growth is a testable hypothesis . Next , the cell-size fluctuation parameter , , was different between regular and high-fat diets; it is larger under high-fat diet , when it is transformed to units appropriate for per unit time change instead of unit fat pad mass change . Thus , the random process for fat cells to release and take up fat occurs more actively under a high-fat diet than under a regular diet . It may be of interest to see if these results can be generalized to other strains and organisms . Compared with the studies observing a single peak in cell-size distributions of fat cells [11] , [15]–[17] , [28] , we have observed bimodal cell-size distributions as reported by others [12] , [13] , [29]–[32] . Most studies [12] , [13] , [31] , [32] observing the bimodality used the Coulter Counter technology which has several advantages to assess the entire distribution of cell sizes [31]: First , the analyzed cells can be proven to be authentic adipose cells based on morphology and flotation; second , the volume of each cell is assessed regardless of shape and free of the artifacts of off-center sectioning as is the rule rather than the exception using histological approaches; finally , sufficient numbers of particles can be counted and sized to provide statistically significant complex curves . In contrast , microscopic methods for histology may not observe small cells due to the influence of microscope magnification [30] , small sample number , and sampling bias . However , when the Coulter Counter is used , non-adipocyte contamination may contribute to the cell-size distribution especially at small sizes , although our minimal cell diameter , 22 µm , is above the possible contamination ranges , 10 to 20 µm , mentioned by Mersmann and MacNeil [31] . To be certain , we again analyzed the modified data using only cell-size distributions above 35 µm diameter with the model , and reached the same conclusions ( data not shown ) . The nadir in the cell-size distribution ( Fig . 3 ) may separate two cell populations . DeMartinis and Francendese defined the small cells , with diameter smaller than 35 µm , as “very small fat cells” [29] . Based on our model , these cells have negligible size-dependent growth , because their size is smaller than the lower critical size , sl = 37 µm . Therefore , the size-dependent growth mechanism can naturally explain the origin of bimodality in the cell-size distribution of fat cells . Cells with size only above can grow with the size-dependent manner , but cells with size below can randomly grow with the size-fluctuation through lipid turnover . This separation causes the cell accumulation below the size , , which gives the lower peak in cell-size distributions . This cell population may serve as a potential reservoir for mature adipose cells . Their maturation process may be interpreted as follows: The fat cells reaching the critical size , , by random size fluctuations , then , can grow with a size-dependent growth mechanism . As mentioned above , the size fluctuation occurs more actively under a high-fat diet; therefore , the reservoir can accelerate the maturation process under the stimulating condition . In the tissue growth model , we included the recruitment of new cells and the growth of existing cells , but not the death of old cells , because the model was consistent with the data without the apoptosis of adipose cells . This result is also consistent with a study observing that epididymal fat tissue of C57BL/6 mice does not show significant cell death before 12 weeks under a high-fat diet [33] . However , extended high-fat diet finally induces apoptosis of fat cells [33] . Furthermore , one recent study has reported that human fat cells turn over on a ten-year time scale [7] . Our model , therefore , still needs enhancement to be more generally applied in various conditions . Cell death should be considered and the diet dependence of the model parameters should be formally incorporated . In this study , we focused on one fat depot , epididymal fat , with several reasons: 1 ) the weight of epididymal fat pads can be accessed more accurately than the weight of inguinal fat pads due to the ease of dissection; 2 ) the morphology of adipose cells in epididymal fat is more homogeneous than in inguinal fat which contains a lot of brown adipose tissue-like cells , particularly in mice resistant to diet-induced obesity; and 3 ) the difference between the genotypes was more evident in growth of epididymal fat , which reaches a plateau at 4 weeks in FVB mice , but continues to grow in C57 mice throughout the course of experiment , in contrast to the inguinal fat shows sustained growth in both strains ( Fig . S1 ) . Although we have not measured the cell-size distribution of other fat depots , we measured the mass change of inguinal and brown fat depots , which shows similar pattern with epididymal fat depot ( Fig . S1 ) . Thus , it is of interest to apply the model to other fat depots that have functional differences [34] , [35] , and to other species such as human , which is left for future study . We expect the model can be applied to such diverse data sets simply by adjusting the model parameters , because the model contains general tissue growth mechanisms for the recruitment of new cells and their subsequent development . Our data suggest that at least three factors may explain why C57 mice gain more fat than FVB mice do under high-fat diet: First , FVB mice have increased metabolic rate and increased rectal temperature , most likely due to the increased sympathetic tone . Although we did not detect significant differences in activity between the strains in our study , more comprehensive behavior measurements suggested that FVB mice are spontaneously hyperactive , compared with C57 mice [26] . They also have increased heart rate [36] and respond with hyperglycemia to a variety of treatments [37] . In addition , they are more responsive to stress associated with restraint and fasting [38] . All these data taken together suggest that the activity of sympathetic nervous system increased more in FVB mice than the activity in C57 mice . Second , compared with the FVB mice , C57 mice clear circulating triglycerides more efficiently [23] , [24] , which at least in part could be attributed to higher serum lipase activity [23] and higher capacity to store triglycerides in the liver [23] , [24] and the adipose tissue ( shown in this study ) . Although molecular mechanisms of triglyceride clearance are not fully understood , adipose tissue clearly contributes to clear triglycerides because the ability to clear circulating triglycerides is impaired in lipoatrophic mice [23] . In particular , it has been reported that high-fat diet enhanced triglyceride clearance [39] , which may be related to the induction of lipoprotein lipase activity in the adipose tissue [40] . Finally , compared with FVB mice , C57 mice showed greater recruitment of small adipose cells , particularly under high-fat diet . It has been suggested that new adipose cells can arise from progenitor cells which reside within the adult white fat depots [41] , [42] , and from other sources such as bone marrow-derived circulating progenitor cells [43] . Recruitment of both types of progenitors has been shown to be stimulated by high-fat diet [41] , [43] . It is possible that greater recruitment of smaller fat cells in C57 mice might be caused by a higher pool of precursor cells or thier higher intrinsic capacity for adipocyte differentiation . However , in vitro mesenchymal stem cell , isolated from the outer ear of FVB and C57 mice , differentiate into adipose cells equally well [44] . The attempt to compare differentiation of bone marrow stromal cell from FVB and C57 mice into adipose cells was not conclusive due to the very low yield and poor proliferative capacity of the cells isolated from C57 mice [45]; however , bone marrow does not appear to be the major source of new fat cells at least in mice [43] . Our model suggests the difference between genotypes in the recruitment of small adipose cells might be fat pad autonomous , but the molecular mechanism underlying this difference is unclear . Fat pad is a complex organ containing a variety of different cell types , including mature adipose , preadipose and vascular cells , nerves , macrophages , and fibroblasts . The number of adipocyte precursors and their proliferation in response to external signals varies between fat depots [35] . Further studies would be needed to determine how genotype specific interaction between different cell type and secreted factors may affect the rate of adipocyte recruitment to the specific fat depots . In summary , we have derived a mathematical model describing the growth of adipose tissue with cell-number and cell-size increases as a function of epididymal fat pad mass . Based on this dynamic model , we examined the effects of genetics and diet on adipose tissue growth . Comparing the cell-size distributions from two strains and two diets , we concluded that cell size change depends on diet , and cell number change depends on genetics and diet , as well as their interaction .
All procedures were approved by the Animal Care and Use Committee of the National Institute of Diabetes and Digestive and Kidney Diseases . Male FVB and C57 mice were obtained from The Jackson Laboratory ( Bar Harbor , ME ) . Mice were reared four per cage on a 12-h light/dark cycle ( lights on 06:00–18:00 ) . At the age of 5 weeks , mice of each strain were split into 2 groups . Half of the mice were fed regular chow NIH-07 diet ( hereafter REG; Zeigler Brothers , Inc . , Gardners , PA ) , containing 4 . 08 kcal/g ( 11% calories from fat , 62% from carbohydrates and 26% from protein ) . The other half was fed high-fat diet , F3282 ( hereafter HF; Bio-Serv , Frenchtown , NJ ) , containing 5 . 45 kcal/g ( 59% fat , 26% carbohydrate , and 15% protein ) . Water and diets were provided ad libitum . Five independent experiments were conducted , each using 4 groups of mice: FVB REG , FVB HF , C57 REG and C57 HF . In three experiments , mice were maintained on controlled diets for 12 weeks and used for body composition analysis , physiological characterization , and cell size distribution . Two additional sets of mice were euthanized after 2 weeks and 4 weeks of high-fat and control feeding for cell-size distribution only . Body composition , food intake , metabolic rate , glucose tolerance , triglyceride clearance , and fatty acid oxidation in isolated soleus muscle were measured as described previously [46] . Whole body fatty acid oxidation was measured as described in Gautam et al . [47] . Blood for biochemical assays was obtained from the tail vein in the non-fasted state . Glucose levels were measured using Glucometer Elite ( Bayer , Elkhart , IN ) . Serum insulin was assayed using radioimmunoassay ( Linco Research , St . Charles , MO ) . Serum triglycerides , cholesterol ( Thermo DMA , Louisville , CO ) and free fatty acid ( FFA ) ( Roche Applied Science , Indianapolis , IN ) were measured according to the manufacturers procedures . Cell-size distribution in epididymal fat was measured after 2 , 4 , and 12 weeks of high-fat and control feeding using Beckman Coulter Multisizer III as previously described [9] . Briefly , 20–30 mg of fat tissue were sampled from the midsection , by dissection and then removing the sample for fixation from the center of the cut epididymal fat . Tissue samples were immediately fixed in osmium tetroxide [48] , incubated in a water bath at 37°C for 48 h , and then adipose cell size was determined by a Beckman Coulter Multisizer III with a 400 µm aperture . The range of cell sizes that can effectively be measured using this aperture is 20–240 µm . The instrument was set to count 6 , 000 particles , and the fixed-cell suspension was diluted so that coincident counting was <10% . After collection of pulse sizes , the data were expressed as particle diameters and displayed as histograms of counts against diameter using linear bins and a linear scale for the x-axis ( Fig . 3 ) . Cell-size distribution was measured in four samples from each group , except for the C57 mice after 4-week high-fat diet exposure , which had only three available samples . A sample was taken from each fat pad and processed separately . Each sample was then counted at least twice . The curves from the two samples are then averaged , but only after examining the reproducibility between the two samples . The cell-size distribution includes all the information related to cell sizes in a tissue and its changes give a statistical view of the detailed growth process of each cell . To examine adipose tissue growth in terms of underlying microscopic processes , we consider a mathematical model quantifying the processes that change the cell-size distribution . The model can predict how many new cells are formed and how cells with different sizes grow as fat pad mass increases . The cell-number density of a certain size ( diameter ) at a given fat pad mass is the specific quantity to be considered . We consider how this cell-size distribution changes with an incremental change in fat pad mass . The evolution of the cell-size distribution with fat pad mass can be modeled by a partial differential equation , ( 1 ) This equation comprises three general components of the adipose tissue growth process . First , we assume that new cell recruitment occurs only at the minimal cell size observed , which is mathematically expressed as the delta function . The recruitment rate with respect to fat pad mass is given by the exponential function , ( 2 ) where is the initial total cell number at a given initial fat pad mass , and is the rate of increase in cell number per unit change in fat pad mass . The change of total cell number is the recruitment rate of new cells if cell death is negligible; we found no need to include apoptosis at any cell size to fit these experimental data . Therefore , this recruitment rate can be directly obtained from the experimental result using the relation between total cell number and fat pad mass by differentiating the function with respect to . Second , there is cell-size dependent cell growth . After maturation of adipose cells to a specific size , they may be able to accumulate fat , causing hypertrophy . In addition to this limiting growth rate of small adipose cells , there may be also an upper growth limit because large adipose cells cannot grow indefinitely by this growth process , though they may attain larger sizes by size fluctuations caused by lipid turnover . The rise and fall of cell-growth rate depending on cell size can be described with the general functional form multiplying two sigmoidal functions , ( 3 ) where represents the maximal growth rate; and are the lower and upper critical sizes , respectively , which give the half-maximal growth rate; and give their scale ( Fig . 6 ) . Finally , the last term in Eq . ( 1 ) represents cell-size fluctuations with the constant rate , , which reflect lipid turnover randomly occurring in adipose cells . This lipid turnover is the only growth mechanism for large cells above the upper critical size . Generally , the size-dependent growth of cells moves their size distribution to larger sizes , while size fluctuations spread the size distribution . The volume-weighted mean cell size was used as a measure of hypertrophy , ( 4 ) where is the cell size of the bin and is the relative frequency corresponding to the size at a given fat pad mass . This measure is meaningful from the functional point of view that the lipid storage capacity is proportional to cell volume . Note that the volume-weighted mean cell size gives an average cell size weighted by the lipid-storage capacity of large cells in the upper peak of the bimodal cell-size distribution ( Fig . 3 ) . In contrast , the usual number-weighted mean cell size , , may give a smaller average cell size due to the considerable contribution of small cells in the lower peak of the bimodal cell-size distribution . Clearly , the volume-weighted mean cell size is a better index of lipid-storage capacity . Total cell number is a direct measure reflecting hyperplasia . In this study , epididymal fat pad mass as well as cell-size distributions were measured . Therefore , the total cell number in the epididymal fat pad could be estimated from the relation between fat mass and volume . Here we used pure trioleine density ρ = 0 . 915 g/ml as the density of adipose cells since the density is similar to the actual fat pad density [49] . The total fat volume is where is the total cell number and is the average cell volume , which could be calculated from the cell-size distribution . Therefore the total cell number , or hyperplasia index , is ( 5 ) To optimize model parameters so that they can closely describe the evolution of cell-size distribution in experiment , we used the minimization of a “cost function” which quantifies the deviation between the model and experimental results . To define the cost function , the normalized cell-size distribution at a given fat mass was compared with simulation data with a parameter set : ( 6 ) where is the total number of cell size bins and is the total number of given fat mass . The scale of the cost function was calculated from the intrinsic fluctuation of experimental data , which can be defined as the squared deviation between the measured cell-size distribution and a smooth fitting function : ( 7 ) This intrinsic fluctuation is numerically about 10 percent of the squared deviation between experimental and model data . As the fitting function in Fig . 3 , we used a sum of two exponentials and a Gaussian , ( 8 ) a form that has been used to fit adipose cell-size distributions [9] . These parameter fits were performed using the nonlinear curve-fitting routine in MATLAB R2007a ( Natick , MA , USA ) . For the optimization process , we specifically used the parallel tempering Monte-Carlo method to find the global minimum of the cost function [50] . We used 10 uniformly spaced values ( 0 . 1 to 1 ) for the tempering parameter and ran ten chains in parallel with the updating probability . At every 20 steps , a pair of adjacent simulations on ten tempering parameters were randomly chosen and their parameter states were exchanged with probability . After equilibrium , 20 , 000 iterations were used with the fixed tempering parameter to estimate the optimal parameter values and their standard errors . We also used this method to estimate the initial total cell number , , and its rate of increase , , from the relation between fat mass and total cell number ( Figs . 4C and 4D ) . In the minimization between the fitting function and experimental data , we used a constraint that the initial cell number is equal regardless of diet conditions , i . e . , regular and high-fat diets . The average fat mass of four control mice before regular and high-fat diets was used as the initial fat mass , , which is 0 . 34 g and 0 . 29 g for FVB and C57 mice , respectively . We estimated the uncertainties , and , by propagating the 10 percent statistical fluctuations observed in the experimental data . We solved the following discrete version of our model , given as a continuous partial differential equation in Eq . ( 1 ) : ( 9 ) with mass interval δm = 0 . 1 mg and size interval δs = 0 . 73 µm . | Obesity is an enlargement of adipose tissue to store excess energy intake . Hyperplasia ( cell number increase ) and hypertrophy ( cell size increase ) are two possible growth mechanisms . The in vivo dynamic change of fat tissue cannot be monitored in real time due to current technical limitations . However , we can measure cell-size distributions of fat cells in individual animals . Our fundamental goal is to extract dynamic features of tissue remodeling from snapshots of cell-size distributions . We develop a mathematical model that interpolates between the cell-size distribution measurements and predicts the continuous change of the cell-size distribution with respect to fat pad mass increase . Our adipose tissue growth model includes three essential components: new cell recruitment , size-dependent cell growth , and cell-size fluctuations . In particular , we compared the adipose tissue growth of obesity-prone and obesity-resistant mice under a standard or a high-fat diet to examine the genetic and diet effect on adipose tissue growth . By applying our model to these different conditions , we found that the size increase of fat cells is dependent on diet . On the other hand , the diet-induced number increase of fat cells is dependent on strain , suggesting a synergy between genetics and diet . |
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Wolbachia are maternally inherited symbiotic bacteria , commonly found in arthropods , which are able to manipulate the reproduction of their host in order to maximise their transmission . The evolutionary history of endosymbionts like Wolbachia can be revealed by integrating information on infection status in natural populations with patterns of sequence variation in Wolbachia and host mitochondrial genomes . Here we use whole-genome resequencing data from 290 lines of Drosophila melanogaster from North America , Europe , and Africa to predict Wolbachia infection status , estimate relative cytoplasmic genome copy number , and reconstruct Wolbachia and mitochondrial genome sequences . Overall , 63% of Drosophila strains were predicted to be infected with Wolbachia by our in silico analysis pipeline , which shows 99% concordance with infection status determined by diagnostic PCR . Complete Wolbachia and mitochondrial genomes show congruent phylogenies , consistent with strict vertical transmission through the maternal cytoplasm and imperfect transmission of Wolbachia . Bayesian phylogenetic analysis reveals that the most recent common ancestor of all Wolbachia and mitochondrial genomes in D . melanogaster dates to around 8 , 000 years ago . We find evidence for a recent global replacement of ancestral Wolbachia and mtDNA lineages , but our data suggest that the derived wMel lineage arose several thousand years ago , not in the 20th century as previously proposed . Our data also provide evidence that this global replacement event is incomplete and is likely to be one of several similar incomplete replacement events that have occurred since the out-of-Africa migration that allowed D . melanogaster to colonize worldwide habitats . This study provides a complete genomic analysis of the evolutionary mode and temporal dynamics of the D . melanogaster–Wolbachia symbiosis , as well as important resources for further analyses of the impact of Wolbachia on host biology .
Heritable symbiotic associations are widespread and important components of animal physiology , development , ecology and evolution [1] . Wolbachia is a facultative endosymbiotic bacterium commonly found in association with insects and other arthropods ( including spiders , scorpions , mites and terrestrial crustaceans ) , as well as filarial nematodes [2]–[4] . Wolbachia are α-Proteobacteria of the order Rickettsiales , a diverse group of bacterial species that exhibit commensal , mutualistic and parasitic relationships with their hosts [5] , [6] . Strains of Wolbachia can increase their frequency in natural populations by manipulating reproductive strategies of their hosts in various ways [5] and , in some species , by conferring resistance to certain viruses [7] , [8] or increased survivorship under nutritional stress [9] . Wolbachia are extremely widespread , with greater than 40% of arthropod species estimated to be infected [10]–[12] , making it one of the most common endosymbionts known among all organisms . Despite being primarily maternally transmitted through the cytoplasm of the egg , over long periods of evolutionary time Wolbachia strains are thought to undergo horizontal transfer among host species [13] and exchange sequences from different Wolbachia lineages by recombination [4] , [14] . However , the extent of these processes on microevolutionary timescales within host populations is less well understood , in part because of the limited resolution of the genetic markers used to study host and Wolbachia diversity [15] . The mode of transmission and other aspects of Wolbachia population dynamics can be examined indirectly by integrating information on Wolbachia infection status with patterns of host mitochondrial DNA ( mtDNA ) [16] , [17] . Since mitochondria and Wolbachia are thought to be maternally co-inherited , mtDNA variation can be used as a proxy to provide insights into the evolutionary history of Wolbachia in a host species such as the timing of an infection , its source , frequency and degree of spread through a population [18] . For example , if infected and uninfected individuals share mtDNA haplotypes , this indicates that there has either been imperfect maternal transmission or horizontal transfer of Wolbachia [19] , [20] , while if they have different mtDNA haplotypes , it suggests maternal transmission rates are extremely high [18] , [21] . Similarly , if Wolbachia is strictly maternally transmitted , then strongly reduced levels of mtDNA variation may indicate that the bacterium has recently invaded the host population [18] , [22]–[24] , while higher levels of mtDNA variation would suggest a more ancient association [25] . However , the fact that mtDNA is only a proxy for the Wolbachia genome itself often limits inferences that can be made from this important molecular marker . Research on Wolbachia in Drosophila melanogaster and related species has made major contributions to our understanding of Wolbachia population dynamics [17] , [21] , [26] , [27] . The Wolbachia endosymbiont from D . melanogaster ( wMel ) was the first Wolbachia strain to have its genome fully sequenced [28] . Natural populations of D . melanogaster on all continents are known to be polymorphic for Wolbachia infection [1] , [17] , [19] , [29]–[32] , but initial studies in D . melanogaster using marker loci revealed no sequence diversity in among Wolbachia isolates [17] , [33] , [34] . The availability of the complete sequence of Wolbachia from D . melanogaster permitted the identification of several structural variants that differentiate Wolbachia genotypes [35] , opening up new possibilities to study the population genetics of Wolbachia in D . melanogaster . By genotyping these polymorphic markers in a panel of stocks isolated from nature at different time points , Riegler et al . [35] found evidence for a global replacement of a putatively ancestral Wolbachia genotype ( called wMelCS ) by a single genotype represented by the reference sequence ( wMel ) sometime in the 20th century . This scenario was reinforced by the work of Nunes et al . [19] who found that mtDNA haplotypes associated with the wMel strain had also increased in frequency during the late 20th century . However , neither of these studies used complete genomic information for either the Wolbachia or mtDNA and thus current inferences about the mode and temporal dynamics of Wolbachia and mtDNA in D . melanogaster remain incomplete . Complete Wolbachia and mitochondrial genomes can be serendipitously assembled from the whole genome shotgun ( WGS ) sequences of host Drosophila species [36] , [37] , and thus it is now possible to investigate the population dynamics of these cytoplasmic genomes using WGS data from population resequencing projects in D . melanogaster [38] , [39] . Here we use WGS sequence data from 290 strains of D . melanogaster to mine complete mitochondrial and ( where present ) Wolbachia genome sequences to study the co-ancestry of Wolbachia and D . melanogaster . Using these genomic resources , we reconstructed the genealogical history of the mitochondrial genome from all 290 strains , which revealed the presence of six major lineages . The pattern of Wolbachia infection across the mtDNA tree suggests that Wolbachia has been lost independently in many different populations through imperfect transmission . For the 179 infected strains , we show that the Wolbachia and mtDNA genealogies are fully congruent , suggesting that there was a single ancestral Wolbachia infection that has been vertically transmitted through the maternal cytoplasm without any paternal or horizontal transmission . The strict maternal transmission of Wolbachia and mtDNA in infected strains allows us to use estimates of the mtDNA mutation rate to calibrate the rates of evolution in Wolbachia . Using Bayesian phylogenetic analysis , we estimate dates for the major clades in the Wolbachia and mtDNA genealogies and show that most recent common ancestor of cytoplasmic lineages arose around 8 , 000 years ago . Using this same approach , we estimate that the rate of sequence evolution in Wolbachia is around 100-fold lower than the mutation rate at synonymous sites in mitochondrial genome and ten-fold lower than the mutation rate at noncoding sites in the nuclear genome of the host species . Patterns of Wolbachia and mtDNA molecular variation in a well-sampled North American population are inconsistent with a standard neutral model of molecular evolution , suggesting the action of natural selection or host population expansion acting on these genomes in the recent past . Finally , we present a biogeographic scenario for the recent evolution of Wolbachia and mtDNA that proposes multiple waves of incomplete replacement of pre-existing cytoplasmic lineages since D . melanogaster left Africa at the end of the Pleistocene , and discuss how the genomic resources reported here can be used to further evolutionary and functional analysis of insect-microbe symbioses .
We used a reference-based short-read mapping pipeline to assemble the consensus sequence of complete mitochondrial and ( where present ) Wolbachia genomes in 290 strains of D . melanogaster: 174 strains from Raleigh , North Carolina provided by the Drosophila Genetic Reference Panel ( DGRP ) and 116 strains from a geographically diverse set of African ( n = 108 ) and European strains ( n = 8 ) provided by the Drosophila Population Genomics Project ( DPGP ) . Detailed information about each strain can be found in Dataset S1 . Overall , we analyzed 14 , 792 , 213 , 317 shotgun sequencing reads totalling 1 , 192 , 414 , 581 , 932 bp of DNA . We predicted a line to be infected with Wolbachia when a consensus sequence covered greater than 90% of the Wolbachia reference genome and the reference-based assembly had a mean depth of read coverage of greater than one . Both of these cutoffs were suggested by natural discontinuities in the distribution of coverage values in the data ( Figure 1 ) . Overall , 179 strains ( 61 . 8% ) were predicted to be infected with Wolbachia by our in silico criteria ( Table 1 ) . The proportion of infected strains differs significantly in the DGRP ( 91/174; 52 . 2% ) and DPGP samples ( 88/116; 75 . 9% ) ( Binomial test; P<1 . 4×10−7 ) . We tested the validity of our in silico method for detecting Wolbachia infection using an experimental assay based on PCR amplification of the Wolbachia wsp gene in 167 of the 174 DGRP lines . Infection status predicted based on PCR was identical with our in silico predictions for 165/167 strains ( 98 . 8% concordance , Dataset S1 ) . Only two lines gave different infection status predictions between procedures ( DGRP38 and DGRP911 ) . Both strains were scored as uninfected using in silico criteria , but scored as infected by PCR . Each of these lines have high overall WGS sequencing depth but very low depth and breadth of coverage in Wolbachia assemblies , suggesting that these discrepancies are not because of poor WGS sampling and may have instead arisen because of cross-contamination of fly stocks or DNA preparations for PCR , after preparation of WGS sequencing libraries . We note that in the DGRP sample , we did observe some strains that have a depth of coverage above one but only intermediate breadth of coverage , and that all of these that were tested by PCR were classified as uninfected . Assemblies of strains with intermediate coverage yield patches of sequence that differ in location across the Wolbachia genome from strain to strain , but which are all highly similar to other sequences from high coverage genomes . Moreover , intermediate coverage strains do not fall out as single clade on the mtDNA phylogeny as would be expected if they were vertically inherited in the cytoplasm ( see below ) . Thus we do not believe intermediate coverage strains represent the presence of Wolbachia sequences in the nuclear genome segregating among DGRP strains , as has been observed in D . ananassae [40] , or a secondary infection from another Wolbachia lineage . Rather we believe these data are consistent with low frequency polymorphic Wolbachia infections in the stocks used for sequencing that have subsequently been lost in culture , as would be predicted to occur occasionally under a mode of vertical inheritance with imperfect transmission . Despite the low rate of discrepancy and presence of some intermediate coverage strains , these results clearly demonstrate the high sensitivity and specificity of predicting Wolbachia infection in natural populations using whole-genome shotgun sequence of individual host strains . The phenotypic effects of Wolbachia on insects often depend on the bacterial density in host cells [17] , [41] . Thus , we attempted to estimate the copy number of Wolbachia and mitochondrial genomes based on the depth of coverage for each assembly scaled relative to the nuclear genome to control for variation in overall WGS throughput ( Figure 2 ) . Relative copy number estimated in this manner represents an average across the tissue sampled , and obscures intra-individual variation across tissues . We found that the relative depth of coverage for both Wolbachia and mtDNA varied substantially among infected strains , but differed systematically between the DGRP strains ( from diploid adult DNA ) and DPGP strains ( from haploid embryonic DNA ) , with a higher depth of coverage for both Wolbachia and mitochondrial DNA in the DGRP strains ( Wilcoxon Rank Sum Tests; P<2 . 2×10−16 ) . This was also true for mtDNA from strains that were not infected with Wolbachia ( Wilcoxon Rank Sum Tests; P<7 . 4×10−9 ) , and thus this pattern is not an artefact of the Wolbachia infection influencing the overall proportion of mtDNA or nuclear reads . For infected DGRP strains , the mean ( standard deviation ) Wolbachia coverage is on 5 . 57 ( 3 . 95 ) times greater than nuclear coverage , and mtDNA coverage is on average 32 . 9 ( 44 . 5 ) times greater than nuclear coverage . For infected DPGP strains , relative Wolbachia coverage is approximately the same as nuclear coverage ( mean: 1 . 02; standard deviation: 1 . 84 ) and relative mtDNA coverage is 9 . 79 ( 24 . 7 ) times greater than nuclear coverage . Assuming nuclear coverage number represents a copy number of 2C in the DGRP strains ( from diploid adults ) and 1C in the DPGP strains ( from haploid embryos ) , values for DPGP and half those for DGRP strains provide an estimate of cytoplasmic genome copy number relative to the haploid DNA content of the cell . To understand the relationship between Wolbachia infection status and mtDNA sequence variation , we reconstructed the recent genealogical history of the complete D . melanogaster mitochondrial genome using the entire set of 290 strains in the combined DGRP and DPGP sample ( Figure 3 ) . This analysis revealed six major intraspecific clades that we label I–VI . Clades I–IV also exhibit well-supported subclades within them , however we focus here on the higher-level aspects of the genealogy represented by these six major clades . Clade I contains the majority of the North America strains , one European strain , and multiple African strains in more basal locations . Clade II contains only African strains . Clade III contains mostly African strains , four European strains , nine North American strains , and the reference mtDNA from the dm3 D . melanogaster genome sequence . Clade IV is a small clade comprised of five strains only from Ethiopia . Clade V is only represented by two European strains . The most divergent clade ( VI ) contains two African strains and two North American strains . The composite mtDNA reference sequence NC_001709 also groups with this clade ( see below ) . D . melanogaster strains infected with Wolbachia are found across the entire mtDNA tree ( Figure 3 ) . All major clades have both infected and uninfected strains ( with the exception of clade V , which comprises a small sample of uninfected strains ) . Assuming this pattern arose by a single infection with imperfect transmission , the distribution of infection status across the mtDNA genealogy provides indirect insight into the degree to which the Wolbachia infection has progressed . When a new Wolbachia strain invades it will tend to be associated with a single mtDNA type . As a new infection spreads , imperfect transmission of Wolbachia will cause mtDNA lineages originally associated with the infection also to be observed in uninfected flies . At equilibrium , the frequency of mtDNA haplotypes in infected and uninfected flies is ultimately expected to be the same [42] . Nunes et al . [19] previously found that mtDNA COI haplotypes in infected and uninfected strains differ significantly in D . melanogaster , which has been attributed to a recent change in the frequency of the Wolbachia infection . We tested for genetic differentiation between mtDNA sequences of infected and uninfected strains using a variant of Kst [43] and found no evidence of subdivision ( weighted mean Kst across sampling locations: Kst = 0 . 01 , P = 0 . 88 ) , suggesting that the infection is not recent and that our sample does not contain mitochondrial lineages that predate the infection . Strict maternal transmission of Wolbachia from a single infection would result in mtDNA and Wolbachia having congruent genealogies , while even low rates of paternal or horizontal transmission will break down this association . Rare paternal transmission has been observed previously in D . simulans laboratory crosses for both mtDNA [44] and Wolbachia [45] . Previous work by Nunes et al . [19] revealed a non-random association between Wolbachia genotypes and mtDNA COI haplotypes , but not a strict congruence . Such a pattern could arise from paternal or horizontal Wolbachia transmission among D . melanogaster mtDNA lineages or from using low-resolution molecular markers that obscure a true pattern of strict maternal inheritance . To test if Wolbachia is evolving under a strict maternal mode of transmission in D . melanogaster , we reconstructed independent genealogies of the Wolbachia and mitochondrial genomes for the 179 infected strains . Genealogies of both Wolbachia and mitochondria from infected strains reveal the presence of five distinct cytoplasmic lineages ( clades I–IV and VI ) in contemporary populations of D . melanogaster , with the majority of strains falling into clades I , II and III . We found that genealogies of Wolbachia and mitochondria genomes reveal perfect congruence for all of the well-supported clades . In other words , no well-supported clade in the mitochondrial genealogy is contradicted , with strong support , in the Wolbachia genealogy and vice versa ( Figure 4 ) . We note that some discrepancies in perfect congruence between the ML topologies can be observed among weakly supported lineages at the tips of the tree , which arise from inherent uncertainties in genealogical inference among closely related lineages . To test the hypothesis that the Wolbachia and mitochondria genealogies are fully congruent , we used a Bayesian approach that assessed whether a model that allows separate topologies for each genome fits the data better than a model that forces a single topology on both genomes . The log marginal likelihood values ( estimated from the harmonic mean ) of the two models were −1 , 300 , 280 . 95 for the two-topology model and −1 , 300 , 271 . 06 for the single topology model . The log10 Bayes Factor of 4 . 30 shows decisive support for the single topology model , as did Akaike's information criterion ( single topology: AIC value 2 , 600 , 722; two topology: 2 , 600 , 916 ) [46] . In contrast , when we randomly assigned Wolbachia to hosts within the same geographical location ( i . e . , Wolbachia strains were associated at random with hosts with same latitude and longitude as their true host ) , we found overwhelming support for the two-topology model ( log10 Bayes Factor −944 ) . Even greater support for two topologies was found when we randomly assigned Wolbachia to hosts within the same continent ( Africa , Europe or USA; log10 Bayes factor −1215 ) , or at random across the tree ( log10 Bayes factor −1539 ) . Results from these randomised data sets suggest that the Bayes factor test would have had the power to detect incongruent genealogies , even if horizontal or paternal transmission was taking place solely within populations . The support for a single genealogy describing both the Wolbachia and mitochondrial genomes from infected strains strongly implies a single ancestral infection followed by strict vertical transmission through the maternal cytoplasm , further supporting the inferences based on mtDNA genealogy of all strains above . Together with the fact that there is no reported evidence for horizontal transfer of mtDNA in D . melanogaster [17] , the strong congruence of Wolbachia and mitochondria rules out the possibility that the divergent cytoplasmic clades IV , V and VI represent independent horizontal transfer events . Furthermore , a phylogeny of mtDNA from infected strains including an allele from the outgroup species D . simulans supports the placement of the root in the same position as is assumed by midpoint rooting without an outgroup ( Dataset S2 ) . Thus we conclude that maternal transmission with recurrent loss characterizes the mode of inheritance Wolbachia over the timescale of the divergence of sequences in this sample . Congruence of Wolbachia and mitochondrial genealogies also underscores the high quality of the genomic data and bioinformatics methods used here , since the biological signal of congruence could only be detected if the data have been processed accurately from WGS library preparation through to phylogenetic reconstruction . Furthermore , the strict maternal transmission implied by the congruence of Wolbachia and mitochondrial genealogies also argues against high rates of within-strain heteroplasmy for either Wolbachia or mtDNA . In fact , the proportion of individual read calls supporting the consensus sequence call at variable sites averaged across strains is very high for both Wolbachia ( 99 . 0% ) and mtDNA ( 99 . 3% ) , suggesting that levels of heteroplasmy ( or WGS sequence contamination ) are very low in these data . Placing an absolute timescale on the evolution of bacterial symbionts allows patterns of bacterial evolution to be related to historical events in the evolution of their hosts . Likewise , insight into the evolutionary forces operating on a species can be substantially improved by understanding its spontaneous point mutation rate . Unfortunately , in Wolbachia there is no estimate of the spontaneous mutation rates that can be used to transform rates of sequence evolution into absolute time . We first attempted to measure the mutation rate directly in Wolbachia by applying our pipeline to D . melanogaster mutation accumulation lines from [47] , but all were found to be uninfected ( data not shown ) . Therefore we developed an indirect phylogenetic approach that assumes strict maternal transmission and incorporates prior information from empirically determined mutation rates in D . melanogaster mtDNA [48] to estimate rates of molecular evolution in the Wolbachia genome . Specifically , we concatenated Wolbachia and mtDNA sequences from the same infected strain and estimated a Bayesian dated phylogeny that placed a lognormal prior on mutation rates at mitochondrial third positions with mean −16 . 59613 and standard deviation 1/3 on the log scale . These values imply a mean rate of 6 . 2×10−8 mutations per site per generation with 95% confidence intervals of approximately 3 . 6×10−8–3 . 6×10−7 , and these values represent the average of the mutation rate estimates obtained from the Florida and Madrid lines studied in [48] . Estimates of dates in number of D . melanogaster generations were then converted into years by assuming ten generations per year ( as in [19] , [49] , [50] ) . Our analysis also included a phylogeographic model , allowing us to infer the geographic locations of the ancestral strains simultaneously with the dated phylogeny . We note that our method estimates rates of sequence change along a coalescent tree , and strictly speaking these are neither mutation rates ( because strongly deleterious mutations will not be observed ) nor are they classical long-term neutral substitution rates ( because they include slightly deleterious mutations that would not be observed in sequence divergence between species ) . Furthermore , we are only able to estimate the rate of sequence change in terms of host , not bacterial , generations . For these reasons , we use the term “short-term evolutionary rate” to describe the rate of sequence evolution estimated here from intraspecific variation , and measure changes in substitutions per site per host generation . Using this approach , we find that the most recent common ancestor ( MRCA ) of all strains in the sample dates to approximately 8 , 000 years ago ( ya ) ( Figure 5 ) , substantially later than the estimated date of 16 , 000 ya for the migration event that allowed D . melanogaster to colonize non-African habitats [51] . The MRCA of clades I , II , III and IV dates to 5 , 000 ya , and is inferred to have arisen in Africa based on our phylogeographic model ( see methods for details ) . The MRCA of the high frequency clades I , II and III dates to 2 , 200 years ago , and is also inferred to have an African origin . The appearance of the MRCA for the high-frequency clades I , II and III occurred within 1 , 000 years of this event ( clade I MRCA: 1 , 800 ya; clade II MRCA: 1 , 400 ya; clade III MRCA: 1 , 200 ya ) . Subclades containing North America strains within clades I and III date to 700 and 375 ya , respectively ( See Dataset S3 for details ) , prior to the estimated time of colonization of North American habitats in the 19th century [52] , [53] . These subclades also contain strains from Europe ( Figure 3 ) and thus the MRCA of these subclades probably arose in Europe prior to their arrival in North America . This analysis also provides estimates of the short-term molecular evolutionary rate for Wolbachia ( Table 2 ) , which we estimate from 3rd codon positions to be 6 . 87×10−10 substitutions/site/generation ( 95% Credible Interval: 2 . 88×10−10–1 . 29×10−9 ) , roughly two orders of magnitude lower than the mutation rate in 3rd codon positions of mtDNA ( Table 2 ) . The median Wolbachia substitution rate is also ten-fold lower than the D . melanogaster mutation rate of 3 . 5–5 . 8×10−9 estimate from noncoding nuclear DNA [47] , [50] . In contrast to the Drosophila mtDNA , in the Wolbachia genome we find no evidence for differences in the estimated substitution rates for 1st and 2nd versus 3rd codon positions ( or noncoding DNA regions ) . We note that the estimates of Wolbachia substitution rates are in terms of D . melanogaster generations , not Wolbachia replications , and are thus directly comparable in molecular terms to the mutation rate estimates based on errors in replication of mitochondrial and nuclear DNA [47] , [48] , [50] . Tables of variable sites in the Wolbachia and mitochondrial genomes can be found in Dataset S4 . Previous work using genetic markers for both Wolbachia and mtDNA has suggested that there has been a global replacement of the Wolbachia strains in D . melanogaster during the 20th century [19] , [35] . Riegler et al . [35] identified several different Wolbachia genotypes ( wMel , wMel2 , wMel3 , wMelCS and wMelCS2 ) on the basis of a small number of structural variants including IS5 transposable elements , copy number variants and genome rearrangements . Subsequent work provided evidence that distinct mitochondrial lineages are associated with these Wolbachia genotypes [19] , [54] . Specifically , Ilinsky et al . [54] found strict association between two SNPs in the mtDNA COI gene and three wMel genotypes ( wMel , wMelCS and wMelCS2 ) in an Ukrainian population , while Nunes et al . [19] found a non-random association between COI haplotypes and wMel genotypes in a worldwide sample . To place our whole genome analyses in the context of this previous work , we identified the location of the Wolbachia IS5-family transposons ( ISWpi1 ) insertions using an in silico transposable element mapping procedure related to that in [55] . Using this approach , we could discriminate between wMel-like ( wMel or wMel2 ) and wMelCS-like ( wMelCS or wMelCS2 ) genotypes for strains that had Illumina read lengths of greater than 75 bp . Based on this analysis , we infer that clades I–V are wMel-like genotypes and the basal clade VI is a wMelCS-like genotype . These inferences are supported by the placement of the Wolbachia reference genome ( GenBank ID: AE017196 ) , which defines the wMel genotype , in a sub-clade of North American strains within clade III ( Figure 4 ) . This placement of the wMel reference is consistent with its isolation from a D . melanogaster stock ( y , w67c23 ) that has its origin in North America [56] . We note that while direct confirmation that clade VI is the wMelCS genotype is in principle possible since this genome has been fully sequenced [57] , we were not able to perform this analysis since these data have not been made publicly available at the time of writing . In terms of mtDNA variants , we found that the diagnostic wMelCS-specific SNPs ( T-2160/C-2187 ) of [54] were only present in clade VI , while all other strains had the diagnostic wMel-specific SNPs ( C-2160/T-2187 ) , further supporting the conclusion that our clades I–V are wMel-like . Likewise , we observe that haplotype 2 , found by Nunes et al . [19] to be at highest frequency worldwide and preferentially associated with wMel-type Wolbachia , is present in clades I , III and IV ( Figure 3 ) . Clade II contains predominantly haplotypes 8 and 9 , which are closely related to haplotype 2 and associated with wMel-type lineages [19] . Clade V , which is only represented by uninfected lineages in our sample , contains only haplotype 10 , which is intermediate to haplotypes 1 and 2 but associated only with wMel-type lineages in [19] . In contrast , we found the rare haplotype 1 , which is associated with wMelCS [19] , to be present only in clade VI . Finally the phylogenetic placement of mtDNA reference sequences also supports the conclusion that clade VI represents the wMelCS lineages . The NC_001709 reference sequence , which is a composite of fragments from Oregon R and Canton S stocks [58]–[60] , falls into clade VI , and complete mtDNA sequences from w1118/Canton S ( GenBank ID: FJ190105 ) and Oregon R ( GenBank ID: AF200828 ) both carry the diagnostic wMelCS-specific 2160/C-2187 SNPs of [54] ( results not shown ) . The placement of these Canton S like mtDNA sequences in clade VI is consistent with the fact that the wMelCS Wolbachia strain was derived from a Canton S strain [35] . Thus , all available evidence from Wolbachia and mitochondrial genomes support the inference that clades I–V represent wMel-like lineages and clade VI represent a wMelCS-like lineage . A recent increase in frequency and spread of new cytoplasmic lineages through worldwide populations of D . melanogaster is expected to lead to low genetic diversity and an excess of low frequency polymorphisms . To test if these predictions are observed in the data , we estimated levels of nucleotide diversity and tested departures from a model of neutral equilibrium in the DGRP strains . We focused on the DGRP strains for this analysis since this project provides a large sample collected from the same location and time and thus fits the assumptions of the standard neutral model better than strains from the DPGP collection . We found low levels of nucleotide variation for both Wolbachia and mitochondrial genomes among DGRP lines ( Table 3 ) relative to that found in the host nuclear genome ( π = 0 . 0056 and θ = 0 . 0067 ) [38] . Levels of mtDNA variation based on π are somewhat lower than previous estimates based on marker loci ( CytB , π = 0 . 0009; ND5 , π = 0 . 00149; COI , π = 0 . 0018 ) [23] , [61] , [62] , however those based on θ are very similar to previous estimates ( CytB , θ = 0 . 0021; ND5 , θ = 0 . 00298 ) suggesting a deeper sampling of rare variants in our sample . Levels of Wolbachia nucleotide diversity are approximately an order of magnitude less than that observed for mtDNA . We also observe an excess of rare variants in both Wolbachia and mtDNA sequences relative to the expectations of the standard neutral model , with Tajima's D being significantly less than zero for all samples ( Table 3 ) . These results are consistent with a non-neutral process operating on Wolbachia and mitochondrial genomes in the DGRP population . This signal could result from the action of a recent selective sweep driving the global replacement of the wMel-like Wolbachia and mitochondrial lineages , with possible fitness effects arising from differential longevity [63] , protection against viruses [7] , [8] , or co-adaptation with the host [64] . Alternatively , the excess of rare variants may be explained by purifying selection on weakly deleterious mutations as has been proposed previously for mtDNA in D . melanogaster [23] . Similar patterns could also have been generated by demographic effects such a population size expansion of the host after D . melanogaster colonized non-African habitats [49] .
Using high-throughput shotgun sequencing data from several hundred strains of D . melanogaster , we have reconstructed complete genome sequences of the Wolbachia endosymbiont and mtDNA to study the recent evolutionary dynamics of these two important model organisms . We use these new genomic resources to estimate copy number of Wolbachia and mitochondrial genomes in the host cell , and to compare patterns of Wolbachia infection across the D . melanogaster mtDNA genealogy . We identify several distinct cytoplasmic lineages that show strong congruence between the Wolbachia and mtDNA genealogies . Our data support a single ancestral Wolbachia infection that has been inherited strictly by vertical transmission in the maternal cytoplasm . This observation allows us to use empirically determined rates of mtDNA evolution to calibrate rates of Wolbachia evolution . We show that the most recent common ancestor of the current Wolbachia infection in D . melanogaster dates to less than 10 , 000 years ago , and that patterns of molecular variation for the Wolbachia and mitochondrial genomes in a well-sampled North American population are inconsistent with a standard neutral population genetic model . Our use of reference-based endosymbiont genome reconstruction from host whole genome shotgun sequences extends previous efforts to identify endosymbiont genomes on the basis of de novo assembly [36] , [65] . Our results also show that it is not necessary to purify Wolbachia prior to whole-genome shotgun sequencing ( e . g [57] ) in order to study the genetics and evolution of this microorganism in D . melanogaster . Furthermore , by demonstrating that host whole-genome shotgun sequences can accurately predict Wolbachia infection status , our work also shows that it is no longer necessary to rely only on indirect mtDNA-based analyses ( e . g . [17] ) or low-resolution techniques like diagnostic PCR or marker-based genotyping methods ( e . g . [19] , [35] ) in order to study Wolbachia , since the ultimate level of genetic resolution – complete Wolbachia genomic sequences – can now be achieved by a relatively easy and scalable protocol . Finally , we show that complete Wolbachia and mitochondrial genomes can be readily obtained from shotgun sequencing libraries of both adult and embryonic DNA in D . melanogaster , however our results would suggest that sampling from adults gives higher yields of cytoplasmic genomes ( see below ) . Furthermore , given the maternal transmission of both mtDNA and Wolbachia , it may not be necessary to use the haploid embryonic DNA preparation technique of Langley et al . [66] to sample cytoplasmic genomes using WGS . WGS provides an opportunity to study differences in copy number of various genomic regions in a library of sequences . By normalizing to a standard nuclear reference sequence ( in order to control for variation in overall sequencing throughput ) we obtained estimates of the abundance of Wolbachia and mitochondrial genomes in each strain relative to the D . melanogaster nuclear genome ( Figure 2 ) . This analysis revealed a greater abundance of mtDNA relative to Wolbachia across both the DGRP and DPGP strains , as well as a greater abundance of both cytoplasmic genomes in DGRP strains relative DPGP strains . The higher abundance of mtDNA is likely to reflect a real biological difference in the relative copy number of these cytoplasmic genomes since this trend is observed in both projects and could arise from a higher titre of mitochondria per cell or because mtDNA is multicopy in a given mitochondria . However , since DGRP strains were prepared from mixed sex adult flies [38] and DPGP samples were prepared from gynogenetic haploid embryos that have undergone whole-genome amplification [66] , differences in relative abundance between these projects could arise either from ( i ) lower abundance of cytoplasmic DNA in embryos relative to adults , ( ii ) biases generated by the whole-genome amplification process that skews the ratio of cytoplasmic to nuclear DNA , or ( iii ) differences in copy number across populations . It is unwarranted to conclude that these observations reflect real geographic variation among populations until differences in sample preparation are excluded . Moreover , if relative abundance correlates with levels of infection or transmission rates we would expect the DPGP sample to have lower infection rates , which is opposite to what is observed ( Table 1 ) . Assuming that the differences between the DGRP and DPGP represent real biological differences between the adult and embryonic stages of the life cycle rather than technical artefacts , our results would suggest a strong reduction in relative cytoplasmic genome copy number during oogenesis or embryogenesis . Recent work using quantitative PCR ( qPCR ) has shown that relative copy number of mtDNA in adults is on the order of 200 copies per nuclear genome [67] , suggesting that studying relative mtDNA copy number using a WGS approach provides lower estimates than qPCR . While this and other caveats prevent the straightforward interpretation of relative abundance in terms of actual copy number in the cell , reduced bacterial titre during oogenesis or embryogenesis would provide a simple stochastic mechanism [41] to explain the relatively high rate of imperfect transmission for Wolbachia in D . melanogaster [68] . Two lines of evidence presented here support the inference that the current Wolbachia infection in D . melanogaster arose once in the past and has been inherited by strict vertical transmission in the maternal cytoplasm , with subsequent loss of the infection in multiple populations worldwide . First , we observe that the Wolbachia infection is found in all major clades across the mtDNA genealogy ( Figure 3 ) , which is most parsimoniously interpreted in terms of a single gain and multiple losses , given that imperfect maternal transmission occurs at a high frequency in the wild [68] . While this conclusion has been made in the past based on mtDNA marker genes [17] , the inference of an infection across the deepest node in a mtDNA tree itself does not exclude the possibility of more than one infection by horizontal transfer . Because maintenance of a Wolbachia infection in the face of imperfect transmission implies some form of positive transmission bias increasing frequency of infected lineages , uninfected mtDNA lineages predating an infection will be rapidly lost [69] . Thus , we only expect to see mtDNA lineages related to those of infected strains in nature , even if there were multiple independent Wolbachia infections . Vertical transmission is secondly supported by the strong congruence between Wolbachia and mtDNA genealogies in infected strains ( Figure 4 ) . Intraspecific genealogical congruence is only consistent with strict vertical co-transmission of both cytoplasmic genomes [70] , but not with horizontal transfer of Wolbachia from another species on a vertically evolving mtDNA lineage . In principle , the pattern of co-transmission we observe could occur through simultaneous introgression of both Wolbachia and mtDNA lineages from a sister species through hybridization . However there is no evidence for introgression of mtDNA from sister species into D . melanogaster [17] , [61] . Taken together , the phylogenetic evidence strongly supports a single infection with vertical transmission through the maternal cytoplasm at least as far back as the time to the MRCA of the sample . Our conclusion of a single infection is consistent with previous genetic evidence that Wolbachia gives rise to a single cytoplasmic incompatibility type in D . melanogaster [17] . Our inference of strict maternal transmission contrasts with previous reports for rare paternal transmission of Wolbachia in D . simulans under laboratory conditions [45] , [71] . However , our conclusion that paternal transmission in nature occurs rarely , if ever , is supported directly by experimental evidence in D . melanogaster [68] and indirectly by analysis of mtDNA frequencies in D . simulans [21] . Two consequences of strict maternal transmission are that heteroplasmy would be expected to be rare or non-existent in nature , and that no paternal lineages would be present in an individual for homologous recombination to occur . Finally , while interspecific horizontal transfer of Wolbachia may occur on large evolutionary timescales [5] and has been inferred to occur within species of some arthropods ( e . g . [72] ) , we find no evidence of horizontal transfer within current D . melanogaster populations , at least within the limits of detection afforded by essentially complete genomic coverage and deep population sampling . However , we cannot reject some horizontal transfer between individuals that have identical or nearly identical sequences using a phylogenetic approach . Our observation that genealogies for Wolbachia and its host D . melanogaster are congruent over short evolutionary timescales ( Figure 4 ) is also important because it allows us to use information from the host species to calibrate rates of sequence evolution for a bacterial species that lacks a fossil record [73] , [74] . To date , this approach has not been applied to Wolbachia because of evidence for horizontal transfer over longer evolutionary time periods [5] . Using a novel Bayesian approach to calibrate Wolbachia evolutionary rates using empirically determined mtDNA mutation rates from D . melanogaster [48] , we find that the evolutionary rate for all classes of sites studied in Wolbachia are a 100-fold lower than silent sites in host mtDNA ( Table 2 ) and ten-fold lower than noncoding sites in host nuclear DNA [47] , [50] . Moreover , in contrast to the five-fold reduction in mutation rates observed for coding sites relative to silent sites in mtDNA , we find no difference in the short-term evolutionary rate for coding and silent sites in Wolbachia . Assuming that changes at silent sites are selectively unconstrained , this observation suggests that our estimates of the rate of short-term sequence evolution for both silent and coding sites are closer to the mutation rate than the long-term neutral substitution rate . This pattern of molecular evolution is consistent with a mode of purifying selection operating on Wolbachia protein sequences that is either relaxed or has had insufficient time to purge newly arising slightly deleterious mutations from the population [75] . We favor the interpretation of the delayed action of purifying selection since sequence divergence in Wolbachia genes between D . melanogaster and D . simulans suggests evidence for purifying selection ( median Ka = 6 . 2×10−3; median Ks = 3 . 2×10−2 [6] ) . Our estimate of the evolutionary rate for Wolbachia is more than 30-fold lower than the short-term evolutionary rate at silent sites estimated for the Buchnera endosymbiont of aphids ( 2 . 2×10−8 substitutions/site/host generation [76] assuming 10 aphid generations per year [77] ) . Moran et al . [76] also found than silent sites in Buchnera had a two-fold higher short-term evolutionary rate relative to the genome-wide estimate that includes coding sites , in contrast to what we observe in Wolbachia . The principal observation that Wolbachia has a much lower short-term evolutionary rate than Buchnera is consistent with Wolbachia having functional DNA repair pathways [28] , and helps explains why Wolbachia has not undergone such extensive genome erosion as is observed in Buchnera [78] . Moreover , the different rate and pattern of mutation between Buchnera and Wolbachia genomes argues against the application of single universal evolutionary model for studying molecular divergence among bacterial endosymbiotic lineages [74] . Our work also provides important insights into the debate about the recent biogeographic history of Wolbachia in D . melanogaster . Solignac et al . [17] first proposed that the Wolbachia infection in D . melanogaster arose once and has been inherited maternally with subsequent loss by imperfect transmission , consistent with the findings presented here . These authors suggested that the infection arose sometime after the split of D . melanogaster from its sister species D . simulans but prior to the MRCA of all D . melanogaster mtDNA sequences , which they estimate to be around 500 thousand ya [17] . We estimate the date for the MRCA for mtDNA to be much younger at around 8 , 000 years , which provides a minimum bound on the age of the infection . However , because of this very recent coalescence event , we cannot say much about the age of the infection prior to this time . Riegler et al . [35] postulated that after this initial infection , a wMel-like lineage arose sometime in the late 19th or early 20th century in North America , which replaced ancestral wMelCS-like lineages on all continents in the 20th century . This scenario of a recent global replacement of wMelCS-like by wMel-like lineages was supported by the work of Nunes et al . [19] , who showed that mtDNA haplotypes preferentially associated with wMel-like lineages have become more prevalent in the late 20th century . Assuming our inferences that cytoplasmic clades I–V represent wMel-like lineages and clade VI represent a wMelCS-like lineage are correct , the widespread geographic distribution of strains in clades I–V ( wMel-like ) and the basal location of clade VI ( wMelCS-like ) is consistent with the Riegler et al . [35] global replacement hypothesis . However , our data and others [19] , [31] demonstrate that wMelCS-like lineages still persist naturally at low frequency in North American and Eurasian populations in the 21st century , and thus this replacement event is clearly incomplete . Furthermore , our inference of the date and geographic location of the ancestor of wMel-like lineages is inconsistent with an origin in North America in the late 19th or early 20th century . Rather , we find that the MRCA of the wMel-like lineages arose several thousand years ago , long enough ago to allow subsequent diversification into distinct clades . The fact that the MRCA of the wMel-like subclades present in North America date to greater than 300 ya provides further evidence for the inference that the replacement of wMelCS-like lineages occurred prior to colonization of North America by D . melanogaster in the late 19th century [52] , [53] . We propose instead that the wMel-like replacement event occurred in the Old World and was incomplete , leaving remnant wMelCS-like and basal wMel-like cytoplasmic lineages in the Afrotropical and Palearctic regions . Sampling of both high frequency wMel-like and low frequency wMelCS-like lineages from these regions during colonization of North America would have led to the mixture of cytoplasmic lineages currently observed in the DGRP sample . The possibility of some populations harbouring remnant cytoplasmic lineages , together with the observation of populations that are entirely free of the Wolbachia infection [17] , [31] , suggests substantial geographic structure with respect to the Wolbachia infection in D . melanogaster . The clearest evidence for this model comes from the clade IV lineage being observed only in the Ethiopian sample from Dodola ( ED ) , which also shows the highest genetic differentiation in the nuclear genome among all DPGP populations [39] . Finally , the resources and approaches presented here offer the possibility for wider application in evolutionary and functional genomics . For example , it is now possible to use the presence or absence of Wolbachia infection as a control factor in genome-wide association studies of host traits based on the DGRP lines [38] . In fact it is also now possible to treat Wolbachia presence/absence or the relative abundance of Wolbachia as traits in genome-wide association studies to identify variation in host genes involved in the modulation of Wolbachia infection . Further studies could investigate the relative rates of various types of mutation observed in the Wolbachia genome , including insertions/deletion and larger structural variants not studied here , or attempt to identify the functional effects of variants in the Wolbachia genome that might underpin traits such as the increased virulence of strains such as the wMelPop “popcorn” strain [79] . Reference-based shotgun sequence assembly using the DGRP and DPGP lines could be also applied to other endosymbiotic and non-endosymbiotic bacteria that are known to be associated with D . melanogaster [80] . For example , a preliminary analysis of Spiroplasma infection in the DGRP and DPGP lines using marker loci as references ( GenBank ID: FJ657061 , FJ657121 , FJ657249 ) revealed that only two closely related strains from Uganda ( UM37 & UM526 ) are likely to be infected with this endosymbiont , both of which are also infected with Wolbachia . This result is consistent with the only other report of Spiroplasma infection in African populations of D . melanogaster being located in Uganda [81] , and the common occurrence of strains that are co-infected with these two endosymbionts [82] . In the absence of a complete D . melanogaster Spiroplasma reference sequence , this result motivates an attempt to reconstruct the Spiroplasma genome from these DPGP strains using a de novo assembly technique [36] , [65] . With continuing advances in high-throughput sequencing and the proof of principle presented here , it is now feasible to consider the comprehensive co-evolutionary analysis of symbionts and their hosts in a population genomics context .
WGS sequences of D . melanogaster strains were downloaded from the NCBI Short Read Archive ( SRA ) from two projects: ( i ) 176 inbred lines sequenced by the Drosophila Genetic Reference Panel ( DGRP; NCBI SRA project: SRP000694 ) from a single population in Raleigh , NC , USA [38]; and ( ii ) 118 “core” isofemale lines sequenced by the Drosophila Population Genomics Project ( DPGP; NCBI SRA project: SRP005599 ) from multiple populations in Africa and a single population in Europe [39] . Two DGRP strains ( SRS003443 and SRS003448 ) that previously have been proposed to contain chimeric Illumina reads [55] were excluded from this analysis . Sequences from strains with multiple SRA sequencing run accessions were concatenated into single fastq files before further processing . Statistics of total read count and total sequence length per strain were calculated using seqtk ( https://github . com/lh3/seqtk ) and faSize ( http://genome . ucsc . edu/admin/git . html ) . Fastq sequences were mapped to reference genomes with BWA version 0 . 5 . 9-r16 [83] using default parameters and converted to BAM format with SAMtools version 0 . 1 . 16 [84] . Reads were mapped to three reference sequences: ( i ) a mitochondrial reference sequence extracted from the Release 5 genome sequence ( chrU:5288528–5305749 ) ; ( ii ) the D . melanogaster Wolbachia endosymbiont reference genome ( GenBank ID: AE017196 ) ; and ( iii ) an equivalently-sized ( 1 . 2 Mb ) nuclear region randomly chosen from the middle of D . melanogaster chromosome arm 3L ( GenBank ID: NT037436; positions 10000000–11200000 ) . We used the chrU version of the mtDNA sequence as our reference since it represents the true mtDNA sequence from the D . melanogaster y1 , cn1 , bw1 , sp1 strain [85] , not the composite mtDNA sequence provided with the Release 5 genome sequence ( GenBank ID: NC_001709 ) . Since not all genomes or runs had paired-end data available , reads were mapped in single-ended mode for consistency . Statistics of total mapped read count and total sequence length per strain and reference file were calculated using SAMtools to ensure that all reads from the input fastq were accounted for in the BAM files . Variant base calling followed a standard SAMtools version 0 . 1 . 16 pileup pipeline [84] . Individual strain consensus fastq sequences were generated where minimum and maximum read depths were set to 10 and 100 , respectively , using pileup2fq . pl , and converted to fasta using a custom PERL script . Insertions relative to the reference sequence were excluded and deletions relative to the reference sequence were coded as N's . Where necessary , individual consensus sequences were extended by adding N's to the 3′ end or deleting nucleotides from the 3′ end to produce consensus sequences with the same length as the reference sequence . Wolbachia infection status was determined automatically by calculating the mean depth of coverage of the assembly and breadth of coverage of the consensus sequences . Depth of coverage at each nucleotide in the reference sequence was estimated from BAM files using the genomeCoverageBed utility from BEDtools version 2 [86] , and mean depth of coverage was calculated from BEDtools output by a custom PERL script . Breadth of coverage is defined as the proportion of nucleotides with non-N base calls in the consensus sequences and was calculated using a custom BioPerl-enabled PERL script [87] . A line was scored as “infected” when breadth of coverage was greater than 90% of the Wolbachia genome and mean depth of coverage was greater than one . Conversely , when a consensus sequence covered less than 90% of the Wolbachia genome and mean coverage was less than one , a line was scored as “uninfected . ” Infection status and other metadata for each strain can be found in Dataset S1 . DNA was extracted from pools of approximately 20 individuals from each DGRP fly line using a method based on Chelex 100 resin [88] . We used a diagnostic PCR to test for the presence of the Wolbachia wsp gene using the primers wsp81F ( 5′-tgg tcc aat aag tga tga aga aac-3′ ) and wsp691r ( 5′-aaa aat taa acg cta ctc ca-3′ ) . The conditions for this diagnostic reaction were 35 cycles of 94°C for 15 seconds , 55°C for 30 seconds and 72°C for 1 minute . Each Wolbachia PCR was repeated twice to check that the results were consistent . The success of DNA extraction was confirmed using the CHK_F-CHK_R and Doc1420_F-CHK_R primer pairs that amplify Drosophila nuclear genomic DNA [89] . The conditions for this control reaction were 35 cycles of 94°C for 15 seconds , 55°C for 30 seconds and 72°C for 30 seconds ) . Multiple alignments were constructed simply by concatenating individual reference-based fasta consensus sequence files . For these analyses , we also included mitochondrial and Wolbachia reference sequences to place them in the context of global sequence diversity . All alignment columns that had an N in any strain ( which can represent either a fully ambiguous character or a deletion relative to the reference ) were then removed . The resulting “essentially complete” multiple sequence alignments were then converted to Phylip format using Seqret ( http://emboss . sourceforge . net/ ) and used to reconstruct phylogenies with RAxML version 7 . 0 . 4 [90] . Maximum likelihood tree searches were conducted using a general time reversible ( GTR ) model of nucleotide substitution with Γ rate heterogeneity , with all model parameters estimated by RAxML . Trees were inferred using a combined approach , with an initial 100 bootstrap replicates and a full ML search for the best-scoring tree , using the rapid bootstrap algorithm [91] . The best-scoring ML trees were visualized and annotated in FigTree version 1 . 3 . 1 ( http://tree . bio . ed . ac . uk/software/figtree ) . Strain identifiers in major clades were selected using Hypertree version 1 . 2 . 2 [92] . Bootstrap maximum likelihood trees in Newick format can be found in Dataset S2 . We predicted the presence or absence of IS5 transposons in Wolbachia genomes at two loci ( WD0516/7 and WD1310 ) defined by [35] to be diagnostic for Wolbachia genotypes . To do this we used a BLAT-based mapping strategy to identify transposable element flanking sequences similar to that reported in [55] . The presence of an IS5 insertion site at WD0516/7 ( AE017196:507322–509812 ) and absence of an IS5 at WD1310 ( AE017196:1251363–1252108 ) indicated a wMel-type Wolbachia strain ( wMel or wMel2 ) , while the converse configuration indicated a wMelCS-type strain ( wMelCS or wMelCS2 ) . If IS5 is absent from both loci , then the infecting strain is predicted to be the wMel3 genotype [35] . Using this procedure , we were able to identify both wMel-type and wMelCS-type strains in our data , but the rare wMel3 was never observed . We extracted sequences corresponding to the cytochrome c oxidase subunit I ( COI ) gene ( ChrU: 5 , 290 , 184–5 , 290 , 738 ) from our mtDNA assemblies and merged them into a previously reported multiple alignment of COI sequences for numerous global populations [19] . Haplotype analysis was then conducted on the resulting alignment in DnaSP version 5 [93] . Sequences in our dataset that were found to be in previously identified haplotypes were given haplotype designations according to Nunes et al . [19] . New haplotypes were numbered sequentially starting from 20 , after the highest haplotype number ( 19 ) identified in [19] . To obtain a dated genealogy from the Wolbachia multiple alignment , we performed a Bayesian molecular evolutionary analysis using BEAST v . 1 . 7 . 1 [94] , [95] incorporating phylogeographic data [96]–[98] . This analysis assumed a three-region phylogeographic model , with each strain labelled as coming from Africa , Europe or North America . We modelled the locations of the ancestral strains over the tree using a continuous-time Markov chain of these three locations , with a non-reversible infinite rate matrix parameterizing the transitions between them , and therefore allowing the rate of transmission from Africa to Europe to differ from the rate of transmission from Europe to Africa , for example [96] , [97] . We chose a constant-population-size coalescent prior for the relative node heights , and all other priors were set at their default values as assigned by the software BEAUti [95] . We note that using an exponentially-changing population-size coalescent prior for the relative node heights made no qualitative difference to our results . To check convergence of the posterior distributions of model parameters , we ran two independent MCMC chains , visualizing results in the program Tracer v1 . 5 , before discarding burn-in as appropriate . For Bayesian phylogenetic analyses , we used an annotated version of the multiple alignments of Wolbachia and mtDNA sequences from infected strains ( omitting reference sequences ) that labelled all alignment columns as one of 5 classes of site: 1st codon position , 2nd codon position , 3rd codon position , noncoding RNA gene , or noncoding DNA intergenic region . We excluded all sites with gaps or fully ambiguous characters in any strain from this annotated alignment ( as above ) and then concatenated Wolbachia and mitochondrial genomes into a single combined sequence from each infected strain . We next performed an initial Bayesian analysis to identify classes that showed poor convergence because of low number of informative sites , which were then removed from the analysis . The final dataset included five rate categories: ( i ) mitochondrial first and second codon positions , ( ii ) mitochondrial third positions , ( iii ) Wolbachia first and second codon positions , ( iv ) Wolbachia third codon positions , and ( v ) Wolbachia noncoding DNA . Following [99] , each of these five rate categories was assigned its own HKY+Γ model of molecular evolution . BEAST XML files and maximum clade consensus trees in Newick format can be found in Dataset S3 . To determine whether Wolbachia and mitochondrial genomes have the same evolutionary history , we implemented a Bayesian incongruence test similar to that of [100] using BEAST v . 1 . 7 . 1 . Specifically , we compared the fit to our data of one- and two-topology models , first with the Wolbachia and mitochondrial alignments constrained to have the same dated topology , then allowing Wolbachia and mitochondrial partitions to have distinct topologies and node ages . All models and partitions were as described above , with the exception of the prior temporal information , which applied to mitochondrial sites only , and so provided no constraints on the Wolbachia data in the two-topology analysis . Accordingly , for the congruence test we set a prior on the root age that applied to all partitions . We chose this prior to correspond loosely to the results of the full dating analysis , and so specified a normal distribution of mean 76 , 287 and standard deviation 10 . To compare the fit of the one- and two-topology models , we computed Bayes Factor values from the difference in log marginal likelihood values estimated using the harmonic mean of the log likelihoods using Tracer v1 . 5 and calculated Akaike's information criterion through MCMC values using the method described in [46] . BEAST XML files for the congruence test can be found in Dataset S3 . Levels of polymorphism for both Wolbachia and mtDNA were estimated as π [101] and θw [102] based on the total number of mutations using Variscan version 2 . 0 . 2 [103] . To test if the frequency spectrum of polymorphisms conformed to predictions of the standard neutral model of molecular evolution , we calculated Tajima's D [104] using Variscan from the DGRP sample . Significance levels were based on 10 , 000 coalescent simulations using ms [105] assuming no recombination and conditioned on the number of variable sites [106] . Tables of variable sites from alignments with indels and fully ambiguous sites removed can be found in Dataset S4 . To test whether there is genetic subdivision in the mtDNA of infected and uninfected flies within a population , we used the mitochondrial genomes to calculated a variant of the Kst statistic [43] between infected and uninfected flies . The genetic distance between mitochondrial genomes was calculated as the patristic distance on the mtDNA genealogy ( inferred using a strict molecular clock in BEAST as described above for the concatenated alignment , but removing Wolbachia partitions ) , and Kst was calculated between the infected and uninfected flies within each population . We assessed the significance of this statistic by permuting the infection status of the flies within each population ( defined as samples collected from the same latitude and longitude ) . | Host–microbe interactions play important roles in the physiology , development , and ecology of many organisms . Studying how hosts and their microbial symbionts evolve together over time is crucial for understanding the impact that microbes have on host biology . With the advent of high-throughput sequencing technologies , it is now possible to obtain complete genomic information for hosts and their associated microbes . Here we use whole-genome sequences from ∼300 strains of the fruitfly Drosophila melanogaster to reveal the evolutionary history of this model species and its intracellular bacterial symbiont Wolbachia . The major findings of this study are that Wolbachia in D . melanogaster is inherited strictly through the egg with no evidence of horizontal transfer from other species , that the genealogies of Wolbachia and mitochondrial genomes are virtually the same , and that both Wolbachia and mitochondrial genomes show evidence for a recent incomplete global replacement event , which has left remnant lineages in North America , Europe , and Africa . We also use the fact that Wolbachia and mitochondrial genomes have the same genealogy to estimate the rate of molecular evolution for Wolbachia , which allows us to put dates on key events in the history of this important host–microbe model system . |
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Chikungunya virus causes mosquito-transmitted infection that leads to extensive morbidity affecting substantial quality of life . Disease associated morbidity , quality of life , and financial loss are seldom reported in resources limited countries , such as Bangladesh . We reported the acute clinical profile , quality of life and consequent economic burden of the affected individuals in the recent chikungunya outbreak ( May to September 2017 ) in Dhaka city , Bangladesh . We conducted a cross-sectional study during the peak of chikungunya outbreak ( July 24 to August 5 , 2017 ) to document the clinical profiles of confirmed cases ( laboratory test positive ) and probable cases diagnosed by medical practitioners . Data related to clinical symptoms , treatment cost , loss of productivity due to missing work days , and quality of life during their first two-weeks of symptom onset were collected via face to face interview using a structured questionnaire . World Health Organization endorsed questionnaire was used to assess the quality of life . A total of 1 , 326 chikungunya cases were investigated . Multivariate analysis of major clinical variables showed no statistically significant differences between confirmed and probable cases . All the patients reported joint pain and fever . Other more frequently reported symptoms include headache , loss of appetite , rash , myalgia , and itching . Arthralgia was polyarticular in 56 . 3% of the patients . Notably , more than 70% patients reported joint pain as the first presenting symptom . About 83% of the patients reported low to very low overall quality of life . Nearly 30% of the patients lost more than 10 days of productivity due to severe arthropathy . This study represents one of the largest samples studied so far around the world describing the clinical profile of chikungunya infection . Our findings would contribute to establish an effective syndromic surveillance system for early detection and timely public health intervention of future chikungunya outbreaks in resource-limited settings like Bangladesh .
Chikungunya is a mosquito-borne ( Aedes species ) , self-limiting , febrile illness with severe debilitating arthropathy caused by chikungunya virus ( CHIKV ) . This virus was first identified during an epidemic of febrile polyarthralgia in Tanzania in 1953 [1] . Since then , CHIKV has been reported to cause several large-scale outbreaks in Africa , India , Southeast Asia , Western Pacific and Americas [2–4] . Before 2000 , chikungunya outbreaks were mostly sporadic and limited . But thereafter , the virus has been frequently causing severe forms of epidemics imposing heavy economic burden and productivity loss [5 , 6] . The CHIKV outbreaks are characterized by a sudden disappearance for a considerably long period of time from a particular geographic area before re-emergence . As such , chikungunya has re-emerged in devastating form of epidemics in and around the Indian Ocean in 2005 , nearly after 30 years of quiescence [7] . In Bangladesh , the first recognized outbreak of chikungunya was reported in 2008 in two villages in the northwest part of the country adjacent to Indian border [8] . Two small-scale outbreaks were documented in rural communities in 2011 [9] and 2012 [10] . Dhaka , the capital of Bangladesh , is one of the most densely populated cities in the world with approximately 18 million inhabitants [11] , have experienced a large-scale chikungunya outbreak in 2017 . Mainstream local media outlets have extensively covered the epidemic [12 , 13] . Despite a lower middle-income country , Bangladesh has achieved remarkable progress in reducing maternal and child mortality by improving primary healthcare access [14] . However , country’s overall public healthcare facilities are not adequate . Approximately 70% patients prefer private clinics/hospitals for seeking medical advice [15 , 16] . It allocates only US$31 per capita on health expenditure [17] . Thus , continuous surveillance of any unprecedented disease like chikungunya is a huge challenge in Bangladesh since most public hospitals lack modern diagnostic facilities and medical documentation system [18] . Therefore , data from public medical facilities alone cannot provide an overall picture of an outbreak resulting in underreporting of cases . A state-run institute named Institute of Epidemiology , Disease Control and Research ( IEDCR ) has monitored the recent chikungunya outbreak based on RT-PCR data sourced from three diagnostic laboratories including the institute itself and two other private facilities . According to IEDCR’s own data on 1003 RT-PCR confirmed cases , the peak of the outbreak was between early May and end of July 2017 [19] . Also , according to their newsletter , about 87 . 3% ( 12060 out of 13814 cases ) of suspected chikungunya patients visited three public hospitals ( DMCH , ShMCH , SSMCMH ) in addition to IEDCR for treatment . Taking other public hospitals and more than 2000 registered private clinics/hospitals in the Dhaka metropolitan are into account [20 , 21] , arguably , there was a widespread chikungunya outbreak in the city . CHIKV infection has emerged as a major public health concern since it often affects a large proportion of the population within an outbreak area and causes considerable pain , distress , and anxiety as well as significant economic burden due to severe clinical manifestations [22–25] . CHIKV infection is usually non-fatal and the sign of clinical symptoms resolves over time . The clinical severity of this disease is associated with reduced quality of life ( QoL ) . It is found that QoL drops dramatically when patients are severely affected , especially during the early phase of chikungunya infection lasting for one or two weeks , while prolonged musculoskeletal manifestations ( chronic arthralgia ) may last for months to years [23] . A study , for instance , has observed a 20-fold reduction of QoL in the non-recovered CHIKV positive group while 5-folds reduction was in recovered group as compared to healthy control group [26] . Poor QoL resulting from CHIKV is linked to immediate and long-term economic burden [24 , 27 , 28] . Due to lack of health insurance system and other socio-economic factors , it is challenging to determine the actual economic burden of chikungunya outbreaks in developing countries . It appears that the actual economic loss is often overlooked because of non-fatality of the disease . CHIKV outbreaks have caused significant financial burdens on society level in India and across Latin America [24 , 29–31] . The impact on household finance was substantial , particularly during acute phase of infection when QoL was found to be at the lowest level . The poor segment of the population generally bears the economic burden most . Consequently , it may aggravate poverty . Long-term arthralgia secondary to CHIKV has also reported to cause significant economic impact [24] . A higher health care utilization has been reported for patients up to six years after acute infection of CHIKV [27] . In this study , we are interested to find out the answers for two primary research questions: a ) the impact of clinical severity on QoL during the acute phase , and b ) the impact of treatment cost on the economic conditions of CHIKV patients during acute phase .
In our study , we investigated patients who experienced typical clinical symptoms of CHIKV infection [32] ( febrile illness with arthralgia/arthritis ) during the peak of recent Dhaka-outbreak ( May-July , 2017 ) . Patients with a positive RT-PCR or serological test ( CHIKV positive but not dengue ) were categorized as confirmed cases . For probable case , we followed the recommendation of the World Health Organization ( WHO ) [33] and standardized European case definition [34] where during an established outbreak , a patient meeting both clinical and epidemiological criteria are considered as probable case . Thus , patients diagnosed by medical practitioners based on characteristic clinical features of CHIKV infections without a RT-PCR or serological tests were documented as probable cases . As discussed earlier , because of the magnitude of the outbreak , the local health authority ( IEDCR ) monitored the outbreak and released health bulletin on a daily basis . There was no sign or warning of any other arboviruses related outbreaks ( such as dengue ) by IEDCR . Unlike developed countries , our healthcare system lacks a functional referral system [18] and organized medical record-keeping system . Therefore , we reached out to every enrolled patient to verify the documents of CHIKV diagnosis . Scanned copies of the laboratory test results were collected from the confirmed cases as a proof . Patients claimed to have CHIKV infection without physicians’ confirmation ( clinical and or laboratory ) were excluded from the study . It is important to note that molecular and serological diagnostic tests for CHIKV infection were available only in a handful of diagnostic facilities ( mostly private ) . The cost of diagnostic test was relatively high considering the average income of city dwellers . In this context , given the insignificant mortality related to CHIKV , the government health regulatory authority ( DGHS , Bangladesh ) discouraged ( non-obligatory instruction ) suspected patients for laboratory tests in order to turn down the panic among city dwellers [35] . As a result , we had more probable cases than confirmed cases . A cross-sectional study was conducted between July 24 and August 5 , 2017 , to investigate the clinical profiles , economic burden , and quality of life of chikungunya affected individuals . Biomedical Research Foundation ( BRF ) , Bangladesh took the initiative to study the impact of this widespread outbreak . The study was conducted by a team of 111 volunteer researchers comprising of clinicians , public health professionals , statisticians as well as undergraduate and postgraduate students . Considering the urgency of the issue and time-sensitive nature of the outbreak , we relied entirely on voluntary services rather than financial support from external sources . Participants were conveniently selected by the members of the research team from within their known circles ( e . g . , friends , friends of friends ) living in Dhaka city , where the outbreak has occurred . Data were collected via face-to-face interview using a structured questionnaire . Enumerators were given one-day training on the use of the questionnaire for face-to-face interviews . Respondents were asked about clinical symptoms , loss of productivity due to missing work hours , and quality of life during their first two-weeks of symptom onset . Data on economic variables were collected from only the earning members of the chikungunya affected families . Descriptive and inferential statistical procedures were used to ascertain the clinical profile of CHIKV infection and its impact on quality of life and economic well-being . Descriptive statistics were reported as percentage and means when applicable along with standard deviation . The intensity of joint pain was evaluated by a 10-point numerical rating scale ( NRS ) , which was categorized as mild ( score between 1 and 3 ) , moderate ( score between 4 and 6 ) and severe ( score between 7 and 10 ) in the analytic stage [36] . To determine the impact of chikungunya infection on quality of life ( QoL ) , World Health Organization ( WHO ) endorsed quality of life questionnaire ( brief version ) , known as WHOQOL-BREF , was used . We used the validated Bengali version of the questionnaire [37] . The responses from WHOQOL-BREF questionnaire were analyzed as per the recommendation and scoring guidelines [38] . Cronbach's alpha coefficient was calculated to check the internal consistency of scores . For assessing the impact of CHIKV on the economic well-being of a family , a 10-point rating scale questions were used to capture granular responses . Missing cases were excluded from bivariate analysis . Completed data collection forms were scrutinized by data collection supervisors . Follow up calls were made within two days of data collection to 10% randomly selected participants to ensure the authenticity of the responses . Verified data were entered and subsequently managed using REDCap electronic data capture tool hosted at BRF [39] . Data were analyzed using R statistical software . For testing association between categorical data , Pearson’s chi-square test was used , and Yate’s correction for continuity was applied where appropriate . We performed independent sample t-test when comparing means of continuous variables . A two-tailed p-value smaller than 0 . 05 was considered statistically significant . The study was approved by the Ethical Review Committee ( ERC ) of Bangladesh University of Health Sciences ( Memo no: BUHS/BIO/EA/17/077 ) . As approved by ERC , verbal and/or written informed consent was obtained from every participant as per their convenience . Trained enumerators first approached and explained consent form to the prospective participants and study questionnaire was shared or discussed with them . After obtaining consent ( oral and/or written ) , participants were registered for face-to-face interview . Some respondents could not sign their names , in which case the questionnaire was marked indicating a case with verbal consent . All adult participants 18 years or above provided informed consent . Although children ( 18 years and below ) were included in the survey , no child was interviewed . Parents or guardians provided information about children’s clinical symptoms . Children were also not included in the economic wellbeing and quality of life section of the study .
A total of 1 , 474 patients were enrolled in our study . After rigorous verification and cross-checking , 148 cases ( 10% ) were discarded due to the incompleteness of data . Finally , of all 1326 verified cases , 18% ( 239/1 , 326 ) constituted confirmed cases ( 214 cases were serologically confirmed , while 25 cases by RT-PCR ) and 82% ( 1087/1326 ) were probable cases . The geospatial distribution of 855 patients ( who provided address ) out of 1326 showed that our study represented most of the administrative zones ( 20 out of 25 ) of Dhaka City ( Fig 1 ) . The mean age of the participants was 33 . 74 years ( SD = 14 . 83 ) and male to female ratio was 1 . 33:1 . Children ( <15 years of age ) represented 6 . 4% of the study subjects , 42 . 5% were adolescents and young adults ( AYA , 15–29 years ) , 44 . 3% were adults ( 30–59 years ) and 6 . 8% of the cases aged over 60 years ( Table 1 ) . About 43 . 7% of the participants were graduates , 33 . 3% completed high school and 4 . 9% had no education . A total of 339 ( 25 . 6% ) participants had comorbidities ( Table 1 ) . Seventy-six patients ( 5 . 7% ) were hospitalized with chikungunya infection , of which 35 were confirmed cases . Over 90% of the respondents reported high-grade fever with an average maximum temperature of 103 . 6°F ( SD = 0 . 89 ) . The mean duration of fever was 4 . 88 days ( SD = 2 . 7 ) . As the first clinical symptom , 74 . 6% ( n = 1326 ) of the respondents experienced pain ( joint and/or muscle pain ) prior to fever ( Table 2 ) . This unique clinical feature was consistent irrespective of age and sex of the patients . About 85% of the patients ( both confirmed and probable cases ) complained severe pain with a mean pain score of 8 . 3 ( out of 10 ) ( SD = 1 . 63 ) , and about 65% patients suffered more than 10 days during the acute phase ( Table 3 ) . Arthralgia was oligoarticular ( 2–4 joints ) in 40 . 1% , and polyarticular ( >5 joints ) in 56 . 3% of the patients . Peripheral small joints were the most common site of involvement . The joint pain was symmetrical in 64 . 8% of the patients ( Table 3 ) . Joint swelling and skin rash were significantly higher among confirmed cases ( 62 . 6% and 78 . 2% respectively ) . Other common symptoms reported were redness of eyes ( over 56 . 5% ) , nausea ( 60% ) , oral ulcer ( 31 . 2% ) , diarrhea ( 25% ) , and edema ( 18% ) ( S1 Table ) . Bivariate analysis showed that the severity of some clinical symptoms was gender ( S1A Fig ) and age group specific ( S1B–S1E Fig ) . For other clinical symptoms analyzed for Dhaka outbreak , see S1 Table and S2 Table . Multivariate analysis of 20 major clinical variables between confirmed and probable cases showed no statistically significant differences between these two patient categories except for rash and swollen joint ( S3 Table ) . This finding suggests that physicians were able to diagnose chikungunya cases effectively based only on typical clinical features during the outbreak . We estimated the overall treatment cost ( including consultation fee , cost of laboratory tests , medicine , transport and special food ) during the acute phase of chikungunya . We found that confirmed cases had to spend around BDT 8 , 192 ( SD = 12 , 127 . 63 ) on an average compared to BDT 2 , 122 ( SD = 3 , 422 ) for probable cases which are equivalent to $99 . 3 ( SD = 147 ) compared to $26 ( SD = 41 . 5 ) , respectively . Approximately 70% ( n = 1 , 302 ) of the patients lost more than 7 productive days while 29 . 6% of them lost more than 10 days in the acute phase of the disease . Our analysis relying on 424 family heads suffering from chikungunya showed that economic impact was most prominent in low-income ( < 10 , 000 BDT ) categories ( Fig 2 , S4 Table ) . We assessed the quality of life ( QoL ) of 1 , 216 respondents who were 18 years and older . The Cronbach's alpha of WHOQOL-BREF was adequate ( 0 . 89 ) for all 26 questions . The average score was highest in the environmental health domain ( mean = 11 . 43 , SD = 2 . 52 ) followed by psychological domain ( mean = 10 . 03 , SD = 2 . 75 ) , social relationship domain ( mean = 10 . 02 , SD = 2 . 94 ) , and physical domain ( mean = 8 . 32 , SD = 2 . 33 ) ( S5 Table ) . Pearson’s correlation coefficient showed a positive linear relationship between Q1 ( represents overall QoL of WHOQOL-BREF ) and all four domains separately ( S6 Table ) . The strongest correlation was found between Q1 and the physical domain ( r = 0 . 46 ) , followed by the psychological domain ( r = 0 . 36 ) . Overall 83 . 2% patients responded ‘very low’ or ‘low’ on Q1 indicating a catastrophic impact on the quality of life during acute-phase CHIKV infection . Average Q1 scores were significantly affected by monthly income ( p = 0 . 0032 ) , marital status ( p<0 . 0001 ) , age ( p<0 . 0001 ) , employment type ( p<0 . 0001 ) . Patients with severe arthralgia had significantly lower QoL compared to mild to moderate arthralgia ( p<0 . 0001 ) during the acute phase ( Fig 3 ) .
The first ever large-scale outbreak of chikungunya in Dhaka led to a rapid spread of infection across the city . To our knowledge , we have analyzed one of the largest samples of 1 , 326 cases to demonstrate the detail clinical profile of acute chikungunya infection . Our study also sheds light on the extent of economic impact on victim families and quality of life of the patients . Both dengue and chikungunya viruses are transmitted by the same mosquito vector and often difficult to differentiate clinically . As discussed earlier , there was no warning of dengue outbreak by health authorities during our study period . Moreover , dengue incidences have been declining since the first large-scale dengue outbreak occurred in Dhaka in 2000 [40] . A seroprevalence study showed that around 80% individuals of Dhaka city had a past history of dengue infection [41] . Furthermore , a serology-based study revealed that approximately 6 . 1% ( n = 271 ) of acute clinical cases ( <7 days ) of chikungunya was positive for dengue infection during recent chikungunya outbreak . Nearly 3 . 1% of these dengue positive patients were co-infected with CHIKV ( obtained from personal communication; Professor Md . Akram Hossain , National Institute of Preventive & Social Medicine , Dhaka , Bangladesh ) . On multivariate analysis , all clinical symptoms ( except skin rash and swollen joints ) were similar among confirmed and probable cases ( Table 2 , Table 3 and S3 Table ) . Compared to other studies conducted on self-reported [22 , 42–44] or hospitalized patients [45–47] , a higher frequency of rash ( 69 . 6% ) and swollen joints ( 52 . 1% ) were documented in the present study . In most reported outbreaks , sudden onset of fever was found to be the initial clinical symptom of chikungunya . However , joint ( and/or muscle ) pain preceded fever in more than 70% of patients in our study irrespective of age and sex ( Table 2 ) . In Kerala outbreak , joint pain was reported as the initial symptom in 17% patients , which varied among different age group reaching up to 77% in patients over 60 years [48] . This initial symptom of pain could be considered as the hallmark of chikungunya infection in Dhaka outbreak . Severe arthropathy is the most consistent clinical feature of chikungunya infection . In our study , all patients experienced joint pain . Polyarthralgia was documented in about 56 . 3% of the patients while oligoarthralgia was present in 40 . 1% cases . In the present study , ankle ( 82 . 6% ) and wrist ( 74 . 8% ) were the most affected joints . In the acute phase , the frequency of incapacitating pain involving certain peripheral joints ( ankle 82 . 6% , feet 63 . 8% , wrist 74 . 8% , fingers 73 . 2% and knee 74 . 5% ) was found to be similar with that of French soldiers cohort and another study conducted in La Reunion [22 , 44] ( S2 Table ) . In contrast , lower frequencies were reported in India and Suriname [42 , 45 , 48] . Over 85% of the patients ( n = 1 , 326 ) experienced severe pain with a median NRS score of 8 . 3 throughout the acute phase . This finding is consistent with a study carried out in La Reunion [44] . About 70% of our patients faced problem in doing routine daily activities and about 65 . 7% reported sleep disturbance due to severe arthropathy ( Table 3 ) . The actual impact of chikungunya fever on daily life activities might be much higher than reported here because of poor health literacy in developing countries . Moreover , socio-culturally , patients often fail to explain their health conditions clearly; rather express them in terms of overall satisfaction from a spiritual standpoint . What it means is that even though they are clinically suffering , people would report that they are alright . The overall severity and the extent of arthralgia related manifestations suggest an aggressive strain of chikungunya virus probably circulated in Dhaka . The sequencing of the viral strain is warranted to find out the lineage of chikungunya virus . The severity of certain clinical manifestations of chikungunya might depend on several factors including age , gender , immune status , genetic predisposition and co-morbid conditions [49] . Bivariate analysis showed that children ( <15 years ) tended to have a higher proportion of oligo-arthralgia and skin rash; while morning stiffness , severity , and duration of pain were proportionally lower among children as compared to other age groups . Joint swelling was most commonly noted in elderly patients ( 60+ years ) , while the severity of pain was highest among adults ( 30–59 years ) . In our study , the number of male participants was higher than female participants . Female patients experienced a higher incidence of skin rash , itching , joint-swelling compared to their male counterparts ( S1 Fig ) . We found that chikungunya infection caused significant loss of productivity due to absenteeism from job , household work and school . Over 95% of the respondents ( including confirmed and probable cases , n = 1 , 302 ) were mostly confined to sickbed . As a consequence , 29 . 6% of the patients lost more than 10 days of productivity during the acute phase ( Fig 2 ) . Notably , no national health insurance system exists in Bangladesh and therefore , all treatment costs were considered as out-of-pocket expenditures of the patients . Considering socio-economic conditions , a significant amount of money had to be spent for treatment purposes . We have not estimated the overall economic burden of the loss of productivity due to this chikungunya outbreak . However , we have attempted to paint a picture of the economic impact on victim families based on responses to a rating scale . Our analysis suggests that low income ( <$303 per month ) families are more likely to face significant economic pressure . Particularly , families of daily workers were the worst hit while there was no significant impact on the families in the higher income category ( >$606 per month ) . Anecdotal evidence suggests that many daily or menial workers lost their jobs due to long absenteeism . It is indispensable to estimate the overall disease burden through systematic epidemic studies to determine the real economic burden of an outbreak like this . Majority of the patients in our study reported low to very low overall quality of life . Although males had slightly higher average scores than females , the difference was not statistically significant ( p = 0 . 138 ) . QoL was significantly lower in patients with severe pain compared to those with moderate pain . Elderly patients reported lower average QoL scores compared to <60 years . In particular , patients in the highest income bracket ( BDT 50 , 000 per month; >$606 per month ) reported the lowest average overall score ( 1 . 66 , p = 0 . 003 ) . This could be due to the fact that most of the respondents in the higher income group are older . Overall QoL scores differed significantly between different job categories ( Fig 3 ) . Not surprisingly , students reported the highest average score ( 2 . 0 ) which is consistent with our findings that younger patients who are mostly students , reported higher scores compared to their older counterparts . The jobless and dependents showed an overall score of 1 . 93 . Interestingly , housewives reported higher QoL score ( 1 . 86 ) compared to those of businessmen ( 1 . 78 ) and service holders ( 1 . 70 ) . It would be of interest to explore the interplay between gender and various socio-cultural factors in terms of quality of life . Our study has some limitations . First , the participants were purposively selected using social connections . Even though it is not uncommon to adopt a non-random sampling design in such circumstances ( considering Bangladesh being one of the healthcare-resource-limited countries ) , the results should be interpreted after taking this into consideration . Second , the representation of confirmed cases was relatively low ( 18% ) . This could be explained by the limited availability of diagnostic facility along with high cost of the tests during CHIKV outbreak . Third , because of the retrospective data collection scheme , there could be recall bias . However , since our study was conducted during the very end of the peak of outbreak , it is likely that the recall bias was minimum . Lastly , it is possible that some participants may have overvalued some clinical symptoms due to massive media coverage of the outbreak . Our work represents one of the largest samples ( n = 1 , 326 ) studied so far around the world describing the clinical profile of chikungunya infection . This study demonstrates the severity of clinical symptoms during the acute phase and how it has impacted on productivity and quality of life of the affected individuals . We found joint pain prior to fever as a unique symptom in the Dhaka ( 2017 ) outbreak . A possible reason could be a novel viral strain that warrants future molecular investigations . Our findings support that during an established outbreak , CHIKV patients can effectively be identified using a set of easily recognisable clinical criteria ( i . e . syndromic approach ) without lab confirmation; an approach also suggested by others [50 , 43]for resource-constrained developing countries . We believe our study would play a pivotal role in devising an effective syndromic surveillance system for CHIKV in Bangladesh that would allow early detection of future outbreaks and timely public health interventions . | A major outbreak of chikungunya virus occurred for the first time in Dhaka , Bangladesh between May and September 2017 . In this study , a face-to-face interview with a structured questionnaire was conducted to collect data to investigate the clinical symptoms , quality of life , and economic aspects of 1 , 326 chikungunya patients during the first two weeks of infection . The severity of the disease was similar to previously reported severe outbreaks elsewhere but joint pain prior to fever emerged as a unique symptom in the Dhaka outbreak . This unique clinical feature was consistent across age and sex of the patients . Some clinical symptoms varied with age . For instance , a higher proportion of skin rash were found among children ( under 15 ) while morning stiffness , severity , and duration of pain were proportionally higher among other age groups . Joint swelling was most commonly noted in elderly patients ( 60+ years ) . About 83% of the patients reported low to very low overall quality of life ( QoL ) during first two weeks of chikungunya infection . Elderly patients reported lower average QoL scores compared to <60 years . Interestingly , housewives reported higher QoL score compared to those of businessmen and service holders . In particular , patients in the highest monthly income category bracket ( BDT 50 , 000 per month; >$606 per month ) reported the lowest average overall score . Nearly 95% of the patients have mostly confined to sickbed and approximately 30% of them lost more than 10 days of productivity due to severe arthropathy . Our study would contribute to establishing an effective syndromic surveillance system for early detection and timely public health intervention of future chikungunya outbreaks in resource-limited countries like Bangladesh . |
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Leptospirosis is the most common zoonotic disease worldwide . The diagnostic performance of a serological test for human leptospirosis is mainly influenced by the antigen used in the test assay . An ideal serological test should cover all serovars of pathogenic leptospires with high sensitivity and specificity and use reagents that are relatively inexpensive to produce and can be used in tropical climates . Peptide-based tests fulfil at least the latter two requirements , and ORFeome phage display has been successfully used to identify immunogenic peptides from other pathogens . Two ORFeome phage display libraries of the entire Leptospira spp . genomes from five local strains isolated in Malaysia and seven WHO reference strains were constructed . Subsequently , 18 unique Leptospira peptides were identified in a screen using a pool of sera from patients with acute leptospirosis . Five of these were validated by titration ELISA using different pools of patient or control sera . The diagnostic performance of these five peptides was then assessed against 16 individual sera from patients with acute leptospirosis and 16 healthy donors and was compared to that of two recombinant reference proteins from L . interrogans . This analysis revealed two peptides ( SIR16-D1 and SIR16-H1 ) from the local isolates with good accuracy for the detection of acute leptospirosis ( area under the ROC curve: 0 . 86 and 0 . 78 , respectively; sensitivity: 0 . 88 and 0 . 94; specificity: 0 . 81 and 0 . 69 ) , which was close to that of the reference proteins LipL32 and Loa22 ( area under the ROC curve: 0 . 91 and 0 . 80; sensitivity: 0 . 94 and 0 . 81; specificity: 0 . 75 and 0 . 75 ) . This analysis lends further support for using ORFeome phage display to identify pathogen-associated immunogenic peptides , and it suggests that this technique holds promise for the development of peptide-based diagnostics for leptospirosis and , possibly , of vaccines against this pathogen .
Leptospirosis is the most common and geographically widespread zoonotic disease worldwide . It is caused by pathogenic strains of the spirochete Leptospira spp . Humans are accidental hosts who get infected through direct contact with body fluids of carrier animals or with contaminated water and soil [1] Human leptospirosis is a major problem in countries of tropical and , less often , subtropical climates . Numerous outbreaks have been reported in association with rainy seasons , floods , and recreational and sports activities [2] . Globally , leptospirosis is estimated to occur at a frequency of approximately 1 . 03 million cases per year with a mortality of 5 . 7% [3] . In Malaysia , incidence is estimated to be 1–10 cases per 100 , 000 individuals per year with case fatality rates around 10% [4] . Leptospirosis was designated a notifiable disease in 2011 , and reported incidence increased subsequently , as illustrated by a reported incidence for 2015 of 17 . 5 cases per 100 , 000 individuals , 86 outbreaks , and case fatality rates of 0 . 6% [5] . The microscopic agglutination test ( MAT ) is the serological ‘gold standard’ for diagnosis of leptospirosis . Unfortunately , this test is not only time-consuming and laborious , but it also can only be performed by a reference laboratory with a collection of serovars endemic in the region . To overcome the drawbacks of the MAT , numerous serological assays have been developed that do not depend on the availability of viable leptospires , particularly IgM ELISA tests based on either whole cell extracts or recombinant proteins [6–8] . Other serological approaches that have been described were agglutination , dipstick , and lateral flow assays [8] . Among these serological tests , in-house Leptospira ELISA using whole cell extracts have achieved the highest specificity and sensitivity [9] . Nevertheless , this method requires the standardized preparation of whole cell extracts from serovars most relevant to the geographical region [10] . Phage display is mainly used for antibody generation [11] , but also for the identification of pathogen-associated immunogenic proteins; for the latter , it is based on using fragmented pathogen genomes or cDNA fragments to allow high-throughput screening for novel potential antigens [12–16] . Immunogenic proteins are often identified using 2D-PAGE of proteins from cultivated bacteria followed by mass spectrometry . However , phage display offers clear advantages , notably better performance for the identification of immunogenic proteins below 10 kD , low abundance proteins , and proteins only expressed in host-pathogen interaction [17 , 18] . ORFeome phage display is an improved variant of the phage display technique in which the library quality of fragmented genomes or cDNA fragments is improved by a unique open reading frame enrichment step [19–22] . Diagnostics for use in hot , resource-limiting environments should be inexpensive to produce and stable at ambient temperature . Peptide antigens fulfil both requirements; besides , they offer the added advantage that they can be synthesized in biotinylated form , thus facilitating their use in streptavidin-based diagnostics . In the present study , we have used ORFeome phage display to identify L . interrogans peptides that are immunogenic in humans and can be used to develop relatively inexpensive diagnostics tools for this challenging pathogen .
The study protocol NMRR-14-1687-23346 ( IIR ) involved the use of patients’ sera obtained for clinically indicated diagnostics to be used for research purposes and was approved by the Medical Research & Ethics Committee , Ministry of Health Malaysia , Malaysia . All human biosamples were anonymized . Sera from 34 patients who presented in the year 2015 with symptoms suggestive of acute leptospirosis were obtained from the Public Health Laboratory of Kota Bharu and Kota Kinabalu , Malaysia . The sera were obtained from hospitalized patients after an average of three to seven days of illness and had MAT titers of 1:800 , which was consistent with the acute phase of leptospirosis . Infection with other pathogens , including Dengue virus , was excluded by the respective diagnostics as suggested by clinical history and other laboratory findings . Dengue fever is the most common infectious disease in this region and it may cause false positive results in Leptospira spp . ELISA tests due to cross-reactivity . All samples were therefore tested for the Dengue virus NS1 protein and anti-Dengue IgM and IgG reactivity and were thus ensured to be negative . The patients presented with fever and one or more of these signs and symptoms: headache , myalgia , arthralgia , conjunctival injection , anuria or oliguria and/or proteinuria , jaundice , pulmonary and/or intestinal hemorrhage , cardiac arrhythmia or failure , skin rash , and gastrointestinal symptoms such as nausea , vomiting , abdominal pain , and diarrhea . Sera from 18 patients were pooled into two groups based on their reactivity to Malaysian strains ( n = 8 ) or WHO reference strains ( n = 10 ) . Two sets of Leptospira-negative control sera were used . The control pool used in S2 Fig was obtained from 7 healthy adult volunteers from Germany with no travel history to Leptospira-endemic countries . The other 16 patient sera were used for the titration ELISAs in the ORFeome procedure . The individual control sera for ELISA validation shown in Fig 2A were obtained from 16 healthy adults of Caucasian origin who participated in an unrelated epidemiological study [23] . Absence of leptospiral seroreactivity in the control sera was confirmed by in-house ELISA with leptospiral culture antigens . Two libraries of Leptospira spp . were constructed using genomic DNA . The first library consisted of five strains of L . interrogans isolated from leptospirosis patients in Malaysia between 2014 and 2015 . The second library consisted of seven WHO reference strains which were obtained from the Leptospirosis Reference Centre ( also known as OIE Reference Laboratory for Leptospirosis , Amsterdam Medical Centre , Amsterdam ) . The strains are listed in Table 1 . Strains from both groups were cultured in Ellinghausen-McCullough-Johnson-Harris ( EMJH ) medium at 30°C for 7–10 days at 250 rpm . Genomic DNA was isolated from pellets of 5 mL culture centrifuged at 8000 x g for 30 minutes ( min ) , using the QiaAmp DNA Mini Kit according to the manufacturer’s instructions ( Qiagen , Hilden , Germany ) . The extracted DNA for each library was mixed and amplified with the illustra™ Ready-To-Go GenomiPhi V3 DNA amplification kit ( GE Healthcare ) according to the manufacturer’s instructions . Twenty μg of DNA from each mixed and amplified genomic library were fragmented by sonication upon extraction . Subsequently , the DNA was concentrated using Amicon Ultra 0 . 5 mL centrifugal filters with a cut-off of 30 kDa . DNA fragments with sizes from 100 to 800 bp were extracted from an agarose gel and the DNA ends were repaired with the Fast DNA End Repair Kit ( Thermo Scientific ) according to the manufacturer’s instructions . 1 . 4 μg of fragmented DNA were then ligated into 1 . 4 μg of the PmeI-digested pHORF3 vector [20] and subsequently transformed into E . coli TOP10F’ ( Invitrogen ) by electroporation . Colony PCR was performed in some of the resulting clones to determine the insert rate of ligation . The library was packaged using Hyperphage [24 , 25] as described before [19 , 20] . By packaging the genomic DNA library with Hyperphage , ORFs are enriched and the resulting oligopeptides are presented on the phage particles for panning . The E . coli XL1-Blue MRF’ containing the library was inoculated into 400 mL 2x YT-GA medium ( 2x yeast-tryptone broth supplemented with 0 . 1 M glucose and 100 μg/mL ampicillin ) to an OD600 <0 . 1 and grown at 37°C , 250 RPM until OD600 ≈0 . 5 . At this point , the culture was infected with Hyperphage ( MOI 1:20 ) for 30 min at 37°C without shaking , and then 30 min under 250 RPM . The culture was then centrifuged , suspended in 400 mL 2x YT-AK medium ( 2x YT containing 100 μg/mL ampicillin and 50 μg/mL kanamycin ) , and phage particles were produced at 30°C and 250 rpm overnight . Cells were then centrifuged for 20 min at 10 , 000 x g , and phage particles in the supernatant were precipitated with 1/5 volume of polyethylene glycol ( PEG ) /NaCl solution ( 20% w/v PEG 6000 ) , 2 . 5 M NaCl ) for 3 hours ( h ) on ice with gentle shaking . Phage particles were then pelleted for 1 h at 10 , 000 x g and suspended in 10 mL phage dilution buffer ( 10 mM TrisHCl pH 7 . 5 , 20 mM NaCl , 2 mM EDTA ) . Remaining bacteria were pelleted by an additional centrifugation step of 10 min at 20 , 000 x g , and the solution was then filtered through a 0 . 45 μm filter to remove residual bacteria . The filtrate was again precipitated with 1/5 PEG/NaCl for 1 h and then centrifuged for 30 min at 20 , 000 x g . Pellets were suspended in 1 mL phage dilution buffer and residual bacteria removed by centrifugation for 1 min at 16 , 000 x g . The final supernatant containing the oligopeptide presenting phages was stored at 4°C . Phage titers were determined as described previously [26] . To check library quality , a number of random E . coli colonies were analyzed by colony PCR and sequenced after packaging with Hyperphage . Therefore , the primers MHLacZPro_f ( 5'-GGCTCGTATGTTGTGTGG-3' ) and MHgIII_r ( 5'-GGAAAGACGACAAAACTTTAG-3' ) were used with the following PCR protocol: 98°C 30 s , 98°C 10 s , 56°C 20 s ( 35 cycles ) , 72°C 60 s and a final extension of 72°C for 2 min . The DNA was separated and analyzed by gel electrophoresis ( Qiaxcel Advanced ) . Additionally , plasmid DNA was sequenced with the primers used for colony PCR to verify the correct inserts and the ORF enrichment after packaging . Two wells of a Maxisorp™ 96 well microplate were coated with 150 μL of goat IgG directed against human IgG , IgA and IgM Fc ( Dianova 109-005-064 , 2 mg/mL , Lot No . 113036 ) diluted in 1:500 phosphate buffered saline ( PBS ) and another six wells were coated with 5 x 1010 CFU Hyperphage in PBS and incubated at 4°C overnight . Subsequently , the coating solutions were removed and the wells were blocked for 30 min with PBS with 0 . 1% Tween , supplemented with 2% ( w/v ) milk powder ( 2% MPBST ) . A pool of twelve patients’ sera was diluted 1:100 in 2% MPBST and pre incubated twice in the Hyperphage coated wells for 1 h to eliminate IgG binding to helper phage . After pre-incubation , the serum pool was incubated for 1 . 5 h in the wells coated with goat anti-human IgG , A , M antibody . The Leptospira spp . phage library ( corresponding to 1 . 1 x 1010 CFU ) was mixed 1:3 with 2% MPBS-T and incubated in the wells with the pooled leptospirosis patients’ sera for 1 . 5 h . Unbound phage and phage with low affinity were removed by stringent washing steps . Three panning rounds were performed , and the wells were washed twice after each step , with one additional wash for the second panning round . After washing , bound phage particles were eluted with 200 μL of 10 μg/mL trypsin in PBS for 30 min at 37°C . Eluted phages of both wells were combined and 10 μL were used for titration . The remaining 390 μL were used to infect 20 mL of an E . coli TOP10F’ culture grown to an OD600 of 0 . 5 . The cells were incubated for 30 min at 37°C and harvested by centrifugation for 10 min at 3250 x g . The pellet was suspended in 250 μL 2xYT-GA . The bacterial suspension was plated onto 15 cm 2xYT-GA agar plates and incubated overnight at 37°C . Colonies were swept off with 5 mL 2xYT-GA medium , then 50 mL of 2xYT-GA medium was inoculated with the bacterial suspension to an OD600 of <0 . 1 and grown to an OD600 of 0 . 5 at 37°C and 250 rpm . For infection , 5 mL of the bacterial culture ( approx . 2 . 5 x 109 cells ) was mixed with Hyperphage infected at an MOI of 1:20 resulting in 5 x 1010 CFU Hyperphage . The suspension was incubated at 37°C for 30 min without shaking and another 30 min at 37°C and 250 rpm . To remove glucose , which inhibits phage expression , the infected cells were harvested by centrifugation for 10 min at 3220 x g . The remaining pellet was resuspended in 30 mL 2x YT-AK and incubated at 30°C and 250 rpm overnight for phage production . The bacterial cells were then pelleted by centrifugation for 20 min at 3220 x g and the remaining supernatant was used to precipitate phage particles with PEG/NaCl ( 20% ( w/v ) PEG 6000 , 2 . 5 M NaCl ) . Thirty mL of supernatant were separated and incubated for 1 h with 6 mL PEG/NaCl on ice with slight shaking on a rocker , followed by centrifugation at 6000 x g for 1 h at 4°C . The phage pellet was resuspended in 500 μL phage dilution buffer ( 10 mM TrisHCl pH 7 . 5 , 20 mM NaCl , 2 mM EDTA ) , centrifuged in a microcentrifuge at 16 , 100 x g for 1 min and the supernatant was used for further panning rounds . For the 2nd and 3rd panning rounds , 150 μL of the amplified phage was used . Eluted phage particles from the 3rd panning round were used for titration without further amplification . Single colonies were then used for single oligopeptide phage production . Single oligopeptide phage clones were produced by inoculating 175 μL 2x YT-GA medium with single colonies from the titration plate in a polypropylene 96-well U-bottom plate ( Greiner bio-one ) . The cultures were incubated at 37°C and 500 rpm shaking overnight . From this plate , 10 μL were used to inoculate another 165 μL 2xYT-GA medium per well , which was incubated at 37°C and 800 rpm for 2 h . Subsequently , the bacteria were infected with 5 x 109 cfu Hyperphage and incubated for 30 min at 37°C without shaking and 30 min at 37°C and 800 rpm . The bacteria were pelleted by centrifugation at 3220 x g for 10 min and the pellets were resuspended in 175 μL/well 2x YT-AK and incubated overnight at 30°C and 800 rpm . The produced phage in the supernatant were transferred to another plate and precipitated with 1/5 volume of PEG/NaCl solution for 1 h at 4°C . Next , precipitated phage particles were pelleted by centrifugation at 3220 x g for 1 h and the pellets dissolved in 150 μL PBS . Remaining bacterial cells were separated by another centrifugation step and the phage-containing supernatants stored in a new plate at 4°C and used for screening ELISA . Two types of ELISA were performed for selection of oligopeptide phage clones . First , a screening ELISA was performed on each genomic library . Oligopeptide phage particles were captured by a monoclonal mouse anti-M13 ( B62-FE2 , Progen ) antibody for screening . For this , 100 μL of a 250 ng/mL solution of antibody in PBS were coated overnight at 4°C and subsequently blocked with 2% MPBST . The wells were washed after each incubation step three times with 300 μL PBST . One hundred μL of the monoclonal phage clones were added to each well and incubated for 2 h at 4°C . 100 μL of pooled patients’ sera reactive to Malaysian strains were added to library 1 while pooled sera reactive to WHO strains were added to library 2 . All sera were diluted in 2% MPBST supplemented with 10% E . coli TOP10F’ lysate and 1 x 1010 CFU/mL Hyperphage . The dilutions were incubated at RT for 2 h prior to use in the ELISA . Then , the dilutions were added onto the captured phage particles for 1 . 5 h and detected via a goat anti-human IgG , A , M antibody conjugated to horseradish peroxidase ( HRP ) ( 1:20 , 000 ) for 1 . 5 h . Visualization was achieved by adding 100 μL TMB ( 3 , 3’ , 5 , 5’-tetramethylbenzidine ) solution and the reaction was stopped with 100 μL 1 N sulfuric acid . A SUNRISE microtiter plate reader ( Tecan , Crailsheim , Germany ) was used to measure absorbance at 450 nm and subtract scattered light at 620 nm . A second titration ELISA was performed on the selected unique oligopeptide clones from the screening ELISA . The method was the same except this time clones were tested for reactivity against each of three aforementioned serum pools . The sera were serially diluted 2-fold from 1:100 to 1:102 , 400 in 2% MPBST supplemented with 10% E . coli TOP10F’ lysate and 1 x 1010 CFU/mL Hyperphage . The lipL32 and loa22 genes were amplified from L . interrogans serovar Copenhageni genomic DNA . Phusion DNA Polymerase ( Thermo Scientific F-530L ) was used to amplify full-length genes according to the following protocol: 98°C 30 seconds ( s ) , 98°C 10 s , annealing temperature primer dependent 20 s , 72°C 20 s , 30 cycles , 72°C 10 min . The amplified genes were digested with Nde1 and Not1 and the resulting fragments resolved by 1% agarose electrophoresis , purified from the gel with NucleoSpin Gel and PCR Clean-Up kit ( Macherey-Nagel 740609 . 250 ) , ligated into the Nde1/Not1 digested vector pET21a ( + ) and the ligation product subsequently transformed into E . coli BLR ( -DE3 ) . Finally , positive clones were identified by colony PCR and confirmed by sequencing . For protein expression , 200 mL 2xYT-GA medium were inoculated with 10 mL overnight culture and incubated at 37°C and 120 rpm in a baffled flask to an OD600 of 0 . 6 . Expression was induced with a final concentration of 1 mM IPTG for 6 h at 30°C , followed by centrifugation at 3 , 000 x g for 20 min for cell harvesting . Cells were then suspended in His-tag binding buffer pH 8 with urea ( 50 mM Na2HPO4 , 100 mM NaCl , 10 mM imidazol , 8 M urea ) and incubated for 1 h under over-head rotation , followed by sonication ( 6 cycles of 10 s 50% power , 10s incubation on ice , Sonotrode MS72 , Bandelin ) . Subsequently 0 . 5 mL Ni-NTA agarose slurry ( Qiagen 30210 ) was added to the disrupted cell solution and incubated for 1 h under over-head rotation . Then , the solution was loaded onto a polypropylene column . The agarose settled by gravity flow and was washed with 10 mM , 30 mM and 50 mM imidazole ( 50 mM Na2HPO4 , 300 mM NaCl , 8 M urea , pH 8 ) . Elution was achieved with 3 x 1 . 25 mL PBS pH 7 . 4 supplemented with 100 mM EDTA and 8 M urea . The proteins were prepared for analysis by 12% SDS-PAGE by heating 0 . 5 μg of protein sample mixed with 5x Lane Marker Reducing Sample Buffer ( Thermo Scientific 39000 ) at 95°C for 5 min . PageRuler Plus Prestained Ladder ( Thermo Scientific 26619 ) and Spectra Multicolor Low Range Protein Ladder ( Thermo Scientific 26628 ) were used as size marker . The samples were stacked for 10 min at 60 V , followed by separation for 60 min at 110 V . The gels were then stained with Coomassie Brilliant Blue R250 solution dye . The protein bands migrated at ~32 kDa and ~22 kDa . Leptospira spp . whole-cell antigen for the whole-cell in house ELISA was prepared using the supernatant of live Leptospira spp . cultures . The antigen was prepared and coated to microtiter plates essentially as described by [27] . To confirm the interaction of each isolated peptide with the sera , 200 ng of synthetic peptide ( Peps4LS GmbH , Heidelberg , Germany ) was diluted in 100 μL of PBS and coated to a high binding 96-well microtiter plate ( Greiner-bio one ) and incubated at 4°C overnight . Blocking was performed with 2% MPBST for 30 min . Then , 16 sera from patients with acute leptospirosis ( MAT titer , 1:800; reactive towards endemic serovars in Malaysia i . e . Australis , Autumnalis , Bataviae , Canicola , Celledoni , Copenhageni , Djasiman , Gryppotyphosa , Hardjobovis , Hardjoprajitno , Icterohaemorrhagiae , Javanica , Lai , Patoc , Terengganu , Sarawak ) and 16 sera from healthy donors were serially diluted 2-fold from 1:100 to 1:102 , 400 in 2% MPBST and added into the wells for 1 . 5 h . After incubation , the wells were washed three times with 300 μL PBST . Bound IgM was detected with mouse IgG anti-human IgM ( CH2 ) -HRPO , MinX none 100 μg; product no . AFC-5349-2 , Dianova ) diluted 1:20 , 000 in 2% MPBST , by incubation for 1 . 5 h at RT . The reaction was developed with TMB solution , stopped with sulfuric acid , and the plates were read at 450 nm as described before . Similar ELISA steps were repeated with control proteins ( rLipL32 and rLoa22 ) and Leptospira antigens . For ELISA of control proteins , the sera were diluted and titrated from 1:100 to 1:102 , 400 in 2% MPBST supplemented with 10% E . coli TOP10F’ lysate and 1 x 1010 CFU / mL Hyperphage and incubated for 2 h prior to use , as done in the ELISA of oligopeptide phage clones described above . Antigen/antibody ELISA signals in control and disease groups were non-normally distributed , and statistical significance of between-group differences was therefore assessed with the Wilcoxon rank sum ( Mann-Whitney U ) test [28] . To evaluate discriminatory biomarker potential , a logistic regression model fitted using Bayesian generalized linear models [29] was used to calculate the area under the receiver operating characteristic ( ROC ) curve ( AUC ) . The AUC and corresponding confidence intervals ( CI ) values were estimated using the cross-validation procedure based on 1000 bootstrap samples as described before [30 , 31] . In addition , multiple logistic regression was used to evaluate the classification performance of each combination of peptides / proteins .
Genomic DNA of Leptospira spp . was fragmented by sonication and cloned into pHORF3 , resulting in libraries with 3 . 0 x 107 ( Malaysian local strains library , i . e . library I ) and 2 . 2 x 107 ( WHO strains library , i . e . library II ) independent clones . The insert rates and sizes were analyzed by colony PCR and sequencing . Thirteen randomly picked clones of each library were used for colony PCR and an additional seven per library were picked for sequencing . The average insert size was 250 bp and 290 bp for library I and library II , respectively . All libraries had insert rates of more than 80% . Both genomic libraries were then packaged with Hyperphage for the selection of open reading frames . The packaged libraries were checked for the number of inserts by colony PCR and for correct in-frame inserts by sequencing . The cloned libraries had an in-frame insert ratio of approximately 50 to 55% and phage titers of 2 . 9 x1010 and 4 . 0 x 1010 cfu/mL . The average DNA fragments were shorter compared to the initial size , i . e . 160 bp and 120 bp for library I and II , respectively . The pooled sera from patients with acute leptospirosis were used as polyclonal antibody source in the panning rounds and screening . Individual clones were picked after the second and third panning rounds during which interaction partners with low affinities were removed , thus selecting the best interaction partners . The results of this panning are summarized in S1 Fig . From both libraries combined , this resulted in the selection of 92 oligopeptide phage clones to be screened by ELISA . Of these , 35 had signals two-fold higher than the negative control and were analyzed further by sequencing ( Fig 1 ) . Eighteen of these 35 clones were identified as unique sequences and matched Leptospira spp . sequences according to BLAST analysis [32] . Their encoded amino acid sequences were translated using Expasy [33] and the corresponding Leptospira spp . proteins identified using BLAST ( Table 2 ) . After three rounds of panning , screening ELISA were performed on phage clones derived from both libraries . Monoclonal phage clones displaying oligopeptides were captured in the wells of a microtiter plate using a monoclonal anti-M13 antibody and screened for reactivity with pooled sera from 18 patients with acute leptospirosis or from 10 healthy donors and detected with a goat anti-human HRP conjugate ( Fig 1 ) . The BLAST results of 18 unique oligopeptide phage clones corresponded to amino acid sequences of peptides consisting of 13–80 amino acids . Six clones corresponded to hypothetical proteins of unknown function; of these , the most frequent one was LEP1GSC042_0155 , which was identical in five clones . Altogether , 18/35 clones ( 51% ) corresponded to hypothetical proteins , which agrees with the report that around 40% of genes of L . interrogans , borgpetersenii and biflexia encode proteins of unknown function [34] . The peptide fragments of known proteins included various enzymes , transporter proteins , and outer membrane protein . The selected 18 oligopeptide phage clones produced as monoclonal phage were tested in ELISA using serial dilutions of pooled sera reactive with Malaysian strains , WHO strains and from healthy donors as described above . The peptides of the five most promising phage clones were then synthesized as biotinylated peptides and tested for immunoreactivity against 16 positive and 16 negative individual sera which were not included in the aforementioned two serum pools . The two most abundant proteins in pathogenic Leptospira spp . , i . e . LipL32 [35] and Loa22 [36] , were used as controls . Based on the signal to noise ratio in the screening ELISA and the BLAST analysis , five peptides ( SIR16-A1 , SIR16-C1 , SIR16-D1 , SIR16-E6 , SIR16-H1; summarized in Table 3 ) were selected for further validation . Of note , all five selected peptides were derived from the Malaysian genomic library . A validation test was carried out by a titration ELISA ( 1:200 ) using individual sera from 16 patients with acute leptospirosis and 16 healthy donors ( Fig 2 ) . The aforementioned two recombinant leptospiral reference proteins , rLipL32 and rLoa22 , were included for comparison . The results were also compared to Leptospira culture supernatant antigen used for in-house ELISA . Two peptides ( SIR16-D1 , SIR16-H1 ) , the two reference proteins , and the leptospiral antigen reacted significantly more strongly with the patient sera than with the control sera , indicating high immunoreactivity ( Fig 2A ) . In addition , signal strength was comparable among the two peptides and reference proteins . ROC curve analysis revealed that the two peptides ( SIR16-H1 and SIR16-D1 ) had AUCs close to or greater than 0 . 8 and thus demonstrated potential as diagnostic biomarkers to differentiate between acute leptospirosis and controls ( Fig 2B ) . Logistic regression was then used to evaluate whether combinations of the peptides with each other and / or with the two recombinant reference proteins would improve classification . First , using a simulation by resampling from the existing dataset [37] we made a posteriori power analysis for the logistic regression model , which includes four predictors and has the highest AUC . For the 32 samples used in the analysis , we detected a power of 98 . 9% at a significance level 0 . 001 . Among the 5 peptides , a combination with better classification than the best single peptide could not be identified ( Table 4 , row 3 ) . This was unexpected , as the reactivity with the individual sera correlated only weakly among the peptides , suggesting that there would be diagnostic synergy ( S3 Fig ) . However , when combining the 5 peptides with the two reference proteins , a classifier consisting of the two best peptides , rLipL32 and the peptide SIR16-A1 was identified that possessed near perfect ( AUC , 0 . 98 ) discrimination between patient and control sera ( Table 4 , rows 4 and 6 ) . When combining only the two best peptides with the reference proteins , classification was somewhat less accurate ( AUC 0 . 93 vs . 0 . 98; compare rows 12 and 14 with 4 and 6 ) , demonstrating the added value of including peptide SIR16-A1 . Inspection of the data then revealed that SIR16-A1 had a lower reactivity with two of the control sera , likely explaining the observed improved classification in this multiple regression model .
ORFeome phage display has been proven to be a successful method for selection of immunogenic peptides to be used for diagnostic purposes . It has previously been used successfully to identify novel biomarkers from Salmonella typhimurium , Mycoplasma pneumoniae and Neisseria gonorrhea [14 , 20 , 38 , 39] . Regarding Leptospira , it has been used successfully to identify ( 1 ) host proteins that interact with LipL32 [40] , ( 2 ) LigB protein acting as heparin binding protein [41] , ( 3 ) adhesin activity of Leptospira lipoprotein [42] , and ( 4 ) mimotopes from monoclonal antibodies specific for Leptospira spp . [43] . Our study is the first to use phage display to identify immunogenic Leptospira antigens from Leptospira spp . genomes . The findings of this study are of particular interest in that they indicate that ORFeome phage display can be used to identify novel peptides for development of leptospiral diagnostics , an approach that promises to be superior to protein or cell extract based methods for use in tropical and resource-limited settings for the reasons outlined in the Introduction . For instance , an ELISA with directly immobilized peptide would constitute a simple peptide-based point-of-care diagnostics for resource-limited settings [44–47] . A very simple antibody-antigen diagnostic based on specially treated paper to immobilize the antigen and a drinking straw as an incubation chamber has been developed for use in resource-limited setting [48] and might constitute an attractive basis for diagnostics based on peptides identified by ORFeome phage display such as ours . Even though the two identified peptides demonstrated good diagnostic performance , even higher accuracy would be desirable for clinical application . Combinations of these peptides with proteins of immunodominant properties , such as LipL32 may result in better coverage of pathogenic serovars . However , adding recombinant proteins to the assay would obviate the clear advantages of using peptide-based assays . Thus , to preserve the “peptide only” aspect of a new diagnostic , it will be important to assess the diagnostic value of the two dominant antigenic epitopes of LipL32 [49] in peptide form in order to assess whether combining them with our peptides would be useful . In addition , future work should include additional screens to identify other immunoreactive peptides that could synergize diagnostically with SIR16-D1 and/or SIR16-H1 or be superior by themselves . Since our goal was to identify peptides for the diagnosis of leptospirosis in the acute phase , we evaluated the peptides for reactivity against IgM only . All patient sera were obtained from patients who presented to the health care system after an average of three to five days of illness symptoms . IgM is the first agglutinating antibody to develop 5 to 14 days after exposure to infection and diagnostically meaningful IgG levels appear 1–3 weeks later [9] . It would now be interesting to test whether these peptides are also reactive with IgG and could therefore be used for seroepidemiological surveys , in addition to diagnostics in the acute phase . It came as a surprise that all five selected peptides were derived from the Malaysian genomic library , but none from the WHO reference genomic library . This is probably because the Malaysian strains had undergone a smaller number of passages in culture than the WHO strains , thus preserving antigen profiles more closely resembling natural infection . This observation has important implications for future work , as it clearly suggests that early passage strains would constitute a better source for ORFeome phage display libraries than extensively subcultured reference strains . Peptide SIR16-H1 corresponded to a predicted protein of unknown function . In contrast , peptide SIR16-D1 turned out to be a fragment of Glucose-1-phosphate cytidylyltransferase , also known as CDP-glucose pyrophosphorylase , the product of the rfbF gene [50–52] . This protein is found in 10 pathogenic leptospiral species and also in some other bacteria . Even though this is an intracellular protein , it is quite feasible that it does lead to a humoral immune response as it might become exposed to the immune system during lysis of leptospirae , during phagocytosis by antigen-presenting cells , or even by being secreted from live leptospirae in the sense of a “moonlighting protein” [53] . This study was limited by the number of sera selected for ELISA and also by the prevalence of serovars in two endemic regions in Malaysia , i . e . Kota Bharu and Kota Kinabalu . Besides , the control group was recruited from healthy donors from a non-endemic region . This is because individuals who had been exposed to leptospirosis in endemic region can produce antibodies from the memory pool , leading to background reactivity and false positive results . In fact , it was reported that healthy individuals in high endemic region have a 15% prevalence of positive anti-Leptospira antibodies detected by MAT [54] . Evaluation of the peptides with sera from patients infected with other known tropical infection diseases such as Dengue Fever , Chikugunya , typhoid etc . should be included , as it is important to assess their practicality and specificity as a leptospirosis diagnostic assay in populations exposed to pathogens that may cause serological cross-reactivity . A more comprehensive study involving sera from patients and healthy individuals from various endemic and non-endemic countries should be included in the future , as the present study is based on patient sera from a limited geographical region in Malaysia only and serological responses to field isolates from other geographic areas may differ . All the samples used here were obtained from hospitalized leptospirosis patients , but the immune response may be different in mild presentations . As there are various serovars causing leptospirosis worldwide , we suggest to apply ORFeome phage display screening to genomically more diverse isolates and to human sera collections from various endemic regions for a more universal selection and characterization of the antigen repertoire . In summary , we report the first study of seroreactive peptides identified by a phage display approach using a combination of endemic Leptospira spp . in Malaysia . The synthetic peptides SIR16-D1 and SIR16-H1 showed good potential for the discrimination of acute phase leptospirosis and healthy patients and can form the basis for the development of peptide-based diagnostics for use in resource-limited settings and hot climates . | Leptospirosis is an infectious disease that is transmitted from animals to humans . It is associated with a broad range of clinical presentations , and diagnostic tests with high diagnostic accuracy are required in order to enable accurate diagnosis . Leptospirosis is diagnosed by detecting DNA of the pathogen or antibodies against it in patients’ blood; the latter are preferred in resource limited regions , and diagnostics based on peptides ( small fragments of proteins ) are advantageous because they are inexpensive to produce and more stable in hot climates than full-length proteins . We used a technique called open reading frame phage display to identify peptides from Leptospira spp . that could be used to detect antibodies against them in human blood . In this method , the pathogen’s genome is fragmented , the corresponding peptides displayed on the surfaces of phages ( viruses that infect bacteria ) , and the peptides that bind most strongly to the patients’ antibodies are then selected by screening . Using this method , we identified 2 leptospiral peptides that accurately identified antibodies against Leptospira spp . in sera from patients with leptospirosis . These results are encouraging because they demonstrate that ORFeome phage display may be a powerful tool to develop better diagnostics for leptospirosis for use in less developed areas . |
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Interferon-inducible GTPases of the Immunity Related GTPase ( IRG ) and Guanylate Binding Protein ( GBP ) families provide resistance to intracellular pathogenic microbes . IRGs and GBPs stably associate with pathogen-containing vacuoles ( PVs ) and elicit immune pathways directed at the targeted vacuoles . Targeting of Interferon-inducible GTPases to PVs requires the formation of higher-order protein oligomers , a process negatively regulated by a subclass of IRG proteins called IRGMs . We found that the paralogous IRGM proteins Irgm1 and Irgm3 fail to robustly associate with “non-self” PVs containing either the bacterial pathogen Chlamydia trachomatis or the protozoan pathogen Toxoplasma gondii . Instead , Irgm1 and Irgm3 reside on “self” organelles including lipid droplets ( LDs ) . Whereas IRGM-positive LDs are guarded against the stable association with other IRGs and GBPs , we demonstrate that IRGM-stripped LDs become high affinity binding substrates for IRG and GBP proteins . These data reveal that intracellular immune recognition of organelle-like structures by IRG and GBP proteins is partly dictated by the missing of “self” IRGM proteins from these structures .
Many intracellular pathogens including the bacterium C . trachomatis and the protozoa T . gondii co-opt the host cell endomembrane system to enclose themselves inside membrane-bound vacuoles . Within the confines of these remodeled PVs , microbes acquire nutrients and replicate [1] . To combat these pathogens , the mammalian host has evolved a large repertoire of cell-autonomous defense mechanisms that kill or restrain the replication of microbes residing within vacuoles [2] , [3] . While these defense mechanisms are effective at targeting foreign or “non-self” vacuoles , they also have the potential to cause organelle damage and must therefore be tightly regulated . Control over these host defenses is executed at two critical steps: ( i ) induction of genes encoding host resistance factors in the context of an infection and ( ii ) targeting of these resistance factors to the appropriate intracellular location , for example to PVs . These two modes of regulation are exemplified by the induction and execution of cell-autonomous defenses by the cytokine Interferon-γ ( IFNγ ) . The importance of IFNγ in host immunity is demonstrated by the severe immuno-deficiencies observed in genetically engineered mouse strains lacking IFNγ or its receptor and in patients carrying rare mutations in genes critical for IFNγ signal transduction [4] , [5] . IFNγ is produced by immune-activated lymphocytes and exerts its antimicrobial effects by dramatically remodeling the transcriptional expression profile of target cells bearing the IFNγ receptor [3] . IFNγ-induced resistance genes include members of two IFN-inducible GTPase families named IRGs and GBPs . Members of both GTPase families have the ability to translocate and to adhere specifically to PVs in order to inhibit intracellular pathogen growth . Although the specificity of this intracellular targeting event is well documented [3] , [6] , the underlying mechanism is unclear . Once docked to PVs , GBP proteins recruit antimicrobial protein complexes that include the NADPH oxidase NOX2 , the autophagy apparatus and the inflammasome [3] . IRG proteins on the other hand can directly disrupt PV membranes , thereby releasing vacuolar pathogens into the cytosol where they can be removed through autophagy [6] , [7] . IRG GTPases are divided into two categories: ( i ) the predominantly cytosolic GKS proteins constitute the most abundant group and harbor a conserved GX4GKS sequence in the first nucleotide-binding motif ( G1 ) , ( ii ) the predominantly membrane-bound IRGM proteins instead contain a non-canonical P-loop sequence GX4GMS [6] . Both GKS and IRGM proteins are essential for cell-autonomous resistance to infections with C . trachomatis and T . gondii in mice but fulfill distinct functions in this process [6] . Whereas GKS proteins directly target and eliminate C . trachomatis and T . gondii PVs , IRGM proteins appear to orchestrate the targeting of GKS proteins to PVs by an incompletely understood mechanism [6] . In addition to their role as regulators of GKS protein function , IRGM proteins also exert antimicrobial activities independently of GKS proteins . Both mouse and human IRGM proteins promote the formation of autophagosomes upon IFNγ stimulation [8]–[10] . Additionally , murine Irgm1 loads onto early phagosomes containing beads or live bacteria [11]–[13] . Vacuolar Irgm1 interacts with target SNARE protein complexes and through these interactions can facilitate the rapid fusion of Irgm1-coated phagosomes with degradative lysosomes . Accelerated lysosomal maturation was shown to result in the destruction of the attenuated pathogen Mycobacterium bovis BCG contained within Irgm1-positive phagosomes in mouse macrophages [13] . Similar to Irgm1 , Irgm3 was implicated as a mediator of direct antimicrobial activities towards T . gondii [14] . To initially establish vacuoles permissive for microbial survival in IFNγ-activated cells , pathogens must have evolved strategies to evade the direct , fast-acting immune responses mediated by membrane-bound Irgm1 and Irgm3 proteins . In agreement with the existence of such evasion mechanisms , we observed that Irgm1 and Irgm3 failed to robustly associate with PVs formed by either C . trachomatis or T . gondii . The absence of substantial amounts of Irgm1/m3 from PVs contrasted with the abundant localization of Irgm1/m3 to “self” structures like LDs . We found that Irgm1/m3-decorated LDs are largely devoid of GKS and GBP proteins , whereas Irgm1/m3-deficient PVs are targets for GKS and GBP proteins . These observations led us to hypothesize that the absence of Irgm1/m3 proteins marked intracellular structures as targets for a “second line of defense” mediated by GKS and GBP proteins . In support of this hypothesis , we demonstrated that stripping LDs of Irgm1/3 resulted in mistargeting of GKS and GBP proteins to LDs independently of an infection . Because IRGM proteins were previously shown to inhibit GKS protein oligomerization [15] , we propose a model in which the missing of “self” IRGM proteins from “non-self” PVs results in the formation of GKS ( and GBP ) protein oligomers with high avidity for membrane binding .
GKS proteins in their GTP-bound state form dimers [6] . Dimerization occurs at the G domain interface and is a prerequisite for the formation of higher order GKS protein oligomers . Mutations that diminish guanine nucleotide binding or disrupt the G domain interface eliminate both protein oligomerization and targeting to T . gondii PVs [16] , [17] . To determine if these findings extended to other PVs , we first tested whether guanine nucleotide binding of the GKS protein Irgb10 was essential for their targeting to “inclusions , ” the PVs formed by C . trachomatis . We replaced the serine on position 82 of Irgb10 in the conserved P-loop GKS motif with asparagine ( Irgb10S82N ) . This mutation is analogous to the Irga6S83N mutation that abrogates guanine nucleotide binding and T . gondii PV localization [16] . We found that Irgb10-GFP-fusion proteins harboring the S82N mutation or a deletion of the central G-domain ( Irgb10ΔG ) failed to localize to C . trachomatis inclusions in infected mouse embryonic fibroblasts ( MEFs ) ( Figure 1A ) . Combined with previous results in T . gondii [16] , our data suggested that protein oligomerization of GTP-bound Irgb10 is essential for tethering this GKS protein to C . trachomatis inclusion membranes . To test whether protein oligomerization of the N- and C-terminal domains of Irgb10 was sufficient to target inclusion membranes , we replaced the G domain of Irgb10 with alternative protein oligomerization domains and monitored the subcellular localization of these protein chimeras . We first substituted the G domain of Irgb10 with the tetramer-forming protein dsRED [18] , which emits red fluorescence exclusively in the oligomerized form [19] . Insertion of dsRED between the N-terminal domain ( NTD ) and C-terminal domain ( CTD ) of Irgb10ΔG ( Irgb10NTD-dsRED-CTD ) restored the association of a GFP-tagged fusion protein with C . trachomatis inclusions ( Figure 1A ) . Inserting dsRED in between the N-terminal myristoylation motif ( Myr ) and the CTD of Irgb10 ( Irgb10Myr-dsRED-CTD ) similarly redirected the mutant variant Irgb10Myr-CTD to inclusions ( Figure 1A ) . Tetramerized Irgb10 localized to IncG-positive inclusion membranes ( Figure 1B ) . Inclusion targeting required the presence of both the Irgb10 myristoylation motif and the C-terminal amphipathic helix αK ( Figure 1A , Figure 2A and data not shown ) . As an alternative mediator of protein oligomerization , we used the highly oligomeric cytoplasmic yeast protein TyA [20] . Insertion of TyA in between the myristoylation domain and the C-terminus of Irgb10 ( Irgb10Myr-TyA-CTD ) similarly re-localized these fusion proteins towards inclusions . Myristoylated-TyA ( Myr-TyA ) localized to microvesicles , as described [21] , but failed to associate with inclusions ( Figure 1C ) , demonstrating that the C-terminus of Irgb10 containing the αK amphipathic helix is essential for inclusion targeting . In summary , these data show that the oligomerization of the N- and C-terminal lipid binding domains of Irgb10 was sufficient to drive localization to inclusions . The targeting of GKS proteins to T . gondii is substantially diminished in the absence of the IRGM proteins Irgm1 and Irgm3 [16] , [22] . We found that the association of Irgb10 and other GKS proteins with C . trachomatis inclusions was similarly reduced in infected MEFs derived from Irgm1−/− , Irgm3−/− and Irgm1/m3−/− mice ( Figure 3 ) . These data indicate that Irgm1 and Irgm3 either directly or indirectly promote the delivery of GKS proteins to inclusions . Because IRGM proteins physically interact with GKS proteins at the G domain interface [16] , we hypothesized that IRGM proteins facilitate the delivery of GKS proteins to inclusions through their interactions with the G domain of GKS proteins . In such a scenario , artificially oligomerized Irgb10 lacking a G domain should target inclusions independently of IRGM proteins . To test the hypothesis , we expressed two tetramerized , chimeric Irgb10ΔG proteins , Irgb10NTD-dsRED-CTD and Irgb10NTD-dsRED-αK , in wildtype and Irgm3−/− MEFs and scored the frequency of dsRED signal on inclusions . We chose Irgm3−/− MEFs for these experiments , because they displayed the most pronounced defect in targeting endogenous Irgb10 to inclusions ( Figure 3 ) . In contrast to endogenous Irgb10 ( Figure 2B and Figure 3 ) , tetramerized Irgb10 lacking a G domain targeted inclusions with the same efficiency in wildtype and Irgm3-deficient cells ( Figure 2A ) . These data suggest that Irgm3 regulates the targeting of Irgb10 to inclusions through its interaction with the G domain of Irgb10 . It is currently unknown where inside a cell IRGM proteins interact with GKS proteins to regulate their function . To determine whether IRGM proteins regulate GKS proteins directly at PV membranes , we first monitored the subcellular localization of IRGM proteins in cells infected with either T . gondii or C . trachomatis . As reported previously [14] , [23] , we found that endogenous Irgm3 but not Irgm1 associated with T . gondii PVs , albeit only weakly relative to its association with endogenous , puncta-like structures ( Figure 4A and data not shown ) . These results are also in agreement with a previous report demonstrating that Irgm3 associates with T . gondii PVs at a lower frequency than GKS proteins do [24] . Next we examined the subcellular localization of endogenous Irgm1 and Irgm3 in C . trachomatis-infected cells . In agreement with a previous report [25] , we detected association of Irgm3 with C . trachomatis inclusions at 2 hpi . However , Irgm3 associated only weakly with inclusions relative to its interactions with endogenous structures ( Figure 4B ) . Similar to the staining pattern of T . gondii PVs , we failed to detect the presence of Irgm1 on inclusions ( data not shown ) . To determine whether Irgm1 or Irgm3 could target established inclusions , we infected MEFs with C . trachomatis and subsequently treated cells with IFNγ at 3hpi . Under these experimental conditions endogenous as well as ectopically expressed Irgm1 and Irgm3 were not present at inclusion membranes in detectable amounts at 20 hpi ( Figure 4C , Figure 5B and data not shown ) . Collectively , these data show that PVs formed by either C . trachomatis or T . gondii are devoid of substantial amounts of Irgm1 and Irgm3 proteins . Because established PVs lack sizeable amounts of Irgm1/m3 , we considered the hypothesis that IRGM proteins regulate Irgb10 and other GKS proteins at sites distinct from PVs . It is known that IRGM proteins localize to various endomembranes , including LDs , a neutral lipid storage organelle [6] . Specifically , Irgm3 was shown to localize to LDs in IFNγ-treated dendritic cells [26] . To determine whether or not Irgm3 also localizes to LDs in IFNγ-treated MEFs , we induced the formation of LDs by supplementing the growth media with oleic acid ( OA ) and subsequently stained these cells with the neutral lipid dye BODIPY493/503 and with anti-Irgm3 antibody . We found that Irgm3 co-localized with the BODIPY dye in IFNγ-treated MEFs ( Figure 5A ) . To determine whether additional IRGM proteins localize to LDs , we monitored the localization of C-terminally V5-tagged Irgm1 , Irgm2 and Irgm3 inside OA-treated MEFs . In addition to Irgm3-V5 , Irgm1-V5 and Irgm2-V5 co-localized with a subset of LDs but not with inclusions ( Figure 5B and Figure S1 ) . Staining for endogenous protein confirmed the presence of Irgm1 but not Irgm2 on a subset of LDs ( Figure S2 and data not shown ) . We next asked if GKS proteins were also found on LDs by immunostaining IFNγ-activated MEFs with antibodies directed against three representative GKS proteins Irga6 , Irgb6 and Irgb10 . We were unable to detect co-localization of these proteins with BODIPY-labeled LDs in wildtype MEFs by immunofluorescence ( Figure 5C and D ) . We independently confirmed these observations by assessing the levels of IRG proteins on purified LDs . LDs purified from IFNγ-treated , wildtype MEFs by sucrose gradient centrifugation displayed significant levels of Irgm1 and Irgm3 ( Figure 5E ) , and relatively small amounts of Irgb10 ( Figure 5E ) , suggesting possible transient interactions between IRGM proteins and Irgb10 on the surface of LDs . In summary , these data indicate that LDs of wildtype cells are decorated with Irgm1 and Irgm3 but only weakly associate with GKS proteins . Although LDs could play an essential role in guiding GKS proteins to inclusions , we thought this was unlikely , because LD-deficient cells lines still target Irgb10 to inclusions ( H . A . S . and R . H . V . , unpublished data ) . Because IRGM proteins inhibit GTP acquisition by GKS proteins and are believed to thereby block the ability of GKS proteins to bind lipids [15] , [16] , we formed an alternative hypothesis in which LD-resident IRGM proteins would prevent GKS proteins from binding to LDs . To test our hypothesis , we examined the localization of Irgb10 in Irgm1/m3−/− MEFs that contain IRGM-deficient LDs . We found that the LDs of Irgm1/m3−/− MEFs were heavily decorated with Irgb10 ( Figure 5D ) . Targeting of Irgb10 to LD in Irgm1/m3−/− MEFs was primarily due to the absence of Irgm3 , because Irgm3−/− MEFs but not Irgm1−/− MEFs displayed a substantial increase in the number of Irgb10-positive LDs ( Figure 5D ) . The simultaneous deletion of both Irgm3 and Irgm1 , however , exacerbated the association of Irgb10 with LDs ( Figure 5D ) suggesting that these proteins fulfill partially redundant functions in protecting LDs against Irgb10 targeting . The role of Irgm1 and Irgm3 in guarding LDs was not limited to Irgb10 but extended to other GKS proteins including Irga6 and Irgb6 ( Figure 5C ) . Again , Irgm3 was predominantly responsible for guarding LDs , because ectopic expression of Irgm3 in either Irgm3−/− or Irgm1/m3−/− MEFs prevented deposition of Irga6 on LDs ( Figure S3 ) . Irgm1 and Irgm3 were also required to prevent Irgb10 accumulation on LD in primary macrophages , indicating that the observed phenomenon is not cell type specific ( Figure S4 ) . Furthermore , endogenous LDs found infrequently in MEFs not treated with OA also acquired Irgb10 in the absence of Irgm1 and Irgm3 ( Figure S5A ) , demonstrating that the aberrant localization of Irgb10 was not induced by OA treatment . Lastly , consistent with our immunofluorescence observations , we detected a robust increase in the amount of Irgb10 protein present in the LD fraction derived from Irgm1/m3−/− MEFs compared to wildtype MEFs ( Figure 5E ) . These data combined demonstrate that GKS proteins target LDs in the absence of Irgm1/m3 . Because IRGM proteins can act as positive regulators of autophagy [8] , [10] , [27] , we also considered the possibility that the mislocalization of GKS proteins to LDs in Irgm1/m3−/− cells was a consequence of disrupted autophagy . To test this hypothesis , we examined the subcellular localization of Irgb10 in autophagy-deficient Atg5−/− MEFs . We did not observe an increase in the association of Irgb10 protein with LDs in Atg5−/− MEFs ( Figure 5E and Figure S6 ) , indicating that a defect in autophagy is not the underlying cause for the mislocalization of GKS proteins to LDs in Irgm1/m3−/− MEFs . Next , we asked whether IRGM proteins exclusively guard LDs . We observed that Irgb10 formed “aggregate-like structures” in Irgm1/m3−/− cells that did not identify as LDs ( Figure S5A ) , suggesting that GKS protein could target additional “self” structures in the absence of Irgm1/m3 . In support of this hypothesis , we found that GKS proteins also targeted mitochondria ( Figure S5B ) and peroxisomes ( Figure S5C ) in Irgm1/m3−/− cells . In wildtype cells mitochondria are decorated with Irgm1 ( G . A . T . , manuscript in preparation ) and subsets of peroxisomes stain positive for Irgm3 ( Figure S5D ) . In summary , our data suggest that IRGM proteins guard LDs and other organelles against the stable association with GKS proteins . We demonstrated that endogenous GKS proteins like Irgb10 stably associate with LDs in the absence of IRGM proteins ( Figure 5 ) . Similarly , ectopically expressed Irgb10 frequently targets LDs in Irgm1/m3−/− MEFs ( Figure S7A ) but not in wildtype MEFs ( Figure 6A ) . Two distinct models could explain the differential targeting of Irgb10 and other GKS proteins to IRGM-deficient but not IRGM-positive LDs: in the first model , the presence of IRGM proteins alters the molecular properties of LDs such that LDs do not serve as binding substrates for GKS proteins; in the second model , IRGM proteins directly interact with GKS proteins on the surface of LDs and block lipid binding . Previous studies have shown that IRGM proteins can transiently interact with GKS proteins and thereby retain GKS proteins in the GDP-bound , inactive state [15] , [16] , thus supporting the second model . We therefore predicted that an Irgb10 variant locked in the active , GTP-bound state should be able to overcome IRGM protein mediated restrictions on lipid binding and be able to target IRGM-positive LDs . To generate a GTP-locked Irgb10 mutant , we replaced the lysine residue of the conserved GKS motif with alanine ( Irgb10K81A ) , as homologous mutations in Irga6 or Irgb6 interfere with GTP hydrolysis and force these GTPases into a GTP-locked state [16] . Similar to previous observations demonstrating the targeting of GTP-locked Irga6 to T . gondii vacuoles [16] , we found that Irgb10K81A co-localized with C . trachomatis inclusions ( Figure 6 ) . However , in contrast to Irgb10WT , Irgb10K81A co-localized with Tip47-positive LDs ( Figure 6A ) and Irgm3 ( Figure 6B ) in wildtype MEFs . In contrast to Irgb10WT and Irgb10K81A , a mutant with low affinity binding for GTP ( Irgb10S82N ) failed to associate with either inclusions or LDs ( Figure S7 ) . Overall , these data are consistent with a model , in which IRGM proteins on LDs block the activation of endogenous Irgb10 on the surface of LDs and prevent their stable association of Irgb10 with LDs . GBP proteins constitute a second large family of IFNγ-inducible GTPases known to target PVs and to provide resistance to infections with vacuolar pathogens [3] . Because we previously observed that the subcellular location of the GBP protein Gbp2 is altered in the absence of IRGM proteins [27] , we hypothesized that IRGM proteins could guard self-membranes against the improper deposition of not only GKS but also GBP proteins . Consistent with this , we found that Gbp2 co-localized with LDs in Irgm1/m3−/− but not in wildtype MEFs ( Figure 7A ) and was enriched in LD fractions obtained from Irgm1/m3−/− cells ( Figure 7B ) . Irgm1 and Irgm3 appeared to fulfill partially redundant functions in guarding LDs against Gbp2 targeting , because Gbp2 localization to LDs was more pronounced in Irgm1/m3−/− MEFs than in Irgm1−/− or Irgm3−/− single gene deletion cells ( Figure 7C and Figure S8 ) . Because both Irgm1 and Irgm3 regulate the formation and/or maturation of autophagosomes [8] , [27] , it was formally possible that the mislocalization of Gbp2 to LDs in Irgm1/m3−/− cells resulted from a defect of these cells in autophagy . However , it is unlikely that defective autophagy is the primary cause for mislocalization of Gbp2 to LDs , because LDs inside autophagy-deficient Atg5−/− MEFs remained exempt from Gbp2 targeting ( Figure 7A and B ) . Similar to endogenous Gbp2 , we found that ectopically expressed , N-terminally tagged FLAG-Gbp1 protein was redirected to LDs in the absence of Irgm1 and Irgm3 proteins ( Figure 7D ) . To determine whether activation of Gbp1 was critical for targeting IRGM-deficient LDs , we expressed a FLAG-Gbp1K51A mutant form that has previously been shown to be defective for nucleotide binding and protein oligomerization [28] . In contrast to wildtype FLAG-Gbp1 , we found that FLAG-Gbp1K51A failed to associate with LDs in Irgm1/m3−/− cells ( Figure 7D ) . These data suggest that the active form of Gbp1 associates with IRGM-deficient LDs . Our data demonstrated that Gbp1 and Gbp2 localized to IRGM-deficient LDs . One of the known effector molecules of Gbp1 is the autophagic adaptor protein p62/sequestosome-1 [29] . We therefore hypothesized that Gbp1 proteins residing on IRGM-deficient LDs would be able to recruit p62 to LDs . In support of our hypothesis we found that 4–5% of LDs in IFNγ-treated Irgm1/m3−/− cells stained positive for p62 ( Figure 8A ) . In contrast to Irgm1/m3−/− cells , we were unable to detect p62 on LDs of IFNγ-treated wildtype cells using immunofluorescence microscopy ( Figure 8A ) . A critical function of p62 is to bind to macromolecular cargo that is destined for autophagic destruction [30] . To deliver its cargo to autophagosomes , p62 also binds directly to the ubiquitin-like protein LC3 , a maker of autophagosomes . To determine whether IRGM-deficient LDs are delivered to autophagosomes upon IFNγ activation , we incubated both wildtype and Irgm1/m3−/− MEFs with OA and IFNγ and subsequently stained cells with anti-LC3 and BODIPY . In these experiments , we frequently observed LDs that were engulfed within ring-like LC3-positive structures in Irgm1/m3−/− but not in wildtype MEFs ( Figure 8B ) . Similarly , LDs purified from Irgm1/m3−/− cells were enriched for LC3-II , the lipidated form of LC3 that is associated with autophagosomes ( Figure 8C ) . Collectively , these data strongly suggested that IRGM-deficient LDs were captured inside autophagosomes upon IFNγ activation . To test this model further , we treated cells with the lysosomotropic H+-ATPase inhibitor bafilomcyicn ( BAF ) , a known inhibitor of autophagic flux [31] . We observed a substantial increase in the number of p62-positive LDs in Irgm1/m3−/− MEFs upon combined treatment with IFNγ and BAF ( Figure 8A ) . BAF treatment also resulted in the appearance of p62-positive LDs in IFNγ-treated wildtype MEFs , however , at a frequency significantly lower than what we observed in BAF-treated Irgm1/m3−/− MEFs ( Figure 8A ) . These data indicated that the targeting of p62 to IRGM-deficient LDs resulted in the degradation of LD-bound p62 . Furthermore , our observations excluded an alternative model in which the increase in the number of p62-positve LDs in Irgm1/m3−/− MEFs was due to a defect in autophagosome maturation in these cells . We then asked whether the increased association of p62 and LC3 with IRGM-deficient LDs would affect the total mass of LDs . To quantify LD mass , we used a flow cytometry approach using BODIPY staining , as previously described [26] . We found that IFNγ treatment resulted in an increase in the BODIPY signal in wildtype MEFs , similar to the observations previously made in dendritic cells [26] . In contrast to the increase in the BODIPY signal observed in IFNγ-treated wildtype cells , the BODIPY signal decreased in IFNγ-treated Irgm1/m3−/− MEFs ( Figure 9 and Figure S9 ) , suggesting increased rates of LD degradation in IRGM-deficient cells . To determine whether the decrease in LD mass in Irgm1/m3−/− MEFs was due to autophagy ( = lipophagy ) , we treated cells with BAF . BAF treatment blocked the IFNγ-induced decrease in LD mass in Irgm1/m3−/− MEFs ( Figure 9 ) . In sum , these data strongly support a model in which p62 targets IRGM-deficient but not IRGM-guarded LDs and delivers IRGM-deficient LDs to autophagosomes for degradation . It has previously been reported that targeting of GBP proteins to T . gondii PVs is facilitated by unknown IFNγ-inducible factors [32] , [33] . Because our data had already established functional interactions between GBP and IRGM proteins , we asked whether IRGM proteins could act as IFNγ-inducible co-factors promoting the recruitment of Gbp2 to PVs . We found that ectopically expressed Gbp2-GFP fusion proteins failed to localize to C . trachomatis inclusions in the absence of IFNγ treatment or in IFNγ-activated Irgm1/m3−/− cells ( Figure 10A ) . Similarly , we observed that both Irgm1 and Irgm3 played critical roles in facilitating targeting of endogenous Gbp2 protein to inclusions ( Figure 10B and C ) . Similar to Gbp2 , recruitment of FLAG-Gbp1 to inclusions was also dependent on IRGM proteins ( Figure 10D ) . To determine whether the regulatory role of IRGM proteins extends to the recruitment of GBP proteins to PVs formed by pathogens other than C . trachomatis , we monitored co-localization of both Gbp2 and , as a control , Irgb10 with T . gondii vacuoles in wildtype , Irgm1−/− , Irgm3−/− and Irgm1/m3−/− MEFs . We observed that the deletion of both Irgm1 and Irgm3 caused a near complete defect in the recruitment of Irgb10 ( Figure 10E ) and Gbp2 to T . gondii PVs at 0 . 5 hpi ( Figure 10F ) . These observations demonstrate that the expression of IRGM proteins is critical for the efficient delivery of GBP proteins to vacuoles formed by distinct intracellular pathogens .
The data presented in this study support a model in which Irgm1 and Irgm3 proteins act as “guard molecules” that block GKS and GBP proteins from stably associating with “self” structures ( Figure 11 ) . On PVs , however , guarding Irgm1 and Irgm3 proteins are present at such low levels that GKS and GBP proteins can firmly attach to these unprotected membranes . In support of our model we found that Irgm1 and Irgm3 , but not GKS and GBP proteins , are present in LDs of wild type cells ( Figure 5 and Figure 7 ) . In the absence of Irgm1 and Irgm3 , however , normally GKS-/GBP-deficient LDs become decorated with various GKS and GBP proteins . We provide evidence that GKS and GBP proteins assemble on IRGM-deficient LDs in their GTP-bound , i . e . “active” state ( Figure 7 and Figure S7 ) . According to our model GKS-/GBP-decorated LDs should resemble GKS-/GBP-decorated PVs and would therefore be expected to become targets of GKS-/GBP-solicited immune responses . Consistent with such a scenario , we demonstrate that the Gbp1 effector protein p62 is recruited to IRGM-deficient LDs . The targeting of p62 to LDs in Irgm1/m3−/− MEFs likely accounts for the enhanced association of IRGM-deficient LDs with the autophagic marker LC3-II and the decrease in LD mass upon IFNγ activation that we observed in Irgm1/m3−/− MEFs . Our observations are consistent with a previous report that showed that the number of LDs is significantly reduced in IFNγ-activated Irgm3−/− dendritic cells compared to IFNγ-activated wildtype dendritic cells [26] . Whereas the authors of this previous study speculated that Irgm3 could play a role in the neoformation of LDs triggered upon IFNγ receptor signaling , our data strongly suggest that the decrease in LDs observed in IRGM-deficient cells primarily results from GBP-mediated autophagy of LDs . However , because our experiments were conducted in MEFs , additional studies are needed to determine whether Irgm3 may also play a role in LD neoformation in dendritic or other cell types . While we propose that IRGM proteins guard self-organelles against misdirected attacks by GKS and GBP proteins , our studies do not exclude additional roles for IRGM proteins in organelle homeostasis . For example , human IRGM protein translocates to mitochondria and induces mitochondrial fission [10] . Because mitochondrial fission not only results in the production of radical oxygen species and the induction of antimicrobial autophagy , but also contributes to the isolation and removal of damaged segments of mitochondria , IRGM proteins may indeed regulate the homeostasis of specific organelles like mitochondria . The question now arises as to why the “guarding” Irgm1 and Irgm3 proteins are present on “self” membranes but largely absent from “non-self” PVs . The answer to this question may be quite obvious , if one considers that IRGM proteins can exert antimicrobial activities directly , once localized to PVs [3] . To escape from IRGM-mediated antimicrobial activities like lysosomal targeting , we propose that vacuolar pathogens have evolved strategies to actively avoid co-localization with IRGM proteins . The absence of Irgm1/m3 from PVs would initially allow pathogens to establish a vacuolar niche permissive for microbial replication . However , the evasion of IRGM proteins would simultaneously mark PVs for immune targeting by GKS and GBP proteins . The principle underlying this type of intracellular immune recognition is similar to the extracellular immune recognition process by which NK cells detect transformed and/or virus-infected cells [34] . NK cells express inhibitory receptors on their cell surface . The ligands for one set of inhibitory NK receptors are MHC class I molecules displayed on the surface of host cells . The primary function of the MHC class I molecules is to display viral or tumor antigens to cytotoxic T cells . To avoid immune recognition by cytotoxic T cells , many tumor cells and viruses have evolved mechanisms to downmodulate MHC class I surface expression . However , the failure of MHC class I-deficient cells to provide an inhibitory signal to NK cells , allows NK cells to recognize the missing of “self” MHC class I in transformed or infected cells . In this analogy IRGM proteins resemble MHC class I molecules: just like MHC class I molecules , IRGM proteins fulfill dual functions in that they can promote antimicrobial activities directly and simultaneously act as inhibitory molecules that block the activation of an alternative defense system . How do Irgm1 and Irgm3 proteins guard membranes against GKS and GBP proteins ? Studies performed by Howard and colleagues indicate that IRGM proteins act as Guanine nucleotide Dissociation Inhibitors ( GDI ) for GKS proteins [15] , [16] . Based on these findings , Howard and colleagues proposed that by maintaining GKS GTPases in the GDP-bound , monomeric state , IRGM proteins reduce the lipid binding capacity of GKS proteins and block their stable association with IRGM-coated membranes . Here , we provide direct evidence in support of this model . As originally proposed by Hunn et al . , our data indicate that the absence of IRGM proteins from PVs promotes the transition of GKS proteins into the GTP-bound , active state and their stable association with IRGM-deficient PVs . We show here that IRGM-deficient membranes are also targets for GBP proteins ( Figure 7 ) . Whether IRGM proteins act as GDIs for GBP proteins or block the ability of GBP proteins to associate with LDs by an alternative mechanism will need to be elucidated in future studies . The lipid binding substrates for GKS and GBP proteins are currently unknown . However , lipid components that are present in LDs as well as in T . gondii parasitophorous and Chlamydia inclusion membranes are obvious candidates to act as GKS- and GBP-interacting molecules . It is tempting to speculate that GKS and GBP proteins might have evolved to preferentially bind to lipids that are frequently found in PVs but infrequently found on the cytosolic face of most endomembranes [35] . According to this model , most “self” structures would be protected against the erroneous attack by GKS and GBP proteins for two reasons: 1 ) the presence of guarding IRGM proteins and 2 ) the relative sparsity of lipid binding substrates on the cytosolic leaflet of “self”-membranes . This model would suggest that GKS and GBP proteins tether specifically to PVs due to the missing of “self” IRGM proteins from PVs and the presence of an unknown “second signal” on PVs . As suggested above , a unique pattern of lipids may provide such a second signal , although other molecules may also be involved . Albeit speculative at this point , we propose that LDs feature such a second signal and therefore become primary targets for GKS and GBP proteins in the absence of IRGM guard molecules . The requirement of a second signal for GKS/GBP membrane targeting as proposed in the model outlined above could in part explain why Irgm1/m3−/− mice and cells are viable in spite of lacking two critical “guard” proteins . Alternatively , expression of Irgm2 in Irgm1/m3−/− cells may provide sufficient protection to assure survival of Irgm1/m3−/− cells upon immune activation . This second model would necessitate that Irgm2 like Irgm1/m3 guards “self” membranes against GKS and GBP proteins . In addition to guarding self-structures , expression of IRGM proteins is required for the efficient targeting of endogenous GKS proteins to C . trachomatis inclusions ( Figure 3 ) and T . gondii PVs [16] . However , tetramerized Irgb10-dsRED , when overexpressed , targets PVs efficiently in IRGM-deficient cells ( Figure 2A ) . These data argue against a direct role for IRGM proteins in delivering GKS proteins to PVs . We therefore propose a model in which IRGM proteins fulfill an indirect role in targeting endogenous GKS proteins to PVs: in this model GKS proteins can bind to an excess of unguarded “self” membranes in IRGM-deficient cells . Consequently , the cellular pool of available GKS proteins is diminished in IRGM-deficient cells and the efficiency of PV targeting is reduced . In addition to GKS proteins , GBP proteins also bind to PVs . The delivery of GBP proteins to PVs requires the presence of a previously unknown IFN-inducible cofactor ( s ) [32] , [33] . Here , we identify IRGM proteins as one such co-factor . We propose that IRGM proteins promote the recruitment of GBP proteins to PVs by a mechanism similar to that which regulates the subcellular localization of GKS proteins . GBP proteins bind to lipids as activated oligomers [36] , [37] and GBP mutants deficient in GTP binding fail to localize to PVs [29] , [33] . In this study , we demonstrate that IRGM proteins on LDs prevent GBP recruitment , suggesting that IRGM proteins interfere with the ability of GBP proteins to transition into the GTP-bound , oligomeric state . In support of this hypothesis , we found that Gbp2 forms high molecular weight aggregates in the absence of Irgm1/m3 ( A . S . P . and J . C . , unpublished results ) . Therefore , IRGM proteins may promote GBP recruitment to PVs by maintaining a pool of GDP-bound , monomeric GBP proteins that are able to diffuse to their target sites . Additional evidence for functional interactions between the GBP and IRG protein families comes from the observation that one or more members of GBP protein family associate with Irgb6 in complexes [38] . Deletion of the chromosomal region containing the genes Gbp1-3 , Gbp5 and Gbp7 causes a partial defect in the recruitment of Irgb6 and Irgb10 but not Irga6 to T . gondii PVs [38] , suggesting that physical interactions between specific GBP proteins and Irgb6/b10 promote targeting of Irgb6/b10 to PVs . In contrast to the partial GKS targeting defects of Gbp-deficient cells , Irgm1/m3−/− cells display a nearly complete deficiency in recruiting either Irgb10 ( Figure 10E ) or Gbp2 protein to T . gondii PVs ( Figure 10F ) . The combined results from both studies suggest that Irgm1 and Irgm3 regulate the recruitment of both GKS and GBP proteins to PVs , while one or more PV-targeted GBP proteins augment the recruitment of a subset of GKS proteins through direct physical interactions . In summary our data demonstrate that IRGM proteins orchestrate the proper targeting of antimicrobial GBP and GKS proteins away from “self” membranes and towards “non-self” PVs .
MEFs derived from wildtype , Irgm1−/− , Irgm3−/− and Irgm1/m3−/− mice were previously described [39] , [40] . MEFs and African green monkey kidney Vero cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) ( Denville and Life Technologies ) . Primary murine bone marrow macrophages were isolated from the tibia and femurs of 2- to 4-months-old mice as described before [41] . C . trachomatis LGV-L2 was propagated as described [39] . A previously described GFP-expression vector [42] was introduced into LGV-L2 for visualizing C . trachomatis at 2 hpi . GFP-expressing Toxoplasma gondii tachyzoites of the type II strain Prugniaud A7 were a generous gift from Dr . John Boothroyd ( Stanford University , Stanford , CA ) [43] . Infections with C . trachomatis were performed at a nominal multiplicity of infection of 1–5 as described [39] . For T . gondii infections cells were incubated overnight with or without 200 U/ml of IFNγ and asynchronously infected with tachyzoites at a nominal multiplicity of infection of 5–10 for thirty minutes . For lipid loading experiments , OA ( Sigma ) was precomplexed with fatty acid-free BSA ( Sigma ) in PBS and emulsified by sonication . OA was added to growth media at final concentration of 100 µM for immunofluorescence experiments . LDs were isolated from MEFs as described before [44] with minor modifications as outlined here . Cells were grown in 150 mm dishes in DMEM +10% FBS and incubated with OA at 300 µM in the presence or absence of 100 U/mL of IFNγ for 14 h before harvesting LDs . Cells were washed with PBS and collected in 5 ml TNE buffer [20 mMTris-HCl ( pH 7 . 5 ) , 0 . 15 M NaCl , and 1 mM EDTA] containing protease inhibitors ( Roche Diagnostics ) . Cells were lysed on ice with ∼30 strokes/150 mm dish in a Dounce homogenizer and 80 µl of total lysates were collected from each sample and stored at −20°C for Western blotting . Cell lysates were then adjusted to 0 . 45 M sucrose , overlaid with 2 ml each of 0 . 25 M , 0 . 15 M , and 0 M Sucrose/TNE and centrifuged at 30 , 000 rpm for 90 min at 4°C in an SW41 rotor ( Beckman Coulter ) . The floating LD-enriched fat layer was collected , diluted in TNE , and refloated at 47 , 000 rpm for 45 min in a TLA55 rotor ( Beckman Coulter ) . LDs were collected , and lipids were extracted with 4 volumes of diethyl ether . Delipidated proteins were precipitated with ice-cold acetone for 1 h , solubilized in 0 . 1%SDS and 0 . 1 N NaOH , and normalized for total protein content by Bradford assay before SDS-PAGE and immunoblot analysis . Following protein transfer to nitrocellulose membranes , membranes were incubated with antibodies as listed below . Densitometric analyses for protein quantification in Western blots were carried out using Image J 1 . 45 s software . Immunocytochemistry was performed essentially as described previously [39] , [44] . Cells were washed thrice with PBS , pH 7 . 4 prior to fixation . Cells were fixed with 3% formaldehyde and 0 . 025% glutaraldehyde for 20 min at room temperature ( RT ) in all experiments that visualized LDs . T . gondii-infected cells were fixed with 4% paraformaldehyde in PBS , pH 7 . 4 , cells for 20 min at RT . In all experiments involving LD staining , fixed cells were permeabilized/blocked with 0 . 05% ( v/v ) saponin and 2% BSA/PBS ( SBP ) for 30 min at RT . When preserving LD structures was not required , fixed cells were permeabilized in 0 . 1% ( v/v ) Triton X-100 in PBS for ten minutes , blocked for 1 h with 2% ( w/v ) BSA ( Equitech-Bio Inc . ) in PBS , and then stained with various primary antibodies , followed by Alexa Fluor-conjugated secondary antibodies ( Molecular Probes/Invitrogen ) . Working solutions of antibodies and BODIPY 493/503 ( 10 µg/ml ) ( Invitrogen ) for immunofluorescence were prepared in SBP ( for LD visualization ) or in 2% ( w/v ) BSA/PBS ( for all other experiments ) . Nucleic and bacterial DNA were stained with Hoechst 33258 according to the manufacturer's protocol . Mitochondria were visualized using MitoTracker Red CMXRos ( Invitrogen ) according to the manufacturer's instructions . Stained cells were washed with PBS , mounted on microscope slides with FluorSave ( Calbiochem ) or ProLong Gold ( Invitrogen ) , and allowed to cure overnight . Cells were imaged using either a Zeiss LSM 510 inverted confocal microscope or a Zeiss Axioskop 2 upright epifluorescence microscope . Co-localization of proteins with PVs was quantified in at least 3 independent experiments . In each experiment at least ten randomly selected fields were imaged for each condition for each cell type . Differential interference contrast images were used to identify extracellular T . gondii tachyzoites because the vacuoles typically contained only one parasite under the experimental conditions used . The fraction of Gbp2- or Irgb10-positive vacuoles was determined for each field by dividing the number of Gbp2- or Irgb10-labeled vacuoles by the total number of vacuoles . Co-localization with C . trachomatis inclusions was quantified using the identical approach . Co-localization of Irgb10 and Gbp2 with LDs was quantified using MBF ImageJ software ( developed by Wayne Rasband , National Institutes of Health , Bethesda , MD; available at http://rsb . info . nih . gov/ij/index . html ) . Images were pre-processed to correct uneven illumination and to minimize noise and background . The co-localization rates were measured based on Manders' coefficient , which varies from 0 to 1 . A coefficient value of zero corresponds to non-overlapping images while a value of 1 reflects 100% co-localization between the images being analyzed . To perform line tracings , i . e . analyze the fluorescence signal intensity profiles of pixels along a selection from images , we used ImageJ software . Cells were treated with or without IFNγ ( 200 U/ml ) in the absence or presence of OA for 20 to 24 hours . Where indicated , BAF was supplemented at a final concentration of 100 nM at 12 hours post IFNγ activation . LD mass was determined by Flow Cytometry as described elsewhere [26] . Briefly , after fixing the cells with 2% PFA , cells were stained with BODIPY 493/503 at 5 µg/ml in FACS buffer ( PBS , 1% BSA and 0 . 1% NaN3 ) for 30 minutes and washed with FACS buffer prior to analysis . The primary antibodies used included anti-Irgm1 mouse monoclonal antibody 1B2 [11] at 1∶10; anti-Irga6 mouse monoclonal antibody 10D7 [12] at 1∶10; anti-Irgb10 rabbit polyclonal antiserum [39] at 1∶1000; anti-Irgb6 rabbit polyclonal antisera [27] at 1∶1000; anti-Irgm3 rabbit polyclonal antisera [45] at 1∶1000; mouse monoclonal anti-Irgm3 antibody ( BD-Transduction Labs ) at 1∶300; FITC-labeled mouse monoclonal anti-C . trachomatis MOMP [39] at 1∶200; rabbit anti-IncG [46] at 1∶50; anti-V5 mouse monoclonal antibody ( Invitrogen ) at 1∶1000; anti-FLAG mouse monoclonal antibody F1804 ( Sigma ) at 1∶500 , rabbit anti-Pmp70 ( abcam ) at 1∶500; anti-TIP47 polyclonal antisera ( Proteintech ) at 1∶1000; anti-p62/SQSTM1 rabbit polyclonal antibody ( MBL International ) at 1∶500; and anti-LC3 rabbit polyclonal antibody ( MBL International ) at 1∶1000 . An affinity-purified polyclonal rabbit anti-Gbp2 antibody was generated against the peptide EVNGKPVTSDEYLEHS of Gbp2 and used at 1∶1000 . An Irgb10-GFP expression construct has been previously described [39] . Site-directed mutagenesis was performed using the QuikChange II Site-Directed Mutagenesis Kit ( Agilent Technologies Inc . ) to introduce the listed point mutations and deletions into the same construct . Standard cloning techniques were used to generate to insert DNA encoding dsRED and the yeast protein TyA into the listed GFP expression constructs . DNA oligonucleotides used for cloning are listed in Table 1 . A previously described TyA expression construct [21] , a kind gift from Dr . Stephen Gould , was used as a template for DNA amplification . The C57BL/6J-derived cDNAs of Irgm1 , Irgm2 and Irgm3 were cloned into pcDNA3 . 1/V5-His-TOPO ( Invitrogen ) following the manufacturer's instructions . FLAG-tagged and GFP-tagged expression constructs of Gbp1 and Gbp2 and Gbp1 mutant variants have been previously described [33] . MEFs were transduced using the MSCV-based delivery system ( Clontech ) or transfected using Attractene ( Qiagen ) following the manufacturers' instructions . Results are represented as means ± SD . All comparisons were evaluated for statistical significance through the use of unpaired two-tailed t tests . When necessary , significant differences between data points were highlighted and the level of significance was depicted as: * , p<0 . 05; ** , p<0 . 01; and *** , p<0 . 005 . | Cell-autonomous host defense pathways directed against vacuolar pathogens constitute an essential arm of the mammalian innate immune defense system . Underlying most of these defense strategies is the ability of the host cell to recognize foreign or pathogen-modified structures and to deliver antimicrobial molecules specifically to these sites . Specific targeting of molecules to pathogen-containing vacuoles ( PVs ) requires host cells to recognize PVs as “non-self” structures that are distinct from intact “self” structures like organelles and other endomembrane components . In this work , we develop a new framework for understanding a critical principle that guides the mammalian immune system in the recognition of PVs as “non-self” structures . Our data indicates that so-called IRGM proteins function as markers of “self” compartments . We find that IRGM proteins act as “guards” that prevent a set of antimicrobial GTPases from stable association with “self” membranes . Because IRGM proteins are largely absent from “non-self” PVs , we propose that intracellular immune recognition of PVs can occur via the missing of “self” IRGM proteins . |
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The mechanism underlying immune system recognition of different types of pathogens has been extensively studied over the past few decades; however , the mechanism by which healthy self-tissue evades an attack by its own immune system is less well-understood . Here , we established an autoimmune model of melanotic mass formation in Drosophila by genetically disrupting the basement membrane . We found that the basement membrane endows otherwise susceptible target tissues with self-tolerance that prevents autoimmunity , and further demonstrated that laminin is a key component for both structural maintenance and the self-tolerance checkpoint function of the basement membrane . Moreover , we found that cell integrity , as determined by cell-cell interaction and apicobasal polarity , functions as a second discrete checkpoint . Target tissues became vulnerable to blood cell encapsulation and subsequent melanization only after loss of both the basement membrane and cell integrity .
The discovery of Toll-like receptors and other categories of pattern recognition receptors has greatly enhanced our understanding of how the immune system recognizes different types of pathogens [1] , [2]; however , it is less clear why the immune system often turns its arsenal toward self-tissues . In fact , the same receptors that were originally found to bind specific types of pathogens are often involved in autoimmune diseases , making this issue more puzzling [3] . To understand this process , it is imperative to molecularly define the notion of the “immunological self” . Autoimmune-like responses are also observed in invertebrates . In Drosophila , degenerating internal tissues are subjected to hemocyte ( insect blood cell ) encapsulation , in which large , flat lamellocytes wrap up the target tissues in layers and melanize them via the phenoloxidase cascade . This process is called melanotic mass formation [4]–[6] . The same process occurs as part of the immunological defense against oversized pathogenic invaders , such as parasitoid wasp eggs , which are too large to be engulfed by the most abundant phagocytic hemocytes , the plasmatocytes [7] , [8] . Currently , about 100 genes have been found to display melanotic masses upon mutation or overexpression ( see FlyBase . org ) . These genes are seemingly unrelated , and the specific triggers of this autoimmune-like reaction are largely obscure . More than 30 years ago , Rizki and Rizki reported that the basement membrane ( BM ) appeared to serve as a barrier against hemocyte attack of self-tissues in Drosophila [9]–[11] . Whereas a same-species implant with the intact BM remained in the host , implants that had been mechanically damaged or pre-treated with collagenase to disrupt the BM triggered lamellocyte encapsulation [11] . Moreover , undamaged implants from sibling species did not induce lamellocyte encapsulation , whereas undamaged implants from distantly related species did , suggesting that hemocytes may recognize the molecular architecture of the BM of its own species . This interesting study raises several important questions as to which molecular component of the BM is crucial for blocking melanotic mass formation of self-tissues , and whether the BM is the sole surface feature for self-tolerance . Furthermore , their experiments were carried out in a sensitized genetic background , tu ( 1 ) Szts , in which the hemocytes were marginally hyperactive at a permissive temperature , which made it unclear whether the mass formation is caused primarily by defects in immune cells or target tissues . These questions have never been probed with genetic tools , largely due to the essential nature of the genes encoding the BM components collagen IV and laminin [12]–[16] . More recently , BM disruption was shown to act as a signal to recruit hemocytes to wound regions or to metastasizing tumors , providing further evidence for the BM-hemocyte relationship [17]–[19] . The BM is located on the basal side of epithelial tissues and serves multiple functions as a cell-supporting matrix , a tissue barrier , and ligands for cell surface receptors [20] , [21] . The composition of the BM varies between tissue types , but in general , the BM contains the following four major components: collagen IV and laminin , which together form a meshwork , and the proteoglycan Perlecan and Nidogen , which function in the scaffold . The BM is maintained by evolutionarily conserved cell surface receptors , such as integrin and dystroglycan [22] . Drosophila melanogaster has two collagen IV genes , Cg25C ( for α1 chain ) and vkg ( α2 ) , and four laminin genes , wb ( for laminin α1 , 2 ) , LanA ( α3 , 5 ) , LanB1 ( β ) , and LanB2 ( γ ) [16] , [23]–[25] . Collagen IV is thought to exist mostly as Cg25C/Vkg heterotrimers , and LanB1 and LanB2 form the common core of the two laminin trimers , laminin W and laminin A . Thus , the mutant phenotypes of these genes are very similar in their own categories , and absence of one subunit is known to prevent BM incorporation of the other ( s ) [14] , [15] , [26] . The major BM components are expressed and secreted predominantly by the fat body and hemocytes [12] , [26] , [27] , although laminins are also expressed in various other tissues [16] , [27] . Here , we genetically removed each of the major BM components using RNA interference ( RNAi ) followed by careful immunohistochemical analysis and examined their roles in melanotic mass formation . We discovered that lamellocyte encapsulation may be blocked by two separate and discrete self-tolerance checkpoints that operate in healthy target tissues . The first checkpoint involves laminin of the BM , and the second involves cell integrity as determined by cell-cell adhesion and apicobasal cell polarity .
To systematically investigate the relationship between the BM and the melanotic mass phenotype , we disrupted the BM using genetic approaches . We knocked down genes for the two collagen IV subunits and the four laminin subunits individually via UAS-RNAi using ubiquitous ( Act5C-GAL4 ) , inducible ( Hsp70-GAL4 ) , and tissue-specific GAL4 drivers ( HmlΔ-GAL4 , FB-GAL4 , and Cg-GAL4 ) ( summarized in Table S1 ) . Knockdown of any one of the six genes consistently induced black masses in the larvae with either Hsp70-GAL4 or Cg-GAL4 drivers ( Figure 1A and 1B; Table S1 ) . Knockdown of the genes for the BM receptor integrins ( scb for αPS3 and mys for βPS ) , Dystroglycan ( Dg ) , or its cytosolic adaptor Dystrophin ( Dys ) similarly induced black masses ( Figure 1A and 1B; Table S1 ) . Melanotic masses formed mainly in fat bodies and salivary glands . We analyzed the fat bodies of these larvae by immunostaining with the lamellocyte-specific L1 antibody . Pale brown-colored fat bodies ( dissected in early stages of melanin deposition ) from larvae in which collagen IV , laminin , or integrin had been knocked down by RNAi were encapsulated by a few lamellocytes ( Figure 1C–F ) . Black nodules ( dissected in late stages of melanin deposition ) recovered from these larvae were also L1-positive ( Figure S1A ) . Using confocal microscopy , we confirmed the complete disappearance or severe disruption of the BM in the fat bodies of these larvae ( Figure 1G–K ) . This immune response did not appear to be caused by pathogen infection , as the larvae did not induce the antimicrobial peptide gene Attacin-A ( Figure S1B; Text S1 ) . Thus , these data indicate that BM loss induced melanotic mass formation . To determine whether loss of the BM is a general feature of the melanotic mass phenotype , we examined various genes that had been previously associated with the melanotic mass using mutant or RNAi-treated larvae . Because we were primarily interested in the target tissues as opposed to hemocytes , we first excluded mutants that might be classifiable as “true blood cell tumors” , in which melanotic mass formation was due to hemocyte hyperactivation [4] . The following four genes were analyzed for the BM: spag [6] , krz [6] , mRpS30 [28] , and hyx [28] . We found that mutant or RNAi-treated larvae for these genes commonly had disrupted BMs in the fat bodies and that the fat bodies were positive for L1 ( Figure S1C and S1D ) . We also examined 30 other melanotic mass-associated genes; however , the RNAi-treated larvae did not reproduce black nodules with the available UAS transgenes and tissue-specific GAL4 drivers or exhibited early lethality with either ubiquitous or stronger GAL4 drivers , thus precluding further analysis ( Table S2 ) . We also analyzed hopTum larvae , in which the JAK kinase Hopscotch is constitutively active and melanotic mass phenotype is dominant at restrictive temperatures ( >25°C ) [29] . Although the melanotic phenotype of this mutant may fit the classification for the blood cell tumors [30] in that hemocyte numbers increase dramatically ( see Figure 2A and 2B ) , we sought to determine its target tissues . At 25°C , only the collagen IV level decreased severely , while at the restrictive temperature ( 29°C ) , both collagen IV and laminin were absent , and numerous lamellocytes attached to the fat body and the salivary gland ( Figure S1C and S1D ) . Altogether , these observations corroborated the evidence that BM-deficient tissues induce melanotic masses . To see whether lamellocyte encapsulation of BM-deficient tissues was a normal hemocyte reaction to abnormal self-tissue or rather due to an abnormality in the hemocyte itself [4] , we assessed the activation state of hemocytes first by counting the cells . The numbers of circulating hemocytes of some of the BM-deficient larvae were 2–2 . 5-fold higher than those of controls ( Figure 2A ) ; however , the numbers of lamellocytes and crystal cells , a third type of hemocytes that contain phenol oxidation enzymes as a crystal form in their cytosol [31] , did not increase significantly in any of the cases ( Figure 2A , S2A , and S2B; Text S1 ) . This result was in stark contrast to the results for hopTum ( Figure 2A ) , TollD , or cactus mutants , which harbor hyperactive hemocytes [5] , [30] . To inhibit hemocyte hyperproliferation displayed by some of the BM-deficient larvae , we expressed a dominant-negative allele for the JAK/STAT pathway receptor Domeless ( domeΔCYT ) in mys RNAi larvae [18] , [32] . Hemocyte numbers were restored to a normal level in these larvae; however , melanotic mass formation was not abrogated or reduced , indicating that the mass phenotype was not due to hemocyte hyperproliferation ( Figure 2A ) . We then examined the larval hematopoietic lymph gland , as robust lamellocyte differentiation in the lymph gland is a common feature of blood cell tumors [28] , [33]–[36] . The lymph glands of wild-type larvae rarely contained the L1-positive lamellocytes [37] ( Figure 2B ) . The lymph glands of collagen IV- or laminin-knockdown larvae occasionally contained 1–5 L1-positive cells , while lymph glands of integrin-knockdown larvae had 5–20 of these cells . The observed levels of activation may be expected for melanotic mass-forming larvae , but the levels were significantly different from that of hopTum in which the cortical zone of the lymph gland was filled with L1-positive cells [38] ( Figure 2B ) . We then examined sessile hemocytes , another source of lamellocytes upon wasp egg infection [39] . Collagen IV knockdown did not change the sessile hemocyte population , as analyzed by plasmatocyte-specific Eater-GFP ( Figure S2C ) . Finally , we knocked down the BM genes using the fat body-specific FB-GAL4 [28] to determine whether gene manipulation at the target site only , and not in the hemocyte or in the hematopoietic organs , still induced melanotic masses . Knockdown using any of the available UAS-RNAi transgenes singly did not induce melanotic masses , presumably due low knockdown efficiencies with FB-GAL4 driver ( Figure 2C ) ; however , knockdown of various combinations of the transgenes induced melanotic masses specifically in the fat body in the absence of a sensitized genetic background [11] . We obtained similar results following fat body-specific RNAi knockdown for integrins or Dystroglycan , which should act strictly in a cell-autonomous manner ( Figure 2C ) . Circulating hemocytes produced collagen IV and laminin , as reported previously [12] , [26] , [27] , but were not enclosed by a sheet of collagen IV or laminin outside of the cell ( Figure S2D; Text S1 ) , excluding the possibility that knockdown of these components in the fat body may have produced the mass phenotype by affecting the surface of the hemocyte rather than the target . Based on these results , we conclude that melanotic mass formation in BM-deficient larvae results from a normal immune response ( operationally defined by FB-GAL4 ) against altered self and is not ascribed to a failure in the immune system . To investigate which component of the BM is crucial in self-tolerance , we individually knocked down each of the BM genes using various GAL4 drivers and analyzed BM integrity and the melanotic mass phenotype . Since melanotic masses in the BM-deficient larvae formed mainly in fat bodies and salivary glands , we focused on these two organs . In fat bodies , collagen IV knockdown with Hsp70-GAL4 reduced not only collagen IV ( the fluorescence intensity was 5 . 23% of the control level ) but also laminin in the BM ( hereafter , BM laminin ) effectively ( 33 . 46%; Figure 1H and 1K ) . Similarly , laminin knockdown ( 11 . 56% ) nearly completely eliminated the BM collagen IV ( 0 . 91%; Figure 1I and 1K ) , indicating that these factors are structurally inter-dependent in this organ . In salivary glands , however , collagen IV knockdown with the same Hsp70-GAL4 eliminated only collagen IV ( 0 . 47% ) , while leaving 75 . 61% of laminin in the BM ( Figure 3B and 3D ) , allowing for separation of the two components in this organ . Laminin knockdown ( 6 . 71% ) effectively removed the BM collagen IV ( 18 . 02% ) in the salivary gland ( Figure 3C and 3D ) , as in the fat bodies . These results were consistent with the fact that laminin is the key component of BM assembly [14] , [20] , [21] . More importantly , collagen IV knockdown induced melanotic masses only in the fat body and not in the salivary gland , whereas laminin knockdown induced melanotic masses in both organs ( Figure 3E–G and L , the first four experiments; melanotic encapsulation often occurred regionally but not in the entire organs , which might be due to incomplete removal of the BM laminin ) . These results strongly suggest that BM laminin and possibly other factors that are tightly associated with laminin block melanotic mass formation , whereas collagen IV is not necessary for blocking this process as the collagen IV-deficient larvae did not form melanotic masses in the salivary gland ( Figure 3F and 3L ) . To further explore the self-tolerance checkpoint function of the BM components , we knocked down the collagen IV and laminin genes using HmlΔ-GAL4 and FB-GAL4 , which are active in hemocytes and fat bodies , respectively [28] , [40] . Laminin knockdown using these GAL4 drivers was inefficient . As for collagen IV knockdown , the changes in BM integrity induced by these two GAL4 drivers were generally the same as those with Hsp70-GAL4 ( Figure 3H–K ) , except that collagen IV knockdown specifically eliminated the BM collagen IV but left considerable laminin at the BM even in the fat bodies ( Figure S3A–D ) . These larvae did not form black masses , further demonstrating that collagen IV is dispensable in the BM for blocking melanotic masses ( Figure 3L , the last four experiments ) . We next examined Perlecan and Nidogen , the other two major components of the BM . Knockdown of the Perlecan-coding gene trol with Act5C-GAL4 did not induce black masses , indicating that Perlecan is not required in this process ( Figure S3E; Table S1 ) . Knockdown of Nidogen using available RNAi transgenes were unsuccessful; however , we found that Nidogen was completely absent in collagen IV-deficient , melanotic mass-free larvae , indicating that Nidogen is not required for blocking melanotic mass formation ( Figure S3F; Table S1 ) . Finally , we knocked down both trol and vkg , leaving laminin as the only major BM component , and found that melanotic masses were not formed ( Figure 3M and 3N ) . Thus , these results indicate that the BM laminin was sufficient to block melanotic mass formation against self-tissue . As an independent approach to determine BM function , we removed the BM by overexpressing Matrix metalloproteinase 2 ( Mmp2 ) . Mmp2 expression in the salivary gland severely disrupted BM integrity , as reported previously [18] ( Figure S4A and S4B ) , but contrary to our expectations , the larvae did not form melanotic masses . Hemocytes attached to the salivary gland , indicating that hemocyte access to the target cells was not blocked ( Figure S4C and S4D ) . We noticed that the salivary gland cells of these larvae looked similar to those of wild-type larvae ( Figure 4A and 4B vs . 4E and 4F ) , whereas the cells of laminin-knockdown larvae were dissociative and round ( Figure 4G and 4H ) , indicative of the loss of cell-cell adhesion . We further examined whether these melanotic mass-containing larvae had defects in cell polarity using apicobasal cell polarity markers [41] . In wild-type larvae , Cora and Dlg localized to the lateral and basal sides of cells ( Figure 4A and 4B ) . A similar pattern was observed in vkg trol RNAi larvae ( Figure 4C and 4D ) . In ptc>Mmp2 larvae in which Mmp2 is expressed in the salivary gland [18] , the lumen often twisted as it expanded , and cell arrangement occasionally became abnormal ( Figure 4F ) . Dlg diffused throughout the membrane , albeit weakly ( Figure 4F ) . Nevertheless , Cora was still excluded from the apical side ( Figure 4E ) , indicating that apicobasal polarity was partially maintained , perhaps due to the remains of the disrupted BM on the cell surface ( Figure S4B ) . In contrast , the salivary gland cells of laminin-knockdown larvae displayed complete loss of apicobasal polarity . Cora and Dlg were localized throughout the cell membrane and were more often lost from the membrane in these larvae ( Figure 4G and 4H ) . Cell-cell contacts were also severely disrupted . These results strongly suggest that loss of cell integrity , in addition to the loss of the BM , may be required for melanotic mass formation . In this report , we will subsequently use the term “cell integrity” to refer to the cellular aspects involving cell-cell adhesion and cell polarity . To examine the possible role of cell integrity as another checkpoint , we used melanotic mass-free AB1>mys-i larvae . Integrin knockdown with the salivary gland driver AB1-GAL4 [42] did not disrupt the BM but resulted in detachment of the BM from the salivary gland tissue ( Figure 4K ) . The cells lost both cell polarity and adhesion properties , and as a result , the BM appeared as a sack containing sticky balls ( Figure 4I–K ) . As expected , hemocytes were not detected on the surface of the salivary gland in these larvae ( Figure 4L ) . To explore this phenotype in more depth , we first mechanically sheared the salivary gland BM by pinching the AB1>mys-i , GFP larva at its anterior side with forceps ( Figure 5A and 5B ) . After two days , these larvae developed black masses in the salivary gland at a rate that was 12-fold higher than that of the pinched control larvae ( Figure 5C ) . Melanized salivary glands dissected from the wounded larvae were positive for L1 ( Figure 5D ) . Second , we enzymatically disrupted the salivary gland BM of ptc>mys-i larvae by overexpressing Mmp2 , as a means to more specifically manipulate the larvae . These larvae developed black masses , and the salivary glands dissected from the larvae were positive for L1 ( Figure 5E–G ) . In these experiments , ptc-GAL4 and mys-i27735 were used instead of the previously used AB1-GAL4 and mys-i33642 because the latter combination with UAS-Mmp2 caused severe growth retardation of salivary glands . The reproducibility of the knockdown phenotypes was confirmed using mys-i27735 ( Figure S5 ) . Third , we tried to wear out the BM by reducing levels of collagen IV in mys-i larvae . Black masses formed only in the fat bodies of FB>vkg-i , mys-i larvae ( Figure 2C ) : but due to the additional AB1-GAL4 driver , a few of FB+AB1>vkg-i , mys-i larvae developed black masses also in the salivary glands , and again , the salivary glands of those larvae were positive for L1 ( Figure 5J ) . Neither mys-i nor vkg-i alone formed black masses in the salivary gland with the same FB+AB1 GAL4 drivers ( Figure 5H and 5I ) . Taken together , our data demonstrate that cell integrity is an additional and discrete checkpoint for tolerance to self-tissues . We sought to define cell integrity in this system by knocking down genes known to be involved in cell-cell adhesion and cell polarity . Knockdown of either scrib , dlg , cora , FasIII , shg , or arm together with Mmp2 overexpression , however , did not induce melanotic mass formation , indicating that loss of any of these components at least singly did not affect cells sufficiently for disrupting the cell-integrity checkpoint function .
Based on the results of these studies , we propose that BM laminin on target tissues functions as a crucial component not only in BM assembly but as a tolerance checkpoint to self-tissues ( Figure 6 ) . As a self-tolerance checkpoint , the BM may either serve as a physical barrier or provide an inhibitory ligand for hemocyte receptors . We speculate that the latter is the case for several reasons . First , the heterospecific implantation experiments described above suggest that the hemocyte recognizes the BM structure of its own species [11] . Second , insect hemocytes are known to encapsulate a wide range of foreign materials , from parasitoid wasp eggs to synthetic beads , when injected into the hemocoel [37] , [43] , [44] . Encapsulation of parasites is faster than encapsulation of non-parasitic , heterospecific implants [discussed in 11] . Thus , hemocytes must be equipped with various cell surface receptors , including some as activating receptors with different binding spectra for pathogen-associated molecular patterns , and others as inhibitory receptors with narrow binding specificities to self-tissues . More specifically , laminin-coating of Sephadex beads inhibit melanotic encapsulation of the beads in mosquito hemocoel [43] . The outer surface of the Plasmodium oocyst appears to bind to mosquito-derived laminin upon passage through the midgut epithelium of the mosquito [45] , suggesting that the insect laminin may indeed serve as an inhibitory ligand to hemocytes of its own species . Furthermore , we have identified cell integrity as another checkpoint for self-tolerance . Self-tissues must lose both the BM and cell integrity in order to be subjected to lamellocyte encapsulation ( Figure 6 ) . The two checkpoints are indeed discrete and experimentally separable . In our experimental system , BM integrity or cell integrity could be disrupted in the salivary gland selectively via ptc>Mmp2 or AB1>mys-i , respectively . The salivary glands of these larvae are subjected to melanotic encapsulation if the tissues fail to acquire the state of self-tolerance from the other , remaining checkpoint . Therefore , multiple failures on sequential self-tolerance checkpoints elicit autoimmune responses , which is analogous to the mechanism of self-tolerance in the mammalian adaptive immune system [46] . In this respect , BM laminin is unique in that knockdown of laminin disrupted both BM integrity and cell integrity simultaneously . During pharyngeal tube formation of the C . elegans embryo , laminin is required for establishment of cell polarity of a group of precursor cells called the double plate [47] . Since this process occurs before the tissue BM is formed and the mutant phenotype is not shared by collagen IV or perlecan mutants , the authors concluded that this function of laminin is distinguished from its role in the BM . Our results indicate that the Drosophila laminin functions similarly and thus is unique among the BM components . Since cell integrity was identified as a discrete self-tolerance checkpoint , it is interesting that target tissue is not subjected to encapsulation during wound healing or developmental processes in which BM integrity is disrupted temporarily . Upon tissue damage , circulating plasmatocytes attach to the wound region as early as 5 min [17] , presumably to help remodel the tissue , including the disrupted BM , as hemocytes also produce BM components [12] , [27] ( Figure 3I ) . According to our model , as long as cell integrity remains intact , this checkpoint would warrant self-tolerance and block lamellocyte encapsulation in these cases , and thus , wound repair would proceed safely ( as in Figure 5C ) . Cancer metastasis is a more complex problem , as the hallmarks of cancer include BM degradation and loss of epithelial polarity [48] , [49] . Additional work will be required to probe this situation , but it is plausible that cell integrity may exert its checkpoint function normally through inhibitory cytokines , and prior to successful metastasis , cancer cells may have to obtain the ability to secret such cytokines independently of the state of cell integrity . While our results were obtained in the invertebrate Drosophila system , we think these findings have relevance to mammalian autoimmunity . In type I diabetes in humans and in a mouse model , leukocyte infiltration and subsequent β cell destruction have been shown to correlate with disruption of the peri-islet BM , suggesting that the BM may protect islets from autoimmune attack [50] , [51] . In Sjögren's syndrome , which mainly affects the exocrine tissues such as tear and salivary glands , increased degradation of the basal lamina is observed in labial salivary glands , and changes in laminin composition of the salivary acini correlates well with disease progression [52] , [53] . A clear causal relationship is still lacking , but it is tempting to speculate that at least some of the autoimmune diseases may be caused by alterations in BM integrity and/or cell polarity and loss of these features may be recognized by the immune system as “missing self” , similar to the way natural killer cells distinguish between self and nonself [54] , [55] .
The following strains were obtained from public stock centers: UAS-vkg-i ( 106812† ) , UAS-Cg25C-i ( 104536 , 28369 ) , UAS-LanA-i ( 18873 ) , UAS-wb-i ( 108020 ) , UAS-LanB1-i ( 23119 , 23121 ) , UAS-LanB2-i ( 42559 , 42560 , 104013† ) , UAS-trol-i ( 22642 ) , UAS-Ndg-i ( 13208 ) , UAS-mew- i ( 44890 ) , UAS-if-i ( 100770 , 44885 ) , UAS-scb-i ( 4891 , 100949 ) , UAS-ItgαPS4-i ( 37172 ) UAS- ItgαPS5-i ( 6646 , 100120 ) , UAS-mys-i ( 29619† ) , UAS-Itgβν-i ( 893 ) , UAS-Dg-i ( 107029 ) , UAS-Dys-i ( 106401 , 106578 ) , and UAS-krz-i ( 103756† ) from Vienna Drosophila RNAi Center; UAS-vkg-i ( 16858R-3 ) , UAS-trol-i ( 12497R-1 ) , UAS-Ndg-i ( 12908R-3 ) , UAS-mew- i ( 1771R-1 ) , UAS- ItgαPS4-i ( 16827R-2 ) , UAS- Itgβν-i ( 1762R-1 ) , UAS-mys-i ( 1560R-1 ) , UAS-mRpS30-i ( 8470R-4† ) , UAS-hyx-i ( 11990R-2† ) , UAS-dlg1-i ( 1725R-1 ) , and UAS-FasIII-i ( 5803R-1 ) from National Institute of Genetics , Japan; and Hsp70-GAL4 ( 1799 ) , Cg-GAL4 ( 7011 ) [56] , HmlΔ-GAL4 ( 30141 ) , ptc-GAL4 ( 2017 ) , AB1-GAL4 ( 1824 ) , Act5C-GAL4 ( 3954 ) , UAS-Dcr2 ( 24650 ) , UAS-Ser . mg5603 ( 5815 ) , UAS-GFP ( 4775 ) , LanB2MI03747 ( 37366 ) , hopTum ( 8492 ) , spagK12101 ( 12200 ) , UAS-LanA-i ( 28071 ) , UAS-trol-i ( 29440† ) , UAS-mys-i ( 33642 , 27735 ) , UAS-cora-i ( 28993 ) , UAS-dlg1-i ( 33620 ) , UAS-scrib-i ( 29552 ) , UAS-shg-i ( 32904 ) , and UAS-arm-i ( 35004 ) from the Bloomington Stock Center . The following stocks were obtained from private collections: Eater-GFP ( X ) from R . A . Schulz [57]; FB-GAL4 from R . P . Kühnlein [58]; and UAS-domeΔCYT , vkgG454 , and UAS-Mmp2 from T . Xu [18] . The RNAi constructs marked with † proved stronger in knockdown experiments than others and were used for further analysis including imaging . All flies were maintained at 25°C unless otherwise indicated on standard cornmeal and agar media . For RNAi knockdown using Hsp70-GAL4 , a single heat pulse of 30 min at 37°C was applied at 24 , 48 , 72 , or 96 h after egg laying ( AEL ) . After counting larval black nodules , 48 h and 96 h time points were chosen as the two optimal conditions for later experiments as 24 h yielded a lower rate of melanotic mass induction and 72 h resulted in a higher rate of lethality . For imaging the BM , a single heat-shock pulse was applied at 48 h AEL for Hsp70>vkg-i , and double pulses were applied at 48 h and 96 h AEL for Hsp70>LanB2-i104013;+/LanB2MI03747 for stronger knockdown . Larval density and stage were tightly controlled during culture due to the fact that melanotic mass formation was affected by overcrowding growth conditions . Ten virgin females of GAL4 strains were crossed to 5–7 males carrying different UAS-RNA-i strains . After 2 days , eggs were collected for 6 h ( approximately 40–60 eggs ) . Melanotic masses were counted at the wandering third instar stage by rotating the larvae under the dissection microscope . The percentages of larvae containing at least one melanotic mass in their bodies were determined after counting 3 or more vials ( n≥150 ) for each genotype or developmental time point . Wandering third instar larvae were dissected on a silicone pad in phosphate-buffered saline ( PBS ) using a pair of forceps . Larval organs were transferred immediately to 4% paraformaldehyde ( PFA ) and fixed for 15–30 min at room temperature . Samples were washed four times in PBS plus 0 . 1% Tween-20 ( PBST ) and incubated in a blocking solution of PBST plus 5% normal goat serum for 1 h ( PBST-NGS ) . Samples were then incubated with primary antibodies in PBST-NGS overnight at 4°C and then washed four times with PBST-NGS . Samples were then incubated with secondary antibodies alone or together with phalloidin-FITC in PBST-NGS for 2 h at room temperature . After four washes , samples were mounted in Vectorshield plus DAPI ( Vector Laboratories , Inc . , Burlingame , CA ) and were subjected to fluorescent microscopy ( Olympus BX40 ) or confocal microscopy ( Zeiss LSM 510 META ) . For junction staining of the salivary gland , PBS plus 0 . 3% Triton X-100 was used as the buffer . The following antibodies and reagents were used: mouse anti-collagen IV [59] ( Col IV ) ( 6G7 , 1∶100 ) , rabbit anti-LanB1 ( 1∶500; Abcam ) , rabbit anti-LanB2 ( 1∶500; Abcam ) , rabbit anti-Trol [60] ( 1∶2000 ) , rabbit anti-Ndg [61] ( 1∶2000 ) , mouse anti-Hemes [62] ( H2 ) ( 1∶500 ) , mouse anti-L1 [63] ( 1∶500 ) , mouse anti-Mys ( 1∶200; Developmental Studies Hybridoma Bank [DSHB] ) , mouse anti-Dlg 4F3 ( 1∶100; DSHB ) , mouse anti-Coracle C615 . 16 ( 1∶100; DSHB ) , anti-mouse IgG-Cy3 ( 1∶200; Jackson ImmunoResearch ) , phalloidin-FITC ( 1∶50 dilution of 400 µM stock; Sigma–Aldrich ) , and anti-rabbit and anti-mouse IgG conjugated to Alexa 488 or Alexa 546 ( 1∶200; Molecular Probes ) . For quantification of the BM fluorescence intensity , 400× magnified confocal images for the middle-marginal part of the salivary gland or region ‘6’ of the fat body were collected [11] . The fluorescence intensity was measured in 5–7 samples per genotype using the Image J software as described previously [26] . Wandering third instar larvae were washed in PBS and dried . A single larva was bled on a silicone pad in a 12-µl drop of PBS by ripping the epidermis with two fine forceps . The PBS/hemocyte drop was swirled gently using a micropipette tip and mounted on the Neubauer hemocytometer for counting . The total hemocytes were counted , and the lamellocytes were counted based on their characteristic morphology . For each genotype , at least 15 larvae were analyzed . In situ wounding was performed as described previously [18] with minor modifications . Third instar larvae were immersed in water on a silicone pad . Under the GFP/dissection microscope , larvae were gently pinched at the salivary glands ( with help of AB1>GFP ) using a pair of forceps ( Fine Science Tools , Cat . No . 11295-00 ) . Larvae were transferred to fresh cornmeal-agar media and incubated at 25°C . Dead or melanized larvae that were identified within the first 3 h were removed . After 48 h of wounding , non-pupariated larvae were dissected and analyzed for melanotic mass formation on the salivary gland . | Autoimmune diseases may be caused by failures in the immune system or by altered selfness in target tissues; however , which of these is more critical is controversial . To better understand such diseases , it is necessary to first define the molecular mechanisms that provide self-tolerance to healthy tissues . As a model system , we used Drosophila melanotic mass formation , in which blood cells encapsulate degenerating self-tissues . By manipulating basement-membrane components specifically in target tissues , not in blood cells , we could elicit autoimmune responses against the altered self-tissues . Moreover , we found that at least two different checkpoints for self-tolerance operate discretely in Drosophila tissues . This parallels mammalian immunity and provides etiological insight into certain autoimmune diseases in which structural abnormalities precede immune system pathology , such as Sjögren's syndrome and type I diabetes mellitus . |
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Hepatitis C virus ( HCV ) is among the most relevant causes of liver cirrhosis and hepatocellular carcinoma . Research is complicated by a lack of accessible small animal models . The systematic investigation of viruses of small mammals could guide efforts to establish such models , while providing insight into viral evolutionary biology . We have assembled the so-far largest collection of small-mammal samples from around the world , qualified to be screened for bloodborne viruses , including sera and organs from 4 , 770 rodents ( 41 species ) ; and sera from 2 , 939 bats ( 51 species ) . Three highly divergent rodent hepacivirus clades were detected in 27 ( 1 . 8% ) of 1 , 465 European bank voles ( Myodes glareolus ) and 10 ( 1 . 9% ) of 518 South African four-striped mice ( Rhabdomys pumilio ) . Bats showed anti-HCV immunoblot reactivities but no virus detection , although the genetic relatedness suggested by the serologic results should have enabled RNA detection using the broadly reactive PCR assays developed for this study . 210 horses and 858 cats and dogs were tested , yielding further horse-associated hepaciviruses but none in dogs or cats . The rodent viruses were equidistant to HCV , exceeding by far the diversity of HCV and the canine/equine hepaciviruses taken together . Five full genomes were sequenced , representing all viral lineages . Salient genome features and distance criteria supported classification of all viruses as hepaciviruses . Quantitative RT-PCR , RNA in-situ hybridisation , and histopathology suggested hepatic tropism with liver inflammation resembling hepatitis C . Recombinant serology for two distinct hepacivirus lineages in 97 bank voles identified seroprevalence rates of 8 . 3 and 12 . 4% , respectively . Antibodies in bank vole sera neither cross-reacted with HCV , nor the heterologous bank vole hepacivirus . Co-occurrence of RNA and antibodies was found in 3 of 57 PCR-positive bank vole sera ( 5 . 3% ) . Our data enable new hypotheses regarding HCV evolution and encourage efforts to develop rodent surrogate models for HCV .
Hepatitis C virus is one of the leading causes of human morbidity and mortality due to hepatitis , liver cirrhosis , and hepatocellular carcinoma [1] , [2] , [3] . It has become the main reason for liver transplantation in developed countries and represents an economic burden exceeding 1 billion US$ of direct health costs [4] , [5] . New estimates of the burden of disease suggest at least 185 million individuals worldwide to have been seropositive in 2005 , with a tendency to increase [6] . Treatment has considerably improved due to the optimization of antiviral regimens and the advent of new antiviral drugs [7] , [8] , [9] . However , treatment in resource-limited settings is hardly accessible [10] . The most effective instrument to prevent new infections with HCV would be a prophylactic vaccine . Unfortunately , chimpanzees are the only known animal species to adequately reflect human HCV infection [11] . Vaccine development is hampered by the lack of a small animal model accessible at early stages of vaccine development [12] , [13] . Mice cannot be infected with HCV [14] , but rats and mice engrafted with human hepatoma cells or transgenic for human CD81 and other co-receptor molecules have been proposed [12] , [15] , [16] , [17] . Mouse-adapted HCV has also been generated [16] , [18] . Still , these models are highly demanding from a technical point of view and reflect only parts of the pathogenesis and lifecycle of HCV , precluding their wide application [12] , [19] . A HCV-related hepacivirus of unknown origin , termed GBV-B , has been used as a surrogate model for HCV infection involving New World monkeys , where it causes hepatitis upon experimental inoculation [20] , [21] . The use of a surrogate model based on a related virus indicates a way to study HCV pathogenesis and immunity , even though neither monkeys nor apes are acceptable laboratory models in terms of accessibility and ethics [12] , [13] , [22] . Non-Primate hepaciviruses related to HCV have also been detected in dogs and horses [23] , [24] . While horses cannot be considered as laboratory models , dogs at least have compatible body sizes . However , additional to ethical controversies , infected dogs showed grossly deviating pathology in that they appeared to have higher virus concentrations in respiratory specimens than in the liver [23] . So far there is no evidence of antibodies against the virus in dogs , limiting their utility as a vaccination challenge model [23] , [24] . No hepaciviruses have been detected in other animals that could be kept in laboratories with reasonable effort , and under ethically acceptable conditions . The targeted identification of animal hepaciviruses might help elucidating the obscure origins of HCV and yield more accessible HCV surrogate models . We have recently demonstrated that the systematic investigation of small mammal reservoirs can yield novel viruses that are genetically closely related to human pathogenic viruses , such as the paramyxoviruses mumps and Nipah virus [25] . Biological and ecological considerations direct research interests to animals with properties supportive of virus maintenance . The close social interaction of certain bat species forming large and dense social groups favors virus maintenance [25] , [26] . Virus spreading by bats may be facilitated by their migratory lifestyle , but also by human activities such as hunting of bats as bushmeat and human invasion of remote habitats [27] , [28] , [29] . Several rodent species are also in focus as potential virus reservoirs , as they constitute habitat generalists and follow human civilization , providing opportunities for virus transmission [30] , [31] . Even though rodents form smaller social groups than bats , some rodent species have a high population turnover , which should enable efficient maintenance of viruses through the continuous replenishment of susceptible individuals [26] , [32] . Among terrestrial mammals , rodents and bats together constitute about two thirds of the 5 , 487 known mammalian species [33] . Screening of wild mammals with a view on laboratory models should be oriented by criteria such as small body size and the ability to adapt to laboratory conditions , which applies to rodents , but not bats [12] , [34] . Here we have investigated 7 , 709 bats and rodents pertaining to 92 species sampled globally in ten tropical and temperate countries . The investigation was complemented by a comparison of virus diversity in 1 , 068 horses , cats and dogs .
All animals were handled according to national and European legislation , namely the EU council directive 86/609/EEC for the protection of animals . For all individual sampling sites , study protocols including trapping , sampling and testing of animals were approved by the responsible animal ethics committees as detailed below . All efforts were made leave animals unharmed or to minimize suffering of animals . Any surgical procedure was performed under sodium pentobarbital/ketamine anesthesia . Trapping of rodents in Germany was conducted in the framework of hantavirus monitoring activities and was coordinated by the Friedrich-Loeffler-Institut , the Federal Research Institute for Animal Health . Rodent monitoring in the federal states Mecklenburg-Western Pomerania , Thuringia , Baden-Wuerttemberg and North Rhine Westphalia was coordinated by the Julius Kühn Institute ( permit numbers LALLF M-V/TSD/7221 . 3-2 . 1-030/09 , TH 15-107/09 , BW 35-9185 . 82/0261 and NW 20 . 09 . 210 . Additional animals were provided by forest institutions and pest management institutions in Mecklenburg-Western Pomerania , Thuringia , Brandenburg , Lower Saxony , Baden-Wuerttemberg , Berlin and Budapest which caught and sacrificed the animals during their official duties without necessity of further permits . Rodents and other small mammals trapped by cats were included in the investigations . Rodent sampling in South Africa was licensed by Cape Nature under permit numbers 317/2003 and 360/2003 . Rodent sampling in The Netherlands was licensed by the Dutch animal ethic committee ( DEC ) under permit numbers 200700119 , 200800113 and 200800053 . Rodent sampling in Thailand was granted by the Agricultural Zoology Research Group , Department of Agriculture , Thailand ( permit no . KU . /14-182 ) . Rodent sampling in Gabon was licensed by the Ministry of Water and Forest , statement 003/MEF/SG/DGEF/DFC from 2011 . Rodent sampling in Mexico was licensed by Secretaría del Medio Ambiente del gobierno de México ( SEMARNAT ) under permit number SGPA/DGVS/08283/12 . Horse , dog and cat samples were collected from regular diagnostic specimens sent to the OIE Reference Laboratory for Equine Influenza and Herpesviruses at the Freie Universitaet Berlin . Sampling and capture of bats as well as sample transfers were done under wildlife permits and ethics clearances: Panama ( Research-Permit STRI: STRI2563 ( PI VC ) - IACUC 100316-1001-18/Research-Permit ANAM: SE/A-68-11/Ethics-Permit: IACUC 100316-1001-18/Export Permits: SEX/A-30-11 , SEX/A-55-11 , SEX/A-81-10 , SEX-A-26-10 ) ; Ghana ( Research Permit: 2008–2010 ( A04957 ) /Ethics-Permit: CHRPE49/09/Export-Permit: State Agreement between Ghana and Hamburg ( BNI ) ) ; Australia ( Research Permit: S11828 and S11762/Ethics-Permit: TRIM 01/1118 ( 2 ) , TRIM 06/3569 , and University of Queensland/Animal Ethics Committee SIB600/05/DEST/Export-Permit: DE201-12 ) ; Papua-New Guinea ( Ethics-Permit: PNG/NatMus/2002/Export-Permit: Conducted by Papua New Guinea National Museum ) ; Gabon ( Ethics-Permit: 00021/MEFEPA/SG/DGEF/DFC ) ; Germany ( Ethics-Permit: LANU 314/5327 . 74 . 1 . 6 ) . For all sampling and exportation of specimens , permission was obtained from the respective authorities ( see Acknowledgement for individual permits ) . Animals were caught with mist nets , live or snap traps , identified by trained field biologists on site or prior to dissection ( where applicable ) , euthanized and dissected in the respective laboratories . Canine , feline and equine samples were routine diagnostic specimens . Between 10–140 µL of blood were extracted using the Qiagen Viral RNA Mini kit ( Qiagen , Hilden , Germany ) . Approximately 30 mg of solid organ tissue were homogenized in a TissueLyser ( Qiagen ) and purified using the RNeasy Kit ( Qiagen ) . Six nested PCR assays for amplification of hepacivirus RNA and two assays targeting the Flaviviridae sister-genera Flavivirus and Pestivirus were used to ensure broad detection . Highly sensitive HCV-specific assays targeting the X-tail , NS5B and 5′-untranslated genomic regions were used in addition ( see Supplementary Table S2 for oligonucleotide sequences and reaction conditions ) . RNA quantification relied on strain-specific real-time RT-PCR assays and photometrically quantified in vitro RNA transcripts generated as described previously [35] . No isolation attempts were made due to the small available specimen quantities and notorious difficulty of hepacivirus isolation . Instead , those rodent specimens with highest RNA concentrations were selected for full genome sequencing . Genome-spanning islets were amplified by PCR using degenerate broadly reactive oligonucleotides ( Supplementary Table S2 ) . Bridging strain-specific oligonucleotide primers ( available upon request ) were then designed to perform long range PCR using the Expand High Fidelity kit ( Roche ) on cDNA templates generated with the SuperScriptIII kit ( Invitrogen ) . Some cDNA templates were enriched using a Phi29-based hexamer-driven amplification using a modified protocol of the Qiagen Whole Transcriptome Amplification kit ( Qiagen ) as described previously [25] . Amplicons were Sanger sequenced using a primer walking strategy . The 5′-genome ends were determined using the Roche rapid amplification of cDNA ends ( RACE ) kit ( Roche ) generating contiguous PCR amplicons encompassing the complete 5′-untranslated region ( 5′-UTR ) and the 5′-terminus of the core gene . 454 junior next generation sequencing was used for confirmation of 5′-UTR sequences . For determination of the 3′-genome end , viral RNA was adenylated using a poly-A-polymerase ( Clontech , Paris , France ) followed by 3′-RACE using the Invitrogen GeneRacer Kit ( Invitrogen ) . Bayesian tree topologies were assessed with MrBayes V3 . 1 [36] using the WAG amino acid substitution matrix and BEAST V1 . 7 . 4 [37] using the GTR model for nucleotide sequences and the FLU model for amino acid sequences . For MrBayes , two million MCMC iterations were sampled every 100 steps , resulting in 20 , 000 trees . For BEAST , 10 , 000 , 000 generations run under a strict clock were sampled every 1 , 000 steps , resulting in 10 , 000 trees . Burn-in was generally 25% of tree replicates . A human pegivirus ( previously termed GBV-C1; GenBank , U36380 ) was used as an outgroup . Maximum Likelihood analyses were used to confirm Bayesian tree topologies using the WAG amino acid substitution model and 1 , 000 bootstrap replicates in PhyML [38] . Trees were visualized in FigTree from the BEAST package and Densitree [39] . RNA secondary structures in viral 5′- and 3′-genome ends were inferred manually basing on covariant base pairing and thermodynamic predictions using mfold [40] in an alignment of rodent , primate and canine/equine hepaciviruses generated with MAFFT [41] . Putative cellular signal peptidase ( SP ) cleavage sites were predicted based on artificial neural networks ( NN ) and hidden Markov models ( HMM ) using the SignalP 3 . 0 Server [42] . N- and O-glycosylation sites were determined using the online tools NetNGlyc 1 . 0 Server and NetOGlyc Server [43] , [44] . Putative genes were annotated based on predicted signal peptidase ( SP ) cleavage sites ( where applicable ) and sequence homology to HCV , GBV-B and canine/equine hepaciviruses . Alignments were generated using MAFFT [41] . Amino acid percentage identity matrices were calculated using MEGA5 [45] with the pairwise deletion option . Comparison of mean virus concentrations was done using an ANOVA analysis with Scheffé post-hoc tests in the SPSS V20 software package ( IBM , Ehningen , Germany ) . Cross-tables were done using EpiInfo7 ( www . cdc . gov/epiinfo ) . RNAScope RNA probes targeting a 978 nucleotide NS3 gene fragment of the M . glareolus clade 1 hepacivirus detected in specimen RMU10-3379 were custom designed by Advanced Cell Diagnostics ( Hayward , CA , USA ) . RMU10-3379 was selected due to best tissue quality and high virus concentration . In-situ hybridization was performed as described by the manufacturer . All virus sequences reported in this study were submitted to GenBank under accession numbers KC411776-KC411814 .
Enough serum for serologic testing was available from 180 bats ( 72 Rousettus aegyptiacus and 108 Eidolon helvum ) and 95 rodents ( 33 Myodes glareolus , 30 Apodemus sylvaticus , 30 Rattus norvegicus , 2 Myocastor coypus ) . In initial tests using immunofluorescence slides containing full recombinant HCV , 13 bats ( 7 . 2% , 9 R . aegyptiacus and 4 E . helvum ) showed reactivity patterns suggestive of antibodies cross-reacting with HCV . Figure 2A exemplifies typical IFA reaction patterns observed . For confirmation , recombinant HCV western blot ( WB ) assays certified for diagnostic application in humans were adapted for use with bat and rodent sera . For 95 of the 180 bat sera , enough serum volume for WB testing was available . This included three of the 13 IFA-positive sera . As shown in Table 1 , between seven ( Core ) to 28 ( NS3/Helicase ) sera were clearly reactive with different WB antigens . 10 sera ( 10 . 6% ) were to be interpreted as antibody-positive upon criteria for the interpretation of western blot results applicable in human diagnostics . Figure 2B provides examples of typical reaction patterns . The three IFA-positive sera were also positive in WB . For rodents , Table 1 shows that two ( Helicase and NS4 ) to six ( NS5B ) sera reacted with individual antigens . Another 45 sera showed borderline reactivities comparable to the intensity of the WB cut-off control ( examples of reactivities in Figure 2C ) . No rodent sera fulfilled the criteria for positive interpretation applicable in human diagnostics . For the molecular analysis of bats , 2 , 939 sera from Gabon , Ghana , Papua-New Guinea , Australia , Thailand , Panama and Germany were tested for Hepacivirus RNA using several broadly reactive and highly sensitive RT-PCR assays , as detailed in Supplementary Table 2 . Despite the apparent relatedness of putative bat hepaciviruses with HCV suggested by the serologic analyses , no hepacivirus RNA was detected in any of the specimens , whereas several PCR fragments from the NS3 gene were obtained which upon sequencing were identified as pegiviruses related to GBV-D [48] . Tested rodent specimens originated from Thailand , Gabon , South Africa , Germany , the Netherlands and Mexico ( Supplementary Table 1 ) . HCV-related sequences from the NS3 gene were detected in 37 out of 4 , 770 specimens ( 0 . 8% ) . Ten of these findings were from South African four-striped grass mice ( Rhabdomys pumilio; 10 of 518 individual animals , 1 . 9% ) . For these and all other positive specimens , a 978 nucleotide NS3 fragment was generated using additional primer pairs ( Supplementary Table 2 ) . The derived sequences pertained to one clade , and were different from each other by 21 . 1% on nucleotide , or 3 . 4% on translated amino acid level . Twenty-seven ( 1 . 8% ) of 1 , 465 individual bank voles ( Myodes glareolus ) from Germany and The Netherlands yielded HCV-related NS3 sequences . The derived sequences fell into two separate clades . Clade 1 contained 23 sequences different from each other by up to 15 . 8% of nucleotides and 2 . 8% of translated amino acids . Clade 2 contained four sequences different by 1 . 6% nucleotides and 0 . 6% translated amino acids . A Bayesian phylogeny of the partial NS3 gene shown in Figure 3A suggested that the M . glareolus hepacivirus clade 1 was monophyletic with HCV and the canine/equine hepaciviruses . M . glareolus hepacivirus clade 2 was most closely related to GBV-B while the R . pumilio-associated clade formed a sister taxon to all other hepaciviruses . An analysis of all replicate trees indicated that the deep phylogenetic nodes were not resolved ( Figure 3A ) . The monophyly of HCV and the M . glareolus clade 1 hepaciviruses was maintained in 68 . 6% of tree replicates ( 3 , 430 of 5 , 000 ) . In another 28 . 9% of trees ( 1 , 446/5 , 000 ) , the two M . glareolus hepacivirus clades clustered together . Monophyly of all three rodent hepacivirus clades and GBV-B was indicated in only 15 of 5 , 000 tree replicates ( 0 . 3% ) . The near full genomes of five representative hepaciviruses from all rodent clades were determined , including two viruses from R . pumilio , two from M . glareolus clade 1 and one from M . glareolus clade 2 ( identified by red squares in Figure 3A ) . The polyprotein genes were of different sizes including 2 , 781; 2 , 887; and 3 , 007 amino acid residues , respectively , compared to 3 , 008–3 , 033 in HCV . All genomes shared the typical hepacivirus polyprotein organization , encoding putative proteins in the sequence C-E1-E2-p7-NS2-NS3-NS4A/4B-NS5A-NS5B ( Figure 3B ) . The putative structural C , E1 , E2 and p7 proteins were predicted by signal peptidase cleavage site analysis ( Supplementary Table S3 ) to be comparable in their sizes to that of known hepacivirus proteins . All rodent viruses had considerably fewer predicted glycosylation sites in their structural proteins , in particular their putative E2 proteins , as opposed to HCV . A detailed genome analysis is provided in Figure 3B . The 5′-terminus of the core gene of the R . pumilio hepacivirus clade contained a putative adenosine-rich slippery sequence at codons 10–14 ( AAAAAAAACAAAAA , Supplementary Figure 3B ) . In HCV , a very similar sequence ( AAAAAAAAAACAAA ) , located at nearly the same positions ( codons 8–12 ) of the core gene induces production of a protein termed F in vitro due to ribosomal frameshift event [49] . Depending on the HCV genotype , the size of the F protein ranges from 126 to 162 amino acid residues which vary considerably in sequence composition [50] . The size of a putative F protein in SAR46 would be 65 amino acid residues and no homology to the HCV F proteins was observed . The total amino acid diversity of all homologous genes within the polyproteins of the three rodent hepacivirus clades was larger than that of all HCV genotypes ( Supplementary Table S4 ) . Similar to HCV , the most variable genomic regions in rodent hepaciviruses were located in the Envelope E2 gene differing in up to 84 . 4% of encoded amino acids between the rodent virus clades; the NS2 gene differing in up to 79 . 8%; and the NS5A gene differing by up to 84 . 6% . The high degree of sequence homology of the RNA-dependent RNA polymerase ( RdRp ) genes between all members of the family Flaviviridae enabled a more comprehensive comparison of the novel viruses . In a Bayesian phylogeny of these genes across the flavivirus family , the rodent viruses formed a monophyletic sister-clade to HCV ( Figure 4A ) . Topological robustness was assessed by the fixation , in parallel Bayesian phylogenies , of two alternative topological hypotheses , the first involving monophyly of HCV with the canine/equine viruses and M . glareolus clade 1 , and the second assuming a separation of HCV and the canine/equine viruses from all rodent viruses and GBV-B . A Bayes factor test comparing the total model likelihood traces of these analyses indicated borderline-significant preference of the second hypothesis over the first ( Log10 Bayes factor = 2 . 94 ) . Figure 4B provides a comparison of RdRp-based amino acid distances within and between Flaviviridae genera . In a Bayesian phylogeny of the full polyprotein , the rodent hepaciviruses and GBV-B were monophyletic , forming a sister clade to the canine/equine hepaciviruses and HCV ( Figure 5 ) . The rodent-associated clade had very long intermediary branches and originated close to the root of all viruses . The full genome tree had a better phylogenetic resolution compared to the partial NS3 phylogeny , but still contained topological uncertainties in some deep nodes leading to rodent-associated taxa ( Supplementary Figure S1 ) . The genome ends of representatives of all three rodent viruses were determined , including virus RMU10-3382 belonging to M . glareolus clade 1 , NLR-AP-70 belonging to M . glareolus clade 2 , and virus SAR-46 belonging to the R . pumilio hepacivirus clade . Figure 5 and Supplementary Figure S2A show that the 5′-genome terminus of RMU10-3382 contained structural elements typical of both pegi- and HCV-like internal ribosomal entry sites ( IRESs ) . Predicted structural similarities with the HCV-like IRES included the first stem-loop element ( termed Ia and highlighted in orange in Figure 5 ) and one of two sites involved in miRNA122 binding [51] , while most of the remaining stem-loop elements ( termed 3 , 4 and 5 and highlighted in blue in Figure 5 ) were more closely related to a pegivirus-like IRES . The 5′-end of AP-70 was identical in structure to RMU10-3382 and contained only a few nucleotide exchanges . SAR-46 contained the typical HCV-like IRES structures including the characteristic stem-loop III ( Figure 5 and Supplementary Figure S2B ) . The observed structural similarity between the first stem-loop of all rodent viruses described here and the prototype hepaciviruses HCV and GBV-B consisted of a hairpin with a six-nucleotide stem and four-five nucleotide loop . The equine/canine hepaciviruses contained a similar structural element located as their second predicted IRES domain , instead of the most 5′-position this domain occupied in all other hepaciviruses . The RMU10-3382 and NLR-365 translation initiation sites contained a cytosine immediately following the putative start codon at position +4 , which is suboptimal in the original Kozak sequence context ( ACCATGG ) but should not block initiation [52] . The 3′-ends of RMU10-3382 and SAR-46 contained three highly ordered stem-loop elements . In RMU10-3382 , these RNA elements did not resemble any known 3′-noncoding sequence RNA structure . In SAR-46 , the 3′-terminal stem-loop structure , but not the preceding structures , resembled that of the HCV X-tail ( Figure 5 and Supplementary Figure S3 ) . A similar 3′-terminal structure could be predicted for GBV-B , but not for the genetically related pegiviruses ( Figure 5 and Supplementary Figure S4 ) . The 3′-end of NLR-AP-70 could not be determined . Contrary to HCV and GBV-B , no poly-uracil stretch was observed in the rodent hepaciviruses . Strain-specific real-time RT-PCR assays were used to determine viral RNA concentrations in tissues of 22 bank voles infected with clade 1 and 2 hepaciviruses . Mean RNA concentrations were highest in liver tissue ( 1 . 8×108 copies/gram; range , 1 . 5×106–4 . 4×109 ) . These concentrations were significantly higher than those in other organs or serum ( ANOVA , F = 7 . 592 , p<0 . 0001; Figure 6A and Supplementary Figure S5 ) . Figure 6B shows M . glareolus clade 1 hepacivirus RNA stained by in-situ hybridization ( ISH ) in liver tissue . Foci of viral RNA were located in the cytoplasm of M . glareolus hepatocytes , while no staining was observed in RT-PCR-negative M . glareolus liver specimens ( Supplementary Figure S6 shows additional ISH details ) . Spleen , kidney , heart and lung tissues yielded no evidence of virus infection by ISH . Histopathological examination of eight RNA-positive and two RNA-negative animals revealed low-grade focal lymphocytic invasion compatible with liver inflammation , such as shown in Figure 6C for two exemplary RNA-positive animals . Serological investigations in wild rodents were complicated by the fact that the vast majority of animals from virus-positive species were not live-trapped , therefore yielding no blood samples . Only post mortem peritoneal lavage fluids were collected from carcasses , but these were not qualified for serology . However , a subset of 97 live-trapped M . glareolus with appropriate blood samples were available . These were analyzed for antibodies against the Myodes hepacivirus clades 1 and 2 in an IFA using cells expressing the NS3 antigens of these viruses . Antibodies against the Myodes hepacivirus clade 1 NS3 antigen were found in eight animals ( 8 . 3% ) at a median end-point titer of 1∶200 ( range , 1∶100–1∶1600 ) . Antibodies against the Myodes hepacivirus clade 2 NS3 antigen were detected in 12 animals ( 12 . 4% ) at a median end-point titer of 1∶600 ( range , 1∶100–1∶12800 ) . The difference in antibody detection rates against clades 1 and 2 was not statistically significant ( X2 = 0 . 5 , p = 0 . 5 ) . Myodes hepacivirus clade 1 antigen specificity was proven by counterstaining with a high-titered rabbit serum raised against the same recombinant NS3 antigen down to dilutions of >1∶20 , 000 . Myodes hepacivirus clade 2 antigen did not cross-react with this rabbit control serum even at high concentrations of 1∶100 , compatible with low NS3 amino acid sequence identity between the NS3 proteins of the two Myodes hepacivirus clades ( 42 . 4% , Supplementary Table S4 ) . Neither hepacivirus clade 1 , nor clade 2 antibody-positive sera cross-reacted with HCV by immunofluorescence and by immunoblot , indicating specific immune reactions against the viruses studied ( exemplary results in Figure 6D ) . This was compatible with low NS3 amino acid sequence identities between both Myodes hepacivirus clades and HCV , ranging from 37 . 9–42 . 2% ( Supplementary Table S4 ) . Only one of the eight sera positive against M . glareolus clade 1 hepaciviruses also contained antibodies against M . glareolus clade 2 hepaciviruses ( titers against clade 1 and clade 2 hepaciviruses were 1∶200 and 1∶3200 , respectively ) . Additional highly sensitive real-time RT-PCR assays were designed specifically for the M . glareolus clade 1 and 2 hepaciviruses and used to analyze the association of viral RNA and antibody status in the 97 M . glareolus sera . No hepacivirus RNA was detected in any of the IFA-positive sera , neither with the broadly reactive screening assays , nor with the additional real-time RT-PCR assay . Therefore , another 239 RNA eluates still containing sufficient volumes to permit screening for M . glareolus clade 1 and 2 hepaciviruses were re-tested with the strain-specific real time RT-PCR assays . Another 57 specimens positive for clade 1 hepaciviruses ( 23 . 9% ) , but no additional clade 2 hepaciviruses were detected . Sera from these PCR-positive animals were obtained and tested for antibodies . Three of the 57 clade 1 RNA-positive sera contained antibodies against clade 1 hepaciviruses ( 5 . 3% ) . Because of previous reports of canine/equine hepaciviruses , all RT-PCR assays used in this study were also applied on specimens from horses , cats and dogs . No HCV-related sequences were found in any of the 858 canine or feline specimens . In seven of 210 horse sera ( 3 . 3% ) , sequences closely related to those equine hepaciviruses described previously from the US and New Zealand [24] were detected ( 9 . 5–15 . 0% exchanges in the 978 nucleotide NS3 gene fragment ) . Most of those nucleotide differences represented synonymous mutations , resulting in low amino acid distances of 0–1 . 2% . The novel hepaciviruses from German horses clustered phylogenetically with the previously described equine viruses ( Figure 3A ) .
Here we found molecular evidence for viruses related to HCV in rodents . Rodent hepaciviruses were detected in four-striped grass mice from South Africa , as well as in bank voles from Central Europe . The latter have already been successfully bred under laboratory conditions , indicating an approach to establish surrogate models for hepacivirus infection [53] , [54] , [55] , [56] . All discovered viruses originated from deep nodes close to the bifurcations separating genera within the flavivirus tree . In phylogenies on whole genome and individual gene alignments , the novel viruses clustered in a monophyletic clade with previously known hepaciviruses and GBV-B . The clade is highly diversified with NS5b amino acid sequence distances between taxa ranging up to 66 . 1% , exceeding that in the well-studied genus Flavivirus ( 55 . 8% ) . Maximal distances within the genera Pegivirus ( 52 . 9% ) and Pestivirus ( 42 . 0% ) are even lower , suggesting a particularly high diversity to exist in a tentative genus defined by the novel clade . Whereas this indicates that some or all of the novel rodent viruses together with GBV-B might alternatively form an independent genus , recent descriptions of novel pegi- and pestiviruses in bats and swine suggest the diversity also within these genera to be understudied [48] , [57] , [58] . Including the novel rodent viruses congeneric with HCV and canine/equine viruses , the minimal distance between the genera Hepacivirus and Pegivirus would be 73 . 5% . While this is lower than the 85–88% between other pairs of genera , it is consistent with a separation threshold of 72 . 2% between all members of the genus Flavivirus and Tamana bat virus , for which a separate genus has been proposed [59] . This also corresponds to inter-generic distances within other well-studied families of plus-strand RNA viruses such as the Picornaviridae , whose twelve genera are mostly separated by 70–80% in the RdRp-encoding 3D gene [60] . The genomic organization of the novel viruses provides additional criteria for tentative classification . Like all hepaciviruses and in contrast to all members of the genus Pegivirus , the novel viruses have a discernible core gene [61] . In contrast to the genus Pestivirus [62] , their genomes contained no putative Npro and Erns genes in any reading frame . Finally , in contrast to the genus Flavivirus [63] , all rodent viruses showed IRES secondary structures in their 5′-genome termini . Some of the novel rodent IRES structures appeared to contain both elements related to type 3 IRES known from hepaci- and pestiviruses and type 4 IRES known from pegiviruses . Additionally , both M . glareolus rodent hepacivirus polyprotein clades were preceded by predominantly pegivirus-related IRES structures , while the R . pumilio hepacivirus clade was preceded by a predominantly hepacivirus-related IRES . This may be compatible with ancient recombination events between Flaviviridae genera , a phenomenon known in the family Picornaviridae [64] , [65] . The genetic elements potentially homologous to HCV detected in rodent viruses also included microRNA-122 binding sites in the 5′-ncr , an X-tail-like element in the 3′-terminus and a putative F gene in an alternative open reading frame ( ORF ) of the R . pumilio–associated virus . The F protein appears to be unessential for HCV replication , but the evolutionary conservation of its ORF suggests that it may play a critical regulatory role in virus propagation and survival [50] . In this regard , the F protein may be considered a counter-defensive security protein that evolved to overcome mechanisms of host resistance [66] . The absence of paramount features typical of other genera and the presence of hepacivirus-like features suggest a tentative classification of the novel rodent viruses within the genus Hepacivirus , rather than a novel genus . Within the genus , phylogeny suggests early divergence of ancestral rodent viruses from a lineage leading up to HCV and canine/equine hepaciviruses . Weakness of resolution in deep bifurcations of the NS3 gene phylogeny and lack of any highly significant preference for deep topological hypotheses in the NS5B gene phylogeny underline the ancestral origin of these viruses . Within the current dataset we can consider them equidistant from HCV and the canine/equine hepaciviruses , suggesting existence of independent taxonomic entities . HCV is one viral species whose genotypes are separated by more than 30% genomic nucleotide distance [67] , which corresponds to about 22–31% AA distance . Different species within the related sister genus Pegivirus , such as GBV-C and GBV-A , are separated from each other by about 45% AA distance [68] . Within the Genus Flavivirus , well-defined species such as dengue virus 1 , West Nile virus , yellow fever virus and tick-borne encephalitis virus are separated from each other by 48–60% AA distance . Comparing these values we could putatively assume that both Myodes-associated clades distant from each other by 70% AA sequence , as well as the Rhabdomys-associated clade separated from both of the aforementioned by 66–69% AA sequence , might form three distinct species . The canine/equine hepacivirus clade separated from HCV by 52–53% AA sequence would then also form a separate species . Furthermore , all rodent hepacivirus clades and specifically M . glareolus hepacivirus clade 2 were slightly more related to GBV-B than to HCV . GBV-B causes hepatitis in experimentally infected New World primates but not in humans and chimpanzees [61] . The true host of this virus is unknown , but our findings suggest that GBV-B might originate from rodents . Nevertheless , the genetic distance of GBV-B even to its closest relative , the Myodes hepacivirus clade 2 ( 63% AA sequence ) , suggests GBV-B to remain a solitary representative of a separate species of hepaciviruses . In the canine/equine clade ( also termed non-primate hepaciviruses or NPHV [23] ) , it is striking that almost identical viruses have been found in horses and dogs . Additionally , horses but not dogs had antibodies against those viruses [24] . In the present study we augmented the number of studied dogs and horses considerably , and investigated cats in addition as these are related in the order of carnivores and have shared domestic habitats with dogs over a long history . The complete absence of viruses in cats and dogs , and the confirmation of highly similar viruses in other geographic regions , here and in another recent study [69] , suggest an actual equine association of the canine/equine clade . Whether acquisition of viruses might have occurred during the domestication of horses , or whether a more generic viral association with the equine stem lineage may exist , could be clarified by testing non-domestic equids such as wild asses or zebras . However , the overall phylogenetic position and monophyly of equine viruses suggest no role as ancestral hepacivirus hosts for horses . While the rodent hepaciviruses greatly extended the genetic diversity of the genus Hepacivirus , their role in the evolution of HCV precursors , if any , remains to be determined . Our serological evidence for hepaciviruses in bats is noteworthy even in absence of direct virus findings . Viruses from all Flaviviridae genera including Pegivirus , Pestivirus and Flavivirus have already been found in bats [48] , [70] , [71] . We could not exclude that the antibodies in bat sera reacting with HCV antigens were directed against viruses from other Flaviviridae genera , rather than bat hepaciviruses . However , there was no cross-reactivity between the NS3 proteins of the more closely related canine/equine hepaciviruses and HCV [24] . Similarly , the two bank vole hepacivirus clades from our study showed no serologic cross-reactivity . These data can therefore serve as very initial suggestions for the existence of bat hepaciviruses only . It should be noted that the degree of genomic similarity necessary for serologic cross-reactivity should have permitted RNA detection by the broadly reactive PCR assays used in this study . Whether bat hepaciviruses indeed exist will therefore require further evidence . A first step to this direction may be an analysis of an expanded bat sample by using the methods presented here . Additional to phylogeny and genomic properties , the novel viruses resemble HCV in important traits of the natural history of infection . The detection of non-identical virus sequences in natural groups of animals , in combination with specific antiviral antibodies , proves continuous transmission of virus among animals . Induction of controlled infections in housed animals should thus be feasible . We found clear in-vivo evidence for hepatic tropism by demonstrating histopathological signs of liver inflammation , excessive viral RNA concentrations in the liver , as well as in-situ hybridizations demonstrating intracellular genome replication in liver cells of bank voles . A somewhat lower degree of hepatic inflammation compared to that in some HCV-infected humans might be due to the shorter life span of bank voles rarely exceeding 1–2 years in the wild , or due to a higher capacity of tissue regeneration [72] , [73] . Interestingly , our serological investigations suggested bank voles might be able to clear hepacivirus infections , as antibodies did not co-occur with RNA in most , but not all animals [1] . Bank voles may therefore be more capable of clearing hepacivirus infection than humans . This would be compatible with infection patterns also observed in other Flaviviridae members , exemplified by the flavivirus West Nile virus in rhesus macaques , the pestivirus BVDV1 in cattle and the hepacivirus GBV-B in experimentally infected tamarins [61] , [74] , [75] . However , it would differ from equine hepaciviruses , in which RNA and antibodies co-occurred [24] . Controlled infection experiments in bank voles might yield relevant scenarios for the study of HCV persistence . Bank voles can be kept in the laboratory with comparatively little effort and have been used for virus infection studies , e . g . , with herpesviruses , bornaviruses , hantaviruses , and flaviviruses [53] , [54] , [55] , [56] . Efforts to establish bank vole infection models may benefit from the discovery of two highly divergent clades in this species . Knowledge of three full genomes in total should enable efficient rescue of virus from cDNA . Notably , the Rhabdomys-associated virus clade has a host in even closer relationship ( on subfamily level ) to Mus musculus commonly kept in laboratories , for which powerful technologies such as gene knock out and in-vivo imaging exist . Also for this virus clade , two different full genomes have been determined . In the present study focusing on viral ecology , however , we have not conducted infection or virus rescue trials in cell cultures or animals . Due to the strict liver tropism those viruses can be expected to be as difficult to cultivate as HCV , and we currently lack any possibilities to generate primary Myodes hepatocytes . The housing of those animals is in preparation , as are attempts to rescue fully sequenced viruses by reverse genetics . Additionally , our finding of presence of hepaciviruses in the Murinae subfamily have triggered more targeted ecological investigations to potentially identify viruses from hosts in even closer relationship to Mus musculus . The availability of rodent surrogate models of HCV infection may obviate one of the most critical obstacles to HCV vaccine development by obviating the need for primate experiments in early stages of experimentation [12] , [34] . | The hepatitis C virus ( HCV ) is one of the most relevant causes of liver disease and cancer in humans . The lack of a small animal models represents an important hurdle on our way to understanding , treating , and preventing hepatitis C . The investigation of small mammals could identify virus infections similar to hepatitis C in animals that can be kept in laboratories , such as rodents , and can also yield insights into the evolution of those ancestral virus lineages out of which HCV developed . Here , we investigated a worldwide sample of 4 , 770 rodents , 2 , 939 bats , 210 horses and 858 cats and dogs for HCV-related viruses . New viruses were discovered in European bank voles ( Myodes glareolus ) and South African four-striped mice ( Rhabdomys pumilio ) . The disease in bank voles was studied in more detail , suggesting that infection of the liver occurs with similar symptoms to those caused by HCV in humans . These rodents might thus enable the development of new laboratory models of hepatitis C . Moreover , the phylogenetic history of those viruses provides fascinating new ideas regarding the evolution of HCV ancestors . |
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Assembling of the membrane-bound viral replicase complexes ( VRCs ) consisting of viral- and host-encoded proteins is a key step during the replication of positive-stranded RNA viruses in the infected cells . Previous genome-wide screens with Tomato bushy stunt tombusvirus ( TBSV ) in a yeast model host have revealed the involvement of eleven cellular ESCRT ( endosomal sorting complexes required for transport ) proteins in viral replication . The ESCRT proteins are involved in endosomal sorting of cellular membrane proteins by forming multiprotein complexes , deforming membranes away from the cytosol and , ultimately , pinching off vesicles into the lumen of the endosomes . In this paper , we show an unexpected key role for the conserved Vps4p AAA+ ATPase , whose canonical function is to disassemble the ESCRT complexes and recycle them from the membranes back to the cytosol . We find that the tombusvirus p33 replication protein interacts with Vps4p and three ESCRT-III proteins . Interestingly , Vps4p is recruited to become a permanent component of the VRCs as shown by co-purification assays and immuno-EM . Vps4p is co-localized with the viral dsRNA and contacts the viral ( + ) RNA in the intracellular membrane . Deletion of Vps4p in yeast leads to the formation of crescent-like membrane structures instead of the characteristic spherule and vesicle-like structures . The in vitro assembled tombusvirus replicase based on cell-free extracts ( CFE ) from vps4Δ yeast is highly nuclease sensitive , in contrast with the nuclease insensitive replicase in wt CFE . These data suggest that the role of Vps4p and the ESCRT machinery is to aid building the membrane-bound VRCs , which become nuclease-insensitive to avoid the recognition by the host antiviral surveillance system and the destruction of the viral RNA . Other ( + ) RNA viruses of plants and animals might also subvert Vps4p and the ESCRT machinery for formation of VRCs , which require membrane deformation and spherule formation .
Plus-stranded ( + ) RNA viruses replicate by assembling membrane-bound viral replicase complexes ( VRCs ) consisting of viral- and host-coded proteins in combination with the viral RNA template in the infected cells . Although major progress has recently been made in understanding the functions of the viral replication proteins , including the viral RNA-dependent RNA polymerase ( RdRp ) and auxiliary replication proteins , the contribution of many host proteins to VRC assembly is far from complete [1]–[7] . The host proteins contributing to VRC assembly likely include translation factors , protein chaperones , RNA-modifying enzymes , and cellular proteins involved in lipid biosynthesis [8]–[14] . Other host proteins , such as the ESCRT proteins , reticulons and amphiphysins could be involved in membrane deformation occurring during VRC assembly [15]–[17] . However , the actual functions of the majority of the identified host proteins involved in VRC assembly have not been fully revealed . To assemble their VRCs , RNA viruses take control of cell membranes by interfering with intracellular lipid metabolism , protein regulation , targeting and transport [7] , [18] . Viral polymerases of many ( + ) RNA viruses interact with membranes and build functional VRCs in spherules that are single-membrane vesicles with a narrow opening to the cytosol . Spherules form as invaginations in a variety of cell organelles [7] , [18] , [19] . Tubulovesicular cubic membranes , double membrane vesicles ( DMV ) and planar oligomeric arrays are some other classes of membranous structures that can harbor VRCs as documented in the literature [18] . TBSV is a small ( + ) RNA virus that has recently emerged as a model virus to study virus replication , recombination , and virus - host interactions using yeast ( Saccharomyces cerevisiae ) as a model host [7] , [20]–[23] . Several systematic genome-wide screens and global proteomics approaches have led to the identification of ∼500 host proteins/genes that interacted with the viral replication proteins or affected TBSV replication and recombination [9] , [11] , [24]–[32] . Subsequent detailed analysis revealed the functions of the two viral replication proteins ( i . e . , p33 and p92pol ) , the viral RNA , the host heat shock protein 70 ( Hsp70 ) and the eukaryotic elongation factor 1A ( eEF1A ) , sterols and phospholipids in the assembly of the tombusvirus VRCs [30] , [31] , [33]–[41] . Hsp70 and eEF1A proteins have been shown to bind to the viral replication proteins [1] , [33] , [42] . The auxiliary p33 replication protein , which is an RNA chaperone , recruits the TBSV ( + ) RNA to the site of replication , which is the cytosolic surface of peroxisomal membranes [43]–[48] . The RdRp protein p92pol binds to the essential p33 replication protein that is required for assembling the functional VRC [22] , [34] , [45] , [49] , [50] . Interestingly , the genome-wide screens and a proteome-wide over-expression approach for host factors affecting TBSV replication in yeast [9] , [11] , [26] have led to the identification of 11 ESCRT ( endosomal sorting complexes required for transport ) proteins involved in multivesicular body ( MVB ) /endosome pathway [51] , [52] . The identified host proteins included Vps27p ( ESCRT-0 complex ) ; Vps23p and Vps28p ( ESCRT-I complex ) , Vps25p and Vps36p ( ESCRT-II complex ) ; Snf7p and Vps24p ( ESCRT-III complex ) ; Doa4p ubiquitin isopeptidase , Did2p having Doa4p-related function; Bro1p ESCRT-associated protein and Vps4p AAA+ ATPase [9] . The identification of ESCRT proteins led to a model that tombusvirus replication depends on hijacking of ESCRT proteins to the peroxisomal membrane . It has been suggested that the protection of the viral RNA is compromised within the VRC assembled in the absence of cellular ESCRT proteins . Altogether , the formation of membranous spherule-like replication structures in infected cells might require co-opted ESCRT proteins . However , the actual functions of the subverted ESCRT proteins in TBSV replication are currently unknown . A set of 20–30 ESCRT proteins is important for the endosomal/multivesicular body ( MVB ) protein-sorting pathway in eukaryotic cells , which down-regulates plasma membrane proteins via endocytosis; and sorts newly synthesized membrane proteins from trans-Golgi vesicles to the endosome , lysosome or the plasma membrane [53]–[55] . The ESCRT proteins are involved in membrane invagination and vesicle formation during the MVB pathway . Defects in the MVB pathway can cause serious diseases , including cancer , early embryonic lethality and defect in growth control [53]–[56] . Also , enveloped retroviruses ( such as HIV ) , ( + ) and ( − ) RNA viruses ( such as filo- , arena- , rhabdo- and paramyxoviruses ) redirect cellular ESCRT proteins to the plasma membrane , leading to budding and fission of the viral particles from infected cells [51] , [52] . The MVB pathway starts with the recognition of monoubiquitinated cargo proteins in the endosome by Vps27p ( ESCRT-0 complex ) , which serves as a signal for proteins to be sorted into membrane microdomains of late endosomes [54]–[57] . Vps27p in turn recruits Vps23-containing ESCRT-I complex and then the ESCRT-II complex , resulting in grouping the cargo proteins together in the limiting membranes of late endosomes and deforming the membranes that leads to membrane invagination into the lumen [58] , [59] . Then , components of the large ESCRT-III complex are recruited from the cytosol , followed by sequential assembly of ESCRT-III monomers into helical lattice on the membrane that leads to the scission of the neck of the invaginated membrane , giving raise to vesicle budding into the lumen of endosome and to MVB formation [60] , [61] . Then , Vps4p recycles the ESCRT proteins , whereas Doa4p recycles the ubiquitin , leading to budding of multiple small vesicles into the lumen [57] . The ESCRT-III and the Vps4p AAA+ ATPase together comprise a conserved membrane scission machinery . The ATP-dependent function of Vps4p is to disassemble and remove the ESCRT-III components from the membranes ( i . e . , recycling them back to the cytosol ) [59] , [62] . Vps4p is a member of the AAA+ ATPase family , which uses ATP to remodel macromolecular structures in various biological processes , such as protein disaggregation , microtubule severing and membrane fusion . The N-terminal part of Vps4p , termed MIT domain , binds to the ESCRT-III components , while the ATPase domain is involved in ATP hydrolysis . Vps4p is present as an inactive dimer in the cytosol and during activation , Vps4p likely forms a dodecamer with two parallel rings and the ESCRT-III components are likely pulled across the central hole of these rings during the Vps4p-driven recycling event [62] . Our previous works have demonstrated that tombusviruses co-opt selected components of the cellular ESCRT machinery for replication via interaction of the p33 replication protein with Vps23p ( similar to Tsg101 in mammals ) and Bro1p ( ALIX ) [15] , [63] . The recruitment of these cellular ESCRT proteins to the sites of tombusvirus replication is assumed to lead to the sequential recruitment of additional selected ESCRT factors , such as ESCRT-III and Vps4p AAA+ ATPase . Indeed , in the absence of these cellular factors in yeast or the expression of dominant negative versions of these proteins in N . benthamiana host plant , tombusvirus replication is decreased by 10-to-20-fold [15] , [63] . Based on the known functions of the ESCRT proteins , it was suggested that the ESCRT proteins are recruited by TBSV to aid the formation of VRCs , which require membrane deformation to induce spherule-like structures . However , the presented data do not explain how the “neck” of the spherule-like replication structure is stabilized ( to maintain an opening towards the cytosol ) and why the recruitment of ESCRT factors does not lead to enclosure of the replicase complex ( i . e . , the VRCs are not converted rapidly into vesicles via scission of the necks of the spherules that bud inside the peroxisomes ) . The latter event is predicted if TBSV would take advantage of the canonical functions of the ESCRT proteins , which always result in budding of the newly formed vesicles away from the cytosol [57] , [59] . In this paper , we identify Vps4p AAA+ ATPase as a major host factor in TBSV replication . We show that the p33 replication protein interacts with Vps4p and three other ESCRT-III proteins . Surprisingly , we find that Vps4p is a permanent member of the tombusvirus VRCs and it also interacts with the viral RNA . EM images revealed that Vps4p is localized in a compartment also containing the viral double-stranded ( ds ) RNA . Altogether , we propose that the interaction of p33 and Vps4p is critical for spherule formation and efficient tombusvirus replication . These data are consistent with the model that TBSV co-opts ESCRT proteins for its replication and these ESCRT proteins play noncanonical functions in aiding VRC formation and TBSV replication .
The formation of spherule-like structures during TBSV replication on the peroxisomal membrane surfaces and the effect of various ESCRT proteins on tombusvirus replication [15] , [48] , suggest that some of the co-opted cellular ESCRT proteins might play noncanonical functions . To gain insights into the functions of the co-opted ESCRT proteins during tombusvirus replication , first we analyzed if p33 replication protein could interact with ESCRT-III components or the Vps4p AAA+ ATPase . The split-ubiquitin-based yeast two-hybrid assay ( membrane-based MYTH assay ) between the tombusvirus p33 and the yeast ESCRT-III components or Vps4p revealed strong interaction between p33 and Vps4p ( Fig . 1A ) . This is surprising , since Vps4p is known to interact with the ESCRT-III components to recycle them from the endosome [59] , [62] , while recycling of the peroxisome-bound p33 to the cytosol is unlikely to happen and not yet documented . In addition , we observed good interactions between p33 and Vps2p , Vps20p , and Vps24p ESCRT-III factors ( Fig . 1A ) . The most abundant ESCRT-III factor , namely Snf7p , whose deletion greatly affected TBSV replication [15] , and Did2p interacted only weakly with p33 ( Fig . 1A ) . Affinity-based co-purification experiments from the membrane fraction of yeast confirmed that Vps4p strongly interacted with p33 ( Fig . 1B , lane 12 ) , while the interaction of Vps2p and Vps20p with p33 was also detectable ( Fig . 1B , lanes 2 and 8 ) . Unlike in the MYTH assay , the co-purification experiments suggested strong interaction between p33 and Vps24p ( Fig . 1B , lane 4 ) . We could not co-purify Snf7p and Did2p with p33 ( Fig . 1B , lanes 6 and 10 ) . Altogether , Vps4p showed consistently the strongest interaction with the p33 replication protein and this interaction is unexpected and could play a direct role in TBSV replication . To confirm that the interaction between the viral replication protein and Vps4p also occurs in plants , we co-expressed the Arabidopsis thaliana AtVps4 in Nicotiana benthamiana leaves together with the FLAG-tagged tombusvirus p33 replication protein ( Fig . 1C ) . After isolation of the membrane-bound replicase from the leaves and solubilization of the membrane fraction with nonionic detergent , we FLAG-affinity purified p33 , followed by Western blotting . This approach revealed co-purification of AtVps4 with the tombusvirus p33 ( Fig . 1C , lane 1 ) , while AtVps4 was missing after purification in the sample prepared from leaves lacking p33 ( lane 2 ) . Thus , similar to yeast , the tombusvirus p33 replication protein also interacts with AtVps4 in plant leaves , suggesting subversion of Vps4 for viral activities . Since Vps4p showed strong interaction with the tombusvirus p33 replication protein , we tested if Vps4p is part of the tombusvirus VRC . Interestingly , the affinity-purified tombusvirus replicase contained Vps4p ( Fig . 2A , lane 2 versus 1 ) . This finding highlights the possibility that Vps4p is a permanent component of the VRC ( i . e . , not rapidly recycled ) , such as Hsp70 [40] . To test this , we added cyclohexamide to yeast to prevent new p33 and p92pol translation , thus formation of new VRCs . Then , we measured the level of Vps4p in the affinity-purified replicase at various time points to study if Vps4p is released from the VRCs . As a control , we used Ssa1p Hsp70 , whose amount did not change within 150 min , confirming that Ssa1p remained stably associated with the existing tombusvirus VRCs ( Fig . 2B , lanes 10–12 ) . In contrast , the amount of Pex19p peroxisomal shuttle protein , which is involved in the delivery of p33 and p92pol to the peroxisomes [64] before its getting recycled to the cytosol , decreased by 60% after 150 min of incubation in the presence of cyclohexamide ( Fig . 2B , lanes 2–4 ) . Interestingly , the amount of Vps4p did not change significantly in the affinity-purified replicase preparations during this time period ( Fig . 2B , lanes 6–8 ) , suggesting that Vps4p is likely a permanent component of the tombusvirus VRCs . Obviously , this is different from the canonical role of Vps4p , which is quickly recycled from the endosomal membranes to the cytosol after the disassembly of the endosome-bound ESCRT-III structures [59] , [62] . Additionally , the N-terminally truncated Vps4p carrying the ATPase domain , but lacking the MIT domain , which is responsible for interaction with the ESCRT-III proteins , was recruited to the VRCs ( Fig . 2A , lane 4 ) , suggesting unique interaction between p33 and Vps4p . To map the binding sites in Vps4p , we used the split ubiquitin assay that revealed that the N-terminal MIT domain , which binds to the ESCRT-III proteins [59] , [62] , bound efficiently to the full-length p33 ( construct 1–100 versus the full-length Vps4 construct 1–437 , Fig . 3A ) . We also observed weaker , but substantial binding between the C-terminal ATPase domain and p33 ( Fig . 3A ) . We confirmed the interaction using the C-terminal ATPase domain and compared with the full-length Vps4p in a pull down assay with p33 ( Vps4-ΔMIT , Fig . 3B ) . The interaction between the ATPase domain and p33 is not abolished by addition of ATP ( not shown ) . Overall , the binding of p33 to Vps4p is different from the binding between Vps4p and the cellular ESCRT-III components that target only the MIT domain and leads to the Vps4p-driven recycling of the ESCRT-III components from the membranes back to the cytosol . We suggest that the unique interaction between the p33 and Vps4p subverts Vps4p for viral replication , leading to association of Vps4p with the membrane-bound replicase , and likely altering the canonical function of Vps4p . Detailed mapping of p33 sites interacting with the Vps4p or the ATPase domain revealed that the very C-terminus of p33 , which contains the p33:p33/p92 interaction sites , is involved in binding to Vps4p ( Fig . 3C–F ) . Binding of the ATPase domain of Vps4p to p33 mostly overlaps with that of the full-length Vps4p in this assay . Altogether , the binding between Vps4p and p33 involves unique interactions that will require high-resolution structural studies . We have developed an EM-based assay to visualize the tombusvirus-induced spherule-like structures in yeast , which are known to form in infected plant tissues [15] , [48] . EM images revealed the presence of single-membrane vesicle-like structures of ∼25–50 nm ( Fig . 4A–C ) that were missing in wt yeast not expressing the tombusvirus replication proteins ( Fig . S1A–B ) . These vesicles likely represent the spherules seen in TBSV-infected plant tissues [15] . To show if Vps4p is localized to the tombusvirus VRCs , we used immuno-gold EM of yeast cells co-expressing HA-tagged Vps4p , and MT ( Metal-binding protein metallothionein ) -tagged p33 that was visualized by Metal-Tagging Transmission Electron Microscopy ( METTEM ) [65] and replicating the TBSV repRNA , which was detected through using a dsRNA-specific antibody [65] ( Fig . 4D–G ) . These samples were processed in the absence of osmium tetroxide that would destroy most protein epitopes and would mask the 1 nm nano-clusters associated to p33-MT . We frequently observed Vps4p in the close vicinity of the dsRNA ( present in the VRCs ) based on using different sized gold particles for immuno-gold EM . It was difficult to detect Vps4p in the areas of the yeast cells lacking dsRNA ( Fig . S2 ) . We suggest that Vps4p is likely concentrated in the VRCs due to binding to p33 , thus facilitating detection of Vps4p in yeast membranous compartments replicating TBSV . For an adequate visualization of gold nanoclusters bound to p33-MT , cells in Fig . 4D and 4E were processed in the absence of osmium tetroxide and contrasting agents as described . Under these conditions intracellular membranes are invisible; however , some membranes can be visualized if these ultra-thin sections are stained with uranyl acetate and lead citrate ( Fig . 4G–I ) ( see Materials and Methods ) . Stained cells showed that Vps4p and the viral dsRNA were surrounded by membranes ( Fig . 4G–I ) , supporting the model that Vps4p is part of the functional membrane-bound tombusvirus VRCs . To enhance the visualization of Vps4p , we over-expressed His6-MT-tagged Vps4p in yeast cells replicating TBSV repRNA . This approach facilitated the frequent observation of Vps4p in the close vicinity of the TBSV dsRNA by immuno-gold EM ( Fig . 5 ) . Interestingly , the spherule-containing membranous structures are frequently connected to form a complex compartment where Vps4p and dsRNA are clustered together ( Fig . 5 ) . Altogether , the co-localization ( proximal location based on immuno-EM ) of Vps4p and the tombusviral dsRNA in yeast cells suggests that Vps4p is recruited to the VRCs actively involved in TBSV RNA replication . If Vps4p is part of the tombusvirus VRCs , it is likely associated with the neck structure of tombusvirus-induced spherules , which is predicted to serve as exit places for the newly synthesized ( + ) RNA from the VRCs . Therefore , we tested if Vps4p is in contact with the tombusviral RNA . Affinity purification of Vps4p from the membrane fraction of yeast containing the tombusvirus VRCs resulted in co-purification of the TBSV ( + ) repRNA ( Fig . 6A , lane 3 ) . Interestingly , affinity purification of Vta1p accessory protein , which facilitates the formation of the functional Vps4p rings with ATPase function from the inactive Vps4p dimers [62] , also resulted in co-purification of the TBSV ( + ) repRNA ( Fig . 6A , lane 4 ) . As a positive control , we also used the tombusvirus p33 replication protein ( purified via FLAG-tag ) , which also resulted in co-purification of the TBSV ( + ) repRNA ( Fig . 6A , lane 5 ) . The Ssa1p Hsp70 chaperone , which is another permanent component of the tombusvirus VRCs also resulted in co-purified ( + ) repRNA , although in a lesser amount than p33 or Vps4p ( Fig . 6A , lane 6 versus 3 and 5 ) . The negative control ( HF , a peptide sequence containing His6-FLAG sequence expressed from the empty expression plasmid ) did not contain any detectable TBSV ( + ) repRNA , excluding that the viral RNA bound nonspecifically to the affinity resin or the FLAG-antibody . We also tested the presence of ( − ) repRNA ( which might be present in a dsRNA form within the VRCs ) in the above samples . As expected , the p33 replication protein preparation contained ( − ) repRNA ( Fig . 6A , lane 15 ) , while Vps4p , Vta1p , Ssa1p and the negative control samples did not contain ( − ) repRNA ( Fig . 6A ) . Based on these data , we suggest that the Vps4p/Vta1p complex might be in direct contact with the TBSV ( + ) repRNA . This contact is unlikely via p33 replication protein since both ( + ) and ( − ) repRNAs were co-purified with p33 . To obtain evidence that Vps4p is involved in the formation of tombusvirus VRCs , we took advantage of a cell-free TBSV replication assay based on TBSV-free yeast cell-free extracts ( CFE ) prepared from wt or vps4Δ yeast [41] . The CFE supports the assembly of the VRCs when purified recombinant p33 and p92 and ( + ) repRNA are added . Micrococcal nuclease was also added for 15 min ( after which it was inactivated ) to the assay at various time points to test the nuclease-sensitivity of the viral repRNA within newly assembled membrane-bound VRCs ( Fig . 7A ) . The CFE from wt yeast was able to assemble nuclease-insensitive VRCs in ∼45–60 min ( Fig . 7B , lane 5 ) . In contrast , the VRCs assembled with CFE from vps4Δ yeast produced only small amounts of repRNA in the presence of nuclease , suggesting that Vps4p is required for the assembly of the nuclease-insensitive tombusvirus VRCs in vitro . The nuclease-insensitivity of the viral RNA requires cellular membranes and ATP/GTP-dependent VRC assembly and is not due to protein coating of the viral RNA , as shown previously [40] , [41] . To visualize the tombusvirus VRCs in yeast lacking Vps4p , we performed EM imaging of vps4Δ yeast replicating TBSV repRNA . Interestingly , unlike the characteristic tombusvirus-induced spherule-like structures in wt yeast ( Fig . 8A and B ) , the membranes from vps4Δ yeast expressing the p33 and p92 replication proteins and the repRNA showed different deformations ( Fig . 8C and D ) . These crescent-shaped structures likely represent incompletely formed spherules with wide openings containing viral replicases . Similar structures ( either spherules or open crescent-shaped structures ) were not visible in control yeasts not expressing the viral proteins ( not shown ) . Similar crescent-shaped membrane-structures were also visualized by EM in vps24Δ yeast replicating TBSV repRNA ( Fig . 8E ) , suggesting that ESCRT-III components are also likely needed for proper deformation of membranes and VRCs formation . We interpret these data that the tombusvirus replication proteins could not induce the formation of complete spherule-like structures in vps4Δ or vps24Δ yeasts . To visualize the tombusvirus p33 replication protein in yeast subcelluar membranes , we used Metal-Tagging Transmission Electron Microscopy ( METTEM ) [66] with yeast expressing MT-tagged p33 replication protein . The MT-p33 molecules were present in elongated structures in vps4Δ yeast ( Fig . 9A ) , while they could form round vesicle-like structures in wt yeast ( Fig . 9B ) . These data are consistent with the model that Vps4p is required for the formation of tombusvirus-induced spherule-like structures in yeast cells . Labeling with anti-dsRNA antibodies revealed weak to moderate signals in vps4Δ yeast ( Fig . 9C ) . These results suggest that , in the absence of Vps4p , the abnormal VRCs are still able to support some repRNA replication . However , the level of repRNA replication is low as shown in the CFE-based replication assay ( ∼75% less repRNA replication in this strain than in wt ) [15] . Thus , replication of repRNA is inefficient in vps4Δ yeast and the repRNAs is less protected in wide open VRCs and much more sensitive to degradation .
In this paper , we document a novel , noncanonical role for the Vps4p AAA+ ATPase during TBSV replication . We propose that tombusviruses not only recruit the cellular ESCRT machinery for assembling the membrane-bound VRCs , but the virus could alter the activities of Vps4p and the ESCRT-III proteins to create new functional structures ( spherules acting as VRCs ) and possibly new activities . The recruitment of Vps4p and additional ESCRT proteins are needed for the assembly of the replicase complex , which could help the virus evade recognition by the host defense surveillance system and/or prevent viral RNA destruction by the gene silencing machinery .
To study co-purification of host ESCRT proteins with p33 replication protein , the yeast strain BY4741 was transformed with plasmids pGBK-FLAGp33-CUP1 ( or pGBK-His33-CUP1 as control , see plasmids used in the Supplementary material ) plus the pYES plasmids expressing 6×His-tagged ESCRT proteins from the GAL1 promoter ( see supplementary Material and Methods S1 for additional information ) . The transformed yeasts were pre-grown in SD minimal media plus 2% glucose for 20 h at 29°C , then transferred to SD minimal media plus 2% galactose for 16 h at 29°C to induce over-expression of ESCRT proteins from the GAL1 promoter . Then , the cultures were supplemented with 50 µM CuSO4 to induce expression of FLAG-p33 or His6-p33 from the CUP1 promoter . The yeasts were collected by centrifugation after 8 h , washed in phosphate buffer saline ( PBS ) and then incubated in PBS plus 1% formaldehyde for 1 h on ice to cross-link proteins . Afterwards , the formaldehyde was quenched by adding glycine to a final concentration of 0 . 1 M . Then , yeasts were washed in PBS and processed to purify the FLAG-tagged p33 protein as described [31] . The FLAG purified fractions were eluted from the FLAG M2-agarose columns with SDS-PAGE loading buffer ( without 2-mercaptoethanol ) . Then the eluted fractions were supplemented with 2-mercaptoethanol ( 5% ) and boiled for 30 minutes to reverse cross-linking . To study co-purification of His6-Vps4p and His6-Vps4101–437 , the yeast strain BY4741 was transformed with plasmids pGBK-FLAGp33-CUP1/DI72-GAL1 ( or pGBK-Hisp33-CUP1/DI72-GAL1 as control ) , pGAD-His92-CUP1 [67] and pYC-NT-VPS4 or pYC-NT-vps4101–437 . Transformed yeasts were pre-grown in SD minimal media plus 2% glucose for 20 h at 29°C , then transferred to SD minimal media plus 2% galactose and 50 µM CuSO4 for 24 h at 29°C . The yeasts are then treated with 1% formaldehyde as above and processed for FLAG-p33 protein purification . To analyze the time dynamics of host proteins association with p33 , BY4741 was transformed with plasmids pGBK-FLAGp33-CUP1/DI72-GAL1 ( or pGBK-Hisp33-CUP1/DI72-GAL1 as control ) , pGAD-His92-CUP1 [67] and either pYES-PEX19 , pYES-VPS4 or pYES-SSA1 . Transformed yeasts were pre-grown in SD minimal media with 2% glucose for 20 h at 29°C and then transferred to media with 2% galactose for 24 h at 29°C . The cultures were supplemented with 50 µM CuSO4 for 2 . 5 h to induce expression of the viral proteins . Then cycloheximide was added ( 100 µg/ml ) to stop protein translation and samples were taken immediately ( time 0 ) , 60 and 150 minutes afterwards . Yeast cultures were treated with formaldehyde and processed for FLAG-p33 purification as above . Purified FLAG-p33 was detected by western blot using anti-FLAG antibody followed by anti-mouse antibody conjugated to alkaline phosphatase . Co-purified His6-tagged host proteins were detected with anti-His antibody followed by anti-mouse antibody conjugated to alkaline phosphatase . Detection was performed with NBT and BCIP . The pMAL c2x-derived plasmids described in the Supplementary materials were transformed into Epicurion Bl21-codon-plus ( DE3 ) -R1L cells ( Stratagene ) . Expression of MBP-tagged proteins was induced by IPTG as described [68] . pGEX-His-VPS4 and pGEX-His-vps4101–437 were also transformed into Epicurion Bl21-codon-plus ( DE3 ) -R1L cells . Expression of GST-His6-tagged Vps4p and Vps4101–437 was essentially done as described [68] with the exception that cultures were incubated at 23°C during IPTG treatment . The cells were broken by sonication as described [68] . The lysates were incubated with GST resin ( Novagen ) for 1 h at 4°C . The GST resin was washed four times with column buffer [68] and then incubated for 3 h at 21°C with column buffer plus 1 mM 2-mercaptoethanol + 1 mM CaCl2 + 1 U Thrombine ( Novagen ) to cleave the His6-Vps4p and His6-Vps4101–437 from the column-bound GST protein . The amylose columns containing the MBP or MBP-tagged p33 portions were then incubated with 15 µg of the purified His6-Vps4p or His6-Vps4101–437 for 1 h at 4°C . The columns were then washed five times with column buffer and the MBP-tagged proteins were eluted with column buffer plus 10 mM maltose . The MBP-tagged proteins were analyzed by SDS-PAGE electrophoresis followed by coomassie staining . Bound His6-Vps4p or His6-Vps4101–437 were detected with anti-His antibody followed by alkaline phosphatase- conjugated anti-mouse and NBT/BCIP . The yeast membrane two-hybrid assay , based on the split-ubiquitin system ( Dualsystems ) has been described before [31] . The plasmid pGAD-BT2-N-His33 was co-transformed with pPR-N-RE derived constructs into the reporter yeast strain NMY51 . Transformed yeasts colonies were suspended in 100 µl of water and serially diluted ( 10-fold ) in water . 6 µl of each dilution were spotted onto TLHA- plates , to score for interaction , or TL- plates , as growth controls . The yeast strain BY4741 was transformed with plasmids pGBK-Hisp33-CUP1/DI72-GAL1 , pGAD-His92-CUP1 and either pYC-HF , pYC-HF-VPS4 , pYC-HF-VTA1 or pYC-HF-SSA1 . Additionally yeast was transformed with pGBK-FLAGp33-CUP1/DI72-GAL1 and pGAD-His92-CUP1 . Transformed yeasts were pre-grown in SD minimal media with 2% glucose for 20 h at 29°C , then transferred to SD minimal media with 2% galactose for 24 h at 29°C . The cultures were then supplemented with 50 µM CuSO4 for 3 h to induce expression of viral proteins . The yeasts were collected by centrifugation and subjected to formaldehyde cross-linking and FLAG purification as described above . The FLAG purified fractions , eluted in SDS-PAGE loading buffer , were treated with phenol/chloroform and ethanol precipitated to recover co-purified RNA . For detection of DI-72 ( + ) RNA , RT reactions were done with SuperScript II ( Life Technologies ) and primer #22 ( GTAATACGACTCACTATAGGGCTGCATTTCTGCAATGTTCC ) , followed by PCR with primers #1165 ( AGCGAGTAAGACAGACTCTTCA ) and #927 ( TAATACGACTCACTATAGG ) . For detection of DI-72 ( − ) RNA , the primer used for RT was #18 ( GTAATACGACTCACTATAGGAGAAAGCGAGTAAGACAG ) , followed by PCR with primers #1190 ( GGGCTGCATTTCTGCAATG ) and #927 . The yeast strains BY4741 and vps4Δ::hphNT1 were transformed with plasmids pGBK-FLAGp33-CUP1 and pGAD-FLAGp92-CUP1 [67] . Transformed yeasts were grown and cell free extracts prepared as described [49] except that the cultures were supplemented with 50 µM CuSO4 1 . 5 h before harvesting , to induce expression of p33 and p92 , and incubated at 37°C for 45 min before harvesting . Replicase reactions were carried out as described [49] . RNA protection was tested by adding 1 mM CaCl2 and 50 ng micrococcal nuclease at selected time points followed by 15 min incubation and then inactivation of the nuclease by addition of 2 . 5 mM EGTA . All reactions were incubated for a total time of 3 h . Yeast strains ( see Supplementary materials ) were pre-cultured from plated single colonies by inoculation in 2 ml of YPG ( yeast extract peptone galactose ) and shaking overnight at 250 rpm at 30°C . For inducing and maintaining viral replication , yeasts cells were grown for 24 h in YPG at 23°C and shaken at 250 rpm . When OD600 was around 2 , cells were collected , centrifuged for 5 min at 4000 g , resuspended in TSD reduction buffer ( Tris-sulfate DTT , pH 9 . 4 ) and either chemically fixed for electron microscopy ( see below ) or processed for removing the cell wall and obtaining spheroplasts . For obtaining spheroplasts , yeast cells were incubated for 10 min at room temperature and treated at 30°C with 0 . 1 µg/µl zymolyase 20T ( AMS Biotechnology ) in spheroplast medium A ( 1× yeast nitrogen base , 2% ( w/v ) glucose , 1× amino acids , 1 M sorbitol , 20 mM TrisCl , pH 7 . 5 ) for 5 or 15 min , depending on the yeast strain . After zymolyase treatment , cells were centrifuged for 5 min , at 1000 g and 23°C and washed once with spheroplast medium B ( 1× yeast nitrogen base , 2% ( w/v ) glucose , 1× amino acids , 1 M sorbitol ) and twice with spheroplast medium A . For ultrastructural studies , yeast cells and spheroplasts were processed . Compared to whole yeast cells , spheroplasts are well infiltrated with fixatives and resins allowing an optimal visualization of intracellular compartments . Cells were first fixed for 20 min in suspension at room temperature with 8% paraformaldehyde and 1% glutaraldehyde followed by a second fixation step of 1 h at room temperature with 4% paraformaldehyde and 0 . 5% glutaraldehyde in HEPES ( pH 7 . 4 ) ; fixed cells were then processed by conventional embedding in the epoxy-resin EML-812 ( Taab Laboratories ) following procedures for an optimal preservation of cell endomembranes [65] , [69] , [70] . Cells were post-fixed for 1 h at 4°C with 1% osmium tetroxide and 0 . 8% potassium ferricyanide in water , washed with HEPES , and 40 min with 2% uranyl acetate at 4°C . During post-fixation , samples were protected from light . Cells were submitted to dehydration steps for 20 min each with increasing concentrations of acetone ( 50 , 70 , 90 , and twice in 100% ) at 4°C and incubated with acetone-resin ( 1∶1 ) with gentle agitation at room temperature . Cells were infiltrated overnight with pure resin for 1 day and polymerized at 60°C for 3 days . Ultrathin ( 50–70 nm ) sections were collected in 300 mesh cooper grids ( G300-C3 , Taab ) with a plastic layer of 0 . 25% formvar in chloroform . Then , grids were stained for 20 min with saturated uranyl acetate and for 2 min with lead citrate following standard procedures . Samples were studied in a Jeol JEM 1011 electron microscope operating at 100 kv . For visualization of MT-tagged p33 in cells and immunogold labeling , cells were processed by embedding in the acrylic resin LRWhite following procedures for an adequate preservation of protein epitopes and optimal visualization of small nano-clusters [66] . Spheroplasts were incubated for 75 min with 0 . 2 mM HAuCl4 ( SIGMA-ALDRICH ) in spheroplast medium A . This treatment builds gold nano-clusters in MT-tagged proteins allowing detection of protein molecules in cells with high sensitivity and at molecular scale resolution [66] , [71] . Cells were washed with spheroplast medium A before fixation with 4% paraformaldehyde and 0 . 2% glutaraldehyde in PHEM ( 20 mM PIPES , 50 mM HEPES , 20 mM EGTA and 4 mM MgCl2 , pH 6 . 9 ) ( 1 h at room temperature ) . Cells were submitted to short dehydration steps of 10 min each in increasing concentrations of ethanol ( 30 , 50 , 70 , 90 and twice with100% ) at 4°C . Spheroplasts were incubated in mixtures of ethanol- LR White acrylic resin ( 2∶1 , 2∶2 , 1∶2 ) with gentle agitation and protected from light and embedded in 100% resin for 24 h . Samples were polymerized for 48 h at 60°C . Ultra-thin sections were collected in 300 mesh Quantifoil holey carbon grids ( R 3 . 5/1 Cu/Rh , Quantifoil Micro Tools ) and studied without staining . For immunogold labeling sections of cells embedded in LR White acrylic resin were processed as described [70] . Briefly , sections were incubated for 6 min with 1% BSA ( Bovine serum albumin ) in PBS , with primary antibodies diluted in 1% BSA and with secondary antibodies conjugated with 5 or 10 nm colloidal gold particles ( from BB International ) and diluted in 1% BSA . Dilutions of antibodies were rabbit anti-HA 9110 from ABCAM ( 1∶200 ) and mouse anti-dsRNA MAb J2 from English & Scientific Consulting ( 1∶200 ) . Secondary antibodies were diluted 1∶40 . | Replication of positive-stranded RNA viruses depends on recruitment of host proteins and cellular membranes to assemble the viral replicase complexes . Tombusviruses , small RNA viruses of plants , co-opt the cellular ESCRT ( endosomal sorting complexes required for transport ) proteins to facilitate replicase assembly on the peroxisomal membranes . The authors show a surprising role for the ESCRT-associated Vps4p AAA+ ATPase during tombusvirus replication . They show that Vps4p is recruited to and becomes a permanent member of the replicase complex through its interaction with the viral replication proteins . Also , EM and immuno-EM studies reveal that Vps4p is required for the formation of single-membrane vesicle-like structures , called spherules , which represent the sites of tombusvirus replication . The authors propose that Vps4p and other ESCRT proteins are required for membrane deformation and replicase assembly . |
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Systematic identification of protein-drug interaction networks is crucial to correlate complex modes of drug action to clinical indications . We introduce a novel computational strategy to identify protein-ligand binding profiles on a genome-wide scale and apply it to elucidating the molecular mechanisms associated with the adverse drug effects of Cholesteryl Ester Transfer Protein ( CETP ) inhibitors . CETP inhibitors are a new class of preventive therapies for the treatment of cardiovascular disease . However , clinical studies indicated that one CETP inhibitor , Torcetrapib , has deadly off-target effects as a result of hypertension , and hence it has been withdrawn from phase III clinical trials . We have identified a panel of off-targets for Torcetrapib and other CETP inhibitors from the human structural genome and map those targets to biological pathways via the literature . The predicted protein-ligand network is consistent with experimental results from multiple sources and reveals that the side-effect of CETP inhibitors is modulated through the combinatorial control of multiple interconnected pathways . Given that combinatorial control is a common phenomenon observed in many biological processes , our findings suggest that adverse drug effects might be minimized by fine-tuning multiple off-target interactions using single or multiple therapies . This work extends the scope of chemogenomics approaches and exemplifies the role that systems biology has in the future of drug discovery .
Identification of protein-ligand interaction networks on a proteome-wide scale is crucial to address a wide range of biological problems such as correlating molecular functions to physiological processes and designing safe and efficient therapeutics [1] . Recent protein-ligand interaction studies have revealed that protein targets involved in entirely different pharmacology can bind similar small molecule drugs [2]–[4] . Large scale mapping of polypharmacology interactions indicates that drug promiscuity is a common phenomenon across the proteome [5] . It has been found that approximately 35% of known drugs or leads were active against more than one target . Moreover , a significant number of promiscuous compounds ( approximately 25% ) have observed activity in completely different gene families . Such drug promiscuity presents both opportunities and challenges for modern drug discovery . On one hand , it is possible to develop high-efficacy drugs by inhibiting multiple targets [6] or to reposition existing drugs to treat different diseases [7] , [8]; on the other hand , the off-target effect may result in adverse drug reactions that account for around one-third of drug failures during development [9] . As a result , there is increasing interest in the identification of multiple targets associated with a phenotype [6] and in developing combinatorial therapies to boost clinical efficacy [10] . Chemogenomics has emerged as a new discipline to systematically establish target relationships based on the structural and biological similarity of their ligands [3] , [11]–[18] . However , the success of chemogenomics depends on the availability of bioactivity data for the receptors and their associated ligands . For new drug targets , such data are either insufficient or unavailable . Further , the adverse drug reaction may involve receptors that are not well characterized . Complementary to chemogenomics methods , we have developed a chemical systems biology approach to identifying off-target binding networks through their ligand binding sites . The method requires 3D-structure information for the protein but not the ligand , thereby extending the scope of existing chemogenomics approaches . Moreover , the identified off-target binding network is integrated with the reconstructed biological pathways so that the effect of the drug on the biological system can be understood at the system level . In brief ( see Methods for further details ) , our chemical systems biology approach proceeds as follows: 1 ) The ligand binding site of the primary target is extracted or predicted from a 3D experimental structure or homology model and characterized by a geometric potential [19] . 2 ) Off-target proteins with a similar ligand binding site to the primary target are identified across the human structural genome using a Sequence Order Independent Profile-Profile Alignment ( SOIPPA ) [20] . The atomic details of the interactions between the drug and the putative off-targets from step 2 are characterized using protein-ligand docking methods . Based on a normalized docking score the high-ranking off-targets are further investigated . 4 ) The identified panel of off-targets is subject to structural and functional cluster analysis and incorporated into a network that includes multiple metabolic , signal transduction , and gene regulation pathways . The first and second steps have been implemented in the software package SMAP , available from http://funsite . sdsc . edu . In this paper , we apply this strategy to identify and analyze a panel of unknown off-targets for Cholesteryl Ester Transfer Protein ( CETP ) inhibitors . CETP inhibitors represent a new preventive therapy for cardiovascular disease through raising HDL cholesterol . However , clinical studies have revealed that one of the CETP inhibitors , Torcetrapib , has deadly off-target effects as a result of hypertension [21]–[25] and consequently was withdrawn from phase III clinical trial . In contrast to Torcetrapib , another CETP inhibitor JTT-705 does not have unwanted side-effects that increases blood pressure [25] . In addition , JTT-705 is able to block cell proliferation and angiogenesis through Ras and P38 kinase pathways [26] . As will be shown , the multiple off-targets of these CETP inhibitors identified here are involved in both positive and negative control of stress regulation and immune response through an interconnected metabolic , signal transduction and gene regulation network . Our predictions are strongly correlated to the observed clinical and in vitro observations , providing a molecular explanation for the difference in side-effect profiles of these two CETP inhibitors . These findings suggest that adverse drug reactions might be modulated by the fine-tuning of the off-target binding network and exemplify the role of systems biology in the future of drug discovery .
The ligand binding site of CETP ( PDB id: 2OBD ) is assumed to be a long tunnel interacting with two cholesteryl oleates ( 2OB ) and two 1 , 2-dioleoyl-Sn-glycero-3-phosphocholines ( PCW ) molecules in the native state ( Fig . S1 ) , however , the exact location of inhibitor binding is unknown . Docking studies using the software Surflex [27] , eHits [28] and AutoDock [29] indicate that the CETP inhibitors are able to bind to all four sites , with a slight preference for the pocket occupied by PCW . Thus , all four sites were used to search for the off-target binding sites of CETP inhibitors . Although only approximately 15% of human proteins have known 3D structures deposited in the Protein Data Bank ( PDB ) [30] , the structural coverage of the human proteome increases to 57% if homologous proteins are included ( e-value less than 1 . 0e-3 and aligned sequence lengths greater than 30 residues using a Blast [31] search ) . The structural coverage is reduced to around 40% if the aligned length is greater than 120 residues ( Fig . S2 ) . After removing structures with redundant sequences ( sequence identity = 100% ) , 5 , 985 structures and models from the PDB were selected for off-target search by SMAP . Besides bactericidal/permeability increasing protein ( PDB Id: 1ewf ) that is classified in the same fold and Pfam [32] family as CETP ( FATCAT [33] p-value = 1 . 26e-11 , RMSD = 4 . 53 ) , 273 off-fold structures are found with similar binding sites to CETP ( SMAP p-value less than 1 . 0e-3 ) . Reverse virtual screening of the 273 structures against JTT-705 , the smallest CETP inhibitor , was carried out with Surflex [27] and eHits [28] ( see Methods ) to detect the binding capability of these proteins . To reduce the impact of protein flexibility , the complex structure , whenever available in PDB , is used for docking . Proteins that have steric crashes with JTT-705 were removed from the list and a panel of CETP off-targets consisting of 204 structures was constructed for further study as shown in Table S1 . The majority of these off-targets have binding sites that match to one of the two sites that are adjacent to PCW in CETP . Excluding cytochrome P450s that bind drugs promiscuously , most of the putative off-targets are involved in lipid/fatty acid transport or binding , signal transduction pathways and immune response . Based on both SMAP p-values and docking scores ( p-value<1 . 0e-3 , Surflex score>3 . 50 and eHiTs score<−4 . 50 ) , six classes of structure were consistently found at the top of the list: CD1B like antigen recognition domains ( CD1B ) ; nuclear hormone receptor ligand binding domains ( NR ) ; lipid transport proteins ( LPTP ) ; fatty acid binding proteins ( FABP ) ; EF hand-like calcium binding proteins ( EF ) ; and heme binding proteins ( HEME ) . The first four classes of proteins are able to bind cognate ligands similar to those that bind to CETP , such as fatty acids , lipoproteins , and lipids [34] . Although these putative off-targets do not have detectable global structural similarities to CETP according to their CE Z-scores ( Fig . S3 ) , they have local structural similarity and are related to each other , forming an interconnected off-target network . As shown in Fig . 1 , 76% of the putative off-targets ( 154/204 ) form the three largest clusters . The largest helix bundle cluster includes NR , EF , HEME and other proteins ( Fig . S4 ) . In this paper , we focus on the six selected classes of proteins and demonstrate how they correlate to the clinical findings . Other putative off-targets are subject to on-going computational and experimental studies . Most of the predicted ligand binding sites of CD1B , LPTP , and FABP have a similar topology to that of CETP . The drug molecule binds to a cavity formed by anti-parallel beta-sheets and capped by other structural components such as a helix . The others , NR , EF , and HEME all have alpha-helical architectures that are completely different from the secondary structure surrounding the binding site of CETP . These differences illustrate the necessity of tools like SMAP that can find local structural similarities even when global similarity is non-existent . From a functional perspective , it is not surprising that lipid binding proteins act as off-targets for CETP inhibitors since they are required to bind similar cognate hydrophobic ligands such as PCW . It is noteworthy that glycolipid transfer protein , one of the lipid binding proteins , has significant structural similarity to nuclear hormone receptors . For example , the FATCAT [33] p-value is 1 . 77e-3 when comparing one glycolipid transfer protein ( PDB id: 1TFJ ) with that of retinoid X receptor ( PDB id: 1YOW ) , but the RMSD is 9 . 82 Å for a rigid superimposition . However , if the components of these structures are allowed to twist , the RMSD drops to 2 . 57 Å when the helices surrounding the binding site are well aligned ( Fig . S5 ) . The structural similarity between glycolipid transfer protein and other all-helical proteins increases confidence in our result that the lipid-activated nuclear receptor ( NR ) is one of the major off-targets of CETP inhibitors . We searched for possible functional correlations between CETP and the putative off-targets using the iHOP [35] literature network ( http://www . ihop-net . org/UniPub/iHOP/in ? dbrefs_1=NCBI_LOCUSLINK__ID|1071 ) . Several top-ranked off-targets appear in the same sentences with each other more than 3 times in the literature . They include phospholipid transfer proteins , nuclear receptors , including PPAR , major histocompatibility complex class II that is similar to CD1B , apolipoprotein A-1 , and angiotension I converting enzyme . The functional similarity between CETP and the off-targets is further quantitatively measured using gene ontology ( GO ) relationships found with the FunSimMat web server [36] ( http://funsimmat . bioinf . mpi-inf . mpg . de/index . php ) . From 204 off-targets , 148 structures had annotated GO terms and 94 structures had detectable similarities with a Resnik score [37] larger than 0 . 0 . Among these 94 structures , lipid transport/binding proteins , CD1B , and nuclear hormone receptors were ranked top , followed by globin-like , EF hand-like and other proteins ( Table S2 ) . To further support our off-target predictions we conducted docking studies on CETP and the identified off-targets , which also provides insights into the molecular mechanisms of off-target binding . It has been established that the binding affinity calculated from docking programs is not necessarily reliable [38]–[40] . When using an energy-based scoring function , the errors come predominantly from the inaccurate parameterization of the individual energy terms . We find that the docking scores for CETP and its putative off-targets are linearly dependent on the number of carbon atoms on the docked molecules because the hydrophobic term dominates the scoring ( Fig . S6 ) . Based on this observation we developed a procedure to minimize the systematic error in the scoring function . Rather than considering the raw docking score we used the z-score to represent the relative binding affinity . The z-score is derived from a large number of random drug-like molecules and is dependent on both the number of carbon atoms in the ligand and the nature of the protein binding site . A large negative z-score indicates a high probability of true binding . Based on this procedure , the normalized docking scores ( NDS ) of the six classes of off-targets are listed in Table 1 . These data indicate that binding of CETP inhibitors to putative off-targets is indeed statistically significant . Furthermore , the vector distance of the carbon atom size dependent average docking score for CETP and the majority of off-targets is less than 1 . 0 ( Table S3 ) . This implies that the ligands are able to bind to CETP and to the off-targets with similar binding affinities , since their predicted binding affinity differences are less than 1 . 0 , which is the standard deviation of docking scores ( see Methods ) . Finally , the correlation of ligand binding profiles between CETP and its off-targets [4] are relatively high ( Table S3 and Fig . S7 ) . Importantly , the binding profiles for the three CETP inhibitors ( Torcetrapib , Anacetrapib , and JTT-705 ) are different from each other across the panel of off-targets . JTT-705 is the most promiscuous inhibitor . In contrast , Torcetrapib failed to dock into some of the off-targets , and Anacetrapib is suitable to be docked into the least number of off-targets . The difference between their off-target binding profiles can be partly explained by their different complexity [41] and sizes . The molecular volumes of JTT-705 , Torcetrapib and Anacetrapib are 407 . 31 , 498 . 42 , and 527 . 28 Å3 , respectively . As shown in Table 1 the estimated volume of the off-target binding pockets varies greatly . Thus , the smallest ligand , JTT-705 , can be accommodated in all of these pockets , but the larger-sized Torcetrapib and Anacetrapib are difficult to fit into the smaller sized pockets . It could be argued that the failure in docking Torcetrapib and Anacetrapib into the smaller sized pockets is because the induce fit of the receptor is not explicitly modeled . However , for most of the NRs , both antagonist and agonist conformations are tested . Thus it is less likely that the unfitness of Torcetrapib and Anacetrapib for some of the off-targets is a result of not specifically considering induced fit in the docking calculation . The different off-target binding profiles of these CETP inhibitors have significant implications for the observed side-effects , as discussed subsequently . By incorporating the predicted off-targets into biological pathways it is possible for us to correlate the predicted off-target interactions with the observed pleotropic effects of Torcetrapib , Anacetrapib and JTT-705 . Among them , the negative effect of Torcetrapib on blood pressure in phase III clinical trials could be deduced . Also deducible was an explanation for the increased death from infection and cancer [21] . Conversely , JTT-705 has gotten encouraging safety results from phase II clinical trials and no side-effects of hypertension have been observed thus far . Similar positive results are observed for Anacetrapib during phase I clinical trials . It should be noted that at this time that JTT-705 and Anacetrapib are in clinical trials involving only a small number of patients during short term studies . Results from long term studies are needed to confirm the absence of negative effects for these two drugs . In addition , JTT-705 is found to be able to block cell proliferation and angiogenesis through Ras and P38 kinase pathways [26] . To illustrate these findings , using a survey of the literature , we constructed a hierarchical biological network that connects drugs , off-targets , pathways and clinical observations . Using this network we could explore the implications of administering CETP inhibitors on different pathways through their interactions with corresponding off-targets ( Fig . S8 ) . The network consists of several interconnected metabolic , signal transduction , and gene regulation pathways . Each component of the network is separately shown in Fig . 2 , Fig . 3 , Fig . 4 , and Fig . S9 , and is discussed in detail in the following sections . It is notable that several predicted off-targets , especially the nuclear hormone receptors , are essential components in the network , involved in both positive and negative controls of several cellular systems . Nuclear hormone receptors are known as lipid-activated transcription factors that play key roles in lipid metabolism , inflammatory processes and the hormone system . The regulatory controls of our predicted nuclear hormone receptors are on pathways involved in hypertension , inflammation and cancer development . Torcetrapib , Anacetrapib and JTT705 showed different binding affinities to these receptors and thus different clinical outcomes resulting from the combinational responses of these receptors in related pathways .
In vitro , in vivo and clinical studies indicate that CETP inhibitors exhibit pleotropic effects in humans through the interaction with unknown off-targets . We have identified a panel of proteins that likely bind to CETP inhibitors leading to the observed clinical indications . The putative off-target interactions are consistent with existing experimental data and provide insights into the molecular mechanisms of the side-effect profile of CETP inhibitors . Drug promiscuity depends not only on the similarity of ligand binding pockets in the related proteins but also the complexity of the drug itself [41] . In general , smaller molecules are able to bind more targets . The same trend has been predicted for CETP inhibitors; the smallest JTT-705 is the most promiscuous and the largest , Anacetrapib , is the least promiscuous . However , in contrast to conventional wisdom that implies the more specific the binding the lesser the side-effects , the most promiscuous inhibitor , JTT-705 , does not cause the side-effect of hypertension that is observed in the more specific Torcetrapib . Considering the regulation of blood pressure by NRs , it is possible that JTT-705 acts as an antagonist of NRs to down-regulate aldosterone . However , our results suggest that CETP inhibitors prefer binding to the agonist rather than the antagonist conformation of the NR . Experimental evidence also implies that JTT-705 actually activates NR to mediate Ras and p38 kinase pathways [26] . Thus , it is more likely that the side-effect of CETP inhibitors is modulated by a combination of biological controls involved in many physiological processes such as cell proliferation [81] , inflammation and hypertension . In other words , JTT-705 is involved in activation of NRs that contribute to both positive and negative controls of aldosterone . Although Torcetrapib is more specific and binds less off-targets than JTT-705 , it only activates those NRs that up-regulate RAAS resulting in hypertension . To fully understand how small molecules can modulate physiological or pathological processes through such combinatorial control , it is necessary to simulate the dynamic properties of the biological system . To this end , it is a critical first step to identify all of the putative molecular receptors involved in the biological process and to connect them into a logical integrated protein-ligand interaction network . The chemical systems biology approach developed here is limited by available protein structures that currently only cover approximately 50% of the human proteome , although the structural coverage of the human proteome will steadily increase with progress in structural genomics [82] and conventional structure determination . As a result , some potential off-targets may be missed because they are not included in the screening . In addition to establishing functional relationships between proteins using their sequences , structures and functional sites , there are significant efforts to relate drug targets to their ligands through chemical genomics analysis [12] . However , the chemical genomics approach is restricted by the availability of bioactivity data . When exploring off-targets that cover the whole human proteome , this limitation becomes obvious since only a small number of target families explored by pharmaceutical companies are in the bioactivity database [5] . Thus our method is complementary to existing chemical genomics approaches . Drug-target networks will be greatly expanded by combining chemical genomics data and a structural genome-wide off-target analysis . Several studies have attempted to extend the target-based method to the domain-based model through similar sequence motifs or global structures [83] . In this study we further expand the scope of the chemical genomics approach beyond sequence and fold similarity by searching for similar ligand binding sites . Hence a ligand binding site-based approach will provide an ever improving way to generate a candidate list of proteins participating in interconnected biochemical pathways and to establish their relationships to biological processes . It is hoped that these approaches will eventually provide the foundation for the in silico simulation of the influence of small molecules on biological systems . In the interim it is noted that the analysis of incomplete networks is still invaluable in making new discoveries in biomedicine as exemplified by several recent studies [3] , [11] . Besides SMAP used in this study , a number of web servers for ligand binding site search are available , for example , SiteEngine [84] , SitesBase [85] , [86] , CavBase [87]–[89] , SuMo [90] , PdbSiteScan [91] , eF-Site [92] , [93] , pvSOAR [94] , and pevoSOAR [95] . Compared with these servers , SMAP has several distinguishing features making it particularly suitable for identifying off-targets on a structural genome-wide scale . First , SMAP does not require prior knowledge of both the location and the boundary of the ligand binding site . Instead , whole proteins are scanned to find the most similar local patch in the spirit of local sequence alignment such as the Smith-Waterman algorithm [96] . This feature makes SMAP appropriate for practical problems since typically the boundary of the ligand binding site is not clearly defined or depends on the ligand in the complex structure . Second , SMAP integrates geometric , evolutionary and physical information into a unified similarity score akin to a sequence alignment score . However , unlike conventional sequence alignment , the SMAP alignment is sequence order independent; a necessary requirement when comparing local binding sites . Third , because SMAP uses the reduced structure representation , it is not sensitive to structural uncertainty and flexibility . Thus SMAP can be applied to homology models and handle flexible ligand binding sites . Finally , we have developed a probability model to efficiently estimate the statistical significance of the binding site similarity . The model allows us to reliably identify similar ligand binding sites in a high throughput fashion . Despite these advantages of SMAP , it is expected that the best results will come from the combination of different tools as demonstrated by many studies in bioinformatics and molecular modeling . Despite the success of ligand binding search algorithms in protein function prediction and drug design [2] , [4] , [20] , [87] , [88] , [95] , [97]–[99] currently no algorithm can retrieve all of the binding sites that bind a cognate ligand such as ATP . However , in the context of searching for off-targets of drug molecules , the actual number of false negatives may be limited based on the nature of the drug . False negatives in the ligand binding site search are due mainly to large conformational changes of the ligand and corresponding physical and geometric changes in the binding site . Most existing drugs are designed to selectively inhibit an exquisite target . They are more rigid and less adaptable to the changing environment of the binding site than the cognate ligand . For example , a protein kinase ATP competitive inhibitor is designed to inhibit only the ATP binding site of the protein kinase , not that of other superfamilies such as P-loop hydrolases . On the other hand , although rational drug design may take the same cognate ligand binding site into account , it rarely explores the cross-reactivity between binding sites that are not naturally designed for the same cognate ligand but are able to bind the same drug . Studies by others have shown that the drug binding site can be considered as a negative image of the drug to screen compound database [100] or vice versa to model the drug binding site [101] . Hence ligand binding site similarity search is a valuable tool to identify off-targets that accommodates only the drug molecule but not necessarily all proteins that bind to the same cognate ligand across gene families . In general , the chemical systems biology approach developed in this paper is specific in identifying potential off-targets for drug-like molecules and could be used in concert with experimental design employing in vitro screening , in vivo screening and clinical trials . Even with the current limited structural coverage of the human proteome , our predications are able to provide a testable hypothesis as to the suitability of a lead compound prior to conducting a clinical trial . Thus our findings have implications for drug discovery and development . In contrast to the conventional drug discovery process in which drug leads are optimized to reduce promiscuous binding , the possible combinatorial control of aldosterone regulation by CETP inhibitors suggests that adverse drug effects can be minimized through fine tuning of multiple off-target interactions . Although it is desirable for a drug to bind the primary target in a highly specific way , this is difficult to achieve considering the inherent similarity among protein binding pockets within and across gene families . Moreover , many biological process involve combinatorial control to provide redundancy and homeostasis [102] . In such cases it becomes very difficult to modulate the systems behavior by inhibiting or activating only one single target protein . Thus , a multiple-target approach [6] and combination therapy [10] have been actively pursued to boost clinical efficacy in the treatment of diseases such as cancer and diabetes . However , these combined approaches are rarely systematic with the purposeful intent of developing therapeutics that bind to a primary target to treat the disease , but at the same time are considered to bind to desirable off-targets that modulate side-effects . In some cases this combined goal is achieved serendipitously as would seem to be the case for JTT-705 . Instead of using a single molecule , it may be more feasible to use multiple components to treat a disease state and at the same time to reduce drug side-effects . Different from conventional combination therapy where all of components target disease related proteins , here only a subset of the molecules are directly therapeutic , other molecules serve the purpose of reducing side-effects by targeting non-disease related proteins . We speculate that many drugs which failed due to off-target effects can be rescued by this target-off-target combination therapy . For example , it is expected that the side-effect of Torcetrapib can be reduced by introducing molecules that binds to molecular components involved in the negative control of aldosterone regulation . Such therapies can be only rationally designed by exploring the system properties of the biological network .
5 , 985 structures or models that cover approximately 57% of the human proteome were searched against CETP ( PDB id: 2obd ) ligand binding sites using the sequence order independent profile-profile alignment ( SOIPPA ) algorithm [20] . A new statistical model was introduced to the original approach to estimate the significance of the alignment score [103] . In brief , the alignment score for a given alignment length is fitted to an extreme value distribution ( EVD ) : ( 1 ) Where: ( 2 ) where S is the raw SOIPPA similarity score . μ and σ are fitted to the logarithm of N , which is the alignment length between two proteins: ( 3 ) ( 4 ) Six parameters a , b , c , d , e , and f are 5 . 963 , −15 . 523 , 21 . 690 , 3 . 122 , −9 . 449 , and 18 . 252 for the McLachlan similarity matrix used in this study , respectively . Using this statistical model , 276 off-targets are identified with p-values less than 1 . 0e-3 . The putative 276 off-targets are subject to further investigation using more computationally intensive protein-ligand docking . After removing three structures with the same fold as CETP , JTT-705 , the smallest CETP inhibitor , is docked to the remaining 273 structures using two commonly used fast docking programs , Surflex 2 . 1 [27] ( default setting ) and eHits 6 . 2 [28] ( fastest setting ) . 69 structures with a Surflex docking score smaller than 0 . 0 or an eHits score larger than 0 . 0 are considered to be difficult to fit JTT-705 due to significant steric crashes ( and hence the other two inhibitors based on size ) and are removed from the putative off-target list . The remaining 204 structures are subject to further investigation using the docking software AutoDock4 . 0 [29] and other more computationally intense methods as described below . An all-against-all global structural similarity analysis between the 204 putative off-targets was computed using CE [104] . A graph is constructed with each of the structures as a node . An edge is formed between two nodes if their CE z-score is larger than 4 . 0 ( a superfamily level similarity ) [104] . The volume of the binding pocket is computed using the CASTp server [105] ( http://sts-fw . bioengr . uic . edu/castp ) with default settings . Drug-like molecules are downloaded from ZINC ( http://zinc . docking . org ) [106] . From this database , six sets of molecules are randomly selected with a fixed number , 5 , 10 , 15 , 20 , 25 and 29 carbon atoms , respectively; each set includes 100 molecules . These molecules are docked to CETP and its putative off-targets using eHiTs [28] and AutoDock4 . 0 [29] . The correlation of the docking score to the number of carbon atoms is derived from linear regression for each of the protein receptors . From the linear fitting curve , the average docking score for molecules with a certain number of carbon atoms can be estimated . Based on the fitted average docking score , a normalized docking score DS is calculated as a z-score: ( 5 ) Where Si is the raw docking score for the molecule with i carbon atoms , μi is the fitted average docking score for the number of carbon atoms i , σ is the standard deviation , which is not dependent on the size of molecules and is approximately 1 . 0 in all cases . The vector distance of the average docking score D between CETP and its off-targets is calculated from the average values of the docking scores for randomly selected molecules with fixed numbers of 5 , 10 , 15 , 20 , 25 and 29 carbon atoms as follows: ( 6 ) where SCETP and Soff are the average values of carbon atom size dependent docking scores to CETP and its off-targets , respectively . In this case study , we identify a panel of off-targets of CETP inhibitors using a chemical systems biology approach . All of the identified off-targets belong to different protein superfamilies from the primary target , but are structurally and functionally related , being mainly involved in lipid metabolism , immune response and signaling networks . Among them , CD1 , nuclear hormone receptors and lipid transport proteins are the most likely off-targets with highly consistent results from multiple resources including functional correlation , ligand binding site similarity , hydrophobic scales , and predicted binding affinities . Moreover , the elucidated off-target effects from these proteins are strongly correlated to clinical and in vitro observations . Their combinatorial control of biological process plays a key role in the modulation of the adverse drug effect of CETP inhibitors . This study demonstrates that a chemical systems biology approach , which systematically explores protein-ligand interactions on a genome-wide scale and incorporates them into biological pathways , will provide us with valuable clues as to the molecular basis of cellular function . At the same time , it will help to transform the conventional single-target-single-drug drug discovery process to a new multi-target-multi-molecule paradigm . | Both the cost to launch a new drug and the attrition rate during the late stage of the drug discovery and development process are increasing . Torcetrapib is a case in point , having been withdrawn from phase III clinical trials after 15 years of development and an estimated cost of US $800 M . Torcetrapib represents a new class of therapies for the treatment of cardiovascular disease; however , clinical studies indicated that Torcetrapib has deadly side-effects as a result of hypertension . To understand the origins of these adverse drug reactions from Torcetrapib and other related drugs undergoing clinical trials , we introduce a systematic strategy to identify off-targets in the human structural proteome and investigate the roles of these off-targets in impacting human physiology and pathology using biochemical pathway analysis . Our findings suggest that potential side-effects of a new drug can be identified at an early stage of the development cycle and be minimized by fine-tuning multiple off-target interactions . The hope is that this can reduce both the cost of drug development and the mortality rates during clinical trials . |
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Poliomyelitis has nearly been eradicated through the efforts of the World Health Organization’s Global Eradication Initiative raising questions on containment of the virus after it has been eliminated in the wild . Most manufacture of inactivated polio vaccines currently requires the growth of large amounts of highly virulent poliovirus , and release from a production facility after eradication could be disastrous; WHO have therefore recommended the use of the attenuated Sabin strains for production as a safer option although it is recognised that they can revert to a transmissible paralytic form . We have exploited the understanding of the molecular virology of the Sabin vaccine strains to design viruses that are extremely genetically stable and hyperattenuated . The viruses are based on the type 3 Sabin vaccine strain and have been genetically modified in domain V of the 5’ non-coding region by changing base pairs to produce a cassette into which capsid regions of other serotypes have been introduced . The viruses give satisfactory yields of antigenically and immunogenically correct viruses in culture , are without measurable neurovirulence and fail to infect non-human primates under conditions where the Sabin strains will do so .
The Global Polio Eradication Initiative ( GPEI ) is the biggest and most ambitious health programme aimed at a single disease in history and is very close to eliminating naturally occurring wild poliovirus from the planet [1] . It has involved the extensive use of both the live attenuated vaccines that can revert to a wild type phenotype [2] , and inactivated polio vaccines ( IPV ) whose production in the main currently requires the growth of very large amounts of virulent wild type poliovirus [3] . The vaccines are therefore a possible source for the re-emergence of poliomyelitis when poliovirus has been eradicated . The manufacture of inactivated polio vaccine by new global producers is being encouraged to improve supply and reduce cost [4 , 5] and this increases the possibility of escape or release of large amounts of virus as a result of accident or because the facilities become terrorist targets . WHO has therefore encouraged novel approaches to vaccine production including the use of the attenuated but genetically unstable Sabin vaccine strains for production of IPV on the grounds that this will be safer than use of the wild type strains [3 , 4 , 5 , 6] . Here we describe a more radical approach to safer IPV production exploiting understanding of the attenuation of poliovirus for humans and animals to design and characterise strains that are genetically stable and able to grow in cells in culture but cannot revert to a form that can paralyse people or , arguably , grow in the human gut . Should such a strain escape the production plant it will therefore be harmless . Polioviruses occur in three serotypes , 1 , 2 and 3 and are positive stranded RNA viruses of the picornavirus family with a genome of approximately 7500 nucleotides . A single large open reading frame is preceded by a highly structured 5’ non coding region of about 750 bases containing an Internal Ribosomal Entry Site ( IRES ) through which the virus initiates protein synthesis [2] . All three of the attenuated vaccine strains developed by Sabin and used in the eradication programme contain mutations that weaken the three or seven base pair adjacent stems between bases 470 to 483 and bases 528 to 538 of a particular region of the IRES designated domain V [2] . This is shown in Fig 1 for the type 3 strain where the base pair between nucleotides 472 and 537 is a UG in the Sabin strain rather than a CG as found in wild type strains . The U rapidly reverts to a C in vaccinees increasing the thermodynamic stability of the structure , and there is a concomitant increase in neurovirulence of the virus relative to the vaccine [2] . The type 1 and type 2 strains also revert mutations or insert compensating changes in the same domain and by day 28 all vaccinees who are excreting type 2 and 3 viruses and over 80% of those excreting type 1 virus are excreting revertants , indicating a strong selection pressure in the gut against mutations in domain V [2] . We believe that a virus in which this region could not revert would be unable to grow in the human gut . In any event it would be stably attenuated . We have previously shown that the temperature sensitivity of growth in certain cell lines is a correlate of attenuation caused by mutation of this region of domain V [7] . The thermodynamic stability of domain V can be adjusted by introducing single base changes or by manipulating base pairs , for instance replacing triple hydrogen bonded GC base pairs with weaker double hydrogen bonded AU pairs . We have previously reported that a structure modified by base pair exchange so that it has the same thermodynamic stability as the Sabin type 3 vaccine strain ( S15 in Fig 1 ) has a similar level of attenuation as the Sabin type 3 vaccine strain [7] . As the base pair requires two simultaneous mutations to revert to the more stable wild type GC ( any other intermediate pairing being less stable ) , the virus is genetically more stable on passage . For maximum genetic stability , however , any single hydrogen bonded GU base pairs must be replaced with Watson-Crick pairs so that they cannot increase the thermodynamic stability by a single mutation . By adjusting relevant base pairs a structure that is predicted to be both minimally stable thermodynamically and maximally stable genetically can be produced . We have constructed a series of such recombinant genomes and investigated the properties of viruses recovered from them including their neurovirulence , temperature sensitivity and genetic stability on passage . Selected strains were further studied for virus yield , infectivity in a primate model and immunogenicity in a rat model compared to strains currently used in production of IPV .
The type 3 vaccine strain was used as a starting point as there is a great deal of data on its attenuation and reversion . Fig 1 shows the successively weaker structures that have been generated , designated S15 , S17 , S18 and S19 . Virus strains based on the type 3 Sabin vaccine strain containing the modified domain V sequences corresponding to S15 , S17 , S18 and S19 were recovered by transfection . Their growth at different temperatures and in different cells was assessed as shown in Fig 2 for L20B cells ( a mouse L cell line stably transfected with the human receptor for poliovirus [8] ) , Vero cells and Hep2C cells . The results are expressed as the log10 reduction in plaque titre at various temperatures relative to that found at the permissive temperature ( 31°C for L20B and Vero or 35°C for Hep2C ) . The unmodified type 3 Sabin vaccine strain and the wild type 3 Saukett strain used in the current production of inactivated polio vaccine ( IPV ) were included for comparison . As previously reported S15 showed similar characteristics to the Sabin type 3 vaccine strain [7] . In L20B cells both viruses grew well at 35° but showed a 1–2 log10 reduction in titre at 37° and a 3–4 log10 reduction at 38° . In Vero cells both viruses showed a 0 . 5–0 . 8 log10 reduction at 37° compared to 33° or 35° and a 2 . 0 log10 reduction at 38° . In Hep2c cells growth was not affected below temperatures of 39 . 5° . The growth of the other viruses was increasingly temperature sensitive in all cells as the domain V structure was progressively weakened , with S19 showing a 2 and 0 . 8 log10 reduction between 31 and 33°C in L20B cells and Vero cells respectively and a reduction of 1 . 5 log10 between 35 and 37°C in Hep 2c cells . The neurovirulence of the strains was assessed by intraspinal inoculation of transgenic mice carrying the human receptor for poliovirus as used for the regulatory testing of oral polio vaccine [9] , using a dilution series to determine the 50% paralytic dose . The results are shown in Table 1 . The Sabin type 3 strain and S15 gave PD50s of 3 . 6 and 3 . 7 log10 CCID50 ( Cell Culture Infectious Dose 50% ) , S17 gave a PD50 of 7 . 4 log10 , about 4 log10 more than S15 , and it was impossible to inoculate sufficient virus for any other strain to paralyse 50% of the mice . Neither S18 nor S19 paralysed any animal and the data demonstrate a sharply increasing degree of virus attenuation as the domain V structure is progressively weakened culminating in S19 . When S18 was passaged ten times in Vero cells at 33 , 35 or 37°C no mutations were induced in domain V but a range of mutations in protein 2A occurred as expected from previous published work[10]Protein 2A is involved in the shut off of cap dependent protein synthesis in poliovirus infected cells . We have previously shown that in certain cells , including Vero , the effect of mutations in domain V of the 5’non coding region can be suppressed by a surprisingly extensive series of coding mutations in protein 2A; the basis for this phenomenon remains unclear . The mutations do not affect neurovirulence in any model or growth in cells of human origin [10] . Substitution of an asparagine by a serine at amino acid 18 in protein 2A ( N18S ) occurred independently several times and the mutation occurs at a base close to a convenient restriction site . While other equally satisfactory suppressors could have been chosen , viruses based on S19 were therefore constructed that included the N18S substitution to allow good growth in Vero cells . S19 and S19/N18S were used as the basis for construction of chimeric viruses in which the capsid proteins were replaced with those of other wild type or Sabin vaccine strain polio viruses . The wild type strains used were the type 1 Mahoney strain , the type 2 polio virus MEF1 strain and the type 3 Saukett strain all of which are used in current classical IPV production . The same genetic backbone was used because there is a good understanding of the properties of the type 3 strain and its attenuation , and the donors of the capsid regions were chosen because use of the Sabin vaccine strains for IPV production is being encouraged with a product licensed in Japan for two years , whilst the clinical properties of vaccines produced from the wild type strains used in current vaccine programmes are well known . The capsids are the immunogenic component of IPV . The virus constructs are summarised in Fig 3 . The neurovirulence of the strains was assessed by intraspinal inoculation of transgenic mice carrying the human receptor for poliovirus . The results are shown in Table 2 . The parental Sabin Vaccine strains had 50% paralytic doses of 2 . 25 , 5 . 9 and 3 . 6 log10 50% Cell Culture Infectious Dose ( CCID50 ) for the type 1 , 2 and 3 strains respectively . These figures are consistent with those expected from commercial vaccines [9] . The PD50s for the wild type strains were much lower as expected being 0 . 5 , 0 . 7 and 0 . 8 for the Mahoney , MEF1 and Saukette strains respectively . None of the constructs based on S19 produced any paralysis in any mouse at the highest dose that could be injected , which ranged from 8 . 10 to 8 . 45 log10 CCID50 . Given the 3-4log10 difference between PD50s of S15 and S17 ( Table 1 ) these results suggest the true PD50s of the S19 strains are in excess of 12 . 0 log10 CCID50 . In contrast to the seeds based on unmodified S19 , those containing the N18S mutation in 2A gave high yields in Vero cells without the need for further adaptation . Previous studies have shown that the mutations do not affect neurovirulence in the tests applied [10] and as can be seen from Table 1 no S19 N18S derived strain caused paralysis at the highest dose that could be inoculated . The novel capsid could have affected the growth properties of the viruses in cells . Growth was studied at a range of temperatures in Vero , Hep2c and MRC 5 cells and the data in Hep2c and MRC5 cells are given in Fig 4a and 4b respectively . For all of the S19 derived strains the curves are super-imposable so that the capsid had no effect on the temperature sensitive growth characteristics . Some of the Sabin strains are known to have mutations conferring a temperature sensitive phenotype in the capsid proteins; however the low temperatures at which the S19 structure has its effect are all fully permissive for the Sabin capsid region mutations , so a lack of effect is to be expected . The same lack of effect of the capsid regions was found for S19 N18S derived strains in Vero cells as shown in Fig 4c . The S19 derived strains without the N18S mutation grew poorly in Vero cells . The S19 N18S seeds were passaged 10 times in Vero cells and in MRC5 cells at 33°C . No mutations were detected in domain V by Sanger sequencing and the virulence of the strains as measured by PD50 assay in transgenic mice was unchanged with no mice paralysed at the highest dose that could be inoculated ( Table 3 ) . Mutations were occasionally found as mixtures in the capsid region and in one instance in the 5’NCR at base 128 in S19 MEF1P1 N18S passaged in Vero ( S1 Table ) . We have previously shown that the Sabin type 3 strain reverts completely both genetically and phenotypically when passaged at 37° [7] . The mutations that had been deliberately inserted in the S19 series did not change on passage . Given the temperature sensitivity of the S19 strains the ease with which the viruses can be grown is crucial to their suitability as seed strains . One step growth yields at 33°C in MRC-5 or Vero cells for the relevant seeds are given in Table 4 in comparison to the wild type strains currently used in conventional production . The S19 strains took longer than the wild type strains , but in all cases complete cpe was reached in 24–48 hours . The effect of passage on yield was not investigated for these constructs . The Sabin strains are not yet widely used in production for IPV but are expected to give slightly lower yields . The yields in MRC-5 cells are slightly lower by 0 . 2 and 0 . 8 log10 for the S19/MahoneyP1 and S19/MEF1P1 strains respectively while the yield for the Saukett strain was slightly higher by 0 . 15 log10 . The strains intended for Vero based production gave titres about 0 . 5 log10 lower in Vero cells than the corresponding wild type . These differences are small but probably real and it is likely that they could be eliminated by optimising the conditions of growth in the course of process development by a manufacturer . The growth of the strains is compatible with production . Mature infectious poliovirus expresses a different antigen ( D antigen ) to the empty capsid ( C antigen ) and the potency of IPV is usually expressed in D antigen units measured by ELISA relative to an International reference established by WHO . The potency of IPV is also assessed by immunising rats and comparing the responses to those produced by the same reference . Different strains of poliovirus differ in the immune response generated for the same mass expressed in D antigen units . Assays were carried out using the S18 versions of the strains which have identical capsid proteins to the analogous S19 strains . Virus preparations were inactivated with formalin imitating the process used commercially [11] . The D antigen content of preparations of inactivated S18 derived strains were measured by ELISA by methods used in house to assay commercial IPV batches [12] . Rats were then immunised with the S18 strains and the wild type IPV International Standard over a similar range of D antigen content [13] . The amount of S18 strain D antigen required to generate an immune response equal to the reference was expressed as a ratio and is shown in Table 5 . The S18 strains gave a ratio of very close to 1 and were therefore of equivalent immunogenicity per unit antigen to the unmodified strains , except for the type 2 Sabin capsid strain which is known to be less immunogenic and gave a ratio of about 0 . 1 [14] . The S18 strains therefore gave figures consistent with the capsid regions they expressed . Humans are more readily infected by the oral route than primates [15] . However an animal model was established in which cynomolgus macacques were fed polio virus and monitored for up to 49 days for virus excretion and then for seroconversion . The results are shown in Table 6 for the Sabin type 3 strain , S19 and the wild type S19 derivatives . Only the Sabin 3 strain showed signs of infecting the animals , being excreted for a period of 14 to 42days by all four animals given 1011pfu in experiment 1 and by two of four animals given 109 . 5pfu in the second experiment . The dose in vaccines for human use is in the region of 105 depending on the serotype Where animals were not infected virus was detected in the stool at declining titers for less than six days consistent with the dose given; where infection was successful the titres were of the order of 100 pfu per gram of stool , which is about two orders of magnitude less than that found in humans . Only animals infected with the Sabin 3 strain seroconverted . The data were consistent with a failure to infect with any of the S19 derived strains and provide some support to the view that the strains would not infect the human gut . However the virus excreted after infection with the Sabin 3 strain retained the U at base 472 throughout the excretion period , whereas it would have rapidly mutated to a C in humans . Other mutations were introduced into the virus excreted by the animals given the Sabin type 3 strain including mutations in protein 2A towards the end of the monitoring period . Such mutations have not been reported in human studies [2] .
We have constructed viruses which are extremely attenuated and genetically stable in cell culture by rational design based on understanding of the attenuation of the Sabin vaccine strains of poliovirus . The viruses grow to titres acceptable for IPV production under appropriate conditions of temperature and cell substrate and have the same antigenic properties based on reactions with panels of monoclonal antibodies in ELISA as well as in regulatory assays for antigen and immunogen content as the strains from which their capsids were derived . It is intended that the S19 strains should be used for production as they represent the most attenuated case; however the properties of S18 make it also suitable for safe production . Should the S19 strains revert by insertion of a double mutation the resulting virus would be S18 like and therefore also safe , providing a margin of security; in fact it would require six paired mutations to produce a virus that was as virulent as the current Sabin strains . It can be argued that the attenuating mutations could be removed by a single recombination event involving a poliovirus or a type C enterovirus . Recombination with a poliovirus that could compensate for the defective 5’non coding region is unlikely since all the production strains have the same basic genetic structure and could not complement each other and the use of other poliovirus strains will be highly constrained after eradication and containment . There is no reason why the viruses should be exposed to an enterovirus species C in the laboratory if good practice is followed , and the most likely exposure would be if a worker was already infected with such a virus and then became exposed to an S19 strain described here . The strains are very highly and stably attenuated and will not cause paralysis . However we believe that there is strong indirect evidence that the strains will be unable to grow in the human gut . This is based in part on their very low fitness in vitro and in vivo , but more importantly on the well documented reversion of the 5’ non coding mutations in vaccinees where recipients of vaccine revert the mutations within a very short time of immunisation , implying a very strong selection pressure against the disruption of the structure ( see reference 2 and references therein ) . The strains are not able to accomplish reversion of this type , requiring multiple simultaneous mutations to regain fitness . The data on oral infectivity in an animal model are consistent with the view that the viruses are of low infectivity by the oral route , although the model is not a perfect reflection of human infection . This suggests that recombination in the human gut with a virus that has an established infection would be difficult and unlikely irrespective of the comparative fitness of a product of such an event . The inferred inability to grow in the human gut makes the strains safe for production; should someone be accidentally exposed through a release from a production facility they will not become infected and will not pass the strain onto others . We consider that the viruses are intrinsically safer and should be fit for use at lower containment than the wild type or Sabin vaccine strains . The recently issued WHO global action plan to minimise poliovirus facility associated risk after type specific eradication of wild polioviruses and sequential cessation of OPV use ( GAP III ) specifies poliovirus containment and risk mitigation strategies as polio is eradicated[5] . Vaccine strains , defined as any strain licensed for use as a live vaccine , i . e . the Sabin strains , are contained by different criteria to wild type strains . Wild type strains encompass everything else including any virus with a wild type capsid or vaccines not licensed for human use such as Cox , CHAT and other strains [5] . The strains described here would be classified as wild type by these criteria as they are not and cannot be licensed as live vaccines on grounds of efficacy at least . However GAP III also leaves open the option of assessing new derivatives by an expert panel that will compare the novel strains to the Sabin strains with respect to degree and stability of attenuation , potential for person to person transmission and neurovirulence in animal models to define the containment required which is not specified . GAP III is also described as an evolving document . Vero cells are the preferred cells for production of IPV as they are robust and easier to adapt to large scale than alternatives such as the human diploid MRC5 cells . However mutations are selected rapidly when the S19 strains are grown in Vero cells; consequently seed constructs were made with one suitable mutation that allowed good growth . The mutation did not affect virulence or growth in other cells . Moreover mutations in 2A have not been seen in viruses excreted by recipients of vaccine [2] . For these reasons we believe that they do not affect the phenotype of S19 so far as human subjects are concerned or affect the considerations of containment required in the use of S19 derived seed in IPV production . Finally , historically live attenuated viral vaccines have been extremely successful but their development has been based on serendipitous identification linked to careful clinical evaluation of their suitability rather than rational manipulation based on understanding of the virus [16] . In contrast the strains described here were derived by application of the molecular biological understanding of the physiology and attenuation of the polio strains used in live vaccines . The properties were correctly predicted a priori .
Hep 2c , Vero , MRC-5 and L20B cells were grown as previously described [17] . Hep2c cells were from stocks maintained at NIBSC since the 1970s , Vero cells originated from ATCC; MRC-5 cells were originally derived at NIBSC in 1968 and L20B cells were originally supplied by Dr Vincent Racaniello , Columbia University New York; the cells used here were part of a cell bank maintained at NIBSC to supply WHO laboratories of the Polio Laboratory Network . Viruses were grown on confluent monolayers at the relevant temperature at a multiplicity of infection of 10 . Cell sheets were incubated in serum free medium supplemented with antibiotics until complete cytopathic effect was seen , when the flasks were frozen and thawed three times followed by removal of cell debris by centrifugation . To establish single cycle growth curves replica MRC-5 or Vero cell monolayers in 6-well plates were inoculated with viruses at multiplicities of infections ( MOI ) of 10 . Infected cell sheets were incubated in MEM containing antibiotics but no serum at 33°C for different periods then frozen at -70°and thawed , three times , and cell debris removed by centrifugation . Supernatants were titrated in HEp2C cells at 33°C by CCID50 assays in 96-well plates . Viruses were assayed by plaque-formation at different temperatures in Hep2C cells , Vero cells and L20B cells . Temperatures were controlled by incubation of inoculated plates in sealed plastic boxes submerged in water baths whose temperatures fluctuated by <0 . 01°C . All viruses were assayed at least twice and control viruses with known phenotypes were always included for validation . The range of temperatures over which variation in domain V stability influences viral growth depends on the cell substrate used [17 , 18] being the lowest in L20B cells and highest in HEp2c cells . MRC-5 or Vero cell monolayers in 25cm2 flasks were inoculated with viruses at multiplicities of infections ( MOI ) of 1 . Infected cell sheets were incubated in MEM containing antibiotics but no serum at 33°C or 37°C until complete cytopathic effect ( cpe ) was apparent . Flasks were frozen at -70°and thawed , three times , and cell debris removed by centrifugation . Supernatants were used for further passage , as above , or for biological and molecular analysis . Derivation of the Sabin 3 cDNA clone and construction of S15 have been described previously [7 , 19] . S17 , S18 and S19 were constructed by PCR mutagenesis; mutations introduced into each sequence are shown in Fig 1 . For each plasmid , three fragments of the 5’ noncoding region of Sabin 3 were amplified by PCR using primers incorporating the necessary sequence changes , located at nucleotides ( a ) 31–50 and 471–489 , ( b ) 471–489 and 522–540 and ( c ) 522–540 and 755–778 . The three overlapping fragments ( a ) - ( c ) were gel-purified , mixed and re-amplified with outer primers then the 747bp fragment comprising the mutated 5’ noncoding region was cloned into pCR2 . 1 ( Invitrogen ) and sequenced . M1uI-SacI ( 279–751 ) fragments with correct sequences were ligated into Sabin 3 clones lacking the SacI-SacI ( 751–1900 ) fragment . Full-length infectious clones were generated by addition of a partial SacI/SmaI ( 2768 ) fragment . In order to replace the P1 coding region a SacII site was introduced without coding change into the S19 clone at nucleotides 3408–13 . Capsid regions from Sabin 1 , Sabin 2 , Mahoney , MEF-1 and Saukett were then introduced precisely using standard PCR methods . Residue 2A-18N was mutated to S using standard methods . All clones were verified by sequencing . Viruses were recovered by transfection of HEp2C monolayers with >2μg T7 transcripts [20] followed by incubation at 33°C for 24–48 hours , by which time complete cytopathic effect was apparent . Sequences of all mutants were confirmed following RNA extraction and RT-PCR . The TgPVR mouse experiments were performed under Home Office licences PPL 80/2478 and PPL 80/2050 which were reviewed and approved by the NIBSC animal ethical committee before submission . High titre virus stocks were prepared by inoculating HEp2C monolayers in 75cm2 flasks ( four per virus ) at multiplicities of infections ( MOI ) of 10 and incubating at 33°C until complete cytopathic effect ( cpe ) was apparent . Flasks were frozen at -70°and thawed , three times , and cell debris removed by centrifugation . Supernatants were centrifuged through 10ml cushions of 30% sucrose in PBS ‘A’ in an SW28 rotor at 25 , 000 rpm overnight . Pellets were resuspended in a total of 1 ml PBS ‘A’ and passed through a 0 . 45μ sterile filter prior to inoculation . Intraspinal inoculation was performed essentially according to the standard operating procedure “WHO neurovirulence test of type 3 live poliomyelitis vaccines ( oral ) in transgenic mice susceptible to poliovirus” available from the WHO ( Coordinator , Quality Assurance & Safety: Biologicals , World Health Organisation , 1211 Geneva 27 , Switzerland ) except that higher doses and fewer mice per dose were used . Briefly , 6–8 week old TgPVR mice in groups of 8 ( weight and sex-matched ) were sedated and inoculated into the lumbar region of the spinal cord with 5μl of each dose and observed for occurrence of paralysis for up to 14 days . Mice with paresis/paralysis were scored positive and mice surviving for 14 days with no clinical signs were scored negative . The experiments were performed under Home Office licence PPL 80/2060 and were specifically approved by the NIBSC animal ethics committee . Cynomolgus macaques ( between 1 . 5 and 3 . 5 kg ) were trained to accept small volumes of fruit cordial by mouth from a syringe on an appropriate signal . The monkeys were then caged in groups of four and starved for at least 12 hours prior to oral inoculation . On day 0 monkeys were given 2ml virus inoculum mixed with fruit cordial , by mouth from a syringe . Separate faecal samples ( 5-10g ) for virus isolation were collected from each animal on days: -1 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 10 , 14 , 17 , 21 , 28 , 35 , 42 & 49 . For sample collection , animals were caged individually at an appropriate time of day , with a clean lined collecting tray placed under the cage , until samples are produced . Blood samples ( 5-10ml ) were taken on days: -1 , 7 , 14 , 21 , 28 , 42 . Faecal samples were processed for virus isolation by adding 1g to10 ml PBS containing 1g of glass beads and 1 ml chloroform in a 50mL Falcon tube . Tubes were shaken vigorously at 4°C for 20 minutes using a mechanical shaker then centrifuged for 20 minutes at 1500 x g in a refrigerated centrifuge . Supernatants were stored at -20°C prior to virus titration and isolation . Faecal virus titres were measured by plaque assay in Hep2C cells . Virus stocks , prepared by inoculating HEp2c cells , were characterised by ( Sanger ) nucleotide sequencing . Neutralising antibodies in monkey sera were determined using standard protocols . Immunogenicity was assessed using Pharmacopieal methods established at NIBSC for the release of IPV lots . Viruses were inactivated by formaldehyde treatment ( 0 . 01% at 37°C , for twelve days [11] ) , D antigen content was measured by ELISA [12] and immunogenicity was assessed in Wistar rats [13] . The neutralising antibody responses to a range of antigen doses were statistically compared to those elicited by a concurrently tested International Standard preparation to derive a relative potency: a potency of 1 . 0 indicates equivalence; vaccine lots are acceptable for release if the statistical 95% confidence interval of the assay of their potency includes 1 . 0 . All animal experiments were performed under licenses granted by the UK Home Office under the Animal ( Scientific Procedures ) Act 1986 revise 2013 and reviewed by the internal NIBSC Animal Etics committee . The TgPVR mouse experiments were performed under Home Office licences PPL 80/2478 and PPL 80/2050 which were reviewed and approved by the NIBSC animal ethical committee before submission . The experiments were performed under Home Office licence PPL 80/2060 and were specifically approved by the NIBSC animal ethics committee . The primates are gang housed with environmental enrichment including toys , swings , television . In the experiments described here the virus was administered in syrup that the animals had been trained to take willingly from a syringe ( i . e . not by gavage ) ; the animals were only housed alone when faecal samples were required . Anaesthesia and sacrifice at the end of the experiment were by scheduled methods as approved by the Home Office . | New polio vaccines will be needed to safeguard global eradication: Sabin strains are known to evolve to fill the niche left by wild-strains so their long-term use is incompatible with eradication; most current inactivated vaccine is made from wild polioviruses so that production presents a significant biosecurity risk . We have developed new strains for Inactivated Polio Vaccine ( IPV ) production with negligible risk to the human population should they escape . Sabin’s live-attenuated vaccines are variants of wild strains selected by the use of unnatural cell substrates , hosts and growth conditions . Unsurprisingly these variants evolve back towards wild-type properties during replication in , and transmission between , their natural hosts . An understanding of the molecular basis of these pathways led us to design novel vaccine strains that are very highly attenuated and arguably cannot replicate in people and whose opportunities for reversion during replication in cell culture are severely restricted . At the same time , the strains can feasibly be produced on a large-scale and they are as immunogenic as current IPV . These attributes allow for safe vaccine production in the post-eradication world . |
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Many aspects of the historical relationships between populations in a species are reflected in genetic data . Inferring these relationships from genetic data , however , remains a challenging task . In this paper , we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations . In our model , the sampled populations in a species are related to their common ancestor through a graph of ancestral populations . Using genome-wide allele frequency data and a Gaussian approximation to genetic drift , we infer the structure of this graph . We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids . In both species , we show that a simple bifurcating tree does not fully describe the data; in contrast , we infer many migration events . While some of the migration events that we find have been detected previously , many have not . For example , in the human data , we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations . In the dog data , we infer that both the boxer and basenji trace a considerable fraction of their ancestry ( 9% and 25% , respectively ) to wolves subsequent to domestication and that East Asian toy breeds ( the Shih Tzu and the Pekingese ) result from admixture between modern toy breeds and “ancient” Asian breeds . Software implementing the model described here , called TreeMix , is available at http://treemix . googlecode . com .
The extant populations in a species result from an often-complex demographic history , involving population splits , gene flow , and changes in population size . It has long been recognized that genetic data can be used to learn about this history [1]–[3] . In humans , early approaches to inferring history from genetics were limited to using a relatively small number of blood group or other protein polymorphisms [1] , [4]–[6] . These types of studies were then superseded by analyses of DNA markers , which have progressed from single marker studies [3] to studies involving hundreds of thousands of markers [7] . It is now feasible to collect genome-wide genetic data in any species; to a large extent it is no longer the data collection , but rather the statistical models used for analysis , that limit the historical insight possible . There are many statistical approaches to demographic inference from genetic data . One approach is to develop an explicit population genetic model for the history of a set of populations , framed in terms of the effective population sizes of the populations , the times of population splits , the times of demographic events ( such as population bottlenecks ) , and other relevant parameters . The values of these parameters can then be learned from the data using a variety of techniques , often involving simulation [8]–[16] . These approaches have the advantage of allowing flexible modeling of a wide variety of demographic scenarios , but the disadvantage that they can only be applied to one or a few populations at a time . Another type of approach to learning about population history uses methods that summarize the major components of genetic variation in a sample by clustering or principal components analysis [17]–[20] . Although these methods do not model history explicitly , the inferred components can often be interpreted post hoc as representing historical populations , and individuals or populations that are mixtures of different components as evidence of admixture between these populations ( e . g . , [17] , [21]–[23] ) . However , these methods are not directly informative about history; indeed , the relationship between the major components of genetic variation and true underlying demography is not always intuitive [24]–[26] . A different class of approaches focuses on the relationships between populations , by representing a set of populations as a bifurcating tree [1] , [27]–[32] . In these models , the details of the demographic histories of the population are absorbed into the branch lengths of the tree [1] , [33] . This approach has the advantage of being applicable to large numbers of populations; however , a major caveat when modeling the history of populations as a tree is that gene flow violates the assumptions of the model [2] , [34] , [35] . It is often difficult to know , a priori , how well the history fits a simple bifurcating tree . Explicit tests for the violation of a tree model have been developed [35]–[40] . These tests have been used , most notably , to infer the existence of gene flow between modern and archaic humans [39] , [41] , [42] , as well as between diverged modern human populations [37] , [43] , [44] . In this paper , we present a unified statistical framework for building population trees and testing for the presence of gene flow between diverged populations . In this framework , the relationship between populations is represented as a graph , allowing us to model both population splits and gene flow . Graph-based models are of growing interest in phylogenetics [45] , [46] , but have been rarely used in population genetics ( with some exceptions [37] , [40] , [47] ) .
In the most simple case , consider a single SNP , and let the allele frequency of one of the alleles at this SNP in an ancestral population be . ( We use a lowercase to denote that this is a parameter rather than a random variable . We initially consider distributions conditional on ) . Now consider a descendant population . We model , the allele frequency of the SNP in population , as: ( 1 ) with ( 2 ) where is a factor that reflects the amount of genetic drift that has occurred between the ancestral population and . This Gaussian model was first introduced by Cavalli-Sforza and Edwards [1] , and the motivation for this model is outlined in Nicholson et al . [33] , if the amount of genetic drift between the two populations is small ( at most on a timescale of the same order as the effective population size ) , then the diffusion approximation to a Wright-Fisher model of genetic drift leads to Equation 2 with , where is the number of generations separating the two populations , and is the effective population size [33] . We do not model the boundaries of the allele frequencies at zero and one , nor do we consider new mutations . This means that this model will be most accurate for alleles that were at intermediate frequency in the ancestral population . Now consider a descendant population of ; let us call this population , and the allele frequency in the population . Using the same model: ( 3 ) ( 4 ) where ( 5 ) We can write down the expectation and variance of as: ( 6 ) ( 7 ) and: ( 8 ) ( 9 ) We then assume that the amount of genetic drift between all the populations is small . This implies that is well-approximated by in Equation 5 , and hence the amount of genetic drift between and is approximately independent of the amount of genetic drift between and [35] . With these simplifications: ( 10 ) ( 11 ) We thus have a model for , conditional on : ( 12 ) We tested the performance of the TreeMix method in simulations . We generated coalescent simulations from several histories; the basic structure was a set of 20 populations produced by a serial bottleneck model like that used by DeGiorgio et al . [51] to model human history ( Figure 2A ) . The parameters of the simulations were chosen to be reasonable for non-African human populations; we used an effective population size of 10 , 000 , and a history where all 20 populations share a common ancestor 2000 generations in the past . Each individual simulation involved 400 regions of approximately 500 kb each , and thus recapitulated many aspects of real data , including hundreds of thousands of loci and the presence of linkage disequilibrium . To test the performance of the TreeMix model with real data , we applied it to humans , whose genetic history has been studied extensively [7] , [21] , [52] , [53] . We applied the model to a dataset consisting of about 125 , 000 SNPs ascertained by low-coverage genome sequencing in a single Yoruban individual and then genotyped in 55 modern and archaic human populations [54] . In all that follows , we excluded the two Oceanian populations because they gave inconsistent results across datasets . We believe this difficulty results from the fact that these populations contain ancestry from multiple sources , making the graph estimation somewhat unstable when they are included ( Text S1 , Figure S12 ) . We first built the tree of all 53 remaining populations ( Figure 3A ) . This tree largely recapitulates the known relationships among population groups [7] , and explains 98 . 8% of the variance in relatedness between populations ( though this is high , it is less than the 99 . 8% observed in the simulations of a true tree model ) . We examined the residuals of the model's fit to identify aspects of ancestry not captured by the tree ( Figure 3B ) . A number of known admixed populations stand out: in particular , these include the Mozabite and Middle Eastern populations . We then sequentially added migration events to the tree . In Figure 4 , we show the inferred graph with ten migration edges; p-values for all reported migration edges are less than ( we show the graph with the maximum likelihood over several independent runs of TreeMix with random orders of input populations ) . This graph model explains 99 . 8% of the variance in relatedness between populations . As expected from examination of Figure 3B , the migration events recapitulate many known events in human history . We infer that the Mozabite are the result of admixture between an African and a Middle Eastern population ( with about 33% of their ancestry from Africa ) , and that Middle Eastern populations also have African ancestry ( Palestinians and Bedouins: from Africa; Druze: ) . This is consistent with previously reported admixture proportions from these populations [43] , [55] . Additionally , we identify the known European ancestry in the Maya ( ) [21] , and infer that the Uyghur and Hazara populations are the result of admixture between west Eurasian and East Asian populations ( and from west Eurasia , respectively ) [20] , [21] , [56] . Several additional migration events in the human data have not been previously examined in detail , but are consistent with previous clustering analysis of these populations [7] , [20] , [21] . These include migration from Africa to the Makrani and Brahui in Central Asia ( ) and from a population related to East Asians and Native Americans ( which we interpret as likely Siberian ) to Russia ( ) . Two inferred edges were unexpected . First , perhaps the most surprising inference is that Cambodians trace about 16% of their ancestry to a population equally related to both Europeans and other East Asians ( while the remaining 84% of their ancestry is related to other southeast Asians ) . This is partially consistent with clustering analyses , which indicate shared ancestry between Cambodians and central Asian populations [7] . To confirm that the Cambodians are admixed , we turned to less parameterized models . The predicted admixture event implies that allele frequencies in Cambodia are more similar to those in African populations than would be expected based on their East Asian ancestry . To test this , we used three-population tests [37] . We tested the trees [African , [Cambodian , Dai]] for evidence of admixture in the Cambodians ( Methods ) . When using any African population , there is strong evidence of admixture ( when using Yoruba , []; when using Mandenka , []; when using San , [] ) . We conclude that the Cambodian population is the result of an admixture event involving a southeast Asian population related to the Dai and a Eurasian population only distantly related to those present in these data . Finally , we infer an admixture edge from the Middle East ( a population related to the Mozabite , a Berber population from northern Africa ) to southern European populations ( ) . This migration edge is the one edge that is not consistent across independent runs of TreeMix on these data ( Figure S8 ) . In particular , an alternative graph ( albeit with lower likelihood ) places the Mozabite as an admixture between southern Europe and Africa ( rather than the Middle East and Africa ) , and does not include an edge from the middle East to southern Europe . We thus hesitate to interpret this result , except to note that the relationship between northern African , the Middle East , and southern Europe involves complex patterns of gene flow that merit further investigation [43] , [57] . To test the robustness of our results to SNP ascertainment , we additionally ran TreeMix on the same set of populations using a set of SNPs ascertained in a single French individual . The inferred graph was nearly identical ( Figure S10 ) . Additionally , as noted above , different random input orders for the populations gave very similar results ( Figure S8 ) . We conclude from this that the model is able to consistently and accurately infer the major mixture events in the history of a species . This approach is computationally efficient: building the tree took around five minutes on a standard desktop computer ( with a processor speed of 3 . 1 GHz ) , and adding ten migration events to the tree took about four and a half hours ( the major computational cost is in the iterative estimation of migration weights ) . While human populations have been extensively studied , we next applied the model to dogs , a species where considerably less is known about population history . In particular , we applied the model to a dataset consisting of about 60 , 000 SNPs genotyped in 82 dog breeds or wild canids [58] . As for humans , we first inferred the maximum likelihood tree ( Figure 5A ) . The differences in history between dogs and humans are striking: there are long terminal branches leading to each dog breed in the inferred tree ( Figure 5A , recall that the terminal branch lengths account for sample size ) . This is consistent with the known strong bottlenecks in the establishment of dog breeds [23] . However , examining the residuals from the model revealed a number of populations that do not fit a strict tree model ( Figure 5B ) ; indeed , the tree model explained 94 . 7% of the variance in relatedness between breeds , somewhat less than between human populations . We sequentially added migration events to the tree in Figure 5A . In Figure 6 , we show the inferred graph with ten migration events , which explains 96 . 8% of the variance in relatedness between breeds ( which suggests that additional events exist in the data ) . In the following paragraphs , we describe some of these events . We infer that the bull mastiff is the result of an admixture event between bulldogs and mastiffs . This is a known event [59]; we estimate the admixture proportions as 33% bulldog and 67% mastiff . We further examined this event using four-population tests for treeness . As expected given the known history , the tree [[boxer , bulldog] , [mastiff , bull mastiff]] fails the four-population test ( , ) , while replacing the bull mastiff with other related breeds that we do not predict to be involved in the admixture event results in trees that pass this test . For example , the tree [[boxer , bulldog] , [mastiff , Boston terrier]] passes the four-population test with . The most visually apparent residuals in Figure 5B are accounted for in the graph by an admixture event from the grey wolf into the basenji , an ancient African breed of dog ( ) . Such a high mixture fraction is consistent with previous clustering analyses of these data [23] , [60] . We again sought to confirm this signal in a less-parameterized model . We tested the four-population tree [[wolf , ancient breed] , [basenji , Afghan hound]] with various “ancient” dog breeds . We could not find a tree that passed the four-population test ( with Akita as the ancient breed , ; with Alaskan Malamute , ) , confirming the presence of gene flow in these trees . Replacing the basenji with the saluki in these analyses resulted in trees that pass the four-population test ( for example , the tree [[wolf , Akita] , [Afghan hound , saluki]] passes with ) . Though we cannot have complete confidence in the precise migration events , these results are consistent with admixture between gray wolves and the basenji . Another breed that stands out in this analysis is the boxer ( Note that many of the SNPs used in this study were ascertained using a boxer individual , so we may have increased power to identify migration events involving this breed ) . We infer a significant genetic contribution from wolves to the boxer ( ) , and migration between the boxer and the Chinese shar-pei , a distantly-related ancient breed ( ) . To further examine these events , we again turned to four-population tests . To evaluate the wolf mixture , we tested the tree [[wolf , ancient breed] , [boxer , bulldog]] . We did not find a tree that passed the four-population test ( with Akita as the ancient breed , ; with Afghan Hound , ) . Replacing the Boxer with the Mastiff in these analyses led to trees that passed the four-population test ( for example , with Akita as the ancient breed , ) . To evaluate the gene flow from the Boxer to the Chinese shar-pei , we tested the tree [[Chinese shar-pei , Akita] , [boxer , bulldog]]; this tree fails the four-population test ( ) , while the tree [[Chow Chow , Akita] , [boxer , bulldog]] passes ( ) . Previous analyses of these data have noted that the “toy breeds” of dog cluster together Vonholdt:2010uq . We find that the Chinese toy breeds ( the Pekingese and the Shi Tzu ) result from admixture between a population related to ancient East Asian dog breeds and a modern population related to the Brussels griffon and the pug ( from the East Asian breeds ) . To confirm the presence of gene flow , we tested four-population trees of the form [[Asian toy breed , Akita/Chow Chow] , [Pug , mastiff]] . These trees fail , with varying levels of significance , ranging from [[Chow Chow , Shi Tzu] , [Pug , mastiff]] ( ) to [[Akita , Pekingese] , [Pug , mastiff]] ( ) . Finally , we noticed that two of the sighthounds ( the Borzoi and the Italian greyhound ) do not cluster with the other sight hounds in the tree , namely greyhound , whippet and Irish wolfhound ( Figure 5A ) ; however , they do show evidence of having sighthound admixture in the graph ( Figure 6 ) . These are the borzoi ( which appear to be admixed between an ancient or spitz-breed dog , with 47% ancestry from the sighthounds ) and the Italian Greyhound ( which appears to be admixed with a toy breed , with 34% ancestry from the sighthounds ) . This is consistent with the known phenotypic characteristics of these dogs; the borzoi is considered a saluki-like breed , and the Italian greyhound is phenotypically a small version of a greyhound [59] . Overall , we conclude that there has been considerable gene flow between dog breeds over the course of domestication; there are many additional migration events that merit further examination ( Figure 6 , Text S1 ) .
There are a number of assumptions , both implicit and explicit , in the interpretation of the TreeMix model . First , we have motivated the model in terms of inferring the historical splits and mixtures of populations . However , a given covariance structure of allele frequencies between populations can be a consequence of either a non-equilibrium demography ( population splits and mixtures ) or an equilibrium demography ( populations at long-term stasis with a fixed migration structure ) [2] . For the species analyzed in this paper , population equilibrium over the entire species range is not a tenable hypothesis; however , some subsets of populations may be at equilibrium , and there may be species where this alternative historical interpretation of the model is plausible . We have also modeled migration between populations as occurring at single , instantaneous time points . This is , of course , a dramatic simplification of the migration process . This model will work best when gene flow between populations is restricted to a relatively short time period . Situations of continuous migration violate this assumption and lead to unclear results ( Figure S14 ) . The relevance of this assumption will depend on the species and the populations considered . In humans , the relevance of continuous versus discrete mixture events is an open question–some aspects of genetic variation appear compatible with continuous migration [61] , while other aspects do not [37] . Indeed , both sorts of models are likely relevant at different time scales [62] . We also rely on the implicit assumption that the history of the species being analyzed is largely tree-like . We have made this assumption to simplify the search for the maximum likelihood graph; additionally , we speculate that in graphs with complex structure , there will be many graphs that lead to identical covariance matrices , and thus several different histories will be compatible with the data . That said , improvements to the search algorithm could allow the assumption of approximate treeness to be somewhat relaxed . Currently , if the number of admixed populations is large relative to the number of unadmixed populations , this assumption breaks down . For example , in the human data , note that we see no evidence of the documented gene flow from Neandertals to all non-African populations [39] ( Figure 3B ) . The reason for this is that the large number of populations with admixture can be accommodated in the tree by allowing the branch from Neandertals to Africans to be slightly underestimated ( additionally , by using SNPs ascertained in Africa , we have selected against sites that are informative about Neandertal ancestry ) . If only a single non-African population is included in the analysis , the relationship between Neandertals and the non-African population is clearer ( Figure S15 ) . A number of extensions to the sort of model described here are of potential interest . First , the historical relationships between populations could be useful as null demographic models for the detection of natural selection [48] , [63] , [64] . Second , in a given individual , the best-fit graph relating the individual to other populations may change along a chromosome; this sort of information could be of use in local ancestry inference . Finally , we have not used the information about demographic history present in linkage disequilibrium; approaches that explicitly use this information may provide additional power to detect migration events and estimate their timing , at an additional computational cost [20] , [53] , [65] .
As described in the Results , we developed an algorithm called TreeMix that uses the composite likelihood in Equation 28 to search for the maximum likelihood graph . Estimation involves two major steps . First , for a given graph topology , we need to find the maximum likelihood branch lengths and migration weights . Second , we need to search the space of possible graphs . First consider a given graph topology . We iterate between optimizing the branch lengths and weights . If the edge weights are known , the observed entries of the covariance matrix can be written down as an overdetermined system of linear equations ( as in Equations 13–15 ) . We solve this system by non-negative least squares [66] . Though the least squares solution is the maximum composite likelihood solution in the case where all entries of the covariance matrix have equal variance , it is not strictly the maximum likelihood solution in cases with unequal variances . The algorithm could be extended to unequal variances using a weighted least squares approach , but we have not implemented this . We then do a golden section search for the optimal weight ( between zero and one ) on each migration edge [67] . At each step in the golden section search , we update the branch lengths . We optimize the weight of each migration edge in turn , and iterate over migration edges until convergence . To search the space of possible graphs , we take a hill-climbing approach . We start by finding a local optimum tree , taking an algorithmic approach similar to Felsenstein [30] . We randomly select three populations , optimize the branch lengths for all three possible trees , and choose the best ( in terms of the composite likelihood ) tree . Then , we add the remaining populations one by one in a random order . To add a population , we try attaching it to all branches of the current tree , optimizing the branch lengths for each one as described above , and find the most likely spot . We then perform a round of local rearrangements ( i . e . , nearest-neighbor interchanges [50] ) around each internal node , keeping the resulting tree only if it increases the likelihood . After adding all populations , we calculate the residual covariance matrix , . We then add migration edges in a directed matter . First , we find the pairs of populations with the maximum residuals ( these are the pairs of populations with the worst fit under the model ) . In the results reported , . We define a “neighborhood” around each population of a pair as the tips within a distance of edges of the focal population . In applications above , we use . This defines a set of pairs of populations that either have a poor fit , or are located in the graph near populations with a poor fit . We take each of these pairs in turn . For each pair , we identify the set of nodes in the path from each member of the pair to the root of the graph . This gives us two sets of nodes . We take all pairwise combinations of nodes in each set , and look at residuals between the populations that are the descendants of each node . If all of the residuals are positive , we add a migration edge between the two nodes and estimate its maximum likelihood weight . We then keep only the single edge that most increases the likelihood of the graph . After adding a migration edge , we attempt nearest-neighbor interchanges at the source and destination of the migration event , attempt changing the source and destination of all migration events , and attempt changing the direction of all migration arrows . Once we have reached the local maximum by this method , we attempt nearest-neighbor interchanges at all internal nodes . We iterate over this procedure for a predetermined number of migration edges . We then test the migration edges for significance as described . The TreeMix source code is available at http://treemix . googlecode . com . We implemented three- and four-population tests as described in Reich et al . [37] . For the relationship between the statistics and the covariance model underlying TreeMix , see the Text S1 . For the three-population test , we estimated as in Reich et al . [37] , and tested whether is it less than zero . We report the Z-score for this test . To obtain a standard error on the estimate of , we used a block jackknife similar to Reich et al . [37] . However , Reich et al . [37] split the genome into blocks based on distance ( with variable numbers of SNPs per block ) ; we split the genome into blocks of SNPs ( and thus the blocks will be of variable size ) . For the four-population test for treeness , we calculate the statistic as in Reich et al . [37] , and test whether it is different than zero . Again , we report a Z-score for this test . Standard errors for the statistic were obtained as for the statistic . The human data we used were downloaded from http://www . cephb . fr/en/hgdp/ on August 16th , 2011 ( the data set labeled Harvard HGDP-CEPH genotypes ) . They consist of several panels of SNPs ascertained from low-coverage genome sequencing of single individual from different populations and then genotyped in the Human Genome Diversity Panel [54] . Additionally , at each site , a single sequencing read from the Denisova and Neandertal genome sequencing projects was sampled and the allele reported . These data have the property that they allow for complete control of the ascertainment strategy , and allow us to test the robustness of inference to different ascertainment schemes . For the main analyses , we used the panel of autosomal SNPs ascertained in a single Yoruban individual; there are 124 , 115 such sites . For some analyses , we also used the panel of autosomal SNPs ascertained in a single French individual; there are 111 , 970 such sites . For all analyses with TreeMix , we used a window size ( ) of 500; this corresponds to a window size of approximately 10 Mb . For all TreeMix analyses , we set the Neandertal and Denisova samples as the outgroups . Since we have only a single allele from the Neandertal and Denisova populations , we cannot calculate heterozygosity in these populations for unbiased estimation of the covariance matrix ( see ) . To account for this , we simply chose a relatively low level of heterozygosity and assigned it to both populations . In the Yoruba ascertained SNPs , we used a heterozygosity of 0 . 13 , and for the French ascertained SNPs , we used a heterozygosity of 0 . 2 . In practice , this only affected the lengths of the terminal branches to Neandertal and Denisova; running TreeMix with a heterozygosity of zero in both populations resulted in the same graph topologies ( not shown ) . Allele counts for the dog breeds and wild canids reported in Boyko et al . Boyko:2010fk were downloaded from http://genome-mirror . bscb . cornell . edu/ on July 30 , 2011 . These data consist of counts of reference and alternate alleles at 61 , 468 sites in 85 dog breeds and wild canids . We removed the Jackal and Scottish Deerhound for having relatively high amounts of missing data , and the village dogs because it is unclear if they represent a coherent population . We also removed all SNPs on the X chromosome . This left us with 60 , 615 SNPs in 82 populations . We ran TreeMix with a window size ( ) of 500 . This corresponds to a window size of approximately 20 Mb . For all TreeMix analyses , we set the coyote as the outgroup . The ascertainment scheme used for SNP discovery in dogs was complicated [68] . The largest set of SNPs were ascertained by virtue of being different between the boxer and poodle assemblies . This should lead to an overestimation of the distance between the boxer and the poodle in our analysis . Indeed , in Figure 5B , a considerable negative residual between the boxer and poodle is visible . Another set of SNPs were ascertained by being heterozygous within a boxer individual , and a third set were ascertained by comparison between a boxer and wild canids . These latter SNPs should lead to an overestimation of the distance between the boxer and the wolf in our analysis ( as we see for the poodle ) ; in fact , we infer migration between the boxer and the wolf . This ascertainment issue may have led us to underestimate the amount of gene flow in the comparison . All simulations were performed using ms [69] . The exact commands used are listed in Text S1 . When running TreeMix on simulations without ascertainment , we used a window size of 5000 SNPs; for simulations with ascertainment we used windows of 1000 SNPs . Consensus trees were generated using SumTrees v . 3 . 1 . 0 [70] . | With modern genotyping technology , it is now possible to obtain large amounts of genetic data from many populations in a species . An important question that can be addressed with these data is: what is the history of these populations ? There is a long history in population genetics of inferring the relationships among populations as a bifurcating tree , analogous to phylogenetic trees for representing the evolution of species . However , it has long been recognized that , since populations from the same species exchange genes , simple bifurcating trees may be an incorrect representation of population histories . We have developed a method to address this issue , using a model which allows for both population splits and gene flow . In application to humans , we show that we are able to identify a number of both previously known and unknown episodes of gene flow in history , including gene flow into Cambodia of a population only distantly related to modern East Asia . In application to dogs , we show that the boxer and basenji breeds have a considerable component of ancestry from grey wolves subsequent to domestication . |
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Taenia solium cysticercosis/taeniosis is emerging as a serious public health and economic problem in many developing countries . This study was conducted to determine prevalence and risk factors of human T . solium infections in Mbeya Region , Tanzania . A cross-sectional survey was conducted in 13 villages of Mbozi district in 2009 . Sera of 830 people ( mean 37 . 9±11 . 3 years ( SD ) ; 43% females ) were tested for circulating cysticerci antigen ( Ag-ELISA ) and antibody ( Ab-ELISA ) . A subset of persons found seropositive by Ag-ELISA underwent computed tomography ( CT ) scan of the brain for evidence of neurocysticercosis . Stool samples from 820 of the same participants were tested for taeniosis by copro-antigens ( copro-Ag-ELISA ) and formol-ether concentration technique . Cases of T . solium taeniosis were confirmed serologically by EITB assay ( rES38 ) . A questionnaire was used for identification of risk factors . Active cysticercosis by positive Ag-ELISA was found in 139 ( 16 . 7% ) persons while anti-cysticercal antibodies were detected in 376 ( 45 . 3% ) persons by Ab-ELISA . Among 55 persons positive for Ag-ELISA undergoing CT scan , 30 ( 54 . 6% ) were found to have structures in the brain suggestive of neurocysticercosis . Using faecal analysis , 43 ( 5 . 2% ) stool samples tested positive for taeniosis by copro-Ag-ELISA while Taenia eggs were detected in 9 ( 1 . 1% ) stool samples by routine coprology . Antibodies specifically against adult T . solium were detected in 34 copro-Ag-ELISA positive participants by EITB ( rES38 ) indicating T . solium taeniosis prevalence of 4 . 1% . Increasing age and hand washing by dipping in contrast to using running water , were found associated with Ag-ELISA seropositivity by logistic regression . Gender ( higher risk in females ) and water source were risk factors associated with Ab-ELISA seropositivity . Reported symptoms of chronic severe headaches and history of epileptic seizures were found associated with positive Ag-ELISA ( p≤0 . 05 ) . The present study indicates T . solium infection in humans is highly endemic in the southern highlands of Tanzania .
Taeniosis and cysticercosis are different stages of Taenia solium infection involving swine as intermediate hosts and humans as definitive and/or intermediate hosts . Taeniosis is the intestinal infection with the adult cestode in humans and is caused by consumption of raw or undercooked pork containing viable cysticerci of T . solium . Ingestion of infective eggs passed by a person with taeniosis either by autoinfection , direct contact with another tapeworm carrier or indirectly via ingestion of contaminated food , water , or hands may also lead to cysticercosis in humans whereby larval tapeworm cysts develop in the muscles , eye and central nervous system . Pigs get cysticercosis by ingesting T . solium eggs primarily as a result of eating feces of a human tapeworm carrier . Human cysticercosis causes a variety of neurological symptoms , most commonly seizures due to cysts in the brain , a condition known as neurocysticercosis [1] , [2] . Cysticercosis imposes substantial global burden on human beings related to epilepsy , ocular disorders , other neurological manifestations , and economic losses related to disability and lost productivity [3] , [4] . A World Health Organization ( WHO ) -commissioned systematic review of studies reporting the frequency of neurocysticercosis ( NCC ) worldwide done by [5] estimated the proportion of NCC among people with epilepsy of all ages to be 29 . 0% ( 95% CI: 22 . 9%–35 . 5% ) , indicating epilepsy is consistently associated with NCC in over one quarter of patients residing in regions where T . solium infections are endemic . In sub-Saharan Africa , people with cysticercosis have been estimated to have a 3 . 4 . to 3 . 8-fold increased risk for developing epilepsy [6] . These estimates confirm the importance of NCC infection in the etiology of epilepsy in developing countries and suggest that NCC may be associated with a very large burden in cysticercosis endemic areas where epilepsy is prevalent , that is , those countries where pork consumption occurs , pigs are managed under free-range conditions , and sanitation is poor or absent enabling pigs to have access to human feces [5] . Although the recognition of its status as a serious and emerging threat to public health in Africa is increasing [7] ( WHO , 2010 ) , the data on incidence in humans is very limited in most endemic areas due to a lack of adequate surveillance , monitoring and reporting systems [8] . In Tanzania T . solium is considered widespread in the northern , central , and southern regions based on porcine cysticercosis surveys [9] , [10] , [11] , [12] , [13] . These surveys provide initial evidence that T . solium infection is of national importance . Despite the high prevalences of porcine cysticercosis reported in different areas of the country , the presence of human T . solium infection has only been confirmed at one hospital in Mbulu district , Manyara region in the northern highlands where 13 . 7% of patients with epilepsy were detected to have probable/definitive NCC based on brain scans using computerised tomography and western blot to detect antibodies to larval T . solium [14] . However , there is still a lack of information on the T . solium situation ( both taeniosis and cysticercosis ) in the general human population [15] , thus the basis for conducting the present cross-sectional survey with the aim of establishing the prevalence and identifying risk factors associated with the transmission of human T . solium infections in rural communities in the southern highlands of Tanzania which is the main pig raising area of the country [16] .
The study was approved ( No . HD/MUH/T . 75/07 ) by the ethical committee at Muhimbili University of Health and Allied Sciences ( MUHAS ) . Permission to conduct the study in the selected villages was obtained from regional , district and local authorities . Consent for selection of a participant in a sampled household was sought from the selected individual as well as the head of the household . Before interview and sample collection , each selected participant was approached individually to obtain written informed consent . For minors ( <18 years ) informed assent to participate in the study was obtained orally , and thereafter a written informed consent for the minor's participation was signed by a parent or guardian . Participants who were to undergo CT scanning were informed about the whole procedure and were scanned only after obtaining their written consent . Participants with a history of epileptic seizures , testing positive by Ag-ELISA and/or detected with cysts in their brain were admitted to Vwawa District Hospital and treated according to standard of care [17] . Participants with taeniosis were treated with a single dose of Niclosamide 2 g orally . For ascariasis and hookworm infections , a single dose of Albendazole 400 mg orally was used while a single dose of Praziquantel 40 mg/kg was used only for participants with intestinal schistosomiasis who were seronegative by Ag-ELISA . The anthelmintic treatment was administered by the researcher ( a nurse ) under supervision of a medical doctor . No adverse effects were reported during or after anthelmintic treatment . The survey was conducted in 13 randomly selected villages in Mbozi district , Mbeya Region following a preliminary study in the district which reported a high prevalence of porcine cysticercosis ( 32% ) based on serum Ag-ELISA ( B158/B60 ) in 300 pigs in 150 households ( Eric Komba , personal communication ) . The district is located in the southern highlands of Tanzania in the southwest of Mbeya region between latitudes 7° and 9° 12′ south of the equator , longitudes 32°7′30″ and 33°2′0″ east of Greenwich Meridian with altitudes between 900–2750 meters above sea level . According to the National Bureau of Statistics ( http://www . nbs . go . tz ) , the human population of Mbozi district was 515 , 270 inhabitants in 2002 . Inhabitants mainly engage in agriculture and keep pigs as a source of income . The total number of pigs in Mbozi district in the year 2009 was estimated to be 25 , 355 ( District Veterinary Officer , Mbozi District - personal communication ) . A community-based descriptive cross-sectional study was conducted between April and July 2009 . Sample size estimation was calculated using the formula [18] , in which n = required sample size , Z = Z score for a given confidence level , P = expected prevalence or proportion , and d = precision . With an estimated prevalence of 9% for taeniosis based on a Nigerian study ( [19] , the sample size was calculated at 385 households ( one person per household ) . To adjust for potential non-compliance and design effect , the sample size was more than doubled such that 900 households were targeted during the survey . A cluster-random sampling with Probability Proportion to Size ( PPS ) technique was used for selection of households according to [20] . In each village , the principal investigator conducted a meeting and explained the purpose of the study to the local authorities and villagers , after which households for inclusion in the survey were selected on the same day . During the following days , the research team visited the selected households and identified eligible household members . Criteria of eligibility were living in the household and being between 15–60 years old . Among all eligible household members who agreed to participate in the study only one person was selected per household by simple random sampling for questionnaire administration and collection of blood and stool samples . After each person was interviewed , 10 ml of venous blood was drawn from cephalic or median cubital vein ( median basilic vein ) by medical laboratory technicians in a labelled plain blood vacutainer tube and allowed to clot at room temperature . The sera were obtained by centrifugation at 3200 rotations per min for 5 min , then aliquoted into 2 ml cryovials and stored at −20°C until tested . Similarly each participant was instructed on how to collect a stool sample and given a stool container which they returned with the sample on the same or following day . All stool samples were fixed with 10% formalin by the researcher and stored at room temperature until tested . A questionnaire based on that of the Cysticercosis Working Group of Eastern and Southern Africa ( www . cwgesa . dk/CWGESA/Action Plan/Research . aspx ) was used to collect information on risk factors and other related information from enrolled participants . The questionnaire addressed demography , pork consumption habits , hygienic and sanitary practices , presence of subcutaneous nodules and history of neurological signs . Responses on hygienic and sanitary practices were confirmed by observation method . The information regarding clinical signs was obtained by asking about the participants' previous histories of subcutaneous nodules , severe chronic headaches , loss of consciousness , epileptic seizures or partial seizures . According to the International League Against Epilepsy's Commission on Epidemiology and Prognosis definitions , epilepsy was defined as recurrent ( two or more ) epileptic seizures , unprovoked by any immediate identified cause while partial seizures were defined as presence of seizures without impairment of consciousness or awareness , i . e . maintained alertness and ability to interact with the environment . Serological analysis was carried out using a monoclonal antibody-based ELISA detecting circulating antigens of T . solium cysticerci ( Ag-ELISA ( B158/B60 ) [21] and an ELISA detecting anti-cysticercal antibodies ( Ab-ELISA ( rT24h ) [22] . Faecal studies were carried out using a copro-Ag-ELISA detecting antigens of adult Taenia species [23] . In addition , formol-ether concentration technique ( microscopic examination ) was used to detect eggs of Taenia species as well as other parasites in stool . Confirmation of T . solium taeniosis was done by detection of adult T . solium specific antibodies in sera of participants tested positive by copro-Ag-ELISA using enzyme-linked immunoelectrotransfer blot ( EITB ) assay ( rES38 ) as described by [24] . All participants with a history of epileptic seizures and seropositive by Ag-ELISA ( n = 28 ) and one-fourth of Ag-ELISA seropositive participants without a history of epileptic seizures ( n = 27 ) , selected at random , were referred to Vwawa District hospital in Mbozi district , Mbeya and then transported to Muhimbili National Hospital in Dar es Salaam to be examined for evidence of NCC using computed tomography ( CT ) scan conducted with intravenous contrast . An image series of slides was made for each patient both before and after the contrast injection which were all examined by the same radiologist . All data were initially entered into EPI info software version 6 . For analysis , the data were converted to Statistical Package for Social Scientist ( SPSS ) version 13 . Bivariate analysis was first performed by Chi-Square test to assess the marginal association between human cysticercosis seropositivity and factors such as age group , gender , religion , presence of latrine , source of drinking water , hand washing practice , pork consumption , boiling of drinking water , history of passing ploglottides , and being positive by copro-Ag-ELISA at a 95% confidence level . To assess potential associations , a multivariate logistic regression analysis was performed by calculating OR and 95% confidence intervals for human cysticercosis seropositivity ( Ag-ELISA/Ab-ELISA ) and identified risk factors . The Ag-ELISA or Ab-ELISA was entered as an outcome and the covariates that were included in the model were age group , gender , presence of latrine , source of water , hand washing practice and being positive by copro-Ag-ELISA .
The risk factors that were significant in the analysis as risks associated with human cysticercosis are presented in Table 3 . Being older age was found to be a risk for seropositivity with human cysticercosis in the regression model , as the Ag-ELISA was higher in the age groups 36–45 years ( OR = 2 . 5 ) and 46–60 years ( OR = 2 . 6 ) as compared to young people ( <25 years ) . In addition , those who reported washing their hands by the dipping method using the same water as others were more likely to be seropositive by Ag-ELISA ( OR = 3 . 8 ) . On the other hand , when fitting the risk factors for seropositivity with human cysticercosis by Ab-ELISA in the regression model , results showed the risk for seropositivity to be significantly higher for males as compared to females ( OR = 1 . 6 ) , and for people using an unsafe source of water ( OR = 1 . 9 ) . Furthermore , being confirmed a T . solium tapeworm carrier by copro-Ag-ELISA substantially increased the risk for being seropositive for cysticercosis by both tests .
Findings of the present study indicate that human T . solium infection is hyper-endemic in Mbozi district , Mbeya Region , Tanzania . Further understanding of the disease distribution and transmission burden in Tanzania is needed as well as strengthening capacity to address it . Correspondingly , cross-sectoral ( stakeholders ) control efforts at local , regional , national level as well as across endemic neighboring countries of the eastern and southern Africa region should be initiated using appropriate , sustainable approaches to decrease or eliminate the burden of the disease . | Cysticercosis caused by the zoonotic pork tapeworm , Taenia solium , is emerging as a serious public health and agricultural problem in sub-Saharan Africa . Surveys have shown cysticercosis in pigs to be highly prevalent in multiple foci in Tanzania , and a hospital-based study in the northern highlands indicated neurocysticercosis as an important cause of epileptic seizures in humans . We present here a cross-sectional community-based survey on the prevalence and risk factors of human cysticercosis and taeniosis conducted in the southern highlands – the major pig-producing area of the country . The most striking findings were that more than 15% of people surveyed were found to have active cysticercosis and nearly half of them were found to have been exposed to larval T . solium indicating a high level of environmental contamination with T . solium eggs . This was supported by finding over 4% of people having had T . solium tapeworms . A subset of persons found positive serologically for active cysticercosis underwent brain scanning and more than half of them were found to have neurocysticercosis . This strong evidence that T . solium cysticercosis/neurocysticercosis/taeniosis is highly endemic in the southern highlands of Tanzania demands urgent attention of regional and national authorities to combat the parasite . |
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BRIT1 protein ( also known as MCPH1 ) contains 3 BRCT domains which are conserved in BRCA1 , BRCA2 , and other important molecules involved in DNA damage signaling , DNA repair , and tumor suppression . BRIT1 mutations or aberrant expression are found in primary microcephaly patients as well as in cancer patients . Recent in vitro studies suggest that BRIT1/MCPH1 functions as a novel key regulator in the DNA damage response pathways . To investigate its physiological role and dissect the underlying mechanisms , we generated BRIT1−/− mice and identified its essential roles in mitotic and meiotic recombination DNA repair and in maintaining genomic stability . Both BRIT1−/− mice and mouse embryonic fibroblasts ( MEFs ) were hypersensitive to γ-irradiation . BRIT1−/− MEFs and T lymphocytes exhibited severe chromatid breaks and reduced RAD51 foci formation after irradiation . Notably , BRIT1−/− mice were infertile and meiotic homologous recombination was impaired . BRIT1-deficient spermatocytes exhibited a failure of chromosomal synapsis , and meiosis was arrested at late zygotene of prophase I accompanied by apoptosis . In mutant spermatocytes , DNA double-strand breaks ( DSBs ) were formed , but localization of RAD51 or BRCA2 to meiotic chromosomes was severely impaired . In addition , we found that BRIT1 could bind to RAD51/BRCA2 complexes and that , in the absence of BRIT1 , recruitment of RAD51 and BRCA2 to chromatin was reduced while their protein levels were not altered , indicating that BRIT1 is involved in mediating recruitment of RAD51/BRCA2 to the damage site . Collectively , our BRIT1-null mouse model demonstrates that BRIT1 is essential for maintaining genomic stability in vivo to protect the hosts from both programmed and irradiation-induced DNA damages , and its depletion causes a failure in both mitotic and meiotic recombination DNA repair via impairing RAD51/BRCA2's function and as a result leads to infertility and genomic instability in mice .
The repair of DNA double-strand breaks ( DSBs ) is critical for maintaining genomic integrity [1] , [2] . DSBs can arise from exogenous agents such as ionizing radiation ( IR ) [3] and endogenous factors such as stalled replication forks [4] . In addition , DSBs can form in a programmed manner during development including meiosis and immunoglobin rearrangements [5] , [6] . During meiosis , DSBs are generated to initiate recombination between homologous chromosomes which leads to the reciprocal exchange of genetic materials between parental genomes . The inability for hosts to respond properly to the breaks or to repair them may trigger physiological defects such as infertility or cause genomic instability . DNA damage response ( DDR ) pathways activated as a result of DSBs conceptually have three components , some with overlapping functions: sensors , signal transducers , and effectors [7] , [8] . Damaged DNA is recognized by sensors; the signal is brought to transducers , which then in turn activate or inactivate the effectors that trigger cell cycle checkpoints , DNA repair or apoptosis . In response to DNA damage , many proteins involved in DDR pathway , including ATM [8] , MDC1 [9] , H2AX [10] , NBS1 [11] , 53BP1 [12] , [13] , RAD51 [14] , BRCA1 [15] , and BRCA2 [16] , quickly accumulate to damage sites and form large nuclear aggregates that appear as IR-induced nuclear foci ( IRIF ) observed microscopically . A variety of evidence suggests that IRIF are required for precise and efficient DSB repair in the context of chromatin . Recent studies suggest that BRIT1 ( BRCT-repeat inhibitor of hTERT expression ) is a key regulator for DNA damage response pathways [17] , [18] . The sequence of BRIT1 was derived from a hypothetical protein that was later matched to a putative disease gene called microcephalin ( MCPH1 ) , one of at least six loci implicated in the autosomal recessive disease primary microcephaly [19] . BRIT1 protein contains three BRCT ( BRCA1 carboxyl terminal ) domains , one in N-terminal and two in C-terminal . Many DDR and DNA repair proteins such as BRCA1 , BRCA2 , MDC1 and NBS1 contain BRCT domains , suggesting that BRIT1 may also play a role in DDR . In fact , knockdown of BRIT1 by specific siRNA in cells showed that BRIT1 was required for DNA damage-induced intra-S and G2/M checkpoints [17] , [20] , [21] . BRIT1 is also a chromatin-binding protein that forms IRIF , which co-localize with ATM , γ-H2AX , MDC1 , NBS1 and 53BP1 , and depletion of BRIT1 by its siRNA impairs the IRIF formation of ATM , MDC1 , NBS1 , and 53BP1 [17] , [18] , indicating that BRIT1 may exert a direct role in transmitting DNA damage signals . In addition , expression levels of BRIT1 are decreased in several types of human cancer including breast and ovarian cancers [18] , suggesting that BRIT1 may function as a novel tumor suppressor gene . To better define its physiological role , here we generated BRIT1−/− mice and demonstrated BRIT1's essential role in regulation of both programmed and IR-induced DNA damage responses .
To characterize the physiological function of BRIT1 , we generated BRIT1−/− mice by gene targeting ( Figure 1A ) . The mice with germ-line transmission of the targeted conditional allele were crossed with Flp mice to eliminate the Neo cassette . To generate the global knockout mice , these mice were bred with transgenic mice carrying a Cre gene under the control of β-actin promoter to eventually generate BRIT1−/− mice , where exon 2 of BRIT1 was deleted , leading to out of reading frame mutation of BRIT1 . We confirmed the loss of both BRIT1 alleles in BRIT1−/− mice by Southern blot analysis ( Figure 1B ) . RT-PCR with primers flanking exon 2 followed by DNA sequencing also revealed the disruption of BRIT1 transcript in BRIT1−/− mice ( Figure 1C ) . We also developed a rabbit antibody specifically against the C-terminal fragment of mouse BRIT1 and anti-BRIT1 Western blot confirmed the loss of BRIT1 in BRIT1−/− mice ( Figure 1D ) . BRIT1−/− mice were able to survive to adulthood , but they were growth-retarded ( Figure 1E ) . The weight of BRIT1−/− mice at postnatal days 56 ( P56 ) was only 80% compared to wild type ( WT ) . In addition , birth rate of the BRIT1−/− mice was ∼10% among the offspring of self-cross of heterozygous mice ( Figure 1F ) , which was much lower than normal Mendelian ratio ( ∼25% ) , suggesting that BRIT1 deficiency may affect early development in mice . One of the hallmarks of defective DNA damage response is increased radiation sensitivity . To assess whether the loss of BRIT1 expression renders mice hypersensitive to IR , we irradiated BRIT1+/+ , BRIT1+/− and BRIT1−/− mice with the dose of 7 Gy . All BRIT1−/− mice died within 9 days after irradiation , while 80% of BRIT1+/− or BRIT1+/+ mice were still alive 4 weeks after irradiation ( Figure 2A ) . We also examined BRIT1−/− MEFs' sensitivity to IR . Passage 2 primary MEFs were used to expose to different dosages of IR . The surviving cells were counted at the 6th day after IR . BRIT1−/− MEFs were more sensitive to IR as compared to BRIT1+/+ cells ( Figure 2B ) . Thus , both BRIT1−/− mice and the MEFs derived from those mice are more sensitive to irradiation . To explore if genomic instability occur in BRIT1−/− cells , we performed metaphase spread assay to examine chromosome aberrations in both BRIT1+/+ and BRIT1−/− MEFs shortly ( 3 h ) and longer time ( 20 h ) after IR . Metaphase spread showed that most of BRIT1−/− MEFs had DNA breaks , while only fewer of BRIT1+/+ cells had breaks at as early as 3 h after IR ( Figure 2C and 2D ) . Remarkably , at 20 h after IR , we still observed more chromosomal aberrations in BRIT1−/− than BRIT1+/+ MEFs ( Figure 2C ) . To assess if BRIT1 plays a role in regulating spontaneous DNA damage , we isolated T cells from both wild-type ( WT ) and mutant mice spleens and compared their genomic stability by metaphase spread , and found more chromosomal aberrations in the BRIT1−/− T cells than in WT ( Table S1 ) . While other types of chromosomal aberrations ( e . g . translocations , polyploidy ) also occurred in IR-treated BRIT1−/− MEFs or T lymphocytes , the majority aberration in these cells was chromatid breaks , a phenomenon associated with defective homologous recombination ( HR ) [22] , [23] . Together with our previous studies [24] , [25] , these results indicate that loss of BRIT1 leads to defective DNA repair in homologous recombination , and eventually cause genomic instability . Thus , BRIT1 is involved in regulating both spontaneous and IR-induced DNA damage responses . BRIT1−/− male mice failed to yield any pregnancies when crossed with the WT female mice , suggesting that BRIT1−/− mice are infertile . Consistently , we found that BRIT1−/− testes were much smaller than WT , especially in the mutant mice at the age of 3- week or older ( Figure 3A and Figure S1 ) . The testicular tubes in BRIT1−/− mice were significantly smaller and thinner than those in WT , and much fewer spermatocytes were produced in BRIT1−/− seminiferous tubules ( Figure 3C , compared with Figure 3B ) , suggesting spermatogenesis is dysregulated due to loss of BRIT1 . In addition , BRIT1−/− female mice are also infertile , and consistently , these mice harbored the smaller ovaries with none of ovarian follicles ( Figure S2 ) . In the male mice , spermatogenesis is divided into several distinct stages: mitotic proliferation of spermatogonia , meiotic division of spermatocytes , and spermiogenesis of spermatids [26] , [27] . To determine which stage of spermatogenesis is disrupted in BRIT1−/− mice , we collected the BRIT1+/+ and BRIT1−/− testes at various developmental stages for histological analysis . At postnatal days 7 ( P7 ) , major cells were Sertoli or Sertoli-like cells ( the supporting cells for testes ) and spermatogonia , but no spermatocytes were found in either BRIT1+/+ or BRIT1−/− testes ( Figure 3D , H&E panel ) , consistent with the fact that spermatocytes do not form in mouse testes until around P10 [26] , [27] . Testes sections at P7 were also double-immunostained with anti-Tra98 and anti-Sox9 antibodies to detect spermatogonia and its surrounding Sertoli cells , respectively . The number of both spermatogonia ( Figure 3D , Tra98 panel ) and Sertoli cells ( Figure 3D , Sox9 panel ) was comparable between BRIT1−/− and BRIT1+/+ testes , indicating that BRIT1 deficiency does not impair spermatogonia or Sertoli cell proliferation . We next examined the development of spermatocytes using the mice at the age of 2- week or older . In testes at P14 , P21 and P28 , although spermatocytes had taken place in seminiferous tubules in both BRIT1−/− and BRIT1+/+ testes , there were considerably fewer spermatocytes in BRIT1−/− as compared to BRIT1+/+ , especially after around P21 ( panels c and d in Figure 3E ) . At P56 , in addition to much smaller seminiferous tubules , remarkably fewer spermatocytes and no spermatids were found in BRIT1−/− testes ( panels e and f in Figure 3E ) . In contrast to seminiferous tubules in WT with a full spectrum of spermatogenic cells ( panels a , c and e in Figure 3E ) , BRIT1−/− tubules just contained two or three layers of darkly stained zygotene-like germ cells and exhibited a complete lack of pachytene spermatocytes and postmeiotic germ cells ( panels b , d and f in Figure 3E ) , suggesting that meiosis in BRIT1−/− spermatocytes may arrest prior to the pachytene stage . Moreover , TUNEL assay revealed dramatically increased apoptosis in BRIT1-deficient tubules ( Figure S1 ) , suggesting degenerated spermatocytes in BRIT1−/− testes may be eliminated through apoptosis . Together , these data indicate spermatocyte meiosis is impaired due to loss of BRIT1 , as a result , leads to the male infertility . To determine the cause of meiotic arrest in BRIT1−/− spermatocytes , we sought to define the actual meiotic stage that was defected in the mutant by examining the assembly of the synaptonemal complex ( SC ) . SC morphology in spermatocyte nuclei can be assessed by immunostaining of synpatonemal complex protein 3 ( SCP3 ) , an integral component of the axial/lateral elements in the SCs [28] , [29] . SCP3-immunostaining of the spermatocyte nuclei spreads showed that all the substages of spermatocytes in meiotic prophase I were detected in WT mice such as leptotene ( Figure 4A ) , zygotene ( Figure 4C ) , pachytene ( Figure 4E ) . In BRIT1−/− mice , spermatocytes at leptotene ( Figure 4B ) or zygotene ( Figure 3D ) stage were also detected . However , no typical pachytene chromosome morphology was detected though there were aberrant pachynema in BRIT1−/− spermatocytes ( Figure 4F ) . In addition , there were much more zygotene spermatocytes found in the mutant testes as compared to those in WT ( Figure 4G ) , indicating that loss of BRIT1 leads to meiotic arrest prior to the pachytene stage . Furthermore , in contrast to WT spermatocytes in which synapsis initiated typically at the distal ends ( or in subtelomeric regions ) of the acrocentric chromosomes [30] ( Figure 4C ) , many mutant spermatocytes in mid- and late- zygotene were characteristic of interstitial initiation of synapsis with asynapsis on either side of the contact ( Figure 4D and 4F ) . Also , the bivalents in the late zygotene or aberrant pachytene were prone to be fragmented ( Figure 4F ) . To confirm the synapsis defects in BRIT1−/− spermatocytes , we performed anti-SCP1/SCP3 double-staining assay . SCP1 mediates synapsis via uniting homologous chromosomes during zygotene and pachytene stages . The accumulation of SCP1 on/around the axial/lateral elements of the whole SCs ( indicated by SCP3 ) is a hallmark for complete synapsis [31] . In the mutant spermatocytes , SCP1/SCP3 double-staining assay showed incomplete , dashed-line like SCP1 pattern occurred in many individual homologs at the late zygotene or zygotene/pachytene transition stage ( panel b in Figure 4H ) while in WT , the intact accumulation of SCP1 protein around the whole SCs was detected ( panel a in Figure 4H ) , revealing a defective synapsis existed in BRIT1-deficient spermatocytes . Collectively , these results indicate that meiosis in BRIT1−/− spermatocytes is arrested prior to the pachytene stage and BRIT1 is required for completing chromosomal synapsis during male meiosis . To further assess BRIT1's role in meiotic recombination , we examined BRIT1 expression pattern in seminiferous tubules as well as its foci formation in response to DSB during meiosis . We found that BRIT1 protein was expressed both in spermatogonia and spermatocytes ( Figure S3A ) . Spermatocyte-chromosome spreading assay showed that BRIT1 foci were formed on the meiotic chromosomes during both leptotene and zygotene . In contrast , during pachytene where synapsis is completed , BRIT1 foci were not found on the chromosomes except in telomeres and non-synapsed sex body ( Figure S3B ) . These data together indicate that BRIT1 is involved in repair of the DSBs occurring at leptotene and zygotene stages during meiotic recombination . To investigate the mechanism mediating the meiotic defect due to loss of BRIT1 , we examined if DSB formation and meiotic recombination repair was impaired . During meiosis , DSBs are introduced by SPO11 at leptotene for initiating meiotic recombination in spermatocytes [32] , and γ-H2AX is an important protein involved in recognition and signaling of the DSBs [33] , [34] . In mice , ∼300 DSBs are generated by SPO11 at leptotene/zygotene in each nucleus [35] . As shown in the Figure 5A , SPO11 foci had the similar pattern in WT and mutant spermatocytes , suggesting DSBs were normally formed in BRIT1-deficient testes . In response to the DSBs , γ-H2AX foci responded equally well and appeared at the damage sites during the leptotene stage of both the WT ( panel a in Figure 5B ) and the BRIT1 mutant ( panel b in Figure 5B ) testes with the same staining pattern and comparable intensity . In consistent with previous report [36] , at late zygotene/pachytene stage of WT spermatocytes , γ-H2AX staining disappeared from synapsed autosomal chromosomes though it still resided in the largely asynapsed sex chromosomes of the XY body ( panel c in Figure 5B ) . However , γ-H2AX staining in the BRIT1-mutant spermatocytes was sustained in asynapsed autosomal homologs at late zygotene ( Figure 5B and 5C ) . Thus , these data suggests that DSBs are normally formed , while DSBs cannot be properly repaired without functional BRIT1 . We next analyzed the homologous recombination process to address why DSBs are not repaired in BRIT1−/− spermatocytes . RAD51 is the homolog of E . coli RecA which binds to DSBs and plays a critical role in both mitotic and meiotic recombination DNA repair [37] , [38] . In response to DNA damages , BRCA2 can facilitate RAD51's loading to the damages sites via their physical interaction and these two proteins form nuclear foci at the DSB sites and execute the DNA repair process [16] . We examined RAD51 foci formation on meiotic chromosomes using by immunostaining of chromosomes from spermatocytes . Interestingly , in BRIT1−/− leptotene/zygotene spermatocytes , the number of RAD51 foci was greatly decreased as compared to the number in WT ( Figure 5D and 5E ) . We found that 80% of leptotene/zygotene spermatocytes in WT contained ∼100–250 RAD51 foci ( Figure 5D and 5E ) . In contrast , 60% of the mutant spermatocytes at these stages exhibited no or only a few RAD51 foci ( Figure 5D and 5E ) . In addition , RAD51 protein level was not changed in BRIT1−/− testes ( Figure S4 ) . Thus , reduction of RAD51 foci on chromosomes in BRIT1-deficient testes might be attributed to decreased localization of RAD51 onto the DSB sites . Furthermore , we found that BRCA2 foci formation in spermatocytes was also disrupted due to BRIT1 deficiency ( Figure 5F and 5G ) . Collectively , these data indicate that depletion of BRIT1 disrupts meiotic recombination repair through abolishing the recruiting and the function of RAD51/BRCA2 , and as a result leads to the catastrophic meiosis failure in BRIT1−/− spermatocytes . To further explore how BRIT1 regulates DNA repair , we assessed the ability of RAD51/BRCA2 to form nuclear foci and their chromatin association in BRIT1−/− MEFs . We first analyzed the IR-induced nuclear foci ( IRIF ) formation of RAD51 and BRCA2 in BRIT1+/+ and BRIT1−/− MEFs . As shown in Figure 6A , we observed typical RAD51 and BRCA2 foci in the BRIT1+/+ MEFs , whereas only diffused pattern of RAD51 and BRCA2 were seen in the BRIT1−/− MEFs , indicating the IRIF formation of RAD51 and BRCA2 was abolished in the BRIT1 null background . In addition , in comparison to WT , the amount of chromatin-bound RAD51 was markedly reduced with or without IR treatment , indicating the requirement of BRIT1 for the basal and IR-induced Rad51 chromatin association . The basal chromatin-binding of BRCA2 was also significantly reduced in the BRIT1 null background while the IR-induced BRCA2 association to the chromatin was modest reduced ( Figure 6B ) . The protein levels of RAD51 and BRCA2 were not altered due to lack of BRIT1 ( Figure 6C ) . These data indicate that BRIT1 is required for increased basal chromatin affinity and the physical assembly of RAD51 and BRCA2 to the DNA damage loci though there is different degree of BRIT1 dependency in terms of IR-induced chromatin binding between RAD51 and BRCA2 . Notably , we found that BRIT1 physically associated with RAD51 and BRCA2 , suggesting BRIT1 being directly involved in DNA repair ( Figure 6D ) . During the course of our study , a very recent report also shows that BRIT1 binds to BRCA2/RAD51 complex and disruption of the interaction between BRIT1 and BRCA2 leads to substantially reduced BRCA2/RAD51 at the DNA repair sites [39] . Altogether , these data indicate that BRIT1 may be directly involved in DNA repair via mediating RAD51/BRCA2's recruitment to the damage sites .
In this report , we generate a BRIT1 knockout ( BRIT1−/− ) mouse model and clearly demonstrate that BRIT1 is crucial for maintaining genomic stability in vivo to protect the hosts from both programmed and irradiation-induced DNA damages . Our studies on BRIT1 null mice also provide convincing evidence to identify a novel and important function of BRIT1 in meiotic recombination DNA repair . BRIT1−/− mice showed significant genomic instability , exemplified by chromosomal aberrations in T lymphocytes and MEFs . In addition , BRIT1−/− mice exhibited growth retardation , male infertility , and increased radiation sensitivity . These phenotypes are virtually identical to those of ATM−/− , MDC1−/− and H2AX−/− mice [40]–[42] , suggesting that these molecules integrate closely in the DDR pathway . Importantly , this is the first reported evidence demonstrating that BRIT1 indeed plays a crucial physiological role in programmed DNA damage response and HR DNA repair in vivo . Our data presented here clearly reveal that BRIT1 is essential for meiotic recombination DNA repair in spermatocytes . We first demonstrated that the male infertility in null mice was caused by catastrophic meiosis failure in spermatocytes and accordingly , no spermatids could be generated ( Figure 3E ) . We also showed that BRIT1−/− spermatocytes exhibited aberrant chromosomal synapsis , and meiosis was arrested before or at the transition of zygotene to pachytene of meiotic prophase I ( Figure 4 ) . In addition , we found that localization of RAD51/BRCA2 to meiotic chromosomes was severely impaired in the mutant spermatocytes while their protein levels were not altered due to loss of BRIT1 ( Figure 5 and Figure S4 ) . Thus , these data together indicate that the DSB generated by SPO11 at leptotene can not be repaired properly and as a result , leads to aberrant chromosomal synapsis and meiosis arrest at late zygotene stage . Interestingly , unlike MDC1 or H2AX whose deficiency only leads to male infertility [41] , [42] , loss of BRIT1 also lead to female infertility with much smaller ovary ( Figure S2 ) , suggesting that BRIT1 may also function as a key regulator in oocyte meiotic recombination . During meiosis , DSB is generated by SPO11 that leads to the initiation of meiotic recombination ( HR DNA repair ) [32] . In the null mice , BRIT1 deficiency only affected foci formation of RAD51/BRCA2 , but not those of SPO11 and γ-H2AX , at leptotene/zygotene stages , Thus , our data support a model in which BRIT1 functions downstream of the SPO11-mediated DSB formation but upstream of RAD51/BRCA2-midiated DSB repair during meiotic recombination . In addition to meiosis , we also found that in response to DSBs induced by IR , the association of RAD51/BRCA2 to chromatin and their foci formation was impaired in MEFs with BRIT1 deficiency while their protein levels were not altered ( Figure 6 ) . All these in vivo and in vitro studies together therefore demonstrate that Brit1 is critical for DNA repair during both meiosis and mitosis . The impaired RAD51 foci formation in both BRIT1−/− MEFs and spermatocytes is not due to the changes of RAD51 protein levels ( Figure 5 and Figure 6 ) . Although BRIT1 has been reported to regulate RAD51 expression [43] , we observed no altered RAD51 protein levels in either BRIT1−/− MEFs or the BRIT1−/− mouse testes which is consistent with our previous studies in BRIT1 knockdown human cells [25] . The mechanisms mediating BRIT1's function on DNA repair may be through multiple levels . Firstly , BRIT1 can be indirectly involved in DNA repair process via regulation of chromatin structure . We recently found that BRIT1 was associated with Condensin II , which modulates BRIT1-mediated HR repair [24] . In addition , we demonstrated BRIT1 being involved in chromatin remodeling via interacting with SWI/SNF , and this interaction relaxes the chromatin structure and increases the access of the repair proteins , including RAD51 , to the DNA damage sites [25] . Indeed , this chromatin remodeling function of BRIT1 may also contribute to the increased accessibility of many other DNA damage responsors , such as ATM , ATR , NBS1 , MDC1 , 53BP1 , RPA as we previously observed [18] . In addition to the generally increased affinity of DNA damage responsors and DNA repair proteins to chromatin , BRIT1 may further directly participate into DNA repair via interacting and recruiting RAD51/BRCA2 complex to the damage sites . Here , we observed that BRIT1 can physically associate with RAD51 or BRCA2 and in the absence of BRIT1 , recruitment of RAD51 and BRCA2 to chromatin was remarkably reduced while their protein levels were not altered , suggesting that BRIT1 being directly involved in DNA repair ( Figure 6 ) . Consistently , in the course of our study , a very recent report also shows that BRIT1 binds to BRCA2/RAD51 complex and this binding is required for recruitment or retention of BRCA2/RAD51 complex at the DNA repair sites [39] . Thus , BRIT1 also functions directly in DNA repair via directing BRCA2/RAD51 foci to the DSBs . In consistent with the role of BRIT1 in regulating the DNA repair function of BRCA2/RAD51 , the meiotic phenotypes in BRIT1−/− mice are virtually the same as those observed in mice with deficiency of BRCA2 [44] and DMC1 ( a homologue of RAD51 ) [45] , [46] . In these mice , spermatocytes are also arrested before or at the transition of zygotene to pachytene with aberrant chromosomal synapsis . In fact , like BRIT1−/− spermatocytes , BRCA2−/− spermatocytes also form DSBs without the consequent recruitment of RAD51 to the meiotic chromosome [44] . Our previous studies show that BRIT1 deficiency is correlated with genomic instability and breast cancer development [18] . Although our BRIT1 knockout mice within one and half years old did not develop any tumor , when we crossed BRIT1 knockout mice to the p53 null background , we found a significant effect of BRIT1 deficiency in enhancing cancer susceptibility ( unpublished data ) . Notably , our very recent preliminary data indicate that low dosage of irradiation can readily induce breast tumors in the mice with conditional knockout of BRIT1 in the mammary gland but not in the control littermates . It will be very interesting to investigate if and to what extent BRIT1 deficiency may contribute to the initiation and progression of cancer with the existence of oncogenic or genotoxic stress . For example , we can assess whether crossing an activated Ras or HER2 allele into the BRIT1 deficient background will result in increased genomic instability and tumorigenesis compared to either mouse strain alone . Thus , our BRIT1 null mouse will be a very valuable model for further assessing BRIT1's role in genome maintenance and tumor suppression in the future .
We isolated BRIT1 BAC clones from a 129/SvEv genomic library to construct the conditional targeting vector ( Figure 1A ) . A 0 . 5 kb fragment consisting of exon 2 ( E2 ) , 3′ end of intron 1 and 5′ end of intron 2 was inserted between two loxP sites in the targeting vector , which allows to remove E2 after introduction of Cre in mice . A 3 . 0 kb fragment from intron 2 was cloned into the same vector as the 3′ homologous arm . A 5′ homologous arm ( a 3 . 0 kb fragment containing E1 and 5′ of intron 1 ) was subsequently cloned into the vector . The Neo selection marker can be excised via the recombination of the Frt sites after introduction of Flp recombinase , which could avoid any unexpected effect of Neo cassette on the normal splicing of BRIT1 . The targeting vector was linear zed by PacI and electroporated into AB2 . 2 ES cells . Neomycin-resistant colonies were selected with Geneticin and analyzed for the expected homologous recombination by EcoRV digestion followed by Southern blot analysis using a 5′ flanking probe . The targeted clones were then confirmed using a 3′ flanking probe after BamHI digestion of the genomic DNA . To generate the mice with BRIT1 conditional targeting allele , two targeting ES cells were injected into C57BL/6J mouse blastocysts . The injection was carried out by the Engineered Mouse Core in department of Molecular and Human Genetics at Baylor College of Medicine [47] . Male chimeras with 95% agouti color were bred with C57BL/6J females and germ-line transmission of the BRIT1 targeting allele was confirmed by agouti coat color in F1 animals and by Southern blot analysis on mouse tail DNA using the two flanking probes for BRIT1 . These heterozygous mice were crossed with Flp mice to generate the mice carrying one BRIT1 conditional allele with the excision of the Neo cassette ( BRIT1+/co ) . These mice were then bred with transgenic mice carrying a Cre gene under the control of β-actin promoter to generate BRIT1+/− mice . BRIT1+/− mice were further bred to generate BRIT1−/− mice . Primary MEFs were obtained from embryonic days E14 . 5 by a standard procedure . MEFs were grown in DMEM supplemented with 10% FBS , 1% penicillin/streptomycin and 0 . 1% Fungazone and kept at a low passage for future use . Three plates of BRIT1+/+ or BRIT1−/− MEFs were exposed to IR ( 0 , 2 , 4 , or 6 Gy ) , and survival rates were calculated 6 days after IR . Whole-body γ-irradiation of mice was carried out as described previously [48] . Ten pairs of BRIT1+/+/BRIT1+/− and BRIT1−/− littermates were irradiated with 7 Gy of whole body IR , and survival rates were calculated every two days after irradiation . Mice were sacrificed at postnatal day P42 , and splenocytes were crushed though a mesh into a collection tube , and lymphocytes were separated using Lymph-M method ( Accurate Chemical & Scientific Corp ) . The separated lymphocytes were activated with ConA and IL-2 . MEFs were γ-irradiated with a dose of 1 Gy , and then collected for metaphase spread 3 hr or 20 hr post-IR . Cytological preparation of the activated T cells and γ-irradiated MEFs was made as described previously [49] . From each sample , at least 30–35 metaphase spreads were analyzed . MEFs on coverslips were treated with or without IR , and underwent immunoflurorescent ( IF ) staining at the indicated time points as described previously [18] . The primary antibodies used here were anti-RAD51 ( BD pharmingen ) and anti-γ-H2AX ( Bethyl ) . The coverslips were finally mounted onto glass slides with VectaShield antifade ( Vector Laboratories ) and visualized by using a Zeiss Axiovert 40 CFL fluorescence microscope . Testes were obtained from BRIT1+/+ and BRIT1−/− mice at different ages ( P7 , P14 , P21 , P28 , and P56 ) , fixed in 4% paraformaldehyde at 4°C , and routinely embedded in paraffin . Haematoxylin-eosin ( H&E ) staining was performed according to the standard procedure . Slides were de-paraffinized and rehydrated , and antigen retrieval was carried out by incubating the slides in 0 . 01 M sodium citrate buffer ( pH 6 . 1 ) in a hot water bath at 95°C for 30 min . If necessary , endogenous peroxidase activity was quenched with 3% hydrogen peroxide in methanol for 10 min at room temperature . Primary antibodies used here are anti-SOX-9 , anti-Tra98 ( gifts from Drs . Pumin Zhang and Xingxu Huang , Baylor College of Medicine , Houston , TX ) . Sections were finally visualized using a Zeiss Axiovert 40 CFL microscope , and images were captured with a Zeiss AxioCam MRc5 digital camera . Seminiferous tubules were collected from 2- to 4-month-old mice , and tubule segments were isolated in 1×DPBS . Chromosome spread of spermatocytes was made following the protocol elsewhere [50] , [51] . The following primary antibodies were used for immunofluorescent analyses: rabbit anti-SPO11 ( Santa Cruz ) , rabbit anti-γ-H2AX ( Bethyl ) , rabbit anti-RAD51 ( Sigma ) , rabbit anti-SCP1 ( GeneTex ) , rabbit anti-SCP3 ( GeneTex ) and guinea pig anti-SCP3 ( gift from Dr . Ricardo Benavente , University of Würzburg , Würzburg , Germany ) . Alexa 488-coupled anti-rabbit IgG and/or Alexa 594-conjugated anti-guinea pig IgG were used as secondary antibodies . The slides were finally counterstained and mounted with antifade mounting medium with DAPI ( Vector Laboratories ) . Foci were visualized microscopically with oil-immersed objectives , captured with a digital camera ( Zeiss AxioCam MRc5 ) , and processed with Photoshop ( Adobe ) . Chromatin isolation assay were performed as previously described [18] . Briefly , MEFs ( totally 5×106 cells ) were treated with or without IR ( 8 Gy ) , collected 1 h later , and then washed with PBS . The cells pellets were resuspended in 200 µl of solution A ( 10 mM HEPES [pH 7 . 9] , 10 mM KCl , 1 . 5 mM MgCl2 , 0 . 34 M sucrose , 10% glycerol , 1mM dithiothreitol , 10m MNaF , 1mM Na2VO3 , and protease inhibitors ) . Triton X-100 was added to a final concentration of 0 . 1% , and the cells were incubated for 5 min on ice . Cytoplasmic proteins were separated from nuclei by low-speed centrifugation . The isolated nuclei ( P1 ) were washed once with solution A and then lysed in 200 µl of solution B ( 3 mM ethylenediamine tetraacetic acid , 0 . 2 mM EGTA , 1 mM dithiothreitol , and protease inhibitors ) . Insoluble chromatin was collected by centrifugation , washed once in solution B , and centrifuged again for 1 min . The final chromatin pellets ( P3 ) were digested by resuspending nuclei in solution A containing 1 mM CaCl2 and 50 units of micrococal nuclease and incubated at 37°C for 1 min , after which the nuclease was stopped by addition of 1 mM EGTA . The chromatin pellets ( MNase-digested P3 ) were resuspended in 2× Laemmli buffer and boiled 10 min at 70°C . Following centrifugation at high speed ( 13 , 000 rpm ) , the chromatin associated proteins in the supernatant were analyzed by SDS-PAGE/Western blotting assay . | The repair of DNA breaks in cells is critical for maintaining genomic integrity and suppressing tumor development . DNA breaks can arise from exogenous agents such as ionizing radiation ( IR ) or can form during the process of germ cell ( sperm and egg ) generation . BRIT1 protein ( also known as MCPH1 ) is a recently identified DNA damage responding protein , and its mutations or reduced expression are found in primary microcephaly ( small brain ) patients , as well as in cancer patients . To investigate BRIT1's physiological functions and dissect the underlying molecular mechanism , we used a genetic approach ( gene targeting technology ) to delete BRIT1 gene in mice and generated a mouse model with BRIT1 deficiency ( called BRIT1-knockout mice ) . Here , we showed that BRIT1 knockout mice are more sensitive to IR due to their inability to repair the IR-induced DNA breaks . These mice are also infertile , and their DNA repair during the process of germ cell generation was impaired substantially . Thus , in this study , we generated a novel mouse model ( BRIT1 knockout mice ) with striking phenotypes related to defective DNA repair and clearly demonstrated the essential role of BRIT1 in DNA repair at organism level . |
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The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release . Here , we use quantitative , extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models . These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors . The analysis of the models’ capability to deal with environmental perturbations revealed three oxotypes , differing in the range of allowable oxygen uptake rates . Interestingly , models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates , which were associated with a glycolytic phenotype . A subset of the melanoma cell models required reductive carboxylation . The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2 , which was an essential gene in the melanoma models , but not IDH1 protein , was detected in normal skin cell types and melanoma . Moreover , the von Hippel-Lindau tumor suppressor ( VHL ) protein , whose loss is associated with non-hypoxic HIF-stabilization , reductive carboxylation , and promotion of glycolysis , was uniformly absent in melanoma . Thus , the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma . Taken together , our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells .
Aerobic glycolysis indicates the incomplete oxidation of glucose to lactate under normoxic conditions [1] and has been a focus of cancer research in recent decades [2] . However , cancer cells are increasingly thought to employ heterogeneous metabolic strategies beyond aerobic glycolysis [3–6] . Many cancer cells generate substantial amounts of energy through mitochondrial oxidative phosphorylation [2 , 7 , 8] . Moreover , cancer cells use additional fuels , such as glutamine and fatty acids , to support proliferation [3 , 9] . These carbon sources can be used in different ways , e . g . , different parts of the tricarboxylic acid ( TCA ) cycle can be employed for glutaminolysis [5 , 8 , 10 , 11] . Reductive carboxylation involves only two TCA cycle reactions that run in reverse direction without producing energy , whereas glutaminolysis in the forward direction does yield energy [5 , 8 , 11] . In addition to various metabolic strategies , cancer cells display robustness towards environmental changes , such as , nutrient supply or oxygenation [12–14] . Even though these differences in metabolic phenotypes are known to exist , the variance in the metabolism of cancer cell lines has not been exhaustively analyzed using extracellular metabolomic data . Liquid chromatography-tandem mass spectrometry ( LC-MS ) was used to determine the metabolites that were consumed and released by the cancer cell lines included in the NCI-60 panel of the National Cancer Institute’s ( NCI’s ) Developmental Therapeutics Program ( DTP; http://dtp . nci . nih . gov ) [15] . By combining the obtained metabolomic profiles with doubling times and transcriptomic data , rapid proliferation was associated with cellular glycine requirements [15] . However , most of the intracellular pathways that gave rise to distinct metabolomic profiles remained undetermined . Metabolism can be investigated using constraint-based modeling [16 , 17] , which involves the application of physico-chemical principles and often assumes the system to be in a steady-state [16] . Limitations on metabolite uptake and secretion rates can be added to the model to increase the precision of the predictions by eliminating network states that exceed these constraints [18] . A reconstruction of the human metabolism is readily available [19 , 20] , and numerous analytical methods are used to investigate the metabolic differences that arise due to the imposed constraints [21 , 22] . Metabolomic data derived from body fluids and cell culture supernatant have previously been integrated with metabolic reconstructions [7 , 23 , 24] . One existing challenge in the integration of extracellular metabolomic data is incomplete data . Analytical techniques identify only a subset of the metabolome due to the chemical diversity among small molecules and because the analysis is often a priori limited to a defined set of targeted metabolites [25] . Hence , the information on which substrates are taken up by the cells is incomplete . Similarly , the management of data derived from cells grown in serum is difficult because the quantitative and qualitative composition of the serum is unknown . However , the quality of computational predictions depends on the extent to which a model’s solution space can be reduced by integrating available data . Ideally , only biologically relevant network states would remain to be investigated [18] . Novel approaches are necessary to overcome these difficulties and enable the rapid classification of metabolic phenotypes based on metabolomic profiles . Such approaches could have a broad impact on many biological fields including biomedicine . We developed a novel method termed minExCard to complete the uptake and secretion profile , by predicting a minimal set of metabolite exchanges in addition to the ones measured , to complete the metabolome . We applied the method to the comprehensive targeted extracellular metabolomic data set from Jain et al . , which was generated from the NCI-60 cell lines grown in medium enriched with serum [15] . Using minExCard we generated 120 condition-specific models from extracellular metabolomic data . Our models utilized different biochemical routes to supply the cells with energy and were distinctively affected by network perturbations . We distinguished different oxotypes based on the range of allowable oxygen uptake rates . We identified a distinctive tissue pattern for melanoma cell lines that was supported by protein and RNA expression levels from melanoma cell lines and primary melanoma . This work demonstrates how analysis of extracellular metabolomic data in the metabolic model context , and the combination of multiple analysis strategies , can lead to unprecedented insight into cell metabolism .
Published metabolomic profiles comprising the uptake and secretion of metabolites from and into the culture medium were integrated with the metabolic model ( Fig 1A ) [15] . The metabolomic data consisted of two samples per cell line . Because there was considerable variation between samples ( Fig A in S1 Text ) , we generated one condition-specific cell line model for each sample rather than averaging the data for each cell line . The metabolome is dynamic and constitutes a snap-shot of the phenotype elicited by the cultivated cells over the duration of the experiment and under a specific set of environmental conditions . We refer to the models as condition-specific since they are tailored only according to the metabolomic profiles . Generic cell-line specific models would need to be generated from data sets of different experimental conditions and the existing literature for the same cell line , to ensure that it can carry out all the functions observed for these cells under any set of environmental conditions . To generate a condition-specific model , the global model was constrained using the metabolite uptake and secretion rates measured for the respective samples . Next , a minimal set of , on average 17 ± 3 , exchange reactions needed to sustain a minimal growth phenotype ( Vbiomass , min = 0 , 008 U ) together with the imposed uptake and secretion rates were identified based on the model structure by minimizing the number of exchange reactions ( using minExCard ) . An analysis of the expression of genes associated with the metabolites additionally required in the MCF-7 models ( which required the highest number of added exchange reactions ) , revealed that extracellular transport and metabolism of these added metabolites could indeed appear in MCF-7 cells ( see S1 Text , S1A Table , [26] ) . However , the gene expression data was only used to validate the added exchanges , but not for the generation of the condition-specific models since the transcriptomic data originated from a different experiment than the metabolomic data . All other metabolite exchanges and internal reactions that were no longer used by the model were removed to produce an individual condition-specific cell line model for each sample ( Fig 1A ) . The 120 models differed with respect to the numbers of reactions , metabolites , and genes ( Fig 1B and 1C , Fig B in S1 Text ) . Many of the models could substantially exceed the maximally possible growth rates expected for any human cell ( S1B Table ) . The capability of the models to grow at realistic rates was analyzed by applying constraints on the biomass objective function based on reported growth rates ( +/-20% ) for the individual cell lines , and flux balance analysis revealed whether the model remained feasible with these constraint . Only 14 models were infeasible when constrained using the experimental growth rates ( see S1 Text , S1C Table ) because the feasible range of flux rates through the biomass reactions exceeded or did not reach up to the experimental growth rates , even when assuming a 20% error range . ACHN-2 and UACC-257 were limited to experimental growth rates just by the metabolite uptake and secretion profile and the minimal growth constraint ( S1 Text , S1B and S1D Table ) . Considering a lower error of 5% or constraining both upper and lower bound to the growth rate , the ACHN-2 model became infeasible ( S1D Table ) . The predicted growth rates for the HCT-116 models using sampling Vmedian , biomass = 0 . 038 U , corresponding to a doubling time of 18 . 2 hrs , deviated at most 7% from the growth rate reported by Jain et al . and others ( S1D Table , [15 , 27] ) . Taken together , the diversity of the models and their ability to predict realistic growth rates suggested that they were a good starting point to investigate metabolic heterogeneity between the cell lines . Metabolic strategies yield different amounts of ATP , e . g . , full oxidation of glucose to CO2 can yield 32 ATP and aerobic glycolysis can yield two ATP [28 , 29] . Herein , we used the ATP yield as an estimator for distinct pathway utilization . For this analysis , we divided the sum of flux through all reactions in the model that produced ATP by the individual glucose uptake . There was a large range of ATP yields across the models ( Fig 2A , ATP yield: min = 2 . 92 , max = 55 . 27 , S1B Table ) that exceeded the theoretical measure for aerobic glycolysis . An exact fit with the theoretical ATP yields was not expected because the models could use all substrates as defined by the uptake profile and ATP-producing reactions present in the condition-specific model and not only glucose ( Fig A in S1 Text ) . As a sanity check , we tested for maximum ATP hydrolysis flux from only O2 and glucose as carbon source . ATP hydrolysis flux from glucose did not exceed the theoretical measures [28 , 29] in any of the 120 cancer models ( S1E Table ) . Upper bounds on exchange reactions were opened for the sanity check . Rank-ordered ATP yields nearly continuously increased and were occasionally interrupted between groups of models ( Fig 2A , Fig C in S1 Text ) . One interruption was associated with the switch of the major ATP-producing reaction . Models with an ATP yield < 4 . 21 ( ’glycolytic’ models , n = 38 , Fig 2A ) produced the highest fraction of ATP through phosphoglycerate kinase ( PGK ) . In contrast , models with an ATP yield > 7 . 26 produced ATP primarily via ATP synthase ( ’OxPhos’ models , n = 82 , Fig 2A ) . Thus , the ATP yield and ATP production strategy divided the models into glycolytic and OxPhos phenotypes . The distinction of the models was significantly associated with the ratios of glucose uptake to lactate secretion ( ttest , p< 0 . 01 ) , and glucose uptake to glutamine uptake ( ttest , p< 0 . 0002 ) . Taken together , the distinction of the glycolytic and the OxPhos models emerged from the ratios of fluxes of metabolites , which are associated with the observed Warburg phenotype and , which were imposed on the models as individual flux constraints . Consideration of differences in the utilization of the TCA cycle , i . e . , ATP production of succinate-CoA ligase , enabled the further identification of two OxPhos subtypes ( Fig D in S1 Text ) . This division was not obvious according to ATP yield ( Fig E in S1 Text ) . In addition to ATP , cells need cofactors to support proliferation . Distinct strategies used in the models produced different cofactors and enabled the division of glycolytic models into two subtypes ( Fig 2B and 2C , S1F–S1J Table ) . The two OxPhos subtypes were further subdivided into a total of six subtypes ( Fig 2B and 2C , S1J Table ) . The glycolytic subtypes differed only in the major FADH2-producing reaction ( Fig 2B , I and II ) . Two OxPhos subtypes were associated with high TCA cycle contribution to ATP production , which was associated with a high utilization of cytosolic malic enzyme as a leading NADPH source ( Fig 2B , IV and VII ) . The four remaining OxPhos subtypes predominantly used either isocitrate dehydrogenase ( IDH , Fig 2B , V and VIII ) or dihydroceramide desaturase ( Fig 2B , III and VI ) for NADPH production . Glyceraldehyde-3-phosphate dehydrogenase was the primary NADH producer in OxPhos models with relatively more glycolysis-based ATP production , whereas 2-oxoglutarate dehydrogenase was favored in models with a higher contribution of ATP synthase ( Fig 2C ) . Thus , the predicted strategies for cofactor production enabled further refinement for the classification of glycolytic and OxPhos models . Thus far , we stratified the models based on the imposed constraints and the distinct use of central metabolic pathways . In the following , we predict the behavior of each model towards environmental and genetic perturbations . Fluctuations of nutrients and oxygen supply during transformation shape the individual metabolic network and may influence the robustness of cancer cells towards environmental changes [30] . Variations of glucose uptake , glutamine uptake , oxygen uptake , and lactate secretion ( phenotypic phase plane analysis ( PhPP ) ) led to two major observations [31] . First , the size and form of the solution spaces varied across models ( Fig 3 ) . Using the form and size of the solution spaces as visual clues ( Fig G in S1 Text ) , we divided the models into six distinct clusters ( Figs 2E and 3 , S1K Table , S1 Text ) . Second , the solution space , which contains all possible network states and which was defined by variations in oxygen uptake , divided the models into three groups ( Fig 2D ) ( i ) Glycolytic models could only grow at low oxygen uptake rates ( Figs 2D and 3 cluster 4 ) . The group of OxPhos models comprised ( ii ) models growing only at high oxygen uptake rates ( Figs 2D and 3 cluster 1–3 ) and ( iii ) models that were indifferent with respect to oxygen uptake rates ( Figs 2D and 3 cluster 5–6 ) . The latter two groups provided a separation of the OxPhos models that was distinct from the previous analysis . Thus , the models could be further divided according to their robustness towards oxygen uptake . In silico gene knock-outs can predict novel drug targets [32] . Single gene deletion of 1215 unique human genes ( all isozymes of one gene were constrained to zero at once ) was performed for each of the 120 models . The number of essential genes varied across models ( min = 132 , max = 272 , S1B Table ) and was not associated with any phenotype . A total of 55 genes were essential to all models and could constitute metabolic targets for all previously defined phenotypes ( S1L Table ) . These numbers of essential genes predicted by our models were higher compared to those predicted for generic cell-or tissue specific models . This was caused by the vast reduction of exchange reactions and fixed uptake and secretion fluxes , which prevented that upon a gene knock-out , the models could switch to using different metabolic fuels or pathways connected to changes in gene expression . The flux ranges and the direction of flux of the exchange reactions were fixed , causing any reaction that was linked to the exchanges to become essential for the model . Whether a gene was essential under changing environmental conditions and whether cells in vivo could evade the effect by changing the metabolic pathways used to generate energy , cannot be answered by our models . However , models build from transcriptomic data could be used instead . Such models have previously revealed the switch to pathways requiring higher oxygen uptake when glycolytic enzymes were inhibited [33] . However , the condition-specific models , which are ‘frozen’ to the metabolic properties elicited at the time , highlight inhibition of which genes necessitate changes in metabolic flux and changes in gene expression . Cancer cells use the TCA cycle in different ways [5 , 8] . Reductive carboxylation involves the TCA cycle reactions isocitrate dehydrogenase and aconitase , and occurs in the mitochondria or the cytosol . The gene IDH1 encodes the cytosolic isocitrate dehydrogenase and the gene IDH2 encodes the mitochondrial isocitrate dehydrogenase . Interestingly , in silico IDH2 knock-out terminated growth in four models ( SK-MEL-28 , SK-MEL-28-2 , MALME-3-2 , and BT-549 ) and reduced growth in 12 additional models . A flux variability analysis ( FVA ) revealed that the four models had to employ reductive carboxylation ( S1M Table [34] , whereas this pathway remained optional for the other models even when constrained to experimental growth rates ( S1D Table ) . In agreement with an observed increase in reductive carboxylation under hypoxic conditions [5] , a reduction of the oxygen uptake rate ( lb = ub = −100 fmol/cell/hr ) rendered 14 additional models dependent on reductive carboxylation ( S1M Table ) . Fifteen models , including the four reductive carboxylation models , belonged to PhPP cluster 4 , which was characterized by a heavily constricted solution space at low oxygen uptake rates compared with , e . g . , the cluster 4C models ( Fig 3 ) . The remainder belonged to cluster 1B . Our models were therefore not only able to predict reductive carboxylation but also able to further reproduce the co-occurrence of low oxygenation and reductive carboxylation in cancer cell lines . Phosphoglycerate dehydrogenase ( PHGDH ) was another essential gene shared among the four models with obligate reductive carboxylation . Interestingly , SK-MEL-28 and MALME-3M had previously been associated with amplifications of PGDH due to 1p12 gain [4 , 35] . The correct prediction of the dependency of SK-MEL-28 and MALME-3M on PHGDH provides additional support for the presented approach and for the predicted dependency of SK-MEL-28 on reductive carboxylation . Because the oxotype played an essential role in determining the phenotype and because tissues are known to be differentially oxygenated [36] , we questioned whether tissue origin impacted the oxotype of the cancer . In total , 49 cell line model pairs had the same oxotype ( Fig 4 ) . Breast , colon , and non-small cell lung cancer models were spread across oxotypes . Leukemia , prostate , renal , and CNS cell line models predominantly depended on high oxygen uptake rates . In contrast , melanoma cell lines were clearly separated from the other cell lines by predominantly relying on low oxygen uptake rates ( Fig 4 ) . Thus , the oxotypes enabled us to distinguish melanoma cell lines from other cancer cell lines . Most melanoma models were predicted to be glycolytic and having a low oxotype ( Fig 4 , S1J and S1K Table ) . A reverse flux through the TCA cycle was essential for a small subset of melanoma models without additional constraints limiting the oxygen uptake . To validate that melanomas indeed use the mitochondrial isocitrate dehydrogenase , we analyzed protein abundance and RNA expression data from the Human Protein Atlas [37] . IDH1 protein abundance was low or not detectable in normal skin cell types ( hypergeometric p ( x = 5 ) = 0 . 047 , Table 1 , 1 ) , skin cancer , and melanoma . In comparison , IDH2 protein levels were medium in normal skin cell types ( hypergeometric p ( x = 5 ) = 0 . 006 , Table 1 , 2 ) and detected in more than 50% of the skin cancers and melanomas ( Table 1 ) . Thus , the data supported a prevalence of IDH2 for normal skin cell types , skin cancers , and melanoma at the protein level . Reductive carboxylation has been associated with the loss of the von Hippel-Lindau tumor suppressor ( VHL ) in renal cancer cell lines [38] . HIF1α protein is no longer degraded , which is associated with the expression of glucose transporters and glycolytic enzymes [39 , 40] . Since the process of HIF stabilization is connected to hypoxia , this process has also been referred to as pseudo-hypoxia [5] . To validate the predicted glycolytic phenotype and the low ‘oxotype’ , we analyzed HIF1α and VHL protein , and RNA levels . HIF1α protein abundance was low in normal skin tissue ( hypergeometric p ( x = 5 ) = 0 . 019 , Table 1 , 3 ) and low or medium in the majority of skin cancers and melanomas ( Table 1 ) . HIF1α RNA expression was overall high in human melanoma and epidermoid carcinoma cell lines ( Table 2 ) . The VHL protein detection was unreliable in all normal skin cell types ( Table 1 ) . Interestingly , VHL protein was not detected in skin cancers or in melanomas ( Table 1 ) . The absence of VHL was even more distinctive in skin cancers as compared to renal cancers where VHL levels were medium or high in 7 out of 12 patient samples ( Table 1 ) . Moreover , RNA expression was low in two melanoma cell lines , an epidermoid carcinoma , an immortalized normal keratinocyte cell lines ( hypergeometric p ( x = 4 ) = 0 . 044 , Table 2 , 1 ) , and A549 cells , which were predicted to be low oxotype ( Table 2 ) . Hence , the lack of VHL emerged as a prominent feature of normal skin and melanoma .
We opted to build our models solely from metabolomic data , without consideration of the genotypic and other omics data , to evaluate whether such models could provide novel biological insights . This approach was particularly interesting for connecting the melanoma cell lines with the reverse flux through the IDH2 and psydohypoxia [5] . However , when only constrained based on the metabolomic data , our 786-O models predicted net reductive carboxylation was optional , which stood in contrast to net reverse flux observed for these cells . Limiting the oxygen uptake in the models to the minimum emphasized the reverse flux in the TCA cycle . Overall , this highlights that oxygen consumption in an experiment determines the observable metabolic phenotype , in addition to the growth medium composition ( or environmental condition ) . Addition of , e . g . , transcriptomic data could further define the phenotype [62] . There is no shortage in transcriptomic data for the NCI-60 cell lines . However , since the models build herein are condition-specific , the data used should originate from the same experiment to resemble the metabolic phenotype displayed in the experiment . The presented computational modeling approach is applicable to many cellular systems and represents a valuable starting point to investigate metabolic strategies of individual cell lines as well as to envision clinical applications . Further development of this approach could help realize personalized clinical applications utilizing metabolomic data . Immortalized cell lines , such as the NCI-60 cell lines , have a limited clinical relevance since they are monoclonal and accumulate mutations due to the high passage numbers [63] . One way to increase the clinical relevance of our work would be to extend the presented work to omics data generated from patient-derived primary tumor cells . Methods exist to cultivate primary tumor cells , or selected sub-populations of the same , e . g . , tumor-initiating stem cells; and to retain phenotype and genotypes using tissue-specific supplements and environmental conditions [63] . Extracellular metabolomic data or multiple omics data derived from such personalized cell cultures could then be used in conjunction with the presented approach to gain a better understanding of an individual’s cancer , and to predict appropriate treatment strategies .
The global model constitutes a subset of Recon 2 [20] . This subset is the same as that used in a previous study [62] . Units ( U ) are given in fmol/cell/hr . The MetaboTools function setMediumConstraints was used to apply the following constraints to the global model [61] . Essentially , infinite constraints were set to lb = −2 , 000 U and ub = 2 , 000 U . All exchange reactions in the model were initially set to lb = −2 , 000 U and ub = 2 , 000 U . Subsequently , constraints were set for exchange reactions of ions ( lb = −100 U ) , vitamins ( lb = −1 U ) , essential amino acids ( lb = −10 U ) and compounds such as water or protons ( lb = −100 U ) . Oxygen uptake was constrained to lb = −1 , 000 U and ub = 0 U . This range was defined based on reported oxygen uptake rates of a cancer cell line ( 2 . 85 ⋅ 10-6ml O2/105cells/min = 646 . 013 U [64] ) . Additionally , the lower bounds of the superoxide anion and hydrogen peroxide exchanges ( i . e . , uptake flux ) were set to zero to prevent the generation of models that did not require oxygen uptake . Reaction fluxes are usually in units of mmol/gDW/hr . Here , however , the metabolite uptake and secretion profiles were mapped in the unit fmol/cell/hr [15] . We assumed a unitary cell weight of 10-12 g , which was in the range of the dry weight ( 3 . 645 ⋅ 10-12 g ) that we calculated for lymphocytes in an earlier study [62] . In that study , the dry weight was inferred from the dry mass ( range 35–60 ng [65] ) and cellular volume ( 4000 μm3 [66] ) of the human osteosarcoma cell line U2OS , which we related to the cell volume of lymphocytes ( 243 μm3 ) [67] . By calculating 4000/243 = 16 . 46 , 60 pg/16 . 46 = 3 . 645 pg ( 3 . 645 ⋅ 10-12 g ) [62] . According to 1mmol/gdw = 1012fmol/1012 cells , no biomass scaling was necessary . The lower bound ( lb ) of the biomass objective function was fixed to a minimal value of 0 . 008 U to match the lb defined for the slowest growing cell line in the data set ( HOP-92 , 88 hrs ) [27] , ensuring that the model building resulted in functional models with non-zero growth . S1Q Table lists the reactions and constraints of the global model . We used published metabolomic data [15] . There were two quantitative extracellular metabolomic profiles for each of the NCI-60 cell lines . These profiles defined the uptake and secretion rates of 115 metabolites [15] . From the entire set of detected metabolites , we used only the calibrated ( quantitative ) uptake and secretion fluxes . Fluxes were provided in the unit fmol/cell/hr ( U ) and were incorporated as such into the model . Throughout the manuscript , fluxes are reported in the unit fmol/cell/hr ( U ) . Metabolite identifiers in the data were mapped to the metabolite abbreviations in the global model . The metabolite aminoisobutyrate was not part of the global model and was excluded . We identified the existing metabolite exchange reactions based on the metabolite abbreviations . If there was no exchange reaction in the model but if the metabolite itself was part of the model , a new exchange reaction was added to the model . In addition to the exchange reactions , transport reactions need to be present in the model to account for transport of metabolites between the extracellular space and the cytosol of the model . Transport reactions need to be added for all metabolites for which we added exchange reactions . These transport reactions were identified from the literature . If no transporter for the metabolite could be identified , we added a diffusion reaction . The additions that we made to the model based on the metabolomic data comprised 44 transport and 37 exchange reactions ( S1R Table ) . The global model used to generate the cancer models comprised 3 , 935 reactions and 2 , 833 metabolites . The Integration of the metabolomic data was performed as detailed in a protocol that provides extensive support ( including workflows , code , and tutorials ) for the data integration , model generation , and model analysis , carried out in this study [61] . Consider the optimization problem min θ ( v ) s . t . S · v = 0 , l b ≤ v ≤ u b , ( 1 ) where v ∈ R n is a vector of reaction rates , θ ( v ) is a scalar valued objective function and S ∈ R m × n is the stoichiometric matrix consisting of m metabolites and n reaction rates as defined by the metabolic reconstruction . The lower and upper bounds , lb and u b ∈ R n respectively , constrain the sign and magnitude of the reaction rate , with the convention that a net forward rate is positive . In flux balance analysis ( FBA [68] ) , the objective is to minimize θ ( v ) : = cT ⋅ v , a linear sum of reaction rates , where c ∈ R is a parameter vector that specifies the linear contribution of each reaction rate to the objective function . When minimizing a single reaction rate , every entry of c is zero , except one . Typically , there is an infinite number of optimal reaction rate vectors that produce an optimal value of the objective function . To obtain a unique flux vector , we first solve Problem ( 1 ) with θ ( v ) : = cT ⋅ v , then fix the rate of the previously optimized reaction and again solve Problem ( 1 ) except with θ ( v ) : = 1 2 v T · v . This procedure returns a unique reaction rate vector that minimizes the square of the Euclidean norm of the reaction rates , subject to optimality with respect to the original objective function [21] . In flux variability analysis ( FVA ) , one uses linear optimization to compute the minimal and maximal rate of each reaction , subject to θ ( v ) : = cT ⋅ v being minimal as computed in Problem ( 1 ) [34] . The presence of an exchange and transport reactions does not ensure that a metabolite can be consumed or secreted by the model because anabolic and/or catabolic pathways may not be present or unknown [20] . We used the MetaboTools function prepIntegrationQuant to generate individual uptake and secretion profiles for each sample in the data set: To identify the subset of metabolites that the model could consume and secrete , we performed FBA while enforcing small uptake ( ub = −0 . 0001 U ) or secretion ( lb = 0 . 0001 U ) for all mapped metabolite exchanges . All metabolites that could not be consumed ( 14 ) or secreted ( 14 ) by the model were discarded ( S1S Table ) . Among them was homoserine 4-hydroxybenzoate , which could be neither consumed nor secreted by the model . Therefore , data for 112 metabolites could be mapped . Note that these 112 metabolites included those that could only be consumed , only be secreted , or by both consumed and secreted ( S1S Table ) . The identification of metabolites that are not part of a metabolic reconstruction is common , and pathways for these metabolites need to be added in future releases of the human metabolic model [20] , which served as a starting point ( see also above ) . If the uptake of a metabolite was possible in the global model but secretion was not , only metabolite secretion was discarded from the metabolic profiles , while uptake remained present , and vice versa . After the sets of ‘qualitatively’ feasible metabolite exchanges were identified , we mapped the sets of metabolite uptake and secretions of a sample to the global model using the MetaboTools function setQuantConstraints [61]: We mapped a minimum of 95 and a maximum of 105 exchanges to the models ( S1T Table ) . These exchanges were split into uptake and secretion . The number of metabolite uptakes mapped to the model ranged between 34 and 58 , and the number of secretions enforced in the model varied between 42 and 67 . We imposed each detected , quantitative flux x as a constraint to the bounds of the respective metabolite exchange reaction while considering a 20% allowance around x ( lb = 0 . 8x U and ub = 1 . 2x U ) . The constraint pairs for one sample were mapped to the global model one by one . After constraints were placed on one exchange reaction , FBA was performed to check if the model was still able to grow . Although the global model was able to perform all qualitative metabolite exchanges that were mapped , certain quantities or combinations of constraints could still render the model infeasible . In case of infeasibility , the original bounds of the model were restored , and we proceeded to the next set of constraints . Quantitative constraints rendered 27 preliminary cell line models infeasible ( Fig 1A ) . Of these 27 models 25x2 , 1x1 , and 1x4 exchange constraints were restored during the data integration ( S1B Table ) . Although 464 of the reactions in the global model can exchange metabolites across the boundary of a cell , the exchange of only 115 metabolites was actually quantified in the metabolomic profiles that we employed . The incompleteness of the metabolic profiles results from limits to the scope of individual metabolomic platforms , e . g . , oxygen uptake rates that were not reported . This issue was compounded due to the use of fresh medium that was undefined with respect to small molecules , e . g . , fresh medium containing serum . In the preliminary model , removing all but the metabolite exchanges ( corresponding to measured , exchanged metabolites ) always led to a model that did not admit a feasible steady-state flux . We hypothesized that the metabolic profiles were likely to be an incomplete representation of the total number of metabolites exchanged with the medium . Therefore , we developed a novel method , deemed minExCard , that takes a preliminary metabolic model as the input and predicts a steady-state flux vector with the minimum cardinality for the reactions corresponding to the missing exchanges . That is , it predicts a minimal number of missing exchange reactions that are required to be active to permit a feasible steady state flux ( S1O Table ) . | Altered metabolism is characteristic for many human diseases including cancer . Disease progression and treatment efficacy vary between patients . Hence , we need personalized approaches to define metabolic disease phenotypes . This definition will enable us to unravel the underlying disease mechanisms and to treat individuals more efficiently . Computational modeling increasingly supports the analysis of disease mechanisms and complex data sets . The interpretation of extracellular metabolomic data sets is particularly promising since this data type is proximal to the actual metabolic phenotype altered in human diseases . Moreover , it might enable us to directly interpret disease states from serum samples in the future . Herein , we took a first step towards this ambitious goal . We generated a large set of cancer metabolic models from extracellular metabolomic data and computationally stratified the models based on their metabolic characteristics into different phenotype groups . Melanoma emerged as an interesting example of how our approach can provide insights into the intracellular metabolism from extracellular measurements . Taken together , this work paves the way to generate condition-specific models from extracellular metabolomic data and demonstrates the many ways by which distinct phenotypes can be stratified and phenotype-specific intervention targets can be predicted . |
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High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution . However , there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data . The challenges arise , at least in part , from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data . Here , we present an algorithm , termed Bycom , a new Bayesian model that can perform methylcytosine calling with high accuracy . Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy . Bycom performance was compared with the performance of Lister , the method most widely used to identify methylcytosines from bisulfite sequencing data . The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels . Bycom also showed higher sensitivity and specificity for low methylation level samples ( <1% ) than Lister . A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94% . This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels . Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines .
DNA methylation is an important epigenetic modification involved in the regulation of gene expression and plays critical roles in cellular processes [1]–[5] . Abnormalities in DNA methylation contribute to the dysregulation of gene expression and have been reported to be associated with tumorigenesis [6] and imprinting disorders [7] . DNA methylation occurs on the cytosine residues in DNA and the accurate identification of methylated cytosines ( methylcytosines ) is essential for studying variance in methylation [8] . Advances in high-throughput bisulfite sequencing ( BS-seq ) [9]–[11] such as whole-genome bisulfite sequencing and reduced representation bisulfite sequencing ( RRBS ) , provide comprehensive and well-fitted ways to identify methylcytosines at single-base resolution . However , the large data sets generated by BS-seq pose data processing challenges for methylcytosine calling . Typically , the first step of methylation analysis with BS-seq data is to map the bisulfite-converted reads to a reference genome using software such as SOAP and BSMAP [12]–[14] . Methylcytosines can then be identified from the reads aligned to the cytosines on the reference genome . However , besides sequencing errors , methylcytosine calling is affected by incomplete bisulfite conversion , which corresponds to the ratio of unmethylated cytosines that were not converted to thymines by the bisulfite treatment . Additionally , cell heterozygosis caused by multicellular sequencing can also influence the precision of methylcytosine detection because the methylation status of the same cytosine site in different cell is probably inconsistent owing to the coexistence of methylation and demethylation [15]–[18] . As a consequence of the factors mentioned above , the false-positive rate for methylcytosines detected by simple filtering using a predefine threshold that the methylation level should be above zero , has been reported to be extremely high [19]–[21] . In recent years , the methylation ascertainment method applied by Lister et al . [9] , which is widely used in the methylation analysis software such as Bismark [22] and Bisulfighter [23] , has been employed to determine the methylcytosines from BS-seq data [5] , [24] and has been shown to have a much lower false positive rate than the simple filtering method [25] , [26] . We termed the method as “Lister” hereafter . Although Lister uses binomial distribution to overcome the impacts of false-positive rate ( sum of non-conversion rate and sequencing errors ) [24] , it does not consider the impact of methylation heterozygosis caused by cell heterozygosis . Besides , as amounts of cytosines , whose methylation level is low , are difficult to be distinguished from unmethylated sites , the methylation status determined by the binomial test is not reliable for samples with extremely low genome-wide methylation levels [5] . Here , we present a novel algorithm , Bycom that can take into account sequencing errors , non-conversion rate , and cell heterozygosis which is initially introduced as the factors to identify precisely the methylcytosines from BS-seq data . Bycom is based on the Bayesian inference model , which is not limited to certain data distribution and has been used successfully for single-nucleotide polymorphism ( SNP ) calling in next-generation sequencing data [27] . Bayesian inference model has also been successfully applied to methylated DNA immunoprecipitation ( MeDIP ) -based studies [28] . We evaluated the performance of Bycom on simulated BS-seq data , publicly available BS-Seq data , and RRBS data and demonstrated that Bycom can identify methylcytosines more precisely than Lister . This methodology of Bycom has been packaged and available at https://sourceforge . net/projects/bycom , which is written in PERL and designed for Linux 64 platform .
The performance of Bycom was evaluated and compared with Lister based on simulated data that were generated from chromosome 10 of the human reference genome sequence ( hg18 , UCSC ) using the bisulfite sequencing simulator for next-generation sequencing BSSim ( http://122 . 228 . 158 . 106/BSSim ) . Six factors that may affect the identification of methylcytosines were considered in the simulation process , namely , total methylation level of the sequenced sample , sequencing depth , bisulfite conversion rate , high quality value ratio , mismatch number , and SNP frequency . With all parameters set to their default values , we found that Bycom had a higher overall accuracy than Lister as shown by the receiver operator characteristic ( ROC ) curves ( Figure 2A ) . Next , we investigated the impact of each single factor on the performance of Bycom . For the total methylation level , we assumed that the number of methylcytosines in the simulated data increased steadily as the total methylation level was increased . When the methylation levels were extremely low ( 0 . 2% , 0 . 4% , 0 . 6% , and 0 . 8% ) , the number of methylcytosines called rose slightly and the false negative rate remained stable at 2% for Bycom and at 15% for Lister . At low methylation levels ( 2% , 4% , 6% , and 8% ) , the number of methylcytosines called by the two methods increased and the false negative rate for Bycom decreased slightly to 0 . 3% compared with 14% for Lister . At the high methylation levels ( 20% , 40% , 60% , and 80% ) , the number of true positive sites increased , while the false negative rates for both Bycom and Lister remained very low ( Figure 2B ) . For the impact of sequencing depth , the total number of methylcytosines called by Bycom increased when the read depth incrementally changed from 5× to 25× . The highest number of methylcytosines called was when the depth was 25×; thereafter , the number of calls remained relatively constant for depths >25× ( Figure 2C ) . In contrast , the total number of methylcytosines called by Lister increased when the read depth went up from 5× to 55× after which the number of calls steadily decreased . The false negative rates for both Bycom and Lister reduced when the higher read depths were introduced and , in all cases , the false positive rates for Lister were higher than the false positive rates for Bycom . The bisulfite conversion rate can affect the methylation level of the cytosines in a genome sequence; therefore , we evaluated the performance of Bycom using different values for this parameter . The number of methylcytosines called by Bycom and Lister remained constant when the non-conversion rate was <1% . When the non-conversion rate was >1% , the total number of methylcytosines called by Bycom increased slightly and the false negative decrease steadily; conversely , the total number of methylcytosines called by Lister increased sharply ( Figure 2D ) . The other three factors , high quality value ratio ( Q20 cut-off ) , mismatch number , and SNP frequency made only small contributions to either the number of methylcytosines called or the false positive rate , both of which remained nearly constant . Here , for the method Lister , the influence of sequencing errors calculated by quality value were reduced as the quality cut-off went up , while for Bycom , the influence was still kept at a relatively low level ( Figure S1B & S1D ) . Overall , compared with the performance of Lister , Bycom generated more true positive sites and fewer false positive sites ( Figure S1 ) . To further assess the ability of Bycom to identify methylcytosines , whole-genome BS-seq data of YH [24] , silkworm [19] and the ascomycetes Aspergillus flavus [5] were used as described in [24] . The YH BS-seq data , generated from the peripheral blood mononuclear cells of an Asian individual , had a high methylation level of 68 . 4% at CpG sites and <0 . 2% at non-CpGs sites with an overall coverage of 12 . 3-fold per strand . 32 methylated CpGs and 18 non-methylated CGs were selected randomly and validated by bisulfite Sanger sequencing . Because the depth of 11 validated cytosines was lower than the threshold ( lowest depth was 4-fold ) [24] , only 24 methylated CpGs and 15 non-methylated CGs gave eligible validated results . The methylcytosine calling results showed that the accuracy for Bycom was 71 . 79% compared with the 66 . 67% accuracy for Lister , and both methods made no false calls ( Figure 3A ) . We also evaluated the performance of Bycom on a species that had a low methylation level . The BS-seq data of silkworm had overall methylation levels of 0 . 67% at CG , 0 . 21% at CHG , and 0 . 24% at CHH sites ( where H is A , C , or T ) with an average depth of 7 . 4-fold per strand . In the BS-seq silkworm data , 598 methylcytosines and 311 cytosines were validated ( ≥4× ) among which 24 methylated and 101 non-methylated sites were verified by bisulfite Sanger sequencing while the others were confirmed using bisulfite-PCR followed by 454 sequencing . The distribution of the validated sites called by Bycom and Lister is shown in Figure 3A . For bisulfite Sanger sequencing , Bycom had 93 . 6% accuracy with a false positive rate of 0 . 99% , while Lister exhibited 83 . 2% accuracy with a false positive rate of 15 . 84% . For 454 sequencing , the Bycom and Lister accuracies were the same at 82 . 53% with false positive rates of 10 . 48% for Bycom and 13 . 81% for Lister ( Figure 3B ) . Finally , we evaluated the performance of Bycom on the BS-seq data of Aspergillus flavus , which had been reported to have a negligible level of methylation with 4-fold overall depth per strand . All the methylcytosines called by Lister on this data were regarded as false positives ( ≥4× ) . An overlapping strategy was applied between the cytosines detected by Bycom and by Lister . The results indicated that the false positive rate for Lister was at least three times the false positive rate for Bycom ( Figure 3C ) . Further , sites that were selected randomly from the methylcytosines called by Lister and verified by Sanger sequencing as non-methylated cytosines were not called by Bycom . Meanwhile , we splited the validated sites of YH , silkworm and Aspergillus flavus into CpG and CpH ( CpA , CpC , and CpT dinucleotides ) context . The strategy was also performed on the validated results of Bycom and Lister in these samples . We found that true positive sites ( mC ) were all in CpG context and the sites in CpH context were all false positive . The results also indicated that Bycom was more precise on the detection of mCpH than Lister ( Table S1 ) . We evaluated the performance of Bycom on RRBS data from T29 cells ( GSE55568 ) . A total of 2 , 110 , 089 methylcytosines were identified in the genome , making up nearly 0 . 18% of the total cytosines; 98 . 58% of the identified methylcytosines were in CG sites with a mean sequencing depth of 15 . 5-fold per strand ( Table S2 ) . To verify the precision of Bycom , seven batches of sequences from the T29 RRBS data were selected randomly for validation by bisulfite-PCR followed by sequencing on the Illumina MiSeq platform . As a result , 68 methylcytosines and 158 cytosines were validated . Bycom identified a high percentage ( 75% ) of the CpG methylcytosines and a low percentage ( 5 . 56% ) of the CpH methylcytosines . Lister identified a relatively low percentage ( 71 . 88% ) of mCpG and the equal proportion of mCpH as Bycom . Besides , a similar low proportion of the validation sites were identified as cytosines by both Bycom and Lister ( Table S3 ) . Bycom and Lister had an accuracy of 93 . 81% and 93 . 36% , respectively , with the same false positive rate of 4 . 12% . Notably , methylcytosines in the CpH context , which were nearly complete absence in the T29 cells [20] , were regarded as background noise ( Table S3 ) . We compared the accuracy of mC calling between Bycom and methylation analysis tools , including Bismark and Bisulfighter , which employed Lister as the algorithm to detect methylcytosines . With all parameters set to default in simulator BSSim , we found that the software based on Bycom run fastest ( Table S4 ) . Besides , Bycom had a highest overall accuracy than others as shown by the ROC curves ( Figure 4A ) . Then , we compared the software based on Bycom with Bismark and Bisulfighter using the previous public data . Firstly , Bycom had a highest accuracy than other two in YH Sanger-sequencing data without false positive results . Secondly , in silkworm data , for 454-sequencing validation , the accuracy of Bycom was lower than that of Bismark and higher than that of Bisulfighter . For Sanger-sequencing validation , Bycom kept highest precision on the detection of methylcytosines than others ( Figure 4B ) . Finally , the validation sites of Aspergillus flavus Sanger sequencing data all proven to be false positive were not called by Bycom and Bismark , and 16 . 67% of them were selected by Bisulfighter as methylcytosines .
Detecting methylcytosines accurately from high-throughput sequencing data is an essential step to calculate the methylation level of genomes at single-base resolution . In this study , we presented a novel computational strategy , Bycom , for identifying precisely methylcytosines from BS-seq data using the Bayes inference model . Bycom not only considers the impacts of sequencing errors and non-conversion rate , both of which are treated as the false-positive rate in the Lister method , but also introduces cell heterozygosis to identify methylcytosines in an unbiased manner . The results on the simulated data showed that the non-conversion rate and total methylation level significantly affected the accuracy of the Bycom and Lister methods , while sequencing errors had limited influence after the relatively stringent quality filtration of the data . Additionally , the accuracy of methylcytosine calling was better with Bycom than with Lister . The results on the public data indicated that the false positive rate of Lister was higher than the false positive rate of Bycom in the genome-wide samples with low methylation levels . Although The validation sites of public data in CpH context were all detected as false positive sites , Bycom had a better performance on the classification of mCpH than Lister . Furthermore , the validation results on the T29 RRBS data showed that Bycom detected the vast majority of methylcytosines and maintained the false calling rate at a low level . The performance of Bycom on the public data with different genome-wide methylation levels revealed that Bycom had no bias on data with high or low methylation levels . Moreover , to further verify the accuracy of our method , we compared the accuracy of Bycom with the methylation analysis tools , Bismark and Bisulfighter , which could also be used to call methylcytosines . The results of simulated data and public data both showed that Bycom behaved better than Bismark and Bisulfighter . However , we noticed that variances of the methylation level on single cytosines between the BS-seq data and the Sanger/454 sequencing data likely led to false/missing calls by both Bycom and Lister . Although the methylcytosines were still detected by Bycom , this finding suggested that the accuracy might improve further if the methylation level of the base was recalibrated depending on mass validated sequencing data . In future work , we aim to refine the model with well-recalibrated sequencing data . Besides , other functions , such as differentially methylated regions ( DMRs ) detection will be added in future , which will be great facilitated by the precise methylated cytosines calling . In conclusion , the results presented here demonstrate that Bycom is an effective statistical framework that is capable of identifying methylcytosines and will be very useful for genome-wide investigations of DNA methylation .
NCBI build 36 . 1 ( hg18 ) was downloaded from the UCSC database ( http://genome . ucsc . edu/ ) and used as the human reference genome . The whole-genome DNA methylation data from an anonymous male Asian Han Chinese ( YH ) were obtained from the NCBI Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo; GSE17972 ) . The silkworm sequence data were downloaded from the GEO database ( GSE18315 ) and the Sequence Read Archive ( SRA; SRP001159 ) . The Aspergillus flavus reference genome sequence was downloaded from the Aspergillus flavus Genome Sequencing Project web site ( http://www . aspergillusflavus . org/genomics/ ) and the DNA methylation data were obtained from GEO ( GSE32177 ) . The raw Illumina T29 RRBS data are available from GEO under accession number GSE55568 . Simulated bisulfite short reads with different levels of six factors ( total methylation level , sequencing depth , bisulfite conversion rate , sequencing quality of base , number of misaligned reads , and mutation rate ) were simulated from chromosome 10 of hg18 using BSSim ( http://122 . 228 . 158 . 106/BSSim/ ) . The default settings of the six factors in BSSim were used for the simulation as: sequencing depth 30×; maximum mismatches 2; bisulfite conversion rate 0 . 998; high quality value ratio 95%; and SNP frequency for homozygote/heterozygote 0 . 0005/0 . 001 . Besides , different methylation levels of 86 . 53% , 2 . 03% , and 2 . 27% were set for the different types of cytosines CG , CHG , and CHH , respectively , where H is A , C , or T . Reads were generated from random locations on chromosome 10 with different settings of the six factors; one factor was changed while the other parameters remained at the default values . Different levels were set as: sequencing depth 5× , 15× , 25× , … , 95×; mismatch number 1 , 2 , 3 , … , 10; bisulfite non-conversion rate 0 . 01 , 0 . 02 , 0 . 03 , … , 0 . 1; and whole-genome methylation level 0 . 002 , 0 . 004 , 0 . 006 , 0 . 008 ( extremely low ) , 0 . 02 , 0 . 04 , 0 . 06 , 0 . 08 ( low ) , and 0 . 2 , 0 . 4 , 0 . 6 , 0 . 8 ( high ) . For SNP frequency , homozygotes were set as 0 . 0005 , 0 . 001 , 0 . 0015 , … , 0 . 005 and heterozygotes were set at double the homozygote frequency . High quality values indicated by ASCII quality values above the char “h” of 10% , 20% , 30% , … , 90% , 99% were assigned to the whole reads . The methylation status and SNP genotypes of each cytosine were then retrieved from the BSSim simulated files . Low quality reads ( Q20 ) were eliminated for the raw data and the clean reads were aligned against the reference genome allowing a maximum of two mismatches . Uniquely mapped reads were retained for the following analysis . For a selected genomic location , the cytosines in that region were set as , where n is the total number of cytosines in the region . Then , the observed base of a mapped read j at cytosine site ci in the genomic sequence was assumed as , where m is the total number of reads that cover site . The quality value of the observed base was set to . In the Bayesian inference model , the posterior probability of expecting base at site on the reference genome was expressed as:where m is the total number of reads covering site . Here , was considered as the actual base according to the observed base of the read when bi was estimated . The independent base was expressed as . When the sequencing error ratio is set to , the likelihood of the site was calculated as: Beside the sequencing error , other factors , including bisulfite conversion rate and methylated heterozygosity , also influence the composition of the bases derived from the reads . After bisulfite treatment , heterozygous methylcytosines , heterozygous cytosines , wrong-sequenced cytosines , correct-sequenced cytosines , converted cytosines , and unconverted cytosines may appear simultaneously at identical cytosine sites in the sequences from distinct cells . Therefore , the likelihood was computed as:The prior probability of each base bi at site on the genome was set as: In this way , the posterior probability derived from the Bayesian inference formula was obtained . The key to the model is the accurate estimation of three parameters , , and , which can change the of base contribution component from the reads covering the cytosine site . To estimate the methylated heterozygosity , a linear fitting equation was built using the methylation level of a 1000-bp genome region of single chromosome in which the methylation status has been reported to be highly correlated [24] , [28] . The of the midpoint in a 1000-bp region was estimated as:where is the methylation level ( methylated reads/total reads ) of site . The values of a and b were obtained by a least square procedure as:where is the methylation level of site in a 1000-bp region centered at sites and is the average methylation level of the 1000-bp region centered at sites . The region centered at contained n cytosine sites . The of cytosine sites in the 500-bp head or tail of the chromosome were estimated using the average methylation level of the cytosine sites in the 1000-bp head or tail chromosome genome regions as:Where p is the total number of the cytosine sites located in the 1000-bp head or tail of the chromosome . and are the number of methylated reads and total reads at site , respectively . The sequencing error ratio , which is indicated by the quality value of the observed base , was calculated as [30] . The bisulfite conversion rate was determined initially by un-methylated lambda DNA spike-ins [25] or by calculating the C to T conversion rate for all cytosines in the CpH context [19] , [24] . After bisulfite treatment , the posterior probability of confirming the cytosine site as base C is actually the probability of cytosine methylation . The threshold T was set to ensure methylcytosine calling was precise , and was computed by a numerical iteration algorithm as: ( * ) where and represent the number of cytosine and methylcytosine sites , respectively , on a chromosome . The false discovery rate ( FDR ) was set as 0 . 01 . In the iteration , was calculated as the number of cytosine sites with a posterior probability above the T threshold . Set for the first iteration , and the posterior probability above the value of the cytosine would be the methylated cytosine . Then we can calculate , substitute it into ( * ) and obtain . For the second iteration , replaces , and acquire ; will tend to the invariant threshold as T after several iterations . Total genomic DNA was extracted from the T29 cell line and converted by sodium bisulfite following an established protocol [31] . Seven batches of regions were selected randomly and amplified with nested RT-PCR primers ( Table S5 ) . The amplified products were sequenced on the Illumina MiSeq platform ( Illumina Inc , San Diego , CA ) . The validation results are summarized in Table S3 . | High-throughput bisulfite sequencing ( BS-seq ) has advanced tremendously the study of DNA methylation and the determination of methylcytosines at single-base resolution . In BS-seq data analysis , sequencing errors , incomplete bisulfite conversion , and cell heterozygosis affect the accuracy of methylcytosine detection in quite a major way . Simple filtering methods using predefined thresholds have proved to have extremely low efficiency . The commonly used Lister uses binomial distribution to overcome the impacts of non-conversion rate and sequencing errors , but the impact of the cell heterozygosis is not considered . Here , we present Bycom , a novel algorithm based on the Bayesian inference model . To improve the accuracy of methylcytosine calling , Bycom considers sequencing errors , non-conversion rate , and cell heterozygosis integratively to identify methylcytosines from BS-seq data . We evaluated the performance of Bycom using different kinds of BS-seq data . Our results demonstrated that Bycom identified methylcytosines more accurately than Lister , especially in BS-seq data with extremely low genome-wide methylation levels . |
You are an expert at summarizing long articles. Proceed to summarize the following text:
Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs . Design of topologically more complex proteins , such as most receptors , channels , and transporters , however , demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions . Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks . The plasma membrane , however , imposes different physical constraints on protein solvation . To understand these constraints , we recently developed a high-throughput experimental screen , called dsTβL , and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane . Here , we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks . Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2 . 5Å root-mean-square deviation within the top-predicted five models ( available online: http://tmhop . weizmann . ac . il ) . Furthermore , in two sequence-design benchmarks , the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure , thereby recommending this new energy function for membrane-protein modelling and design .
Membrane proteins have essential biological roles as receptors , channels , and transporters . Over the past decade , significant progress has been made in the design of membrane proteins , including the first design of membrane-integral inhibitors [1] , a transporter [2 , 3] , and a de novo designed structure based on coiled-coil motifs [4] . Despite this exciting progress , however , modelling , design , and engineering of membrane proteins lag far behind those of soluble proteins [5] . This lag is due , in part , to the relatively small number of high-resolution membrane-protein structures [6] and is exacerbated by these proteins’ typically large size . Clearly , however , the heterogeneity of a membrane protein’s environment , comprising water , lipid , and polar headgroups , is the most significant complication [7] . Therefore , modelling solvation is a fundamental problem that impacts all membrane-protein structure prediction and design . Current energy functions used in modelling and design incorporate simplified solvation models [8] . For instance , RosettaMP uses information inferred from water-to-cyclohexane partitioning [9] as a proxy for amino acid solvation in the plasma membrane [10 , 11] . Due to these simplifications , expert analysis has been a prerequisite for accurate membrane-protein modelling and design [12 , 13] . Automating modelling and design processes and extending them to complex membrane proteins will likely require an accurate energy function that correctly balances intra-protein interactions , membrane solvation and water solvation [14–16] . To understand the contributions to membrane-protein solvation , we recently established a high-throughput experimental screen , called deep sequencing TOXCAT-β-lactamase ( dsTβL ) , which quantified apparent amino acid transfer energies from the cytosol to the E . coli plasma membrane [17] . From the resulting data , we inferred apparent position-specific insertion profiles for each amino acid relative to alanine , reconciling previously conflicting lines of evidence [18] . Foremost , the lipophilicity inferred for hydrophobic residues , such as Leu , Ile , and Phe , was greater than previously measured in some membrane mimics , including the water-to-cyclohexane transfer energies that are the basis for membrane solvation in Rosetta [9 , 11 , 16] ( approximately 2 kcal/mol according to dsTβL compared to ½ kcal/mol ) , and in line with theoretical considerations [19 , 20] . Second , the profiles exhibited a strong 2 kcal/mol preference for Arg and Lys in the intracellular side of the plasma membrane compared to the extracellular side . While this preference , known as the “positive-inside” rule , was revealed based on sequence analysis 30 years ago [21–23] , the dsTβL assay was the first to indicate a large energy gap favouring positively charged residues in the intracellular relative to the extracellular membrane leaflet . The accuracy and generality of the dsTβL apparent transfer energies were partly verified by demonstrating that they correctly predicted the locations and orientations of membrane spans directly from sequence even in several large and complex eukaryotic transporters [24] . Taken together , these results provided reassurance that the dsTβL apparent insertion energies correctly balanced essential aspects of membrane-protein solvation . As the next step towards accurate all-atom membrane-protein modelling and design , we develop a new lipophilicity-based energy term based on the dsTβL amino acid-specific insertion profiles and integrate this energy term in the Rosetta centroid-level and all-atom potentials . We furthermore develop a strategy to enhance conformational sampling of membrane-spanning helical segments and of helix-tilt angles observed in naturally occurring membrane proteins . Encouragingly , the new energy function outperforms previous ones in three benchmarks essential to modelling and design: atomistic ab initio structure prediction starting from completely extended chains of single-spanning membrane homo-oligomers of known structure , prediction of mutational effects on stability , and sequence recovery in combinatorial sequence design . An automated web server for structure prediction in transmembrane homooligomeric proteins ( TMHOP ) is available at ( http://tmhop . weizmann . ac . il ) and may enable modelling of the membrane-spanning domains of receptors . We conclude that the combination of lipophilicity and energetics developed for soluble proteins provides a basis for accurate structure prediction and design of membrane proteins .
The recent all-atom energy function in Rosetta , ref2015 , is dominated by physics-based terms , including van der Waals packing , hydrogen bonding , electrostatics and water solvation [25] . This energy function was parameterized on a large set of crystallographic structures and experimental data of water-soluble proteins and was shown to outperform previous energy functions in several structure-prediction benchmarks . For membrane-protein modelling and design , however , the ref2015 solvation potential is relevant only to the water-embedded regions of the protein; a different potential is required to model the energetics of amino acids near and within different regions of the plasma membrane . Accordingly , we sought to replace the ref2015 solvation model with one that encodes a gradual transition from the default water-solvation that evaluates regions distant from the plasma membrane and the dsTβL insertion profiles near and within the plasma membrane . The dsTβL profiles were inferred from an experimental mutation analysis of a monomeric membrane span into which each of the 20 amino acids were individually introduced at each position [17]; the profiles were then normalized to express the apparent transfer energy for each amino acid at each position relative to a theoretical poly-Ala membrane span , yielding apparent ΔΔGAla—>mut at each position across the plasma membrane ( Fig 1 ) . As a first step to encoding these energy profiles in Rosetta , we smoothed these profiles and symmetrised them with respect to the presumed membrane midplane , except the profiles for Arg , His , and Lys , for which the “positive-inside” rule applies ( S1 Fig ) . Next , we implemented an iterative strategy to encode the dsTβL energetics in a modified ref2015 all-atom energy function which we called ref2015_memb . To enable efficient conformational search as required in ab initio structure prediction and de novo design , we also encoded this energetics in the centroid-level energy function [26] . As a reference state in both all-atom and centroid-level modelling , we generated an ideal poly-Ala α helix and placed it perpendicular to the membrane plane . At each position along the helix ( including the aqueous and membrane phases ) , we introduced each of the 19 point mutations , relaxed the models using the all-atom or centroid-level energy function , and computed the energy difference due to each single-point mutation ΔΔGAla—>mut . In the first iteration of these calculations , the unmodified ref2015 or centroid-level energy functions were used , resulting , as expected , in large deviations from the apparent energies observed in the dsTβL profiles ( red lines in Fig 1 ) . We then added a new context-dependent 1-body energy term , called MPResidueLipophilicity , which encoded the difference between the computed and dsTβL energies for each mutation at each position , ΔΔΔGAla—>mut . We iterated mutation , relaxation , energy calculations , and MPResidueLipophilicity updates for each of the mutations at each position up to ten times , noting that the computed energies converged with the trends observed in the experiment ( blue and red lines in Fig 1 , respectively ) . Scripts for calibrating the all-atom and centroid energy functions are available at github . com/Fleishman-Lab/membrane_protein_energy_function to enable adapting future improvements of the Rosetta energy functions to encode the dsTβL energetics . The dsTβL apparent energy profiles were inferred from a monomeric segment [17] . Consequently , the profiles express the lipophilicity of each amino acid relative to Ala across the membrane when that amino acid is maximally solvent-exposed . To account for amino acid burial in multispan or oligomeric membrane proteins , we derived a continuous , differentiable and easily computable weighting term that expresses the extent of a residue’s burial in other protein segments . For any amino acid , this weighting term is based on the number of heavy-atom neighbours within 6 and 12 Å distance of the amino acid’s Cβ atom ( Eq 1 ) resulting in a weight that expresses the extent to which a residue is buried in other protein segments or exposed to solvent ( 0 to 1 , respectively ) . Water-embedded and completely buried positions are treated with the ref2015 solvation energy; fully membrane-exposed positions are treated with the MPResidueLipophilicity energy , and positions of intermediate exposure are treated with a linearly weighted sum of the two terms . In summary , the actual contribution from solvation of an amino acid is a function of its exposure to the membrane and depends on the amino acid’s lipophilicity according to the dsTβL apparent energy and the position’s location relative to the membrane midplane . Note that this energy term averages lipophilicity contributions in the plasma membrane and does not express atomic contributions to solvation that are likely to be important in calculating membrane-protein energetics in different types of biological membranes [11 , 27] , in non-helical membrane-exposed segments , or surrounding water-filled cavities [28] . The dsTβL assay reports on residue-specific insertion into the plasma membrane . Ab initio modelling and de novo design , however , also require a potential that addresses the protein backbone solvation . Although the low-dielectric environment in the core of the membrane enforces a strong tendency for forming canonical α helices [7] , deviations from canonical α helicity can make important contributions to membrane-protein structure and function [29] . Therefore , we encoded an energy term , called MPHelicality , that allows sampling backbone dihedral angles and penalises deviations from α helicity ( Eq 5 ) . MPHelicality enforces strong constraints on the dihedral angles in the lipid-exposed surfaces at the core of the membrane and is attenuated in regions that are buried in other protein segments and in the extra-membrane environment ( using the same weighting as for lipophilicity , Eq 1 ) ; this term thus allows significant deviations from α helicity only in buried or water-embedded regions . In preliminary ab initio calculations starting from a fully extended chain , we noticed that conformational sampling significantly favoured large helical tilt angles relative to the membrane normal ( θ in Fig 2 ) . By contrast , 50% of naturally observed membrane spans exhibit small tilt angles in the range 15–30° . The skew in conformational sampling towards large tilt angles is expected from previous theoretical investigations according to which the distribution of helix-tilt angles in random sampling is proportional to sin ( θ ) , substantially preferring large angles compared to the distribution observed in natural membrane proteins [30] . To eliminate this skew in conformational sampling , we introduced another energy term , called MPSpanAngle ( Eq 4 and Fig 2 ) , that strongly penalised large tilt angles , guiding ab initio sampling to tilt angles observed in natural proteins . In summary , ref2015_memb encodes three new energy terms relative to the soluble energy function ref2015: ( 1 ) a lipophilicity term based on amino acid type , membrane-depth , and burial; ( 2 ) a penalty on deviations from α helicity in backbone-dihedral angles; and ( 3 ) a penalty on the sampling of large tilt angles with respect to the membrane-normal ( S1 Table ) . In the calculations reported below , the penalties on deviations from α helicity and helix-tilt angles are implemented in all centroid-level ab initio structure prediction simulations; all-atom calculations use the ref2015 energy modified with the lipophilicity term ( ref2015_memb ) . Previous structure-prediction benchmarks started from canonical α helices or from monomers obtained from experimental structures of homodimers and used the bound-structures in grid search or rigid-body docking [11 , 16 , 31–34] . Additionally , structure-prediction studies used experimental constraints , conservation analysis or correlated-mutation analysis to predict residue contacts in order to constrain conformational sampling [12 , 13 , 35–40] . Several automated predictors dedicated to single-span dimers used shape complementarity [41 , 42] , sequence-packing motifs [43] or comparative modelling [44] , but to the best of our knowledge , ab initio modelling calculations , starting from a fully extended chain , have not been described . Given that deviations from canonical α helicity make important contributions to membrane-protein structure and function [29] , we decided to apply a more stringent test using ab initio modelling , sampling all symmetric backbone , sidechain , and rigid-body degrees of freedom . To test ab initio modelling using the new energy function , we applied the fold and dock protocol [45] , which has been successfully applied in a variety of soluble-protein structure prediction and design studies [46–49] . Briefly , fold and dock starts from an extended chain and conducts several hundred iterations of symmetric centroid-level backbone-fragment insertion and relaxation moves . It then applies symmetric all-atom refinement including all dihedral sidechain and backbone degrees of freedom ( S1 Movie ) . To generate an energy landscape , we ran 5 , 000 independent trajectories ( 50 , 000 for high-order oligomers ) for every 19 and 21 residue subsequence of each homooligomer , filtered the resulting models according to energy and structure parameters ( Methods ) , and isolated the lowest-energy 10% of the models . Models were then clustered according to their energies and conformations , and five cluster representatives were compared to the experimental structures ( Figs 3 and 4 , Table 1 ) . For comparison , we applied the described methodology using ref2015_memb , ref2015 and the current membrane-protein energy function in Rosetta , RosettaMP [11] . The Protein Data Bank ( PDB ) contains 17 nonredundant ( sequence identity <80% ) NMR and X-ray crystallographic structures ( adopted from Lomize et al . [44] ) of natural single-span homodimers , two tetramers and one pentameric structure . Of the 20 cases in the benchmark , fold-and-dock simulations using ref2015_memb predicted near-native ( <2 . 5 Å root-mean-square deviation [RMSD] ) low-energy models for 14 homooligomers compared to nine using RosettaMP; of the 14 oligomers accurately predicted by ref2015_memb , the soluble energy function ref2015 also resulted in nine correct predictions . Prediction success rate using ref2015_memb was somewhat higher for right-handed relative to left-handed homodimers ( 80 and 50% , respectively; S2 Table ) , reflecting the tendency of right-handed homodimers to be more tightly packed [31] , and in 11 cases , a near-native prediction was found among the top 3 lowest-energy predicted models ( Fig 3 ) . Of the three high-order oligomers tested , ref2015_memb successfully recapitulated the structures of the M2 tetramer and phospholamban pentamer . The PREDDIMER [42] and TMDIM [43] structure-prediction web servers , which do not use ab initio modelling , found models at <2 . 5 Å RMSD for nine and eight of the 17 homodimers , respectively . Thus , ab initio calculations using ref2015_memb accurately predict structures in two-thirds of the homooligomers in our benchmark , including high-order oligomers that cannot be predicted by other automated methods . Given the high success rate of the ab initio calculations , we developed a web-accessible server for predicting the structures of membrane-spanning homo-oligomers such as are observed in receptor tyrosine kinases and other membrane proteins ( http://tmhop . weizmann . ac . il ) . The successfully predicted homooligomers exhibit different structural packing motifs . The majority of the homodimer interfaces are mediated by the ubiquitous Gly-xxx-Gly motif [50] , in which two small amino acids separated by three positions on the primary sequence enable close packing between the helices . There is uncertainty whether these motifs additionally form stabilising Cα hydrogen bonds [51 , 52] . Our structure-prediction analysis cannot resolve this uncertainty; note , however , that the new energy function ref2015_memb does not encode terms for Cα hydrogen bonds and yet recapitulates a large fraction of the homodimer structures ( Figs 3 and 4 , and Table 1 ) . The underlying reason for successful prediction is that the dsTβL energetics encodes a strong penalty on exposing Gly residues to the lipid bilayer ( approximately 2 kcal/mol/Gly at the membrane mid-plane; Fig 1 ) , driving the burial of Gly amino acids within the homodimer interface ( i . e . , “solvophobicity” ) . Thus , lipophilicity and interfacial residue packing are sufficient for accurate structure prediction in a large fraction of the targets we examined . In several single-spanning membrane receptors , conformational change in the membrane domain is thought to underlie receptor activation . For instance , past modelling of the ErbB2 membrane domain suggested two non-overlapping interaction sites involving two small-xxx-small motifs within the membrane domain and a molecular switching mechanism that underlies receptor activation [54] . The only experimental structure for ErbB2 involves the N-terminal small-xxx-small motif [55] , which is recapitulated by the second predicted cluster ( Fig 5A ) , whereas in the fourth predicted cluster , dimerisation is mediated via the C-terminal motif ( Fig 5B ) , suggesting that in some cases , TMHOP may provide structural hypotheses for alternative binding modes for receptor homooligomeric domains . Using the dsTβL assay , we also examined the effects of dozens of point mutations in glycophorin A on apparent association energy ( ΔΔGbinding ) in the bacterial plasma membrane [17] . As a stringent test of the new energy function , we conducted fold-and-dock calculations using both ref2015_memb and RosettaMP starting from the sequences of each of the point mutants . To reduce uncertainty in interpreting the experimental results , we focused on 32 mutations that exhibited large apparent energy changes in the experiment ( |ΔΔGbinding| ≥ 2 kcal/mol ) and compared the median computed ΔΔGbinding of the lowest-energy models to the experimental observation ( Fig 6 , S3 Table ) . ref2015_memb outperformed RosettaMP , correctly assigning 81% of mutations as stabilizing or destabilizing compared to 66% for RosettaMP . The six false-positive predictions using ref2015_memb are due to mutations at position Gly86 , which is exposed to the membrane , explaining why our simulations predict these mutations to be neutral or favourable . Note that as observed in studies of mutational effects on stability in soluble proteins , the correlation coefficient between computed and observed values is low ( Pearson r2 = 0 . 21 and 0 . 02 for ref2015_memb and RosettaMP , respectively ) [56–59] . Such low correlation coefficients provide an impetus for improving the energy function; however , as we previously demonstrated , discriminating stabilizing from destabilizing mutations is sufficient to enable the design of accurate , stable , and functionally efficient proteins [59–64] . We next tested sequence-recovery rates using combinatorial sequence optimisation based on ref2015 , ref2015_memb , and RosettaMP in a benchmark of 20 non-redundant structures ( <80% sequence identity ) ranging in size from 124–765 amino acids [65] . ref2015_memb outperformed the other energy functions , exhibiting 83% sequence recovery , on average , when each design was compared to the target’s natural homologs ( Table 2 ) . To our surprise , the soluble energy function ref2015 outperformed RosettaMP in this test and was almost as successful as ref2015_memb ( 78% overall success ) , implying that the packing and electrostatic models of ref2015 [25] enabled at least some of the improvement observed in sequence recovery by ref2015_memb ( see S1 Table for a comparison of the energy functions ) . High sequence recovery in both buried and exposed positions implies that ref2015_memb may be applied effectively to design large and complex membrane proteins .
An accurate energy function is a prerequisite for automated modelling and design , and solvation makes a critical contribution to protein structure and function . The recent dsTβL apparent energies of insertion into the plasma membrane [17] enabled us to derive an empirical lipophilicity-based energy function for Rosetta . The results demonstrate that ref2015_memb outperforms RosettaMP in three benchmarks that are important for structure prediction and design . As ref2015_memb is based on the current state-of-the-art water-soluble Rosetta energy function , prediction accuracy is high for ref2015_memb both in soluble regions and in the core of the membrane domain . Thus , the lipophilicity preferences inferred from the dsTβL energetics together with the residue packing calculations in Rosetta enable accurate modelling in several ab initio prediction cases . The current energy function and the fold and dock procedure accurately model homooligomeric interactions in the membrane and the effects of point mutations , suggesting that they may enable the accurate design of homooligomeric single-span receptor-like transmembrane domains . The high accuracy models generated by the TMHOP method also suggest that laborious and often failed experiments to determine the structures of homooligomeric receptor membrane domains may be circumvented through ab initio modelling . Nevertheless , certain important attributes of membrane-protein energetics are not yet addressed by ref2015_memb; for instance , atomic-level solvation and the impact on electrostatic interactions due to changes in the dielectric constant in various parts of the membrane are currently not treated [16 , 28] and warrant further research . Furthermore , the dsTβL profiles are based on measurements conducted on α-helical proteins in E . coli inner membranes . Ref2015_memb may , therefore , not perform as well in outer-membrane proteins or in proteins residing in membranes with a substantially different lipid composition . The benchmark reported here provides a basis on which improvements in the energy function can be verified . Furthermore , structure prediction in heterooligomers is important for understanding receptor cross-activation and for the design of membrane inhibitors [1] . In preliminary calculations , however , we found that fold and dock simulations of heterooligomeric systems fail to converge due to the much larger conformation space open to a non-symmetric system . A potentially exciting extension of the current work is to use the information on preferred crossing angles between membrane helices to constrain conformational sampling in heterooligomers [66 , 67] . We recently showed that evolution-guided atomistic design calculations , which use phylogenetic analysis to guide the design processes [68] , enabled the automated , accurate and effective design of large and topologically complex soluble proteins . Designed proteins exhibited atomic accuracy , high expression levels , stability [59 , 60 , 69] , binding affinity , specificity [64] , and catalytic efficiency [62 , 63] . Membrane proteins are typically large and challenging targets for conventional protein-engineering and design methods . Looking ahead , we anticipate that evolution-guided atomistic design using ref2015_memb may enable reliable design in this important but often formidable class of proteins .
All code is available in the Rosetta release at www . rosettacommons . org ( git version: b210d6d5a0c21208f4f874f62b2909f926379c0f ) . Command lines and RosettaScripts [70] are available in the supplement . The original dsTβL insertion profiles [17] were modified to generate smooth and symmetric functions [24] . The polar and charged residues Asp , Glu , Gln and Asn , which exhibited few counts in the deep sequencing analysis , were averaged such that the insertion energy at the membrane core ( -10 to 10 Å; negative values correspond to the inner membrane leaflet and positive values to the outer leaflet ) was applied uniformly to the entire membrane span . The profile for His was capped at the maximal value observed in the experiment ( 2 . 3 kcal/mol ) between 0Å ( membrane midplane ) and 20 Å . The dsTβL profile for Cys is unusually asymmetric . Cys residues are rare in membrane proteins [71] and are likely to have similar polarity to Ser . We , therefore , applied the profile measured for Ser to Cys . To convert the values from the dsTβL insertion profiles to Rosetta energy units ( R . e . u . ) they were multiplied by 2 . 94 following the interpolation reported in ref . [25] . The dsTβL profiles spanned 27 positions , and we correspondingly translated them to span -20 to +20 Å relative to the membrane midplane . The context-dependent , one-body energy term MPResidueLipophilicity was implemented to encode the dsTβL insertion profiles in ref2015 . Starting from an ideal poly Ala α helix embedded perpendicular to a virtual membrane , every position was mutated to all 19 identities , relaxed , and the energy difference between the ref2015 energy and the dsTβL energy was implemented in MPResidueLipophilicity . This process was repeated ten times to reach convergence , and the resulting energy profiles were fitted by a cubic spline [72] , generating continuous , differentiable functions for all 19 amino acids relative to Ala , which was assumed to be 0 throughout the membrane . The splines were recorded in the Rosetta database and are loaded at runtime . Insertion -profile adjustments were done using a python3 script available at github . com/Fleishman-Lab/membrane_protein_energy_function . The number of protein atoms within 6 and 12 Å of each amino acid’s Cβ atom is computed and transformed to a burial score ( Eq 1 ) . We used sigmoid functions which range from 0 to 1 , corresponding to completely buried and completely lipid-exposed , respectively . Where N is the number of heavy atoms and S and O determine the slope and offset of the sigmoids and are different for all-atom and centroid calculations . Each parameter has different thresholds at 6 or 12 Å . For all-atom calculations , S = 0 . 15 and 0 . 5 and O = 20 and 475 , for 6 and 12 Å radii , respectively . For centroid-level calculations , S = 0 . 15 and 5 and O = 20 and 220 for 6 and 12Å radii , respectively . For each amino acid , the product of the 6 and 12Å sigmoid functions is taken , producing a continuous , differentiable function that transitions from buried to exposed states . These parameters were determined by visualising the burial scores of all amino acids in several polytopic membrane proteins of known structure . All membrane-spanning helices reported in the PDBTM [73] dataset ( version 20170210 ) were analyzed for their tilt angles with respect to the membrane normal . A second-degree polynomial was fitted to this distribution using scikit-learn [74] . As Bowie noted , the expected distribution function of helix-tilt angles is sin ( Θ ) [30] . We , therefore , used a partition function to convert the expected distribution ( sin ( Θ ) ) and observed one ( Eq 2 ) to energy functions , finally subtracting the expected energy from the observed one to derive the helix-tilt penalty function: penalty=− ( ln ( −2 . 36×10−4×θ2+0 . 01095×θ+0 . 0202 ) −ln ( sin ( θ ) ) ) ( 3 ) Where θ is given in degrees . In order to simplify runtime calculations , we approximated Eq 3 using a third-degree polynomial ( using scikit-learn ) ( Fig 2 ) . The MPHelicality energy term penalizes the energy of every position that exhibits ϕ-ψ torsion angles significantly different from ideal α helices . A paraboloid function was manually calibrated to express a penalty for any given ( ϕ , ψ ) . The paraboloid centre , for which the penalty is 0 , was set to the centre of the helical region according to the Ramachandran plot ( ϕ = 60° , ψ = 45° ) [75] . The paraboloid curvature was set to 25 , such that the penalty is low throughout the ϕ-ψ torsion angles space observed for α helices [75] . As segments buried against the protein should not be penalized to the same extent as those completely exposed to the membrane , the burial approximation of Eq 1 is used to weight MPHelicality . Moreover , as the protein extends outside of the membrane , the penalty is attenuated with a function that follows the trend observed for the hydrophobic residues , Leu , Ile , and Phe ( see Fig 1A ) . In effect , the MPHelicality term favours α helicity in lipid-exposed surfaces in the core of the membrane , thereby enforcing some of the electrostatic and solvophobic effects that are essential for correctly modelling the backbone but are not expressed in the residue-specific dsTβL energy profiles . Where ϕ and ψ are given in degrees , z is the distance from the membrane midplane of residue i , and burial is calculated as in Eq 1 . 17 structures of single-span homodimers , two homotetramers and one pentamer were selected from the PDB ( S2 Table ) . For each structure , a 20–30 residue segment comprising the membrane-spanning domain was manually chosen . A sliding window then extracted all 19 or 21 residue subsequences . For each subsequence , three and nine residue backbone fragments were generated using the Rosetta fragment picker application [76] . The fold-and-dock protocol [45] was used to compute 5000 models ( 50 , 000 models for tetramers and the phospholamban pentamer ) , and the lowest-energy 10% of the models were subsequently filtered using structure and energy-based filters ( solvent accessible surface area >500 Å; shape complementarity [77] Sc>0 . 5; ΔΔGbinding <-5 R . e . u . ; rotameric binding strain [78] < 4 R . e . u . ; helicality <0 . 1 R . e . u . ( computed using Eq 5 ) ; and closest distance between the interacting helices < 9 Å , as calculated by the filter HelixHelixAngle ) . For each target , the filtered models from all subsequences were then pooled together and clustered using a score-wise clustering algorithm . This is an iterative process , where each iteration calculates the RMSD of all unclustered models to the best-energy model , and removes the ones closer than 4 Å . RMSD to NMR structures were calculated with respect to the first model in the PDB entry . Glycophorin A mutants that exhibited |ΔΔGbinding| > 2 kcal/mol according to the dsTβL study [17] were modelled using the same fold-and-dock protocol described for the structure prediction of homodimers . To reduce computational load , we used a single sequence ( 73-ITLIIFGVMAGVIGTILLI-91 ) , and the median of computed ΔΔGbinding for the top models was reported ( models were filtered using structure and energy based filters; solvent accessible surface area > 600 Å , packing > 0 . 4 , shape complementarity > 0 . 5 , ΔΔGbinding < -10 R . e . u . , binding strain < 4 R . e . u . MPHelicality < 0 . 1 , minimal atomic distance between helices < 4 . 5 Å and minimal distance between helix vectors < 8 Å . Of these models , only the top 10% scoring models were used ) . 20 structures of polytopic membrane-spanning proteins were taken from ref . [65] , 11 of which were symmetric complexes . All were refined ( eliminating sidechain conformation information before refinement ) , and for each protein , 100 designs were computed using combinatorial sequence design followed by sidechain and backbone minimization , and the lowest-energy 10 designs were checked for the fraction of mutations relative to the target protein . For each target protein , a multiple-sequence alignment was prepared: homologous sequences were automatically collected using BLASTP [79] on the nonredundant sequence database [80] with a maximal number of targets set to 3 , 000 and an e-value ≤ 10−4 . All sequences were clustered using CD-hit [81] with a 90% sequence identity threshold . Sequences were then aligned using MUSCLE [82] with default parameters . A position-specific scoring matrix ( PSSM ) was calculated using PSI-BLAST [83] . In the sequence-recovery benchmark , where homologous sequences are considered , the substitution of a given position to an identity with a PSSM score ≥ 0 is considered a match . | Membrane proteins comprise a third of the genome and have essential roles as intermediaries between the cell and its environment . Despite exciting recent progress in membrane-protein modelling and design , however , these fields lag far behind advances in soluble proteins , chiefly because of inaccurate modelling of the membrane environment . Recently , our lab developed an assay , called dsTβL , that used high-throughput experimental screening to infer the energetics of each amino acid across the bacterial plasma membrane . Here , we encode the dsTβL energetics in the Rosetta software suite for biomolecular modelling and design and subject the energy function to three structure prediction and design benchmarks . The new energy function consistently outperforms the previous Rosetta membrane energy function . Additionally , ab initio structure prediction of homooligomeric membrane proteins results in accurate predictions in ⅔ of the examples in our benchmark . Therefore , we present a web server , called TMHOP , to compute the structures of single-pass homooligomeric membrane proteins directly from sequence . The results suggest that the automated design of large and complex membrane proteins is within reach . |
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Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals . Here , we identify epidemiological traits of self-limited infections ( i . e . infections with an effective reproduction number satisfying ) that correlate with transmissibility . Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases . Our approach provides insight into a variety of scenarios , including the transmission of Middle East Respiratory Syndrome Coronavirus ( MERS-CoV ) in the Arabian peninsula , measles in North America , pre-eradication smallpox in Europe , and human monkeypox in the Democratic Republic of the Congo . When applied to chain size data for MERS-CoV transmission before 2014 , our method indicates that despite an apparent trend towards improved control , there is not enough statistical evidence to indicate that has declined with time . Meanwhile , chain size data for measles in the United States and Canada reveal statistically significant geographic variation in , suggesting that the timing and coverage of national vaccination programs , as well as contact tracing procedures , may shape the size distribution of observed infection clusters . Infection source data for smallpox suggests that primary cases transmitted more than secondary cases , and provides a quantitative assessment of the effectiveness of control interventions . Human monkeypox , on the other hand , does not show evidence of differential transmission between animals in contact with humans , primary cases , or secondary cases , which assuages the concern that social mixing can amplify transmission by secondary cases . Lastly , we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination . Our studies lay the foundation for future investigations regarding how infection source , vaccination status or other putative transmissibility traits may affect self-limited transmission .
Many infections only occur as isolated cases , short chains of transmission , or as small infection clusters ( i . e . intertwined transmission chains ) . Examples include zoonotic infections with relatively weak human-to-human transmission as well as vaccine-preventable infections in settings of high vaccination coverage [1]–[7] . Even though transmission is limited , these diseases are an important public health concern . For example , zoonotic infections can adapt for increased human-to-human transmission and then cause greater or even pandemic spread [8]–[10] . In addition , decreased voluntary vaccination , difficulty with vaccine delivery or changes in vaccine efficacy can allow growth of the number of individuals susceptible to preventable diseases and thus cause larger outbreaks [3] , [11] . Self-limited ( or subcritical ) transmission also characterizes diseases that are on the brink of elimination such as smallpox during its worldwide eradication campaign or polio today [12]–[14] . Despite a need to monitor disease burden , manage the risk of disease emergence or enhance disease elimination , the surveillance and control of subcritical infections can be challenging . Resource-poor countries , which are home to many zoonoses , have many logistical hurdles that impact the quality of surveillance and control interventions . Meanwhile , even in developed countries , reactive control strategies such as isolation protocols for vaccine-preventable diseases have significant sociological impact beyond the immediate financial costs . Because of these challenges , the overarching goal is to optimize control interventions for the least amount of effort and expense . It is therefore important to gain as much quantitative information about disease transmission as possible from existing surveillance data . This includes monitoring how transmission varies with time , location and other epidemiological characteristics of individual cases . By improving the understanding of mechanisms of disease transmission , finer tuning within the spectrum of intervention strategies becomes possible [15] , [16] . Such mechanistic understanding can guide the response to a diverse range of threats that include emerging infections ( e . g . , Middle East respiratory syndrome coronavirus ) , vaccine-preventable infections ( e . g . , measles ) and antibiotic resistance [17] , [18] . For ethical and logistical reasons , population-level studies of infectious disease transmission in humans typically involve retrospective statistical analysis rather than controlled prospective experimentation . Given this constraint , one approach for evaluating mechanisms underlying transmission patterns is to compare the transmissibility of two distinct , but related populations . In this manuscript , we demonstrate how the strength and heterogeneity of transmission can be compared for two different populations or types of infection sources . We then show how our framework provides insight into the transmission patterns of a variety of subcritical diseases . This analysis builds upon earlier studies that were limited to estimating transmission parameters from chain size distributions and addressing issues of surveillance bias [19] , [20] . Mathematically , the transmissibility of a group of infected individuals can be quantified by determining the group's effective reproduction number , . This number represents the mean number of secondary cases caused by an infected case . However , because of the stochastic nature of disease transmission , the realized numbers of secondary infections caused by a given infected individual will vary . is a more general parameter than the oft cited basic reproduction number , which more specifically represents the mean number of secondary cases caused by the first infected case in a completely susceptible population [21] . When , transmission cannot reach epidemic proportions , whereas if there is a potential for epidemic spread . Thus , our focus on subcritical diseases implies that , overall , will be less than one and transmission will be characterized by self-limited clusters of infection . However , our method still permits the possibility that cases can be divided into two groups in which one group has a , and the other group has a . Our study builds upon the prior success of inferring from the size distribution of observed transmission chains [1] , [2] , [22] . The same distributions can also be used to infer the degree of transmission heterogeneity , represented by the dispersion parameter , [19] , [20] , [23] . A high degree of heterogeneity represents a scenario where some individuals are predisposed to spreading infection to a larger number of people ( i . e . , ‘superspreaders’ ) . When models of chain size distributions incorporate both and , excellent agreement can often be found between observed data and model predictions [19] , [20] , [23] . Our goal is to evaluate specific hypotheses regarding disease transmission by testing whether and differ between two groups of cases . Our analyses differ from more traditional epidemiological approaches based on case-control studies ( and many other study designs ) in that we focus on transmissibility instead of individual-level risk factors for disease susceptibility . We demonstrate our methodology by considering four subcritical infections ( MERS-CoV , measles , monkeypox and smallpox ) and three types of data ( size distribution of infection clusters , transmission chain data and infection source classification ) to answer four different questions based on published data . For MERS-CoV , we use chain size distributions to determine whether an apparent decrease in during the latter half of 2013 was statistically significant . Assessing temporal trends of has important implications for evaluating the risk of endemic MERS-CoV transmission and the impact of control interventions . For measles , we use chain size distributions to compare two locations ( United States and Canada ) and test whether there is a significant difference in , which would suggest important differences in vaccine distribution , social connectedness , and/or demographics . For smallpox and monkeypox , we use case series resolved by infection generation to determine whether there are significant differences between the first and subsequent generations of spread [24] , [25] . This analysis allows us to assess whether variation in the number of contacts or the timing of control interventions can be linked to changes in . It also allows us to test the validity of a specific ‘random network’ model that relates the contact patterns of primary and secondary cases . We then test whether there is a significant difference between inferred transmission parameters for animal-to-human and human-to-human transmission of monkeypox , which provides insight into the mechanisms of zoonotic spillover . Our analysis of chain size distributions also provides perspective on the surveillance required to detect a change in , such as the expected increase in human monkeypox transmission following the eradication of smallpox . Each of the scenarios considered represents a unique example of how quantitative characterization of transmissibility can provide insight into the effectiveness of control interventions and risk assessment for future spread .
The stochastic nature of infectious disease transmission is particularly important when , as it can result in substantial variation in the size distribution of transmission chains . In this case it is helpful to model transmission as a branching process [26] . In this formulation , the offspring distribution specifies the probability that an infected individual will cause new infections . We specify the corresponding offspring probabilities to be , with . To facilitate likelihood calculations ( as seen below ) , the offspring distribution can be represented as a generating function , , in which the polynomial coefficients are the offspring probabilities [26]–[28] . In line with research demonstrating how the strength and variability of transmission can be modeled [23] , we assume the qi's follow a negative binomial offspring distribution with a mean of and a dispersion parameter of . The dispersion parameter represents the degree of transmission heterogeneity , with lower values of corresponding to higher variance . The supplementary methods ( Text S1 ) explains how our simple model of disease transmission can be used to calculate the likelihood for various types of observed data . These likelihood calculations permit inference of the strength and variability of transmission for individual cases , in terms of and . All calculations were conducted with either Matlab or R . Code for all analyses is available at: https://github . com/sbfnk/nbbpchainsizes . By calculating the likelihood of an observed set of transmission events , we can probe whether there is statistical support for differences in transmission between two pre-specified populations , and . In our general model , the two types of individuals have distinct negative binomial characterizations and thus there are four parameters in total . We label these four parameters , , and with the subscripts corresponding to the type of individual . Five simpler models that are nested within the 4-parameter model can be constructed by assuming , and/or ( Figure 1 ) . The specific test case of is chosen for the nested models because this corresponds to a geometric offspring distribution which is the expectation for a traditional SIR or SEIR model . These models assume homogenous mixing with constant infectivity over an exponentially distributed infectious period [29] . For each model , we determine the parameter values ( MLE ) that maximize the log-likelihood . The 95% confidence intervals and confidence regions shown in the figures were found by profiling on and/or and employing the likelihood ratio test [30] . Model comparison is accomplished via the Akaike Information criterion ( AIC ) [31] . To identify whether there is statistical support for a difference in for two data sets , the AIC scores were computed for all six aforementioned models . A difference in was deemed statistically significant according to the rule that the model with the best AIC score cannot be within two AIC units of a model that supports identical values of for the two sets of simulations . This rule is in approximate alignment with the commonly used likelihood ratio test for establishing statistical support for the use of an extra parameter with 95% confidence , but we could not employ the likelihood ratio test explicitly because some pairs of models we consider are not nested . We verified the internal consistency of our modeling framework by applying this method to simulated data ( Supplementary material , Text S1 ) . We used parametric bootstrapping to evaluate the type I error and the power for detecting a change in for our analyses . Specifically , for every analysis we simulated 20 , 000 new data sets . Each simulated data set replicated the two populations involved in the analyses ( e . g . MERS-CoV chains before and after June 1 , 2013 ) . Two models were simulated . Half of the simulations used two distinct values of and that matched the inferred values of our unrestricted four-parameter model . The other half of the simulations used a single value of and that matched the inferred values of our two-parameter model , which requires both and to be the same for all cases seen in the observed data . Our inferential algorithm for ascertaining a statistically significant difference in the inferred value of was then applied to all simulations . The type I error of an analysis ( i . e . the probability that the analysis would falsely claim that is different for the two types of cases considered ) was estimated as the proportion of simulations based on the two-parameter model that were found to have a statistically significant difference in for the two types of cases . The parametric bootstrap probability ( or power ) of detecting a change in was estimated as the proportion of simulations based on the four-parameter model that were found to have significant difference in for the two types of cases .
Since 2011 , there have been over 500 confirmed cases of MERS-CoV , and over 140 associated deaths , suggesting a case fatality rate of 28% [32] . The persistent occurrence of small outbreaks is due to zoonotic spillover [33]–[35] . MERS-CoV may be a new virus , as the most recent common ancestor of viral samples from infected patients was estimated to have occurred after September 2010 [34] . The novelty of this virus and its high case fatality rate underscore the significance of monitoring the transmission of MERS-CoV . Although human-to-human transmission has been relatively limited so far , with likely less than one , there is concern that future adaptation that could lead to spread similar to sudden acute respiratory syndrome ( SARS ) in 2003 . Health authorities have prudently instituted a variety of infection control policies and procedures and a trend towards decreasing has been reported [34] . Since verification of the effectiveness of control has important implications , we reconsidered the evidence for a trend towards decreasing . To avoid artifacts of assembling multiple data sources , we restricted our analysis to the previously reported chain size distribution for all MERS-CoV cases in the Arabian Peninsula occurring before August 8 , 2013 [34] . Previous analysis of these data shows that is 0 . 74 ( 95% CI 0 . 53–1 . 03 ) before June 1 , 2013 and 0 . 32 ( 95% CI 0 . 14–0 . 65 ) after June 1 , 2013 . Our results replicate the finding that independent evaluation of cases before and after June 1 , 2013 results in an estimate of 0 . 7 and 0 . 3 for respectively ( Figure 2 and Table 1 ) . When our six models are compared , we do not find statistical support for models with different values of before and after June 1 , 2013 . This is again consistent with the results of prior studies that determined a p-value of 0 . 07 for change in , but our analysis allows the possibility of a high degree of transmission heterogeneity . Local elimination of measles is dependent on vaccination programs , and the potential for re-emergence necessitates continued surveillance and re-assessment of vaccination strategy [1] , [3] , [36]–[38] . Even where elimination has been achieved , there can be sporadic clusters of infection due to a combination of geographic importation and pockets of susceptibility [39]–[41] . Geographical differences in transmission may arise due to differences in cultural practices , public health guidelines , population density and other factors . Methods that delineate whether differences in are statistically significant for two different regions can therefore help to identify key differences in transmission potential and thus pinpoint opportunities for improved control . Measles data in the United States ( 1997–1999 ) and Canada ( 1998–2001 ) are reported according to the size of infection clusters [39] , [40] . Most infection clusters have a single primary infection , but even when multiple primary infections exist ( as in the case of a cluster with six cases in the United States ) , the likelihood calculation needed for assessing differences in is straightforward ( Supplementary Material , Text S1 ) . When the two data sets are compared , the results indicate that for the United States and Canada are significantly different ( Figure 3 and Table 2 ) . Meanwhile , the results also confirm previous studies that infer a high degree of transmission heterogeneity in measles transmission [19] , [23] . This can be seen from Table 2 since the MLE estimates for and are less than one and the value of the model with is large . On the other hand , there is negligible statistical support for distinct values of in the two countries . The type I error for this situation was estimated to be 4 . 9% by parametric bootstrapping . Smallpox is the only human disease to have been eradicated and thus represents a tremendously successful use of control [12] . During the endgame of smallpox eradication in the middle of the 20th century , smallpox cases in Europe resulted in rapid implementation of quarantine and control procedures . Transmission data for smallpox infections in Europe that occurred during this period provide an opportunity to investigate how control interventions impacted the transmissibility of primary cases caused by geographic importation relative to secondary cases resulting from local transmission [12] . Smallpox clusters were tabulated according to the number of cases in each generation of spread [12] . The inference results indicate that secondary cases transmitted significantly less than primary cases ( seen by the lack of overlap of contours with the grey line in Figure 4 and by the statistical selection of the non-restricted model in Table 3 ) . In fact , the effectiveness of control procedures can be quantified by looking at the ratio of reproduction numbers for primary and secondary transmission ( Figure 4 inset ) . The ratio of the maximum likelihood values for to suggests that control reduced by 75% . Meanwhile , for both primary and secondary transmission , a high degree of transmission heterogeneity is evident ( since the MLE estimates of and are substantially less than one and the value of the model is large ) . Based on selection of the unrestricted model , and the associated estimates of , there appears to be significantly more heterogeneity of disease transmission for secondary cases than for primary cases . The type I error for this analysis was estimated to be 5 . 1% by parametric bootstrapping . Following the eradication of smallpox in 1979 , the World Health Organization was concerned that subsequent cessation of smallpox vaccination would allow other diseases to flourish [42] . Monkeypox was of particular concern because exposure to smallpox or smallpox vaccination provided protection against monkeypox . Estimates of , extrapolated from contact tracing data gathered during rigorous surveillance in the Democratic Republic of Congo ( formerly Zaire ) during 1981–1984 , provided re-assurance that endemic transmission would not be sustainable even when population immunity to monkeypox waned [43] . The initial analysis of monkeypox transmission did not quantitatively compare the transmission of primary cases ( i . e . those caused by animal-to-human transmission ) to the transmission of secondary cases ( i . e . those caused by human-to-human transmission ) . Since the characteristics of these cases differ ( i . e . only primary cases required exposure to infected animals ) , differences in transmission are possible . Increased transmission of secondary cases could also arise from population structure [25] , or evolutionary adaptation [8] , [10] . For example , network models have proposed that social structure impacts the effective reproduction number of individual cases [44]–[48] . In particular , the random network model that we have considered ( Supplementary material , Text S1 ) predicts that secondary cases transmit more than primary cases since highly-connected individuals are most likely to both acquire and spread infection . If this aspect of the random network model is accurate , the risk of endemic spread as population immunity wanes may be higher than previously expected . This is because for secondary transmission would be expected to increase more than for primary transmission . It is thus important to ascertain whether there is a difference between primary and secondary transmission that is consistent with the random network hypothesis . As part of the monkeypox surveillance efforts , transmission was tabulated according to the number of cases in each generation of spread [43] , [49] . These data can be used to ascertain whether there is a statistically significant difference in primary versus secondary transmission ( Figure 5 and Table 4 ) . The results indicate a lack of evidence for a difference between the of primary and secondary cases ( seen by noting the overlap of contours with the grey line in Figure 5 and because the preferred model in Table 4 has ) . The low values for the maximum likelihood estimates of are consistent with previous studies that infer a high degree of transmission heterogeneity in monkeypox transmission [20] , [23] . Animal-to-human transmission of monkeypox is an important contributor to overall disease burden . Determining the factors that allow continual introduction of monkeypox into human populations requires knowledge of how monkeypox maintains itself in reservoir hosts and the mechanisms that allow its transmission to humans [6] , [50] . In this section we assess whether an infected animal in contact with humans has a distinct set of inferred transmission parameters than infected humans . The relationship between infection source and transmissibility is an active area of research for many multi-host diseases systems [51]–[55] , particularly for zoonotic infections . Since the infection cluster data for monkeypox contains information on how many primary infections are in each cluster , it can be used to infer the amount of animal-to-human transmission that occurs when infected animals make contact with humans . To accomplish this , we assume that the negative binomial offspring distribution that has been shown to be a good description of human-to-human transmission [23] is also an effective model of animal-to-human transmission . We let represent the average number of primary cases caused by an infected animal that has contact with humans . Our results indicate that the for human-to-human transmission is similar to ( Figure 6 and Table 5 ) . There is also evidence that animal-to-human transmission is relatively homogeneous ( since the for the preferred model ) . If one takes the MLEs of and for the preferred model at face value , then we estimate that at least one infection occurs 25% of the time that a infected animal has contact with humans . Recently , a 20-fold increase in the incidence of monkeypox has been reported in the Democratic Republic of Congo [56] , and there is concern that for monkeypox may have increased . The lack of cross-protective immunity to monkeypox from either smallpox vaccination or natural exposure to smallpox provides a mechanism for why would increase [57] . However , land-use changes that impact the potential for animal-human transmission have also been suggested as a cause of an increase in monkeypox incidence [58] , [59] , and could do so without changing . There are no active interventions in place for monkeypox , so it is important to determine if has changed in order to understand the source of increased incidence . Due to logistical barriers and the rare nature of the disease , acquiring data on monkeypox is a challenge [42] , [56] . In the wake of smallpox eradication , the infrastructure for monkeypox surveillance in 1980–1984 was strong and well funded [42] . The detailed transmission data from this surveillance effort provide an estimate of 0 . 30 for ( 95% CI: 0 . 21–0 . 42 ) and 0 . 33 for ( 95% CI: 0 . 17–0 . 75 ) [20] . For the 2005–2007 surveillance effort , specific data on cluster sizes and individual-level transmission are unavailable , so an assessment of cannot be made . However , we can quantify the amount of data that would be needed in order to detect a change in relative to 1980–1984 [42] , [43] , [49] . Simulations show that 200 clusters would provide 70% power to detect an increase in from 0 . 3 to 0 . 5 ( Figure 7A ) . As the number of observations increase , smaller changes are more readily noticeable . Consideration of the relationship between , the number of chains and the number of cases provides perspective on the power of the recent surveillance efforts ( 2005–2007 ) to detect a change in [56] . It appears that there is 95% power to detect an increase in from 0 . 3 to 0 . 55 with analysis of the 760 observed cases ( Figure 7B ) .
To reduce the burden of MERS-CoV and reduce the risk of global spread , effective control procedures are of obvious importance . Given the large amount of resources and effort that have already been directed towards the control of MERS-CoV , it would be reassuring to see a statistically significant decrease in . When analyzing data on MERS-CoV cases that presented before Aug 8 , 2013 , the unrestricted model had the best score . This unrestricted model suggested that because decreased from 0 . 7 to 0 . 3 , control is over 50% effective . However , there is not enough data to show statistical significance for this result . Meanwhile , our analysis is likely biased by the large outbreak that initiated the observational period for the data , so further studies are needed to more accurately evaluate the impact of control interventions [60] . Unfortunately , the number of recent confirmed MERS-CoV cases remains significant and the overall incidence may be increasing [32] . An increase in the number of cases can be caused by an increased , an increased rate of primary cases , or a combination of these effects [61] . Based on our observation that is more likely to be decreasing after June 2013 than increasing , the paradigm of emergence that is most consistent with the previously published data we have analyzed is that MERS-CoV incidence may be increasing in its non-human reservoir , but that human-to-human transmission remains stable . In fact , sequence data support the possibility of an expanding epidemic in animal hosts of MERS-CoV that could lead to an increased incidence of primary cases [34] . However , other factors , such as seasonal drivers of transmission could also impact the temporal trend of . An increased case load could also be observed if transmission patterns have not changed much , but greater interest in and knowledge of MERS-CoV has led to improved surveillance . This could paradoxically lead to both an increase in the number of observed cases and a decrease in the observed value of because of a greater chance of seeing a larger proportion of smaller outbreaks [19] , [62] . Given the relative paucity of cases and uncertainties regarding case observation probability , it would be inappropriate to make a definitive statement concerning the cause of the apparent increase in MERS-CoV incidence at this time . However , as more data on MERS-CoV are reported , the types of analyses presented in this manuscript can be rapidly applied to address hypothesis-driven questions concerning the temporal trends of incidence and the impact of control intervention . In particular there may be concerns that certain subgroups of MERS-CoV cases may have increased transmission , such as those occurring in health care settings where nosocomial transmission is higher or in geographic regions where control interventions are harder to implement . Alternatively , as we have shown with smallpox , there may be a difference in the transmissibility of primary cases versus secondary cases . With more data , our method can help to quantify differences in transmission , and evaluate whether certain population subgroups may have an that exceeds the critical value of one . While it is not necessary for future data to be resolved to the level of individual transmission events , the types of analyses we have presented do require knowledge of chain size distributions rather than aggregate epidemic curve data . Meanwhile , an important gap in the currently available data is a quantitative assessment of the case reporting probability for MERS-CoV cases and whether this is increasing with time . Improved knowledge of the reporting probability would permit adjustments to the likelihood calculations and reduce the bias of imperfect case ascertainment [19] . Our comparison of measles transmission in the United States and Canada provides a framework for elucidating geographic differences in transmission ( Figure 3 ) . Interestingly , while our analysis supported a difference in between the two countries , a difference in the degree of transmission heterogeneity ( as quantified by the dispersion parameter ) was not identified . This apparent disassociation between the strength of transmissibility and the mechanisms of transmission heterogeneity may occur if the heterogeneity is due to intrinsic biological processes such as variability in viral shedding . However , the relationship between the value of dispersion parameter and various mechanisms of transmission heterogeneity is not straightforward so the interpretation of similar values of dispersion is unclear . There are many reasons why the value of may differ between the United States and Canada . One consideration is a potential difference in the timing of the introduction of two-dose vaccination . The Advisory Committee on Immunization Practices and the American Academy of Pediatrics recommended two-dose coverage in 1989 [63] . Although the coverage in 2004 appeared similar between the United States and Canada [38] , it is unclear whether this level of coverage was achieved at the same time in both countries . To assess whether a difference in vaccine coverage explains the difference in observed here , it would be helpful to run a similar analysis on more recent data . Other factors that could contribute to the difference in include a greater tendency in the United States to conduct contact tracing for susceptible cases and vaccinate close contacts , a greater sensitivity in Canada for reporting milder cases of measles , or greater difficulty of detecting isolated cases via passive surveillance in Canada [37] , [38] . More detailed information of the impact of contact investigation , stratification of cases based on disease severity , and quantitative comparison of case ascertainment in passive versus active surveillance would provide additional insight . Smallpox control is already known to have been very effective; however , our analysis of smallpox transmission in Europe around the time of eradication quantifies the impact of interventions for control ( Figure 4 ) showing that there was a reduction of for secondary cases by 75% compared to primary cases . This effect of control may be an underestimate because it does not account for the possibility of late arrival of imported cases during the course of infection . Since the infectious period of imported primary cases may have occurred outside of the country of residence , the actual for primary cases might be higher than seen in the data and thus the effect of control may be even greater than our estimates indicate . Here we have shown how for each generation can be quantitatively compared , using published transmission data . Our analysis of differences in the transmissibility of cases as an outbreak develops is not unique ( see for example [64] ) . However , previously published methods rely on symptom-onset data to determine at various stages of an outbreak and thus these approaches could not be performed on the smallpox data set . Aside from the change in , the marked increase in degree of transmission heterogeneity for secondary cases ( as evidenced by a decreased in the observed value of ) suggests that control tended to be individual-specific rather than population-wide . Here , individual-specific control refers to an intervention that is completely effective for 75% of cases but not effective at all for the remaining cases , whereas population-wide control refers to an intervention that reduces the transmissibility of each case by 75% [23] . For individual-specific control , a large number of cases become dead ends for infection so the observed degree of heterogeneity increases [19] , [20] . In contrast , the observed degree of transmission ( as quantified by the dispersion parameter ) would not change for population-wide intervention . The support for individual-specific control is highly consistent with the quarantine and ring vaccination methods employed during smallpox elimination efforts [12] . These observations show how understanding the variation in both the strength and heterogeneity of transmission can provide insight into disease dynamics . Our analysis of monkeypox in the Democratic Republic of Congo demonstrates how our method can be used to inform surveillance planning . In particular , by determining the number of chains that needed to be observed in order to detect various degrees of change in , we provide perspective regarding the extent to which the 760 monkeypox cases observed between 2005 and 2007 [56] can provide enough information to detect increased transmissibility ( Figure 7 ) . Based on our power analysis , it appears that a change in due to declining population immunity should be detectable , since is expected to approach [43] . However , this result needs to be interpreted in context because our model assumes that the probability of case observation is high and that distinct infection clusters can be determined . Given the logistical challenges of recent surveillance efforts [56] , these assumptions are unlikely to have been met , so the realized power for detecting a change in is probably lower . Nevertheless , this simulation analysis provides perspective concerning the trade-offs of thoroughness in detecting and characterizing cases versus observing cases within a greater catchment area for any future surveillance efforts for which measurement of is of interest . When we focused on more detailed generation-level data for monkeypox transmission from 1980–1984 , we found no support for enhancement of by highly-connected individuals in secondary generations ( Figure 5 ) . This suggests that the high degree of transmission heterogeneity may be caused by biological factors , rather than variability in social contact . However , a key assumption of the network model we tested is that primary cases are infected at random relative to their degree ( as might reasonably be expected for a zoonotic infection ) . It may be that high-connected individuals are also more likely to get a primary infection . If this were the case , then highly connected individuals would contribute to heterogeneity of both primary and secondary transmission . Meanwhile , the lack of increased for secondary transmission provides assurance that significant viral adaptation is not occurring , although local depletion of susceptible individuals within small sub-networks such as households could obscure signals of viral adaptation . We found that humans and animals in contact with humans produce similar numbers of human cases ( Figure 6 ) . Moreover , we estimated that 25% of human exposure to an infected animal lead to at least one detected human case . While the truncated negative binomial distribution produces unbiased estimates of transmission parameters , the confidence intervals can be quite large [19] . Furthermore , the a priori specification that the offspring distribution will be characterized by negative binomial distribution is a strong assumption . Thus the inferred proportion of animal-to-human exposures leading to infection deserves cautious interpretation . Nevertheless , this type of analysis could be useful for informing surveillance and detection efforts in wildlife species . In particular , since the overall incidence of monkeypox is quite low ( 14 . 42 per 10 , 000 per year [56] ) , the observation that there may be only 4-fold more infected animals in contact with humans than the number of observed infection clusters provides perspective on the fact that monkeypox virus has only been isolated from one wild animal ( as of 2011 ) [58] . If contacts with infected animals account for a small proportion of overall human contact with reservoir species , the use of targeted-surveillance strategies that can exploit spatial-temporal data to identify likely hotspots of incidence [58] , [59] , [65] may be essential to improve detection efforts in wildlife hosts . As with any model selection or measurement scheme , a small portion of the data , or even a single data point , can have a particularly large influence . For example , the largest transmission chain in the Canadian measles data consists of 155 cases while the second largest chain has just 30 cases . Moreover , the chain with 155 cases was associated with a religious community that resisted immunization , thus it could be argued that this chain is not representative of the population as a whole . If the 155-case chain were excluded from the analysis , our method would no longer find statistical support for a difference in between the United States and Canada ( Supplementary material , Text S1 ) . However , rather than excluding a possible outlier , our preference is to treat the data at face value . From a modeling perspective , it is often unclear whether the mechanism responsible for a purported outlier is absent in the rest of the data . For example , in the case of Canadian measles data set , the second largest chain of 30 cases was also associated with a religious community . In addition , a particularly large chain does not represent a single large transmission event , but rather an entire group of individuals who collectively had relatively high transmission . Mathematically , a high degree of transmission heterogeneity ( represented by low values of ) is expected to have a big tail for the distribution of the number of cases that each case causes [23]; thus , a large transmission event or chain in a set of data will increase the estimated value of , but will also decrease the estimated value of . A lower will be associated with a wider confidence interval for and this would make it harder for our analysis to find a statistically significant difference in [19] , [20] . Thus our modeling framework has a built-in mechanism that compensates for large transmission events and chains that are consequences of intrinsic population-level or individual-level mechanisms of heterogeneity . A key caveat of our analyses is that we have assumed perfect observation of cases . Some surveillance programs , such as measles in the United States , have documented evidence of high case observation [36] . However , this level of case ascertainment cannot be expected of all diseases , particularly those such as MERS-CoV that are quite new . Meanwhile , even meticulously collected data are prone to multiple sources of observation bias due to limited surveillance resources , subclinical infections , laboratory error , or other factors . When the limitations of observation can be quantified , likelihood calculation for observed transmission events can be adjusted appropriately [19] , [20] . The challenge is that the limitations of surveillance systems and case ascertainment are often difficult to quantify . An alternative to explicit correction of observation bias is to simply consider what level of observation bias would impact key results . For example , in our analysis of the difference between animal-to-human and human-to-human transmission of monkeypox , it is quite possible that a number of animal-to-human infections are unobserved — particularly if the resulting primary infection is mild and has no further transmission . When we treated observation of an infection cluster as an all-or-none process with an independent probability , , that each case would activate surveillance ( thus implying many isolated cases would be unobserved ) , our preferred model of transmission remained stable even for a of 0 . 1 ( Supplementary material , Text S1 ) . This provides re-assurance that our methodology is not necessarily sensitive to imperfect observation . However , different data sets or a different type of observation bias could yield less stable results . In our analyses , we have allowed for at most two values of and in a data set rather than permitting additional stratification or a continuous distribution of values . These simplifications are not always valid assumptions . However , modifications to the likelihood calculation can often be made in order to accommodate more complicated data sets so that our framework for detecting a difference in can be utilized . For example , the offspring generating function used for the likelihood calculation can be written in terms of a continuous variable that provides a smooth transition between the extreme limits of classification . In fact this approach has been used to investigate whether there is a temporal trend of measles transmissibility in the United States [61] . Although we have mainly focused on differences in between two populations , our method can also be used to identify whether these populations differ in the observed degree of heterogeneity . Clustering of individuals with higher transmissibility may favor models with two distinct values for whenever two distinct values of are observed . Meanwhile , situations that would favor a model with two distinct values of and one value of could arise if different mechanisms of control were used to maintain below a given threshold , as seen in the smallpox example . Regardless of which model is the preferred model for a given data set , the estimated or assigned value of can be useful to assess the overall degree of transmission heterogeneity and the likely presence of super-spreaders [20] , [23] . On the other hand , the specific mechanism of heterogeneity ( e . g . differences in transmission potential among cases versus clustering of susceptible individuals ) cannot be ascertained from estimation of alone . Our analysis is focused on determining whether there is statistical support for a difference in for individuals having a specific trait . Also , as exemplified by our direct comparison to the random network model ( Figure 5 and Table 4 ) , we can evaluate specific models of transmission . However , in the absence of a mechanistically derived model , our analysis cannot identify the cause of differences in . For-example , population-level factors favoring transmission ( e . g . increased human density ) cannot be directly distinguished from biological factors ( e . g . evolutionary adaptation ) . Furthermore , the decrease in secondary transmission due to local depletion of susceptibles cannot be directly distinguished from decreases due to control mechanisms . Instead , our method needs to be considered as a tool that can identify differences in transmission ( e . g . temporal trends for MERS-CoV , and geographic distinctions in measles ) or quantify changes in transmission that are expected to occur ( e . g . decreased transmission due to quarantine of smallpox cases or ring vaccination ) . By addressing diverse questions within varied data sets , we have demonstrated that a set of inter-related models within a branching process framework allows rigorous statistical assessment of whether particular characteristics of infectious cases impact transmission potential . We have focused on subcritical diseases , in large part because the type of surveillance data gathered for these diseases is most compatible with our computational approach . For MERS-CoV , we evaluated the possibility of a temporal trend towards decreasing that may indicate stronger control , but did not find enough statistical evidence to confirm this finding . For measles , we found evidence of geographic variability that provides potential insight into the effectiveness of surveillance and public health interventions . For smallpox , we identified signatures of effective control by comparing primary and secondary transmission . For monkeypox , we found that the most parsimonious models are ones that incorporate a high degree of transmission heterogeneity , but do not differentiate between animal-to-human transmission , transmission of primary cases , and transmission of secondary cases . In general , the statistical support we observed for models that allow flexible inference of both and reinforces the importance of quantifying both the strength and variability of disease transmissibility . By providing a diverse array of applications and analyses , the method we have demonstrated can increase the value of existing surveillance data and improve strategies for future data collection . Through identifying specific risk factors for transmissibility and by assessing different sources of transmission heterogeneity , we hope that disease monitoring and control interventions can become more targeted and thus more effective . | The goal of this paper is to identify epidemiological factors that correlate with either an increased or decreased risk of transmitting a particular disease . We are particularly interested in identifying such factors for diseases that are self-limited ( meaning that infections tend to occur in isolated clusters ) , because targeted control of these diseases can facilitate public health goals for minimizing the risk of disease emergence or promoting disease elimination . For example , we show that there is a significant difference in the transmission of measles between the United States and Canada . In contrast , we find that an observed decrease in the transmission of Middle East respiratory syndrome coronavirus during the latter half of 2013 cannot be ascertained with sufficient confidence . We then quantify the degree to which control was effective in eradicating smallpox in Europe . We also consider how the transmission of monkeypox in humans depends on whether the infection source is an animal or a human . Finally , we demonstrate how our approach can be used by surveillance programs to detect changes in transmission that may occur over time . |
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In Drosophila melanogaster , as in many animal and plant species , centromere identity is specified epigenetically . In proliferating cells , a centromere-specific histone H3 variant ( CenH3 ) , named Cid in Drosophila and Cenp-A in humans , is a crucial component of the epigenetic centromere mark . Hence , maintenance of the amount and chromosomal location of CenH3 during mitotic proliferation is important . Interestingly , CenH3 may have different roles during meiosis and the onset of embryogenesis . In gametes of Caenorhabditis elegans , and possibly in plants , centromere marking is independent of CenH3 . Moreover , male gamete differentiation in animals often includes global nucleosome for protamine exchange that potentially could remove CenH3 nucleosomes . Here we demonstrate that the control of Cid loading during male meiosis is distinct from the regulation observed during the mitotic cycles of early embryogenesis . But Cid is present in mature sperm . After strong Cid depletion in sperm , paternal centromeres fail to integrate into the gonomeric spindle of the first mitosis , resulting in gynogenetic haploid embryos . Furthermore , after moderate depletion , paternal centromeres are unable to re-acquire normal Cid levels in the next generation . We conclude that Cid in sperm is an essential component of the epigenetic centromere mark on paternal chromosomes and it exerts quantitative control over centromeric Cid levels throughout development . Hence , the amount of Cid that is loaded during each cell cycle appears to be determined primarily by the preexisting centromeric Cid , with little flexibility for compensation of accidental losses .
Many eukaryotes , like humans and Drosophila , have chromosomes with a single regional centromere . Faithful propagation of this centromere during chromosome replication and cell proliferation is crucial . Loss of centromere function or extra centromeres cause aneuploidy . Therefore , the molecular mechanisms that control centromere replication have attracted considerable attention recently ( for reviews see [1]–[4] ) . Importantly , these analyses have indicated that centromere identity in regional centromeres is specified epigenetically . Centromere-specific histone H3 variants ( CenH3s ) are thought to be an essential component of the corresponding epigenetic mark . In humans and Drosophila , the CenH3s have been named CENP-A and Centromere identifier ( Cid ) ( FlyBase accession number FBgn0040477 ) , respectively [5] , [6] . Nucleosomes with these CenH3s instead of other histone H3 variants are stably incorporated exclusively within the centromeric region of the chromosome during unperturbed cell cycle progression . The precise structural details of these special centromeric nucleosomes may vary in different cell cycle phases and organisms ( reviewed in [1] ) . Based on the analysis of stretched chromatin fibres , blocks of chromatin containing CenH3 alternate with blocks that lack it [7] . The molecular mechanisms that control the number and size of these blocks and the centromere region overall are not understood . While the gradual depletion of CenH3 does not appear to have immediate effects [8] , an enforced acute increase in centromeric Cid has been shown to result in severe chromosome missegregation during mitosis [9] . A conceptually simple mechanism that might maintain the centromere during cell proliferation is “template-governed . ” After random distribution of centromeric CenH3 nucleosomes during chromosome replication onto the two sister chromatids , these old nucleosomes may act as a template , allowing the local stoichiometric loading of new CenH3 nucleosomes during each cell cycle . Such a mechanism for maintenance of centromere position and size would lack flexibility for correction of occasional errors . In contrast , “homeostatic” mechanisms controlling the loading of new CenH3s to a target level that is set independently from the actual amount that is already present at the centromere would allow for correction of accidental fluctuations . Elegant experiments in Drosophila have provided clear evidence for template-governed CenH3 loading . Cid-GFP-LacI targeting to lac operator arrays was shown to recruit endogenous Cid that appeared to be maintained independently of Cid-GFP-LacI at least to some extent [10] . On the other hand , recent findings from C . elegans and plants have indicated that centromere maintenance during meiosis and onset of embryogenesis can be mechanistically distinct . Cenp-A nucleosomes are transiently eliminated from chromosomes in the Caenorhabditis elegans germline and not required for subsequent Cenp-A incorporation in nontranscribed regions throughout the holocentric chromosomes [11] , [12] . Although this independence on pre-existing Cenp-A in C . elegans might represent a derived state resulting from the evolution of the holocentric chromosomes , a similar transient absence of centromeric CenH3 has also been described in egg cells of Arabidopsis thaliana [13] , which has regional centromeres . In addition , de novo formation of centromeres can occasionally occur in humans and various experimental systems [14]–[20] . These findings emphasize that in animals , the uncharacterized role of CenH3 in regional centromeres during meiosis and fertilization might not necessarily be the same as during mitotic cell proliferation , where it is both required and sufficient according to the evidence obtained in case of Cid [7] , [10] , [21]–[23] . To address significance , composition , and transgenerational maintenance of epigenetic centromere marking during sexual reproduction in Drosophila melanogaster , we analyzed Cid behavior during spermatogenesis and early embryogenesis . Drosophila spermatogenesis begins at the closed apical end of the testis tube ( Figure 1a ) [24] , [25] . Germline stem cells located there divide asymmetrically . The resulting differentiating daughter cell , the gonioblast , progresses through four mitotic cell cycles with incomplete cytokinesis , and thereby generates a cyst with 16 interconnected spermatocytes . Premeiotic S phase is completed very soon after the last of these four mitotic divisions . Thereafter extensive spermatocyte growth occurs during an extended meiotic G2 phase before progression through the first and second meiotic division . The haploid cell nucleus of postmeiotic spermatids , which remain interconnected within each cyst , is extensively remodeled . Nucleosomes are massively replaced with sperm-specific proteins such as protamines and the genetic material is highly compacted ( 200-fold ) into a needle-shaped sperm head [26] . After complete elongation of the sperm tails , mature sperm is individualized and released in a motile form into the seminal vesicle at the distal end of the testis tube . After fertilization , the sperm nucleus is once more extensively remodeled [27] , [28] . Protamines are rapidly replaced with nucleosomes concomitant with transformation into a round male pronucleus . Thereafter progression through the first S phase occurs . In parallel , female meiosis is completed . After S phase and pronuclear migration , the female pronucleus and the closely apposed male pronucleus enter into the first mitosis by forming a gonomeric spindle [29] . The reformation of daughter nuclei in telophase combines the two parental genomes within the first two daughter nuclei . Subsequent progression through the rapid and synchronous cleavage cycles generates a syncytium because cytokinesis is omitted during early Drosophila embryogenesis . After cellularization of the syncytial blastoderm nuclei at the onset of gastrulation , additional cell proliferation involves progression through cell cycles including cytokinesis . Here we show that Cid survives the radical nucleosome replacement process that accompanies spermatogenesis . Centromeric Cid in sperm also perdures during formation of the male pronucleus after fertilization . Finally , analyses after experimental changes of centromeric Cid levels in sperm demonstrate its crucial role in centromere specification and quantitative maintenance .
In case of epigenetic specification of centromere identity , all essential components of the corresponding mark have to be preserved when the bulk of nucleosomes are replaced with protamines during postmeiotic spermatid differentiation . Otherwise paternal chromosomes could not be propagated after fertilization . Cid , the Drosophila CenH3 , which is essential for centromere maintenance during mitotic proliferation [21] , [22] , was therefore expected to be present in mature sperm if Cid is also crucial for transgenerational centromere maintenance . In earlier attempts Cid was not detected in sperm , but technical problems with antigen accessibility during immunolabeling were suspected [30] . To avoid such problems , we analyzed testis from transgenic cid mutant males that expressed functional Cid-EGFP under control of the normal cid cis-regulatory region instead of endogenous Cid . Specific dot-like EGFP signals were clearly observed in mature cid; cid-EGFP sperm ( Figure 1b , c ) , indicating that Cid is indeed present in sperm . While centromeres are strongly clustered close to the chromocenter in most somatic Drosophila interphase cells , Cid-EGFP dots were found to be predominantly unclustered in mature sperm ( 46% , 42% , and 12% with 4 , 3 , and 2 signals , respectively; n = 24 ) . In contrast to Cid-EGFP , we were unable to detect Cenp-C-EGFP in mature sperm ( Figure 1b , d ) . During earlier stages , Cenp-C-EGFP was readily detectable ( Figure 1b , d ) . For comparison of Cid and Cenp-C changes during spermatogenesis , centromeric EGFP signal intensities observed in S4–6 spermatocytes were set to 1 arbitrary unit in Figure 1c and d . During the S4–6 stages , however , centromeric Cenp-C-EGFP signals were at least as strong as those observed for Cid-EGFP ( unpublished data ) . Our failure to detect Cenp-C-EGFP in mature sperm is therefore not simply a result of limited detection sensitivity . We conclude that centromeric Cenp-C ( FlyBase accession number FBgn0086697 ) is eliminated during sperm head formation . It is either absent or very low in mature sperm . Another centromere protein described in Drosophila apart from Cid and Cenp-C is Cal1 ( FlyBase accession number FBgn0038478 ) [31] . Cal1-EGFP could also not be detected in sperm ( see below ) . Therefore , Cenp-C and Cal1 do not appear to be essential components of the suspected epigenetic centromere mark . To analyse the fate of paternal Cid protein after fertilization , cid; cid-EGFP males were crossed with wild-type females , followed by analyses during the initial cleavage cycles in the resulting embryos . Cid-EGFP signals in up to four discrete spots were readily detected during male pronucleus formation ( Figure 2a–c ) . At metaphase of the first mitosis , Cid-EGFP was present on four pairs of sister centromeres in one of the two chromosome sets within the gonomeric metaphase plate ( Figure 2d ) . Cid-EGFP signals in essentially all of the analyzed paternal pronuclei ( 11 out of 12 ) were also observed when males hemizygous for the cid-EGFP transgene were crossed to wild-type females . If Cid-EGFP signals in paternal pronuclei , however , were to reflect zygotic expression of the paternally inherited transgene after fertilization , at most 50% of the progeny of hemizygous fathers would be expected to display Cid-EGFP at paternal centromeres . We conclude that the Cid protein of mature sperm remains associated with paternal centromeres during chromatin remodeling and male pronucleus formation , followed by equal distribution onto sister centromeres during the first S phase . During metaphase of mitosis 2 , centromeric Cid-EGFP was still detectable but again on only one half of the chromosomes and with reduced intensity ( unpublished data ) . During mitosis 3 , paternal Cid-EGFP was no longer detectable ( Figure 2e ) . Progression through the cleavage stages therefore appears to be accompanied by dilution of the inherited paternal Cid-EGFP during each cell cycle by newly recruited unlabeled Cid from maternally provided stores . In contrast to Cid , but as expected from the absence of Cenp-C in mature sperm described above , we did not detect EGFP signals in early embryos after crossing Cenp-C-EGFP , Cenp-C males with wild-type females ( Figure 2f ) . To evaluate the functional significance of paternal Cid inherited with sperm , we applied deGradFP [32] for Cid protein depletion during spermatogenesis . In deGradFP , depletion of GFP fusion proteins is achieved by expression of a GFP-specific recombinant ubiquitin ligase ( NSlmb-vhhGFP4 ) with the UAS/GAL4 system . For expression of this ubiquitin ligase specifically in late spermatocytes , we generated a topi-GAL4-VP16 driver . Using this driver for deGradFP in cid; cid-EGFP males , we were able to obtain sperm in which EGFP signals were no longer above background ( Figure 3a ) . We assume that some centromeric Cid was still present at least during the preceding meiotic divisions , as these were clearly successful . The resulting Cid-depleted sperm allowed successful fertilization , as evidenced by analyses of embryos collected from crosses of deGradFP cid; cid-EGFP males with control females . Around 90% of progeny developed to the syncytial blastoderm stage , when thousands of nuclei are regularly arranged just below the egg cell membrane . As fertilization is required for the initiation of embryonic development in D . melanogaster , we conclude that fertilization with sperm is still possible after Cid elimination . However , careful cytological analyses of embryos derived from deGradFP cid; cid-EGFP fathers indicated that development after fertilization is not normal . When in control experiments cid; cid-EGFP males without deGradFP were crossed to cid; cid-EGFP females , we observed normal progeny development with centromeric Cid-EGFP signals in both chromosome sets within all of the analyzed gonomeric metaphase plates of mitosis 1 ( Figure 3a; n = 10 ) , as expected . However , when deGradFP was active in the cid; cid-EGFP males that were crossed to cid; cid-EGFP females , one of the two chromosome sets within all of the analyzed gonomeric metaphase plates of mitosis 1 did not display centromeric Cid-EGFP signals ( Figure 3a; n = 9 ) . This indicates that paternal centromeres cannot acquire maternally derived Cid-EGFP after degradation of Cid-EGFP during spermatogenesis . Mitotic figures in anaphase and telophase of mitosis 1 indicated that Cid-EGFP-free paternal chromosomes did not attach normally to the mitotic spindle . Only the Cid-EGFP containing chromatids were oriented towards the spindle poles in all of the analyzed late mitosis 1 figures ( Figure 3a; n = 11 ) . We conclude that Cid elimination from sperm results in the loss of paternal chromosomes during the initial syncytial cycles of early embryogenesis . Gynogenetic haploid embryos obtained from various mutant genotypes ( mh , ms ( 3 ) K81 , Hira ) all progress through 14 instead of the normal 13 syncytial blastoderm cycles before cellularization , and they eventually arrest late in embryogenesis [33] , [34] . The progeny from cid; cid-EGFP fathers with deGradFP expressed these traits as well . First , none of the progeny obtained from these fathers reached the larval stages . We point out that expression of the GFP-specific recombinant ubiquitin ligase ( NSlmb-vhhGFP4 ) with the topi-GAL4-VP16 driver did not affect male fertility when cid function was provided by the endogenous wild-type cid gene instead of the cid-EGFP transgene . The sterility of cid; cid-EGFP fathers with deGradFP therefore does not reflect a Cid-EGFP independent deGradFP effect . Second , compared to progeny derived from wild-type or cid; cid-EGFP fathers without deGradFP , the nuclear density during cellularization was 2-fold higher in embryos obtained from cid; cid-EGFP fathers with deGradFP ( Figure S1 ) . Counting the number of Cid-EGFP dots during mitosis revealed only four pairs of sister centromeres in the large majority ( >90% ) of the syncytial blastoderm embryos obtained from a cross of cid; cid-EGFP males with deGradFP during spermatogenesis and cid; cid-EGFP females ( Figure 3b ) . In contrast , the expected eight pairs of sister centromeres characteristic for the normal diploid karyotype were detected with control fathers lacking deGradFP ( Figure 3b ) . Centromere counting revealed that a minority ( <10% ) of progeny from cid; cid-EGFP fathers with deGradFP contained nuclei with five pairs of sister centromeres with comparable amounts of Cid-EGFP . Such nuclei were often in patches next to regions with nuclei containing four pairs of sister centromeres . Similarly , a minority of embryos fertilized with Cid-depleted sperm displayed a mosaic of nuclear densities during cellularization with patches of wild-type next to patches with 2-fold higher density ( Figure S1 ) , as characteristically observed in near-haploid embryos [35] . While it is not excluded that these near-haploid embryos reflect occasional neocentromere formation or postzygotic centromere restoration by maternal Cid , we favor alternative explanations as discussed below . Our analysis of the consequences of Cid-EGFP degradation during spermatogenesis demonstrates that the paternally contributed Cid protein on centromeres of paternal chromosomes is required for normal function of these centromeres . Evidently , the maternally derived Cid supplies present in early embryos cannot be used for restoration of centromere function on paternal chromosomes contributed by Cid-depleted sperm , at least in the great majority of cases . This finding argues against efficient homeostatic compensation of centromeric Cid losses and supports template-governed regulation where Cid recruitment is strictly dependent on already present centromeric Cid . Therefore , the amount of old Cid nucleosomes partitioned onto the two sister chromatids during chromosome replication might determine the loading of a precisely equivalent amount of new Cid into the centromere during cell cycle progression . Cid recruitment into the centromere occurs during exit from M phase according to our earlier analyses of the syncytial blastoderm cycles [36] . As meiosis includes progression through two consecutive M phases without an intervening S phase , meiotic Cid loading attracted our attention . If new Cid was loaded during both meiotic M phases in amounts precisely equal to the already present centromeric Cid protein , an increase of centromeric Cid levels with each generation had to occur unless compensated by periodic reduction . To analyse meiotic Cid loading , we quantified centromeric EGFP signals during spermatogenesis in cid; cid-EGFP males . Interestingly , this did not reveal any net Cid loading during exit from MI and MII ( Figure 1c ) , suggesting the possibility of compensatory loading during other developmental stages . Indeed , analysis of early spermatocytes revealed net centromeric Cid loading between stage S1 and S4 ( Figure 4a ) —that is , during G2 well after the premeiotic S phase [25] . The expression pattern of Cal1 , a protein required for Cid loading [9] , [37] , appeared to be entirely consistent with the observed meiotic Cid loading pattern . Cal1-EGFP expressed from a transgene under control of the normal cal1 cis-regulatory region in a cal1 null mutant background was detected at centromeres of spermatocytes between S1 and S3 but not during progression through the meiotic divisions ( Figures 4b and S2 and unpublished data ) . Moreover , Cal1 depletion in early spermatocytes by RNAi abolished the increase in Cid-EGFP levels that normally occurred between S1 and S4 ( Figure 4c ) , supporting our conclusion that this Cid-EGFP increase in centromeres of early spermatocytes represents compensatory Cid loading during G2 . Apart from Cid loading during spermatogenesis , we also analyzed the initial phase of embryogenesis when sperm nucleus remodeling occurs concomitant with completion of female meiosis . Given that Cenp-C was found to be no longer present on centromeres of mature sperm ( see above ) and given that this centromere protein provides an essential link between Cid and outer kinetochore components [38] , [39] , loading of maternally derived Cenp-C onto paternal centromeres during the first cell cycle following fertilization was expected . Therefore , we crossed wild-type males to Cenp-C-EGFP; Cenp-C females and analyzed progeny during early embryogenesis in order to evaluate whether centromere loading of maternally derived GFP fusion proteins onto paternal centromeres is detectable . Indeed , maternally derived Cenp-C-EGFP was observed to associate very soon after fertilization with the sperm nucleus ( Figure 5a , b ) . Cenp-C-EGFP spots were already observed in sperm nuclei that had not yet attained a regular round shape . Cenp-C-EGFP spots were also present in the paternal pronucleus during S phase and pronuclear apposition ( Figure 5c ) . Moreover , in the first metaphase , Cenp-C-EGFP was present in paternal centromeres just like in the maternal centromeres ( Figure 5d , e ) . In contrast to Cenp-C , paternal Cid is still present in mature sperm and remains stably associated with paternal centromeres after fertilization , as shown above . Therefore , rapid association of maternally derived Cid before mitosis 1 as in the case of Cenp-C was not necessarily expected . However , in analogous analyses with progeny obtained from cid; cid-EGFP mothers and wild-type fathers , such early association of Cid-EGFP was clearly observed ( Figure 5f–j ) . In contrast to the Cenp-C-EGFP experiments , where signal intensities during metaphase 1 were comparable on maternal and paternal centromeres , this was not the case in the Cid-EGFP experiments . Cid-EGFP signal intensities were clearly weaker in paternal compared to maternal centromeres . While both maternal and paternal centromeres contain exclusively the EGFP-tagged version in the Cenp-C experiments , this is only true for the maternal centromeres in case of the Cid experiments , where the paternal centromeres also contain unlabeled wild-type Cid inherited from the father apart from newly loaded maternally derived Cid-EGFP . We conclude that in addition to the net loading of Cid in G2 spermatocytes described above , the rapid association of maternally derived Cid onto paternal centromeres soon after fertilization might provide additional compensation for the absence of Cid loading during the male meiotic divisions . However , we point out that precise quantification of total centromeric Cid-EGFP levels in early embryos is precluded by various factors ( like sample thickness , high and variable autofluorescence levels ) . Thus , we cannot exclude the possibility that the rapid association of maternal Cid-EGFP with paternal centromeres might be balanced by loss of paternal Cid in early embryos . Similarly , we cannot exclude the occurrence of dynamic Cid-EGFP turnover at centromeres during the stages of spermatogenesis where we have not detected any net loading . By a more detailed quantification of Cid levels during spermatogenesis we addressed yet another aspect of the control of centromeric Cid levels—that is , chromosome-specific variation . Drosophila testis provides a unique advantage for the analysis of chromosome-specific variation of centromeric Cid because of the characteristic segregation of chromosome bivalents into discrete subnuclear territories in late spermatocytes [25] . In principle , an observation of reproducible chromosome-specific differences in centromeric Cid amounts would argue in favor of template-governed control of centromeric Cid levels . Such control would readily propagate distinct chromosome-specific amounts of centromeric Cid . In contrast , homeostatic mechanisms might be expected to equalize occasional fluctuations and keep a uniform level of Cid in all of the centromeres . Therefore , to evaluate whether centromeric Cid amounts vary on different chromosomes , we quantified EGFP signals in individual centromeres of S5/6 spermatocytes in cid; cid-EGFP testis preparations . At the S5/6 stage , DNA staining revealed the three characteristic chromosome territories within the large spermatocyte nucleus . Two of these territories represent the bivalents of chromosome 2 and 3 , respectively . Their DNA labeling is more homogenous than that of the third territory , which is formed by an association of the bivalent of chromosome 4 with the X chromosome and those parts of the Y that are not involved in Y loop formation [25] . The territories with the bivalents of chromosome 2 and 3 both contained two Cid-EGFP spots ( Figure 6a ) . Each spot is known to represent the tightly associated sister centromeres of one homolog [40] . Double labeling with anti-ModC [41] , [42] allowed the identification of the X-Y bivalent ( Figure 6a ) . The X-Y region was observed to be associated with two spots of obviously unequal Cid-EGFP intensity . An additional bright spot was usually observed in close association with a dot of very bright DNA staining near the X-Y region ( Figure 6a ) . This bright Cid-EGFP spot represents the paired centromeres of the small dot-like chromosome 4 bivalent . The characteristic unequal intensity of the two Cid-EGFP spots within the X-Y chromosome territory suggested that either the X or the Y centromere is associated with higher levels of centromeric Cid . To clarify this issue we crossed cid-EGFP into X/0 males . Apart from the paired centromeres of chromosome 4 , the X/0 spermatocytes no longer contained a second bright Cid-EGFP spot ( Figure 6b ) , as characteristically present in normal X/Y spermatocytes ( Figure 6a ) . Therefore , we conclude that the Y centromere contains more Cid than all the other centromeres . A quantification of the Cid-EGFP signals on the different chromosomes revealed that the Y centromere contains ∼2-fold more Cid than the other centromeres . Analyses with Y chromosomes introgressed from different Drosophila strains into the cid; cid-EGFP background indicated that the increased Cid levels on the Y centromere are not strain-specific ( Figure S3 ) . Analogous quantification of Cenp-C revealed that the level of this centromere protein was also ∼2-fold higher on the Y centromere ( Figure 6c ) . To evaluate whether the ∼2-fold higher levels of the centromere proteins Cid and Cenp-C on the Y centromere were accompanied by a corresponding increase in kinetochore components , we analyzed Spc25-EGFP signals . Spc25 is a component of the Ndc80 complex , which represents the major microtubule binding site of the kinetochore . Before the onset of the meiotic divisions , we did not detect dot-like Spc25-EGFP signals . However , during prometaphase of meiosis I , spermatocytes often displayed eight distinct Spc25-EGFP signals , as expected . In such prometaphase I figures , one of the eight signals was always considerably stronger than all the others ( Figure 6d ) . In contrast , in X/0 testis , prometaphase I figures with seven distinct Spc25-EGFP signals did not include such a conspicuously stronger signal ( Figure 6d ) , suggesting that the especially strong Spc25-EGFP signals in X/Y testis represent the Y kinetochore . As predicted by this interpretation , prometaphase II figures in X/Y testis with 4 Spc25-EGFP signals could readily be grouped into two classes: a first class with a conspicuously strong signal , and a second class without such an intensity outlier . In all likelihood , these two classes represent early spermatids that had inherited the Y and the X chromosome , respectively , in the preceding meiosis I . Finally , a quantification of kinetochore signal intensities in mitotic chromosomes released from early syncytial embryos provided a further confirmation that the Y centromere has higher levels of Cid , Cenp-C , and Spc25 ( Figure 6e ) . Thus , the increased levels of centromere and kinetochore proteins on the Y centromere are not a peculiarity of the spermatocyte stages . Moreover , these observations argue against the existence of efficient homeostatic mechanisms that enforce identical Cid amounts on all the different centromeres . For a direct evaluation of the role of centromeric Cid for quantitative maintenance , we generated sperm with either moderately increased or decreased levels of Cid on centromeres and analyzed whether the altered centromeric levels were maintained during development of the next generation . To raise centromeric Cid levels in sperm , we used the UAS/GAL4 system for targeted cid-EGFP overexpression during spermatogenesis . Overexpression was driven in a cid; cid-EGFP background that did not produce any untagged wild-type Cid . Therefore , the accurately quantifiable Cid-EGFP was the only Cid species produced . Concomitantly with UAS-cid-EGFP , we also expressed UAS-cal1 because increased Cid deposition in centromeres was previously found to depend on simultaneous overexpression of cid and cal1 [9] . bam-GAL4-VP16-driven co-expression of UAS-cid-EGFP and UAS-cal1 in cid; cid-EGFP testis resulted in a strong increase in centromeric Cid-EGFP signals in sperm compared to controls lacking the UAS transgenes ( Figure 7a ) . Quantification revealed almost 7-fold higher Cid-EGFP levels after overexpression . Judging from the number and size of the observed Cid-EGFP spots , Cid-EGFP was still primarily confined to the centromeric region . Males with “high Cid-EGFP” sperm as well as control males lacking the UAS transgenes were crossed with cid; cid-EGFP; Cenp-C-Tomato females and progeny was aged to the syncytial blastoderm stage before fixation and quantification of centromeric Cid-EGFP signals in prometa- and metaphase embryos . The total centromeric Cid-EGFP intensity per nucleus was found to be ∼1 . 7-fold higher in embryos generated with high Cid-EGFP sperm compared to embryos generated with control sperm ( Figure 7b ) . Centromeric Cenp-C-Tomato was increased to a comparable extent ( unpublished data ) . Considering that only one half of the centromeres in the embryo are of paternal origin , we conclude that the increased Cid-EGFP levels on paternal centromeres appear to be maintained during progression through the early embryonic cell cycles , although not quantitatively . The level of Cid-EGFP during embryogenesis might not be sufficiently high to support a complete maintenance of the paternally increased centromeric Cid-EGFP levels during postzygotic development . Results from an analysis of the effects of the zygotic cid-EGFP gene dose on centromeric Cid-EGFP levels in wing imaginal disc cells of third instar wandering stage larvae and in spermatocytes of adult males supported the notion that the expression level governed by the normal cid regulatory region is not much higher than what is required for maintenance of physiological centromeric Cid levels . In the absence of endogenous Cid , cells with only one cid-EGFP copy were observed to display centromeric signals that were 40% weaker than those in cells with two cid-EGFP copies ( Figure S4 ) . As limiting cid expression might have prevented complete maintenance of increased Cid levels on paternal centromeres , we also analyzed whether decreased Cid levels on paternal centromeres in sperm are maintained during development of the next generation . Transgenic RNAi allowed a partial Cid depletion during spermatogenesis . Targeted depletion using bam-GAL4-VP16 in combination with a UAS-cidRNAi transgene was achieved in a cid; cid-EGFP background lacking untagged wild-type Cid . Quantification of centromeric signals in sperm indicated that RNAi resulted in a reduction of Cid-EGFP to about 33% of its level in controls lacking the UAS-cidRNAi transgene ( Figure 7c ) . In a second independent experiment , a somewhat lower reduction to about 50% was obtained , and the centromeres of X , Y , and autosomes were found to be affected to a comparable degree ( Figure S5a , b ) . Males producing low Cid-EGFP sperm and control males were crossed with cid; cid-EGFP; Cenp-C-Tomato females , and centromeric Cid-EGFP levels in progeny were determined at the syncytial blastoderm stage . The total centromeric Cid-EGFP intensity per nucleus in the embryos derived from low Cid-EGFP sperm was found to be ∼72% of the intensity observed in the controls ( Figure 7d ) . Considering that only one half of the centromeres are of paternal origin , the reduced Cid-EGFP levels on paternal centromeres appeared to have been quantitatively maintained during progression through the early embryonic cell cycles . To evaluate whether the reduced Cid-EGFP levels were also maintained during subsequent development , we analyzed wing imaginal discs from third instar wandering stage larvae . These measurements revealed that the reduced Cid-EGFP levels were indeed maintained beyond embryogenesis ( Figures 7e and S5c , d ) . Finally , we measured centromeric Cid-EGFP levels in sperm of adult male progeny . As in embryos and imaginal discs , only ∼71% of the control levels were observed in sperm of males fathered by Cid-depleted sperm ( Figure 7f ) . Since chromosome territory formation in spermatocytes is accompanied by conversion of chromocenter-associated centromere clusters into well-separated centromeres , we were able to quantify Cid-EGFP levels in individual centromeres in this special cell type . Because the X and Y chromosomes are of maternal and paternal origin , respectively , only the Y but not the X centromere is expected to have reduced centromeric Cid-EGFP , if the reduction reflects propagation on paternal centromeres after Cid depletion during spermatogenesis in the father . Indeed reduction of Cid-EGFP in paternal sperm was found to result in a significant decrease of Cid-EGFP in the Y but not in the X centromere in two independent experiments ( Figure 7g , and unpublished data ) . In case of chromosome 2 and 3 territories , parental origin could not be assigned to the two signals within a territory . Under the assumption that in control spermatocytes Cid amounts in maternally and paternally derived centromeres of chromosome 2 and 3 are usually equal on average , the results obtained after quantification of centromeric Cid-EGFP signal intensities in major autosome territories were not in accord with the findings concerning the X and Y centromeres . Under this assumption it is expected that the intensity difference between the stronger and weaker centromere signal within a major autosome territory should be greater after reduction in sperm and subsequent propagation of reduced Cid on paternal centromeres in comparison to control spermatocytes . However , the average intensity difference between the two signals of a major autosome territory was not increased after reduction of Cid-EGFP in paternal sperm ( Figure 7g ) . In principle , this result might argue for chromosome-specific differences in the control of centromeric Cid levels on sex chromosomes and autosomes . However , this apparent support for chromosome-specific discrepancies is completely abolished under the following alternative assumption . If centromeric Cid levels in control spermatocytes on average are usually higher on paternal compared to maternal centromeres , our results are clearly consistent with quantitative propagation of centromeric Cid not only on the Y but on all paternal centromeres . According to this alternative assumption , the stronger of the two signals in each major autosome territory within control spermatocytes in general corresponds to the paternal and the weaker to the maternal centromere . After reduction in sperm and subsequent propagation of reduced centromeric Cid , only the paternal ( i . e . , the stronger ) but not the maternal ( i . e . , the weaker ) centromere signals should be decreased . This expectation is borne out by our data ( Figure 7g ) . While statistical analyses did not favor one over the other assumption , we propose that our other findings ( Figures 5 and 6 ) provide support for the second assumption , as discussed below . Our comparison of spermatocytes in males with either two or only one Cid-EGFP gene copies also corroborated the second interpretation . After reduction of the zygotic Cid-EGFP gene dose , centromeric Cid-EGFP was no longer decreased exclusively on the Y centromere ( as after centromeric Cid-EGFP reduction in paternal sperm ) , but equally on both sex chromosomes , and also on all autosomal centromeres ( Figure S4C ) . Based on our analysis of the consequences after reduction of centromeric Cid in sperm , we conclude that centromeric Cid-EGFP is not replenished to normal levels during development of progeny , at least in case of the Y centromere and presumably also on all other centromeres .
Among the known Drosophila centromere proteins ( Cid , Cenp-C , Cal1 ) , only Cid survives the excessive chromatin remodeling that accompanies the compaction of the haploid genome into sperm heads . We demonstrate that this centromeric Cid in sperm is essential for the propagation of the paternal genome in the next generation . When normal oocytes are fertilized with sperm lacking centromeric Cid , paternal chromosomes fail to recruit the maternally provided Cid and cannot generate functional kinetochores during mitosis 1 . As a result , gynogenetic haploid embryos develop . These findings demonstrate that a minimal amount of pre-existing centromeric Cid is required for centromere propagation during cell cycle progression . Moreover , by partial depletion of centromeric Cid in sperm , in combination with precise quantification , we establish that pre-existing centromeric Cid not only functions as a permissive factor but actually exerts quantitative control over centromeric Cid maintenance during cell proliferation . Reduced centromeric Cid levels in sperm are maintained throughout development of the next generation . They are not restored to the normal amount . The presence of CenH3 in sperm has previously been demonstrated in mammals and Xenopus [43]–[45] . Similarly , the absence of Cenp-C in sperm has been observed in Xenopus [45] . A future analysis of the mechanism that selectively maintains all or at least a substantial amount of centromeric CenH3 during the radical chromatin re-organization that accompanies genome compaction into sperm heads will be of interest . The fact that CenH3 nucleosomes are not exchanged for protamines , in contrast to bulk nucleosomes , is crucial , at least in case of Drosophila sperm where centromeric Cid is an essential component of an epigenetic centromere mark for paternal chromosome maintenance in progeny . The demonstration that Cid is indispensible for epigenetic centromere marking in sperm may appear trivial in the light of the clear evidence that Cid is required and sufficient for centromere maintenance during mitotic proliferation [10] , [21] , [22] . However , recent findings in C . elegans [11] , [12] and A . thaliana [13] have indicated that functional gametes do not necessarily require centromeric CenH3 . While the large majority of progeny generated after Cid elimination in sperm are gynogenetic haploid embryos , a fraction appears to have an extra chromosome with normal centromeric Cid levels . We cannot rule out that these near-haploid embryos represent cases where normal Cid amounts have been restored postzygotically on a particular paternal chromosome at the original centromere or at an ectopic location . The successful production of human artificial chromosomes ( HACs ) , for example , is a clear case for de novo CenH3 acquisition and subsequent maintenance [20] . While the alpha-satellite arrays used in HAC production are completely CenH3-free before transfection , the centromeres in Cid-depleted sperm might have residual Cid below the level of detection in our experiments . A partial Cid depletion might also explain the apparently normal chromosome segregation during the two meiotic divisions . These meiotic divisions reduce Cid intensity per spot by a factor of at least four ( Figure 1c ) and thereby in our deGradFP experiments perhaps below our detection limit . Alternatively , it is not excluded that Cid depletion continues after the meiotic divisions in these deGradFP experiments . However , even if the near-haploid embryos were to result from postzygotic restoration after partial or complete Cid elimination in sperm , such centromere restorations would be rare exceptions and not the rule . Since postzygotic replenishment is not even effective after far more moderate Cid reduction in sperm by RNAi , we consider centromere restoration to be an unlikely explanation for the observed near-haploid embryos . Perhaps these embryos arise after missegregation of maternal chromosomes during the first embryonic mitoses because occasionally the lagging paternal chromosomes might affect the function of the gonomeric spindle . Consistent with this interpretation , embryos fathered by Cid-EGFP-depleted sperm often displayed a reduced and irregular nuclear density during the syncytial stages within the anterior region where fertilization occurs ( 33% versus 5% in controls ) . Similarly , polar body morphology in this anterior region was also often abnormal ( 64% versus 20% in controls ) . It appears therefore that the lagging paternal chromosomes somehow cause local cell cycle defects in a considerable fraction of the progeny . The fact that centromeric Cid , after moderate reduction in sperm to 33%–50% of its normal level , is not restored back to normal during development of progeny with normal levels of maternal and zygotic cid expression demonstrates that the pre-existing level of centromeric Cid is a major determinant for quantitative control over centromeric Cid levels during cell cycle progression . Some restoration occurs within one generation according to our data , and Cid on the Y centromere no longer seems to be significantly reduced in spermatocytes of grandsons and great-grandsons of fathers with Cid-depleted sperm ( N . R . and C . F . L . , preliminary observations ) . However , it is clear that the efficiency of this restoration is poor . Starting from sperm , generation of F1 spermatocytes requires more than 2 wk of development , including progression through about 20 or more cell cycles . This is insufficient to replenish centromeric Cid to the normal level . Thus our data clearly support the idea that the Cid nucleosomes , which remain after random partitioning of pre-existing centromeric Cid nucleosomes onto the two sister chromatids during chromosome replication , instruct the local loading of an equivalent amount of new CenH3 nucleosomes during each cell cycle . Accordingly , centromeric Cid nucleosomes might be licensed for loading in a first cell cycle period , followed by actual loading and concomitant license consumption during a later cell cycle period . Overproduction of Cid and its loading factor Cal1 might by-pass the license requirement . Thus , the proposed quantitative dependence of Cid loading on pre-existing amounts is not necessarily incompatible with our finding that a centromeric Cid increase can be induced . Apart from the fact that pre-existing centromeric Cid is critical for quantitative regulation , our overexpression experiments and the effects of cid-GFP transgene dose indicate that the level of cid expression is also a critical factor . We demonstrate that a single copy of this transgene under control of the cid cis-regulatory region ( in a cid mutant background with Cid-EGFP as the only Cid source ) is not sufficient for maintenance of centromeric Cid-EGFP at the level established in the presence of two copies . Therefore , the normal level of cid expression does not seem to be in great excess over what is required for centromere maintenance . Our previous analyses have clearly revealed cell-cycle-dependent control of centromeric Cid deposition [9] , [36] . In syncytial Drosophila embryos , Cid loading occurs during and depends on exit from mitosis . Studies in vertebrates [46]–[49] have similarly suggested that Cid loading in animal cells might generally depend on exit from M phase and that it occurs early in the cell cycle . However , here we demonstrate that cell-cycle coupling of Cid loading is subject to developmental control . Exit from M phase during the meiotic divisions in testis is not accompanied by Cid loading and expression of the loading factor Cal1 . Instead , we observe Cal1-dependent loading during G2 before the onset of the meiotic divisions . Similarly , recent data have suggested that Cid loading in cultured Drosophila cells occurs already during metaphase ( i . e . , before exit from M phase ) [50] . Moreover , observations from plant and fungal cells [51]–[53] have also indicated that the control of CenH3 loading during eukaryotic cell cycle progression is not governed by an invariant universal mechanism . Although presently precluded by background problems , a precise quantitative understanding of Cid loading throughout female gametogenesis would be of great interest . The quantitative control of centromeric Cid during male and female gametogenesis might not be precisely identical and subtly subvert the quantitative control exerted by pre-existing Cid . Our quantification of centromeric Cid on Y , X , and autosomes is clearly consistent with the notion that centromeres are somewhat overloaded during passage through the male germline . This might explain the fact that the Y centromere , which is transmitted exclusively through the male germline , has about 2-fold higher levels of centromeric Cid . Moreover , the X centromere , which is transmitted more frequently through the female germline than any other centromere , seems to have the lowest amount of centromeric Cid . A possible reason for the postulated sex-specific difference in Cid loading might be linked to the fact that paternal centromeres experience exit from meiotic M phase not only in the testis but also again in the egg after fertilization during completion of female meiosis . Indeed we find that maternal Cid associates with paternal centromeres very early after fertilization during completion of the meiotic divisions of the oocyte . Importantly , mathematical analysis ( Text S1 ) demonstrates that if the extent of over- and underloading are equal in the male and female germline , respectively , then a stable difference between Cid levels on paternal and maternal autosomal centromeres is reached within only two generations . Such a difference is also required for compatibility of our quantitative measurements ( Figure 7g ) with the parsimonious interpretation that centromeric Cid levels on autosomes ( where we cannot assign parental origin ) behave in the same way as revealed by our results concerning X and Y ( where parental origin is known ) . Our mathematical analysis also implies that overloading in the male germline will result in a continuous increase of Y-centromeric Cid in the absence of counterbalancing mechanisms . In the case of the Y chromosome , Cid underloading in the female germline will of course not act as a counterbalancing process , but we speculate that the observed limited level of Cid expression might be involved . In addition , the Drosophila Y centromere contains unique telomere-related satellite repeats [54] that may have chromosome-specific effects . Even though centromeres in animals are specified primarily in an epigenetic manner , centromeric and pericentromeric DNA sequences are unlikely to be irrelevant and they have been implicated in meiotic drive and speciation [55] . Some aspects of centromere control that we have defined in Drosophila are presumably not valid or of minor importance in case of humans . In contrast to Drosophila , the Y centromere in human cell lines appears to have the lowest level of centromeric Cenp-A , while the X has average amounts [56] . Cenp-A levels on a given chromosome might vary considerably within the human population and appear to correlate with the size of the alpha-satellite region [57] . While our experiments concur with the notion that limited variation in the level of centromeric Cid is not necessarily detrimental , we also demonstrate that the variation of centromeric Cid on different chromosomes correlates with the amount of recruited kinetochore proteins , as previously found in some [58] , [59] but not all experiments [60] with fungi . Moreover , evidence from human cancer cells has implicated Cenp-A overexpression in chromosome mis-segregation [61] , [62] . Further clarification of the mechanisms that control centromeric CenH3 levels can therefore be expected to provide important insights into evolution of rogue cells , as well as of new species .
Most of the mutant alleles and transgenes used here have been characterized previously . cidT12-1 and cidT22-4 [22] carry premature stop codons . cidG5950 ( Bloomington Drosophila Stock Center #29695 ) has a P element insertion within the coding sequence . Moreover also Cenp-Cprl41 [63] , cal1MB04866 [9] , and Spc25c00064 [38] are known or predicted to abolish the production of gene products that can localize to centromeres . The transgenes P{w+ , gcid-EGFP-cid}III . 2 [36] , P{w+ , giEGFP-Cenp-C}II . 1 [64] , P{w+ , gi2xtdTomato-Cenp-C}II . 3 and III . 1 [65] , P{w+ , gcal1-EGFP}II . 2 [9] , and P{w+ , gSpc25-EGFP} II . 1 [38] have been shown to complement recessive lethal mutations in the corresponding endogenous loci , demonstrating the functionality of the encoded fluorescently tagged centromere and kinetochore proteins . P{w+ , His2Av-mRFP}II . 2 [36] and P{w+ , pUASt-mCherry-nls}III were used for genotype marking in some experiments . P{w+ , pUASt-cal1}III . 1 [9] was used for ectopic cal1 expression . The C ( 1;Y ) , y1 v1 f1 B1: y+/C ( 1 ) RM , y2 su ( wa ) 1 wa stock for generation of X/0 males was kindly provided by Terry Orr-Weaver ( Whitehead Institute for Biomedical Research , Cambridge , MA , USA ) . P{w+ , bamP-GAL4-VP16}III [66] , P{w+ , UASt-NSlmb-vhh-GFP4} III [32] , and P{w+ , Cid-RNAiGD4436}v43857 were kindly provided by D . McKearin , E . Caussinus , and the Vienna Drosophila RNAi Center ( VDRC ) , respectively . The P{w+ , gtopi-GAL4-VP16 }III line was obtained by PhiC31-mediated germline transformation with pattB-topi-GAL4-VP16-topi . In this construct , the cis-regulatory sequences of the spermatocyte-specific gene matotopetli ( topi ) [67] control the production of a Gal4-VP16-Topi fusion protein . The topi cis-regulatory sequences were isolated by enzymatic DNA amplification with the primers NT15 ( 5′-CTTG GGATCC CTCGCAGATCGAATGTCTTG-3′ ) and NT16 ( 5′-CTTC AGATCT TTTCATGGCGCTAGTCCGAT-3′ ) , the GAL4-VP16 sequences with the primers NT17 ( 5′-CGACC AGATCT ATGAAGCTACTGTCTTCTATCG-3′ ) , and NT19 ( 5′-GTTTA GCGGCCGC CCCACCGTACTCGTCAATTC-3′ ) from a bamP-GAL4-VP16 plasmid ( kindly provided by D . McKearin ) , and the topi coding and 3′UTR sequences with NT20 ( 5′-AAGAG GCGGCCGCG ATGAAAGTCAAAGTTTCGGG-3′ ) and NT21 ( 5′-AATTC GCGGCCGC CGCTATCTTGCCGCTTTATTT-3′ ) . The UAS-Cid-EGFP lines were obtained after germline transformation with a pUAST construct where the sequences coding for Cid with an internal EGFP insertion were inserted after enzymatic amplification using pCaSpeR4-gcid-EGFP-cid [36] as a template in combination with the primers NT41 ( 5′-CTTTAA GCGGCCGC TTAAGCAAATACCGAAAATTTG-3′ ) and NT42 ( 5′-GCAAA TCTAGA AACTAAGCCTAAACTTCTCTTTTGG-3′ ) . The UAS-cal1RNAi lines were obtained after PhiC31-mediated germline transformation with a Valium20 [68] construct with an insert generated by annealing the oligonucleotides 5'-ctagcagt ACGAGTGTAGTTGCTGCAATA tagttatattcaagcata TATTGCAGCAACTACACTCGT gcg-3′ and 5′-attcgc ACGAGTGTAGTTGCTGCAATA tatgcttgaatataacta TATTGCAGCAACTACACTCGT actg-3′ . The testis squash preparations for the quantification of EGFP signals at centromeres and kinetochores were made with males of the following genotypes: w*; cidT12-1/cidT22-4; P{w+ , gcid-EGFP-cid}III . 2 ( Figure 1b , c , Figure 4a , Figure 6a ) ; w*; P{w+ , giEGFP-Cenp-C}II . 1; FRT82B Cenp-Cprl41 ( Figure 1b , d , Figure 6c ) ; w*; P{w+ , gSpc25-EGFP}II . 1; Spc25c00064 ( Figure 6d ) ; and w*; P{w+ , gcal1-EGFP}II . 2; cal1MB04866 ( Figure 4b , Figure S2 ) . Males with the first two genotypes were also crossed to w1118 females for the analysis of the transmission of paternal centromere proteins in progeny embryos ( Figure 2 ) . Moreover , females with these genotypes were crossed to w1118 males for the analysis of the association of maternally derived centromere proteins with sperm DNA ( Figure 5 ) . The squash preparations for the quantification of EGFP signals at centromeres and kinetochores of mitotic chromosomes ( Figure 6e ) were made with 1–2-h embryos collected from parents with the first three genotypes . For deGrad Cid-EGFP during spermatogenesis ( Figure 3 ) , we generated w*; cidT12-1/cidG5950 , P{w+ , gcid-EGFP-cid}II . 1; P{w+ , UASt-NSlmb-vhhGFP4}III/P{w+ , gtopi-GAL4-VP16-topi}III , P{w+ , gcid-EGFP-cid}III . 2 males by standard crossing schemes . In parallel , we generated w*; cidT12-1/cidG5950 , P{w+ , gcid-EGFP-cid}II . 1; +/P{w+ , gtopi-GAL4-VP16-topi}III , P{w+ , gcid-EGFP-cid}III . 2 males for control experiments . The males were crossed with w*; cidT12-1/cidT22-4; P{w+ , gcid-EGFP-cid}III . 2 females for analysis of the subsequent generation . For the analysis of X/0 spermatocytes , we used testis isolated from v+ , f+ , B+ males obtained after crossing C ( 1;Y ) , y1 v1 f1 B1: y+ males with either w*; cidT12-1/cidT22-4; P{w+ , gcid-EGFP-cid}III . 2 ( Figure 6b ) or w*; P{w+ , gSpc25-EGFP}II . 1; Spc25c00064 females ( Figure 6d ) . To increase Cid-EGFP levels on sperm centromeres ( Figure 7a , b ) , we generated w*; cidT12-1/cidG5950 , P{w+ , gcid-EGFP-cid}II . 1; P{w+ , pUASt-cal1}III . 1 , P{w+ , pUASt-cid-EGFP-Cid} III . 1/P{w+ , bamP-GAL4-VP16}III , P{w+ , gcid-EGFP-cid}III . 2 males by standard crossing schemes . In parallel , w*; cidT12-1/cidG5950 , P{w+ , gcid-EGFP-cid}II . 1; +/P{w+ , bamP-GAL4-VP16}III , P{w+ , gcid-EGFP-cid}III . 2 males were generated for control experiments . For analysis in the next generation ( Figure 7b ) , the males were crossed to w*; cidG5950 , P{w+ , gcid-EGFP-cid}II . 1/cidG5950 , P{w+ , gi2xtdTomato-Cenp-C}II . 3; P{w+ , gcid-EGFP-cid} III . 2/Cenp-Cprl41 , P{w+ , gi2xtdTomato-Cenp-C}III . 1 females . To decrease Cid-EGFP levels on sperm centromeres ( Figure 7c–g ) , we generated w*; cidT12-1/cidG5950 , P{w+ , gcid-EGFP-cid}II . 1; P{w+ , cid-RNAiGD4436}v43857/P{w+ , bamP-GAL4-VP16}III , P{w+ , gcid-EGFP-cid}III . 2 males . In parallel , w*; cidT12-1/cidG5950 , P{w+ , gcid-EGFP-cid}II . 1; +/P{w+ , bamP-GAL4-VP16}III , P{w+ , gcid-EGFP-cid}III . 2 males were generated for control experiments . For analyses during embryogenesis of the next generation ( Figure 7d ) , the males were crossed to w*; cidG5950 , P{w+ , gcid-EGFP-cid}II . 1/cidG5950 , P{w+ , gi2xtdTomato-Cenp-C}II . 3; P{w+ , gcid-EGFP-cid} III . 2/Cenp-Cprl41 , P{w+ , gi2xtdTomato-Cenp-C}III . 1 females . For analyses with wing imaginal discs of the next generation ( Figure 7e ) , the males were crossed to w*; cidT12-1 , P{w+ , His2Av-mRFP}II . 2/CyO , Dfd-EYFP females . Wing discs of larvae with His2Av-mRFP expression were mounted and imaged [9] . The rest of the larvae was used for further genotype analysis by PCR using primers specific for the bam-GAL4-VP16 transgene and the P insertion in cidG5950 , respectively . The data shown in Figure 7e are from the genotype w*; cidG5950 , P{w+ , gcid-EGFP-cid}II . 1/cidT12-1 , P{w+ , His2Av-mRFP}II . 2; {w+ , bamP-GAL4-VP16}III , P{w+ , gcid-EGFP-cid}III . 2/+ . We point out that this genotype , which does not include the cid-RNAiGD4436 transgene , results from crosses with both the experimental and the control males . The data obtained with this genotype therefore cannot be affected by cid-RNAiGD4436 expression during zygotic development . As shown in Figure S5c , d , data from additional progeny genotypes were fully consistent with the findings made with the genotype displayed in Figure 7e . For analyses with testis of the next generation ( Figure 7f , g ) , the males were crossed to P{w+ , pUASt-mCherry-nls}III females followed by isolation of testis from male progeny with the genotype w*; cidG5950 , P{w+ , gcid-EGFP-cid}II . 1/+; P{Cid-RNAiGD4436}v43857/P{w+ , pUASt-mCherry-nls}III or w*; cidG5950 , P{w+ , gcid-EGFP-cid}II . 1/+; +/P{w+ , pUASt-mCherry-nls}III in case of the control experiments . These testes were characterized by the presence of green centromeric signals and absence of red nuclear signals . Testis squash preparations were made , fixed , and stained essentially as described [69] with the following modifications . After dissection in testis buffer ( 183 mM KCl , 47 mM NaCl , 10 mM Tris-HCl , pH 6 . 8 ) , testes were transferred to a 5 µl drop of phosphate buffered saline ( PBS ) on a poly-l-lysine-treated slide and cut open to spill the contents . The sample was squashed very gently after addition of 15 µl of 4% formaldehyde in PBS under a 22×22 mm siliconized cover slip . Fixation was continued for 6 min . Testes whole mount immunolabeling was done as described [70] with the following modifications . After testis dissection ( see above ) , fixation was done in 4% formaldehyde in PBS for 10 min . Antibody incubations were performed in a humid chamber . For immunolabeling , rabbit antiserum against ModC [41] was diluted 1∶4 , 000 in PBS . Affinity-purified rabbit antibodies against Cenp-C [63] were diluted 1∶5 , 000 . Hybridoma supernatant containing mouse monoclonal antibody eya10H6 ( generated by S . Benzer and N . M . Bonini and kindly provided by the Developmental Studies Hybridoma Bank developed under the auspices of the NICHD and maintained by The University of Iowa , Department of Biology , Iowa City , IA 52242 ) or 38F3 against NopI/Fibrillarin ( Abcam , ab4566 ) was diluted 1∶100 and 1∶300 , respectively , Secondary antibodies were Cy5 or Alexa568-conjugated goat antibodies against rabbit or mouse IgG . The images shown in Figure 1b , Figure 3a , Figure 4a , b , Figure S2 , and Figure 6a , b represent projections of image stacks assembled using Adobe Photoshop . Deconvolution was performed before maximum projection in case of Figure 3a . To reveal the weaker signals in advanced stages of spermatogenesis , increasing adjustment of brightness and contrast was applied to the progressive stages shown in Figure 1b . Therefore , the EGFP signals displayed in Figure 1b do not reflect their quantified intensities ( Figure 1c , d ) . However , to document differences between Cid-EGFP and Cenp-C-EGFP intensities , images were treated equally at a given stage . Concerning X/0 spermatocytes , we point out that the Cid-EGFP signals of the X and the fourth chromosomes were often tightly associated in a single cluster during S5 ( in 65% of the spermatocytes , n = 25 ) . The data displayed in Figure 6b were obtained from spermatocytes with separate X and chromosome 4 signals . For analyses during the very early embryonic stages , eggs were collected for 30 min at 25°C . For analyses during the syncytial blastoderm cycles , eggs were collected for 1 h and aged for an additional hour . For analyses of nuclear densities during cellularization , the embryos were aged for an additional 1 . 5 h . After dechorionation , embryos were fixed and released from the vitelline membrane by shaking in methanol . After DNA staining with Hoechst 33258 ( 1 µg/ml in PBS ) , we mounted the embryos under a coverslip in 70% glycerol , 1% n-propyl gallate , and 0 . 05% p-phenylenediamine . For the preparation of mitotic chromosome spreads , eggs were collected for 1 h and aged for an additional hour . Embryos were dechorionated in 1 . 4% sodium hypochlorite and extensively rinsed with deionized water . After transfer into an Eppendorf tube containing a 1∶1 mixture of heptane and Schneider’s tissue culture medium with 10 µM demecolcine ( Sigma D7385 ) , embryos were incubated on a rotating wheel . In case of the analysis of Cenp-EGFP and Spc25-EGFP , the incubation in demeocolcine was omitted . After 30 min , embryos were transferred to 75 mM KCl in a depression slide and incubated for 10 min . Embryos were then transferred into a 5 µl drop of polyamine buffer [71] on a glass slide and torn apart using fine tungsten needles . A drop of 5 µl of 4% formaldehyde in PBS was added . After addition of a coverslip , the sample was inverted onto a filter paper and squashed for a few seconds to spread the embryos . After a 5-min incubation , the sample was frozen in liquid nitrogen . After flipping away the coverslip , the slide was immediately placed into chilled 100% ethanol and incubated for 10 min at −20°C . Excess ethanol was removed by tapping the slide onto a paper towel . After washing the sample area with PBS for 5 min , DNA staining was performed with 0 . 5 µg/ml Hoechst 33258 in PBS for 10 min . After a 5-min wash in PBS , the sample was mounted under a coverslip in 70% glycerol , 1% n-propyl gallate , and 0 . 05% p-phenylenediamine . Immunostainings of eggs and embryos shown in Figures 2 and 5 was performed as described [72] . Briefly , embryos were dechorionated in bleach , fixed in methanol , and rehydrated in 1× PBS , 0 . 15% Triton X-100 . Embryos were then incubated overnight in the same buffer with rabbit anti-GFP antibody ( Invitrogen ) at a 1∶200 dilution . They were then washed three times in 1× PBS , 0 . 15% Triton X-100 , and incubated overnight in secondary antibody ( AlexaFluor 488 goat anti-rabbit ( Molecular Probes ) at 1∶1 , 000 ) . After an incubation step in a RNAse A solution ( 2 mg/ml in PBS ) for 1 h at 37°C , embryos were mounted in a mounting medium ( DAKO ) containing propidium iodide ( 5 µg/ml ) to stain DNA . Male and female pronuclei at the pronuclear apposition stage and during the first prometaphase were identified based on their position . As previously revealed by immunolabeling using an antibody against actetylated histone H4 , a histone mark that is enriched in paternal chromatin , the female pronucleus ( or the maternal set of chromosomes ) is known to be systematically oriented toward the polar bodies [27] . Accordingly , the first pronucleus encountered along the virtual line from polar bodies to the apposed pronuclei was considered to be the female pronucleus . Quantification of EGFP signals on centromeres and kinetochores was performed after acquiring stacks ( 20–28 sections , 250 nm spacing ) from squashed testis preparations using a 63×/1 . 4 oil immersion objective on a Zeiss Cell Observer HS microscope . Stacks were converted into maximum projections using ImageJ . Signal quantification was performed essentially as described previously [9] with the following modifications . For quantification of centromeric signal intensities during spermatogenesis , all centromeric signals within a cell were surrounded with the free hand tool followed by measurement of area ( As ) and integrated pixel intensity ( Is ) of the selected regions . For subtraction of diffuse signals ( background and GFP signals from any noncentromeric pools ) , the selected region was slightly enlarged yielding Al and Il . Total centromeric signal intensity per cell was then calculated as Is−[As× ( Il−Is ) / ( Al−As ) ] . An analogous subtraction of diffuse signals was performed for quantification of intensities of individual centromeres in spermatocytes where each centromeric spot was surrounded individually . The characteristics of the DNA staining pattern during the S5 spermatocytes stage provided the basis for an assignment of Cid-EGFP signals to different chromosomes . While the centromeres of the two chromosome 4 homologs in the large majority of all S5 spermatocytes analyzed are paired into a single Cid-EGFP spot next to a strongly staining DNA dot , each homolog of all the other chromosomes usually displays a single Cid-EGFP dot . The X centromere Cid-EGFP signal is usually also close to a region of intense DNA labeling , which however is more irregular in shape and not as intense as in case of chromosome 4 . In contrast , the Y centromere is very rarely associated with a region of intense DNA staining presumably as a result of the Y loops present during the S5 stage . Finally , the territories of chromosome 2 and 3 display a far more homogenous DNA staining than the regions with chromosomes X , Y , and 4 . We would like to point out that quantification of centromeric signals obtained after immunofluorescent labeling with anti-Cid , anti-Cenp-C , or anti-GFP resulted in far more noisy data . Moreover , comparison of GFP fluorescence signals and immunofluorescent signals after double labeling of cells expressing only Cid-EGFP or Cenp-C-EGFP with antibodies recognizing these GFP fusions indicated that immunofluorescent signal variability is likely to be caused by problems with antibody accessibility that at least in part also reflect the kinetochore attachment status . Accurate centromere signal quantification in combination with DNA fluorescent in situ hybridization ( FISH ) for chromosome identification was therefore not an option , also because GFP fluorescence does not survive the FISH procedure . In case of the analyses in syncytial blastoderm embryos , stack size was 16 focal planes with 250 nm spacing , in case of wing discs , 20 focal planes with 250 nm spacing . For all quantitative analyses of EGFP signal intensities , data were acquired from at least three different slides . The data displayed in Figure 7b , d are from embryos in prometaphase or metaphase of mitosis 11 and 12 . As we did not observe significant intensity differences between mitosis 11 and 12 , values were pooled for preparation of the s . | Genetic information in eukaryotic cells is parceled into chromosomes . These information strings are precisely transmitted to daughter cells during mitotic and meiotic cell divisions , but only if the centromere , a specialized chromosomal region , is functional . The centromere region within chromosomes of many species—including humans and the fly Drosophila melanogaster—is thought to be specified epigenetically by incorporation of a centromere-specific histone H3 variant ( CenH3 ) . After chromosome replication , the centromeres in the resulting two sister chromatids might be expected to be composed of a mixture of pre-existing CenH3 evenly distributed onto the two copies during replication and new CenH3 recruited by the partitioned pool in a stoichiometric manner . Here , we have addressed whether centromeres are indeed replicated in this manner by experimentally altering the levels of centromeric CenH3 in Drosophila sperm . We show that centromeres on paternal chromosomes cannot recruit new CenH3 in embryos fertilized with sperm lacking CenH3 . By using sperm with increased or reduced amounts of centromeric CenH3 , we demonstrate that altered CenH3 levels are at least partially propagated on paternal centromeres throughout development of the offspring . We conclude that pre-existing CenH3 in Drosophila sperm is therefore not only required for transgenerational centromere maintenance , but that it also exerts quantitative control of this process . |
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Chronic kidney disease ( CKD ) is an important public health problem with a genetic component . We performed genome-wide association studies in up to 130 , 600 European ancestry participants overall , and stratified for key CKD risk factors . We uncovered 6 new loci in association with estimated glomerular filtration rate ( eGFR ) , the primary clinical measure of CKD , in or near MPPED2 , DDX1 , SLC47A1 , CDK12 , CASP9 , and INO80 . Morpholino knockdown of mpped2 and casp9 in zebrafish embryos revealed podocyte and tubular abnormalities with altered dextran clearance , suggesting a role for these genes in renal function . By providing new insights into genes that regulate renal function , these results could further our understanding of the pathogenesis of CKD .
Chronic kidney disease ( CKD ) affects nearly 10% of the global population [1] , [2] , and its prevalence continues to increase [3] . Reduced estimated glomerular filtration rate ( eGFR ) , the primary measure used to define CKD ( eGFR<60 ml/min/1 . 73 m2 ) [4] , is associated with an increased risk of cardiovascular morbidity and mortality [5] , acute kidney injury [6] , and end stage renal disease ( ESRD ) [6] , [7] . Using genome-wide association studies ( GWAS ) in predominantly population-based cohorts , we and others have previously identified more than 20 genetic loci associated with eGFR and CKD [8]–[11] . Although most of these genetic effects seem largely robust across strata of diabetes or hypertension status [9] , evidence suggests that some of the loci such as the UMOD locus may have heterogeneous effects across these strata [11] . We thus hypothesized that GWAS in study populations stratified by four key CKD risk factors - age , sex , diabetes or hypertension status - may permit the identification of novel eGFR and CKD loci . We carried this out by extending our previous work [9] to a larger discovery sample of 74 , 354 individuals with independent replication in additional 56 , 246 individuals , resulting in a total of 130 , 600 individuals of European ancestry . To assess for potential heterogeneity , we performed separate genome-wide association analyses across strata of CKD risk factors , as well as in a more extreme CKD phenotype .
Meta-analyses of GWAS on the 22 autosomes were performed for: 1 ) eGFR based on serum creatinine ( eGFRcrea ) and CKD ( 6 , 271 cases ) in the overall sample , 2 ) eGFRcrea and CKD stratified by the four risk factors , and 3 ) CKD45 , a more severe CKD phenotype defined as eGFRcrea <45 ml/min/1 . 73 m2 in the overall sample ( 2 , 181 cases ) . For the stratified analyses , in addition to identifying loci that were significant within each stratum , we performed a genome-wide comparison of the effect estimates between strata of the four risk factors . A complete overview of the analysis workflow is given in Figure S1 . All studies participating in the stage 1 discovery and stage 2 replication phases are listed in Tables S1 and S2 . The characteristics of all stage 1 discovery samples by study are reported in Table S3 , and information on study design and genotyping are reported in Table S4 . Results of the eGFRcrea analyses are summarized in the Manhattan and quantile-quantile plots reported in Figures S2 and S3 . A total of 21 SNPs from the discovery stage were carried forward for replication in an independent set of 56 , 246 individuals ( Tables S5 and S6 ) . These SNPs were selected for replication for the following ( Figure S1 ) : 5 reached genome-wide significance in either eGFRcrea overall or stratified analyses , 1 based on a test of direction-consistency of SNP-eGFR associations across the discovery cohorts for eGFRcrea overall , 4 demonstrated a P value≤10−6 and high between-study homogeneity ( I2<25% ) in the CKD45 analysis ( Table S7 ) , and 11 demonstrated between-strata P value≤5×10−5 along with a P value≤5×10−5 for association with eGFRcrea in at least one of the two strata ( Table S8 ) . While none of the loci identified for CKD45 or the test for between-strata difference analyses replicated , all 6 loci identified from the eGFRcrea overall analysis , stratified analyses , and the direction test did ( Table 1 ) . These 6 loci were identified and replicated in the overall analysis ( rs3925584 , located upstream of the MPPED2 gene; rs6431731 near the DDX1 gene ) , in the diabetes-free sub-group ( rs2453580 in an intron of the SLC47A1 gene ) , in the younger age stratum ( rs11078903 in an intron of the CDK12 gene; rs12124078 located near the CASP9 gene ) , and the direction test ( rs2928148 , located in the INO80 gene , see Methods for details ) . In the combined meta-analysis of all 45 studies used in the discovery and replication stages , all six SNPs met the genome-wide significance threshold of 5×10−8 , with individual P values ranging from 4 . 3×10−8 to 8 . 4×10−18 ( Table 1 ) . The imputation quality of these SNPs is reported in Table S9 , and Figure S4 shows the regional association plots for each of the 6 loci . We also confirmed all previously identified renal function loci in the current data ( Table S10 ) . Brief descriptions of the genes included within the 6 new loci uncovered can be found in Table S11 . Forest plots for the associations between the index SNP at each of the 6 novel loci and eGFR across all discovery studies and all strata are presented in Figures S5 and S6 . Most of the 6 new loci had similar associations across strata of CKD risk factors except for the CDK12 locus , which revealed stronger association in the younger ( ≤65 years of age ) as compared to the older age group ( >65 years of age ) . We further examined our findings in 8 , 110 African ancestry participants from the CARe consortium [12] ( Table 2 ) . Not surprisingly , given linkage disequilibrium ( LD ) differences between Europeans and African Americans , none of the 6 lead SNPs uncovered in CKDGen achieved significance in the African American samples . Next , we interrogated the 250 kb flanking regions from the lead SNP at each locus , and showed that 4 of the 6 regions ( MPPED2 , DDX1 , SLC47A1 , and CDK12 ) harbored SNPs that achieved statistical significance after correcting for multiple comparisons based on the genetic structure of each region ( see Methods for details ) . Figure 1 presents the regional association plots for MPPED2 , and Figure S7 presents the plots of the remaining loci in the African American sample . Imputation scores for the lead SNPs can be found in Table S12 . We observed that rs12278026 , upstream of MPPED2 , was associated with eGFRcrea in African Americans ( P value = 5×10−5 , threshold for statistical significance: P value = 0 . 001 ) . While rs12278026 is monomorphic in the CEU population in HapMap , rs3925584 and rs12278026 have a D′ of 1 ( r2 = 0 . 005 ) in the YRI population , suggesting that these SNPs may have arisen from the same ancestral haplotype . We also performed eQTL analyses of our 6 newly identified loci using known databases and a newly created renal eSNP database ( see Methods ) and found that rs12124078 was associated with cis expression of the nearby CASP9 gene in myocytes , which encodes caspase-9 , the third apoptotic activation factor involved in the activation of cell apoptosis , necrosis and inflammation ( P value for the monocyte eSNP of interest = 3 . 7×10−13 ) . In the kidney , caspase-9 may play an important role in the medulla response to hyperosmotic stress [13] and in cadmium-induced toxicity [14] . The other 5 SNPs were not associated with any investigated eQTL . Additional eQTL analyses of 81 kidney biopsies ( Table S13 ) did not reveal further evidence of association with eQTLs ( Table S14 ) . Of the 6 novel loci identified , 2 ( MPPED2 and DDX1 ) were in regions containing only a single gene , and 1 ( CASP9 ) had its expression associated with the locus lead SNP . Thus , to determine the potential involvement of these three genes during zebrafish kidney development , we independently assessed the expression of 4 well-characterized renal markers following morpholino knockdown: pax2a ( global kidney ) [15] , nephrin ( podocyte ) [16] , slc20a1a ( proximal tubule ) [17] , and slc12a3 ( distal tubule ) [17] . While we observed no abnormalities in ddx1 morphants ( Figure S8 ) , mpped2 and casp9 knockdown resulted in expanded pax2a expression in the glomerular region in 90% and 75% of morphant embryos , respectively , compared to 0% in controls ( P value<0 . 0001 for both genes; Figure 2A versus 2F and 2K; 2B versus 2G and 2L; and 2P ) . Significant differences were also observed in expression of the podocyte marker nephrin ( Figure 2C versus 2H and 2M; 80% and 74% abnormalities for mpped2 and casp9 , respectively , versus 0% in controls , P value<0 . 0001 for both genes ) . For mpped2 , no differences were observed in expression of the proximal or distal tubular markers slc20a1a and slc12a3 ( P value = 1 . 0; Figure 2D versus 2I and 2E versus 2J ) . Casp9 morphants and controls showed no differences in proximal tubular marker expression ( Figure 2D versus 2N ) , but abnormalities were observed in distal tubular marker expression in casp9 knockdown embryos ( 30% versus 0%; Figure 2E versus 2O; P value = 0 . 0064 ) . Casp9 morphants displayed diminished clearance of 70 , 000 MW fluorescent dextran 48 hours after injection into the sinus venosus compared to controls , revealing significant functional consequences of casp9 knockdown ( Figure 2Q–2V ) . No clearance abnormalities were observed in mpped2 morphants . The occurrence of abdominal edema is a non-specific finding that is frequently observed in zebrafish embryos with kidney defects . We examined the occurrence of edema in mpped2 and casp9 knockdown embryos at 4 and 6 days post fertilization ( dpf ) , both in the absence and presence of dextran , and observed a significant increase in edema prevalence in casp9 with ( P value<0 . 0001 ) and without ( P value = 0 . 0234 ) dextran challenge but not in mpped2 morphants ( Figure 2W ) . In order to further demonstrate differences in kidney function in response to knockdown of mpped2 and casp9 , we injected the nephrotoxin gentamicin which predictably causes edema in a subset of embryos . Casp9 morphants were more susceptible to developing edema compared to both controls and mpped2 morphants ( Figure 2X ) . In addition , edema developed earlier and was more severe , encompassing a greater area of the entire embryo ( Figure S9 ) . Together , these findings suggest that casp9 and mpped2 knockdowns result in altered kidney gene expression and function . Specifically , abnormal expression of pax2a and nephrin in casp9 morphants in addition to dextran retention and edema formation suggest loss of casp9 impacts glomerular development and function . The lead SNP at the MPPED2 locus is located approximately 100 kb upstream of the gene metallophosphoesterase domain containing 2 ( MPPED2 ) , which is highly evolutionary conserved and encodes a protein with metallophosphoesterase activity [18] . It has been recognized for a role in brain development and tumorigenesis [19] but thus far not for kidney function . To determine whether the association at our newly identified eGFRcrea loci was primarily due to creatinine metabolism or renal function , we compared the relative associations between eGFRcrea and eGFR estimated using cystatin C ( eGFRcys ) ( Figure S10 , File S1 ) . The new loci showed similar effect sizes and consistent effect directions for eGFRcrea and eGFRcys , suggesting a relation to renal function rather than to creatinine metabolism . Placing the results of these 6 loci in context with our previously identified loci [8] , [9] ( 23 known and 6 novel ) , 18 were associated with CKD at a 0 . 05 significance level ( odds ratio , OR , from 1 . 05 to 1 . 26; P values from 3 . 7×10−16 to 0 . 01 ) and 11 with CKD45 ( OR from 1 . 08 to 1 . 34; P values from 1 . 1×10−5 to 0 . 047; Figure S11 and Table S15 ) . When we examined these 29 renal function loci by age group , sex , diabetes and hypertension status ( Tables S16 , S17 , S18 , and S19 ) , we observed consistent associations with eGFRcrea for most loci across all strata , with only two exceptions: UMOD had a stronger association in older individuals ( P value for difference 8 . 4×10−13 ) and in those with hypertension ( P value for difference 0 . 002 ) , and CDK12 was stronger in younger subjects ( P value for difference 0 . 0008 ) . We tested the interaction between age and rs11078903 in one of our largest studies , the ARIC study . The interaction was significant ( P value = 0 . 0047 ) and direction consistent with the observed between-strata difference . Finally , we tested for associations between our 6 new loci and CKD related traits . The new loci were not associated with urinary albumin-to-creatinine ratio ( UACR ) or microalbuminuria [20] ( Tables S20 and S21 ) , with blood pressure from the ICBP Consortium [21] ( Table S22 ) or with myocardial infarction from the CARDIoGRAM Consortium [22] ( Table S23 ) .
We have extended prior knowledge of common genetic variants for kidney function [8]–[11] , [23] by performing genome-wide association tests within strata of key CKD risk factors , including age , sex , diabetes , and hypertension , thus uncovering 6 loci not previously known to be associated with renal function in population-based studies ( MPPED2 , DDX1 , CASP9 , SLC47A1 , CDK12 , INO80 ) . In contrast to our prior genome-wide analysis [8] , [9] , the majority of the new loci uncovered in the present analysis have little known prior associations with renal function . This highlights a continued benefit of the GWAS approach by using large sample sizes to infer new biology . Despite our hypothesis that genetic effects are modified by CKD risk factors , most of the identified variants did not exhibit strong cross-strata differences . This highlights that many genetic associations with kidney function may be shared across risk factor strata . The association of several of these loci with kidney function in African Americans underscores the generalizability of identified renal loci across ethnicities . Zebrafish knockdown of mpped2 resulted in abnormal podocyte anatomy as assessed by expression of glomerular markers , and loss of casp9 led to altered podocyte and distal tubular marker expression , decreased dextran clearance , edema , and enhanced susceptibility to gentamicin-induced kidney damage . These findings demonstrate the potential importance of these genes with respect to renal function and illustrate that zebrafish are a useful in vivo model to explore the functional consequences of GWAS-identified genes . Despite these strengths , there are some limitations of our study that warrant discussion . Although we used cystatin C to separate creatinine metabolism from true filtration loci , SNPs within the cystatin C gene cluster have been shown to be associated with cystatin C levels [8] , which might result in some degree of misclassification in absolute levels . While we used standard definitions of diabetes and hypertension in the setting of population-based studies , these may differ from those definitions used in clinical practice . In addition , we were unable to differentiate the use of anti-hypertension medications from other clinical indications of these agents or type 1 from type 2 diabetes . The absence of association between our six newly discovered SNPs and the urinary albumin to creatinine ratio , blood pressure , and cardiovascular disease may have resulted from disparate genetic underpinnings of these traits , the overall small effect sizes , or the cross-sectional nature of our explorations; and we were unable to differentiate between these potential issues . Finally , power was modest to detect between-strata heterogeneity . With increased sample size and stratified analyses , we have identified additional loci for kidney function that continue to have novel biological implications . Our primary findings suggest that there is substantial generalizability of SNPs associations across strata of important CKD risk factors , specifically with hypertension and diabetes .
Serum creatinine and cystatin C were measured as detailed in Tables S1 and S2 . To account for between-laboratory variation , serum creatinine was calibrated to the US nationally representative National Health and Nutrition Examination Study ( NHANES ) standards in all discovery and replication studies as described previously [8] , [24] , [25] . GFR based on serum creatinine ( eGFRcrea ) was estimated using the four-variable MDRD Study equation [26] . GFR based on cystatin C ( eGFRcys ) was estimated as eGFRcys = 76 . 7× ( serum cystatin C ) −1 . 19 [27] . eGFRcrea and eGFRcys values<15 ml/min/1 . 73 m2 were set to 15 , and those >200 were set to 200 ml/min/1 . 73 m2 . CKD was defined as eGFRcrea <60 ml/min/1 . 73 m2 according to the National Kidney Foundation guidelines [28] . A more severe CKD phenotype , CKD45 , was defined as eGFRcrea <45 ml/min/1 . 73 m2 . Control individuals for both CKD and CKD45 analyses were defined as those with eGFRcrea >60 ml/min/1 . 73 m2 . In discovery and replication cohorts , diabetes was defined as fasting glucose ≥126 mg/dl , pharmacologic treatment for diabetes , or by self-report . Hypertension was defined as systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg or pharmacologic treatment for hypertension . Genotyping was conducted as specified in Table S4 . After applying quality-control filters to exclude low-quality SNPs or samples , each study imputed up to ∼2 . 5 million HapMap-II SNPs , based on the CEU reference samples . Imputed genotypes were coded as the estimated number of copies of a specified allele ( allelic dosage ) . Additional , study-specific details can be found in Table S1 . A schematic view of our complete analysis workflow is presented in Figure S1 . Using data from 26 population-based studies of individuals of European ancestry , we performed GWA analyses of the following phenotypes: 1 ) loge ( eGFRcrea ) , loge ( eGFRcys ) , CKD , and CKD45 overall and 2 ) loge ( eGFRcrea ) and CKD stratified by diabetes status , hypertension status , age group ( ≤/>65 years ) , and sex . GWAS of loge ( eGFRcrea ) and loge ( eGFRcys ) were based on linear regression . GWAS of CKD and CKD45 were performed in studies with at least 25 cases ( i . e . all 26 studies for CKD and 11 studies for CKD45 ) and were based on logistic regression . Additive genetic effects were assumed and models were adjusted for age and , where applicable , for sex , study site and principal components . Imputation uncertainty was accounted for by including allelic dosages in the model . Where necessary , relatedness was modeled with appropriate methods ( see Table S1 for study-specific details ) . Before including in the meta-analysis , all GWA data files underwent to a careful quality control , performed using the GWAtoolbox package in R ( www . eurac . edu/GWAtoolbox . html ) [29] . Meta-analyses of study-specific SNP-association results , assuming fixed effects and using inverse-variance weighting , i . e . : the pooled effect is estimated as , where is the effect of the SNP on the outcome in the ith study , K is the number of studies , and is the weight given to the ith study . The meta-analyses were performed using METAL [30] , with genomic control correction applied across all imputed SNPs [31] if the inflation factor λ>1 at both the individual study level and after the meta-analysis . SNPs with minor allele frequency ( MAF ) <1% were excluded . All SNPs with a meta-analysis P value≤5×10−8 for any trait or any stratum were deemed genome-wide significant [32] . In the eGFRcrea analyses , after excluding loci that were previously reported [8] , [9] , we selected for replication all SNPs with P value<5×10−8 in any trait or stratum that were independent ( defined by pairwise r2<0 . 2 ) , in the primary association analysis . This yielded five SNPs in five independent loci . The same criterion was applied to the CKD analysis , where no SNPs passed the selection threshold . Given the smaller number of cases with severe CKD resulting in less statistical power , a different selection strategy was adopted for the CKD45 analysis: selected for replication were SNPs with discovery P value≤5×10−6 , MAF≥5% , and homogeneous effect size across studies ( I2≤25% ) . Four additional SNPs were thereby selected for replication from the CKD45 analysis . In addition to identifying SNPs for replication based on the genome-wide significance threshold from a fixed effect model meta-analysis , we performed a “direction test” to identify additional SNPs for which between-study heterogeneity in effect size might have obscured the overall association that was nevertheless highly consistent in the direction of allelic effects . Under the null hypothesis of no association , the a priori probability that a given effect allele of a SNP has either a positive or negative association with eGFRcrea is 0 . 5 . Because the meta-analysis includes independent studies , the number of concordant effect directions follows a binomial distribution . Therefore , we tested whether the number of discovery cohorts with the same sign of association ( i . e . direction of effect ) was greater than expected by chance given the binomial distribution and a null expectation of equal numbers of associations with positive and negative sign . The test was only applied for eGFRcrea in the overall analysis . Multiple testing was controlled by applying the same P value threshold of 5×10−8 as in the overall GWAS . Given that no SNP met this criterion , we selected for replication one novel SNP with the lowest P value of 4 . 0×10−7 . Based on the results of the stratified GWAS of eGFRcrea and CKD , for each SNP we tested the hypothesis whether the effect of a SNP on eGFRcrea or CKD was the same between strata ( null hypothesis ) , i . e . diabetes versus non-diabetes subjects , hypertensive versus normotensive , younger versus older , females versus males . We used a two-sample test defined as Z = ( b1−b2 ) / ( SE ( b1 ) 2+SE ( b2 ) 2 ) 0 . 5 , with b1 and b2 indicating the effect estimates in the two strata and SE ( b1 ) and SE ( b2 ) their standard errors [33] . For large samples , the test statistic follows a standard normal distribution . SNPs were selected for replication if they had a between-stratum difference P value≤5×10−5 , an association P value≤5×10−5 in one of the two strata , and MAF≥10% . Independent loci were defined using the same criteria as described above . Eleven further SNPs , one per locus , were selected for replication from the between-strata difference test . Replication was performed for a total of 21 SNPs including 5 from the overall and stratified eGFRcrea analyses , 1 from the direction test on eGFRcrea , 4 from the overall CKD45 analysis , and 11 from the between-strata difference test . Replication studies used the same phenotype definition , and had available genotypes from imputed in silico genome-wide SNP data or de novo genotyping . The same association analyses including the identical stratifications were performed as in discovery studies . Details can be found in the Tables S2 , S5 and S6 . Study-specific replication results for the selected SNPs were combined using the same meta-analysis approach and software as in the discovery stage . One-sided P values were derived with regard to the effect direction found in the discovery stage . Based on the P value distribution of all SNPs submitted for replication ( the 10 from eGFRcrea and CKD45 and the 11 from the between strata difference test ) , we estimated the False Discovery Rate as a q-value using the QVALUE [34] package in R . SNPs with q-value<0 . 05 were called significantly replicating , thus specifying a list of associations expected to include not more than 5% false positives . Finally , study-specific results from both the discovery and replication stage were combined in a joint inverse-variance weighted fixed-effect meta-analysis and the two-sided P values were compared to the genome-wide significance threshold of 5×10−8 to test whether a SNP was genome-wide significant . Between-study heterogeneity of replicated SNPs was quantified by the I2 statistic [35] . For de novo genotyping in 10 , 446 samples from KORA F3 , KORA F4 , SAPHIR and SAPALDIA , the MassARRAY system at the Helmholtz Zentrum ( München , Germany ) was used , using Assay Design v3 . 1 . 2 and the iPLEX chemistry ( Sequenom , San Diego , USA ) . Assay design failed for rs1322199 and genotyping was not performed . Ten percent of the spectra were checked by two independent , trained persons , and 100% concordance between investigators was obtained . SNPs with a P value<0 . 001 when testing for Hardy-Weinberg equilibrium ( rs10490130 , rs10068737 , rs11078903 ) , SNPs with call rate <90% ( rs500456 in KORA F4 only ) or monomorphic SNPs ( rs2928148 ) were excluded from analyses without attempting further genotyping . The call rates of rs4149333 and rs752805 were near 0% on the MassARRAY system . These SNPs were thus genotyped on a 7900HT Fast Real-Time PCR System ( Applied Biosystems , Foster City , USA ) . Mean call rate across all studies and SNPs ranged from 96 . 8% ( KORA F4 ) to 99% ( SAPHIR ) . Duplicate genotyping was performed in at least 14% of the subjects in each study with a concordance of 95–100% ( median 100% ) . In the Ogliastra Genetic Park Replication Study ( n = 3000 ) de novo genotyping was conducted on a 7900HT Fast Real-Time PCR System ( Applied Biosystems , Foster City , USA ) , with a mean call rate of 99 . 4% and 100% concordance of SNPs genotyped in duplicate . Twenty-nine SNPs , including the 6 novel loci reported in the current manuscript along with 23 previously confirmed to be associated with renal function [9] , were tested for differential effects between the strata . The same Z statistics as described for discovery ( above ) was used and the Bonferroni-adjusted significance level was set to 0 . 10/29 = 0 . 003 . SNP-by-age interaction , for the one SNP showing significantly different effects between strata of age , was tested in the ARIC study by fitting a linear model on log ( eGFRcrea ) adjusted for sex , recruitment site , the first and the seventh genetic principal components ( only these two were associated with the outcome at P value<0 . 05 ) . Both the interaction term and the terms for the main effects of age and the SNP were included in the model . To assess genome-wide between-strata differences , with alpha = 5×10−8 and power = 80% , the maximum detectable difference was 0 . 025 when comparing nonDM versus DM and 0 . 015 when comparing nonHTN versus HTN . Similarly , when testing for between-strata differences the 29 known and new loci ( Bonferroni-corrected alpha = 0 . 003 ) in the combined sample ( n = ∼125 , 000 in nonDM and n = ∼13 , 000 in DM ) we had 80% power to detect differences as large as 0 . 035 . For each of the 6 lead SNPs identified in our European ancestry samples , we extracted eGFR association statistics from a genome-wide study in the CARe African ancestry consortium [12] . We further investigated potential allelic heterogeneity across ethnicities by examining the 250 kb flanking region surrounding each lead SNP to determine whether other SNPs with stronger associations exist in each region . A SNP with the smallest association P value with MAF>0 . 03 was considered the top SNP in the African ancestry sample . We defined statistical significance of the identified lead SNP in African ancestry individuals based on a region-specific Bonferroni correction . The number of independent SNPs was determined based on the variance inflation factor ( VIF ) with a recursive calculation within a sliding window of 50 SNPs and pairwise r2 of 0 . 2 . These analyses were performed using PLINK . For each replicating SNP , we obtained association results for urinary albumin-to-creatinine ratio and microalbuminuria from our previous genome-wide association analysis [20] , and for blood pressure and myocardial infarction from genome-wide association analysis from the ICBP [21] and CARDIoGRAM [22] consortia , respectively . Significant renal SNPs were searched against a database of expression SNPs ( eSNP ) including the following tissues: fresh lymphocytes [36] , fresh leukocytes [37] , leukocyte samples in individuals with Celiac disease [38] , lymphoblastoid cell lines ( LCL ) derived from asthmatic children [39] , HapMap LCL from 3 populations [40] , a separate study on HapMap CEU LCL [41] , peripheral blood monocytes [42] , [43] , adipose [44] , [45] and blood samples [44] , 2 studies on brain cortex [42] , [46] , 3 large studies of brain regions including prefrontal cortex , visual cortex and cerebellum ( Emilsson , personal communication ) , liver [45] , [47] , osteoblasts [48] , skin [49] and additional fibroblast , T cell and LCL samples [50] . The collected eSNP results met criteria for statistical significance for association with gene transcript levels as described in the original papers . A second expression analysis of 81 biopsies from normal kidney cortex samples was performed as described previously [51] , [52] . Genotyping was performed using Affymetrix 6 . 0 Genome-wide chip and called with GTC Software ( Affymetrix ) . For eQTL analyses , expression probes ( Affymetrix U133set ) were linked to SNP probes with >90% call-rate using RefSeq annotation ( Affymetrix build a30 ) . P values for eQTLs were calculated using linear multivariable regression in both cohorts and then combined using Fisher's combined probability test ( see also [52] ) . Pairwise LD was calculated using SNAP [53] on the CEU HapMap release 22 . Zebrafish were maintained according to established IACUC protocols . Briefly , we injected zebrafish embryos with newly designed ( mpped2 , ddx1 ) or previously validated ( casp9 [54] ) morpholino antisense oligonucleotides ( MO , GeneTools , Philomath OR ) at the one-cell stage at various doses . We fixed embryos in 4% PFA at the appropriate stages for in situ hybridization ( http://zfin . org/ZFIN/Methods/ThisseProtocol . html ) . Different anatomic regions of the kidney were visualized using a panel of 4 established markers: pax2a ( global kidney marker ) [15] , nephrin ( podocyte marker ) [16] , slc20a1a ( proximal tubule ) [17] , and slc12a3 ( distal tubule marker ) [17] . Abnormalities in gene expression were independently scored by two investigators . We compared the number of abnormal morphant embryos to control embryos , injected with a standard control MO designed by GeneTools , with the Fisher's exact test , at the Bonferroni-corrected significance level of 0 . 0125 , i . e . : 0 . 05/4 markers . We documented the development of gross edema at 4 and 6 days post-fertilization in live embryos . We performed dextran clearance experiments following previously described protocols [55] . Briefly , 80 hours after MO injection , we anesthetized embryos in 4 mg/ml Tricaine in embryo water ( 1∶20 dilution ) , then positioned embryos on their back in a 1% agarose injection mold . We injected an equal volume of tetramethylrhodamine dextran ( 70 , 000 MW; Invitrogen ) into the cardiac sinus venosus of each embryo . We then returned the embryos to fresh embryo water . Using fluorescence microscopy , we imaged the embryos at 2 hours post-injection ( 82 hpf ) to demonstrate equal loading , then at 48 hours post-injection ( 128 hpf ) to evaluate dextran clearance . Embryos were injected with control , mpped2 , or casp9 MOs at the one-cell stage . At 48 hpf , embryos were manually dechorionated , anesthetized in a 1∶20 dilution of 4 mg/ml Tricaine in embryo water , and oriented on a 1% agarose injection mold . As previously described [56] , embryos were injected with equal volumes of 10 mg/ml gentamicin ( Sigma ) in the cardiac sinus venosus , returned to fresh embryo water , and subsequently scored for edema ( prevalence , time of onset ) over the next 3 days . | Chronic kidney disease ( CKD ) is an important public health problem with a hereditary component . We performed a new genome-wide association study in up to 130 , 600 European ancestry individuals to identify genes that may influence kidney function , specifically genes that may influence kidney function differently depending on sex , age , hypertension , and diabetes status of individuals . We uncovered 6 new loci associated with estimated glomerular filtration rate ( eGFR ) , the primary measure of renal function , in or near MPPED2 , DDX1 , SLC47A1 , CDK12 , CASP9 , and INO80 . CDK12 effect was stronger in younger and absent in older individuals . MPPED2 , DDX1 , SLC47A1 , and CDK12 loci were associated with eGFR in African ancestry samples as well , highlighting the cross-ethnicity validity of our findings . Using the zebrafish model , we performed morpholino knockdown of mpped2 and casp9 in zebrafish embryos and revealed podocyte and tubular abnormalities with altered dextran clearance , suggesting a role for these genes in renal function . These results further our understanding of the pathogenesis of CKD and provide insights into potential novel mechanisms of disease . |
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Drosophila telomeres are sequence-independent structures that are maintained by transposition to chromosome ends of three specialized retroelements ( HeT-A , TART and TAHRE; collectively designated as HTT ) rather than telomerase activity . Fly telomeres are protected by the terminin complex ( HOAP-HipHop-Moi-Ver ) that localizes and functions exclusively at telomeres and by non-terminin proteins that do not serve telomere-specific functions . Although all Drosophila telomeres terminate with HTT arrays and are capped by terminin , they differ in the type of subtelomeric chromatin; the Y , XR , and 4L HTT are juxtaposed to constitutive heterochromatin , while the XL , 2L , 2R , 3L and 3R HTT are linked to the TAS repetitive sequences; the 4R HTT is associated with a chromatin that has features common to both euchromatin and heterochromatin . Here we show that mutations in pendolino ( peo ) cause telomeric fusions ( TFs ) . The analysis of several peo mutant combinations showed that these TFs preferentially involve the Y , XR and 4th chromosome telomeres , a TF pattern never observed in the other 10 telomere-capping mutants so far characterized . peo encodes a non-terminin protein homologous to the E2 variant ubiquitin-conjugating enzymes . The Peo protein directly interacts with the terminin components , but peo mutations do not affect telomeric localization of HOAP , Moi , Ver and HP1a , suggesting that the peo-dependent telomere fusion phenotype is not due to loss of terminin from chromosome ends . peo mutants are also defective in DNA replication and PCNA recruitment . However , our results suggest that general defects in DNA replication are unable to induce TFs in Drosophila cells . We thus hypothesize that DNA replication in Peo-depleted cells results in specific fusigenic lesions concentrated in heterochromatin-associated telomeres . Alternatively , it is possible that Peo plays a dual function being independently required for DNA replication and telomere capping .
Telomeres are nucleoprotein complexes that counterbalance incomplete replication of terminal DNA and protect chromosome ends preventing both activation of cell cycle checkpoints and fusion events . In most organisms , the end replication problem is solved by telomerase , which mediates the addition of short GC-rich repeats to chromosome ends . These repeats specifically bind a discrete number of proteins , which recruit a series of additional factors to form large protein assemblies that ensure proper telomere function and homeostasis ( reviewed in [1–4] ) . Drosophila telomeres are not elongated by telomerase but by targeted transposition of three specialized retroelements called HeT-A , TART and TAHRE ( collectively abbreviated with HTT ) . However , Drosophila telomere formation does not require the HTT arrays; abundant evidence indicates that fly telomeres are epigenetically-determined structures that can assemble at the ends of the chromosomes independently of their terminal DNA sequence ( reviewed in [5–7] ) . With the exception of budding yeast , in organisms with telomerase telomeres are protected by the conserved shelterin complex . Human shelterin is a six-protein complex ( TRF1 , TRF2 , POT1 , TIN2 , TPP1 and Rap1 ) that specifically associates with the telomeric TTAGGG repeats . TRF1 , TRF2 directly bind the TTAGGG duplex and POT1 the single stranded overhang . TIN2 and TPP1 do not bind DNA and interconnect TRF1 and TRF2 with POT1 . TRF2 interacts with hRap1 , a distant homologue of S . cerevisiae Rap1 . The shelterin subunits share properties that distinguish them from the non-shelterin telomere-associated proteins: they are specifically enriched at telomeres throughout the cell cycle and appear to function only at telomeres [1] . The human non-shelterin proteins , which are not telomere-specific in localization and function , include the conserved CST complex , HP1a , and proteins involved in DNA repair and/or replication such as the ATM kinase , the Ku70/80 heterodimer , the MRE11/RAD50/NBS1 ( MRN ) complex , Rad51 , the ERCC1/XPF endonuclease , the Apollo exonuclease , the FEN1 nuclease , the RecQ family members WRN and BLM; the RTL1 helicase , RPA70 , the Timeless component of the replisome , and the subunits of the conserved ORC prereplication complex . Depletion of any one of the shelterin subunits or the shelterin-associated proteins leads to visible telomere defects ranging from altered packaging of telomeric chromatin ( multi telomere signals after FISH ) , telomere loss and telomere fusion ( reviewed in [1–4]; see also [8 , 9] ) . Most of the Drosophila telomere-capping proteins have been identified by molecular cloning of genes specified by mutations that cause telomeric fusions ( TFs ) in larval brain cells . Genetic and molecular analyses have thus far identified 11 loci that are required to prevent TF ( henceforth they will be designated as TF genes ) . These are effete ( eff; also called UbcD1 ) that encodes a highly conserved E2 enzyme that mediates protein ubiquitination [10 , 11] , Su ( var ) 205 that encodes Heterochromatin Protein 1a ( HP1a ) [12] , the Drosophila homologues of the ATM , RAD50 , MRE11 and NBS1 DNA repair genes [13–19] , without children ( woc ) that specifies a transcription factor [20]; caravaggio ( cav ) , modigliani ( moi ) , verrocchio ( ver ) and hiphop that encode the components of the terminin complex [21–25] . HOAP ( the cav product HP1/ORC-Associated Protein [21–26] ) , Moi and HipHop are fast evolving non-conserved proteins that do not share homology with any known telomere-associated protein [22–24]Ver is also a fast evolving protein; however , it contains an OB-fold motif that is structurally homologous to the OB fold of the Stn1 protein of the conserved CST complex [23] . Although the structural characterization of terminin is still incomplete , the extant data suggest that HOAP and HipHop are primarily bound to the DNA duplex while Ver is associated with the single-stranded overhang [6 , 22 , 23 , 26] . In contrast with the other telomere capping proteins ( Eff , HP1a , ATM , Mre11 , Rad50 , Nbs , and Woc ) that have multiple localizations and functions , HOAP , HipHop , Moi and Ver localize only at telomeres and appear to function only in telomere maintenance . These properties are similar to the shelterin properties , suggesting that terminin is a functional analog of shelterin [25] . Furthermore , the findings that shelterin subunits are not conserved in flies , that terminin components have no homologues ( with the possible exception of Ver ) outside Drosophilidae , and that terminin subunits are encoded by fast evolving genes have suggested a hypothesis on terminin evolution . We proposed that the transition between a telomerase-driven and a transposon-driven telomere elongation mechanism generated a divergence in terminal DNA sequences , which exerted a strong selective pressure towards the evolution of sequence-independent telomere binding proteins such as those that comprise terminin . We also hypothesized that non-terminin Drosophila telomere-capping proteins with multiple localizations and functions correspond to ancestral telomere components that did not evolve as rapidly as terminin because of the functional constraints imposed by their participation in diverse cellular processes [23–25] . In addition to their peculiar telomere elongation mechanism , Drosophila telomeres are also characterized by striking variations in their subtelomeric regions . Recent work has shown that subtelomeric regions play important regulatory roles in mammalian telomere behavior . For example , it has been reported that most human telomeres are replicated by forks progressing from subtelomere to telomere [27] and that the timing of telomere replication depends on the type of subtelomeric DNA; the telomeres associated with satellite-like subtelomeric sequences replicate later than telomeres that are not associated with this type of subtelomeric DNA [28] . Furthermore , recent work has shown that chimpanzee telomeres carrying subtelomeric heterochromatin replicate later than telomeres devoid of heterochromatic subtelomeres [29] . However , the fusigenic properties of mammalian telomeres carrying different subtelomeres have never been investigated . Drosophila is an ideal model organism for investigating the influence of subtelomeric regions on telomere behavior . All Drosophila telomeres terminate with HTT arrays that are capped by terminin; these HTT arrays are juxtaposed to different types of chromatin: canonical constitutive heterochromatin ( the Y , XR , and 4L telomeres ) , clusters of repetitive telomere-associated sequences ( designated as TAS , and present at the XL , 2L , 2R , 3L and 3R telomeres ) , or sequences with both euchromatic and heterochromatic features ( 4R telomeres ) . Here we describe a Drosophila gene , pendolino ( peo ) , identified by mutations that preferentially induce TFs between telomeres associated with constitutive heterochromatin . The Peo protein binds terminin but does not have the typical terminin properties , as it is conserved in mammals and associates with several chromosomal sites . In addition , Peo is required for PCNA recruitment and for general DNA replication . However , both the present and previous results strongly suggest that telomere lesions generated by general defects in DNA replication are unable to induce TFs in Drosophila cells . We thus propose that loss of peo function results in specific fusigenic lesions concentrated in heterochromatin-associated telomeres , and that these lesions might be generated during telomere replication .
The pendolino1 ( peo1 ) mutation was isolated by a cytological screen of 120 late lethal mutants mapping to the second chromosome , recovered after I element mobilization by I-R dysgenic crosses ( see Materials and Methods ) . Mitotic cells of DAPI-stained brain preparations from peo1/peo1 larvae displayed very frequent telomeric fusions ( TFs; Fig 1A and 1B ) , often resulting in multicentric linear chromosomes that resemble little “trains” of chromosomes . The pendolino gene was named after this phenotype just as caravaggio , modigliani and verrocchio , which are all names of Italian trains . Recombination analysis with visible markers and deficiency mapping placed peo1 in the 46B-C polytene chromosome interval uncovered by Df ( 2R ) X3 and Df ( 2R ) B5 but not by Df ( 2R ) X1 ( Fig 1C ) . Previous studies mapped to this interval 3 lethal complementation groups [30] . peo1 failed to complement the 1527 and 2723 mutant alleles ( henceforth designated as peo1527 and peo2723 ) of group III for both the lethality and the TF phenotype but complemented representative alleles of groups I and II ( Fig 1D ) . peo1 also failed to complement the P element insertion p112 ( henceforth peop112 ) and the small p221 and p520 deficiencies ( henceforth peo∆221 and peo∆520 ) all generated by the remobilization of the P{w+ , ry+}AJN2 insertion [31] , originally localized proximally to the Df ( 2R ) X3 breakpoint ( Fig 1C and 1D ) . Finally , we identified another peo mutant allele ( peoh ) by a cytological screen of a collection of 193 late lethal mutants that arose in the Zucker’s collection of heavily mutagenized viable lines ( see Materials and Methods for details ) . peoh is homozygous viable but male and female sterile , and it is lethal in combination with peo1 ( peo1/peoh ) ; the lethal phases of selected combinations of peo mutant alleles and deficiencies are reported in Fig 1D . To identify the peo gene at the molecular level we exploited the peop112 allele that carries a P{w+ , ry+}construct inserted into the gene [30] . Using inverse PCR we found that the P construct is inserted into the 5’ UTR of the longest transcript of the CG10536 gene ( Fig 2 ) . CG10536 was originally named ms ( 2 ) 46C [32] an then renamed crossbronx ( cbx ) [33] . However , CG10536 does not correspond to ms ( 2 ) 46C/cbx . The phenotype associated with the ms ( 2 ) 46C/cbx mutation was attributed to the P{PZ}05704 insertion that maps just proximal to the UTR region of CG10536 ( Fig 2 ) . However , this attribution was only tentative because the male sterile phenotype was neither mapped over deficiency nor reverted by P element excision [32] . We found that males homozygous for the P{PZ}05704 insertion are sterile and show the spermatid abnormalities previously described [32] . In contrast , males bearing the same insertion over Df ( 2R ) B5 that uncover the 46B-C interval were fully fertile and displayed normal spermatids ( Fig 1D ) . We thus conclude that the ms ( 2 ) 46C/cbx mutation maps outside the 46B-C region , and that CG10536 actually corresponds to pendolino . The organization of the peo locus is rather complex . The gene encodes three different transcripts; the long introns of two of these transcripts contain genes ( Ntmt and CG18446 ) with an opposite transcriptional orientation to peo/CG10536 ( FlyBase; Fig 2 ) . The three putative peo transcripts encode proteins of 183 , 214 and 244 aa and are identified by 2 cDNAs ( FlyBase , Fig 2 ) . These proteins share homology with the E2 variant ubiquitin-conjugating enzymes , which are devoid of the catalytic cysteine that mediates ubiquitin transfer [34] . Interestingly , peo has an intronless paralogue ( CG16894 ) that maps to the 56F9 polytene chromosome region . The CG16894 protein shares 35 . 4 identity and 51 . 6 similarity with Peo and its function is currently unknown ( FlyBase ) . Sequencing of peo1 did not revealed nonsense , frameshift , splice-site or missense mutations in the protein-coding sequences of CG10536 with respect to the FlyBase sequences . In addition , sequencing of approximately 2000 bp upstream of the gene ATG and in situ hybridization experiments did not reveal I element insertions . Nonsense , frameshift or splice-site mutations were also absent from the protein coding sequences of peo1527 and peoh mutant genes . All peo mutant alleles displayed several genomic single nucleotide polymorphisms ( SNPs ) with respect to the Fly Base sequence . However these SNPs were all present in DPGP natural populations ( www . dpgp . org/dpgp3 ) , suggesting that they are associated with little if any phenotypic consequences . Collectively , these results suggest that peo1 , peo1527 and peoh are regulatory mutations that lower the intracellular amount of the Peo protein ( see below ) . However the molecular lesions in these mutant alleles remain to be identified . To unambiguously determine the identity of peo we performed rescue experiments using the LD08052 cDNA clone ( Fig 2; see also FlyBase ) . Sequencing confirmed that this cDNA contains the entire coding sequence of the CG10536-RB transcript , which produces the larger protein isoform encoded by the locus ( Fig 2 ) . The LD08052 cDNA was fused in frame with the heat shock ( hsp70 ) promoter or used to make a construct containing the tubulin promoter and a 3HA sequence at the 3’ of the gene . Both constructs were then used to make transgenic flies and both rescued the lethality and the TF phenotype of the peo1 mutant flies . In hs-CG10536; peo1/peo1 flies exposed to heat shocks ( 1h at 37°C every 24 h throughout development ) , survival of peo1/peo1 individuals with respect their peo1/CyO siblings was 18% of the Mendelian expectation , and the TF frequency dropped to less than 1 per cell from more than 5 per cell ( Fig 1B ) . In the presence of the Tub-CG10536-3HA construct , the survival rate of peo1/peo1 homozygotes with respect to peo1/CyO heterozygotes was 10% and the TF frequency less than 1/cell ( Fig 1B ) . To confirm these rescue data we generated peoh/peoh larvae bearing the Tub-CG10536-3HA construct . The brains of these larvae displayed a 5-fold reduction in TF frequency compared to those of peoh/peoh larvae ( 0 . 2 vs 1 TF/ cell ) . Thus , our results collectively indicate that CG10536 corresponds to peo . In most colchicine-treated metaphases from peo1/peo1 , peo1/ peo1527 , peo1/peo2723 , peo1/peop112 , peo1/ peo∆221 , peo1/peo∆520 and peo1/Df ( 2R ) BSC298 ( henceforth Df ( 2R ) BSC298 will be abbreviated with Df ) , the majority of telomeres were involved in fusions , often forming tangles of chromosomes difficult to resolve ( Fig 1A and 1B ) . However , a careful examination of these tangles led us to estimate the average number of TFs per cell and to determine the relative frequencies of single telomere associations ( STAs ) and double telomere associations ( DTAs ) . STAs involve a single telomere that fuses with either its sister telomere or another single nonsister telomere . In DTAs , two sister telomeres fuse with another pair of sister telomeres . STAs are likely to be generated during the S-G2 phase , while DTAs are thought to result from the replication of TFs generated during G1 [10 , 35] . In peo mutants , DTAs were much more frequent than STAs ( Fig 1B ) just as in the other TF mutants in which the relative frequencies of STAs and DTAs have been determined , namely eff , Su ( var ) 205 , cav , mre11 , rad50 , nbs , tefu ( ATM ) , woc , moi and ver [10 , 12 , 15 , 16 , 21 , 20 , 23 , 24] . This bias towards DTAs may reflect the proximity of Drosophila telomeres during G1 that would be progressively lost as cells proceed through S and G2 [10 , 36] . It has been also suggested that telomere fusion occurs primarily during G1 , because the DNA repair pathways that join chromosome ends are more active in G1 than in S or G2 [37] . Although peo mutants show a high DTA/STA ratio as the other TF mutants , they exhibit a specific pattern of TFs . The analysis of the peoh mutant that shows ~1 TF/cell allowed a very precise definition of the telomeres involved in fusion events . In peoh homozygous brains of males and females , nearly all TFs involved the telomeres associated with the heterochomatic regions of the chromosomes , namely those of the entirely heterochromatic Y chromosome ( YS and YL ) , the telomere of the right arm of the X chromosome ( XR ) and the fourth chromosome telomeres ( Fig 1A ) . Of the two telomeres of the fourth chromosome only one is associated with constitutive heterochromatin ( 4L ) but we were not able to distinguish between 4L and 4R , as the DAPI-stained fourth chromosomes appear as brightly fluorescent dots in which the chromosome arms are not discernible . High frequencies of TFs between heterochromatin-associated telomeres ( henceforth abbreviated with Ha-telomeres ) were also observed in peoh/Df and peoh/peo1 brains ( Fig 3 ) . We note that we classified as DTAs all TFs between Ha-telomeres , because the close apposition of the sister chromatids in heterochromatin does not allow a distinction between STAs and DTAs . Thus , the apparent lack of STAs observed in weak peo mutants ( Fig 1B ) might not reflect a real absence of this type of TFs . The high incidence of TFs between Ha-telomeres is not due to an allele-specific effect of peoh , as a high frequency of TFs involving the Ha-telomeres was also observed in peo1/peo1 mutant cells bearing the Tub-peo+-3HA rescue construct ( Fig 3 ) . This suggests that the Ha-telomeres are preferentially affected by an impairment of peo function and that this effect is partially masked in strong mutants in which most telomeres are fused . The TF pattern observed in peo mutants is highly specific , as none of the TF mutants we characterized in the past showed a prevalence of TFs between the Ha-telomeres . To precisely compare the patterns of fusions we re-examined all extant Drosophila TF mutants ( eff , cav , Su ( var ) 205 , mre11 , rad50 , nbs , tefu , woc , moi and ver ) . All these mutants displayed an inverse TF pattern compared with peo mutants , with lower than expected frequencies of fusions involving the Y , the XR , and the 4th chromosome telomeres and higher than expected frequencies of TFs between the telomeres of the major autosomes ( Fig 3 and S1 Table ) . We also confirmed that eff mutants do not exhibit a prevalence of fusion between Ha-telomeres ( Fig 3 ) [10] . This finding strongly suggests that the canonical E2 ubiquitin conjugating enzyme encoded by eff and the E2 variant encoded by peo do not function in the same telomere protection pathway . In interphase nuclei , the heterochromatic regions of the chromosomes aggregate to form an irregular mass of chromatin called chromocenter [38] . Thus , the specific pattern of TFs observed in peo mutants could depend either on an abnormal organization and compaction of the chromocenter or on a specific chromatin composition of heterochromatic telomeres . To discriminate between these possibilities we introduced in a peoh mutant background a Bs w+ y+ Y chromosome that carries a euchromatic fragment marked with Bs w+ and y+ at its YL end [39] ( see also FlyBase ) . We found that in peoh mutant brains the frequency with which the Bs w+ y+ Y chromosome is involved in TFs is more than 5-fold lower than that of a normal Y ( Fig 4 ) . Specifically , the formation of Y rings , which in peoh/peoh mutants represents ~90% of the total fusions involving the Y ( that together are more than 40% of the observed TFs ) , was drastically reduced in the presence of a Bs w+ y+ Y . Because the cells bearing a Bs w+ y+ Y chromosome do not exhibit detectable morphological variation in the chromocenter compared to wild type , these results strongly suggest that the preferential involvement of the Ha-telomeres in peo-induced TFs is a consequence of their association with heterochromatin and not of their arrangement within the interphase nucleus . The particularly high frequency of Y rings in peo mutants is another peculiar feature of their TF pattern . Indeed , the Y rings are rare or virtually absent in mutants in genes such as ver , tefu , woc , and nbs ( Fig 4 ) . On the assumption of a random involvement of telomeres in TFs , the expected frequency of Y ring is 1/15 of the total fusions involving the Y chromosome and 1/6 of the fusions involving the Y and either the XR , or the 4th chromosome telomeres . Thus , formation of Y rings in peo mutants is specifically and strikingly frequent . This could reflect a particularly high frequency of fusigenic lesions on the Y telomeres , or the contemporary presence of these lesions on the opposite Y telomeres , or both . We have recently observed that brains from larvae heterozygous for both Su ( var ) 20505 and cav exhibit ~ 0 . 1 TFs/cells , while in wild type , Su ( var ) 20505/+ , cav/+ brains the TF frequency was virtually zero ( 300 metaphases analyzed in each case ) . This finding suggests that Su ( var ) 205 and cav genetically interact and that a simultaneous reduction of HP1a and HOAP results in a low level of TFs . We thus asked whether peo exhibits similar genetic interactions with other TF mutants . Double heterozygotes for peo1 and either cav1 , moi1 , ver2 , Su ( var ) 20504 or Su ( var ) 20505 displayed TF frequencies ranging from 0 . 05 to 0 . 1 per cell , whereas heterozygotes for peo1 and either effΔ73 , wocB111 , mre11DC , rad50Δ5 . 1 , nbs1 or tefuatm6 did not exhibit TFs ( in all cases , we examined at least 300 metaphases ) . We next asked whether the Peo protein physically interacts with the terminin components . A preliminary experiment using the yeast two-hybrid assay suggested that Peo directly interacts with HOAP but not with HP1a ( S1 Fig ) . To confirm this result we performed a GST pulldown assay using bacterially expressed 6His-Peo and GST-tagged HOAP polypeptides of different length . As shown in Fig 5A and 5B , the intact HOAP protein and the HOAP fragments containing the N-terminal region of the protein ( aa 1–145 ) precipitated Peo , while a larger HOAP fragments including the 3 repeated segments of the protein ( aa 109–343 ) failed to bind Peo . We then investigated whether Peo interacts with Moi and Ver . We performed GST pulldown experiments with extracts from human 293T cells expressing Peo-FLAG and either GST alone , GST-Moi , GST-Ver or GST-HOAP . We chose to express Drosophila tagged proteins in human cells because a heterogeneous cellular environment is likely to reduce the probability of indirect interactions among fly proteins . As shown in Fig 5C , Peo-FLAG is precipitated by GST-Moi , GST-Ver and GST HOAP but not GST alone . Collectively , these results provide strong evidence that Peo directly binds HOAP; in addition , they suggest that Peo directly interacts with Moi and Ver . peo encodes an E2 variant ( UEV ) enzyme; the UEV proteins are similar to the E2 ubiquitin conjugating enzymes ( UBCs ) but lack the catalytic cysteine residue that mediates the interaction between ubiquitin and E2 [40] . We elaborated a three-dimensional model of Peo exploiting a series of bionformatic analyses ( Fig 6A; see also Material and Methods and S2 Fig ) . We confirmed that Peo lacks the catalytic cysteine of E2 enzymes . In addition , 8 residues before the catalytic cysteine site , Peo exhibits an HPH tripeptide ( S2 Fig ) instead of HPN , which is a canonical signature of the E2 superfamily [41] . Peo contains a UEV domain of ~150 amino acids resembling the canonical E2 fold in its hydrophobic core and active site region . This domain consists of three helices packed against a four-stranded antiparallel β-sheet . Next to the UEV domain , Peo contains two C-terminal helices that are present in all E2 proteins but missing in other E2 variant enzymes such as Tsg101 and Mms2 . Finally , prediction of potentially disordered regions revealed that Peo also contains a long ( 50 aa ) disordered region at the C-terminus ( Figs 6A and S2; see also Materials and Methods ) . To map the Peo sites that interact with the terminin proteins , we subdivided Peo into 3 GST-tagged fragments: Peo 1 ( aa 1–31 ) that contains the Peo N terminal region which is absent in the short isoform ( see Fig 2 ) ; Peo 2 ( aa 31–180 ) that includes the central UEV domain of the protein; and Peo 3 ( aa180-244 ) that contains C-terminal disordered region of Peo ( Fig 6B ) . These bacterially expressed GST-tagged Peo fragments were then used to probe Drosophila S2 cell extracts expressing HOAP-HA , Moi-HA or Ver-FLAG ( Fig 6C–6E ) . GST pulldown showed that both Moi and Ver interact with the entire Peo protein ( GST-Peo ) , GST-Peo 1 and GST-Peo 2 but not with GST-Peo 3 or GST alone ( Fig 6D and 6E ) . HOAP did not display the same interaction pattern as Moi and Ver . It was precipitated by GST-Peo and GST-Peo 1 but not by GST-Peo 2 and GST-Peo 3 and thus failed to interact with the Peo UEV domain ( Fig 6C ) . The finding that Peo binds HOAP , Moi and Ver prompted us to ask whether Peo is required for terminin localization at chromosome ends . Although Peo does not appear to interact with HP1a , we also asked whether loss of the wild type function of peo affects HP1a localization at telomeres . Of the latter proteins , only HOAP is clearly detectable at both mitotic and polytene chromosome telomeres; HP1a , Moi and Ver can be easily detected at polytene chromosome ends but not at mitotic telomeres [23 , 24 , 42] . We thus analyzed HOAP localization in both mitotic and polytene chromosomes of peo1 homozygous mutants; HP1a , Moi and Ver localization was instead studied only in peo1 polytene chromosomes . An analysis of mitotic chromosomes immunostained for HOAP revealed that mutations in peo do not substantially affect HOAP localization at telomeres , as the frequency of HOAP-stained telomeres in peo mutants and the intensity of the signals were similar to those observed in wild type controls ( Fig 7A ) . Consistent with these results , immunostaining with anti-HOAP and ant-HP1a antibodies showed that peo1/Df mutants exhibit normal concentrations of these proteins at polytene chromosome telomeres ( Fig 7B ) . Because antibodies to Moi or Ver are not currently available , to analyze the localization of these proteins in peo mutants we constructed flies expressing GFP-tagged forms of Moi or Ver in a peo1 mutant background . The analysis of unfixed polytene chromosome nuclei from peo1/peo1 flies expressing either GFP-Moi or Ver-GFP revealed that they exhibit 6 discrete GFP signals ( Fig 7C ) , which we have previously shown to correspond to the 6 euchromatic telomeres of polytene chromosomes [23 , 24] . In addition , we found that the intensities of these signals were comparable to those observed in peo1/+ heterozygotes ( Fig 7C ) . Collectively , these results indicate that the wild type function of peo is not required for telomeric localization of HOAP , Moi , Ver and HP1a , and that the strong telomere fusion phenotype observed in peo mutants is not due to the absence of any of these proteins . As pointed out in the introduction , terminin subunits are non-conserved fast-evolving proteins that localize and function only at telomeres . Peo is not a terminin-like protein because it does not share any of these properties . Peo is well conserved in Drosophila species ( S3 Fig ) and has homologues in mouse and humans ( Ft1 and AKTIP , respectively ) . In addition , a three-dimensional model of Drosophila Peo is rather similar to an AKTIP model [43] . To determine the subcellular localization of Peo we raised a rabbit polyclonal antibody against the entirety of Peo and affinity purified it against a GST-Peo fusion protein ( see Material and Methods ) . Western blotting analysis showed that this antibody recognizes 3 bands of ~ 32 , ~28 and ~ 25 kDa . In extracts from peo1/Df , and peo1527/Df larval brains , these 3 bands were reduced by approximately 70% compared to +/Df controls ( Fig 8A and 8B ) . Thus , the band with the highest molecular weight is likely to correspond to the longest Peo isoform , while the other two bands might correspond to the shorter isoforms ( see Fig 2 ) . In peoh/Df mutant brains , the Peo bands were reduced by approximately 20% with respect to +/Df brains , consistent with the relatively low TF frequency observed in peoh mutants ( Fig 1 ) . These results indicate that peo1 and peo1527 are strong hypomorphs compared to the weaker peoh allele . Indirect immunofluorescence experiments on wild type polytene nuclei showed that the anti-Peo antibody stains the nucleolus and many bands along the polytene chromosomes ( Fig 8C and 8D ) . In peoh/Df and peo1/Df nuclei , both the nucleolus and chromosome staining were significantly reduced compared to either +/Df or wild type nuclei , confirming the specificity of the antibody . In addition , the polytene chromosomes of peoh/Df larvae were more intensely stained than those of peo1/Df larvae ( Fig 8C and 8D ) , confirming that the peo1 allele is stronger than peoh . Although Peo is enriched at numerous polytene bands and interbands , we were not able to detect clear Peo accumulations at chromosome ends . Given that Peo interacts with terminin and it is required to prevent telomere fusion , the most likely explanation for this finding is that Peo is present at the telomere caps in amounts that are not detected by the antibody and the immunostaining technique used here . We have recently found that AKTIP/Ft1 is required for proper telomeric DNA replication [43] . This finding suggested that peo mutations could also impair DNA replication and specifically affect heterochromatic telomeres , which are likely to replicate at the end of the S phase together with the bulk of heterochromatin ( reviewed in [38] ) . To test this possibility we examined DNA replication in brain cells by analyzing the incorporation of the EdU ( 5-ethynyl-2’-deoxyuridine ) analog of thymidine . Brains were incubated in saline containing 10 mM EdU for 1 h , immediately fixed and then stained with the Click-It Alexa Fluor method to detect EdU ( see Methods ) . In wild type brains , 10% of the nuclei were actively replicating their DNA and incorporated EdU , whereas in peoh/peoh and peo1/peo1 brains the frequency of EdU-labeled nuclei dropped to 7% and 5 . 5% , respectively ( Fig 9A and 9B ) . In contrast , in woc and ver mutants , the frequency of EdU-positive nuclei was not significantly different from wild type controls , suggesting that the DNA replication defect observed in peo mutants is a specific outcome of the reduced peo activity and not a general consequence of impaired telomere protection . EdU staining also allowed subdivision of the S phase according to the incorporation pattern . We distinguished nuclei in early/mid S ( S1 ) in which the nucleus was partially or completely stained with the exception of the heterochromatic chromocenter , nuclei in mid/late S ( S2 ) in which both the chromocenter and the less compact nuclear areas were stained , and nuclei in late S ( S3 ) in which only the chromocenter displayed EdU incorporation ( Fig 9 ) . We found that wild type and peo mutant brains do not differ in the relative frequencies of nuclei showing S1 , S2 and S3 EdU incorporation patterns . Thus , we conclude that the wild type function of peo is required for general DNA synthesis and not for completion of specific sub-phases of DNA replication . To gather additional information on the role of Peo in DNA replication we analyzed the distribution of PCNA ( proliferating cell nuclear antigen ) in peo1/peo1 and peoh/peoh mutant nuclei . PCNA is a processivity factor for DNA polymerases; in nuclei PCNA is either present in a soluble form that can be extracted by detergent treatment or in a detergent-resistant form tightly associated with DNA replication forks ( chromatin-bound PCNA ) [44] . The analysis of Triton X-extracted brain preparations immunostained for PCNA showed that in wild type 7% of the nuclei were PCNA-positive , while in peo1/peo∆520 and peoh/peoh brains the frequencies of PCNA-stained nuclei were 1 . 2% and 0 . 8% , respectively ( Fig 10 ) . These results are consistent with those on EdU incorporation and show that in peo mutants the frequency of nuclei with chromatin-bound PCNA is much lower than in control . Collectively our results suggest three possibilities: ( i ) that any impairment of Drosophila telomere replication results in telomere fusion , ( ii ) that Peo is required for a specific step of telomere replication and that an incorrect execution of this steps results in fusigenic lesions , or ( iii ) that Peo plays a dual function being independently required for telomere replication and telomere capping . To discriminate between these possibilities we treated wild type and peoh/peoh mutant brains with the DNA polymerase inhibitor aphidicolin ( APH ) , which is known to impair human telomere replication [45–47] . As shown in Fig 11 , control and mutant brains were treated in three different ways . Dissected brains were incubated for 1 . 5 hours in 110 mM APH in NaCl 0 . 7% . They were then washed , transferred into 3 ml of APH-free saline and fixed 1 , 2 or 3 hours after the end of the APH treatment; in all cases , 1 hour before fixation , we added colchicine to the saline to collect metaphases . None of the APH treatments caused TFs in wild type brains or increased the TF frequency in peoh/peoh mutants . We note that the APH treatment was highly effective as it caused a strong reduction in the frequency of mitoses in brains fixed 1 h after the end of the APH treatment ( Fig 11B ) . Our previous work has shown that mutations in Drosophila timeless2 ( tim2 ) result in chromosome breakage but not in TFs [48] . Tim2 is the Drosophila ortholog of mammalian TIM , a replisome component that facilitates DNA synthesis [49 , 50] and is required for telomere replication [8] . These results prompted us to examine the cytological phenotype of brains from mutants in the Blm ( formerly mus-309 ) gene [51] , the Drosophila homolog of the Bloom syndrome gene , which encodes a DNA helicase required for mammalian telomere replication [46 , 47] . We found that BlmD2/BlmD3 mutant brains exhibit chromosome breaks ranging from 4 to 7% but not TFs ( 500 metaphases scored from 7 brains ) . These results suggest that a general impairment of Drosophila telomere replication does not result in TFs .
We have shown that strong peo mutants exhibit an average of 5 TFs/cell; these TFs appear to involve all the telomeres of the Drosophila chromosome complement making difficult a reliable assessment of the relative involvement of individual telomeres in fusion events . However , in weak peo mutants or in strong mutants bearing a peo+ rescue construct , which exhibit ~ 1 TF /cell , the majority of fusions involved the XR , the Y and the 4th chromosome telomeres . These results suggest that these telomeres are preferentially affected by mutations in peo and that this effect is partially masked in strong mutants where most telomeres are fused . In all the other Drosophila mutants we characterized ( eff , Su ( var ) 205 , cav , mre11 , rad50 , nbs , tefu , woc , moi and ver ) individual telomeres were engaged in TFs with frequencies that are different from those expected for a random involvement . The telomeres of the major autosomes were involved in TFs more frequently than expected , while participation of the XL telomere in fusions was either slightly lower or conformed to the expected frequency . In contrast , the Y , the XR , and the 4th chromosome telomeres were engaged in TF less than expected ( Fig 3 and S1 Table ) . peo mutants displayed an inverse TF pattern , with higher than expected frequencies of fusions involving the Y , the XR , and the 4th chromosome telomeres ( Fig 3 and S1 Table ) . To the best of our knowledge , this is the first case in which individual telomeres of an organism exhibit different fusigenic capabilities in response to the genetic background . We believe that this finding reflects the peculiar structural differences between the telomeric regions of the different Drosophila chromosomes . All Drosophila chromosomes of wild type strains terminate with HTT arrays of variable length ( made of complete and incomplete HeT-A , TART and TAHRE elements ) ( reviewed in [5 , 52 , 53] ) and are capped by the multiprotein terminin complex ( reviewed in [6] ) . However Drosophila telomeres differ from each other in both the type of subtelomeric chromatin and in the properties of their HTT arrays ( Fig 12 ) . One of the most straightforward features that contradistinguish some of the Drosophila telomeres is their association with constitutive hetrochromatin . Approximately one-third of the Drosophila genome is made of constitutive heterochromatin; the entire Y chromosome , the short arm ( XR ) and the proximal 40% of the long arm ( XL ) of the X chromosome , the short arm ( 4L ) and the proximal 70% of the long arm ( 4R ) of the 4th chromosome , and the centric 25% of chromosomes 2 and 3 are heterochromatic [38 , 54] . Thus the HTT arrays of YL , YS , XR and 4L are linked to constitutive heterochromatin ( Fig 12 ) . The HHT blocks of XL and those of the major autosomes are not directly associated with euchromatin but are instead juxtaposed to divergent clusters of subtelomeric repeats , known as telomere associated sequences ( TAS ) ( reviewed in [55] ) . The TAS are not only different in sequence but are also occasionally absent from the subtelomeric regions , suggesting that their presence is not essential for proper telomere function [55] . Finally , the 4R telomere is joined to a special type of chromatin that has peculiar features , as well as features shared with both euchromatin and heterochromatin; for example the 4R distal chromatin is enriched in the 4th chromosome-specific Painting of four ( Pof ) protein and the heterochromatic markers HP1a and histone 3 methylated at lysine 9 ( H3K9 ) ( reviewed in [56] ) ( Fig 12 ) . Additional features that differentiate Drosophila telomeres are the silencing properties of their HTT arrays ( Fig 12 ) . Several studies have shown that white+ transgenes inserted within or next to the TAS are partially silenced leading to a variegated eye phenotype ( a phenomenon known as telomere position effect or TPE ) ( reviewed in [5] ) . However , white+ transgenes inserted into the HTT arrays behave differently depending on the insertion site . Insertions into the HTTs of telomeres not directly joined to heterochromatin such as those of the 2L , 3L and 3R arms do not appear to be subject to TPE [57] . In contrast white+ transgenes inserted within the 4R ( only one tested ) and YS ( 6 tested ) HTTs lead to a variegated eye phenotype [57 , 58] . Furthermore , the white+ transgenes inserted into the HTT array of YS and those embedded into or near the TAS respond differently to genetic modifiers . For example , mutations in Su ( var ) 205 ( HP1a ) , which suppress variegation of genes relocated next to constitutive heterochromatin ( position effect variegation or PEV; reviewed in [59 , 60] ) do not affect the TAS-associated TPE [5 , 55] but strongly suppress the TPE of transgenes inserted into the HTT of YS ( 58 ) . These results indicate that the chromatin of the YS HTT shares some features with constitutive heterochromatin and has different properties from the chromatin that includes the autosomal HTT arrays and the TAS . These results suggest that the HTT arrays of XR , YL , and 4th chromosome telomeres have properties similar to those of YS , namely they are subject to a “heterochromatinization” process related to their particular location . Numerous studies on PEV have shown that proteins that are typically enriched in the heterochromatin can spread into neighboring euchromatic genes changing their chromatin composition and packaging and downregulating their expression [59 , 60] . Thus , although the properties of the HTT arrays of YL , XR and 4th chromosome have not been tested , it is quite likely that they are similar to those of YS . This would suggest that in a peo mutant background the preferential involvement of these telomeres in TF is a consequence of their “heterochromatinization” . This in turn implies that the heterochromatic markers of the HTT regions of the YL , XR and 4h chromosome telomeres extend to the terminin-coated chromosome ends . However , “heterochromatinization” does not extend to the HTT arrays at the end of the left arm end of the BSw+y+Y chromosome , because in peo mutants this marked Y forms much fewer Y rings than a normal Y . Our findings on ring Y formation in peo mutants also suggest that the different “heterochromatic” telomeres respond differently to Peo depletion . The higher than expected frequency of ring Ys observed in peo mutants could be a consequence of the particularly high fusigenic properties of the Y telomeres . We would like to note that the “heterochromatinization” of the YL , YS , XR and 4th chromosome termini is the natural condition of these telomeres and that we only know that this condition makes them more fusigenic than the other Drosophila telomeres . However , we have no information on the actual properties of these telomeres . Namely , we do not know the properties of their terminin-associated regions . For example , we do not know whether the terminin-coated chromosome ends of YL , YS , XR and 4L have different silencing properties and different responses to PEV and TPE modifiers compared to the terminin-bound regions of the other chromosome ends . We have shown that mutations in peo genetically interact with mutation in genes that encode the terminin subunits . Consistent with these results , we have also shown that Peo directly binds terminin and mapped the Peo-HOAP interacting domains . Thus , although Peo does not share the properties of the terminin proteins , it is clearly a component of the Drosophila telomere capping machinery . What is then the role of peo in telomere protection ? The finding that mutations in peo do not affect HOAP , Moi and Ver localization at telomeres strongly suggest that loss of peo function does not cause TFs by affecting terminin recruitment at telomeres . Similarly , the normal accumulation of HP1a at the telomeres of peo mutants suggests a telomere fusion mechanism independent of HP1a . More in general , the fact that peo , but not the other TF genes ( eff , Su ( var ) 205 , cav , mre11 , rad50 , nbs , woc , moi and ver ) , preferentially affect “heterochromatic” telomeres suggests that peo might either act upstream to these genes or function in a telomere protection pathway that does not involve them . The findings that mutations in peo impair DNA replication and preferentially affect late replicating “heterochromatic” telomeres raise the possibility that defective telomere duplication might be fusigenic in Drosophila . However , there are several reasons that lead us to exclude this possibility . Should it be correct , one would expect that impairment of some of the many factors that mediate DNA replication would results in TFs . However , in addition to the aphidicolin treatment and Blm mutations described here , several additional DNA replication factors have been described whose loss fails to induce TFs . Hydroxyurea ( HU ) , which blocks DNA replication by inducing a deoxyribonucleotide triphosphate ( dNTP ) pool depletion , does not induce TFs in brain cells [48] . In addition , a number of mutations ( or RNAi treatments ) disrupting different aspect of DNA replication do not results in TFs in Drosophila brain cells or S2 tissue culture cells , although they cause more or less extensive chromosome breakage . These include lesions in the genes/RNAs encoding the origin recognition ( ORC ) and the minichromosome maintenance ( MCM ) prereplication complexes , DNA primase , the Cul4 replication licensing factor , the replisome components DNA polymerase alpha , Rpa70 , Tim2 and PCNA , as well as the chromatin assembly factor Caf1 that assists in loading the histone tetramer after DNA replication [21 , 48 , 61–64] . We thus hypothesize that the peo-dependent TFs are generated by a peculiar defect in telomeric DNA replication that creates specific fusigenic lesions . Heterochromatin replication is likely to be different from that of euchromatin , as it requires not only DNA duplication but also reinstallment of specific epigenetic markers such as heterochromatin-associated proteins and histone modifications ( reviewed in [65] ) . It is thus possible that in the presence of weak peo mutations replication of “heterochromatic” telomeres is preferentially affected leading to specific Peo-dependent fusigenic lesions concentrated in the XR , the Y and the 4th chromosome ends . In strong peo mutants , these fusigenic lesions would be generated also in “euchromatic” telomeres resulting into a more general involvement of telomeres in fusion events . However , we cannot exclude the possibility that peo plays a dual function being independently required for DNA replication and telomere capping . In conclusion , we propose that Peo is a Drosophila telomere-capping protein that preferentially protects chromosome ends associated with heterochromatic markers . Our results also indicate that Peo is required for general DNA replication and most likely also for telomere replication . However , while it is possible that loss of Peo generates specific fusigenic lesions during telomere replication , it is unlikely that a general impairment of Drosophila telomere duplication leads to telomere fusion . In this respect Drosophila telomeres are similar to human telomeres , which fail to fuse following defective replication [8 , 46 , 47 , 66] .
The pendolino1 ( peo1 ) mutation was isolated by a cytological screen of 120 late lethal mutants mapping to the second chromosome , recovered after I element mobilization by I-R dysgenic crosses [67] . The late lethal mutations l ( 2 ) 1527 , l ( 2 ) 2723 , the insertion lines p112 , p221 and p520 were described previously [30] and were kindly provided by P . Taghert ( Washington University , MO ) . The peoh allele was isolated from a cytological screen of a collection of 193 late lethal mutants that arose in the Zucker’s collection of heavily mutagenized viable lines [68] . The Df ( 2R ) X3 , Df ( 2R ) B5 , Df ( 2R ) X1 and Df ( 2R ) BSC298 deletions , the insertion line cbx05704 , were obtained from the Bloomington Stock Center and are described in FlyBase . All peo alleles and the deficiencies of 2R were balanced over CyO Tb balancer [69] . The eff , Su ( var ) 205 , cav , mre11 , rad50 , nbs , woc , moi and ver mutations were previously described [10 , 12 , 15 , 16 , 21 , 23 , 24] . The DNA sequences flanking the peop112 insertion were obtained by inverse PCR using standard procedures . To obtain the rescue constructs , the LD08052 full lenght peo cDNA was cloned into pCASPER4 transformation vectors . Germline transformation was carried out by the BestGene Company . A pCasper4 [w+ , peo+] insertion ( with a Hsp70 promoter ) on the X chromosome was used to establish the [w+ , peo+]; peo1/CyO Tb stock . Animals from this stock were heat-shocked for 1h at 37°C every day starting from the embryonic stages; we then examined the brains of non-Tb third instar larvae for the presence and frequency of TFs and the adult flies for the presence of w+ , peo+; peo1/peo1 individuals . For the rescue experiments we also employed a pCASPER4 [w+ , peo+-HA] insertion ( with a Tubulin promoter ) on the third chromosomes . We established [w+ , peo+-HA]/TM6C; peo1/CyO Tb and [w+ , peo+-HA]/TM6C; peoh/CyO Tb stocks and examined them for TFs in non-Tb larvae . The [w+ , peo+-HA]/TM6C; peo1/CyO Tb stock was also examined for the presence of peo1/peo1 non Cy flies . Information on the genetic markers and balancers used in this study is available at FlyBase ( http://flybase . bio . indiana . edu/ ) . Stocks were maintained and crosses were made on standard Drosophila medium at 25°C . To generate anti-Peo polyclonal antibodies , rabbits were immunized with bacterially expressed 6His-Peo . Immunization and production of anti-Peo antibodies were carried out by the Agro-Bio Company ( France ) . To purify the anti-Peo antibody we used a bacterially expressed GST-Peo protein . About 1 mg of this tagged protein was run on a polyacrylamide gel and then blotted onto a nitrocellulose membrane . The membrane strip containing GST-Peo was cut out , washed in 100 mM glycine/HCl pH 2 . 5 for 5 min , washed for 5 min in TBS , blocked by incubation with 3% BSA for 1 hour , and washed again for 4 min in TBS . The membrane was then cut into small pieces and incubated overnight with rotation at 4°C in 2 ml of serum diluted 1:5 in TBS . After centrifugation and removal of supernatant the membrane pieces were washed for 15 min in 50 mM Tris/HCl pH 7 . 5 , 500 mM NaCl , for 5 min in 50 mM Tris/HCl pH 7 . 5 , 100 mM NaCl , and for 10 min in TBS . The membrane was then incubated for 30 min in 1ml of 100 mM glycine/HCl pH 2 . 5 to elute the antibody . The eluate was then mixed to a proper volume 1M Tris pH 8 . 8 to bring the final pH to 8 . 0 , and then kept at 4°C before use . DAPI-stained colchicine-treated larval brain chromosomes were prepared according to [10] . Preparation and immunostaining of mitotic and polytene chromosomes were carried out as described previously [10 , 20] , with minor modifications . For anti-PCNA immunostaining , dissected larval brains were incubated in PBS with 0 . 5% Triton X-100 for 3 min , and then fixed in 3 . 7% formaldehyde according to [70] . Before immunostaining , brain squash preparations were Triton-X extracted by incubating the slides in 0 . 1% Triton X-100 containing PBS ( PBT ) , 2 times for 10 min . The primary antibodies used for immunostaining were: the rabbit anti-Peo described above diluted 1:10; rabbit anti-HOAP ( 1:100 ) , mouse anti-HP1a ( C1A9; 1:10 ) , and mouse anti-PCNA ( 1:20; Abcam ab29 ) . The anti-HP1a antibody C1A9 was obtained from the Developmental Studies Hybridoma Bank , created by the NICHD of the NIH and maintained at The University of Iowa , Department of Biology , Iowa City , IA 52242 . After an overnight incubation at 4°C with the primary antibody , slides were washed twice in TBS-Tween 0 . 1% for 15 min and then incubated for 2 h at room temperature with FITC-conjugated goat anti-mouse ( 1:20; Jackson Laboratories ) , or AlexaFluor 555-conjugated donkey anti-rabbit ( 1:200; Invitrogen ) antibodies . All slides were then mounted in Vectashield medium H-1200 with DAPI to stain DNA . in vivo detection and immunostaining of GFP-tagged proteins on polytene chromosomes were carried out as previously described [24] . Chromosome preparations were analyzed using a Zeiss Axioplan epifluorescence microscope ( CarlZeiss , Obezkochen , Germany ) , equipped with a cooled CCD camera ( CoolSnap HQ , Photometrics , Woburn , MA ) . Gray-scale digital images were collected separately , converted to Photoshop format , pseudocolored , and merged . To quantify the polytene chromosome fluorescence intensity after Peo immunostaining , we used the ImageJ software ( National Institute of Mental Health , Bethesda , Maryland , USA ) . Given that the distribution of fluorescent bands along the chromosomes was rather uniform , for each polytene nucleus we selected 3–4 different chromosome regions of similar length , and measured both their fluorescence and the fluorescence of a close chromosome-free region to correct for background fluorescence . For each genotype ( wild type , +/Df , peoh/Df and peo1/Df ) shown in Fig 8 we measured at least 30 polytene regions from at least 10 nuclei . The coding sequences of Peo , Hp1a and HOAP were PCR-amplified and cloned into pGBKT7 or pGAD-T7 vectors ( Clontech ) . The S . cerevisiae AH109 strain was transformed with the indicated combinations of plasmids and assayed for growth on SD/–His/–Trp/–Leu selection plates supplemented with 20 mM 3-amino-1 , 2 , 4-triazole ( 3-AT ) , according to the manufacturer’s instructions . To obtain the GST-Moi , GST-Ver , GST-HOAP and GST-Peo fusion proteins , the corresponding full length cDNAs were cloned in either pGEX-6P1 or pGEX-3X , expressed in bacteria and purified as described previously [24] . The GST-Peo1 , GST-Peo2 , GST-Peo3 , GST-HOAP ∆3 , GST-HOAP ∆2 , 3 , GST-HOAP ∆1–3 and GST-HOAP ∆N-TERM truncated proteins were obtained by cloning the corresponding PCR-generated sequences in pGEX-6P1; bacterially expressed GST fusion proteins were then purified by incubating crude lysates with glutathione sepharose beads ( QIAGEN ) as recommended by the manufacturer . To generate 6His-Peo , the LD08052 peo full-length cDNA was cloned into the pQE32 expression vector ( QIAGEN ) , and expressed in bacteria; 6His-Peo was affinity purified with a Ni-NTA resin using standard procedures . To obtain extracts for Western Blot analysis , 50 dissected third instar larval brains were lysed in an ice-cold buffer containing 20 mM Hepes KOH pH 7 . 9 , 1 . 5 mM MgCl2 , 10 mM KCl , 420 mM NaCl , 30 mM NaF , 0 . 2 mM Na3VO4 , 25 mM BGP , 0 . 5 M PMSF , 0 . 1% NP40 , and 1X protease inhibitor cocktail ( Roche ) . To obtain HOAP-FLAG , Ver-FLAG and Peo-FLAG expressing S2 cells , cav , ver or peo cDNAs were cloned in the pAWF vector ( DGRC ) in frame with the FLAG-coding sequence . For the expression of Moi-HA , the moi full lenght cDNA was fused in frame with the HA-coding sequence and then cloned into a pCASPER4 vector . All constructs were transfected in S2 tissue culture cells using Cellfectin ( Invitrogen ) , and cells were harvested 72 h after transfection . Extracts were lysed in 20 mM Tris pH 8 . 0 , 420 mM NaCl , 1 mM MgCl2 , 1 mM DTT , 0 . 1% NP40 , and 1X protease inhibitor cocktail ( Roche ) . For preparation of human cell extracts , HeLa cells expressing Peo-FLAG cloned in the pCDNA vector were harvested after 72hr transfection and lysated in 20 mM Tris pH 8 . 0 , 420 mM NaCl , 1 mM MgCl2 , 1 mM DTT , 0 . 1% NP40 and 1X protease inhibitor cocktail ( Roche ) . For GST-pulldown assays , protein extracts were incubated with 2 μg of each GST fusion protein bound to sepharose beads in a buffer containing 20 mM Hepes KOH , 20 mM NaF and 0 . 8% NP40 for 1h at 4°C . Sepharose-bound GST proteins were collected by centrifugation , washed several times with 20 mM Hepes KOH , 20 mM NaF and 1 . 8% NP40 , and resuspended in Laemli buffer in a 30μl final volume for Western Blot analysis . For immunoblotting , protein samples were run into SDS polyacrilammide gels and electro-blotted on a nitrocellulose membrane ( Bio-Rad ) in a phosphate buffer ( 390 mM NaH2PO4 /610 mM Na2HPO4 ) . For the detection of HOAP-FLAG , Ver- FLAG , Peo- FLAG , and Moi-HA , membranes were probed with anti-FLAG HRP-conjugated ( 1:1000; Roche ) , and anti-HA HRP-conjugated ( 1:500; Roche ) antibodies; Peo and His-Peo were detected with our rabbit anti-Peo ( 1:100 ) , and Giotto with our anti-Giotto ( 1:5000; [71] ) . Secondary antibodies were sheep anti-mouse IgG HRP-conjugated ( 1:5000 ) , or donkey anti-rabbit IgG HRP-conjugated ( 1:5000 ) ( both from Amersham Biosciences ) . The blots were developed using the ECL or ECL Plus method ( Amersham Biosciences ) and signals were detected with the ChemiDoc scanning system ( BioRad ) . Band intensities were quantified using the image acquisition and analysis Image lab 4 . 0 . 1 software ( Biorad ) . EdU labeling was performed as per the manufacturer's instructions ( Invitrogen , ClickiT Alexa Fluor 488 Imaging kit ) . Larval brains were cultured with 10μM EdU in 1 X PBS for 60 min prior to fixation and detection . Brains from third instar larvae were dissected in saline ( 0 . 7% NaCl ) , incubated in saline with 110 μM Aphidicolin ( APH ) for 1 . 5 hours , rinsed in saline , and then transferred into a 33 mm Petri dish containing 3 ml of Schneider’s medium ( SIGMA ) supplemented with 10% fetal bovine serum ( FBS , Gibco BRL ) for 1 , 2 or 3h . Colchicine at a final concentration of 10-5M was added to the medium 1 hour before fixation according to [10] . Control wild type and peoh/peoh brains were incubated in saline without APH for 1 . 5 , and processed like the APH-treated brains; they were fixed after 2 hours incubation n the medium . As a first step towards the construction of a three-dimensional model of Peo , we used its full-length sequence ( Accession code: Q7K4V4 ) as a query to search the UniProtKB database ( http://www . uniprot . org/ ) using CSI-BLAST [72] , with an Expectation ( E ) value threshold of 10–5 . Iterative searches of the database yielded 59 unique sequences . The retrieved sequences were aligned with the CLUSTALW software [73] with default parameters . The multiple sequence alignment ( MSA ) was next used as a seed to construct a Hidden Markov Model ( HMM ) of the family . The HMM was employed to search the Pfam database ( http://pfam . sanger . ac . uk/ ) via the HHpred server [74] . The highest scoring hit was the ubiquitin-conjugating enzyme ( UBC ) family also known as the E2 enzyme family [34] ( probability to be a true positive more than to 99% , E-value equal to 1 . 2 x10-42 ) . The third scoring hit was the ubiquitin E2 variant ( UEV ) family ( Pfam ID: PF05743 ) that includes UBC homologs such as Tsg101 , Mms2 and UEV1 ( probability to be a true positive equal to 96 . 44% , E-value equal to 1 . 3 x10-4 ) . Peo belongs to the UEV family because it contains an aspartic acid residue ( at position 106 , according to SwissProt numbering ) in place of the E2 active site cysteine , and it is unable to catalyze ubiquitin transfer as it lacks the cysteine that forms a transient thioester bond with the C-terminus of ubiquitin ( Ub ) . Prediction of potentially disordered regions using the GeneSilico MetaDisorder server ( http://iimcb . genesilico . pl/metadisorder/ ) revealed that at the C-terminus of Peo there is a stretch of ~ 70 aa ( from residue 177 to 244 , according SwissProt numbering ) that has the tendency to be intrinsically disordered ( i . e . lack a unique three dimensional structure at least in the absence of a binding partner ) , while the region including residues 16–176 shows propensity to form a folded globular domain with a well-defined pattern of secondary structures as revealed by the Quick2d web server analysis [75] . Because no homologous structure with sufficiently high sequence identity with Peo is available , we performed the Peo modeling using the composite approach implemented in I-TASSER server ( Iterative Threading ASSEmbly Refinement ) [76] . The Peo sequence from residue 16 to 176 ( predicted to fold in a globular domain ) was submitted to the server and the model with the best confidence score ( C-score = 0 . 5 ) returned by I-TASSER was selected . We added hydrogen atoms in this model using HAAD software [77] and refined it close to the native structure using FG-MD molecular dynamics based algorithm [78] . Our final refined model of Peo was evaluated as a potentially extremely good model ( with a predicted LGscore of 2 . 50 ) by the PRO-Q model quality assessment program [79] . The QMEAN score [80] was 0 . 6 ( the variability range is 0–1 , with 1 being a perfect model ) . Collectively these parameters indicate that the Peo three-dimensional model is sufficiently accurate for making functional inferences . | Telomeres are specialized structures that protect chromosome ends from incomplete replication , degradation and end-to-end fusion . Abnormalities in telomere structure or maintenance can promote a variety of human diseases including premature aging and cancer . Although all human telomeres contain the same DNA sequences , they differ from each other in the subtelomeric regions or subtelomeres . Recent work has shown that human subtelomeres control telomere replication and that abnormalities in these structures can lead to localized chromosome instability and disease . However , the relationships between subtelomeres and telomeres are currently poorly understood . Here , we have addressed this problem using the fruit fly Drosophila melanogaster as model system . Drosophila subtelomers are very different from each other as they contain different types of chromatin . We have found that mutations in a gene we called pendolino ( peo ) cause telomeric fusions ( TFs ) and that these fusions preferentially involve the telomeres associated with a tightly packed form of chromatin called heterochromatin . Interestingly , none of the 10 mutants with TFs so far described in Drosophila shows the pattern of TFs observed in peo mutants . Thus , our data provide the first demonstration that subtelomeres can affect telomere fusion . We believe that these results will stimulate further studies on the role of subtelomeres in the maintenance of genome stability . |
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Gene expression regulators , such as transcription factors ( TFs ) and microRNAs ( miRNAs ) , have varying regulatory targets based on the tissue and physiological state ( context ) within which they are expressed . While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts , methods for transferring regulator target genes across contexts are lacking . Further , regulator target gene lists frequently are not curated or have permissive inclusion criteria , impairing their use . Here , we present a method called iterative Contextual Transcriptional Activity Inference of Regulators ( icTAIR ) to resolve these issues . icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context , quantifying that regulator’s activity for that context . It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity , removes the uncorrelated genes from the list , and iterates the process until it derives a stable list of refined target genes . To validate and demonstrate icTAIR’s power , we use it to refine the MSigDB c3 database of TF , miRNA and unclassified motif target gene lists for breast cancer . We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3 , 430 patients . We uncover many novel prognostic regulators that were obscured prior to refinement , in particular NFY , and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome . We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts . The icTAIR algorithm can be downloaded from https://github . com/icTAIR .
The major gene expression regulators , DNA-binding transcription factors ( TFs ) and mRNA-binding microRNAs ( miRNAs ) , have long been known to play critical roles in cellular physiology and pathophysiology , especially cancer [1–4] . By modulating transcription ( TFs ) or complementarily binding to the 3’ UTR of mRNA transcripts and decreasing or silencing their expression ( miRNAs ) , they affect the cellular state by the integration of the induced changes in protein levels of their regulatory targets . Far from monotonous , their targets and functions vary based on the tissue and cellular state ( hereafter referred to as the “context” ) within which they are expressed . This reflects contextual variability in hetero- and eu-chromatin composition , promoter methylation status , concurrent exogenous regulatory activity , baseline gene expression , and post-translational modifications , among other factors [5 , 6] . In a widespread effort to understand their activities , numerous experiments and tools seeking to identify and characterize regulators have emerged over the past decade . Among these , the Encyclopedia of DNA Elements ( ENCODE ) [7] project has and continues to conduct Chromatin ImmunoPrecipitation followed by Sequencing ( ChIP-Seq ) studies to explore genomic regions of TF binding , from which TF regulatory target genes can be inferred [8]; many groups have conducted similar experiments leading to databases of TFs and their targets , such as TRANSFAC [9–11] , ReMap [12] and ChEA [13] . On the miRNA side , both experimental techniques ( knockdowns or inductions , combined with gene expression analysis ) and computational methods , like miRanda [14] , PicTar [15] , PITA [16] , RNAhybrid [17] , and TargetScan [18] , have led to the miRBase [19] , TarBase [20–23] , and miRTarBase [24 , 25] databases of miRNAs and their predicted targets , among others . Still more databases , such as the Molecular Signatures Data Base ( MSigDB ) [26 , 27] , compile results across regulators , contexts and other databases . While these sources of regulators and their targets promise to enhance understanding of regulator activities and their implications , currently the full extent of this promise is unfulfilled . This is due to two intertwined issues: ( 1 ) target gene inaccuracy and ( 2 ) problems of contextual transference . Without doubt , the accurate determination of regulator target genes is difficult , with much experimental noise and room for identification algorithm improvement [28] , leading to high false positive and negative rates of a regulator’s identified target genes . Second , target gene lists are generated and confirmed using context-specific experiments , if experimentally generated and confirmed at all , creating the challenge of transferring and combining results from one context to another . While this challenge has been approached with success through set operation intersection of target gene lists to create “core” target gene sets for use in a new context [29] , this approach necessarily drops context-specific target genes , even if genuine , and results in the rapid shrinkage of target gene lists as more and varying contexts are intersected . As such , set operation unions of target gene lists may be performed instead; however , this results in the opposite issue of creating non-specific gene lists that increase in size and inaccuracy as more and varying contexts are combined . All told , these issues highlight the need for a new approach for target gene list refinement and contextual transference . In this paper , we present a method called iterative Contextual Transcriptional Activity Inference of Regulators ( icTAIR ) to jointly solve these issues . icTAIR’s key insight is simple: a strong , genuine target of a regulator for a context will have its expression correlate with the regulator’s activity in that context . Put another way , each target gene’s degree of expression should contribute to the degree of a regulator’s activity . icTAIR applies this insight by calculating a regulator’s activity level for a context and correlating it with each of its target genes’ expression levels , thereby discerning strong , genuine targets from false targets for that context . icTAIR then iteratively drops false and/or weak targets from the target gene list and repeats the process until it achieves a stable target gene list . As a crucial additional outcome , this refinement greatly improves the precision of the calculation of a regulator’s activity to enhance downstream analysis . To validate icTAIR and demonstrate the power of this precision improvement , we employ it to contextually refine the MSigDB c3 database of 825 TF , miRNA , and unclassified regulatory motif target gene lists for breast cancer using The Cancer Genome Atlas ( TCGA ) ’s gene expression dataset of 590 breast cancer ( BRCA ) samples . First , we use the output of each iteration of icTAIR to see how each regulator’s list of target genes changes across consecutive iterations . Concurrently , we employ these consecutive lists to calculate regulator activity levels and survival analyses in the independently-generated METABRIC dataset [30] of 1992 breast cancer samples , finding that icTAIR refinement creates stable gene lists that markedly improve the analyses . We then use icTAIR’s final output for survival analysis for each of the 825 regulatory motifs in 7 independent datasets covering 3 , 430 patient samples . We find icTAIR greatly improves our analysis’ resolution and reveals numerous prognostic regulators that were obscured prior to icTAIR refinement . Gains in resolution are especially large for miRNAs . Further validation checks of specific results are passed and give us full confidence in the icTAIR method and results . Next , we follow-up by using these results to look in detail at the composition and relationships among the breast cancer prognostic regulome , and conclude by identifying 29 regulatory motifs that are prognostic in all datasets even after clinicopathological multivariate adjustment . Excitingly , we find that regulatory motifs associated with the E2F and NFY TF families have the greatest and most significant effects on breast cancer prognosis , in alignment with past results for E2F [29] and novel for NFY . Given the strength of these findings , especially in contrast to those achieved without icTAIR refinement , we anticipate icTAIR will be of general use for contextual refinement of regulator target genes and discovery of novel activities and implications across many biological contexts .
A schematic overview of icTAIR and its integration into regulator functional analysis is presented in Fig 1 . icTAIR is an iterative algorithm that takes as its input previously-defined target genes of regulators and gene expression data from a dataset of samples for a given context ( Fig 1A ) . It then employs the previously described Binding Association with Sorted Expression ( BASE ) method [31] to integrate these inputs and calculate individual Regulatory Activity Scores ( iRASs ) for each sample for that regulator . It then correlates each target gene’s expression with the regulator’s iRAS across the samples , drops the weakly and uncorrelated target genes to create an updated target gene list , and repeats the whole process . Once the number of pre-specified iterations are complete , the resultant contextually-refined target gene lists can be used for final regulator iRAS discernment for any dataset of samples within that context . The specific use of icTAIR for this work is represented in Fig 1B . Each MSigDB c3 target gene list was contextually refined by icTAIR for the TCGA BRCA two-channel gene expression dataset . icTAIR parameters were default set as follows: minimum spearman correlation , 0 . 1; minimum target gene list length , 20; maximum number of icTAIR iterations , 10 . These parameters were empirically chosen to establish relaxed refinement criteria yet maintain sufficient numbers of target genes for downstream analysis . Once the 10 iterations were completed , the final , BRCA-refined regulator target gene lists were combined using BASE with gene expression data from the three independent datasets of METABRIC ( 1 , 992 tumors ) [30] , Ur-Rehman ( 1 , 118 tumors ) [32] and Vijver ( 260 samples ) [33] to calculate regulator iRASs for each of the tumors in the datasets . These scores were used for the rest of the analysis . To understand the process of icTAIR refinement we investigate the output of each consecutive iteration on the composition of the regulator target gene lists and associated survival analyses . For each icTAIR iteration , we counted the number of target genes on each regulator’s list and plotted how this number evolved across iterations . Fig 2A shows a boxplot of the results for each iteration across the regulators , revealing that the numbers of regulator target genes fall asymptotically and stabilize by the fifth icTAIR iteration . This trajectory matches what one would expect if icTAIR were accurately refining the lists to true contextual targets . To further validate these results , we input each of these regulator target gene lists into BASE using the METABRIC dataset , and then employ the derived iRASs for survival analysis for each regulator for each iteration . To do this , univariate Cox proportional hazards ( PH ) models were constructed of the consecutive regulator iRASs vs . survival for the samples , yielding Hazard ratios ( HRs ) and associated p-values that were subsequently corrected for multiple comparisons into Q-values . Ranking these Q-values , we selected three most-prognostic and three least-prognostic regulators ( one TF , one miRNA , and one unclassified motif for each group ) and plotted how icTAIR refinement iteratively affects each regulator’s number of target genes and Q-value of prognostic significance ( Fig 2B and 2C ) . For both the most ( 2B ) and least ( 2C ) significant regulators , the target gene lists stabilize around 5 iterations , matching the overall results ( Fig 2A ) . Moreover , for the most significant regulators , the Q-values of the results increase in significance with each iteration ( Fig 2B ) , whereas for the least significant regulators , the Q-values decrease in significance ( Fig 2C ) . These results suggest that icTAIR effectively improves the statistical “signal-to-noise” ratios of downstream analyses , as the regulators with initially-significant prognoses see gains in significance and those initially lacking in significance become increasingly insignificant . To utilize the icTAIR refinement , the final , BRCA-refined target gene lists were inputted into BASE for iRAS calculation into the METABRIC [30] , Ur-Rehman [32] , and Vijver [33] breast cancer datasets . For each of the regulators , univariate survival analyses were conducted in each dataset . A summary of the results and comparison across datasets is shown in Fig 3 . Considering the different sample sizes in the different datasets , we set conservative Q-value thresholds of 1e-06 for the METABRIC ( 1 , 992 tumors ) , 1e-03 for the Vijver ( 260 samples ) , and 1e-03 for the Ur-Rehman ( 1 , 118 tumors ) results to reflect the corresponding differences in statistical power . Using these thresholds , 352 unique survival-associated regulatory programs were found ( Fig 3A ) , of which 59 were significant in all three datasets and deemed pan-dataset univariate prognostic ( S1 Table ) . Examining the makeup of the prognostic regulators , Fig 3B shows the Cox PH results for the 200 regulators in the METABRIC dataset , with each dot a unique regulator color-coded by type and plotted based on its HR and Q-value . Given the dot distribution , it is seen that the TFs ( green dots ) tend to have more favorable prognostic implications than do miRNAs ( red dots ) . Repeat analyses for the Vijver and Ur-Rehman datasets show similar results ( S1 Fig ) . Further analysis revealed complete concordance of regulator motif HR direction ( positive vs . negative ) between all pairwise comparisons of the datasets ( Fig 3C ) , indicating that the identified survival-associated regulators have stable pan-dataset prognostic implications ( favorable or unfavorable ) . To benchmark these results , we repeated this analysis instead using the raw MSigDB c3 target gene lists , with no icTAIR refinement ( S2 Fig ) . Each panel in S2 Fig was drawn with the same parameters ( Q-value thresholds and plot areas ) as in Fig 3 . As seen with the volcano plots ( Figs 3B and S2B ) , Q-value significance of all results is dramatically improved with the use of icTAIR ( Fig 3B , smallest Q-value <1e-15; S2B Fig , smallest Q-value >1e-09 ) . Further , without icTAIR the analysis fails to identify any prognostic regulators in the Vijver dataset , resulting in 0 pan-dataset prognostic regulators discovered . Of the datasets with results ( METABRIC and Ur-Rehman ) , the fraction of prognostic regulators that overlaps increases after icTAIR refinement ( 50% vs . 48% overlap METABRIC , 43% vs . 21% Ur-Rehman , Figs 3A vs . S2A ) , indicating the improved strength of results . Notably , an examination of the volcano plots shows an especially remarkable improvement in prognosis discernment of miRNAs following the use of icTAIR ( Figs 3B vs . S2B , number of red dots above horizontal Q-value threshold line ) . icTAIR refinement’s biological sensibility was examined via a detailed analysis of the Estrogen Receptor ( ER ) TF regulatory motif program . MSigDB c3 contains a target gene list for ER named V$ER_Q6_01 , for which it was reasoned that a statistically significant , favorable prognosis should be observed in ER+ tumors and no prognostic implications observed for ER- tumors , given that standard breast cancer hormonal therapy targets the ER pathway to great effect and the pathway would be inactive in ER- tumors . If icTAIR refinement is effective and operates as intended , the icTAIR refined V$ER_Q6_01 regulatory program should demonstrate this pattern . The results of this analysis are shown in Fig 4 . For construction of all plots , the V$ER_Q1_06 iRASs were dichotomized into two groups around 0 , stratified into ER+ and ER- groups based on pathological data annotation , and combined with clinical survival data to construct Kaplain-Meier ( KM ) curves for each dataset as shown . For both the METABRIC ( Fig 4A and 4D ) and Ur-Rehman ( Fig 4B and 4E ) datasets , the ER regulatory motif is highly favorably prognostic within ER+ samples ( p-value < 1e-06 , log-rank test ) but not within ER- ones ( p-value = 0 . 24 , METABRIC dataset; p-value = 0 . 99 , Ur-Rehman dataset; log-rank test ) . For the Vijver dataset ( Fig 4C and 4F ) , the pattern holds but is less pronounced ( ER+ p-value = 0 . 048 , ER- p-value = 0 . 54 , log-rank test ) . Further , the fraction of iRASs>0 is much higher in ER+ vs . ER- samples across all datasets: 2 . 7 , 1 . 8 , and 5 . 6 times higher for METABRIC , Ur-Rehman , and Vijver , respectively ( bottom-left of all panels ) , consistent with the expectation that ER+ tumors would have greater ER regulatory activity than ER- ones . Examination of the 59 pan-dataset univariate prognostic regulators ( Fig 3B ) reveals the presence of both novel and previously identified regulators ( S1 Table ) . Closer analysis of the E2F ( a family of TFs ) , HIF1 ( a TF ) , and MIR-19 ( a miRNA ) regulatory motifs is provided in Fig 5 as representative members of the group . Dichotomizing each of these regulatory motif’s iRASs around 0 and constructing KM curves for each survival dataset demonstrates the prognostic effects of these three regulators ( p-value <0 . 0001 , all regulators and datasets , log-rank test ) . These results match those from prior studies [29 , 34–36] . Network analysis was pursued to investigate the relationships among the pan-dataset univariate prognostic regulators . For each regulator , the associated icTAIR-refined target gene lists were examined for the presence of other members in the MSigDB c3 database and used to establish regulator-target relationships . Regulator-redundant motifs were collapsed by regulator and mapped to visualize the regulatory network of the MSigDB c3 regulome . Maps of the regulatory relationships of the pan-dataset univariate prognostic regulators targeting all other members of the MSigDB c3 regulome ( Fig 6A ) and restricted to regulatory relationships among themselves ( Fig 6B ) are shown . Of note , all unclassified regulatory motifs are necessarily excluded from the right . Size of circles indicate the number of relations of the regulator and color indicates direction of prognosis ( blue , more favorable; purple , less favorable ) . A follow-up multivariate analysis of each univariate prognostic regulator to adjust for clinicopathological factors was pursued . For each of the 59 pan-dataset univariate prognostic regulatory motifs , a multivariate Cox PH model was constructed for it and all available non-redundant clinicopathological factors ( see Materials and Methods ) . Results were corrected for multiple comparisons and all motifs whose hazard ratios were significant at Q-value thresholds ( see Materials and Methods ) in all datasets were kept to create Table 1 , ranked in descending order of HR . For the complete multivariate-adjusted results of the 59 pan-dataset univariate prognosic motifs , see S2–S4 Tables . As each of the listed regulators remains prognostic after full clinicopathological variable adjustment , they all contribute prognostic information beyond clinicopathological variables currently collected as part of breast cancer patient diagnosis and care . Certain regulatory programs appear particularly prognostic: the regulatory motifs associated with the TF families E2F and NFY appear multiple times and are ranked at the top of the list . Comparing these results with Fig 6 puts these prognostic effects into a relational context . Each of the 59 prognostic regulators that were identified across the three datasets ( Fig 3A ) using univariate Cox PH modeling were selected for multivariate Cox PH adjustment incorporating clinicopathological data . Variables adjusted for were all available , non-redundant factors for each dataset as follows: METABRIC , patient age at diagnosis , disease stage , tumor grade , ER status ( positive or negative ) , PR status ( positive or negative ) , HER2 status ( positive or negative ) , and non-surgical treatment status ( whether the patient received treatment beyond surgery ) ; Ur-Rehman and Vijver: patient age at diagnosis , tumor size , tumor grade , lymph node status ( positive or negative ) , ER status ( positive or negative ) , and non-surgical treatment status ( whether the patient received treatment beyond surgery ) . P-values were adjusted for multiple comparisons into Q-values using the FDR method . 29 regulatory motifs met Q-value significance thresholds ( < 0 . 01 for METABRIC , and < 0 . 05 for Ur-Rehman and Vijver , reflecting differences in statistical power ) and are listed . Motifs are ordered by descending magnitude of the adjusted hazard ratio ( HR ) in the METABRIC dataset ( second column ) . Survival information is disease-specific for METABRIC ( n = 1 , 481 ) , relapse-free for Ur-Rehman ( n = 762 ) , and overall for Vijver ( n = 260 ) , respectively .
Dysregulation of gene expression is integral to essentially all disease states , making the characterization of regulator activity of central importance in understanding pathophysiology . While past efforts have successfully introduced methods to infer regulator activity levels based on the expression levels of their target genes [31 , 37] , these methods necessarily require regulator target gene lists or binding profiles [8] . This makes the accuracy of target gene lists and binding profiles crucial for accurate regulatory activity inference and downstream analysis of its implications . As a regulator’s targets vary across contexts , regulator-characterizing experiments are expensive and noisy , and target gene list curation lacks stringency ( if it is even pursued ) , achieving this accuracy across contexts is a challenge . The icTAIR method introduced here addresses this challenge . By using contextual gene expression data to infer a regulator’s activity from the expression levels of an associated target gene list in that context , it can then analyze via spearman correlation the degree to which each gene’s expression level contributes to the inference of the regulator’s activity . The iterative removal of uncorrelated genes from the target gene list , re-inference of regulator activity , and re-correlation of each remaining target gene with the re-inferred activity progressively refines the target list until only genes that affect the regulator’s end activity score are included . As such , icTAIR distills target gene lists for contexts to greatly enhance their context-specific accuracy . Examining the consecutive output of the icTAIR algorithm shows this process at work ( Fig 2 ) : with repeated iterations , the regulator target gene lists stabilize ( Fig 2A ) and downstream analyses achieve parallel leaps in accuracy ( Fig 2B and 2C ) . Utilizing icTAIR’s output for downstream breast cancer survival analysis demonstrates the performance improvement and findings it makes achievable . The MSigDB c3 database of target gene lists , representing targets of the known evolutionarily conserved regulome in mammals [38] , should itself be accurate and enable robust analysis . Nevertheless , icTAIR refinement of the database achieves an improvement in statistical significance of regulatory motif breast cancer survival HRs of roughly 7 orders of magnitude with a resultant enormous increase in identified prognostic regulatory motifs ( Figs 3 vs . S1 ) . Most impressive are the gains achieved for miRNAs ( red dots , Figs 3B and S1 ) . This is perhaps unsurprising , as miRNA targets tend to be predicted based on complementary sequence homology more than experimentally confirmed , suggesting that they may have the greatest opportunity for accuracy improvement . It is additionally noted that this analysis employed icTAIR refinement in a dataset distinct from those in which prognostic studies were undertaken , that these gains were seen in all datasets , and that the datasets were generated across seven independent studies using both one- and two- channel microarray platforms . The likelihood that the refined output and its results are thus artifacts of any given dataset , experimental system , or the icTAIR algorithm is improbable . Further increasing confidence in the icTAIR-enabled analysis are specific result examples . For one , the uncovered prognostic effect of the ER regulatory motif matches the expected result , conferring a favorable prognosis in ER positive tumors and no effect on prognosis in ER negative ones ( Fig 4 ) . The ER regulatory motif iRASs are also remarkably lower in ER negative vs . ER positive samples . Continued examination of the results shows that E2F , HIF1 , and MIR-19 associated regulatory motifs are uncovered as unfavorably prognostic in all datasets , results that reconfirm those of prior studies conducted by us and others using independent approaches and data sources [29 , 34–36] . Beyond statistical and general icTAIR validation achieved with comparing Fig 3 to S2 Fig , these findings demonstrate the biological sensibility of the analysis’s output . This confidence provides for continued exploration of the results and derivation of their meaning . Examining regulator prognosis by regulator type ( Fig 3B ) shows that miRNAs ( red dots ) overwhelmingly have positive HRs , indicating that general miRNA functional overexpression may be of broadly unfavorable breast cancer prognostic significance . Whether this is instigative ( mechanistic of a worse outcome ) , reactionary ( an induced , failing attempt by regulatory systems to reduce aberrant mRNA overexpression ) or a mixture of the two requires further investigation . Turning to the relationships of the prognostic regulators , a regulatory network constructed of and among them ( Fig 6 ) highlights their interconnectedness and their particularly central members ( Fig 6A , sizes of spheres; Fig 6B , number of connecting lines ) . The FOXO4 , E2F , TFAP4 , TCF , and NFYA regulatory programs particularly stand out . In contrast , the miRNA programs tend to surround the periphery , although this may be artefactual and represent decreased information regarding their personal expression regulatory regions . A detailed analysis of regulator prognostic significance when controlling for clinicopathological variables is perhaps of greatest clinical importance . Taking the 59 pan-dataset univariate prognostic regulatory motifs identified from univariate survival analysis ( Fig 3A ) , multivariate adjustment for all available clinicopathological factors and a repeat of significance testing with multiple comparisons correction finds 29 regulatory motifs that remain prognostic in all datasets ( Table 1 ) . These motifs provide prognostic information for breast cancer beyond currently assessed factors and likely represent entirely new mechanistic pathways of tumorigenesis and potential avenues for therapy . Given past results , it is unsurprising and confirmatory that E2F-associated regulatory motifs are extensively repeated across the list . More intriguingly is the repetition of NFY regulatory motifs and their placement at the very top ( largest magnitude of prognostic effect ) of the cohort . This recasts the NFY target-to-regulator relationship to E2F and autoregulatory behavior ( Fig 6B ) in a new light . By others , the NFY TF family has been previously studied as a mediator of tissue invasion in granulocytes via regulation of cell-to-cell adhesion through induction of CD34 [39 , 40] , has known roles in angiogenesis [41] , is negatively regulated by p53 [42] , and has been implicated in colorectal adenocarcinoma formation and metastasis [43] . Still , to our knowledge the finding here is the first time NFY activity has been identified as dysregulated and of key prognostic importance in breast cancer . In this study , we focused on MSigDB c3 regulator target gene list refinement and their implications for improving downstream analyses in breast cancer . A limitation of these gene lists is that target genes are predicted based on the presence of binding motifs in promoter proximal DNA regions for TFs and in the 3’UTR of mRNAs for miRNAs , without further experimental validation . Thus , these gene lists are generally associated with both high false positive ( by lacking experimental validation ) and false negative ( by restricting all regulator activity to binding in target promoter regions ) rates . However , we note that icTAIR is a flexible method that can take as input gene expression and target gene list data from any source to provide contextual refinement . There are numerous possibilities for this , not least of which is improving long-distance ( enhancer ) TF target gene prediction from ChIP-Seq data . We hope that icTAIR will help power many genomic-based analyses across many contexts going forward .
Datasets used in this study were of two types: breast tumors gene expression and regulatory motif target gene lists . Tumor gene expression datasets were selected based on completeness of associated survival and clinicopathological data , sample size , and coverage of differing survival endpoints and microarray types . For the icTAIR contextual refinement dataset , TCGA level 3 breast cancer ( BRCA ) data covering 590 samples [44] were downloaded from the TCGA data portal ( https://tcga-data . nci . nih . gov/tcga/ ) . For survival analysis datasets , annotation and collation efforts from prior work [29 , 45] led to the inclusion of the filtered , renormalized meta-analysis dataset from [32] ( Ur-Rehman ) containing 1 , 118 samples with survival and clinicopathological data from 5 independent datasets [46–50] and the dataset from [33] ( Vijver ) containing 260 samples with survival and clinicopathological data . Extending beyond prior efforts , the METABRIC dataset from [30] containing 1 , 992 samples was additionally selected due to its size and richness of clinicopathological data . Download sources were as follows: Ur-Rehman , the NCBI GEO database ( www . ncbi . nlm . nih . gov/geo , GSE47561 ) ; Vijver , The Netherlands Cancer Institute ( http://ccb . nki . nl/data/ ) ; METABRIC , the European Genome-phenome Archive ( https://ega . crg . eu/studies/ , study ID EGAS00000000083 ) . Datasets were both one-channel ( Ur-Rehman and METABRIC ) and two-channel ( TCGA and Vijver ) . Survival endpoints spanned relapse-free ( Ur-Rehman ) , disease-specific survival ( METABRIC ) , and overall survival ( Vijver ) . For the regulatory motif target gene lists , the MSigDB [26 , 27] was queried and the c3 database of 825 TFs , miRNAs , and unclassified regulatory motif target gene lists selected as the most complete repository of evolutionarily-conserved [38] regulators available . Evolutionary conservation of regulators was desired to enrich for biological importance and accuracy of motifs . Target gene lists for each motif were downloaded from the GSEA repository ( http://www . broadinstitute . org/gsea/msigdb/collections . jsp#C3 ) . All data collection took place in December 2014 , with dataset sizes reflecting all then-available samples / lists . icTAIR requires as input two data sources , 1 ) a preliminary target gene list of a regulator and 2 ) a dataset of gene expression profiles of samples from a context of interest , and two parameters , I ) the minimum allowable length of a target gene list and II ) the minimum correlation threshold ( 0–1 ) . An optional parameter , a maximum number of allowed iterations , may also be specified . The first step of icTAIR is to input data sources 1 ) and 2 ) into the BASE algorithm for iRAS calculation ( equivalent to the AC from [31] , with the slightly-modified background function as introduced in [37] ) . Briefly , BASE works by sorting the gene expression data in descending order of expression level and generating two non-decreasing functions , the first to encapsulate the expression activity of the genes on the target gene list ( foreground function ) and the second to do the same for those that are not ( background function ) . It then calculates the maximum division of the two functions to get a preliminary score . This preliminary score is similar in concept to the D-statistic of the Kolmogorov-Smirnov test and is representative of the expression activity of the target genes relative to background expression , with a higher preliminary score indicating relatively higher target gene activity and a lower preliminary score indicating relatively lower target gene activity . This preliminary score is then further normalized against the average of the absolute value of the preliminary scores from 1 , 000 randomly permuted target gene list to ultimately generate an iRAS for a given regulator . Of note , in this context icTAIR provides BASE the target gene list by creating a vector of all genes for the given genome ( e . g . , human RefSeq ) and assigning a weight of 0 to the non-targets and 1 to the targets , i . e . , the input is as a list g = [g1 , g2 , g3 , … , gj , … , gn] where gj = 0 if a non-target , 1 if a target , and n = number of genes in the genome; this is so the target gene list matches the format of BASE’s binding affinity data ( see [31] ) . Of further note , the expression level e of each gene gj for each sample si , ej , i is normalized to either its internal reference ( for two-channel microarray expression data ) or the median of its expression across the samples ( for one-channel microarray expression data ) . Once BASE generates the list of iRASs for the dataset of samples , icTAIR computes the spearman correlation coefficient ρ between each target gene tj’s expression and the sample iRASs , namely , ρ ( tj ) =1−6∑i=1sdi2s ( s2−1 ) , where s is the total number of samples and di is the difference in the rank parameter between the sorted values of tj’s ei and the iRASi . For each tj in the target list , t = [t1 , t2 , t3 , … , tj , … , tn] , where n is the total number of target genes , icTAIR compares ρ ( tj ) to the minimum correlation threshold parameter value . If the parameter is set as a ρ threshold ( 0–1 ) , for all tj for which ρ ( tj ) is less than the threshold , its value in the gene list g is converted to 0 from 1 and it is dropped from the target list t . With these updated lists g and t , iRASs are recalculated and the entire process repeated until either i ) the length ( n ) of the target list t shrinks to the minimum allowable length parameter , ii ) membership in t stabilizes , or ( optional ) iii ) the maximum allowable iterations are reached . The icTAIR algorithm has been implemented as a R function and can be downloaded from https://github . com/icTAIR . Using both the raw MSigDB c3 target gene lists and the output from icTAIR-refinement , BASE was employed to generate iRASs for each regulatory motif in the 3 overall survival analysis datasets ( METABRIC , Ur-Rehman , and Vijver ) . Of note , while icTAIR does not require that its refinement be conducted in a gene expression dataset distinct from the datasets used for downstream analysis , this was done to reduce any potential dataset-specific source of confounding and because of a paucity of survival information for the TCGA data . For BASE , parameters were set at 1 , 000 permutations for all iRAS calculations and median normalization was implemented for the one-channel array datasets ( METABRIC and Ur-Rehman ) . Univariate Cox Proportional Hazards ( PH ) regression modeling was subsequently performed in each dataset for each regulatory motif’s iRAS . P-values of each hazard ratio ( HR ) for each motif were corrected for multiple comparisons ( across all regulatory motifs ) within each dataset into Q-values using the FDR method . Thresholds for statistical significance were set at Q-value < 1e-6 for METABRIC and Q-value < 1e-3 for Ur-Rehman and Vijver , reflecting differences in statistical power across these differently-sized and collected datasets and a desire to be highly conservative . Regulatory motifs that met these threshold criteria in all 3 datasets were deemed pan-dataset univariate prognostic . For visualization of selected pan-dataset univariate prognostic regulatory motifs , Kaplan-Meier ( KM ) survival curves were generated by dichotomizing the motifs’ associated iRASs around 0 into two groups for plotting . KM p-values were generated using the log-ranks test . Regulatory motifs whose HRs met statistical significance thresholds in univariate Cox PH analyses in all three datasets ( i . e . , those deemed pan-dataset univariate prognostic ) were chosen for follow-up clinicopathological multivariate Cox PH adjustment . Variables included for adjustment were all the non-redundant factors available for each dataset . For METABRIC , this included patient age at diagnosis , disease stage , tumor grade , ER status ( positive or negative ) , PR status ( positive or negative ) , HER2 status ( positive or negative ) , and non-surgical treatment status ( whether the patient received treatment beyond surgery ) ; for Ur-Rehman and Vijver , this included patient age at diagnosis , tumor size , tumor grade , lymph node status ( positive or negative ) , ER status ( positive or negative ) , and non-surgical treatment status ( whether the patient received treatment beyond surgery ) . As for univariate analyses , p-values of each motif’s multivariate-adjusted HR were corrected for multiple comparisons ( across all adjusted motifs ) within each dataset into Q-values using the FDR method . Thresholds for statistical significance were set at Q-value < 0 . 01 for METABRIC and Q-value < 0 . 05 for Ur-Rehman and Vijver , reflecting differences in statistical power across these datasets . Thresholds were lower than in univariate analyses due to a loss of power from multivariate correction and a decrease in sample count due to only including samples with full clinicopathological data . Sample sizes were 1 , 481 for METABRIC , 762 for Ur-Rehman , and 260 for Vijver . For all survival analyses , survival endpoints were disease-specific survival in METABRIC , relapse-free survival in Ur-Rehman , and overall survival in Vijver . All work was performed using R software and its survival package , specifically the survreg ( ) , survdiff ( ) , and coxph ( ) functions . Venn diagrams were constructed using the VennDiagram package . icTAIR-refined univariate prognostic regulatory motif target gene lists were searched for the presence of other members in the MSigDB c3 regulome and used to assign regulator-to-target relationships: if a regulatory motif target gene list included another member of the MSigDB c3 regulome , it was deemed a regulator and the other member its target . This process was undertaken for all univariate prognostic regulatory motifs , searching among all other members in the MSigDB c3 regulome ( i . e . , 825 total ) and within the restricted corpus of pan-dataset univariate prognostic regulatory motifs only . Regulator-target relational maps were subsequently constructed by referring to the annotated TFs of motifs . Due to the limitations of the unclassified regulatory motif data , these motifs were necessarily excluded from the more restricted analysis . All motifs redundant on a regulator were collapsed down to the regulator level . | Gene expression regulators , such as transcription factors and microRNAs , are critical actors in cellular physiology and pathophysiology and act by modulating the expression levels of sets of target genes . Given their significance , numerous experiments have sought to characterize the specific target genes of specific regulators , which in turn has led to regulator target gene list databases . Unfortunately , these lists are plagued by poor curation and validation . Further , all lists suffer from the fundamental issue that regulator targets vary across tissue type and physiological state , or “context” , making them poor for conducting downstream , context-specific analyses . To address this issue , here we present a method called icTAIR that contextually-refines regulator target gene lists . To demonstrate its value , we use icTAIR to take the largest-available database of regulator target gene lists , refine it for the breast cancer context , and use both the pre-refined and refined lists for downstream survival analyses in over 3 , 400 tumors . We find that icTAIR improves the statistical power of the analyses by multiple orders of magnitude . This in turn lets us map the relational network of breast cancer regulators and identify regulators with prognostic effects even after clinicopathological adjustment . We anticipate icTAIR will be broadly useful in regulator studies . |
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Alternative polyadenylation ( APA ) can for example occur when a protein-coding gene has several polyadenylation ( polyA ) signals in its last exon , resulting in messenger RNAs ( mRNAs ) with different 3′ untranslated region ( UTR ) lengths . Different 3′UTR lengths can give different microRNA ( miRNA ) regulation such that shortened transcripts have increased expression . The APA process is part of human cells' natural regulatory processes , but APA also seems to play an important role in many human diseases . Although altered APA in disease can have many causes , we reasoned that mutations in DNA elements that are important for the polyA process , such as the polyA signal and the downstream GU-rich region , can be one important mechanism . To test this hypothesis , we identified single nucleotide polymorphisms ( SNPs ) that can create or disrupt APA signals ( APA-SNPs ) . By using a data-integrative approach , we show that APA-SNPs can affect 3′UTR length , miRNA regulation , and mRNA expression—both between homozygote individuals and within heterozygote individuals . Furthermore , we show that a significant fraction of the alleles that cause APA are strongly and positively linked with alleles found by genome-wide studies to be associated with disease . Our results confirm that APA-SNPs can give altered gene regulation and that APA alleles that give shortened transcripts and increased gene expression can be important hereditary causes for disease .
In protein-coding genes , the polyadenylation process consists of cleaving the end of the 3′ untranslated region ( UTR ) of precursor messenger RNA ( pre-mRNA ) and adding a polyadenylation ( polyA ) tail . Alternative polyadenylation ( APA ) can occur when several polyadenylation ( polyA ) signals lie in the last exon of a protein-coding gene . Many APA signals are evolutionary conserved [1] , and Expressed Sequence Tag ( EST ) data suggest that 54% of human genes have alternative polyadenylation signals [1] . The polyA signals themselves are hexamer DNA sequences that usually lie 10 to 30 nucleotides upstream from the cleavage site [2] , but a GU-rich region 20 to 40 nucleotides downstream of the cleavage site is also important for the polyA-process [2] . One functional consequence of APA is transcripts with different 3′UTR lengths and different microRNA ( miRNA ) regulation [3] , [4] . Shortened transcripts tend to have increased expression compared with longer transcripts , and the same expression increase can be achieved by deleting miRNA target sites in non-shortened transcripts [5] . Data on APA can be used as an efficient biomarker for distinguishing between cancer subtypes and for prognosis [6] , and seems to play an important role in gene deregulation and in many human diseases [7] . One such mechanism for deregulation is mutations in the polyA signal or GU-rich downstream region [7] . A single nucleotide polymorphism ( SNP ) in the GU-rich region downstream of an alternative polyA signal in the FGG gene has for example been shown to affect the usage of this polyA site , and has been associated with increased risk for deep-venous thrombosis [8] . Similarly , a mutation in the 3′UTR of the CCND1 gene has been shown to create an alternative polyA signal and is associated with increased oncogenic risk in mantle cell lymphoma [9] . Hypothesizing that mutations in DNA elements such as the polyA signal can be an important cause of altered APA , we investigated to what extent SNPs can create or disrupt APA signals ( APA-SNPs ) . Specifically , we tested whether APA-SNPs can give shorter 3′UTRs , increased gene expression through loss of miRNA regulation ( Fig . 1 ) , and be associated with disease . Our hypothesis focuses on shorter 3′UTRs rather than longer ones , because the loss of functional miRNA sites in the 3′UTR is more likely than the gain of new sites downstream of the gene . First , by analysing EST data , we found that SNPs can create polyA motifs and affect 3′UTR length . Second , differential allelic expression from RNA-seq data , as well as mRNA and miRNA microarray expression data revealed an association between alternative polyA site strength ( signal and GU-content ) , loss of miRNA target sites , and transcript expression . Third , based on these analyses we also identified significant APA-SNPs . Fourth , we mapped the identified SNPs to disease-associated SNPs and found that APA alleles were significantly correlated with disease-risk alleles . Together , these results suggest that APA-SNPs can be a significant causative mechanism in disease ( Fig . S1 ) .
The distribution of SNPs within 3′UTRs is fairly uniform [10] ( Fig . S2A ) . The main exceptions are microRNA target sites and the start and end of the 3′UTR , which have decreased SNP diversity that is consistent with these regions containing functional elements under selective pressure [10] . Indeed , when specifically investigating the region around the transcription end site , we found that the position containing the polyA signal has a markedly decreased SNP density ( Fig . S2B , C ) , indicating that SNPs arising there could have a high functional impact . To analyse SNPs in alternative polyadenylation signals , we first identified a set of SNPs that potentially create new APA signals in 3′UTRs . Specifically , we searched for any Hapmap SNP [11] that could create or disrupt one of the 13 known polyA signal hexamers [1] in any coding gene's 3′UTRs ( see Methods ) . We found 1954 SNPs , including 755 SNPs that are mono-allelic in the CEU population from Hapmap [11] ( see Datasets ) . We kept only the APA-SNPs that change from no signal to one signal in the locus , by discarding loci with several signals in the 40 nucleotides around the SNP , discarding SNPs that change one signal into another , and discarding mono-allelic SNPs . After filtering , 412 SNPs that can create or delete potential polyadenylation signals remained . We will from now refer to them as our candidate SNPs . To investigate whether SNPs can create functional alternative polyA sites , we analysed the EST-based polyA sites from the PolyA_Db database [12] , [13] . In the PolyA_Db database , there are several polyA sites which do not have any noticeable polyA signal ( according to the reference genome ) in the 40 , 80 , and 100 nucleotides upstream from the reported cleavage site position ( Table S1 ) . In those regions , we used different SNP data to look for SNPs that could create a polyA signal with the non-reference allele . When considering regions of 100 nucleotides and SNPs from NCBI dbSNP Build 130 [14] , we could identify polyA signals with the alternative allele for more than of the missing signals . Some of the remaining sites can probably be explained by SNPs further upstream , and some other by exon splicing , by alterations in ESTs that are not registered in dbSNP , or as false positive sites . Since EST-based annotated polyA sites can be affected by SNPs , we wanted to test whether alleles in polyA sites could be associated with EST ending positions . Specifically , we first took the intersection between the polyA signals from our 412 candidate SNPs , and the polyA sites from PolyA_Db database [12] , [13] . We identified 18 intersecting polyA sites that have a polyA signal for either the reference or the non-reference allele . These sites corresponded to 18 SNPs in 18 genes . Five SNPs were discarded because they lie within the 20 last nucleotides of the reference 3′UTR . The following 13 genes remained: ABCC4 , AKAP13 , FANCD2 , KY , MIER1 , OSTM1 , PNN , RASGRP3 , RHOJ , SELS , SHMT1 , SLBP , and SLC11A2 . Second , for each of these genes , we identified and imputed ( see Methods ) alleles at the SNPs in the EST sequences when possible , and tested if the proportion of alleles with polyA signal ( APA alleles ) was different for EST sequences ending within the interval nucleotides around the polyA site , compared to EST sequences ending further downstream ( see Methods ) . The two genes MIER1 ( SNP rs17497828 ) and PNN ( SNP rs532 ) were significant ( Fig . 2 , Table 1 ) . After correcting for multiple testing ( Benjamini & Hochberg correction ) , the genes remained significant when including alleles imputed based on haplotype ( Table 1 ) . For MIER1 , 12 of the 16 EST sequences ending near the annotated APA site had the APA allele ( including 2 with a clear polyA tail ) , whereas 3 had the non-APA allele ( none of them had a clear polyA tail ) . Similarly , for PNN , all of the 34 EST sequences ending near the annotated APA site had the APA allele ( including 10 with a clear polyA tail ) . Together , these results suggest that SNPs can create functional APA sites and thereby affect 3′UTR length . EST data can be used to identify alleles and transcript ending positions ( Fig . 1 ) , but EST data seldom have sufficient resolution to quantify transcript expression levels . In contrast , RNA-seq data can both be used to genotype SNPs [15] and to analyse transcript length and expression patterns . The main challenge with RNA-seq data compared with ESTs , however , is the shorter sequence reads , which makes it challenging to distinguish between homozygotes , heterozygotes with strong expression differences between its alleles ( allelic imbalance ) , sequencing errors , and alignment errors . To explore whether RNA-seq data could reveal whether APA-SNPs affect transcript expression , we therefore developed and validated an RNA-seq-based genotyping approach ( see Supporting Text S2 ) . We then used this approach to show that APA-SNPs can affect transcript expression and that this effect is associated with loss of miRNA regulation . Specifically , we first show that homozygous APA-SNPs have significantly shorter 3′UTRs than have heterozygous or homozygous wildtype SNPs . Second , we show an association between allelic imbalance of heterozygous APA-SNPs and the two following important features of polyA sites: signal strength and GU level downstream of the cleavage site . Third , we show that the loss of miRNA target sites can be the missing link in this association . Fourth , we use allelic imbalance to detect potentially functional APA-SNPs . Fifth , we show that APA-SNPs at strong sites ( strong APA signal and high GU level ) that have a strong predicted effect on miRNA regulation , have higher allelic imbalance and higher transcript expression than have other APA-SNPs . To confirm the results from the RNA-seq-based allelic imbalance analyses , we turned to gene expression data from the well characterised Hapmap population . We looked at human gene expression profiling of EBV-transformed lymphoblastoid cell lines from 270 unrelated Hapmap individuals [17] , and genotypes of the same individuals , from the Hapmap database [11] . Specifically , we first investigated whether genotypes of SNPs in strong polyA sites that affect miRNA targeting in general are associated with increased gene expression . Second , we investigated whether individual APA-SNP genotypes correlate significantly with gene expression . Since SNPs can alter polyadenylation and affect miRNA target sites and gene expression , we wondered whether they can also play an important role in human diseases . We therefore tested if any of our APA-SNPs were linked to trait-associated SNPs from the NHGRI GWAS catalogue [20] , [21] , which consists of SNP-trait associations from published genome-wide association studies ( GWAS ) ( accessed Apr . 18 , 2011 ) . Specifically , we mapped our 412 APA-SNPs to the 4304 GWAS SNPs , by using the mapping method described in Thomas et al . [22] . The mapping was based on linkage disequilibrium ( LD ) data from the Hapmap database ( CEU population release 27 ) . We identified 135 APA-SNP/GWAS-SNP pairs ( consisting of 84 unique APA-SNPs and 123 unique GWAS SNPs ) that had available haplotype data in Hapmap and one known and unique risk allele in the GWAS catalogue . For each APA-SNP/GWAS-SNP pair , we computed the correlation between the APA allele and risk allele as the LD value [23] , where , , and are respectively the APA allele frequency of the APA-SNP , the risk allele frequency of the GWAS SNP , and the “APA allele risk allele” haplotype frequency in the CEU Hapmap population . For each of the 84 unique APA-SNPs , we computed as the mean of when an APA-SNP was linked to several GWAS SNPs , and similarly as the mean of . We hypothesised that if APA-SNPs play a role in diseases , then APA alleles would be positively ( ) and strongly ( high ) correlated with risk alleles , particularly for the significant APA-SNPs that we identified in the previous sections , as they are more likely to be functional , and particularly those that are linked to GWAS-SNPs from CEU-population-related studies , since the values are based on CEU haplotypes . Among the 84 APA-SNPs , 60 were paired to GWAS-SNPs that are trait-associated in CEU-related populations . Nine of those SNPs were identified in the previous sections as significant APA-SNPs , and those nine SNPs had a significantly high number of positive ( more positive correlations between APA and risk alleles than expected ) and a significantly high number of greater than 0 . 2 ( higher number of correlations between APA and risk alleles than expected ) ( Table 2 ) . In contrast , for computed from CEU haplotypes but for GWAS-SNPs that are trait-associated in non-CEU-related populations , binomial test p-values were not significant , suggesting that GWAS and haplotype data should be matched according to population , to detect potential disease-related APA-SNPs . Those results show that a significantly high proportion of our candidate SNPs is in LD with trait-associated SNPs and their APA alleles are positively correlated with risk alleles of trait SNPs . This suggests that those APA-SNPs can potentially be the cause of their corresponding disease-association signals measured and registered in the GWAS catalogue .
Our analyses confirmed the hypothesis ( presented in Fig . 1 ) that SNPs can create functional alternative polyadenylation signals and thereby affect miRNA-based gene regulation and give increased gene expression . Both EST and RNA-seq analyses supported our hypothesis , despite some limitations . Additionally , the microarray analysis could further confirm these results and strengthen our hypothesis . Given the results from these three analyses , we estimate the proportion of functional APA-SNPs to be ( ) . The EST analysis supports our hypothesis but has some limitations . Specifically , we analysed EST data for 13 genes and found that 2 of them had an APA-SNP that could create polyA motifs and affect 3′UTR length . However , the EST analysis does not take into account the presence of a polyA tail in the EST sequence . Moreover , the ESTs came from a mix of tissues , which could also affect the results . Segregating ESTs based on tissue origin or filtering on sequences with clear tails in the “short” group , reduces sample size and affects statistical power . However , when combining sequences from our two significant genes , all of the 12 EST sequences ending at the alternative cleavage site and that have a polyA tail , had the APA allele . This number is significant ( binomial test p-value of , where the expected proportion of the APA allele is the combination of weighted allele frequencies of APA alleles for the 2 SNPs ) , and tells that the shortened transcripts arose from functional APA signals from the APA alleles . Similarly , RNA-seq data from the Burge Lab , matched to miRNA expression data showed association between alternative polyA site strength ( signal and GU-content ) , loss of miRNA target sites , allelic imbalance , and transcript expression . The Burge dataset was generated by using cDNA fragmentation , which gives a good coverage of 3′UTRs [24] . An increased allelic imbalance towards the APA allele could come from the loss of miRNA target sites , but also from the fragmentation method . This is because cDNA fragmentation gives a good coverage at the end of the transcript , and , in case of alternative polyadenylation , the transcript is shorter for the APA allele , which results in a high coverage at the SNP locus . In contrast , a longer transcript with the non-APA allele could have a higher coverage downstream , but a lower coverage at the SNP locus . Bias from cDNA fragmentation would therefore give an increased allelic ratio towards the APA allele simply because of transcript length differences . Consequently , we cannot exclude that some of the overall RNA-seq trends can be attributed to cDNA fragmentation bias . The independent microarray data strongly support the EST and RNA-seq results , however . Specifically , the mRNA and miRNA microarray expression data showed association between alternative polyA site strength ( signal and GU-content ) , loss of miRNA target sites , and transcript expression . This microarray analysis had the advantage of directly using genotype data from Hapmap , instead of genotyping SNPs through mapped RNA-seq reads . Furthermore , the microarray analysis focused on transcript expression differences between individuals and therefore required data from a unique cell type , whereas the RNA-seq analysis focused on allelic expression differences within a sample and could therefore involve different cell types . As expected , the microarray analysis showed similar results as the RNA-seq analysis , suggesting that the increased allelic ratios from RNA-seq data did not come from a potential bias due to the cDNA fragmentation method , but from the loss of functional miRNA target sites . One clear disadvantage of using the RNA-seq data for genotyping and allelic-imbalance-based detection , was false positive homozygotes . We could detect potentially functional candidate SNPs by testing for allelic imbalance , which takes into account the number of reads and their quality , while testing for unusual allele proportion patterns . The difficulty was to find extreme allelic imbalance , as we could miss extreme imbalance by classifying a locus as homozygote because of too few reads ( ) corresponding to the alternative allele . This was a conscious trade-off , however , since we wanted to maximise true positive heterozygotes and avoid false positives ( i . e . predicted heterozygotes that were in fact homozygous ) . RNA-seq data enabled us to genotype SNPs in expressed genes and compute allelic imbalance . Genotype classification could be checked with known genotypes from the Heap dataset and with mono-allelic SNPs . However the Heap dataset could not be used in the allelic imbalance analysis , because the library was generated by using RNA fragmentation , which gives a good coverage for the coding regions [24] , but not for the UTRs . Since we were interested in SNPs in 3′UTRs , and particularly at the end of potentially alternative transcripts , RNA fragmentation would affect allelic imbalance . The whole analysis is limited to SNPs that can make the reference 3′UTR shorter , lose miRNA sites and upregulate genes , because the loss of functional miRNA sites within the 3′UTR is more likely than the gain of new ones downstream of the annotated 3′UTR . However , it could be interesting to consider the hypothesis where SNPs in the signals at the end of the reference transcript could make 3′UTR longer having more miRNA target sites further downstream , and down-regulate the gene . Alternative polyadenylation alleles play a role in 3′UTR shortening , gene deregulation , and increased disease risk ( Fig . 1 ) . Our analyses confirm that APA is an important factor for miRNA-mediated gene regulation [4] . EST data suggest that SNPs can create polyA motifs and affect 3′UTR length , and allelic imbalance from RNA-seq data coupled to miRNA expression data suggest an association between alternative polyA site strength ( signal and GU-content ) , loss of miRNA target sites , allelic imbalance and transcript expression . Similarly , mRNA microarray expression data and matched genotypes of the same individuals , coupled with miRNA expression data could confirm association between alternative polyA site strength ( signal and GU-content ) , loss of miRNA target sites , genotype and transcript expression . Each of our analyses could also be used to detect potentially functional APA-SNPs . The detected APA-SNPs could further be linked to GWAS-SNP markers and a significant part of these APA-SNPs had their APA allele positively correlated with disease-risk alleles . We propose that these APA SNPs are potential disease-causative variants .
We used SNP data from the CEU population ( CEPH - Utah residents with ancestry from northern and western Europe ) from the human haplotype map project ( HapMap database [11] ) , release 22 for haplotype data , and release 27 for the genotype , allele , frequency , and linkage disequilibrium data . We used the human genome assembly version 18 ( hg18 ) [25] , RefSeq gene annotations ( hg18 version ) , and EST sequences from the UCSC Genome browser [26] . We used human APA sites from PolyA_Db [12] , [13] . We used disease-associated SNPs from the NHGRI GWAS catalogue [20] , [21] . RNA-seq data came from Heap et al . [15] and from the Burge Lab [27] . Human miRNA profiles came from Landgraf et al . [16] ( their Table S5 ) and from Wang et al . [18] . MicroRNA data came from the MirBase database release 16 [28] . Thirteen polyA signal motifs are known in human genes: AAUAAA , AUUAAA , UAUAAA , AGUAAA , AAGAAA , AAUAUA , AAUACA , CAUAAA , GAUAAA , AAUGAA , UUUAAA , ACUAAA , and AAUAGA [1] ( ordered by strength ranks ) . We detected SNPs in potential APA signals , by a motif search that looks if any CEU Hapmap SNP in the 3′UTR of any coding gene would create/disrupt one of those 13 motifs . For a given SNP , the motif search looks for a given motif in an mRNA sub-sequence consisting of the SNP and its flanking sequences ( 6 nucleotides up/downstream ) , for each allele . We downloaded the 28 . 857 APA sites ( human ) from PolyA_Db [12] , [13] from the UCSC track ( hg18 ) [26] . We downloaded knownToLocusLink . txt and knownToRefSeq . txt from UCSC ( hg18 ) [26] to convert entrez gene ID to RefSeq gene ID . We took the intersection between our APA signals and polyA sites from PolyA_Db , by taking all the sites from PolyA_Db that lie up to 40 bp downstream of our signals . For each of the 13 candidate genes , we downloaded the EST sequences ( Expressed sequence tag ) from UCSC ( hg18 , tables ‘all_mrna’ and ‘all_est’ ) [26] that lie within their 3′UTR region . We also downloaded their alignment to their reference mRNA sequence from UCSC [26] , and the list of EST that support the considered polyA site from PolyA_Db2 [13] . We used sequence alignment to identify the allele and haplotype of each sequence , when possible . Otherwise , the APA-SNP allele was imputed , by using haplotypes from the CEU Hapmap population [11] ( see Dataset ) . We tested the proportion of APA alleles that support the candidate APA site , versus longer transcripts , by using a 2×2 contingency table . If the 4 expected values were greater than 5: we used the 2×2 , and Fisher's exact test otherwise . Given a 3′UTR region of a gene of interest , we took all the phased SNPs from Hapmap [11] in that region , as well as their haplotypes in the CEU population [11] . For each of those SNPs , we identified the allele in the EST sequence when possible , to identify the EST haplotype . We discarded EST haplotypes that had zero identified allele . For each remaining EST haplotype , we selected haplotypes from Hapmap that fit the identified alleles in the EST haplotype . The APA-SNP could be imputed if there was only one unique allele at that SNP in all the selected haplotypes from Hapmap . We downloaded RNA-seq data from human primary cells from 4 individuals [15] ( Short read archive accession number: SRA008367 ) , reads in FASTQ format , length of 45 bp . We downloaded Burge lab RNA-seq [27] ( Short read archive: SRA002355 , and Gene expression omnibus: GSE12946 ) : Human tissue samples ( brain , liver , heart , skeletal muscle , colon , adipose , testes , lymph node , breast , MAQC , 6 Cerebellum ) , immortalised and cancer cell lines ( BT474 , HME , MCF-7 , MD435 , T47D , MAQC UHR ) , reads in FASTQ format , length of 36 bp . MAQC is a mixture of brain cell types from several donors , MAQC UHR is a mixture of several cancer cell lines , and MD435 is thought to be contaminated by the M14 melanoma cell line . Therefore those 3 cell lines were discarded from the allelic imbalance analysis . We mapped RNA-seq reads using the RMAP software [29] , with option ‘-Q’ for position weight matrix matching , based on quality score . Alignment was stored in BED files . We used the default options: 2 mismatches allowed in the 32 first nucleotides , 10 mismatches allowed in the whole read . Ambiguous reads were discarded . Paired-End reads were mapped as Single-End reads . We mapped those reads to 3′UTR 50 bp: the reference sequence is all 3′UTR DNA sequences ( from the human genome assembly HG18 [25] ) from all coding genes ( excluding Y chromosome because of overlap with X ) , including introns , extended of 50 nucleotides up- and downstream . Overlapping sequences were merged ( 19012 regions ) . We mapped reads to a second version of the reference sequence , where reference alleles of APA-SNPs were replaced by non-reference alleles . We counted base calls based on base quality probability score and sequence alignment score: We discarded reads mapped with an alignment score , and reads that had a quality score accuracy at the SNP . Quality score probability of accuracy at a SNP was computed as follows: , where is the ASCII character of one base call in a read in FASTQ file format [30] . We computed the mapping score as , where is the alignment score given by RMAP . We counted alleles as for each allele ( for all the FASTQ files of each individual ) . We discarded alleles that do not fit Hapmap bi-allelic SNPs . If there was only one allele left , we classified the SNP as homozygous . If there were two alleles left , with both proportions greater than 0 . 15 , we classified the SNP as heterozygous . If there were two alleles but one had its proportion lower than 0 . 15 , we classified the SNP as homozygous with the allele having the biggest proportion . We mapped reads from the Burge dataset using the alignment software Bowtie [31] version 0 . 12 . 7 with default options . Bowtie generated alignments in the SAM format [32] . The transcript assembly software Cufflinks version 1 . 3 . 0 [33] was then used with the SAM files to generate a list of expressed exons for each run ( default options ) . Those exons were then mapped back to annotated RefSeq genes . Exons that mapped to several different genes were discarded; the corresponding genes they overlapped were also discarded . For a given gene and a given run , the 3′ end of the exon that mapped the most downstream on the gene was used as an estimate of the gene's 3′ end . Finally , the distance between the estimate and the annotated transcript end was computed for each gene and each run . This distance is negative when the transcript is shorter than the annotation and had a logarithmic distribution for negative s . Few transcripts were longer than the annotated transcription end site , resulting in positive values . To handle these few positive values , we put a threshold at 30 , so that all were truncated to 29 . We then converted the s to the logarithm scale by using the following formula: . Log Allelic Ratio for each heterozygous SNP is defined as , where counts of alleles are computed in a similar way as in the genotyping section ( by taking base quality and alignment score into account ) . is positive when the transcripts with APA alleles are up-regulated compared to non-APA allele . However , to avoid that a mapping bias towards reference alleles affects allelic ratios , we used a corrected allelic imbalance in our analyses , by combining allelic ratios computed from reads mapped to the reference genome with reference alleles , and allelic ratios computed from reads mapped to the same genome but with non-reference alleles at candidate SNPs . We defined it as the mean of the two log-ratios: where is the allelic ratio , and are the counts of APA alleles mapped to respectively the genome with reference alleles , and the one with non-reference alleles . Similarly and are the counts of non-APA alleles . We took all the known coding genes from the UCSC RefSeq gene database ( hg18 ) [26] . To define the precise region of GU-analysis , for each gene , we computed the GU proportion in a 5-nucleotide long window sliding from the polyA signal downstream in a 70-nucleotide long region . Those curves represent the variation of GU proportion in the region for each gene . We then took the mean of all the curves , which showed that the increased GU region was from the window to the window ( Fig . S4 ) . We therefore defined the GU level as the mean of the GU-proportions in the 5-nucleotide windows , from the to the downstream of the polyA signal . We downloaded human gene expression profiling of EBV-transformed lymphoblastoid cell lines from 270 unrelated Hapmap individuals [17] ( Gene expression omnibus: GSE6536 , data normalised across populations ) , and genotypes for the same individuals , from the Hapmap database release 27 . We mapped probe IDs to RefSeq genes using the BioConductor package for R [35] , [36] ( R version 2 . 10 . 1 , AnnotationDbi package version 1 . 8 . 2 [37] and the annotation file illuminaHumanv1 . db version 1 . 4 . 0 ) . One candidate SNP could have one or several RefSeq gene IDs , which could be mapped to one or several probe IDs . Among those probe IDs , we selected the one with maximum variance across all the individuals in the dataset , and assigned it to the given SNP in the 3′UTR . Genotype was encoded as 0 , 1 , and 2 for non-APA homozygotes , heterozygotes , and APA homozygotes , respectively . We computed bootstraps of median differences: Given two groups with different sizes , we resampled with replacement in each group with their actual original size . We took the median in each resampling and computed the difference . We repeated this procedure 1000 times to create a median difference distribution , which was then used to compute the 95% confidence interval ( CI ) . We mapped APA-SNPs to GWAS SNPs , using the mapping method described in Thomas et al . [22] . The mapping was based on linkage disequilibrium ( LD ) data from the Hapmap database ( CEU population release 27 ) . The mapping parameter was the threshold ( see Thomas et al . [22] ) , to identify all neighbouring APA-SNP/GWAS-SNP pairs . | Variants in DNA that affect gene expression—so-called regulatory variants—are thought to play important roles in common complex diseases , such as cancer . In contrast to variants in protein-coding regions , regulatory variants do not affect protein sequence and function . Instead , regulatory variants affect the amount of protein produced . The 3′ untranslated region ( UTR ) is one gene region that is critically important for gene regulation; cancers for example , often express genes with shortened 3′UTRs that , compared with full-length 3′UTRs , have higher and more stable expression levels . We have investigated one kind of regulatory variant that can affect the 3′UTR length and thereby cause disease . We identified several such variants in different genes and found that these variants affected the genes' expression . Some of these variants were also strongly linked with known markers for disease , suggesting that these regulatory variants are important hereditary causes for disease . |
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Decades of study have revealed more than 100 ribonucleoside structures incorporated as post-transcriptional modifications mainly in tRNA and rRNA , yet the larger functional dynamics of this conserved system are unclear . To this end , we developed a highly precise mass spectrometric method to quantify tRNA modifications in Saccharomyces cerevisiae . Our approach revealed several novel biosynthetic pathways for RNA modifications and led to the discovery of signature changes in the spectrum of tRNA modifications in the damage response to mechanistically different toxicants . This is illustrated with the RNA modifications Cm , m5C , and m22G , which increase following hydrogen peroxide exposure but decrease or are unaffected by exposure to methylmethane sulfonate , arsenite , and hypochlorite . Cytotoxic hypersensitivity to hydrogen peroxide is conferred by loss of enzymes catalyzing the formation of Cm , m5C , and m22G , which demonstrates that tRNA modifications are critical features of the cellular stress response . The results of our study support a general model of dynamic control of tRNA modifications in cellular response pathways and add to the growing repertoire of mechanisms controlling translational responses in cells .
The complexity of the transfer RNA ( tRNA ) system confers great potential for its use in cellular regulatory programs . There are hundreds of tRNA-encoding genes in S . cerevisiae and human genomes , with extensive post-transcriptional processing that includes enzyme-mediated ribonucleoside modifications [1] . Considering both tRNA and ribosomal RNA ( rRNA ) , there are more than 100 known ribonucleoside modifications across all organisms in addition to the canonical adenosine , guanosine , cytidine and uridine [2] , [3] . In general , tRNA modifications enhance ribosome binding affinity , reduce misreading and modulate frame-shifting , all of which affect the rate and fidelity of translation [4]–[7] . However , information about the higher-level biological function of ribonucleoside modifications has only recently begun to emerge . We have approached this problem with a systems-level analysis of changes in the spectrum of ribonucleosides in tRNA as a function of cell stress , which has revealed novel insights into the biosynthesis of tRNA modifications and their role in cellular responses . Emerging evidence points to a critical role for tRNA and rRNA modifications in cellular responses to stimuli , with evidence for a role in tRNA stability [8] , [9] , cellular stress responses [10]–[12] and cell growth [13] . We recently used high-throughput screens and targeted studies to show that the tRNA methyltransferase 9 ( Trm9 ) modulates the toxicity of methylmethanesulfonate ( MMS ) in S . cerevisiae [11] , [14] . This is similar to the observed role of Trm9 in modulating the toxicity of ionizing radiation [15] and of Trm4 in promoting viability after methylation damage [14] , [16] . Trm9 catalyzes the methyl esterification of the uracil-based cm5U and cm5s2U to mcm5U and mcm5s2U , respectively , at the wobble bases of tRNAUCU-ARG and tRNACCU-GLU , among others [17] . These wobble base modifications in the tRNA enhance binding of the anticodon with specific codons in mixed codon boxes [18] . Codon-specific reporter assays and genome-wide searches revealed that Trm9-catalyzed tRNA modifications enhanced the translation of AGA- and GAA-rich transcripts that functionally mapped to processes associated with protein synthesis , metabolism and stress signalling [11] . The resulting model proposes that specific codons will be more efficiently translated by anticodons containing the Trm9-modified nucleoside and that tRNA modifications can dynamically change in response to stress . To assess the dynamic nature of tRNA modifications proposed by this model , we developed a systems-oriented approach using liquid chromatography-coupled , tandem quadrupole mass spectrometry ( LC-MS/MS ) to quantify the full set of tRNA modifications in an organism . Mass spectrometry-based methods have recently emerged as powerful tools for identifying and quantifying RNA modifications [19] , [20] . We applied such an approach to quantify changes in the spectrum of tRNA modifications in yeast exposed to four mechanistically dissimilar toxicants . Multivariate statistical analysis of the data reveals dynamic shifts in the population of RNA modifications as part of the response to damage , with signature changes for each agent and dose . Further , analysis of yeast mutants lacking specific modification enzymes revealed novel biosynthetic pathways and compensatory or cooperative shifts in the levels of other modifications .
As shown in Figure 1 , we developed an LC-MS/MS method capable of quantifying 23 of the ∼25 known ribonucleoside modifications in cytoplasmic tRNA in S . cerevisiae [2] , [3] . The method begins with isolation of small RNA species ( <200 nt ) and quantification of the tRNA content ( ∼80–90% of small RNA species ) . Individual ribonucleosides in enzymatic hydrolysates of tRNA were resolved by HPLC and identified by high mass accuracy mass spectrometry , by fragmentation patterns with collision-induced dissociation ( CID ) and by comparison to chemical standards . Each ribonucleoside was subsequently quantified by pre-determined molecular transitions during CID in the LC-MS/MS system . We were able to quantify 23 of the 25 tRNA modifications in yeast , with 2′-O-ribosyladenosine phosphate ( Ar ( p ) ) not detected in positive ion mode , possibly due to the negatively charged phosphate , and only tentative identification of ncm5Um by CID due to weak signal intensities . A critical feature of our approach is quantitative rigor given the need for highly precise measurement of even small changes in the relative quantities of ribonucleosides . To this end , we used an Agilent Bioanalyzer ( microfluidics-based sizing and quantification against an internal standard ) for quantification of total tRNA species in the mixture of small RNA ( 85±5% , N = 39 ) and an internal standard ( [15N5]-2′-deoxyriboadenosine ) to minimize variation in the levels of the individual ribonucleosides . One caveat here is low-level contamination ( a few percent ) with 5S rRNA that also contains ribonucleoside modifications . We were able to obtain highly reproducible data for the signal intensity associated with each ribonucleoside ( see Figure S1 for linearity of signal intensity for the 23 ribonucleosides ) . Multiple reaction monitoring ( MRM ) mode yielded no detectable background signal in the absence of tRNA hydrolysates except for i6A ( 9±2% ) . The method proved to be highly precise: 3±1% intra-day variance in average signal intensity and 12±10% inter-day variance in average fold-change values for each ribonucleoside in treated and untreated cells ( 294 analyses in three biological replicates over several weeks ) . Analysis of tRNA from wild type cells revealed a three-log range of signal intensity , with I and ac4C producing the highest intensity and ncm5Um the lowest ( Figure 1 ) . In general , modifications can be categorized in high ( I , ac4C , m1A , m22G , Am , Y ) , medium ( Cm , m5C , Gm , m1G , t6A , m7G , m2G , m3C , i6A ) and low signal intensities ( m1I , D , m5U , ncm5Um , mcm5U , mcm5s2U , Um , yW , ncm5U ) , with signal intensity reflecting both the abundance and mass spectrometric sensitivity for each ribonucleoside . To quantify the dynamics of tRNA modifications in cellular responses , we selected four well studied chemicals that possess distinct mechanisms of toxicity: MMS , hydrogen peroxide ( H2O2 ) , sodium arsenite ( NaAsO2 ) , and sodium hypochlorite ( NaOCl , pKa 7 . 5; ref . [21] ) . The behavior of yeast upon exposure to MMS , NaAsO2 and H2O2 has been extensively studied in terms of transcriptional response and cytotoxicity phenotyping [11] , [22] , [23] . We also chose NaOCl since it produces an oxidative stress distinct from that of H2O2 and could thus affect the tRNA modification spectrum differently . We then performed cytotoxicity dose-response studies in S . cerevisiae exposed to agents ( Figure S2 ) , choosing concentrations ( Figure 2 ) that produced ∼20% , 50% and 80% cytotoxicity to ensure a common phenotypic endpoint for comparison . One important issue with the methylating agent , MMS , was the possibility that changes in methyl-based modifications in tRNA could be due to both enzymatic methylation and direct chemical methylation . Literature precedent indicates that MMS reacts with DNA to form adducts mainly at guanine N7 ( 68% ) , adenine N1 ( 18% ) and cytosine N3 ( 10% ) [24] , [25] . To address the extent of direct methylation of RNA by MMS , control studies were performed and revealed that direct alkylation by MMS contributes <25% to the cellular burden of m7G in small RNA , with the bulk of m7G arising by enzymatic methylation of tRNA ( Figure S3 ) . No other agent affected tRNA modifications in this manner , with changes in the relative quantities of the modifications resulting from alterations in biosynthesis , tRNA gene transcription or tRNA degradation . With exposure and analytical parameters established , we tested the hypothesis that the spectrum of tRNA modifications would dynamically change as a function of the S . cerevisiae stress response . In addition , we predicted that these changes would serve as biomarkers of each exposure . Cells were exposed to three concentrations of each chemical and 23 tRNA modifications were quantified by LC-MS/MS , with the results shown in Tables S1 and S2 , the latter as the ratio of treated to control signal intensities . A crude analysis of the data shows fold-changes ranging from 0 . 2 to 4 , with 25% and 36% of the exposure data significantly different from control values by Student's t-test at p<0 . 05 and p<0 . 1 , respectively ( Table S2 ) . These results point to the non-random and regulated nature of the exposure-induced changes in the levels of the tRNA modifications . Multivariate statistical analyses revealed important patterns or signatures in the toxicant-induced changes in tRNA modifications . As shown in Figure 2 , hierarchical clustering distinguished both agent- and dose-specific changes in the modification spectra , with unique patterns of increase and decrease apparent in all cases . H2O2 consistently increased the levels of m5C , Cm and m22G and , at the highest concentration , t6A , with dose-dependent decreases in m5U , m1G , m2G , mcm5s2U , i6A , yW and m1A . MMS consistently increased the level of m7G , and decreased Am , m5C , Cm , mcm5s2U , i6A , and yW . NaAsO2 caused only decreases in modification levels at the highest concentration , most notably for mcm5U , m3C , m7G , mcm5s2U , i6A , yW , m5C , and Cm . Interestingly , the dose-response for NaOCl showed an inverse correlation between concentration and increased levels of Am and Um and decreased levels of m5C . Given the reproducibility of the data , the changes in tRNA modification spectra can be considered signature biomarkers of exposure for these four classes of chemical stressor . Principal component analysis ( PCA ) creates a model that reduces the complexity of a data set by identifying hidden correlations ( the principal components ) comprised of weighted , linear combinations of the original variables , with the first principal component ( P1 ) accounting for the largest portion of the variation of the data and so on . The results of PCA of the dataset of nucleoside fold-change values ( Table S2 ) are shown in Figure 3 . With 88% of the variability expressed in the first 3 principal components ( 56% , 22% and 10% , respectively ) , individual agents contributed variance to each as shown in Table S3 , with H2O2 contributing 74% in P1 , MMS and NaOCl each contributing >40% in P2 and NaAsO2 contributing 53% in P3 . The scores plots ( Figure 3A , 3C ) clearly distinguish the four agents , with H2O2-induced changes as the major determinant of P1 and with MMS , NaOCl and NaAsO2 distinguished best in P2 . While H2O2 and NaOCl are negatively correlated in P1 , they are more closely grouped in P2 and P3 , which suggests that the changes in tRNA modifications reflect both common and unique facets of the toxic mechanism of each agent . For example , H2O2 and NaOCl are both oxidizing agents , but H2O2 generates hydroxyl radicals by Fenton chemistry while the protonated form of NaOCl yields hydroxyl radicals , chloramines and singlet oxygen [26]–[29] . Similarly , MMS and NaAsO2 are negatively correlated in P3 and more positively correlated in P2 , with the latter consistent with recent evidence for alkylation-like adduction of arsenic to DNA and proteins following its metabolism [30] , [31] . This would also explain the negative correlation of NaAsO2 and H2O2 in P1 , while the recognized oxidative stress caused by arsenite [32] is consistent with a positive correlation between NaAsO2 and H2O2 in P2 . Both PCA ( Figure 3B , 3D ) and cluster analysis ( Figure 2 ) revealed that m5C , m22G , Cm and t6A are major features of the H2O2 response , while m1A , m3C and m7G were associated with MMS . Increases in Gm , Um , I and Am were responsible for the variance induced by NaOCl , which is consistent with the inversely related doses and levels for Am and Um observed in cluster analysis . NaAsO2 was poorly distinguished in P2 , with only m2G accounting for variance only at the highest concentrations ( Figure 2 ) . The observation of toxicant- and dose-dependent changes in the levels of the 23 tRNA modifications is consistent with a model in which cells respond to toxicant exposure by modifying tRNA structure to enhance the synthesis of proteins critical to cell survival , as has been proposed in our earlier work with yeast exposure to MMS [11] . In this case , the conversion of cm5U to mcm5U by Trm9 was found to be critical for surviving MMS exposure [11] . To define the roles of specific tRNA modifications in the toxicant response , cytotoxicity phenotypic analyses were performed with yeast mutants lacking each of 13 trm tRNA methyltransferase genes and 3 other types of RNA modification biosynthetic genes . As shown in Figure 4 , heightened sensitivity to H2O2 was observed in mutants lacking Trm4 and Trm7 , which catalyze formation of two modifications elevated by H2O2 exposure: m5C and Cm , respectively [33] , [34] . The simple explanation is that the increase in a specific tRNA modification is needed to promote an efficient stress response . However , m22G was also elevated by H2O2 ( Figure 2 , Figure 3 ) , yet loss of an enzyme involved in its biosynthesis , Trm1 [35] , [36] , did not confer H2O2 sensitivity ( Figure 4 ) . This behavior draws a comparison to mRNA , as it has been reported that many of the transcripts induced in response to a stress are not essential for viability during a challenge from that stress [37] , [38] . MMS sensitivity was identified in trm1 , trm4 and trm9 mutants , the latter as shown previously [11] , whose corresponding proteins synthesize m22G , m5C and mcm5U/mcm5s2U , respectively . However , these modifications were not strongly associated with MMS exposure in PCA ( Figure 2 , Figure 3 ) . Somewhat surprisingly , loss of Trm1 , Trm4 , Trm7 and Trm9 conferred NaAsO2 sensitivity . These methyltransferases are responsible for m22G , m5C , m1G ( position 37 ) and mcm5u/mcm5s2U , respectively , of which only m2G was found to vary significantly in PCA ( Figure 3 ) . For NaOCl , only trm4 was sensitive to exposure and the m5C product of Trm4 was not associated with NaOCl exposure ( Figure 3 ) . Again , this behavior parallels that of mRNA transcripts the levels of which do not change after exposure but that encode proteins important for viability after exposure [37] , [38] . These results reveal a complex and dynamic control of tRNA modifications in cellular survival responses and suggest models for homeostasis of the modifications . One example involves modifications for which the biosynthetic mutant is sensitive to exposure but the modification level does not change in wild type cells following exposure ( e . g . , MMS exposure and trm1/m22G , trm4/m5C , trm9/mcm5U or mcm5s2U; Figure 2 , Figure 3 , Figure 4 ) . The simplest explanation here is that the modification change occurs in a single tRNA species and the change is masked by an inverse change in the level of the modification in the larger population of tRNA molecules . As noted in Table S4 , both m22g and m5C occur in multiple tRNAs . A second explanation parallels the idea of both pre-existing mRNA and stressor-induced transcription during a stress response . We have observed stress-induced increases in the levels of several modifications required for the survival response ( Figure 2 , Figure 3; ref . [11] ) . However , other modifications may already exist on tRNA molecules involved in selective translation of stress response messages . In both cases , the modifications are absolutely required for survival , but some are already present in unstressed cells and others are induced . Finally , it is possible that a modification , though its level may not change , is required for the subsequent synthesis of other modifications that are critical to the survival response . Such “cooperativity” is suggested by data from mod5-deficient cells , in which i6A decreases by ∼75-fold while D is reduced by ∼2-fold . The presence of i6A may signal downstream biosynthetic events , with deficiencies promoting a general reprogramming of tRNA . Similarly , cells deficient in Trm82 , a subunit of m7G methyltransferase , had a ∼7-fold reduction in m7G and a >1 . 5-fold increase in m3C , mcm5U , m1G , m2G , t6A , mcm5s2U and m22G ( Figure 5 ) , which raises the possibility that Trm82 itself or m7G inhibits other tRNA modifying enzymes . With the caveat of possible increases in tRNA copy number , the ∼50% increase in these modifications suggests a pool of unmodified tRNA molecules , an observation supported by increases in m3C after exposure to MMS , mcm5U after exposure to NaOCl , and both t6A and m22G after exposure to H2O2 ( Figure 2 , Figure 3 ) . Cooperativity could also explain the case in which the level of a modification changes significantly following exposure yet the mutant strain is not sensitive to the exposure . For example , loss of trm1 did not confer sensitivity to H2O2 but its product , m22G , rose significantly with H2O2 exposure ( Figure 2 , Figure 3 , Figure 4 ) . The stress-induced change in m22G may be a response to a change occurring with another modification for which the mutant strain might be sensitive to the exposure . In support of this argument , m5C modifications increase along with m22G after H2O2 exposure and deficiencies in the m5C-producing methyltransferase Trm4 confer sensitivity to H2O2 . Wohlgamuth-Benedum et al . have also demonstrated such cooperativity among RNA modifications in their observation of the negative regulation of wobble position C-to-U editing by thiolation of a U at position 33 outside the anticodon in T . brucei [39] . Finally , there is the case in which a modification decreases with exposure to a stressor and a deficiency in the enzyme responsible for that modification confers sensitivity , as in the case of m5C , trm4 and NaOCl ( Figure 2 , Figure 3 , Figure 4 ) . The population level of m5C may decrease with NaOCl exposure in spite of a protective increase in the level of m5C at some critical tRNA location . This may reflect a decrease in the transcription of tRNA substrates of Trm4 or the targeted degradation of specific tRNA species . It is important to note that biosynthetic redundancy , as in the case of Gm with Trm3 and Trm7 , could mask any major changes in tRNA modification levels that are associated with mutational loss of one enzyme ( Figure 5 ) , yet loss of one of the redundant enzymes can induce sensitivity , such as the case of H2O2 and trm7 ( Figure 2 , Figure 3 , Figure 4 ) . These observations lead to many questions that obviously require more mechanistic study to define the precise role of tRNA modifications in cellular responses to stress . One consistent feature that arose from our studies of modifications affected by or protecting against toxicant exposure was the frequent involvement of the wobble position , 34 ( Tables S4 , S6 ) . The correlation between the wobble modification and the importance of a corresponding enzyme after toxicant exposure is not surprising in light of recent observations of the critical role played by these modifications and anticodon loop ribonucleosides in translational fidelity and efficiency [4] . Controlled alteration of ribonucleoside structure at position 34 , and that at the conserved purine at position 37 , is proposed to allow reading of degenerate codons by modulating the structure of the anticodon domain to facilitate correct codon binding [4] . As the most frequently modified ribonucleosides , positions 34 and 37 also have the largest variety of modifications [40] , [41] , so it is reasonable that they would be extensively involved in translational control of the survival response . This is also consistent with our previous observation that mcm5U at the wobble position was critical to the translation of key protein synthesis and DNA damage response genes [11] . Perhaps more interesting is a potential role for putative non-anticodon loop ribonucleoside modifications in the survival response . For example , Trm44 is the 2′-O-methyltransferase in yeast responsible for formation of 2′-O-methyl-U ( Um ) , which occurs only at position 44 in yeast tRNA [42] , [43] . Loss of Trm44 conferred sensitivity to NaAsO2 exposure . This observation suggests three possibilities: ( 1 ) that Trm44 synthesizes or influences the synthesis of modifications at other positions in tRNA; ( 2 ) that Um occurs in positions other than 44 ( e . g . , anticodon loop ) ; or ( 3 ) that Um ( 44 ) plays a role in modulating translation in response to NaAsO2 exposure . Another example involves Trm1 and m22G at position 26 . Current evidence suggests that m22G occurs only at position 26 in yeast tRNA [43] and that Trm1 is the methyltransferase responsible for its formation [44] . The fact that loss of Trm1 conferred sensitivity to MMS and NaAsO2 exposure and that H2O2 exposure increased the level of m22G again suggest the three possibilities analogous to those for Trm44 and Um . Similar arguments can be made for Trm3 and Gm at position 18 with NaOCl exposure , for Trm11 and m2G at position 10 with NaOCl and NaAsO2 exposure , and for Trm8/82 and m7G at position 46 with MMS exposure . All of these observations point to participation of wobble and non-wobble RNA modifications in a complex and dynamic network of translational mechanisms in cellular responses . This expands the repertoire of translational control mechanisms , which includes recent discoveries about the effect of ribonucleoside modifications on tRNA stability [8] , [9] . In this model , cell stress leads to rapid degradation of specific tRNAs and subsequent effects on translational efficiency . Another similar stress response involves cleavage of cytoplasmic transfer RNAs by ribonucleases released during the stress [10] . One consequence of these degradation pathways would be to decrease the amount of modified ribonucleoside detected in our assay , which may explain some of our observations with the toxicant stresses . Our approach to quantifying tRNA modifications provides information only about population-level changes , so the observed changes could result from modification of existing tRNA molecules or changes in the number of tRNA copies . Of particular importance here is the observation by Phizicky and coworkers that loss of m7G at position 46 leads to degradation of specific tRNAs [9] , which suggests that our observation of changes in the levels of RNA modifications could be amplified by both reduction in the activity of modifying enzymes and by tRNA degradation . On the other hand , one argument against large increases in tRNA copy number arises from recent observations of repressed tRNA transcription during S-phase and , of direct relevance to the present studies , during replication stress induced by MMS , hydroxyurea and likely other toxicants [45] . Finally , our findings may also parallel recent work on tRNA charging . Reactive oxygen species have been implicated as a methionine misacylation trigger and modification status could help promote these programmed changes to the genetic code [12] . As we are beginning to appreciate the precision and coordinated nature by which cells mount a regulated stress-response , it is most likely the observed changes in tRNA modification levels promote multiple biological responses . As recognized by several groups [19] , [20] , the LC-MS/MS platform facilitates definition of biosynthetic pathways for RNA modifications . This is illustrated in Table S5 , which contains ratios of the basal levels of tRNA modifications in yeast mutants lacking various tRNA modification enzymes compared to wild type yeast , and in a heat map visual depiction of these ratios in Figure 5 . These data corroborate known substrate/enzyme pairs [43] and further demonstrate the highly quantitative nature of our approach . For example , the level of m1I drops to nearly undetectable levels with loss of Tad1 , the adenosine deaminase producing the inosine precursor to m1I [46] . That a diploid heterozygous mutant of trm5 , the product of which catalyzes N-methylation of I [47] , caused a ∼40% reduction in total m1I attests to the accuracy of our assay and demonstrate that gene dosage effects alter the level of tRNA modification . A similar ∼50% reduction in yW occurred in the trm5 mutant due to the absence of the m1G ( 37 ) precursor to yW [47] , while complete loss of Trm12 , which methylates the 4-demethylwyosine precursor of yW , made yW undetectable . Other pathways critical to yW are apparent in the smaller decreases in yW ( 0 . 3– to 0 . 5-fold ) occurred in cells deficient in other enzymes ( Trm8 , Trm82 , Tad1 , Mod5 , Tan1 , Trm11 , Trm5; Figure 5 , Table S5 ) . The data in Figure 5 also reveal several novel observations . Pintard et al . observed that Trm7 catalyzes 2′-O-methylation of G and C nucleosides at positions 32 and 34 , but they could not detect the ncm5Um product of 2′-O-methylation of ncm5U [34] . While we could only tentatively identify ncm5Um , we observed a quantifiable signal for a species with the correct molecular transition for ncm5Um and observed that loss of Trm7 led to a lowering of putative ncm5Um to undetectable levels ( Figure 5 , Table S5 ) . This supports their prediction that Trm7 catalyzes formation of ncm5Um in yeast . Another example involves the formation of Um . While Trm44 catalyzes synthesis of Um at position 44 in tRNA ( ser ) [42] , analysis of trm mutants in Figure 5 and Table S5 suggests a redundancy in methyltransferase activity capable of 2′-O-methylation of U ( 44 ) , including Trm7 , which methylates U at positions 32 and 34 [34] , and Trm13 methylation of C and A at position 4 in several yeast tRNAs . Cells lacking Trm44 , Trm7 or Trm13 have 53% , 50% and 76% of wild type levels of Um , respectively . More striking evidence for this redundancy arises in correlation analysis that revealed a strong covariance in the levels of tRNA modifications in cells lacking either Trm 44 or Trm 13 ( Table S7; C = 0 . 87 ) . This correlation ranks second highest in our analysis behind the two subunits of the m7G methyltransferase ( Trm8 and Trm82; C = 0 . 95 ) , which suggests possible functional redundancy for Trm44 and Trm13 , with broader substrate specificities for either or both enzymes . In summary , a quantitative bioanalytical approach to the study of tRNA modifications has revealed several novel biosynthetic pathways for RNA modifications and has led to the discovery of signature changes in the spectrum of tRNA modifications in the damage response to different toxicant exposures . The results support a general model of dynamic control of tRNA modifications in cellular response pathways and add to the growing repertoire of mechanisms controlling translational responses in cells [8]–[10] , [13] . Further , these cellular response mechanisms almost certainly involve parallel changes in spectrum of ribonucleoside modifications in rRNA and perhaps other RNA species .
All chemicals and reagents were of the highest purity available and were used without further purification . 2′-O-Methyluridine ( Um ) , pseudouridine ( Y ) , N1-methyladenosine ( m1A ) , N2 , N2-dimethylguanosine ( m22G ) , and 2′-O-methylguanosine ( Gm ) were purchased from Berry and Associates ( Dexter , MI ) . N6-Threonylcarbamoyladenosine ( t6A ) was purchased from Biolog ( Bremen , Germany ) . N6-Isopentenyladenosine ( i6A ) was purchased from International Laboratory LLC ( San Bruno , CA ) . 2′-O-Methyladenosine ( Am ) , N4-acetylcytidine ( ac4C ) , 5-methyluridine ( m5U ) , inosine ( I ) , 2-methylguanosine ( m2G ) , N7-methylguanosine ( m7G ) , 2′-O-methylcytidine ( Cm ) , 3-methylcytidine ( m3C ) , 5-methylcytidine ( m5C ) , alkaline phosphatase , lyticase , RNase A , ammonium acetate , geneticine and desferrioxamine were purchased from Sigma Chemical Co . ( St . Louis , MO ) . Nuclease P1 was purchased from Roche Diagnostic Corp . ( Indianapolis , IN ) . Phosphodiesterase I was purchased from USB ( Cleveland , OH ) . PureLink miRNA Isolation Kits were purchased from Invitrogen ( Carlsbad , CA ) . Acetonitrile and HPLC-grade water were purchased from Mallinckrodt Baker ( Phillipsburg , NJ ) . All strains of S . cerevisiae BY4741 were purchased from American Type Culture Collections ( Manassas , VA ) . Cultures of S . cerevisiae BY4741 were grown to mid-log phase followed by addition of toxicants to the noted final concentrations ( cytotoxicity of ∼20% , 50% and 80% ) : H2O2 , 2 , 5 or 12 mM; MMS , 6 , 12 or 24 mM; NaAsO2 , 20 , 40 or 60 mM; NaOCl , 3 . 2 , 4 . 0 or 4 . 8 mM . The sensitivity of the following mutant strains to toxicant exposure was also determined ( doses producing ∼80% cytotoxicity in wild-type: 12 mM H2O2 , 24 mM MMS , 60 mM NaAsO2 , or 4 . 8 mM NaOCl ) : trm1 , trm2 , trm3 , trm4 , trm7 , trm8 , trm9 , trm10 , trm11 , trm12 , trm13 , trm44 , trm82 , tad1 , mod5 , and tan1 . Since trm5 is essential , a diploid strain ( GBY1 ) lacking one copy of trm5 was used . After a 1 h , cells were collected and viability determined by plating . Following lyticase treatment ( 50 units ) in the presence of deaminase inhibitors ( 5 µg/ml coformycin , 50 µg/ml tetrahydrouridine ) and antioxidants ( 0 . 1 mM desferrioxamine , 0 . 1 mM butylated hydroxytoluene ) , tRNA-containing small RNA species were isolated ( Invitrogen PureLink miRNA kit ) and the tRNA quantified ( Agilent Series 2100 Bioanalyzer ) . Following addition of deaminase inhibitors , antioxidants and [15N]5-2-deoxyadenosine internal standard ( 6 pmol ) , tRNA ( 6 µg ) in 30 mM sodium acetate and 2 mM ZnCl2 ( pH 6 . 8 ) was hydrolyzed with nuclease P1 ( 1 U ) and RNase A ( 5 U ) for 3 h at 37°C and dephosphorylated with alkaline phosphatase ( 10 U ) and phosphodiesterase I ( 0 . 5 U ) for 1 h at 37°C following addition of acetate buffer to 30 mM , pH 7 . 8 . Proteins were removed by filtration ( Microcon YM-10 ) . Ribonucleosides were resolved with a Thermo Scientific Hypersil GOLD aQ reverse-phase column ( 150×2 . 1 mm , 3 µm particle size ) eluted with the following gradient of acetonitrile in 8 mM ammonium acetate at a flow rate of 0 . 3 ml/min and 36°C: 0–18 min , 1–2%; 18–23 min , 2%; 23–28 min , 2–7%; 28–30 min , 7%; 30–31 min , 7–100%; 31–41 min , 100% . The HPLC column was coupled to an Agilent 6410 Triple Quadrupole LC/MS mass spectrometer with an electrospray ionization source where it was operated in positive ion mode with the following parameters for voltages and source gas: gas temperature , 350°C; gas flow , 10 l/min; nebulizer , 20 psi; and capillary voltage , 3500 V . The first and third quadrupoles ( Q1 and Q3 ) were fixed to unit resolution and the modifications were quantified by pre-determined molecular transitions . Q1 was set to transmit the parent ribonucleoside ions and Q3 was set to monitor the deglycosylated product ions , except for Y for which the stable C-C glycosidic bond led to fragmentation of the ribose ring; we used the m/z 125 ion for quantification [48] , [49] . The dwell time for each ribonucleoside was 200 ms . The retention time , m/z of the transmitted parent ion , m/z of the monitored product ion , fragmentor voltage , and collision energy of each modified nucleoside and 15N-labeled internal standard are as follow: D , 1 . 9 min , m/z 247→115 , 80 V , 5 V; Y , 2 . 5 min , m/z 245→125 , 80 V , 10 V; m5C , 3 . 3 min , m/z 258→126 , 80 V , 8 V; Cm , 3 . 6 min , m/z 258→112 , 80 V , 8 V; m5U , 4 . 2 min , m/z 259→127 , 80 V , 7 V; ncm5U , 4 . 3 min , m/z 302→170 , 90 V , 7 V; ac4C , 4 . 4 min , m/z 286→154 , 80 V , 6 V; m3C , 4 . 4 min , m/z 258→126 , 80 V , 8 V; ncm5Um , 5 . 5 min , m/z 316→170 , 90 V , 7 V; Um , 5 . 1 min , m/z 259→113 , 80 V , 7 V; m7G , 5 . 1 min , m/z 298→166 , 90 V , 10 V; m1A , 5 . 7 min , m/z 282→150 , 100 V , 16 V; mcm5U , 6 . 4 min , m/z 317→185 , 90 V , 7 V; m1I , 7 . 3 min , m/z 283→151 , 80 V , 10 V; Gm , 8 . 0 min , m/z 298→152 , 80 V , 7 V; m1G , 8 . 3 min , m/z 298→166 , 90 V , 10 V; m2G , 9 . 4 min , m/z 298→166 , 90 V , 10 V; I , 10 . 9 min , m/z 269→137 , 80 V , 10 V; mcm5s2U , 14 . 2 min , m/z 333→201 , 90 V , 7 V; [15N]5-dA , 14 . 4 min , m/z 257→141 , 90 V , 10 V; m22G , 15 . 9 min , m/z 312→180 , 100 V , 8 V; t6A , 17 . 2 min , m/z 413→281 , 100 V , 8 V; Am , 19 min , m/z 282→136 , 100 V , 15 V; yW , 34 . 2 min , m/z 509→377 , 80 V , 5 V , and i6A , 34 . 4 min , m/z 336→204 , 100 V , 17 V . The mass spectrometer monitored ions with the molecular transitions of D , Y , m5C , and Cm from 1 to 4 min; molecular transitions of m5U , ncm5U , ac4C , m3C , ncm5Um , Um , m7G , m1A , and mcm5U from 4 to 7 min; molecular transitions of m1I , Gm , m1G , and m2G from 7 to 10 min; molecular transitions of I , mcm5s2U , [15N]5-dA , m22G , t6A , and Am from 10 to 30 min; molecular transitions of yW and i6A from 30 to 40 min . The identities of individual ribonucleosides were established by comparison to commercially available synthetic standards , high mass accuracy mass spectrometry , fragmentation patterns generated by collision-induced dissociation ( CID ) in a quadrupole time-of-flight mass spectrometer ( QTOF ) or MSn analysis by ion trap mass spectrometry , with comparison to literature data ( e . g . , ref . [48] ) . To assess the direct and indirect effects of MMS on levels of methylated ribonucleosides , the absolute levels of m7G were quantified in small RNA hydrolysates isolated from MMS-exposed and unexposed mutant and wild type strains of yeast by the LC-MS/MS method described above . Calibration curves were generated by mixing variable amounts of m7G ( final concentrations of 0 , 5 , 50 , 300 , 600 , 1000 , and 2000 nM ) with a fixed concentration of [15N]5-dA ( 40 nM ) . A volume of 10 µl of each solution was analyzed with the LC-MS/MS system described earlier . Differences in the levels of ribonucleosides in exposed versus unexposed and in mutant versus wild-type yeast were analyzed by Student's t-test . Hierarchical clustering analyses were performed using Cluster 3 . 0 . Data were transformed to log2 ratios of modification levels in treated cells relative to unexposed controls . Clustering was carried out using the centroid linkage algorithm based on the distance between each dataset measured using the Pearson correlation , with heat map representations produced using Java Treeview . Principal component analysis was performed using XLStat ( Addinsoft SARL , Paris , France ) , with a Pearson correlation matrix consisting of data that were mean-centered and normalized to the standard deviation . Correlation analysis was used to assess the degree of covariance among the various sets of fold-change values for each mutant ( Table S5 ) , with correlation coefficients calculated using Excel ( Microsoft ) . | While the genetic code in DNA is read from four nucleobase structures , there are more than 100 ribonucleoside structures incorporated as post-transcriptional modifications mainly in tRNA and rRNA . These structures and their biosynthetic machinery are highly conserved , with 20–30 present in any one organism , yet the larger biological function of the modifications has eluded understanding . To this end , we developed a sensitive and precise mass spectrometric method to quantify 23 of the 25 ribonucleosides in the model eukaryotic yeast , Saccharomyces cerevisiae . We discovered that the spectrum of ribonucleosides shifts predictably when the cells are exposed to different toxic chemical stimulants , with these signature changes in the spectrum serving as part of the cellular survival response to these exposures . The method also revealed novel enzymatic pathways for the synthesis of several modified ribonucleosides . These results suggest a dynamic reprogramming of the tRNA and rRNA modifications during cellular responses to stimuli , with corresponding modifications working as part of a larger mechanism of translational control during the cellular stress response . |
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Gene expression is subject to stochastic noise , but to what extent and by which means such stochastic variations are coordinated among different genes are unclear . We hypothesize that neighboring genes on the same chromosome co-fluctuate in expression because of their common chromatin dynamics , and verify it at the genomic scale using allele-specific single-cell RNA-sequencing data of mouse cells . Unexpectedly , the co-fluctuation extends to genes that are over 60 million bases apart . We provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity , which is much closer intra-chromosomally than inter-chromosomally . We further show that genes encoding components of the same protein complex tend to be chromosomally linked , likely resulting from natural selection for intracellular among-component dosage balance . These findings have implications for both the evolution of genome organization and optimal design of synthetic genomes in the face of gene expression noise .
Gene expression is subject to considerable stochasticity that is known as expression noise , formally defined as the expression variation of a given gene among isogenic cells in the same environment [1–3] . Gene expression noise is a double-edged sword . On the one hand , it can be deleterious because it leads to imprecise controls of cellular behavior , including , for example , destroying the stoichiometric relationship among functionally related proteins and disrupting homeostasis [4–8] . On the other hand , gene expression noise can be beneficial . For instance , unicellular organisms may exploit gene expression noise to employ bet-hedging strategies in fluctuating environments [9 , 10] , whereas multicellular organisms can make use of expression noise to initiate developmental processes [11–13] . By quantifying protein concentrations in individual isogenic cells cultured in a common environment , researchers have measured the expression noise for thousands of genes in the bacterium Escherichia coli [14] and unicellular eukaryote Saccharomyces cerevisiae [15] . Nevertheless , because genes are not in isolation , one wonders whether and to what extent expression levels co-vary among genes at a steady state , which unfortunately cannot be studied by the above data . By simultaneously tagging two genes with different florescent markers , Stewart-Ornstein et al . discovered strong co-fluctuation of the concentrations of some functionally related proteins in yeast such as those involved in the Msn2/4 stress response pathway , amino acid synthesis , and mitochondrial maintenance , respectively [16] , and the expression co-fluctuation of these genes is facilitated by their sharing of transcriptional regulators [17] . Here we explore yet another mechanism for expression co-fluctuation . We hypothesize that , due to the sharing of chromatin dynamics [18] , a key contributor to gene expression noise [18–20] , genes that are closely linked on the same chromosome should exhibit a stronger expression co-fluctuation when compared with genes that are not closely linked or unlinked ( Fig 1 ) . We refer to this potential influence of chromosomal linkage of two genes on their expression co-fluctuation as the linkage effect . The linkage-effect hypothesis is supported by two pioneering studies demonstrating that the correlation in expression level between two reporter genes across isogeneic cells in the same environment is much higher when they are placed next to each other on the same chromosome than when they are placed on separate chromosomes [21 , 22] . However , neither the generality of the linkage effect nor the chromosomal proximity required for this effect are known . Furthermore , the biological significance of the linkage effect and its potential impact on genome organization and evolution have not been investigated . In this study , we address these questions by analyzing allele-specific single-cell RNA-sequencing ( RNA-seq ) data from mouse cells [23] . We demonstrate that the linkage effect is not only general but also long-range , extending to genes that are tens of millions of bases apart . We provide evidence that three-dimensional ( 3D ) chromatin proximities are responsible for the long-range expression co-fluctuation through mediating chromatin accessibility covariations . Finally , we show theoretically and empirically that the linkage effect has likely impacted the evolution of the chromosomal locations of genes encoding members of the same protein complex .
Let us consider two genes A and B each with two alleles respectively named 1 and 2 in a diploid cell . When A and B are chromosomally linked , without loss of generality , we assume that A1 and B1 are on the same chromosome whereas A2 and B2 are on its homologous chromosome ( Fig 2A ) . Expression co-fluctuation between one allele of A and one allele of B ( e . g . , A1 and B2 ) is measured by Pearson's correlation ( re , where the subscript "e" stands for expression ) between the expression levels of the two alleles across isogenic cells under the same environment . Among the four possible pairs of an A and a B allele , A1-B1 , A2-B2 , A1-B2 , and A2-B1 , the former two pairs are physically linked whereas the latter two pairs are unlinked . The linkage-effect hypothesis asserts that , at a steady state , expression correlations between linked alleles ( cis-correlations ) are greater than those between unlinked alleles ( trans-correlations ) . That is , δe = [re ( A1 , B1 ) +re ( A2 , B2 ) −re ( A1 , B2 ) −re ( A2 , B1 ) ]/2>0 . Note that this formulation is valid regardless of whether the two alleles of the same gene have equal mean expression levels . While each of the four correlations could be positive or negative , in the large data analyzed below , they are mostly positive and show approximately normal distributions across gene pairs examined ( S1 Fig ) . To verify the above prediction about δe , we analyzed a single-cell RNA-seq dataset of fibroblast cells derived from a hybrid between two mouse strains ( CAST/EiJ × C57BL/6J ) [23] . Single-cell RNA-seq profiles the transcriptomes of individual cells , allowing quantifying stochastic gene expression variations among isogenic cells in the same environment [24–26] . DNA polymorphisms in the hybrid allow estimation of the expression level of each allele for thousands of genes per cell . The dataset includes data from seven fibroblast clones and some non-clonal fibroblast cells of the same genotype . We focused our analysis on clone 7 ( derived from the hybrid of CAST/EiJ male × C57BL/6J female ) in the dataset , because the number of cells sequenced in this clone is the largest ( n = 60 ) among all clones . We excluded from our analysis all genes on Chromosomes 3 and 4 due to aneuploidy in this clone and X-linked genes due to X inactivation . To increase the sensitivity of our analysis and remove imprinted genes , we focused on the 3405 genes that have at least 10 RNA-seq reads averaged across cells mapped to each of the two alleles . Note that this gene set constitute >30% of all expressed genes in the cells concerned . While most of the 3405 genes tend to be highly expressed , 16% of them have lower expressions than the median expression of all expressed genes . The 3405 genes form 3404×3405/2 = 5 , 795 , 310 gene pairs , among which 377 , 584 pairs are chromosomally linked . For each pair of chromosomally linked genes , we computed their δe by treating the allele from CAST/EiJ as allele 1 and that from C57BL/6J as allele 2 at each locus . The fraction of gene pairs with δe > 0 is 0 . 61 ( Fig 2B ) . This fraction has a rather narrow 95% confidence interval ( Fig 2B ) , demonstrating that the fraction is significantly higher than the null expectation of 0 . 5 . Because a gene can appear in multiple gene pairs , which are not mutually independent , we applied a binomial test in a subset of gene pairs where each gene appears only once . Specifically , we randomly shuffled the relative positions of all genes on each chromosome and considered from one end of the chromosome to the other end non-overlapping consecutive windows of two genes . The observed fraction of gene pairs with positive δe still significantly exceeds the null expectation of 0 . 5 ( P < 2 . 4×10−16 , binomial test ) . That most gene pairs exhibit δe > 0 holds in each of the 17 chromosomes examined , with the trend being statistically significant in six chromosomes even by the above conservative test ( nominal P < 0 . 05; Fig 2C ) . As a negative control , we analyzed gene pairs located on different chromosomes , treating alleles the same way as described above . As expected , this time the fraction of gene pairs with δe > 0 is not significantly different from 0 . 5 ( P = 0 . 25; Fig 2B ) . The fraction of gene pairs with δe > 0 appears to vary among chromosomes ( Fig 2C ) . To assess the significance of this variation , we compared the fraction of independent gene pairs with δe > 0 between every two chromosomes by Fisher's exact test . After correcting for multiple testing , we found no significant difference between any two chromosomes . To examine the generality of the findings from clone 7 , we also analyzed clone 6 ( derived from the hybrid of CAST/EiJ male × C57BL/6J female ) , which has 38 cells with RNA-seq data . Because 10 of these cells are aneuploidy for different chromosomes ( see the supplementary materials in [23] ) , we analyzed the remaining 28 cells . Similar results were obtained ( S2A and S2B Fig ) . Because clone 6 was from a male whereas clone 7 was from a female , our results apparently apply to both sexes . We also analyzed 47 non-clonal fibroblast cells with the same genetic background ( cell IDs from 124 to 170 , derived from the hybrid of CAST/EiJ male × C57BL/6J female ) , and obtained similar results ( S2C and S2D Fig ) . These findings establish that the linkage effect on expression co-fluctuation is neither limited to a few genes in a specific clone nor an epigenetic artifact of clonal cells , but is general . The linkage effect on co-fluctuation ( and the decrease of the effect with genomic distance shown below ) is robust to the definition of δe , because similar results are obtained when correlation coefficients are replaced with squares of correlation coefficients in the definition of δe . We next investigated how close two genes need to be on the same chromosome for them to co-fluctuate in expression . We divided all pairs of chromosomally linked genes into 100 equal-interval bins based on the genomic distance between genes , defined by the number of nucleotides between their transcription start sites ( TSSs ) . The median δe in a bin is found to decrease with the genomic distance represented by the bin ( Fig 2D ) . Furthermore , even for the unbinned data , δe for a pair of linked genes correlates negatively with their genomic distance ( Spearman's ρ = -0 . 029 ) . To assess the statistical significance of this negative correlation , we randomly shuffled the genomic coordinates of genes within chromosomes and recomputed the correlation . This was repeated 1000 times and none of the 1000 ρ values were equal to or more negative than the observed ρ . Hence , the linkage effect on expression co-fluctuation of two linked genes weakens significantly with their genomic distance ( P < 0 . 001 ) . Surprisingly , however , median δe exceeds 0 for every bin except when the genomic distance exceeds 150 Mb ( Fig 2D ) . Hence , the linkage effect is long-range . To statistically verify the potentially chromosome-wide linkage effect , we focused on linked gene pairs that are at least 63 Mb apart , which is one half the median size of mouse chromosomes . The median δe for these gene pairs is 0 . 017 , or 68% of the median δe for the left-most bin in Fig 2D . We randomly shuffled the genomic positions of all genes and repeated the above analysis 1000 times . In none of the 1000 shuffled genomes did we observe the median δe greater than 0 . 017 for linked genes of distances >63 Mb , validating the long-range expression co-fluctuation in the actual genome . The same can be said even for linked genes of distances >90 Mb ( P < 0 . 001 , shuffling test ) . The above observations are not clone-specific , because the same trend is observed for cells of clone 6 ( S2B Fig ) . Notably , a previous experiment in mammalian cells [21] detected a linkage effect for chromosomally adjacent reporter genes ( δe = 0 . 834 ) orders of magnitude stronger than what is observed here . This is primarily because expression levels estimated using single-cell RNA fluorescence in situ hybridization in the early study [21] are much more precise than those estimated using allele-specific single-cell RNA-seq [27] here . We thus predict that the linkage effect detected will be more pronounced as the expression level estimates become more precise . As a proof of principle , we gradually raised the required minimal number of reads per allele in our analysis , which should increase the precision of expression level estimation but decrease the number of genes that can be analyzed . Indeed , as the minimal read number rises , the fraction of chromosomally linked gene pairs with a positive δe ( Fig 2E ) , median δe for all chromosomally linked gene pairs ( Fig 2F ) , and median δe for the left-most bin ( Fig 2F ) all increase significantly . Furthermore , the low capturing efficiency of single-cell RNA-seq substantially reduces the observed size of the linkage effect , and our lower-bound estimate of the median true δe is 0 . 15 ( see Materials and Methods ) . Because what matters to a cell is the total number of transcripts produced from the two alleles of a gene instead of the number produced from each allele , we also calculated the pairwise correlation in expression level between genes using either the total number of reads mapped to both alleles of a gene or normalized expression level of the gene . We similarly found a long-range linkage effect ( S3 Fig ) , with trends and effect sizes close to the observations based on allele-specific expressions . Previous studies reported that the relative transcriptional orientations of neighboring genes influence their expression co-fluctuation [28] . This impact , however , is unobserved in our study ( S4 Fig ) , which may be due to the limited precision of the expression estimates and the fact that only 422 pairs of neighboring genes satisfy the minimal read number requirement . What has caused the chromosome-wide expression co-fluctuation of linked genes ? One simple explanation is the asynchronous DNA replication in dividing cells , where closely linked genes tend to be replicated at the same time so show positively correlated gene copy numbers and hence expression levels . But a simple calculation demonstrates that this explanation is not tenable . There are 104 to 105 replication origins per mammalian cell [29] . Given the size of the mammalian genome ( ~3×109 bases ) , DNA segments within 0 . 03–0 . 3 Mb share a replication origin . The asynchronous DNA replication could result in the expression co-fluctuation of genes in the range of 0 . 03 to 0 . 3 Mb , which cannot explain our observation of expression co-fluctuation at the scale of >60 Mb . Individual chromosomes in mammalian cells are organized into territories with a diameter of 1~2 μm [30] , whereas the diameter of the nucleus is ~8 μm [30] . Thus , the physical distance between chromosomally linked genes is below 1~2 μm , whereas that between unlinked genes is usually > 1~2 μm and can be as large as ~8 μm . Because it takes time for macromolecules to diffuse in the nucleus , linked genes tend to have similar chemical environments and hence similar transcriptional dynamics ( i . e . , promoter co-accessibility and/or co-transcription ) when compared with unlinked genes . We thus hypothesize that the linkage effect is fundamentally explained by the 3D proximity of linked genes compared with unlinked genes ( Fig 3A ) . Below we provide evidence for this model . Note , while our computational analyses cannot prove causation , they examine correlations among various quantities that are predicted by our hypothesis and hence can provide strong evidence for or against the hypothesis when performed rigorously and interpreted appropriately . We started by comparing the 3D distances between linked alleles with those between unlinked alleles . The 3D distance between two genomic regions can be approximately measured by Hi-C , a high-throughput chromosome conformation capture method for quantifying the number of interactions between genomic loci that are nearby in 3D space [31] . The smaller the 3D distance between two genomic regions , the higher the interaction frequency between them [32] . It is predicted that the interaction frequency between the physically linked alleles of two genes ( cis-interaction ) is greater than that between the unlinked alleles of the same gene pair ( trans-interaction ) . To verify this prediction , we analyzed the recently published allele-specific 500kb-resolution Hi-C interaction matrix [33] of mouse neural progenitor cells ( NPC ) . For any two linked loci A and B as depicted in the left diagram of Fig 2A , we computed δi = [F ( A1 , B1 ) +F ( A2 , B2 ) −F ( A1 , B2 ) −F ( A2 , B1 ) ]/2 , where F is the interaction frequency between the two alleles in the parentheses and the subscript "i" refers to interaction . We found that 99% of pairs of linked loci have a positive δi ( P < 2 . 2×10−16 , binomial test on independent locus pairs; Fig 3B ) . By contrast , among unlinked gene pairs , the fraction with a positive δi is not significantly different from that with a negative δi ( P = 0 . 90 , binomial test on independent locus pairs; Fig 3B ) . In the analysis of unlinked loci , we treated all alleles from one parental strain of the hybrid as alleles 1 and all alleles from the other parental strain of the hybrid as alleles 2 in the above formula of δi . These results clearly demonstrate the 3D proximity of genes on the same chromosome when compared with those on two homologous chromosomes . To examine if the above phenomenon is long-range , we plotted δi as a function of the distance ( in Mb ) between two linked loci considered . Indeed , even when the distance exceeds 63 Mb , one half the median size of mouse chromosomes , almost all locus pairs still show positive δi ( Fig 3C ) . Similar to the phenomenon of the linkage effect on gene expression co-fluctuation , we observed a negative correlation between the genomic distance between two linked loci and δi ( ρ = -0 . 81 for unbinned data ) . This correlation is statistically significant ( P < 0 . 001 ) , because it is stronger than the corresponding correlation in each of the 1000 negative controls where the genomic positions of all genes are randomly shuffled within chromosomes . As mentioned , 3D proximity should synchronize the transcriptional dynamics of linked alleles . Based on the bursty model of gene expression [34] , transcription involves two primary steps . In the first step , the promoter region switches from the inactive state to the active state such that it becomes accessible to the transcriptional machinery . In the second step , RNA polymerase binds to the activated promoter to initiate transcription . In principle , the synchronization of either step can result in co-fluctuation of mRNA concentrations . Because the accessibility of promoters can be detected using transposase-accessible chromatin using sequencing ( ATAC-seq ) [35] in a high-throughput manner , we focused our empirical analysis on promoter co-accessibility . Note that although the bursty model does not consider certain details of transcription such as polymerase pausing and productive elongation , there is mounting evidence that the bursty model provides a good approximation of stochastic gene expression [21 , 36–38] , which is what matters to our study . To verify the potential long-range linkage effect on chromatin co-accessibility , we should ideally use single-cell allele-specific measures of chromatin accessibility . However , such data are unavailable . We reason that , the accessibility covariation of genomic regions among cells may be quantified by the corresponding covariation among populations of cells of the same type cultured under the same environment . In fact , it can be shown mathematically that , under certain conditions , chromatin co-accessibility of two genomic regions among cells equals the corresponding chromatin co-accessibility across cell populations ( see Materials and Methods ) . Based on this result , we analyzed a dataset collected from allele-specific ATAC-seq in 16 NPC cell populations [39] . We first removed sex chromosomes and then required the number of reads mapped to each allele of a peak to exceed 50 for the peak to be considered . This latter step removed imprinted loci and ensured that the considered peaks are relatively reliable . About 3500 peaks remained after the filtering . This sample size is comparable to the number of genes used in the analysis of expression co-fluctuation . For each pair of ATAC peaks , we computed δa = [ra ( A1 , B1 ) +ra ( A2 , B2 ) −ra ( A1 , B2 ) −ra ( A2 , B1 ) ]/2 , where ra is the correlation in ATAC-seq read number between the alleles specified in the parentheses ( following the left diagram in Fig 2A ) across the 16 cell populations and the subscript "a" refers to chromatin accessibility . The fraction of peak pairs with a positive δa is significantly greater than 0 . 5 for linked peak pairs but not significantly different from 0 . 5 for unlinked peak pairs ( binomial test on independent peak pairs; Fig 3D ) . Furthermore , after grouping ATAC peak pairs into 100 equal-interval bins according to the genomic distance between peaks , we observed a clear trend that δa decreases with the genomic distance between peaks ( ρ = -0 . 05 for unbinned data , P < 0 . 001 , within-chromosome shuffling test; Fig 3E ) . In addition , even for linked peak pairs with a distance greater than 63 Mb , their median δa is significantly greater than that of unlinked peak pairs ( P < 0 . 001 , among-chromosome shuffling test ) . Together , these results demonstrate a long-range linkage effect on chromatin co-accessibility . Similar to δe , the observed δa is small . This is again at least in part a result of low capturing efficiencies in high-throughput sequencing . We estimated that the median true δa is at least 0 . 03 ( see Materials and Methods ) , an order of magnitude larger than the observed value . Because we hypothesize that the linkage effect on expression co-fluctuation is via 3D chromatin proximity that leads to chromatin co-accessibility ( Fig 3A ) , we should verify the relationship between 3D proximity and chromatin co-accessibility for unlinked genomic regions to avoid the confounding factor of linkage . To this end , we converted ATAC-seq read counts to a 500kb resolution by summing up read counts for all allele-specific chromatin accessibility peaks that fall within the corresponding Hi-C bin , because the resolution of the Hi-C data is 500kb . Because alleles from different parents are unlinked in the hybrid used for ATAC-seq , for each pair of bins , we computed the mean correlation in chromatin accessibility between the alleles derived from different parents among the 16 cell populations , or trans-ra = ra ( A1 , B2 ) /2 + ra ( A2 , B1 ) /2 . For the same reason , we computed the sum of Hi-C contact frequency between the alleles derived from different parents , trans-F = F ( A1 , B2 ) /2+F ( A2 , B1 ) /2 . Because interaction frequencies in Hi-C data are generally low for unlinked regions , we separated all pairs of bins into two categories , contacted ( i . e . , trans-F > 0 ) and uncontacted ( i . e . , trans-F = 0 ) . We found that trans-ra values for contacted bin pairs are significantly higher than those for uncontacted bin pairs ( P < 0 . 0001; Fig 3F ) , consistent with our hypothesis that 3D chromatin proximity induces chromatin co-accessibility . The above statistical significance was determined by performing a Mantel test using the original trans-ra matrix of the aforementioned allele pairs and the corresponding trans-F matrix . Corroborating our finding , a recent study of single-cell ( but not allele-specific ) chromatin accessibility data also found that the co-accessibility of two loci rises with their 3D proximity [40] . To test the hypothesis that chromatin co-accessibility leads to expression co-fluctuation ( even for unlinked alleles ) ( Fig 3A ) , we analyzed the allele-specific ATAC-seq data and single-cell allele-specific RNA-seq data together . Although these data were generated from different cell types in mouse , we reason that , because the 3D chromosome conformation is highly similar among tissues [41] , chromatin co-accessibility , which is affected by 3D chromatin proximity ( Fig 3F ) , may also be similar among tissues . Hence , it may be possible to detect a correlation between chromatin co-accessibility and expression co-fluctuation . To this end , we used unbinned ATAC-peak data to compute trans-ra but limited the analysis to those peaks with at least 10 reads per allele . We used the allele-specific RNA-seq data to compute trans-re = re ( A1 , B2 ) /2+re ( A2 , B1 ) /2 for pairs of linked genes . We then assigned each gene to its nearest ATAC peak and averaged trans-re among gene pairs assigned to the same pair of ATAC peaks . We subsequently grouped ATAC peak pairs into 100 equal-interval bins according to their co-accessibilities , and observed a clear positive correlation between median trans-ra and median trans-re across the 100 bins ( Fig 3G ) . For unbinned data , trans-ra and trans-re also show a significant , positive correlation ( ρ = 0 . 021 , P = 0 . 027 , Mantel test ) . Although the assignment of a gene to its nearest ATAC peak may not be biologically meaningful in some cases , such potential errors only add noise to our analysis , meaning that the true signal should be stronger than what is observed . As predicted , there is also a positive correlation between δi and δe ( ρ = 0 . 014 , P = 1 . 6×10−17 ) . In this analysis , for each linked pair of Hi-C bins , we computed δi . We assigned each gene in our dataset to its nearest Hi-C bin , estimated δe for each pair of genes that are respectively located in the two Hi-C bins considered , and computed the average δe between the two bins . The correlation between δi and δe can be visualized in Fig 3H , where Hi-C bin pairs are separated into 100 equal-size groups based on δi . Note that although the impact of 3D proximity ( Fig 3H ) appears weaker than the impact of genomic distance ( Fig 2D ) on δe , these two plots are not directly comparable because of the following reasons . First , the Hi-C contact frequency is not an accurate measure of 3D proximity , especially for region pairs that rarely contact , which apply to the vast majority of region pairs ( Fig 3C ) . By contrast , genomic distance is measured accurately . Second , the resolution of the Hi-C data used is much lower ( 500 kb ) than that of the genomic distance ( 1 bp ) . Third , the Hi-C data and gene expression co-fluctuations data are not from the same cell type , which reduces the observed impact of 3D proximity . Together , the above results support our hypothesis that , compared with unlinked genes , linked genes have a shared chemical environment due to their 3D proximity and hence chromatin co-accessibility , which leads to their expression co-fluctuation ( Fig 3A ) . However , 3D proximity can lead to promoter co-accessibility by several means , which have been broadly summarized into three categories of mechanisms [30]: 1D scanning , 3D looping , and 3D diffusion . 1D scanning refers to the spread of chromatin states along an entire chromosome , but 1D scanning is rare , with only a few known examples such as X-chromosome inactivation [30] . Hence , 1D scanning is unlikely to be the mechanism responsible for the broad linkage effect discovered here . 3D looping refers to the phenomenon that a chromosome often forms loops to bring far-separated loci into contact , whereas 3D diffusion refers to chromosome communication by local diffusion of transcription-related proteins . For tightly linked loci , our data do not allow a clear distinction between 3D looping and 3D diffusion in causing the linkage effect discovered here . But 3D diffusion seems more likely for the long-range effect , because the range of 3D looping seems limited to loci separated by no more than 200 kb simply due to the rapid decrease of the contact frequency with the physical distance between two loci [42] , evident in Fig 3C ( note the log scale of the Y-axis ) . It has been estimated that loci separated by 10 Mb behave essentially the same as two loci that are on different chromosomes in terms of the contact frequency [30] , and any contact-based mechanism is unlikely to be long-range ( e . g . , topologically associating domains ) [41] . Therefore , the most likely cause of our observed long-range linkage effect is 3D diffusion . In the 3D diffusion mechanism , which molecule is most likely responsible for the observed long-range linkage effect on expression co-fluctuation ? If the chemical influencing transcription has a diffusion time in the nucleus much shorter than the interval between transcriptional bursts , two genes have essentially the same environment with respect to that chemical regardless of their 3D distance [43] and hence no linkage effect is expected ( top cell in Fig 3I ) . On the contrary , if the chemical diffuses too slowly , the linkage effect will be local [43] and hence cannot be chromosome-wide ( bottom cell in Fig 3I ) . Therefore , the diffusion rate of the chemical responsible for the long-range linkage effect cannot be too low or too high such that they become evenly distributed in a chromosome territory but not the whole nucleus in a time comparable to the interval between transcriptional bursts ( middle cell in Fig 3I ) . The typical transcriptional burst interval is 18–50 minutes in mammalian cells [37 , 38] . The time for a chemical to distribute evenly in a given volume with radius R is on the order of R2/D , where D is the diffusion coefficient of the chemical [34] . Most molecules in the nucleus are rapidly diffused . For example , transcription factors typically have a diffusion coefficient of 0 . 5–5 μm2/s in the nucleus [34 , 44] , meaning that they can diffuse across the whole nucleus in about 3 to 30 seconds . By contrast , core histone proteins such as H2B proteins diffuse extremely slowly due to their tight binding to DNA . They are usually considered immobilized because diffusion is rarely observed during the course of an experiment [44 , 45] . Therefore , none of these molecules are responsible for the long-range linkage effect observed . Interestingly , linker histones , which include five subtypes of H1 histones in mouse that play important roles in chromatin structure and transcription regulation [46] , have a diffusion coefficient of about 0 . 01 μm2/s [47] . Thus , it takes H1 proteins 25–100 seconds to diffuse through a chromosome territory , but about 30 minutes to diffuse across the whole nucleus . The former time but not the latter is much smaller than the typical transcriptional burst interval . Hence , it is possible that H1 diffusion in the nucleus is the ultimate cause of the linkage effect . We provide empirical evidence for this hypothesis in a later section . Our finding that chromosomal linkage leads to gene expression co-fluctuation implies that linkage between genes could be selected for when expression co-fluctuation is advantageous . Due to the complexity of biology , it is generally difficult to predict whether the expression co-fluctuation of a pair of genes is beneficial , neutral , or deleterious . However , the expression co-fluctuation of genes encoding components of the same protein complex is likely advantageous . To see why this is the case , let us consider a dimer composed of one molecule of protein A and one molecule of protein B; the heterodimer is functional but monomers are not . We denote the concentration of dissociated protein A as [A] , the concentration of dissociated protein B as [B] , and the concentration of protein complex AB as [AB] . At the steady state , [AB] = K[A][B] , where K is the association constant [48] . Furthermore , the total concentration of protein A , [A]t , equals [A] + [AB] , while the total concentration of protein B , [B]t , equals [B] + [AB] . Based on these relationships , we simulated 10 , 000 cells , where the mean and coefficient of variation ( CV ) are respectively 1 and 0 . 2 for both [A]t and [B]t ( see Materials and Methods ) . We assumed K = 105 based on empirical K values of stable protein complexes [49] . We found that , as the correlation between [A]t and [B]t increases , mean [AB] of the 10 , 000 cells rises ( Fig 4A ) . We also considered a wide range of other K values ( 10−1 , 100 , 101 , 102 , 103 , and 104 ) and found the result largely unchanged . The lower-bound mean [AB] is about 3% higher under co-fluctuation than under no co-fluctuation . Furthermore , the effect size rises substantially with CV . For example , when CV = 0 . 5 , which is not unusual in eukaryotes [49] , the effect increases to 20% . We also considered protein complexes with other stoichiometries and the scenario when the mean [A]t to mean [B]t ratio deviates from the stoichiometry ( see Materials and Methods ) . In all parameter combinations examined , the mean [AB] increases with the correlation between [A]t and [B]t , albeit with a wide range of effect size ( 0 . 001% to 27% higher mean [AB] under co-fluctuation than under no co-fluctuation ) . If we assume that fitness rises with [AB] , the co-fluctuation of [A]t and [B]t is beneficial , compared with independent fluctuations of [A]t and [B]t . In addition , because mean [A] and mean [B] must decrease with the rise of mean [AB] , the co-fluctuation of [A]t and [B]t could also be advantageous because it lowers the concentrations of the unbound monomers that may be toxic . Indeed , past studies found better expression co-fluctuations of genes encoding members of the same protein complex than random gene pairs [50 , 51] , suggesting that expression co-fluctuation of members of the same protein complex is selectively favored . Because our simulation considers protein concentrations instead of gene expressions , it directly applies to both haploid and diploid cells . The only difference is that protein concentrations such as [A]t and [B]t have a lower CV in diploid than haploid cells [6] . Further , as shown in S3 Fig , mouse cells analyzed here do show a higher correlation in the total expression level of two alleles between linked than unlinked genes . To test if genes encoding components of the same protein complex tend to be linked , we used the mouse protein complex data from CORUM and downloaded the chromosomal positions of all mouse protein-coding genes from Ensembl [52] . Because genes may be linked due to their origins from tandem duplication [53] , the data were pre-processed to produce a set of duplicate-free mouse protein-coding genes ( see Materials and Methods ) . We then randomly shuffled the genomic positions of the retained genes encoding protein complex components among all possible positions of the duplicate-free mouse protein-coding genes . The observed number of linked pairs of genes encoding components of the same protein complex is significantly greater than the random expectation ( Fig 4B ) . For comparison , we also computed the number of linked pairs of genes encoding components of different protein complexes . This number is not significantly greater than the random expectation ( Fig 4C ) . Thus , the enrichment in gene linkage is specifically related to coding for subunits of the same protein complex . Interestingly , the observed median distance between the TSSs of two linked genes encoding protein complex subunits is not significantly different from the random expectation , regardless of whether subunits of the same ( Fig 4D ) or different ( Fig 4E ) protein complexes are considered . The phenomenon that members of the same protein complex tend to be encoded by linked genes could have arisen for one or both of the following reasons . First , selection for co-fluctuation among proteins of the same complex has driven the evolution of gene linkage . Second , due to their co-fluctuation , products of linked genes may have been preferentially recruited to the same protein complex in evolution . Under the first hypothesis , originally unlinked genes encoding members of the same protein complex are more likely to become linked in evolution than originally unlinked genes that do not encode members of the same complex . To verify this prediction , we examined mouse genes using rat and human as outgroups ( Fig 4F ) , because our δe estimates are from the mouse . We obtained pairs of genes encoding components of the same protein complex in both human and mouse . Hence , these pairs likely encode members of the same protein complex in the common ancestor of the three species . Among them , 875 pairs are unlinked in human and rat , suggesting that they were unlinked in the common ancestor of the three species . Of the 875 pairs , 25 pairs become linked in the mouse genome , significantly more than the random expectation under no requirement for gene pairs to encode members of the same complex ( P = 0 . 005; Fig 4F; see Materials and Methods ) . Therefore , the first hypothesis is supported . Under this hypothesis , the result in Fig 4D may be explained by the long-range linkage effect on expression co-fluctuation , such that once two genes encoding components of the same protein complex move to the same chromosome , selection is not strong enough to drive them closer to each other . To test the second hypothesis , we need gene pairs encoding proteins that belong to the same protein complex in mouse but not in human nor rat , which require such low false negative errors in protein complex identification that no current method can meet . Hence , we leave the test of the second hypothesis to future studies . Note that the above tests have two caveats . First , it is possible that some tandem duplicates remain in our data , which will compromise the analysis in Fig 4B . However , the result in Fig 4F is robust to such potential errors because the gene pairs concerned are originally unlinked so could not have arisen by tandem duplication . Thus , our evidence for positive selection for gene linkage holds even when the data are contaminated by tandem duplicates . Second , we inferred ancestral gene linkage by the parsimony principle , which may occasionally be incorrect . But such errors add only random noise to our analysis , suggesting that the actual strength of evidence for our hypothesis is likely stronger than what is shown here . As mentioned , our theoretical consideration suggests that , due to their intermediate diffusion coefficients , H1 histones may be responsible for the observed chromosome-wide expression co-fluctuation . Because the local H1 concentration fluctuates more when its cellular concentration is lower , we predict that the benefit and the selection coefficient for linkage of genes encoding members of the same protein complex are greater in tissues with lower H1 concentrations . Given that gene expression is costly , for a given gene , it is reasonable to assume that the relative importance of its function in a tissue increases with its expression level in the tissue [54 , 55] . Hence , we predict that , the more negative the across-tissue expression correlation is between a protein complex member gene and H1 histones , the higher the likelihood that the gene is driven to be linked with other genes encoding members of the same protein complex . To verify the above prediction , we used a recently published RNA-seq dataset [56] to measure Pearson's correlation between the mRNA concentration of a gene that encodes a protein complex subunit and the mean mRNA concentration of all H1 histone genes across 13 mouse tissues . Indeed , the linked protein complex genes show more negative correlations than the unlinked protein complex genes ( P = 0 . 012 , one-tailed Mann-Whitney U test; Fig 4G ) . The disparity is even more pronounced when we compare linked protein complex genes that become linked in the mouse lineage with unlinked protein complex genes ( P = 0 . 00068 , one-tailed Mann-Whitney U test; Fig 4G ) . This is likely owing to the enrichment of genes that are linked due to the linkage effect in the group of evolved linked protein complex genes ( Observed−nullexpectationnullexpectation=25−1313=92% ) when compared with the group of linked protein complex genes ( Observed−nullexpectationnullexpectation=200−161161=24% ) . The above three groups of genes ( evolved linked protein complex genes , linked protein complex genes , and unlinked protein complex genes ) were constructed using stratified sampling to ensure that their mean expression levels across tissues are not significantly different ( see Materials and Methods ) . For comparison , we performed the same analysis but replaced H1 histones with TFIIB , a general transcription factor that is involved in the formation of the RNA polymerase II preinitiation complex and has a high diffusion rate [57] . The trends shown in Fig 4G no longer holds ( unlinked vs . linked: P = 0 . 11 , one-tailed Mann-Whitney U test; unlinked vs . evolved linked: P = 0 . 63 , one-tailed Mann-Whitney U test ) . We also performed the same analysis but replaced H1 histones with core histone proteins , which are immobilized [45] . Again , the trends in Fig 4G disappeared ( unlinked vs . linked: P = 0 . 48 , one-tailed Mann-Whitney U test; unlinked vs evolved linked: P = 0 . 89 , one-tailed Mann-Whitney U test ) . These results support our hypothesis about the role of H1 histones in the linkage effect of expression co-fluctuation .
Using allele-specific single-cell RNA-seq data , we discovered chromosome-wide expression co-fluctuation of linked genes in mammalian cells . We hypothesize and provide evidence that genes on the same chromosome tend to have close 3D proximity , which results in a shared chemical environment for transcription and leads to expression co-fluctuation . While the linkage effect on expression co-fluctuation is likely an intrinsic cellular property , when the expression co-fluctuation of certain genes improves fitness , natural selection may drive the relocation of these genes to the same chromosome . Indeed , we provide evidence suggesting that the chromosomal linkage of genes encoding protein complex subunits is beneficial owing to the resultant expression co-fluctuation that minimizes the dosage imbalance among these subunits and has been selected for in genome evolution . Although many statistical results in this study are highly significant , the effect sizes appear small in several analyses , most notably the δe and δa values for linked genes . The small effect sizes are generally due to the large noise in the data , less ideal types of data used , and mismatches between the data sets co-analyzed . For instance , δe between linked genes estimated here ( Fig 2D ) is much smaller than what was previously estimated for a pair of linked florescent protein genes [21] , due in a large part to the inherently large error in quantifying mRNA concentrations by single-cell RNA-seq [58] . The small size of δa ( Fig 3E ) is likely caused at least in part by the low efficiency of ATAC-seq in detecting open chromatin ( see Materials and Methods ) . The positive correlation between trans-ra and trans-re ( Fig 3G ) is likely an underestimate due to the use of different cell types in RNA-seq and ATAC-seq . As shown in Fig 2E and 2F , the actual effect sizes would be much larger should better experimental methods and/or data become available . Hence , it is likely that many effects are underestimated in this study . Indeed , we estimated that the true effect sizes of δe and δa are at least an order of magnitude larger than observed ( see Materials and Methods ) . In addition , the co-fluctuation effect detected by Raj et al . may be unusually large because in that study the chromosomal distance between the two genes was extremely small and the two genes used identical regulatory elements [21] . Regardless , we stress that whether an effect is large/important depends on whether it is detectable by natural selection , and our results in Fig 4 suggest that the effects appear visible to natural selection , as reflected in the preferential chromosomal linkage of genes encoding protein complex subunits . Note that natural selection can detect a selective differential as small as the inverse of the effective population size , which is about 70 , 000 in mouse [59] . Because the expression co-fluctuation of two genes can be achieved by the sharing of regulatory elements and/or linkage , it is important to understand the relative contributions of the two mechanisms . But this question is generally difficult to answer because the extent to which two genes share regulatory elements is usually unknown . However , a lower bound contribution of the linkage effect can be estimated by examining two ( equally regulated ) alleles of the same gene . In this extreme case , the contribution of the linkage effect on expression co-fluctuation is about one thirteenth the contribution of sharing regulatory elements ( see Materials and Methods ) . Although linkage likely makes a smaller contribution than regulatory element sharing to the expression co-fluctuation of genes , linkage can increase the expression co-fluctuation to a level that regulatory element sharing alone cannot reach . This additional improvement can be important under certain circumstances , as shown recently for genes encoding enzymes of the yeast galactose use pathway [60] . Because we used RNA-seq to measure expression co-fluctuation , our results apply to the co-fluctuation of mRNA concentrations . In the case of protein complex components , it is presumably the co-fluctuation of protein concentrations rather than mRNA concentrations that is directly beneficial . Although the degree of covariation between mRNA and protein concentrations is under debate [61 , 62] , the two concentrations correlate well at the steady state [21] . One key factor in this correlation is the protein half-life , because , when the protein half-life is long , mRNA and protein concentrations may not correlate well due to the delay in the effect of a change in mRNA concentration on protein concentration [21] . It is interesting to note that in Raj et al . 's study [21] , mRNA and protein concentrations still correlate reasonably well ( r = 0 . 43 ) when the protein half-life is 25 hours , which is much longer than the reported mean protein half-life of 9 hours in mammalian cells [63] . Corroborating this finding is the recent report [64] that mRNA and protein concentrations correlate well across single cells in the steady state ( mean r = 0 . 732 ) . Note that , although the correlation between mRNA and protein concentrations measured at the same moment may not be high when the protein half-life is long , the current protein level can still correlate well with a past mRNA level [65] . Because our study focuses on cells at the steady state , co-fluctuation of mRNA concentrations is expected to lead to co-fluctuation of protein concentrations . We attributed the preferential linkage of genes encoding protein complex subunits to the benefit of expression co-fluctuation , while a similar phenomenon of linkage was previously reported in yeast and attributed to the potential benefit of co-expression of protein complex subunits across environments [66] , where co-expression refers to the correlation in mean expression level . In mammalian cells , our hypothesis is more plausible than the co-expression hypothesis for five reasons . First , across-environment ( or among-tissue ) variation in mean mRNA concentration does not translate well to the corresponding variation in mean protein concentration [62 , 67] , but mRNA concentration fluctuation explains protein concentration fluctuation quite well [21 , 64] . Hence , gene linkage , which enhances mRNA concentration co-fluctuation and by extension protein concentration co-fluctuation , may not improve protein co-expression across environments . Second , co-expression of linked genes appears to occur at a much smaller genomic distance than the linkage effect on co-fluctuation detected here [68] . Thus , if selection on co-expression were the cause for the non-random distribution of protein complex genes , these genes should be closely linked . This , however , is not observed ( Fig 4D ) . Hence , the previous finding that genes encoding members of ( usually not the same ) protein complexes tend to be clustered is best explained by the fact that certain chromosomal regions have inherently low expression noise and that these regions attract genes encoding protein complex members because stochastic expressions of these genes are especially harmful ( i . e . , the noise reduction hypothesis ) [4 , 69] . Third , the protein complex stoichiometry often differs among environments , which makes co-expression of complex components disfavored in the face of environmental changes [70 , 71] . Nonetheless , under a given environment , protein concentration co-fluctuation remains beneficial because of the presence of an optimal stoichiometry at each steady state . Fourth , gene linkage is not necessary for the purpose of co-expression , because the genes involved can use similar cis-regulatory sequences to ensure co-expression even when they are unlinked . In fact , a large fraction of co-expression of linked genes is due to tandem duplicates [68] , which have similar regulatory sequences by descent . However , even for genes with the same regulatory sequences , linkage improves expression co-fluctuation at the steady state . Finally , the co-expression hypothesis or noise reduction hypothesis cannot explain our observation of the relationship between the expression levels of H1 histones and those of linked genes encoding protein complex members across tissues ( Fig 4G ) . Taken together , these considerations suggest that it is most likely the selection for expression co-fluctuation rather than co-expression across environments that has driven the evolution of linkage of genes encoding members of the same protein complex . Several previous studies reported long-range coordination of gene expression [62 , 72–79] , but most of them was about co-expression above explained . One study used fluorescent in situ hybridization of intronic RNA to detect nascent transcripts in individual cells [72] . The authors reported independent transcriptions of most linked genes with the exception of two genes about 14 Mb apart that exhibit a negative correlation in transcription . Their observations are not contradictory to ours , because they measured the nearly instantaneous rate of transcription , whereas we measured the mRNA concentration that is the accumulated result of many transcriptional bursts . As explained , having a similar biochemical environment makes the activation/inactivation cycles of linked genes coordinated to some extent , even though the stochastic transcriptional bursts in the activation period may still look independent . Our work suggests several future directions of research regarding expression co-fluctuation and its functional implications . First , it would be interesting to know if the linkage effect on expression co-fluctuation varies across chromosomes . Although we analyzed individual chromosomes ( S5 Fig ) , addressing this question fully requires better single-cell expression data , because the current single-cell RNA-seq data are noisy . This also makes it difficult to detect any unusual chromosomal segment in its δe distribution . Second , our results suggest that 3D proximity is a major cause for the linkage effect on expression co-fluctuation . In particular , diffusion of proteins with intermediate diffusion coefficients such as H1 histones is likely one mechanistic basis of the effect . However , the diffusion behaviors of most proteins involved in transcription are largely unknown . A thorough research on the diffusion behaviors of proteins inside the nucleus will help us identify other proteins that are important in the linkage effect . As mentioned , our data do not allow a clear distinction between 3D looping and 3D diffusion in causing the linkage effect on tightly linked genes . To distinguish between these two mechanisms definitively , we would need allele-specific models of mouse chromosome conformation [80] , which require more advanced algorithms and more sensitive allele-specific Hi-C methods . Third , our study highlights the importance of the impact of sub-nucleus spatial heterogeneity in gene expression . This can be studied more thoroughly via real-time imaging and spatial modeling of chemical reactions [43 , 81] . The lack of knowledge about the details of transcription reactions prevents us from constructing an accurate quantitative model of gene expression , which can be achieved only by more accurate measurement and more advanced computational modeling . Fourth , we used protein complexes as an example to demonstrate how the linkage effect on expression co-fluctuation influences the evolution of gene order . Protein complex genes are by no means the only group of genes for which expression co-fluctuation can be advantageous . Previous work suggested that expression co-fluctuation of genes on the same signaling or metabolic pathway can be beneficial [82 , 83] , which was recently experimentally confirmed for the yeast galactose catabolism pathway [60] . But , to understand the broader evolutionary impact of the linkage effect , a general prediction of the fitness consequence of expression co-fluctuation is necessary . To achieve this goal , whole-cell modeling may be required [84] . Note that some other mechanisms such as cell cycle [85] can also lead to gene expression co-fluctuation , so should be considered in the study of the relationship between gene expression and fitness . Fifth , physical proximity might impact aspects of gene expression regulation other than co-fluctuation . For instance , previous research found that selective expression of genes that are clustered on the same chromosome ( i . e . , stochastic gene choice ) is strongly dependent on intrachromosomal looping , which alters the pairwise physical distance between genes in the same gene array [86–88] . It will be interesting to explore whether the principle that governs the linkage effect studied here applies to stochastic gene choice . Sixth , because expression co-fluctuation could be beneficial or harmful , an alteration of expression co-fluctuation should be considered as a potential mechanism of disease caused by mutations that relocate genes in the genome . Seventh , our analysis focused on highly expressed genes more than lowly expressed ones due to the limited sensitivity of single-cell RNA-seq . Because lowly expressed genes are affected more than highly expressed genes by expression noise [89] , expression co-fluctuation may be more important to lowly expressed genes than highly expressed ones . More sensitive and accurate single-cell expression profiling methods are needed to study the expression co-fluctuation of lowly expressed genes . Eighth , we focused on mouse fibroblast cells because of the limited availability of allele-specific single-cell RNA-seq data . To study how expression co-fluctuation impacts the evolution of gene order , it will be important to have data from multiple cell types and species . Last but not least , as we start designing and synthesizing genomes [90] , it will be important to consider how gene order affects expression co-fluctuation and potentially fitness . It is possible that the fitness effect associated with expression co-fluctuation is quite large when one compares an ideal gene order with a random one . It is our hope that our discovery will stimulate future researches in above areas .
The processed allele-specific single-cell RNA-seq data were downloaded from https://github . com/RickardSandberg/Reinius_et_al_Nature_Genetics_2016 ? files=1 ( mouse . c57 . counts . rds and mouse . cast . counts . rds ) . The Hi-C data [33] were downloaded from https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE72697 , and we analyzed the 500kb-resolution Hi-C interaction matrix with high SNP density ( iced-snpFiltered ) . The processed ATAC-seq data were provided by authors [39] , and the data from 16 NPC populations were analyzed . All analyses were performed using custom programs in R or python . The mouse protein complex data were downloaded from the CORUM database ( http://mips . helmholtz-muenchen . de/corum/ ) [91] . The coordinates for all mouse protein-coding genes were downloaded from Ensembl BioMart ( GRC38m . p5 ) [52] . To produce duplicate-free gene pairs , we also downloaded all paralogous gene pairs from Ensembl BioMart . Note that these gene pairs can be redundant , meaning that a gene may be paralogous with multiple other genes and appear in multiple gene pairs . We then iteratively removed duplicate genes based on the following rules . First , if one gene in a pair of duplicate genes has been removed , the other gene is retained . Second , if neither gene in a duplicate pair has been removed and neither encodes a protein complex component , one of them is randomly removed . Third , if neither gene in a duplicate pair has been removed and only one of them encodes a protein complex member , we remove the other gene . Fourth , if neither gene in a duplicate pair has been removed and both genes encode protein complex components , one of them is randomly removed . Applying the above rules resulted in a set of duplicate-free genes with as many of them encoding protein complex members as possible . We obtained all mouse genes that have one-to-one orthologs in both human and rat , and acquired from Ensembl their chromosomal locations in human , mouse , and rat . Gene pairs are formed if their products belong to the same protein complex in human as well as mouse , based on protein complex information in the CORUM database mentioned above . Among them , 875 gene pairs from 342 genes are unlinked in both human and rat , of which 25 pairs become linked in mouse . To test whether the number 25 is more than expected by chance , we compared these 342 genes with a random set of 342 genes that also form 875 unlinked gene pairs in human and rat . These unlinked pairs are highly unlikely to encode members of the same complex , so serve as a negative control . Because of the difficulty in randomly sampling 342 genes that form 875 unlinked gene pairs , we adopted Gibbs sampling [92] , one kind of Markov-Chain Monte-Carlo sampling [93] . The procedure was as follows . Starting from the observed 342 genes , represented by the vector of ( gene 1 , gene 2 , … , gene 342 ) , we swapped gene 1 with a randomly picked gene from the mouse genome such that the 342 genes still satisfied all conditions of the original 342 genes described above . We then similarly swapped gene 2 , gene 3 , … , and finally gene 342 , at which point a new gene set was produced . To allow the Markov chain to reach the stationary phase , we discarded the first 1000 gene sets generated . Starting the 1001st gene set , we retained a set every 50 sets produced until 1000 sets were retained; this ensured relative independence among the 1000 retained sets . In each of these 1000 sets , we counted the number of gene pairs that are linked in mouse . The fraction of sets having the number equal to or greater than 25 was the probability reported in Fig 4F . Let us consider the chromatin accessibilities of two genomic regions , A and B , in a population of N cells ( N = 50 , 000 in the data analyzed ) [39] . Let us denote the chromatin accessibilities for the two regions in cell i by random variables Ai and Bi , respectively , where i = 1 , 2 , 3 , … , and N . We further denote the corresponding total accessibilities in the population as random variables AT and BT , respectively . We assume that Ai follows the distribution X , while Bi follows the distribution Y . We then have the following equations . AT=∑i=1NAiandBT=∑i=1NBi . ( 1 ) Pearson's correlation between AT and BT across cell populations all of size N is Corr ( AT , BT ) =E ( AT⋅BT ) −E ( AT ) E ( BT ) Var ( AT ) Var ( BT ) =E ( ∑i=1N∑j=1NAiBj ) −N2E ( X ) E ( Y ) N2Var ( X ) Var ( Y ) =∑i=1N∑j=1NE ( AiBj ) −N2E ( X ) E ( Y ) NVar ( X ) Var ( Y ) . ( 2 ) Because cells are independent from one another , when i ≠ j , E ( AiBj ) =E ( Ai ) E ( Bj ) . ( 3 ) Thus , ∑i=1N∑j=1NE ( AiBj ) =∑i=1NE ( AiBi ) +∑i=1N∑j=1j≠iNE ( Ai ) E ( Bi ) =NE ( XY ) + ( N2−N ) E ( X ) E ( Y ) . ( 4 ) Combining Eq ( 2 ) with Eq ( 4 ) , we have Corr ( AT , BT ) =NE ( XY ) −NE ( X ) E ( Y ) NVar ( A ) ⋅Var ( B ) =E ( XY ) −E ( X ) E ( Y ) Var ( X ) ⋅Var ( Y ) =Corr ( X , Y ) . ( 5 ) Hence , if the number of cells per population is a constant and there is no measurement error , correlation of chromatin accessibilities of two loci among cells is expected to equal the correlation of total chromatin accessibilities per population of cells among cell populations . To examine how violations of some of the above conditions affect the accuracy of Eq ( 5 ) , we conducted computer simulations . We assume that the accessibility of a genomic region in a single cell is either 1 ( accessible ) or 0 ( inaccessible ) . This assumption is supported by previous single-cell ATAC-seq data [40] , where the number of reads mapped to each peak in a cell is nearly binary . Now let us consider two genomic regions whose chromatin states are denoted by A and B , respectively . The probabilities of the four possible states of this system are as follows . Pr ( A=0 , B=0 ) =p , Pr ( A=0 , B=1 ) =q , Pr ( A=1 , B=0 ) =r , andPr ( A=1 , B=1 ) =s , ( 6 ) where p + q + r + s = 1 . Hence , we have E ( A ) =r+s , E ( B ) =q+s , E ( AB ) =s , Var ( A ) = ( r+s ) ( p+q ) , Var ( B ) = ( q+s ) ( p+r ) . ( 7 ) With Eq ( 7 ) , we can compute Corr ( A , B ) . In other words , for any given set of p , q , r , and s , we can compute the among-cell correlation in chromatin accessibility between the two regions . We then generated 10 , 000 random sets of p , q , r , s from a Dirichlet distribution . For each set of p , q , r , and s , we simulated the state of a cell by a random sampling from the four possible states . We did this for 16 cells as well as 16 cell populations each composed of 50 , 000 cells . We computed the total accessibility of each region in each cell population by summing up the corresponding accessibility of each cell . As expected , the among-cell correlation between the two regions in accessibility matches the true correlation ( S6A Fig ) . The deviation from the true correlation is due to sampling error . Based on Eq ( 5 ) , the among-cell-population correlation between the two regions in total accessibility approximates the true correlation , which is indeed observed in our simulation ( S6B Fig ) . Nevertheless , accessibility of a region may be undetected due to low detection efficiencies of high-throughput methods , which makes the observed correlation between the accessibilities of two regions lower than the true correlation . To assess the impact of such low detection efficiencies on the correlation , we simulated a scenario with a 10% detection efficiency , which is common in high-throughput methods [58] . That is , for every accessible region , it is detected as accessible with a 10% chance and inaccessible with a 90% chance; every inaccessible region is detected as inaccessible with a 100% chance . Our simulation showed that the observed correlation between the accessibilities of two regions is weaker than the true correlation regardless of whether the data are from individual cells ( S6C Fig ) or cell populations ( S6D Fig ) . The framework developed in the above section allows using computer simulation to acquire a lower-bound estimate of the true δa . We simulated δa by considering two pairs of regions simultaneously . For each pair of regions , we first randomly sampled p , q , r , and s , followed by the computation of the true correlation using Eq ( 7 ) . The difference between the true correlations of the two pairs of regions is equivalent to the true δa . Then , for each pair of regions , we can obtain the estimated δa from estimated correlations using simulation . In our allele-specific ATAC-seq data , only 55% of all reads are allele-specific . Given that in high-throughput sequencing data , the detection efficiency is 10 to 20% when all reads are considered [94] , we choose 8 . 25% ( = 0 . 15 × 0 . 55 ) as the detection efficiency in our simulation . We repeated this procedure 10 , 000 times and plotted the result in S7A Fig . We inferred the corresponding true δa from the observed median δa in the actual data using the regression in S7A Fig . To obtain a lower-bound estimate of the true δe , we performed a simulation incorporating the known parameters of single-cell RNA-seq in our dataset . The simulation was performed as follows . First , we determined the mean expression levels for a pair of genes , A and B , by sampling from the distribution of mean expression levels of genes analyzed , which was obtained based on the estimation that 1 RPKM corresponds to 1 transcript per cell in the original dataset [23] . Note that the mean expression level of each allele ( A1 , A2 , B1 , and B2 ) is one half the above sampled value . Second , we generated the expression levels across 60 cells for a pair of alleles ( A1 and B1 ) from the joint multivariate normal distribution . The multivariate normal distribution can be uniquely determined once the correlation coefficient between the two alleles and their CV are chosen . We fixed the CV of the two alleles at 0 . 5 , based on sm-FISH experiments in mammalian cells for genes whose expression levels are similar to the genes we analyzed [95] . Note that the CV used here is the mRNA CV , not the protein CV . The correlation between the two alleles was randomly sampled from the range ( -1 , 1 ) . We name this correlation r1 . Third , for each allele in each cell , we used binomial sampling to determine the detected transcript level . In our data set , only 17% of the reads are allele-specific . Because the capturing efficiency is around 10–20% for full-length single cell RNA-seq data [94] , we used 2 . 55% ( = 0 . 15 × 0 . 17 ) as the sampling probability . Fourth , we computed the observed correlation between A1 and B1 across cells after binomial sampling . Fifth , we repeated the above steps 2 to 4 . We named the newly sampled correlation r2 . The true δe would be r1−r2 , and the observed δe is the difference between the observed correlations . Sixth , we repeated 10 , 000 times steps 1 to 5 , with all true δe and observed δe recorded ( S7B Fig ) . We inferred the corresponding true δe from the observed median δe in the actual data using the regression in S7B Fig . Let the concentration of protein complex AB be [AB] . To study the average [AB] across cells in a population , we first simulated the concentrations of subunit A and subunit B in each cell . We assumed that the total concentrations of A and B , denoted by [A]t and [B]t respectively , are both normally distributed with mean = 1 and CV = 0 . 2 . We used CV = 0 . 2 because this is the median expression noise measured by CV for enzymes in yeast [6] , the only eukaryote with genome-wide protein expression noise data [15] . Thus , the joint distribution of [A]t and [B]t is multivariate normal , which can be specified if the correlation ( r ) between [A]t and [B]t is known . With a given r , we simulated [A]t and [B]t for 10 , 000 cells by sampling from the joint distribution . We set the concentration to 0 if the simulated value is negative . We computed [AB] in each cell by solving the following set of equations . [A]t=[A]+[AB] , [B]t=[B]+[AB] , and[AB]=K[A][B] , ( 8 ) where we used K = 105 based on the empirical values of association constants of stable protein complexes [49] . The mean complex concentration is the average [AB] among all cells . We also performed the simulation with other K values ( 10−2 , 10−1 , 100 , 101 , 102 , 103 , and 104 ) . The above simulation can be extended for studying protein complexes with various stoichiometries . In general , for protein complex AMBN , we have [A]t=[A]+M[AMBN] , [B]t=[B]+N[AMBN] , and[AMBN]=K[A]M[B]N . ( 9 ) We considered ( M , N ) = ( 1 , 1 ) , ( 1 , 2 ) , ( 1 , 3 ) , ( 2 , 2 ) , ( 2 , 3 ) , and ( 3 , 3 ) , respectively . In addition , we studied the consequence of having suboptimal mean [A]t or mean [B]t . That is , we set the ratio of mean [A]t to mean [B]t at M/N , 2M/N , or 0 . 5M/N . We considered CV = 0 . 2 or 0 . 5 .
The maximum effect of sharing regulatory elements on expression co-fluctuation , referred to as δre , can be estimated by the median correlation coefficient in expression level between two alleles of the same gene minus the corresponding value for two alleles of different genes . We found that median δe is approximately one thirteenth of δre . Thus , the linkage effect improves expression co-fluctuation brought by regulatory element sharing by at least one thirteenth . Software used and the underlying numerical data for all figures can be downloaded from Github ( https://github . com/mengysun/Linked_noise ) . | Gene expression is subject to substantial stochastic noise or fluctuation . We hypothesize that expressions of neighboring genes on the same chromosome co-fluctuate because of their common chromatin dynamics . To test this hypothesis , we make use of the fact that each diploid cell contains a maternal and a paternal copy of the same chromosome that are differentiable by their DNA sequences . Hence , allele-specific single-cell RNA-sequencing can quantify the expression level of each allele in individual diploid cells , allowing measuring the expression co-fluctuation of linked alleles as well as that of unlinked alleles of the same genes . Using such data from mouse cells , we discover chromosome-wide gene expression co-fluctuation and provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity . We show that genes encoding protein complex subunits tend to be chromosomally linked , likely resulting from natural selection for intracellular among-component dosage balance . Thus , minimization of the deleterious effect of gene expression noise has probably produced a nonrandom distribution of genes in the genome . These findings have implications for the evolution of genome organization and optimal design of synthetic genomes . |
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The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex ( V1 ) based solely on the statistics of natural scenes . In typical sparse coding models , model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity . As these networks develop to represent a given class of stimulus , the receptive fields are refined so that they capture the most important stimulus features . Intuitively , this is expected to result in sparser network activity over time . Recent experiments , however , show that stimulus-evoked activity in ferret V1 becomes less sparse during development , presenting an apparent challenge to the sparse coding hypothesis . Here we demonstrate that some sparse coding models , such as those employing homeostatic mechanisms on neural firing rates , can exhibit decreasing sparseness during learning , while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state . We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development . To make comparisons between model and physiological receptive fields , we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques .
A central question in systems neuroscience is whether optimization principles can account for the architecture and physiology of the nervous system . One candidate principle is sparse coding ( SC ) , which posits that neurons encode input stimuli efficiently: stimuli should be encoded with maximum fidelity while simultaneously using the smallest possible amount of neural activity [1] , [2] . Much evidence suggests that primary visual cortex ( V1 ) forms sparse representations of visual stimuli [1] , [3]–[7] . For example , when trained with natural scenes , SC models have been shown to learn the same types of receptive fields ( RFs ) as are exhibited by simple cells in macaque primary visual cortex ( V1 ) [1] , [8] . Throughout this paper , we make reference to the notion of “sparseness” . Intuitively , sparseness is related to there being either a small subset of neurons active at any time ( population sparseness ) , or to each neuron being active only a small fraction of the time ( lifetime sparseness ) [9] . In the Methods section , we define the precise notions of sparseness that we use in this paper . In further support of the SC hypothesis , measurements of the firing rates of V1 neurons in response to videos of natural scenes show that those rates are low , and that the firing rate distributions are sharply peaked near zero [7] . Similarly , cell-attached recordings in auditory cortex show highly sparse levels of activity [10] . Conversely , other experimenters [11] have observed non-sparse ( dense ) neuronal activity in visual cortex , although the boundary between “sparse” and “dense” activity is open to interpretation and thus it is unclear how sparse the activity must be in order to confirm the SC hypothesis [12] . Importantly , however , it has been observed that stimulating larger portions of the visual field leads to sparser , and less correlated , V1 neuronal responses [4]–[6] . It has been suggested that this effect arises because of inhibitory recurrent connections between excitatory cells , mediated by the appropriate interneurons [6] . In simulating the development of a sparse coding model , one typically [1] , [3] initializes the receptive fields with random white noise — so as to not bias the shapes of the RFs learned by the network — and then presents the network with natural images , in response to which the RFs get modified . As the model ( e . g . , [1] , [3] , [13] ) modifies itself in response to the stimuli , neurons gradually learn features that allow for a better encoding of the stimuli , so the sparseness is expected to increase over time . This point was emphasized in recent work [14] . Physiology experiments , however , show something different in the developing visual cortex . Recently , Berkes and colleagues measured multi-unit V1 activity in awake young ferrets viewing natural movies , and found that , as the animals matured , their stimulus-driven V1 activity became less sparse [14] , [15] ( Fig . 1 ) . The above discussion hints at a major source of confusion in this area of research . In particular , sparseness is discussed as both a relative measure ( i . e . : “Is network A sparser than network B ? ” ) , and as an absolute descriptor ( i . e . : “Is network A sparse ? ” ) . In this paper , we will first study relative measures of sparseness , and observe how these measures change as a result of development in our recently published SAILnet model [12]; do they increase , or decrease over time ? The absolute sparseness values of the final ( mature ) networks – which vary between 0 ( not sparse ) , and 1 ( maximally sparse ) – will be used to infer whether the final network is sparse at all . This mirrors the way that Berkes and colleagues discussed sparseness in the developing ferret . The ferret sparseness-over-time data appears to contradict the SC hypothesis . At the same time , that hypothesis has otherwise been quite successful in explaining some key features of peripheral sensory systems . It is therefore natural to ask whether sparse coding models necessarily must exhibit increasing sparseness in order to learn V1-like receptive fields and perform sparse coding in the mature state . In this work , we focus primarily on a recently published variant of sparse coding called SAILnet [12] in which homeostasis regulates the neuronal firing rates while synaptically local plasticity rules modify the network structure , leading to V1-like receptive field formation . We will demonstrate that , depending on the initial conditions of the simulation , SAILnet can exhibit either increasing , or decreasing sparseness , while learning RFs that are in quantitatively good agreement with those observed in V1 , and having a reasonably sparse final state . The choices of parameter values in the model determine the equilibrium state to which the network ultimately converges . If the initial conditions are even sparser than this equilibrium point , sparseness will decrease during development , and yet the final state can still be sparse in an absolute sense . We will also see that , for appropriately chosen initial conditions , the same can be true of the canonical SparseNet model of Olshausen and Field [3] . Thus , the apparent contradiction between the ferret developmental sparseness data , and SC models [14] does not necessarily mean that SC is implausible as a theory for sensory computation . Later in this paper , we discuss plausible alternatives for sensory coding other than SC models .
Since this paper focuses primarily on our SAILnet model ( Fig . 2 ) , we will now provide a brief overview that model , which is described in detail elsewhere [12] and summarized in the Methods section . The model consists of a network of leaky integrate-and-fire ( LIF ) neurons , which receive feed-forward input from image pixels , in a rough approximation of the thalamic input to V1 . The neurons inhibit each other via recurrent inhibitory connections , the strengths of which are learned so as to reduce correlations amongst the units , consistent with recent physiology experiments [4]–[6] . We note that one can modify SAILnet so that interneurons mediate the inhibition between excitatory cells so as to satisfy Dale's law ( E-I Net; [16] ) . The neurons' firing thresholds are modified over time so as to maintain a target lifetime-average firing rate . For our LIF neurons , this is similar to synaptic rescaling , which has been proposed as a mechanism to stabilize correlation-based learning schemes [17] , [18] , and has been observed in physiology experiments [17] . Alternatively , the variable firing threshold can be thought of in terms of a modifiable intrinsic neuronal excitability , another well-known homeostatic mechanism [19] . Finally , the feed-forward weights are learned by the network , so that the neuronal activities form an optimal linear generative model of the input stimulus , subject to the constraints imposed by limited firing rates and minimal correlations . The derivation of our learning rules from this objective function is presented in [12] . All information needed for the model's plasticity rules is available locally at the synapse being modified — updates depend only on the pre- and post-synaptic activity levels . To study the change in sparseness over time , we ran SAILnet simulations , starting with randomized feed-forward weights , recurrent connection strengths , and firing thresholds that were initialized with Gaussian-distributed white noise . At different times during the development process , we recorded the simulated neuronal activity in response to randomly selected batches of natural images . Following a recent experimental study [14] , we computed from these network activities three sparseness measures , which are discussed in more detail in the Methods section . Each of these measures varies between ( not sparse at all ) and ( as sparse as possible ) . The first of these , the “activity sparseness , ” , measures the fraction of units that are inactive in response to a given stimulus , averaged over different stimuli . If this quantity is near 1 , then only a small subset of units responds to each stimulus . If every unit is active in response to every stimulus , then . The “population sparseness” [5] , [14] , [20] , , measures the degree to which the population response to a given stimulus is restricted to a small subset of the population , averaged over all stimuli . If only a small number of units have large activities in response to a given stimulus , then will be near , even if many units have small but non-zero activities . By contrast , would be small in that case , because there are not many completely inactive units . If the units all respond equally to every stimulus , then . For the same level of representation error , the formation of efficient representations demands relatively high values of and , such that only a small fraction of the available neural resources are utilized in representing each image . Finally , the “lifetime sparseness” [5] , [14] , [20] measures how much the responses of individual units tend to be concentrated over a small subset of stimuli , averaged over units . If the units respond very selectively , so that they have strong responses to a small number of stimuli , and weak responses to most stimuli , then will be near 1 . Conversely , if each unit responds equally to all stimuli , then . Intuitively , all of these measures are somewhat related ( although , see [9] for notable exceptions ) . At the same time , when it comes to the efficiency of the neural representation , the more relevant quantities are the activity sparseness and the population sparseness , which both have to do with the fraction of neural resources used to represent each image [9] . Furthermore , the values of “lifetime” sparseness one obtains will vary with the time scale over which one performs the measurement . As such , it is a somewhat more ambiguous quantity than are the activity and population sparseness measures . Despite these issues , we include results for the lifetime sparseness for our model in keeping with the experimental study [14] that motivated this theoretical project . In order to further facilitate meaningful comparison with the experiment of Berkes and colleagues , we mimicked a multi-unit activity measurement by randomly grouping together sets of 8 SAILnet neurons , whose activities were then summed to form a multi-unit response . These “multi-unit” activities were used for computing our sparseness measures . This procedure yielded results ( Fig . 3 ) that were qualitatively similar to the single-unit sparseness measures ( not shown ) , but with larger changes in sparseness values . A direct quantitative comparison between our model multi-unit sparseness data and the ferret data is difficult because it is not clear how best to estimate the relevant number of neurons to group together , or even whether all groupings should have the same number of neurons . Furthermore , in the ferret data , the neurons grouped together are physically nearby , which means that , due to retinotopic and orientation maps in V1 , they will have similar receptive fields . The SAILnet model has no such notion of spatial organization and our random grouping of cells misses that aspect of the ferret experiment . In Fig . 3 , we show a random subset of 196 ( out of the 250 total ) receptive fields of SAILnet neurons both before and after the network is trained with natural scenes . We also show the evolution of our multi-unit sparseness measures during that training process . Contrary to the idea that sparse coding models must show strictly increasing sparseness during learning [14] , our SAILnet model can display decreasing sparseness by all three measures while it is learning localized and oriented receptive fields . We will later show that the popular SparseNet model of Olshausen and Field [3] can also exhibit decreasing sparseness over time . The time course of the sparseness measures depends on the learning rates ( parameter modification step sizes ) , with smaller learning rates leading to slower changes in sparseness measures , as expected ( data not shown ) . The depth of the observed “undershoot” also depends on the initial conditions and the learning rates . The specific activity sparseness values ( ) depend on the chosen threshold: higher thresholds lead to higher sparseness values . The receptive fields learned by the model , while displaying decreasing sparseness , are in good quantitative agreement with a measured corpus of 250 macaque monkey V1 simple cell receptive fields , as we will demonstrate in the section on comparisons of receptive field shapes . The model discussed in this section and shown in Fig . 3 has relatively high firing thresholds and a relatively large amount of lateral inhibition in the initial state . Those properties lead to highly sparse firing . As the network learns , the homeostatic firing rate regulation reduces the thresholds and then inhibitory connections are modified by their own plasticity rules ( see Methods section for details ) . These have the effect of reducing the model's sparseness over time . For contrast , in the next section we will consider a model that is identical to the one presented here , but with the following modifications to the initial conditions: the firing thresholds are initialized at smaller values and there is initially less lateral inhibition . We find that SAILnet does not require sparseness to decrease over time; rather , it is compatible with decreasing sparseness . To demonstrate this point , we repeated our SAILnet simulations and sparseness measurements with different initial conditions ( see Methods section for details ) . As discussed in the previous section , these initial conditions have lower firing thresholds and less lateral inhibition than in the model shown in Fig . 3 . In this case , the relatively low firing thresholds and relatively small amount of lateral inhibition lead to the initial network state being less sparse than the final ( equilibrium ) state , so sparseness increases over time ( Fig . 4 ) . Similar to Fig . 3 , in Fig . 4 we show a random subset of 196 neuronal receptive fields both before and after the training procedure and , as we demonstrate below , those RF shapes are in quantitative agreement with those measured in macaque V1 . We emphasize that , compared to the model discussed in Fig . 3 , which exhibited increasing sparseness , the model discussed here ( and in Fig . 4 ) differs only in the initial conditions; all other parameters were the same for the two models . Consequently , after a long training period , over which the effects of the initial conditions gradually disappear , these two models have very similar final sparseness levels and receptive fields . For the models studied in this paper ( Figs . 3 and 4 ) , the final multi-unit sparseness values ( after the final training batch ) are for the model in which sparseness increases over time ( Fig . 4 ) , and for the model in which sparseness decreases over time . While our SAILnet model [12] and a recent extension that obeys Dale's law [16] are more biophysically realistic than previous sparse coding models , the increasing or decreasing sparseness over time we describe above ( Figs . 3 and 4 ) is not unique to SAILnet . To explore this issue more fully , we return to the canonical SparseNet model of Olshausen and Field [3] , [21] . We first note that , as in SAILnet , the “equilibrium” sparseness level in SparseNet is determined by a free parameter in the model ( in [3] ) . Furthermore , in the SparseNet model , there is a homeostatic mechanism that adjusts the magnitudes of the feed-forward weights so as to keep the units' activities near some pre-defined set point . Similar to SAILnet , this process is not instantaneous . In what follows , we use the SparseNet code of Olshausen and Field [3] , [21] “out of the box , ” without modifying any parameters except the initialization of the basis functions — these are analogous to the feed-forward weights , or receptive fields , of SAILnet units . We begin by initializing these basis functions with Gaussian white noise of variance , so that the bases have norms of approximately . In this case , sparseness increases over time ( Fig . 5a ) and the basis amplitudes decrease: the mean norm of these bases is approximately 0 . 5 once the model converges , after the training period . These changes can be understood by recalling that , during inference — where the activities of the units are determined in response to a given image — the activities are chosen to minimize the following cost function: . The error , as in SAILnet , is the sum over pixels of the squared error of the difference between the image and a linear generative model formed by multiplying each unit's activity by its basis function . Thus , if the basis has a small magnitude , then large activations are needed in order to form a decent linear generative model . However , these large activations are punished by the “activities” term in the cost function . As a result , some of the units that add only a modest improvement to the representation are turned off , or nearly so . If the basis has a large magnitude , then only small activations are needed to form the linear generative model , and these small activations are less strongly penalized than are large activations . As a result , the activation can be more distributed over the network when the filters have large magnitudes . To summarize: large basis function magnitudes lead to less sparse network activity and the basis magnitudes are modified with a non-zero timescale . Putting all of this together , if we initialize the bases with Gaussian white noise of variance , so that they initially have norms in the neighborhood of , then the basis norms increase over time during training . After this model converges , the mean norm of the bases is again around 0 . 5 . Consequently , the sparseness decreases over time ( Fig . 5b ) . As in SAILnet , one way to understand these trends is to recall that the model parameters dictate the final “equilibrium” state of the model , but the initial conditions can be chosen independently of the final state . As such , initial conditions can be chosen to be either more or less sparse than the equilibrium condition , leading to sparseness either decreasing or increasing over time . In many theoretical studies ( e . g . , [1] , [12] , [22] , [23] ) , one learns a sparse coding dictionary for natural stimuli , then compares the shapes of the resultant basis functions to the shapes of the physiologically measured receptive fields . Typically , this comparison is either done by eye ( as in [22] ) , or by fitting both the model RFs and the experimentally measured ones to some parameterized shape functions , and then comparing ( again , typically by eye ) the distributions of the resultant shape parameters for both the model and the data [1] , [12] , [23] . More rigorously , one can quantitatively compare the distributions of these shape parameters ( Rehn , Warland , and Sommer , CoSyNe 2008 abstract ) between the model and the experimental data , although that method fails if one has too few RFs with which to perform the comparison . The by-eye comparisons are not very quantitative , even if they first involve fitting parameterized shape models , and any fitting of parameterized shape models is vulnerable to failures of the shape function: any RFs whose shapes are not well described by the parameterized function will yield nonsense best fit parameter values . To get around these difficulties , we introduce a novel method for directly comparing the shapes of theoretical and experimental receptive fields , using image registration . In this technique , we assume that receptive fields may differ by a translation , rotation , and/or global size rescaling , yet still have the same shape . For example , consider an equilateral triangle within a bounding box . A shifted , rotated , and resized version of that shape is still an equilateral triangle . We apply this intuition to the comparison between our model receptive fields , and a set of 250 macaque V1 receptive fields courtesy of D . Ringach . We do this by taking each experimentally measured V1 receptive field and then for each model RF we find the combination of translation , rotation , and overall rescaling that gives the best match between the experimental and transformed-model RF . We quantify the match by the value ( square of the correlation coefficient ) between the pixel values of the model and the experimental RF . Choosing the -maximizing RF is similar to seeking the model RF that can account for the largest fraction of the variance of the experimental RF , but allows for an overall multiplicative constant in front of the model RF . Specifically , the value tells us the fraction of the experimental RF variance that could be explained by a linear function of the best model RF ( i . e . , possibly including an additive constant to all pixel values and an overall amplitude change ) . Once we have done this for all model RFs , we take the one whose best transform yields the largest and take that as the best-fit model RF for the given experimental RF . In this way , we answer the question: Once we account for possible translations , rotations , and size rescalings , how much of the variance in the experimental RF pixel values can be accounted for by the library of model RFs ? values near indicate that the experimental RF is reproduced perfectly by the theoretical model , while values near indicate that it is not . We then repeat this procedure for all of the RFs in the experimental dataset , yielding one value per experimental RF . Below , we discuss the averages over these values . Looking at the macaque RFs in Fig . 6A , it is clear that the region of support of the RF is often smaller than the size of the image window over which the RF is measured , and any noise outside of the region of support ( or within it , for that matter ) will be unaccounted for by the image registration process , thus lowering the apparent goodness-of-fit . To attempt to quantify this level of noise and to give a solid benchmark with which to assess our experiment-vs . -model comparison , we repeat the image registration fitting described above , but instead of using model RFs as comparators , we compare each experimental RF against the corpus of other experimental RFs . In other words , we do a leave-one-out analysis where we try to fit each macaque RF and , for that fit , we take the 249 other macaque RFs and pretend that they are “model” RFs . We then find the largest value possible for the best transformation of each of the 249 comparison RFs and use that number to quantify how well we could realistically expect to fit the macaque RF . We term this maximal value for the macaque-to-macaque comparison the fraction of variance in the data that is “explainable” by our image-registration technique ( although not necessarily captured by the dictionary of shapes learned by our sparse coding model ) . In using this technique to estimate the noise level , we essentially assume that the RF shapes are repeated in the experimental data ( which one can see in Fig . 6 ) , and use that intuition to ask , “How much of the data variance is due to noise rather than the RF properties themselves ? ” In Fig . 6 , we show the experimental RFs , the best-transformed ( translation , rotation , and overall size rescaling ) model RFs learned with either increasing or decreasing sparseness values , and the quantitative comparisons between the RF shapes ( average values for how well the macaque RFs can be explained by the model RFs ) . The macaque-to-macaque comparisons ( Fig . 6E ) show that , on average , of the variance in the RF pixel values can be explained using other RFs from the macaque V1 dataset . We term this the “explainable” variance and it sets an upper bound on how well we could expect our model RFs to match the macaque RFs . For comparison , the model RFs account for , on average , of the variance in the RF pixel values , regardless of whether the learning of those RFs was accompanied with either increasing or decreasing sparseness . The difference between the average explained variance for the two different models ( those of Figs . 3 and 4 ) is not statistically significant ( , paired t test; ) , while the differences between each of the model's mean values and that of the macaque-vs-macaque comparison are statistically significant ( , paired t test; ) . Because the model RFs can explain an average of roughly ( ) of the explainable variance in the RF data , regardless of whether the model experienced increasing or decreasing sparseness during training , we conclude that the model RFs are in quantitatively good agreement with the experimental RFs , independent of whether the learning of shapes was accompanied with increasing or decreasing sparseness . Generally , larger networks have a greater diversity of receptive field shapes ( this can be easily seen by comparing the RFs shown in this paper to those in [12] ) , and will thus tend to perform better in our image-registration comparisons . The networks studied in this paper were relatively small , in order to allow us to study the evolution of networks with many different initial conditions . Thus , we expect that even better model-to-experiment RF matches are possible if one were to study larger networks . On the other hand , the macaque V1 database to which we compared our model contains only 250 receptive fields , so a “fair” comparison to a larger simulated network would require more experimental data , or the selection of a random subset of the simulated RFs . Our quantitative non-parametric RF comparison method could be used to compare many different theories to experimental data , and thus to ascertain which ones provide the best fit . That comparison is beyond the scope of this paper .
We have demonstrated that a computational model ( SAILnet [12] ) can learn V1-like receptive fields while simultaneously exhibiting either a decrease , or an increase , in the sparseness of neuronal activities . In both cases , the sparseness of the final ( mature ) network state is high enough to be reasonably considered “sparse . ” We further showed that these same trends in sparseness over time can be achieved with the less biophysically realistic SparseNet model , despite the fact that it does not incorporate the same form of homeostasis as SAILnet . In order to quantify the similarity between experimentally measured V1 receptive fields and the receptive fields learned by our SAILnet model , we have further introduced a novel non-parametric RF comparison tool based on image registration techniques . Since sparseness can decrease during development , with the mature network state still performing sparse coding , the type of active sparseness maximization disproven by recent experiments [14] is not necessary to produce observed V1 receptive field shapes , nor is it required to learn a sparse representation of natural scenes . The trends in sparseness over time can be so strongly affected by the initial conditions of the network that those trends are not very informative about the objective function being optimized . Thus , developmental data , such as those shown in Fig . 1 , cannot strictly rule out the general notion that V1 simple cell receptive fields develop so as to form sparse , efficient , representations of natural scenes . One possibility that requires consideration is that the sparseness data of Berkes and colleagues [14] should not be compared at all with theoretical models when assessing the hypothesis that V1 performs sparse coding . Recall that the sparse coding models criticized by Berkes and colleagues showed increasing sparseness during receptive field formation , and that their claim was that , since the ferret data instead showed decreasing sparseness during development , the sparse coding models do not provide a good description of V1 . In order for this comparison to be “fair , ” one must ensure that receptive fields undergo significant change during the developmental period over which Berkes and colleagues measured sparseness . Indeed , other experimenters have observed that the orientation and direction selectivity of the neurons in ferret V1 increase [24] , [25] during the same developmental period over which Berkes and colleagues observed decreasing sparseness levels , and that the mature state of the visual cortex for both ferrets and cats is sensitive to visual experiences in this period [25]–[28] . Combining these observations , we note that sparseness in ferret V1 seems to decrease while the visual cortical maps and receptive fields are being refined by experience , in apparent contradiction to the SC hypothesis . The largest contribution of this paper is to resolve that apparent contradiction . Of course , there could always be other reasons — beyond the scope of this paper — why the developmental data fail to be relevant to the sparse coding hypothesis . We leave that question for future work . For the sake of completeness , we note that Rochefort and colleagues have observed that spontaneous slow-wave activity in the anesthetized mouse visual cortex becomes sparser immediately after eye-opening [29] . At first glance , this might appear to contradict the ferret data of Berkes and colleagues [14] . However , since the ferret data is stimulus-evoked activity and the mouse data is spontaneous activity , it is not clear that a comparison between these datasets is meaningful . Because the spontaneous activity is not very easily related to the sparse coding hypothesis , which does not have much to say about activity in the absence of sensory input , we have focused on the ferret data ( stimulus-evoked activity ) in this paper . We are not the first to propose that homeostasis might underlay experience-dependent modification of the nervous system . Indeed , Marder and others have strongly and persuasively argued that neural systems might have a desired operating point such that when perturbed they use homeostatic mechanisms to return to that desired functional state [18] , [19] . Moreover , Miller and others have shown that homeostatic activity regulation can facilitate learning in model-neuronal systems [30]–[32] . Finally , recent work by Perrinet [30] also used homeostatic activity regulation in learning sparse codes , so that model may also show either increasing or decreasing sparseness over time , for appropriately chosen initial conditions . We have demonstrated that the mature network state can perform sparse coding regardless of whether learning is accompanied by an increase or decrease in sparseness . At the same time , sparse coding is not the only principle that has been proposed in order to understand V1 function . Of particular interest in this regard is a recent study by Berkes and colleagues [15] . In that work , the authors showed that both stimulus-evoked and spontaneous ( in the absence of any stimulus ) activity in V1 became more similar as the animals aged , suggesting that V1 might be learning a Bayesian prior on the statistics of the environment . They further observed that part of this change in activity distributions came about due to increases in the correlations between V1 neurons with age . This observation is contrary to the redundancy reduction arguments often used to support efficient coding models , such as traditional sparse coding models and SAILnet . Thus , there is some evidence that sparse coding may not be the best theory of V1 function , and that others , such as that advanced in [15] , may be better in some regards . Moreover , it is not clear how the sparse coding hypothesis could account for many of the properties of V1 complex cells despite its success for simple cells , and differences in the level of activity between awake and anesthetized V1 may pose additional challenges for sparse coding models . However , sparse coding models have successfully accounted for the shapes of V1 simple cell receptive fields , while the Bayesian-type optimality models have yet to do so . There is thus much room for further advances in our understanding of sensory coding , and measurements that can rule out theoretical models are key to that advancement . Our results show that the decrease in sparseness during development , which has been argued to rule out sparse coding in V1 [14] , is not , on its own , sufficient to make that claim .
There are many different ways to measure sparseness . In this work , we follow the experimental study of Berkes and colleagues [14] and use the following three measures . First , the “activity sparseness” ( ) , which is the fraction of units inactive in response to any given stimulus: ( 1 ) where is the number of units whose activities rose above some threshold number of spikes in response to the stimulus , and N is the total number of units for which data were recorded . We set the threshold to 4 spikes for the multi-unit sparseness data shown herein . We performed this measurement by averaging over different input stimuli . The activity sparseness is very similar to the norm – which is actually a pseudo-norm – of the unit activities; low norms correspond to high values . In addition , we recorded two other sparseness measures , originally due to Treves Rolls [20] ( TR ) , and subsequently modified by Vinje Gallant [5] . First , let us consider what we will call the “TR population sparseness” measure [15] , , ( 2 ) where is the activity of unit . Note that is assessed in response to a single image , although for our purposes , we will average this measure over different image stimuli , to infer the average TR population sparseness . Similarly , we will define the “TR lifetime” sparseness of a given unit , , the same way ( Eq . 2 ) , but with the replacement that represents the unit's activity in response to a given image , and will be the number of different image stimuli ( ) for which activities are recorded . Similar to the TR population sparseness , we will average these values over the entire population for our measurement . The SAILnet model [12] consists of a network of leaky integrate-and-fire neurons that receive feed-forward input from image pixels ( a rough approximation of the thalamic input to V1 ) , and inhibit each other through recurrent connections . The feed-forward weight from pixel ( with value ) to neuron ( ) and the inhibitory recurrent connections between neurons and ( ) are learned by the network . In response to a given image , neuron emits some number of spikes , which can be zero . Homeostasis , enforced via modifiable firing thresholds forces the neurons to all have the same lifetime-average firing rate of spikes per image . After the image presentation , the network parameters are updated via ( 3 ) where the ( positive ) constants , and define the rates at which the parameters are learned . In order for them to remain inhibitory , after each update , the recurrent connections are rectified so that . For all simulations shown herein , the feed-forward weights were initialized with Gaussian white noise , and the learning rates were set to . We used neurons which viewed pixel image patches , hence the neuronal representation was approximately critically sampled ( overcomplete ) with respect to the number of pixels . The target firing rate was set to spikes per neuron per image in all cases . In all cases , the norm of the feed-forwards weights to each neuron was in the initial condition . For the data shown in Fig . 4 , the initial recurrent connection strengths were drawn randomly from a normal ( Gaussian ) distribution with zero mean and unit variance , the firing thresholds were initialized to , and the model neuronal activity became more sparse during training . For the results shown in Fig . 3 , the initial recurrent connection strengths were drawn randomly from a lognormal distribution ( the negative of their logarithms were drawn from a normal distribution with zero mean and unit variance ) , the firing thresholds were drawn uniformly over , and the model neuronal activity became less sparse during training . We note that in all cases , the specific shape of the sparseness vs . time plot depends on the choice of initial conditions . However , for a large class of initial conditions , the sparseness will decrease over time , and for another large class of initial conditions , it will increase ( data not shown ) . Thus , our qualitative conclusion is not particularly sensitive to the exact numerical values described above . The SparseNet results were generated using code publicly distributed by Bruno Olshausen ( http://redwood . berkeley . edu/bruno/sparsenet/ ) . The code was used “out of the box” , without modifying the parameter values . For the data shown in Fig . 5a , then bases ( matrix A in the code ) was initialized with Gaussian white noise of variance 1 . For the data shown in Fig . 5b , matrix A was initialized with Gaussian white noise of variance 0 . 01 . In both cases , 256 units were used , and the model was trained on pixel image patches: the model is overcomplete with respect to the number of input pixels . To perform the quantitative comparison of receptive field shapes , we used the image registration tool in MatLab [33] . This package allowed us to quickly and easily compute the optimum combination of translation , rotation , and stretch – called a “similarity” transform in MatLab – to match each physiology RF with an RF from the appropriate comparison class ( either model data or other experimentally measured RFs ) . For the optimizer , we used the “monomodal” option . Our data consists of 250 Macaque V1 receptive fields , measured using reverse-correlation methods in the lab of Dario Ringach [34] . These data are either , , or pixel images , showing the extent to which the neuron responds to each pixel . In order to standardize these data for the comparison , and because many of the RFs occupy only a tiny fraction of the image , we pre-process those RFs that are larger than pixels , as follows . First , we find the peak absolute pixel value in the image – nominally , this is somewhere in the region of support of the “real” RF – , and we cut out a pixel region surrounding that peak . We then perform our image registration fitting on these standard-sized RFs . For each macaque RF , we performed an exhaustive search over all model RFs , wherein we found the best similarity transform to match each model RF to the macaque RF , then took the best-matching model RF ( with the appropriate best similarity transform ) , as the fit . The value between this best-fit transformed-model RF and the data RF was used to quantify the goodness of fit . To generate a benchmark to assess how good a “good” value is for this problem , we repeated our fitting process , but instead of fitting the data to SAILnet model RFs , we fit each macaque RF with the corpus of other macaque RFs . In so doing , we could estimate the fraction of data variance that could be explained in a best-case scenario . The ratio between these numbers – the data-vs . -model value and the data-vs . -data value – gives us an estimate of the fraction of explainable variance that is captured by the model . | The popular sparse coding theory posits that the receptive fields of visual cortical neurons maximize the efficiency of the neural representation of natural images . Models implementing this idea typically minimize a combination of the error in reconstructing natural images from neural activities , and the average level of activity in the model neurons . In simulations , these models are presented with natural images and the RFs then develop so as to increase representation efficiency . After a long developmental period , the model RFs typically agree well with those observed experimentally in visual cortex . Since the models seek to minimize ( for a given level of reconstruction error ) the neural activity levels , the average levels of neural activity might be expected to decrease as the models develop . In the developing mammalian cortex , visual RFs are also modified during development , so the sparse coding hypothesis might appear to suggest that activity levels should decrease during development . Recent experiments with young ferrets show the opposite trend: mature animals tend to have more active visual cortices . Herein , we demonstrate that , depending on the models' initial conditions , some sparse coding models can exhibit increasing activity levels while learning the same types of RFs that are observed in visual cortex: the developmental data do not preclude sparse coding . |
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Susceptibility loci identified by GWAS generally account for a limited fraction of heritability . Predictive models based on identified loci also have modest success in risk assessment and therefore are of limited practical use . Many methods have been developed to overcome these limitations by incorporating prior biological knowledge . However , most of the information utilized by these methods is at the level of genes , limiting analyses to variants that are in or proximate to coding regions . We propose a new method that integrates protein protein interaction ( PPI ) as well as expression quantitative trait loci ( eQTL ) data to identify sets of functionally related loci that are collectively associated with a trait of interest . We call such sets of loci “population covering locus sets” ( PoCos ) . The contributions of the proposed approach are three-fold: 1 ) We consider all possible genotype models for each locus , thereby enabling identification of combinatorial relationships between multiple loci . 2 ) We develop a framework for the integration of PPI and eQTL into a heterogenous network model , enabling efficient identification of functionally related variants that are associated with the disease . 3 ) We develop a novel method to integrate the genotypes of multiple loci in a PoCo into a representative genotype to be used in risk assessment . We test the proposed framework in the context of risk assessment for seven complex diseases , type 1 diabetes ( T1D ) , type 2 diabetes ( T2D ) , psoriasis ( PS ) , bipolar disorder ( BD ) , coronary artery disease ( CAD ) , hypertension ( HT ) , and multiple sclerosis ( MS ) . Our results show that the proposed method significantly outperforms individual variant based risk assessment models as well as the state-of-the-art polygenic score . We also show that incorporation of eQTL data improves the performance of identified POCOs in risk assessment . We also assess the biological relevance of PoCos for three diseases that have similar biological mechanisms and identify novel candidate genes . The resulting software is publicly available at http://compbio . case . edu/pocos/ .
Genome-wide association studies ( GWAS ) have a transformative effect on the search for genetic variants that are associated with complex traits , since they enable screening of hundreds of thousands of genomic variants for their association with traits of interest [1] . Recently published GWAS lead to the discovery of susceptibility loci for many complex diseases , including type 2 diabetes [2] , psoriasis [3] , multiple sclerosis [4] , and prostate cancer [5] . For improved identification of risk variants , researchers draw information from clinical , microarray , copy number , and single nucleotide polymorphism ( SNP ) data to build disease risk models , which are then used to predict an individual’s susceptibility to the disease of interest [6 , 7] . Several companies , such as deCODE genetics ( http://www . decode . com ) and 23andme ( https://www . 23andme . com ) have started using SNPs identified by GWAS , to provide personal genomic test services in the United States and health related genomic test services in Canada and the United Kingdom . An important problem with GWAS is that the identified variants account for little heritability [8 , 9] . However , empirical evidence from model organisms [10] and human studies [11] suggests that the interplay among multiple genetic variants contribute to complex traits . Epistasis among pairs of loci , i . e . , significantly improved association with the phenotype when two loci are considered together , is also shown to provide provide further insights into disease mechanisms [12–14] . Therefore , recent studies focus on identifying the interactions among pairs of genomic loci , as well as among multiple genomic loci [15–17] . These studies suggest that consideration of more than one locus together can better capture the relationship between genotype and phenotype . For this reason , genetic markers that aggregate multiple genomic loci can be used to design effective strategies for risk assessment and guide treatment decisions [18] . The Polygenic score is a commonly used method to identify the joint association of a large mass of the loci to predict disease risk [19] . The first application of polygenic score on GWAS data shows that the genetic risk for schizophrenia is a predictor of bipolar disorder [20] . There are also several studies demonstrating that polygenic risk score is a powerful tool in risk prediction [20–22] . However , polygenic score does not make use of prior biological knowledge , which may be useful in generating more robust features by incorporating the functional relationships among individual variants . Furthermore , according to a recent comparative assessment of various classification algorithms , there are no statistically significant differences between state-of-the-art classification algorithms in terms of performance in risk assessment [23] . This observation suggests that research on construction of features for risk assessment can be useful in improving the classification performance of these algorithms . Since detection of epistasis and higher order interactions is computationally expensive , many methods first assess the disease association of individual loci and then use functional knowledge to integrate these associations [24–26] . The key idea behind these methods is that functionally related variants , e . g . , those that induce dense subnetworks in protein-protein interaction ( PPI ) networks , can provide stronger statistical signals when they are considered together [27] . Based on similar insights , some researchers integrate GWAS with pathway information to identify statistically significant pathways that are associated with the disease [28 , 29] . Recently , Azencott et al . propose a method to discover sets of genomic loci that are associated with a phenotype while being connected in an underlying biological network [30] . They use an additive model to integrate the genotypes of loci and use connectivity patterns in the network to select a functionally coherent set of disease associated SNPs . While this method works on a network of genomic loci , the network is constructed based on the interactions among genes and mapping of loci to genes . For this reason , the application of these methods is limited to the variants in coding regions or in regions that are in close proximity to genes . However , 88 percent of genotyped variants in GWAS fall outside of coding regions [31] . Several risk variants are found in non-coding regions of the genome and it is shown that the functional effects of these variants are regulatory ( e . g . , mRNA expression , microRNA expression ) as opposed to directly influencing protein structure or function [32] . In this paper , we propose a new algorithm for the identification of multiple functionally related genomic variants that are collectively associated with a phenotype . The proposed method builds on the concept of “Population Covering Locus Sets” ( PoCos ) [33 , 34] . A PoCo is a set of loci that harbor at least one susceptibility allele in samples with the phenotype of interest . Here , we extend the notion of PoCos to enable adaptive identification of “susceptibility genotype” ( as opposed to susceptibility allele ) for each locus . We also develop a method for aggregating the genotypes of multiple loci in a PoCo to compute representative genotypes for use in risk assessment . Finally , in order to capture the functional relationship between genomic loci , we integrate GWAS data with human protein-protein interaction ( PPI ) network and regulatory interactions identified via expression quantitative trait loci ( eQTL ) . We use the PoCos identified by the proposed framework to construct features that can be used in risk assessment . We evaluate the performance of PoCos in risk assessment via cross-validation on seven GWAS case-control data sets obtained from the Wellcome Trust Case-Control Consortium ( WTCCC ) . We compare the risk assessment performance of models built using PoCos to that of models built using individual loci and polygenic score . Our experimental results show that PoCos significantly outperform individual loci and polygenic score in risk assessment . Furthermore , we assess the information added by the incorporation of PPI and eQTL and observe that inclusion of these data leads to more parsimonious models for risk assessment . In the next section , we describe the proposed procedure for modeling the genotypes and identifying PoCos . Then we describe how we use PoCos to develop a model for risk assessment . Subsequently , we present comprehensive experimental results on GWAS data sets for Type 2 Diabetes ( T2D ) , Psoriasis ( PS ) , Type 1 Diabetes ( T1D ) , Hypertension ( HT ) , Bipolar Disorder ( BD ) , Multiple Sclerosis ( MS ) and Coronary Artery Disease ( CAD ) . Our results show that the proposed method significantly outperforms individual variant based risk assessment model as well as the state-of-the-art polygenic score . We also observe that integrating prior biological information leads to more parsimonious models for risk assessment .
The input to the problem is a genome-wide association ( GWA ) dataset D = ( C , S , g , f ) , where C denotes the set of genomic loci that harbor the genetic variants ( e . g . , single nucleotide polymorphisms or copy number variants ) that are assayed , S denotes the set of samples , g ( c , s ) denotes the genotype of locus c ∈ C in sample s ∈ S , and f ( s ) denotes the phenotype of sample s ∈ S . Here , we assume that the phenotype variable is dichotomous , i . e . , f ( s ) can take only two values: if sample s is associated with the phenotype of interest ( e . g . diagnosed with the disease , responds to a certain drug etc . ) , s is called a “case” sample ( f ( s ) = 1 ) , otherwise ( e . g . , was not diagnosed with the disease , does not respond to a certain drug etc . ) , s is called a “control” sample ( f ( s ) = 0 ) . We denote the set of case samples with S1 and the set of control samples with S0 , where S1 ∪ S0 = S . While we focus on qualitative traits here for brevity , the proposed methodology can also be extended to quantitative traits ( i . e . , when f ( s ) is a continuous phenotype variable ) . The minor allele for a locus is usually defined as the allele that is less frequent in the population . While it is common to focus on the minor allele as the risk allele , specific genotypes can also be associated with a phenotype [35–37] . Different types of encoding may represent different biological assumptions . In an additive model , each genotype is encoded as a single numeric feature that reflects the number of minor alleles ( homozygous major , heterozygous , and homozygous minor are respectively encoded as 0 , 1 and 2 ) . This model does not capture combinatorial relationships between locus genotypes and phenotype , since the assumption is that one of the alleles quantitatively contributes to risk . In the recessive/dominant model , each genotype is encoded as two binary features ( presence of minor allele and presence of major allele ) . This model does not capture the difference between homozygous and heterozygous genotypes , since it only accounts for the presence of an allele . Here , we argue that considering the effect of all possible genotype combinations can provide more information in distinguishing case samples from control samples . The five models proposed here capture all potential relationships , in that differences in heterozygosity vs . homozygosity , presence vs . absence of a specific risk allele are represented by different genotype models . This notion is particularly useful when the genotypes of multiple loci are being integrated . For example , heterozygosity on one locus can be associated with increased susceptibility to a disease , while homozygous minor allele on another locus may be protective at the presence of heterozygosity in the former locus [38] . In this case , the interaction between the two loci can be detected by considering the association of all possible genotype combinations with the phenotype . We adaptively binarize the genotypes of each locus by considering all possible allele combinations . Given the genotype of a locus , we consider five different binary genotype models m ( i ) , i ∈ {1 , … 5} . Based on each model , we generate a binary genotype profile for each locus . Namely , we consider the following genotype models: 1 . Homozygous Minor Allele: This corresponds to the case when the possible effect of the minor allele is “recessive” , i . e . , the locus is considered to harbor a genotype of interest if both copies contain the minor allele . m ( 1 ) ( c , s ) = 1 2 if g ( c , s ) ∈ { a a } 0 otherwise ( 1 ) 2 . Heterozygous Genotype: The locus is considered to harbor a genotype of interest if the two copies contain different alleles . m ( 2 ) ( c , s ) = 1 2 if g ( c , s ) ∈ { A a } 0 otherwise ( 2 ) 3 . Homozygous Major Allele: The locus is considered to harbor a genotype of interest if both copies contain the major allele . m ( 3 ) ( c , s ) = 1 2 if g ( c , s ) ∈ { A A } 0 otherwise ( 3 ) 4 . Presence of Minor Allele: This corresponds to the case when the possible effect of the minor allele is “dominant” , i . e . , the locus is considered to harbor a genotype of interest if at least one copy contains the minor allele . This is the complement of m ( 3 ) . m ( 4 ) ( c , s ) = 1 2 if g ( c , s ) ∈ { A a , a a } 0 otherwise ( 4 ) 5 . Presence of Major Allele: The locus is considered to harbor a genotype of interest if at least one copy contains the major allele . This is the complement of m ( 1 ) . m ( 5 ) ( c , s ) = 1 2 if g ( c , s ) ∈ { A a , A A } 0 otherwise ( 5 ) Note that , although models m4 and m5 are complements of other models , we consider them separately . This is because , as we discuss in the next section , the 1s and 0s in the binary genotype profiles are considered asymmetrically while integrating the genotypes of multiple loci . Also note that “homozygous minor allele or homozygous major allele” is not considered since it is not associated with a specific risk allele . To select a genotype model for each locus , we separately assess the association of the resulting five genotype profiles with the phenotype of interest . Subsequently , we choose the model that leads to greatest discrimination between cases and controls , and use the respective binary genotype profile as the representative genotype of that locus . This process is illustrated in Fig 2 . For each locus c , binarization according to the five different genotype models produces five |S|-dimensional binary genotype profiles m ( i ) ( c ) , i ∈ {1 , … 5} . For each binary genotype profile m ( i ) ( c ) , we compute the difference in the fraction of case and control samples that harbor the genotype of interest as follows: D ( i ) ( c ) = 〈f , m ( i ) ( c ) 〉 | S 1 | - 〈 1 - f , m ( i ) ( c ) 〉 | S 0 | . ( 6 ) where 1 denotes a vector of all 1’s and < . > denotes the inner product of two vectors . We then determine the binary genotype model for each locus as the model that maximizes the difference of relative coverage between case samples and control samples , i . e . : k ( c ) = argmax i ∈ { 1 ⋯ 5 } { | D ( i ) ( c ) | } . ( 7 ) Based on the selected model for each locus , we compute the binary genotype profile accordingly: M ( c , s ) = m ( k ( c ) ) ( c , s ) . ( 8 ) Once we compute the binary genotype profiles for all loci , we identify Population Covering Locus Sets ( PoCos ) . In previous work , we define and use PoCos in the context of prioritizing locus pairs for testing epistasis [33] . In this earlier definition , the genotypes of interest are limited to the presence of the minor or major allele; i . e . , only the last two models described in the previous section are used to determine the binary genotype profile of each locus . Here , we generalize the concept of PoCo to utilize five different models for determining the genotypes of interest , as described in the previous subsection . A PoCo is a set of genomic loci that collectively “cover” a larger fraction of case samples while minimally covering control samples . Namely for a given set P ⊆ C of loci , we define the set of case and control samples covered by P respectively as E ( P ) = ∪ c ∈ P { s ∈ S 1 : M ( c , s ) = 1 } ( 9 ) and T ( P ) = ∪ c ∈ P { s ∈ S 0 : M ( c , s ) = 1 } . ( 10 ) We define a PoCo as a set P of loci that satisfies |E ( P ) | = |S1| while minimizing |T ( P ) | . Note that , since we are interested in finding all sets of loci with potential relationship in their association with phenotype , we do not define an optimization problem that aims to find a single PoCo with minimum |T ( P ) | . We rather develop an algorithm to heuristically identify all non-overlapping PoCos with minimal |T ( P ) | . To identify all non-overlapping PoCos , we use a greedy algorithm that progressively grows a set of loci to maximize the difference of the fraction of case and control samples covered by the loci that are recruited in a PoCo . In another words , we initialize P to ∅ and at each step , add to P the locus that maximizes δ ( c ) = E ( { c } ) ∩ S ′ | | S 1 | - | T ( { c } ) ∩ S ′ | | S 0 | ( 11 ) where S′ = S\ ( E ( P ) ∪ T ( P ) ) . The algorithm stops when all case samples are covered . We then record P , remove the loci in P from the dataset , and identify another PoCo . This process continues until it is not possible to find a set of loci that covers all case samples . We develop two methods to identify two different types of PoCos . The first type of PoCos ( named “network-free PoCos” ) are identifed using the greedy algorithm described above , without the use of any prior biological information . The second type of PoCos are NetPocos , which are identified by restricting the search space to connected subgraphs of a network of potential functional relationships among genomic loci . As we describe below , this network is constructed by integrating established locus-gene associations from eQTL studies and protein-protein interaction ( PPI ) data that contains functional relationships among genes . One potential utility of the PoCos is risk assessment . By construction , PoCos ( NetPocos ) contain ( functionally associated ) loci that exhibit improved power in distinguishing cases from control . Consequently , as compared to individual variants , they may provide more robust and reproducible features to be used in predictive models . To investigate the utility of these multi-locus features in risk assessment , we use PoCos to build a model for risk assessment using L1 regularized logistic regression classifier .
For each dataset , we divide the population into 5 groups while preserving the proportion of case and control samples in each group . We reserve one group for testing and we identify NetPocos on the remaining four groups . Then , we use these four groups for feature selection and model building . Finally , we test the performance on the group reserved for testing . All of the reported performance figures are averages across five different cross-validation runs . The number of PoCos identified on each dataset and the size of these PoCos are presented in Table 2 . Please note that the variance in number of PoCos does not have a significant effect on the performance ( S1 Fig ) .
In this paper , we propose a novel criterion to assess the collective disease-association of multiple genomic loci ( PoCos ) and investigate the utility of these multiple-loci features in risk assessment . We also perform extensive experiments to evaluate the effect of using network information to drive the search for multi-locus features on risk assessment . We also investigate the effect of the variants that have regulatory effects ( i . e . eQTL data ) on performance for risk assessment . Moreover , we compare the proposed method with the polygenic score which has been shown to be successful in different studies . Our result show that our method is significantly more powerful in risk assessment . Our results show that multi-locus features improve prediction performance as compared to individual locus based features . We also observe that integrating functional information provided by protein-protein interaction data and expression quantitative trait loci ( i . e . eQTL ) data leads to more parsimonious models for risk assessment . However , inclusion of functional data does not yield significant improvement in prediction performance . This may be indicative of the limitations of genomic data in risk assessment . Furthermore , since PoCos contain loci that are related to each other in the context of a phenotype , PoCos that are discovered without the inclusion of functional information also likely contain functionally related loci . However , utilization of functional information reduces the search space to render the problem computationally feasible , and brings forward PoCos that are more functionally relevant and robust , thereby leading to more parsimonious models . Based on the success of multi-locus genomic features in risk assessment , we conclude that combining these features with non-genetic risk factors and other biological data may lead to further improvements in risk assessment . The proposed method is implemented in MATLAB and provided in the public domain ( http://compbio . case . edu/pocos/ ) as open source software . | Several studies try to predict the individual disease risk using genetic data obtained from genome wide association studies ( GWAS ) . Earlier studies only focus on individual genetic variants . However , studies on disease mechanisms suggest the aggregation of genomic variants may contribute to diseases . For this reason , researchers commonly use prior biological knowledge to identify genetic variants that are functionally related . However , these approaches are often limited to variants that are in the coding regions of genes . However , several risk variants are in the regulatory region . Here , we incorporate known regulatory and functional interactions to find sets of genetic variants which are informative features for risk assessment . Our result on seven complex diseases show that our method outperforms individual variant based risk assessment models , as well as other methods that integrate multiple genetic variants . |
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In prion diseases , the cellular form of the prion protein , PrPC , undergoes a conformational conversion to the infectious isoform , PrPSc . PrPC associates with lipid rafts through its glycosyl-phosphatidylinositol ( GPI ) anchor and a region in its N-terminal domain which also binds to heparan sulfate proteoglycans ( HSPGs ) . We show that heparin displaces PrPC from rafts and promotes its endocytosis , suggesting that heparin competes with an endogenous raft-resident HSPG for binding to PrPC . We then utilised a transmembrane-anchored form of PrP ( PrP-TM ) , which is targeted to rafts solely by its N-terminal domain , to show that both heparin and phosphatidylinositol-specific phospholipase C can inhibit its association with detergent-resistant rafts , implying that a GPI-anchored HSPG targets PrPC to rafts . Depletion of the major neuronal GPI-anchored HSPG , glypican-1 , significantly reduced the raft association of PrP-TM and displaced PrPC from rafts , promoting its endocytosis . Glypican-1 and PrPC colocalised on the cell surface and both PrPC and PrPSc co-immunoprecipitated with glypican-1 . Critically , treatment of scrapie-infected N2a cells with glypican-1 siRNA significantly reduced PrPSc formation . In contrast , depletion of glypican-1 did not alter the inhibitory effect of PrPC on the β-secretase cleavage of the Alzheimer's amyloid precursor protein . These data indicate that glypican-1 is a novel cellular cofactor for prion conversion and we propose that it acts as a scaffold facilitating the interaction of PrPC and PrPSc in lipid rafts .
Creutzfeldt-Jakob ( CJD ) disease of humans , bovine spongiform encephalopathy of cattle and scrapie of sheep are all examples of prion diseases . These diseases propagate through the misfolding of the normal cellular form of the prion protein ( PrPC ) into the disease-associated isoform ( PrPSc ) [1] . The conversion of PrPC to PrPSc is accompanied by a large increase in the β-sheet content of the protein and a propensity to aggregate into larger macromolecular structures . PrPC is post-translationally modified with a glycosyl-phosphatidylinositol ( GPI ) anchor attached to the C-terminus . The GPI anchor facilitates the association of PrPC with cholesterol- and sphingolipid-rich membrane microdomains , termed lipid rafts ( reviewed in [2] ) . Lipid rafts are characterised biochemically by their resistance to solubilisation with detergents , such as Triton X-100 , at low temperature , with the resulting detergent-resistant membranes ( DRMs ) enriched in raft resident proteins and lipids [3] . PrPC also associates with lipid rafts by virtue of raft targeting determinants within its N-terminal domain [4] , [5] . However , the identity of the raft interacting partner ( s ) for the N-terminal domain of PrPC remains unknown . A number of studies suggest that the formation of PrPSc takes place in lipid rafts . For example , PrPSc , like PrPC , is present in DRMs isolated from cultured cells [6] , [7] . Furthermore , when the GPI anchor of PrPC is replaced by a transmembrane anchor , the protein redistributes to non-raft regions of the plasma membrane and no conversion occurs [7] , [8] . In addition , depletion of cellular cholesterol levels to disrupt lipid rafts leads to a reduction in the PrPSc-load in infected cell culture models [8]–[10] . The presence of prion conversion cofactors in a given subcellular location may rationalise why a particular site is favoured for conversion [11] . Of these potential cofactors , there is evidence linking proteoglycans and their glycosaminoglycan ( GAG ) side chains to PrPC metabolism [12] . Sulfated GAGs , including heparan sulfate , were identified as constituents of PrPSc plaques in the brains of CJD , Gerstmann-Straussler-Scheinker disease and kuru cases , as well as in hamster brains infected with scrapie [13] , [14] . The N-terminal half of PrPC has been shown to bind heparan sulfate [15] , [16] , with the basic residues at the extreme N-terminus of mature PrPC constituting a particularly strong site of interaction [17]–[19] . GAGs stimulated the endocytosis of chicken PrPC [15] and heparin reduced the level of cell surface human PrPC by an unknown mechanism [17] . Furthermore , the incorporation of PrPSc into Chinese Hamster Ovary cells required endogenous GAG expression [20] and heparan sulfate acted as a cellular receptor for prion rods in neuroblastoma cells [21] . However , despite all these observations , the identity of the cellular heparan sulfate involved in the interaction with PrPC and/or PrPSc , whether it is lipid raft-associated and involved in the conformational conversion of PrPC to PrPSc remain to be determined . In the current study we show that heparin promotes the endocytosis of mammalian PrPC and displaces it from lipid rafts , suggesting that heparin competes with an endogenous raft-resident heparan sulfate proteoglycan ( HSPG ) for binding to PrPC . We then utilised a transmembrane-anchored construct of PrP , PrP-TM [22] , which associates with DRMs solely through the raft targeting determinant in its N-terminal domain [5] , to identify the GPI-anchored HSPG glypican-1 as a molecule that targets PrPC to detergent-resistant lipid rafts . In addition , we show that glypican-1 directly interacts with both PrPC and PrPSc and that depletion of glypican-1 in scrapie-infected murine neuroblastoma N2a ( ScN2a ) cells inhibits the formation of PrPSc .
To investigate whether heparin promotes the endocytosis of mammalian PrPC , and to determine the mechanism involved , we utilised human neuroblastoma SH-SY5Y cells stably expressing murine PrPC ( tagged with the 3F4 antibody epitope ) and an established endocytosis assay [23] , [24] . It should be noted that the SH-SY5Y cells do not express detectable levels of endogenous PrPC ( see Fig . 1E ) as reported previously [19] , [25] , [26] . In the endocytosis assay , plasma membrane proteins are selectively labelled using a cell-impermeable biotin reagent , thus allowing their distinction from proteins either in the secretory pathway or already endocytosed . Cells were then incubated with heparin for 1 h at 37°C and subsequently treated with trypsin to remove residual cell surface PrPC prior to lysis . Any PrPC endocytosed during the course of the experiment is protected from trypsin digestion . In the absence of heparin , negligible ( 4% ) cell surface PrPC was endocytosed , whereas heparin stimulated the endocytosis of PrPC in a dose-dependent manner , with 77% internalised by 100 µM heparin in 1 h ( Fig . 1A and B ) . Cu2+ ions stimulate the clathrin-dependent endocytosis of PrPC by a mechanism which is initiated by the protein dissociating from lipid rafts [24] . Therefore , we determined whether heparin could likewise displace PrPC from lipid rafts . Cells were first surface biotinylated and then incubated with heparin before homogenisation in the presence of Triton X-100 followed by buoyant sucrose density gradient centrifugation . Consistent with previous reports [5] , [24] , PrPC in cells incubated in the absence of heparin resided almost exclusively at the 5%/30% sucrose interface in the DRM fractions containing the raft-associated protein , flotillin-1 ( Fig . 1C ) . However , in cells incubated with heparin , a significant amount ( 37% ) of PrPC redistributed to detergent-soluble fractions of the plasma membrane isolated at the bottom of the sucrose gradient containing the transferrin receptor ( TfR , Fig . 1C and D ) . To determine whether the heparin treatment had any effect on other aspects of PrPC metabolism , we investigated the effect of the 1 h heparin treatment on the expression and shedding of PrPC in untransfected and PrPC-transfected SH-SY5Y cells ( Fig . 1E ) . Endogenous PrPC expression in untransfected SH-SY5Y cells remained undetectable following heparin treatment ( Fig . 1E ) . In the SH-SY5Y cells transfected with PrPC , 1 h heparin treatment led to 19 . 5% reduction in the total cellular amount of PrPC ( Fig . 1E and F ) which may be attributable to its degradation following endocytosis . No PrPC was detected in concentrated conditioned media in the presence or absence of heparin treatment ( data not shown ) . Together , these data indicate that the addition of heparin to cells for 1 h does not increase the expression or shedding of PrPC but stimulates the endocytosis of mammalian PrPC by its lateral displacement from detergent-resistant lipid rafts into detergent-soluble regions of the plasma membrane . From this , we hypothesised that the lateral movement of PrPC from rafts may occur as exogenous heparin competes with an endogenous raft-resident HSPG for binding to PrPC . To test this hypothesis we employed a transmembrane-anchored construct of PrP ( PrP-TM ) in which the GPI anchor signal sequence of murine PrP is replaced with the transmembrane and cytosolic domains of the non-raft protein angiotensin converting enzyme [22] . PrP-TM also contains the 3F4 epitope . While wild-type PrPC is targeted to DRMs by virtue of both its GPI anchor and lipid raft targeting determinants in its N-terminal domain , PrP-TM localises to DRMs solely by means of its N-terminal domain interacting with raft resident molecule ( s ) [5] . Thus , PrP-TM offers a unique system to identify cellular components that interact with the N-terminal domain of PrPC and target it to lipid rafts . SH-SY5Y cells expressing PrP-TM were surface biotinylated and then incubated with heparin prior to DRM isolation . As reported previously [5] , [24] , PrP-TM resided exclusively in the DRM fractions of the plasma membrane ( Fig . 2A ) . However , in those cells that had been incubated with heparin , 44% of PrP-TM was now localised to the detergent-soluble fractions at the base of the sucrose gradient ( Fig . 2A and C ) . As with wild type PrPC , the heparin treatment did not increase the expression or stability of PrP-TM ( Fig . 1E and F ) . In parallel , SH-SY5Y cells expressing PrP-TM were incubated with bacterial phosphatidylinositol-specific phospholipase C ( PI-PLC ) to determine whether the lipid raft targeting of PrP-TM was due to raft-resident GPI-anchored proteins ( Fig . 2B ) . Treatment of cells with PI-PLC after cell surface biotinylation almost completely abrogated PrP-TM association with the DRMs but had no effect on the DRM distribution of flotillin-1 ( Fig . 2B and C ) , indicating that gross disruption of the rafts had not occurred . One interpretation of the ability of both exogenous heparin and PI-PLC treatment to inhibit the association of PrP-TM with DRMs is that a GPI-anchored HSPG is responsible for the lipid raft targeting of PrP-TM . The glypicans are a family of GPI-anchored HSPGs [27] , of which glypican-1 is particularly abundant in neurons [28] . To assess whether glypican-1 is involved in the interaction of PrP-TM with DRMs , SH-SY5Y cells expressing PrP-TM were transfected with either a control siRNA or a siRNA pool directed against glypican-1 . The specific siRNAs reduced the amount of glypican-1 in the cells by 84% ( Fig . 3A ) . The siRNA treated cells were surface biotinylated prior to DRM isolation . In glypican-1 siRNA treated cells a large proportion ( 64% ) of PrP-TM was displaced from the DRMs , instead localising with the detergent-soluble fractions ( Fig . 3B and C ) , indicating that glypican-1 plays a role in the lipid raft targeting of PrP-TM . Next , we sought to determine if glypican-1 was involved in the lipid raft targeting of wild type PrPC . We reasoned that if glypican-1 is involved in the raft targeting of PrPC , then its depletion should increase the endocytosis of PrPC . SH-SY5Y cells expressing PrPC were treated with either control siRNA or siRNA against glypican-1 . Cells were then surface biotinylated , incubated for 1 h at 37°C and the amount of PrPC endocytosed determined . In those cells treated with the control siRNA , incubation in serum-free medium resulted in little detectable endocytosis of PrPC ( Fig . 4A and B ) . However , in the cells treated with the glypican-1 siRNA , a modest but significant amount ( 19% ) of PrPC was endocytosed ( Fig . 4A and B ) . In the cells treated with the glypican-1 siRNA , a significant knockdown ( 88% ) of glypican-1 was clearly apparent , with no detectable effect on the amount of PrPC ( Fig . 4C ) . We also assessed the raft distribution of PrPC in cells treated with control or glypican-1 siRNA . Cells were then surface biotinylated and incubated for 1 h at 37°C in the presence of tyrphostin A23 , to block clathrin-mediated endocytosis [24] . In control siRNA treated cells , cell surface PrPC resided exclusively in the DRMs ( Fig . 4D ) . In contrast , in cells treated with glypican-1 siRNA a significant amount ( 20 . 6% ) of cell surface PrPC localised to the detergent-soluble fractions of the sucrose gradient ( Fig . 4D and E ) . Immunofluorescence microscopy revealed that PrPC and glypican-1 extensively colocalised on the surface of the cells ( Fig . 4F ) . Collectively these data indicate that PrPC and glypican-1 co-localise on the cell surface and that knockdown of glypican-1 allows PrPC to translocate out of the detergent-insoluble rafts into detergent-soluble regions of the plasma membrane prior to its endocytosis . We next sought to determine if glypican-1 is able to interact directly with PrPC and PrPSc by co-immunoprecipitation . When cell lysates prepared using Triton X-100 from SH-SY5Y cells stably expressing PrPC ( Fig . 5A ) or N2a cells endogenously expressing PrPC ( Fig . 5B ) were incubated with a polyclonal antibody against glypican-1 , PrPC was co-immunoprecipitated in both cases ( Fig . 5A ) . The interaction between glypican-1 and PrPC was disrupted by prior treatment of the cell lysates with heparinase I and heparinase III to digest the heparan sulfate chains on glypican-1 [29] , [30] or exogenous heparin ( Fig . 5A and B ) , indicating that the interaction between PrPC and glypican-1 involves the heparan sulfate sidechains on the latter . In order to provide further evidence that glypican-1 and PrPC are directly associated , co-immunoprecipitation experiments were performed using cells lysed with sarkosyl or octylglucoside , detergents known to completely solubilise lipid rafts [31]–[33] . PrPC co-immunoprecipitated with glypican-1 using lysates prepared with either detergent ( Fig . 5A and B ) . To address whether glypican-1 was also able to interact with PrPSc we performed co-immunoprecipitation experiments using cell extracts prepared from mouse neuroblastoma cells persistently infected with PrPSc ( ScN2a cells ) . Immunoprecipitation of glypican-1 led to the co-immunoprecipitation of PrP from cells lysed with either Triton X-100 or octylglucoside ( Fig . 5C ) . Sarkosyl is unsuitable for use in co-immunoprecipitation experiments in ScN2a cells owing to its ability to aggregate PrPSc [34] . Co-immunoprecipitation of PrP with glypican-1 in Triton-extracted cells was inhibited by co-incubation with exogenous heparin or removal of the heparan sulfate sidechains on glypican-1 with heparinase I and heparinase III treatment ( Fig . 5C ) . When the protein A-sepharose pellets were treated with proteinase K ( PK ) prior to subsequent analysis , PrPSc was found to have precipitated with glypican-1 in both the Triton X-100 and octylglucoside extracted cells ( Fig . 5D ) . Similarly , the interaction between glypican-1 and PrPSc was inhibited by addition of exogenous heparin to the cell lysates or pre-treatment of samples with heparinase I and heparinase III ( Fig . 5D ) . These data indicate that glypican-1 is capable of interacting via its heparan sulfate chains with both normal and protease-resistant forms of PrP . Next we investigated if the interaction with glypican-1 could modulate the conversion of PrPC to PrPSc . ScN2a cells were treated with the control siRNA or one of four siRNA duplexes targeted to mouse glypican-1 . After a total incubation period of 96 h , cells were harvested , lysed , and then the cell lysates digested with PK prior to SDS-PAGE and subsequent immunoblotting ( Fig . 6A ) . Treatment with the control siRNA did not alter the level of PK-resistant PrPSc . However , in cells treated with each of the four glypican-1 siRNA duplexes an average 50% reduction in PK-resistant PrPSc was consistently observed ( Fig . 6A and B ) . Knockdown ( 78–87% ) of glypican-1 by the siRNA duplexes in the ScN2a cells was confirmed by immunoblotting ( Fig . 6C ) . The reduction in PK-resistant PrPSc seen in the glypican-1 depleted cells was not a consequence of a more general reduction in PrP levels , as when non-PK digested samples were immunoblotted the amount of total PrP was unaltered relative to the control siRNA treated cells ( Fig . 6C ) . Next we sought to confirm the specificity of glypican-1 in modulating the conversion of PrPC to PrPSc by testing whether another HSPG , syndecan-1 , could influence PrPSc accumulation in ScN2a cells . When cells were treated with syndecan-1 siRNA over an incubation period of 96 h , the level of PK-resistant PrPSc remained comparable to control siRNA treated cells ( Fig . 6D and E ) . In contrast , treatment of ScN2a cells with glypican-1 siRNA once more led to 50% reduction in PK-resistant PrPSc ( Fig . 6D and E ) . Knockdown of glypican-1 ( 73 . 3±5 . 3% ) and syndecan-1 ( 60 . 7±6 . 3% ) was confirmed by immunoblotting ( Fig . 6F ) . Cell division can modulate the accumulation of prions in ScN2a cells [35] and glypican-1 depletion has been reported to inhibit cell proliferation in endothelial cells by arresting cell cycle progression [36] . Therefore , we investigated whether the effect of glypican-1 knockdown was altering the amount of PrPSc in the ScN2a cells due to modulation of cell division . However , glypican-1 depletion did not significantly affect cell proliferation compared to mock-treated and control siRNA-treated cells ( Fig . 7A ) . As the cell surface is the probable site for the initial interaction between the two isoforms of PrP [11] , we assessed whether the reduction in PrPSc associated with glypican-1 depletion was due to an alteration in the steady state level of PrP at the cell surface . ScN2a cells were surface biotinylated following treatment with either control siRNA or glypican-1 siRNA . Although there was a reduction in the amount of PrPSc in the glypican-1 siRNA treated cells , there was no difference in the amount of biotinylated cell surface PrP ( Fig . 7B and C ) . We have recently reported that PrPC inhibits the cleavage of the amyloid precursor protein ( APP ) by the β-secretase BACE1 [19] . This effect was dependent on the lipid raft localisation of PrPC and was mediated by the N-terminal polybasic region of PrPC through interaction with GAGs [19] . Therefore , we hypothesised that glypican-1 may be involved in the mechanism by which PrPC regulates the BACE1 cleavage of APP . To address this , control siRNA or siRNA directed against glypican-1 were transfected into SH-SY5Y cells expressing PrPC . Parallel experiments were performed in SH-SY5Y cells transfected with an empty pIRESneo vector . In the media from control siRNA-treated cells expressing PrPC the level of sAPPβ , the product of BACE1 cleavage of APP , was dramatically reduced relative to cells lacking PrPC ( Fig . 8A and B ) , as seen previously [19] . In media from the empty vector cells treated with glypican-1 siRNA there was no alteration in the level of sAPPβ relative to control siRNA-treated cells ( Fig . 8A and B ) . Significantly , levels of sAPPβ in the cells expressing PrP that had been treated with the glypican-1 siRNA were not altered relative to the control siRNA treated cells ( Fig . 8A and B ) , implying that the GAG sidechains of glypican-1 are not critical in mediating the inhibition of BACE1 by PrPC .
By utilising a transmembrane anchored form of PrP , PrP-TM , which associates with DRMs solely by means of its N-terminal domain and not also via the GPI anchor as in wild type PrPC , we have identified the GPI-anchored HSPG , glypican-1 , as involved in targeting PrPC to detergent-resistant lipid rafts in the plasma membrane of neuronal cells . Furthermore , we have shown that depletion of glypican-1 by siRNA knock down significantly inhibits the formation of PrPSc in a scrapie-infected cell line . Thus , we have identified glypican-1 as a novel cofactor involved in the cellular conversion of PrPC to PrPSc . Glypican-1 appeared to be interacting via its heparan sulfate chains with both PrPC and PrPSc , as the interaction was inhibited by digestion of its GAG chains with heparinase I and heparinase III . This would be consistent with a previous study , where treatment of ScN2a cells with heparinase III caused a profound reduction in the level of PrPSc [37] . We propose a model where glypican-1 acts as a scaffold for prion propagation by binding to both PrPC and PrPSc , thereby bringing them into close enough proximity within lipid rafts to facilitate prion conversion ( Fig . 9A ) . This is supported by the observation that both PrPC and PrPSc co-immunoprecipitated with glypican-1 and that both isoforms of PrP are localised in lipid rafts [6] , [7] . Glypican-1 may be acting as a catalyst , increasing the rate of conversion of PrPC to PrPSc . In this respect , a previous study has demonstrated that pentosan polysulfate destabilised protease-sensitive PrP with a loss of α-helical structure [38] . Thus , a potential mechanism by which glypican-1 may be acting is through its heparan sulfate side chains exerting a similar destabilising effect on the structural stability of PrPC , thereby facilitating its refolding in the presence of PrPSc . Although depletion of glypican-1 did not alter the cell surface level of PrP , we cannot exclude the possibility that glypican-1 may also have an effect on the conversion of PrPC to PrPSc by altering the trafficking or clearance of PrPSc . Glypican-1 itself is internalised from the plasma membrane by a mechanism involving caveolin-1 [39] . During its intracellular trafficking the heparin sulfate chains of glypican-1 are removed and degraded , either by heparanase cleavage or a non-enzymatic deaminative cleavage which is nitric oxide-catalysed and Cu2+-/Zn2+-dependent [40] . The nitric oxide is derived from nitrosylated cysteine residues within the glypican-1 core protein , formed via a Cu2+-dependent redox reaction [41] . In both cell-free experiments and cell models , Cu2+-loaded PrPC was shown to support the nitrosylation of glypican-1 [40] . Recently , it was proposed that glypican-1 autoprocessing is involved in the cellular clearance of PrPSc [42] . These authors used scrapie-infected hypothalamic ( ScGT1 ) cells and immunofluorescence microscopy to show that PrPSc-associated immunofluorescence was increased in cells treated with a glypican-1 specific siRNA , though no direct quantification was provided [42] . In addition , when ScGT1 cells were treated with reagents to inhibit glypican-1 autoprocessing , western blot analysis revealed increased levels of PrPSc [42] . From this , these authors argued that glypican-1 autoprocessing may contribute to the cellular clearance of PrPSc , thus inhibiting this process would lead to an increase in PrPSc accumulation [42] . However , prevention of the autoprocessing of glypican-1 may increase the half-life of the protein , and thus lead to the observed rise in PrPSc if glypican-1 is a cofactor in prion conversion as our data indicate . Interestingly , PrPSc still propagated following the substantial knock down of glypican-1 by siRNA; PrPSc levels were reduced by at most 55% despite glypican-1 protein levels being reduced by over 80% . One possibility is that the remaining glypican-1 is sufficient to support prion conversion . Alternatively , these data may be interpreted with respect to the protein-only hypothesis in that PrPSc is sufficient on its own to convert PrPC , with glypican-1 acting merely as a catalyst [1] . Another possibility is the existence of other conversion-favouring cellular cofactors , which may include other HSPGs although the lack of effect of syndecan-1 depletion on PrPSc formation argues against the involvement of this family of proteoglycans . In this context it is also interesting to note that siRNA knock down of glypican-1 did not completely abolish the association of PrP-TM with DRMs , suggesting the existence of additional lipid raft targeting molecules . Such components may be proteins , such as other members of the glypican family [27] or lipids . In support of the latter , PrP lacking its GPI anchor was shown to interact with cholesterol and sphingomyelin containing raft-like lipid vesicles [4] . Whether the same additional molecules involved in the raft targeting of PrPC are also involved in facilitating the conversion of PrPC to PrPSc awaits their identification and subsequent determination of their role in these processes . To our knowledge the only other protein to date shown to be capable of influencing prion accumulation in cultured cells is the 37 kDa/67 kDa laminin receptor [43] . When infected cells were depleted of the 37 kDa/67 kDa laminin receptor , PrPSc levels were significantly reduced , although this may be the result of reduced PrPC expression seen after these treatments rather than the loss of a direct role for the 37 kDa/67 kDa laminin receptor in prion conversion [43] . Interestingly , PrPC interacted with the 37 kDa/67 kDa laminin receptor via both a direct interaction involving residues 144–179 and an indirect interaction via an unidentified HSPG involving residues 53–93 of PrPC [44] . Whether glypican-1 is this intermediate HSPG awaits further study . Like heparan sulfate , many of the anti-prion compounds identified to date are , or have the capacity to stack into , large polyanionic chains [45] . This suggests a common mechanism of inhibition of PrPSc accumulation by these compounds , perhaps by binding to the same or overlapping sites on PrP . In support of this , sulfated glycans have been shown to compete with both Congo red and phosphorothioated oligonucleotides for binding to PrPC [46] , [47] . It is possible that some of the anti-prion compounds identified to date exert their effects by antagonising the binding of glypican-1 to PrPC and PrPSc . The ability of exogenous heparin to inhibit the co-immunoprecipitation of glypican-1 with both PrPC and PrPSc supports such a hypothesis . Thus , such anti-prion compounds may directly disrupt the interaction between PrPC/PrPSc and glypican-1 which is required for optimal prion conversion . Alternatively , the observation that heparin promotes the endocytosis of PrPC , may suggest that some anti-prion compounds could act by a similar mechanism , promoting the endocytosis of PrPC , thereby directing the protein to late endosomes and/or lysosomes where conversion to PrPSc is inefficient [15] and/or removing PrPC from the cell surface lipid rafts where glypican-1 facilitates conversion . Following from these results , the design of specific small molecule inhibitors to antagonise the binding of glypican-1 and PrPC/PrPSc may represent a viable therapeutic avenue for the treatment of prion diseases . With any potential treatment for prion diseases that targets PrPC , there is the concern that the normal functions of the protein may be adversely affected . In this respect , the lack of effect of glypican-1 depletion on the ability of PrPC to inhibit the cleavage of APP by BACE1 [19] suggests that at least one of the proposed physiological functions of PrPC [48] is independent of its interaction with glypican-1 . Although heparan and other GAGs disrupt prion conversion in both cell and animal models [49]–[54] , such compounds stimulate the conversion of PrPC to PrPSc in cell free systems [38] , [55] . From our data this apparent paradox can be explained by exogenous GAGs competitively inhibiting the binding of PrPC/PrPSc to glypican-1 in the cell and animal models , thus not allowing the cellular and infectious forms of PrP to come into close enough contact for conversion to occur ( Fig . 9B ) . While in the cell-free systems the GAGs provide a scaffold to promote the interaction of PrPSc with PrPC thereby facilitating conversion . In conclusion , we have identified the cell surface HSPG , glypican-1 , as a novel lipid raft targeting determinant for PrPC . In addition , we show that depletion of glypican-1 inhibits the formation of PrPSc in scrapie-infected cells , implying that glypican-1 is a novel cellular cofactor in prion conversion . Furthermore , we show that glypican-1 is not required for one of the proposed physiological functions of PrPC , inhibition of the β-secretase cleavage of APP .
Insertion of the coding sequence of murine PrP containing a 3F4 epitope tag into pIRESneo ( Clontech-Takara Bio Europe ) and generation of the PrP-TM construct have been reported previously [22] . For stable transfection of the cDNA encoding the PrP constructs , 30 µg DNA was introduced into human SH-SY5Y neuroblastoma cells by electroporation and selection was performed in normal growth medium containing G418 selection antibiotic . Mouse N2a neuroblastoma cells , N2a cells infected with the mouse-adapted 22L scrapie strain ( ScN2a ) [56] and SH-SY5Y cells were routinely cultured in Dulbecco's modified Eagle's medium supplemented with 10% foetal bovine serum , 50 U/ml penicillin and 0 . 1 mg/ml streptomycin ( all from Invitrogen ) . Cells were maintained in a humidified incubator at 37°C with 5% CO2 . For analysis of cell-associated proteins , cells were washed with phosphate-buffered saline ( 20 mM Na2HPO4 , 2 mM NaH2PO4 , 0 . 15 M NaCl , pH 7 . 4 ) and scraped from the flasks into phosphate-buffered saline . Cells were pelleted by centrifugation at 500 g for 5 min . Unless indicated otherwise , pelleted cells were lysed in ice cold lysis buffer ( 150 mM NaCl , 0 . 5% ( v/v ) Triton X-100 , 0 . 5% ( w/v ) sodium deoxycholate , 50 mM Tris-HCl , pH 7 . 5 ) supplemented with a complete protease inhibitor cocktail ( Roche Applied Science , Burgess Hill , U . K ) . Cells at confluency were incubated for 1 h at 4°C with 0 . 5 mg/ml Biotin sulfo-NHS ( Sigma-Aldrich , Poole , U . K . ) . Cells were then incubated for 30 min at 37°C in OptiMEM . Prior to cell lysis , PrP remaining on the cell surface was removed by digestion with trypsin as described previously [23] , [24] . Cells at confluence were surface biotinylated and then treated as described in individual experiments in the presence of Tyrphostin A23 to prevent PrPC endocytosis [24] . Media was then removed and cells rinsed in phosphate-buffered saline . Cells were subsequently harvested and then resuspended in Mes-buffered saline ( 25 mM Mes , 150 mM NaCl , pH 6 . 5 ) containing 1% ( v/v ) Triton X-100 . Cells were then homogenised by passing through a Luer 21-gauge needle . After centrifugation at 500 g for 5 min to pellet cell debris , the supernatant was harvested and made 40% ( v/v ) with respect to sucrose by addition of an equal volume of 80% ( v/v ) sucrose . A 1 ml aliquot of the sample was then placed beneath a discontinuous sucrose gradient comprising 3 ml of 30% sucrose and 1 ml of 5% sucrose , both in Mes-buffered saline . The samples were centrifuged at 140 , 000 g in an SW-55 rotor ( Beckman Coulter Inc . , CA , U . S . A . ) for 18 h at 4°C . The sucrose gradients were harvested in 0 . 5 ml fractions from the base of the gradient and the distribution of proteins monitored by western blot analysis of the individual fractions . Where indicated , biotin-labelled PrP was detected by subsequent immunoprecipitation of epitope-tagged PrP from the individual fractions using antibody 3F4 ( Eurogentec Ltd . , Southampton , U . K . ) and subsequent immunoblotting using horseradish peroxidase-conjugated streptavidin ( Thermo Fisher Scientific , Cramlington , U . K . ) . Protein was quantified using bicinchoninic acid [57] in a microtitre plate-based assay using bovine serum albumin as standard . To detect PK-resistant PrPSc , cell lysate samples containing 200 µg of protein in a total volume of 200 µl were incubated for 30 min at 37°C with 4 µg PK . The reaction was terminated by the addition of phenylmethanesulfonyl fluoride to a final concentration of 3 mM . Protein in the samples was precipitated by the addition of 800 µl of ice cold methanol and incubated overnight at 4°C . Precipitated protein was pelleted by centrifugation and then resuspended in dissociation buffer ( 125 mM Tris-HCl pH 8 . 0 , 2% ( w/v ) sodium dodecyl sulfate , 20% ( v/v ) glycerol , 100 mM dithiothreitol , bromophenol blue , pH 6 . 8 ) prior to SDS-PAGE . To remove heparan sulfate sidechains from HSPGs prior to detection of glypican-1 and syndecan-1 core proteins by immunoblotting , cell lysates ( 50 µg protein ) were incubated for 5 h at 37°C in the presence of 1 m unit heparinase I and 1 m unit heparinase III ( both Sigma ) . To remove cell surface GPI-anchored proteins , cell monolayers were incubated for 1 h at 4°C with 1 U/ml Bacillus thuringiensis phosphatidylinositol-specific phospholipase C ( PI-PLC ) ( Sigma-Aldrich ) . Samples were prepared in dissociation buffer and boiled for 5 min . Proteins were resolved by SDS-PAGE using either 7–17% polyacrylamide gradient gels or 14 . 5% polyacrylamide gels . For western blot analysis , resolved proteins were transferred to Immobilon P polyvinylidene difluoride membrane ( Amersham , Little Chalfont , U . K . ) . The membrane was blocked by incubation for 1 h with phosphate-buffered saline containing 0 . 1% ( v/v ) Tween-20 and 5% ( w/v ) dried milk powder . Antibody incubations were performed in phosphate-buffered saline-Tween containing 2% ( v/v ) bovine serum albumin . Antibody 3F4 recognises the 3F4 epitope tag ( corresponding to amino acid residues 109–112 of human PrP ) at residues 108–111 of the chimeric murine PrP and was used at a dilution of 1∶4000; antibody 6D11 ( Eurogentec Ltd . ) recognises an epitope within amino acids 93–109 of PrP and was used at 1∶10000; antibody 22C11 ( Millipore ( UK ) Ltd , Livingston , U . K . ) recognises amino acid residues 66–81 at the N-terminus of APP and was used at 1∶2500; antibody 1A9 ( GlaxoSmithKline , Harlow , U . K . ) recognises a neoepitope on the soluble ectodomain fragment of APP derived from β-secretase cleavage ( sAPPβ ) and was used at 1∶2500; antibody S1 against human glypican-1 was a kind gift from Professor G . David ( Flanders Institute for Biotechnology , Belgium ) and was used at 1∶2000; polyclonal anti-glypican-1 and anti-syndecan-1 ( both Abcam plc , Cambridge , U . K . ) were used at 1∶1000; and anti-actin antibody ( Sigma ) at 1∶5000 . Horseradish peroxidase-conjugated streptavidin was used at 1∶2000 . Bound antibody was detected using peroxidase-conjugated secondary antibodies in conjunction with the enhanced chemiluminescence detection method ( Amersham Biosciences , Amersham , U . K . ) . SH-SY5Y cells expressing PrPC were seeded into T80 flasks at 70% confluency and incubated with 500 pmol of a 2 µM Smartpool siRNA solution against glypican-1 or a control Smartpool reagent ( Dharmacon Inc . , Chicago , U . S . A . ) complexed with DharmaFECT-1 transfection regent ( Dharmacon Inc . ) in serum-free medium . After 4 h , serum was added to 10% ( v/v ) . Cells were then incubated for a further 44 h prior to experimentation . To analyse the role of glypican-1 in the inhibitory action of PrPC on the BACE1 cleavage of APP , SH-SY5Y cells expressing APP695 were transfected with the cDNA encoding PrPC or an empty pIRESneo vector and treated with glypican-1 or control Smartpool reagents . After 30 h the medium was changed to OptiMEM ( Invitrogen ) , and the cells incubated for a further 24 h . Cells were pelleted and the medium harvested and centrifuged at 1000 g for 5 min to remove residual cell debris . Medium was then concentrated 50-fold using Vivaspin centrifugal concentrators ( 10 000 MW cut off ) . To assess the role of glypcian-1 on prion conversion ScN2a cells were treated with 500 pmol of the following 2 µM siRNA solutions: Smartpool glypican-1 solution; one of four individual duplexes targeted to glypican-1; Smartpool syndecan-1 solution or control Smartpool siRNA reagent . Cells were treated twice over a 96 h period with siRNA ( at 0 and 48 h ) . After 96 h cells were harvested and lysed . Co-immunoprecipitation of PrP isoforms and glypican-1 were performed as described previously [58] . Briefly , SH-SY5Y cells expressing PrPC , N2a cells or ScN2a cells were grown to confluence and then harvested . Cells were lysed using chilled immunoprecipitation lysis buffer ( 150 mM NaCl , 10 mM EDTA , 10 mM KH2PO4 , pH 7 . 5 ) containing , where indicated 2% ( v/v ) Triton X-100 , 1% n-octyl-β-D-glucopyranoside ( octyl glucoside ) or 1% N-laurylsarcosine ( sarkosyl ) . Cell debris were removed by centrifugation and then lysates precleared for 30 min with 0 . 5% ( w/v ) protein A-Sepharose . The protein A-Sepharose was removed by centrifugation and the supernatant incubated overnight at 4°C with anti-PrP antibody ( 6H4 ) or anti-glypican-1 antibody . Protein A-Sepharose was added to 0 . 5% ( w/v ) to the samples and incubation proceeded at 37°C for 1 h . Immunocomplexes were pelleted and the pellet washed six times with 150 mM NaCl , 10 mM Tris-HCl , pH 7 . 4 containing 0 . 2% ( v/v ) Tween-20 . Cells were seeded onto coverslips and grown to 50% confluency . Cells were then fixed with 4% ( v/v ) paraformaldehyde/0 . 1% ( v/v ) glutaraldehyde in PBS for 15 min , and then blocked for 1 h in PBS containing 5% ( v/v ) fish skin gelatin ( Sigma-Aldrich ) . Coverslips were then incubated with anti-PrP antibody 3F4 and anti-glypican-1 antibody . Finally , coverslips were incubated with the appropriate fluorescent probe-conjugated secondary antibodies ( Molecular Probes , Eugene , U . S . A . ) for 1 h and mounted on slides using fluoromount G mounting medium ( SouthernBiotech , Alabama , U . S . A ) . Cells were visualised using a DeltaVision Optical Restoration Microscopy System ( Applied Precision Inc . , Washington , USA ) . Data were collected from 30–40 0 . 1 µm thick optical sections , and 3-D datasets were deconvolved using the softWoRx programme ( Applied Precision Inc . ) . The presented images represent individual Z-slices taken from the middle of the cell . ScN2a cells ( 1×104 per well ) in 96-well tissue culture plates were cultured overnight in complete medium . After 24 h , cells were treated with the indicated siRNA duplexes . Cells were fixed in 70% ethanol at room temperature for 5 min , and the adherent cell monolayers were stained with the DNA-binding fluorochrome Hoescht 33342 ( 8 . 8 µM ) . Once dry , the fluorescence of each well was measured on a Synergy HT ( Bio-Tek ) fluorescent plate reader ( 350 nm excitation and 450 nm emission wavelengths ) in order to determine the cell number in each well . Data are expressed as means ( ± s . e . m . ) . Experiments were performed in triplicate and repeated on at least three occasions . Statistical analysis was performed using student's t-test . P-values <0 . 05 were taken as statistically significant . | The prion diseases are unique in that their infectious nature is not dependent on nucleic acid but is instead attributed to a misfolded protein , the prion protein . This misfolded prion protein is capable of inducing the misfolding of the normal form of the prion protein that is present on the surface of neurons and other cells in the body . However , the site in the cell at which this misfolding occurs and whether other proteins are involved remains controversial . We have addressed these questions by investigating how the normal form of the prion protein is targeted to specialised domains on the plasma membrane termed cholesterol-rich lipid rafts . We show that targeting is due , in part , to a particular heparin sulfate proteoglycan called glypican-1 . Significantly , reducing the levels of glypican-1 in an infected cell line reduced the accumulation of misfolded prion protein . We propose that glypican-1 acts as a scaffold facilitating the favourable interaction of the misfolded , infectious form of the prion protein with the normal cellular form within cholesterol-rich lipid rafts . Our results indicate that glypican-1 is intimately involved in the misfolding of the prion protein , the critical event in the pathogenesis of prion diseases such as Creutzfeldt-Jakob disease in humans . |
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Gastric cancer development is strongly correlated with infection by Helicobacter pylori possessing the effector protein CagA . Using a transgenic Drosophila melanogaster model , we show that CagA expression in the simple model epithelium of the larval wing imaginal disc causes dramatic tissue perturbations and apoptosis when CagA-expressing and non-expressing cells are juxtaposed . This cell death phenotype occurs through activation of JNK signaling and is enhanced by loss of the neoplastic tumor suppressors in CagA-expressing cells or loss of the TNF homolog Eiger in wild type neighboring cells . We further explored the effects of CagA-mediated JNK pathway activation on an epithelium in the context of oncogenic Ras activation , using a Drosophila model of metastasis . In this model , CagA expression in epithelial cells enhances the growth and invasion of tumors in a JNK-dependent manner . These data suggest a potential role for CagA-mediated JNK pathway activation in promoting gastric cancer progression .
Infection with Helicobacter pylori is the strongest risk factor for the development of gastric carcinoma , which is the second most common cause of cancer-related death worldwide [1] . Although approximately half the world's population is infected with H . pylori , most of those individuals will develop simple gastritis and remain asymptomatic . However , 10–15% of infected subjects will develop duodenal ulcers and 1% will develop gastric adenocarcinoma [2] . This dramatic variability in clinical outcome of H . pylori infection is not well-understood , but likely results from the consequences of long-term interactions between the bacterium and its human host . Specific bacterial and host genetic factors have been shown to affect H . pylori pathogenesis . Strains that possess the cag pathogenicity island ( cag PAI ) , which encodes a type IV secretion system used to inject the CagA effector protein directly into gastric epithelial cells , are much more virulent [3] . Once inside host cells , CagA is tyrosine phosphorylated on conserved carboxyl terminal EPIYA motifs by Src family kinases . Variability in the number and composition of these phosphorylation motifs also correlates with differences in the carcinogenic potential of H . pylori strains [4] . Host genetic factors that can influence the progression and ultimate disease outcome of H . pylori pathogenesis include polymorphisms that enhance expression of certain cytokines [2] , and genetic changes that occur during progression from normal mucosa to gastric carcinoma such as loss of tumor suppressors and activation of oncogenes [5] . Although development of a complex disease like gastric cancer requires the cooperation of many bacterial and host genetic factors , it is clear that the CagA effector protein is an important driver of disease progression . CagA has been shown to interact with a multitude of host cell proteins belonging to several conserved signaling pathways [6] , and these interactions are thought to promote carcinogenesis upon H . pylori infection . The majority of these interactions were discovered using cell culture models in which CagA expression can disrupt processes such as tight junction formation , motility and cytoskeleton dynamics . However , which interactions between CagA and host cell signaling pathways trigger the processes that lead to gastric cancer remains unclear [4] . Obtaining more specific information about the relative importance of CagA's interactions with host cell proteins will require investigation of their downstream effects on intact epithelial tissue . In order to examine the effects of both bacterial and host genetic factors , our group has developed a system in which Drosophila melanogaster is used to model pathogenesis of the H . pylori virulence factor CagA [7] . There are several properties that make this model organism well-suited for studying the pathogenic effects of CagA expression . First , many canonical cell signaling pathways have been extensively characterized in Drosophila and show high conservation with the homologous pathways in humans . Also , genetic tools like the GAL4/UAS system allow expression of CagA in specific cells within an epithelium and examination of how CagA-expressing cells interact with neighboring wild type cells . Finally , we can easily manipulate host genes using resources generated by the rich Drosophila research community to assess potential effects on CagA-induced phenotypes . In addition , our model allows us to test whether CagA's interactions are phosphorylation-dependent through expression of a mutant form of CagA known as CagAEPISA , in which the EPIYA phosphorylation motifs have been deleted or mutated [8] . Use of this model has already provided insight into CagA's role in manipulating receptor tyrosine kinases , the Rho signaling pathway and epithelial junctions [7]–[10] . Epithelial polarity is one important feature of host cells known to be perturbed by CagA . Strains of H . pylori that encode CagA are exclusively able to cause localized disruption of apicobasal polarity in order to colonize a polarized monolayer of tissue culture cells [11] . CagA-positive strains of H . pylori have also been shown to cause apoptosis in both cultured gastric cancer cells and human gastric biopsies [12] , [13] , although the role of CagA-dependent apoptosis in H . pylori pathogenesis remains controversial . Loss of epithelial cell polarity has been shown to induce apoptotic cell death or promote tumorigenesis in different cellular and genetic contexts [14] . Cell death resulting from polarity disruption can trigger compensatory proliferation in order to replace lost cells , but this process can become tumorigenic in the presence of genetic alterations that block apoptosis [15] . This mechanism has been proposed to explain how the ability of CagA to disrupt cell polarity and induce apoptosis may be linked to its tumorigenic potential , but the host cell signaling pathways that could mediate these downstream effects have not been identified [16] . An important host signaling pathway that triggers apoptosis downstream of cell polarity disruption is the c-Jun NH2-terminal kinase ( JNK ) pathway . JNK is a stress-activated protein kinase with numerous upstream activators including cytokines , mitogens , osmotic stress , ultraviolet radiation and loss of cell polarity [17] . JNK-mediated apoptosis plays a role in several physiological processes including morphogenetic apoptosis and classical cell competition in which slow-growing cells are eliminated by their wild type neighbors . The JNK pathway also triggers apoptosis in response to a unique form of cell competition known as intrinsic tumor suppression where JNK activation performs a cell editing function by removing aberrant cells that arise within an epithelium , thus enhancing the resilience of epithelia to insult . Both expression of the tumor necrosis factor ( TNF ) homolog Eiger ( Egr ) and the presence of wild type cells within an epithelium are required for JNK pathway activation downstream of cell polarity disruption , and their absence can lead to tumor formation [18] . Furthermore , JNK signaling has been shown to switch from a proapoptotic to a progrowth role in the presence of oncogenic Ras [19] . These functions of the JNK pathway are well-established in Drosophila , and likely also relevant in mammals given the high conservation of this pathway throughout evolution [20] . Bacterial activation of JNK signaling has also demonstrated importance in enhancing epithelial robustness . During oral infection of Drosophila with the human pathogen Pseudomonas aeruginosa , the bacterium activates JNK signaling in the intestinal epithelium to trigger apoptosis and subsequent compensatory proliferation , thereby stimulating epithelial renewal . The same effect was not seen during infection with an avirulent strain of P . aeruginosa that does not secrete the virulence factor pyocyanin , suggesting a role for this effector protein in activating JNK signaling in response to damage induced by the bacterium [21] . Similar to the adult Drosophila intestine , the larval imaginal disc epithelia are particularly resistant to the effects of stress-induced apoptosis and can recover after losing over 50% of their cells during development to produce normal adult structures [22] . This inherent epithelial resilience makes the imaginal discs a relevant tissue in which to examine potential effects of JNK-dependent apoptosis mediated by a bacterial virulence factor . In this study , we discovered a role for the CagA virulence factor in activating JNK signaling . We used transgenic Drosophila to express CagA in the developing wing imaginal disc , a simple polarized epithelial structure formed during larval stages of development . We found that CagA expression caused a distinct pattern of cell death in which apoptotic cells are basally extruded from the epithelium . In addition we showed that this apoptosis phenotype is enhanced by coexpression with Basket ( Bsk ) , the Drosophila homolog of JNK , and suppressed by coexpression with a dominant-negative form of Bsk . From these results , we conclude that expression of CagA triggers JNK pathway activation which causes apoptosis in an intact epithelium . Furthermore , we used a Drosophila model of metastasis to show that CagA expression can enhance the growth and invasion of tumors generated by expression of activated Ras . This increase in tumorigenic capacity is suppressed by coexpression with dominant-negative Bsk , leading us to conclude that CagA promotes tumor growth and invasion through JNK pathway activation .
In order to examine the effects of expressing the H . pylori effector protein CagA on an intact epithelium , we used the GAL4/UAS system to drive its expression in the wing imaginal disc . The Drosophila wing begins to form during early larval life when it exists as a primordial sac which contains both a simple columnar epithelium and the squamous epithelium of the peripodial membrane [23] . Cells within the wing imaginal disc proliferate extensively in larval stages followed by disc evagination during pupation , resulting in the adult wing structure . This developmental process is distinct from that of the eye imaginal disc used to model CagA pathogenesis previously [7]–[10] , which undergoes systematic differentiation during larval stages . In addition , the fate of imaginal disc cells is specified early in development [24] which allowed us to express CagA in distinct regions of the wing disc ( Figure 1A ) . We expressed CagA with various GAL4 drivers specific to the wing ( Figure S1A ) , and determined that both the level of CagA protein and the region in which it is expressed affect the resulting larval and adult wing phenotypes ( Figure S1B–S1L ) . We focused our subsequent analysis on two different GAL4 drivers which express CagA either in a subset of wing cells or throughout the wing imaginal disc: beadex-GAL4 ( bx-GAL4 ) is expressed specifically in cells of the columnar epithelium that give rise to the dorsal surface of the wing blade ( Figure 1B ) , and 765-GAL4 is expressed ubiquitously throughout the wing . A membrane-localized GFP construct ( mGFP ) was used to visualize the expression domain . Expressing CagA with the 765-GAL4 ubiquitous wing driver did not cause any observable phenotype ( Figure 1C ) . However , expressing CagA with the bx-GAL4 dorsal wing driver caused clusters of apoptotic cells to form near the center of the expression domain in wing imaginal discs from third instar larvae ( Figure 1D ) . This phenotype was dose-dependent , since expressing two copies of CagA increased both the size and number of apoptotic clusters formed ( Figure 1E ) . A similar phenotype has been shown to result from localized JNK pathway activation in the wing imaginal disc epithelium but does not occur upon more ubiquitous activation [25] . Interestingly , although expressing one copy of CagAEPISA with the bx-GAL4 driver did not cause a phenotype ( Figure 1F ) , expressing two copies induced formation of small apoptotic clusters within the expression domain ( Figure 1G ) . This reduction in apoptosis induction suggests that the phenomenon does not require phosphorylated CagA , but that CagAEPISA is a less potent activator of cell death . This observation is consistent with data obtained from transgenic expression of CagAEPISA in the eye imaginal disc epithelium , where less severe phenotypes were shown to result from differential cellular localization of the phosphorylation-resistant form of CagA . Whereas wild type CagA was highly enriched at the apical membrane in eye imaginal disc epithelial cells , CagAEPISA was expressed diffusely throughout the cytoplasm . We propose that the inability of phosphorylation-resistant CagA to localize apically within an epithelium influences its interactions with host cell proteins and their resulting effects on the epithelial tissue [9] . Cells within the apoptotic clusters generated by CagA expression were extruded from the basal surface of the wing imaginal disc epithelium . Further examination of this tissue revealed an enrichment of matrix metalloproteinases , which break down basement membrane , specifically in cells located directly apical to the apoptotic clusters ( Figure 1H ) . This observation indicates that apoptotic cells generated by CagA expression are actively removed from the wing epithelium and not passively lost during development of the imaginal disc . Many complex cellular interactions are required during wing disc development to ensure proper formation of the adult wing structure ( Figure 1I ) . While this process did not appear to be disrupted by ubiquitous expression of CagA in the wing ( Figure 1J ) , CagA expression specifically in the dorsal wing caused a dose-dependent disruption of the imaginal disc epithelium ( Figure S1M and S1N ) which affected the overall appearance of the adult wing ( Figure 1K and 1L ) . This phenomenon also did not require phosphorylated CagA since expression of CagAEPISA caused a less severe dose-dependent disruption of the adult wing ( Figure 1M and 1N ) . The observation that ubiquitous expression of CagA in the wing does not cause apoptosis or epithelial disruption suggests that wild type cells surrounding those which express CagA are required to produce both phenotypes . This is consistent with the previous observation that JNK-dependent apoptosis is only triggered when aberrant cells within an epithelium are surrounded by wild type cells [26] . Taken together , these data prompted us to examine a potential role for JNK signaling in the apoptosis and epithelial disruption phenotypes resulting from localized expression of CagA in the wing imaginal disc . Several aspects of the apoptosis phenotype caused by CagA expression in the wing imaginal disc suggested an interaction between CagA and the JNK pathway . In order to determine the nature of this potential interaction , we examined the effects of expressing several forms of Bsk , the Drosophila homolog of JNK , on the CagA-induced wing phenotype . Ectopic overexpression of wild type Bsk with the bx-GAL4 dorsal wing driver generated small apoptotic clusters ( Figure 2A ) , indicating that the presence of excess JNK in the wing can phenocopy CagA expression . Furthermore , the cell death phenotype caused by CagA expression in the wing was dramatically enhanced by coexpression with wild type Bsk ( Figure 2B ) . Coexpression of Bsk with CagAEPISA also caused a substantial amount of apoptosis in the wing imaginal disc , suggesting that this interaction is not dependent on phosphorylated CagA ( Figure 2C ) . As expected , expression of a dominant-negative form of Bsk ( BskDN ) alone did not cause apoptosis in the wing imaginal disc ( Figure 2D ) . Significantly , coexpression of BskDN with CagA almost completely suppressed the apoptosis phenotype caused by CagA expression ( Figure 2E ) , indicating that blocking JNK signaling suppresses CagA-dependent cell death in the wing . These data suggest that CagA expression triggers wing imaginal disc apoptosis through JNK pathway activation . We also examined the effects of JNK pathway modulation on the epithelial disruption phenotype caused by CagA expression . Although ectopic overexpression of wild type Bsk with bx-GAL4 caused only a minor adult wing phenotype in the form of extra vein material ( Figure 2F ) , coexpression of Bsk with CagA dramatically enhanced the epithelial disruption phenotype ( Figure 2G ) . Ectopic overexpression of Bsk with CagAEPISA was not sufficient to induce epithelial disruption ( Figure 2H ) . Expression of BskDN also gave rise to only subtle vein defects in an otherwise normal adult wing ( Figure 2I ) . Interestingly , BskDN expression was not able to rescue but instead enhanced the epithelial disruption caused by CagA expression ( Figure 2J ) . One explanation for this apparent contradiction is that blocking JNK signaling prevents the induction of apoptosis that is required to remove aberrant CagA-expressing cells from within the epithelium , which are then allowed to accumulate and lead to a more severe disruption of the adult structure . We tested this hypothesis using the apoptosis inhibitor p35 , a baculovirus-derived suicide substrate for effector caspases . Overexpressing p35 alone with bx-GAL4 did not produce a phenotype ( Figure S2A and S2C ) , while coexpressing p35 with CagA effectively blocked apoptosis but enhanced disruption of the adult wing epithelium ( Figure S2B and S2D ) . This observation is consistent with the inhibition of apoptosis resulting in more severe CagA-dependent adult phenotypes . Enhancement and suppression of CagA-induced apoptosis in the wing imaginal disc was quantified using a method we developed to measure the percentage of the expression domain that is caspase-positive . These quantitative data showed that both the enhancement of CagA-induced apoptosis seen with coexpression of ectopic Bsk , and its suppression upon expression of BskDN were statistically significant ( Figure 2K ) . In order to further examine the genetic interaction between CagA and JNK signaling , we used a lacZ reporter allele of puckered ( puc ) , the main component of a negative feedback loop in the JNK pathway . This construct has been used extensively as a readout for JNK pathway activation in Drosophila tissue using antibody staining for β-galactosidase ( β-gal ) . Expressing CagA in combination with puc-lacZ in the dorsal wing imaginal disc demonstrated that cells adjacent to those undergoing apoptosis are activating JNK signaling ( Figure 2L ) . Upregulation of puc-lacZ correlated with phosphorylation of JNK , verifying that specific activation of JNK signaling results from CagA expression ( Figure S2E ) . These data provide additional evidence that CagA expression activates JNK signaling in the wing imaginal disc epithelium . JNK signaling is activated by a complex set of signals including TNF and loss of epithelial polarity ( Figure 2M ) . To examine the mechanism through which CagA activates JNK signaling , we used the bx-GAL4 driver to express CagA in combination with RNAi-mediated knockdown of known epithelial polarity determinants and examined wing imaginal discs for enhancement of the apoptosis phenotype ( Figure 3A ) . We tested a panel of polarity proteins , many of which caused apoptosis when knocked down in the absence of CagA expression ( Table S1 ) . We chose to target a protein from each of the previously described complexes whose localization and function establish epithelial cell polarity [27] , and to simplify our analysis we selected polarity proteins that did not cause an apoptosis phenotype when knocked down on their own ( Figure S3A–S3C ) . When tested in combination with CagA expression , we found that RNAi-mediated knockdown of neither the junctional protein Bazooka ( Baz ) , nor the apical protein Crumbs ( Crb ) enhanced apoptosis ( Figure S3D and S3E ) . In addition , knockdown of Par1 , which has been shown to interact with CagA in tissue culture cells [28] , did not enhance the apoptosis phenotype caused by CagA expression in this context ( Figure S3F ) . Interestingly , RNAi-mediated knockdown of the basolateral protein Discs Large ( Dlg ) did not cause a significant phenotype ( Figure 3B ) but markedly enhanced the apoptosis caused by CagA expression ( Figure 3C ) . The same effect was seen with knockdown of Lethal Giant Larvae ( Lgl ) , another basolateral protein ( Figure S3G and S3H ) . The genes encoding these polarity proteins are known as neoplastic tumor suppressor genes ( nTSGs ) because their loss causes tumor formation in Drosophila [29] , and generating clones of cells which lack this specific class of polarity determinants has been shown to trigger JNK-dependent apoptosis in imaginal discs [30] . Our data suggest that nTSGs normally suppress CagA-mediated JNK pathway activation and subsequent apoptosis in the wing imaginal disc . Disruption of the nTSGs activates JNK signaling through endocytosis of the TNF homolog Egr [31] . Homozygous egr mutant animals are viable and , as expected , no apoptosis was observed in their wing imaginal discs ( Figure S3I ) . Conversely , ectopic overexpression of wild type Egr in the dorsal wing imaginal disc caused a severe apoptosis phenotype ( Figure S3J ) , consistent with previous data showing Egr to be a potent activator of cell death in Drosophila epithelia [32] . We made the unexpected observation that expression of CagA in the dorsal wing disc of an egr mutant animal enhanced the apoptosis phenotype ( Figure 3D ) . Interestingly , RNAi-mediated knockdown of Egr alone in the dorsal wing with bx-GAL4 did not cause a phenotype ( Figure S3K ) or enhance apoptosis when coexpressed with CagA ( Figure S3L ) . This observation suggests that loss of Egr in wild type cells surrounding the CagA expression domain is responsible for the enhanced apoptosis phenotype seen in the wing imaginal discs of egr mutant animals expressing CagA . Recent data has demonstrated that loss of nTSGs in clones of imaginal disc cells causes Egr-dependent activation of nonapoptotic JNK signaling in their wild type neighbors . JNK activation in surrounding wild type cells leads to induction of a phagocytic pathway which triggers engulfment of polarity-deficient cells within the clone [30] . A similar mechanism can be invoked to explain the enhancement of CagA-induced apoptosis seen in egr mutant wing imaginal discs . Loss of Egr in the wild type cells surrounding the expression domain may prevent engulfment of CagA-expressing cells . This would increase the number of aberrant cells available to undergo apoptosis upon CagA-mediated activation of JNK signaling via another parallel upstream pathway . We hypothesize that multiple cellular consequences of CagA expression can activate JNK signaling combinatorially . Supporting this view , we demonstrated that CagA-induced apoptosis was enhanced by ectopic overexpression with a wild type form of the small GTPase Rho1 ( Figure 3E ) , another upstream activator of the JNK pathway that did not cause a phenotype when overexpressed alone ( Figure S3M ) , and which our group has shown is activated by CagA [9] . Enhancement of CagA-induced apoptosis in the wing imaginal disc was quantified using the previously described method . These data showed significant enhancement of apoptosis with coexpression of CagA and knockdown of nTSGs , ubiquitous loss of Egr or overexpression of Rho1 . Knockdown of several other polarity proteins or Egr in CagA-expressing cells did not enhance the apoptosis phenotype ( Figure 3F ) . Overexpression of Rho1 , ubiquitous or localized loss of Egr and knockdown of the other polarity proteins alone did not induce significant apoptosis in the wing imaginal disc ( Figure S3N ) . These observations suggest that specific polarity protein complexes within the cell , as well as other upstream activators are responsible for transducing the signals that lead to JNK pathway activation upon CagA expression in the wing imaginal disc ( Figure 3G ) . The finding that CagA activates the JNK pathway is intriguing in light of recent evidence indicating that activation of JNK signaling can switch from proapoptotic to progrowth in the presence of oncogenic Ras [19] . In order to examine a potential role for CagA-mediated JNK pathway activation in promoting tumorigenesis , we used a slight variation of a previously established Drosophila metastasis model to create whole eye clones expressing an activated form of the Ras oncogene ( RasV12 ) in epithelial cells of the eye imaginal disc using the eyeless ( ey ) driver with the FLP/FRT system to generate primary tumors [33] . We then evaluated the size of GFP-marked tumors in whole larvae ( Figure 4A ) and dissected cephalic complexes ( Figure 4B ) in order to determine whether coexpression of CagA could enhance the growth and invasive potential of these tumor cells through activation of the JNK signaling pathway . Expression of RasV12 alone in whole eye clones caused overgrowth of eye imaginal disc cells which resulted in tumor formation ( Figure 4C ) , as previously described [34] . Although generating whole eye clones expressing either GFP alone ( Figure S4A ) or with CagA ( Figure S4B ) was not tumorigenic , coexpression of CagA enhanced the growth of tumors generated by RasV12 expression ( Figure 4D ) . Whole eye clones expressing CagAEPISA were also not tumorigenic ( Figure S4C ) , and when combined with RasV12 expression caused only a minor enhancement of tumor growth ( Figure 4E ) . As expected , coexpression of BskDN did not affect the growth of tumors generated by RasV12 expression alone ( Figure 4F ) . However , BskDN expression caused a severe reduction in the growth of tumors expressing both RasV12 and CagA ( Figure 4G ) . Quantification of these data was accomplished by determining the size of dissected cephalic complexes of each genotype and showed a significant growth enhancement with combined expression of RasV12 and CagA , which was suppressed by coexpression of BskDN ( Figure 4H ) . These data demonstrate that expression of CagA can enhance the growth of tumors generated by expression of RasV12 in a JNK-dependent manner . Generating whole eye clones that express RasV12 alone most commonly caused either a mildly invasive phenotype characterized by the migration of a small number of GFP-positive cells along one edge of the ventral nerve cord ( VNC ) , or a noninvasive phenotype in which cells within the optic lobe approached but did not migrate into the VNC ( Figure 5A ) . Whole eye clones expressing either GFP alone ( Figure S5A ) or with CagA ( Figure S5B ) were not invasive , but coexpression of CagA with RasV12 resulted in a much larger number of GFP-positive tumor cells migrating from both optic lobes into the VNC ( Figure 5B ) . These cells were not terminally differentiated , as indicated by a lack of staining with the neuron-specific ElaV antibody , and phalloidin staining showed a morphology distinct from other cells in the VNC ( Figure S5D ) . Expressing CagAEPISA in whole eye clones also did not produce an invasive phenotype ( Figure S5C ) , and coexpression of CagAEPISA with RasV12 caused a less pronounced enhancement of the mild invasion caused by expression of RasV12 alone ( Figure 5C ) , suggesting that the phosphorylation-resistant form of CagA is less effective at promoting tumor progression . Coexpression of BskDN did not affect the invasive phenotype generated by RasV12 expression alone ( Figure 5D ) , but BskDN expression caused a dramatic reduction in the invasive capacity of tumors expressing both RasV12 and CagA ( Figure 5E ) . These data show that CagA expression can enhance the invasion of RasV12-expressing tumor cells through JNK activation . In order to determine the significance of CagA's enhancement of invasion , we used a previously described method [35] to categorize invasive phenotypes into four distinct classes which represent a progression from non-invasive to severe invasion of the VNC ( Figure 5F ) . Quantitation of the percentage of cephalic complexes exhibiting each class of VNC invasion showed a significant difference between expression of RasV12 alone and in combination with CagA , which was suppressed by coexpression of BskDN ( Figure 5G ) .
Infection of tissue culture cells with H . pylori has been shown to activate JNK signaling , but a role for CagA in this process remains controversial [36]–[38] . Additionally , these experiments were performed in nonpolar AGS cells , so if polarity disruption plays a role in JNK pathway activation downstream of CagA , as our data suggest , these cell culture models may not reveal this interaction . JNK pathway activation has also been shown to result from infection with several pathogenic bacteria in epithelial cell culture models of infection [39] . Interestingly , the enteroinvasive bacterium Shigella flexneri was shown to activate JNK and upregulate TNFα expression in both infected and adjacent uninfected epithelial cells in culture [40] , similar to our data showing that JNK-mediated tissue responses to CagA expression involve a cell-nonautonomous requirement for TNF/Egr . The distribution of H . pylori during infection of the gastric epithelium is known to be heterogeneous [2] . We therefore hypothesize that interactions between cells containing CagA protein and uninfected neighboring cells could also be important for pathogenesis of H . pylori . Our data suggest that CagA is an important mediator of JNK pathway activation during H . pylori infection , and identify several host proteins involved in this process . We observe genetic interaction between CagA and nTSGs , but not junctional proteins involved in polarity . This is consistent with recent data from tissue culture cells which demonstrated that CagA-positive strains of H . pylori specifically disrupt apicobasal polarity in a polarized monolayer prior to affecting the integrity of cellular junctions [11] . Disruption of nTSGs has been shown to cause JNK-dependent apoptosis , and more recent data indicates that elimination of polarity-deficient cells is dependent on their location within the wing imaginal disc due to varying levels of dMyc throughout the tissue [41] . The extent of aberrant cell removal differs significantly with respect to established gradients of Wnt/Wingless , dMyc and Hippo-Salvador-Warts pathway activation that ensure proper development of the wing [42] . We propose that the extent of variation observed upon CagA expression in the wing with different GAL4 drivers is due to spatial variation in these host cell signaling pathways . Our data also suggest that CagA can activate JNK-dependent apoptosis through multiple upstream pathways . The observation that overexpression of Rho1 enhances CagA-dependent apoptosis in the wing imaginal disc epithelium is consistent with previous data from our group demonstrating a role for CagA in activating the Rho pathway to disrupt epithelial patterning [9] . Use of the unique genetic tools available in Drosophila has provided important insight into potential interactions between CagA-expressing cells and neighboring wild type cells . Our observation that loss of TNF/Egr in wild type cells surrounding those expressing CagA can enhance apoptosis , presumably by reducing engulfment of CagA-expressing cells , indicates that the genetic state of uninfected cells may also play a role in H . pylori pathogenesis . This finding is important with respect to the established function of TNF/Egr-dependent JNK activation in cell competition induced by intrinsic tumor suppression . Our data suggest that the presence of CagA protein induces changes in signaling and morphology which cause an epithelial cell to be outcompeted by its wild type neighbors through a local mechanism that requires TNF/Egr in the neighboring epithelial cells . Interestingly , Drosophila immune cells known as hemocytes have also demonstrated the ability to remove polarity-deficient cells from an epithelium through a more global extrinsic tumor suppression mechanism that is TNF/Egr-dependent [43] . Although we have not explored a role for hemocytes in removal of CagA-expressing wing epithelial cells , it is possible that a related mechanism may occur during H . pylori infection of the human stomach through immune surveillance mediated by TNF . Although this specific cytokine is an important component of the initial immune response to infection with a pathogen , TNF is also known to promote tumor progression specifically in the context of chronic inflammation or in the presence of activated Ras [43] , [44] . We hypothesize that TNF functions to suppress tumor initiation resulting from the presence of CagA protein in gastric epithelial cells through several mechanisms , but that the inflammatory environment created by prolonged infection with H . pylori and the emergence of oncogenic mutations over time cause TNF to promote progression of gastric cancer . Since it was first discovered , JNK has been demonstrated to have both pro-tumorigenic and tumor suppressor functions in different cell types and organs . Studies in Drosophila have helped shed light on the genetic contexts in which JNK activation functions to promote tumor progression , namely in the presence of oncogenic Ras [45] . Recently , JNK was shown to be required for activated KRas-induced lung tumor formation in mice [46] , suggesting a conserved function of JNK activation in cooperating with activated Ras to promote tumorigenesis in mammals . A potential role for JNK pathway activation has also been explored in mammalian gastric cancer . Activation of JNK signaling has been detected in human gastric cancer samples , and mice lacking JNK1 exhibit a decrease in gastric apoptosis and an attenuation of gastric tumor development induced by the chemical carcinogen N-methyl-N-nitrosourea [47] . A role for H . pylori in the context of mammalian gastric cancers induced by cooperation between JNK and Ras signaling has not been explored . Our finding that CagA expression can induce JNK-dependent apoptosis in a polarized epithelium is interesting with respect to data suggesting that JNK signaling has evolved as a cell editing mechanism to remove aberrant cells from within an epithelium [18] . Activation of JNK signaling could represent a host response aimed at removing cells containing CagA protein from the gastric epithelium . Similarly , P . aeruginosa-mediated activation of JNK signaling in the intestinal epithelium of Drosophila can trigger epithelial renewal as a host defense mechanism . However , this process can become pathogenic and lead to dramatic overproliferation of intestinal cells in animals harboring oncogenic Ras mutations [21] . In H . pylori infection , which can persist for many years before the development of gastric cancer , JNK-mediated apoptosis could be an effective mechanism to limit pathogenic effects on the gastric epithelium . However , this process of tissue editing can also increase cell turnover , contributing to accumulation of genetic mutations in host cells . Our data show that acquisition of an oncogenic mutation in host epithelial cells experiencing CagA-mediated JNK pathway activation can promote tumor progression , suggesting that this potential host defense strategy can become tumorigenic in certain genetic contexts ( Figure 6B ) . Transgenic expression of CagA was recently found to cause neoplastic transformation in a mouse model , providing evidence for CagA's role as a bacterial oncoprotein in mammals [48] . The low incidence and delayed development of gastrointestinal tumors in these mice was attributed to lower expression of CagA in the surviving animals , as higher expression was assumed to be lethal during embryogenesis . Additionally , secondary mutations were identified in the tumors , but their potential cooperation with host cell signaling pathways activated by CagA expression was not addressed [48] . Infection with CagA-positive H . pylori is also known to induce an invasive phenotype in tissue culture cells [49] , but potential effects of the oncogenic mutations present in these immortalized cell lines is unknown . Although we did not demonstrate the sufficiency of CagA to induce tumor phenotypes in our Drosophila model , our data support a crucial role for CagA in promoting tumor progression in combination with oncogene activation . We believe that using an inducible expression system in Drosophila allowed us to bypass the toxicity observed upon CagA expression in mice and cell culture models , thus revealing novel interactions between CagA and host cell proteins with downstream effects on apoptosis and tumorigenesis . Although half the world's population is thought to be infected with H . pylori , a small percentage of those individuals will develop gastric cancer [2] . This observation indicates that , in addition to the presence of the cag PAI in more virulent strains , host genetics must also play a crucial role in determining the outcome of H . pylori infection . Our results suggest that a change in host genetics during long-term association with H . pylori could cause JNK activation to switch from conferring a protective function against CagA-induced cellular changes to enabling tumor progression . Data collected from tissue biopsies indicate that Ras mutation may play a role in the development of gastric cancer in human patients [50] , and our data put forward the idea that enhanced tumorigenic potential created by cooperation between JNK pathway activation via the bacterial genetic factor CagA and sporadic activation of Ras in host cells could drive gastric cancer formation in a subset of H . pylori infections .
The following fly stocks were used: UAS-CagA , UAS-CagAEPISA [7]; bx-GAL4 , sd-GAL4 , ap-GAL4 , en-GAL4 , ptc-GAL4 , hs-FLP , Act>y+>GAL4 , UAS-GFP . S65T , UAS-mCD8::GFP ( mGFP ) , UAS-bsk . B ( Bsk1 ) , UAS-bsk . A-Y ( Bsk2 ) , UAS-bsk . K53R ( BskDN ) , egrMB06803 ( egr−/− ) , UAS-Dlg-RNAi , UASp-FLAG . Rho1 ( Rho1 ) , UAS-Ras85D . V12 ( RasV12 ) , UAS-Crb-RNAi , UAS-Patj-RNAi , UAS-Cora-RNAi , UAS-Cdc42-RNAi ( from Bloomington Stock Center ) ; 765-GAL4 ( provided by Ross Cagan , Mount Sinai School of Medicine ) ; pucE69 ( puc-lacZ ) , Regg1GS9830 ( UAS-Egr ) ( provided by Michael Galko , MD Anderson Cancer Center ) ; UAS-Lgl-RNAi , UAS-Baz-RNAi , UAS-Par1-RNAi , UAS-Scrib-RNAi , UAS-Par6-RNAi , UAS-aPKC-RNAi , UAS-Mir-RNAi ( provided by Chris Doe , University of Oregon ) ; UAS-Egr-RNAi ( from Vienna Drosophila Resource Center ) ; ey-FLP; Act>y+>GAL4 , UAS-GFP ( provided by Tory Herman , University of Oregon ) . Flies were raised at 25°C using standard methods . Whole eye clones were generated as previously described [33] without the GAL80 repressor to express transgenes in all cells that give rise to the eye-antennal disc . FLP-out clones were generated by subjecting each 4–6 hour collection of embryos to one hour of heat-shock at 37°C , then dissecting wing discs approximately 96–120 hours later . Larval tissues were fixed and stained using standard protocols . The following primary antibodies were used: rabbit anti-active caspase-3 ( 1∶200; BD Pharmingen ) , mouse anti-Mmp1 ( 1∶50; Developmental Studies Hybridoma Bank ) , mouse anti-β-galatosidase ( 1∶500; Sigma ) rat anti-ElaV ( 1∶10; Developmental Studies Hybridoma Bank ) , rabbit anti-β-galatosidase ( 1∶200; MP Biomedicals ) and mouse anti-phospho-SAPK/JNK ( 1∶100; Cell Signaling Technology ) . Both Cy3 and Cy5-conjugated secondary antibodies were used ( 1∶200; Jackson ImmunoResearch ) , as well as Alexa Fluor 546 and Alexa Fluor 633 phalloidin ( 1∶40; Molecular Probes ) . Intact adult wings were mounted in a 1∶1 mixture of lactic acid and ethanol . Adult wings , intact larvae and whole cephalic complexes were visualized using light microscopy or GFP fluorescence on a Zeiss dissecting microscope . Wing imaginal discs , ventral nerve cords and cephalic complexes were visualized on a Nikon confocal microscope . Images were processed using Adobe Photoshop , where levels were adjusted to optimize the signal-to-noise ratio in each color channel while maintaining similar levels of background noise and desired signal between channels and images . Adult wing images were removed from their background using the Extract filter in Adobe Photoshop . XZ confocal planes were created using the Reslice function in Image J . Projections of confocal cross sections were created using the Merge to HDR command in Adobe Photoshop . Apoptosis was quantified by selecting the single confocal cross section of each wing imaginal disc exhibiting the highest level of active caspase-3 staining and manually tracing the expression domain , then determining the percentage of this domain showing active caspase-3 staining using the Threshold function in Image J . Cephalic complex size was quantified using the Threshold function in Image J to determine the area of the tissue in µm2 . Graphs were created with GraphPad Prism software , which was also used to calculate two-tailed p values using the unpaired t test with Welch's correction for apoptosis quantitation . The statistical significance of differences in metastatic potential for each genotype was calculated using Excel to determine two-tailed p values using the unpaired t test . | The gastric pathogen Helicobacter pylori infects an estimated 50% of the world's population and is a major risk factor for the development of gastric cancer . Strains of H . pylori that can inject the CagA effector protein into host cells are known to be more virulent , but the potential contributions of host genetics to pathogenesis are not well-understood . Using transgenic Drosophila melanogaster , we show that the genetic context of both the host cells in which CagA is expressed and their neighboring cells changes CagA's effects on epithelial tissue . When CagA is expressed in a subset of cells within an epithelium , it disrupts tissue integrity and induces apoptosis through activation of JNK signaling , a pathway that functions to remove aberrant cells from an epithelium . CagA's proapoptotic effects are inhibited by neoplastic tumor suppressor genes in CagA-expressing cells , and by the tumor necrosis factor homolog Eiger in neighboring cells . In contrast , when CagA is coexpressed with oncogenic Ras in a Drosophila model of metastasis , it enhances the growth and invasion of tumors in a JNK-dependent manner . Our study demonstrates how changes in host genetics can cooperate with activation of JNK signaling by the bacterial virulence factor CagA to promote tumorigenesis . |
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HIV-1 is internalized into mature dendritic cells ( mDCs ) via an as yet undefined mechanism with subsequent transfer of stored , infectious virus to CD4+ T lymphocytes . Thus , HIV-1 subverts a DC antigen capture mechanism to promote viral spread . Here , we show that gangliosides in the HIV-1 membrane are the key molecules for mDC uptake . HIV-1 virus-like particles and liposomes mimicking the HIV-1 lipid composition were shown to use a common internalization pathway and the same trafficking route within mDCs . Hence , these results demonstrate that gangliosides can act as viral attachment factors , in addition to their well known function as cellular receptors for certain viruses . Furthermore , the sialyllactose molecule present in specific gangliosides was identified as the determinant moiety for mDC HIV-1 uptake . Thus , sialyllactose represents a novel molecular recognition pattern for mDC capture , and may be crucial both for antigen presentation leading to immunity against pathogens and for succumbing to subversion by HIV-1 .
Dendritic cells ( DCs ) are the most potent antigen-presenting cells found in the organism and play a paramount role in initiating immune responses to assaulting pathogens [1] , [2] . DCs that patrol the mucosal tissue display an immature phenotype and are able to capture incoming pathogens , which leads to DC activation , maturation , and migration to the secondary lymphoid tissue , where DCs acquire the mature phenotype required to efficiently induce adaptive immune responses [1] . Given the unique role of DCs in initiating primary immune responses [1] , it is generally believed that these antigen-presenting cells are critical to induce resistance to infection [2]–[4] . In the specific case of viral infections , murine DC depletion models have provided in vivo evidence of the DC requirement to induce both humoral and cellular antiviral immune responses [5] , [6] . However , some viruses , including HIV-1 , have evolved strategies to subvert DC antiviral activity [7] , [8] . DCs can even promote HIV-l dissemination , both through the direct release of new virus particles after productive infection , and through transmission of captured viruses to susceptible T cells without DC infection , a process known as trans-infection ( reviewed in [9] ) . Direct infection and trans-infection occur to a different extent in immature DCs ( iDCs ) and mature DCs ( mDCs ) ( reviewed in [9] , [10] ) . The initial Trojan horse hypothesis suggested that HIV-1 capture by iDCs in the mucosa may protect the virus from degradation and allow its transport to secondary lymphoid organs , facilitating trans-infection of CD4+ T cells and fueling viral spread [11] , [12] . However , iDCs show rapid degradation of captured viral particles [13]– , and several lines of evidence suggest now that the long-term ability of iDCs to transfer HIV-1 relies on de novo production of viral particles after productive infection [14] , [16] , [17] . HIV-1 replication in DCs is generally less productive than in CD4+ T cells ( reviewed in [9] , [18] ) , however , probably due to the presence of cellular restriction factors such as SAMHD1 that limit reverse transcription following viral entry [19] , [20] . Maturation of DCs further reduces their ability to support HIV-1 replication [21]–[24] , but potently enhances their capacity to trans-infect bystander CD4+ T cells [15] , [21] , [25]–[27] . Trans-infection occurs via the infectious synapse , a cell-to-cell contact zone that facilitates transmission of HIV-1 by locally concentrating virus and viral receptors [26] , [28] . Strikingly , poorly macropinocytic mDCs [29] sequester significantly more complete , structurally intact virions into large vesicles than actively endocytic iDCs [30] , and retain greater amounts of virus 48 h post-pulse than iDCs immediately after viral wash [15] . Thus , enhanced mDC trans-infection correlates with increased HIV-1 capture and a longer life span of trapped viruses [15] , [27] . Furthermore , mDCs efficiently interact with CD4+ T cells in lymphoid tissues; key sites of viral replication , where naïve CD4+ T cells are activated and turn highly susceptible to HIV-1 infection [31] . Accordingly , carriage of HIV-1 by mDCs could facilitate the loss of antigen-specific CD4+ T cells [32] , [33] , favoring HIV-1 pathogenesis . However , the molecular mechanism underlying HIV-1 uptake by mDCs remains largely uncharacterized . We have previously identified an HIV-1 gp120-independent mechanism of viral binding and uptake that is upregulated upon DC maturation [15] . Furthermore , HIV-1 Gag enhanced green fluorescent protein ( eGFP ) –expressing fluorescent virus-like particles ( VLPHIV-Gag-eGFP ) follow the same trafficking route as wild-type HIV-1 in mDCs [34] , and hence share a common molecular pattern that governs entry into mDCs . In addition , we also reported that treatment of HIV-1 or VLP producer cells with inhibitors of glycosphingolipid biosynthesis yielded particles with less glycosphingolipids , which exhibited reduced entry into mDCs [34] , [35] , without affecting net release from producer cells [36] . Thus , we hypothesize that gangliosides in the outer monolayer of HIV-1 and VLP membranes could act as viral attachment factors and allow viral recognition and capture by mDCs . Here we sought to investigate the molecular determinants involved in viral binding and internalization mediated by mDCs . Using liposomes to mimic the lipid composition and size of HIV-1 , we demonstrate that gangliosides are the key molecules that mediate liposome uptake . We extended these observations to VLPs and HIV-1 , characterizing a new role for these glycosphingolipids as viral attachment factors . Furthermore , we identify sialyllactose on HIV-1 membrane gangliosides as a novel molecular recognition pattern that mediates virus uptake into mDCs .
Considering that glycosphingolipids are enriched in raft-like plasma membrane domains [37]–[39] from where HIV-1 is thought to bud ( reviewed in [40] ) , we investigated the potential role of glycosphingolipids for HIV-1 capture by mDCs . The ganglioside GM3 was previously identified in the membrane of different retroviruses including HIV-1 [41] , [42] . We were able to confirm the presence of GM3 in HIVNL4-3 derived from the T-cell line MT-4 by mass spectrometry ( Figure 1A ) . In addition , we detected several other gangliosides including GM1 , GM2 , and GD1 in the HIV-1 membrane ( Figure 1A ) . To test whether gangliosides in the outer leaflet of HIV-1 or vesicular membranes can act as attachment factors yielding mDC uptake , we prepared Texas Red ( tRed ) -labeled large unilamellar vesicles ( LUV ) mimicking the size and lipid composition of HIV-1 ( referred to as LUVHIV-tRed and prepared as in [43] ) and containing different gangliosides ( Figure S1 ) . All LUVs displayed equal fluorescence intensities ( Figure S2 ) . mDCs were pulsed with either LUVHIV-tRed or VLPs for 4 h at 37°C and the percentage of fluorescent cells was determined by fluorescence activated cell sorting ( FACS ) . Similar to our previous results with infectious HIV-1 [15] , a high percentage of mDCs captured the fluorescent VLPHIV-Gag-eGFP ( Figure 1B ) . Furthermore , VLPs produced in the CHO cell line , which is only able to synthesize gangliosides up to GM3 [44] , were also efficiently captured by mDCs ( Figure 1C ) . Uptake into mDCs was further observed for the murine retrovirus MuLV ( Figure 1D ) , which was previously shown to also contain gangliosides [41] . On the other hand , no significant uptake into mDCs was observed for LUVHIV-tRed , which contained the main lipid constituents of HIV-1 , but were devoid of gangliosides ( p<0 . 0001 , paired t test ) ( Figures 1B and S3 ) . Uptake into mDCs remained negative for LUVHIV-tRed containing ceramide ( Cer ) ( p<0 . 0001 , paired t test ) ( Figures 1B and S3 ) . This was completely different when monosialogangliosides such as GM3 , GM2 , or GM1a were incorporated into the LUVs; mDCs were able to capture these liposomes with equal efficiency as VLPHIV-Gag-eGFP ( Figures 1B and S3 ) . To ensure that this capture was not merely due to electrostatic interactions between negatively charged gangliosides and surface charges on mDCs , LUVHIV-tRed containing negatively charged phosphatidylserine ( PS ) were analyzed in parallel and were found to be negative for mDC capture ( p = 0 . 0081 , paired t test ) ( Figure 1B ) . These results reveal that monosialogangliosides mediate LUV capture by mDCs , and that the carbohydrate head group is essential for this process . To determine whether ganglioside-containing LUVHIV-tRed and VLPHIV-Gag-eGFP exploit a common entry mechanism into mDCs , we performed competition experiments . mDCs were pulsed with decreasing amounts of GM2-containing LUVHIV-tRed and a constant amount of VLPHIV-Gag-eGFP for 4 h at 37°C . After extensive washing , the percentage of eGFP- and tRed-positive cells was determined by FACS . GM2-containing LUVHIV-tRed efficiently competed for the uptake of VLPHIV-Gag-eGFP into mDCs in a dose-dependent manner ( p<0 . 0001 , paired t test ) ( Figure 1E ) . However , no competition for VLP uptake was observed for LUVHIV-tRed containing Cer or lacking glycosphingolipids ( Figure 1E ) . Hence , GM-containing LUVHIV-tRed and VLPHIV-Gag-eGFPuse a common entry mechanism to gain access into mDCs , which is dependent on the carbohydrate head group . We next investigated whether GM-containing LUVHIV-tRed and VLPHIV-Gag-eGFP reach the same compartment in mDCs using spinning-disc confocal microscopy . We had previously described three types of patterns for HIV-1 captured into mDC: random , polarized , or sac-like compartments [15] , [45] . The same patterns were also observed for GM-containing LUVHIV-tRed and the percentage of mDCs displaying the different patterns was similar regardless of the particle used ( Figure 2A ) . Thus , VLPHIV-Gag-eGFP and GM-containing LUVHIV-tRed not only compete for internalization , but also traffic to an analogous compartment within mDCs . To determine whether VLPHIV-Gag-eGFP and GM-containing LUVHIV-tRed are captured into the same compartment , mDCs were pre-incubated 3 h at 37°C with GM-containing LUVHIV-tRed and subsequently incubated with VLPHIV-Gag-eGFP for three additional hours . Confocal microscopy of fixed cells revealed that GM-containing LUVHIV-tRed and VLPs polarized towards the same cell area in mDCs ( Figure S4 ) . Furthermore , VLPs extensively co-localized with GM-containing LUVHIV-tRed ( including either GM1a , GM2 , or GM3 ) in the same intracellular compartment ( Figure 2B; Video S1 ) . The HIV-1 envelope is a liquid-ordered membrane and gangliosides are presumably enriched in this type of membranes [41] , [43] , [46] , [47] . Moreover , ganglioside interaction with cholesterol in lipid rafts [37] , [38] , [47] is known to influence ganglioside conformation and alter its activity as a cellular receptor [48] . We therefore assessed whether membrane structure or the specific lipid composition ( other than gangliosides ) of the particle membrane influenced mDC capture . mDCs were incubated with LUVPOPC-tRedcomposed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) with or without different gangliosides ( Figure 3A ) . In contrast to LUVHIV-tRed , LUVPOPC-tRed have a liquid-disordered membrane structure [38] . Results for LUVPOPC-tRed were very similar to LUVHIV-tRed with efficient capture if either GM1a , GM2 , or GM3 was present , while no uptake was observed for Cer containing LUVPOPC-tRed or LUVPOPC-tRed lacking gangliosides ( Figure 3A ) . Furthermore , the percentage of mDCs displaying particles in random , polarized or sac-like compartment capture patterns was again very similar for the different particles ( Figure 3B ) . These results show that ganglioside-containing LUVs use the same trafficking pathway as VLPHIV-Gag-eGFP regardless of their membrane structure , indicating that gangliosides themselves are the key molecules responsible for mDC capture . To gain further insight into the molecular determinant structure required for efficient recognition by mDCs , LUVHIV-tRed carrying more complex gangliosides were produced , including two , three , and four sialic acid groups at diverse positions in the carbohydrate polar head group ( di- , tri- , and tetra-sialogangliosides ) ( Figure 4A ) . mDCs pulsed with an equal amount of LUVHIV-tRed-containing gangliosides with two or three sialic acids ( GD1b and GT1b , respectively ) captured these particles with the same efficiency as GM1a-LUVHIV-tRed ( Figure 4A ) . However , capture was almost completely lost for LUVHIV-tRed containing a ganglioside with four sialic acids ( GQ1b ) ( Figure 4A ) . Accordingly , LUVHIV-tRed carrying GD1b or GT1b efficiently competed for mDC uptake with VLPHIV-Gag-eGFP , while no competition was observed for LUVHIV-tRed carrying GQ1b , PS , or Cer ( Figure 4B ) . These results indicate that complex gangliosides with up to three sialic acids located in distinct positions of the carbohydrate head group share a common structure determinant for mDC uptake , which is lost in GQ1b . The lack of internalization of Cer-containing LUVs indicated that the carbohydrate head group is specifically required for mDC capture . Sialic acid has previously been identified as a cellular receptor for certain viruses [49] . We therefore tested its importance for mDC capture . Incubation of mDCs with equal concentrations of LUVHIV-tRed containing Cer , GM1a , or GM1 without the sialic acid group ( Asialo GM1 ) revealed sialic acid-dependent capture ( Figure 5A ) . In addition , in situ neuraminidase treatment of GM3-containing LUVHIV-tRed and VLPHIV-Gag-eGFP , significantly reduced particle capture ( Figure 5B ) and LUV binding to mDCs ( Figure S5 ) . Thus , the sialic acid moiety in gangliosides is necessary for specific recognition by mDCs . To assess the contribution of other components of the carbohydrate head group , we prepared LUVHIV-tRed containing either GM4 ( lacking the glucose moiety of GM3 ) ( Figure 5C ) or GalCer ( lacking both the glucose and sialic acid moieties of GM3 ) ( Figure 5C ) . mDCs incubated with GM4- or GalCer-containing LUVHIV-tRed showed only background levels of liposome capture ( Figure 5C ) , indicating that the glucose moiety of sphingolipids is also necessary for mDC capture . Given that the carbohydrate moiety within gangliosides constitutes the molecular recognition determinant for mDC capture , these head groups should compete for VLP and LUV uptake . Capture of GM3-containing LUVHIV-tRed or VLPHIV-Gag-eGFP by mDCs was completely blocked in the presence of the GM3 polar head group ( sialyllactose ) , while equal concentrations of lactose ( lacking the sialic acid group ) had no effect ( Figure 5D ) . Taken together , these data clearly show that the sialyllactose moiety of gangliosides is the molecular determinant required for efficient VLP and LUV recognition and capture by mDCs . Noteworthy , the high concentrations of the GM3 head group required for competition in Figure 5D compared to the low concentrations of gangliosides in LUVs needed to outcompete VLPs ( ∼1 , 000-fold less ) ( Figure 1C ) suggests that the attachment of sialyllactose to Cer within membranes confers a higher binding avidity . This is not surprising since viruses and toxins are multivalent: for instance , the VP1 capsid protein of SV40 is pentameric and the cholera toxin is pentavalent; both bind five GM1 molecules [50]–[52] . In addition , the hydrophilic moiety of Cer in the membrane interface could be part of the recognition domain , directly increasing the binding affinity to mDCs . Modeling of the 3-D structure of gangliosides ( Figure S6 ) suggested that mDC recognition required an exposed sialyllactose domain and that the lack of recognition of GQ1b could be caused by steric hindrance ( Figure S6 ) . Thus , although the lipid and protein context of surrounding membranes might influence the biologically active conformation of gangliosides [48] , [53]; 3-D reconstructions provide a structural basis to explain the distinct recognition patterns observed for different gangliosides . The sialyllactose recognition domain defined in this study differs from the NeuAc α 2 , 3Gal β 1 , 3GalNAc on gangliosides identified as host cell receptors for SV40 and Sendai virus , and for cholera and tetanus toxin [50] , [52] , [54] , but is identical with the α 2 , 3-sialyllactose identified as host cell receptor for other paramyxoviruses [55] , [56] . To determine whether the results obtained with LUVs and VLPs also hold true for the authentic virus , we performed experiments with wild-type HIVNL4-3 produced in primary T cells . Similar to our previous data [15] , a high percentage of mDCs captured HIV-1 , while uptake into iDCs was much less efficient ( p = 0 . 0047 , one sample t test ) ( Figure 6A ) . To confirm the importance of viral gangliosides for mDC capture , we purified HIVNL4-3 from primary CD4+ T cells pre-treated or not with the glycosphingolipid biosynthesis inhibitor NB-DNJ . HIV-1 capture was strongly reduced for virus obtained from inhibitor-treated cells compared to control virus ( p<0 . 0001 , one sample t test ) ( Figure 6B ) . To directly determine the importance of the sialyllactose head group for mDC capture of authentic HIV-1 , we performed competition experiments showing a strong reduction of virus capture in the presence of the GM3 polar head group , but not in the presence of lactose ( p<0 . 0001 , one sample t test ) ( Figure 6C ) . These data confirm the observations obtained with liposomes and VLPs for authentic HIV-1 from primary CD4+ T cells . HIV-1 capture by mDCs has been shown to promote trans-infection of CD4+ T cells and other target cells [15] , [21] , [25]–[27] , and we therefore analyzed whether sialyllactose recognition by mDCs is also important for viral transmission . Co-culturing mDCs that had been exposed to an equivalent amount of infectious HIVNL4-3 derived from NB-DNJ-treated or untreated CD4+ T cells with the TZM-bl reporter cell line revealed a strong reduction of trans-infection for the virus from inhibitor-treated cells compared to control virus ( p = 0 . 0404 , paired t test ) ( Figure 6D ) . We also observed a strong reduction of trans-infection for mDCs pulsed with HIVNL4-3 in the presence of the GM3 polar head group and subsequently incubated with TZM-bl cells ( Figure 6E ) ( p = 0 . 0197 , paired t test ) . These results were further confirmed when we co-cultured HIV-1-pulsed mDCs with activated primary CD4+ T cells . Co-cultures were performed in the presence or absence of the protease inhibitor saquinavir to distinguish net trans-infection from re-infection events ( Figure 6F and 6G; left and right panels , respectively ) . Infection of primary CD4+ T cells was strongly enhanced when they were co-cultured with HIV-1 pulsed mDCs ( Figure 6F and 6G; filled bars ) . This effect was abrogated when mDCs were pulsed with virus produced from NB-DNJ-treated cells ( Figure 6F ) or cultured with the GM3 polar head group ( Figure 6G ) . These data indicate that the sialyllactose moiety of gangliosides is the molecular determinant required for efficient HIV-1 capture by mDCs and for subsequent viral trans-infection .
Sialic acid on gangliosides has previously been shown to function as host cell receptor for several viruses [54] , [57] , [58] and for human toxins [52] . The current study clearly identifies a novel role for sialylated gangliosides in the membrane of viruses or LUVs as determinants for specific capture by mDCs . This recognition is reminiscent of the engulfment of apoptotic cells by phagocytes such as DCs , which is triggered by PS , a phospholipid normally found in the inner leaflet of the plasma membrane of living cells , but exposed on the surface of dying cells [59] . However , the viral capture described here is dependent on an exposed sialyllactose moiety on gangliosides , which we identified as a novel molecular recognition pattern . The ganglioside GM3 was previously detected in the membrane of HIV-1 and several other viruses ( SFV , VSV , MuLV ) [41] , [42] , and this was extended to GM1 , GM2 , and GD1 for HIV-1 in the present study . Gangliosides are significant components of the plasma membrane lipidome [42] , suggesting that all enveloped viruses , which bud from the plasma membrane of infected cells , may be captured into mDCs by the reported mechanism unless they exclude sialyllactose-containing gangliosides . Most studies of viral lipidomes have not included gangliosides so far , however , and it will be important to determine whether certain viruses have developed mechanisms to prevent sialyllactose presentation on their membrane lipids . This would be conceivable for influenza virus , which carries a viral neuraminidase needed for removal of the sialic acid receptor from the producer cell surface , thus allowing virus release . This neuraminidase may also remove sialic acid groups from viral gangliosides , thus preventing mDC uptake and potential antigen presentation . Consequently , neuraminidase inhibitor treatment should lead to increased DC capture and potentially enhance immunogenicity of influenza virus or VLPs . Moreover , viral ganglioside content may vary depending on the membrane composition of the producer cell . Viral replication in the nervous system , where gangliosides are particularly enriched [60] , may thus lead to the insertion of an increased amount of distinct gangliosides into virions , affecting mDC recognition and local immunosurveillance . The potential immunological role of mDC uptake implies efficient antigen capture and processing throughout the antigen presentation pathway . Interestingly , antigen-bearing cellular secreted vesicles known as exosomes also follow the same trafficking route as HIV-1 [34] and contain gangliosides such as GM3 or GM1 [61] . Hence , sialyllactose-carrying gangliosides in the membrane of viruses and cellular vesicles are targeting molecules for mDC uptake , a pathway that may normally lead to antigen processing and presentation and has been subverted by HIV-1 for infectious virus storage and transmission . Furthermore , although downregulation of endocytosis is considered a hallmark of DC maturation ( reviewed in [3] ) , there is increasing evidence that under inflammatory conditions mDCs capture , process , and present antigens without exclusively relying on prior pathogen exposure [62] , [63] . This scenario might be particularly relevant in chronic infections , such as the one caused by HIV-1 , where translocation of bacteria from the intestinal lumen [64] could stimulate DCs systemically and contribute to sustained antiviral immune responses . Remarkably , HIV-1 infected patients show enhanced GM3 content in the plasma membrane of T lymphocytes and high titers of anti-lymphocytic GM3 antibodies [65] , [66] . Paradoxically , HIV-1 capture into mDCs appears to also critically enhance viral dissemination in lymphoid tissue by efficient release of infectious virus to CD4+ T cells in the DC-T-cell synapse , thus promoting pathogenesis and disease progression through trans-infection . Hence , although myeloid cells are largely refractory to productive HIV-1 infection due to the presence of cellular restriction factors such as the recently identified SAMHD1 [19] , [20] , the sialyllactose driven trans-infection process characterized here in mDCs seems to exploit a pre-existing cellular-trafficking machinery that avoids the activation of these intrinsic immune pathways . The efficient capture of ganglioside-carrying cellular vesicles or virions suggests a model where a specific receptor present on the cell surface of mDCs ( and possibly other cells ) recognizes the sialyllactose moiety on virions or vesicle membranes . Gangliosides have been reported to function as cell adhesion molecules [67] , and this may also involve such a receptor . Specific recognition of vesicular gangliosides would then trigger uptake of the respective particles into an intracellular compartment from where they are either recycled to the surface ( as in HIV-1 transmission to CD4+ T cells ) or fed into the antigen presentation pathway . The results of the current study identify sialyllactose on membrane gangliosides as the relevant molecular recognition pattern , explaining the specificity of this process and providing the basis for its future exploitation for interventional or vaccine purposes .
The institutional review board on biomedical research from Hospital Germans Trias i Pujol approved this study . MT-4 cells were infected with HIVNL4-3 and co-cultured with uninfected cells . Virus was harvested before cytopathic effects were observed , and purified essentially as described in [43] . Briefly , medium was cleared by filtration , and particles were concentrated by ultracentrifugation through a cushion of 20% ( w/w ) sucrose . Concentrated HIV-1 was further purified by velocity gradient centrifugation on an OptiPrep gradient ( Axis-Shield ) . The visible virus fraction was collected and concentrated by centrifugation . The final pellet was resuspended in 10 mM Hepes , 150 mM NaCl , pH 7 . 4 ( hepes-sodium buffer ) , rapidly frozen in liquid nitrogen and stored at −80°C . For lipid composition analysis , samples were resuspended in methanol upon thawing and then assessed in a UPLC coupled to an orthogonal acceleration time-of-flight mass spectrometer with an electrospray ionization interface ( LCT Premier; Waters ) using the procedure previously described in [68] . Data were acquired using positive ionization mode over a mass range of m/z 50–1 , 500 in W-mode . A scan time of 0 . 15 s and interscan delay of 0 . 01 s were used at a nominal instrument resolution of 11 , 500 ( FWHM ) . Leucine enkephalin was used as the lock spray calibrant . Peripheral blood mononuclear cells ( PBMCs ) were obtained from HIV-1-seronegative donors and monocyte populations ( >97% CD14+ ) were isolated with CD14+-positive selection magnetic beads ( Miltenyi Biotec ) . DCs were obtained culturing these cells in the presence of 1 , 000 IU/ml of granulocyte-macrophage colony-stimulating factor ( GM-CSF ) and interleukin-4 ( IL-4; R&D ) . mDCs were differentiated by culturing iDCs at day five for two more days in the presence of 100 ng/ml of lipopolysaccharide ( LPS; Sigma ) . DCs were immunophenotyped at day 7 as previously described [15] . Adequate differentiation from monocytes to iDCs was based on the loss of CD14 and the acquisition of DC-SIGN , while DC maturation upregulated the expression of CD83 , CD86 , and HLA-DR . CD4+ T lymphocytes required for trans-infection experiments were isolated from PBMCs with CD4-negative selection magnetic beads ( Miltenyi Biotec ) and stimulated for 72 h in the presence of 10 IU/ml of IL-2 ( Roche ) and 3 µg/ml of phytohaemagglutinin ( PHA; SigmaAldrich ) . Primary cells were maintained in RPMI with 10% fetal bovine serum ( FBS ) , 100 IU/ml of penicillin and 100 µg/ml of streptomycin ( all from Invitrogen ) . HEK-293T and TZM-bl ( obtained through the US National Institutes of Health [NIH] AIDS Research and Reference Reagent Program , from JC Kappes , X Wu , and Tranzyme Inc . ) were maintained in D-MEM ( Invitrogen ) . CHO cell line was maintained in α-MEM . MT4 cell line was maintained in RPMI . All media contained 10% FBS , 100 IU/ml of penicillin , and 100 µg/ml of streptomycin . VLPHIV-Gag-eGFP were obtained transfecting the molecular clone pGag-eGFP ( obtained through the NIH AIDS Research and Reference Reagent Program , from MD Resh ) . VLPMLV-Gag-YFP were obtained transfecting the molecular clone pGag-MLV wt [69] . HEK-293T cells were transfected with calcium phosphate ( CalPhos , Clontech ) in T75 flasks using 30 µg of plasmid DNA . CHO cells were electroporated ( 0 . 24 Kv and 950 µF ) using 7×106 cells and 40 µg of plasmid DNA . Supernatants containing VLPs were filtered ( Millex HV , 0 . 45 µm; Millipore ) and frozen at −80°C until use . For studies with concentrated VLPs , medium was harvested , cleared by filtration , and particles were concentrated by ultracentrifugation ( 28 , 000 rpm 2 h at 4°C in SW32 rotor ) through 20% ( w/w ) sucrose . The final pellet was resuspended in hepes-sodium buffer , rapidly frozen in liquid nitrogen , and stored at −80°C . The p24Gag content of the MT4-derived viral stock and VLPHIV-Gag-eGFPwas determined by an ELISA ( Perkin-Elmer ) and by a quantitative Western blot . Detection was carried out with a LiCoR Odyssey system employing our own Rabbit anti-capsid polyclonal antibody and a purified Gag protein ( kindly provided by J Mak ) as a standard . To produce HIVNL4-3 in PBMCs , cells were stimulated with 3 µg/ml PHA and 10 IU/ml of IL-2 for 72 h prior to infection with HIVNL4-3 . To generate HIVNL4-3 in CD4+ T cells , whole blood from three HIV-1–seronegative donors were CD8+ T cell depleted using Rossettesep anti-CD8+ cocktail ( Stem cell ) . Enriched CD4+ T cells were pooled and stimulated under three different conditions: low-dose PHA ( 0 . 5 µg/ml ) , high-dose PHA ( 5 µg/ml ) , or plate-bound anti-CD3 monoclonal antibody OKT3 ( e-Bioscience ) [70] . After 72 h , cells were mixed together , infected with HIVNL4-3 and resuspended to a final concentration of 106 cells/ml in RPM1 with 10% FBS supplemented with 100 IU/ml of IL-2 . To produce glycosphingolipid deficient HIVNL4-3 , enriched CD4+ T cells were kept in the presence or absence of 500 µM of NB-DNJ ( Calbiochem ) and 10 IU/ml of IL-2 for 6 d before stimulation with 3 µg/ml of PHA and subsequent infection with HIVNL4-3 . Virus growth was monitored by p24Gag ELISA ( Perkin Elmer ) . Supernatants were harvested when the concentration of p24Gag was at least 102 ng/ml , filtered and stored at −80°C until use . Titers of all viruses were determined using the TZM-bl indicator cell line as described elsewhere [71] . LUVs were prepared following the extrusion method described in [72] . Lipids were purchased from Avanti Polar Lipids and gangliosides were obtained from Santa Cruz Biotechnology . Ganglioside source was bovine brain with the exception of GM4 , which was from human brain . The LUVHIV-tRed lipid composition was: POPC 25 mol%: 1 , 2-dipalmitoyl-sn-glycero-3-phosphocholine ( DPPC ) 16 mol%: brain sphingomyelin ( SM ) 14 mol%: cholesterol ( Chol ) 45 mol% , and when Cer , PS , or gangliosides were present ( 4 mol% ) the SM amount was reduced to 10 mol% . The LUVPOPC-tRed lipid composition was 96 mol% POPC containing or not 4 mol% of Cer , GM3 , GM2 , or GM1a . All the LUVs contained 2 mol% of 1 , 2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine ( DHPE ) -tRed ( Molecular Probes ) . Lipids were mixed in chloroform∶methanol ( 2∶1 ) and dried under nitrogen . Traces of organic solvent were removed by vacuum pumping for 1–2 h . Subsequently , the dried lipid film was dispersed in hepes-sodium buffer and subjected to ten freeze-thaw cycles prior to extruding ten times through two stacked polycarbonate membranes with a 100-nm pore size ( Nucleopore , Inc . ) using the Thermo-barrel extruder ( Lipex extruder , Northern Lipids , Inc . ) . In order to perform mDC pulse with equal concentrations of LUV displaying similar fluorescence intensities , tRed containing LUVs concentration was quantified following the phosphate determination method of [73] and the fluorescence emission spectra was recorded setting the excitation at 580 nm in a SLM Aminco series 2 spectrofluorimeter ( Spectronic Instruments ) . All capture experiments were performed pulsing mDCs in parallel at a constant rate of 100 µM of distinct LUVtRed formulations and 75 ng of VLPHIV-Gag-eGFP Gag quantified by western blot ( 2 , 500 pg of VLPHIV-Gag-eGFP p24Gag estimated by ELISA ) per 2×105 cells for 4 h at 37°C . After extensive washing , positive DCs were acquired by FACS with a FACSCalibur ( BD ) using CellQuest software to analyze collected data . Forward-angle and side-scatter light gating were used to exclude dead cells and debris from all the analysis . Competition experiments were done incubating 2×105 mDCs with 75 ng of VLPHIV-Gag-eGFP Gag at a final concentration of 1×106 cells/ml for 4 h at 37°C in the presence of decreasing amounts of GM2-containing LUVHIV-tRed or 100 µM of Cer- and PS-containing LUVHIV-tRed . Alternatively , cells were incubated with 75 ng of VLPHIV-Gag-eGFP Gag and 100 µM of LUVHIV-tRed including or not GM1a , GD1b , GT1b , GQ1b , Cer , and PS . Cells were then analyzed by FACS as previously described . Co-localization experiments were done pulsing mDCs sequentially with LUVHIV-tRed and VLPHIV-Gag-eGFP for 3 h as described in the capture assays section . Capture patterns were analyzed similarly , incubating cells with distinct LUVHIV-tRed and VLPHIV-Gag-eGFP separately . After extensive washing , cells were fixed and cytospun into glass slides , mounted in DAPI-containing fluorescent media and sealed with nail polish to analyze them in a spinning disk confocal microscope . Z-sections were acquired at 0 . 1-µm steps using a 60× Nikon objective . Spinning disk confocal microscopy was performed on a Nikon TI Eclipse inverted optical microscope equipped with an Ultraview spinning disk setup ( PerkinElmer ) fitted with a two CCD camera ( Hamamatsu ) . The co-localization signals in percentages of the compartment area and the thresholded Manders and Pearson coefficients were calculated for each image using Volocity 5 . 1 software ( Improvision ) . To obtain 3-D reconstructions , confocal Z stacks were processed with Volocity 5 . 1 software , employing the isosurface module for the nucleus and the maximum fluorescent intensity projection for LUVs and VLPs . A total of 2×105 DCs were pulsed for 2 h at 37°C with 25 µM of GM3-containing LUVHIV-tRed and 75 ng of sucrose-pelleted VLPHIV-Gag-eGFP Gag treated or not during 12 h at 37°C with 100 or 50 mU of neuraminidase from Clostridium perfringens Factor X ( Sigma Aldrich ) . The 12-h incubation was done in a glass-coated plate ( LabHut ) in hepes-sodium buffer , and the reaction was stopped adding RPMI media containing FBS . Cells were washed and assessed by FACS to obtain the percentage of tRed- and eGFP-positive cells . mDCs were preincubated with or without 5 or 10 mM of lactose ( Sigma-Aldrich ) and soluble GM3 carbohydrate head group ( Carbosynth ) for 30 min at RT . Cells were then pulsed with 50 µM of GM3-containing LUVHIV-tRed and 75 ng of sucrose-pelleted VLPHIV-Gag-eGFP Gag for 2 h at 37°C , at a final concentration of 5 or 10 mM for the compounds tested . Cells were analyzed by FACS as described previously . For experiments with HIVNL4-3 generated in primary CD4+ T cells , mDCs were equally pre-incubated with lactose and the GM3 head group . Minimal energy structures in vacuum were computed using Chem3D Ultra software employing the MM2-force field and the steepest-descent algorithm . Minimum root mean square gradient was set to 0 . 1; minimum and maximum move to 0 . 00001 and 1 . 0 , respectively . mDCs and iDCs ( 5×105 cells ) were exposed to 30 ng p24Gag of HIVNL4-3 obtained from stimulated PBMCs for 4 h at 37°C . Cells were washed thoroughly to remove unbound particles , lysed , and assayed for cell-associated p24Gag content by an ELISA . mDCs ( 2 . 5×105 cells ) were exposed to 50 ng p24Gag of HIVNL4-3 obtained from CD4+ T cells treated or not with NB-DNJ , incubated and assayed as previously described to detect the cell-associated p24Gag . Alternatively , mDCs ( 2 . 5×105 cells ) pre-incubated or not with GM3 or lactose as previously indicated were exposed to 90 ng p24Gag of HIVNL4-3 obtained from CD4+ T cells and assayed equally . For trans-infection assays , mDCs were pulsed equally but with a constant multiplicity of infection ( MOI ) of 0 . 1 , extensively washed and co-cultured in quadruplicate with the TZM-bl reporter cell line at a ratio of 104∶104 cells in the presence of 0 . 5 µM of saquinavir to assay luciferase activity 48 h later ( BrightGLo luciferase system; Promega ) in a Fluoroskan Ascent FL luminometer . Background values consisting of non-HIV-1 pulsed co-cultures were subtracted for each sample ( mean background of 8 . 668 RLUs×100 ) . Trans-infection to primary cells was performed similarly , co-culturing pulsed mDCs with activated primary CD4+ T cells for 48 h on a 96-well U-bottom plate without removal of unbound viral particles . Co-cultures were performed in the presence or in the absence of 0 . 5 µM of saquinavir . Infection of activated primary CD4+ T cells was detected with FACS , measuring the intracellular p24Gag content within the CD2-positive CD11c negative population of CD4+ T cells employing the monoclonal antibodies p24Gag-FITC ( KC57-FITC , clone FH190-1-1 , Beckman Coulter ) , CD2-PerCP Cy5 . 5 ( clone RPA-2 . 10 , BD Pharmingen ) , and CD11c-APC Cy7 ( clone Bu15 , BioLegend ) . Co-cultures containing non-pulsed cells were used as a background control for p24Gag labeling and used to set up the marker at 0 . 5% . To detect the possible cell free virus infection of activated CD4+ T cells , an equal MOI was added directly to control wells lacking mDCs . Statistics were performed using GraphPad Prism v . 5 software . | Antigen-presenting cells such as dendritic cells ( DCs ) are required to combat infections , but viruses including HIV have evolved strategies to evade their anti-viral activity . HIV can enter DCs via a non-infectious endocytic mechanism and trick them into passing infectious virus on to bystander CD4+ T cells . Immature DC ( iDCs ) are characterized by high endocytic activity and low T-cell activation potential . Interestingly , several groups have shown that DCs that have undergone “‘maturation’” ( mDCs ) , a process that occurs on contact with a presentable antigen , capture higher numbers of HIV-1 particles than iDCs when they are matured in the presence of lipopolysaccharide . mDCs move to the lymph nodes where they have more opportunity to interact with T cells than iDCs , and thus to pass on infectious virus . But the molecular mechanism underlying HIV-1 uptake by mDCs has until now been elusive . Here we show that gangliosides , basic components of the host cell's plasma membrane , have an important role in this process . Gangliosides are known to be incorporated into the viral envelope membrane during the process of viral particle budding and here we show that they serve as viral attachment factors: they are recognized and enable HIV-1 uptake by mDCs . Thus , in addition to the well-known function of gangliosides as host cell receptors that mediate virus ( e . g . , polyoma and SV40 ) attachment and transport from the plasma membrane to the ER , we now demonstrate that they can also act as determinants for capture by mDCs . Furthermore , we identify a moiety composed of sialyllactose on HIV-1 membrane gangliosides as the specific domain recognized by mDCs . We propose that this novel recognition moiety might be crucial for inducing immune responses , but also critical to disseminate HIV-1 and other ganglioside-containing viruses . |
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Here , we have investigated the possible effect of B-1 cells on the activity of peritoneal macrophages in E . cuniculi infection . In the presence of B-1 cells , peritoneal macrophages had an M1 profile with showed increased phagocytic capacity and index , associated with the intense microbicidal activity and a higher percentage of apoptotic death . The absence of B-1 cells was associated with a predominance of the M2 macrophages , reduced phagocytic capacity and index and microbicidal activity , increased pro-inflammatory and anti-inflammatory cytokines production , and higher percentual of necrosis death . In addition , in the M2 macrophages , spore of phagocytic E . cuniculi with polar tubular extrusion was observed , which is an important mechanism of evasion of the immune response . The results showed the importance of B-1 cells in the modulation of macrophage function against E . cuniculi infection , increasing microbicidal activity , and reducing the fungal mechanisms involved in the evasion of the immune response .
Microsporidia are obligate intracellular spore-forming microorganisms that can infect a wide range of vertebrate and invertebrate species . These fungi have been recognized as human pathogens and are particularly harmful to immunodeficient patients infected with HIV . Since then , interest among researchers of in vitro culture techniques has increased , with more people studying their biology and immune response against them [1] . Encephalitozoon cuniculi is one of the most common microsporidian species , in humans or animals . It is considered to be an emerging zoonotic and opportunistic pathogen in immunocompromised as well as immunocompetent individuals [2] . Spores of E . cuniculi can survive in macrophages , spread throughout the host , and cause lesions in organs of the urinary , digestive , respiratory , and nervous systems [3] . The adaptive immune response is critical for the elimination of E . cuniculi , but the innate immune response forms the first line of defense against these pathogens . The infection by E . cuniculi induces CD8+ cytotoxic T lymphocyte ( CTL ) response , which lyses the infected cells by perforin-dependent mechanisms [1] . Although antibody response during E . cuniculi infection has been recorded , it is clearly not sufficient to prevent mortality or cure the infection , making cell-mediated immunity critical for the survival of host infected by E . cuniculi [4] . The survival and replication of certain species of microsporidia within macrophages may be associated with the absence of phagosome-lysosome fusion [5] . Internalized microsporidium spores are normally destroyed within macrophages by the toxic activity of reactive oxygen and nitrogen species produced by the respiratory burst , and cytokines released by macrophages may be important in the protection against microsporidia [6] . B-1 cells are a subtype of B cells that account for 35%-70% of the B cells in the peritoneal cavity of mice [7] . They differ from B-2 cells in the expression of surface markers and function [8] . B-1 cells act as antigen-presenting cells , phagocytes , expressing myeloid ( CD11b ) and lymphoid markers ( CD45/B220 , CD5 , CD19 and IgM ) , but not CD23 , unlike B-2 cells [9] . The main function of B-1 cells in the innate immune system is the spontaneous secretion of natural antibodies , thereby maintaining immunoglobulin levels in the body without any stimulus or immunization [10] . In addition , B-1 cells also spontaneously secrete IL-10 , while GM-CSF and IL-3 are secreted after lipopolysaccharide stimulation [10] . B-1 cells also regulate acute and chronic inflammatory diseases through the production of several immunomodulatory molecules , such as interleukin-10 ( IL-10 ) , adenosine , granulocyte-macrophage colony-stimulating factor ( GM-CSF ) , IL-13 , and IL-35 , in the presence or absence of stimulus [11] . The possible role of B-1 cells in the dynamics of the inflammatory process of various etiologies is unknown and researchers have demonstrated the role of these cells in the functional regulation of macrophages . Also , B-1 cells are able to differentiate into phagocytes ( B-1CDP ) , characterized by the expression of F4/80 and increased phagocytic activity [12 , 13] . Furthermore , Popi et al . [14] demonstrated that BALB/c mice were more susceptible to Paracoccidioides brasiliensis infection compared to XID ( B-1 cell deficient ) mice and attributed the down-regulation of macrophage function to IL-10 secreted by B-1 cells . In recent studies from our group , we demonstrated that B-1 cell deficient XID mice were more susceptible to experimental encephalitozoonosis than BALB/c mice , evidenced histologically with more prominent inflammatory lesions and fungal burden [15 , 16] . Although the mechanism of action of B-1 cells in the resistance of BALB/c mice to encephalitozoonosis is not fully understood , a significant increase in the population of peritoneal macrophages was reported in BALB/c mice infected with E . cuniculi [15] . We hypothesized that B-1 cells from the peritoneal cavity ( PerC ) could differentiate in infected animals into B-1 cell-derived phagocytes ( B-1 CDP ) , which could then promote the phagocytosis of E . cuniculi spores and also influence the macrophage function in this context . Herein , we tested this hypothesis using the ultrastructure and phenotypic analysis of adherent peritoneal cells ( APerC ) to evaluate their in vitro behavior in BALB/c and B-1 cell-deficient XID mice against the fungus E . cuniculi . The presence of B-1 cells facilitated the phagocytic and microbicidal activity and increased apoptosis in the APerC cultures . These cells were associated with the presence of M1 macrophages with increased proinflammatory cytokine production . Electron micrographs showed an intimate physical relationship between B-1 cells and macrophages , indicating communication and modulation of activity , demonstrating that the presence of B-1 cells drives the behavior of macrophages and consequently the innate immune response in encephalitozoonosis .
To assess the influence of B-1 cells on the activity of macrophages in E . cuniculi infection , APerC obtained from PerC of B-1 cell-containing BALB/c and B-1 cell-deficient XID mice were co-infected with E . cuniculi for ultrastructural analyses at 1 , 48 , and 144 h . The phagocytosis of spores occurred in cell cultures of both BALB/c and XID APerC groups , characterized by projections of the cellular membrane of macrophages near or around the spores ( Fig 1A and 1B ) , and remained within phagosome vacuoles that were dispersed throughout the cytoplasm . XID macrophages showed large numbers of vacuoles in the cytoplasm and several membrane projections ( pseudopods ) , indicating widespread and strong phagocytic activity ( Fig 1A ) . Intact and degenerating internalized spores surrounded by a vacuolar membrane , which is a typical phagosome vacuole , were observed ( Fig 1C ) . Under normal circumstances , the interaction of phagosomes with each other and with other organelles is tightly regulated , and it is well documented that phagosomes containing inert particles are not subjected to homotypic fusion [17] . The important finding of this study is the formation of megasomes by the fusion of homotypic phagosomes containing a single E . cuniculi spore ( Fig 1D ) . Therefore , the ability of E . cuniculi to induce the fusion of phagosomes resembles that of Helicobacter pylori [18] and Chlamydia trachomatis [19] . In AperC cultures of both BALB/c and XID mice , the microbicidal activity was identified by the presence of a large amount of amorphous and electron-dense material either inside the phagocytic vacuoles and megasome in the cytoplasm of macrophages or undergoing exocytosis ( Fig 1B and 1D ) . The same findings were seen at 48 h ( Fig 2A and 2D ) with an increase in the ratio of degenerated spores , and at 144 h with only a few intact spores inside the phagocytic cells in case of XID APerC ( S1A and S1B Fig ) . Myelin figures indicating spore degeneration were recorded ( Fig 2B ) . Another important finding was the presence of degenerating macrophages ( Fig 2D ) . At 144 h , the APerC of BALB/c showed an absence of macrophages and mature spores of E . cuniculi outside the cells ( S1C and S1D Fig ) and degenerated lymphocytes ( S1D Fig ) . The intracellular proliferative stages of E . cuniculi ( meronts , sporonts , or sporoblast ) were not observed . The B-1 cells were identified in BALB/c APerC by their ultrastructural morphological characteristics such as a lobed nucleus with bridges of the nuclear membrane joining the lobules and a well-developed endoplasmic reticulum with few mitochondria [20] ( Fig 2E ) . We also observed the presence of cytoplasmic projections of pre-B-1 CDP cells and B-1 lymphocytes adhered to or near extracellular E . cuniculi spores in BALB/c APerC ( Fig 2E and insert ) . Intercellular communication involving other lymphocytes and mast cells and macrophages in BALB/c ( Fig 1B ) and XID APerC ( Fig 1D ) . Using Calcofluor stain , we observed increased phagocytic capacity and index in BALB/c APerC compared to XID APerC ( Fig 3 ) , suggesting the involvement of B-1 cells in the increase in phagocytic activity . After 1 h , large numbers of preserved spores were observed within the macrophages from BALB/c and XID , but after 48 h , there were fewer intact spores within the phagocytic vacuoles from BALB/c APerC , while the spores remained preserved in XID APerC . As the ultrastructural analysis revealed that the BALB/c APerC phagocytes were no longer viable after 144 h , we evaluated the level of apoptosis and necrosis after 48 and 96 h for all experimental groups in order to examine the possible influence of E . cuniculi in the process of cell death ( Fig 4 ) . Given the abundance of cell death accompanying intracellular infection , two possibilities have been described as evasion mechanisms: 1 ) the pathogen can be destroyed along with the engulfed apoptotic cell ( host antimicrobial activity ) or 2 ) the pathogen can use the process of efferocytosis to disperse into new cellular hosts ( “Trojan Horse” model ) [21] . About 50–60% of cell death was observed in BALB/c AperC , which was higher than that in XID APerC ( 30–40% ) after 48 h ( Fig 4A ) . At 96 h , the percentage of the death of uninfected BALB/c and XID APerC was similar ( about 70% ) , but a 40 to 50% reduction in the rate of cell death in the infected groups was evident ( Fig 4A ) . In addition , after the infection , the BALB/c and XID APerC behaviors were antagonistic and the rate of apoptosis identified in the infected BALB/c APerC was higher than that observed in XID APerC at both the time intervals ( Fig 4B ) , and infected XID APerC had more necrosis than the other groups ( Fig 4C ) . Similar to our findings , Martin et al . [21] reported that M . tuberculosis-infected macrophages that die by apoptosis are rapidly engulfed by uninfected macrophages via efferocytosis and this process is responsible for the bactericidal effect associated with apoptosis . The spores that were internalized by macrophages in XID APerC cultures had a contact area between the phagocytic vacuolar membrane of the cell and the spore wall ( Fig 5A and insert ) , suggesting intimate contact with the phagocytic cells . Furthermore , we observed rarely extrusion of the polar filament by an internalized spore from the phagosome vacuole at 1 h ( Fig 5B and insert ) . The ejection of the polar filament from the phagosome into the cell has been reported in the literature [1] , but has not been demonstrated by ultrastructure analysis . This finding suggests an evasion of the microbicidal activity . Therefore , we hypothesize that the absence of B-1 cells could direct the macrophages to M2 profile and favor the dissemination of E . cuniculi . To test the influence of B-1 cells in trigger macrophage profile , we evaluated the expression of CD40 and CD206 in macrophages from BALB/c and XID APerC cultures , which may suggest the classical activation—M1 profile ( CD40+high CD206+low ) or the alternative activation—M2 profile ( CD206+high CD40+low ) process . We also analyzed the mean fluorescence intensity of CD80 and CD86 co-stimulatory molecules to measure the activation of these macrophages and aid in the characterization of M1/M2 . The proportion of macrophages in XID APerC ( approximately 60% ) was significantly higher than that in BALB/c APerC ( approximately 20% ) , regardless of microsporidial infection ( Fig 6A ) . Since the BALB/c culture also contains B-1 cells that adhere together with the macrophages in the first incubation [15 , 16] , this result was expected . After infection , the percentage of macrophages in XID APerC was 80% , which is higher than that observed in uninfected cultures , both at 48 and 96 h ( Fig 6A ) . In BALB/c APerC , no difference was observed in this parameter . At 48 h , approximately 80% of the BALB/c APerC macrophages expressed CD40+ while approximately 40% of XID APerC macrophages expressed CD40+ ( Fig 6B ) . At 96 h , the percentage of these cells decreased to about 60% of CD40+ cells in infected BALB/c AperC and about 40% in infected XID APerC . At 48 h , the CD206+ expression on macrophages was higher in XID APerC than in BALB/c APerC ( Fig 6C ) . At 96 h , there was no difference between the groups . We also analyzed the mean fluorescence intensity ( MFI ) ratio of CD40 to CD206 molecules and observed a higher ratio in the infected BALB/c than in XID ( Fig 6D ) , confirming the predominance of CD40 expression in BALB/c APerC . In addition , we observed that infected BALB/c APerC macrophages expressed more CD80 and CD86 costimulatory molecules ( Fig 6F ) than macrophages from the other groups . These molecules are expressed on the surface of activated macrophages , especially M1 macrophages , which present a higher level of expression . These results together indicate a higher proportion of M1 profile macrophages in BALB/c APerC and M2 profile in XID APerC . The prevalence of E . cuniculi infection reinforces these profiles . In general , both the pro-inflammatory cytokines ( TNF-α and MCP1 ) and the anti-inflammatory cytokines IL-6 and IL-10 increased 1 hour of infection in the XID infect APerC ( Fig 7 ) . While the pro-inflammatory cytokines TNF-α and MCP1 showed no difference in relation to the BALB/c groups , the levels of IL-6 and IL-10 in XID APerC infected were about 4-fold higher than that observed in BALB/c APerC infected . At 30 min , only IL-10 levels were significantly higher in XID than BALB/c , in infected groups . At 48 h , XID APerC infected still had higher levels of cytokines ( except TNF-α ) than BALB/c APerC infected . At 96 hours , low or undetectable levels of cytokines were observed in all groups . It was previously demonstrated that B-1 cells differentiate to acquire a mononuclear phagocyte phenotype following attachment to a substrate in vitro , which are named B-1 cell-derived phagocytes ( B-1CDPs ) [22 , 23] . It has already been demonstrated that these cells are able to phagocytose various pathogens in vivo and in vitro [23 , 24 , 25 , 26] . We used B-1 CDP cultures to evaluate the participation of B-1-derived phagocytes in encephalitozoonosis . In B-1 CDP cultures , we observed B-1 CDPs and B-1 cells with preserved characteristics after 1 h and 48 h ( Fig 8 ) , indicating that a part of the B-1 cells had become phagocytes . Mussalem et al . [27] demonstrated that infection with Propionibacterium acnes induced the commitment of B-1 cells to the myeloid lineage and their differentiation into phagocytes . We observed the contact of spores with B-1 cells and pre-B-1-CDPs ( Fig 8A ) . B-1 CDPs with spores of E . cuniculi in the process of lysis within phagocytic vacuoles , amorphous material in megasomes ( Fig 8B ) , and intact spores were also visualized in the B-1 CDP cultures ( Fig 8C ) . Other authors also demonstrated the phagocytic and microbicidal ability of peritoneal B-1 cells [28 , 29] . The ultrastructure of E . cuniculi spores was typical of non-germinated mature spores and showed of a thick wall composed of an electron-dense outer layer ( exospore ) , an electron-lucent inner layer ( endospore ) , and a plasma membrane enclosing the cytoplasm ( Fig 8C ) . B-1 cells and pre-B-1 CDP have abundant microvesicles in their membranes ( Fig 8D and 8E ) , indicating exocytosis . Exosomes are nanosized membrane microvesicles that have the capability to communicate intercellularly and to transport cell components [30] . Thus , here we identified two forms of intercellular communication through contact and release of extracellular vesicles ( Fig 8A and 8E , respectively ) . In B-1 CDP cultures , the percentage of dead cells increased at 96 h , as a result of necrosis and apoptosis , regardless of the infection ( Fig 9A ) . Spores outside the cells were identified under light microscopy and low index of phagocytosis was determined after one hour with an increasing trend across the time intervals ( Fig 9B ) . We observed a considerable increase in the pro-inflammatory cytokine TNF-α at 30 min and 1 h in the infected cultures and an increase in MCP1 at 48 and 96 hours ( Fig 9C ) .
The role of B-1 cells in immunity against fungal , protozoan , bacterial , and helminthic infections has been described by us as well as by other researchers . Popi et al . [14] demonstrated that BALB/c mice were more susceptible to experimental infection by Paracoccidioides brasiliensis than XID mice , suggesting that B-1 cells may favor infection . This was consistent with other experimentally induced infections , such as Mycobacterium bovis bacillus Calmette-Guerin ( BCG ) [31] and Trypanosoma cruzi [32] . B-1 cells secrete IL-10 and use it as an autocrine growth factor . Popi et al . [12] demonstrated in vitro that this cytokine decreases the production of nitric oxide and hydrogen peroxide by macrophages , which decreases their phagocytic capacity , compromising the innate immune response and antigenic presentation . Nevertheless , B-1 cells are critical for the early control of infections with encapsulated bacteria such as Streptococcus pneumoniae [33] , viruses such as the Influenza virus [34] , or fungi [35 , 36] . In line with these studies , our group demonstrated that XID mice were more susceptible to encephalitozoonosis than BALB/c mice , suggesting that the pathogenicity caused by E . cuniculi depends on the relationship between the multiplication of parasites and the host immune response [15 , 16] . In addition , da Costa et al . [15] showed that BALB/c mice infected with E . cuniculi showed an increase in the number of macrophages and plasma levels of IFN-γ , which was responsible for the activation of macrophages and elimination of the pathogens . Confirming this , here we demonstrated in vitro that B-1 cells upregulated the macrophage activity against E . cuniculi , characterized by higher phagocytic index and microbicidal capacity , as well as increased macrophage death by apoptosis . The presence of intimate contact between B-1 cells and macrophages suggested communication between these cells and modulation of activity . In addition , B-1 CDP also showed intense phagocytic and microbicidal activities , a fact that may explain the numerical increase of macrophages in BALB/c mice , as previously described by our group [15] . B-1 cells from the peritoneal cavity of mice or cultures of adherent peritoneal cells can be clearly identified on the basis of their distinct morphology and cell surface phenotype . The main morphological characteristic of these cells is the formation of bridges of the nuclear membrane , suggesting a lobular organization of the nucleus . In addition , B-1 cells are characterized by small membrane projections and a large number of ribosomes , a predominance of euchromatin in B-1 cell nuclei , and more condensed chromatin in the nuclear periphery [20] . In this study , we identified B-1 cells by TEM in BALB/c APerC and B-1 CDP cultures . The B-1 CDPs decrease the expression of immunoglobulin M ( IgM ) but retain the expression of heavy-chain gene-variable VH11 or VH12 , an immunoglobulin gene rearrangement that is predominantly expressed by B-1 cells [23] . The maintenance of lymphoid characteristics in B-1 CDPs is the characteristic of a unique type of phagocyte that is not related to monocyte-derived macrophages . Cell-to-cell communication is required to guarantee proper coordination among different cell types within tissues . Studies have suggested that cells may also communicate by circular membrane fragments called extracellular vesicle that are released from the endosomal compartment as exosomes or shed off from the surface membranes of most cell types [37] . In this study , the identified phagocytic cells of B-1 CDP cultures had abundant extracellular vesicles in their membranes , indicating cell communication . In addition , we observed cells in the process of communicating by cell membrane projections ( pseudopodia ) or adhered cell-to-cell membrane between different types of cells in BALB/c APerC . The plasticity of macrophages has been demonstrated over the last few years . Depending on the stimulus received by macrophages ( pathogens or injured tissue ) , their receptors trigger a decision to kill ( M1 ) or repair ( M2 ) [38] . The data from transcriptome analysis demonstrated the existence of distinct polarization phenotypes of macrophages associated with specific pathological conditions [39 , 40] . It is widely accepted that the phenotype of macrophages reflects the immediate microenvironment , wherein other cells can participate in this process . In addition , the role of B-1 cells in the polarization of peritoneal macrophages to an M2 profile in tumor condition was proposed by Wong et al . [41] . In contrast , we demonstrated that macrophages from BALB/c APerC present M1 profile ( CD40 high CD206 lowCD80/86 high ) while macrophages from XID APerC present M2 profile ( CD206 high CD40lowCD80/86low ) . These results may partly explain the susceptibility of XID mice to encephalitozoonosis when compared to BALB/c mice , as previously demonstrated by our group [15] . As demonstrated in this study , M1 macrophages possess intense phagocytic activity . The presence of megasomes , amorphous material , degenerated spores , and myelin figures within phagocytes in BALB/c APerC confirmed the high microbicidal potential of these macrophages . Furthermore , the expression of CD80 and CD86 produces a second signal necessary for the proliferation and activation of T lymphocytes , a fact that demonstrates the importance of innate response in the production of acquired effective response against microsporidia . APerC infected with E . cuniculi showed higher expression of these molecules . M2 macrophages produce ornithine that promotes proliferation and tissue repair , increasing levels of TGF-β , IL-10 , IFN , chitinases , matrix metalloproteinases , scavenger receptors , and have a poor microbicidal function [38 , 42] . Herein , apart from the M2 profile phenotypically associated with XID APerC macrophages , we observed a lower rate of phagocytosis and delayed microbicidal activity associated with a balance in the production of pro- and anti-inflammatory cytokines , a phenomenon linked with the absence of B-1 cells and E . cuniculi infection . Th2 cytokines have been demonstrated in E . cuniculi infection [15 , 43 , 44] and an increase in the mRNA for IL-10 was observed in the splenocytes of infected animals [43] . This cytokine has been reported to be involved in the regulation of Th1 immune response against Toxoplasma gondii [45] and possibly has a similar role in E . cuniculi infection . In an in vitro study on cell cultures of human macrophages incubated with E . cuniculi spores , an increase in the levels of IL-10 was observed in supernatants of infected cultures . Our findings demonstrated an increase in levels of IL-10 in XID group after 30 minutes and 1 hour of E . cuniculi infection , indicating an anti-inflammatory profile . IL-10 is an anti-inflammatory cytokine . During infection it inhibits the activity of Th1 cells , NK cells , and macrophages , all of which are required for optimal pathogen clearance but also contribute to tissue damage [46] . Macrophages are potent antimicrobial effector cells that can participate in both pro-inflammatory ( classical M1 ) and fibrotic ( alternatively activated M2 ) responses [38 , 47] . Consequently , it is not surprising that pathogens like E . cuniculi have evolved mechanisms to subvert macrophage function or that macrophages are a major source of IL-10 during infection , but in this case it is emphasized that the same occurred in the absence of the B-1 cell in XID group . IL-6 is a pleiotropic cytokine that mediates several biological functions , including regulation of the immune system by anti-inflammatory and pro-inflammatory production [48] . In a previous study , we demonstrated that diabetes mellitus ( DM ) increased the susceptibility of C57BL/6 mice to encephalitozoonosis and DM mice infected with E . cuniculi showed higher levels of IL-6 than DM-uninfected mice , suggesting that DM may also modulate a pro-inflammatory state of the organism [49] . In the current study , we observed an increase in IL-6- in XID APerC after 1 h and 48 h of cultures infection , associated with an increase in the production of IL-10 , a fact that suggests an anti-inflammatory effect of IL-6 corroborating the descriptions referring to its pleiotropic behavior , sometimes as a pro-inflammatory cytokine and sometimes as an anti-inflammatory cytokine . In this study , pro-inflammatory cytokine productions also showed an upward trend in Infected XID , suggesting a balance between the production of anti-inflammatory ( IL-10 and IL-6 ) and pro-inflammatory cytokines ( TNF and MCP1 ) , however with predominance of the anti-inflammatory profile characteristic of M2 macrophages . Chemokines are a group of small molecules that regulate cell trafficking of leukocytes . They mainly act on monocytes , lymphocytes , neutrophils , and eosinophils , and play an important role in host defense mechanisms [50] . MCP-1 , also known as CCL2 , was the first human chemokine to be characterized [51] . This molecule attracts cells of the monocyte lineage , including macrophages , monocytes , and microglia [52] . Mice deficient in MCP-1 were reported to be incapable of effectively recruiting monocytes in response to an inflammatory stimulus , despite the presence of normal numbers of circulating leukocytes [53] . Chemokine production has been documented to be induced by microsporidian infections in human macrophages . A primary human macrophage culture from peripheral blood mononuclear cells was infected with E . cuniculi , revealing the involvement of several chemokines , including MCP-1 , in the inflammatory responses [54] . The results showed that in B-1 CDP cultures an increase in MCP1 production occurs at 48 and 96 hours , indicating that the presence of B-1 cells may favor the recruitment of macrophages and the targeting for an M1 phenotype . The infection process of E . cuniculi involves the forced eversion of a coiled hollow polar filament that pierces the host cell membrane , allowing the passage of infectious sporoplasm into the host cell cytoplasm . If a spore is phagocytosed by a host cell , germination will occur and the polar tube can pierce the phagocytic vacuole , delivering the sporoplasm into the host cell cytoplasm [1] . In XID APerC , we observed the extrusion of the polar filaments of intracellular mature spores from the phagolysosomes at 1 h , suggesting that spores may germinate after phagocytosis and escape . This extrusion of the polar tubule has been described in the literature by immunofluorescence [55] . Herein , we showed ultra-micrographic of this process . These findings indicate an intimate relationship between spores and the phagocytic vacuole , suggesting that some XID APerC macrophages have less microbicidal activity . We hypothesize that these macrophages can be polarized to M2 profile in the absence of B-1 cells and promote the maintenance of E . cuniculi . With the results obtained herein , we have demonstrated that B-1 cells modulate the activity of peritoneal macrophages infected with E . cuniculi to an M1 profile . Furthermore , part of these cells become B-1 CDPs having microbicidal activity against the pathogen , which explains the lower susceptibility of BALB/c mice to encephalitozoonosis associated with innate immune response .
Inbred specific pathogen free ( SPF ) BALB/c and BALB/c XID female mice at 6–8 weeks of age were obtained from the animal facility at Centro de Desenvolvimento de Modelos Experimentais ( CEDEME ) , UNIFESP , Brazil . The animals were housed in polypropylene microisolator cages with a 12-hour light-dark cycle , maintained at 21 ±2°C and >40% humidity , and fed on standard chow and water ad libitum . All the experimental procedures were performed in accordance with guidelines of Conselho Nacional de Controle de Experimentação Animal ( CONCEA ) and were approved by the Ethics Committee for Animal Research at Paulista University , under protocol number 385/15 . Spores of E . cuniculi ( genotype I ) ( from Waterborne Inc . , New Orleans , LA , USA ) that were used in this experiment were previously cultivated in a rabbit kidney cell lineage ( RK-13 , ATCC CCL-37 ) in Eagle medium supplemented with 10% of fetal calf serum ( FCS ) ( Cultilab , Campinas , SP , Brazil ) , pyruvate , nonessential amino acids , and gentamicin at 37°C in 5% CO2 . The spores were purified by centrifugation and cellular debris was excluded by 50% Percoll ( Pharmacia ) as described previously [56] . The APerC were obtained from the peritoneal cavities ( PerC ) of BALB/c and B-1 cell-deficient XID mice . PerC were washed using RPMI-1640 medium and 0 . 5×106 cells were dispensed in each well of 24-well plates and incubated at 37°C in 5% CO2 for 40 min . Non-adherent cells were discarded and RPMI-1640 supplemented with 10% FCS ( R10 ) was added to the adherent fraction . To obtain B-1 CDP , APerC from BALB/c mice were cultured and the enriched B-1 cells in the floating medium were collected from the third day [57] . Cultures of 0 . 5 × 106 cells/well were re-suspended in R10 and re-cultured under the same conditions as described above . The culture of BALB/c APerC , XID APerC , and B-1 CDP were infected simultaneously with E . cuniculi ( 1×106 spores/mL ) in the proportion of two spores per cell ( 2:1 ) . The cultures were incubated at 37°C in 5% CO2 for 30 min , 1h , 48 h , 96 h , and 144 h after infection , following which the supernatants were collected and stored at –80°C to measure the level of NO and cytokines . The cultures uninfected with E . cuniculi were incubated for the same time intervals and used as a group control . The production of NO was measured using a colorimetric indirect method based on the detection of nitrite ( nitrate was initially converted to nitrite by nitrate reductase ) as a product of the Griess reaction ( R&D Systems ) in the supernatants of cell cultures . Briefly , supernatants were mixed with the Griess reagent [equal volumes of 0 . 2% ( w/v ) naphthylethylenediamine in 60% acetic acid and 2% ( w/v ) sulfanilamide in 30% ( v/v ) acetic acid] and incubated at room temperature for 10 min . A spectrophotometer was used to measure the absorbance at 540 nm and fresh culture medium was used as a blank in all the experiments . The amount of nitrite in the test samples was calculated from a sodium nitrite standard curve ( 0 . 78–100 μM ) . Cytokines were measured in culture supernatants using Cytometric Bead Array ( CBA ) Mouse Inflammation Kit ( BD Bioscience , San Jose , CA , USA ) . The kit was used for the simultaneous detection of mouse monocyte chemoattractant protein-1 ( MCP-1 ) , interleukin-4 ( IL-4 ) , IL-6 , IFN-γ , tumor necrosis factor ( TNF-α ) , IL-10 , and IL-12p70 , according to the manufacturer’s instructions . Briefly , the supernatant samples were added to bind to allophycocyanin ( APC ) -conjugated beads specific for the cytokines listed above and phycoerythrin ( PE ) -conjugated antibodies . The samples were incubated for 2 h at room temperature in the dark , then measured using FACS Canto II Flow Cytometer , and analyzed by FCAP ArrayTM Software ( BD Bioscience ) , version 3 . 0 . Individual cytokine concentrations ( pg/mL ) were indicated by the intensity of PE fluorescence and cytokine standard curves . Infected and uninfected APerC were detached using a cell scraper and washed with PBS . The cell suspensions were centrifuged and subsequently washed with PBS and re-suspended in 100 μL PBS supplemented with 1% bovine serum albumin ( BSA ) ( PBS-BSA 1% ) . Each sample was incubated at 4°C for 20 min with anti-CD16/CD32 to block the Fc II and III receptors . After incubation , the cells were washed , divided into two aliquots , and re-suspended in PBS-BSA 1% . Each sample was then incubated with the following monoclonal antibodies: 1 ) fluorescein-isothiocyanate ( FITC ) -conjugated rat anti-mouse CD80/CD86 , 2 ) PE-Cyanine 5 ( PE-Cy5 ) -conjugated anti-mouse CD40 , 3 ) APC-Cy7-conjugated rat anti-mouse CD11b , and 4 ) Alexa Fluor 647 rat anti-mouse CD206 ( BD-Pharmingen , San Diego , CA , USA ) for analysis of the surface markers . The cell pellet was incubated with fluorochrome-conjugated antibodies for 20 min at 4°C , washed with PBS-BSA 1% , re-suspended in 500 μL of PBS , and analyzed with BD Accuri C6 flow cytometer . To investigate the phagocytic capacity and index , 10 μL Calcofluor ( Sigma-Aldrich , St . Louis , USA ) was added per milliliter of the cell cultures to visualize the spores inside the phagocytic cells . Phagocytic capacity and index were calculated according to the formula: FC = number of phagocytes containing at least one ingested spore/100 phagocytes and FI = total number of phagocytic spores/100 phagocytes containing spores . Cell cultures were washed twice with cold PBS and then resuspended in 1x annexin-binding buffer ( BD Biosciences ) at a concentration of 1 × 106 cells/mL . Thereafter , 100 μL of the solution ( 1 × 105 cells ) was transferred to a 5-mL culture tube , to which 1 μL of PE Annexin V and 1 μL of 7-AAD were added and followed by incubation for 15 min on ice in dark conditions . Subsequently , 400 μL of 1x annexin binding buffer was added to the samples , incubated for 30 min , and all the resultant cell suspensions were analyzed using the BD Accuri C6 flow cytometer . The cell volume of BALB/c APerC , XID APerC , and B-1 CDP cells , cultured as described above , was adjusted to 1×107 cells . Each culture was transferred to 25 cm2 bottles and incubated in the same medium containing 10% FCS ( R10 ) at 37°C with 5% CO2 for 40 min . The culture medium was then removed and fresh R10 medium containing E . cuniculi spores ( 2:1 ) was introduced into the bottles . The cultures were collected after 1 h , 48 h , and 144 h of incubation and fixed using 2% glutaraldehyde in 0 . 2 M cacodylate buffer ( pH 7 . 2 ) at 4°C for 10 h . They were then post-fixed in 1% OsO4 buffer for 2 h . Semi-thin sections stained with toluidine blue were made for visualization by light microscope , and ultrathin sections were made for TEM analysis . The groups were compared using the two-way analysis of variance ( ANOVA ) and the significance of the mean difference within and between the groups was evaluated by multiple comparisons using the Bonferroni or Tukey's post-tests . All the experimental data were expressed as mean ± standard error mean , indicated by bars in the figures . P values ≤0 . 05 were considered statistically significant . All the graphs and statistical analyses were made using GraphPad Prism software version 6 . 0 for Windows ( GraphPad Software , San Diego , California , USA ) . | The adaptive immune response plays a key role against Encephalitozoon cuniculi , an opportunistic fungus for T cells immunodeficient patients . The role of B cells and antibody play in natural resistance to Encephalitozoon cuniculi remains unknown . Previously , we demonstrated that B-1 deficient mice ( XID ) , an important component of innate immunity , were more susceptible to encephalitozoonosis , despite the increase in the number of CD4+ and CD8+ T lymphocytes . Here we observed that the absence of B-1 cells was associated with a larger population of M2 macrophages , a balance between anti-inflammatory and pro-inflammatory cytokines profile , which had lower microbicidal activity against E . cuniculi infection . However , in the presence of B-1 cells , peritoneal macrophages had a M1 profile with showed increased microbicidal activity and a higher percentage of apoptotic death . |
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The catalytic activity of GDP/GTP exchange factors ( GEFs ) is considered critical to maintain the typically high activity of Rho GTPases found in cancer cells . However , the large number of them has made it difficult to pinpoint those playing proactive , nonredundant roles in tumors . In this work , we have investigated whether GEFs of the Vav subfamily exert such specific roles in skin cancer . Using genetically engineered mice , we show here that Vav2 and Vav3 favor cooperatively the initiation and promotion phases of skin tumors . Transcriptomal profiling and signaling experiments indicate such function is linked to the engagement of , and subsequent participation in , keratinocyte-based autocrine/paracrine programs that promote epidermal proliferation and recruitment of pro-inflammatory cells . This is a pathology-restricted mechanism because the loss of Vav proteins does not cause alterations in epidermal homeostasis . These results reveal a previously unknown Rho GEF-dependent pro-tumorigenic mechanism that influences the biology of cancer cells and their microenvironment . They also suggest that anti-Vav therapies may be of potential interest in skin tumor prevention and/or treatment .
Rho guanosine nucleotide exchange factors ( Rho GEFs ) promote the transition of Rho family GTP hydrolases ( GTPases ) from the inactive ( GDP bound ) to the active ( GTP bound ) state during signal transduction [1] , [2] . These enzymes can be subdivided into the Dbl-homology ( DH ) and the Dedicator of Cytokinesis ( Dock ) families based on the catalytic domain utilized for the GDP/GTP exchange reaction . A common feature of these two families is their extreme diversity because , in mammals , they are composed of 54 and 11 members , respectively . These family members vary widely in terms of catalytic specificity , presence of regulatory and effector domains , mechanism of activation , and expression patterns . As a consequence , they are key elements to adapt the activation of Rho GTPases to specific cell types , membrane receptors , or subcellular localizations [1] , [2] . Rho GEFs have been traditionally regarded as important for tumorigenesis and , thereby , as potential drug targets [3] . However , the large number of Rho GEFs and their regulatory complexity have made it difficult to identify which ones were the most important for the development and/or progression of specific tumors . Inferences from sequencing data have not been useful in this case , because their genes seldom undergo mutations in cancer cells [3] . The use of animal models in this functional context has been also rather limited . However , the few studies available do support the idea that these enzymes have pro-tumorigenic functions . Thus , the T-cell lymphoma invasion and metastasis-inducing protein 1 ( Tiam1 , ID number: 21844 ) has been shown to be important for both cutaneous squamous and colorectal [4] , [5] tumors . The adenomatous polyposis coli-stimulated exchange factor 1 ( Asef1 , ID number: 226970 ) and Asef2 ( ID number: 219140 ) proteins have been linked to colorectal cancer [6] . Finally , Vav3 ( ID number: 57257 ) and phosphatidylinositol 3 , 4 , 5-triphosphate-dependent Rac exchanger 1 ( P-Rex1 , ID number: 277360 ) are involved in the development of p190Brc/Abl-driven acute lymphoblastic leukemia and melanoma , respectively [7] , [8] . Vav proteins exemplify well the complexity existing in the large Rho GEF family . Thus , this subfamily has three members in vertebrates ( Vav1 [ID number: 22324] , Vav2 [ID number: 22325] , Vav3 ) that display overlapping but not identical expression patterns . They all share similar structures that encompass a complex array of regulatory , catalytic , and protein–protein interaction domains [9] . These domains enable them to interact with and become activated by receptors with either intrinsic or associated tyrosine kinase activity , activate GDP/GTP exchange on Rho GTPases , and in addition , engage parallel routes in GTPase-independent manners [9]–[17] . The physiological role of Vav proteins in the immune , nervous , and cardiovascular systems are now well established thanks to the use of genetically engineered mice [9] , [18]–[25] . By contrast , the genetic analysis of the role of these proteins in cancer has been restricted so far to acute lymphoblastic leukemia and polyomavirus middle T-antigen-induced breast cancer . These studies have revealed that Vav3 and Vav2 plus Vav3 were required for the development of each of those tumors , respectively [7] , [17] . In the present work , we aimed at expanding the spectrum of Vav family-dependent tumors by focusing our attention on cutaneous squamous tumors ( CSTs ) , the second most frequent type of skin cancer worldwide [26] . To this end , we decided to use Vav family knockout mice to evaluate the role of these proteins in the development of 7 , 12-dimethylben[a]antracene ( DMBA ) /12-O-tetradecanoylphorbol-13-acetate ( TPA ) - and DMBA/DMBA-triggered skin tumors . In the former model , a single topic administration of DMBA induces oncogenic mutations ( Q61L ) in the HRas locus ( ID number: 15461 ) in a small pool of keratinocytes ( initiation phase ) . Subsequent serial topic applications of TPA are then applied to expand this pool of transformed keratinocytes to generate papillomas ( promotion phase ) and , depending on the genetic background of mice , cutaneous squamous cell carcinomas ( cSCCs ) ( progression phase ) . Such pro-tumorigenic effect is mediated by the stimulation of intracellular signaling cascades in the initiated keratinocytes and , in addition , through autocrine/paracrine-based crosstalk between cancer and tumor-associated stromal cells that ultimately favor the expansion of the initial pool of transformed keratinocytes [27]–[30] . The latter model uses serial topic applications of DMBA that increase the frequency of cSCC development at the end of the carcinogenic protocol . We selected these models for our experiments because: ( i ) they are known to be Rac1- ( ID number: 19353 ) and Tiam1-dependent [4] , [31]; ( ii ) high levels of two Vav family proteins and a large number of additional Rho GEFs are present in normal and tumoral skin ( see Figure S1 ) , thus making a perfect working model to address intra-GEF family redundancies in a tumorigenic context; and ( iii ) they are compatible with the analysis of the role of the proteins under study in the tumor initiation , promotion , and progression phases [28] . Using this strategy , we have unveiled a Vav2/Vav3-dependent and cancer-specific autocrine/paracrine program that contributes to the initiation and promotion phases of skin tumors .
Expression analyses indicated that mouse papillomas and cSCCs expressed large numbers of Rho GEFs , including Vav2 and Vav3 ( Figure S1 ) . To assess if these two Vav family proteins played nonredundant roles with other Rho GEFs in the skin , we used compound Vav2−/−;Vav3−/− mice to evaluate the impact of the systemic inactivation of these two proteins in both epidermal maintenance and tumorigenesis . Unlike the case of Rac1−/− mice [32]–[34] , we could not find any defect in skin development , histological structure , or self-renewal in those mice regardless of the genetic background used ( Figure S2 and unpublished data ) . Wild-type-like parameters were also found in triple Vav1−/−;Vav2−/−;Vav3−/− C57BL/10 mice , indicating that the lack of an epidermal phenotype was not due to functional compensation events by Vav1 ( unpublished data ) . Hence , other Rho/Rac GEFs must be in charge of stimulating Rac1 in skin stem cells and keratinocytes under physiological conditions . By contrast , we observed that Vav2−/−;Vav3−/− FVB mice displayed lower kinetics of tumor development ( Figure 1A ) , a ≈5-fold reduction in the total number of tumors developed per mouse ( Figure 1B , C ) , and 10-fold lower levels of carcinoma in situ ( Table S1 ) when subjected to the two-step DMBA/TPA carcinogenic method . This effect was independent of the mouse genetic background , because C57Bl/10 Vav2−/−;Vav3−/− mice also showed statistically significant reductions in tumor burden when compared to control animals ( Figure 1D–F ) . Vav2−/−;Vav3−/− FVB mice also displayed lower kinetics of tumor development ( Figure 1G ) , a 2-fold reduction in tumor burden per mouse ( Figure 1H , I ) , and a decrease in the percentage of cSCC development ( Table S2 ) when subjected to the complete DMBA/DMBA tumorigenic method . Taken together , these results indicate that Vav2 and Vav3 play important roles in CST development but not in normal epithelial development and homeostasis . The lower tumor burden observed in DMBA/DMBA-treated Vav2−/−;Vav3−/− mice indicated that Vav proteins may have direct roles during the initiation phase of CSTs . To investigate this possibility , we analyzed the short-term response of the epidermis of wild type and Vav2−/−;Vav3−/− mice to DMBA ( Figure 2A ) . Using immunostaining with antibodies to the cleaved fragment of caspase 3 , we observed that DMBA induced ≈2-fold higher apoptotic cell numbers in the epidermis of Vav2−/−;Vav3−/− mice than in controls ( Figure 2B , C ) . This was not due to enhanced absorption and/or metabolization of the carcinogen , because immunohistochemistry experiments with antibodies to phospho-histone H2AX ( ID number: 15270 ) indicated that the DMBA treatment triggered similar levels of DNA double-strand breaks in mice of both genotypes ( Figure 2D , E ) . We also found that primary Vav2−/−;Vav3−/− keratinocytes were more susceptible to programmed cell death upon DMBA treatment or serum starvation in tissue culture , suggesting that the defects detected in vivo were keratinocyte autonomous ( Figure 2F ) . This was a stimulus-dependent defect , because wild-type and mutant keratinocytes displayed similar cell death rates when challenged with other pro-apoptotic agents such as radiomimetic ( bleomycin ) and endoplasmic reticulum stress-inducing ( dithiothreitol ) drugs ( Figure 2F ) . The ectopic expression of either HA-tagged Vav2 or Myc-tagged Vav3 , but not of the control green fluorescent protein ( GFP , ID number: P42212 ) , restored wild-type-like apoptotic rates in both DMBA-treated and serum-starved Vav2−/−;Vav3−/− keratinocytes ( Figure 2G , H ) , further indicating that this survival defect was a direct effect of the Vav2;Vav3 gene deficiency in keratinocytes . We hypothesized that Vav proteins could be also involved in the TPA-dependent promotion phase of skin tumors , an idea consistent with our prior observations indicating that the effect of the double Vav2;Vav3 gene deficiency in the reduction of tumor burden was significantly more conspicuous in DMBA/TPA- than in DMBA/DMBA-treated mice ( Figure 1; compare panels B and H ) . To investigate this possibility , we evaluated the short-term proliferative and inflammatory reaction induced by TPA in the skin of control and Vav2−/−;Vav3−/− mice ( Figure 3A ) . The TPA-induced proliferative response of the epidermis was severely attenuated in the absence of these two proteins , as demonstrated by the limited hyperplasia ( Figure 3B , C ) and the low levels of BrdU incorporation into keratinocytes ( Figure 3D ) detected in the epidermal layers of TPA-treated Vav2−/−;Vav3−/− mice . In vivo BrdU pulse-chase experiments indicated that those proliferative defects were associated with delayed kinetics and a reduced efficiency in the G1/S phase transition induced by TPA in the mutant keratinocytes ( Figure 3E ) . Consistent with this , immunoblot analyses using total cellular extracts obtained from the epidermis of TPA-treated mice showed that the activation ( extracellular regulated kinase , [Erk , ID numbers: 26417 , 26413] , signal transduction and activator of transcription 3 [Stat3 , ID number: 20848] ) or abundance ( cyclin E , ID number: 12447 ) of proteins involved in such cell cycle transition did not take place efficiently in Vav2−/−;Vav3−/− mice ( Figure 3F ) . Furthermore , we observed that the dermis of these animals did not show any sign of neutrophil infiltration ( Figure 3G , H ) or edema-associated thickening ( Figure 3G , I ) , indicating that the inflammatory response that takes places during the tumor promotion phase is totally abated in the absence of Vav proteins . The lack of an inflammatory response led us to explore the potential contribution of Vav2−/−;Vav3−/− inflammatory cells to the proliferative defects found in the epidermis of Vav2−/−;Vav3−/− mice . We observed that such defects still persisted in Vav2−/−;Vav3−/− C57BL/10 mice carrying a wild-type C57BL/6-Ly5 . 1 hematopoietic system ( Figure 4A–C ) , ruling out the possibility that the defective proliferation of the epidermis of the knockout animals could be an indirect consequence of dysfunctional hematopoietic cells . In agreement with those results , we also found that the grafting of Vav2−/−;Vav3−/− C57BL/10 bone marrow cells into lethally irradiated wild-type C57BL/6-Ly5 . 1 mice did not have any detectable effect on the responsiveness of the epidermis of host animals to TPA ( Figure 4D–F ) . To further assess the keratinocyte autonomous nature of the proliferative defects found in the epidermis of Vav2−/−;Vav3−/− mice , we evaluated the proliferative response of wild-type and Vav2;Vav3-deficient primary keratinocytes to TPA in cell culture . In agreement with the in vivo data , we observed that quiescent Vav2−/−;Vav3−/− keratinocytes incorporated less efficiently the S phase marker 5-ethynyl-2′-deoxyuridine ( EdU ) than their wild-type counterparts upon TPA stimulation ( Figure 5A ) . This was a TPA-specific defect , because Vav2−/−;Vav3−/− keratinocytes showed normal cell cycle progression when stimulated with complete growth media ( Figure 5A ) . Western blot and GTPase-linked immunosorbent ( G-LISA ) assays revealed that the Vav2;Vav3 gene deficiency was associated to reduced amounts of activation of Erk ( Figure 5B , upper panel ) , Stat3 ( Figure 5B , third panel from top ) , and Rac1 ( Figure 5C , upper panel ) in TPA-stimulated cells . It also reduced the basal levels of RhoA ( ID number: 11848 ) activation in nonstimutated cells and , in addition , eliminated the inactivation of RhoA that was typically observed in TPA-stimulated wild-type keratinocytes ( Figure 5C , lower panel ) . Normal levels of Erk and Rac1 activation were observed upon overexpression of HA-tagged Vav2 ( Figure S3A , C ) or Myc-tagged Vav3 in Vav2;Vav3-deficient keratinocytes ( Figure S3B , C ) , thus confirming that those defects were directly due to the Vav2;Vav3 gene deficiency . Furthermore , and consistent with the TPA-specific deficiency of the cell cycle transitions , we observed that those signaling responses were normal when Vav2−/−;Vav3−/− keratinocytes were stimulated with either serum or synthetic CnT07 media ( Figure 5D , E ) . We surmised that a tyrosine kinase had to be involved in this process , because Vav proteins cannot be activated by direct TPA/diacylglycerol binding or protein kinase C ( PKC ) –mediated serine/threonine phosphorylation 9 , 35 . In agreement with this idea , we observed that TPA triggered the tyrosine phosphorylation of both endogenous and ectopically expressed Vav2 in keratinocytes ( Figure 5F ) . This phosphorylation was blocked when cells were pre-incubated with either general PKC ( GF109203X ) or Src family ( PP2 ) inhibitors ( Figure 5F ) prior to the TPA stimulation step . An inactive PP2 analog ( PP3 ) did not have such inhibitory effect on Vav2 tyrosine phosphorylation ( Figure 5F ) . Similar results were obtained with the TPA-mediated stimulation of Erk route ( Figure 5G ) . Although many PKC family members are present in keratinocytes ( Figure S4A ) , we believe that a classical PKC ( cPKC ) must be involved , because we could reproduce the lack of Rac1 activation typically seen in Vav2−/−;Vav3−/− keratinocytes when we incubated the wild-type counterparts with Gö6976 ( Figure 5H ) , a cPKC-specific inhibitor that does not inactivate PKCs belonging to the novel or atypical subclasses [36] . A similar effect was observed when Fyn ( ID number: 14360 ) was knocked down in wild-type cells using short hairpin RNA ( shRNA ) techniques ( Figures 5H and S4B ) , indicating that this kinase was the Src family member preferentially involved in this signaling response . This is consistent with previous results reporting the TPA-mediated stimulation of this kinase in mouse keratinocytes [37] . These results indicate that Vav proteins act downstream of a cPKC/Fyn signaling route that mediates the pro-mitogenic effects of TPA in keratinocytes . We next considered the possibility that Vav proteins could control , in addition to the intrinsic signaling programs of keratinocytes described above , the stimulation of long-range autocrine/paracrine programs in the skin . This idea was consistent with the long-term defects seen in the activation/expression of G1/S phase-related signaling proteins in the epidermis of TPA-stimulated Vav2;Vav3-deficient mice ( Figure 3F ) and , in addition , by the total lack of inflammatory response found in the skin of those mice ( Figure 3G–I ) . To explore this idea , we carried out microarray experiments to identify the fraction of the TPA-induced transcriptome of the skin ( epidermis plus dermis ) that was Vav-dependent . We found a significant subset of TPA-regulated transcripts whose upregulation ( Figure S5A; right panel , A1 cluster; for functional annotation , see Table S3A , B ) or repression ( Figure S5A; right panel , A2 cluster; for functional annotation , see Table S3C , D ) was Vav2/Vav3-dependent . Interestingly , bioinformatics analyses using a skin tumor microarray dataset [38] revealed that most A1 cluster transcripts displayed a similar up-regulation in DMBA/TPA-induced papillomas and/or cSCCs when compared to normal skin ( Figure S5B , see clusters 2 and 3 ) . Conversely , most A2 cluster mRNAs were expressed in normal skin and down-regulated in papillomas and/or cSCCs ( Figure S5B , see clusters 5 and 6 ) . These results indicated that the short-term Vav2/Vav3-dependent gene signature identified in the above microarray experiments is mostly conserved in fully developed tumors . The functional annotation of the A1 gene cluster revealed a statistically significant enrichment of genes encoding extracellular ligands , including EGF family members ( i . e . , amphiregulin [Areg , ID number: 11839] , tumor growth factor α [TGFα , ID number: 21802] , heparin-binding EGF-like growth factor [HbEGF , ID number: 15200] ) , hepatocyte growth factor ( HGF , ID number: 15234 ) , fibroblast growth factor 7 ( FGF7 , ID number: 14178 ) , vascular endothelial growth factor β ( VEGFβ , ID number: 22340 ) , and a large cohort of cytokines and chemokines ( i . e . , IL1β [ID number: 16176] , interleukin 6 [IL6 , ID number: 16193] ) ( Table S4 ) . The detection of cytokine-encoding transcripts was not due to the infiltration of hematopoietic cells in the samples analyzed , because the A1 cluster did not include myeloid- or lymphocyte-specific genes ( Table S3A ) . In silico analyses indicated that many of the genes for those ligands could be regulated by transcriptional factors belonging to the Stat ( p = 6 . 4×10−6 ) , nuclear factor of activated T-cells ( NFAT; p = 0 . 002 ) , nuclear factor kappa-light-chain-enhancer of activated B cells ( NFκB; p = 0 . 004 ) , AP1 ( p≤0 . 05 ) , and E2F ( p = 0 . 03 ) families . We confirmed by quantitative RT-PCR ( qRT-PCR ) that Vav proteins were important for the TPA-mediated induction of mRNAs for EGF family ligands , HGF , FGF7 , IL6 , and IL1β both in vivo ( Figure S5C ) and in vitro ( Figure S5D ) . The only exception found for the correlation between whole skin and cultured keratinocytes was the Tgfa mRNA , which showed a Vav-dependent expression pattern in TPA-stimulated skin ( Figure S5C ) but not in isolated primary keratinocytes ( Figure S5D ) . These results suggest that some of the transcripts detected in the skin microarray experiments probably represent secondary waves of transcriptional activation set in place upon the engagement of other Vav2/Vav3-dependent extracellular ligands . Defects in the production of HGF and IL6 by Vav2;Vav3-deficient mice were confirmed at the protein level using ELISA determinations in skin and serum samples obtained from TPA-treated animals ( Figure S6A ) and , in the case of IL6 , by carrying out immunohistochemical analyses in skin sections ( Figure S6B ) . The TPA-mediated up-regulation of these Vav-dependent transcripts was abolished in wild-type keratinocytes upon the Fyn mRNA knockdown ( Figure S7 ) , indicating that this autocrine/paracrine program is one of the downstream responses triggered by the TPA/cPKC/Fyn/Vav pro-mitogenic route previously characterized in keratinocytes ( see above , Figure 5 ) . This Vav-dependent autocrine/paracrine program was keratinocyte-specific , since it was mostly absent in the recently described Vav-dependent transcriptome of mouse breast cancer cells [17] . Indeed , these two transcriptomes only shared the Areg , Hbegf , Tnfa , Il24 ( ID number: 93672 ) , Il23a ( ID number: 83430 ) , Osm ( ID number: 18413 ) , and Cxcl14 ( ID number: 57266 ) transcripts ( Table S4 ) . By contrast , we observed that the majority of the Vav2/Vav3-dependent transcripts previously found to be involved in the lung-specific metastasis of breast cancer cells was also present in the Vav-dependent transcriptome of TPA-stimulated skin ( Inhba [ID number: 16323] , Ptgs2 [ID number: 19225] , Tacstd2 [ID number: 56753]; Figure S8 ) [17] . The only exception was Ilk ( integrin-linked kinase , ID number: 16202 ) , which was repressed rather than induced by TPA independently of the expression status of Vav proteins ( Figure S8A ) . This indicates that the Vav-dependent transcriptome contains both cell-type-dependent ( i . e . , the autocrine/paracrine program ) and -independent ( i . e . , lung metastasis-related genes ) subsets . Given the critical roles that extracellular factors play in both the initiation and promotion phase of skin tumors [29] , [30] , we investigated whether they could be involved in the tumorigenic defects observed in Vav2−/−;Vav3−/− keratinocytes . If so , we speculated that the experimental manipulation of the extracellular environment had to rescue wild-type-like responses in them . To this end , we first checked whether the survival defects exhibited by DMBA-treated keratinocytes could be overcome when co-culturing them in the presence of equal numbers of wild-type cells . To distinguish each cell subpopulation in the mixed cultures , one of the subpopulation was labeled with a cell permeable chromophore prior to the co-culturing step . Using annexin V flow cytometry , we found that wild-type cells restored normal survival rates to both DMBA and serum deprivation in the co-cultured Vav2−/−;Vav3−/− keratinocytes ( Figure 6A ) . A similar protective effect was found when we included , without wild-type cells , ligands for transmembrane tyrosine kinase receptors ( epidermal growth factor [EGF , ID number: 13645] , TGFα , HGF , FGF7 ) in the culture media of Vav2−/−;Vav3−/− keratinocytes ( Figure 6B ) . By contrast , the addition of IL6 protected wild type but not Vav2−/−;Vav3−/− keratinocytes in the same type of experiments ( Figure 6B ) . The cell cycle defects shown by those cells under TPA-stimulation conditions were also eliminated when the serum-free media was supplemented with either transmembrane tyrosine kinase receptor ligands or IL1β ( Figure 6C ) . However , as in the apoptotic assays , we observed that IL6 could induce cell cycle entry in wild-type but not in Vav-deficient keratinocytes ( Figure 6C ) . This lack of responsiveness was not due to abnormal expression of any of the two IL6 receptor ( IL6-R ) subunits ( Figure S9 ) , indicating that Vav2;Vav3-deficient keratinocytes have , in addition to the general defect in the generation of the autocrine/paracrine program , a specific signaling defect downstream of the IL6-R . Consistent with this idea , immunoblot analyses indicated that EGF ( Figure 6D ) , but not IL6 ( Figure 6E ) , could trigger proper phosphorylation levels of Erk and Stat3 in Vav2−/−;Vav3−/− keratinocytes . This was a direct consequence of the Vav2;Vav3 gene deficiency , because we could restore normal phosphorylation levels of Erk and Stat3 downstream of the IL6-R upon the re-expression of either HA-tagged Vav2 ( Figure 6F ) or Myc-tagged Vav3 ( Figure 6G ) in Vav2−/−;Vav3−/− cells . The implication of Vav proteins in the signaling of the IL6-R was further demonstrated by the observation that IL6 triggered tyrosine phosphorylation of endogenous Vav2 in wild-type keratinocytes ( Figure 6H ) . To further evaluate the relevance of the Vav-dependent autocrine/paracrine program , we investigated whether the intradermal injection of mitogens could eliminate the proliferative and inflammatory defects seen in the skin of TPA-treated Vav2−/−;Vav3−/− mice . We observed that the injection of EGF , TGFα , or FGF7 induced similar levels of hyperplasia ( Figure 7A ) and BrdU immunoreactivity ( Figure 7B ) in the epidermis of wild-type and Vav2;Vav3-deficient mice . The simultaneous application of TPA resulted in a synergistic proliferative response in the epidermis of animals of both genotypes ( Figure 7C , D ) . The intradermal injection of IL6 could trigger a robust , TPA-like mitogenic response in the epidermis of control but not Vav2−/−;Vav3−/− animals ( Figure 7A–D ) , thus recapitulating the signaling defects observed in primary keratinocyte cultures ( see above; Figure 6 ) . IL6 , but not the other ligands tested , did induce a potent infiltration of neutrophils in the skin of both wild-type and knockout mice ( Figure 7E , F ) . This indicates that , unlike the case of keratinocytes , the IL6-R does not require the presence of Vav2 and Vav3 for proper signaling in myeloid cells . This is not due to functional compensation events from Vav1 , because IL6-treated Vav1−/−;Vav2−/−;Vav3−/− mice showed neutrophil infiltration rates similar to both wild-type and Vav2−/−;Vav3−/− animals ( Figure 7F , right panel ) . Finally , we investigated whether the tumors that developed in Vav2;Vav3-deficient mice could be the result of the outgrowth of cancer cells that , due to selection events , could have compensated the lack of Vav proteins by the exacerbation of other signaling routes . We suspected that such compensation could occur in this case because the few tumors that developed in mutant mice displayed a high similarity to those found in control animals in terms of size distribution , proliferation rates , and differentiation stage ( unpublished data ) . Based on the above , we decided to compare the abundance of transcripts encoding a variety of mitogenic ligands in papilloma and cSCC samples obtained from FVB mice of both genotypes . In addition , we monitored the expression pattern in those samples of Vav2/Vav3-dependent genes that were common between breast cancer cells and TPA-stimulated keratinocytes [17] . We observed that the Vav2/Vav3-dependent Tgfa , Hgf , Fgf7 , and Il6 transcripts showed reduced abundance in papillomas derived from Vav2/Vav3-deficient mice when compared to the levels present in control mouse tumors ( Figure 8A ) . This reduction , however , was milder than the defect originally seen in the TPA-stimulated skin of Vav2−/−;Vav3−/− mice ( see above , Figure S5C ) . Other Vav2/Vav3-dependent ( Areg , Hbegf ) and -independent ( Egf , Btc [ID number: 12223] ) transcripts displayed similar levels in papillomas regardless of the Vav2/Vav3 expression status , further suggesting that the autocrine/paracrine defect was ameliorated in these tumors ( Figure 8A ) . Such compensation event was exacerbated in carcinomas , since we observed a striking up-regulation of the abundance of EGF-R family ligand transcripts in all cSCC samples derived from mutant mice ( Figure 8B ) . Such up-regulation took place irrespectively of whether the ligands were of the Vav-dependent ( Tgfa , Areg , Hbegf ) or Vav-independent ( Egf , Btc ) subclasses ( Figure 8B ) . Carcinomas from Vav2;Vav3-deficient mice also displayed up-regulation of the Hgf mRNA ( Figure 8B ) . This was not a sign of a total elimination of the Vav2/Vav3 signaling deficiency , because lower levels of Fgf7 and Il6 transcripts were still detected in cSCCs collected from Vav2−/−;Vav3−/− mice ( Figure 8B ) . A similar behavior was observed in the case of the Inhba and Ptgs2 when their abundance was compared between papilloma and cSCCs ( Figure 8C , D ) . However , the Tacstd2 and Ilk transcripts showed no variations in these experiments ( Figure 8C , D ) . These results suggest that the HRasQ61L-transformed keratinocytes have to progressively bypass part of the Vav2/Vav3 signaling deficiency to generate fully developed tumors . This compensation event is not due to Vav1 overexpression , because this transcript shows a similar 3-fold increase in abundance in cSCCs of both wild-type and Vav2−/−;Vav3−/− mice ( n = 4 ) .
Our work indicates that Vav2 and Vav3 play first- and second-level signaling roles in keratinocytes that , although mechanistically different , act in a concerted manner to favor the initiation and promotion phases of CSTs ( Figure 8E ) . From a signaling hierarchical point of view , the earliest role of Vav proteins is to work in a cPKC- and Fyn-dependent signaling cascade that leads to the downstream stimulation of Rac1 and optimal phosphorylation kinetics of both Erk and Stat3 ( Figure 8E , step 1 ) . This route has a direct impact on the G1/S transition of primary keratinocytes . A more distal endpoint of this Vav-dependent route is the engagement of a wide autocrine/paracrine program that favors keratinocyte survival to DNA damage , epithelial hyperplasia , and the formation of an inflammatory microenvironment ( Figure 8E , step 2 ) . This program is composed of extracellular factors ( i . e . , HGF , FGF7 , IL1β ) involved in the monovalent regulation of keratinocyte survival and proliferation during the initiation and promotion phases , respectively . In addition , it contains bivalent extracellular factors ( i . e . , IL6 ) that regulate keratinocyte mitogenesis and the engagement of inflammatory responses during the tumor promotion phase . Finally , Vav proteins play a second-level , keratinocyte-specific role in the signaling route of one of the Vav2/Vav3-dependent extracellular factors , the cytokine IL6 ( Figure 8E , step 3 ) . This biological program may play roles in both inchoate and fully established tumors , as indicated by the similar regulation of the TPA-induced Vav2/Vav3-dependent gene signature in papillomas and cSCCs obtained from DMBA/TPA-treated mice . By contrast , we have observed that the compound Vav2;Vav3 gene deficiency does not induce any overt dysfunction in normal skin development and homeostasis , indicating that the Vav2/Vav3-dependent route only becomes functionally relevant in the epidermis under conditions that require increased signaling thresholds for the assembly of new , pathophysiological-specific programs . To our knowledge , this is the first demonstration of the implication of Vav proteins , Rho GEFs , or any Rho GTPase in the assembly of such large autocrine/paracrine program in either a physiological or pro-tumorigenic scenario . Several indications support the idea that the upstream and downstream roles of Vav proteins in this autocrine/paracrine program are critical for the initiation and promotion phase of inchoate skin tumors and , probably , for long-term tumor sustenance . Thus , previous reports have shown that many of the Vav2/Vav3-dependent extracellular factors regulated by Vav2 and Vav3 contribute to keratinocyte survival , proliferation , and tumorigenesis [29] , [30] . Furthermore , we have shown that all the defects detected in cultured Vav2−/−;Vav3−/− keratinocytes can be effectively bypassed upon their co-culture with wild-type keratinocytes or , alternatively , upon the addition of specific Vav2/Vav3-dependent ligands ( EGF family ligands , HGF , FGF7 , IL1β ) to their cultures . Similar results were observed when these rescue experiments were carried out in vivo using intradermal injections of those extracellular factors in Vav2−/−;Vav3−/− mice . However , and in good agreement with the critical role of Vav proteins downstream of the IL6-R , IL6 could not restore the survival and mitogenic defects of Vav2/Vav3-deficient keratinocytes when used in vitro or in vivo . It is likely that these second-level signaling defects also contribute to the lower tumor burden observed in Vav2−/−;Vav3−/− animals , since previous reports have shown that IL6-mediated signaling is essential for skin tumorigenesis [39] . Interestingly , we observed that IL6 did restore the defective inflammatory response observed in the skin of TPA-stimulated Vav2−/−;Vav3−/− animals . This result indicates that IL6 is indeed part of the Vav2/Vav3-dependent pro-mitogenic and pro-inflammatory program of keratinocytes and , in addition , that the critical role of Vav proteins downstream of the IL6-R is keratinocyte-specific . The use of Vav1−/−;Vav2−/−;Vav3−/− mice has ruled out the possibility that the normal chemoattraction of Vav2−/−;Vav3−/− neutrophils induced by IL6 could be due to functional compensation events mediated by the hematopoietic-specific Vav1 protein [9] . Thus , these cell-type-specific differences could be due to the presence of other IL6-regulated Rho GEFs in neutrophils ( e . g . , P-Rex1 ) [40] or , alternatively , reflect the implication of Vav proteins in a proliferative/survival signaling branch of the IL6-R that is not important for migration and the induction of the pro-inflammatory program . Additional experiments using wild-type and Vav family-deficient neutrophils will be needed to discriminate those possibilities . Finally , the pathophysiological importance of this program during skin tumorigenesis is further highlighted by our in silico analyses indicating that the Vav2/Vav3-dependent transcriptomal signature is conserved in fully developed tumors and , in addition , by the exacerbated up-regulation of most transcripts for those autocrine/paracrine factors consistently seen in cSCCs from Vav2−/−;Vav3−/− mice ( Figure 8E ) . We surmise that these experiments have only revealed the tip of the iceberg of this biological program , because the Vav2/Vav3-dependent transcriptomal signature encodes other pro-mitogenic and pro-inflammatory factors that have not been yet analyzed . It also contains factors with assigned roles in angiogenesis ( i . e . , Cyr61 [ID number: 16007] , IL6 , VEGFβ ) , the polarization of the TH cell response towards the TH1 ( i . e . , Ccl3 [ID number: 20302] , Ccl4 [ID number: 20303] , Cxcl5 [ID number: 20311] , IL1β , Spp1 [ID number: 20750] , TNFα ) , and TH17 ( i . e . , Csf3 [ID number: 12985] , Cxcl2 [ID number: 20310] , Cxcl5 , IL6 , IL10 [ID number: 16153] ) subtypes or the tumor-induced “education” of dermal fibroblasts ( i . e . , IL1β ) [29] , [30] , [41]–[43] , thus suggesting that the engagement of the Vav2/Vav3 route in keratinocytes could result in a widespread reprogramming of the tissue microenvironment ( Figure 8E ) . This biological program is also endowed with multiple positive feedback mechanisms that can lead to self-amplification and long-term signal sustenance upon its initial engagement in keratinocytes . An obvious signaling relay is the Vav→IL6→IL6-R→Vav→Stat3 route , since it is known that the stimulation of phospho-Stat3 re-feeds this autocrine loop by activating transcriptionally the Il6 gene [44] , [45] . The targeted inflammatory and stromal cells provide additional options for amplification and diversification of signals , since those cells can turn on additional paracrine mechanisms that target keratinocytes and stromal cells [41]–[43] . Thus , it is likely that the initial activation of the Vav route in keratinocytes will kindle a “butterfly effect” that will induce widespread and reciprocal signaling interactions among keratinocytes , resident stromal cells , and newcomer inflammatory and immune cells . Our work also sheds light on issues related to functional redundancies with the Rho GEF family and Vav subfamily during the initiation and promotion phases of skin tumors . On the one hand , the cell reconstitution experiments indicate that Vav2 and Vav3 seem to act redundantly in all the biological processes analyzed in this work . Consistent with this idea , we have observed that single Vav2−/− and Vav3−/− knockout mice do not show the initiation and promotion defects found in the compound Vav2;Vav3-deficient mice ( M . M . -M . and X . R . B . , unpublished data ) . On the other hand , this program seems to be quite idiosyncratic for Vav proteins as inferred by the detection of a phenotype despite the large number of Rho GEFs that , according to our bioinformatic array analyses , are present in normal skin , papilloma , and cSCCs . The cancer-linked phenotype of Vav2/Vav3-deficient mice is also quite different from that previously reported for Tiam1-deficient mice during both tumor initiation [4] , [5] and papilloma/cSCC malignant progression [4] . In this context , the observation that subsets of Rho GEFs are differentially regulated in normal skin , papilloma , and cSCCs suggests the specific engagement of physiological- and tumor-stage-specific Rac1- , RhoA , and Cdc42 ( ID number: 12540 ) -dependent programs that may contribute to both skin homeostasis and pathophysiology . These results emphasize the importance of extending animal-model-based genetic analysis to all Rho GEF family members . Our results suggest that the pharmacological targeting of Vav proteins could be a potentially useful strategy in skin cancer . Since our data have been generated using mice lacking Vav proteins from the initiation stage , they could only be formally used to establish the value of such therapies at the prevention rather than the remediation level . However , preventive therapies are interesting in this case because CSTs are known to develop at high frequency and multiplicity in individuals with actinic keratosis or in patients treated with some immunosuppressants , antifungal antibiotics , or antitumoral therapies ( i . e . , B-Raf ( ID number: 109880 ) inhibitors ) [46] . Assessing the value of such potential therapies in the case of fully developed or metastasized CSTs will require the generation of chemically inducible knock-in systems . In any case , we can anticipate from the present data that anti-Vav therapies will elicit much fewer intrinsic side effects in the skin than those based on the inactivation of either Tiam1 or Rac1 .
All animal work has been done in accordance with protocols approved by the Bioethics committees of both the University of Salamanca and CSIC . To analyze the expression of Rho GEFs mRNAs in skin and tumors , raw data containing samples from normal tail skin ( n = 83 ) , papillomas ( n = 60 ) , and carcinomas ( n = 68 ) was downloaded from the Gene Expression Omnibus website ( Accession number: GSE21264 ) . These data were obtained in Balmain's laboratory using Affymetrix Mouse Genome 430 2 . 0 arrays and animals belonging to a mixed Mus musculus FVB/Mus spretus genetic background [38] . Signal intensity values were obtained from CEL files after RMA . Probesets corresponding to Rho/Rac GEFs were extracted from the dataset and ANOVA analysis used to identify Rho/Rac GEF-encoding transcripts that were differentially expressed between normal tail , papillomas , and carcinomas ( p value <0 . 01 ) . For those genes with more than one probeset significantly deregulated , we selected the one with the lowest p value for graphic representation . Total RNA was extracted from the indicated cells using Trizol ( Sigma ) and quantitative RT-PCR performed using the QuantiTect SYBR Green RT-PCR kit ( Qiagen ) and the iCycler machine ( Bio-Rad ) or , alternatively , the Script One-Step RT-PCR kit ( BioRad ) and the StepOnePlus Real-Time PCR System ( Applied BioSystems ) . Raw data were then analyzed using either the iCycler iQ Optical System software ( Bio-Rad ) or the StepOne software v2 . 1 ( Applied Biosystems ) . We used the abundance of the endogenous Gapdh mRNA as internal normalization control . Primers used for transcript quantitation included 5′-TTG CCC AGA ACA AAG GAA TC-3′ ( forward for mouse Vav1 ) , 5′-AAG CGC ATT AGG TCC TCG TA-3′ ( reverse for mouse Vav1 ) , 5′-AAG CCT GTG TTG ACC TTC CAG-3′ ( forward for mouse Vav2 ) , 5′-GTG TAA TCG ATC TCC CGG GAT-3′ ( reverse for mouse Vav2 ) , 5′-GGG TAA TAG AAC AGG CAC AGC-3′ ( forward for mouse Vav3 ) , 5′-GCC ATT TAC TTC ACC TCT CCA C-3′ ( reverse for mouse Vav3 ) , 5′-GGC AAA AAG TCA GTC CGA CC-3′ ( forward for mouse Fyn , transcript variants 1 and 2 ) , 5′-AAA GCG CCA CAA ACA GTG TC-3′ ( reverse for mouse Fyn , transcript variants 1 and 2 ) , 5′-TCG TGG CAA AAG AGC TTG GA-3′ ( forward for mouse Fyn , transcript variant 3 ) , 5′-TAG GGT CCC AGT GTG AGA GG-3′ ( reverse for mouse Fyn , transcript variant 3 ) , 5′-CGT CCG CCA TCT TGG TAG AGA GAG CAT-3′ ( forward for mouse Cd3e ) , 5′-CTA CTG CTG TCA GGT CCA CCT CCA C-3′ ( reverse for mouse Cd3e ) , 5′-ATG CTA GCG ATG CAT GAG TG-3′ ( forward for mouse Tgfa ) , 5′-CAG GGA CTT TCT TGC CTG AG-3′ ( reverse for mouse Tgfa ) , 5′-CGG TGG AAC CAA TGA GAA CT-3′ ( forward for mouse Areg ) , 5′-TTT CGC TTA TGG TGG AAA CC-3′ ( reverse for mouse Areg ) , 5′-GCT GCC GTC GGT GAT GCT GAA GC-3′ ( forward for mouse Hbegf ) , 5′-GAT GAC AAG AAG ACA GAC G-3′ ( reverse for mouse Hbegf ) , 5′-GCA GAC ACC ACA CCG GCA CAA-3′ ( forward for mouse Hgf ) , 5′-GCA CCA TGG CCT CGG CTT GC-3′ ( reverse for mouse Hgf ) , 5′-TTT GGA AAG AGC GAC GAC TT-3′ ( forward for mouse Fgf7 ) , 5′-GGC AGG ATC CGT GTC AGT AT-3′ ( reverse for mouse Fgf7 ) , 5′-CTT CCT ACC CCA ATT TCC AAT G-3′ ( forward for mouse Il6 ) , 5′-ATT GGA TGG TCT TGG TCC TTA GC-3′ ( reverse for mouse Il6 ) , 5′-ACG GAC CCC AAA AGA TGA AGG GCT-3′ ( forward for mouse Il1b ) , 5′-GGG AAC GTC ACA CAC CAG CAG G-3′ ( reverse for mouse Il1b ) , 5′-CGC TGC TTT GTC TAG GTT CC-3′ ( forward for mouse Ereg ) , 5′-GGG ATC GTC TTC CAT CTG AA-3′ ( reverse for mouse Ereg ) , 5′-CCC AGG CAA CGT ATC AAA GT-3′ ( forward for mouse Egf ) , 5′-CCC AGG AAA GCA ATC ACA TT-3′ ( reverse for mouse Egf ) , 5′-GGA ACC TGA GGA CTC ATC CA-3′ ( forward for mouse Btc ) , 5′-TCT AGG GGT GGT ACC TGT G-3′ ( reverse for mouse Btc ) , 5′-TGC ACC ACC AAC TGC TTA GC-3′ ( forward for mouse Gapdh ) , and 5′-TCT TCT GGG TGG CAG TGA TG-3′ ( reverse for mouse Gapdh ) . Primers used for quantitating Inhba , Ptgs2 , Tacstd2 , and Ilk transcripts were described before [17] . To calculate the number of copies of mouse Vav family mRNAs in cell/tissue samples , the Ct values obtained for the amplified Vav1 , Vav2 , and Vav3 cDNA fragments by qRT-PCR in each sample were compared with those obtained using serial dilutions of plasmids of known concentration containing the Vav1 ( pJLZ52 ) [13] , Vav2 ( pCCM33 ) [17] , and Vav3 ( pCCM31 ) [17] cDNAs . The number of copies obtained for each transcript was finally calculated using this titration curve and the size of each plasmid . Single and compound Vav family knockout mice were described elsewhere [19] , [23] , [47]–[49] . For long-term carcinogenesis experiments , the backs of animals of the indicated genotypes were shaved and , 2 d later , the two-step carcinogenic DMBA/TPA protocol initiated using a single topic application of DMBA ( 25 µg diluted in 200 µl of acetone , both from Sigma ) . The promotion phase consisted of biweekly applications of TPA ( 200 µl of a 1×10−4 M solution in acetone , Sigma ) during a 20-wk-long period . For complete carcinogenesis , mice were treated biweekly with 5 µg of DMBA alone in 200 µl of acetone during 20 wk . The number , size ( measured with a digital caliper ) , and incidence of papilloma was determined weekly . At the end of the experiment , animals were injected intraperitoneally with BrdU ( 100 µg/g body weight , Sigma ) and euthanized 1 h later . For short-term studies of in vivo epidermal apoptosis , the dorsal skin of mice was treated with a single application of either DMBA ( 25 nmol in 200 µl of acetone ) or acetone ( 200 µl ) 2 d after shaving . For short-term in vivo proliferation assays , the dorsal skin of mice was treated with either one or four applications of either TPA ( 6 . 8 nmol in 200 µl acetone ) or carrier solution 2 d after shaving . Animals were injected with BrdU and euthanized , as indicated above . For the determination of in vivo cell cycle transitions , animals were treated with a single topic application of TPA and , 1 h before euthanasia , injected intraperitoneally with BrdU . Alternatively , TPA-treated animals were euthanized and the epithelial layer obtained to generate tissue cell extracts . For intradermal injection of mitogens and cytokines , mice were anesthetized and injected under the epidermal layer of their skin with 10 µl of either the appropriate protein-containing saline solution or the control buffer injected using a 30-gauge needle and a Hamilton microsyringe . The concentration of ligands ( Peprotech ) in the injection solution was 100 ng/ml . Mice were then BrdU labeled and killed as above . For bone marrow reconstitution experiments , 3–5×106 cells of donor bone marrow cells were injected intravenously into recipient mice that were previously subjected to sublethal doses of irradiation ( 600 rad; 1 Gy = 100 rads ) . In these experiments , we used “wild-type” C57BL/6 Ly5 . 1 mice in order to distinguish the hematopoietic populations derived from them ( which express the CD45 . 1 surface marker ) and from the knockout C57BL/10 mice ( expressing the CD45 . 2 surface marker ) . Reconstitution experiments were done in both orientations . Eight weeks after injection , peripheral blood samples were collected from the cheek vein and the proper reconstitution of the immune system by the injected donor cells evaluated using flow cytometry . Surface markers used in the cytometry experiments included an allophycocyanin-labeled mouse monoclonal antibody to CD45 . 2 , a biotin-labeled mouse monoclonal antibody to mouse CD45 . 1 , a biotin-labeled rat monoclonal antibody to mouse CD45R/B220 , a biotin-labeled rat monoclonal antibody to mouse CD19 , a phycoerytrin-labeled rat monoclonal antibody to mouse CD11b , a phycoerytrin-labeled hamster monoclonal antibody to mouse CD3ε ( all from BD Pharmingen ) , a biotin-labeled hamster monoclonal antibody to mouse TCRβ , and a phycoerytrin-labeled rat monoclonal antibody to mouse CD19 ( both from eBioscience ) . Bone-marrow-reconstituted animals were then subjected to topic TPA treatments in the skin as indicated above . Whereas the long-term carcinogenesis assays were done in animals of both the FVB and C57BL/10 genetic backgrounds , we selected animals of the C57BL/10 background for the short-term studies . This facilitated comparative studies with other Vav family knockout animals ( i . e . , single Vav1−/− and triple Vav1−/−;Vav2−/−;Vav3−/− mice , which were all homogenized in that background ) as well as the bone marrow reconstitution experiments ( which required transplantation between genetically compatible donor and recipient mice ) . At the beginning of each experimental procedure , cohorts of 6–8-wk-old animals with an even gender distribution of each genotype subset were used . Samples from tumors and short-term skin experiments were processed following three independent protocols: ( i ) Fixed in 4% paraformaldehyde ( Sigma ) , ( ii ) cryoprotected and stored at −70°C , and ( iii ) snap-frozen and stored at −70°C for molecular analyses ( i . e . , RNA and protein extraction ) . Tissues were extracted , fixed in 4% paraformaldehyde ( Sigma ) , cut in 2–3 µm thick sections , and stained with hematoxylin/eosin . Tumor sections were analyzed by pathologists to classify them according to malignancy grade ( benign , benign plus carcinoma in situ , malignant ) and level of differentiation ( high , mild poor ) . In short-term experiments , the thickness of the epidermal layer and total skin thickness was measured in vertical cross-sections in at least 10 different locations in each mouse to determine epidermal hyperplasia and inflammatory response ( edema ) , respectively . For standard immunohistochemical staining , paraffin-embedded sections were dewaxed , microwaved in citrate buffer , blocked with 10% nonimmune horse serum ( Gibco ) , and incubated overnight with the appropriate primary antibody at 4°C . After exhaustive washing in 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 2 mM KH2PO4 , and 0 . 1% Tween-20 ( PBST ) , sections were incubated with appropriate biotin-coupled secondary antibodies ( all used at a 1∶1 , 000 dilution in PBST ) followed by avidin-peroxidase ( ABC Elite Kit Vector , Vector labs ) . Positive staining was determined using diaminobenzidine as a substrate ( DAB Kit Vector , Vector labs ) following the manufacturer's recommendations . Sections were then counterstained with hematoxylin and mounted . Images were captured using an Olympus BX51 microscope coupled to an Olympus DP70 digital camera . Staining quantification was blindly assessed by two independent investigators and classified according to intensity ( 0–10 ) and estimation of ratio of cells stained ( 0–1 ) . The relative value for each section was scored as the product of intensity×ratio given a general score ranging from 0 to 10 . For immunofluorescent staining , sections treated as before were incubated with either a Alexa Fluor 488–labeled goat anti-rabbit IgG antibody ( Molecular Probes , 1∶200 dilution ) or a Cy3-conjugated goat anti-mouse IgG antibody ( Jackson Immunoresearch laboratories , Inc , 1∶200 dilution ) , washed , and countersatined with 4′ , 6-diamidino-2-phenylindole ( DAPI; 2 ng/ml , Sigma ) with or without rhodamine-labeled phalloidin ( Molecular Probes , 1∶200 dilution ) . Primary antibodies used in these experiments included rabbit polyclonal antibodies to keratin 1 , keratin 5 , keratin 14 , and filaggrin ( Covance , 1∶500 dilution in each case ) , rabbit polyclonal antibodies to IL6 ( Genzyme , 1∶100 dilution ) , rat monoclonal antibodies to keratin 8 ( Troma1 , not commercial; 1/5 dilution of the hybridoma supernatant ) and CD45 ( BD Biosciences , 1∶50 dilution ) , mouse monoclonal antibodies to keratin 10 ( Santa Cruz Biotechnology , 1∶50 dilution ) , keratin 13 ( Sigma , 1∶50 dilution ) and histone H2A . X ( Cell Signaling , 1∶100 dilution ) , and a rabbit polyclonal antibody to myeloperoxidase ( Abcam , 1∶250 dilution ) . To detect proliferating cells , dewaxed sections were denaturalized in 2N HCl at 37°C for 1 h , washed extensively in 0 . 1 M borate buffer , blocked with 2% nonfat dry milk ( Nestlé ) in 0 . 1% Triton-PBS , and incubated overnight with a mouse monoclonal antibody to BrdU ( BD Biosciences , 1∶400 dilution ) . To detect apoptotic cells , we used two different approaches . In some cases , tissue sections were deparafinized , hydrated , digested with proteinase K ( Dako ) for 30 min at 37°C , and subjected to the Tunel reaction using the Tunel-based In Situ Cell Detection kit ( Roche ) as indicated by the manufacturer's instructions . Alternatively , tissues sections were cryoprotected using stepwise immersions in 15%–30% sucrose-PBS solutions , embedded in Tissue-Tek OCT ( Sakura Finetek Europe ) , and stored frozen at −70°C . Upon thawing , slides were blocked as described before and incubated with a rabbit polyclonal antibody to cleaved caspase 3 ( Cell Signaling , 1∶50 dilution ) . Images from immunofluoresce experiments were captured using a Leica CTR600 microscope . Quantifications of both standard and immunoflurescence signals were done with the Metamorph-Metaview software ( Universal Imaging ) . Epidermis from neonates of the indicated genotypes were treated with 250 U/ml of dispase ( Roche ) overnight at 4°C and keratinocytes prepared using CnT07 media ( CELLnTEC ) according to the manufacturer's instructions . Keratinocytes were maintained in CnT07 media on type I collagen-precoated plates ( BD Biosciences ) . When genotypically mixed cultures were utilized , the keratinocytes of one the genotypes used were labeled in culture with a cell permeable chromophore ( CellTracker green CMFDA , Invitrogen ) for 30 min according to the manufacturer's instructions . Labeled and nonlabeled cultures were trypsinized and plated either as genotypically pure or mixed populations in tissue culture plates . Apoptotic rates in vitro were determined by flow cytometry using the Annexin V kit ( Immunostep ) . In vitro cell cycle transitions were determined using the Click-iT EdU Alexa Fluor 647 Flow Cytometry Assay Kit ( Invitrogen ) . For in vitro apoptotic assays , exponentially growing keratinocytes of the indicated genotypes were either serum starved in Eagle's minimal essential medium ( EMEM , Lonza ) supplemented with 0 . 05 mM CaCl2 ( Sigma ) or , alternatively , treated with 100 µM DMBA , 0 . 15 µM bleomycin ( Sigma ) , or 2 mM dithiothreitol ( Sigma ) . Cells were then harvested 8–24 h later and the number of apoptotic cells determined by flow cytometry using the Annexin V kit ( Immunostep ) . In apoptotic experiments using keratinocytes ectopically expressing proteins , cells that had integrated the lentiviral particle ( GFP+ ) were gated away from noninfected ( GFP− ) cells . A similar gating strategy ( presence/absence of the CellTracker green CMFDA chromophore ) was used to characterize apoptotic rates in genotypically mixed cell cultures . In rescue experiments with extracellular ligands , these factors were added at the beginning of the starvation period at a concentration of either 10 ng/ml ( EGF , TGFα , HGF , FGF7; all obtained from Peprotech ) or 100 ng/ml ( IL6 , Peprotech ) and apoptosis quantified as above after an overnight incubation . For keratinocyte G1/S phase transition assays , cells were starved for 3 h as above and then stimulated by the addition EMEM supplemented with 0 . 2 µM TPA , 10 ng/ml of receptors for transmembrane tyrosine kinases ( EGF , TGFα HGF , FGF7 ) , 10 ng/ml IL1β ( Peprotech ) , 100 ng/ml IL6 , or 8% calcium-chelated fetal calf serum . After 4 h , cells were incubated with the EdU reactive ( Invitrogen ) for 30 min , and the percentage of cells in S phase ( EdU+ ) determined using the Click-iT EdU Alexa Fluor 647 Flow Cytometry Assay Kit ( Invitrogen ) . For intracellular signaling experiments , exponentially growing primary cells of the indicated genotypes were washed and starved for 3 h in EMEM supplemented with 0 . 05 mM CaCl2 . For stimulation , we used EMEM supplemented with 0 . 2 µM TPA , 10 ng/ml EGF , or 100 ng/ml IL6 for the indicated periods of time . When indicated , the pan-PKC GF109203X ( 5 µM , Sigma ) , the classical PKD-specific Gö6976 ( 3 µM , Calbiochem ) , and the Src family PP2 ( 2 µM , Calbiochem ) inhibitors were added to the starved keratinocytes 30 min before the stimulation with TPA . The PP3 molecule was used as negative control for the PP2 experiments ( 2 µM , Calbiochem ) . When indicated , HA-Vav2 , Myc-Vav3 with or without GFP were expressed in wild-type ( GFP ) or Vav2−/−;Vav3−/− ( each of the above proteins ) keratinocytes using lentiviral delivery methods . To this end , the pCCM33 [17] , pCCM31 [17] , or pCQS1 [21] vectors were transfected into Lenti-X 293T cells ( Clontech ) using the Lenti-X HT packaging mix ( Clontech ) . Viral particles were collected 48 h after transfection , concentrated using the Lenti-X concentrator kit ( Clontech ) , and then used to infect keratinocyctes of the indicated genotypes during 3 consecutive days using centrifugation at 1 , 500× g in the presence of 8 µg/ml polybrene ( Sigma ) . As controls , we carried out infections of wild-type and mutant keratinocytes using either the empty pLVX-IRES-Hyg or the GFP-encoding pcDH1-MCS1-EF1-coGFP ( System Biosciences ) lentivirus . The shRNA-mediated knockdown of Fyn transcripts was carried out using lentiviral particles ( TRC lentiviral Mouse Fyn shRNA; Thermo Scientific ) as previously described [17] . The TRC number and the shRNA sequence yielding the greatest knockdown was clone number TRCN0000023382 ( 5′-AAA CCC AGG GCT GCC TTG GAA AAG-3′ ) . This clone was used in the experiments presented in this work . In the case of tissue extracts , skin samples were excised from the euthanized animals , placed on ice-cold glass plates , the epidermis removed with a razor blade , transferred into a lysis buffer ( 10 mM Tris-HCl [pH 8 . 0] , 150 mM NaCl , 1% Triton X-100 , 1 mM Na3VO4 , 10 mM β-glycerophosphate , and a mixture of protease inhibitors [Cømplete , Roche] ) , and mechanically homogenized using the GentleMACS dissociator ( Miltenyi Biotec ) . In the case of primary keratinocytes maintained in culture , cells were washed with chilled PBS , scrapped in lysis buffer , and disrupted by extensive vortexing ( Ika ) . Extracts were precleared by centrifugation at 14 , 000 rpm for 10 min at 4°C , denatured by boiling in SDS-PAGE sample buffer , separated electrophoretically , and transferred onto nitrocellulose filters using the iBlot Dry Blotting System ( Invitrogen ) . Membranes were blocked in 2% BSA ( Sigma ) in TBS-T ( 25 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 0 . 1% Tween-20 ) for at least 1 h and then incubated overnight with the appropriate antibodies . Those included rabbit polyclonal antibodies to phospho-Erk1/2 ( residues Thr202/Tyr204; Cell Signaling , 1∶1 , 000 dilution ) , total Erk1/2 ( Cell Signaling , 1∶1 , 000 dilution ) , phospho-Stat3 ( residue Tyr705; Cell Signaling , 1∶1 , 000 dilution ) , total Stat3 ( Cell Signaling , 1∶1 , 000 dilution ) , cyclin E ( Abcam , 1∶1 , 000 dilution ) , the Myc epitope ( Upstate/Millipore , 1∶1 , 000 dilution ) , Gp130 ( Cell Signaling , 1∶1 , 000 dilution ) , PKC and PKD ( all from Cell Signaling , 1∶1 , 000 dilution ) , as well as mouse monoclonal antibodies to α-tubulin ( Calbiochem , 1∶1 , 000 dilution ) , phosphotyrosine ( Santa Cruz Biotechnology , dilution 1∶1 , 000 ) , the HA epitope ( Covance , 1∶1 , 000 dilution ) , and IL6-Ra subunit ( Santa Cruz Biotechnology , 1∶500 dilution ) . Homemade rabbit polyclonal antibodies to Vav2 and Vav3 have been previously described [16] , [20] , [24] . After three washes with TBS-T to eliminate the primary antibody , the membrane was incubated with the appropriate secondary antibody ( GE Healthcare , 1∶5 , 000 ) for 30 min at room temperature . Immunoreacting bands were developed using a standard chemoluminescent method ( ECL , GE Healthcare ) . In the case of immunoprecipitations , keratinocyte lysates obtained as above were incubated overnight at 4°C using either a rabbit polyclonal antibody to Vav2 or a monoclonal antibody to HA . Immunocomplexes were collected with Gammabind G-Sepharose beads ( GE Healthcare Life Biosciences ) , washed , and analyzed by immunobloting as above . ELISAs were used to measure the amount of IL6 ( IL6 Mouse ELISA Kit , Invitrogen ) and HGF ( HGF Mouse ELISA kit , Abnova ) according to the manufacturer's instructions . Absorbance at 450 nm was measured immediately at the end of the protocol using a plate reader ( Ultraevolution , Tecan ) . Total cellular lysates were obtained as above , snap frozen , thawed , quantified , and analyzed using Rac1 and RhoA G-LISA assay kits according to the manufacturer's instructions ( Cytoskeleton ) . Absorbance at 490 nm was measured using a Ultraevolution plate reader . All microarray experiments were performed by the personnel of the Genomics and Proteomics Unit of our Institution . Total cellular RNA was extracted from the back skin of wild-type or knockout mice treated as described before ( n = 3 for each treatment ) using the RNAeasy kit ( Qiagen ) , quantified using 6000 Nano Chips ( Agilent ) , and used ( 2 . 3 µg/sample ) to generate labeled cRNA probes according to the manufacturer's instructions ( Affymetrix ) . Purified RNA was processed and hybridized to Affymetrix GeneChip Mouse Gene 1 . 0 ST as indicated elsewhere [17] , [50] . Signal intensity values were obtained from CEL files after robust multichip average [51] , [52] . We performed Pavlidis template matching analysis , in which the Pearson's correlation coefficient is computed between the intensities measured for each gene and the values of an independent variable [53] . The p values to test for the null hypothesis that the correlation is zero are provided . Corrected p values were also calculated ( Q value ) . For functional annotation purposes , genes were considered differentially regulated if their Q values were lower than 0 . 05 . For metagenomic analysis , we used as threshold significance values a Q≤0 . 025 and fold change variations relative to control skin ≥1 . 5 . Graphical representation of microarray data was generated using hierarchical clustering analysis and the BioConductor HCLUST tool . Further bioinformatics were used to check the expression of Vav2/Vav3-dependent mRNAs in normal skin and skin tumors . To that end , probesets within GSE21264 dataset corresponding to A1 and A2 gene clusters were analyzed from differential expression between normal tail , papillomas , and carcinomas using the microarray database indicated above and ANOVA analysis ( p≤0 . 01 ) . For those genes with more than one probeset significantly deregulated , we selected the one with the lowest p value for representation . The raw microarray data generated in this work have been uploaded to the GEO database ( www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=pjcxliqugwowqfq&acc=GSE40849 ) . Differences in tumor multiplicity and incidence were analyzed by the Mann-Whitney U test and the χ2 test , respectively . Other wet lab data were processed using the Student's t or the one tail Mann-Whitney tests . In all cases , p values lower than 0 . 05 were considered statistically significant . p values were represented in all figures as * ( when ≤0 . 05 ) , ** ( when ≤0 . 01 ) , and *** ( when ≤0 . 001 ) . Data obtained are given as the mean ± the s . e . m . | GDP/GTP exchange factors ( GEFs ) involved in Rho GTPase activation have been traditionally considered as potential anticancer drug targets . However , little is known about the best GEFs to inhibit in different tumor types , the pro-tumorigenic programs that they regulate , and the collateral effects that inactivation may induce in healthy tissues . Here , we investigate this issue in HRas-dependent skin tumors using genetic techniques . Despite the large number of Rho GEFs present in both normal and tumoral epidermis , we demonstrate that the co-expression of the exchange factors Vav2 and Vav3 is critical for the development of this tumor type . We also identify a previously unknown Vav-dependent autocrine/paracrine program that favors keratinocyte survival/proliferation and the formation of an inflammatory state during the initiation and promotion phases of this tumor . Finally , our results indicate that inactivation of Vav proteins is innocuous for the homeostasis of normal epidermis . Taken together , these results imply that Vav protein-based therapies may be of interest for skin tumor prevention and/or treatment . |
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High blood pressure ( BP ) is the most common cardiovascular risk factor worldwide and a major contributor to heart disease and stroke . We previously discovered a BP-associated missense SNP ( single nucleotide polymorphism ) –rs2272996–in the gene encoding vanin-1 , a glycosylphosphatidylinositol ( GPI ) -anchored membrane pantetheinase . In the present study , we first replicated the association of rs2272996 and BP traits with a total sample size of nearly 30 , 000 individuals from the Continental Origins and Genetic Epidemiology Network ( COGENT ) of African Americans ( P = 0 . 01 ) . This association was further validated using patient plasma samples; we observed that the N131S mutation is associated with significantly lower plasma vanin-1 protein levels . We observed that the N131S vanin-1 is subjected to rapid endoplasmic reticulum-associated degradation ( ERAD ) as the underlying mechanism for its reduction . Using HEK293 cells stably expressing vanin-1 variants , we showed that N131S vanin-1 was degraded significantly faster than wild type ( WT ) vanin-1 . Consequently , there were only minimal quantities of variant vanin-1 present on the plasma membrane and greatly reduced pantetheinase activity . Application of MG-132 , a proteasome inhibitor , resulted in accumulation of ubiquitinated variant protein . A further experiment demonstrated that atenolol and diltiazem , two current drugs for treating hypertension , reduce the vanin-1 protein level . Our study provides strong biological evidence for the association of the identified SNP with BP and suggests that vanin-1 misfolding and degradation are the underlying molecular mechanism .
Hypertension ( HTN ) or high blood pressure ( BP ) is common in populations worldwide and a major risk factor for cardiovascular disease ( CVD ) and all-cause mortality [1] . Although it is observed across ethnically diverse populations , the prevalence of HTN in the US varies from 27% in persons of European ancestry to 40% among those of African ancestry [2] . BP is a moderately heritable trait and affected by the combined effects of genetic and environmental factors , with heritable factors cumulatively accounting for 30–55% of the variance [3] . After age 20 , African Americans have higher BP than other US race/ethnicities [4]–[6] and the progression from pre-HTN to HTN occurs one year eariler on average [7] . Increased rates of HTN among African Americans are the main factor contributing to their greater risk of CVD and end-stage renal disease compared to US whites [8] , [9] . Given the widespread occurrence of this condition , and our as yet limited ability to reduce the disease burden , identifying the genetic variants of BP phenotypes could elucidate the underlying biology of high BP and reduce the CVD prevalence . Identification of genetic variants of consequence for HTN remains a significant challenge , owing in large part to the complex and polygenic nature of the disorder and the imprecision with which the phenotype is measured [10] . Using admixture mapping analysis of data from the Family Blood Pressure Program , we recently identified a genomic region on chromosome 6 harboring HTN-associated variants [11] . The same region on chromosome 6 was replicated in an admixture mapping analysis based on the African Americans enrolled in the Dallas Heart Study [12] . By further genotyping the functional variants in the region of interest on chromosome 6 , the VNN1 gene , in particular SNP rs2272996 ( N131S ) was found to account for the association with BP in both African Americans and Mexican Americans , but this association was not observed in European Americans [12] . Fava et al . [13] recently argued that rs2294757 ( T26I ) , rather than N131S , was a more likely functional variant accounting for the effect on BP because it is located in a splicing regulation site in VNN1 , but these investigators only found a weak association between T26I and both DBP and HTN in one of the two studies that they carried out . The results of this study are consistent with the lack of evidence for association observed in European Americans in the Dallas Heart Study [12] . VNN1 encodes the protein vanin-1 , a glycosylphosphatidylinositol ( GPI ) -anchored membrane protein [14] , [15] . Vanin-1 is widely expressed in a variety of tissues , with higher expression in liver , kidney and blood [16] . Vanin-1 is a pantetheinase , a member of the biotinidase branch of the nitrilase superfamily [17] . Vanin-1 hydrolyzes pantetheine to pantothenic acid ( vitamin B5 ) and cysteamine , a potent regulator of oxidative stress . In vanin-1 null mice free cysteamine is undetectable , indicating vanin-1's indispensable role in generating cysteamine under physiological conditions [18] . Therefore , vanin-1 plays an essential role in regulating oxidative stress via cysteamine generation . A linkage between oxidative stress and HTN has been hypothesized for many years [19]–[21] . Furthermore , vanin-1 was reported to be involved in cardiovascular diseases [22] , [23] . Overexpression of vanin-1 was associated with progression to chronic pediatric immune thrombocytopenia ( ITP ) [24] , and was shown to lead to hyperglycemia [25] . Vanin-1−/− mice showed protective effects against a variety of phenotypes , such as oxidative stress [26] , intestinal inflammation [27] , and colon cancer [28] , mostly due to higher glutathione storage to maintain a more reducing environment . As a consequence , vanin-1's pantetheinase activity may offer a physiologic rationale for BP regulation with loss of vanin-1 function . In this study , we first investigated the association evidence of the missense variant rs2272996 ( N131S ) in VNN1 and BP phenotypes by performing a meta-analysis of nearly 30 , 000 African ancestry subjects from 19 independent cohorts from the Continental Origins and Genetic Epidemiology Network ( COGENT ) . We next examined whether there were other variants in VNN1 associated with BP traits . Lastly , we conducted molecular experiments to establish a functional connection between N131S vanin-1 and HTN .
The study samples were the African-ancestry subjects from the COGENT , which includes 19 discovery cohorts . The details are described elsewhere by Franceschini et al [29] . Briefly , the phenotype-genotype association analysis was performed in each cohort separately . Systolic BP ( SBP ) and diastolic BP ( DBP ) were treated as continuous variables . For individuals reporting the use of antihypertensive medications , BP was adjusted by adding 10 and 5 mmHg to SBP and DBP respectively [30] . SBP and DBP were adjusted for age , age2 , body mass index ( BMI ) and gender in linear regression models . The results of association between SNP rs2272996 and SBP or DBP for the 18 cohorts are presented in Figure 1 . This SNP was not available in the GeneSTAR cohort . The corresponding allele frequencies in the different studies are listed in Supplementary Table S1 . Among the 18 cohorts , 12 and 10 have positive effect sizes for SBP ( P = 0 . 048 ) and DBP ( P = 0 . 24 ) , respectively , comparing to 9 expected under null hypothesis of no association between this SNP and BP . We next performed meta-analysis by applying both fixed-effect [31] , [32] and random-effect [33] models to estimate the overall effect . SNP rs2272996 was significantly associated with SBP in both fixed-effect ( P = 0 . 01 ) and random-effect ( P = 0 . 04 ) models ( Table 1 ) . However , we did not observe evidence of genotype-phenotype association for DBP . Among the individual cohort analyses , the Maywood cohort had a sample size of 743 and was the only cohort that showed significant association with rs2272996 for both SBP ( P = 0 . 016 ) and DBP ( P = 0 . 0003 ) , however the direction of the association was opposite to what was found in the test for overall effects ( Figure 1 ) . The distributions of SBP , DBP , age and BMI did not suggest that the Maywood was an outlier in epidemiologic characteristics ( Supplementary Figure S1 ) , with the exception that the sampling strategy for this cohort was based on exclusion of persons on antihypertensive medications ( antihypertensive medication rate was 0 . 7% ) . The Nigeria cohort also included a low antihypertensive medication rate but this was a result of inaccessibility to medications ( Supplementary Figure S2 ) . When analyses were repeated after exclusion of the Maywood cohort , the association of BP and rs2272996 was substantially improved ( P = 0 . 003 for SBP , Table 1 ) . The different association evidence between Maywood and other cohorts may suggest genetic heterogeneity or possible interaction between gene and environment factors , although further studies are needed to address this possibility . Since additional genetic variants in VNN1 might be associated with BP , we examined available known variants in VNN1 including 10 kb up- and down-stream of the gene . A total of 105 other SNPs were available in the 19 cohorts . SNP rs7739368 had the smallest p values for association with SBP using either fixed-effect model ( P = 0 . 004 ) or random-effect model ( P = 0 . 004 , Supplementary Table S2 ) , but this was not significant after correcting for multiple comparisons . This SNP is ∼7 k bp's upstream of VNN1 adjacent to the PU . 1 transcription factor binding region ( 306 bp's upstream ) . To understand the function of N131S vanin-1 in relation to HTN , plasma samples from Nigeria HTN patients and normotensives with WT ( TT ) or homozygous N131S ( CC ) vanin-1 were collected ( 6 samples per group , 4 groups ) . The same amount of total plasma protein from each sample was subjected to Western blot analysis: a clean vanin-1 protein band appeared at 70 kD ( Figure 2A ) , consistent with previous reports [14] , [34] . The plasma vanin-1 protein in homozygous N131S vanin-1 was significantly lower than that in WT vanin-1 in both hypertensive and normotensive groups ( P = 1 . 64×10−5 and 0 . 014 , Figure 2A , see Figure 2B for quantification ) , indicating that the N131S mutation is a functional variant that is associated with substantially less steady-state vanin-1 protein . Furthermore , the plasma vanin-1 protein in normotensive groups with WT vanin-1 ( samples 13–18 ) was significantly lower than that in HTN patients with WT vanin-1 ( samples 1–6 ) ( P = 0 . 042 ) ( Figure 2A , see Figure 2B for quantification ) . These results demonstrated that vanin-1 expression is associated with both the genotypic N131S mutation and phenotypic HTN , with the former exerting stronger effect . Lastly , the plasma vanin-1 protein in normotensive groups with homozygous N131S vanin-1 ( samples 19–24 ) is also lower than that in HTN patients with homozygous N131S vanin-1 ( samples 7–12 ) although it was not statistically significant ( P = 0 . 13 ) , probably due to the already exceedingly low vanin-1 quantity . These results suggest that the WT vanin-1 is associated with increased plasma vanin-1 protein expression , and increased HTN risk . We tested two variants , N131S and T26I , as regards how they influence the total vanin-1 protein levels because other investigators have suggested that T26I may be a candidate variant for BP variation as well [13] . We utilized the human embryonic kidney 293 ( HEK293 ) cells stably expressing these vanin-1 variants because HEK293 cells have high transfection efficiency and physiologically-relevant cell environment for vanin-1 protein expression [23] . Significantly lower total vanin-1 proteins were detected in the cells expressing N131S vanin-1 , whereas similar vanin-1 protein levels were detected in cells expressing T26I vanin-1 compared to cells expressing WT vanin-1 ( Figure 3A , quantification shown below ) . Because vanin-1 is a GPI-anchored membrane protein , it needs to traffic efficiently to the plasma membrane for its pantetheinase activity . We hypothesized that N131S substantially reduces the trafficking of vanin-1 protein to the plasma membrane , whereas T26I does not . Using a surface biotinylation assay [35] , we observed that the N131S mutation led to significantly lower plasma membrane expression , whereas in cells expressing the T26I mutation , vanin-1 surface expression was similar to that observed in WT cells ( Figure 3B , quantification shown below ) . We further confirmed that the variation in vanin-1 protein expression resulted in corresponding functional consequences for N131S and T26I mutations . A cell-based fluorescence assay was carried out to record the kinetics of pantetheinase activity by vanin-1 variants [36] . The cells expressing T26I vanin-1 had similar pantetheinase activity compared to cells expressing WT vanin-1; however , cells expressing N131S vanin-1 retained approximately 9% of the pantetheinase activity , by quantifying the fluorescence signals at the kinetic steady state at 57 minutes ( Figure 3C ) . These data taken together provide evidence of less protein , less membrane trafficking , and lower enzymatic activity of the N131S protein as compared to both the wild type and the T26I variant . To determine the mechanism of loss of surface N131S vanin-1 , we sought to confirm that N131S vanin-1 is rapidly degraded . A cycloheximide ( CHX ) chase assay was used to quantify the half-life of vanin-1 variants in HEK293 cells: WT vanin-1 had a half-life of 240 min; T26I vanin-1 , 232 min; N131S vanin-1 , 76 min , respectively ( Figure 4A , quantification in Figure 4B ) . Thus , N131S vanin-1 has a much faster degradation rate than WT vanin-1 , whereas T26I vanin-1 is degraded at a rate similar to that of WT vanin-1 . To confirm that misfolded N131S vanin-1 is subjected to ERAD , we applied MG-132 to the cells , which is a potent proteasome inhibitor . MG-132 treatment resulted in the accumulation of ubiquitinated proteins and substantially more total vanin-1 proteins ( Figure 4C , cf . lane 2 to lane 1 ) , indicating that efficient proteasome inhibition prevents the degradation of N131S vanin-1 . Furthermore , using immunoprecipitation against vanin-1 , we confirmed that MG-132 treatment resulted in ubiquitination of N131S vanin-1 ( Figure 4C , cf . lane 5 to lane 4 ) . These data indicate that N131S vanin-1 is subjected to rapid ERAD , resulting in loss of functional vanin-1 on the plasma membrane . We hypothesize that rapid degradation of N131S vanin-1 resulted from its misfolding in the endoplasmic reticulum ( ER ) . The endoglycosidase H ( endo H ) enzyme selectively cleaves vanin-1 after asparaginyl-N-acetyl-D-glucosamine ( GlcNAc ) in the N-linked glycans incorporated in the ER . After the high-mannose form is enzymatically remodeled in the Golgi , endo H is unable to remove the oligosaccharide chain . Therefore , endo H-resistant vanin-1 bands ( with higher molecular weight ) represent properly folded , post-ER vanin-1 glycoforms , which traffic at least to the Golgi compartment . The N131S mutation resulted in much less intense endo H-resistant bands than WT vanin-1 ( Figure 4D , cf . lane 6 to lane 2 ) , whereas T26I did not ( Figure 4D , cf . lane 4 to lane 2 ) . The ratio of endo H-resistant to total vanin-1 serves as a measure of vanin-1 trafficking efficiency . The trafficking efficiency of N131S vanin-1 was less than WT vanin-1 , indicating that N131S vanin-1 does not fold properly in the ER . These data support the conclusion that N131S vanin-1 is misfolded in the ER and subsequently degraded by the ERAD pathway . To determine whether vanin-1 is a target of current anti-hypertensive drugs , we tested the effect of two commonly prescribed HTN drugs with different known drug mechanisms on endogenous vanin-1 protein level . Human monocyte THP-1 cells were used because they were derived from human blood and have high endogenous WT vanin-1 protein expression levels . Two HTN drugs used are diltiazem [37] , an L-type calcium channel blocker , and atenolol , a selective β1 adrenergic receptor blocker [38] . Treatment of THP-1 cells with diltiazem ( 10 µM ) or atenolol ( 10 µM ) for 1d or 3d decreased the endogenous total vanin-1 protein significantly in a time-dependent manner ( Figure 5A , quantification shown below ) . Furthermore , application of diltiazem for 3d decreased the endogenous total vanin-1 protein significantly in a dose-dependent manner ( Figure 5B , quantification shown below ) . This indicates that vanin-1 is a molecular target of current HTN drugs , which was previously unknown and confirms the relevance of vanin-1 to the regulation of blood pressure . Therefore , exploring other compounds that decrease vanin-1 level may lead to discovery of novel antihypertensive drugs , especially those with previously unknown function in HTN .
A major methodological issue that has greatly increased the challenges faced in the genetic epidemiology of BP is the high noise-to-signal ratio in the phenotype . This problem has numerous causes , including variation in measurement protocols of SBP and DBP across studies , the dynamic nature of BP levels , and concurrent use of antihypertensive medications . In addition , as with all polygenic disorders , the effect size for any single gene variant is very small and a large number of genes/variants are involved [10] . Recent large-scale BP genome-wide association studies ( GWAS ) of European , Asian and African ancestry populations demonstrated that the identified genetic variants together explain only 1–2% of BP variation [29] , [39] , [40] . It is thus not surprising that a large sample size is often necessary to detect genome-wide significant effects . An analysis method complementary to GWAS is admixture mapping , which has been successfully applied to detect BP loci [11] , [12] , [41] . Our group reported that the missense variant rs2272996 ( N131S ) in VNN1 was associated with BP through admixture mapping , and we conducted a follow-up association analysis in African and Mexican American samples [11] , [12] . The association evidence in European-ancestry population is however less convincing [12] , [13] In the current study , we performed meta-analysis using the COGENT consortium consisting of 19 studies with a total sample size of nearly 30 , 000 African ancestry subjects and confirmed the association evidence between rs2272996 and SBP ( P = 0 . 01 , Table 1 ) . However , statistical evidence alone cannot explain the role of a given variant on disease risk and drug response . Therefore , in our study we decided to analyze the functional effects of the N131S variant . Vanin-1 is a pantetheinase generating cysteamine , which regulates the glutathione-dependent oxidative stress response . We showed that the HTN-associated N131S mutation in vanin-1 significantly reduces vanin-1 total and cell surface expression . Consequently , the N131S vanin-1 only has fractional pantetheinase activity on the plasma membrane , which is associated with decreased HTN risk . Our result is consistent with the recognized link between impaired reduction-oxidation status and the development of HTN [19]–[21] , and the observed protective effects in vanin-1−/− mice in a variety of diseases , including oxidative stress [26] , intestinal inflammation [27] , and colon cancer [28] , mostly due to higher glutathione storage to maintain a more reducing environment . We further tested the drug effects of atenolol and diltiazem in human monocyte THP-1 cells , which have high endogenous WT vanin-1 protein expression level . Atenolol is a selective β1 adrenergic receptor blocker and developed as a replacement for propranolol in treating hypertension; diltiazem is a nondihydropyridine member of calcium channel blockers used in treatment of hypertension . We found that both drugs reduce the vanin-1 protein level in the THP-1 cells . The anti-hypertensive drugs may have different and complex mechanisms leading to reduced BP , but whether vanin-1 is targeted was previously unknown . Our experiments filled this gap and showed vanin-1 is involved in the BP regulation pathway . Therefore , other potent vanin-1 inhibitors may prove to have BP reducing effects , which is especially useful given that these inhibitors have not been studied in HTN and thus may provide new therapeutics for HTN . Our study presented the first functional studies of vanin-1 in HTN association , and provides compelling evidence for the essential role of its N131S mutation . Nonetheless , it has been demonstrated that multiple variants in a gene may contribute to a phenotypic variation [42] , [43] , and it is possible that other closely linked variants may have similar or analogous effects , or act in combination with N131S to regulate the vanin-1 protein expression and function . Current GWAS of HTN related traits mainly focus on testing common variants ( MAF: minor allele frequency ≥5% ) through pre-built chips and imputations based on HapMap [44] data; to date those findings in general have modest effect sizes [39] . Other functional and rare variants may be identified by deep sequencing , in combination with publicly available databases , such as the 1000 Genome Projects [45] and the Encyclopedia of DNA Elements ( ENCODE ) [46] . Identification of additional functional SNPs in VNN1 and their association with BP should provide further evidence for vanin-1 function in the regulation of BP . Cell lines were used to determine that ERAD is the underlying mechanism for vanin-1's loss of function due to the N131S mutation . Cell lines are commonly used for the study of molecular mechanisms because they typically provide efficient transfection and a physiologically-relevant cell environment for the target protein . However , BP has a complex etiology with the involvement of a variety of organs , such as heart , brain and kidney , which cannot be recapitulated solely in cell lines . Although knowledge gained from our cell system provides essential cellular mechanistic insights into the regulation of vanin-1 and its function , the study of BP regulation by vanin-1 calls for studies in animal models . A hypertensive mouse or rat model , vanin-1 knockout mouse or rat model , and N131S vanin-1 knockin mouse or rat model would be of great interest to study the effects of vanin-1 and its mutation in the complex physiological and metabolic systems . Vanin-1 provides a potential candidate to be manipulated to ameliorate HTN . Vanin-1 is a pantetheinase that contains the conserved catalytic triad residue–glutamate , lysine and cysteine–within the nitrilase family [47] . Based on the sequence alignment of vanin-1 with other nitrilase family members , the conserved catalytic triad of vanin-1 is composed of glutamate 79 , lysine 178 and cysteine 211 [23] . A three-dimensional atomic model of vanin-1 was built using the I-TASSER server ( Figure S3 ) [48] . Neither T26 nor N131 is in the vicinity of the catalytic sites of vanin-1 . Therefore , the T26I and N131S mutations per se are not expected to change the vanin-1 enzyme activity significantly . Indeed , we showed that the T26I mutation did not influence vanin-1 maturation or enzymatic activity . The N131S mutation has much weaker pantetheinase activity , presumably due to exceedingly low concentration of N131S vanin-1 on the plasma membrane; however , the activity is still evident , implying that the catalytic triad is not disrupted by this mutation . Loss of function of vanin-1 is caused by misfolding and rapid degradation of vanin-1 due to a single missense mutation from Asn to Ser at position 131 . As a GPI-anchored protein , to function properly , vanin-1 needs to be trafficked efficiently to the plasma membrane , where it acts as a pantetheinase . In accordance with the maturation of general GPI-anchored proteins [15] , vanin-1 is co-translationally translocated into the ER for folding . Because human vanin-1 has six potential N-linked glycosylation sites , its maturation is presumably dictated by glycoprotein processing machinery in the ER [49] , [50] . Properly folded vanin-1 is trafficked out of the ER , through the Golgi and to the plasma membrane in a fully functional state . Misfolded vanin-1 is recognized by the ER quality control machinery and subjected to ERAD , being retrotranslocated to the cytosol , ubiquitinated and degraded by the proteasome [51]–[54] . Cells need to maintain a delicate balance between protein synthesis , folding , trafficking , aggregation and degradation for individual proteins that make up the proteome in normal physiology . This balance is dictated by the cellular protein homeostasis ( proteostasis ) network , composed of a variety of sub-networks , including the chaperone , degradation and trafficking networks , and cellular signaling pathways that regulate proteostasis as the core layers [55]–[57] . Therefore , further elucidation of the proteostasis network for vanin-1 should provide a valuable fine-tuning control of vanin-1 expression , function and BP .
19 cohort studies contributed to the meta-analysis of BP and genetic variants in VNN1 in African-Americans as detailed in Franceschini et al [29] , including Biological Bank of Vanderbilt University ( BioVU ) ; Atherosclerosis Risk In Communities ( ARIC ) ; Coronary Artery Risk Development in Young Adults ( CARDIA ) ; Cleveland Family Study ( CFS ) ; Jackson Heart Study ( JHS ) ; Multi-Ethnic Study of Atherosclerosis ( MESA ) ; Cardiovascular Health Study ( CHS ) ; Genetic Study of Atherosclerosis Risk ( GeneSTAR ) ; Genetic Epidemiology Network of Arteriopathy ( GENOA ) ; The Healthy Aging in Neighborhoods of Diversity Across the Life Span Study ( HANDLS ) ; Health , Aging , and Body Composition ( Health ABC ) Study; The Hypertension Genetic Epidemiology Network ( HyperGEN ) ; Mount Sinai , New York City , USA Study ( Mt Sinai Study ) ; Women's Health Initiative SNP Health Association Resource ( WHI ) ; Howard University Family Study ( HUFS ) ; Bogalusa Heart Study ( Bogalusa ) ; Sea Islands Genetic Network ( SIGNET ) ; Loyola Maywood Study ( Maywood ) ; and Loyola Nigeria Study ( Nigeria ) . Each study received IRB approval of its consent procedures , examination and surveillance components , data security measures , and DNA collection and its use for genetic research . We selected 24 plasma samples from the International Collaborative Study on Hypertension in Blacks ( ICSHIB ) , in which the study participants were recruited from Igbo-Ora and Ibadan in southwest Nigeria as part of a long-term study on the environmental and genetic factors underlying hypertension [58] . The ICSHIB included 1 , 188 subjects who were genotyped using Affymetrix platform 6 . 0 chip [59] . We selected 6 subjects per group from the high and lower SBP traits in each of TT and CC genotype groups of SNP rs2272996 . For each of these 24 subjects , western blot analysis was performed by controlling the same amount of total plasma protein . The detailed statistical analysis of each cohort can be found in Franceschini et al [29] . In brief , each study cohort received a uniform statistical analysis protocol and analyses were conducted accordingly . BP was measured in mmHg . For individuals reporting use of antihypertensive medications , BP was imputed by adding 10 and 5 mmHg for SBP and DBP , respectively . For unrelated individuals , SNP associations for SBP or DBP were assessed by linear regression assuming an additive model , adjusting for age , age2 , body mass index ( BMI ) and gender . Population stratification was controlled by adjusting for the first 10 principal components obtained from selected ancestry informative markers [60] , [61] . For family data , association was tested using a linear mixed effect model , where random effects account for family structure [62] . Meta-analysis across the 19 cohorts was performed by applying both fixed-effect [31] , [32] and random-effect [33] models to estimate the overall effect . The fixed-effect model assumes that the effect size is the same for all the included studies; the only source of error is the random error within studies , which depends primarily on the sample size for each study . Because the inverse variance is roughly proportional to sample size , the fixed-effect model provides a weighted average of the effect sizes , with the weights being the estimated inverse of the variance of the estimate in each study . The random-effect model assumes that the effect sizes from studies are similar but not identical , dependent on each study protocol; the source of error includes within-study and among-study error [33] . It is more conservative and thus provides relatively wider 95% confidence intervals when heterogeneity across studies exists . All experimental data are presented as mean ± SEM , and any statistical significance was calculated using two-tailed Student's t-test . MG-132 , diltiazem , and atenolol were obtained from Sigma-Aldrich . The pCMV6 plasmids containing human vanin-1 and pCMV6 Entry Vector plasmid ( pCMV6-EV ) were obtained from Origene . The human vanin-1 missense mutations , N131S and T26I , were constructed using QuickChange II site-directed mutagenesis Kit ( Agilent Genomics ) , and the cDNA sequences were confirmed by DNA sequencing , showing the single-site mutation of these variants . The rabbit polyclonal anti-vanin-1 antibody came from Pierce antibodies , the mouse monoclonal anti-transferrin antibody from Santa Cruz Biotechnology , the mouse monoclonal anti-β-actin antibody from Sigma , and the rabbit polyclonal anti-ubiquitin antibody from Cell Signaling . Human embryonic kidney 293 ( HEK293 ) cells and human monocytic THP-1 cells came from ATCC . THP-1 cells were maintained in RPMI-1640 medium ( Hyclone ) with 10% heat-inactivated fetal bovine serum ( Sigma-Aldrich ) and 1% Pen-Strep ( Hyclone ) at 37°C in 5% CO2 . HEK293 cells were maintained in Dulbecco's Modified Eagle Medium ( DMEM ) ( Hyclone ) with 10% heat-inactivated fetal bovine serum ( Sigma-Aldrich ) and 1% Pen-Strep ( Hyclone ) at 37°C in 5% CO2 . Monolayers were passaged upon reaching confluency with TrypLE Express ( Life Technologies ) . HEK293 cells were grown in 6-well plates or 10-cm dishes and allowed to reach ∼70% confluency before transient transfection using Lipofectamine 2000 ( Life Technologies ) according to the manufacturer's instruction . Stable cell lines expressing vanin-1 variants ( WT , N131S or T26I ) were generated using the G-418 selection method . Briefly , transfected cells were maintained in DMEM supplemented with 0 . 8 mg/mL G418 ( Enzo Life Sciences ) for 15 days . G-418 resistant cells were selected for follow-up experiments . Cells were harvested and then lysed with lysis buffer ( 50 mM Tris , pH 7 . 5 , 150 mM NaCl , and 1% Triton X-100 ) supplemented with Roche complete protease inhibitor cocktail . Lysates were cleared by centrifugation ( 15 , 000× g , 10 min , 4°C ) . Protein concentration was determined by MicroBCA assay ( Pierce ) . Endoglycosidase H ( endo H ) or Peptide-N-Glycosidase F ( PNGase F ) ( New England Biolabs ) enzyme digestion was performed according to published procedure [35] . Aliquots of cell lysates or human plasma samples were separated in an 8% SDS-PAGE gel , and Western blot analysis was performed using the appropriate antibodies . Band intensity was quantified using Image J software from the NIH . HEK293 cells stably expressing vanin-1 variants were plated in 10-cm dishes for surface biotinylation experiments according to published procedure [35] . Intact cells were washed twice with ice-cold PBS and incubated with the membrane-impermeable biotinylation reagent Sulfo-NHS SS-Biotin ( 0 . 5 mg/mL; Pierce ) in PBS containing 0 . 1 mM CaCl2 and 1 mM MgCl2 ( PBS+CM ) for 30 min at 4°C to label surface membrane proteins . To quench the reaction , cells were incubated with 10 mM glycine in ice-cold PBS+CM twice for 5 min at 4°C . Sulfhydryl groups were blocked by incubating the cells with 5 nM N-ethylmaleimide ( NEM ) in PBS for 15 min at room temperature . Cells were solubilized for 1 h at 4°C in lysis buffer ( Triton X-100 , 1%; Tris–HCl , 50 mM; NaCl , 150 mM; and EDTA , 5 mM; pH 7 . 5 ) supplemented with Roche complete protease inhibitor cocktail and 5 mM NEM . The lysates were cleared by centrifugation ( 16 , 000× g , 10 min at 4°C ) to pellet cellular debris . The supernatant contained the biotinylated surface proteins . The concentration of the supernatant was measured using microBCA assay ( Pierce ) . Biotinylated surface proteins were affinity-purified from the above supernatant by incubating for 1 h at 4°C with 100 µL of immobilized neutravidin-conjugated agarose bead slurry ( Pierce ) . The samples were then subjected to centrifugation ( 16 , 000×g , 10 min , at 4°C ) . The beads were washed six times with buffer ( Triton X-100 , 0 . 5%; Tris–HCl , 50 mM; NaCl , 150 mM; and EDTA , 5 mM; pH 7 . 5 ) . Surface proteins were eluted from beads by boiling for 5 min with 60 µL of LSB/Urea buffer ( 2× Laemmli sample buffer ( LSB ) with 100 mM DTT and 6 M urea; pH 6 . 8 ) for SDS-PAGE and Western blotting analysis . The cell-based fluorescence assay to evaluate vanin-1's pantetheinase activity was performed according to published procedure with modifications [36] . The substrate , pantothenate-7-amino-4-methylcoumarin ( pantothenate-AMC ) was chemically synthesized according to published method [36] . As a pantetheinase , vanin-1 catalyzed the release of AMC , giving a fluorescence signal at excitation 350 nm and emission 460 nm . HEK293 cells expressing vanin-1 variants were lysed with lysis buffer ( 50 mM Tris , pH 7 . 5 , 150 mM NaCl , and 1% Triton X-100 ) supplemented with Roche complete protease inhibitor cocktail . Enzyme activity was performed using 10 µg of total proteins containing the substrate pantothenate-AMC ( 5 µM ) , 0 . 5 mM DTT , 5% DMSO in a 100 µL final volume in PBS , pH 7 . 5 . Fluorescence signals at excitation 350 nm and emission 460 nm measuring the released AMC were recorded every 3 min at 37°C in 96-well plates ( Greiner Bio-One ) using a fluorescence plate reader . A 60-min kinetic assay in four replicates and three biological replicates was carried out . Buffer only and HEK293 cells transfected with empty vector ( EV ) were used as negative controls for non-specific pantetheinase activity . HEK293 cells stably expressing vanin-1 variants were seeded at 2 . 5×105 cells per well in 6-well plates and incubated at 37°C overnight . To stop protein translation , cells were treated with 100 µg/mL cycloheximide ( Ameresco ) and chased for the indicated time . Cells were then lysed for SDS-PAGE and Western blot analysis . Cell lysates ( 500 µg ) were pre-cleared with 30 µL of protein A/G plus-agarose beads ( Santa Cruz ) and 1 . 0 µg of normal rabbit IgG for 1 hour at 4°C to remove nonspecific binding proteins [63] . The pre-cleared cell lysates were incubated with 2 . 0 µg of rabbit anti-vanin-1 antibody ( Pierce ) for 1 hour at 4°C , and then with 30 µL of protein A/G plus agarose beads overnight at 4°C . The beads were collected by centrifugation at 8000×g for 30 s , and washed four times with lysis buffer . The vanin-1 protein complex was eluted by incubation with 30 µL of SDS loading buffer in the presence of 100 mM DTT . The immunopurified eluents were separated in 8% SDS-PAGE gel , and Western blot analysis was performed . | Hypertension ( HTN ) or high blood pressure ( BP ) is common worldwide and a major risk factor for cardiovascular disease and all-cause mortality . Identification of genetic variants of consequence for HTN serves as the molecular basis for its treatment . Using admixture mapping analysis of the Family Blood Pressure Program data , we recently identified that the VNN1 gene ( encoding the protein vanin-1 ) , in particular SNP rs2272996 ( N131S ) , was associated with BP in both African Americans and Mexican Americans . Vanin-1 was reported to act as an oxidative stress sensor using its pantetheinase enzyme activity . Because a linkage between oxidative stress and HTN has been hypothesized for many years , vanin-1's pantetheinase activity offers a physiologic rationale for BP regulation . Here , we first replicated the association of rs2272996 with BP in the Continental Origins and Genetic Epidemiology Network ( COGENT ) , which included nearly 30 , 000 African Americans . We further demonstrated that the N131S mutation in vanin-1 leads to its rapid degradation in cells , resulting in loss of function on the plasma membrane . The loss of function of vanin-1 is associated with reduced BP . Therefore , our results indicate that vanin-1 is a new candidate to be manipulated to ameliorate HTN . |
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Phenotypes proximal to gene action generally reflect larger genetic effect sizes than those that are distant . The human metabolome , a result of multiple cellular and biological processes , are functional intermediate phenotypes proximal to gene action . Here , we present a genome-wide association study of 308 untargeted metabolite levels among African Americans from the Atherosclerosis Risk in Communities ( ARIC ) Study . Nineteen significant common variant-metabolite associations were identified , including 13 novel loci ( p<1 . 6×10−10 ) . These loci were associated with 7–50% of the difference in metabolite levels per allele , and the variance explained ranged from 4% to 20% . Fourteen genes were identified within the nineteen loci , and four of them contained non-synonymous substitutions in four enzyme-encoding genes ( KLKB1 , SIAE , CPS1 , and NAT8 ) ; the other significant loci consist of eight other enzyme-encoding genes ( ACE , GATM , ACY3 , ACSM2B , THEM4 , ADH4 , UGT1A , TREH ) , a transporter gene ( SLC6A13 ) and a polycystin protein gene ( PKD2L1 ) . In addition , four potential disease-associated paths were identified , including two direct longitudinal predictive relationships: NAT8 with N-acetylornithine , N-acetyl-1-methylhistidine and incident chronic kidney disease , and TREH with trehalose and incident diabetes . These results highlight the value of using endophenotypes proximal to gene function to discover new insights into biology and disease pathology .
The power to detect genetic effects for complex traits is influenced by , among other things , the study sample size and the effect size of a particular locus . Most contemporary genome-wide association studies ( GWAS ) have achieved increased power by increasing the size of the discovery sample to tens of thousands of individuals [1] . Besides expanding the sample size , focusing on variants with large effects is an alternative strategy for novel gene discovery . The human metabolome consists of a collection of small molecules resulting from a variety of cellular and biologic processes , the activity of which is regulated by coordinated enzyme action [2] . In addition , as metabolites reflect multiple metabolic and physiological activities in the body , they hold promise to discover intermediate traits between gene action and disease processes [3] . GWASs of known risk factor phenotypes of clinical disease , such as cholesterol or urate levels , have shown that genetic association with functional intermediate traits , as opposed to the clinical endpoint itself , are often more highly powered and may provide information into the biological mechanism of disease [4]–[7] . Untargeted metabolomic approaches simultaneously measure numerous known and unknown metabolites present in a study sample . Recent studies combining genetics and metabolomics have identified multiple common variant-metabolite associations with large effect sizes in populations of European ancestry , and provided new functional insights into common complex disease . [8]–[11] . African ancestry-derived populations have higher levels of genetic variation and population substructure , and lower levels of linkage disequilibrium ( LD ) compared to European ancestry-derived populations , so studies in African-Americans may lead to identification of new genes or variants and fine map of existing loci [12]–[14] . To date , no such study has been conducted in African Americans , a population that bears a disproportionate burden of disease , such as cardiovascular disease , diabetes and chronic kidney disease [15]–[17] . Our goal here is to identify common genetic variations influencing the human metabolome in African Americans among the Atherosclerosis Risk in Communities ( ARIC ) Study in order to reveal novel pathways underlying disease etiology and possible avenues of disease prevention and treatment .
A total of 308 known serum metabolites including 83 amino acids , 16 carbohydrates , 9 cofactors and vitamins , 7 energies , 136 lipids , 12 nucleotides , 25 peptides and 20 xenobiotics ( Table S1 ) were included and a set of 2 , 341 , 704 common autosomal SNPs were tested in 1 , 260 African Americans ( demographics in Table S2 ) for each metabolite levels . Nineteen significant ( p-value<1 . 6×10−10 after correction for multiple testing ) common variant-metabolite associations were identified ( locus association summaries are presented in Table 1 , regional association plots and quantile-quantile plots are presented in Figures S1 and S2 , respectively ) , including 13 novel loci which have not been reported in previous metabolomics studies . Depending on the particular metabolite , these loci were associated with 7–50% of the difference in metabolite levels per allele ( average at 25% ) , and the variance explained ranged from 4% to 20% . Fourteen genes were mapped within the nineteen significant genetic loci; eight of them encode enzymes that catalyze the reaction of the corresponding metabolite as a substrate or product ( gene names shown in red in Figure 1 ) . Four of the associated loci contained non-synonymous substitutions in four enzyme-encoding genes ( KLKB1 , SIAE , CPS1 , and NAT8 ) . The other significant loci consist of eight other enzyme-encoding genes ( ACE , GATM , ACY3 , ACSM2B , THEM4 , ADH4 , UGT1A , and TREH ) , a transporter gene ( SLC6A13 ) and a polycystin protein gene ( PKD2L1 ) . Two protease-encoding genes , ACE and KLKB1 , showed pleiotropic effects on multiple oligopeptide metabolites , and the UDP-glucuronosyltransferases gene , UGT1A , contributed to the levels of several bile pigments ( Figure 1 ) . Nineteen significant common variant-metabolite associations were compared with previously published SNP-metabolite associations in Caucasians [10] . Eleven out of nineteen metabolites were shared between the published study and the data presented here , and six of them showed the same significant SNP-metabolite associations in both ethnicities ( Table 2 ) . A CPS1-glycine association was reported in the Caucasion metabolomic GWAS , but the sentinel SNP was different ( r2<0 . 5 ) from that reported here ( Table 2 ) . A CPS1-glycine association was also reported in a recent genetic study for glycine metabolism among Caucasians [18] . The other four shared metabolites had different signals in African-Americans when compared to Caucasians ( Table S3 ) . We identified a missense mutation in NAT8 ( rs13538 ) that was significantly associated with N-acetylornithine levels ( p = 4 . 0×10−66 ) . A recent biochemical study has shown that NAT8 catalyzed the N-acetylation of cysteine conjugates [19] . We next asked whether the presumed specificity of NAT8's function could be used to identify the identity of any unknown metabolites by analyzing its effect on 294 unknown metabolites . Two metabolites , X-11333 and X-11787 reached our a priori defined level of significance ( p = 1 . 0×10−61 and p = 2 . 5×10−25 , respectively ) . By targeted mass spectroscopy , X-11333 was determined to be N-acetyl-1-methylhistidine ( Figure S3 ) , a type of N-acetyl amino acid; and X-11787 was an isoform of either hydroxy leucine or isoleucine , as reported previously [20] . Among nineteen metabolites that reached genome-wide significance , we identified four potential disease-associated paths among African Americans for cardiovascular disease , chronic kidney disease ( CKD ) and diabetes , including two direct longitudinal associations ( Figure 2 , detailed estimates in Table S4 ) . As described above , a missense mutation in NAT8 ( rs13538 ) , a known susceptibility locus for chronic kidney disease [21] , was significantly associated with N-acetylornithine and N-acetyl-1-methylhistidine levels . We identified a pronounced relationship of both N-acetylornithine and N-acetyl-1-methylhistidine levels with kidney function , whereby higher levels of of N-acetylornithine and N-acetyl-1-methylhistidine were related to lower eGFR ( p = 9 . 0×10−13 and 1 . 6×10−21; respectively ) and higher risk of incident CKD after 19 average years of follow-up among 1 , 921 African Americans ( demographics in Table S5 , HR = 1 . 64 , p = 0 . 003 and HR = 1 . 34 , p = 0 . 03 , respectively ) . However , the longitudinal associations with the metabolites were attenuated and no longer significant after further adjusting for eGFR ( data not shown ) . Finally , trehalose levels were significantly associated with TREH gene variation . Trehalose can be cleaved to two molecules of glucose . In this study , trehalose levels were significantly associated with glucose levels ( p = 2 . 9×10−17 ) , and showed a 1 . 34 fold increased risk of incident diabetes after an average 7 years of follow-up ( p = 2 . 0×10−5 ) in a sample of 1 , 430 ARIC African Americans ( demographics in Table S5 ) . With further adjustment of glucose levels , trehalose levels persisted to show an apparent association with incident diabetes , although the effect size was lessened ( HR = 1 . 16 , p = 0 . 02 ) .
By combining high-throughput metabolomic and genomic technologies , we identified nineteen common variant-metabolite associations among African Americans with p-values ranging from 6 . 0×10−11 to 4 . 0×10−66 . We inferred the structure of an unknown metabolite to be N-acetyl-1-methylhistidine using knowledge of the associated gene's function and targeted mass spectroscopy . We further established potential novel disease-associated pathways for cardiovascular disease risk factors , CKD and diabetes . The results offer new evidence about the genetic impact on metabolites and disease among African Americans , which advance our understanding of disease causation and progression . Most loci identified by GWA studies of complex disease traits contribute relatively small effects and the variance explained remains modest [14] , [22] , [23] . Thus , contemporary GWAS are shifting focus to phenotypes that more immediately reflect the effects of gene action . For example , although the effect sizes of genetic loci related to coronary heart disease ( CHD ) are relatively small ( OR from 1 . 08 to 1 . 47 ) [24]–[26] , loci related to plasma triglyceride and cholesterol levels explained a meaningful proportion of the variance ( 9–13% ) [4] . The human metabolome , the ultimate downstream product of gene and environment interaction , holds the promise to identify genes that directly reflect gene action with large effects sizes [8] , [10] , [27] . Our results show relatively large effect sizes of nineteen identified genetic loci related to human metabolome among African Americans ( average at 25% shift per allele copy ) . In addition , the majority of identified loci ( 15/19 ) are located in or near genes , and these loci explained up to 20% of the variance of each trait . Twelve out of fourteen genes that were significantly associated with metabolite levels were enzyme-encoding genes , including four genes involved in disease-associated processes . These data underscore the important role of enzyme activity and regulation in controlling metabolite levels . As metabolite levels are closely related to disease process , to understand whether the underlying mutations detected here lead to gain-of-function or loss-of-function for these enzyme-encoding genes offers new opportunities for disease treatment and prevention ( e . g . design an antagonist/agonist of the gene as a drug candidate ) . The majority of the gene-metabolite associations are consistent with the gene's known function , but the direction of effect of the coded allele does not provide direct evidence as to whether or not the variant represents gain of function or loss of function . Future investigation of the functional impact of the underlying causal variants is critical and is an area of intense research . NAT8 is expressed mainly in the kidney and liver [28] , but its function is not fully understood . Several previous , seemingly unrelated , observations have found that mutations in N-acetyltransferase 8 ( NAT8 ) , are contributed to N-acetylornithine levels , creatinine levels , kidney function and CKD [10] , [21] , [29] , [30] . Our results show that an amino acid substitution in NAT8 is related to N-acetylornithine , N-acetyl-1-methylhistidine and eGFR , which in-turn influence risk to incident CKD . These findings provide evidences that N-acetylation plays a role in the development of CKD [10] . Trehalose is a food ingredient with the ability to prevent protein denaturation [31] . Because of its ability to inhibit lipid and protein misfolding , trehalose has become a potential therapeutic in neurodegenerative studies [32] , [33] . Animal safety studies concluded that trehalose is safe for use as an ingredient in consumer products [34] , and it is now widely used in food and cosmetics . Here , we report that trehalose levels are regulated by TREH , which encodes the trehalase enzyme which hydrolyzes trehalose to two glucose molecules . In addition , we show that trehalose is associated with glucose levels and the onset of incident diabetes . Environment factors , in addition to and interacting with genetic factors , ( e . g . dietary intake ) explain part of the variability of human metabolome . Follow-up investigations of the interactions between the genes identified here and possible environment factors are likely to provide new insight into the understanding of disease etiology and its metabolism . For example , alcohol dehydrogenase 4 ( ADH4 ) contributes to esophageal squamous-cell carcinoma ( ESCC ) through an interaction with alcohol consumption [35] . Here , we reported that ADH4 is associated with hexadecanedioate levels , a metabolite with an antitumor activity [36] . Moreover , studies have shown that coffee consumption is associated with lower bilirubin levels [37] and UGT1A is contributed to bilirubin levels as well [10] . Our data show that mutations in UGT1A are associated with the levels of several bile pigments . Thus , future investigations of genes related to metabolite levels with environment interaction are of interest . Untargeted metabolomics approaches measure numerous known and unknown metabolites presented in a sample simultaneously . Since the chemical identities for unknown metabolites have not been elucidated , previous GWAS on metabolomic traits largely ignored unknown metabolites for the analysis . In our study , we show an example of unknown metabolite identification ( i . e . X-11333 ) by combining GWAS results ( i . e . NAT8 ) with existing knowledge about the function of the gene product ( i . e . N- acetylation ) . A recent study has used GWAS results and Gaussian graphical modeling to predict unknown metabolite identities [38] . These two examples demonstrate the feasibility for unknown compounds structure identification by combing genetic and metabolomics information . Limitation of this study warrants consideration . To our knowledge , the ARIC study is the only cohort with serum metabolome measurements in African-Americans , so it is unlikely to find an independent sample for replication . In our study , the SNP-metabolite associations identified were compared with the results from a published study in Caucasians [10] as a surrogate replication . Six distinct SNP-metabolite associations were replicated out of eleven shared metabolites , indicating homogeneous genetic effects on several metabolites regardless of ethnicities . Differences in the site frequency spectrum between African-Americans and Caucasians and lower LD in African-Americans may explain the lack of significant association at the other loci . As a consequence of lack of replication , the proportion of variance explained by the SNPs was reported from the discovery sample , which may be an over-estimate . Future studies are needed to replicate our findings in independent samples of African-Americans . Despite limitations , the data presented here have important strength . Previously published GWAS on human metabolites estimate only cross-sectional relationships between metabolites and clinical endpoints . In contrast , the data presented here originate from a large , well-defined , longitudinal cohort study , allowing establishments of longitudinal predictive relationships . In summary , we report here the first genome-wide association study of untargeted metabolome in African-Americans . The genetic variant-metabolite associations along with the disease path reported here will continue to be improved with further use of contemporary omics technologies . Our study highlights the value of utilizing omics studies in deeply phenotyped individuals to provide new insights into gene function , disease etiology and epidemiology .
The Atherosclerosis Risk in Communities ( ARIC ) study is a longitudinal cohort study designed to ascertain the etiology and predictors of cardiovascular disease ( CVD ) . The ARIC study enrolled 15 , 792 middle-aged adults from four U . S . communities ( Forsyth County , NC; Jackson , MS; suburbs of Minneapolis , MN; and Washington County , MD ) between 1987–89 and followed by four completed visits with each approximately three years apart , in 1987–89 , 1990–92 , 1993–95 , and 1996–98 . In general , each visit included interviews and a physical examination . A detailed description of the ARIC study design and methods was published elsewhere [39] . Metabolomic profiles were measured in baseline serum from 1 , 977 African-Americans selected from the Jackson , MS field center . Participants were excluded if they did not give consent for use of DNA information . Metabolite profiling was completed in June 2010 using fasting serum samples which had been stored at −80° since collection at the baseline examination in 1987–1989 . In total , detection and quantification of 602 metabolites was completed by Metabolon Inc . ( Durham , USA ) using an untargeted , gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry ( GC-MS and LC-MS ) -based metabolomic quantification protocol [40] , [41] . Prior to the analyses presented here , a rigorous assessment of the metabolomic data was done . Metabolites were excluded if: 1 ) more than 50% of the samples had values below the detection limit; or 2 ) they had unknown chemical structures , except for X-11333 and X-11787 which were followed-up as part of more detailed NAT8 investigations . After this assessment , a total of 308 named metabolites were included in the present study . Structural identifications for X-11333 and X-11787 were proposed using a mass spec-based structural approach , including targeted accurate mass and MSn fragmentation with accurate mass [41] . In the present study , common ( minor allele frequency , MAF≥5% ) autosomal single-nucleotide polymorphisms ( SNPs ) were genotyped on the Affymetrix 6 . 0 chip and were imputed to 2 , 341 , 704 SNPs based on a panel of cosmopolitan reference haplotypes from HapMap CEU and YRI . MACH v1 . 0 was used to do imputation and allele dosage information was summarized in the imputation results . SNPs were excluded before imputation if they had no chromosomal location , were monomorphic , had a call rate <95% , or had a Hardy-Weinberg equilibrium p-value<10−5 . For each SNP , the ratio of the observed versus expected variance of the dosage served as a measure of imputation quality . A total of 308 metabolites were included in this analysis . Metabolite levels below the detectable limit of the assay were imputed with the lowest detected value for that metabolite in all samples , and all metabolites values were natural log-transformed prior to the analyses . Linear regressions and an additive genetic model were applied to each metabolite , adjusting for age , sex and the first 10 principal components . The significant threshold was defined as a p-value<1 . 6×10−10 ( 5 . 0×10−8/308 ) based on Bonferroni correction . SNPs with MAF<5% were excluded . Quantile-quantile ( QQ ) plots were generated for each analysis to illustrate the distribution of the observed and expected p-values for all eligible SNPs . Regional plots showing LD and the location of nearby genes ( if any ) were generated for the top ranking SNPs for each metabolite . If more than one significant SNP clustered at a locus , the SNP with the smallest p-value was reported as the sentinel marker . All analyses were performed using ProbABEL and R ( www . r-project . org ) . The identified sentinel SNPs were further compared with the metabolite-SNP association from the KORA and TwinsUK studies [10] using their public GWAS server ( http://metabolomics . helmholtz-muenchen . de/gwa/index . html ) and other published GWA studies through NHGRI GWAS Catalog ( http://www . genome . gov/gwastudies/ ) . Analyses included all African-American samples with metabolomic data were conducted to estimate the association between genome-wide significant metabolite levels and relevant clinical risk factors and endpoints , including incident chronic kidney disease and incident type 2 diabetes . Nine associations , including six cross-sectional associations with clinical risk factors and three longitudinal associations with clinical endpoints , were tested . In each analysis , metabolite levels were natural log-transformed . The cross-sectional associations were assessed using linear regression with adjustment for age and gender . Longitudinal associations with disease endpoints were estimated using Cox proportional hazards models adjusting for age , gender , systolic blood pressure ( SBP ) , antihypertensive medication use , diabetes , high-density lipoprotein , low-density lipoprotein , current smoking and prevalent CHD for incident the CKD analysis; and age , gender , SBP , antihypertensive medication use , body mass index , total cholesterol for the incident type 2 diabetes analysis . The proportional hazards assumption was examined and not rejected using the methods developed by Grambsch and Therneau [42] . Covariates were measured at baseline ( 1987–1989 ) and The Chronic Kidney Disease Epidemiology Collaboration equation was applied to estimate glomerular filtration rate ( eGFRCKD-EPI ) [43] . For the disease association analyses , the significant threshold was defined as p<0 . 005 using Bonferroni correction ( 0 . 05/9 ) and the analyses were performed using R ( www . r-project . org ) . | Most contemporary GWAS studies have achieved increased power by increasing the size of the discovery sample to tens of thousands of individuals . An alternative approach for detecting the effects of novel loci is to measure phenotypes that more immediately reflect the effects of gene function . The metabolome consists of a collection of small molecules resulting from a variety of cellular and biologic processes , which can be considered intermediate phenotypes proximal to gene function . Here , we report a genome-wide association study identifying nineteen genetic loci influencing untargeted metabolomes traits among African Americans in the Atherosclerosis Risk in Communities ( ARIC ) Study . Fourteen genes mapped within nineteen loci , including twelve enzyme-encoding genes ( KLKB1 , SIAE , CPS1 , NAT8 , ACE , GATM , ACY3 , ACSM2B , THEM4 , ADH4 , UGT1A and TREH ) , a transporter gene ( SLC6A13 ) and a polycystin protein gene ( PKD2L1 ) . In addition , four potential disease-associated paths were identified , including two direct longitudinal predictive relationships: NAT8 with N-acetylornithine , N-acetyl-1-methylhistidine and incident chronic kidney disease , and TREH with trehalose and incident diabetes . These results highlight the value of using phenotypes proximal to gene function to promote novel gene discovery . |
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Neisseria meningitidis ( Nm ) and N . gonorrhoeae ( Ng ) are adapted to different environments within their human host . If the basis of this difference has not yet been fully understood , previous studies ( including our own data ) have reported that , unlike Ng , Nm tolerates high manganese concentrations . As transition metals are essential regulators of cell growth and host pathogen interactions , we aimed to address mechanisms of Nm Mn2+ tolerance and its pathogenic consequences . Using bioinformatics , gene deletion and heterologous expression we identified a conserved bacterial manganese resistance factor MntX ( formerly YebN ) . The predicted structure suggests that MntX represents a new family of transporters exporting Mn . In the Neisseria genus , this exporter is present and functional in all Nm isolates but it is mutated in a majority of Ng strains and commonly absent in nonpathogenic species . In Nm , Mn2+ export via MntX regulates the intracellular Mn/Fe ratio and protects against manganese toxicity that is exacerbated in low iron conditions . MntX is also important for N . meningitidis to resist killing by human serum and for survival in mice blood during septicemia . The present work thus points to new clues about Mn homeostasis , its interplay with Fe metabolism and the influence on N . meningitidis physiology and pathogenicity .
It is largely accepted that access to metals impacts on the equilibrium of host pathogen interface [1] , [2] . In fact , bacteria must acquire nutrients for survival from the host environment during the course of the interaction . These nutrients comprise transition metals ( such as Fe , Mn , Zn , Ni , Cu , Co and Mo ) [3] which have the specific characteristic of an incompletely filled “d” orbital . This permits different state of oxidation ( e . g . Fe2+ and Fe3+ ) and their use for proteins structural stabilization or as enzymes cofactors in a majority of metabolic processes [4] . Accordingly , transition metals are essential for the survival of bacteria . However , their accumulation can be toxic if the quantity , state of oxidation or intracellular localization and regulation are inadequate [5] . In these cases , metals can cause deleterious oxido-reduction reactions with proteins or other compounds ( e . g . H202 and iron also known as Fenton reaction ) , generating toxic compounds ( e . g . OH . OH- ) that alter macromolecular structures such as proteins , membranes and DNA , leading to cell death [5] . In bacteria , this duality has forced selection of strategies to orchestrate essential transition metals homeostasis by sensing , acquiring , storing or , when needed , exporting them properly . On the host side , the duality of metal functionality is also true and metal homeostasis is also tightly controlled . Furthermore , prokaryote-eukaryote co-evolution has selected immune strategies aimed at controlling metals availability and restricting bacterial growth [3] , [6] , [7] . If the dominant role of iron in host-pathogen interactions has been extensively documented the impact of other transition metals and the interplay between their metabolisms are just emerging . One can cite the example of Mn for which the importance in bacterial physiology and pathogenesis just became apparent [8] , [9] , [10] . In addition to its role as cofactor in several bacteria , manganese has been suggested to quench reactive oxygen species ( ROS ) [11] , [12] which could be endogenous ( generated during bacterial metabolism ) or exogenous ( host immune defense mechanism ) . Bacterial manganese importers have been shown to influence host-pathogen interactions but depending on the infectious model and type of pathogen they were found to either contribute or not to virulence [13] , [14] , [15] , [16] . Their definition in terms of general bacterial pathogenicity determinants is therefore unclear . Besides , the homeostasis of manganese has to be considered in a more complex situation when several metals are acting in concert . Examples of metals interplay have been described with manganese control of bacterial iron homeostasis [17] or more importantly , adequate intracellular Mn/Fe ratio critical to resist to certain stress [18] , [19] , [20] . The impact of metal availability on pathogenesis is particularly exemplified by Neisseria human pathogens . The Neisseria genus is composed of bacteria which are part of the normal human microbiome and live in harmless symbiosis with humans . Unfortunately , in some cases this relation may evolve to parasitism . This is particularly the case of two species namely N . meningitidis and N . gonorrhoeae . These two closely related bacteria are exclusively found in humans but in different ecological niches consequently causing distinct diseases . N . meningitidis is frequently isolated from the upper respiratory tract of asymptomatic carriers but can also be the causative agent of life threatening invasive infections such as septicemia and meningitis . N . gonorrhoeae is isolated from the genitourinary tract and is the causative agent of gonorrhoeae a sexually transmitted disease generally characterized by a localized inflammation with , in some circumstances , severe consequences . In both cases , the importance of Fe acquisition for Neisseria pathogenesis has been previously established . As a matter of fact , deletion of genes encoding metallo-transporters [21] , [22] , [23] , [24] , [25] leads to a decreased of virulence in model of infection . Regarding N . meningitidis , an additional approach has been used by providing a compatible iron source that enhances virulence in a mice model [26] , [27] . In contrast , the role of Mn and its potential interplay with Fe metabolism remains unclear in this pathogen . Some studies have demonstrated that N . gonorrhoeae is more sensitive to manganese than N . meningitidis [28] , [29] , [30] . The work in this article aims to extend this observation by: 1 ) identifying the genetic determinant responsible for Mn sensitivity and 2 ) describing the impact of its alteration on N . meningitidis pathogenesis . Our hypothesis was that Mn homeostasis may be different between Neisseria spp . , which evolved to fill distinct host niches , and these differences might give clues about N . meningitidis virulence .
To confirm that N . gonorrhoeae is more sensitive to manganese than N . meningitidis [28] , [29] , [30] , we tested the sensitivity of a sample ( ≈20 strains ) of each species isolated during the last twenty years by the Centre National de Reference des Meningocoques ( Table S1 ) . The Figure 1 presents Mn-dependent growth inhibition of these isolates measured by disk assay . Approximately 60% of strains of N . gonorrhoeae were Mn-sensitive compared to none in the case of N . meningitidis isolates . This result suggests that the capacity to resist to manganese toxicity is globally present in both species but that this capacity is strongly conserved only in N . meningitidis . In both bacteria , genes encoding the manganese/zinc transporter system MntABC are present and conserved [30] , implying a similar capacity for manganese import . Thus we postulated the existence of a new actor in the manganese homeostasis . Such an actor in manganese homeostasis should be regulated by Mn2+ to act when required . At the transcriptional level , the best known manganese regulators are the DtxR-related factor MntR [31] or Mur , derived from the general ferric uptake regulator ( Fur ) [17] , [32] . Since , the genome of N . meningitidis does not code for these specialized metallo-regulators ( our observations ) , we have preferred an alternative strategy which has consisted to first identify new actors in a model organism and to later verify their presence and evolution in the Neisseria genus . The bold speculation was that this factor may be present and regulated by MntR in other proteobacteria pathogens . We chose the phytopathogen Xanthomonas campestris , which possesses a simple predicted Mn regulon , including an mntRH locus , but lacks other Fur-derived transcription factor such as the oxido-metallo-regulator PerR or additional Mn importers such as the P-type ATPase MntA and the ABC-driven , periplasmic binding protein-based SitABCD transport system ( our observation ) . Thus , we deleted mntH ( Mn-importer ) , mntR ( Mn-regulator ) or both genes and measured Mn sensitivity by disk diffusion assay . The results are illustrated in Figure 2A . The mntH deletion did not alter growth in the presence of Mn . In contrast , a drastic Mn-dependent growth inhibition was observed when mntR was deleted . First , we attributed this effect to an over-expression of MntH in absence of MntR but the double mutant ΔmntH-R was as affected as the single ΔmntR mutant . These results argued for the presence of at least one other MntR-regulated actor in manganese homeostasis in X . campestris . To identify candidates , we used MntR-regulated promoters [33] , [34] , [35] to derive a matrix for MntR binding site ( Figure 2B ) , which allowed consistent detection of one DNA motif ( Figure 2C ) in the 5′ region of a Xanthomonas gene termed XCC4075 ( formerly yebN; in this article designated mntX ) . To our knowledge MntXNm and MntXXc are the first members of a large family of highly hydrophobic putative membrane proteins ( Figure 4A ) of previously unknown function . As a first clue toward the mechanism of MntX-mediated manganese tolerance , we examined the phylogenetic distribution of 202 MntX homologues by Neighbor Joining ( NJ ) analysis [36] . The result , presented as a tree topology in the Figure S2A , shows significant discrepancy between the phylogenetic and taxonomic distributions of the sequences studied . The simplest explanation can be that mntX were frequently transmitted by horizontal gene transfer . Nevertheless , MntXXc and MntXNm are found in separate subgroups among most of the other proteobacterial sequences from α- , β- and γ-divisions . To gain functional information on MntX , we generated logos of aligned protein sequences presented in Figure S2B . These graphical representations of amino acid frequencies by position allow simultaneous visualization of patterns and variations among MntX homologues that correspond to selected phylogenetic groups [37] . To identify features typical of the MntX family , we also included a phylogenetic outgroup of divergent sequences ( in dark blue ) [38] . Transmembrane segments ( TMS ) are the most conserved parts of the molecule and logos were obtained for each ( Figure S2B , TMS1-3 , top; TMS4-6 , bottom ) . Examining these logos provided important structural information that is summarized in the predicted topology presented in Figure 4B . First , similar patterns of conservation between TMS1-3 and TMS4-6 could be observed ( underlined in the Figure S2B ) . This corresponds to the two directly repeated Domains of Unknown Function 204 ( DUF204 ) [39] that form the MntX structure . In addition , conserved accumulations of positive charges found between TMS1/2 , 3/4 and 5/6 ( Figure S2B ) were detected . Based on the ‘positive-inside’ rule , it was predicted that the two halves may adopt an inverted transmembrane configuration forming an overall pseudo-symmetric structure ( Figure 4B ) . We also noticed periodicity of residue conservation in TSM2/3 and TSM5/6 compatible with α-helical structure and also the abundance of conserved glycine implying tight inter-helix packing interactions [40] . In comparison , the sequence conservation pattern in TMS1&4 is more local and central and involves adjacent residues which could form short extended segments . As reviewed in [41] , such a configuration of repeated structures with a central extended segment is a characteristic of several membrane transporters . In these cases , the extended segments may provide contact points for substrate binding . In accordance with this hypothesis , MntX putative extended peptides ( TMS1&4 as schematized in Figure 4B ) contain invariant D residues which are know to be preferred ligands for divalent metals and polar moieties ( S , T ) also known to interact with inorganic cations [42] . In conclusion , this structural prediction suggests that MntX family is composed of metallo-transporters with conserved features putatively implicated in metal binding and transport MntX can protect bacteria against manganese toxicity and sequences analyses suggest transport function . To test whether MntX encodes a metallo-exporter , we first used heterologous expression in E . coli using a model strain that reports on gene expression controlled by the intracellular pools of metals . In this strain ( schematized in the top of Figure 5A ) , the mntH Orf was replaced by the firefly luciferase Orf . Furthermore , it is in a Δfur background . Hence , luminescence is repressed exclusively by MntR ( Figure 5A bar 1 ) , with Mn2+ as co-repressor , and less efficiently with other metals as Fe2+ [43] ( Bergevin and Cellier , unpublished ) . In this system , incubation of bacteria with a membrane permeant Me2+ chelator ( 2 , 2′-dipyridyl: DP ) [16] , [44] rapidly de-repressed significantly PmntH-luc expression ( Figure 5A bar 9 ) , and this could be prevented by co-incubation with some Me2+ ( Figure 5A bar 13 ) . With this assay , it is expected that a difference in luciferase expression may be observed if metals are pumped out of the cell via an exporter ( i . e . the metal will not reverse the effect of DP ) . To measure metal export in this E . coli strain , we expressed MntXXc , MntXNm and MntXNg ( strain 16626; frameshift mutated ) via a L-arabinose inducible promoter present in the pBad plasmid ( depicted in the top of Figure 5A ) . As a negative control we used bacteria transformed with an empty plasmid . In rich medium , ( LB broth ) metals are in sufficient amount to control the expression of luciferase . The basal expression ( D-arabinose ) without production of MntX proteins was similar in all strains ( bar 1 to 4 ) . In contrast , when the production of MntX was induced ( bar 5 to 8 ) the expression of luciferase was significantly increased only in the case of MntXXc ( bar 6 ) . This suggests that MntXXc is strongly active and able to deplete enough intracellular metals for MntR to be free from all possible metal cofactors ( including Fe2+ ) in this rich medium . Other strains , in particular the one harboring mntXNm did not differently express the luciferase when the production of MntX was induced ( bar 3 compared to 7 ) . This implies that MntXNm is not active or it is not able to deplete , in rich medium , all possible MntR metallo-cofactors ( including Fe2+ ) . In metallo-depleted medium ( using DP bars 9 to 12 ) , a maximum expression of the luciferase , independent of the presence of MntX , was observed . To test export of specific metal , bacteria were co-incubated with a Me2+ concentration allowing to significantly reverse DP intracellular action ( Mn2+ Fe2+ or Ni2+ ) . This reversion of the DP effect with metals could be observed for strains carrying the empty plasmid ( bars 13 , 17 and 21 ) or the plasmid encoding MntXNg ( bars 16 , 20 and 24 ) . In contrast , this reversion of the DP effect by Mn2+ , Fe2+ and Ni2+ was not observed for the strain expressing MntXXc ( bars 14 , 18 and 22 ) . On the other hand , the luciferase activity was de-repressed in presence of MntXNm only when DP was co-incubated with Mn2+ ( bar 15 ) but not with Fe2+ ( Bar 19 ) or Ni2+ ( bar 23 ) . As a note , similar results were observed for MntXXc and MntXNm using up to 300 µM of Mn2+ or Fe2+ ( Figure S3 ) . Overall , these results strongly suggest that MntXXc exports a broad variety of metals whereas MntXNm exports only Mn2+ when expressed in E . coli . To confirm these results and examine MntXNm endogenous function we performed ICP-MS analyses of metal content in N . meningitidis and N . gonorrhoeae harboring a functional MntX or not . We quantified intracellular atoms of manganese using bacteria grown in rich media with added Mn and using Mg or Fe content for normalizing the metal measurements ( Figure 5B and 5C ) . For both strains , the lack of MntX activity ( white ) correlated with ∼two fold increase in Mn intracellular content compared to their parental or complemented strains ( black ) . Similar results were obtained when Mn intracellular content was normalized with Fe ( Figure 5C ) suggesting that MntXNm did not interfere with iron homeostasis and support E . coli data showing lack of MntXNm specificity for Fe . Thus N . meningitidis MntX native activity supports the prediction that it is a membrane transporter that exports Mn and was therefore renamed MntX . Of note , the intracellular amount of Mn measured in WT N . gonorrhoeae and N . meningitidis were close ( respectively 3 . 0×105 and 6 . 5×105 Mn atoms by bacteria ) . However , about 10-fold more Fe was associated with WT N . gonorrhoeae compared to WT N . meningitidis ( respectively 3 . 9×106 and 4 . 9×105 Fe atoms by bacteria ) , suggesting unsuspected differences in metal homeostasis between these close relatives , including an unusually high Mn/Fe ratio for N . meningitidis . Manganese is typically considered as less toxic transition metal than iron ( less Fenton's reaction ) and beneficial in the context of an oxidative stress [45] . Thus , the existence of a manganese exporter in the genome of a bacterium which is only found in human , in metallo-restricted condition seemed rather surprising . Consequently , we tested if MntX could protect from oxidative stress . In X . campestris and E . coli , we were able to correlate the presence of MntX ( expected to decrease intracellular manganese ) with reduced resistance to organic peroxide toxicity ( Figure S4 ) . However , this was not true for N . meningitidis ( Figure S4 ) as previously reported by others [30] . More importantly this trait was specific to N . meningitidis since N . gonorrhoeae was shown to accumulate manganese in defense against oxidative species [30] , [46] . As N . meningitidis does not seems to rely on Mn to detoxify oxidative compounds , we reasoned that Mn over-accumulation may be deleterious , for example by competing with other essential metals when available in limited quantity ( such as iron or zinc ) for the binding to the active site of N . meningitidis proteins . Using a N . meningitidis strain harboring , in its native locus , the mntX promoter fused to the luciferase orf we observed that regulation of mntX was consistent with this hypothesis . The Figure 6A presents the key experiment which has consisted in growing N . meningitidis PmntX::luc in media with increased concentrations of manganese and Desferal ( iron chelator ) . The results show that luciferase activity increased in presence of manganese but more importantly this expression was potentiated in low iron condition ( i . e . manganese 10 µM and Desferal 5 µM ) ( Figure 6A ) . To test a possible role for MntX under low iron/high manganese condition , we performed a serial dilution plate assay under increased concentrations of Desferal in combination with increased amount of manganese . The results presented in Figure 6B revealed that the growth of N . meningitidis harboring mntX ( wild type or complemented ) was neither affected by manganese alone ( up to 40 µM ) nor by Desferal alone ( up to 15 µM ) . Most importantly , a drastic inhibitory effect of Desferal ( 15 µM ) on the growth of N . meningitidis was strictly dependent on the presence of manganese ( Figure 6 , bottom right of panel B ) . This strongly suggests that with low iron availability manganese excess becomes lethal for N . meningitidis . Indeed , ΔmntX could not grow in manganese concentrations above 40μM , independently of iron concentration . To determine whether the ΔmntX mutation could increase sensitivity toward Desferal in the presence of sublethal Mn concentration ( <40μM ) we performed CFU counting experiments . The results presented in Figure 6C showed reduced survival in strains lacking MntX only in the presence of both Desferal and manganese but not in the presence of either compound alone . Measurements of intracellular metal content showed that an active MntX decreases the Mn/Fe ratio by 38 . 0 +/- 6 . 4% in the case of N . meningitidis and 35 . 1 +/- 0 . 3% for N . gonorrhoeae ( Figure 5C ) . All together , these data illustrate the importance of MntX to maintain an optimal Mn/Fe ratio in N . meningitidis via Mn export and in proportion with intracellular iron pool depletion . To test whether reduced fitness of bacteria lacking MntX under low iron/high manganese condition could be due to the abnormal replacement of Fe cofactors by Mn , we monitored the expression of Fur regulated genes . Accordingly , in absence of MntX , a miss-regulation of Fur regulated genes was observed in low iron/high manganese , illustrating these Mn interferences ( Figure S5 ) . Next we addressed the possibility that , since iron availability is restricted in the human body , N . meningitidis may require manganese export to survive in the host . First , we used the murine model of sepsis to determine if the gene was expressed in blood . To avoid residual expression due to culture media , we performed an in vivo passage ( infecting mice with blood from a previously infected mouse ) and extracted RNA . We employed two types of mice: wild type BALB/c mice and BALB/c mice expressing the human transferrin ( hTf ) [26] . N . meningitidis iron acquisition from transferrin is host-specific [47] . Therefore we used mice expressing the hTf ( which is a compatible iron source ) as an alternate model which sustains a better bacterial survival [26] . The Figure 7A presents the Δ ( Δ Cycle threshold or Ct ) of bacterial genes expressed in blood , normalized with gyrA and relative to the expression level upon growth on culture media . As a note , the Ct is defined as the number of cycles required for the fluorescent signal to cross the threshold . In this experiment , we measured the expression of mntX and genes encoding the pillin pilE , the global iron regulator fur , the transferrin binding protein A tbpA and the porin porA . The Δ ( Δ Ct ) of mntX was positive and similar between Balb/C and hTf mice . Thus , mntX was expressed in mouse blood suggesting that export of manganese is required in these conditions . Secondly , we infected mice ( BALB/c wild type or hTf ) i . p . with N . meningitidis harboring or not mntX and determined the amount of bacteria in the blood at early ( 2h after infection ) and later time point ( 4 h or 6h as indicated ) . The results of these experiments are expressed as % bacteria that survived in blood compared to those found at earlier time point ( 2 h ) and are depicted in Figure 7C for hTf mice and Figure 7D for wild type mice . Median survival of N . meningitidis mntX mutant was significantly reduced compared to the wild type or complemented strains in both models of murine infection . Interestingly , the number of bacteria at 2h post-infection was similar independent of mntX indicating that the ΔmntX strain could still cross the epithelial barrier from the peritoneal cavity to the circulation . Nevertheless , to support a reduced survival of N . meningitidis ΔmntX in host blood , we assayed a human serum bactericidal activity with N . meningitidis wild type , ΔmntX or the complemented strain . As shown in Figure 7B , there was 3 to 4 times less mntX mutant surviving in the presence of this specific serum compared to the wild type or complemented bacteria , therefore supporting a role for manganese export in the survival of N . meningitidis during septicemia .
The present work establishes the manganese export function of MntX ( formerly YebN ) , the first characterized members of a new family of metal exporter . Members of the MntX family have been found so far only in prokaryotes and phylogeny suggests pervasive mntX horizontal transfer . While these observations require functional validation , it is of interest that several known major pathogenic bacteria were found to harbor a homologue of MntX such as Actinobacillus , Bacillus , Corynebacterium , Clostridum , Campylobacter , Fusobacteria , Legionella , Proteus , Pseudomonas and enterobacteria ( such as Escherichia , Klebsiella , Yersinia , Salmonella , Shigella ) species ( data not shown ) . In addition , members from this novel family display sequence characteristics that could be related to the evolution of their functional properties . MntX protein sequences are composed of direct repeats with inverted topology . A common evolutionary scenario reminiscent of the MFS , EmrE and LeuT superfamilies [48] , [49] , [50] , [51] would propose that initially , an ancestral 3 TMS repeat ( i . e . , DUF204 ) could adopt inverted topologies and dimerize pseudo-symmetrically to catalyze a substrate translocation process . Subsequent steps of genetic fusion and evolution would have yielded a fixed 6TMS topology , and allowed substrate-driven divergence . MntX xenologs display appreciable sequence conservation despite inverted symmetrical organization; it is tempting to suggest that this family may have relatively recent origins , following the genetic fusion of two DUF204 domains . Future studies will establish whether N . meningitidis MntX Mn2+ selectivity can be related to site-specific sequence divergence within TMS and if other members could have evolved toward another specific metal export . Beyond physico-chemistry , the novel family of manganese exporters MntX sheds new light on manganese impact on bacterial physiology . In this work , we studied the importance of Mn export via MntX in N . meningitidis . Unlike N . gonorrhoeae , N . meningitidis is not known to use Mn as quenching compounds during oxidative stress [30] . The selection of different strategies may reflect conditions encountered within their specific ecological niche . In fact , N . meningitidis ( but also N . lactamica ) are frequently isolated from the human nasopharynx , which is aerobic . The strong conservation of manganese export attribute in N . meningitidis suggests a damaging effect of manganese and a need for export in some conditions . It is generally recognized that the homeostasis of manganese and iron are linked; manganese importers expression is influenced by iron ( e . g . via fur ) [34] and , inversely , iron importer could be regulated by manganese [17] . In addition , several studies have recently correlated the ratio of Mn/Fe with the resistance toward specific stresses [18] . The results presented in this study , strongly suggest that in order to survive in iron restricted environment , N . meningitidis needs to regulate the Mn/Fe intracellular ratio and that MntX is contributing by exporting manganese . Consistent with this , inactivation of mntX reduces N . meningitidis virulence by limiting survival during septicemia in mouse model of infection . Similar attenuation has been observed using another human pathogen , Streptococcus pneumoniae and another family of manganese exporter . Lack of the CDF manganese exporter MntE reduced S . pneumoniae virulence by diminishing nasal colonization , blood invasion and mice mortality [10] . In the absence of MntX ( or other manganese exporters ) , the replacement of iron by manganese in proteins may lead to suboptimal enzymatic activities or inadequate regulation . In stringent environments where bacteria need to be high-performers , a lack of competitiveness may reduce survival of the microorganism due to absence of vigorous responses . However , a different strategy has been preferred for N . gonorrhoeae in order to survive in the genitourinary tract . This environment is anaerobiotic and rich in peroxides produced by host cells ( e . g . , neutrophils ) and other host-adapted bacteria such as Lactobacillus spp . which compete for the same ecological niche [52] . Lactobacilli are well-known for their high intracellular manganese content and peroxide stress resistance [11] . It is therefore not surprising that N . gonorrhoeae uses also Mn-dependent peroxide detoxification strategies ( reviewed in [53] ) . However , though Lactobacilli strategy relies on redundant Mn uptake systems [54] , N . gonorrhoeae approach seems to exploit mntX gene mutation . Importantly , the mntX gene is not deleted from the genome and a reversion of the mutation ( s ) by phase variation may be possible if a modification of the local environment occurred ( e . g . , less H202 ) . During our study we have also noticed that N . gonorrhoeae contains more intracellular iron than N . meningitidis . This result is similar to previous N . gonorrhoeae measurement [18] and may suggest that because N . gonorrhoeae accumulates more Fe than N . meningitidis , the Mn quantities tolerated in the cytoplasm are higher and the need for MntX is not as crucial as it is in N . meningitidis . In addition , this situation may have favored the use of a different strategy for peroxide stress protection aiming to increase manganese concentration during oxidative stress [46] . As our results reveal , this is not possible in N . meningitidis possibly due to lower intracellular iron content not permissive for this Mn-increase . Bacterial homeostasis of iron and manganese or modulations of the Mn/Fe ratio are complex phenomena as they have to integrate information from both the extracellular environment and bacterial intrinsic physiology . In addition , the metabolism of other divalent metals such as Zn may also be coordinately regulated . In this sense , MntX and other manganese exporters have a crucial role to play in the interplay of metal metabolisms . In the future , it will be also important to precisely determine how the innate immune system is interfering with this ratio ( versus iron alone ) to understand how host defense could affect pathogenicity and if perturbation of this equilibrium could inspire new therapeutic strategies .
This study was carried out in strict accordance with the European Union Directive 2010/63/EU ( and its revision 86/609/EEC ) on the protection of animals used for scientific purposes . Our laboratory has the administrative authorization for animal experimentation ( Permit Number 75–1554 ) and the protocol was approved by the Institut Pasteur Review Board that is part of in the Regional Committee of Ethics of Animal Experiments of Paris region ( Permit Number: 99–174 ) . All surgery was performed under sodium pentobarbital anaesthesia , and all efforts were made to minimize suffering . Xanthomonas campestris pv campestris str . ATCC 33913 was purchased at the ATCC and was grown in SB ( Silva Buddenhagen ) medium at 30°C . Neisseria meningitis MC58 and clinical isolates of N . meningitidis and N . gonorrhoeae were obtained from the Centre National de Reference des Meningocoques ( CNRM , Institut Pasteur , Paris ) . The description of these clinical isolates is provided in Table S1 . All Neisseria were grown in GCB medium with Kellogg supplements . For cloning experiments , E . coli DH5α was grown at 37°C in Luria-Bertani Media ( Difco ) . As required , antibiotics were added as follows: chloramphenicol ( 10 µg . ml−1 ) , Spectinomycin ( 100 µg . ml−1 ) , streptomycin ( 50 µg . ml−1 ) , tetracyclin ( 10 µg . ml−1 ) , gentamycin ( 30 µg . ml−1 ) , kanamycin ( 50 µg . ml−1 for E . coli; 100 µg . ml−1 for Neisseria sp . ) and Erythromycin ( 300 µg . ml−1 for E . coli; 3 µg . ml−1 for Neisseria sp . ) . The mntH ( XCC2171 ) and mntR ( XCC2170 ) genes are in the same locus and in an opposite direction in the genome of X . campestris . Therefore , to construct the plasmid used for double recombination , the 3′ part of mntH ( amplified using the primers XcaSmaIF and XcampR2 ) was first inserted into pK18mobsacB [55] ( gift of CVector ) using the enzymes SmaI and EcoRI . Secondly , the plasmid generated , called pK18clon1 , has served to generate all the other constructs by adding a specific 5′ part: as a part of mntR ( to construct ΔmntH-R ) , or mntR and the rest of mntH ( complemented ) or the full mntR and a 5′ part of mntH ( ΔmntH ) . These specifics 5′ parts were generated by specific enzymatic digestion of the same PCR product called PCR2 ( obtained using XcaHindIIIF and XcaNoMutR primers ) . In more details , the construction of plasmids was as follow: 1 ) pK18mntH::Tet used to make the mntH deletion: PCR2 was inserted into pK18clon1 using SmaI and BamHI . Following this , the tetracycline resistance cassette from p34S-tet [56] was inserted between the 5′ and 3′ fragments using SmaI . 2 ) pK18mntH-R::Gm used to make the mntH-R deletion: The chloramphenicol resistance cassette from p34s-Cm [56] was inserted into pK18clon1 using SmaI . The resulting plasmid was ligated with PCR2 , after a digestion with SalI and HindIII , to generate pK18mntH-R::Cm . Due to spontaneous resistance , the chloramphenicol resistance gene was changed by the gentamycin cassette . This one was extracted from p34S-Gm [56] using SmaI and inserted into pK18mntH-R::Cm digested with SmaI to generate the plasmid pK18mntH-R::Gm . 3 ) pRH used to complement the mntR-H deletion: PCR2 was inserted into pK18clon1 using PstI and XbaI to generate pK18comp this has led to the reconstruction of the complete locus . The mntH-R genes were extracted to pK18comp using NheI and XbaI and were transferred into a X . campestris compatible plasmid , pBBR1-Tet digested by XbaI . Of note , pBBR1-Tet was generated by the ligation of pBBR1-Tp [57] digested with NheI and NcoI and treated with T4 DNA polymerase with the SmaI-digested tetracyclin cassette from p34S-Tet [56] . 4 ) pK18mntR::Sm used to make the mntR deletion: The plasmid pK18comp was digested with AgeI and SalI and , after treatment with T4 DNA polymerase , was ligated to a SmaI-digested spectinomycin resistance cassette from p34S-Sm3 [56] . 5 ) pK18yebN::Tet used to make the yebN deletion: A PCR product containing yebN and obtained with XCC4075F and XCC4075R was digested by NheI and XbaI and cloned in pK18mobsacB digested with the same enzyme . This new plasmid was digested by BglI ( two sites in yebN ) , treated with T4 DNA polymerase and ligated with the tetracycline resistance cassette obtained by SmaI digestion of p34S-Tet , to finally generate pK18yebN::Tet [56] . All these plasmids were introduced in X . campestris using E . coli S17λpir as previously described [58] . The allelic exchange events were selected in SB medium containing 10% sucrose , ampicillin 100 µg . ml-1 ( in order to inhibit E . coli ) and the specific antibiotic for the deletion . As a first step , the kanamycin and erythromycin resistance genes were amplified using Km6-KmUp and ERAM3-ERAMUp and subcloned into pGEM-T-easy ( Promega ) to give pGEM::Km and pGEM::Ery respectively . Secondly , the 5′ and 3′ DNA fragments were amplified from genomic DNA of the N . meningitidis MC58 , using respectively KOYebN5′F-KOYebN5′R or KOYebN3′F-KOYebN3′R couple of primers . Lastly , the 5′ and 3′ DNA fragments were inserted sequentially into this pGEM::Km using respectively , NcoI and SphI or NdeI and PstI to finally generate p5′KOYebN3′::Km . To construct the plasmid for the complementation , the full length yebN gene was amplified using YebNNsiIF and YebNPstIR and was inserted into p5′KOYebN3′::Km after a digestion with NsiI and PstI to generate p5Y3::Km . Subsequently , the kanamycine resistance gene has been removed and replaced by the erythromycin gene from pGEM::Ery using EcoRI to generate p5Y3::Ery . The luciferase was amplified using LucF and LucR , digested with NsiI-PvuII and ligated to p5Y3::Km digested with NsiI-BsaBI to generate p5L3::Km ( luciferase under the control of the yebN promoter ) . The p5′KOYebN3′::Km and p5L3::Km were then used to transform N . meningitidis MC58 , and transformants were selected on GCB medium in the presence of 100 µg/ml Km . After verification of the deletion of yebN , the plasmid p5Y3::Ery was used to transform N . meningitidis MC58 ΔyebN and transformants were selected on GCB medium in the presence of 3 µg/ml Ery . Therefore , the functional yebN gene was introduced back in its original locus in the genome of N . meningitidis ΔyebN . Additionally , the plasmid p5Y3::Km was used to transform and complement N . gonorrhoeae 16626 . First , to evaluate the metal sensitivity of X . campestris , Neisseria sp . and respective mutants , a disk assay of metal sensitivity was used . X . campestris and mutants were grown during a 16-h period at 30°C and 250 rpm . The cultures were then diluted in GTA broth to obtain a final OD580 of 0 . 1 and were further incubated at 30°C until OD580 0 . 4 . A volume of 1 ml was then taken , mixed with 9 ml of Top-SB medium and poured in 15 cm Petri plates containing 40 ml of solidified SB medium . After 15 min , a disk containing 10 µl of solution was placed on the center of the plates . For Neisseria , an aliquot of a 16h old culture ( on plate ) was taken and diluted on GCB media plus supplements to an OD600 of 1 . Following this , 100 µl of this suspension were spread on a Petri dish containing 20 ml of GCB agar medium plus supplements . After 15 min , a disk containing 10 µl of solutions was deposed on the center of the plates . A standard t-test was done in order to assess statistically significant differences . Secondly , growth sensitivity to the ratio manganese/iron was evaluated for the different strains of N . meningitidis . Serial dilutions of bacteria were spotted on plate containing increasing amount of Desferal and manganese . These plates were incubated overnight at 37°C after what , pictures were taken . To measure more precisely the effect of the ratio on bacterial growth , ≈250 bacteria were plated on GCB agar plate containing the indicated amount of manganese and Desferal , and the percentage of growth was calculated compared to the GCB agar with no manganese . A standard t-test was done in order to assess statistically significant differences . For in vitro expression , we used the firefly luciferase fused to the yebN promoter and inserted in the genome as described above . The strain was grown on GCB agar plates overnight at 37°C and subculture in GCB agar plates containing the indicated concentration of MnCl2 and Desferal during 5 h . The bacterial layer was harvested in physiologic water to obtain an OD600 . The luciferase activity was measured as described in the “Luciferase assay system” protocol ( Promega ) and a standard t-test was applied to assess statistical significance . For in vivo expression , we performed qRT-PCR . Briefly , RNA was extracted using bacterial RNA protect and RNeasy Mini Kit ( Qiagen ) from blood of mice infected i . p . with 500 ml of blood from a mice previously infected i . p . with 10° bacteria . A passage from one mouse to another was done in order to attenuate the GCB media interference . qRT-PCR was performed using Power SYBR Green PCR master mix and StrepOne plus ( Applied Biosystems ) using the primers listed in table 1 . In addition , a standard t-test was applied to assess statistically significant difference in comparison to the gyrA ΔCt . The amount of metals accumulated in Neisseria sp . cells was determined by growing cells overnight on complete GCB and sub-culturing them in GCB agar plate containing 10 µM of Mn . After an incubation of 6h for N . meningitidis and 16h for N . gonorrhoeae ( slow grower ) the cells were resuspended in PBS and centrifuged . The pellets were subjected to acidic digestion using 500 µL of 65% acid nitric during one hour at 80°C . The preparation has been diluted with H2O ( HPLC grade , Fisher ) to obtain a concentration of 2% acid nitric . The samples were sent to Intertek Analytical Services ( Chalon sur Saone; France ) and analyzed by inductively coupled plasma mass spectrometry ( ICP-MS ) with Agilent 7500 cx ( Agilent Technologies , USA ) . Results were expressed as the calculated ratio of number of atoms of the specified metal ( MW used for Mn: 54 , 938 ) normalized with the number of Mg atoms ( MW: 24 , 305 ) or Fe ( MW: 55 , 845 ) . Each strain has been cultured in triplicates for N . meningitidis and in duplicates for N . gonorrhoeae and a standard t-test was applied to assess significance . To measure the percentage of survival , a specific human serum was selected due to its capacity to induce death in 30 to 50% of bacteria without addition of external source of complement . The assay was done as previously described [59] by incubating ≈1000 bacteria in HSSB2+ containing 50% of human serum during 30 min at 37°C . The results of several experiments ( at least three ) were pooled and one tailed Mann–Whitney test was used to determine statistical significance of observed differences ( GraphPad Prism v5 . 0; GraphPad Software , CA ) . The day of infection , inocula of N . meningitidis strains were prepared in PBS to obtain a final concentration of 2×107 CFU/ml ( OD600 0 , 1 ) . Six week-old BALB/c ( Janvier; France ) or six to nine week-old human transferrin ( hTf ) transgenic mice [26] were infected by intraperitoneal challenge with 500 µl of inocula ( 1×107 CFU ) . Bacterial counts in the blood were determined at 2h and 4h or 6h ( for wild type or hTf mice respectively ) after meningococcal challenge by plating serial dilutions of blood samples on GCB medium . Results are expressed in percentage of bacteria counted at 4 h or 6 h ( for wild type or hTf mice respectively ) compare to those counted at 2 h post infection . The results of several experiments ( at least two ) were pooled and one tailed Mann–Whitney test was used to determine statistical significance of observed differences ( GraphPad Prism v5 . 0; GraphPad Software , CA ) . To search for MntR regulated genes in bacterial genomes , we first sought the presence of putative conserved MntR binding sites using several predictive approaches . For this purpose , the sequence of the promoters of mntH , mntR , sitA from different proteobacteria were aligned using the AlignX program from VectorNTI ( Invitrogen ) , and the resulting multiple alignment of similar motifs was used as a matrix to detect conserved binding sites in different genomes using the programs PredictRegulon [60] and PREDetector [61] . The protein hydropathy profile was calculated using the server TOPPRED [62] using a 20-residue long sliding window ( core window: 14 residues , wedge windows: 4 residues ) . The default parameters suggested for predicting the presence of transmembrane segments in prokaryotic proteins were used , such as the Goldman , Engelman , Steitz hydropathy scale cut-off values ( Lower , 0 . 6; putative; Upper , 1 . 0; certain ) . Protein transmembrane topology prediction was calculated using the MEMSAT-SVM server [63] . Molecular evolutionary sequence analyses were performed as previously described [38] . Sequences were classified by similarity clustering using CLANS [64] and multiple sequence alignment and phylogenetic analyses were conducted using the package Mega [36] . Briefly , BlastP searches using MntX revealed numerous similar bacterial sequences showing unexpected taxonomic distribution . To perform tractable sequence analyses we selected from databases of reduced complexity ( entries <70% identity ) candidate homologs co-linear with and displaying more than 30% identity to MntX . Clustering and tree-making approaches validated our sequence set as representative of the putative MntX family and including a possible phylogenetic outgroup [38] . To visualize MntX sequence conservation patterns we used a multiple-logo alignment tool [37] to compare the variability/homogeneity of aligned sequences representing hierarchically defined subclusters , such as the outgroup and the MntX family , as well as MntX family subgroups . | Neisseria meningitidis is an obligate resident of the human nasopharynx but can also be responsible for septicemia and meningitis . During our efforts to understand the specific selective pressure underwent by N . meningitidis to survive in its human niche , we have brought to light a new family of bacterial manganese-exporters ( MntX ) strongly conserved in N . meningitidis but often inactivated or absent in other Neisseria species . As iron , manganese is an essential metallo-nutrient for bacteria . Thus , the N . meningitidis need for a manganese-exporter seemed rather surprising . In fact , we were able to show that MntX is an important player in the regulation of the manganese/iron equilibrium and that this regulation via MntX is critical to survive in presence of manganese in particular when iron is rare . It is expected that excessive iron replacement by manganese into the active site of enzymes would handicap bacteria . Accordingly , MntX is required for full virulence of N . meningitidis in a mice model of septicemia or to resist killing by human serum . More generally , this equilibrium may be tightly regulated in other respiratory tract pathogens such as S . pneumoniae and therefore , interferences with this balance may be a promising strategy for infectious diseases therapy . |
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In metabolic networks , metabolites are usually present in great excess over the enzymes that catalyze their interconversion , and describing the rates of these reactions by using the Michaelis–Menten rate law is perfectly valid . This rate law assumes that the concentration of enzyme–substrate complex ( C ) is much less than the free substrate concentration ( S0 ) . However , in protein interaction networks , the enzymes and substrates are all proteins in comparable concentrations , and neglecting C with respect to S0 is not valid . Borghans , DeBoer , and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0 . We extend this description , which Borghans et al . call the total quasi-steady state approximation , to networks of coupled enzymatic reactions . First , we analyze an isolated Goldbeter–Koshland switch when enzymes and substrates are present in comparable concentrations . Then , on the basis of a real example of the molecular network governing cell cycle progression , we couple two and three Goldbeter–Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases . Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks , because ( 1 ) it unveils the modular structure of the enzymatic reactions , ( 2 ) it suggests a simple algorithm to formulate correct kinetic equations , and ( 3 ) contrary to classical Michaelis–Menten kinetics , it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively .
Our approach to PINs relies on a modified QSSA introduced by Borghans , DeBoer , and Segel in 1996 [7] . They proposed that , for conditions when ET and S0 are comparable numbers , the proper intermediate timescale variable is Ŝ ( t ) = S ( t ) + C ( t ) . In terms of this variable , the governing equations are Borghans , DeBoer , and Segel called this the total QSSA ( tQSSA ) . A sufficient condition for the uniform validity of the tQSSA was derived by Tzafriri [8]: where ST = S ( 0 ) + C ( 0 ) , ɛ ( ET , ST ) = ( k2/2k1ST ) · f ( r ( ET , ST ) ) , f ( r ) = ( 1 – r ) −1/2 − 1 , and r ( ET , ST ) = 4ETST ( Km + ET + ST ) −2 . Tzafriri showed that Hence , Equation 3 is satisfied if k−1 ≫ k2; i . e . , the dissociation rate of the enzyme–substrate complex is much faster than the catalytic conversion of substrate into product . Notice that Equation 3 is not badly violated even if k–1 = 0; so the tQSSA is likely to be an excellent approximation for any ratio of enzyme to substrate and for any ratio of timescales . It is of course possible , using the quadratic formula , to solve Equation 1 exactly for C as a function of Ŝ and ET and to substitute this formula into Equation 2 for dŜ/dt . One may instead use the expression which is a good approximation so long as C2 ≪ ETŜ . Defining ρ = ( Ŝ + Km ) /ET , we can write the condition C2 ≪ ETŜ as which is certainly satisfied for ρ ≫ 1 and ρ ≪ 1 . At its minimum , the left-hand side of Inequality 6 is 4 , so the approximate Equation 5 would seem to be quite good for any values of ET and ST . Per Borghans , DeBoer , and Segel , we call Equation 5 the Padé approximant , and we use it whenever possible . Tzafriri [8] provides a careful discussion of the conditions for validity of the tQSSA and the more restrictive Padé approximant ( which he calls the first-order tQSSA ) . Recently , Pedersen et al . applied the tQSSA to the case of an enzyme converting two different substrates into products [9] . Continuing along this line , we apply the tQSSA to an isolated Goldbeter–Koshland ( GK ) switch [10] and then to PINs of increasing complexity .
In 1981 , Goldbeter and Koshland [10] introduced the notion of an ultrasensitive switch , composed of a substrate–product pair ( say , S and Sp ) that are interconverted by two enzymes ( say , E and D ) ; see Figure 1A . ( Think of Sp as a phosphorylated form of S and of E and D as a kinase and a phosphatase , respectively . ) Assuming the MM conditions , ET ≪ S ( 0 ) + Kme and DT ≪ Sp ( 0 ) + Kmd , Goldbeter and Koshland wrote a single dynamical equation for the time evolution of the switch: In Equation 7 , x = Sp ( t ) /ST , ST = S ( 0 ) + Sp ( 0 ) , Je = Kme/ST , Jd = Kmd/ST , Ve = k2eET/ST , Vd = k2dDT/ST , and the subscripts “e” and “d” refer to the kinase and phosphatase reactions , respectively . Goldbeter and Koshland showed that the steady state solution of Equation 7 is given by the GK function , The steady state response ( Sp* ) as a function of stimulus strength ( kinase level , ET ) is simply If Je and Jd ≪ 1 , then Sp* is a steeply sigmoidal function of ET . Goldbeter and Koshland called this signal-response curve zero-order ultrasensitivity . Goldbeter and Koshland's analysis of phosphorylation–dephosphorylation cycles is fine for metabolic control systems , where metabolite concentrations ST are orders of magnitude larger than enzyme concentrations , ET and DT . But for PINs , the condition for the classical MM rate law is not valid , and we must keep track of the enzyme–substrate complexes ( Figure 1B ) . A similar argument has recently been suggested for the mitogen-activated protein kinase pathway [11] . Re-deriving Equation 7 , using the tQSSA and the Padé approximation to the algebraic equations , we find Equation 7 subject to some new definitions: The signal–response curve , is ultrasensitive if Je and Jd ≪ 1 , but this requires that the total enzyme concentrations be small with respect to the total substrate concentration: the standard MM requirement . For PINs , we cannot expect this requirement to be satisfied , which suggests that protein phosphorylation–dephosphorylation cycles are unlikely to be ultrasensitive . In Figure 1C we plot the signal–response curve , Ŝp/ST , as a function of ET/DT given by Equation 8 , for three different values of DT . As expected , the response function is ultrasensitive for Je and Jd small , but ultrasensitivity is lost as Je and Jd increase . This conclusion about ultrasensitivity being lost in PINs , based as it is on the Padé approximant , is not reliable when enzymes and substrates are present in similar concentrations [12] . In fact , numerical calculations ( Figure 1D ) , based on the full tQSSA equations ( Table 1 ) , show that a GK switch may still be ultrasensitive for reasonable values of Je and Jd ( see Figure 1D ) . Therefore , the issue of ultrasensitivity for covalent modifications in PINs should be settled using exact steady state calculations , without making the Padé approximation . In the next section we show that the tQSSA , besides giving insights into the steady state behavior of the network , provides a good approximation of its temporal dynamics as well . In Table 2 ( columns D and E ) we assign specific values to the rate constants and total concentrations for the GK module discussed in the previous section . Our choices are not consistent with MM requirements and are only partly consistent with favorable conditions for the tQSSA . In Figure 2 we compare a numerical solution of the full set of governing differential equations with approximate solutions computed from the usual QSSA and the tQSSA ( the equations are provided in Table 1 and in machine-readable form in File Collection S1 ) . In addition to a time course ( Figure 2C ) , we plot ( Figure 2A and 2B ) one of the fast variables ( [E:S] or [Dp:Sp] ) as a function of the relevant slow variable ( S or Sp for QSSA and Ŝ or Ŝp for tQSSA ) , to highlight the presence of two different timescales . If the timescales are clearly separated , a sudden increase of the complex ( displacement along the y-axis ) will precede the slower conversion of substrate into product ( displacement along the x-axis ) ; see [7] . From the exact solution ( black curves in Figures 2A and 2B ) we see that , at the beginning of the reaction , Sp forms a complex with D , and before D:Sp reaches a maximum , Sp starts to be converted into S . As soon as S is produced , E:S is created and converted back into Sp , with most of E forming a stable complex with S ( [E:S]/ET = 0 . 935 at steady state ) . Although the fast and slow dynamics are not perfectly separated in either QSSA or tQSSA , the timescale separation is clearly more pronounced in tQSSA ( red curve in Figure 2B ) than in QSSA ( blue curve in Figure 2A ) . Time courses ( Figure 2C ) show that throughout the simulation tQSSA does a better job in reproducing the dynamics of the network than QSSA . Notice that at the beginning of the simulation , QSSA gives negative values to S and [E:S] to comply with both initial conditions and conservation relations . In this section we study the steady state behavior of a system of two coupled GK switches ( Figure 3A ) . Building upon the previous network , we consider the possibility that E exists in phosphorylated ( Ep ) and unphosphorylated ( E ) forms . Suppose that Ep is a less active form , and E → Ep is catalyzed by S . S and E are antagonists since they phosphorylate and inactivate each other . F is a phosphatase that converts Ep back to E . ( In our notation for complexes , A:B , the enzyme comes first and substrate follows; for example , for the reaction whereby S phosphorylates E , the enzyme–substrate complex is denoted S:E . ) This is no arbitrary example; it describes exactly the interactions between two regulators of the G2-to-mitosis ( G2/M ) transition in the eukaryotic cell cycle [13] . In that case , S = MPF ( M-phase promoting factor , a dimer of Cdc2 and cyclin B ) and E = Wee1 ( a kinase that phosphorylates and inactivates Cdc2 ) . The parameter values that we choose for these coupled enzymatic reactions ( Table 2 ) are taken , for the most part , from a careful study of the biochemical kinetics of these reactions in Xenopus egg extracts , done by Marlovits et al . [14] . Novak , Tyson , and collaborators implemented these values into a mathematical model for the G2/M transition during early embryonic cell cycles . The equations of the model were derived from an implicit application of the QSSA , but to our knowledge an explicit derivation has never been done: this observation prompted us to investigate the matters addressed in this paper . The antagonism between E and S ( Figure 3A ) constitutes a positive feedback loop ( sometimes called a double-negative feedback loop ) . Novak and Tyson proposed that this loop contributes to the bistability that characterizes the G2/M transition in the eukaryotic cell cycle [15–17] . In fact , according to their original model , the antagonism between Wee1 and MPF alone could sustain bistability . However , this conclusion was drawn from an improper application of the QSSA; hysteresis is lost once the network is unpacked to its elementary steps ( Figure 3B ) . Even more , according to Advanced Deficiency Theory ( performed with the software package CRNT [Chemical Reaction Network Toolbox] developed by Feinberg [18] ) , this network ( Figure 3B ) cannot have bistable behavior for any positive values of the kinetic rate constants . Is it possible to recover hysteresis from the antagonism between S and E ? In Novak and Tyson's original model , both Sp and Ep had some residual activity , a feature that in their model was not essential to generate hysteresis and that we have not taken into account so far . This residual activity becomes essential when the network is reduced to elementary steps: we find that bistability is recovered when we add the reactions E + Sp ↔ Sp:E → Ep + Sp , i . e . , when Sp retains some limited kinase activity . ( In Figure 3B the additional reactions are drawn in grey , and in Figure 3C , top panel , the signal–response curve is drawn with and without the additional reactions . ) Bifurcation analysis suggests that the new reactions create a steady state where S is inhibited and E is active , which is paradoxical because the additional reactions provide an alternative path for inactivating E . The paradox is resolved when we realize that a large fraction of S is sequestered in Sp:E complexes , thus helping E to inactivate S . Indeed , hysteresis is possible ( unpublished data ) with the association–dissociation reactions alone ( E + Sp ↔ Sp:E ) without the catalysis step ( Sp:E → Ep + Sp ) . Of course , bistability can also be restored by allowing Ep to phosphorylate S . To apply the tQSSA to such networks , we first define “hat” variables to include a single free molecular species plus all the complexes in which this species appears . Defined thus , the association–dissociation reactions and all the reactions where a chemical species serve as a catalyst cancel out . Here we define whose rates of change are given by Our definition of hat variables extends what was originally proposed for a single enzymatic reaction by Borghans , DeBoer , and Segel . In terms of the hat variables , we can describe each catalytic reaction in the network with equations similar to Equations 1 and 2 . For example , the rate of phosphorylation of S in Figure 3B is described by Equation 2 with k2C → e2 [E:S] and with the concentration of enzyme–substrate complex given by Equation 1 with C = [E:S] , , and ( see equations in Table 3 ) . Concerning this network: we perform no numerical simulations to compare tQSSA and QSSA; we will do that in the next section for a larger and biologically more significant network . Rather , we use the model reduced with tQSSA to perform phase plane analysis ( Figure 3C , bottom panel ) , which confirms that the additional reaction strengthens the negative effect that E exerts on S , thereby bending the Ŝ nullcline and creating three intersection points . In the G2/M network , the antagonism between Wee1 and MPF is aided by a second positive feedback loop , involving MPF and Cdc25 , a phosphatase that removes the inactivating phosphate group from Cdc2 [13] . ( In our notation , Wee1 is E , MPF is S , and Cdc25 is D . ) Because S can phosphorylate D , and Dp is a more active form , S and Dp activate each other . Finally , we have C , an unregulated phosphatase that converts Dp back to D . Altogether , the network consists of three GK modules: the first ( C/D/S ) controls D's phosphorylation , the second ( D/S/E ) affects the phosphorylation state of S , and the last ( S/E/F ) controls the activity of E ( Figure 4A and 4B ) . Again , we take most of the parameter values for the additional reactions from Marlovits et al . [14] . In Table 4 we provide the governing equations for these coupled GK switches in three versions: full ( no approximations ) , QSSA , and tQSSA . Bifurcation analysis and CRNT show that the network is bistable even with no residual activity for Sp or Ep ( unpublished data ) . Indeed , the positive feedback between Dp and S suffices , by itself , to generate hysteresis , as confirmed by CRNT . Interestingly , given the parameter values in Table 2 , the network is bistable ( unpublished data ) in a region very similar to that determined experimentally by Sha et al . [17] . Since the model performs satisfactorily concerning its steady state behavior , we move on to compare the dynamics of both QSSA and tQSSA to the exact solution ( Figure 4C ) . To apply tQSSA , we define the hat variables whose dynamics are described by the following equations: The concentrations of the enzyme–substrate intermediates are given by solving simultaneously a set of six coupled quadratic algebraic equations ( see Table 4 ) . Notice how the modularity of the network ( composed of six identical MM reactions ) is mirrored by the modularity of the tQSSA equations . Numerical simulations ( Figure 4C ) demonstrate the superiority of tQSSA ( red lines ) over QSSA ( blue lines ) in accurately capturing the exact dynamics ( black lines ) of this regulatory network . To compare further the QSSA and tQSSA with the exact solution , we plot in Figure 5 the complexes ( [Dp:Sp] and [E:S] ) as functions of the slow variables ( Sp and S for QSSA and Ŝp and Ŝ for tQSSA ) . The initial hump of [Dp:Sp]—due to phosphorylation of D by S followed by dephosphorylation of Dp by C— ( black curves in Figure 5A and 5B ) , is captured qualitatively by the tQSSA ( red curve ) but completely missed by the QSSA ( blue curve ) . Similarly , the initial accumulation of [E:S] is closely approximated by the tQSSA but badly overshot by the QSSA . Both approximations capture the latter part of the time course reasonably well . The QSSA completely misses the initial stages of the simulation because it assigns negative values to Ep , [F:Ep] , and D .
Coupled enzymatic reactions ( e . g . , interacting kinases and phosphatases ) are common features of PINs . We analyze one of these networks , composed of three phosphorylation and three dephosphorylation reactions , altogether comprising 14 chemical species . The network was originally proposed by Novak and Tyson [13] to model the activation of MPF in early embryos of the frog Xenopus . In their original work , Novak and Tyson neglected the contribution of enzyme–substrate complexes . Using a combination of mass-action and MM kinetics , they showed that the network may have multiple steady state solutions . We have relaxed the assumptions , writing down differential equations for each species , including the enzyme–substrate complexes . Using parameters obtained indirectly from published data , we confirmed that the model exhibits bistability in the same parameter range as predicted theoretically by Novak and Tyson [13] and measured experimentally [16 , 17] . As for the dynamics , our simulations show that the enzyme–substrate complexes , neglected in the original model , are present in non-negligible concentrations . The complexes play an important role because of the topology of the network . In the cell cycle model , some molecular species are at the same time enzymes and substrates of each other . If for one reaction enzyme concentration is negligible compared with substrate concentration , then the opposite must be true when the roles are exchanged , allowing for a significant fraction of the substrate to be sequestered in the complex . Similarly , when some molecules form complexes with several enzymes , even if each complex is not present in a large amount , their sum may not be negligible . The role played by enzyme–substrate complexes in PINs could be more important than currently appreciated . In the cell cycle network , Wee1 and MPF are antagonists that phosphorylate and inhibit each other . In our simulations , the concentrations of Wee1:MPF and MPF:Wee1 are not negligible . The presence of high concentrations of such complexes can be interpreted as a second way for Wee1 to inhibit MPF , by sequestration . In this sense , Wee1 behaves as both an inhibitory kinase and a stoichiometric cyclin dependent kinase ( CDK ) inhibitor . In our simulation we notice that MPF:Cdc25 is also present in high concentration ( 40% of total MPF , 10% of total Cdc25 ) . If confirmed , that would suggest an intrinsic way to attenuate the positive feedback loop between MPF and Cdc25 . Finally , trimeric complexes might form as well ( e . g . , Wee1:MPF:Wee1 ) , and such molecular species may have great effects on the qualitative behavior of PINs ( Sabouri-Ghomi , Ciliberto , Novak , and Tyson , unpublished data ) . Of course , one can follow the exact dynamics of a control system by solving the full system of ordinary differential equations ( ODEs ) . However , a full description of the system comes with many equations , making any qualitative analysis difficult . A typical way to reduce the number of equations is the QSSA originally formulated for an isolated enzyme-catalyzed reaction , when substrate is in great excess over enzyme . When applying the QSSA to a network of coupled catalytic reactions , with realistic values of rate constants and total protein concentrations , we found that the reduced system of ODEs obtained by the QSSA does not faithfully reproduce the dynamics of the full system of ODEs . The tQSSA works for a larger range of parameter values than does the QSSA; in particular , it is valid even when the enzyme is in excess compared with the substrate . Such an approximation is particularly appealing in PINs where , as mentioned above , enzymes and substrates often exchange roles . Given its appeal , we applied tQSSA to a full model of the PIN that regulates the G2/M transition in the cell cycle . We found by numerical simulations that the tQSSA does a good job describing coupled catalytic reactions . Moreover , applying tQSSA to PINs generates a set of differential–algebraic equations of standard format , Equations 1 and 2 , suggesting that a computer algorithm may be devised whereby tQSSA is used to reduce a full set of multi-timescale ODEs to a smaller set of slow equations . The reduced set of equations may be especially useful for stochastic simulations of PINs by Gillespie's algorithm . Summarizing , we propose that large networks of coupled enzymatic reactions should first be written in full and then reduced by applying the tQSSA . This way it will be possible to reduce the number of dynamic equations while maintaining the complexity of the network ( i . e . , including enzyme–substrate complexes ) and simultaneously to achieve reliable approximate solutions for the transient dynamics of the network .
All calculations have been made using XPPAUT , software developed by Ermentrout [19] and freely available online at http://www . math . pitt . edu/∼bard/xpp/xpp . html . XPPAUT solves the algebraic part of differential–algebraic equations by Newton's method , given an initial guess for the unknowns . In File Collection S1 we provide . ode files , readable by XPPAUT , for reproducing the results in this paper . | The physiological responses of a cell to its environment are controlled by gene–protein interaction networks of great complexity . To understand how information is processed in these networks requires accurate mathematical models of the dynamical behavior of large sets of coupled chemical reactions . To avoid producing large and hardly manageable models , such reaction networks are often simplified using phenomenological reaction rate laws , such as the Michaelis–Menten rate law for an enzyme-catalyzed reaction . We show that , in regulatory networks where proteins swap places as enzymes and substrates , such simplifications must be carried out with care , keeping track of enzyme–substrate complexes . The risk is to provide a simplified description of the molecular networks that at best is correct for the long-term behavior but fails to represent the short-term dynamics of the real network . To avoid such a possibility , we suggest using an alternative approach called the total quasi-steady state approximation . We apply this alternative formalism to a model of the network controlling the entry into mitosis in the eukaryotic cell cycle , composed of three coupled protein modification cycles . Whereas the classical Michaelis–Menten formalism fails to represent the dynamics of this network correctly , the one we propose captures the behavior with economy and accuracy . |
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Negative regulation of receptor signaling is essential for controlling cell activation and differentiation . In B-lymphocytes , the down-regulation of B-cell antigen receptor ( BCR ) signaling is critical for suppressing the activation of self-reactive B cells; however , the mechanism underlying the negative regulation of signaling remains elusive . Using genetically manipulated mouse models and total internal reflection fluorescence microscopy , we demonstrate that neuronal Wiskott–Aldrich syndrome protein ( N-WASP ) , which is coexpressed with WASP in all immune cells , is a critical negative regulator of B-cell signaling . B-cell–specific N-WASP gene deletion causes enhanced and prolonged BCR signaling and elevated levels of autoantibodies in the mouse serum . The increased signaling in N-WASP knockout B cells is concurrent with increased accumulation of F-actin at the B-cell surface , enhanced B-cell spreading on the antigen-presenting membrane , delayed B-cell contraction , inhibition in the merger of signaling active BCR microclusters into signaling inactive central clusters , and a blockage of BCR internalization . Upon BCR activation , WASP is activated first , followed by N-WASP in mouse and human primary B cells . The activation of N-WASP is suppressed by Bruton's tyrosine kinase-induced WASP activation , and is restored by the activation of SH2 domain-containing inositol 5-phosphatase that inhibits WASP activation . Our results reveal a new mechanism for the negative regulation of BCR signaling and broadly suggest an actin-mediated mechanism for signaling down-regulation .
B lymphocytes are a key component of the immune system and responsible for generating antibody responses against foreign invaders . B-cell–mediated antibody responses are activated by signals generated from B-cell antigen receptor ( BCR ) and from T helper cells through antigen presentation . Antigen binding induces self-aggregation of BCRs into microclusters and BCR association with lipid rafts , which lead to the recruitment of signaling molecules to BCRs . First the tyrosine kinases Lyn and Syk are recruited followed by phospholipase Cγ2 ( PLCγ2 ) , phosphatidyinositol-3-kinase , Bruton's tyrosine kinase ( Btk ) , and the guanine nucleotide exchange factor Vav for the GTPases Rac and Cdc42 , which activate signaling cascades [1] , [2] . BCR microclusters grow over time and subsequently merge into each other , resulting in the formation of a BCR central cluster at one pole of the cell [3]–[5] . After initial signaling activation , inhibitory signaling molecules , including the tyrosine and phosphatidylinositol phosphatases SHP , SHIP , and PTEN , are activated , down-regulating signaling [6]–[9] . Defects in the negative regulation of BCR signaling are associated with losses of B-cell self-tolerance and increases in the susceptibility to autoimmune diseases [10] , [11] . However , the molecular details of the negative regulation of BCR signaling have not been well defined . The self-clustering of surface BCRs into microclusters is an essential event for triggering signaling activation and a target for regulation . While surface BCRs have been shown to exist as tight but inhibitory oligomers at the nanoscale before activation [12] , [13] , antigen-induced coalescence and transformation of the nano-clusters into microclusters is required for BCR activation . Spontaneous formation of BCR microclusters induced by actin depolymerization leads to signaling activation in the absence of antigen [14]–[16] . BCRs with high affinity to an antigen cluster induce signaling with faster kinetics and to higher levels than those with low affinity to the antigen [17] , [18] . Conversely , the co-engagement of the BCR with the inhibitory coreceptor FcγRIIB by antigen–antibody complexes inhibits both BCR clustering and signaling [19] , [20] . We have recently shown that while the formation of BCR microclusters induces signaling , the coalescence of BCR microclusters into the BCR central cluster is associated with signaling attenuation at the B-cell surface . Both the attenuation of BCR signaling and the coalescence of BCR microclusters into the central cluster are inhibited in B cells where the gene of SH2 domain-containing inositol 5-phosphatase ( SHIP-1 ) is specifically deleted [21] . These results suggest that the formation of the BCR central cluster is a down-regulatory mechanism for BCR signaling . BCR self-clustering at the B-cell surface depends on actin reorganization . Actin can regulate BCR clustering by controlling the lateral mobility of surface receptors and B-cell morphology [5] , [15] , [17] , [22] . Perturbing the cortical actin increases the lateral mobility of surface BCRs and facilitates BCR self-clustering and BCR signaling [15] , while stabilizing the actin network does the opposite [16] . In response to membrane-associated antigen , B cells undergo actin-dependent spreading and contraction . B-cell spreading expands the area of contact between the B-cell surface and the antigen-tethered membrane , thereby increasing the number of antigen-engaged BCRs [17] . The merger of BCR microclusters into the central cluster appears to depend on B-cell contraction following spreading since the BCR central cluster fails to form when cell contraction is inhibited [21] . Conversely , BCR clustering and B-cell spreading are regulated by BCR signaling . B cells with genetic deletion of signaling molecules , CD19 , PLCγ2 , Btk , Vav , or Rac , exhibit impaired BCR clustering and B-cell spreading [23]–[25] . We have demonstrated that the stimulatory kinase Btk is essential for the activation of the actin regulator Wiskott–Aldrich syndrome protein ( WASP ) , B-cell spreading , and BCR clustering . In contrast , the inhibitory phosphatase SHIP-1 inhibits WASP activation by suppressing Btk activation , which promotes B-cell contraction and the coalescence of BCR microclusters into the central cluster [21] . Furthermore , actin reorganization is essential for BCR internalization [26] , which is not only the initiation step of antigen processing but also a mechanism for down-regulation of receptor signaling . Therefore , actin can provide both positive and negative feedback to BCR signaling . WASP is an actin-nucleation–promoting factor that is specifically expressed in hematopoietic cells [27] , [28] . WASP mutations that ablate its expression cause a severe and complicated X-linked immune disorder , WAS [29]–[31] , which demonstrates the critical role of actin in immune regulation . WAS patients suffer from recurrent bacterial infections , which are associated with defective T-independent antibody responses against polysaccharide and impaired maturation of T-dependent antibody responses [30] , [32]–[34] . In addition , a large portion of WAS patients develop autoimmune diseases , which are associated with a higher risk of leukemia and lymphoma [35] , [36] . In the WASP knockout ( KO ) mouse model , there is no significant defect in T- and B-cell development , except for a reduction in marginal zone B cells [37]–[39] . The in vitro activation of T cells but not B cells from WASP KO mice is decreased [40] . WASP-deficient B cells from both mice and WAS patients exhibit defective migration [34] , [37] , [38] . We have found that there were significant reductions in cell spreading , BCR clustering , surface tyrosine phosphorylation [21] , and BCR internalization [41] in WASP KO B cells as compared to those of wild type ( wt ) B cells . Recent studies have clearly demonstrated a critical and B-cell–intrinsic role for WASP in controlling B-cell self-tolerance [39] , [41] . However , the molecular mechanism underlying the loss of self-tolerance in WASP-deficient B cells has not yet been elucidated . In addition to hematopoietic-specific WASP , B cells , like all immune cells , also express the ubiquitous N-WASP . These two proteins share ∼50% sequence homology [42] and are reported to have the same cellular function , activating actin polymerization and branching by binding to Arp2/3 complexes [43] , [44] . They are capable of linking cell signaling to actin dynamics via their GTPase binding ( GBD ) , proline rich ( PRD ) , and pleckstrin homology ( PH ) domains . The binding of GTP–Cdc42 and phosphatidylinositol-4 , 5-biphosphate ( PI ( 4 , 5 ) P2 ) releases the proteins from an autoinhibitory conformation [43] , [45] , [46] . Signaling-induced phosphorylation of WASP and N-WASP at tyrosines in the GBD domain and serines in the VCA ( verprolin homology , cofilin homology , and acidic ) domain is required for their optimal activity [47]–[49] . In B cells , Btk is responsible for activating WASP by activating Vav , a guanine nucleotide exchange factor for Cdc42 , stimulating the production of PI ( 4 , 5 ) P2 , and inducing the phosphorylation of WASP [50] . While WASP and N-WASP have been well studied individually , how these two proteins function when coexpressed is largely unknown . Recent studies have demonstrated that both WASP and N-WASP are critical for the development and function of B cells [38] . However , why both WASP and N-WASP are required and how N-WASP and WASP functionally coordinate with each other during BCR activation is not well understood . In this study , we examined the cellular function of N-WASP and its functional relationship with WASP during BCR activation using WASP KO , B-cell–specific N-WASP KO , and double KO mice , as well as primary human B cells from WAS patients . We found that while both WASP and N-WASP are required for optimal signaling activation and internalization of the BCR , the two proteins have distinct functions . N-WASP is critical for the down-regulation of BCR signaling . N-WASP promotes signaling attenuation by facilitating B-cell contraction and coalescence of BCR microclusters into a central cluster and by mediating BCR internalization . Surprisingly , WASP and N-WASP functionally suppress each other and are inversely regulated by stimulatory and inhibitory signals . These results reveal a new function for N-WASP in the negative regulation of receptor signaling and demonstrate a unique functional relationship between N-WASP and WASP during BCR activation .
To understand the function of N-WASP in BCR activation , we examined the activation status of N-WASP in relation to WASP activation in human peripheral blood ( PBMC ) and mouse splenic B cells in response to BCR cross-linking . We labeled and cross-linked surface BCRs using Alexa Fluor 546–labeled , monobiotinylated Fab′ fragment of anti-mouse or human IgG+M antibody ( AF546–mB-Fab′–anti-Ig ) that was tethered to planar lipid bilayers by streptavidin to mimic membrane-associated antigen or mB-Fab′–anti-Ig plus soluble streptavidin to mimic soluble antigen . We chose this activation system because it activates the BCR on mouse and human B cells in a similar way and because it has similar capability of inducing BCR activation as bona fide Ag , such as hen egg lysozyme ( HEL ) tethered to lipid bilayers that activates B cells from MD4 transgenic mice ( Figure S1 ) . Active WASP and N-WASP were detected using antibodies specific for their phosphorylated forms that did not show cross-reactivity between phosphorylated WASP and N-WASP ( Figure S2 ) . Human and mouse B cells were activated with membrane-tethered or soluble mB-Fab′–anti-Ig plus streptavidin before staining for phosphorylated WASP ( pWASP ) and N-WASP ( pN-WASP ) . Using total internal reflection fluorescence microscopy ( TIRFM ) , we analyzed the distribution and the relative levels of pWASP and pN-WASP at the surface of B cells in contact with the mB-Fab′–anti-Ig–tethered lipid bilayer ( B-cell contact zone ) . We found that both pWASP and pN-WASP were detected in the contact zone of mouse ( Figure 1A , B ) and human B cells ( Figure 1C , D ) , and they have a punctate appearance . As Ag–BCR complexes coalesced and merged into a central cluster accompanied by B-cell contraction , most of the pWASP staining ( Figure 1A , C ) , but not pN-WASP ( Figure 1B , D ) , moved away from the center to the edge of antigen–BCR central clusters . The mean fluorescence intensity ( MFI ) of pN-WASP in the B-cell contact zone increased over time similar to that of pWASP . However , the pN-WASP MFI reached its maximal level 2 min later than pWASP , concurrent with the pWASP levels returning to the basal levels ( Figure 1E , G ) . Flow cytometry analysis was used to determine the overall levels of pWASP and pN-WASP in response to mB-Fab′-anti-Ig plus soluble streptavidin . We found similar patterns of WASP and N-WASP activation , where the MFI of pN-WASP peaked 3–5 min later than that of pWASP until the pWASP levels decreased ( Figure 1F , H ) . These results indicate that N-WASP is transiently activated at locations where BCRs interact with antigen similar to WASP , but its activation does not peak until the level of WASP activation decreases . Previous studies have shown that WASP is dispensable for antigen-induced BCR clustering and B-cell morphological changes [21] , [41] , implying a compensatory role for N-WASP in WASP KO B cells . To investigate this hypothesis , we utilized WASP KO mice ( WKO ) , B-cell–specific N-WASP KO mice ( cNKO ) , and double KO mice where N-WASP is selectively deleted in B cells ( cDKO ) , established previously by Westerberg et al . [38] . Using reverse transcription PCR and Western blot , we were unable to detect any mRNA and protein of N-WASP in B cells sorted from the splenocytes of cNKO and cDKO mice ( Figure S3 ) , confirming the effective deletion of loxP flanked n-wasp gene by CD19Cre . We examined the effect of WASP and/or N-WASP KO on BCR clustering and B-cell morphology in response to membrane-tethered Fab′–anti-Ig . Surface BCR clustering was analyzed by TIRFM . As we have shown previously [16] , [21] , upon being bound by the BCR , AF546–mB-Fab′–anti-Ig clustered , coalesced , and formed a polarized central cluster at 7 min ( Figure 2A ) , and the total fluorescence intensity ( TFI ) of AF546–mB-Fab′–anti-Ig in the contact zone of littermate control B cells ( CD19Cre/+ or N-WASPFlox/Flox ) increased over time ( Figure 2D ) . There was no significant BCR clustering and accumulation in the contact zone of B cells interacting with transferrin ( Tf ) -tethered lipid bilayer ( Figure 2A , D ) , indicating that Fab′–anti-Ig aggregates reflects BCR clustering . In WKO and cNKO B cells , the TFI of labeled BCRs in the contact zone was significantly decreased compared to that of littermate control B cells ( Figure 2D ) . While BCR accumulation in the contact zone of WKO and cNKO B cells was decreased to similar levels , the BCRs showed distinct distribution patterns . BCRs in the contact zone of WKO B cells formed a central cluster smaller than that of control B cells ( Figure 2A ) , while in cNKO B cells they appeared punctate , failing to merge into a central cluster ( Figure 2A ) . Treating MD4 B cells stimulated with membrane-associated HEL with the N-WASP inhibitor wiskostatin resulted in similar phenotypes as seen in cNKO B cells ( Figure S1 ) . The deletion of both wasp and n-wasp genes caused a further decrease in the BCR TFI in the B-cell contact zone , similar to the levels in unstimulated B cells ( Figure 2A , D ) . Similarly , treating WKO B cells with the N-WASP inhibitor wiskostatin ( Figure 2D ) [51] or A20 lymphoma B cells with siRNAs targeted to WASP and N-WASP ( Figure 2B , F ) reduced the BCR TFI in the contact zone to levels similar to that in cDKO B cells . Furthermore , the BCR TFI in the contact zone of human B cells was decreased by wiskostatin treatment to levels similar to that of cNKO mouse B cells ( Figure 2C , H ) . We examined the effect of WASP and/or N-WASP KO on B-cell morphology by quantifying the changes in the contact area between B cells and Fab′–anti-Ig–tethered lipid bilayer , using interference reflection microscopy ( IRM ) . In littermate control B cells , the contact area increased rapidly during the first 3 min , indicating cell spreading , and then decreased after reaching a maximal spread area , indicating cell contraction ( Figure 2A , E ) . The change of WKO B-cell contact area over time was qualitatively similar to that of control B cells , but with a smaller magnitude of increase ( Figure 2A , E ) . Both the kinetics and magnitude of the increase in the contact area of cDKO B cells were dramatically slower and smaller than those of control and WKO B cells ( Figure 2A , E ) . Surprisingly , the contact area of cNKO B cells was significantly larger and decreased much later ( 7 min ) than that of control B cells ( Figure 2A , E ) . Again , treating WKO B cells with the N-WASP inhibitor wiskostatin ( Figure 2E ) or A20 B cells with WASP/N-WASP siRNAs ( Figure 2B , G ) reduced the B-cell contact area to sizes similar to that of cDKO B cells . Treating human primary B cells ( Figure 2C , I ) or HEL-stimulated mouse MD4 B cells ( Figure S1 ) with wiskostatin caused an increase in B-cell spreading and a delay of B-cell contraction , similar to what we observed in cNKO B cells ( Figure 2A , E ) . Taken together , these results indicate that both WASP and N-WASP are indispensable for optimal BCR clustering and B-cell spreading , but N-WASP exhibits two opposing functions , supporting BCR clustering and B-cell spreading in the absence of WASP and promoting the coalescence of BCR microclusters into a central cluster and B-cell contraction in the presence of WASP . N-WASP exhibits similar functions in mouse and human primary B cells . The effects of WASP and/or N-WASP KO on BCR clustering and B-cell morphology suggest their involvement in BCR signaling . To test this hypothesis , we analyzed the impact of WASP and/or N-WASP KO on tyrosine phosphorylation ( pY ) at the cell surface in response to membrane-tethered Fab′–anti-Ig using TIRFM . Similar to what we have shown previously [21] , pY was first detected at BCR microclusters at early times during the interaction of littermate control B cells with membrane-tethered Fab′–anti-Ig ( ∼3 min ) and then at the outer edge of the BCR central cluster at later times ( ∼7 min ) ( Figure 3A ) . The MFI of pY staining rapidly increased upon BCR binding , peaked at 3 min , and then decreased ( Figure 3E ) . The distribution and levels of pY in the contact zone of WKO B cells followed a qualitatively similar pattern as in control B cells , but the increasing magnitude of pY MFI in the contact zone of WKO B cells was significantly smaller than that of control B cells ( Figure 3B , E ) . Double KO of WASP and N-WASP caused a further reduction in the levels of pY in the B-cell contact zone ( Figure 3D , E ) . However , the pY staining in the contact zone of cNKO B cells remained punctate and colocalized with BCR clusters at 7 min ( Figure 3C ) . The peak level of pY in the contact zone of cNKO B cells was similar to that of control B cells , but its attenuation was significantly delayed ( Figure 3E ) . These results suggest that N-WASP is involved in both stimulation and attenuation of BCR signaling . Our previous studies show a two-phase relationship between BCR clustering and signaling , where BCR aggregation into small clusters stimulates signaling activation , but the merger of small clusters into a central cluster leads to signaling attenuation at the cell surface [21] . The effects of cNKO on BCR clustering and B-cell contraction led us to hypothesize that N-WASP may regulate signaling via modulating the clustering of surface BCRs . To investigate this hypothesis , we determined the relative size of BCR clusters based on the TFI of BCR labeling in individual clusters and the relative signaling levels of BCR clusters based on the fluorescence intensity ratio ( FIR ) of the pY to the BCR in individual clusters . A nonparametric regression method , LOWESS , was used to examine the trend between the size and signaling level of BCR clusters . In littermate control B cells , the FIR of pY to BCR increased as the size of BCR clusters increased when their sizes were relatively small . After the clusters reached a certain size , the FIR decreased as the sizes of BCR clusters further increased ( Figure 3F ) . Compared to control B cells , BCR clusters in the contact zone of cNKO B cells were limited to smaller sizes , and the FIR of pY to BCR in individual microclusters of cNKO B cells was much higher than that of control B cells ( Figure 3G ) . These results show that the delayed attenuation of tyrosine phosphorylation in cNKO B cells is associated with the inhibition of the growth of BCR clusters , suggesting that N-WASP can down-regulate BCR signaling by promoting the growth and coalescence of BCR microclusters into the central cluster . In order to confirm the roles of WASP and N-WASP in BCR signaling , we determined the effect of WASP and/or N-WASP KO on the phosphorylation of stimulatory kinase Btk ( pBtk ) and inhibitory phosphatase SHIP-1 ( pSHIP-1 ) in response to membrane-tethered Fab′–anti-Ig by TIRFM and calcium flux in response to soluble Fab′–anti-Ig plus streptavidin by flow cytometry . Similar to the effect of WASP and/or N-WASP KO on pY , the MFI of pBtk was significantly reduced in the contact zone of WKO B cells and further reduced in that of cDKO B cells , compared to that of littermate control B cells ( Figure 3H ) . However , the MFI of pBtk in the contact zone of cNKO B cells was not only significantly higher at the 3-min peak time , but the attenuation of pBtk was also significantly delayed , compared to that of control B cells ( Figure 3H ) . In contrast , the MFI of pSHIP-1 in the contact zone was significantly increased in WKO B cells but reduced in cNKO B cells ( Figure 3I ) . Double KO also caused a decrease in pSHIP-1 levels in the B-cell contact zone , but the magnitude of the decrease was similar to that in cNKO B cells ( Figure 3I ) . Consistent with the changes in levels of pBtk and pSHIP-1 in the contact zone , the level of calcium influx was reduced in both WKO and cDKO B cells , with a much more dramatic reduction in cDKO than WKO B cells . In contrast , calcium influx was increased in cNKO B cells ( Figure 3J ) . These results collectively indicate critical roles for WASP and N-WASP in regulating BCR signaling in response to both membrane-associated and soluble antigen , and suggest dual functions for N-WASP in positive and negative signaling regulation . The negative regulatory function of N-WASP in BCR activation implies a role for N-WASP in controlling B-cell self-tolerance . To investigate this possibility , we analyzed the serum levels of anti-nuclear and anti–double strand ( ds ) DNA antibody . Using an immunofluorescence test , we found that the sera of 50% cNKO mice were positive with anti-nuclear antibody ( n = 4 ) , compared to none of littermate control mice at 6 mo of age ( Figure 4A ) . Consistent with this result , quantitative ELISA analysis detected an elevated level of anti-dsDNA antibody in the sera of cNKO mice , compared to littermate control mice at 6 and 9 mo old ( Figure 4B ) . However , we did not detect any significant increase in the levels of anti-nuclear and anti-dsDNA antibody in the sera of WKO and cDKO mice compared to those of littermate control mice ( Figure S4 ) . Since the n-wasp gene deletion in cNKO mice is B-cell specific , our data indicate a critical and B-cell–intrinsic role for N-WASP in maintaining B-cell tolerance . Since the major function of WASP and N-WASP is to activate actin polymerization , we examined the effects of WASP and/or N-WASP KO on the distribution patterns and levels of F-actin in the B-cell contact zone using phalloidin staining and TIRFM analysis . When littermate control B cells spread on Fab′–anti-Ig–tethered lipid bilayer , F-actin formed smaller clusters throughout the contact zone and partially colocalized with BCR clusters . When B cells contracted and BCR microclusters coalesced into a central cluster , F-actin accumulated at the outer edge of the contact zone ( Figure 5A ) . The level of F-actin in the contact zone of control B cells rapidly increased over time and peaked at 3–5 min when B-cell spreading reached maximal magnitude , followed by a significant reduction at 7 min when B cells contracted ( Figure 2E and 5E ) . Gene KO of WASP or both WASP and N-WASP did not significantly change the distribution pattern of F-actin ( Figure 5B , D ) but reduced the level of F-actin accumulation in the B-cell contact zone ( Figure 5E ) . The reduction was much more drastic in cDKO B cells and WKO B cells treated with the N-WASP inhibitor wiskostatin than in WKO B cells ( Figure 5E ) . In contrast , the level of F-actin accumulation in the contact zone of cNKO B cells was significantly increased at 5 min and remained high at 7 min ( Figure 5E ) . F-actin clusters formed in the contact zone of cNKO B cells appeared much more prominent than those in control B cells , and they displayed sustained colocalization with BCR clusters ( Figure 5C ) up to 7 min . These results suggest that N-WASP not only synergizes with WASP in generating and mobilizing F-actin to BCR clusters during signal activation , but is also critical for removing F-actin from the contact zone during B-cell contraction and surface signaling attenuation . Since WASP and N-WASP share the same cellular function , activation of actin polymerization by binding to Arp2/3 , we next asked how these two molecules exhibit opposing roles in actin remodeling during BCR activation . We analyzed the behavior of Arp2/3 in response to membrane-tethered Fab′–anti-Ig and its spatial relationship with pWASP and pN-WASP at the B-cell surface using TIRFM and Arp2-specific antibody . We found that Arp2 was readily recruited to the contact zone of littermate control B cells , and the timing of its recruitment , levels , and distribution patterns were similar to those of F-actin ( Figure 5F , I ) . Consistent with the effect of WASP and/or N-WASP KO on F-actin accumulation , the MFI of Arp2 staining decreased slightly in the contact zone of WKO B cells and was significantly reduced in that of cDKO B cells , but increased in that of cNKO ( Figure 5F , I ) . This result supports the notion that antigen-induced F-actin accumulation in the B-cell contact zone is mediated through the activation of Arp2/3 by WASP and N-WASP . The spatial relationship of Arp2 with active WASP and N-WASP were analyzed using Pearson correlation coefficients between the staining of Arp2 and pWASP or pN-WASP in the B-cell contact zone . The results showed that pWASP exhibited a significantly higher level of colocalization with Arp2 than pN-WASP in control B cells ( Figure 5G , H , J ) . WASP KO caused a significant increase in the colocalization between pN-WASP and Arp2 , close to the colocalization level of pWASP with Arp2 in control B cells ( Figure 5H , J ) , but N-WASP KO did not change the level of colocalization between pWASP and Arp2 ( Figure 5G , J ) . These data suggest that activated WASP predominately colocalizes with Arp2/3 while inhibiting the colocalization of active N-WASP with Arp2/3 at the B-cell surface . BCR activation induces receptor internalization , which attenuates receptor signaling by removing the receptor from surface signaling microdomains . Since BCR internalization requires actin reorganization [26] , we investigated whether WASP and N-WASP are involved in this process . BCR internalization was evaluated qualitatively by the colocalization of surface-labeled BCRs with the late endosomal marker LAMP-1 using immunofluorescence microscopy and quantitatively by the amount of surface-labeled BCRs remaining at the cell surface after internalization using flow cytometry . In littermate control B cells , 70% of surface-labeled BCRs disappeared from the cell surface after internalization ( Figure 6D ) , and they colocalized with LAMP-1–labeled late endosomes ( Figure 6A , C ) that coalesced in response to signaling [52] . In WKO B cells , the colocalization of surface-labeled BCRs with LAMP-1 was slightly decreased and the amount of the BCR remaining on the cell surface was increased ( Figure 6A , C , D ) , indicating a reduction in BCR internalization . In cDKO B cells , the colocalization of the BCR with LAMP-1 was decreased from 0 . 4 to 0 . 1 , and the amount of the BCRs remaining at the cell surface increased ( ∼80% ) , compared to those in littermate control B cells ( ∼30% ) , showing a dramatic reduction of BCR internalization ( Figure 6A , C , D ) . Similarly , double knockdown of WASP and N-WASP by siRNA reduced the colocalization of the BCR with LAMP-1 in A20 cells ( Figure 6B–C ) . Noticeably , cNKO B cells showed a similar level of reduction in the colocalization of BCR with LAMP-1 and a similar level of increase in surface BCRs as those observed in cDKO B cells , indicating that cNKO causes a decrease in BCR internalization similar in magnitude as cDKO . These results demonstrate that N-WASP plays a major role in this process . The involvement of both WASP and N-WASP in BCR activation suggests a functional coordination between these two proteins . To understand their functional relationship , we compared the activation levels and kinetics of one of the two proteins in the presence and absence of the other using TIRFM and flow cytometry . Both the MFI of pWASP in the contact zone of cNKO B cells ( Figure 7A , C ) and the cellular MFI of wiskostatin-treated mouse B cells ( Figure 7E ) were increased compared to those in untreated control B cells . Conversely , the pN-WASP levels in the contact zone of WKO B cells ( Figure 7B , D ) and in WKO B cells ( Figure 7F ) were significantly higher than those in littermate control B cells . In both cases , the time taken for pN-WASP to peak was not changed . While the levels of pN-WASP and pWASP were changed , the overall protein levels of N-WASP and WASP did not change in WKO and cNKO B cells ( Figure S5 ) , indicating that the increased phosphorylation level is not due to an increase in protein expression . Consistent with the data from mouse models , treating human B cells from healthy subjects with the N-WASP inhibitor wiskostatin resulted in an increase in the cellular level of pWASP ( Figure 7G ) . Furthermore , the level of pN-WASP was increased in PBMC B cells from WAS patients that did not express or expressed low levels of WASP , compared to that of healthy human controls ( Figure 7H ) . These results indicate that WASP and N-WASP negatively regulate each other during BCR activation in both mouse and human B cells . The activation of WASP and N-WASP in response to BCR cross-linking suggests that BCR signaling triggers their activation . We have previously shown that Btk is responsible for activating WASP , while SHIP-1 suppresses WASP activation by inhibiting Btk [21] . To investigate whether N-WASP activation is controlled in the same manner as WASP , we determined the effect of Btk or SHIP-1 deficiency on antigen-triggered phosphorylation of N-WASP using xid mice where the PH domain of Btk contains a point mutation that blocks Btk activation [53] and B-cell–specific SHIP-1 KO mice [21] , [54] . In sharp contrast with the effect of Btk and SHIP-1 deficiency on WASP activation , the MFI of pN-WASP was increased in the B-cell contact zone and B cells from xid mice ( Figure 8A–C ) , while it was decreased in the B-cell contact zone and B cells from SHIP-1 KO mice ( Figure 8D–F ) . These results suggest that the activation of WASP and N-WASP is regulated inversely by BCR signaling: Btk , which activates WASP , suppresses N-WASP activation , while SHIP-1 , which inhibits the activation of Btk and WASP , promotes N-WASP activation .
In this study , we demonstrate that in addition to the overlapping function with WASP in signaling activation , N-WASP plays a unique role in the down-regulation of BCR signaling at the cell surface . This is shown by enhanced and/or prolonged tyrosine and Btk phosphorylation , increased calcium influx , and reduced SHIP-1 phosphorylation in cNKO B cells . Importantly , the enhanced signaling in response to in vitro antigenic stimulation is associated with elevated levels of anti-nuclear and anti-dsDNA autoantibodies in the serum of cNKO mice . Since N-WASP is exclusively deleted from B cells , the increased autoantibody and BCR signaling is the result of B-cell–intrinsic defects . These results indicate that N-WASP–mediated signal attenuation of surface BCRs is critical for the maintenance of B-cell self-tolerance . Our studies find that there are more severe defects in BCR clustering , B-cell spreading , and signal activation in cDKO B cells than in WKO B cells , indicating overlapping functions between N-WASP and WASP in these processes . This is consistent with their shared cellular function in promoting actin polymerization . However , WASP KO alone results in significant decreases in these processes , even though they are much less severe than those in cDKO B cells . These results suggest that N-WASP is unable to completely compensate for WASP in WKO B cells . Our data further show that the inhibition of the attenuation of BCR surface signaling is concurrent with increases in B-cell spreading , delays in B-cell contraction and the coalescence of BCR microclusters into the central cluster , and a blockage of BCR internalization in cNKO B cells . These results suggest that N-WASP promotes signaling attenuation via modulating BCR clustering , B-cell morphology , and BCR internalization . Similar to what we have previously shown in SHIP-1−/− B cells [21] , BCR clusters in cNKO B cells remain smaller in size and exhibit much higher levels of tyrosine phosphorylation than those in littermate control B cells , while cNKO B cell contraction is delayed in comparison with littermate control B cells . These data collectively demonstrate that B-cell contraction , which can provide a force for the merger of small BCR microclusters into polarized central clusters , is an important mechanism for down-regulating BCR signaling at the cell surface . The exact mechanism by which the size of BCR clusters regulates receptor signaling is unknown . Our results show that the levels of pBtk and pSHIP-1 are increased and decreased , respectively , in the contact zone of cNKO B cells , suggesting an association of the activation of positive and negative signaling molecules with the size of BCR clusters and N-WASP expression . N-WASP has been suggested to have a role in regulating the cellular location and activation of SHIP [55] . The dominant role of N-WASP in BCR internalization enables the removal of antigen–BCR complexes from the cell surface , which leads to both surface signaling attenuation and antigen presentation that provides another layer of control over B-cell activation by T cells . While N-WASP has both an overlapping role with WASP in signaling activation and a unique role in signaling down-regulation in B cells , we demonstrate here that N-WASP exhibits the two opposing functions under different circumstances . N-WASP predominantly displays its function for signaling down-regulation in B cells that have normal expression of WASP , since cNKO B cells have no defects in B-cell spreading and signaling activation . However , it switches to signaling activation function in B cells lacking WASP expression , since cDKO B cells have more severe signaling defects than WKO B cells . The different functionalities of N-WASP in the presence or absence of WASP expression suggests that WASP is involved in the functional switch of N-WASP . Indeed , we found that in littermate control B cells , the pWASP level increases first , and the pN-WASP level does not peak until pWASP returns near to its basal level . In the absence of WASP , the pN-WASP level rises earlier and significantly higher in WKO B cells than that in littermate control B cells . Conversely , in the absence of N-WASP , the pWASP level increases earlier and is sustained longer in cNKO B cells than that in littermate control B cells . Moreover , we found similar results in human B cells from healthy subjects and WAS patients . These results indicate that WASP and N-WASP mutually suppress each other for activation in both human and mouse B cells . Previous studies have shown that WASP and N-WASP share the same activation mechanisms , including the interaction with the GTPase Cdc42 or Rac via the GBD domain and PI ( 4 , 5 ) P2 via the PH domain , and tyrosine and serine phosphorylation [43] , [46] , [56] . We have previously shown that Btk is responsible for the activation of WASP in B cells by activating guanine nucleotide exchange factor Vav , increasing PI ( 4 , 5 ) P2 , and inducing WASP phosphorylation [50] . A surprising finding of this study is that N-WASP is not activated by the same pathway as WASP , since the level of pN-WASP is increased rather than decreased in Btk-deficient B cells and is decreased rather than increased in SHIP-1−/− B cell as seen for pWASP . This suggests that SHIP-1 instead of Btk is involved in N-WASP activation . During BCR activation , Btk is activated before SHIP-1 [2] , [10] , [57] , which provides an explanation for the activation of WASP before N-WASP . SHIP-1 inhibits Btk activation via dephosphorylating PI ( 3 , 4 , 5 ) P3 , the docking site of Btk at the plasma membrane [58] , consequently suppressing WASP activation . The molecular mechanisms by which SHIP-1 activates N-WASP are unknown . Based on our data , we postulate that releasing N-WASP from WASP suppression by inhibiting Btk-dependent WASP activation is one possible mechanism for SHIP-1 to facilitate N-WASP activation . In addition , SHIP-1 potentially regulates the location and activation of N-WASP via the signaling adaptor protein Grb2 . Grb2 has been reported to bind both N-WASP and the SHIP adaptor protein Dok [42] , [59] , [60] . Further , the product of SHIP-1–mediated hydrolysis PtdIn ( 3 , 4 ) P2 has been shown to regulate N-WASP by recruiting Tks5-Grb2 scaffold [61] . SHIP may also indirectly regulate the subcellular location and activity of kinases that phosphorylate N-WASP . These possible mechanisms remain to be further examined . Studies accumulated from the last decade have clearly demonstrated that WASP and N-WASP share the same cellular function: stimulating actin polymerization by activating Arp2/3 [44] , [46] , [62] . This raises the question of how these two proteins could possibly have opposing roles in B-cell morphology and BCR clustering and activation . It should be noted that almost all of the studies so far have examined the cellular function of WASP and N-WASP individually in the absence of their homolog . Consistent with these studies , we found that WASP and N-WASP appear to play a similar role in actin accumulation at the B-cell contact zone , shown by a smaller decrease in the level of F-actin in the contact zone of WKO B cells than that of cNKO B cells . Surprisingly , N-WASP KO alone causes a significant and sustained increase in the level of F-actin in the B-cell contact zone , which is inconsistent with the current dogma that N-WASP functions as an actin-nucleation–promoting but not inhibitory factor . Our examination of the spatial relationship of activated WASP and N-WASP with Arp2/3 shows that pWASP has a significantly higher level of colocalization with Arp2 than pN-WASP , and that WASP KO increases the colocalization between pN-WASP and Arp2 , but N-WASP KO does not lead to further increases in the colocalization of pWASP with Arp2 . These results suggest a competition between WASP and N-WASP for binding to Arp2/3 complexes . Since WASP is activated and recruited to the B-cell contact zone by Btk first , this allows WASP to win the competition for binding to Arp2/3 , consequently competitively inhibiting the binding of N-WASP to Arp2/3 . How N-WASP reduces the amount of F-actin in the B-cell contact zone and inhibits B-cell spreading is an interesting question . When Takenawa's group discovered N-WASP , N-WASP was identified as an actin depolymerizing protein since the VCA domain of N-WASP was capable of depolymerizing F-actin in vitro in the absence of Arp2/3 [42] . This raises the possibility that the VCA domain of active N-WASP , when it is not bound by Arp2/3 as it loses the competition to activated WASP , can depolymerize F-actin at the B-cell contact zone . In addition , N-WASP has a unique function in tethering F-actin to budding and moving vesicles [63] , [64] . We have previously demonstrated that the membrane fission step of BCR endocytosis requires actin [26] . The dominant role of N-WASP in BCR internalization implicates that N-WASP is responsible for this actin-dependent step , probably by recruiting F-actin from the B-cell contact zone to and/or activating actin polymerization at budding vesicles containing BCRs [65] , [66] . Furthermore , the involvement of N-WASP in SHIP-1 activation reported here and the capability of N-WASP to interact with BCR adaptor proteins , such as Grb2 [42] , suggest that N-WASP may inhibit actin polymerization and B-cell spreading by facilitating or enhancing the activation of inhibitory signaling molecules . WASP deficiency due to gene mutations causes an X-linked immune disorder , exhibiting immune deficiency , autoimmunity , and lymphoma [30] , [32] , [36] . While defects in other immune cells contribute to the disease , recent studies have demonstrated that B-cell–intrinsic defects are critical for the development of autoimmunity in mouse models [39] , [41] . Here we show that WASP and N-WASP behave similarly in mouse and human B cells , including the sequential activation of N-WASP and WASP and the mutual regulation between the two . The B-cell–intrinsic roles of N-WASP in the down-regulation of BCR signaling and B-cell tolerance demonstrated here suggest a critical contribution of N-WASP to disease development . It is possible that without the competitive inhibition of WASP , the signaling promoting function of N-WASP is enhanced and its signaling attenuation function is reduced , leading to deregulation of BCR and B-cell activation . This hypothesis will be pursued in our future studies . It is well known that the genetic background influences the characteristics of the mouse immune system and the susceptibility of mice to autoimmune diseases [67] , [68] . Due to triple genetic manipulations , the mouse models used in this study were comprised of a mixed background of C57BL/6 and 129Sv . Given our breeding strategy ( described in the Materials and Methods section ) , the CD19Cre/Cre mice on a C57BL/6 background bring B6 genes into WASP−/− and N-WASPFlox/Flox mice on a 129Sv background . The CD19 Cre allele from CD19Cre/+ mice generates a systematic bias for B6 mouse chromosome 7 , where CD19 is located , while n-wasp and wasp genes are located in chromosome 6 and X chromosome of mice , respectively . We utilized CD19Cre/+ mice for the final crossing step , which enables us to generate CD19+/+ littermates with similar numbers of B6 alleles as CD19Cre/+ littermates , thereby providing littermate controls . By using more than 15 sets of littermate controls to compare with WKO , cNKO , and cDKO mice , we found a consistent and significant increase in the level of serum autoantibody in cNKO mice as well as increased spreading of cNKO B cells . While CD19Cre/+ C57BL/6 mice would provide an additional control for ruling out any contribution of genetic background to the results , our data with 15–18 littermate controls improves confidence that the dosage of B6 genes is not biasing the results regarding the negative regulation mediated by N-WASP . Taking the results of this study and previous studies together enables us to propose a working model for the functional coordination of WASP and N-WASP during BCR activation ( Figure 9 ) . Antigen binding to the BCR induces an early activation of Btk ( Figure 9A ) that in turn activates and translocates WASP to the cell surface . Activated WASP stimulates actin polymerization by binding to Arp2/3 , which modulates BCR lateral mobility and drives B-cell spreading . Together , these facilitate BCR clustering and signaling ( Figure 9B ) . The activation of SHIP-1 after the initial signaling inhibits Btk activation , which decreases the level of active WASP and releases N-WASP from the suppression of WASP . The activated N-WASP reduces the surface level of F-actin probably by depolymerizing and/or transferring F-actin to BCR containing budding vesicles . Reductions in F-actin at the B-cell contact zone allow B-cells to contract , which promotes the coalescence of BCR microclusters into a central cluster . Tethering F-actin to endocytosing vesicles by N-WASP is essential for the endocytosis of antigen–BCR complexes . The formation of BCR central clusters and BCR endocytosis lead to the down-regulation of BCR signaling at the cell surface ( Figure 9C ) . While molecular details of this working model require further investigation , this study reveals a novel function of N-WASP in the down-regulation of BCR signaling and a unique functional coordination between WASP and N-WASP during receptor signaling .
Wild-type ( wt ) ( CBA/CaJ ) , xid ( CBA/CaHNBtkxid/J ) , MD4 Ig transgenic , and CD19Cre/+ ( B6 . 129P2 ( C ) -Cd19tm1 ( cre ) Cgn/J ) mice were purchased from Jackson Laboratories ( Bar Harbor , ME ) . B-cell–specific SHIP-1 knockout mice ( CD19Cre/+ SHIP-1Flox/Flox ) were kindly provided by Dr . Silvia Bolland at NIH [21] , [54] . WASP knockout ( WASP−/− CD19+/+ N-WASPFlox/Flox , WKO ) , B-cell–specific N-WASP knockout ( CD19Cre/+ N-WASPFlox/Flox , cNKO ) , WASP and N-WASP double conditional knockout ( WASP−/− CD19Cre/+ N-WASPFlox/Flox , cDKO ) mice were bred as previously described [38] . These mice are generated by breeding WKO mice on a 129Sv background , N-WASPFlox/Flox mice on a 129Sv background , and CD19Cre/Cre mice on a C57BL/6 background . The data were generated from breeding littermates of CD19Cre/+ N-WASPFlox/Flox with CD19Cre/+ N-WASPFlox/Flox for littermate control mice ( CD19+/+ N-WASPFlox/Flox ) and cNKO mice ( CD19Cre/+ N-WASPFlox/Flox ) and of WASP−/− CD19Cre/+ N-WASPFlox/Flox with WASP−/− CD19Cre/+ N-WASPFlox/Flox for WKO ( WASP−/− CD19+/+ N-WASPFlox/Flox ) and cDKO ( WASP−/− CD19Cre/+ N-WASPFlox/Flox ) . The littermates of cNKO breeding that have neither WASP nor N-WASP deficiency were used as controls ( CD19+/+N-WASPFlox/Flox ) . B-cell lymphoma A20 IIA1 . 6 cells ( H-2d , IgG2a+ , FcγIIBR− ) were cultured and splenic B cells were isolated as previously described [50] . Human peripheral blood mononuclear cells ( PBMCs ) were collected from WAS patients , age-matched healthy controls , and healthy adults . B cells were isolated from human PBMCs using a Miltenyi Human B-cell negative selection kit by AutoMACS ( Miltenyi Biotec Inc . , Auburn , CA ) . Mono-biotinylated Fab′ fragment of anti-mouse or human IgM+G antibody ( mB-Fab′–anti-Ig ) was generated from the F ( ab′ ) 2 fragment ( Jackson ImmunoResearch , West Grove , PA ) using a published protocol [69] . The planar lipid bilayer was prepared as described previously [70] , [71] . Liposomes were made by sonicating 1 , 2-dioleoyl-sn-Glycero-3-phosphocholine and 1 , 2-dioleoyl-sn-Glycero-3-phosphoethanolamine-cap-biotin ( Avanti Polar Lipids , Alabaster , AL ) in a 100∶1 molar ratio in PBS . Coverslip chambers ( Nalge Nunc International , Rochester , NY ) were incubated with the liposomes before coating with 1 µg/ml streptavidin ( Jackson ImmunoResearch ) and 2 µg/ml AF546–mB-Fab′–anti-Ig mixed with 8 µg/ml mB-Fab′–anti-Ig antibody . For a nonantigen control , surface BCRs were labeled with AF546-Fab–anti-Ig ( 2 µg/ml ) . The labeled B cells were then incubated with biotinylated holo-transferrin ( Tf; 16 µg/ml , Sigma , St . Louis , MO ) tethered to lipid bilayers by streptavidin . Images were acquired using a Nikon TE2000-PFS microscope equipped with a 60× , NA 1 . 49 Apochromat TIRF objective , a Coolsnap HQ2 CCD camera ( Roper Scientific ) , and two solid-state lasers of wavelength 491 and 561 nm . Interference refection images ( IRM ) , AF488 , and AF546 were acquired sequentially . B cells were incubated with AF546–mB-Fab′–anti-Ig–tethered lipid bilayers at 37°C , and then were then fixed with 4% paraformaldehyde , permeabilized with 0 . 05% saponin , and stained for phosphotyrosine ( Millipore ) , phosphorylated Btk ( BD Bioscience , San Jose , CA ) , SHIP-1 ( Cell Signaling Technology , Inc . , Danvers , MA ) , WASP ( S483/S484 or Y290 ) ( Bethyl Laboratory , Inc . , Montgomery , TX or Abcam , Cambridge , MA ) , N-WASP ( Y256 , Millipore ) , and AF488-phalloidin . The B-cell contact area was determined based on IRM images using MATLAB software ( The MathWorks , Inc . Natick , MA ) . The total and mean fluorescence intensity in the B-cell contact zone was determined using Andor iQ software ( Andor Technology , Belfast , UK ) . Background fluorescence generated by antigen or secondary antibody was subtracted . For each set of data , >50 individual cells from three independent experiments were analyzed . The intracellular calcium flux was measured by flow cytometry using the calcium-sensitive dyes Fluo4 AM and Fura Red ( Invitrogen ) using manufacturer-recommended protocols . The relative levels of intracellular calcium were determined by a ratio of Fluo4 to Fura Red emission using FlowJo software ( Tree Star , Inc . , Ashland , OR ) . B cells were incubated with FcR blocking antibodies ( BD ) and then with PE-Cy7–anti-CD19 ( BD ) at 4°C . B cells were activated with F ( ab′ ) 2–anti-Ig ( M+G ) ( 10 µg/ml , Jackson ImmunoResearch ) at 37°C . The cells were fixed , permeabilized , and stained with anti-phosphorylated WASP ( S483/S484 ) and N-WASP ( Y256 ) antibodies for mouse B cells , anti-phosphorylated WASP ( Y290 ) ( Abcam , Cambridge , MA ) and N-WASP ( Y256 , Millipore ) antibodies for human B cells , and anti-WASP or N-WASP ( Santa Cruz , CA ) antibody for total protein . Stained cells were analyzed by a BD FACS Canto . Anti-phosphorylated WASP antibody showed no significant staining in B cells from WKO mice and anti-phosphorylated N-WASP antibody showed no staining in B cells from cNKO mice ( Figure S2 ) , indicating that there is no cross-reactivity of these two antibodies between phosphorylated WASP and N-WASP . B cells were pretreated with wiskostatin B ( 10 µM , EMD Bioscience , Gibbstown , NJ ) for 1 h at 37°C . The inhibitor was also included in the incubation media . Anti-nuclear antibody in sera was tested using the ANA slide test kit from MBL-Bion ( Des Plaines , IL ) . The serum levels of anti-dsDNA antibody were quantified by ELISA using a published protocol [41] . B cells were incubated with FcγR blocking antibodies ( BD ) and then with PE-Cy7-anti-CD19 ( BD ) at 4°C . CD19 positive cells were sorted with BD FACS Aria II , and mRNAs were extracted using Trizol ( Invitrogen ) . RT-PCR was carried out using the SuperScript III One-Step RT-PCR System ( Invitrogen ) . WASP and N-WASP were amplified using specific primers from Santa Cruz Biotechnology , and β-tubulin was amplified as a control . Lysates were generated from sorted CD19 positive cells and analyzed by SDS-PAGE and Western blotting . Blots were probed with WASP- ( Cell signaling ) and N-WASP–specific antibodies ( Santa Cruz ) , and β-tubulin–specific antibody ( Sigma ) as loading controls . Statistical significance was assessed by the two-tailed student's t test using Prism software ( GraphPad Software , San Diego , CA ) . | Mechanisms to shut down B-cell activation are necessary to ensure termination of an immune response when an infection has been cleared . When this negative regulation goes wrong , it can also lead to autoimmunity . To understand how this inhibitory process is regulated , here we utilized knockout mice containing B cells that are deficient for proteins potentially involved in their negative regulation . We focus on Wiskott–Aldrich syndrome protein ( WASP ) , a key cytoskeletal regulator of hematopoietic cells , and neural WASP ( N-WASP ) , which shares 50% homology with WASP and is ubiquitously expressed . Our study shows that mouse B cells that lack N-WASP protein are activated to a greater level and for longer periods than B cells that express this protein . Furthermore , in mice where B cells do not make N-WASP , the numbers of self-reactive B cells are elevated . We went on to identify molecules that promote or inhibit N-WASP activation and to examine the cellular mechanisms by which N-WASP inhibits B-cell activation . Based on these findings we propose that N-WASP is a critical inhibitor of B-cell activation and serves to suppress self-reactive B cells . |
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The competence of the tsetse fly Glossina pallidipes ( Diptera; Glossinidae ) to acquire salivary gland hypertrophy virus ( SGHV ) , to support virus replication and successfully transmit the virus depends on complex interactions between Glossina and SGHV macromolecules . Critical requisites to SGHV transmission are its replication and secretion of mature virions into the fly's salivary gland ( SG ) lumen . However , secretion of host proteins is of equal importance for successful transmission and requires cataloging of G . pallidipes secretome proteins from hypertrophied and non-hypertrophied SGs . After electrophoretic profiling and in-gel trypsin digestion , saliva proteins were analyzed by nano-LC-MS/MS . MaxQuant/Andromeda search of the MS data against the non-redundant ( nr ) GenBank database and a G . morsitans morsitans SG EST database , yielded a total of 521 hits , 31 of which were SGHV-encoded . On a false discovery rate limit of 1% and detection threshold of least 2 unique peptides per protein , the analysis resulted in 292 Glossina and 25 SGHV MS-supported proteins . When annotated by the Blast2GO suite , at least one gene ontology ( GO ) term could be assigned to 89 . 9% ( 285/317 ) of the detected proteins . Five ( ∼1 . 8% ) Glossina and three ( ∼12% ) SGHV proteins remained without a predicted function after blast searches against the nr database . Sixty-five of the 292 detected Glossina proteins contained an N-terminal signal/secretion peptide sequence . Eight of the SGHV proteins were predicted to be non-structural ( NS ) , and fourteen are known structural ( VP ) proteins . SGHV alters the protein expression pattern in Glossina . The G . pallidipes SG secretome encompasses a spectrum of proteins that may be required during the SGHV infection cycle . These detected proteins have putative interactions with at least 21 of the 25 SGHV-encoded proteins . Our findings opens venues for developing novel SGHV mitigation strategies to block SGHV infections in tsetse production facilities such as using SGHV-specific antibodies and phage display-selected gut epithelia-binding peptides .
Tsetse flies ( Glossina sp . ) are found exclusively in sub-Saharan Africa and are efficient vectors of African trypanosomes , causative agents of sleeping sickness in humans and nagana in domesticated animals [1]–[3] . Sleeping sickness is invariably fatal if untreated and , until now , the available drugs for sleeping sickness have been unsatisfactory , some being toxic and all difficult to administer [4] , and resistance to drugs is increasing [5] . Hence , the search for novel strategies must continue among which are vector-based strategies [6] . Tsetse control remains the most feasible management technique to combat trypanosomiasis and the application of the sterile insect technique ( SIT ) within the concept of area-wide integrated insect management ( AW-IPM ) , has had promising successes [7] , [8] . This strategy relies heavily on colony mass rearing of flies in contained production facilities . The problem is that the production of some species of tsetse such as Glossina pallidipes colonies are vulnerable to infections by a salivary gland hypertrophy virus ( SGHV ) [9]–[12]; which in a proportion of infected flies leads to hypertrophy ( hyperplasia ) of the salivary glands ( hereafter referred to as SGs ) and gonadal lesions . Consequently , fly productivity and fecundity drastically drops , often leading to colony collapse , making colony rearing and SIT applications difficult to implement . A critical step during SGHV infection of tsetse is the viral replication following ingestion of virus-contaminated blood meals [13] . Although it is yet to be established how long after infection the virus is transmitted , it is likely that a requisite to the transmission of the virus is replication and secretion of the virus into the SG lumen . It is currently unknown whether virus transmission is modified by tsetse saliva that is also deposited at the feeding site to enable the blood feeding process [14] , [15] . Further , it is currently unknown how SGHV influences fly gene expression in the SGs or how exactly tsetse immune system defends the fly from the injurious consequences of SGHV infection . To date , the non-redundant ( nr ) protein database of GeneBank has 156 Glossina proteins , 17 of which are annotated as found in the fly's SGs [16] . This is in addition to 8 proteins from a previous limited transcriptome analysis of G . morsitans morsitans saliva [17]–[20] . Given that knowledge on the mechanisms behind virus replication and transmission processes remains very limited , further studies are required to characterize the molecular interactions between Glossina and its SGHV . The hypothesis of this study is that the competence of Glossina sp . to acquire SGHV and to successfully transmit mature virions to its offspring and to other flies in the colony depends on interactions between Glossina and SGHV macromolecules . Hence , the Glossina SG secretome must encompass a spectrum of proteins required for all the different facets of the SGHV infection cycle . In this study , we investigated the secretome of hypertrophied and non-hypertrophied SGs of the tsetse fly Glossina pallidipes to gain a deeper understanding of the composition and putative roles of the fly's saliva in the Glossina-SGHV interactions , aiming particularly at the identification of potential targets for development of virus mitigation strategies in mass rearing facilities of G . pallidipes .
Two groups of G . pallidipes flies , teneral ( i . e . within 24 h post emergence ) and ten-day old were obtained from the IAEA Insect Pest Control Laboratory Seibersdorf , Austria . To detect the presence of Glossina pallidipes salivary gland hypertrophy virus ( GpSGHV ) infections in teneral flies , total DNA was extracted from one intermediate excised leg of individual flies using the ZR Genomic DNA kit ( Zymo Research , USA ) . The DNA was amplified by PCR using primers and conditions described previously [21] , [22] . Flies with negative PCR results were considered as non-infected ( hereafter referred to as non-hypertrophied ) , while flies with positive PCR results were considered as symptomatically infected . The symptomatically SGHV-infected flies ( hereafter referred to as hypertrophied ) used in this study were naturally infected during the fly mass-rearing , i . e . they acquired SGHV from their mothers . These naturally-infected flies were used in this study because artificial infection of Glossina using SGHV preparations from hypertrophied salivary glands either orally or by injection does not lead to hyperplasia in the same generation ( unpublished data ) . The hyperplasia of the SGs was confirmed microscopically during subsequent dissections . The ten-day old experimental flies were divided into eight groups based on hours post feeding ( hereafter referred to as hpf ) and whether they were hypertrophied or not . These were groups 1A and B at 0 hpf ( non-fed = teneral , non-hypertrophied or hypertrophied ) , and groups of ten-day old flies at 48 ( groups 2A and B ) , 72 ( groups 3A and B ) and 96 ( groups 4A and B ) hpf . Flies in groups 1A and B were dissected immediately after PCR results . The flies in the other groups were given a blood meal and maintained in the insectaria in standard rearing conditions [23] until the dissections of the SGs at the respective hpf . Harvesting of saliva was performed by adaptation of previously described methods [13] , [17] , [24] , [25] . Briefly , flies were anesthetized by a cold shock ( 10 min; 4°C ) , and dissected . For each group , 10 and 40 pairs of intact SGs were aseptically collected from hypertrophied and non-hypertrophied flies , respectively , in 500 µl ice-cold , sterile PBS ( pH 7 . 4 ) supplemented with EDTA-free protease inhibitor cocktail ( Roche Applied Sciences , Germany ) . Saliva fluid was allowed to diffuse out of the glands into the buffer for 2 . 5 h on ice and the buffer was subsequently separated from the SGs by brief centrifugation ( 500 rpm; 2 min; 4°C ) . The supernatants ( i . e . , saliva = diffusate from intact SGs ) were filtered ( 0 . 45-µm filter ) and immediately frozen in 100 µl aliquots at −80°C until further analysis . To determine the tsetse SGs protein profiles , the saliva samples were separated using SDS-PAGE ( 12% acrylamide ) [26] for 1 . 1 cm . The gel was stained with Coomassie Brilliant Blue using the Colloidal Staining Kit ( Invitrogen ) . To prepare the proteins for each of the eight groups described above , equal portions of the central third of each entire gel lane , i . e . the middle section spanning the complete gel lane , and containing the saliva proteins was excised . We selected this middle region of the lanes to avoid contaminations from neighboring lanes . The resultant gel sections were cut into approximately 1 mm3 cubes and in-gel protein digestions performed as previously described [27] . Supernatants were transferred to fresh Eppendorf microtubes , and the remaining peptides were extracted by incubating the gel pieces with 5% trifluoroacetic acid ( TFA ) /H2O , followed by 15% acetonitrile ( ACN ) /1% TFA . The extracts were combined , reduced in volume in a speed vacuum and dissolved in 20 µl of 0 . 1% formic acid/H2O . The peptides resulting from this digestion were analyzed by LTQ-Orbitrap Nano liquid chromatography coupled to electrospray and tandem mass spectrometry ( nanoLC-MS/MS ) as previously described [28] . Raw MS/MS files from the LTQ-Orbitrap were generated by MaxQuant software version 1 . 1 . 1 . 36 , supported by Andromeda as the database search engine for peptide identification [28]–[31] . MS/MS spectra were searched against a concatenated G . m . morsitans decoy database generated by reversing the protein sequences . The database used for peptide/protein searches was derived from the SGs expressed sequence tag ( EST ) library available from the International Glossina Genome Initiative ( IGGI ) ( http://old . genedb . org/genedb/glossina/ ) . Protein sequences of common contaminants , e . g . trypsin and keratins were used in MaxQuant's “contaminants . fasta” database . MaxQuant was used with a peptide tolerance of 10 parts per million while all other settings were kept as default with one extra addition of asparagine or glutamine de-amidation as variable modification [32] to allow de-amidated peptides to be used for quantification . Bioinformatic analysis of the MaxQuant/Andromeda Work flow output and the analysis of the abundances of the identified proteins were performed with the Perseus module ( available at the MaxQuant suite ) . We accepted peptides and proteins with a false discovery rate ( FDR ) of less than 1% and proteins with at least 2 unique peptides . To suggest putative functions for individual tsetse SGs secretome components , the accepted proteins were inputted into Blast2GO v . 2 . 4 . 8 ( http://www . blast2go . org/ ) [33] and categorized by molecular function ( MF ) , biological process ( BP ) and cellular component ( CC ) . Gene Ontology ( GO ) term mapping was based on sequence similarity to previous GO mapped sequences available in the Uniprot database and by merging GOs identified after the InterProScan searches . Signal secretion peptides were predicted from the results arising from InterProScan searches [34] , while potential secretion signal peptide sequences and determination of cleavage site positions predicted using SignalP v . 3 . 0 . ( http://www . cbs . dtu . dk/services/SignalP/ ) . To predict the conserved domains of SGHV-encoded proteins , the viral protein sequences were inputted into InterPro suite ( http://www . ebi . ac . uk/Tools/pfa/iprscan/ ) . Structural and functional annotations were determined by pasting the single-letter amino acid codes of the proteins into the Sequence Annotated by Structure ( SAS ) interface ( http://www . ebi . ac . uk/thornton-srv/databases/sas/ ) [35] . Further annotations of the information obtained from the PDB output were performed at the pfam site ( http://pfam . sanger . ac . uk/ ) .
For a comprehensive analysis of the G . pallidipes SG proteins , we adopted a four-step strategy . ( 1 ) Electrophoretic profiling and identification of tsetse saliva proteins by nanoLC-MS/MS , ( 2 ) cataloguing of the MS-supported proteins by gene ontology mapping using Blas2GO suite , ( 3 ) confirmation of the presence of N-terminal signal peptide sequences in these proteins by InterProScan and SignalP suites , and ( 4 ) prediction of the potential Glossina-SGHV interactions by analyzing the proteins expressed in hypertrophied SGs . The rationale of using 10 and 40 pairs of intact glands from hypertrophied and non-hypertrophied salivary glands respectively , follows from documented reports that hypertrophied salivary glands are enlarged at least four times their normal thickness [36] , [37] . The saliva was harvested from hypertrophied and non-hypertrophied SGs dissected at various time points after feeding as described in the materials and methods and electrophoretic profiles were made ( Figure 1 ) . In general , the electrophoretic protein expression profiles detected in the non-hypertrophied and hypertrophied glands correlated well with the viral loads that we have reported in our previous study for these glands [36] , [37] . The proteins ranged from <10 kDa to >170 kDa . Visual observation of the SDS-PAGE gel revealed not only clear similarities , but also quantitative differences between the protein profiles of hypertrophied and non-hypertrophied SGs in terms of both proteic band intensities and the presence or absence of several protein bands . Three trends are demonstrated from the protein profiles: ( 1 ) A fairly constant protein quantity , albeit a slight decrease at 96 hpf for non-hypertrophied SGs and a maximal quantity at 72 hpf for hypertrophied SGs , ( 2 ) A ( multiple ) high intensity protein band ( 26 kDa region ) in the profile of non-hypertrophied SGs relative to the hypertrophied SGs , and ( 3 ) at all the time points , the majority of protein bands in the 19–25 kDa , 29–43 kDa , 55–70 kDa and >95 kDa range present in the secretome of hypertrophied SGs have low abundance or are not detectable in the non-hypertrophied SGs . A MaxQuant/Andromeda search of LC-MS/MS data against the nr NCBI database was performed for all eight saliva protein samples in the gel lanes ( Figure 1 ) . The saliva samples at 72 hpf were arbitrarily chosen for further analysis are they seemed to contain maximum amount of saliva proteins and may also contain the maximum amount of SGHV proteins . This selection criterion is in agreement with previous studies indicating that tsetse saliva production is at peak production 3 days after taking a blood meal [17] . Analysis of these samples by the Perseus module of the MaxQuant suite yielded 521 protein hits , 31 of which were SGHV-encoded based on the known genome and predicted proteome of the virus [28] , [38] . Based on an FDR limit of <1% and detection of at least 2 unique peptides per protein ( see methods ) , a further search of G . m . morsitans SG EST dataset identified 317 protein hits . Twenty-five of these proteins were SGHV-encoded ( Table S1 ) . Based on the MS/MS data and the electrophoretic protein profile in Figure 1 , a comparison of the protein expression patterns in the non-hypertrophied and hypertrophied SGs within and between the different time points revealed noteworthy findings . Firstly , the relative composition of saliva proteins in the non-hypertrophied SGs remained fairly constant from 0 hpf to 72 h pf , followed by a slight drop at 96 hpf , unlike in the case of the non-hypertrophied SGs ( Figure 1 ) . This is in agreement with a previous report by Van Den Abbeele and his group [17] . Secondly , at all the time points , at least 32 Glossina proteins are highly up regulated i . e . they are expressed in high abundance in the hypertrophied SGs but were either detected in very low abundance or not detectable at all in the non-hypertrophied SGs ( Table 1 ) . Thirdly , some Glossina proteins are down regulated in the hypertrophied SGs relative to the non-hypertrophied SGs . Nine of the most down-regulated proteins at all the time points are indicated in Table 2 . Taken together , these differential protein expressions between hypertrophied and non-hypertrophied SG implies that infection of Glossina by SGHV greatly alters the protein expression pattern in flies with hypertrophied SGs . Lastly , the maximal expression of proteins was found in the hypertrophied SGs at 72 hpf ( Figure 1 ) . This agrees well with previous reports that saliva production in Glossina reaches maximum 2–3 days after a blood meal [17] . Based on the analysis of the differential protein expression patterns , we focused on the abundances of saliva proteins from hypertrophied and non-hypertrophied SGs at 72 hpf relative to 0 hpf in order to investigate perturbations of the protein expression in the fly SGs . We first compared Glossina and SGHV protein abundance ratios between hypertrophied and non-hypertrophied SGs ( Figure 2 ) to find the really significance differences between the two samples . Three abundance patterns could be deduced . First , 39 . 4% ( 115/292 ) of Glossina and 52% ( 13/25 ) SGHV proteins were abundantly expressed in hypertrophied flies relative to non-hypertrophied flies ( Figure 2A ) . Second , 14 . 4% ( 42/292 ) of Glossina proteins showed relatively low abundance regardless of SGHV infection ( Figure 2B ) . Lastly , 46 . 2% ( 135/292 ) and 48% ( 12/25 ) of Glossina and SGHV proteins were specifically expressed in the hypertrophied SGs , respectively ( Figure 2C ) . A plot of the shift in the abundance of a selection of seven SGHV proteins uniquely expressed in hypertrophied flies at 72 hpf relative to teneral flies is shown in Figure 3 . The host proteins abundantly expressed in hypertrophied SGs at 72 hpf showed a similar trend ( data not show ) . These uniquely expressed proteins at 72 hpf have implications in the Glossina-SGHV molecular interactions ( see discussion ) . Secondly , we conducted gene ontology ( GO ) mapping on the saliva proteins . At least one GO term could be assigned to 285 of the 317 detected saliva proteins . Five ( 1 . 8% ) Glossina proteins were deemed of unknown function after blast searches against the nr databases and annotation augmentation . The GO terms assigned to individual Glossina proteins are shown in Table S1 , while the terms assigned to SGHV proteins are shown in Table S2 . Based on the GO annotations , the MS/MS-supported proteins were grouped into three categories: ( 1 ) the broad biological processes ( BP ) the proteins are involved in , ( 2 ) the predicted molecular functions ( MF ) they perform , and ( 3 ) the sub-cellular structures/locations/macromolecular complexes or components ( CC ) these proteins associate with ( Figure 4 ) . The three most common BPs were metabolic processes ( 24 . 7% ) , cellular processes ( 23% ) and biological regulation ( 10 . 1% ) whereas the three most common MFs were nucleotide/nucleoside-binding ( combined percentage of 30 . 7% ) , hydrolase activity ( 21 . 7% ) , protein binding ( 16 . 2% ) and transferase activity ( 13 . 2% ) . The multilevel CC analysis returned five sub-categories , of which the highest proportion ( 27 . 2% ) of the proteins showed association with lipid-related metabolism ( sub-categories of CC , Figure 4 ) . These observations are not entirely unexpected . Other studies have shown considerable changes in cellular organization and metabolism in diseased insects . For instance , during the infection of mosquitoes by densonucleosis ( DNV ) and crane fly by Tipula iridescent ( TIV ) viruses , an enlarged nucleus in target cells was noted to be accompanied by a large increase in nuclear DNA synthesis and a massive enlargement of fat body cells [39] . We also noted that a relatively high proportion ( 23% ) of the identified tsetse SG proteins is associated with cellular processes ( BP ) . Although a full explanation for this observation remains to be confirmed , cellular modifications would be expected in SGHV-infected Glossina SG cells , in terms of the formation of vesicles and/or multivesicular bodies associated with various organelles such as the endoplasmic reticulum . Indeed , Sang et al . , showed that the cytoplasm of SGHV-infected cells of G . m . centralis are heavily vacuolated , show complete disintegration of cytoplasmic organelles , including the smooth and rough ER and the mitochondria , leaving the nuclei scattered around [40] . These membrane and organelle changes could also be involved in processes such as viral mRNA translation , assembly of protein complexes of both SGHV and Glossina origin , as well as cell-to-cell transport . Lastly , we used the InterProScan suite to determine which of the identified saliva proteins contained predicted secretion signals . This analysis revealed that 96 . 5% ( 282/292 ) of Glossina and ( 6/25 ) and 24% of SGHV MS/MS-supported proteins contained predicted secretion signals , respectively . Further , SignalP analysis of these proteins confirmed that 23% ( 65/282 ) of these Glossina proteins and the 6 SGHV proteins contained N-terminal signal peptide sequences . Table S1 shows the 65 G . pallidipes saliva proteins with signal peptides predicted by SignalP ( hereafter referred to as the SG secretome proteins ) . Structural and functional annotation of all detected SGHV-encoded proteins revealed that 14 were non-structural ( NS ) an 11 were structural/capsid ( VP ) proteins , respectively ( Table S2 ) . It is noteworthy that some of the detected Glossina proteins may not be synthesized in the SGs . For this , alternative mechanisms for the translocation of proteins into SGs should be taken into account . We expected Glossina to have a large number of saliva effector macromolecules with complex effects due to their unique blood-sucking nature . Perhaps some of the identified proteins and macromolecules are synthesized in other organs and then transported into the SGs through hemolymph , by yet-to-be identified mechanisms . This is likely because SGs are capable of sequestering proteins from the hemolymph and then secreting them [41]–[43] .
Functional analysis of the Glossina proteins that are abundantly expressed in the hypertrophied SGs but are either not detectable or are expressed in very low abundance in the non-hypertrophied SGs revealed that approximately 31% ( 10/32 ) may be involved in DNA replication ( see Table 1 ) . These uniquely expressed proteins also include molecular chaperones ( transitional ER ATPase ) , and proteins involved in regulation of signalling ( serine protease inhibitor 4 ) , and protein-protein interactions ( Hsp70/Hsp90 organizing protein ) productive protein folding ( Hsp60 or GroEL_like chaperonin protein ) . Other noteworthy proteins include quiescin-sulfylhydryl oxidase 4 , a protein usually localized in high concentrations in cells with heavy secretory loads , Ras-like GTP binding protein ( Rho1 ) which is involved in the control of cytoskeletal changes , and imaginal disc growth factor 3 , a chitinase-related GH18 protein involved in the interactions with surface glycoproteins . Some of the down-regulated proteins such as tsetse salivary gland proteins 1 and 2 ( Tsal1/2 ) and tsetse antigen 5 protein are of unknown function . It is possible that the need for bulk synthesis of proteins for maturation of the virions in hypertrophied SGs could have led to the down regulation of the larval serum protein-2 , which serves as a store of amino acid for synthesis of adult proteins . Additionally , the down regulation of salivary apyrase , which has been postulated to facilitate blood location and blood feeding [44] , is understandable because flies with hypertrophied SGs almost stop feeding 10–15 days post emergence ( unpublished data ) . Overall , the up-regulation of these proteins , coupled to the down-regulation of the proteins described in Table 2 signifies the alteration of protein expression in hypertrophied Glossina SGs as SGHV hijacks key molecular processes as discussed later in this article . Our approach in establishing the Glossina SG secretome was based on the assumption that the SG proteins would have N-terminal signal sequences . Two clear trends were inferred from the cataloguing of saliva proteins in this study: ( 1 ) a high proportion of Glossina SG secretome proteins indeed have signal peptide sequences , and ( 2 ) only a low proportion of the secreted proteins associated with Glossina saliva ( ∼4 . 9% ) are of unknown function . The percentage of non-annotatable Glossina SG secretome proteins is substantially lower than the average of non-annotatable proteins across other systems such as the pea aphid Acyrthosiphon pisum genome ( 30% ) [45] . This high percentage of annotable proteins in tsetse SGs is probably due to the highly specific function of Glossina saliva in blood feeding . It is likely that our stringent analysis and assumption that the secretome proteins must have N-terminal sequences to substantiate their inclusion into the Glossina secretome may have excluded some important proteins from our secretome pool . These proteins do not necessarily need to be excluded from being part of the Glossina secretome . As this is the first study of the secretome of the Glossina SGs , there is insufficient data available to provide insights into the roles of a majority of the identified proteins . However , potential effector roles of many of these candidate proteins , particularly in the interactions between Glossina and SGHV macromolecules , can be predicted based on their homology or similarity to other proteins involved in vector-virus interactions . In this regard , we discuss a selection of the secretome proteins in the context of six categories of functional proteins , and in relation to SGHV infection . A high abundance of heat shock proteins ( HSPs ) was identified in the Glossina hypertrophied SG secretome . HSP induction by viruses is not the result of a meager “canonical” HSP-mediated heat shock response ( HSR ) , but rather the effect of virus-controlled transcriptional/translational switches , sometimes involving individual viral products [46] . Our data present strong indication of hijacking of the host cell chaperon machinery for correct folding of abundant SGHV proteins rapidly synthesized in bulk , and for their correct assembly into viral components during the different phases of viral replication . These observations have been documented in previous studies ( see [47]–[49] for review ) . For instance , we detected Torsin-like-protein-precursor in hypertrophied SGs as opposed to the non-hypertrophied SGs . This protein is localized in the endoplasmic reticulum ( ER ) lumen and is involved in unfolded protein binding as well as chaperone-mediated protein folding [50] , [51] . Many viruses interact with HSPs at different infection stages . In Epstein-Barr Virus ( EBV ) , virus attachment at cell membrane receptors activates signal transduction pathways interfering with the heat shock response ( HR ) [52] . Surface exposed hsc70 and hsp70 proteins are involved in virus entry into cells [53]–[55] and hsc70 may play an active role in virus entry into the host cell as well as at a post-attachment step [56] . In addition , hsp70 chaperones are involved in disassembly of oligomeric protein structures and viral internalization into host cells ( see review in [57]–[63] ) . Viral proteins , including E1A of Adenovirus [64] , large T antigen ( T ag ) of Simian vacuolating virus 40 [65] , [66] , ICP4 of Human simplex virus ( HSV-1 ) , IE2 of human cytomegalovirus , and nuclear antigen 3 ( EBNA3 ) of EBV [67]–[72] , modulate hsp70 by direct interaction with different components of the basal transcription apparatus . Additionally HSP70-cognate 4 ( HSP70-4 ) plays an important role in the homeostasis and suppression of O'nyong-nyong virus ( ONNV ) replication and in the establishment of latent infections in the mosquito Anopheles gambiae [73] . It is noteworthy that HSP70-4 was detected in the secretome of hypertrophied SG , as opposed to the non-hypertrophied SGs . Although it remains to be established in the case of SGHV infection of Glossina , this protein may play roles in the establishment of asymptomatic SGHV infections in Glossina [13] . We also identified some members of inducible secreted polypeptides , including C-type lectins ( CTLs ) ( Table S1 ) . CTLs play important roles in insect defense by recognizing pathogen-associated molecular patterns ( PAMPS ) [74] , [75] in invading pathogens [76] , [77] . The expression of lectins during virus infection would be expected because they have been shown to mediate immune functions [78] , including activation of the lectin complement pathway that also exists in arthropods , thus binding to carbohydrates expressed on viral glycoproteins . In this regard , we detected members of thioester-containing proteins ( TEPs ) ( Table S1 ) which have been described in the complement system of Drosophila melanogaster and An . gambiae [79] . It is therefore likely that the hypertrophied SGs express CTLs to block glycoprotein-mediated attachment of SGHV to non-infected SG cells . Although CTLs generally diffuse during SGHV infection , their binding to circulating virus could effectively reduce viral infection of lectin-expressing SG cells . Binding of CTLs to virus and subsequent deposition of complement components on the virus membrane can also lead to enhanced infection of cells that express complement receptors . ADP-ribosylation factor ( ARF ) is an abundant protein that reversibly associates with Golgi membranes , and is implicated in the regulation of membrane traffic through the secretory pathway [80]–[82] . This pathway is important for processing of viral contents into complexes capable of nuclear penetration . ARFs have been shown to be up-regulated and involved in virus infection [83]–[85] , and possibly explains the detection of ARF in the hypertrophied SGs at 48 , 72 and 96 hpf , as opposed to non-hypertrophied and teneral-hypertrophied SGs at 0 hpf . The data presented here indicate the recruitment/hijacking of ARFs to membranes by SGHV and may provide clues for future identification of the pathways utilized by the virus in the replication process in hypertrophied SG . Six types of serine proteases were detected in the secretome of hypertrophied SG but not in the non-hypertrophied SG . Additionally , a precursor of phosphenol oxidase activating factor of the prophenol-oxidase-activating system ( proPO-AS ) , an important component of the innate immune response in insects [86]–[88] , was also detected . These two proteins are involved in initiating a signal cascade that eventually leads to melanization reaction which includes the formation of toxic intermediary compounds to kill invading viruses [87] . Studies have shown that the baculovirus P74 is a viral attachment protein [89]–[91] . Cleavage of P74 by trypsin is crucial for infection , probably by to exposing a receptor binding domain [92] , enabling interaction with host receptors . SGHV P74 was not detected in this study , which is probably due to its low abundance as demonstrated in our previous study [28] . The expression of the serine proteases in hypertrophied SG is probably required for the interaction of SGHV with the host SG cells receptors as has been reported for several entomopathogens [93] , [94] . RNA polymerase II general transcription factor ( BTF3 ) , the translation elongation factor EF-1 gamma ( EF1γ ) , and the ATP-dependent RNA helicase were among the factors detected in the secretome of hypertrophied SG . BTF3 is a general transcription factor necessary for activation of a number of viral promoters by RNAP II [95] , while EF1γ is involved in the regulation of protein assembly and folding [96] , [97] . The detection of these proteins in hypertrophied SGs , coupled to the presence of RNA helicase is desirable for the expression of replication- and maturation-related genes . In addition , proteins involved in signal transduction were also detected ( in SGs dissected 48 , 72 and 96 hpf ) including GTPase-activating protein ( GAP ) , cAMP-dependent protein kinase , and Ras-related small GTPase ( Rho type ) . GAP is known to be necessary for efficient virus infection and replication [98] , and is implicated in the regulation of anterograde traffic between the ER and the Golgi complex , while cAMP-dependent protein kinase is implicated in the regulation of virus infection and virus-induced cell-cell fusion . Most viruses use components of the host cytoskeleton to move within cells . Upon virus infection , virions or sub-viral nucleoprotein complexes are transported from the cell surface to the site of viral transcription and replication . During viral escape , particles containing proteins and nucleic acids move again from the site of their synthesis to that of virus assembly and further to the plasma membrane [99] , [100] . Viral ( sub ) particles , particularly in members of herpesviridae , adenoviridae , parvoviridae , poxviridae and baculoviridae use the microtubule and the actin cytoskeleton . In this study , actin 5C , actin 87E and actin depolymerizing factor were detected in all saliva samples except in the non-hypertrophied SG collected 96 hpf . F-actin capping protein was detected in the saliva of hypertrophied SGs 48 hpf , while actin 57B was detected , albeit in low abundance , in hypertrophied SGs 72 hpf . Myosin heavy chain , which drives transport along actin filaments [99] was detected in the saliva of hypertrophied SGs except at 96 hpf , which probably indicates reduced active transport of SGHV virions at this time point . While all the 6 cytoskeletal proteins were detected in the SGs 96 hpf , none were detected in the secretome of non-hypertrophied SGs 96 hpf . It is possible that this could be due to the hyperplasia of the SGs by SGHV , which could potentially lead to lysis of SG cells during advanced stages of viral infection . Further studies are required to investigate these observations . The movement and/or replication of viruses in insect vectors require specific interactions between viruses and host components . DNA viruses have evolved mechanisms to evade the host restrictions at entry , cytoplasmic transport , replication , protein synthesis , innate ( and for mammalian viruses , adaptive immune ) recognition , and egress from the infected cells . The SGHV genome is a circular double-stranded ( ds ) DNA molecule [38] . Nuclear-replicating viruses with ds DNA genomes such as herpesvirus engage in all aspects of cellular metabolism [101] . Although it has yet to be established for SGHV , DNA viruses adopt the host transcriptional apparatus and all cellular pathways required for processing and transport of their mRNAs . The host cellular mechanisms translate and turn over viral proteins , while transport of viral macromolecules takes place through cellular organelles and structures . In this study , structural and functional annotation of the identified SGHV proteins also indicate their engagement with Glossina cellular metabolism . In the following sections , we briefly discuss potential roles played by these SGHV proteins and their possible interactions with Glossina SG secretome proteins during the different facets of the viral infection cycle . Inferences below are drawn from other nuclear-replicating DNA viruses , including adenoviruses , hepadnaviruses , herpesviruses , papillomaviruses , and polyomaviruses . The stepwise entry of DNA viruses into host cells requires viral attachment to cell surface receptors and lateral movements of the virus-receptor complex to specialized sites on the plasma membrane [102]–[105] . In closely-related baculoviruses , per os infectivity factor proteins ( PIFs ) have been shown to be involved in viral attachment to the host cells . [38] , [106]–[110] . In the current study , PIFs had very low abundance as was also noted in our previous proteomics study of GpSGHV [28] . In this study , SGHV085 was annotated to be a tyrosine kinase-dependent signaling structural protein , and is probably involved in transport of SGHV polypeptide into the nucleus ( see next section ) [111] . Additionally , viruses in general elicit signals following attachment to the host cell membrane to circumvent the host defense mechanism . In this regard , annotation revealed SGHV046 as a glutathione S-transferase-like protein , thus pointing to its involvement in this type of signaling . DNA viruses that replicate their genomes in the nucleus use microtubule motors for trafficking towards the nucleus and the periphery during egress after replication [111]–[113] . Bidirectional transport allows precise delivery of capsids to ensure nuclear targeting , and has been demonstrated in HSV-1 ) [114] , [115] and in human adenovirus 2/5 ( Ad2/5 ) [116]–[118] . Incoming DNA viruses expose proteins on the capsid that preferentially recruit microtubule motor complexes [112] , and may release tegument proteins before they traffic to the nucleus [119] . To regulate capsid transport , protein phosphorylation by viral and/or host cellular kinases modulate tegument protein composition as in the case of vaccinia virus [120] . In this study , cAMP-dependent protein kinase detected in the SG secretome is probably involved in anterograde trafficking of SGHV . Additionally , the SGHV041 protein detected here is a Casein kinase ( isoform-δ ) , which is likely to be involved in phosphorylating cytoskeletal components both in anterograde and egress [121] , [122] . Early during infection , some viruses such as δ-2 human herpes virus 8 and hepatitis C virus induce Rho GTPases [98] , [123] , which alter the dynamics by increasing the acetylation of actin microfilaments thereby enhancing viral capsid trafficking transport to the nucleus and establishment of successful infection . Again , Ras-related small GTPase ( Rho type ) as well as GTPase-activating protein ( GAP ) were detected in the Glossina hypertrophied SGs , and suggest their participation in viral trafficking towards the nucleus . Finally , although the role of spectrins in cytoplasmic transport is not clear , this study identified SGHV010 to have spectrin repeat domains , indicating its potential involvement in SGHV anterograde trafficking . Cytoplasmic transport is followed by viral genome docking and uncoating at the nuclear pore complex ( NPC ) , a stepwise programme involving partial proteome degradation of incoming capsid or tegument proteins [124] , [125] . Although it is not clear how uncoating at the NPC occurs , experiments with some viruses such as herpes B virus have indicated that capsids are transported to the nuclear membrane where they bind to NPCs and release their genome into the nucleus [126] . Additionally , cytoplasmic processing of incoming capsids makes them competent for docking to the NPC [114] , and probably prevents the naked viral chromatin from traveling through the cytoplasm , which could trigger DNA-sensing host innate immune responses as has been demonstrated in adenovirus [127] . The SGHV006 protein detected in this study was determined to have α/β-hydrolase catalytic domain , a signature domain for lecithin∶cholesterol acyltransferase ( LACT ) which is involved in membrane docking of viruses to NPC , as well as in nucleocytoplasmic transport of capsids ( see [114] , [128]–[131] for review ) . Upon infection , some viruses such as Autographa californica multicapsid nucleopolyhedrovirus ( AcMNPV ) establish centers for transcription , DNA replication and progeny nucleocapsid assembly [101] , and others express at least one regulatory protein that interacts directly with similar domains such as the promyelocytic leukemia protein nuclear bodies ( PML-NBs ) [101] . In this study , SGHV112 annotation revealed the presence of the helix-turn-helix characteristic domains of regulatory proteins [132] involved in DNA-protein interactions . Gamma-interferons were detected in this study in the hypertrophied SG dissected as early as 48 hpf , as well as a 19 . 3 kDa Glossina protein encoded by GMsg-6444 ( a SUMMO-binding protein ) . The ubiquitin-like protein SUMO is a partner protein to viral replication center and are dramatically enhanced by interferons [133] . Viral proteins associating with these centers have the ability to stimulate lytic infection and induction of reaction from quiescence [134] . This supports observations that majority of SGHV infections in tsetse colonies are in fact asymptomatic [13] , and that there is virus induction in infected flies after an initial blood meal ( unpublished data ) . Also detected in this study were SGHV035 and SGHV036 , homologs of thymidylate synthase and deoxycytidylate hedroxymethylase , respectively . The former is involved in regulating a balanced supply of dNTPs during DNA replication [135] , [136] , while the latter is involved in pyrimidine metabolism [137] , [138] . Further , our annotation predicted that SGHV039 ( a HSP90-like ATPase ) is possibly involved in regulation of unwinding of DNA supercoil strands . Additionally , SGHV062 , a p53 transcription factor-like protein containing β-sandwich domain of the sec23/24 superfamily was detected . Proteins of this family are involved in chromosomal segregation and are induced early during the S-phase of the cell cycle [139]–[142] , and hence have direct roles in viral DNA transcription and replication . Further , detected in this study was an ABC ATPase-like family protein ( SGHV064 ) . Studies have implicated members of this protein family to be involved in translation initiation , ribosome biosynthesis and virus capsid assembly of other viruses such as HIV [143] , [144] . Taken together , the presence of these Glossina and SGHV proteins in the hypertrophied SGs are an indication that SGHV genome associates with the periphery of PML-NBs , and that viral replication compartments would develop from these sites , as has been observed in other viral systems for instance in HSV-1 [145] , [146] . SGHV049 detected in this study was predicted to be a pre-mRNA splicing factor-9-like protein . The WD40/G-β-repeats represent in this protein is a signature domain for proteins that associate with the spliceosome [147] ) . Other detected proteins that have potential roles in SGHV maturation were: ( 1 ) SGHV072 , a FAD-dependent sulfhydryl oxidase ( with a late promoter motif and hence likely to be involved in virion maturation , [148] ) ; ( 2 ) SGHV093 , an uncharacterized endonuclease type-IIe-like protein with DNA-binding and cleavage activities [149]; ( 3 ) SGHV027 , a chitinase-II ( O-glycosyl hydrolase ) protein , which like the chitinases family-18 proteins may be involved in virus maturation ( see [150]–[152] for review ) ; ( 4 ) SGHV038 , a protein containing α-β-barrel active site , thus likely to be involved in the expression of receptor proteins for membrane transport ( egress ) [153] and ( 5 ) SGHV096 , a metal ( Mn2+ ) and ion ( S2O4 2− ) -binding protein with multiple TMs , thus likely to be part of SGHV ion-channel proteins . Newly assembled enveloped viruses recruit periphery directed motors , and are transported to the plasma membrane on the microtubules upon binding of the outer membrane [113] proteins and fuse with plasma membrane . With involvement of host tyrosine kinases [112] , the virus may eventually be launched away from the infected cell and spread the viral progeny . Although it is unclear whether SGHV travel in vesicles or as capsid as baculoviruses do , SGHV097 was predicted to be a vesicle-associated membrane protein and could be involved in targeting and/or fusion of virus-containing vesicles to the target membranes [119] , [154] . The Glossina pallidipes species is found in several African countries and rearing facilities have been established in Kenya , Ethiopia and Tanzania with the aim of strengthening the fly eradication campaigns in African . Salivary gland hypertrophy virus infection leads to drastic negative effects on the productivity and stability of the G . pallidipes colonies and in certain cases to colony collapse . In addition , a recent study [155] has demonstrated that this virus is widely distributed in the wild populations of G . pallidipes which further complicates the tsetse eradication campaigns because the mass-reared colonies are normally established from flies collected from the target wild populations . Due to this negative effect , it is necessary to develop virus management strategies to enable sustenance of a healthy and productive colony size for the fly eradication campaigns . Designing a strategy that would interrupt the replication/transmission cycle of the virus in the colonies requires a comprehensive understanding of the mechanism involving the vector-virus interactions . The aim of our study was to establish an extensive protein map of G . pallidipes salivary gland secretome proteins and SGHV proteins in hypertrophied salivary glands , using stringent mass spectrometry criteria to validate the potential proteins , and to establish possible Glossina-SGHV interactions . The substantial differences between the protein profiles of the secretomes of hypertrophied and non-hypertrophied salivary glands , and the analysis of the nanoLC-MS/MS-supported secretome proteins data obtained demonstrate that a large proportion of the proteins identified are indeed secreted . The data presented in this study demonstrates that the G . pallidipes salivary gland secretome encompasses a wide spectrum of proteins that may be required for the different facets of the SGHV infection cycle from viral attachment to egress of the virions from infected Glossina cells . On the basis of our previous proteomic analysis of GpSGHV virions [28] and the data presented in this study , some interactions between Glossina and SGHV proteins can be predicted ( Table 3 ) . It is to be noted that the per os infectivity factors ( PIFs ) were only detected in very low abundance , and although they are not included in our final pool of the salivary proteins , they probably play a vital role in the oral infection of Glossina sp . One other SGHV protein that was noted to be present in very low abundance was the baculovirus ODV-E66 ( PIF4 ) homolog . This protein is known to be involved in the initial attachment of the baculovirus in the mid-gut epithelia of infected insects , and it is likely to play a similar role in the case of Glossina . Taken together , the identification of putative host-viral protein interactions opens novel avenues for the development of mitigation strategies against GpSGHV infections . Such strategies could include immune-interventions whereby virus-specific antibodies against PIF proteins could be supplemented in the blood meals used in the membrane-feeding in tsetse production facilities . This would lead to immuno-complexion of SGHV virions in the blood meals , which would block the horizontal transmission of SGHV from fly to fly . In addition , phage display-selected gut epithelia-binding peptides such as derived from the ODV-e66 protein ( PIF-4 ) homolog could be designed to impede the attachment of SGHV to the Glossina mid-gut and subsequent movement of the virus into the fly hemocoel . Such chemically-synthesized oligopeptides or the phage display library expressing the active peptides could be supplemented in the flies' blood meals and , thereby , upon ingestion of the meal , the peptides would out-compete the viral homologs ( ODV-e66 ) in the attachment to the mid-gut receptors [156] . By this approach , vertical transmission of the virus from mother-to-offspring would be interrupted . Finally , SGHV-specific genes could also be targets for RNA interference ( RNAi ) . The roles of Glossina and SGHV proteins identified in this study need to be experimentally established . Still to be investigated are questions particularly with regard to Glossina specificity and how SGHV overcomes various transmission barriers in the tsetse fly . Also to be resolved are the roles played in Glossina specificity by SGHV proteins , Glossina proteins , virus receptors , Glossina symbionts , as well as the role of hemolymph and other tissues in the viral transmission process . In addition , the barriers to transovarial ( vertical ) transmission of SGHV in tsetse remain grossly under-investigated . We hope that with the cataloguing of Glossina salivary glands secretome proteins , the detection of SGHV proteins in hypertrophied salivary glands and the advent of new molecular technologies , that such roles can be elucidated further and eventually exploited to initiate novel strategies for controlling SGHV infections in tsetse mass rearing facilities . | Tsetse fly ( Diptera; Glossinidae ) transmits two devastating diseases to farmers ( human African Trypanosomiasis; HAT ) and their livestock ( Animal African Trypanosomiasis; AAT ) in 37 sub-Saharan African countries . During the rainy seasons , vast areas of fertile , arable land remain uncultivated as farmers flee their homes due to the presence of tsetse . Available drugs against trypanosomiasis are ineffective and difficult to administer . Control of the tsetse vector by Sterile Insect Technique ( SIT ) has been effective . This method involves repeated release of sterilized males into wild tsetse populations , which compete with wild type males for females . Upon mating , there is no offspring , leading to reduction in tsetse populations and thus relief from trypanosomiasis . The SIT method requires large-scale tsetse rearing to produce sterile males . However , tsetse colony productivity is hampered by infections with the salivary gland hypertrophy virus , which is transmitted via saliva as flies take blood meals during membrane feeding and often leads to colony collapse . Here , we investigated the salivary gland secretome proteins of virus-infected tsetse to broaden our understanding of virus infection , transmission and pathology . By this approach , we obtain insight in tsetse-hytrosavirus interactions and identified potential candidate proteins as targets for developing biotechnological strategies to control viral infections in tsetse colonies . |
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Human influenza viruses replicate almost exclusively in the respiratory tract , yet infected individuals may also develop gastrointestinal symptoms , such as vomiting and diarrhea . However , the molecular mechanisms remain incompletely defined . Using an influenza mouse model , we found that influenza pulmonary infection can significantly alter the intestinal microbiota profile through a mechanism dependent on type I interferons ( IFN-Is ) . Notably , influenza-induced IFN-Is produced in the lungs promote the depletion of obligate anaerobic bacteria and the enrichment of Proteobacteria in the gut , leading to a “dysbiotic” microenvironment . Additionally , we provide evidence that IFN-Is induced in the lungs during influenza pulmonary infection inhibit the antimicrobial and inflammatory responses in the gut during Salmonella-induced colitis , further enhancing Salmonella intestinal colonization and systemic dissemination . Thus , our studies demonstrate a systemic role for IFN-Is in regulating the host immune response in the gut during Salmonella-induced colitis and in altering the intestinal microbial balance after influenza infection .
Influenza is a highly contagious viral infection that has a substantial impact on global health . Notably , outbreaks of influenza infection are usually associated with an increased incidence or severity of secondary bacterial infections responsible for high levels of morbidity during seasonal influenza episodes . We and others have previously shown that IFN-Is play a critical role in the development of secondary bacterial pneumonia after influenza infection [1] . Since their discovery in 1957 , IFN-Is have been recognized as the central antiviral cytokines in vertebrates [2] . The type I IFN family mainly consists of numerous subtypes of IFNα and a single IFNβ , whose induction appears to be ubiquitous in most cell types . Toll-like receptor ( TLR ) -mediated IFN-I induction plays a key role in facilitating antiviral responses [3] . IFN-Is bind to a common heterodimeric receptor , IFN-α/β receptor ( IFNAR ) , composed of two subunits , IFNAR1 and IFNAR2 . Binding activates the JAK/STAT pathway , which induces pro-inflammatory genes that inhibit viral replication and boost adaptive immunity [4] , and regulates the transcription of multiple interferon-stimulated genes ( ISGs ) [5] . A recent study has shown that influenza infection can alter the composition of the intestinal flora , resulting in immunological dysregulation that may promote inflammatory gut disorders [6] . The mammalian gut harbors a complex microbiota that plays a key role in host health through its contribution to nutritional , immunological , and physiological functions . Intestinal commensals are required for maintaining gut homeostasis through dynamic interactions with the host’s immune system [7] . Resident microbiota promote gut immune homeostasis by regulating T regulatory cells ( Tregs ) [8] and T helper 17 cells ( Th17 ) [9] . The gut microbiota can inhibit infection through direct microbial antagonism or by stimulating several host effectors and injury responses [10 , 11] . However , the intestinal commensals also pose an enormous challenge to the host that needs to remain “ignorant” to a selection of microbial antigens and keep the bacterial load anatomically contained , while remaining responsive to its dissemination [12] . Reciprocal interactions between gut microbiota and the host immune system shape the microbial community and influence imbalances that can lead to disease [13] . These changes are often characterized by a reduction of obligate anaerobic bacteria , and a proliferation of facultative anaerobic Enterobacteriaceae [14] . Furthermore , some people with pulmonary influenza infections also experience symptoms of gastrointestinal disorders , especially children [15] . Influenza RNA is rarely recovered from their stool [15] , so it is unclear whether the symptoms develop from swallowed respiratory secretions or from active infection of the gastrointestinal tract . In order to investigate the role of IFN-Is induced during influenza infection in modulating the endogenous intestinal microbiota , we established a model of influenza pulmonary infection using genetically modified animals with defective IFNAR signaling ( Ifnar1–/–mice ) . Remarkably , we found that influenza infection alters the intestinal microbial community supporting gut Proteobacteria pathobionts through a mechanism dependent on IFN-Is . While the importance of IFN-Is in antiviral defense is well established , their role during bacterial infection is more ambiguous . Moreover , we wanted to test whether primary influenza infection can predispose the host to secondary intestinal bacterial infections . We therefore developed a model of sequential influenza pulmonary infection followed by secondary Salmonella-induced colitis using Ifnar1–/–mice to investigate the effects of IFN-Is induced during influenza infection on intestinal host defense against Salmonella . Interestingly , we found that lung induced IFN-Is enhanced the growth of Salmonella in the inflamed gut and increased its systemic dissemination to secondary sites . Furthermore , we found that influenza pulmonary infection resulted in a profound inhibitory effect on the intestinal antibacterial and inflammatory responses against Salmonella infection in a IFN-I dependent manner .
Previously , it was shown that influenza infection causes intestinal injury through microbiota-dependent inflammation [6] . Considering that IFN-Is are essential components of the host antiviral response , we hypothesized that these molecules might also mediate changes in the intestinal microbiota during viral influenza infection . To study this , we infected wild-type ( WT ) and Ifnar1 knockout ( Ifnar1-/- ) mice by non-surgical intratracheal instillation [16 , 17] with a sublethal dose ( 200 infectious units ) of influenza A/Puerto Rico/8/34 ( PR8 ) . Mice were monitored daily for 17 days after infection . We assessed the microbiota composition in the fecal content of WT and Ifnar1-/- mice before PR8 or mock infection and at 9 day post infection ( dpi ) ( Fig 1A ) since the peak weight loss was observed at 9 dpi in WT and Ifnar1−/− mice . PR8 viral load was quantified after non-surgical intratracheal instillation at 1 dpi , and we detected live virus only in the lungs , neither in the colon content nor in the cecum tissue ( S1A and S1B Fig ) . MiSeq Illumina analysis of microbial DNA extracted from fecal samples confirmed observations reported by others [18] that the mouse intestinal microbiota , independent of the genotype , consists of two major bacterial phyla , the Bacteroidetes and the Firmicutes ( Fig 1B ) , with the most relevant classes being Bacteroidia and Clostridia ( Fig 1C ) . No statistical differences were found in the fecal microbiota composition between WT and Ifnar1-/- mice , either before infection at day 0 or after mock infection at day 9 . Moreover , we observed low abundance of Proteobacteria in the intestinal microbiota of the uninfected and mock-infected mice , previously reported by others [19] , independent of the mouse genotype ( Fig 1B ) . Furthermore , at day 9 post PR8 infection , Bacteroidetes and Firmicutes were still the most dominant colonizers in both mouse genotypes ( Fig 1B ) . Our findings , however , uncovered a significant blooming of Proteobacteria at day 9 after PR8 infection only in the WT mice , whereas no significant increase was noted in the Ifnar1-/- mice , irrespective of the infection ( Fig 1B ) . Indeed , while Proteobacteria represented 1% on average in uninfected and mock-infected mice , regardless of the genotype , they comprised approximately 15% of the total fecal microbiota in the PR8-infected WT mice ( p = 0 . 0340 One-Way ANOVA after Bonferroni correction ) ( Fig 1B ) . The most striking change in the fecal microbial community of WT mice after PR8 infection was the increased abundance of the genus Escherichia , being however mostly undetectable in uninfected and mock-infected mice of both genotypes ( p = 0 . 0011 One-Way ANOVA after Bonferroni correction ) ( Fig 1C ) . Overall , greater Proteobacteria colonization levels after influenza infection in WT mice were not caused by differences in Proteobacteria abundance between WT and Ifnar1-/- mice prior to PR8 infection . Moreover , the thriving of Proteobacteria after PR8 infection in the WT but not Ifnar1-/- mice supports our hypothesis that influenza virus is able to alter the intestinal microbiota , and that this action is dependent on IFN-Is . In addition , using 16S quantitative PCR ( qPCR ) analysis we confirmed a significant increase in Enterobacteriaceae in the stool samples of the PR8-infected WT mice , but not in the PR8-infected Ifnar1-/- mice ( Fig 1D ) , however no significant difference was found between WT and Ifnar1-/- mice at day 0 prior to infection ( Fig 1D ) . Furthermore , we detected a significant lower level of Segmented Filamentous Bacteria ( SFB ) in the stool samples of the PR8-infected WT mice compared to the uninfected WT mice ( S1C Fig ) . SFB are Clostridia-correlated bacteria closely attached to the intestinal epithelium , which are able to activate a range of host defenses , including the production of antimicrobials , development of Th17 cells and increased colonization resistance to the intestinal pathogen Citrobacter rodentium [9] . However , uninfected WT and Ifnar1-/- mice were found similarly colonized with SFB; furthermore , the SFB abundance did not significantly change in the Ifnar1-/- mice , despite PR8 infection ( S1C Fig ) . In summary , our findings indicate that differences in the fecal microbiota between WT and Ifnar1-/- mice prior to influenza infection are insufficient to explain the PR8-mediated changes in specific endogenous bacterial population in WT mice . Similar results , as observed with influenza , were obtained when synthetic stimulators of IFN-Is such as poly I:C ( pIC ) [20 , 21] were administered to WT and Ifnar1-/- mice by non-surgical intratracheal instillation at day 0 and at day 2 ( S1D Fig ) . Using 16S qPCR analysis we found a significant increase in Enterobacteriaceae at day 4 and day 5 in the fecal samples of the pIC-treated WT mice , but not in the pIC-treated Ifnar1-/- mice ( S1E Fig ) . However , lower level of SFB was found at day 4 only in pIC-treated WT mice , but not in the pIC-treated Ifnar1-/- mice ( S1F Fig ) . Collectively , our findings highlight a critical role of type I IFN-mediated signaling induced in the lungs during pulmonary influenza infection in predisposing the host to dysbiosis . Our analysis specifically demonstrates a flourishing of resident bacteria belonging to Proteobacteria pathobionts , and a depletion of a subset of indigenous SFB . Since we demonstrated that IFNAR1-mediated signaling increased the abundance of endogenous Enterobacteriaceae during influenza infection , we aimed to test whether they could similarly affect the growth of Salmonella Typhimurium ( S . Typhimurium ) , a leading cause of acute gastroenteritis and inflammatory diarrhea , using a mouse model of acute colitis . One of the hallmarks of S . Typhimurium virulence in mice is its systemic manifestations resembling typhoid fever; in the typhoid model no intestinal inflammation is observed , and subsequently Salmonella numbers in the colon content are low and extremely variable [22 , 23] . To achieve colitis , S . Typhimurium must be administered to mice pretreated with the antibiotic streptomycin , this results in its effective colonization of the intestinal lumen , followed by high density growth and mucosal inflammation [22] . In our colitis model , WT and Ifnar1-/- mice were treated with streptomycin 1 day prior to S . Typhimurium infection in order to achieve acute inflammation of the cecal mucosa . On day 0 , mice were first infected with a sublethal dose of PR8 virus or PBS by non-surgical intratracheal instillation , then secondarily infected by oral gavage with 107 CFU of S . Typhimurium or given LB medium alone at 5 dpi ( Fig 2A ) . Mice were monitored daily until euthanasia at 8 dpi . At that time point , WT mice infected with PR8 , followed by S . Typhimurium , referred to as “secondarily infected” , were noted to have significantly more weight loss than those infected only with S . Typhimurium ( Fig 2B ) , and no difference in weight loss between the two groups of Ifnar1-/- mice was detected at 8 dpi . The extent of the weight loss seen in secondarily infected WT mice at 8 dpi was also greater than in secondarily infected Ifnar1-/- group ( Fig 2B ) . Lung viral load measured by plaque assay ( Fig 2C ) and qPCR ( S2A Fig ) revealed no difference between the WT and Ifnar1-/- groups , implying that the influenza infection was not the direct cause of the weight loss . However , at 7 and 8 dpi , corresponding to 48 and 72 hours ( h ) post S . Typhimurium infection , respectively , we found a significant increase ( 12-fold and 18-fold , respectively ) in the S . Typhimurium burden in the colons of the WT mice previously infected with PR8 ( Fig 2D and 2E ) . In contrast , infection with PR8 did not enhance S . Typhimurium gut colonization in the Ifnar1-/- mice ( Fig 2D and 2E ) . Using 16S qPCR analysis , we also detected a significant increase in the Salmonella copy number in the colon content of secondarily infected WT mice at 8 dpi compared to the S . Typhimurium-only infected WT mice . No difference was detected in the Salmonella gene copies between secondarily infected Ifnar1-/- and S . Typhimurium-only infected Ifnar1-/- mice at 8 dpi ( Fig 2F ) . These results were confirmed by 16S-Denaturing Gradient Gel Electrophoresis ( DGGE ) analysis performed from the microbial DNA extracted from the colon content 8 dpi ( S2B Fig ) . Likewise , at 8 dpi , a significant increase was noted in the total Enterobacteriaceae copy number only in the colon content of secondarily infected WT mice , compared to the S . Typhimurium-only infected WT mice ( Fig 2I ) . We interpret this rise in total Enterobacteriaceae gene copies to represent an increase in the population of Salmonella , since a rise in other Enterobacteriaceae was not detected . This is in accordance with previous observations [24] showing no overgrowth of commensal Enterobacteriaceae , despite high levels of inflammation , in mice infected with S . Typhimurium . Finally , we enumerated S . Typhimurium in the mesenteric lymph nodes ( MLN ) and lungs of WT and Ifnar1-/- mice at 8 dpi . Similar to our findings regarding the colonic burden , prior infection with PR8 enhanced the ability of S . Typhimurium to disseminate to the MLN and the lungs in WT but not Ifnar1-/- mice ( Fig 2G and 2H ) . Indeed there was significantly less dissemination of S . Typhimurium overall in the Ifnar1-/- mice compared to WT mice ( Fig 2G and 2H ) . Similar results in S . Typhimurium intestinal colonization were obtained when mice were infected at day 5 with 103 CFU of S . Typhimurium , without streptomycin pretreatment ( typhoid model ) ( S2C and S2D Fig ) . Indeed , as expected , S . Typhimurium numbers in the colon content were low and highly variable in S . Typhimurium-only infected WT and Ifnar1-/- mice , whereas prior infection with PR8 was still able to increase S . Typhimurium gut colonization in WT , but not in Ifnar1-/- mice ( S2D Fig ) . Overall , these results further support our hypothesis that PR8 infection predisposes mice to secondary Salmonella infection in a IFN-I-dependent manner . We further tested whether pIC could elicit similar effects in our acute colitis mouse model as shown with influenza . WT and Ifnar1-/- mice were intraperitoneally ( i . p . ) injected with pIC or saline 1 day prior and 2 days after the oral gavage administration of 107 CFU of S . Typhimurium or LB alone ( S3A Fig ) . Alternatively , WT and Ifnar1-/- mice were treated with pIC through non-surgical intratracheal instillation 1 day prior and 1 day after the oral gavage administration of 107 CFU of S . Typhimurium ( S4A Fig ) . S . Typhimurium CFUs were measured in the colon content , MLN , lungs and spleen at 72 h post bacterial infection . We found that pIC treatment significantly increased the S . Typhimurium burden in the luminal colon of the WT mice , but not Ifnar1-/- mice ( S3B Fig and S4B Fig ) . Moreover , we found that pIC enhanced S . Typhimurium dissemination in the WT but not in the Ifnar1-/- group ( S3C–S3E Fig and S4C–S4E Fig ) . These findings imply that the pro-bacterial effects induced by pIC during S . Typhimurium infection are largely mediated by IFN-Is , and that these can potently enhance S . Typhimurium pathogenicity . In summary , our studies have consistently shown that IFN-Is confer a fitness advantage to Salmonella in colonizing the intestine and disseminating to systemic sites . We next investigated whether IFN-Is might augment Salmonella intestinal colonization and dissemination through the suppression of specific well-characterized antimicrobial genes . To this end , we analyzed the expression of the following: Ifnγ , the gene that is of pivotal importance in host defense against intramacrophage pathogens [25] , in Salmonella-induced colitis [26–28] and in the systemic control of Salmonella infections [29 , 30]; S100A9 , the gene that encodes one of the two subunits of calprotectin , an antimicrobial heterodimer that acts as a metal-sequestering protein , which can starve Salmonella and many other microorganisms of critical nutrients , such as zinc and manganese [24 , 31]; Lcn2 , the gene that encodes the antimicrobial peptide lipocalin-2 , which sequesters iron-laden siderophores to inhibit Enterobacteriaceae growth [32] . We and others had already noted that transcript levels of Ifnγ , S100A9 , and Lcn2 were increased in the ceca of WT streptomycin-pretreated mice during S . Typhimurium infection [24 , 33] . We next chose to compare transcript levels in WT and Ifnar1-/- mice , which were both previously PR8- or mock-infected , and secondarily infected with S . Typhimurium , following the infection model depicted in Fig 2A . At 8 dpi , cecal tissue was excised from the large intestines of WT and Ifnar1-/- mice , and transcription of the candidate antimicrobial genes was measured by qPCR . Although basal transcription of Ifnγ , S100A9 and Lcn2 was similar between mock-infected WT and mock-infected Ifnar1-/- mice ( Fig 3A , 3C and 3E ) , the transcription of Ifnγ and Lcn2 was significantly higher in the ceca of the Ifnar1-/- group , compared to the WT group , after infection with S . Typhimurium alone ( Fig 3A and 3E ) . Moreover , PR8 infection alone did not change the induction level of these genes in the ceca of WT and Ifnar1-/- mice ( Fig 3A , 3C and 3E ) . However , WT mice secondarily infected with S . Typhimurium had a significant reduction in the transcription levels of all three genes , especially S100A9 , compared to those only infected with S . Typhimurium ( Fig 3A , 3C and 3E ) . By contrast , Ifnar1-/- mice showed no difference in the transcript levels of these genes when comparing secondarily S . Typhimurium-infected mice with S . Typhimurium-only infected mice ( Fig 3A , 3C and 3E ) . Differences were also confirmed in WT mice at the protein level; Western Blot revealed drastically reduced production of S100A9 and Lcn2 in the ceca of the secondarily S . Typhimurium-infected mice compared to the S . Typhimurium-only infected mice ( Fig 3B and 3F ) . As expected , Ifnar1-/- groups showed no difference in the expression of these antimicrobial peptides when comparing the two infection groups ( Fig 3D and 3H ) . The concentration of Ifnγ in the serum of either WT and Ifnar1-/-mice infected only with PR8 or PBS did not rise above basal levels ( 36 pg/ml for WT-PR8 and 66 pg/ml for Ifnar1-/--PR8-infected groups; Fig 3G ) . Yet , the serum level of Ifnγ was found drastically reduced in the secondarily infected WT group , in comparison with the S . Typhimurium-only infected WT group; by contrast , no disparity was detected between the same groups in the Ifnar1-/- cohort ( Fig 3G ) . Similar effects were observed when the i . p . pIC model instead of the PR8 infection was employed ( S5 Fig ) . S100A9 and Lcn2 were both strongly inhibited at the transcript ( S5A and S5B Fig ) and protein level ( S5E and S5F Fig ) in the ceca of WT but not Ifnar1-/- mice following pIC treatment then S . Typhimurium infection . Cecal Ifnγ transcription levels were also reduced by pIC treatment in the WT mice infected with S . Typhimurium , but not in the S . Typhimurium-infected Ifnar1-/- mice ( S5C Fig ) . Moreover , pIC treatment dramatically lowered the Ifnγ serum level in the S . Typhimurium-infected WT mice , but not in the S . Typhimurium-infected Ifnar1-/- mice ( S5D Fig ) . All together , our findings demonstrate that IFNAR1-mediated signaling can inhibit the host antimicrobial response to Salmonella infection . To broaden our study of the effects IFN-Is have on the level of cytokine expression in an inflammatory setting such as Salmonella-induced colitis , we examined the expression of the pro-inflammatory genes Il6 and Cxcl2 , and the anti-inflammatory genes Il10 and Muc2 . IL-6 and CXCL2 play important roles in macrophage activation and neutrophil function and recruitment . IL-10 and MUC2 are considered essential immunoregulators in the intestinal tract . IL-10 mainly functions to dampen excessive inflammatory responses that risk damaging the host [34 , 35] . MUC2 is critical for colon protection , as Muc-2 deficient mice spontaneously develop colitis [36 , 37] . Their transcript level in the cecum was assessed in both WT and Ifnar1-/- mice at 8 dpi after PR8 or mock infection , and following a secondary S . Typhimurium infection . After S . Typhimurium infection , both pro-inflammatory genes were induced in the cecum of WT and Ifnar1-/- mice ( Fig 4A and 4C ) . Significantly higher transcription of Il6 was observed in S . Typhimurium-only infected Ifnar1-/- mice compared to S . Typhimurium-only infected WT mice ( Fig 4A ) . Interestingly , in WT but not Ifnar1-/- mice , the secondarily infected S . Typhimurium group showed significantly lower levels of pro-inflammatory gene transcription than the S . Typhimurium-only infected group ( Fig 4A and 4C ) . By contrast , PR8 infection enhanced cecal Il10 levels in an IFNAR1-dependent manner ( Fig 4B ) . Basal transcript expression of Il10 was overall similar in mock-infected WT and Ifnar1-/- mice . However , we observed a lower level of induction of Il10 in S . Typhimurium-only infected Ifnar1-/- mice compared to S . Typhimurium-only infected WT mice ( Fig 4B ) . We did not detect upregulation of Muc2 in S . Typhimurium-only infected WT or Ifnar1-/- mice , although our samples showed a high level of variability ( Fig 4D ) . However , in WT but not Ifnar1-/- mice , PR8 infection enhanced cecal Muc2 levels . Furthermore , we observed an approximately 10-fold upregulation of Muc2 in the secondarily infected WT group compared to the S . Typhimurium-only infected WT group ( Fig 4D ) . To further study the contribution of IFN-Is in the modulation of the intestinal host response during Salmonella infection , we examined the transcription of Ifnβ and Ifnα4 in both the lungs and cecum of mice infected with PR8 . We found their induction in the lungs but not in the cecum . We also examined the transcription level of Cxcl10 and Mx1 , ISGs strongly induced by IFN-Is . Strikingly , we observed approximately 10-fold upregulation of Cxcl10 in the cecum of PR8-infected WT mice compared to mock-infected WT mice . In contrast , no difference was detected between Ifnar1-/- groups ( Fig 4E ) . Moreover , in WT but not Ifnar1-/- mice secondarily-infected with S . Typhimurium , the level of induction of Cxcl10 in the cecum was approximately 4-fold higher than S . Typhimurium-only infected mice ( Fig 4E ) . Similarly , the cecal expression of Mx1 was largely upregulated after PR8 infection in the WT mice , but not in the Ifnar1-/- mice , and as anticipated the level of induction of Mx1 tended to be lower overall in the Ifnar1-/- mice compared to WT mice ( Fig 4F ) . Remarkably , we observed a 12-fold upregulation of Mx1 in the secondarily infected WT mice compared to the secondarily infected Ifnar1-/- mice ( Fig 4F ) . In conclusion , the dissimilar regulation of ISGs in the cecum of WT and Ifnar1-/- mice after PR8 infection suggest that type I IFN-mediated signaling significantly contributes to the host response against S . Typhimurium infection . Histopathology from the cecum extracted at 8 dpi illustrates that WT and Ifnar1-/- mice develop severe inflammation 72 h after infection with S . Typhimurium , whereas no abnormalities are seen after PR8 or mock infection . However , WT mice infected with PR8 followed by S . Typhimurium infection had reduced inflammation compared to WT mice infected with S . Typhimurium alone ( Fig 5A ) . Indeed , the mucosal integrity ( assessed by cryptitis and epithelial erosions ) , inflammatory cell infiltration and submucosal edema were exacerbated in the S . Typhimurium-only infected WT group compared to secondarily infected WT group ( Fig 5B and 5C ) . However , no difference in the histopathology was noted between S . Typhimurium-only infected Ifnar1-/- group compared to secondarily infected Ifnar1-/-group ( Fig 5A , 5C and 5D ) . Similar differences in the cecal inflammatory score detected by histopathology were noted when pretreating WT mice with pIC before S . Typhimurium infection ( S6A–S6C Fig and S7A–S7C Fig ) . However , pIC did not reduce the inflammatory score in the cecum of S . Typhimurium-infected Ifnar1-/- mice ( S6A , S6C and S6D Fig; S7A , S7C and S7D Fig ) . To summarize , during S . Typhimurium-induced colitis , prior infection with influenza alters gut immune response promoting anti-inflammatory cytokines and reducing pro-inflammatory cytokines through an IFNAR1-dependent mechanism . The end result is a decrease , mediated by IFN-Is , of the intestinal tissue damage in mice that were first infected with influenza prior to S . Typhimurium infection .
IFN-Is are primarily considered to be antiviral and immunomodulatory cytokines [38]; their effects during bacterial infection are still controversial . Although IFN-Is have been shown to protect against and limit infection with certain bacterial pathogens [39 , 40] , they can also impair the clearance of others [41 , 42] . This suggests a complex and bacterium-specific mechanism of action . Influenza virus is a major cause of respiratory illness in humans , with the potential to cause lung damage and sensitize the host to secondary pulmonary infections [1] . Although gastroenteritis symptoms have been reported during infection , the mechanism through which influenza virus would affect the gut is not completely clear . Our studies have shown that influenza pulmonary infection has an effect on the mouse fecal microbiota and promotes secondary infection with the intestinal pathogen , S . Typhimurium . Importantly , we have shown that these effects are dependent on IFN-Is , which are induced in the lungs during influenza pulmonary infection . These in turn can alter the gut microbial composition and suppress host immunity to a secondary Salmonella intestinal infection . In line with a previous study [6] , we found that PR8 infection and poly I:C treatment increased the relative abundance of Enterobacteriaceae and decreased the number of SFB in the fecal content of WT mice . However , we found that the relative abundance of these bacterial groups remained unchanged after PR8 infection or poly I:C treatment in mice deficient in IFN-I signaling , demonstrating that IFN-Is play an essential role in regulating their numbers . Based on these results and consistent with previous observations [43] , we speculate that IFN-Is might regulate the populations of the major bacterial phyla within the intestinal tract . Moreover , our analysis showed that only particular members of the fecal microbiota were affected by PR8-induced IFN-Is . While anaerobes , such as Bacteroidetes and Firmicutes , make up the majority of the healthy microbiota , IFN-Is released during influenza infection promote the blooming of indigenous Proteobacteria pathobionts to the detriment of restricted anaerobic commensals , leading to significant intestinal disbyosis . This is in accordance with previous findings that show that bacterial imbalance characterized by an enrichment of Proteobacteria is observed during intestinal inflammatory disorders in humans , including Crohn's disease [44] and enteropathy in human immunodeficiency virus ( HIV ) -infected subjects [45] . In addition , we show that influenza- and poly I:C-induced IFN-Is promote the intestinal overgrowth of the bacterial pathogen S . Typhimurium , which is an important component of Enterobacteriaceae and clinically associated with severe gastroenteritis and inflammatory diarrhea in humans [46] . Furthermore , we showed that Ifnar1-/- mice were more resistant to weight loss during S . Typhimurium secondary infection than WT mice , and allowed less translocation of S . Typhimurium across the intestinal wall . Not surprisingly , we also reported an increased resistance in Ifnar1-/- mice against single S . Typhimurium infection , as indicated by reduced weight loss and bacterial dissemination , as well as higher induction of specific antibacterial genes , compared to S . Typhimurium-only infected WT mice . This is in agreement with previous reports where S . Typhimurium has been shown to induce the expression of IFN-Is by macrophages [47] , along with improved host survival and enhanced control of S . Typhimurium in Ifnar1-/- mice in a necroptosis-dependent mechanism [48] . While there were differences between S . Typhimurium-only infected WT and Ifnar1-/- mice , these differences were strongly exacerbated when mice were previously infected with PR8 , indicating the contribution of IFN-Is produced during influenza infection to secondary S . Typhimurium infection . Considering that the number of S . Typhimurium that disseminated systemically was greatly increased following influenza infection in WT , but not in Ifnar1-/- mice , this suggests that IFN-Is may potentially relax the intestinal barrier to allow for Salmonella systemic dissemination . In support of this , we found that the host inflammatory and antimicrobial responses against S . Typhimurium were reduced in the intestine after influenza infection in a mechanism dependent on IFNAR . Host inflammatory responses are essential to keep Salmonella localized to the gut and to limit the systemic spread of the pathogen . The immunosuppression mediated by IFN-Is would diminish local host surveillance , resulting in poor control of Salmonella in the gut and enhanced bacterial dissemination . We demonstrated that the induction of IFN-γ was decreased at the transcriptional level in the inflamed cecum and at the protein level systemically in an IFN-I-dependent manner . This is important given the key role IFN-γ plays in intestinal antibacterial immunity , host survival and resolution of Salmonella infection [49] . The host inflammatory response against Salmonella in the gut [50] includes the synthesis of antimicrobial peptides , some of which may possess a secondary function as regulatory molecules [51] . Two critical components of the mammalian nutritional immune response against S . Typhimurium are lipocalin-2 and calprotectin , which are both highly induced by this pathogen in the inflamed gut . Lipocalin-2 and calprotectin sequester essential nutrients from microorganisms and exert an antimicrobial effect against several bacteria , including intestinal commensals . Both antimicrobials were dramatically suppressed in the inflamed gut after PR8 infection in a mechanism that depended on IFNAR signaling . Paradoxically , we know that the metal deficiency induced by high levels of both antimicrobials can be evaded by Salmonella through expression of high-affinity metal transporters [24 , 52] . However , metal starvation may still have a critical defensive role against Salmonella by forcing infected host cells in the local microenvironment to undergo apoptosis [53 , 54] , thereby destroying the internalized Salmonella . Furthermore , both antimicrobial peptides act as crucial paracrine chemoattractants to recruit neutrophils [55 , 56] , which play a major role in preventing systemic dissemination of S . Typhimurium , as suggested by clinical and experimental data [57] . Additionally , the induction in the gut of anti-inflammatory Il10 after influenza infection might , not only inhibit the bactericidal response of macrophages , but also cause infected macrophages to function as hosts for bacterial replication , as previously shown [58] . IL-10 has been shown to be important in the pathogenesis of Salmonella infection and regulation of subsequent host immune responses . IL-10 levels are elevated in susceptible strains of mice [59] suggesting that those strains producing IL-10 at high levels cannot adequately control Salmonella infection . Moreover , the relevance of the anti-inflammatory role mediated by IFN-Is in the gut during Salmonella infection was confirmed by histopathology , which underscores the concept of the double-edged sword IFN-Is represent during secondary intestinal infections . Although IFN-I-mediated effects promote Salmonella intestinal colonization and systemic dissemination , they also limit the damage triggered by exacerbated inflammation induced by Salmonella infection . Furthermore , several clinical trials could not completely define the therapeutic effects of IFN-Is in patients with ulcerative colitis , an IBD with a complex etiology that includes genetic and environmental factors leading to chronic inflammatory responses against the gut microbiota [60 , 61] . This raises important questions about the potential mechanisms of action of IFN-Is in IBDs such as ulcerative colitis . Notably , IFNβ therapy markedly attenuates the course and severity of disorders such as Multiple Sclerosis ( MS ) . Indeed , in vitro studies previously showed that IFNβ induced the release of the anti-inflammatory cytokine IL-10 from lymphocytes acquired from patients with MS [62 , 63] , which might indicate that IFNβ could eventually induce an anti-inflammatory response in the colonic mucosa as well . Many investigators have reported a variety of both beneficial and detrimental immune functions for IFN-Is during bacterial infections , and this clearly expands the old misguided notion that IFN-Is serve “only” as antiviral cytokines . Specifically , we have examined the effects of IFN-Is induced by pulmonary influenza infection in intestinal bacterial homeostasis and during secondary enteric infection . We propose that during influenza infection , this family of cytokines alters the intestinal microbiota composition , leading to an overgrowth of pathobionts which puts the host at risk to develop intestinal bacterial disorders . This is particularly significant as IFN-Is are currently being used as anti-inflammatory therapies for several immunological disorders such as IBD and MS . Furthermore , we propose that influenza-induced IFN-Is enhance susceptibility to Salmonella intestinal colonization and dissemination during secondary Salmonella-induced colitis through suppression of host intestinal immunity . Our work highlights the critical importance of further studies that clarify the roles and effects IFN-Is play in balancing host susceptibility to bacterial infection and inflammatory control , as well as the potential risk associated with influenza infection in predisponing the host to Salmonella infections and intestinal disorders .
IR715 is a fully virulent , nalidixic acid-resistant derivative of Salmonella enterica serotype Typhimurium WT isolate ATCC 14028 [64] . The strain was cultured aerobically in Luria Bertani ( LB ) broth at 37°C . Bacterial strains and plasmids used in this study are listed in S1 Table . Carbenicillin was added to final 100 mg/L as needed . To render all strains equally resistant to streptomycin , pHP45omega plasmid [65] was introduced by electroporation . 7–9 week old mice with a C57BL/6J genetic background were used in all experiments . Ifnar1−/−mice were generated as previously reported [42] . The same number of males and females were used in each treatment group . Colonies of Ifnar1−/− and WT mice were maintained and housed in the same pathogen-free facilities at UCLA . The mouse studies described in this manuscript were performed under the written approval of the UCLA Animal Research Committee ( ARC ) in accordance to all federal , state , and local guidelines . All studies were carried out under strict accordance to the guidelines in The Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and the accreditation and guidelines of the Association for the Assessment and Accreditation of Laboratory Animal Care ( AALAC ) International under UCLA OARC Protocol Number 2009-012-21 . Mice were infected with a sublethal dose ( 200 PFU ) of the mouse-adapted influenza A/Puerto Rico/8/34 ( PR8 ) virus strain by non-surgical intratracheal instillation [66] . Briefly , mice were anesthetized with a mixture of ketamine and xylazine ( 100 mg/10 mg/kg ) , suspended by an incisor wire on an angled stand , and then a fixed volume containing 200 PFU of PR8 in pharmaceutical grade PBS was instilled inside the trachea [66] . Following aspiration of the inoculum into the lungs , mice were maintained on warming pads and monitored until completely ambulatory . Mice were monitored daily and weight was recorded . On day 1 , 8 or 17 after PR8 ( or mock ) infection , mice were euthanized by CO2 asphyxiation . Lungs were excised , lobes were separated and placed in a 2 mL FastPrep homogenization tube containing lysing matrix D . Sterile DPBS was added to give a final 20% w/v suspension and homogenized using a FastPrep-24 Instrument , 6 m/s , 45 s ( MP Biomedicals , Santa Ana , CA ) . Lung homogenate was diluted 1:10 in TRIzol Reagent ( Life Technologies , Grand Island , NY ) for analysis of gene expression , or serially diluted with DPBS for quantification of bacterial burden by CFU analysis and viral titer by plaque assay . Viral titer was determined by plating a monolayer of MDCK cells in 6-well tissue culture treated plates , then incubating with lung homogenate , serially diluted in virus dilution buffer ( PBS with 1% Pencillin/Streptomycin , 0 . 2% BSA , 0 . 005% DEAE Dextran , 1X CaCl2/MgCl2 ) , for 1 h at 37°C . Extracellular virus was removed by gently washing the monolayer , then an overlay containing 2% low melting point agarose in virus growth medium ( MEM containing BME vitamins , 10 mM HEPES , 1% Pencillin/Streptomycin , 0 . 15% NaHCO3 , 0 . 2% BSA , 0 . 0015% DEAE Dextran , 0 . 7 mg/ml TPCK-treated Trypsin ) was applied and plates were incubated for 2 days at 37°C . The overlay was gently aspirated , then the plates were incubated with 0 . 3% crystal violet in 20% ethanol and plaque forming units ( PFU ) were enumerated . Mice were administered 200 PFU of PR8 virus or PBS through non-surgical intratracheal instillation [66] . Mice were monitored daily at the same time until day 17 , when all the mice were euthanized . Fecal samples were collected from WT and Ifnar1−/− mice before PR8 or mock infection on day 0 and at 9 dpi , then snap frozen in liquid nitrogen . The fecal DNA was subsequently extracted using the QIAamp DNA stool kit ( Qiagen ) , according to the manufacturer’s instructions . The fecal microbial DNA was used for 16S quantitative real-time PCR ( qPCR ) analysis and for Illumina MiSeq analysis . Two μl of extracted fecal bacterial DNA was used as a template for 16S qPCR reaction with the primer pairs previous developed and presented in S2 Table . The 16S gene copy numbers per μl of DNA from each sample ( one fecal pellet collected from each mouse ) was determined using the plasmids described in S1 Table . For MiSeq analysis , bacterial DNA was amplified by a two-step PCR enrichment of the 16S rDNA ( V4 region ) encoding sequences from each sample with primers 515F and 806R modified by addition of barcodes for multiplexing . Libraries were sequenced using an Illumina MiSeq system . Following quality filtering , the sequences were demultiplexed and trimmed before performing sequence alignments , identification of operational taxonomic units ( OTU ) , clustering , and phylogenetic analysis using QIIME open-source software ( http://qiime . org ) . Mice were infected on day 0 with 200 PFU of PR8 influenza strain or PBS through non-surgical intratracheal instillation [66] . WT and Ifnar1-/- mice were orally gavaged with 0 . 1 ml of a 200 mg/ml streptomycin/sterile water solution on day 4 , prior to mock infection in LB or oral infection with 1×107 CFU of S . Typhimurium in LB on day 5 . Colon content was collected at 48 h postbacterial infection , weighed , homogenized in 1 ml of sterile PBS , serial diluted and plated on LB agar containing appropriate antibiotics . At 72 h post-bacterial infection the cecum was harvested for mRNA , protein , and histopathology . The colon contents , spleen , lungs and mesenteric lymph nodes ( MLN ) were collected , serially diluted , and plated on appropriate antibiotic LB agar plates to determine bacterial counts . Lungs were harvested for mRNA isolation and plaque assay . Blood was collected by cardiac puncture , allowed to clot at room temperature; serum was isolated by centrifugation , transferred to a sterile tube and stored at -80°C until ELISA cytokine analysis . Groups of 4–6 mice were used for each experiment . Mouse weight was taken daily until euthanasia . For the typhoid model , WT and Ifnar1-/- mice were infected on day 0 with 200 PFU of PR8 influenza strain or PBS through non-surgical intratracheal instillation [66] . Mice were gavaged with 1×103 CFU of S . Typhimurium in LB , without streptomycin pre-treatment , on day 5 . The colon contents were collected at 72h post bacterial infection , serially diluted , and plated on appropriate antibiotic LB agar plates to determine bacterial counts . Polyinosinic polycytidylic acid ( #tlrl-pic ) was purchased from Invivogen ( San Diego , CA ) . Mice were administered 50 μg of pIC or saline through non-surgical intratracheal instillation [66] on day 0 and on day 2 . Fecal samples were collected from WT and Ifnar1−/− mice on day 0 before treatment and on day 4 and day 5 , then snap frozen in liquid nitrogen . The fecal DNA was subsequently extracted using the QIAamp DNA stool kit ( Qiagen ) , according to the manufacturer’s instructions . The fecal microbial DNA was used for 16S qPCR analysis , as described above , and the copy numbers’ fold increase from each mock and pIC-treated sample ( one fecal pellet collected from each mouse ) on day 4 and day 5 over the baseline before treatment on day 0 were calculated . In the i . p . pIC model , WT and Ifnar1-/- mice were intraperitoneal injected with 150 μg of pIC or saline on day -1 , and intragastrically treated with streptomycin ( 0 . 1 ml of a 200 mg/ml solution in sterile water ) [22] on day -1 . Alternatively , in the non-surgical intratracheal instillation pIC model , WT and Ifnar1-/- mice were injected with 50 μg of pIC or saline on day -1 , and intragastrically treated with streptomycin ( 0 . 1 ml of a 200 mg/ml solution in sterile water ) [22] on day -1 . In both models , mice were then orally gavaged with a dose of 107 CFU of S . Typhimurium in 0 . 1 ml of LB on day 0 or mock-infected . A booster dose of 100 μg or 50 μg of pIC was administered on day 2 or on day 1 in the i . p pIC or in the non-surgical intratracheal instillation pIC models , respectively . On day 3 , corresponding to 72 h post S . Typhimurium infection , mice were euthanized; the cecum was collected for mRNA and protein isolation and also for histopathological analysis . Serum was separated from blood and collected for ELISA cytokine detection . CFU was enumerated from homogenates of colon content , MLN , lungs and spleen serially diluted and plated on agar plates containing the appropriate antibiotic selection . Total RNA was extracted from mouse cecal tissue with TRIzol Reagent ( Life Technologies ) . Reverse transcription of 1 μg of total RNA was performed with the iScript cDNA Synthesis kit ( Bio-Rad ) . qPCR was performed using iTaq Universal Sybr Green Supermix ( Bio-Rad ) . For analysis , target gene expression of each sample was normalized to the respective level of L32 mRNA . Fold changes in gene expression values were then calculated using the mean from the control samples as a baseline and determined using the ΔΔ Ct method . A list of qPCR primers used in this study is provided in S2 Table . See also S1 References . Mouse IFNγ ELISA Ready-SET-Go ! was purchased from eBioscience . Cytokine serum levels were measured according to the manufacturer’s instructions . Total protein was extracted from mouse cecum tissue using TRIzol Reagent ( Life Technologies , Grand Island , NY ) . 15 μg of total protein was resolved using 12 . 5% SDS-PAGE gels and transferred to PVDF membranes . The membranes were blocked with 2% nonfat dried milk and incubated at 4°C with primary antibodies . Detection of mouse HSP90 α/β was performed with primary rabbit polyclonal antibodies ( Santa Cruz Biotechnology ) , while detection of S100A9 was performed with polyclonal goat anti-mouse S100A9 ( R&D Systems ) . Lcn-2 was detected by polyclonal goat anti-mouse Lcn2 ( R&D Systems ) . After overnight incubation at 4°C , the blots were washed and then incubated for 1 h at room temperature with secondary goat anti-rabbit and donkey anti-goat antibodies conjugate to horseradish peroxidase ( HRP ) ( Southern Biotech and Santa Cruz Biotechnology , respectively ) . After washing , bands were developed using the SuperSignal West Pico Chemiluminescent Sustrate ( Thermo Scientific ) per manufacturer’s instructions and visualized using Gel Doc ( BioRad ) . Total genomic bacterial DNA was isolated using the MasterPure DNA purification kit ( Epicentre ) . DNA quality and quantity were determined with a Spectronic Genesys UV spectrophotometer at 260 nm and 280 nm ( Spectronic Instruments , Inc . Rochester , NY ) . Amplification of bacterial 16S rDNA was carried out by PCR as described previously [67] . Briefly , the universal primer set Bac1 and Bac2 [68] ( S2 Table ) was used to amplify an approximately 300-base-pair internal fragment of the 16S rDNA . Each 50 μl PCR contained 100 ng purified genomic DNA , 40 pmole each primer , 200 μM of each dNTP , 4 . 0 mM MgCl2 , 5 μl 10 X PCR buffer , and 2 . 5 U Taq DNA polymerase ( Invitrogen ) . Cycling conditions were 94°C for 3 min , followed by 30 cycles of 94°C for 1 min , 56°C for 1 min and 72°C for 30 s , with a final extension period of 5 min at 72°C . The resulting PCR products were evaluated by electrophoresis through 1 . 0% agarose . DGGE was performed by use of the Bio-Rad DCode System ( Hercules , CA , USA ) . A 40% to 60% linear DNA denaturing gradient ( 100% denaturant is equivalent to 7 M urea and 40% de-ionized formamide ) was formed in 8% ( w/v ) polyacrylamide gels . Approximately 300 ng PCR product was applied per lane . The gels were submerged in 1 X TAE buffer ( 40 mM Tris base , 40 mM glacial acetic acid , 1 mM EDTA ) and the PCR products were separated by electrophoresis for 17 h at 58°C using a fixed voltage of 60 V . After electrophoresis , the DNA bands were stained with 0 . 5 μg/ml ethidium bromide and DGGE profile images were digitally recorded using the Molecular Imager Gel Documentation system ( Bio-Rad ) . DIVERSITY DATABASE Software ( Bio-Rad ) was used to assess the change in the relative intensity of bands corresponding to bacterial species of interest . The DNA bands of interest were excised from the DGGE gels and transferred to a 1 . 5-ml microfuge tube containing 20 μl sterile ddH2O . Tubes were incubated at 4°C overnight before the recovered DNA samples were re-amplified with the universal primer set Bac1 and Bac2 . The PCR products were purified using the QIAquick PCR purification kit ( Qiagen ) and sequenced at the UCLA Core DNA Sequencing Facility . The sequences obtained were subjected to nucleotide BLAST searches against the NCBI ( http://blast . ncbi . nlm . nih . gov/ ) and Human Oral Microbiome ( http://www . homd . org/index . php ) databases . The differences between treatment groups were analyzed by non-parametric 2-tailed Mann-Whitney U test , non-parametric Kruskal-Wallis test ( Dunn's Multiple Comparison Test ) and One-Way Analysis of variance ( ANOVA ) with Bonferroni correction , as specified in each Fig legend . Data were expressed as the geometric mean or mean ± SEM , as indicated in each Fig legend , and the results were considered statistically significant when the p value was < 0 . 05 . All calculations were performed using GraphPad Software , unless indicated otherwise . Tissue samples were fixed in formalin for 24 h , processed according to standard procedures for paraffin embedding , sectioned at 4 mm , and stained with hematoxylin and eosin . The pathology score of cecal samples was determined by blinded examinations of cecal sections by a board-certified pathologist using previously published methods [22] . Each section was evaluated for the presence of neutrophils , mononuclear infiltrate , submucosal edema , epithelial erosions and cryptitis . Inflammatory changes were scored from 0 to 4 according to the following scale: 0 = none; 1 = low; 2 = moderate; 3 = high; 4 = extreme . The inflammation score was calculated as a sum of each parameter score and interpreted as follows: 0–3 = within normal limits; 4–8 = mild; 9–14 = moderate; 15–20 = severe . The mouse studies described in this manuscript were performed under the written approval of the UCLA Animal Research Committee ( ARC ) in accordance to all federal , state , and local guidelines . All studies were carried out under strict accordance to the guidelines in The Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and the accreditation and guidelines of the Association for the Assessment and Accreditation of Laboratory Animal Care ( AALAC ) International under UCLA OARC Protocol Number 2009-012-21 . Influenza infections were performed under ketamine/xylazine anesthesia and all efforts were made to minimize animal pain and discomfort . | Influenza is a respiratory illness . Symptoms of flu include fever , headache , extreme tiredness , dry cough , sore throat , runny or stuffy nose , and muscle aches . Some people , especially children , can have additional gastrointestinal symptoms , such as nausea , vomiting , and diarrhea . In humans , there is no evidence that the influenza virus replicates in the intestine . Using an influenza mouse model , we found that influenza infection alters the intestinal microbial community through a mechanism dependent on type I interferons induced in the pulmonary tract . Futhermore , we demonstrate that influenza-induced type I interferons increase the host susceptibility to Salmonella intestinal colonization and dissemination during secondary Salmonella-induced colitis through suppression of host intestinal immunity . |
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Trypanosoma brucei , the agents of African trypanosomiasis , undergo density-dependent differentiation in the mammalian bloodstream to prepare for transmission by tsetse flies . This involves the generation of cell-cycle arrested , quiescent , stumpy forms from proliferative slender forms . The signalling pathway responsible for the quorum sensing response has been catalogued using a genome-wide selective screen , providing a compendium of signalling protein kinases phosphatases , RNA binding proteins and hypothetical proteins . However , the ordering of these components is unknown . To piece together these components to provide a description of how stumpy formation arises we have used an extragenic suppression approach . This exploited a combinatorial gene knockout and overexpression strategy to assess whether the loss of developmental competence in null mutants of pathway components could be compensated by ectopic expression of other components . We have created null mutants for three genes in the stumpy induction factor signalling pathway ( RBP7 , YAK , MEKK1 ) and evaluated complementation by expression of RBP7 , NEK17 , PP1-6 , or inducible gene silencing of the proposed differentiation inhibitor TbTOR4 . This indicated that the signalling pathway is non-linear . Phosphoproteomic analysis focused on one pathway component , a putative MEKK , identified molecules with altered expression and phosphorylation profiles in MEKK1 null mutants , including another component in the pathway , NEK17 . Our data provide a first molecular dissection of multiple components in a signal transduction cascade in trypanosomes .
Cells respond to their external environment in order to regulate their proliferation , developmental fate , specialisation or death . This can be in response to environmental cues such as temperature , pH or light , or can be driven by chemical signals generated by other cells of the same species or from competing or co-operating cells occupying the same niche [1] . To respond to such signals , single-celled and multicellular organisms have evolved elaborate signalling pathways in which surface receptors often transduce a signal to protein kinases and protein phosphatases , these transduction cascades eventually driving changes in gene expression either in their nucleus , or by generating phenotypic responses through changes in the abundance or activity of mRNAs or proteins [2–4] . The organisation of these signalling cascades is relatively well conserved in overall structure in eukaryotic organisms , although the individual receptors and transducer kinases and phosphatases are different . In particular , the ability of cells to become quiescent through exiting the cell cycle is a central feature of eukaryotic life , enabling cells to withstand periods of nutrient restriction , or to prepare for cell differentiation [5] . Parasitic protozoa also respond to extracellular signals to regulate their proliferation , tropism and development to ensure their successful transmission to new hosts [6–8] . Among major pathogens such as malaria , many signalling proteins have been identified and individual components have been assigned functions in different cellular processes [9 , 10] . However , in most cases the interactions between different components of the signalling pathways are unknown and the connections between regulators of particular processes are not understood , preventing assembly of a coherent regulatory pathway despite high throughput mutant selection and analysis [11] . The ability to assemble these pathways can also be limited by the evolutionary divergence of protozoan pathogens such that the conventional framework established in the crown group eukaryotes may not apply [12 , 13] . In consequence , the environmentally regulated control of virulence , transmission competence , tissue tropism or metabolic adaptation is poorly understood in eukaryotic microbial pathogens . Environmental sensing has particular importance in the life cycle of Trypanosoma brucei in the bloodstream of their mammalian hosts . These parasites are responsible for African trypanosomiasis in humans and animals [14 , 15] and live extracellularly in the bloodstream , adipose tissue [16] and skin [17] of hosts where they evade immune destruction by a sophisticated antigenic variation process [18] . This exchange of antigen types during chronic infections contributes to the waves of parasitaemia that characterise infection with these parasites [19 , 20] . However , a further contributor is environmentally regulated , that being the development of the parasite in the mammalian bloodstream in preparation for its transmission by tsetse flies [21] . Specifically , as trypanosomes proliferate they generate a soluble factor , stumpy induction factor ( SIF ) , that accumulates as parasite numbers increase [22 , 23] . At a given density , the SIF signal stimulates the bloodstream parasites to exit their proliferative cell cycle and differentiate to morphologically stumpy forms that are adapted for transmission by tsetse flies [24] . Therefore , this signal-response pathway serves two purposes- it restricts proliferation of the parasite in the host , prolonging host survival , and it also optimises the parasite for its uptake by the disease vector [21] . The soluble signal driving parasite development is not identified despite many years of effort . However , the pathway that transduces the signal is quite well characterised at least in terms of its molecular composition . This is because the action of SIF can be mimicked in vitro by cell permeable cAMP analogues which cause cells to arrest but not undergo full development to stumpy forms [23 , 25] . This enabled us to carry out a genome-wide RNAi screen for molecules whose silencing renders parasites unresponsive to the SIF mimic signal [26] . Analysis of the genes contributing to resistance identified approximately 30 components and , for several of them , their involvement in physiological stumpy formation was confirmed in vivo . Thus , where cells silenced the expression of identified genes they became highly virulent in mice because they did not arrest with the accumulation of SIF . This validated the selectional screen and provided a large compendium of molecules linked to developmental signalling in the parasite . Furthermore , analysis of the molecules identified revealed that they fell into a potential hierarchy with signal processing molecules ( specific to cell permeable cAMP ) identified , as well as protein kinases and phosphatases and predicted post transcriptional gene expression regulators [24] . Complementing this screen for activators of stumpy formation , a number of inhibitors of stumpy formation have been described [27 , 28] , including a novel component of the TOR family whose gene silencing drives parasites to become arrested as stumpy like forms , although this was only tested in monomorphic parasites that are naturally incompetent to undergo this developmental transition [29] . Signalling may involve AMPK ( also identified in the genome-wide screen for positive regulators of stumpy formation ) , which becomes phosphorylated during the slender to stumpy differentiation and whose activity regulates quiescence [30] . Despite the identification of molecules linked to stumpy formation , their positioning with respect to one another is unknown . Here we have applied extragenic suppression as a strategy to dissect the quorum sensing signalling pathway in African trypanosomes . This has exploited our ability to create null mutants for pathway components that have lost the ability to respond to the SIF signal and to simultaneously express in the same cells other pathway components under doxycycline regulatable expression both in vitro and in vivo . This approach has determined the dependency relationships between distinct components of the pathway and the existence of complex non-linear relationships in the pathway . The further analysis of one of the null mutants in the signalling pathway has provided a molecular characterisation of the difference between differentiation competent cells and those lacking a central developmental signalling component .
To investigate the dependency relationships between identified components of the SIF signalling pathway in Trypanosoma brucei we used pairwise gene deletion and inducible overexpression to assess the ability of one gene to compensate for the loss of another ( Fig 1A ) . This required the creation of null mutants for the target genes because the kinetics of simultaneous inducible RNA interference and inducible ectopic expression complicate the interpretation of any resulting phenotypes . It also required the use of pleomorphic trypanosome lines since these are competent for developmental progression to stumpy forms in vivo , unlike monomorphic lines which are far more easily manipulated but developmentally incompetent . The creation of null mutants in T . brucei EATRO 1125 AnTat1 . 1 90:13 involved a sequential allelic replacement using nested integrative cassettes to replace each target gene copy in the trypanosome’s diploid genome . Thereafter , ectopic overexpression in the null mutant lines was achieved through integration of a construct allowing doxycycline inducible expression in vivo , this generating a protein with a TY epitope tag permitting its expression to be monitored ( Fig 1B ) . Each generated cell line was assessed in vivo for their ability to produce stumpy forms , this involving scoring the virulence of the parasites in mice , their accumulation as quiescent G1 arrested cells as parasite numbers increased ( indicated by an accumulation of cells with 1 kinetoplast and 1 nucleus ) , their morphological differentiation to stumpy forms and their expression of the stumpy form specific cell surface marker PAD1[31] . The ability of cells to generate procyclic parasites once harvested from infections and exposed to cis aconitate ( CA ) was also assessed , this confirming the developmental competence of the parasites: stumpy forms uniformly express the procyclic form surface marker EP procyclin within 2–4 hours of exposure to CA whereas slender forms either do not differentiate , or differentiate asynchronously and inefficiently over 24 hours [32] . In all cases , triplicate infections were carried out for both ‘induced’ and ‘non induced’ infection profiles . Our earlier RNAi analysis had indicated that the predicted RNA binding protein RBP7 ( encoded by two tandemly arranged gene copies , RBP7A and RBP7B ) was required for the sensitivity of trypanosomes to cell permeable cAMP and for the SIF-induced differentiation of pleomorphic trypanosomes to stumpy forms in vivo[26] . To confirm this , we initially created pleomorphic null mutants for RBP7AB using integrative constructs targeting the 5’UTR of RBP7A and the 3’UTR of RBP7B , the resulting cells being validated by genomic PCR amplification of a fragment of the coding regions using gene specific primers ( S1A Fig; other null mutants described in this study are validated in S1B and S1C Fig ) . Thereafter , the RBP7AB null mutants were assessed in vivo for their developmental capacity in parallel with wild type T . brucei EATRO 1125 AnTat1 . 1 90:13 which are differentiation competent . Fig 2A demonstrates that the null mutant parasites proliferated in vivo until infections were terminated on day 4 on humane grounds due to their rapidly ascending parasitaemia . In contrast , the parental control infection showed restricted growth from day 3 and began to develop morphological stumpy forms on day 4 . Consistent with this , the control cells showed a significantly higher proportion of parasites with a 1 kinetoplast 1 nucleus configuration of DNA containing organelles[33] reflecting G1 arrest ( 89%±0 . 9 vs . 69±2 . 3% 1K1N in the null mutant on day 4; p = 0 . 0014 , Fig 2B ) and , when harvested , expressed the stumpy form marker PAD1 on 72±8 . 5% of cells compared with 21±5 . 5% of the RBP7AB null mutant cells ( Fig 2E ) . Furthermore , upon incubation with 6mM CA , 6±3 . 8% of the RBP7AB null mutant parasites expressed EP procyclin after 4 hours , contrasting with 70±3 . 4% of the control cells ( Fig 2F ) . All of these parameters confirmed that RBP7AB gene deletion reduces the ability of the parasites to arrest as stumpy forms in vivo , matching our previous RNAi analysis[26] . However , whilst the inability to completely eliminate stumpy formation seen previously could have been attributable to incomplete gene silencing using RNAi lines , the use of null mutants demonstrated that RBP7AB loss reduced differentiation capacity , but did not completely ablate either stumpy formation or differentiation competence . Having established the effect of RBP7AB deletion on stumpy formation , we analysed the interaction between RBP7 and YAK ( Tb927 . 10 . 15020 ) , encoding a predicted protein kinase of the DYRK family . As with RBP7AB , our earlier analysis had demonstrated that YAK RNAi reduces stumpy formation [26]and consistent with this , a null mutant for YAK resulted in parasites that were virulent in vivo ( Fig 2C ) and showed reduced accumulation in the 1K1N configuration ( 74±1 . 2% vs . 89±0 . 96% in the control , p = 0 . 0008; Fig 2D ) . The cells also did not express PAD1 as assessed by flow cytometry ( 0 . 7±0 . 1% vs . 72±8 . 5%; Fig 2E ) and less than 1% of cells expressed EP procyclin 4 hours after exposure to CA ( 0 . 4±0 . 1%; Fig 2F ) . Hence , contrasting with RBP7AB null mutants , where inefficient differentiation was observed , YAK null mutants had very significantly abrogated their ability to differentiate to stumpy forms . The ability of RBP7B ectopic expression to restore differentiation competence to the RBP7AB and YAK null mutant was then examined . Initially , the consequences for induction of RBP7B ectopic expression were evaluated in vitro by growth analysis . S2A and S2B Fig , demonstrate that when RBP7B ectopic expression was induced in either the RBP7AB null mutant line , or the YAK null mutant line , growth was inhibited there being equivalent RBP7B expression in each mutant ( S2C Fig ) . This matched our prior demonstration that RBP7B ectopic expression in wild type T . brucei EATRO 1125 AnTat1 . 1 90:13 cells caused arrested growth in vitro and in vivo , the latter being accompanied by an accumulation of cells with 1K1N configuration and increased expression of EP procyclin when exposed to 6mM CA [26] . It also indicated that the ectopic expression of Ty-tagged RBP7B alone could restore differentiation in the RBP7AB null mutant . The consequences of RBP7B ectopic expression in the YAK knockout line were then assessed in vivo ( Fig 3A ) . Here , the uninduced parasites were highly virulent such that infections were humanely terminated as the parasitaemia ascended beyond 3x108 parasites/ml . In contrast , with RBP7B ectopic expression , the parasites showed slow growth reaching 1x108 parasites/ml on day 4 of infection and a small proportion of cells ( ~10% ) cells with an intermediate/stumpy morphology were observed ( Fig 3B ) . Furthermore , they accumulated with a 1K1N configuration , indicative of cell cycle arrest ( 86±1% induced vs . 74±5% uninduced on day 4 , p = 0 . 019; Fig 3C left panel ) . When PAD1 was examined , the capacity of some cells to express this marker was observed ( 9±1 . 4 induced vs . 0 . 3±0 . 1% uninduced on day 4 , p = 0 . 0029; Fig 3C right panel ) , this reflecting the proportion of morphologically stumpy forms seen in the population at this relatively early time point . Hence , RBP7B ectopic expression can restore growth arrest and the ability to undergo at least some stumpy transformation in YAK KO cells , indicating that RBP7B induced arrest and stumpy formation is not dependent on YAK . In addition to RBP7AB and YAK , genome-wide RNAi screens for resistance to the cell permeable cAMP analogue pCPTcAMP identified protein phosphatase 1 as potentially necessary for stumpy formation , this being confirmed in vivo by the simultaneous RNAi-mediated silencing of three PP1 genes ( PP1-4 , 5 , 6; respectively , Tb927 . 4 . 3640 , Tb927 . 4 . 3630 , Tb927 . 4 . 3620; >94 . 90% identity ) , whereupon stumpy formation was lost[26] . The creation of null mutants for the PP1-4 , 5 , 6 genes , individually or together , was not successful potentially due to the tandem arrangement of these genes . However , it was possible to explore the ability of PP1-6 ectopic expression to restore stumpy formation in the RBP7 and YAK null mutant lines evaluated above . Initially we investigated the consequences of PP1-6 ( Tb927 . 4 . 3620 ) ectopic expression in a wild type T . brucei EATRO 1125 AnTat1 . 1 90:13 background in vitro ( S3A Fig ) and in vivo ( Fig 4A ) . Here , induction of PP1-6 expression resulted in the rapid cessation of parasite growth ( Fig 4A , left ) and the appearance of morphologically stumpy forms in rodent infections , despite the low parasitaemia of the induced population . The induced parasites also showed an accumulation of cells with 1K1N configuration ( p<0 . 0001: S4A Fig ) and elevated expression of PAD1 ( Fig 4D ) . Their capacity for differentiation to procyclic forms in vitro as assessed by EP procyclin expression was also significantly enhanced ( p = 0 . 0002; Fig 4A , right ) . Having demonstrated that PP1-6 ectopic expression could precipitate premature differentiation to stumpy forms in a parental background , inducible expression of PP1-6 was tested in the RBP7AB and YAK null mutant lines . In an RBP7AB null mutant background , PP1-6 ectopic expression was accompanied by reduced parasitaemia ( Fig 4B ) and the appearance of morphologically intermediate-stumpy forms on day 4 of infection ( Fig 4C ) . This was matched by the expression of PAD1 ( Fig 4D ) , the accumulation of 1K1N cells ( 92±4% vs . 73±1%; p = 0 . 0095; S4B Fig ) , and increased capacity for differentiation to procyclic forms when exposed to CA ( 82±4% vs . 33±10% , p = 0 . 0114; Fig 4B , right ) . Hence , ectopic expression of PP1-6 restored differentiation competence to RBP7AB null mutants and demonstrated that PP1-6 induced differentiation was RBP7 independent . With a YAK null mutant background the picture was more complex . In vitro , reduced growth accompanied PP1-6 overexpression ( S3B Fig ) and in vivo , the induction of PP1-6 expression caused the parasitaemia to arrest ( Fig 5A ) although this was not accompanied by a large accumulation of 1K1N cells ( 65±2 . 3% -DOX vs . 73±1% +DOX; p = 0 . 029; ( S4C Fig ) ) nor effective expression of PAD1 ( 4±1% -DOX vs . 16±3% +DOX , p = 0 . 015 as determined by immunofluorescence; Fig 5B shows a western blot of PAD1 expression ) . Furthermore , the capacity of the cells to differentiate to procyclic forms was much less than when PP1-6 was expressed in the parental background ( 19±6 . 7% compared to 91±1 . 4% , p = 0 . 05; Figs 4A and 5A right panels ) . Also , when the morphology of parasites induced to ectopically express PP1-6 was examined in the YAK null mutant line , stumpy cells were not observed ( Fig 5C ) . Rather , the parasites remained slender but demonstrated aberrant nuclear morphologies , this appearing elongate and often curved or irregular in shape ( Fig 5C , lower DAPI stained samples ) . This morphological phenotype was quantitated using two descriptors- aspect ratio and solidity . The aspect ratio defines the length as a proportion of the width of the organelle , whereas the solidity measures the area of the nucleus divided by its convex area , providing a measure of how concave the nucleus was . An analysis of more than 100 cells ( +dox , 102; -dox , 145 ) demonstrated that the doxycyline treated cells induced to express PP1-6 in the YAK background showed a significantly increased aspect ratio and decreased solidity ( p<0 . 00005 ) ( Fig 5D ) . This nuclear phenotype was not present when PP1-6 was ectopically expressed in the parental cell line , indicating that it was generated only in the absence of YAK or with the high level of PP1-6 expression in that line . Overall this analysis demonstrated that PP1-6 can drive premature differentiation in parental cells and RBP7AB null mutants but not in the YAK null mutant , where aberrant cells were generated . This indicates that PP1-6 induced stumpy formation is RBP7AB independent and YAK dependent . In addition to YAK kinase , a member of the NEK kinase family was also identified in the screen for stumpy inducers , NEK17 [26 , 34] ) . NEK17 is encoded by a cluster of three almost identical genes ( Tb927 . 10 . 5950; Tb927 . 10 . 5940; Tb927 . 10 . 5930; e = 0 . 0 ) indistinguishable by RNAi . Therefore , the ability of one representative , Tb927 . 10 . 5950 to restore stumpy formation to the RBP7AB and YAK null mutants analysed above was explored by ectopic expression . In vitro , the inducible expression of NEK17 in the parental T . brucei EATRO 1125 AnTat1 . 1 90:13 background prevented population growth ( S5A Fig ) . When the same assay was carried out in the RBP7AB null mutant , however , the parasites grew as well as uninduced parasites ( S5B Fig ) despite the effective expression of NEK17 , which was at higher level than in the parental background ( S5D Fig , left panel ) . In contrast , when examined in a YAK null mutant background , NEK17 ectopic expression resulted in slowed parasite growth ( S5C Fig ) , and expression of the ectopically expressed protein was at the elevated levels seen in the RBP7AB null mutant ( S5D Fig , right panel ) . In addition to cell growth in vitro , the ability of NEK17 expression to drive physiological stumpy formation was analysed in mouse infections . As in culture , NEK17 overexpression in RBP7AB null mutants did not arrest growth in vivo; the parasites continued to proliferate , albeit more slowly ( Fig 6A ) , and remained slender in morphology ( Fig 6B ) despite effective expression of the protein ( Fig 6C ) . The cells also did not accumulate in a 1K1N cell cycle configuration ( Fig 6D ) nor express PAD1 at an elevated level ( Fig 6E ) . When NEK17 was expressed in a YAK KO background in vivo cells showed greatly reduced growth ( Fig 7A ) , as seen in vitro ( S5C Fig ) . However , this was not linked to G1 arrest; rather there was appearance of the aberrant cytological configuration 1K2N ( Fig 7B ) and PAD1 expression on some cells albeit at low frequency ( ~5% ) ( Fig 7C ) . Hence , in an RBP7AB null mutant background , neither arrest nor morphological change or PAD1 expression was seen , demonstrating arrest mediated by NEK ectopic expression is RBP7AB dependent . In contrast , NEK17 expression arrested growth independently of YAK but this caused only very limited development to stumpy forms and cell cycle abnormalities , demonstrating that effective NEK17 induced differentiation is also YAK dependent . So far , our studies had focused on interactions between different positive regulators of stumpy formation . However , inhibitors of stumpy formation have also been identified . Amongst these , TbTOR4 is a ‘slender retainer’[24] , whose depletion causes stumpy forms to appear in monomorphic populations which normally cannot undergo this development [29] . To explore whether the same effect was present in physiologically-relevant pleomorphic cells , we generated an inducible RNAi line able to reduce the expression of TbTOR4 . This line was then analysed in mice , where induction of TbTOR4 RNAi generated reduced growth over 6 days ( S6A Fig ) when the levels of TbTOR4 mRNA were reduced ( S6B Fig ) . This indicated that , as in monomorphs , TbTOR4 depletion arrested cells prematurely , albeit incompletely , perhaps related to the remaining TbTOR4 after RNAi induction . The effect of TbTOR4 is expected to be close to the top of a signal transduction pathway if organised similarly to other eukaryotes . Also expected to be close to the top of a conventional signalling pathway are MAP kinase kinase kinases ( MEKK ) and MAP kinase Kinases ( MEK ) . One such molecule was identified in the genome-wide screen for stumpy inducers , MEKK1 ( Tb927 . 2 . 2720 ) whose Leishmania major orthologue LmjF02 . 0570 is annotated as a Ste11/MEKK ( the T . brucei protein is also named FlaK [34] for its flagellar location; www . Tryptag . org ) . Therefore , we investigated the dependency , if any , between MEKK1 and TbTOR4 by creating a MEKK1 null mutant and then activating TbTOR4 RNAi in the same cell line . This was anticipated to determine whether the absence of MEKK1 overrides TbTOR4 silencing-induced arrest , or not . As expected , the MEKK1 null mutant was virulent in mice such that infections had to be terminated by day 5 ( Fig 8A , MEKK1 , middle panel ) , this being consistent with their incapacity to generate stumpy forms . However , when RNAi against TbTOR4 was induced in the MEKK1 null mutant background , the growth of the null mutant was reduced , indicating that TbTOR4 silencing overrides the virulence generated by MEKK1 deletion ( Fig 8A; MEKK1 KO TOR4 RNAi ) . This was supported when the accumulation of cells in a 1K1N configuration was examined ( Fig 8B ) . Thus , MEKK1 null mutants showed reduced levels of 1K1N cells compared to parental T . brucei EATRO 1125 AnTat1 . 1 90:13 cells ( Fig 8B centre ‘MEKK1KO’ ) , whereas the induction of TbTOR4 RNAi in the MEKK1 null mutant resulted in significantly more 1K1N cells on day 3 and day 4 of infection with respect to when TbTOR4 RNAi was not induced ( Fig 8B right ) . This demonstrated that TbTOR4 depletion results in the arrest of parasites independently of the presence of MEKK1 . Interestingly , however , when the expression of PAD1 was investigated , it was found that TbTOR4 RNAi in the MEKK1 null mutant resulted in much less PAD1 expression than when TbTOR4 was depleted in the presence of MEKK1 ( Fig 8C ) . Thus , on day 4 of infection , TbTOR4 RNAi in the parental cell line generated 38 . 4±7 . 6% PAD1+ve cells , whereas TbTOR4 RNAi in the MEKK null mutant at the same time point , few ( 11 . 2±2 . 2% ) cells were PAD1 +ve ( Fig 8C ) . A similar phenomenon was observed on day 5 of infection ( TbTOR4 RNAi induced in parental cells , 57 . 4±10 . 2%; TbTOR4 RNAi induced in the MEKK1 null mutant , 19 . 7±1 . 6% ) . Apparently therefore , TbTOR4 depletion causes the arrest of cells independently of the presence of MEKK1 , but MEKK1 is necessary for the efficient expression of the stumpy marker PAD1 . Since arrest precedes the expression of PAD1 protein in the development pathway that results in the formation of stumpy cells , this indicates that both TbTOR4 and MEKK1 are involved in cell cycle arrest , but that MEKK1 is required for efficient development to stumpy forms . Our genetic dissection above attempted to identify dependency relationships between different components of the differentiation control pathway . However , these studies do not inform on molecular interactions between the components , nor distinguish direct or indirect molecular consequences of the perturbations generated . To explore molecular changes associated with a loss of developmental competence in slender forms we analysed the phosphoproteomic changes that accompany deletion of MEKK1 as this was expected to operate early in the signalling pathway . Hence , parental T . brucei EATRO 1125 AnTat 1 . 1 90:13 and the MEKK1 null mutant cells were cultured in duplicate at equivalent cell density in vitro and protein extracted and analysed after isobaric tandem mass tagging ( Fig 9A ) . In total 4767 unique proteins and 2499 unique phosphoproteins were identified; correlations between the replicates were >99% at the peptide level , and at the phosphopeptide level were 0 . 91 ( T . brucei EATRO 1125 AnTat1 . 1 90:13 ) and 0 . 88 ( MEKK1 null mutant ) respectively , demonstrating excellent reproducibility ( Fig 9B ) . The datasets ( S1 Table ) were then analysed for peptides with >1 . 5-fold changes in phosphorylation , with an adjusted P value of <0 . 05 , regardless of the direction of change ( i . e . more phosphorylated or less phosphorylated in the MEKK1 null mutant compared to T . brucei EATRO 1125 AnTat1 . 1 90:13 cells ) ( Fig 9C ) . This identified 19 proteins , of which 17 were 1 . 5-fold less phosphorylated in the null mutant and 2 were 1 . 5-fold more phosphorylated . Most interestingly , in the MEKK1 null mutant we observed significantly reduced phosphorylation of peptides derived from another component of the quorum sensing signalling pathway , NEK 17 ( Log2–1 . 68; adj P = 0 . 0128 ) . Further , analysis of the phosphosite change revealed that NEK 17 was less phosphorylated in the absence of MEKK1 at the activation loop threonine 195 ( S7 Fig ) , consistent with reduced kinase activity when cells are less able to generate stumpy forms . Although indirect phosphorylation through other molecular components of the pathway is possible , this invokes the potential for MEKK1 to directly phosphorylate and activate NEK17 in the trypanosome quorum sensing signalling pathway . Other proteins with reduced phosphorylation in the absence of MEKK1 included the kinetoplastid kinetochore protein KKT4 and RNA regulators ALPH1 and RBP31 .
Genome-wide RNAi screens identify the genes whose silencing renders parasites resistant to an imposed selection[35] . This approach was applied to identify genes that confer resistance to cell permeable cAMP , acting as an in vitro proxy for SIF-induced differentiation in vivo[26] . The resulting screen identified approximately 30 genes , many of which were subsequently validated in individual RNAi lines , and tested for their inability to undergo natural stumpy formation in vivo . This confirmed the involvement of many of the hits from the original selection as molecules linked to developmental competence in bloodstream form trypanosomes . However , whilst the list of genes identified suggested the existence of a potential signalling pathway , the ordering and interactions between components of the pathway could not be assumed . Furthermore , it was unclear whether resistance to cell permeable cAMP was enacted through a simple processional linear pathway or whether there was more complexity . One established approach to address this question in conventional genetic systems has been to explore the ability of one molecular mutant to suppress a second mutant and thereby gain understanding of their relative positioning with respect to one another; this also often highlights direct molecular interactions between the respective molecules[36–38] . An alternative approach , epistasis or extragenic suppression , can use the ectopic expression of a wild type or mutant protein to restore a phenotype lost by mutation of another component in a pathway . This also orders the molecules with respect to one another , but does not necessarily imply direct molecular interaction[37] . Here we have used extragenic suppression and phosphoproteomic analysis of a null mutant for a signalling component to dissect the pathway responsible for the generation of stumpy forms in response to the quorum sensing signal , stumpy induction factor , and assigned pairwise dependency relationships between several molecules involved in the process . The analysis of gene function in trypanosomes has been enormously assisted by the use of inducible gene silencing via RNA interference[39] . However , this has limitations where gene silencing is incomplete , and because the kinetics of gene silencing are such that loss of protein function might not be observed for over 24 hours . These limitations thwarted our early attempts to understand molecular ordering in the stumpy formation pathway , since the ectopic expression of rescue molecules occurred more rapidly than the effective depletion of the partner molecule . Although this issue is now less problematic due to the development of alternative inducible systems based on vanillic acid [40] or cumate [41] control , such that independent gene silencing and ectopic expression can be activated , null mutants provide the best background for complementation studies despite the technical challenge associated with this in pleomorphic cell lines . This was highlighted in our analysis of the differentiation capacity of RBP7AB null mutants that retained the capacity for expression of PAD1 in a small proportion of cells , and these were able to differentiate to procyclic forms albeit inefficiently . With the creation of null mutants , low level protein remaining after incomplete gene silencing by RNAi can be eliminated as an explanation for this inefficient differentiation and it is clear that loss of these molecules does not completely eliminate differentiation capacity . The importance of the use of null mutants for the interpretation of pathway dependencies has been well recognised in the genetic dissection of cell lineage determination in C . elegans[37] . Analysis of the combination of gene knock out and ectopic expression for several components identified from our RNAi screen highlighted potential features of the control pathway ( Fig 10A ) . Thus , both RBP7AB and YAK knockout lines prevented efficient development to stumpy forms , and this was restored when RBP7B was ectopically expressed in either line . This confirms the involvement of both genes in the stumpy formation in vivo and potentially places RBP7B downstream of YAK , an order that might be anticipated given the predicted role of RBP7B as a gene expression regulator[24 , 42] . However , when PP1-6 was ectopically expressed in the same null mutant lines , stumpy formation was observed for the RBP7AB null mutant indicating that PP1-6 can promote stumpy formation independently of the presence of RBP7AB . For YAK , the picture was more complex . Although growth inhibition was observed in vitro , analysis of PPI-6 expression in the YAK KO line in vivo highlighted that growth retardation was not accompanied by the hallmarks of stumpy formation: the cells did not activate the expression of PAD1 , nor were they capable of efficient differentiation to procyclic forms . Instead , the parasites exhibited a slender morphology and cytological abnormalities , with the nucleus becoming elongate and misshapen . Interestingly , this phenotype was not seen when PP1-6 was expressed in a parental background or the RBP7AB null mutant but was restricted to the YAK null mutant . The trypanosome genome contains 7 PP1 genes , of which a cluster of 3 genes , indistinguishable by RNAi , were identified in the screen for regulators of stumpy formation ( PP1-4 , PP1-5 , PP1-6 ) [26] . The predicted encoded proteins share 95–99% identity . RNAi silencing of all PP1 genes in procyclic forms generates slow growth ( 40% of control growth ) and an accumulation of mitotic and post mitotic cells[43] , whereas specifically depleting PP1-1 to PP1-3 in procyclic forms generates altered positioning of the nucleus and kinetoplast[44] . Okadaic acid treatment ( inhibiting PP1 and PP2 activities in vitro ) also caused an accumulation of multinucleate cells[45] . Hence , the phenotype observed with PP1-6 expression in the YAK null mutant background is similar to organellar defects seen with other perturbations of PP1 genes , and may represent activity of PP1-6 against inappropriate targets in the absence of YAK . Contrasting with PP1-6 , the slow growth and differentiation phenotype induced by NEK17 ectopic expression was RBP7AB dependent , being absent in the RBP7AB null mutant . NEK17 is a member of the expanded NEK family in T . brucei which includes NEK12 . 2 ( RDK2 ) , that has been proposed to act as a negative regulator of differentiation to procyclic forms such that its depletion precipitates procyclic formation[34] . When ectopically expressed in a YAK null mutant , NEK17 inhibited the growth of parasites but only a few stumpy cells were generated . This indicates NEK17 induced stumpy formation is RBP7AB-dependent and that , as with PP1-6 , its normal function may also be YAK-dependent . Interestingly , the expression level of NEK17 that was detectable in the parental and YAK or RBP7AB null mutants differed . Thus , parental cells , with an intact stumpy formation pathway consistently expressed less NEK17 than either of the null mutant lines . Although individual differences between distinct cell lines cannot be eliminated , NEK17 may be subject to regulatory feedback such that its expression is restricted in developmentally-competent cells . As well as promoters of the differentiation step , inhibitors of the differentiation from slender to stumpy forms have been identified . In particular , a component of a novel TORC complex , TbTOR4 , has been identified that prevents the differentiation of monomorphic cells to stumpy forms[29] . To evaluate this finding in a physiologically relevant context , we initially explored the consequences of TbTOR4 inducible depletion in pleomorphic bloodstream forms and confirmed that they too exhibited slow growth and premature G1 arrest upon TbTOR4 loss , although this was incomplete probably due to incomplete transcript depletion . Thereafter , we explored the interaction between TbTOR4 and the predicted MAP Kinase Kinase Kinase ( MEKK1 ) since this too would be predicted to operate early in a signalling cascade if structured in a similar way to conventional eukaryotic pathways . As expected , the MEKK1 null mutant was virulent in vivo and did not generate stumpy forms . However , when TbTOR4 was depleted in the MEKK null mutant , slowed growth and G1 arrest was observed similar to the depletion of TbTOR4 alone . This demonstrates that the growth effects of TbTOR4 depletion act independently of MEKK1 . However developmental competence after TbTOR4 depletion was not complete in the absence of MEKK1; instead , the cells expressed low levels of PAD1 . This may reflect parasites entering cellular quiescence reversibly before committing to irreversible stumpy formation and full PAD1 expression . This would be consistent with our previous mathematical modelling of the transition from proliferative slender forms where cells undergo commitment to differentiation only after cell cycle arrest[46] . It is also consistent with PAD1 being a marker for later stages in the differentiation programme , its mRNA being expressed prior to the expression of PAD1 protein that accompanies morphological transformation[21 , 46] . This highlights that TbTOR4 may be needed to prevent reversible cell cycle arrest , similar to the signal-induced reversible quiescence in several eukaryotic systems[47–51] . This is also compatible with the effects of AMPK on trypanosome cellular quiescence observed previously [30] . In contrast , MEKK1 is needed for both effective cell cycle arrest and stumpy formation . The molecular consequences of depletion of MEKK1 were also evaluated by phosphoproteomic analysis of isobaric mass tag labelled samples . In this analysis , it was important to use pleomorphic slender cells grown in culture so that the mutant and parental ( MEKK1+ ) population could be assessed at an equivalent cell density and growth phase , maximising the ability to detect phosphorylation differences between cells with or without MEKK1 . As a protein kinase , direct substrates of MEKK1 might be detected in the null mutant line as molecules demonstrating reduced phosphorylation , although phosphorylation changes in either direction could also result indirectly from the absence of MEKK1 . Interestingly , we found that another SIF pathway component , NEK17 , showed less phosphorylation in the MEKK1 null mutant line . Furthermore , analysis of the phosphorylation site indicated that it was positioned at the activation loop threonine 195 of the NEK kinase and therefore potentially able to regulate its activity . This provides support for NEK17 being positioned downstream of MEKK1 in the differentiation control pathway although direct interaction remains to be demonstrated . Also identified were several hypothetical proteins , and also proteins linked to gene expression ( ALPH1 , an mRNA decapping enzyme[52] , and the post transcriptional repressor RBP31[53] ) and cytoskeletal or protein trafficking control . Regulation of development through the action of RNA binding proteins would be expected given the emphasis of post transcriptional control of the trypanosome genome , such that differential phosphorylation of these proteins may affect their ability to bind RNA or interact with other regulatory or RNA binding proteins to retain cells in a slender form , or permit differentiation to stumpy forms . All of these molecules change phosphorylation status in the absence of MEKK1 but their involvement as slender retainers or stumpy inducers awaits individual analysis . In Fig 10A we summarise the data generated in our study and in Fig 10B and 10C present alternative ( non-exhaustive ) models for the combinatorial interactions explored . In one interpretation ( Fig 10B ) , there are two branches leading to G1 arrest and then stumpy formation , one dependent upon YAK , the second dependent on RBP7 . Thus , PP1-6 can drive stumpy formation in the absence of RBP7AB , but not in the absence of YAK , where cell cycle defects are generated . In contrast , NEK17—potentially a direct substrate of MEKK1—is dependent upon both RBP7 and YAK , such that RBP7 is needed to generate arrest and stumpy formation , whereas the absence of YAK generates cell cycle defects and the cells remain slender . Although bifurcated , each branch is not redundant , however , because the loss of individual pathway components on either branch ( e . g . YAK or RBP7AB ) prevents efficient stumpy formation . Perhaps , therefore , the amount of signal through each branch determines the differentiation response . Notably , the ability of RBP7AB null mutants to differentiate at very low level may be contributed by SIF-independent differentiation , a mechanism proposed to operate with disruption of VSG expression site activity[54] . An alternative pathway structure ( Fig 10C ) is unbranched , with NEK17 and PP1-6 acting downstream of YAK but with each dependent on the presence of YAK for their normal activity; in the absence of YAK , cell cycle defects and growth inhibition arise when either NEK17 or PP1-6 are ectopically expressed . Inevitably a complication in the interpretation of these interactions is that the molecules may have other functions in the cell that are disrupted upon their expression or deletion , exemplified by the inability to knock down the expression of PP1-4 , 5 , 6 without inducing a strong growth phenotype in bloodstream form cells[26] . In conclusion , our work has highlighted ( i ) the potential for the differentiation signalling pathway to be non-linear , and ( ii ) that cell cycle arrest does not inevitably lead to stumpy formation , as exemplified by the TbTOR4-induced arrest , that does not generate PAD1+ cells unless there is MEKK1 present . It has also demonstrated that to accurately interpret the phenotypes resulting from perturbation in molecules proposed to be involved in differentiation requires pleomorphic cell lines and detailed monitoring of physiological stumpy formation through several cytological parameters ( G1 arrest , PAD1 expression , morphology and differentiation capacity or procyclic formation ) . When combined with extragenic suppression and phosphoproteomic analysis in a null mutant background , the systematic piecing together of the components of the signalling pathway becomes possible , as do the wider molecular events linked to remaining as slender forms , or committing to development .
All animal experiments were carried out after local ethical approval at the University of Edinburgh Animal Welfare and Ethical Review Body ( approval number PL02-12 ) and were approved under the United Kingdom Government Home office licence P262AE604 to satisfy requirements of the United Kingdom Animals ( Scientific Procedures ) Act 1986 . Infections were carried out in adult MF1 mice . Experiments were carried out using the Trypanosoma brucei brucei EATRO 1125 AnTat1 . 1 . 90:13 line[55] . In vitro , parasites were cultured in HMI-9[56] . Transfection of pleomorphic AnTat 90:13 strains was performed using either cells grown in vitro or harvested from mouse blood[57] . In the latter case , whole blood was harvested from a mouse showing slender parasitaemia and added to 25 ml HMI-9 + 10% FCS and allowed to settle for 4–6 hours . 20 ml of this culture was then carefully pipetted from the top and transferred to a new flask containing another 20 ml media . Any remaining blood was allowed to settle overnight . The following day media was pipetted from the top , transferred to a new flask and used for transfection . 2-3x107 parasites at a density of 6x105-1 . 2x106/ml were centrifuged for 5 minutes at 1000g and washed in TDB ( Trypanosome Dilution Buffer; Trypanosome Dilution Buffer: 5 mM KCl , 80 mM NaCl , 1 mM MgSO4 , 20 mM Na2HPO4 , 2 mM NaH2PO4 , 20 mM glucose , pH7 . 4 ) . They were resuspended in 50–100 μl Amaxa buffer ( Lonza ) mixed with the DNA . The sample was then transferred to a cuvette and electroporated in an Amaxa Nucleofector II using program Z-001 –Free Program Choice . The cells were immediately transferred to a flask containing 30ml pre-warmed media and serially diluted 1:10 and 1:100 in 2 additional flasks . After incubation at 37°C for minimum 6 hours , antibiotic was added to each flask for selection of transfectants . Antibiotic concentrations used for parasite transfection were: Hygromycin ( 0 . 5μg/ml ) , puromycin ( 0 . 5μg/ml ) , phleomycin ( 1 . 5μg/ml ) , G418 ( 2 . 5μg/ml ) , Blasticidin ( 10μg/ml ) . The cultures were plated in 1ml volumes in 24-well plates . Positive transfectants were identified microscopically and diluted into antibiotic-containing media 4–8 days post transfection . For inducible expression and/or RNAi induction , Tetracycline was used at 1μg/ml . Constructs were generated using standard molecular biology protocols or by Gibson assembly . For the latter , the assembly reaction comprised 50–100 ng of vector with 2–3 fold excess of each insert in a 10 μl volume . 10 μl of 2x Gibson Assembly Master Mix was then added and the reaction incubated at 50°C for 15 minutes . Null mutants were created by replacing the YFP and TY tags of the pEnT6B-Y and pEnT6P-Y vectors[58] with fragments of the 3’ and 5’UTRs of each target gene . Restriction digestion of these plasmids between the 3’UTR and 5’UTR sequences produced 2 linear constructs , containing resistance markers to blasticidin and puromycin respectively , which were capable of homologous recombination with , and replacement of , target gene alleles . The pEnT6B-Y construct contained external UTR fragments located distal to the ORF relative to the internal UTR fragments in the pEnT6P-Y construct . This meant that when the pEnT6B-Y construct was transfected into the AnTat 1 . 1 90:13 strain in the first of 2 sequential transfections , one allele of the target gene including the internal UTR fragments was deleted . When the second pEnT6P-Y construct was transfected , recombination occurs only with the remaining target gene allele , producing a double knockout ( dKO ) line resistant to both antibiotics . Ectopic expression was achieved using as a construct backbone pDEX577Y[58] , that integrates in to the 177bp repeat minichromosome region . Drug selection was mediated by a bleomycin resistance cassette transcribed by a constitutive rRNA promoter; inducible expression was achieved using a T7 promoter with a tetracycline operator sequence . Parasites purified from whole blood were resuspended at 3x106/ml in SDM-79 +10% FCS media supplemented with 6mM cis-aconitate ( Sigma ) to induce procyclic differentiation and incubated at 27°C/5% CO2 . After 0 , 4 and 24 hours , 1 ml culture was removed , washed and fixed in 2% formaldehyde/0 . 05% gluteraldehyde for EP procyclin expression analysis by flow cytometry . Samples were prepared in 5ml FACS tubes ( BD Biosciences ) . Cells were fixed in 2% formaldehyde/0 . 05% gluteraldehyde at 4°C overnight . Samples were centrifuged at 2000g for 7 minutes , washed in PBS and blocked in 200 μl 2% BSA/PBS for 40 minutes or overnight . They were centrifuged again and resuspended in primary antibody in 2% BSA/PBS for 1 hour or overnight at 4°C . Cells were then washed again and resuspended in fluorescently conjugated secondary antibody for 40 minutes at 4°C . The cells were washed again and finally resuspended in 500 μl 0 . 2 μg/ml 4’ , 6-diamidino- 2-phenylindole ( DAPI ) /PBS . Antibody concentrations are listed below . At least 2x105 cells were centrifuged at 1000g for 5 minutes , washed in ice-cold PBS and resuspended in 125 μl ice-cold PBS . 75 μl 8% paraformaldehyde in PBS was added and the tube mixed by inverting . The sample was incubated on ice for 10 minutes . 500 μl PBS was added and the tube centrifuged at 1000g for 5 minutes . The cells were resuspended in 0 . 1 M glycine/PBS and stored at 4°C . Samples were resuspended in 15 μl PBS and pipetted onto slide wells pre-treated with 15 μl 0 . 1 mg/ml poly-L-lysine ( Biochrom ) . Slides were incubated in a humid chamber for 1 hour to allow the cells to adhere . For intracellular proteins , 0 . 05% Triton X-100 was added for 5 minutes . The wells were washed 3 times in PBS and then blocked in 20% FCS/PBS for 45 minutes . Excess liquid was removed and primary antibody in 20% FCS/PBS was applied for 1 hour . The wells were washed 5 times and then fluorescent dye conjugated secondary antibody in 20% FCS/PBS added for 1 hour . Secondary antibody was aspirated and 10 μg/ml DAPI applied for 1 minute . The wells were washed 5 times in PBS . The slides were removed from the humid chamber and dried for 10 minutes at 37°C , then mounted and dried . Antibody concentrations used are shown in Table 1 . Lysates were prepared by centrifuging cells at 1000g for 5 minutes , washing in PBS and resuspending in Laemmli buffer at 3x105 cells/μl . For PAD1 expression analysis , samples were sonicated using a Bandelin Sonorex Ultrasonic bath to reduce viscosity . All other samples were incubated at 95°C for 5 minutes . Lysates were stored at -20°C until use . Protein was transferred from acrylamide gels to nitrocellulose membrane ( GE Healthcare ) using a Bio-Rad Mini Trans-Blot cell according to the manufacturer’s instructions , using cold transfer buffer ( 25 mM Tris , 200 mM glycine , 20% methanol ) and run at 200 mA for 90 minutes with stirring . For reaction , the membrane was blocked at room temperature with agitation for at least 30 minutes in 5% milk in PBS-0 . 1% Tween 20 ( PBS-T ) . It was then incubated in primary antibody in 5% milk in PBS-T for minimum 1 hour with agitation and washed 3 times in PBS-T . Secondary detection of the antibody used one of two systems . Either the membrane was incubated in secondary antibody conjugated to horseradish peroxidase ( HRP ) diluted in 5% milk in PBS-T for 1 hour with agitation and washed three times with PBS-T; the membrane was then reacted with Pierce ECL2 Western Blotting substrate for 2 minutes and exposed to X-ray film ( Fujifilm ) . Alternatively , the signal was visualised using the LI-COR Odyssey system . In this case , the secondary antibody was conjugated to a fluorescent dye and diluted in 50% Odyssey Blocking Buffer/50% PBS-T . After 1 hour incubation in secondary antibody , the membrane was washed twice in PBS-T and once in PBS , and scanned using a LI-COR Odyssey imager . For Northern blotting , RNA was extracted from Trypanosoma brucei using the QIAGEN RNeasy kit according to the manufacturer’s instructions and run on a formaldehyde gel in MOPs buffer . RNA was then transferred from the gel to a positively charged nylon membrane and cross-linked using a UV Stratalinker . The blot was then pre-hybridised in 10 ml formaldehyde hybridisation buffer ( 5XSSC , 50% formamide , 0 . 02% SDS , 2% DIG block ) at 68°C for 1 hour in a hybridisation oven ( Techne Hybridiser HB-1D ) . This was then replaced with 7 ml hybridization buffer containing 1 μl DIG labelled riboprobe ( Roche; prepared according to the manufacturer’s instructions ) , which had been denatured for 5 mins at 95°C , and hybridised overnight . The blot was washed at 68°C in the hybridisation tube , first in 2x SSC/0 . 1% SDS three times for 30 minutes and then once in 0 . 5x SSC/0 . 1% SDS for 30 minutes . It was then removed from the tube and rinsed in maleic acid buffer with 0 . 3% Tween 20 for 1 minute at room temperature . Signal was detected according to the Roche DIG-labelling protocol . For preparation of phosphoproteomic samples , low binding tips and low binding eppendorfs were used . Samples were extracted from 2 replicates each of T . brucei EATRO1125 AnTat 1 . 1 90:13 or MEKK1 KO lines . For each , 2 . 7x108 cells at 9x105/ml were spun at 1000 g for 10 minutes at 4°C , washed three times in PBS and resuspended in 100 μl lysis buffer ( 4% SDS; 25 mM Tris ( 2-carboxyethyl ) phosphine ( TCEP ) ( Thermo ) ; 50 mM N-ethylmaleimide ( Thermo ) ; 150 mM NaCl;1x PhosSTOP phosphatase inhibitor ( Roche ) ; 10 mM Na2HPO4 pH6 ) . The samples were sonicated using a Bioruptor ( Diagenode; 10 cycles of 30 seconds alternating sonication/rest at 18°C ) . The samples were then chloroform:methanol precipitated . After 10 minutes incubation at 65°C , 400 μl methanol was added and the samples vortexed . 100 μl of chloroform was added and the samples vortexed again . 300 μl of ddH2O was added and the samples vortexed for 1 minute . They were then centrifuged for 5 minutes at 9000g and the upper phase aspirated . A further 300 μl of methanol was added and the samples vortexed and centrifuged as before . The supernatants were aspirated and the pellets air dried . For tryptic digestion , the pellets were resuspended in 150 μl 8M urea/0 . 1 M Tris pH8/1 mM CaCl2 and quantified by Bradford assay . The protein concentration was determined by comparison to a bovine serum albumin ( BSA ) standard curve ( 0 . 1–2 . 5 mg/ml ) . 125 μl of Bradford Reagent ( Sigma ) was added to 2 . 5 μl protein sample in triplicate in a 96 well plate and absorbance read at 595 nm using a Fluostar Omega plate reader ( BMG Labtech ) . 800 μg of protein for each sample was then diluted with 0 . 1 M Tris pH8/1 mM CaCl2 to 1 M urea and 8 μl LysC ( Wako ) added . The samples were digested overnight at 37°C . 8 μg trypsin ( Pierce ) in 50 mM acetic acid was then added and the samples incubated for 4 hours at 37°C . Finally , 1% v/v trifluoroacetic acid was added . Labelling and mass spectrometry were carried out at the FingerPrints Proteomics Facility at the University of Dundee . The samples purified by solid phase extraction and then quantified by bicinchoninic acid assay prior to labeling with isobaric tandem mass tags ( 6-plex TMT ) . The samples were pooled and fractionated into fractions using hydrophilic interaction liquid chromatography . 5% of each fraction was analysed by nLC-MS/MS ( nano liquid chromatography-tandem mass spectrometry ) using a Thermo Q-Exactive HF mass spectrometer to generate proteomic quantitation . Phosphopeptides were enriched in the remaining 95% of each fraction and these samples were then also analysed by nLC-MS/MS to generate quantitation of phosphopeptides . Statistical analysis of the fold change in phosphorylation between MEKK1 KO and parental replicates was performed using the R package limma [59 , 60] . This uses the empirical Bayes method which calculates a moderated t-statistic . In this method , posteriors residual SDs replace ordinary SDs , effectively compressing the variance of the peptides with similar means towards a common value , allowing a more stable statistical interference when only few measurement are available [61] . Prior the statistical analysis , data were normalised using the voom methodology included in the limma library . All phosphoproteins that showed an adjusted p-value < 0 . 05 and a greater than 1 . 5 fold change compared to parental T . brucei replicates were considered to be differentially regulated . Statistical analysis for all parasitaemia , cell cycle , PAD1 IFA , FACS data collected in triplicate was carried out using either Minitab version 15 or GraphPad Prism version 6 . Where only a single timepoint was analysed , the data was analysed by students t test . In the case of multiple timepoints , the data was analysed by general linear model with Tukey’s test for multiple comparisons . P values of less than 0 . 05 were considered statistically significant . | African trypanosome parasites respond to density sensing information in the bloodstream of their mammalian hosts to generate their transmission stage , the stumpy form . Components of this ‘quorum sensing’ signalling cascade are known but their interactions and ordering are not . Here we have dissected the dependency relationships between molecules in the pathway by combinatorial gene knockout and ectopic expression , as well as by detailed phosphoproteomic analysis of one component . Our results provide a first analysis of the signal pathway architecture , revealing that it is non-linear . Moreover , phosphoproteome analysis reveals pathway hierarchy through identifying that the phosphorylation of a NEK kinase component of the pathway is reduced when a predicted upstream kinase is absent . This provides a framework for the coherent dissection of a signal transduction cascade in these parasites that use quorum sensing to control disease spread . |
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Using molecular dynamics simulations , we show that the prion protein ( PrP ) exhibits a dual behavior , with two possible transition routes , upon protonation of H187 around pH 4 . 5 , which mimics specific conditions encountered in endosomes . Our results suggest a picture in which the protonated imidazole ring of H187 experiences an electrostatic repulsion with the nearby guanidinium group of R136 , to which the system responds by pushing either H187 or R136 sidechains away from their native cavities . The regions to which H187 and R136 are linked , namely the C-terminal part of H2 and the loop connecting S1 to H1 , respectively , are affected in a different manner depending on which pathway is taken . Specific in vivo or in vitro conditions , such as the presence of molecular chaperones or a particular experimental setup , may favor one transition pathway over the other , which can result in very different monomers . This has some possible connections with the observation of various fibril morphologies and the outcome of prion strains . In addition , the finding that the interaction of H187 with R136 is a weak point in mammalian PrP is supported by the absence of the residue pair in non-mammalian species that are known to be resistant to prion diseases .
The misfolding of the prion protein ( PrP ) , which is a key aspect of transmissible spongiform encephalopathies ( TSE ) , has been the subject of intense research during the past decades . Nonetheless , little is known about the underlying molecular mechanism . One serious hurdle remains the determination of the structure of the resulting misfolded isoform ( ) [1] . As a consequence , various models have been suggested with substantially different packing arrangements and monomer structures , and a consensus about the structure of is far from being reached [1] . A particular subject of controversy is about the actual region of PrP that undergoes a deep refolding during the PrP conversion . According to the so-called “spiral” [2] and “” [3] , [4] models , extended are formed in the N-terminal region and at the beginning of the C-terminal domain up to H1 ( H1 is kept intact in the former and is refolded in the latter model ) . However , it has been recently shown that the H2H3 core is also highly fibrillogenic by itself [5] , [6] . Finally , it has also been suggested that could be entirely refolded in an in-register extended [7] . Many in vitro [5] , [8]–[11] and computational [2] , [12]–[16] studies have tackled this issue using acidic conditions . They have consistently shown that low pH destabilizes PrP and favors its misfolding . This represents biologically relevant conditions insofar as endosomal organelles , whose typical pH is about 5 but can be as low as 4 . 3 [17] , have been highlighted as possible locations for growth [18]–[20] . Importantly , mammalian PrP contain one slightly buried residue , H187 , that titrates right in the range of endosomal pH [11] , [13] . Several lines of evidence indicate that its protonation [13] , or more generally the addition of a positive charge at site 187 [11] , destabilizes the protein fold . Whereas many theoretical studies have been performed on the globular C-terminal domain ( residues 121–231 using the numbering of the human sequence ) of mouse PrP ( mPrP , Fig . 1-A ) , it is worth noting that the cellular form of PrP ( ) also contains a long unstructured N-terminal tail ( residues 23–120 ) [21]–[29] , a glycosylphosphatidyl-inositol ( GPI ) anchor [30]–[32] and can be mono or diglycosylated [27] , [33] . Nevertheless , previous MD simulations have suggested that the structure and dynamics of the globular domain of is rather independent of the anchoring to the membrane and the glycosylation [34] . In addition , our previous study of the misfolding propensity of mPrP using extensive REMD simulations [16] has revealed that various monomers can be formed from the C-terminal domain alone , which is also consistent with the results of Ref . [5] , [6] . Here , we have performed microsecond MD simulations of the structured C-terminal domain of mPrP at pH 4 . 5 , which corresponds approximately to the lowest pH value observed in endosomes [17] . To this end , we assigned the protonation state of all titrable residues with the program PROPKA [35] ( see also Materials and Methods section ) . The only buried residue for which the protonation state cannot be uniquely assigned is H187 . The quantitative evaluation of its is challenging , because the protonation/deprotonation of a buried residue usually affects the protein structure drastically [36] , [37] . Nevertheless , several semi-quantitative estimates of the of H187 have been obtained [13] , [38] and they all indicate that mPrP coexists in both , neutral and protonated-H187 forms at pH 4 . 5 . Thus we have performed two sets of acidic pH simulations , with H187 in either its neutral or protonated form . It is worth noting that other residues in mPrP also titrate at pH 4 . 5 . However , they are all located at the protein surface , so that their electrostatic effect on the global structure of the protein is much less important than that of H187 . Thus , we have considered only one protonation state for these residues ( see Materials and Methods section ) . Our micorsecond simulations show that the mechanism of mPrP destabilization upon protonation of H187 involves R136 as a key partner ( Fig . 1-B , C ) . There is an electrostatic repulsion between the imidazole ring of the protonated H187 ( ) and the guanidinium group of R136 ( ) , to which the system responds by pushing away either or . Because R136 and H187 belong to two very different structural regions of the protein , namely the loop connecting S1 to H1 ( ) and the C-terminal part of H2 ( H2 ( Cter ) , Fig . 1-A ) , the effect on the structure is different depending on which of the two transition routes is taken . It is possible that specific in vivo or in vitro conditions may favor one route over the other , which could lead to completely different structures . Our findings thus seems to provide some rational to the various conclusions reached by different authors regarding the actual region of the protein that is refolded upon misfolding .
Fig . 2 shows the effect of protonating H187 on the backbone of mPrP . The structure is very stable and remains close to the NMR structure when H187 is neutral , whereas simulations with the protonated H187 exhibit important backbone fluctuations and reorganizations . As depicted in Fig . 2-C , these enhanced fluctuations are mainly located in two specific regions of the protein , namely H2 ( Cter ) , which hosts H187 , and . Fig . 2-E shows that the protonation of H187 induces a drastic change in the free energy surface . The projection of the free energy on the of H2 ( Cter ) and shows a single minimum when H187 is neutral , which corresponds to the native structure of PrP , and a complicated multiple minima landscape when H187 is protonated . The new free energy basins are located Å away from the native basin , thus corresponding to substantial conformational changes . The two example snapshots provided in Fig . 2-B , D show that this reorganization is accompanied by a significant modification of the secondary structure of the protein . We will provide a more detailed analysis of the secondary structure changes later in the following sections . For the time being , it is interesting to rationalize how the perturbation that is introduced at one side of the protein ( the protonation of H187 located in H2 ( Cter ) ) is transmitted through the macromolecule and affects strongly the structure at the opposite side ( ) . In order to understand the mechanism by which the protonation of H187 induces the reorganization of the protein structure , it is necessary to have a closer look to the environment of H187 in PrP . It is particularly interesting to focus on nearby charged residues because they are expected to play a major role in the reorganization of the protein when H187 gets a positive charge upon protonation . In the NMR structure of mouse PrP , the closest charged residues are R136 , R156 , K194 , E196 and D202 ( Fig . S5-A ) . R136 is somewhat isolated in terms of proximity with charged residues other than H187 ( when protonated ) , whereas K194 , E196 , R156 , and D202 form a network of salt-bridge interactions . These four latter residues have been pointed out has possible key residues in the misfolding of PrP [13] , [39] . As shown in Fig . S5-B , our simulation provide a consistent picture with that of Ref . [13] , because the protonation of H187 leads to the disruption of the salt bridge between E196 and R156 and the transient formation of a new salt bridge between the protonated E196 and H187 , while a tight salt bridge is maintained between R156 and D202 ( K194 is highly solvated , independently of the protonation state of H187 , and never interact strongly with E196 ) . Nevertheless , the fact that R136 does not have any close alternative partner makes it more sensitive to the positive electric field created by the protonated H187 , as we shall see in the next section . The observation of structural rearrangements in , which is located far from H187 , has motivated us to perform a thorough analysis of the mobility of each residue in this region . It turns out that R136 is a key partner of H187 in the destabilization of mammalian PrP upon protonation of H187 . In the NMR structure of mPrP ( Fig . 1 ) , and are about 8 Å apart and loop ( residues 154–158 ) is located in between . is stabilized by a series of dipole-charge interactions with four peptide bonds while is H-bonded to one carbonyl group and establishes van der Waals contacts with the ring of P158 ( Fig . 1-B ) . Because of the proximity of and in the native structure of mPrP , the protonation of the former should induce an electrostatic repulsion between the two groups . A discussion of the corresponding energetics is provided in Text S1 . Fig . 3 shows the effect of the protonation of H187 on the position of ( or ) and . When H187 is neutral , and are mostly located in their respective native cavities , whereas they cover a much wider portion of conformational space upon protonation of H187 . We define four conformational states according to the position of ( or ) and inside or outside their respective native cavities . To do so we consider the bivariate histogram of the distances between ( or ) and from their respective cavities ( Fig . 3-C ) . The conformational state in which both groups stay close to their original location will be termed , and we define states and according to the departure of or , respectively . Interestingly , the state is almost not populated . The picture that is the most consistent with these data is that PrP exhibits a dual response to the protonation of H187 , by pushing away either or ( but not both at the same time ) , thus decreasing the electrostatic repulsion between them . Because H187 and R136 are attached to H2 ( Cter ) and , respectively ( Fig . 1-A , B ) , the local reorganization of either or affects the global structure of these two regions ( Fig . 2 ) . We stress that , once H187 is protonated , the dynamics of the system proceeds smoothly through a series of locally thermalized states giving rise , in a reproducible way , to either the or state . Fig . S6 and S7 show that and remain in their native pockets during at least 100 ns before one of the two moves out . A similar electrostatic repulsion can be expected for the H187R mutation , for which the positive charge of the introduced arginine has been suggested to destabilize the overall fold of human PrP [11] , [40] , [41] . An interesting aspect of this finding is that none of the non-mammalian PrP exhibit this specific spatial arrangement ( Fig . S4 ) . In other words , these non-mammalian proteins do not have this pH-sensitive “weak point” in there structure and this probably explains the fact that non-mammalian species do not exhibit TSEs . Due to the buried character of H187 and the fact that its protonation induces a substantial modification of the protein structure , the quantitative evaluation of its ( and the corresponding contributions of other residues ) during the misfolding is challenging [36] , [37] . Nevertheless , PROPKA calculations [35] provide physically sound estimates that can help to rationalize the underlying physics . Such calculations for representative snapshots of our simulations are provided in Fig . S8 . The of H187 is systematically shifted up as soon as the protein starts to misfold , independently of the pathway ( vs ) that is taken . This is in agreement with the fact that the proximity with the positive charge of in the native structure of mPrP induces a down-shift of the of H187 ( this is supported by the fact that our PROPKA calculations report R136 as a key residue in the electrostatic environment of H187 , see the corresponding PROPKA output file for a representative structure in Dataset S1 ) . As soon as moves out of its cavity ( state ) the electrostatic repulsion between and decreases and the protonated form of H187 becomes much more stable ( shifted up ) . When the protein adopts a state , is much more solvated by water and the of H187 approaches the corresponding value in water ( ) . The positioning of ( or ) and has a strong influence on the length of the S1 , S2 , as shown in Table 1 . Typically , both and states correspond to structures with a short native , while the state is characterized by the preference of an elongated . This is illustrated by the simulation depicted in Fig . 4 . At the beginning of the simulation , the protein is in its native conformation . As depicted in the insets of Fig . 4 , the native location of at is a key aspect of the protein fold because it forms a sort of “clip” that forces the backbone to remain packed against the rest of the protein ( Fig . 1-A ) in a specific conformation . The permanent departure of out of its cavity at ns induces an important release of backbone constraint and the system is consequently more prone to reorganize in this region . Then the system relaxes during about 400 ns , and and come close together . The number of hydrogen bonds between the two strands increases concomitantly and the elongates ( Fig . 4-A , B ) . As shown in Fig . 5 , both and states are characterized by an unraveling of H2 ( Cter ) . However , the underlying mechanisms ( and the corresponding transition pathways ) differ substantially . The portion of the helix that undergoes an unraveling is represented by a dashed purple arrow in Fig . 1-A ( see also the example snapshot depicted in Fig . 2-D ) . The departure of ( conformation ) out of its cavity obviously destabilizes H2 because the helix looses a key tertiary contact with loop ( Fig . 1-A ) . The unraveling of H2 ( Cter ) in the state has its roots in the polar interactions of with the nearby residues . A closer look to the shape of the cavity ( Fig . 1 ) reveals that it is a narrow groove at the bottom of which lies the carbonyl group of T183 . The contact analysis shown in Fig . 6 reveals that the neutral is H-bonded to R156 only , consistent with the NMR structure of mPrP [21] , whereas new contacts are formed with the CO group of T183 when H187 is protonated . A key aspect of these extra contacts is that they involve not only the group of H187 , but also the group . They reflect dipole-charge interactions between the extra positive charge of and the dipole moments of the 156–157 and 182–183 peptide bonds . In other words , the imidazole ring can take two conformations around the bond and still maintain a significant interaction with one of the two nearby backbone CO groups , which results in four stable conformations inside the pocket . The formation of new contacts between and T183 ( CO ) has two effects that explain the loss of helical character in H2 ( Cter ) . First of all , it weakens the tertiary contact between H2 ( Cter ) and . Second , the native intra-helix H-bond between T183 ( CO ) and H187 ( NH ) is lost . The tighter the interaction between and T183 ( CO ) the weaker the local stability of H2 . In this paper we have shown that the protonation of H187 in mPrP at pH 4 . 5 , which corresponds approximately to the lowest pH observed in endosomes [17] , leads to extensive conformational changes on the microsecond time scale . The picture that emerges from our simulations is that the protonation of H187 leads to an electrostatic repulsion between the positive charges of and , which results in conformational transitions in the regions to which H187 and R136 are linked , namely H2 ( Cter ) and respectively . Our findings hence highlight two possible routes for PrP misfolding with either the unraveling of H2 ( Cter ) alone ( route ) or the unraveling of H2 ( Cter ) with simultaneous elongation of S1 , S2 ( route ) . This dual behavior seems to reconcile the various observations and proposals that have been made regarding the actual PrP region that undergoes a deep refolding upon conversion to [2]–[5] . It is indeed possible that a particular computational or experimental setup favors one of the or substates at the beginning of the misfolding process . Such conformational shift could be assisted in vivo by molecular chaperones such as polyanionic molecules [1] , [42] . This variability in misfolding pathways may also be connected to the fact that prion exhibits a variety of strains , because it is believed that changes in conformations of encodes for strain properties [30] , [43] , [44] . Finally , it is interesting to note that the pattern is not present in those non-mammalian species who are known to resist to TSEs . This is a possible explanation for the observed resistance to TSEs in these species .
All simulations were started from the NMR structure of mPrP published by Riek et al . ( PDB code 1AG2 ) . We aimed at modeling mPrP with a neutral or protonated H187 at pH 4 . 5 . The protonation state of titrable residues apart from H187 was first estimated from PROPKA [35] calculations . The protonation state of most of them can be determined without ambiguity . All buried or semi-buried residues other than H187 are all aspartic or glutamic acids whose side chains are hydrogen-bonded to other groups in the protein . This has the effect to shift up their above the typical values of that they adopt in water , i . e . significantly above the pH we want to model . Hence they are expected to be protonated . The solvent-exposed histidines are expected to exhibit a of so they can be considered protonated at a pH of 4 . 5 . The remaining solvent-exposed aspartic or glutamic acids are more ambiguous because their is close to the pH we want to model . Nevertheless , their solvent-exposed character makes them much less important for the global fold of the protein . We chose their protonation state according to the estimated with PROPKA [35] . The relevance of this choice was verified a posteriori by observing that the fold of the protein is very well conserved over microsecond simulations with a neutral H187 . Two topologies ( one with H187 neutral and one with H187 protonated ) were built with the GROMACS 4 . 0 . 7 [45]–[47] suite of programs . For each of them , the protein was immersed in a rhombic dodecahedral water box . The size of the box was chosen so that the distance between the protein and the edge of the box was Å . The system was neutralized by adding 2 or 3 chloride counterions ( depending on the protonation state of H187 ) . The resulting system contained about 30000 atoms . The AMBER99SB force field [48] was used to describe the protein and the TIP3P model [49] was employed for the water molecules . The force field was included in GROMACS thanks to the ports provided by Sorin and coworkers [50] , [51] . The particle mesh Ewald method [52] together with a Fourier grid spacing of 1 Å and a cutoff of 12 Å was used to treat long-range electrostatic interactions . A cutoff of 12 Å was used for van der Waals interactions . The water box was first relaxed by means of NpT simulations with restraints applied to the positions of the heavy atoms of the protein . Then the system was optimized in a series of energy minimization runs in which the restraints on the protein were progressively removed . Finally , we run eight simulations with a time step of 2 fs . Three and five of them were conducted with a neutral or protonated H187 , respectively . Each simulation was initiated with a set of velocities taken at random from a Maxwell-Boltzmann distribution corresponding to a temperature of 10 K . Then the system was heated up to 300 K in 300 ps using two Berendsen thermostats [53] ( one for the protein and one for the solvent ) with a relaxation time of 0 . 1 ps each . The simulation was prolonged for 100 ps and the Berendsen barostat with a relaxation time of 2 ps was switched on during 100 ps . Finally , we switched to production phase using a Nose-Hoover [54] , [55] thermostat and a Parrinello-Rahman barostat [56] with relaxation times of 0 . 5 and 10 . 0 ps , respectively . The total simulation lengths were 1 . 9 , 1 . 3 and 1 . 6 for simulations with a neutral H187 , and 1 . 9 , 1 . 5 , 1 . 6 , 1 . 2 and 1 . 2 for simulations with a protonated H187 . The plot of each simulation is provided in Figure S1 . All the representations were done with the program VMD [57] . Secondary structure assignments were done using the STRIDE algorithm [58] . | Transmissible spongiform encephalopathies , which include the “mad cow” disease and the Creutzfeldt-Jakob disease , are related to the abnormal folding of a host protein termed the prion protein ( PrP ) . Many aspects of the underlying molecular mechanism still remain elusive . Among the hypotheses that have been put forward in the past few years , it has been suggested that PrP could be destabilized by the protonation of a specific residue , H187 , when the protein passes through acidic cell organelles . We have modeled PrP at the atomistic level , with the neutral and protonated forms of H187 . Our simulations show that the destabilization process can follow two alternative pathways that could lead to different final structures . This discovery may shed some light on one of the most puzzling aspect of prion diseases , the fact that they exhibit various strains encoded in the structure of misfolded PrP . In addition , the atomistic details provided by our model highlight a key interactions partner in the destabilization process , R136 . The residue pair is not present in non-mammalian species that do not develop prion diseases . |
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The coordinated activities at centromeres of two key cell cycle kinases , Polo and Aurora B , are critical for ensuring that the two sister kinetochores of each chromosome are attached to microtubules from opposite spindle poles prior to chromosome segregation at anaphase . Initial attachments of chromosomes to the spindle involve random interactions between kinetochores and dynamic microtubules , and errors occur frequently during early stages of the process . The balance between microtubule binding and error correction ( e . g . , release of bound microtubules ) requires the activities of Polo and Aurora B kinases , with Polo promoting stable attachments and Aurora B promoting detachment . Our study concerns the coordination of the activities of these two kinases in vivo . We show that INCENP , a key scaffolding subunit of the chromosomal passenger complex ( CPC ) , which consists of Aurora B kinase , INCENP , Survivin , and Borealin/Dasra B , also interacts with Polo kinase in Drosophila cells . It was known that Aurora A/Bora activates Polo at centrosomes during late G2 . However , the kinase that activates Polo on chromosomes for its critical functions at kinetochores was not known . We show here that Aurora B kinase phosphorylates Polo on its activation loop at the centromere in early mitosis . This phosphorylation requires both INCENP and Aurora B activity ( but not Aurora A activity ) and is critical for Polo function at kinetochores . Our results demonstrate clearly that Polo kinase is regulated differently at centrosomes and centromeres and suggest that INCENP acts as a platform for kinase crosstalk at the centromere . This crosstalk may enable Polo and Aurora B to achieve a balance wherein microtubule mis-attachments are corrected , but proper attachments are stabilized allowing proper chromosome segregation .
Executive decisions concerning when cells enter and exit mitosis are made by Cdk1 with cyclins A and B as cofactors . Once cells have entered mitosis , Plk1 and the Aurora kinases direct spindle formation , regulate chromosome attachments to spindle microtubules , ensure the operation of the spindle checkpoint , and enable daughter cells to complete cytokinesis ( reviewed in [1]–[4] ) . Plk1 and Aurora A also function in the regulation of mitotic entry ( reviewed in [5] ) . In higher eukaryotes , Plk1 and Aurora B have potentially antagonistic activities during the early stages of chromosome attachment and alignment on the mitotic spindle . Plk1 phosphorylation of kinetochore components and microtubule plus-end-associated proteins is required for the establishment of stable kinetochore-microtubule ( KT-MT ) interactions . Electron micrographs of human cells treated with the Plk1 inhibitor BI2536 show fewer microtubule connections per kinetochore [6] . Tension-sensitive phosphorylation of BubR1 by Plk1 regulates the initial stability of KT-MT interactions [7] , as do phosphorylation of CLIP-170 [8] and NudC [9] . Plk1 also phosphorylates components of the Ska and KNL-1/Mis12/Ndc80 ( KMN ) kinetochore complexes as well as centromere proteins CENP-B , CENP-C , CENP-E , and CENP-F . However , the function of these phosphorylations is not known [10] . The chromosomal passenger complex ( CPC ) , consisting of Aurora B kinase , INCENP , Survivin , and Borealin [11] , has a role in the correction of kinetochore-microtubule attachment errors by promoting the release of kinetochore-microtubule attachments [11]–[15] . The localization of the CPC relative to kinetochores is critical for regulation of kinetochore-microtubule attachments [16] . CPC targeting to inner centromeres occurs as a result of Survivin binding to histone H3 phosphorylated on Thr3 by Haspin kinase [17]–[19] and is helped by an Aurora B-dependent positive feedback loop [20] . The temporal and spatial regulation of Plk1 activation is complex . While Polo activity is not required for mitotic entry in unperturbed cell cycles , its activation by T-loop phosphorylation is needed for its functions during mitosis . At centrosomes , Aurora A kinase-Bora phosphorylates Plk1 on Thr210 , thereby activating Plk1 at the G2-M transition in human cells [21] , [22] . Subsequently , Plk1 triggers the degradation of both Bora [23] , [24] and Aurora A [25] . The regulation of Plk1 activity at kinetochores is a critical and largely unstudied question . Here , we have examined the mechanism of Polo kinase activation at Drosophila and human kinetochores . We show that Aurora B is required for Polo T-loop phosphorylation at the centromere , but not at centrosomes . Our studies identify a new regulatory link between the Aurora B and Polo kinases mediated by INCENP . Furthermore , we demonstrate that this mechanism of regulation of Polo kinase at the kinetochore by the CPC is conserved in human cells . These results support our previous hypothesis that INCENP acts as a platform coordinating the activities of these kinases on chromosomes during early mitosis [26] .
As a starting point to examine the relationship between Polo and the CPC in Drosophila , we compared their localization in space and time . INCENP and the CPC are diffuse during early prophase in Drosophila cultured cells ( Figure 1A2 ) . In contrast , Polo kinase in these early stages is localized at centromeres ( Figure 1 , arrows; Figure S1A ) , at centrosomes , where it colocalizes with Aurora A ( Figure 1A–D , asterisks; Figure S2 ) , and also at the nuclear envelope ( Figure 1A3 , blue arrowhead ) , where it has been proposed to promote nuclear envelope breakdown ( NEB ) [8] . Later during prophase and early in prometaphase , INCENP concentrates at specific , brightly stained chromosomal regions that probably correspond to heterochromatin . At this time , Polo kinase is already concentrated at centromeres ( Figure 1B3 ) . This is the earliest stage at which we observe partial colocalization between Polo ( Figure 1B3 , 4 white arrows ) and the CPC . In metaphase , when chromosomes are bioriented and under tension , INCENP is concentrated on inner centromeric threads that extend between bioriented sister kinetochores , running parallel to microtubules ( Figure 1C2 , 4; Figure S1D ) . At this stage , most chromosome-associated Polo is detected in the outer kinetochore ( Figure 1C3 , 4 , arrows ) and does not colocalize with INCENP . Later , in early anaphase , INCENP and Polo are observed on threads parallel to central spindle microtubules ( Figure 1D and unpublished data ) , although prominent Polo labelling is still detected at the kinetochore . We conclude that Polo concentrates at centromeres before INCENP does and that both proteins transiently colocalize there during early prometaphase . To ask if this colocalisation of INCENP and Polo reflects a direct interaction between the two proteins , soluble , bacterially expressed , full-length Drosophila GST-INCENP ( Figure 1E right panel ) was mixed with in vitro-translated Polo kinase using Aurora B and luciferase as positive and negative controls , respectively . The mixture was then incubated with glutathione beads and bound proteins detected by SDS-PAGE . Robust binding was observed between GST-INCENP and Polo or Aurora B , but not with the luciferase control ( Figure 1E ) . Interestingly , this interaction did not require CDK phosphorylation of INCENP , as previously described for the binding of mammalian INCENP to Plk1 [27] . We confirmed that a physical interaction between INCENP and Polo also occurs in vivo by immunoprecipitation from cell extracts . Cell lines stably expressing either Polo-GFP or Aurora B-GFP ( positive control ) were lysed and the tagged protein immunoprecipitated with anti-GFP . INCENP was readily detected in both immunoprecipitates by immunoblotting ( Figure 1F ) . To determine more precisely when and where INCENP and Polo interact during the cell cycle , we used a proximity ligation assay ( PLA ) to map sites where INCENP , Polo , and Aurora B are in close proximity . PLA is based on conventional double staining using primary antibodies raised in different species . The secondary antibodies used for detection are tagged with short DNA oligonucleotides . If those oligonucleotides are close enough to allow them to be bridged by hybridization with circle-forming oligonucleotides ( distance between antigens of 10–30 nm , [28] ) , the circle can be amplified by rolling circle DNA synthesis and a positive PLA signal is obtained . That signal requires not only the close proximity of the antigens but also their favourable spatial conformation and absence of structural obstacles so that the oligos can interact and subsequent reactions take place . Thus , only a subset of actual interactions between proteins is detected with the PLA technique . We validated the PLA assay by first confirming the known interaction between endogenous INCENP and Aurora B [29]–[32] . Indeed , we readily observed PLA signals associated with chromosomes in prophase and prometaphase cells ( Figure 1G1 ) . This confirmed a recent study that found positive PLA signals between various members of the CPC in all phases of mitosis in human cells [33] . In Drosophila , the positive PLA signals overlapped with Aurora B-GFP , confirming that epitopes on INCENP and Aurora B are in close proximity in the inner centromere ( Figure 1G4 ) . A parallel assay using antibodies against INCENP and GFP confirmed this close association of INCENP with exogenous GFP-tagged Aurora B ( unpublished data ) . In two negative controls , we failed to observe PLA signals using antibodies to INCENP and γ-tubulin and between Polo and the centromere histone CENP-A/CID ( Figure 1J , K ) . We next used PLA to define where in cells interactions occur between INCENP and Polo . Cells co-stained for INCENP and Polo showed PLA signals on chromosomes in early mitosis ( Figure 1H1 ) . These signals colocalized with Aurora B-GFP , confirming that the interaction occurs at inner centromeres ( Figure 1H4 ) . PLA also detected a close association between INCENP and PoloT182ph , the activated form of Polo kinase , using an anti-phospho-epitope-specific antibody ( Figure 1I1 , see validation of the antibody below ) . These positive PLA signals were also present in inner centromeres ( Figures 1I4 ) . We conclude that INCENP and Polo physically interact and are in close proximity at inner centromeres during early mitosis in Drosophila . The association between INCENP and Polo described above suggested that INCENP and other CPC components might have a role in Polo activation by T-loop phosphorylation . To test this hypothesis , we asked whether reducing INCENP protein levels by RNAi affected the localization and activation of Polo kinase , as detected by monitoring Polo T-loop phosphorylation . Plk1 phosphorylation on the highly conserved T-loop residue Thr-210 ( Thr-182 in Drosophila Polo , Figure 2A ) is crucial for kinase activation [34] . T182 of Drosophila Polo is a major phosphorylation site detected by mass spectrometry , and a phosphomimetic mutation at that site ( PoloT182D ) increases Polo kinase activity in vitro ( VA , unpublished observations ) . To examine Polo activation , we used a phospho-epitope-specific antibody raised against human Plk1T210ph that also recognises Drosophila PoloT182ph ( Figure 2B ) . In asynchronous cultures , PoloT182ph was barely detectable by Western blotting . However , we could readily observe endogenous PoloT182ph using this antibody after treatment with the phosphatase inhibitor okadaic acid ( OA; Figure 2B ) . This signal disappeared following Polo depletion by RNAi , confirming that it comes from the Polo protein ( see below ) . Moreover , Polo-Myc gives a signal at the expected higher molecular weight , while PoloT182A-Myc and PoloT182D-Myc are not recognized by this antibody ( Figure 2B ) . In these experiments , Polo was detected as a doublet by Western blotting . This mobility shift is not caused by T182 phosphorylation and may be caused by an as-yet uncharacterized modification . We used this antibody to examine the distribution of PoloT182ph by immunofluorescence in cycling DMel-2 cells that had not been treated with okadaic acid . The antibody detected PoloT182ph at centromeres/kinetochores , centrosomes , the cleavage furrow , and midbody . This staining was largely abolished following Polo RNAi-depletion ( Figure S3 ) . Although most Polo accumulates at prometaphase kinetochores ( Figure 2C3; Figure S2B , C; Figure S4A–C linescans ) , a minor fraction of the kinase localizes to inner centromeres ( Figure 2C3 arrows; Figure 2C linescan ) . Indeed , the active kinase ( detected with anti-PoloT182ph ) is clearly detectable at inner centromeres ( Figure 2C4+inset , arrows ) , where it colocalizes with INCENP , as predicted by the PLA results ( Figure 2C4; linescan ) . We first detect this inner-centromeric pool of active Polo in late prophase cells ( Figure S4A–C ) . PoloT182ph is no longer detected at the inner centromeres of chromosomes aligned at the metaphase plate . Instead it accumulates at kinetochores in metaphase cells ( Figure 2D4 , arrows; linescan ) . Depletion of INCENP by RNAi substantially reduced levels of activated Polo T182ph at kinetochores ( Figure 2E4 , F4; linescans ) . In contrast , total Polo localized normally to kinetochores following INCENP knockdown ( Figure 2E3 , F3; linescans ) . This is consistent with the observation that Polo localization to this region precedes that of INCENP ( Figure 1A ) . Importantly , we could still readily detect active PoloT182ph at centrosomes of cells following INCENP knockdown ( Figure 2E4 , F4 , asterisks ) . These experiments reveal that INCENP is required for T182 phosphorylation and activation of Polo kinase at inner centromeres in early mitosis . Activation of centrosomal Polo does not require INCENP . In order to investigate the function ( s ) of Polo T182 phosphorylation in mitosis , we established stable cell lines allowing inducible expression of PoloWT-GFP or PoloT182A-GFP . Endogenous Polo could be depleted in those cells by RNAi against the 3′UTR of the native transcript ( Figure 3A ) . Expression of PoloWT-GFP rescued the viability and proliferation of cells depleted of endogenous Polo . However , expression of PoloT182A-GFP did not , and cells died ( unpublished data ) . Thus , Polo T-loop phosphorylation is essential for viability . Polo-depleted cells accumulated in mitosis , exhibiting phenotypes similar to those observed for the first polo mutants [35] . Expression of PoloWT-GFP restored mitotic progression in cells depleted of endogenous Polo ( Figure 3B ) , but expression of PoloT182A-GFP did not . Cells expressing only PoloT182A-GFP accumulated in prometaphase/metaphase ( Figure 3C ) , often with unaligned chromosomes ( Figure 3D–F ) . Interestingly , while the loss of Polo led to an increase in monopolar spindles , substitution of endogenous Polo with PoloT182A-GFP did not ( Figure 3D–F ) . This suggests that T-loop phosphorylation of Polo may be dispensable for its role in bipolar spindle assembly . The observation that INCENP-dependent activation of Polo by phosphorylation at T182 at centromeres/kinetochores is required for chromosome alignment in prometaphase is consistent with the known role of Polo in regulating kinetochore function . Because the best known role of INCENP is to activate Aurora B kinase in the CPC , we next asked whether Aurora B has a role in Polo T-loop phosphorylation at centromeres . Drosophila Polo T182 ( corresponding to human Plk1 T210 ) is preceded by a conserved stretch of basic residues resembling the consensus site for Aurora kinases ( Figure 2A ) [31] , [36] , [37] . Indeed , Drosophila Aurora B complexed with a fragment of INCENP can directly phosphorylate Polo in vitro ( Figure 4A ) . A T182A mutation in the Polo used as a substrate reproducibly reduced its phosphorylation by about one half . Thus , Polo T182 is a major phosphorylation site for Aurora B ( Figure 4A ) . Similar results were obtained using human Aurora B on GST-PoloWT or GST-PoloT182D ( unpublished data ) . Kinase inhibition studies suggest that Aurora B is responsible for PoloT182 phosphorylation in vivo . Binucleine 2 is the only specific Aurora B kinase inhibitor described to date that is effective in Drosophila cells [38] , [39] . When DMel-2 cells were treated with 20 µM Binucleine 2 for 2 h , H3S10ph was undetectable in mitotic cells ( unpublished data; [39] ) and INCENP and Aurora B were dispersed in clumps on the chromosomes ( [38] , [39]; Figure S5B , C , compare with Figure S5A ) . Both of these phenotypes are characteristic of the loss of Aurora B function [40] , [41] . Aurora B kinase activity is required for Polo activation at kinetochores , and levels of kinetochore-associated PoloT182ph were greatly reduced in Binucleine 2-treated mitotic cells ( Figure 4B4–D4; Figure 4E ) . In contrast , we observed no obvious difference in the localization of bulk Polo kinase in those cells ( Figure 4B3–D3; Figure 4E ) . Importantly , as in the case of INCENP RNAi , we could still detect activated Polo kinase at centrosomes in the same cells ( Figure 4B4–D4 asterisks ) . As independent confirmation of the inhibitor studies , RNAi-mediated depletion of Aurora B also led to disappearance of the PoloT182ph signal observed in Western blots after OA treatment of cells , while total Polo levels remained unchanged ( Figure 4F ) . In striking contrast , the PoloT182ph signal actually increased after partial Aurora A depletion ( Figure 4Fa ) , perhaps because cells accumulated in mitosis . The above results suggested that Aurora B rather than Aurora A plays a major role to promote PoloT182 phosphorylation at centromeres in Drosophila cells . In order to exclude that our failure to detect PoloT182ph by Western blotting following Aurora B depletion was due to a cell cycle block outside mitosis caused by OA treatment , we examined the effects of RNAi depletion of Aurora A , Aurora B , and INCENP on the PoloT182ph signal at centromeres in individual mitotic cells without okadaic acid treatment . Brief ( 3 d ) dsRNA treatments were used to avoid an accumulation of binucleate cells caused by failure in CPC function in cytokinesis . Depletion of Aurora B or INCENP led to a significant reduction of the PoloT182ph signal at centromeres ( Figure 4G ) . This effect was specific to centromeres , and PoloT182ph levels at centrosomes were unaffected following depletion of Aurora B or INCENP ( Figure S6A ) . In contrast , Aurora A depletion had no effect on levels of PoloT182ph at centromeres , but led to a modest reduction in PoloT182ph levels at centrosomes . Together , these results confirm that Aurora B and INCENP are required for Polo activation at the centromere/kinetochore in early mitosis and strongly implicate Aurora B as the kinase responsible . The CPC is required for Polo kinase activation at centromeres in live animals , and not only in aneuploid cultured cells . To demonstrate this , we examined flies homozygous for the hypomorphic female-sterile allele incenpQA26 , a point mutation in the highly conserved IN-box domain [42] . We observed a strong signal of PoloT182ph concentrated at kinetochores in wild-type mitotic neuroblasts ( Figure 5A3 ) . In third instar larval neuroblasts from the incenpQA26 mutant , 27% of mitoses ( n = 290 ) showed obvious defects in INCENP localization , with the protein spreading onto chromosome arms ( Figures 5B2 , S5E ) . This was never observed in wild-type neuroblasts ( n = 303; Figures 5A , S5D ) . The incenpQA26 mitotic phenotype ( Figure S5E ) resembles the Binucleine 2-induced phenotype , with INCENP dispersed in clumps on the chromosome arms in affected cells ( Figure 4C , D; Figure S5B , C ) . Levels of PoloT182ph at kinetochores were substantially reduced in incenpQA26 mutant mitoses showing this characteristic incenp phenotype ( Figure 5B3; Figure 5E ) . In contrast , overall levels of Polo at kinetochores remained similar to wild type ( Figure 5G and I ) . To test if PoloT182 phosphorylation requires Aurora B activity in vivo , we dissected whole brains , treated them with Binucleine 2 , and processed them for immunostaining as above . After a 2-h incubation in 20 µM Binucleine 2 , Histone H3S10ph ( a reporter for Aurora B activity ) was undetectable in mitotic cells ( unpublished data ) . Drug-treated neuroblasts also showed the characteristic dispersion of INCENP in clumps on chromosome arms ( Figure 5C2 , D2 arrowheads; Figure 5H ) . As predicted , PoloT182ph was virtually undetectable at kinetochores of Binucleine 2-treated neuroblasts , but remained readily observable at centrosomes ( Figure 5C3 , D3 asterisks ) . Total Polo levels at kinetochores remained similar to wild type in these cells . Thus , both Aurora B activity and INCENP are required for Polo T182 phosphorylation at kinetochores in mitotic Drosophila neuroblasts . As independent confirmation that the CPC contributes to Polo regulation in vivo , we conducted a genetic experiment in a sensitized background . Tubulin-Gal4-driven overexpression of Polo mutated in a conserved destruction box ( PoloΔdb—Figure S7 ) is semi-lethal at the pupal stage , suggesting that excessive Polo activity is detrimental to development . Interestingly , a decrease in the levels of functional INCENP rescued the semi-lethality of PoloΔdb-expressing flies . This was observed when one wild-type allele of incenp was replaced with either of the alleles incenp EC3747 or incenp QA26 ( Figure S7 ) . A heterozygous deletion removing Aurora B did not cause a significant rescue in the same assay , suggesting that INCENP may be the limiting CPC component for Polo regulation . Together , these results confirm that the CPC contributes to the regulation of Polo function at kinetochores in vivo . Importantly , the regulation of Polo T-loop phosphorylation described above for Drosophila is conserved in human cells . We used siRNAs to deplete either Aurora B ( Figure 6A ) or INCENP ( Figure 6C ) in HeLa cells and subsequently measured the levels of total Plk1 and Plk1T210Ph at kinetochores . Both INCENP and Aurora B depletion caused a dramatic reduction in Plk1T210Ph levels in early mitosis ( Figure 6Ad3 , B , Cd3 , D ) . Levels of Plk1 were also slightly reduced , confirming that the CPC is at least partly required for the stable localization of Plk1 to mammalian kinetochores [27] . Comparable results were obtained when we treated cells with the Aurora B kinase inhibitor ZM447439 ( Figure S8 ) , confirming that Aurora B activity is indeed required for the presence of Plk1T210Ph at kinetochores in human cells . Our results thus indicate that the CPC activates Polo kinase by T-loop phosphorylation at centromeres in both flies and humans .
Coordination of Polo and Aurora B activity at kinetochores is critical in early mitosis , as the two kinases play potentially antagonistic but complementary roles in regulating kinetochore-microtubule interactions . Aurora B is essential for the correction of aberrant attachments [13]–[15] , and indeed , tethering Aurora B too close to kinetochores interferes with the formation of stable attachments [16] . In contrast , Plk1 activity is required for initial stabilisation of microtubule attachments to kinetochores [7]–[9] . We suggest that interactions with INCENP may provide a mechanism to coordinate the activities of these two essential kinases during early mitosis . Recent studies suggest that Plk1 is activated at centrosomes when its T-loop ( T210 ) is phosphorylated by Aurora A kinase–Bora , and that this promotes the G2/M transition upstream of Cdk1 [21] , [22] , although Polo activity is not required for mitotic entry ( [3]; this paper–Figure 3 ) . How Plk1 is activated at kinetochores remained an important unsolved question . Our present results show that Aurora B and INCENP , which are concentrated at inner centromeres [43] , [44] , function there to activate Polo by phosphorylating its T-loop . Plk1 recruitment to centromeres in late G2 has been variously proposed to be mediated by Bub1 [45] , INCENP [27] , and BubR1 [7] . Another report implicated the self-primed interaction of Plk1 with PBIP1/CENP-U [46] . This could potentially explain why Plk1 activity is reportedly required for its localisation to kinetochores in human cells [47] . Our RNAi studies confirmed that Plk1 is partially dependent on the CPC for its centromeric localization in human cells . However , this appears not to be the case in Drosophila , where Polo is present at centromeres before NEB , at a time when INCENP is not yet concentrated at inner centromeres and before PoloT182ph , the active form of the kinase , is detected there . Indeed , we observed no significant decrease in kinetochore-associated Polo levels after INCENP RNAi in Drosophila cells . Although Polo targeting to kinetochores is independent of the CPC in Drosophila , its activation there does require the CPC with active Aurora B . Our data suggest that INCENP binding to Polo facilitates its subsequent activation by Aurora B kinase ( Figure 7B , C ) . Indeed , INCENP and Polo interact physically in vitro and co-immunoprecipitate in mitotic cell extracts . Although most centromeric Polo kinase is concentrated in the outer kinetochore in prophase and prometaphase , active Polo ( PoloT182ph ) is also found in inner centromeres , where it overlaps with INCENP as confirmed by a proximity ligation assay ( PLA ) . A range of evidence presented here suggests that Aurora B is the upstream kinase responsible for Polo kinase activation at centromeres . Firstly , Aurora B phosphorylates Polo at Thr182 in vitro . Secondly , RNAi depletion of INCENP or Aurora B , but not Aurora A , reduces levels of active PoloT182ph at kinetochores . Thirdly , tissue culture cells and third larval instar neuroblasts treated with a specific inhibitor of Drosophila Aurora B kinase show decreased levels of PoloT182ph at kinetochores . In all of the preceding experiments , PoloT182ph levels are affected at kinetochores but not at centrosomes , where Polo is presumably activated by Aurora A [21] , [22] . Importantly , this involvement of Aurora B in Polo activation at centromeres discovered in Drosophila is conserved for Plk1 in human cells . Our results suggest a model for interactions between Polo kinase and the CPC at centromeres ( Figure 7 ) . In Drosophila cells , Polo targets to centromeres before the CPC is recruited by Survivin binding to histone H3T3ph [17]–[19] . At inner centromeres of chromosomes whose kinetochores are not under tension , Polo now binds to INCENP . This promotes Polo kinase activation , as Aurora B phosphorylates PoloT182 . We suggest that interactions with INCENP link the two complementary kinase activities , thereby potentially creating a microtubule attachment/detachment cycle at kinetochores . Such a cycle would not be possible without a balancing phosphatase activity , and PP2A-B56 has recently been shown to oppose both Aurora B and Plk1 activities at kinetochores to promote stable attachments [48] . At metaphase , when chromosomes are bioriented and under tension , the CPC and Polo kinase exhibit only a partial overlap . A weakening of the INCENP/Polo PLA signals in metaphase suggests that Polo may be released from INCENP after its activation—possibly moving to the outer kinetochore ( Figure 7D ) . During metaphase , the CPC localizes in the inner centromere , stretching between sister kinetochores , whereas Polo and PoloT182ph concentrate mainly at the kinetochores . This separation may be necessary to allow Polo-mediated stabilisation of kinetochore-microtubule attachments . The coordinated activities of both kinases at kinetochores and their tension-mediated separation might facilitate a dynamic equilibrium between attached and unattached kinetochores , selectively stabilizing proper chromosome attachments . In summary , our results reveal that INCENP and Aurora B are responsible for Polo kinase activation at centromeres but not at centrosomes during mitosis . These experiments support the hypothesis that INCENP acts as a scaffold integrating the cross-talk between these two important mitotic kinases [26] .
Fly strains were grown at 25°C in standard Drosophila medium . The following stocks were used: Canton-S; incenpQA26/SM6a , incenpEC3747/SM6a , Tubulin Gal-4/TM3 . UASp-POLOΔDB-MYC transgenic flies were generated by BestGene Inc . Immunostaining of testes and larval neuroblasts was performed as described previously [49] . Primary antibodies and dilutions for immunofluorescence analysis were as follows: mouse monoclonal B512 anti-αTubulin ( SIGMA , 1∶2 , 000 ) ; Rabbit Polyclonals Rb-801 , Rb-803 [41] , 1∶500 ) ; mouse monoclonal anti-PhosphoT210 Plk1 ( Abcam ab39068 , 1∶100 ) ; mouse monoclonal anti-Polo Mab294 ( kindly provided by A . Tavares and David Glover , 1∶100 ) ; rabbit polyclonal anti-Aurora A ( 1∶100 ) and anti-Aurora B ( 1∶500 ) [50] , [51]; and monoclonal anti-Myc 9E10 ( Santa Cruz ) . Secondary antibodies were obtained from Jackson Immunoresearch . The AC5-Polo-GFP cell line was described previously [52] , and the AC5-Aurora B-GFP , AC5-Polo-Myc , AC5-Polo-T182A-Myc , and AC5-Polo-T182D-Myc stable cell lines were generated following the same protocol . Cell lines were grown in Express-Five medium ( GIBCO ) containing 20 µg/ml blasticidin . Cells were treated with either DMSO or 20 µM Binucleine-2 for 2 to 4 h before being processed for immunostaining as described previously [41] . For experiments shown in Figures 3 , 4F , G , and S6 , 1 . 2×106 D-Mel2 cells were transfected in 6-well plates with 20 µg of dsRNA using Transfast reagent ( Promega ) . Cells were analysed 3 or 4 d later by immunofluorescence and immunoblotting . The control dsRNA was generated against the sequence of the bacterial Kanamycin resistance gene . For experiments shown in Figures 3 , 4G , and S6A , cells were seeded on coverslips and treated for 10 s in BRB-80+0 . 1% NP-40 before a 20 min fixation in BRB-80+4% formaldehyde . Cells were then permeabilized for 10 min in BRB-80+0 . 1% Triton X-100 and blocked for 1 h in PBS+0 . 1% Tween20+1% BSA . Primary antibodies were diluted in PBS+0 . 1% Tween20+1% BSA and incubated overnight at 4°C . Secondary antibodies were incubated 2 h at room temperature . Coverslips were mounted with Vectashield+DAPI . Images were taken using an AxioImager epifluorescence microscope . Proximity Ligation Assay was performed using Duolink QL ( Olink , Uppsala , Sweden ) following the manufacturer's protocol . Duolink anti-rabbit plus probe , anti-mouse minus probe , and anti-rat minus probe were used . The following antibody pairs were used for the assay: Rabbit polyclonal anti-Incenp Rb801 [41] , 1∶500/mouse monoclonal anti-Polo Mab294 ( kind gift of A . Tavares , 1∶100 ) ; Rabbit polyclonal anti-Incenp Rb801 ( [41] , 1∶500 ) /mouse monoclonal anti-PhosphoT210Plk1 ( Abcam , 1∶100 ) ; Rat monoclonal anti-Incenp ( Kind gift of Kim McKim , 1∶300 ) /Rabbit polyclonal anti-Aurora B 963 ( [41] , 1∶500 ) ; Rabbit polyclonal anti-Incenp Rb801 ( [41] , 1∶500 ) /mouse monoclonal anti-GFP ( Roche , 1∶500 ) ; Rabbit polyclonal anti-Incenp Rb801 ( [41] , 1∶500 ) /mouse monoclonal anti-γTubulin ( Sigma 1∶50 ) ; and Rabbit polyclonal anti-CID ( a gift from S . Henikoff , 1∶500 ) /mouse monoclonal anti-Polo Mab294 ( 1∶100 ) . In each experiment a negative control using only one antibody of each pair was included . For each antibody pair , exponentially growing DMel-2 cells were seeded on Con-A treated coverslips and fixed for immunostaining as described previously [41] . After overnight incubation with primary antibody at 4°C , half of the samples were processed following the normal immunostaining protocol [41] and the other half was used for the PLA assay . Imaging was performed using Olympus IX-71 microscope controlled by Delta Vision SoftWorx ( Applied Precision , Issequa , WA , USA ) . Image stacks were deconvolved , quick-projected , and saved as tiff images to be processed using Adobe Photoshop . Linescans were generated using Image-Pro software . POLO-T182A , T182D , and POLOΔDB ( R309A , L312A ) were generated in the pDONR221 ( Invitrogen ) using QuickChange ( Stratagene ) . The expression vectors pAC5-POLO-MYC , pAC5-POLO-T182A-MYC , pAC5-POLO-T182D-MYC , and pUASp-POLOΔDB-MYC were generated by Gateway recombination ( Invitrogen ) of pDONR-based entry clones into pDEST-AC5-Cterm-MYC and pDEST-UASp-Cterm-MYC , respectively . POLO-WT and POLO-T182A were cloned into pETDuet for expression as N-terminal fusions with a HIS tag at the MCS1 position . Aurora B was cloned into pDONR221 , which was then recombined into pDEST-AC5-Cterm-GFP to generate pAC5-AURORA B-GFP . GST tagged full-length Drosophila Incenp was expressed in bacteria ( BL21 ) and purified on Glutathione sepharose beads as described previously [42] . Polo , Aurora B , and Luciferase were in vitro translated using a coupled transcription/translation reticulocyte lysate system ( Promega's TNT system ) . Binding buffer—50 mM Tris pH 7 . 5 , 10 mM MgCl2 , 1 mM EGTA , 1 mM DTT , 0 . 1% Triton X-100 , 0 . 5 mM PMSF , and 1 mg/ml CLAP . Mouse anti-GFP ( Roche ) and mouse IgG ( Abcam ) —negative control—antibodies were crosslinked to protein G Dynabeads ( Invitrogen ) at 0 . 5 µg of antibody/1 µl of beads . Exponentially growing D-Mel2 cells were lysed on ice in lysis buffer ( for Polo-GFP cell line: 40 mM Tris-Cl [pH 7 . 5] , 100 mM NaCl , 1 mM PMSF , 1 mM DTT , 10 mM EGTA , 1% Triton-X-100 , and protease inhibitor cocktail ( Roche , UK ) ; for GFP-Aurora B cell line: 50 mM Tris-Cl ( pH 8 . 0 ) , 150 mM NaCl , 1 mM EDTA , 1% NP40 , 0 . 5% deoxycholate , and protease inhibitor cocktail -Roche ) . Cell lysates were separately incubated with either mAb anti-GFP or mouse IgG bound to Dynabeads protein G for 1 h at 4°C . Samples were spun down , then washed first with lysis buffer , and then twice with wash buffer ( 40 mM Tris-Cl [pH 7 . 5] , 100 mM NaCl , 1 mM PMSF , 10 mM EGTA , 0 . 1% Triton-X-100 , protease inhibitor cocktail , Roche , UK ) . Finally the beads were boiled in Laemmli sample buffer . All samples were subjected to SDS-PAGE and analyzed by immunoblotting as described before . HIS-Polo and HIS-Polo-T182A were expressed in BL21 bacteria from pETDuet-based constructions ( see above ) . Protein purification was done using Talon resin ( Clontech ) and purified proteins were stored on the resin at −80°C . For the kinase assay , HIS-Polo and HIS-Polo-T182A on the resin were incubated with Drosophila Aurora B in complex with INCENP645–755 [39] for 15 min at 30°C in 20 mM K-HEPES pH 7 . 5 , 2 mM MgCl2 , 1 mM DTT , 500 mM ATP , 5 mCi 32P-g-ATP . Reactions were initiated by the combination of the substrate-bound resins to a fixed volume of a master mix containing all other reagents . Reactions were stopped by the addition of Laemmli sample buffer . Samples were separated by SDS-PAGE and transferred to nitrocellulose . Quantitative , sub-saturation measurements of radioactivity and Polo Western blot signals were obtained using a PhosphorImager and a Typhoon luminescence reader , respectively . HeLa Kyoto were grown in Dulbecco's modified Eagle's medium , supplemented with 10% foetal calf serum , 0 . 2 mM l-Glutamine , 100 U/ml penicillin , and 100 µg/ml streptomycin . RNAi experiments were performed using annealed siRNA oligos ( Qiagen ) diluted in serum free OptiMem and transfected using HiPerFect reagent ( Qiagen ) according to the manufacturer's protocol . HeLa cells were seeded on coverslips at a density of 1×105 cells/ml and diluted siRNA was added to cells so that the final concentration of siRNA was 40 nM . Coverslips were fixed at 48 h . For control transfections non-silencing random scramble siRNA oligos were used at the same concentration . The full sequences of siRNA oligos used are as follows: for Aurora B siRNA , 5′-AACGCGGCACTTCACAATTGA-3′; for INCENP siRNA 5′-AGATCAACCCAGATAACTA-3′ . For drug treatments , ZM447439 ( Tocris Bioscience ) or DMSO as control were added to the cells at the concentration of 3 µM for 1 h . All fixation , permeabilisation and immunostaining were performed at room temperature , as previously described [53] . Anti-Aurora B rabbit polyclonal at 1∶100 ( Abcam , ab2245 ) , anti-INCENP rabbit polyclonal at 1∶100 ( Upstate ) , anti-Plk1 1∶100 mouse monoclonal ( Abcam ) , anti-P-Plk1 ( T210 ) 1∶100 mouse monoclonal ( Abcam ) , and anti-phospho-Histone H3 ( Ser10 ) rabbit polyclonal ( Upstate ) . All affinity purified donkey secondary antibodies ( labelled either with FITC , TRITC , or CY5 ) were purchased from Jackson Immunoresearch . Quantification of Plk1 and Plk1T210Ph on approximately 1 , 000 centromeres per condition was carried out as follows: Deconvolved images were imported into OMERO [54] and segmentation of centromere foci ( ACA , Cy5 , reference channel ) performed using Otsu segmentation implemented in Matlab . Masks stored in OMERO were then used to calculate intensities , and output into comma-separated value file for plotting in Excel . | When cells divide , their chromosomes segregate to the two daughter cells on the mitotic spindle , a dynamic macromolecular scaffold composed of microtubules . Each chromosome consists of two sister chromatids . Microtubules attach to the chromatids at structures called kinetochores , which assemble at the surface of the constricted centromere region where the sister chromatids are most closely paired . To segregate correctly , sister kinetochores must attach to microtubules emanating from opposite spindle poles . Kinetochore attachment to microtubules occurs randomly and mistakes occur frequently . For example , both sister kinetochores may attach to one pole , or one kinetochore may attach to both poles simultaneously . Two protein kinases , Aurora B and Polo , have essential roles in regulating this process: Aurora B triggers the release of incorrect attachments and Polo strengthens the grip that correctly attached kinetochores have on microtubules . In this work , we have investigated the potential functional links between these two crucial enzymes at centromeres in cells of the fruitfly . We found that early in division , Aurora B and Polo both interact with a structural partner protein named INCENP at centromeres . This allows Aurora B to phosphorylate Polo , thereby activating it . We show that coordinating the activities of these two central mitotic kinases is crucial for successful cell division , and that this mechanism is conserved in human cells . |
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Japanese encephalitis ( JE ) virus infection can cause severe disease in humans , resulting in death or permanent neurologic deficits among survivors . Studies indicate that the incidence of JE is high in northwestern Bangladesh . Pigs are amplifying hosts for JE virus ( JEV ) and a potentially important source of virus in the environment . The objectives of this study were to describe the transmission dynamics of JEV among pigs in northwestern Bangladesh and estimate the potential impact of vaccination to reduce incidence among pigs . We conducted a comprehensive census of pigs in three JE endemic districts and tested a sample of them for evidence of previous JEV infection . We built a compartmental model to describe JEV transmission dynamics in this region and to estimate the potential impact of pig vaccination . We identified 11 , 364 pigs in the study area . Previous JEV infection was identified in 30% of pigs with no spatial differences in the proportion of pigs that were seropositive across the study area . We estimated that JEV infects 20% of susceptible pigs each year and the basic reproductive number among pigs was 1 . 2 . The model suggest that vaccinating 50% of pigs each year resulted in an estimated 82% reduction in annual incidence in pigs . The widespread distribution of historic JEV infection in pigs suggests they may play an important role in virus transmission in this area . Future studies are required to understand the contribution of pig infections to JE risk in humans and the potential impact of pig vaccination on human disease .
Japanese encephalitis ( JE ) virus is an arthropod borne viral zoonosis that is endemic throughout eastern , south-eastern and southern Asian countries [1] , [2] . JE virus ( JEV ) infection can cause irreversible damage to the central nervous system of humans , who serve as incidental ‘dead end’ hosts because they do not produce sufficient viremia to infect mosquitos [3] . Approximately , 60% of the world's population lives in JE endemic regions [4] and a 2011 review estimated that the annual incidence was 1 . 8/100 , 000 and 5 . 4/100 , 000 for children 0–14 years old in 24 JE endemic countries [5] . The majority of human infections are asymptomatic , and only a small ratio ( 1∶25 to 1∶1 , 000 ) develop clinical infections [6] . However , the prognosis for people who develop encephalitis is poor: approximately 25% die , and 30% to 60% of survivors suffer from neurological sequelae [7] , [8] . JEV transmission is complex , involving numerous vertebrate and mosquito species , and is poorly understood in Bangladesh . Studies from other Asian countries show that Culex species are the primary vectors driving transmission; Aedes mosquitoes are also competent vectors but likely play only a minor role [9] , [10] . Host species for the virus include ardeid wading birds and some domestic animals . In particular , pigs appear to play a major role in transmission cycles due to large-scale viral amplification and relatively high viral titers [11] , promoting onward transmission [12] , [13] . Several other domestic animal and bird species including cattle , goats , dog , ducks and chickens also become infected , but because they produce low level of viremia for a brief time , they are unlikely to play a significant role in transmission [14]–[17] . Hospital based acute meningoencephalitis surveillance in Bangladesh began in 2003 and identified that JEV infection was responsible for 6% of all encephalitis at surveillance hospitals [18] . Further work to characterize the burden of JE in 2009 estimated that the incidence was highest in the northwest part of the country , with 2 . 7 cases per 100 , 000 population per year [19] , which is similar to its incidence in other JE endemic countries before the introduction of JE vaccine into national immunization programs [20] , [21] . Although Bangladesh is a predominantly Muslim country , pigs are raised by some ethnic minority communities and in nomadic herds [22] . Understanding the distribution of pigs and the extent to which they get infected with JEV will help to define their role in the JEV transmission cycle . The objectives of this study were to describe the transmission dynamics of JEV among pigs in the northwestern part of Bangladesh , where human incidence is high , [19] and to estimate the potential impact of vaccinating pigs on JE incidence in the pig population .
From May through September 2009 we used snowball sampling to identify all pig-raisers in Rajshahi , Nawabgonj , and Naogaon Districts and counted their pigs ( Figure 1 ) [23] . Previous research suggested that pig-raising communities maintained close kinship networks and we relied on these networks to identify all pig-raising households in these districts [22] . We initially identified 10 pig-raisers who previously participated in a study of other diseases with us in 2007–2008 [22] . In addition , we collected a list of 100 pig-raisers identified through a local livestock office's preliminary survey to identify pig-raisers . Once a pig-raising household was identified , we asked them if they knew other pig-raisers in the study area . All identified pig-raising households were listed and visited and asked to identify other households . This process was continued until no additional pig-raisers were found . We then visited 20 randomly selected unions ( smallest administrative unit consisting of multiple villages ) where no pig-raising was reported and went to places where residents congregate , such as markets , tea stalls and shops , to ask if they knew about any pig-raising communities in those areas . The presence of pigs reported through these interviews was confirmed by household visits . At each pig-raising household , we recorded the number of pigs , their age , sex , and location . We collected blood samples from pigs over six months of age for JE IgG antibody testing . We approached the first pig raising household in the village and sampled a pig after obtaining an informed consent . We continued the sampling in the nearest households until they sampled 10 pigs per village . If no more than 10 pig raising households were identified in a village , we collected samples from multiple pigs from a household . If there were less than 10 pigs in a village , we sampled all of the pigs . The sampling continued until we reached a sample size of 260 . We used the same snowball technique to identify any mobile pig herds in our study area . Regardless their health status , we recorded the number of pigs in each herd and collected blood from at least three pigs but no more than 10 . Although we plan to sample 10 adult pigs per herd , the herders often provided less than that , or the herds did not have that many adult pigs for testing . The serum samples were tested by using a commercially available enzyme-linked immunosorbent assay ( ELISA ) according to manufacturer's protocol ( GENTAUR BVBA – Genoprice , Belgium . http://www . genoprice . com/ ) . With guidance from herders from each herd , we recorded longitude and latitude of the grazing locations and travelling paths ( from April 2008 and March 2009 ) of nine pig herds . These herds were particularly chosen for mapping because their grazing covered the majority of the nomadic pig herds feeding sites , and represented geographical variation of the feeding sites for the three districts . We also conducted informal interviews with all nomadic pig herders to explore the commercial trade of pigs at markets and backyard raisers . We visited commercial pig traders at the markets and some backyard pig-raisers to verify the frequency and timing of the sale . We described the demographics of pigs in our study area and the proportion with evidence of historic exposure to JE . We mapped the locations of pig-raising households , nomadic pig-raising routes , and sampled pigs by JE IgG status . All pig-raisers provided informed consent for participation . The study protocol was reviewed and approved by icddr , b′s Ethical Review Committee , Animal Experimentation Ethics Committee of Bangladesh and CDC's Institutional Animal Care and Use Committee .
We identified 11 , 364 pigs ( Sus scrofa ) in the study area with a mean of 1 . 5 pigs per km2; the majority ( 61% , n = 6963 ) were over 12 months of age and 50% were female . Most ( 88% , n = 9977 ) of the pigs were raised in backyards ( median per backyard: 1 pig , range: 1–25 ) with the remainder ( 12%; n = 1387 ) raised in 28 nomadic pig herds ( median pigs per herd: 39 , range: 6–145 ) ( Table 2 ) . Of the 28 herds , we mapped grazing routes of 9 herds . The pig herding routes had an average length of 108 km per year ( range: 30–329 km per year ) . We also visited 20 ( 52% ) of the 111 unions ( administrative cluster of villages ) where we did not receive any information on pig raising through pig raiser's social informational network and could only identify an additional five pigs , suggesting our approach was able to capture the vast majority of pigs . We identified 20 locations in our study area where pork and live pigs were sold . Pork was sold either daily or weekly in a pig slaughterer's backyard , which in most cases ( 80% ) was near an established live pig market . In addition , there was one pig fair in Naogaon District held once a year during December-January . All nomadic pig herders participated in that fair to sell their pigs to backyard pig-raisers and other pig herders . Commercial pig dealers who sold pigs and pork at the markets throughout the country also participated in this fair . Other than local pig trade between the backyard farmers , the pig-raisers received the majority of their piglets from the herders . Backyard raisers mostly bought 2–3 months old piglets , which they raised for about a year or two before slaughtering for their consumption or for money . On special occasions , such as weddings and spiritual ceremonies , the backyard raiser's preferred to buy older pigs for immediate slaughter . The pig traders who sold the pigs in markets for slaughter usually bought the 5–6 month old pigs from the herders , and supplied the pork markets across the country . We tested 312 pigs for JEV antibodies and 30% had evidence of previous infection ( Table 2 ) . Pig-raising households were primarily located in the central part of our study area ( Figure 1 ) . We failed to reject the null hypothesis of no spatial heterogeneity in the location of seropositive pigs ( p-value of 0 . 3 , Figures S1 and S2 in text S1 ) , suggesting that the extent to which pigs were infected did not differ across the study region . Evidence of past JEV infection increased consistently with age , supporting a constant average force of infection in the pig population . Approximately 50% of pigs had evidence of past infection by 3 years of age ( Figure 2 ) . A force of infection of 0 . 20 per year best fitted the age distribution of seropositive cases ( 1/8 likelihood interval of 0 . 16–0 . 25 , Figure S3 in text S1 ) indicating that 1 in 5 susceptible pigs got infected each year . To describe the dynamics of JEV transmission in pigs we built a compartmental model and fitted it to the force of infection estimate . Using the model , we estimated that the basic reproductive ratio ( R0 ) among pigs was 1 . 2 . Therefore , on average , each infected pig would transmit to 1 . 2 other pigs ( via mosquitoes ) in a completely susceptible population . We subsequently explored the impact of vaccinating pigs . We found that vaccinating only half of all pigs each year , a potentially feasible goal from a vaccination campaign , would result in an 82% reduction in incidence , assuming that the vaccine efficacy was 95% and that 5% of pig infections result from external introductions; we would expect a small marginal benefit if we were able to vaccinate 75% of pigs ( 89% reduction in annual incidence ) ( Figure 3 ) . In the base model , vaccinating only a quarter of pigs would result in a 61% reduction in annual incidence . We conducted sensitivity analysis that varied both the rate of external introductions and the vaccination coverage ( Figure 3C ) . We found that even if 10% of pig infections were externally introduced , vaccinating 50% of the pigs would still result in a 72% reduction in annual incidence after five years . In a second sensitivity analyses we assumed 50% vaccine coverage , but assumed a lower vaccine efficacy of 50% ( compared to 95% in the base model ) . Under these assumptions , vaccinating half the pigs would still result in a 54% reduction in JE incidence ( compared to 82% in the base model ) .
Characterizing incidence patterns in pigs is crucial to understanding both the dynamics of JEV transmission in pig populations and the potential impact of intervention efforts . Here we conducted an intensive pig census across an area that has experienced high levels of JEV infection in humans [19] . We identified over ten thousand pigs with at least one pig found in approximately half of the unions in the study area . Evidence of past JEV infection in the pigs was found across the study area with no spatial heterogeneity in the location of seropositive pigs . There was a clear increase in seropositivity by age , characteristic of endemic diseases and we estimated that 20% of susceptible pigs get infected each year . We are not aware of previously published estimates of the basic reproductive number or the force of infection of Japanese encephalitis in pig populations . Our model shows that by vaccinating only half ( or 4 , 000 ) of the susceptible pigs each year , the incidence among pigs would be reduced by >70% , even if up to 10% of pig infections were from other animal reservoirs , such as wading birds . In other parts of Asia , pig immunization against JEV was discontinued because of logistical difficulties in keeping large herds with high annual turnovers vaccinated [29] . However , in this setting , there were fewer pigs than on large commercial pig raising operations and pigs lived for one year , on average , meaning that vaccination once per year could be sufficient to provide protection . These factors might make pig vaccination more logistically feasible in these communities . Pig-raisers in this part of Bangladesh are marginalized populations and economically disadvantaged [22] and JEV infection among sows can result in abortions and deaths of newborn pigs [30] . Although there are no published reports of using JE pig vaccination as an effective intervention to improve poverty among pig raisers , the small reduction in pig miscarriages and stillbirths from vaccination could translate into meaningful reduction in economic losses for this economically disadvantaged group . Pig raising communities are typically wary of outside intervention , and currently receive very little in the way of veterinary services provided by government-owned livestock clinics in the country [22] . Therefore , interventions to vaccine pigs could be more difficult to implement than in the setting of large commercial farms where previous vaccination efforts were attempted . Nonetheless , a pig vaccination strategy might be feasible and acceptable , particularly if it would provide some benefit to pig raisers . Pig-raising communities maintain strong relationships with one another , including through the pig trade . These close networks may provide an opportunity to efficiently access pigs and disseminate communication messages for a vaccination campaign , such as during the yearly pig-marketing event in Naogaon . The findings from this study are subject to some important limitations . First , the number of pigs in this area may be underreported by our census if large pig-raising communities were not identified through our snowball approach . However , this appears unlikely as efforts to identify pigs in unions where no pig-raisers were reported through our snowball approach yielded very few additional pigs . Second , it is possible that we have overestimated the seroprevalence of pigs in our study sample due to cross-reactivity of antibodies to other flaviviruses [16] , although there is no evidence that these viruses are endemic in pig populations in Bangladesh . A study of human encephalitis patients residing in this area found no evidence of infection with West Nile Virus ( ES Gurley , personal communication ) , a possible viruses that often produce cross-reactions in the region [16] . Finally , we do not know the proportion of pig infections that originate from other host species . However , sensitivity analyses that varied this assumption widely still resulted in substantial reduction in pig infections from vaccination . The high level of seropositivity in pigs in the study area suggests they may be a key reservoir for the virus and could contribute importantly to JE risk in humans . If pig infections do increase human risk substantially , reducing incidence in pigs could also result in reduced incidence in humans . All countries that have effectively reduced the burden of JE in human populations have done so through human vaccination campaigns [31] . However , there are currently no plans to introduce JE vaccine into the expanded program for immunization in Bangladesh . Given the poor prognosis for humans with JE disease and the lack of human vaccine in this area , vaccinating pigs may represent an opportunity to reduce disease risk in humans . For example , one study from Assam , India [32] , which borders Bangladesh in the north , showed that human risk of JEV infection was reduced by 66% in areas where insecticide treated mosquito nets were used to prevent infections in pigs . Further studies are required to estimate the contribution of pigs in determining JE risk in humans in this area and to understand if preventing pig JEV infections could also confer a human health benefit , in the absence of human vaccination . | Japanese encephalitis ( JE ) virus infection can cause severe neurological disease in man . More JE cases are seen in northwestern districts in Bangladesh . Pigs are the most common amplifying host of the virus and can act as a potential environmental source . We conducted a comprehensive census of pigs in three JE endemic districts and tested a sample of them for evidence of previous JEV infection . We built a compartmental model to describe JEV transmission dynamics in this region and to estimate the potential impact of pig vaccination . We identified 11 , 364 pigs in our study area , mostly raised in backyards . About 30% of the pigs had evidence of previous JE virus infection . Our model suggests that vaccinating 50% of pigs each year resulted in an estimated 82% reduction in annual incidence in pigs . Pigs in northwestern Bangladesh may play a significant role in JE virus transmission . JE incidence may be substantially reduced through reasonable pig vaccination coverage . |
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Current drugs to treat African sleeping sickness are inadequate and new therapies are urgently required . As part of a medicinal chemistry programme based upon the simplification of acetogenin-type ether scaffolds , we previously reported the promising trypanocidal activity of compound 1 , a bis-tetrahydropyran 1 , 4-triazole ( B-THP-T ) inhibitor . This study aims to identify the protein target ( s ) of this class of compound in Trypanosoma brucei to understand its mode of action and aid further structural optimisation . We used compound 3 , a diazirine- and alkyne-containing bi-functional photo-affinity probe analogue of our lead B-THP-T , compound 1 , to identify potential targets of our lead compound in the procyclic form T . brucei . Bi-functional compound 3 was UV cross-linked to its target ( s ) in vivo and biotin affinity or Cy5 . 5 reporter tags were subsequently appended by Cu ( II ) -catalysed azide-alkyne cycloaddition . The biotinylated protein adducts were isolated with streptavidin affinity beads and subsequent LC-MSMS identified the FoF1-ATP synthase ( mitochondrial complex V ) as a potential target . This target identification was confirmed using various different approaches . We show that ( i ) compound 1 decreases cellular ATP levels ( ii ) by inhibiting oxidative phosphorylation ( iii ) at the FoF1-ATP synthase . Furthermore , the use of GFP-PTP-tagged subunits of the FoF1-ATP synthase , shows that our compounds bind specifically to both the α- and β-subunits of the ATP synthase . The FoF1-ATP synthase is a target of our simplified acetogenin-type analogues . This mitochondrial complex is essential in both procyclic and bloodstream forms of T . brucei and its identification as our target will enable further inhibitor optimisation towards future drug discovery . Furthermore , the photo-affinity labeling technique described here can be readily applied to other drugs of unknown targets to identify their modes of action and facilitate more broadly therapeutic drug design in any pathogen or disease model .
Protozoan parasites of the Trypanosoma genus cause widespread disease and death across large regions of the developing world . In sub-Saharan Africa Trypanosoma brucei gambiense and T . b . rhodesiense are the causative agents of human African trypanosomiasis ( HAT , or African sleeping sickness ) in humans while several species cause disease in livestock and wild animals , creating a major socio-economic burden to the African continent . The parasites are spread through the bites of infected tsetse flies and , if left untreated , infection is usually fatal . Over 65 million people who live in the tsetse fly habitat are at risk of infection and each year there are an estimated 15–20 , 000 new cases [1] . In the early 1900’s African trypanosomes became one of the first subjects of “modern drug discovery” when Paul Ehrlich , following his observations on differential cell stains , hypothesised that some molecules could be developed to target pathogens but not their hosts ( a term he coined “chemotherapy” ) , and screened a library of synthetic dyes in trypanosome-infected animals to find a “magic bullet” [2 , 3] . Through a combination of rational synthetic chemistry and phenotypic screening his pioneering work led to the discoveries by others of suramin in 1917 and melarsoprol in 1949 [4] , both of which are still front-line drugs for the treatment of early stage ( suramin ) and late stage ( melarsprol ) infection by T . b . rhodesiense [5] . Pentamindine , which is currently the first-line treatment for early stage infection by T . b . gambiense [5] , was likewise developed from the anti-diabetic synthalin in 1937 [6 , 7] . However , HAT has been neglected over the past half century and all of these antiquated non-oral drugs are difficult to administer , are sometimes ineffective and are themselves toxic , often causing undesirable side effects with melarsoprol causing the death of up to 5% of those treated [5 , 8] . Furthermore , melarsoprol resistance is a growing issue [9–14] and new drugs are therefore urgently needed , particularly for late stage infection . Despite their antiquity and widespread use , the targets and modes of action of these currently used drugs are poorly understood , making it difficult to design to safer analogues . Investment from the pharmaceutical industry has been slow in forthcoming for this and related neglected diseases , which affect many of the poorest and most underdeveloped countries in the world , and efforts so far have been driven instead by charities and non-profit organisations . Advances in automated liquid handling , cell culture and detection technology has allowed researchers and the pharmaceutical industry to return to phenotypic screening-based practices , as those pioneered by Ehrlich , for the latest drug discovery efforts . We recently reported the total synthesis and trypanocidal activity of the acetogenin , chamuvarinin [15 , 16] and non-natural bis-tetrahydropyran 1 , 4-triazole ( B-THP-T ) analogues thereof including compound 1 ( [17]; Fig 1A ) using a phenotypic screening approach . Acetogenins are a family of over 400 structurally related fatty acid-derived natural products isolated from tropical plants of the Annonaceae family ( for review , see [18] ) , and characteristically bear one to three tetrahydropyran ( THP ) and/or tetrahydrofuran ( THF ) rings flanked by a terminal γ-lactone head and a hydrophobic tail . Many members have been reported to display high inhibition of mitochondrial complex I [19–21] , making them cytotoxic to a wide range of organisms [22 , 23] , and their particularly high potency against ATP-hungry tumour cells ( reviewed in [24] ) has led to their investigation as potential anti-cancer chemotherapeutics; despite mammalian cells requiring complex I activity , pre-clinical trials with select acetogenins are encouraging , with some proving as effective and selective as Taxol , a first-line treatment for some cancers , at reducing solid tumours in mice [25] . Cytotoxic activities vary among acetogenins and between cell lines/organisms but several studies have demonstrated that both γ-lactone and THP/THF moieties are essential for complex I inhibition [26–28] . Intriguingly , chamuvarinin and B-THP-Ts are toxic to procyclic form ( PF ) and bloodstream form ( BSF ) T . brucei [15–17] with EC50 values in the low micromolar range ( Fig 1 ) , however , complex I is not essential in either form of the parasite [29 , 30] , and our B-THP-Ts lack the terminal γ-lactone indicating that our compounds must have a different mode of action in kinetoplastids . In order to further optimise the potency and selectivity of our trypanocidal B-THP-T compounds this study set out to determine their precise target ( s ) in T . brucei . The identification of targets of lead compounds is often necessary to allow subsequent lead optimisation , but target identification is a major bottleneck in the drug discovery pipeline following phenotypic screens . Photo-affinity labelling provides an elegant solution by covalently attaching a bifunctional photo-affinity probe analogue of the lead compound to the target for its ultimate detection ( for review see [31] and Fig 1A & 1B ) and can readily be achieved by incorporating photoreactive and reporter moieties to the lead compound . Azides , benzophenones and diazirines make excellent photoreactive substituents as they decompose upon UV-irradiation to form short-lived reactive nitrenes , diradicals or carbenes respectively , which rapidly react with neighbouring molecules to form covalent bonds . Hence , by photo-activating the probes once they have been trafficked to their targets , these protein targets can be preferentially labelled . Several radiolabled leads have been functionalised with photoreactive azides [32 , 33] , benzophenones [34] or diazirines [21 , 35 , 36] and their targets detected by autoradiograph following protein separation , i . e . SDS-PAGE . A similar , but radiolabel free , methodology has also been employed by functionalising an inhibitor with both a photoreactive diazirine and a fluorescent reporter moieties , allowing the target to be visualised both in gel and within intact cells , thus adding the benefit of target localisation determination . The major drawback of using a radiolabel or fluorescent tags is that , while it allows one to visualise the target , it rarely provides a means to isolate or identify the target . Thus , many [37–44] have used bi-functional photo-affinity probes containing a photoreactive moiety coupled with biotin affinity tag which allows the photo-affinity labelled target to be purified from other proteins using streptavidin beads and identified by tandem mass spectrometry . More versatile yet is the functionalization of the lead compound with photo-affinity group and alkyne handle which allows the subsequent attachment of any desired reporter , such as fluorophore [45–47] or biotin [48 , 49] using the Cu ( II ) -catalysed azide-alkyne cycloaddition , also known as the “click reaction” [50] once the photo-affinity probe had been UV-cross-linked to its target . To identify the target ( s ) of B-THP-T compounds we synthesised two photo-affinity probe analogues of our lead compound ( Fig 1A , synthesis to be reported elsewhere ) : mono-functional compound 2 contains a photoreactive diazirine , but has no reporter capability; while bi-functional compound 3 contains the same photoreactive diazirine and an alkyne handle to allow subsequent identification through appendage with biotin affinity or Cy5 . 5 fluorescent reporter tags . Importantly , the diazirine and alkyne encompass only minor changes to our lead B-THP-T , compound 1 , and all compounds show similar levels of toxicity ( Fig 1 and S1 Fig ) indicating that the photo-affinity probes work through the same mode of action and are thus suitable for target identification purposes . Here we employed an in vivo pulse-chase photo-affinity labelling methodology to covalently attach bi-functional compound 3 to its target within its biological context and , coupled with liquid chromatography-tandem mass spectrometry identified proteins that are potential targets of B-THP-T compounds . Subsequently , using several metabolic and biochemical assays with lead B-THP-T , compound 1 , we validated two of those protein hits as binding partners and now allows us to begin work to optimise the potency and selectivity of our inhibitors towards these targets . Furthermore , it demonstrates the strength of this photo-affinity labelling technique , which can be readily applied to any drug discovery program to accelerate the drug discovery process .
To identify the target ( s ) of the trypanocidal B-THP-T , compound 1 , we developed compound 3 , a bi-functional photo-affinity probe analogue of compound 1 ( Fig 1A and 1B , synthesis to be published elsewhere ) . In addition to the core structure of compound 1 , compound 3 contains a diazirine to covalently bind to its target upon UV-activation and an alkyne handle to which an azide reporter , such as a fluorophore or affinity tag can subsequently be added via copper catalysed azide-alkyne cycloaddition , often referred to as the ‘click reaction’ [31 , 50] . As a negative control , we also developed the mono-functional compound 2 ( Fig 1A ) , which contains the diazirine , but lacks the alkyne and therefore cannot conjugate the azide reporter . Crucially , the addition of the diazirine and alkyne moieties encompass only minor changes to the parent molecule and have minimal effect on overall compound potency ( Fig 1A and S1 Fig ) , suggesting that compounds 1–3 have the same mode of action and that photo-affinity probe 3 is therefore suitable for the identification of target ( s ) of our B-THP-T compounds . Compounds 1–3 were pulse-chased to their targets in live PF T . brucei cells and UV-irradiated to covalently attach the diazirine-containing compounds to their targets ( Fig 1B ) . The pulse-chase methodology was employed rather than labelling in cell lysates so that inhibitors would be trafficked to their target ( s ) and labelling would therefore occur in its biological context , minimising the likelihood of identifying high affinity targets that do not form in vivo . Once the extracted proteins had been subjected to azide-alkyne cycloaddition with Cy5 . 5-azide , proteins that had been appended with Cy5 . 5 were detected by SDS-PAGE coupled with fluorescence imaging ( Fig 1C ) . As expected , when either the diazirine or alkyne were absent ( as in compounds 1 and 3 ) no proteins fluoresced as there was no mechanism for them to covalently bind the Cy5 . 5 reporter molecule , and this absence of fluorescence indicated that there was minimal non-specific binding of Cy5 . 5 reporter to cellular proteins . Proteins only incorporated the Cy5 . 5 reporter tag when diazirine and alkyne were both present on the B-THP-T compound ( i . e . , with the bi-functional compound 3 , lane 3 of Fig 1C ) confirming their essentiality for photo-affinity labelling of protein targets . 18 protein bands were detected to varying degrees , with most prominent bands around 70 and 55 KDa , suggesting major targets of these sizes . To identify the cellular compartments housing B-THP-T protein targets , Cy5 . 5-azide was “clicked on” to B-THP-T-conjugated proteins in fixed whole cells following pulse-chase photo-affinity labelling , and detected by fluorescence microscopy ( Fig 2 ) . Bi-functional compound 3 without UV-conjugation and lead compound 1 , were used as negative controls . As with Fig 1C , Cy5 . 5 labelling only occurred when compound 3 was cross-linked by UV-activation , again indicating specific labelling of target proteins ( s ) . Cy5 . 5 labelling was absent from the nucleus , but showed good co-localisation with the MitoTracker staining , suggesting that B-THP-T compounds predominantly target proteins associated with the mitochondria . Next a series of pull-down experiments were performed to identify the proteins tagged with bi-functional photo-affinity probe , compound 3 . For pull-down of the target ( s ) , biotin azide was used as reporter and “clicked” on to the B-THP-T-labelled proteins following the same pulse-chase in vivo photo-affinity labelling methodology as used above . Tagged proteins were then enriched with streptavidin-agarose , digested on-bead with trypsin , analysed by LC-MSMS , and identified through Mascot searches of the T . brucei proteome . Compound 1 and mono-functional compound 2 , which both lacked the alkyne handle required for biotin-azide cycloaddition , were used as negative controls and any proteins pulled down with either of these were eliminated from the bi-functional compound 3 list of potential protein targets as they represented proteins bound non-specifically to the beads rather than through the biotin-streptavidin interaction . Following elimination of low scoring proteins and unknown proteins ( which would be difficult to validate in the timeframe of this study due to their unknown nature ) , twenty-five T . brucei proteins were identified as potential targets in three replicate pull-downs from whole cell protein extracts ( S1 Table ) . Of those , seventeen ( 68% ) utilised nucleotides as a co-factor , indicating that B-THP-Ts may mimic nucleotides . As localisation studies indicated the target was primarily localised in the mitochondria , attention was focused on the six mitochondrial proteins identified in the pull-down data ( Table 1 ) . Pleasingly , bands corresponding to proteins of the masses in Table 1 appear to be Cy5 . 5-labelled via compound 3 cross-linking in lane 3 of Fig 1C , with the most intense bands ( ~70 KDa and ~55 KDa ) matching closely with the masses of two of the top three hits ( 72 KDa mitochondrial heat shock protein 70 , mHSP70; and 56 KDa ATP synthase F1 β-subunit ) . While this supports the likelihood that the proteins identified in Table 1 were captured via the streptavidin-biotin/compound 3 interaction rather than non-specific interaction with the beads , the list only serves as a guide and each of the hits needs to be validated before being identified as a bona-fide target . The most intense Cy5 . 5-labelled band in Fig 1C matched the mass the top-scoring hit in Table 1 ( 72 KDa mHSP70 ) , which is under investigation as a B-THP-T target and will be reported elsewhere . Of the remaining five mitochondrial pull-down hits , four ( hits 2–5 ) have roles in the production of ATP through proline metabolism ( Fig 3 ) and are the subject of further investigation herein as they can be validated or eliminated through a series of related follow-up assays . Proline is one of the principle amino acids used as a carbon source for PF T . brucei in the insect midgut [51 , 52] . It is taken into the PF mitochondrion and converted to pyruvate through eight enzymatic steps , and then further catabolised to alanine or acetate end products ( Fig 3 , [53] ) . ATP is produced at two points during this process: first through substrate-level phosphorylation at succinyl-CoA synthetase ( Fig 3 , step 5 ) as intermediate succinyl-CoA is converted to succinate by succinyl-CoA synthetase; and second through oxidative phosphorylation ( the coupling of the electron transport chain ( ETC ) with ATP production ) at the FoF1-ATP synthase ( mitochondrial complex V ) as the reduction of succinate to fumarate by mitochondrial complex II feeds electrons into the electron transport chain ( ETC ) . Two of the pull-down hits , δ-pyrroline-5-carboxylate dehydrogenase ( δPCDH , hit 4 in Table 1 , Fig 3 ) and dihydrolipoamide succinyltransferase ( DLST , hit 2 in Table 1 , Fig 3 ) have roles prior to substrate-level ATP production , while two other hits , the F1 α- and β-subunits ( hits 5 and 3 respectively in Table 1 , Fig 3 ) , form the ADP-binding regulatory and ADP-binding catalytic domains respectively of the FoF1-ATP synthase and have direct roles in ATP production from oxidative phosphorylation downstream of substrate-level ATP production . Targeting of any of these four hits by B-THP-T compounds would consequently impact cellular ATP production to varying extents . Therefore , a series of biochemical phenotypic studies were undertaken to validate or exclude the pull-down of these four hits . We first set out to determine if B-THP-T compounds affected cellular ATP levels . PF T . brucei were incubated in buffered PBS with/without proline as the sole carbon source and various inhibitors for 2 h . Cells were harvested and their ATP content was determined by bioluminescence assay . Two ETC inhibitors were used as positive control: malonate is a competitive inhibitor of mitochondrial complex II and prevents the reduction of succinate to fumarate and entry of electrons to the ETC; and antimycin A ( AA ) is a mitochondrial complex III inhibitor , which prevents the transfer of electrons to cytochrome c and ubiquinone recycling . Both ETC inhibitors significantly inhibited cellular ATP production by 62 ± 15% and 71 ± 9% respectively ( Fig 4 ) , consistent with them shutting down oxidative phosphorylation but leaving upstream substrate-level phosphorylation at succinyl-CoA synthetase unaffected . Compound 1 inhibited ATP production in a dose-response manner to similar levels ( by 55 ± 6% ) , supporting the hypothesis from pull-down data that its target is involved in the ATP production pathway from proline . We next sought to identify the point at which ATP production was blocked using digitonin-permeabilised cells . Digitonin permeabilises the plasma membrane of trypanosomatids , but crucially , leaves the glycosomes and mitochondria intact and fully functional , allowing them to be probed with compartment-specific ATP-yielding substrates [54 , 55] . Digitonin-permeabilised PF T . brucei were probed with succinate , which is readily transported across the PF T . brucei mitochondrial membrane by the dicarboxylic acid carrier [56] and can yield only oxidative phosphorylation-derived ATP through the FoF1-ATP synthase ( complex V ) . As expected , the mitochondrial complex II inhibitor , malonate , completely eliminated all ATP production from succinate , while downstream inhibitors antimycin A ( AA ) , complex V inhibitor oligomycin A ( OA ) and protonophore 2 , 4-dinitrophenol ( DNP ) almost abolished ATP production ( Fig 5A ) . Lead B-THP-T , compound 1 , decreased ATP production from succinate to similar levels , while rotenone and salicylhydroxamic acid ( SHAM ) , which inhibit the ETC at different entry points and are therefore irrelevant inhibitors , had no significant effect , indicating that compound 1 inhibits oxidative phosphorylation , and is consistent with the F1 α- and/or β-subunits being targets . However , this result on its own does not confirm that the F1 subunits are targets , as inhibition of substrate ( ADP or succinate ) uptake or any of the mitochondrial complexes could have the same effect . Next , mitochondria were incubated with α-ketoglutarate , which is also taken into the mitochondria by the dicarboxylic acid carrier , to examine the effects of B-THP-T compounds on substrate-level phosphorylation . The multiprotein α-ketoglutarate dehydrogenase complex , of which dihydrolipoamide succinyltransferase ( DLST , hit 2 in Table 1 ) is a component , converts α-ketoglutarate into succinyl-CoA ( Fig 3 ) , allowing succinyl-CoA synthetase to generate substrate-level ATP . In addition , electrons from resulting succinate can enter the ETC to generate oxidative phosphorylation-derived ATP . As complexes III-IV export six protons for every α-ketoglutarate metabolised and the FoF1-ATP synthase imports only four protons with every ATP synthesised , each mole of α-ketoglutarate can yield 1 . 5 moles ATP through oxidative phosphorylation . Thus , 40% of the ATP produced from α-ketoglutarate is by substrate-level phosphorylation and 60% is through oxidative phosphorylation . With α-ketoglutarate as substrate , the four oxidative phosphorylation inhibitor controls only blocked up to 48 ± 12% of ATP production ( Fig 5B ) as they were unable to block upstream substrate-level ATP production by succinyl-CoA synthetase . Compound 1 inhibited ATP production by 67 ± 13% , achieving a significantly lower level of inhibition than that achieved when succinate was used as substrate ( Fig 5A ) , suggesting that the blockade is not due to inhibition of substrate uptake , but is due to inhibition of one of the mitochondrial complexes . The greater level of inhibition achieved by compound 1 over the other oxidative phosphorylation inhibitors suggests that compound 1 may also be inhibiting substrate-level phosphorylation to some extent , and is , perhaps , an indication that compound 1 also targets DLST ( pull-down hit 2 in Table 1 ) . However , the more significant effect of compound 1 is , by far , on oxidative phosphorylation and the F1 subunits are therefore likely to be more significant targets . Use of the cytosolic oxidative phosphorylation substrate glycerol-3-phosphate ( Gly-3P ) allowed us to establish whether substrate uptake was a target of our compounds . Mitochondrial Gly-3P dehydrogenase ( mGPDH ) is located in the intermembrane space of the mitochondrion and converts cytosolic Gly-3P into dihydroxyacetone phosphate ( DHAP ) while transferring electrons to the ETC via ubiquinone [54] . Gly-3P can therefore be used to probe oxidative phosphorylation at a different entry point from succinate and is not reliant on substrate uptake . When Gly-3P was used as substrate , malonate had no effect on ATP production ( Fig 5C ) as complex II is irrelevant at this ETC entry point . Likewise , other irrelevant inhibitors rotenone and SHAM had no effect on ATP production . However , AA , OA and DNP , which all inhibit oxidative phosphorylation downstream of mGPDH and mitochondrial complex II , reduced ATP production by 75 ± 14% , 65 ± 21% and 67 ± 13% respectively . The inability of oxidative phosphorylation inhibitors to abolish ATP production from Gly-3P has been noted previously [54] and may be the result of glycolytic ATP production following glycosomal uptake of Gly-3P or its product , DHAP . Lead B-THP-T , compound 1 , acted similarly , inhibiting ATP production by 82 ± 16% . This confirmed that inhibition of oxidative phosphorylation by compound 1 is not due to inhibition of substrate uptake as Gly-3P is not taken into the mitochondrion and , furthermore , it indicates that the inhibition is downstream of electron entry to the ETC , or more specifically , that compound 1 inhibits one of complexes III-V . The potency of lead B-THP-T , compound 1 , was determined against oxidative phosphorylation in dose-response assays in digitonin-permeabilised PF T . brucei . The IC50 of compound 1 was 57 ± 15μM and 39 ± 5 μM when succinate and Gly-3P were used respectively ( Fig 5D ) . Although these values are higher than in whole cell EC50 assays ( 8 . 4 μM ) , it is noteworthy that they cannot be compared directly as the concentrations of proteins and substrates differ greatly between experiments , as do the time-scales of the experiments: digiton-permeabilised cells were plated at over 1000-fold greater density than live cells and used an acute incubation ( 30 min ) rather than the chronic 72 h incubation of the cell viability assay . To determine which mitochondrial complex of oxidative phosphorylation was targeted by B-THP-T compounds , their effects on the mitochondrial membrane potential ( Δψm ) were monitored in live PF T . brucei ( Fig 6A ) . MitoTracker Red CMXRos , like tetramethylrhodamine and safranine , accumulates in active mitochondria and the degree of accumulation is dependent on the Δψm [57 , 58] , however , accumulation of MitoTracker Green FM is independent of the Δψm and the ratio of red/green uptake can therefore be used to determine the Δψm normalised to the number and size of mitochondria present [59] . In PF T . brucei mitochondrial complexes III-IV generate a Δψm by pumping protons out of the mitochondrion during the ETC ( Fig 3 ) , and thus ETC inhibitors ( malonate or AA ) prevent proton export and decrease the Δψm ( Fig 6B ) . Conversely , mitochondrial complex V ( FoF1-ATP synthase ) uses this potential to generate ATP as it pumps protons back in ( Fig 3 ) , so inhibition of complex V by OA prevents proton import and elevates the Δψm ( Fig 6B and [58] ) . Similarly , compounds 1 and 3 elevated the mitochondrial membrane potential ( by 2-3-fold ) , confirming that they , like OA , reduce oxidative phosphorylation by the inhibition of the FoF1-ATP synthase . The F1 α- and β-subunits of the FoF1-ATP synthase were both identified as potential B-THP-T targets through biotin pull-down ( Table 1 ) , and the FoF1-ATP synthase was shown above to be inhibited by lead B-THP-T compound 1 , but to confirm whether both , or just one of the F1 subunits is a target of our compounds , we generated cell lines endogenously expressing α- or β-subunits with C-terminal GFP-PTP tags . The PTP affinity tag comprises epitopes for proteins C and A separated by a TEV protease cleavage site for efficient purification of the target protein and the cloning methodology facilitates recombination within the endogenous targeted gene rather than exogenous integration , allowing for endogenous levels of expression [60] . The endogenous expression produces tagged protein at physiological levels , allowing it to be trafficked and form complexes in the same way as untagged endogenously expressed protein , as previously demonstrated through the purification of intact FoF1-ATP synthase with a C-terminally TAP-tagged F1 β-subunit [61] . In addition , we cloned green fluorescing protein ( GFP ) into the tag to act as an additional reporter and for antibody-free localisation studies . Fluorescence imaging ( Fig 7A ) confirms that both tagged subunits correctly localise to the mitochondria . Pulse-chase in vivo photo-affinity labelling with bi-functional compound 3 ( and lead compound 1 as negative control ) was performed on cells from both cell lines expressing GFP-PTP-tagged F1 α- or β-subunits . Tagged subunits were immobilised on IgG-sepharose beads , purified , and Cy5 . 5 fluorescent reporter clicked on , and proteins were detected by western blotting ( Fig 7B ) . Mouse anti-GFP coupled with anti-mouse-DyLight-800 identified tagged F1 α-GFP-PTP and F1 β-GFP-PTP subunits migrating as expected at approximately 89 KDa and 101 KDa respectively . In trypanosomatids ( but not in other organisms ) the native α-subunit is expressed as a 63 KDa protein which is then cleaved by an unknown protease to 20 KDa N-terminal and 43 KDa C-terminal domains , both of which remain associated with the FoF1-ATP synthase complex [61 , 62] . Pleasingly the 109 KDa GFP-PTP C-terminally tagged α-subunit also appears to be cleaved at the same position yielding the 89 KDa GFP-PTP-tagged C-terminal domain of the α-subunit , indicating that it is localised and processed within the cell as the native untagged protein . Fig 7B shows that approximately equal amounts of GFP-PTP-tagged protein had been captured between compound 1/3 pairs and between α- and β-subunits ( Fig 7B ) , indicating that α- and β-subunits were expressed ( and captured ) to similar levels . However , for both α- and β-subunits , Cy5 . 5 fluorescence at 700 nm was only detected appreciably in samples photo-affinity labelled with bi-functional compound 3 , and not with lead compound 1 . This indicates that compound 3 can UV-conjugate to both F1 α- and β-subunits and corroborates their pull-down in Table 1 . F1 of mitochondrial complex V is a multi-protein complex composed of α- , β- , γ- , δ- and ε-subunits with stoichiometry 3:3:1:1:1 [63 , 64] . The α- and β-subunits form a catalytic heterohexamer of alternating subunits with ADP/ATP binding sites at their interfaces ( S2 Fig and Fig 8A and 8B ) while γ- and ε-subunits form an asymmetrical central stalk connected to the Fo moiety which together , driven by the proton-motive force , rotate within the α/β hexamer to drive ATP production by introducing conformational changes at the catalytic sites and changing their affinities for ADP and ATP [63 , 65–67] . X-ray crystal structures of F1 have been determined for several species , but no high-resolution structure of trypanosomatid F1 have been solved . Consequently , to determine likely B-THP-T binding sites over the entire F1 moiety , B-THP-T compound 1 , which displays only modest selectivity towards T . brucei over mammalian HeLa cells ( Fig 1 ) , was docked into the entire F1 crystal structures from Sacharomyces cerevisiae ( PDB entry 2WPD [68] ) and Bos taurus ( PDB entry 1BMF [63] ) using AutoDock Vina [69] . For both crystal structures , catalytic and regulatory ATP binding sites were identified as sites of lowest binding energy and therefore most likely binding positions of B-THP-T compounds . The F1 α- and β-subunits are paralogues of one another with sequence identity of 20–25% ( value depending on species ) , and while the β-subunit has catalytic activity , the α-subunit has lost this ability and instead plays a regulatory role in ATP synthesis and hydrolysis . They share a common RecA-like protein fold along with other classes of ATP-binding proteins including kinases , phosphatases , ATPases , heat shock proteins , transfer/transport ATPases , and permeases [70 , 71] , in which the adenine of the nucleotide binds within a sequence-non-specific hydrophobic pocket and the B-phosphate within the amine nest of the conserved Walker A motif ( sequence GxxxxGKT/S ) ( Fig 8A and 8B ) . Although the mode of ATP-binding is similar for α- and β-subunits , their three-dimensional shapes differ due to differences in amino acid sequence . The three ATP-binding regulatory sites of the α-subunits ( top panel of S3A Fig ) are all relatively similar , however , the three ATP-binding catalytic sites of the β-subunits ( lower panel of S3A Fig ) are different as their shapes are dependent on the position of the central stalk ( and thus state of bound nucleotide ) . The most significant differences are in the distances between Tyr345 and Phe424 , and in the position of the neighbouring α-subunit which completes the catalytic site . The empty site of the β-subunit ( βE ) is open , while the ATP-bound ( βTP ) and ADP + Pi-bound ( βDP ) conformations are closed and somewhat more similar to one another . Compound 1 docked similarly within all three regulatory ATP-binding pockets of the yeast α-subunits ( αE , αDP and αTP , top panel of S4 Fig and Fig 8C ) . The triazole H-bonded with the amine-rich phosphate nest , THP2 occupied a similar position to the ATP ribose and hydrophobic tail bound within the hydrophobic adenine-binding pocket . THP1 could potentially form an H-bond via a water bridge , while the terminal hydroxyl could H-bond with Thr214 , Lys211 of the α-subunit or Ala327 of the neighbouring β-subunit . Interactions between compound 1 and the bovine regulatory subunits were similar . Compound 1 docked similarly within the nucleotide-bound β-subunits ( βDP and βTP , lower panel of S4 Fig and Fig 8D ) of yeast F1 with THP2 sandwiched between Tyr345 and Phe424 and the hydrophobic tail buried within the hydrophobic adenine-binding pocket lined by V165 , F166 , P346 and F418 . The triazole adopted a similar position to the nucleotide’s ribose , allowing THP1 to sit close to the phosphate-binding Walker A nest and the terminal hydroxyl to form extensive H-bonds with Pi-coordinating Arg190 , Glu189 , Arg260 and Arg’375 ( or Adp256 and Thr164 ) . However , in the open BE conformation , Tyr345 and Phe424 lie too far apart to sandwich THP2 and the triazole instead forms interactions with the Walker A nest and THPs potentially forming H-bonds via water bridges . Docking of compound 1 to the ATP-binding sites of the α- and β-subunits reveals that compound 1 can form similar interactions as ATP and suggests that B-THP-T compounds mimic ADP , a finding which is mirrored by the pull-down of predominantly nucleotide utilising proteins during target identification ( Table 1 ) . Intriguingly , only one other protein ( phosphoenolpyruvate carboxykinase–a glycosomal protein shown in S1 Table ) identified as a potential target during pull-down experiments has the Walker A motif , suggesting that the B-THP-T compounds probably do not target all members of this protein family , but instead bind to specific nucleotide-binding sites . Until recently drug discovery efforts focussed on the identification of compounds targeting proteins specific to the target organism with the view that such compounds would have low toxicity to the human hosts . Such studies typically began with screening and lead optimisation against recombinant target protein , but projects often derailed during subsequent cell-based testing when compounds were shown to be equally toxic to mammalian cells or compounds failed to reach their targets within the cells . Phenotypic screening removes all previous bias towards targets and brings into play targets that were previously though undruggable by testing compounds in their context within the cell . However , the major bottleneck in phenotypic screening lies in identifying the target after initial screens , often making it difficult to further improve compound potency and selectivity . We previously identified compound 1 as a trypanocidal compound during phenotypic screening and this study has successfully identified its protein target and mode of action using a photo-affinity labelling approach . Using a compound 1-like bi-functional photo-affinity probe ( compound 3 ) , we showed that the target is localised to the mitochondria and biotin pull-down experiments identified the α- and β-subunits of the FoF1-ATP synthase ( mitochondrial complex V ) as potential targets . We next used compound 1 to validate our photo-affinity labelling experiments and showed that compound 1 inhibits cellular ATP production by blocking oxidative phosphorylation at the FoF1-ATP synthase . Photo-affinity labelling with bi-functional compound 3 confirmed that both α- and β-subunits are targeted and modelling suggests that compound 1 binds at their ATP-binding regulatory and catalytic sites respectively . While it is possible that both sites could be targeted , it must be noted that regulatory and catalytic sites sit at the interfaces between α- and β-subunits and it may therefore be possible for photo-affinity probe compound 3 to covalently bind both of the subunits from either catalytic or regulatory site . Regardless , future investigations can target both sites to tailor new compounds that bind specifically to each , and this study highlights the usefulness of photo-affinity labelling in target identification for structure-based drug discovery . The overall architecture of the FoF1-ATP synthase is well conserved among different species . Although the α- and β-subunits of T . brucei F1 share 47% and 67% sequence identity respectively with their mammalian homologues , structural differences exist between them . In particular , the trypanosomatid α-subunit is cleaved into 20 KDa N-terminal and 44 KDa C-terminal chains and the recent low-resolution structure of T . brucei FoF1 obtained by cryo-electron tomography [62] suggests that this causes a major shift in the position of the α-subunit within the complex with respect to the β-subunit . Crucially , as the catalytic and regulatory binding sites are positioned at the interfaces between α- and β-subunits this is likely to have an effect on the overall shapes and sizes of those sites and a high-resolution structure is warranted to facilitate future structure-based drug design . The essentiality of the mitochondrial respiration complexes in human hosts may make the FoF1-ATP synthase ( complex V ) appear an unattractive target despite the possibility of significant differences between human and trypanosomatid homologues . However , the mitochondrial complexes are being investigated as targets in other diseases . For example , complex I-targeting acetogenins have shown promise as anti-cancer agents in preclinical studies [25] , while complex V-targeting Bedaquiline has also recently been approved for the treatment of tuberculosis [72] . The FoF1 ATP synthase has been shown to be essential in both PF [61] and BSF [73 , 74] T . brucei , although its role in each parasite form differs . In the procyclic form ( and in mammalian hosts ) , it is chiefly involved in oxidative phosphorylation whereby mitochondrial complexes III and IV generate a mitochondrial membrane potential ( Δψm ) by exporting protons from the mitochondrion during the electron transport chain ( ETC ) , and mitochondrial complex V ( the FoF1 ATP synthase ) generates ATP while pumping protons back in . Furthermore , the Δψm is used for other functions , such as protein trafficking [75] and tRNA import [76] . BSF T . brucei lacks much of the oxidative phosphorylation machinery ( namely , complexes III and IV , [77] ) as it generates sufficient ATP through glycolysis [78] and generates the essential Δψm by running FoF1-ATP synthase in reverse , hydrolysing ATP instead of producing it [73 , 79 , 80] . The difference in function between mammalian and BSF T . brucei FoF1 ATP synthase ( ATP production versus mitochondrial membrane polarisation respectively ) could be exploited for the creation of synergistic drug combinations that do not affect the mammalian hosts .
PF double marker strain 29–13 were maintained as reported previously [81] in full growth medium ( SDM-79 medium supplemented with 10% foetal bovine serum ( FBS ) ( Gibco ) , 2 g/L sodium bicarbonate , 7 . 5 mg/L haemin , 15 μg/mL neomycin ( G418 ) and 50 μg/mL hygromycin ) at 27°C with 5% CO2 . For experiments using low-glucose growth medium , PFs were adapted to glucose-free SDM-79 supplemented with 10% FBS , 2 g/L sodium bicarbonate , 7 . 5 mg/L haemin and maintained as above . ATP production assays were carried out in duplicate , and three independent assays were conducted . Cells were digitonin-permeabilised as previously described [55] . Briefly , cells were washed in PBS and permeabilised for 5 min at a density of 1x108 cells/mL in SoTE buffer ( 20 mM Tris . HCl ( pH 7 . 5 ) , 2 mM EDTA pH 7 . 5 , 600 mM sorbitol ) supplemented with 0 . 015% ( w/v ) digitonin . Permeabilised cells were pelleted at 5000 g for 3 min at 4°C and suspended to 8 . 4x107 cells/mL in ATP assay buffer ( 20 mM Tris . HCl ( pH 7 . 4 ) , 15 mM KH2PO4 , 0 . 6 M sorbitol , 10 mM MgSO4 , 2 . 5 mg/mL fatty acid-free BSA . ATP production assays were carried out in 75 μl volumes in 96-well plates with a final PF T . brucei cell density of 6 . 7 x 107 cells/mL as previously reported [55] . Unless otherwise stated , inhibitors were used at high concentration ( exceeding their EC90 values in live cells ) because plating density was >1000-fold higher than with live cell EC50 determination and to ensure that the target was fully inhibited . B-THP-Ts , antimycin A ( AA ) and oligomycin A ( OA ) were used at 200 μM , Dinitrophenol ( DNP ) at 1 mM , malonate at 5 mM , rotenone at 100 μM and salicylhydroxamic acid ( SHAM ) at 100 μM . 59 . 25 μL digitonin-permeabilised cells ( prepared above ) were added to 0 . 75 μl inhibitor dissolved in DMSO ( or 0 . 75 μL DMSO without inhibitor for no inhibitor control ) and incubated for 10 min to allow inhibitors to take effect . Substrate was added to 2 . 5 mM and incubated for 10 min . Reactions were started with the addition of ADP to 60 μM and incubated at RT for 30 min . Reactions were quenched by the addition of 1 . 75 μL of 60% perchloric acid and the plate was incubated on ice for 30 min . The acid was then neutralised with 11 . 5 μL of 1 M KOH . ATP production was quantified using ATP bioluminescence assay kit CLS II ( Roche ) . 10–20 μL from each well was transferred in duplicate to a black 96-well plate . Volumes were made to 50 μL with 0 . 5 M Tris-acetate pH 7 . 75 . 50 μL luciferase was added to each well , mixed , and precise luminescence was recorded with a Spectramax Gemini XPD ( Molecular Devices ) set to high sensitivity . Background luminescence was subtracted from each well and ATP levels calculated relative to the uninhibited controls . Effects of compounds on proline metabolism were determined using a methodology adapted from Podolec et al [85] . Briefly , glucose-free-conditioned PF T . brucei cells were washed into buffered PBS and incubated in 200 μl volumes with inhibitors for 10 min . 3 mM proline was added as the sole carbon source and cells were incubated for 2 h , after which cultures were spiked with 1 mM citric acid ( as internal standard ) and cells were pelleted . Supernatants containing metabolic end-products and citrate standard were analysed to evaluate effects on proline metabolism ( see below ) , while the ATP content of cells was determined as follows . Cells were resuspended in 75 μL PBS and lysed with the addition of 1 . 75 μL 60% perchloric acid . Reactions were incubated on ice for 30 min , then neutralised with 11 . 5 μL 1 M KOH . ATP content was determined as above . F1 α- and β-subunits were purified using a similar procedure to that previously described for TAP-tagged subunits [61] . Briefly , PF T . brucei cells from 50 mL culture endogenously expressing GFP-PTP-tagged F1 subunits were photo-affinity probed with 50 μM compound 3 or compound 1 as outlined above , except cells were lysed in IP buffer ( 50 mM Tris . HCl ( pH 7 . 6 ) , 150 mM NaCl , 1% TX-100 ) supplemented with ‘Complete’ EDTA-free protease inhibitors ( Roche ) . GFP-PTP-tagged proteins were solubilsed for 1 h with end-over-end agitation at 4°C and insoluble material pelleted at 16000 g for 10 min at 4°C . The supernatant was applied to 20 μL IgG-sepharose ( GE Healthcare Life Sciences ) and incubated with end-over-end agitation for 1 h at 4°C . Beads were pelleted at 16000 g for 1 min and non-bound material was discarded . Beads were washed 4 times in 1 mL IP buffer to remove contaminating proteins . Compound 1 was docked into F1 from Bos taurus ( PBD entry 1BMF [63] ) and Saccharomyces cerevisiae ( PDB entry 2WPD [68] ) using AutoDock Tools [90] and AutoDock Vina [69] as per the user manuals . Briefly , the energy-minimised 3-dimensional coordinates of compound 1 were generated with ChemBio 3D ( Perkin Elmer ) and converted to PDBQT format with AutoDockTools . All non-protein atoms ( e . g . , waters , nucleotides and metals ) were removed from the F1 PDB coordinates and hydrogens were added with AutoDockTools . Initially compound 1 was docked into the entire F1 complex using a grid-box of dimensions 126 x 126 x 126 Å containing the entire complex and an exhaustiveness of 48 to account for the large search area . Compound 1 was next docked into the ATP-binding sites of each α- and β-subunit using a grid-box of dimensions 17 x 15 x 23 Å covering the binding site . Positions of docked compound 1 were evaluated using Pymol ( Schrodinger ) . | Millions of people are at risk of developing African sleeping sickness through infection with the parasite Trypanosoma brucei , which is fatal if untreated . Current therapies are antiquated and inadequate , requiring hospitalization to deliver them through complex regimens , thus new effective , cheap and easy to administer therapies are urgently required . We recently reported the generation of a new compound that is selectively active against the parasite responsible for the disease , but its mechanism of action was unknown . This body of work identifies the specific target within the parasite as the essential FoF1-ATP synthase ( mitochondrial complex V ) , first by analysing the proteins bound to a tagged analogue of our active compound ( in a process known as photo-affinity labelling ) , and secondly by determining the effects of our active compound on cellular metabolism . With the target now identified work can begin to improve inhibitor potency and selectivity with the aid of rational drug design and structural optimisation towards future drug discovery . |
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According to recent experimental evidence , promoter architecture , defined by the number , strength and regulatory role of the operators that control transcription , plays a major role in determining the level of cell-to-cell variability in gene expression . These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect variability in gene expression in a systematic rather than case-by-case fashion . In this article we make such a systematic investigation , based on a microscopic model of gene regulation that incorporates stochastic effects . In particular , we show how operator strength and operator multiplicity affect this variability . We examine different modes of transcription factor binding to complex promoters ( cooperative , independent , simultaneous ) and how each of these affects the level of variability in transcriptional output from cell-to-cell . We propose that direct comparison between in vivo single-cell experiments and theoretical predictions for the moments of the probability distribution of mRNA number per cell can be used to test kinetic models of gene regulation . The emphasis of the discussion is on prokaryotic gene regulation , but our analysis can be extended to eukaryotic cells as well .
A fundamental property of all living organisms is their ability to gather information about their environment and adjust their internal physiological state in response to environmental conditions . This property , shared by all organisms , includes the ability of single-cells to respond to changes in their environment by regulating their patterns of gene expression . By regulating the genes they express , cells are able to survive , for example , changes in the extracellular pH or osmotic pressure , switch the mode of sugar utilization when the sugar content in their medium changes , or respond to shortages in key metabolites by adapting their metabolic pathways . Perhaps more interesting is the organization of patterns of gene expression in space and time resulting in the differentiation of cells into different types , which is one of the defining features of multicellular organisms . Much of this regulation occurs at the level of transcription initiation , and is mediated by simple physical interactions between transcription factor proteins and DNA , leading to genes being turned on or off . Understanding how genes are turned on or off ( as well as the more nuanced expression patterns in which the level of expression takes intermediate levels ) at a mechanistic level has been one of the great challenges of molecular biology and has attracted intense attention over the past 50 years . The current view of transcription and transcriptional regulation has been strongly influenced by recent experiments with single-cell and single-molecule resolution [1]–[11] . These experiments have confirmed the long-suspected idea that gene expression is stochastic [12] , [13] , meaning that different steps on the path from gene to protein occur at random . This stochasticity also causes variability in the number of messenger RNAs ( mRNA ) and proteins produced from cell-to-cell in a colony of isogenic cells [11] , [14]–[17] . The question of how transcriptional regulatory networks function reliably in spite of the noisy character of the inputs and outputs has attracted much experimental and theoretical interest [18] , [19] . A different , but also very relevant , question is whether cells actually exploit this stochasticity to fulfill any physiologically important task . This issue has been investigated in many different cell types and it has been found that stochasticity in gene expression plays a pivotal role in processes as diverse as cell fate determination in the retina of Drosophila melanogaster [20] , entrance to the competent state of B . subtilis [7] , resistance of yeast colonies to antibiotic challenge [17] , maintenance of HIV latency [21] , promoting host infection by pathogens [22] or the induction of the lactose operon in E . coli [23] . Other examples have been found , and reviewed elsewhere [24] , [25] . The overall conclusion of all of these studies is that stochasticity in gene expression can have important physiological consequences in natural and synthetic systems and that the overall architecture of the gene regulatory network can greatly affect the level of stochasticity . A number of theoretical and experimental studies have revealed multiple ways in which the architecture of the gene regulatory network affects cell-to-cell variability in gene expression . Examples of mechanisms for the control of stochasticity have been proposed and tested , including the regulation of translational efficiency [8] , the presence of negative feedback loops [26] , [27] , [28] , or the propagation of fluctuations from upstream regulatory components [29] . Another important source of stochasticity in gene expression is fluctuations in promoter activity , caused by stochastic association and dissociation of transcription factors , chromatin remodeling events , and formation of stable pre-initiation complexes [5] , [15] , [16] , [23] , [30] . In particular , it has been reported that perturbations to the architecture of yeast and bacterial promoters , such as varying the strength of transcription factor binding sites[17] , the number and location of such binding sites [11] , [31] , the presence of auxiliary operators that mediate DNA looping [23] , or the competition of activators and repressors for binding to the same stretch of DNA associated with the promoter [32] , may strongly affect the level of variability . Our goal is to examine all of these different promoter architectures from a unifying perspective provided by stochastic models of transcription leading to mRNA production . The logic here is the same as in earlier work where we examined a host of different promoter architectures using thermodynamic models of transcriptional regulation [33] , [34] . We now generalize those systematic efforts to examine the same architectures , but now from the point of view of stochastic models . These models allow us to assess the unique signature provided by a particular regulatory architecture in terms of the cell-to-cell variability it produces . First , we investigate in general theoretical terms how the architecture of a promoter affects the level of cell-to-cell variability . The architecture of a promoter is defined by the collection of transcription factor binding sites ( also known as operators ) , their number , position within the promoter , their strength , as well as what kind of transcription factors bind them ( repressors , activators or both ) , and how those transcription factors bind to the operators ( independently , cooperatively , simultaneously ) . We apply the master-equation model of stochastic gene expression [35] , [36] , [37] , [38] to increasingly complex promoter architectures [30] , and compute the moments of the mRNA and protein distributions expected for these promoters . Our results provide an expectation for how different architectural elements affect cell-to-cell variability in gene expression . The second point of this paper is to make use of stochastic kinetic models of gene regulation to put forth in vivo tests of the molecular mechanisms of gene regulation by transcription factors that have been proposed as a result of in vitro biochemical experiments . The idea of using spontaneous fluctuations in gene expression to infer properties of gene regulatory circuits is an area of growing interest , given its non-invasive nature and its potential to reveal regulatory mechanisms in vivo . Different theoretical methods have recently been proposed , which could be employed to distinguish between different modes ( e . g . AND/OR ) of combinatorial gene regulation , and to rule out candidate regulatory circuits [27] , [39] , [40] based solely on properties of noise in gene expression , such as the autocorrelation function of the fluctuations [27] or the three-point steady state correlations between multiple inputs and outputs [39] , [40] . Here , we make experimentally testable predictions about the level of cell-to-cell variability in gene expression expected for different bacterial promoters , based on the physical kinetic models of gene regulation that are believed to describe these promoters in vivo . In particular , we focus on how varying the different parameters ( i . e . , mutating operators to make them stronger or weaker , varying the intracellular concentration of transcription factors , etc . ) should affect the level of variability . This way , cell-to-cell variability in gene expression is used as a tool for testing kinetic models of transcription factor mediated regulation of gene expression in vivo . The remainder of the paper is organized as follows: First we describe the theoretical formalism we use to determine analytic expressions for the moments of the probability distribution for both mRNA and protein abundances per cell . Next , we examine how the architecture of the promoter affects cell-to-cell variability in gene expression . We focus on simple and cooperative repression , simple and cooperative activation , and transcriptional regulation by distal operators mediated by DNA looping . We investigate how noise in gene expression caused by promoter activation differs from repression , how operator multiplicity affects noise in gene expression , the effect of cooperative binding of transcription factors , as well as DNA looping . For each one of these architectures we present a prediction of cell-to-cell variability in gene expression for a bacterial promoter that has been well characterized experimentally in terms of their mean expression values . These predictions suggest a new round of experiments to test the current mechanistic models of gene regulation at these promoters .
For proteins , the picture is only slightly more complicated . As shown in the Text S1 , in the limit where the lifetime of mRNA is much shorter than that of the protein it encodes for ( a limit that is often fulfilled [30] ) , the noise strength of the probability distribution of proteins per cell takes the following form ( where we define n as a state variable that represents the copy number of proteins per cell ) : ( 13 ) where stands for the protein degradation rate , and the constant b is equal to the protein burst size ( the average number of proteins produced by one mRNA molecule ) . The mean protein per cell is given by ( 14 ) and the vector is the solution of the algebraic equation: ( 15 ) The reader is referred to the Text S1 for a detailed derivation and interpretation of these equations . In the previous section we have shown that the noise for proteins and mRNA take very similar analytical forms . Indeed , if we define and , as the vector and matrix containing the average rates of protein synthesis for each promoter state , it is straightforward to see that equations ( 8 ) and ( 15 ) are mathematically equivalent , with the only difference being that in equation ( 15 ) the matrix represents the rates of protein synthesis , so all the rates of transcription are multiplied by the translation burst size b . Therefore , the vectors and are only going to differ in the prefactor b multiplying all the different transcription rates . We conclude that the promoter contribution to the noise takes the exact same analytical form both for proteins and for mRNA , with the only other quantitative difference being the different rates of degradation for proteins and mRNA . Therefore , promoter architecture has the same qualitative effect on cell-to-cell variability in mRNA and protein numbers . All the conclusions about the effect of promoter architecture on cell-to-cell variability in mRNA expression are also valid for proteins , even though quantitative differences do generally exist . For the sake of simplicity we focus on mRNA noise for the remainder of the paper . In order to evaluate the equations in our model , we use parameters that are consistent with experimental measurements of rates and equilibrium constants in vivo and in vitro , which we summarize in Table 1 . Although these values correspond to specific examples of E . coli promoters , like the Plac or the PRM promoter , we extend their reach by using them as “typical” parameters characteristic of bacterial promoters , with the idea being that we are trying to demonstrate the classes of effects that can be expected , rather than dissecting in detail any particular promoter . The rate of association for transcription factors to operators in vivo is assumed to be the same as the recently measured value for the Lac repressor , which is close to the diffusion limited rate [48] . In order to test whether the particular selection of parameters in Table 1 is biasing our results , we have also done several controls ( See Figures 2–4 in Text S1 ) in which the kinetic parameters were randomly sampled . We found that the conclusions reached for the set of parameters in Table 1 are valid for other parameter sets as well . Operator strength reflects how tightly operators bind their transcription factors , and it is quantitatively characterized by the equilibrium dissociation constant . The dissociation constant has units of concentration and is equal to the concentration of free transcription factor at which the probability for the operator to be occupied is 1/2 . is related to the association and dissociation rates by , where koff is the rate ( i . e . , the probability per unit time ) at which a transcription factor dissociates from the promoter , and is a second order rate constant , which represents the association rate per unit of concentration of transcription factors , i . e . , . Note that in the last formula , which has units of s−1 , is written as the product of two quantities: , which is the concentration ( in units of ( mol/liter ) ) of transcription factors inside the cell , and , a second order rate constant that has units of ( mol/liter ) −1s−1 . For simplicity , we assume that the binding reaction is diffusion limited , namely , is already close to its maximum possible value , so the only parameter that can differ from operator to operator is the dissociation rate: strong operators have slow dissociation rates , and weak operators have large dissociation rates . Throughout this paper , we also make the assumption that the mean expression level is controlled by varying the intracellular concentration of transcription factors , a scenario that is very common experimentally [49] , [50] , [51] . We also assume that changing the intracellular concentration of transcription factors only affects the association rate of transcription factors to the operators , but the dissociation rate and the rates of transcription at each promoter state are not affected . In other words , is a constant parameter for each operator , and it is not changed when we change the mean by titrating the intracellular repressor level . All of these general assumptions need to be revisited when studying a specific gene-regulatory system . Here our focus is on illustrating the general principles associated with different promoter architectures typical of those found in prokaryotes . To generate mRNA time traces , we applied the Gillespie algorithm [52] to the master equation described in the text . A single time step of the simulation is performed as follows: one of the set of possible trajectories is chosen according to its relative weight , and the state of the system is updated appropriately . At the same time , the time elapsed since the last step is chosen from an exponential distribution , whose rate parameter equals the sum of rate parameters of all possible trajectories . This process is repeated iteratively to generate trajectories that exactly reflect dynamics of the underlying master equation . For the figures , simulation lengths were set long enough for the system to reach steady state and for a few promoter state transitions to occur . To generate the probability distributions , it is convenient to reformulate the entire system of mRNA master equations in terms of a single matrix equation . To do this , we first define a vector ( 16 ) where is the joint probability of having mRNAs while in the th promoter state . Then the master equation for time evolution of this probability vector is ( 17 ) where each element of the matrix is itself an N by N matrix as described in the text . Then finding the steady-state distribution is equivalent to finding the eigenvector of the above matrix associated with eigenvalue 0 . To perform this calculation numerically , one must first choose an upper bound on mRNA copy number in order to work with finite matrices . In this work , we chose an upper bound six standard deviations above mean mRNA copy number as an initial guess , and then modified this bound if necessary . Computations were performed using the SciPy ( Scientific Python ) software package .
We first investigate a promoter architecture consisting of a single repressor binding site , and examine how operator strength affects intrinsic variability in gene expression . Although this particular mode of gene regulation has been well studied theoretically before [1] , [16] , [36] , [37] , [45] , it is a useful starting point for illustrating the utility of this class of models . Within this class of models , when the repressor is bound to the operator , it interferes with transcription initiation and transcription does not occur . When the repressor dissociates and the operator is free , RNAP can bind and initiate transcription at a constant rate . The probability per unit time that a bound repressor dissociates is , and the probability per unit time that a free repressor binds the empty operator is , where is the second-order association constant and is the intracellular repressor concentration . The rate of mRNA degradation per molecule is . This mechanism is illustrated in Figure 2B . We compute the mean and the Fano factor for this architecture following the algorithm described in the Mathematical Methods section . The kinetic rate and transcription rate matrices and are shown in Table S1 in Text S1 . For this simple architecture , the mean of the mRNA probability distribution and the normalized variance take simple analytical forms: ( 18 ) ( 19 ) Using the relationship between and the intracellular concentration of repressor , we can write the mean as: ( 20 ) Here we have defined the equilibrium dissociation constant between the repressor and the operator as: . It is interesting to note that equation ( 20 ) could have been derived using the thermodynamic model approach [33] , [34] , [41] , [53] . In particular we see that this expression is equal to the product of the maximal activity in the absence of repressor , and the so-called fold-change in gene expression: [34] . The fold-change is defined as the ratio of the level of expression in the presence of the transcription factor of interest , and the level of expression in the absence of the transcription factor . The Fano factor for the mRNA distribution can be computed from equation ( 12 ) and we obtain: ( 21 ) which is also shown as the first entry of Table S2 in Text S1 . In many experiments [4] , [15] , [31] , [50] , the concentration of repressor inside the cell ( and therefore the association rate ) can be varied by either expressing the repressor from an inducible promoter , or by adding an inducer that binds directly to the repressor rendering it incapable of binding specifically to the operators in the promoter region . When such an operation is performed , the only parameter that is varied is typically , and all other kinetic rates are constant . The Fano factor can thus be re-written as a function of the mean mRNA , and we find: ( 22 ) Therefore , for any given value of the mean , the Fano factor depends only on two parameters: the maximal mRNA or protein expression per cell , and a parameter that reflects the strength of binding between the repressor and the operator: . Equations ( 20 ) and ( 21 ) reveal that changes in the mean due to repressor titration affect the noise as well as the mean . Since neither the repressor dissociation rate nor the mRNA degradation rates are affected by the concentration of repressors , is a constant parameter that will determine how large the cell-to-cell variability is: The Fano factor is maximal for promoters with very strong operators , ( <<γ , and it goes to 1 ( i . e . , the distribution tends to a Poisson distribution ) when the operator is very weak and the rate of dissociation extremely fast ( >>γ ) . In the latter limit of fast promoter kinetics , the fast fluctuations in promoter occupancy are filtered by the long lifetime of mRNA . Effectively , mRNA degradation acts as a low-pass frequency filter [54] , [55] , and fast fluctuations in promoter occupancy are not propagated into mRNA fluctuations . Therefore , promoters with strong operators are expected to be noisier than promoters with weak operators [56] . From this discussion it should also be clear that the mRNA degradation rate critically affects cell-to-cell variability . Any processes that tend to accelerate degradation will tend to increase noise , and mRNA stabilization ( i . e . , protection of the transcript by RNA binding proteins ) leads to reduction of variability . However , the focus of this article is on promoter architecture and transcriptional regulation . Therefore , we do not consider regulation of transcription by mRNA degradation , and assume that all the promoters transcribe the same mRNA as is often the case in experimental studies . The effect of operator strength on the output of transcription and translation is illustrated in Figure 2A , where we show results from a stochastic simulation of the model depicted in Figure 2B , for the case of a weak and a strong operator . The simulation yields trajectories in time for the promoter state , the mRNA , and protein number , as well as the steady state distribution of mRNA number . Concentrations of repressor in the simulations were chosen so that the mean expression level was equal for the two different promoter architectures . As expected from the general arguments presented above , we clearly see that the level of variability is smaller for the weak operator than for the strong operator , due to faster promoter switching leading to smaller mRNA fluctuations and a more Poisson-like mRNA distribution ( Figure 2A , weak promoter ) . Slow dissociation from a strong operator , on the other hand , causes slow promoter state fluctuations and a highly non-Poissonian mRNA distribution , with few cells near the mean expression level ( see Figure 2A , strong promoter ) . In order to show that the effect of operator strength on the cell-to-cell variability is general and does not depend on the particular set of parameters chosen in the simulation , in figures 2C and 2D , we show the normalized variance and the Fano factor as a function of the fold-change in the mean mRNA concentration for a strong operator whose dissociation rate is ( a value that is representative of well characterized repressor-operator interactions such as the Lac repressor-lacOid , or the cI2- ) , and for a single weak operator whose dissociation rate is 10 times larger . The Fano factor has a characteristic shape whereby it takes values approaching 1 at low and high transcription levels with a peak at intermediate values . The reason for this shape is that for very low transcription levels the promoter is nearly always inactive , firing only very rarely . In this limit successive transcription events become uncorrelated and the time in between them is exponentially distributed , leading to a distribution of mRNA per cell that approaches a Poisson distribution characterized by a Fano factor equal to 1 . In contrast , for very high transcription levels the promoter is nearly always active , switching off very rarely and staying in the off state for short times . In this limit , transcription events are again uncorrelated and exponentially distributed , leading once again to a Poisson distribution of mRNA number . It is only for intermediate values of the mean that the promoter is switching between a transcriptionally active and an inactive state . This causes transcription to occur in bursts , and the mRNA distribution to deviate from Poisson , leading to a Fano factor that is larger than 1 . In Figure 2E we plot the fold-change in protein noise due to gene regulation for the simple repression architecture . As expected , we find that the effect of operator strength in protein noise is qualitatively identical to what we found for mRNA . Since the same can be said of all the rest of architectures studied , we will limit the discussion to mRNA noise for the rest of the paper , with the understanding that for the class of models considered here , all the conclusions about the effect of promoter architecture in cell-to-cell variability that are valid for mRNA , are true for intrinsic protein noise as well . In Figure 2 , and throughout this paper , we plot the Fano factor as a function of transcription level , which is characterized by the fold-change in gene expression . The fold-change in gene expression is defined as the mean mRNA number in the presence of the transcription factor , normalized by the mean mRNA in the absence of the transcription factor . For architectures based on repression , the fold-change in gene expression is always less than 1 , since the repressor reduces the level of transcription . For example , a fold-change in gene expression of 0 . 1 means that in the presence of repressor , the transcription level is 10% of the value it would have if the repressor concentration dropped to 0 . For the case of activators , the fold-change is always greater than 1 , since activators raise the level of transcription . An example of the single repressor-binding site architecture is a simplified version of the PlacUV5 promoter , which consists of a single operator overlapping with the promoter . Based on a simple kinetic model of repression , in which the Lac repressor competes with RNAP for binding at the promoter , we can write down the and matrices and compute the cell-to-cell variability in mRNA copy number . The matrices are presented in Table S1 in Text S1 . Based on our previous analysis , we know that stronger operators are expected to cause larger noise and higher values of the Fano factor than weaker operators . Therefore , we expect that if we replace the wild-type O1 operator by the 10 times weaker O2 operator , or by the ∼500 times weaker operator O3 , the fold-change in noise should go down . Using our best estimates and available measurements for the kinetic parameters involved , we find that noise is indeed much larger for O1 than for O2 , and it is negligible for O3 . This prediction is presented as an inset in Figure 2C . Dual repression occurs when promoters contain two or more repressor binding sites . Here , we consider three different scenarios for architectures with two operators: 1 ) repressors bind independently to the two operators , 2 ) repressors bind cooperatively to the two operators and 3 ) one single repressor may be bound to the two operators simultaneously thereby looping the intervening DNA . At the molecular level , cooperative repression is achieved by two weak operators that form long-lived repressor-bound complexes when both operators are simultaneously occupied . Transcription factors may stabilize each other either through direct protein-protein interactions [53] , or through indirect mechanisms mediated by alteration of DNA conformation [57] . Transcriptional activators bind to specific sites at the promoter from which they increase the rate of transcription initiation by either direct contact with one or more RNAP subunits or indirectly by modifying the conformation of DNA around the promoter [57] . The simplest example of an activating promoter architecture consists of a single binding site for an activator in the vicinity of the RNAP binding site . When the activator is not bound , transcription occurs at a low basal rate . When the activator is bound , transcription occurs at a higher , activated rate . Stochastic association and dissociation of the activator causes fluctuations in transcription rate which in turn cause fluctuations in mRNA copy number . This simple activation architecture is illustrated in Figure 1A . The and matrices for this architecture are given in Table S1 in Text S1 . Solving equations ( 5–8 ) for this particular case , we find that the mean mRNA per cell for this simple mechanism takes the form: ( 23 ) The mean mRNA can be changed by adjusting the intracellular concentration of the activator . The rate at which one of the activators binds to the promoter is proportional to the activator concentration: . Following the same argument as we used in the simple repression case , the equilibrium dissociation constant for the activator-promoter interaction is given by . Finally , it is convenient to define the enhancement factor: the ratio between the rate of transcription in the active and the basal states . The mean mRNA can be written in terms of these parameters as: ( 24 ) The Fano factor can be computed using equations ( 5–12 ) and it is shown as entry 2 of Table S2 in Text S1 . We can rewrite the equation appearing in Table S2 in Text S1 by writing as a function of the mean: ( 25 ) With these equations in hand , we explore how operator strength affects noise in gene expression in the case of activation . Stronger operators bind to the activator more tightly than weak operators , leading to longer residence times of the promoter in the active state . In Figure 5A we plot the Fano factor as a function of the fold-change in mean expression for a strong operator as well as a 10 times weaker operator . We have used the parameters in Table 1 . Just as we saw for the simple repression architecture , it is also true for the simple activation architecture that stronger operators cause larger levels of noise for activators than weaker operators . To get a sense of the differences between these two standard regulatory mechanisms , we compare simple repression with simple activation . In Figure 5B , we plot the Fano factor as a function of the mean for a repressor and an activator with identical dissociation rates . We assume that the promoter switches between a transcription rate in its inactive state ( which happens when the repressor is bound in the simple repression case , or the activator is not bound in the simple activation case ) , and a rate equal to ( see Table 1 ) in the active state ( repressor not bound in the simple repression case , activator bound in the simple activation case ) . As shown in Figure 5B , at low expression levels the simple activation is considerably ( >20 times ) noisier than the simple repression promoter . At high expression levels both architectures yield very similar noise levels , with the simple repression architecture being slightly noisier . A low level of gene expression may be achieved either by low concentrations of an activator , or by high concentrations of a repressor . Low concentrations of an activator will lead to rare activation events . High concentrations of a repressor will lead to frequent but short-lasting windows of time for which the promoter is available for transcription . As a result , and as we illustrate in Figure 5C , the activation mechanism leads to bursty mRNA expression whereas the repressor leads to Poissonian mRNA production . This result suggests that in order to maintain a homogeneously low expression level , a repressive strategy in which a high concentration of repressor ensures low expression levels may be more adequate than a low activation strategy . We confirmed that this statement is true for other parameter sets in addition to the particular choice used above . We randomly sampled the rates of activator and repressor dissociation , as well as the rates of basal and maximum transcription . As shown in Figure 3 in Text S1 , the statement that the simple activation architecture is noisier than the simple repression architecture at low expression ( less than 10 mRNA/cell ) levels is valid for a wide range of parameter values , with over 99% of the conditions sampled leading to this conclusion . An example of simple activation is the wild-type promoter , which is activated by CRP when complexed with cyclic AMP ( cAMP ) . CRP is a ubiquitous transcription factor , and is involved in the regulation of dozens of promoters , which contain CRP binding sites of different strengths [66] . In the inset of Figure 5A we include CRP as an example of simple activation , and make predictions for how changing the wild-type CRP binding site in the promoter by the CRP binding site of the promoter ( which is ∼8 times weaker [67] ) , should affect the Fano factor . As expected from our analysis of this class of promoters , the noise goes down . Dual activation architectures have two operator binding sites . Simultaneous binding of two activators to the two operators may lead to a larger promoter activity in different ways . For instance , in some promoters each of the activators may independently contact the polymerase , recruiting it to the promoter . As a result , the probability to find RNAP bound at the promoter increases and so does the rate of transcription [33] , [68] . In other instances , there is no increase in enhancement factor when the two activators are bound . However , the first activator recruits the second one through protein-protein or protein-DNA interactions , stabilizing the active state and increasing the fraction of time that the promoter is active [59] . These two modes are not mutually exclusive , and some promoters exhibit a combination of both mechanisms [69] . We first investigate the effect of dual activation in the limit where binding of the two transcription factors is not cooperative . Assuming that activators bound at the two operators independently recruit the polymerase , we compare this architecture with the simple activation architecture . The mechanism of activation is depicted in Figure 6A , and matrices and are presented in Table S1 in Text S1 . For simplicity , we assume that both operators have the same strength , and both have the same enhancement factor . When the two activators are bound , the total enhancement factor is given by the product of the individual enhancement factors , which in this case is [33] . All of the other relevant kinetic parameters are given in Table 1 . The Fano factor is plotted in Figure 6B . We find that compared to the single operator architecture , the second operator increases the level of variability , even when binding to the operators is non-cooperative . We then ask whether this is also true when the binding of activators is cooperative . We assume a small cooperativity factor . Just as we found for repressors , cooperative binding of activators generates larger cell-to-cell variability than independent binding , which in turn generates larger cell-to-cell variability than simple activation . This is illustrated in the stochastic simulation in Figure 6C . As expected the dual activation architectures are noisier than the simple activation , characterized by rare but long lived activation events that lead to large fluctuations in mRNA levels . In contrast , the simple activation architecture leads to more frequent but less intense activation events . Together with the results from the dual repressor mechanism , these results indicate that multiplicity in operator number may introduce significant intrinsic noise in gene expression . Multiple repeats of operators commonly appear in eukaryotic promoters [1] , [70] , [71] , but are often found in prokaryotic promoters as well [59] , [68] , [72] . It is interesting to note that this prediction of the model is in qualitative agreement with the findings by Raj et al . [2] who report an increase in cell-to-cell variability in mRNA when the number of activator binding sites was changed from one to seven . An example of cooperative activation is the lysogenic phage-λ PRM promoter [59] . This promoter contains three operators ( OR1 , OR2 and OR3 ) for the cI protein , which acts as an activator . When OR2 is occupied , cI activates transcription . OR1 has no direct effect on the transcription rate , but it helps recruit cI to OR2 , since cI binds cooperatively to the two operators . Finally , OR3 binds cI very weakly , but when it is occupied , PRM becomes repressed . There are variants of this promoter [50] that harbor mutations in OR3 that make it unable to bind cI . In Figure 6D , we include one of these variants , r1-PRM [51] as an example of dual activation , and we present a theoretical prediction for the promoter noise as a function of the mean mRNA . We examine the role of cooperativity by comparing the wild-type cI , with a cooperativity deficient mutant . We find that the cooperative activator causes substantially larger cell-to-cell variability than the mutant , emphasizing our expectation that cooperativity may cause substantial noise in gene expression in bacterial promoters such as PRM .
There are two different classes of sources of cell-to-cell variability in gene expression . The first class has its origins in the intrinsically stochastic nature of the chemical reactions leading to the production and degradation of mRNAs and proteins , including the binding and unbinding of transcription factors , transcription initiation , mRNA degradation , translation , and protein degradation . The noise coming from these sources is known as intrinsic noise [79] . A different source of variability originates in cell-to-cell differences in cell size , metabolic state , copy number of transcription factors , RNA polymerases , ribosomes , nucleotides , etc . This second kind of noise is termed extrinsic noise [79] . The contributions from intrinsic and extrinsic sources can be separated experimentally , and the total noise can be written as the sum of intrinsic and extrinsic components [3] . In this paper we focus exclusively on intrinsic noise , and the emphasis is on bacterial promoters . This double focus requires us to discuss to what extent intrinsic noise is relevant in bacteria . The experimental evidence gathered so far indicates that intrinsic noise is the dominant source of cell-to-cell variability in bacteria of the mRNA copy number . In a recent single-molecule study , transcription was monitored in real time for two different E . coli promoters , PRM and Plac/ara [4] . The authors measured the rates of mRNA synthesis and dilution , as well as the rates of promoter activation and inactivation in single cells . The intrinsic noise contribution was calculated from all of these rates . It was found to be responsible for the majority of the total cell-to-cell variability , accounting for over 75% of the total variance . Another recent experiment in B . subtilis [7] found that mRNA expressed from the ComK promoter is also dominated by intrinsic noise . Furthermore , this study indicated that intrinsic mRNA noise is responsible for activation of a phenotypic switch that drives a fraction of the cells to competence for the uptake of DNA [7] . A third recent report investigated the activation of the genetic switch in E . coli , which drives the entrance of a fraction of cells into a lactose metabolizing phenotype [23] . The authors of the study found evidence that stochastic binding and unbinding of the Lac repressor to the main operator was responsible for the observed cell-to-cell variability in gene expression and , consequently the choice of phenotype . Furthermore , the authors discovered that the deletion of an auxiliary operator that permits transcriptional repression by DNA looping , leads to a strong increase in the level of cell to cell variability in the expression of the lactose genes , indicating that promoter architecture plays a big role in determining the level of noise and variability in this system . Taken all together , these experiments suggest that intrinsic mRNA noise is dominant and may have important consequences for cell fate determination . In addition , at least in one case , promoter architecture has been shown to be of considerable importance . At the protein level , the contribution of extrinsic and intrinsic noise to the total cell-to-cell variability has also been determined experimentally for a variety of promoters and different kinds of bacteria . The first reports examined intrinsic and extrinsic protein noise in E . coli and found that extrinsic noise was the dominant source of cell-to-cell variability in protein expressed from a variant of the PL promoter in a variety of different strains [3] . However , the intrinsic component was non-negligible and for some strains , dominant [3] . A second team of researchers examined a different set of E . coli promoters involved in the biosynthetic pathway of lysine [80] . The authors found that the intrinsic noise contribution was significant for some promoters ( i . e . lysA ) , but not for others . In a third study the total protein noise was measured for a Lac repressor-controlled promoter in B . subtilis , and it was reported that the data could be well explained by a model consisting only of intrinsic noise [8] . The authors found that the rates of transcription and translation could be determined by directly comparing the total cell-to-cell variability to the predictions of a simple stochastic model that considered only intrinsic sources of noise . They also found that the model had predictive power , and that mutations that enhanced the rate of translation or transcription produced expected effects in the total noise . In summary , all studies that have measured mRNA noise in bacteria so far report that intrinsic noise contributes substantially to the total cell-to-cell variability . This is further supported by observations that most of the mRNA variability comes from intrinsic sources in yeast [31] and mammalian cells [1] . The issue is less clear for protein noise . Some reports indicate that it is mostly extrinsic [3] , but others suggest that intrinsic noise may also be important [8] , [23] , [80] . It seems likely that the relative importance of intrinsic and extrinsic noise depends on the context , and that for some promoters and genes extrinsic noise will be larger , whereas for others the intrinsic component may dominate . In any case , it is clear that both contributions are important , and both need to be understood . The aim of this paper is to formulate a set of predictions that reflect the class of kinetic models of gene regulation in bacteria that one routinely finds in the literature [42] , [64] , [81]–[84] . Our analysis indicate that if these models are correct , and if the kinetic and thermodynamic parameters that have been measured over the years are also reasonably close to their real values in live cells [85] , the effect of promoter architecture in cell-to-cell variability in bacteria should be rather large and easily observable . In this sense , our intention is more to motivate new experiments than to explain or fit any currently available data . We only know of one published report in which the effect of perturbing the architecture of a bacterial promoter on the cell-to-cell variability in gene expression has been determined [23] . Given that there are several examples of promoters in bacteria for which a molecular kinetic mechanism of gene regulation has been formulated [42] , [64] , [81]–[84] , [86] , we hope that the computational analysis in this paper may serve as an encouragement for researchers to do for bacteria the same kind of experiments that have been already performed in eukaryotes [1] , [11] , [15] , [17] , [31] . Indeed , several different studies have examined the effect of promoter architectural elements in cell-to-cell variability in protein and mRNA in eukaryotic cells . Although our efforts in this paper have focused on bacterial promoters rather than eukaryotic promoters , it is worthwhile to discuss the findings of these studies and compare them ( if only qualitatively ) with the predictions made in this paper . Two recent studies measured intrinsic mRNA noise in yeast [31] and mammalian cells [1] . Both papers concluded that stochastic promoter activation and inactivation was the leading source of intrinsic noise . While stochastic chromatin remodeling is suspected to be the origin of those activation events , neither one of these studies was conclusive about the precise molecular mechanism responsible for promoter activation . However , both studies found that promoter architecture had an important role and strongly affected the level of total mRNA noise . In both studies , the authors found that when the number of binding sites for a transcriptional activator was raised from one to seven , the normalized variance increased several-fold . This qualitative behavior is in agreement with our prediction that dual activation causes larger intrinsic mRNA noise than simple activation . It is possible that this agreement is coincidental , since the actual mechanism of gene regulation at these promoters could be much more complicated than the simple description of gene activation at a bacterial promoter adopted here . Other studies [11] , [15] , [17] have measured the total protein noise from variants of the GAL1 promoter in yeast , and found that their data could be well explained by a model that considered only intrinsic noise sources . These studies also concluded that the main sources of intrinsic noise were stochastic activation and inactivation of the promoter due to chromatin remodeling . However , it was also found that the stable formation of pre-initiation complex at the TATA box and the stochastic binding and unbinding of transcriptional repressors contributed to the total noise [11] , [15] , [17] . The authors of these studies found that for point mutations in the TATA box of the GAL1 promoter in yeast , which made the box weaker , the level of cell to cell variability went down significantly . This is also in good agreement with our prediction that the stronger the binding site of a transcriptional activator , the larger the intrinsic noise should be . However , since this study measured the total noise strength , and did not isolate the intrinsic noise , the observed decrease in noise strength as a result of making the TATA box weaker may have other origins . These experiments were conducted under induction conditions that minimize repression by nucleosomes and activation by chromatin remodeling . A more recent report by the same lab [11] found that the copy number and location of a transcriptional repressor binding site greatly affects the total protein noise . The authors found that when they increased the number of repressor binding sites , the noise went up . This is also in qualitative agreement with our prediction that operator number positively correlates with intrinsic noise in the case of dual repression . However , the same caveat applies here as in the previous case studies , which is that only the total noise was measured . Although the authors of this study attributed all of the noise to intrinsic sources , it is still possible that extrinsic noise was responsible for the observed dependence of noise strength on operator number . Finally , it is worth going back to bacteria , and discussing the only study that has yet examined the effect of a promoter-architecture motif on cell-to-cell variability in gene expression . In this paper , the authors investigated the effect of DNA looping on the total cell-to-cell variability for the PlacUV5 promoter in E . coli [23] . Using a novel single-protein counting technique , Choi and co-workers measured protein distributions for promoters whose auxiliary operator had been deleted ( leaving them with a simple repression architecture ) , and compared them to promoters with the auxiliary operator O3 present , which allows for DNA looping . They report a reduction in protein noise due to the presence of O3 , which according to our analysis , may indicate that the dissociation of the repressor from the looped state is faster than the normal dissociation rate . The authors attributed this looping-dependent decrease in noise to intrinsic origins , related to the different kinetics of repressor binding and rebinding to the main operator in the presence of the auxiliary operator , and in its absence . However , their measurements also reflect the total noise , and not only the intrinsic part , so the explanation may lie elsewhere . These results emphasize the need for more experiments in which the intrinsic noise is isolated and measured directly . More recently , several impressive experimental studies have measured the noise in mRNA in bacteria for a host of different promoters ( [87] , and Ido Golding , private communication ) . In both of these cases , simplified low-dimensional models which do not consider the details of the promoter architecture have been exploited to provide a theoretical framework for thinking about the data . Our own studies indicate that the differences between a generic two-state model and specific models that attempt to capture the details of a given architecture are sometimes subtle and that the acid test of ideas like those presented in this paper can only come from experiments which systematically tune parameters , such as the repressor concentration , for a given transcriptional architecture . Some recent theoretical work has analyzed the effect of cooperative binding of activators in the context of particular examples of eukaryotic promoters [88] , [89] . The main focus of this study is bacterial promoters . The simplicity of the microscopic mechanisms of transcriptional regulation for bacterial promoters makes them a better starting point for a systematic study like the one we propose . However , many examples of eukaryotic promoters have been found whose architecture affects the cell-to-cell variability [1] , [11] , [17] , [31] , [32] . Although the molecular mechanisms of gene regulation in these promoters are much more complex , with many intervening global and specific regulators [90] , the stochastic model employed in this paper can be applied to any number of promoter states , and thus can be applied to these more complex promoters . Recent experimental work is starting to reveal the dynamics of nucleosomes and transcription factors with single-molecule sensitivity [91] , [92] , allowing the formulation of quantitative kinetic and thermodynamic mechanistic models of transcriptional regulation at the molecular level [73] , [77] . The framework for analyzing gene expression at the single-cell level developed in this paper will be helpful to investigate the kinetic mechanisms of gene regulation in eukaryotic promoters , as the experimental studies switch from ensemble , to single-cell . Although the model of transcriptional regulation used in this paper is standard in the field , it is important to remark that it is a very simplified model of what really happens during transcription initiation . There are many ways in which this kind of model can fail to describe real situations . For instance , mRNA degradation requires the action of RNases . These may become saturated if the global transcriptional activity is very large , and degradation the becomes non-linear [55] . Transcription initiation and elongation are assumed to be jointly captured in a single constant rate of mRNA synthesis for each promoter state . This is an oversimplification also . When considered explicitly , and in certain parameter ranges , the kinetics of RNAP-promoter interaction may cause noticeable effects in the overall variability [46] . Similarly , as pointed out elsewhere [93] , [94] , [95] , translational pausing , backtracking or road-blocking may also cause significant deviations in mRNA variability from the predictions of the model used in this paper . How serious these deviations are depends on the specifics of each promoter-gene system . The model explored in this paper also assumes that the cell is a well-mixed environment . Deviations from that approximation can significantly affect cell-to-cell variability [56] , [96] . Another simplification refers to cell growth and division , which are not treated explicitly by the model used in this paper: cell division and DNA replication cause doubling of gene and promoter copy number every cell cycle , as well as binomial partitioning of mRNAs between mother and daughter cells [3] . In eukaryotes , mRNA often needs to be further processed by the splicing apparatus before it becomes transcriptionally active . It also needs to be exported out of the nucleus , where it can be translated by ribosomes . To study the effect of transcription factor dynamics on mRNA noise we assume that the unregulated promoter produces mRNA in a Poisson manner , at a constant rate . This assumption can turn out to be wrong if there is another process , independent of transcription factors , that independently turns the promoter on and off . In eukaryotes examples of such processes are nucleosome positioning and chromatin remodeling , while in prokaryotes analogous processes are not as established , but could include the action of non-specifically bound nucleoid proteins such as HU and HNS , or DNA supercoiling . Experiments that measure cell-to-cell distributions of mRNA copy number in the absence of transcription factors ( say without Lac repressor for the lac operon case ) can settle this question . In case the Fano factor for this distribution is not one ( as expected for a Poisson distribution ) this can signal a possible transcription factor-independent source of variability . The stochastic models studied here can be extended to account for this situation . For example , the promoter can be made to switch between an on and an off state , where the transcription factors are allowed to interact with promoter DNA only while it is in the on state . In this case the mRNA fluctuations produced by an unregulated promoter will not be Poissonian . One can still investigate the affect of transcription factors by measuring how they change the nature of mRNA fluctuations from this new base-line . Comparison of this extended model with single-cell transcription experiments would then have the exciting potential for uncovering novel modes of transctriptional regulation in prokaryotes . For the purpose of isolating the effect of individual promoter architectural elements on cell-to-cell variability in gene expression , we have artificially changed the value of one of those parameters , while keeping the other parameters constant . For instance , we have investigated the effect of altering the strength of an operator on the total cell-to-cell variability . In order to do this , we ask how changes in the dissociation rate of the transcription factor alter the cell-to-cell variability , given that all other rates ( say the rate of transcription , or mRNA degradation ) remain constant . This assumption is not necessarily always correct , since very often the operator sequence overlaps the promoter , and therefore changes in the sequence that alter operator strength also affect the sequence from which RNAP initiates transcription , which can potentially affect the overall rates of transcription . As is usually the case , biology presents us with a great diversity of forms , shapes and functions , and promoters are no exception . One needs to examine each promoter independently on the basis of the assumptions made in this paper , as many of these assumptions may apply for some promoters , but not for others . For the same reason of isolating the effect of promoter architecture and cis-transcriptional regulation on cell-to-cell variability in gene expression , when we compare different architectures we make the simplifying assumption that they are transcribing the same gene , and therefore that the mRNA transcript has the same degradation rate . Care must be taken to take this into account when promoters transcribing different genes are investigated , since the mRNA degradation rate has a large effect on the level of cell-to-cell variability . We have also assumed that when transcription factors dissociate from the operator , they dissociate into an averaged out , well-mixed , mean-field concentration of transcription factors inside the cell . The possibility of transcription factors being recaptured by the same or another operator in the promoter right after they fall off the operator is not captured by the class of models considered here . Recent in vivo experiments suggest that this scenario may be important in yeast promoters containing arrays of operators [31] . In spite of all of the simplifications inherent in the class of models analyzed in this paper , we believe they are an adequate jumping off point for developing an intuition about how promoter architecture contributes to variability in gene expression . Our approach is to take a highly simplified model of stochastic gene expression , based on a kinetic model for the processes of the central dogma of molecular biology , and add promoter dynamics explicitly to see how different architectural features affect variability . This allows us to isolate the effect of promoter dynamics , and develop an intuitive understanding of how they affect the statistics of gene expression . It must be emphasized , however , that the predictions made by the model may be wrong if any of the complications mentioned above are significant . This is not necessarily a bad outcome . If the comparison between experimental data and the predictions made by the theory for any particular system reveals inconsistencies , then the model will need to be refined and new experiments are required to identify which of the sources of variability that are not accounted for by the model are in play . In other words , experiments that test the quantitative predictions outlined stand a chance of gaining new insights about the physical mechanisms that underlie prokaryotic transcriptional regulation . | Stochastic chemical kinetics provides a framework for modeling gene regulation at the single-cell level . Using this framework , we systematically investigate the effect of promoter architecture , that is , the number , quality and position of transcription factor binding sites , on cell-to-cell variability in transcription levels . We compare architectures resulting in transcriptional activation with those resulting in transcriptional repression . We start from simple activation and repression motifs with a single operator sequence , and explore the parameter regime for which the cell-to-cell variability is maximal . Using the same formalism , we then turn to more complicated architectures with more than one operator . We examine the effect of independent and cooperative binding , as well as the role of DNA mechanics for those architectures where DNA looping is relevant . We examine the interplay between operator strength and operator number , and we make specific predictions for single-cell mRNA-counting experiments with well characterized promoters . This theoretical approach makes it possible to find the statistical response of a population of cells to perturbations in the architecture of the promoter; it can be used to quantitatively test physical models of gene regulation in vivo , and as the basis of a more systematic approach to designing new promoter architectures . |
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Neuronal computations strongly depend on inhibitory interactions . One such example occurs at the first retinal synapse , where horizontal cells inhibit photoreceptors . This interaction generates the center/surround organization of bipolar cell receptive fields and is crucial for contrast enhancement . Despite its essential role in vision , the underlying synaptic mechanism has puzzled the neuroscience community for decades . Two competing hypotheses are currently considered: an ephaptic and a proton-mediated mechanism . Here we show that horizontal cells feed back to photoreceptors via an unexpected synthesis of the two . The first one is a very fast ephaptic mechanism that has no synaptic delay , making it one of the fastest inhibitory synapses known . The second one is a relatively slow ( τ≈200 ms ) , highly intriguing mechanism . It depends on ATP release via Pannexin 1 channels located on horizontal cell dendrites invaginating the cone synaptic terminal . The ecto-ATPase NTPDase1 hydrolyses extracellular ATP to AMP , phosphate groups , and protons . The phosphate groups and protons form a pH buffer with a pKa of 7 . 2 , which keeps the pH in the synaptic cleft relatively acidic . This inhibits the cone Ca2+ channels and consequently reduces the glutamate release by the cones . When horizontal cells hyperpolarize , the pannexin 1 channels decrease their conductance , the ATP release decreases , and the formation of the pH buffer reduces . The resulting alkalization in the synaptic cleft consequently increases cone glutamate release . Surprisingly , the hydrolysis of ATP instead of ATP itself mediates the synaptic modulation . Our results not only solve longstanding issues regarding horizontal cell to photoreceptor feedback , they also demonstrate a new form of synaptic modulation . Because pannexin 1 channels and ecto-ATPases are strongly expressed in the nervous system and pannexin 1 function is implicated in synaptic plasticity , we anticipate that this novel form of synaptic modulation may be a widespread phenomenon .
Natural scenes contain a large amount of redundant information both in space and time . For optimal coding of the visual scene , most of these redundancies need to be removed . The inhibitory interaction between horizontal cells ( HCs ) and cones is the first step in this process . HCs estimate the global properties of the stimulus . This information is subtracted from the local information sampled by the cones . The result is a strong reduction of redundant information in the cone output signal . In order to reduce spatial redundancies , this feedback mechanism needs to be very fast . If this were not the case , the surround of the bipolar cell receptive field would lag the center for moving stimuli . In contrast , to reduce temporal redundancies , the inhibition needs to be very slow; otherwise , long-lasting activity would not be removed selectively . In this article we show that these two seemingly incompatible requirements are merged into a feedback mechanism that consists of a very fast ephaptic component and a very slow component that modulates the synaptic efficiency by changing the pH buffering in the synaptic cleft of cones . Cones project to HCs that are strongly coupled to each other electrically and provide negative feedback to the cones . Hyperpolarization of HCs by light shifts the activation potential of the presynaptic Ca2+ current ( ICa ) in cones to more negative potentials [1]–[5] , leading to a larger ICa . This increases the Ca2+ concentration in the cone synaptic terminal so more glutamate is released . This modulation is not mediated by GABA [1] , [3] . Two hypotheses have been put forward to account for the modulation of ICa: an ephaptic mechanism [6]–[8] and a proton-mediated mechanism [4] , [9] , [10] . The ephaptic feedback mechanism depends on connexin ( Cx ) hemichannels and possibly pannexin 1 ( Panx1 ) channels expressed at the tips of HC dendrites [6] , [7] , [11] , [12] . These channels form large pores in the cell membrane , leading to an inward current . Given the finite resistance of the synaptic cleft , this inward current creates a slight negativity in the synaptic cleft . This negativity is sensed by the voltage-gated Ca channels of the cones as a slight depolarization of the cone membrane potential . HC hyperpolarization will increase the inward current through the Cx hemichannels , thus increasing the negativity in the synaptic cleft . This will be visible as an inward current in voltage clamped cones , reflecting a shift of ICa to more negative potentials . A major unresolved issue with the ephaptic feedback hypothesis is that it predicts a very fast feedback signal , whereas other studies indicate a relatively slow process [13] , [14] . The proton-mediated feedback hypothesis is based on the pH sensitivity of ICa [15] . The activation potential of ICa shifts to more positive potentials in an acidic condition and to more negative potentials in an alkaline condition . To test the proton-mediated feedback hypothesis , Hirasawa and Kaneko [4] added 10 mM HEPES to the medium to reduce pH changes in the synaptic cleft and were able to show that the feedback-induced shift of ICa was reduced and sometimes even absent . They suggested that hyperpolarized HCs take up protons , which leads to an alkalization of the synaptic cleft . However , these experiments were not conclusive as more recent research found that 20 mM HEPES also induced a number of nonspecific effects , such as intracellular acidification and a direct inhibition of Cx hemichannels [16] . Moreover , the mechanism responsible for the proposed changes in proton concentration in the synaptic cleft has not yet been identified . The aim of this article is to determine the synaptic mechanisms underlying lateral inhibition in the vertebrate outer retina . We show that the negative feedback pathway from HCs to cones is a synthesis of an ephaptic feedback mechanism and a proton-mediated mechanism . The ephaptic mechanism is one of the fastest inhibitory systems known and is especially suitable for spatial redundancy reduction in a dynamic scene . The proton-mediated mechanism depends on extracellular hydrolysis of ATP . HCs release ATP via Panx1 channels located on their dendritic tips that invaginate the synaptic terminals of cones . Ecto-ATPases hydrolyze ATP , which generates protons and a phosphate pH buffer , leading to an acidification of the synaptic cleft that inhibits ICa . This pathway is very slow ( time constant of about 200 ms ) and does not involve purinergic or adenosine receptors . It is especially suitable for reducing temporal redundancies . Our findings not only resolve the longstanding controversy about the mechanism of negative feedback from HCs to cones , they also demonstrate a novel mechanism of synaptic modulation involving ATP released from Panx1 channels .
In zebrafish , the Cx hemichannels at the tips of the HC dendrites are formed by Cx55 . 5 . Zebrafish lacking this Cx ( Cx55 . 5−/− mutant ) have reduced feedback from HCs to cones [7] . Figure 1C shows feedback responses measured in cones of wild-type ( WT , black ) and Cx55 . 5−/− mutant ( red ) zebrafish . The feedback-induced current was reduced by 48±11% in the Cx55 . 5−/− mutant zebrafish ( n = 22 ) compared to WT ( n = 25 ) . To quantify the contribution of both feedback components , double exponential functions were fitted through the feedback responses . Although the time constants of both components were independent of the genotype ( Figure 1D ) , the amplitude of the fast feedback component was reduced in Cx55 . 5−/− mutants such that the fast and slow feedback components now equally contributed to the feedback response . The amplitude of the slow feedback component in Cx55 . 5−/− mutants ( 1 . 62±0 . 39 pA; n = 9 ) did not differ significantly from WT ( 1 . 87±0 . 39 pA; n = 13; p = 0 . 67 ) , illustrating that the reduction of feedback in the Cx55 . 5−/− mutant can be fully accounted for by the reduction of the fast feedback component . These experiments show that the fast component depends on Cx55 . 5 , whereas the slow component does not . Previously we have shown that carbenoxolone , a Cx antagonist , blocks light-induced feedback responses in cones completely [6] . Interestingly , carbenoxolone is also a potent blocker of Panx1 channels [11] . Could Panx1 channels mediate the slow component of feedback ? Panx1 channels are open at the resting membrane potential of HCs ( −35 mV ) , reduce their conductance with hyperpolarization [11] , [20] , [21] , and are modulated by intracellular Ca2+ [11] , [22] . Figure 2A shows that Panx1-IR ( green ) was present as punctated labeling in the outer plexiform layer ( OPL ) , suggesting Panx1 channels were localized at the tips of HC dendrites , which invaginate the cone's synaptic terminals [11] . Immuno-electron microscopy confirms this location . Panx1 labeling ( black dots ) was found in lateral elements flanking the synaptic ribbons ( R ) ( Figure 2B and C ) . Next we determined whether Panx1 channels were active in HCs . Goldfish HCs were dissociated according to a modified protocol of Dowling et al . [23] , [24] ( Figure 3A ) . Such a preparation consists of about 90% of HCs , and it has been used previously to quantify GABA release by HCs [24] . Probenecid is a specific inhibitor of Panx1 channels that does not block Cx hemichannels [25] ( see also Figure S3A ) . Whole-cell voltage clamp experiments on these dissociated HCs show that 500 µM probenecid blocks a current in the physiological membrane potential range with similar properties as previously described for Panx1 channels [11] , [20] , [21] ( Figure 3B; black , control; red , probenecid; green , probenecid blocked current ) . On average probenecid reduced the whole-cell current statistically significant amounts at positive and negative potentials and in the physiological range ( n = 6; asterisk means p<0 . 05 ) . In three of the six cells , where stable recordings could be maintained , partial recovery was obtained . Similar results were obtained by application of another specific Panx1 blocker , 20 µM BB FCF ( Figure S3B ) [26] . The Panx1-mediated current was small , most likely because most of the Panx1 channels will have been lost during the dissociation process , as Panx1 is preferentially expressed at the tips of the HC dendrites . Panx1 channels have been shown to mediate ATP release in many cell types [27] , [28] . Can HCs release ATP upon depolarization ? A luciferin–luciferase ATP detection assay was used to measure changes in the ATP release from the dissociated HCs . Bioluminescence was measured using a luminometer . After obtaining a baseline value , HCs were depolarized with 50 µM AMPA and the ATP concentration in the medium increased by 11 . 5±2 . 5% . The subsequent addition of 100 µM probenecid decreased the ATP concentration to 6 . 8±2 . 8% below the baseline value ( n = 74; p<0 . 001 ) . These data show that upon depolarization HCs release ATP via Panx1 channels ( Figure 3C ) . Finally , we determined whether Panx1 channels are active under physiological conditions . Panx1 channels mediate a current with a reversal potential more positive than the dark resting membrane potential of HCs [28] , suggesting that these channels keep the HCs slightly depolarized and that their closure should cause HCs to hyperpolarize . We found that this was indeed the case . Inhibiting Panx1 channels with 500 µM probenecid hyperpolarized HCs on average −8 . 2±1 . 8 mV ( n = 9 ) . HCs are part of a closed feedback loop with cones . Hyperpolarization of HCs will lead to an increase in feedback , leading to more glutamate release by the cones , which limits the extent to which HCs will hyperpolarize . To isolate the effect of Panx1 on the HC membrane potential , we opened this closed loop by applying 28 mM HEPES , which is known to inhibit feedback substantially [4] , [10] , [16] . When feedback was inhibited in this way , HCs hyperpolarized significantly more when 500 µM probenecid was applied , compared to its application while feedback was intact ( −19 . 2±2 . 7 mV; n = 9; p = 0 . 0047 ) . These experiments confirm that HCs express Panx1 channels that are functional at physiological membrane potentials . It has been suggested that HCs take up protons upon hyperpolarization and that the resulting increase in pH modulates the ICa of cones [4] . Extensive evidence indicates that feedback is inhibited by HEPES [4] , [10] , [16] . Could ATP be involved in modulating the pH in the synaptic cleft ? ATP can be hydrolyzed to ADP and AMP by ecto-ATPases . These reactions generate protons and phosphate groups , which constitute a phosphate pH buffer with a pKa of 7 . 2 . Therefore , ATP released by HCs might lead to an acidification of the synaptic cleft relative to the extrasynaptic medium ( 7 . 6–7 . 8 ) and an increase in the pH buffer capacity of the synaptic cleft . This leads to an inhibition of ICa of the cones . Upon hyperpolarization of HCs , Panx1 channels will reduce their conductance and the release of ATP will decrease , resulting in a drop of the pH buffer capacity and an alkalization of the synaptic cleft . The inhibition of ICa will be relieved . This hypothesis depends on three critical aspects: ( 1 ) release of ATP , ( 2 ) hydrolysis of ATP , and ( 3 ) changes in pH and pH buffer capacity in the synaptic cleft . The dependence of the slow component of feedback on these three aspects was tested next . Panx1 channels were inhibited with 500 µM probenecid , and feedback responses in cones were measured . Figure 4A shows mean responses ( control , black; probenecid , red ) . Examples of the responses of individual cells are given in Figure S4A ( wash , green ) . In the presence of probenecid , the amplitude of feedback responses reduced by 26±8% ( n = 9; p = 0 . 0009 ) and the shape of the response became more square ( Figure 4A and I ) . The slow component of feedback often reduced to such an extent that a two exponential fit could not be performed reliably . After application of probenecid , a single exponential function fitted the response significantly better than a double exponential function in seven of the nine cells tested , whereas this was the case for only one cell in the control condition . Therefore , to quantify the reduction of the slow component , we determined the difference in amplitude measured at 160 ms and 460 ms after stimulus onset ( see Materials and Methods ) . Application of probenecid resulted in a 51±10% reduction of the slow component ( n = 9; p = 0 . 003 ) ( Figure 4A and J ) . Figures 4E and S4A show that these effects were reversible . To test whether the slow component of feedback was dependent on ATP , we applied 100 µM ATP , a concentration that does not block Panx1 channels [20] , and measured feedback responses . The amplitude of the feedback response reduced by 21±5% ( n = 6; p = 0 . 009 ) ( Figures 4B , I and S4B ) and became squarer in shape . The size of the slow component reduced by 67±15% ( n = 6; p = 0 . 036 ) ( Figure 4B and J ) and recovered after washing out the drug ( Figures 4F and S4B ) . These experiments indicate that ATP is able to modulate the slow component of feedback . ATP hydrolysis by ecto-ATPases should lead to acidification . First we tested whether ecto-nucleoside triphosphate diphosphohydrolase ( NTPDase1 ) was present in the cone synaptic cleft . Figure 2D shows NTDPase1-IR ( green ) in horseshoe-shaped structures in the OPL , at a similar localization as the Panx1-IR shown in Figure 2A . Double labeling of anti-NTPDase1 with an antibody against GluR2 , the glutamate receptor expressed at the HC dendrites , resulted in complete overlap ( Figure 2E ) . This shows that NTPDase1 is expressed at the tips of HC dendrites . Because NTPDase1 is a protein with two membrane spanning domains with its catalytic domain located extracellularly , these experiments show that enzymes that hydrolyze extracellular ATP are present within the synaptic complex of cones , allowing for the synthesis of protons and a phosphate buffer and thus acidification of the synaptic cleft . ATP hydrolysis eventually leads to the formation of adenosine . Adenosine deaminase ( ADA ) is the enzyme that extracellularly degrades adenosine to inosine . Figure 2F shows ADA-IR in similar horseshoe-shaped structures in the OPL , as was found for NTDPase1-IR . Double labeling with an antibody against the glutamate receptor GluR2 shows strong co-localization ( Figure 2G ) , suggesting that ADA is also expressed on the HC dendrites invaginating the cone synaptic terminal . These two enzymes allow for effective hydrolyzation of ATP in the synaptic cleft . Is the hydrolysis of ATP an essential step in the feedback pathway ? Blocking NTPDase1 with 50 µM ARL67156 , a specific blocker of NTPDases [29] , made the feedback response more square and reduced the amplitude of the slow component of feedback ( 49±20%; n = 7; p = 0 . 045 ) ( Figures 4C , J and S4C ) . This effect was reversible ( Figures 4G and S4C ) . Contrary to the effect seen when ATP was applied , the amplitude of the total feedback response did not decrease but may even have increased ( 113±10%; n = 7; p = 0 . 23 ) ( Figure 4I ) . This experiment shows that the hydrolysis of ATP is involved in the generation of the slow component of feedback . Finally , we tested whether the slow component of feedback depends on the pH gradient between the synaptic ( pH 7 . 2 ) and the extrasynaptic ( pH 7 . 6 ) compartments . We decreased the pH of the Ringer's solution by changing the ratio of CO2 and O2 , with which the Ringer's solution was gassed . This might cause two changes: the pH gradient is reduced and the pH in the synaptic cleft might reduce . Figures 4D , J and S4D show that the slow component was reduced by acidifying the extrasynaptic compartment ( 35±6%; n = 6; p = 0 . 010 ) . This effect was reversible ( Figures 4H and S4D ) . The feedback amplitude was reduced by 29±8% ( n = 6; p = 0 . 017 ) ( Figure 4D and I ) . The pharmacological manipulations we applied are expected to change the pH and the pH buffer capacity in the synaptic cleft . Barnes and Bui [15] showed that pH modulates the activation potential of ICa of cones . Therefore , it is expected that the half activation potential of ICa will have shifted in the various pharmacological conditions used in this study . According to the proposed hypothesis , ATP should acidify the synaptic cleft , whereas ARL67156 alkalizes it , predicting that ICa will shift to more positive potentials in ATP and to more negative potentials in the ARL67156 . Indeed this was found . Application of ATP shifted the activation of ICa 0 . 8±0 . 3 mV ( n = 8; p = 0 . 027 ) to more positive potentials , whereas ARL67156 shifted the activation of ICa −1 . 4±0 . 4 mV ( n = 8; p = 0 . 013 ) to more negative potentials ( Figure 4K ) . Consistent with these results is that acidifying the extracellular medium also leads to acidification of the synaptic cleft and thus to a shift of the activation potential of ICa to positive potentials ( 2 . 0±0 . 3 mV; n = 5; p = 0 . 002 ) . Interestingly , application of probenecid did not lead to a significant shift of ICa ( −0 . 7±0 . 7 mV; n = 9; p = 0 . 34 ) . It has been suggested the vacuolar H+-ATPase ( V-ATPase ) in the HC plasma membrane is involved in mediating negative feedback from HCS to cones [30] . However , no direct effects of blocking V-ATPase on the feedback-induced modulation of ICa in cones have been published . To see whether negative feedback indeed depends on the activity of V-ATPase , we applied the specific V-ATPase blocker bafilomycin A1 ( BFA1 ) . BFA1 did not significantly affect either the total feedback amplitude ( 113±11%; n = 8; p = 0 . 27 ) or the slow component ( 138±22%; n = 8; p = 0 . 14 ) of the feedback response , making it unlikely that V-ATPase is involved in feedback-induced modulation of the cone ICa within the physiological range . It is expected that negative feedback from HCs to cones will affect the kinetic properties of HCs . HC responses consist of a fast hyperpolarization followed by a slow rollback response ( Figure 5A , left trace , arrow ) . It has been suggested that the HC rollback response correlates , at least partly , with negative feedback from HCs to cones [13] , [31] , [32] . However , some caution is warranted , as the rollback response is also influenced by many other processes , such as the transient nature of the cone response [33] , [34] and voltage-gated currents [35] , [36] in HCs . Because the rollback response is relatively slow , the slow component of feedback might be influenced most by the slow component . First , we tested whether the rollback response depended on the pH gradient in the synaptic cleft . Figure 5D shows that changing the pH of the extrasynaptic medium from 7 . 6 to 7 . 2 hyperpolarized HCs ( −9 . 1±1 . 1 mV; n = 4 ) . In all four cells tested , the rollback response reduced but did not disappear completely and recovered after returning to pH 7 . 6 ( Figure 5A; black , control; red , pH 7 . 2; green , wash ) . This shows that the HC rollback response depends on the pH gradient in the synaptic cleft just as the slow component of feedback does ( Figures 4D , H , J and S4E ) . Next we tested if application of 500 µM probenecid would also reduce the rollback response . Application of probenecid hyperpolarized the HC membrane potential ( Figure 5D ) , and even though there was a high degree of variability in the rollback responses , it was reduced in 10 out of 18 HCs ( Figure 5B; black , control; red , probenecid; green , wash ) . However , the rollback response never completely disappeared . Note that this result resembles the effect of probenecid on the slow component of feedback , which only reduced by about 45% but never disappeared completely ( Figure 4J ) . This is to be expected as probenecid is only a partial blocker of Panx1 channels [37] . Furthermore , because probenecid does not affect Cx hemichannels , feedback via Cx hemichannels is still present . This might account for the remaining rollback response in HCs . To test this , we determined the effect of probenecid in conditions when a major part of feedback was blocked by 28 mM HEPES . HEPES most likely has two effects on the feedback system . It inhibits the proton-mediated feedback by buffering the pH in the synaptic cleft , and it leads to the closure of Cx hemichannels [16] . When 28 mM HEPES was present in the medium , applying probenecid hyperpolarized HCs significantly more ( Figure 5D ) and no sign of rollback could be seen ( Figure 5C; black , HEPES; red , HEPES+probenecid; green , HEPES ) . This result indicates that feedback is blocked ( almost ) completely in this condition . These experiments indicate that the rollback response depends , at least partly , on the slow feedback component . In many systems ATP released from Panx1 channels activates purinergic receptors [38] . Some evidence exists for expression of P2X [39] and P2Y [40] receptors on photoreceptors . Activation of P2X receptors by ATP should induce a nonspecific cation current [41] . Whole-cell IV relations were constructed for cones in control conditions and when 100 µM ATP ( n = 5 ) ( Figure 6A ) or 50 µM ARL67156 ( n = 5 ) ( Figure 6B ) was added to the bath solution . Neither ATP nor ARL67156 appeared to modulate a cation conductance , making it unlikely that ATP acted directly on a P2X receptor . The data presented in Figure 4 also show that purinergic receptors are unlikely to be involved in mediating negative feedback from HCs to cones . Either applying ATP or blocking NTPDase1 with ARL67156 will increase the ATP concentration . If ATP affected feedback by modulating photoreceptor purinergic receptors , ATP and ARL67156 would have had similar effects . Although both ATP and ARL67156 reduced the slow component of feedback , the shift of ICa and the size of the total feedback response moved in opposite directions ( Figure 4I , J , and K ) . This implies that ATP hydrolysis and not ATP itself exerts an effect on the slow component of feedback . In addition , A2 receptors have been suggested to modulate photoreceptor function [42]–[44] . We excluded a possible role for adenosine by measuring feedback in the presence of the specific A2 receptor blocker , ZM 241385 , at a similar concentration as used by Stella and co-workers ( Figure 6C ) [42]–[44] . In this condition , neither the feedback amplitude nor its kinetics were affected ( p>0 . 2; n = 4 ) , indicating that A2 receptors do not mediate feedback .
Figure 7 summarizes the proposed mechanism of negative feedback from HCs to cones . Glutamate receptors , Cx hemichannels , and Panx1 channels are expressed in the postsynaptic HC membrane . Voltage-gated Ca channels are expressed on the presynaptic cone membrane . In the dark , HCs and cones rest at about −35 mV . As ICa in cones is activated at that potential , cones release glutamate and HC glutamate receptors are activated , depolarizing HCs . Glutamate-gated channels , Cx hemichannels , and Panx1 channels are open in this condition and current flows into the HC . Because the extracellular space has a finite resistance , this current makes the potential deep in the synaptic cleft slightly negative . This is sensed by the voltage-gated Ca channels in the presynaptic membrane of the cones as a slight depolarization . The effect will be that the activation potential of ICa has shifted to more negative potentials relative to the cone's overall membrane potential [6] , [7] , [45] . At the same time , ATP is released via Panx1 channels . ATP is hydrolyzed to ADP , AMP , and eventually to adenosine by ecto-ATPases . This hydrolysis leads to an acidification of the synaptic cleft and the formation of a phosphate pH buffer ( pKa of phosphate buffer is 7 . 2 ) . Acidification of the synaptic cleft inhibits the cone ICa [15] and shifts its activation potential to more positive potentials . Interestingly , the ephaptic mechanism shifts the activation potential of ICa to more negative potentials , while the Panx1/ATP-mediated mechanism shifts it to more positive potentials . These two opposing mechanisms together set the activation potential of ICa in the dark ( Figure 7C , black line ) . Hyperpolarization of HCs due to surround stimulation will have two effects on the system . First , the current flowing through the Cx hemichannels , the Panx1 channels , and the glutamate-gated channels will increase . This increases the negativity of the synaptic cleft , shifting ICa to more negative potentials ( Figure 7C , red line ) . This process is as fast as the change in HC membrane potential and does not have a synaptic delay . In due time , the Panx1 channels will reduce their conductance and the release of ATP will diminish . Gradually the proton concentration and the pH buffer capacity in the synaptic cleft reduce , and the resulting alkalization disinhibits ICa . As a consequence , ICa increases and its activation potential shifts to even more negative potentials ( Figure 7C , blue line ) . This process is the slow feedback component and has a time constant of about 200 ms . The form of synaptic modulation we propose here is novel as it exerts its actions by regulating the local pH buffer capacity . Changing the pH buffer capacity consequentially changes the pH in the synaptic cleft . The feedback-induced pH change in the synaptic cleft is therefore indirect . This has an enormous advantage over direct modulation of the proton concentration in the synaptic cleft in an unbuffered system . To illustrate this , we calculated the number of protons involved in a feedback response . The maximal feedback-induced shift of ICa is about 9 mV [16] , and a pH change of 0 . 1 units shifts ICa by 1 mV [15] . Here we show that about 35% of the total feedback current in cones is pH-mediated . This implies that the slow component of feedback is responsible for about 3 mV of the shift of ICa , which translates to a pH change in the synaptic cleft of about 0 . 3 pH units . Vandenbranden [46] estimated that the volume of the extracellular space within the synaptic terminal of goldfish cones is 0 . 88 µm3 . Given these numbers , a 0 . 3 pH unit change in the pH in the synaptic cleft would involve the movement of about 19 protons from the total pool of around 38 free protons . In an unbuffered system , this would produce an unreliable noisy signal . However , the phosphate buffer is most likely present in the range of hundreds of µM , making the number of free protons a well-controlled statistical parameter . On the other hand , if the synaptic pH was strongly buffered by static pH buffers , proton-mediated feedback would be largely inefficient . Any pH change induced by HC hyperpolarization would be counteracted by the activity of the static pH buffers . This suggests that changing the pH buffer capacity is possibly the only way proton-mediated synaptic transmission can work . In this way , a pH-signaling mechanism can be highly reliable and noise free . In the dark , cones continuously release glutamate . Because glutamate and protons are co-released by the cones , this would lead to an acidification of the synaptic cleft in the dark and an alkalization in the light . Negative feedback leads to an increase in glutamate release and hence an increase in proton release as well—that is , an acidification . The opposite is found , suggesting that the pH near the Ca channels in the synaptic cleft depends more on HC activity than on cone activity . This is highly unexpected as glutamate is released continuously very close to the Ca channels of the cones . The reason why the pH in the synaptic cleft depends more on HC activity than on cone activity might be that the pH in the synaptic cleft is set by the pH buffer capacity , which is modulated by HCs , and not directly by proton release or uptake . Noise reduction is of great importance for an optimally performing visual system . The feedback pathway from HCs to cones modulates the output of the photoreceptors . Any noise added at this level of the visual system will considerably decrease the visual performance of the whole visual system . In later processing stages , noise can be lowered by convergence and by distributing the signals over various parallel channels . Such options are not available for the photoreceptors . Both feedback mechanisms we describe here have low noise properties; for instance , neither of them depends on vesicular release of neurotransmitters or activation of postsynaptic receptors . This unusual feedback synapse thus seems to be optimally adapted for its function in the outer retina . Wang et al . [14] studied feedback-induced pH changes in the synaptic cleft of zebrafish cones using a fluorescent method . They found that HC depolarization leads to acidification of the synaptic cleft . The time course ( τ∼200 ms ) of this pH change is remarkably similar to the time course of the second feedback component ( τs = 189±25 ms ) we find . Also , the size of the pH change they describe is very similar to the pH change we predict . In other words , the results of Wang et al . [14] are fully consistent with our experimental data . Wang et al . [14] did not find the fast component of feedback we have described here . However , they solely used fluorescent measurements to study pH changes in the synaptic cleft . As such , these types of experiments would be unable to detect the fast feedback component we describe , as it is mediated by an ephaptic mechanism and not by a pH-dependent mechanism . Wang et al . [14] suggested that the pH changes are mainly due to V-ATPase activity because they could inhibit the feedback-induced pH changes in the synaptic cleft with BFA1 . We tested the effect of BFA1 on light-induced feedback responses measured in the cones and found that neither the fast nor slow feedback component was affected by BFA1 . These results indicate that light-induced feedback does not depend on V-ATPase activity . This discrepancy may relate to the transgenic approach Wang et al . [14] used . They studied pH changes in the synaptic cleft in a transgenic zebrafish line that expressed Na channels ( FaNaChannels ) in HCs . To depolarize the HCs , FMRFamide , an agonist for the FaNaChannels , was applied . Because the reversal potential for sodium is very positive , it is highly likely that HCs in their experiments were depolarized to potentials far outside their physiological operating range . The HC membrane potentials might even have become positive during the depolarization , which is a very unphysiological condition . We show that under physiological conditions BFA1 does not significantly affect feedback , making it unlikely that V-ATPase has a role in negative feedback from HCs to cones . The question arises as to why two feedback mechanisms are present in the outer retina instead of one . To reduce spatial redundancies in the visual scene , the mean activity of all cones within the large receptive field of HCs is subtracted from the output of individual cones . If this process was not extremely fast , the surround of BC receptive fields would lag the center when responding to moving stimuli . Conversely , reducing temporal redundancies requires a slow mechanism so that lasting activity can be subtracted from the cone output . The feedback system we present here fulfills these requirements . The ephaptic feedback mechanism is extremely fast and will be prominently involved in reducing spatial redundancies . The Panx1/ATP-mediated mechanism is especially suitable for reducing temporal redundancies . As the slow component of feedback can only contribute when relatively static stimuli are used , it will not compromise the fast spatial redundancy reduction via the ephaptic feedback mechanism . In a first order approximation it is expected that inhibiting the slow component of feedback will always reduce the amplitude of feedback . However , this is not necessarily the case . For example , enhancing the amount of pH buffer experimentally by application of ATP will keep the pH in the synaptic cleft low and so ICa remains inhibited . Because the pH in the cleft no longer depends on HC hyperpolarization , the slow component of feedback is lost and the total feedback response becomes smaller . In contrast , when the formation of the pH buffer is prevented by ARL67156 , the pH in the synaptic cleft is again no longer dependent on HC hyperpolarization , but now the pH is high and ICa disinhibited . Because of this larger ICa , the total feedback responses will increase , even though the slow component is lost . Indeed this is what was found experimentally ( Figure 4B and C ) . One would also expect that probenecid and ARL67156 would affect the feedback amplitude similarly , as both drugs decrease the pH buffer concentration in the synaptic cleft . However , this was not the case as probenecid decreased the total feedback response amplitude , whereas ARL67156 did not ( Figure 4A , C , and J ) . The difference between probenecid and ARL67156 application is that probenecid also modulates the conductance of the Panx1 channels , whereas ARL67156 does not . This suggests that Panx1 channels also participate in the ephaptic component of negative feedback together with Cx hemichannels [7] , [11] , [12] and glutamate-gated channels [47] . Reducing the Panx1 conductance will affect both the fast and the slow feedback component , whereas application of ARL67156 only affects the slow component . Blocking the ephaptic part of feedback will lead to a shift of the activation potential of ICa to more positive potentials , whereas inhibiting the ATP release leads to alkalization and thus a shift to more negative potentials . The overall effect of inhibiting Panx1 channels will therefore depend on the relative strength of both effects . Our data show that application of probenecid indeed does not significantly shift ICa , indicating that both Panx1-mediated feedback signals affect ICa equally but in opposite directions ( Figure 4K ) . Why does probenecid have a greater effect on the HC membrane potential when the feedback is blocked with 28 mM HEPES ? The cone-HC system is a closed loop . Glutamate release by cones sets the membrane potential of HCs ( feedforward signal ) , whereas feedback from HC to cones determines the amount of glutamate released by the cones . In other words , the feedforward signal from cones to HCs is kept in its working range by the feedback signal from HCs to cones . They keep each other balanced . Therefore , it is expected that when feedback is blocked completely , ICa is no longer kept in its working range and cones stop releasing glutamate . Indeed this happens when feedback is blocked by carbenoxolone [6] . Carbenoxolone completely blocks feedback by closing both the Cx hemichannels and Panx1 channels . This causes a large shift of the activation potential of ICa to more positive potentials , which induces a strong reduction of the glutamate release by cones and hyperpolarization of HCs . Because cones no longer release glutamate , HC light responses are lost . The fact that a high concentration of HEPES does not strongly hyperpolarize HCs or completely block their light responses suggests that HEPES does not block feedback completely . This was confirmed by Fahrenfort et al . [16] , who showed that 10 mM HEPES or more reduces feedback to about 40% of its maximum . The consequence is that in the presence of 28 mM HEPES , the remaining part of feedback can keep the cone-HC system in its working range and HC light responses remain ( Figure 5C ) . When the ephaptic Panx1/ATP-mediated feedback component is inhibited as well by adding probenecid in the presence of HEPES , the cone-HC system can no longer remain in its working range . The cones stop releasing glutamate and HCs hyperpolarize strongly and lose their light responses . On the other hand , applying probenecid in the absence of HEPES has only a relatively minor effect on the HC membrane potential , as in this condition the Cx-hemichannel-mediated feedback pathway will not be affected and can keep the cone output in its working range and HC light responses remain present . Stella and co-workers [42]–[44] report that adenosine decreased ICa in cones via an A2 receptor interaction . In this article we showed that the slow component of feedback did not depend on this pathway . In fact , we found no effect at all on the feedback responses of cones when adenosine receptors were blocked . This disparity may have occurred as a result of the different experimental conditions used . We used relatively light-adapted goldfish or zebrafish retinas , whereas Stella and co-workers [42]–[44] worked with salamander retinas that were dark-adapted , a condition where adenosine signaling might be most prominent [48] . Furthermore , at present there is no direct evidence for the expression of adenosine receptors in the cone synaptic terminal , whereas expression is found in the inner retina [48]–[51] . It is possible that the effects seen by Stella and co-workers [42]–[44] are mediated by activation of A2 receptors in the inner retina affecting the outer retina via , for instance , interplexiform cells . Are the feedback mechanisms we describe here also present in other vertebrates , or are they specific for fish ? There is general agreement that in all vertebrates , ranging from salamander and fish to mice and primates , negative feedback from HCs modulates the ICa of cones [1]–[5] . In zebrafish , knocking out the hemichannel forming Cxs ( Cx55 . 5 ) leads to a severe reduction of feedback from HCs to cones [7] . Interestingly , the rollback response remained intact [7] . Similarly , the HC rollback response is unaffected in mice when the HC-specific Cx ( Cx57 ) is knocked out [52] . This led the authors to conclude that there was no Cx-hemichannel-mediated ephaptic feedback in mice . However , because the ephaptic feedback component is very fast and most likely does not contribute strongly to the rollback response in HCs , the conclusion that ephaptic feedback does not occur in mice might be premature . In the present study , we show that the rollback response in HCs , at least partly , depends on the slow Panx1/ATP-mediated component of feedback . Similar to zebrafish and goldfish , mice also express Panx1 channels at the HC dendrites [12] . It is therefore likely that in mice the slow component of feedback is also mediated by Panx1 and ATP . It is tempting to speculate that both mechanisms are present in all vertebrates but in different ratios and that this ratio reflects a species' visual capability and needs . In some animals , the ephaptic component might be the largest , whereas in others the Panx1/ATP component might dominate . For example , as the spatiotemporal correlation function of natural scenes is inseparable [53] , faster temporal signals received by the retina of species like mice with low visual acuity will be limited under normal conditions . In animals such as these , the slow Panx1/ATP component of feedback may dominate as their retina has less need to manage the faster temporal aspects of the scene . On the other hand , in animals strongly depending on vision , such as zebrafish and goldfish , the fast component might be dominating . Whether the ratios of the two feedback mechanisms indeed correlate with the visual performance of the various animals is an intriguing question that awaits further study . Could the Panx1/ATP mechanism presented here also function outside the retina ? In the central nervous system , Panx1 and NTPDase1 are both abundantly present and their localization is concentrated in synapses [54]–[56] . This suggests that the Panx1/ATP system described in this article might also occur in other brain regions . The hippocampus is one region where Panx1 expression levels are high , and recently it was suggested that Panx1 channels are involved in synaptic plasticity stabilization in that brain area , although the underlying mechanism has not been clarified [57] . As NMDA receptors are very sensitive to changes in pH [58] , the proposed Panx1/ATP system may also be acting as a modulator of synaptic strength in the hippocampus and other brain regions . Panx1 is also expressed in astrocytes and astrocytes are known to release ATP [27] . Processes of astrocytes and pre- and postsynaptic structures of neurons form a tripartite synapse . Astrocytes play an active role in such a complex; they modulate the signal flow between pre- and postsynaptic cells and in that way influence the neuronal network properties [59] . This modulation depends on the intracellular Ca2+ concentration in the astrocytes . Because Panx1 channels are gated by intracellular Ca2+ , and because any voltage-gated channel , such as the presynaptic voltage-gated Ca channels , is pH sensitive due to pH-induced changes in surface charge , astrocytes might modulate the synaptic efficiency by changing the pH buffering in the synaptic cleft utilizing the Panx1/ATP system described in this article . Furthermore , because both Panx1 and NTPDase1 are also coexpressed in other organs like the kidneys and heart [21] , [55] , [60] , similar extracellular pH modulation systems might also modulate cellular activity in nonneuronal tissues .
All animal experiments were carried out under the responsibility of the ethical committee of the Royal Netherlands Academy of Arts and Sciences acting in accordance with the European Communities Council Directive of November 24 , 1986 ( 86/609/EEC ) . Goldfish , Carassius auratus , or zebrafish , Danio rerio , were euthanized and the eyes enucleated . Wild-type and Cx55 . 5 mutant zebrafish ( C54X , hu1795 , ZFIN ID: ZDB-ALT-110920-1 ) , all in a TL background , were used . For the isolated retina preparation , control Ringer solution contained ( in mM ) 102 . 0 NaCl , 2 . 6 KCl , 1 . 0 MgCl2 , 1 . 0 CaCl2 , 28 . 0 NaHCO3 , 5 . 0 glucose , and 0 . 1 picrotoxin and was continuously gassed with 2 . 5% CO2 and 97 . 5% O2 to yield a pH of 7 . 6 . HEPES Ringer solution contained ( in mM ) 102 . 0 NaCl , 2 . 6 KCl , 1 . 0 MgCl2 , 1 . 0 CaCl2 , 28 . 0 HEPES , 5 . 0 glucose , and 0 . 1 picrotoxin , and the pH was set to 7 . 6 with NaOH . The pipette solution contained ( in mM ) 85 K-gluconate , 21 KCl , 1 MgCl2 , 0 . 1 CaCl2 , 1 EGTA , 10 HEPES , 10 ATP-K2 , 1 GTP-Na3 , 20 phosphocreatine-Na2 , 50 units ml−1 creatine phosphokinase , adjusted with NaOH to pH 7 . 3 , and resulting in a ECl of −50 mV when used in conjunction with the Ringer solution . All chemicals were supplied by Sigma-Aldrich ( Zwijndrecht , the Netherlands ) , except for Papain ( Worthington Biochemical Company , Lakewood , NJ ) and ARL67156 and ZM241385 ( Tocris Biosciences , Bristol , UK ) . For the electrophysiological experiments with dissociated HC , the following solutions were used . The control Ringer solution contained ( in mM ) 110 NaCl , 5 KCl , 10 CsCl , 2 . 5 CaCl2 , 2 MgCl2 , 10 HEPES , 10 Glucose , pH 7 . 8 . Intracellular recording solution contained ( in mM ) 10 NaCl , 120 CsCl , 5 CaCl2 ( free 100 µM ) , 5 EGTA , 10 HEPES , 2 ATP-Mg , pH 7 . 4 . Concentrations of the drugs used are indicated in the legends . HCs of goldfish were isolated using an optimized version of the procedure originally described by Dowling et al . [23] and optimized by Ayoub and Lam [24] . Retinas were dissected as described above . The isolated retinas were placed in a solution of L-15 containing 5 mg/ml papain ( Worthington , no . 3126 ) . They were incubated at room temperature for 35 min while being shaken at a low frequency . Subsequently , the retinas were washed in DMEM+Glutamax ( GIBCO , no . 61965 ) containing 10% fetal bovine serum ( GIBCO , no . 10270 ) to inactivate the papain . Then the retinas were washed in L-15 and finally placed in a tube containing 3 ml L-15 . The retinas were mechanically dissociated by trituration using a wide plastic Pasteur pipette first and a narrower fire polished glass pipette for later fractions . For each new fraction , the cells were allowed to settle for a minute before the lower heavier section was transferred to a new tube . The fractions containing an HC concentration of 85% or higher were used for luminescence measurements . ATP bioluminescence was measured using the ATP Bioluminescence Assay Kit CLS II purchased from Roche . Luciferase luminescence was measured with a Varioscan Flash ( Thermo Scientific ) using luminometry . After obtaining a baseline measure , 50 µM AMPA was added and subsequently 100 µM probenecid . A 20 µm white light spot ( 0 log ) was focused via a 60× water immersion objective on the cone outer segment and a 4 , 500 µm “full field” white spot ( −1 . 5 log ) projected through the microscope condenser . The light stimulator consisted of two homemade LED stimulators based on a three-wavelength high-intensity LED ( Atlas , Lamina Ceramics Inc . , Westhampton , NJ ) . The peak wavelengths of the LEDs were 624 , 525 , and 465 nm , respectively , with bandwidths smaller than 25 nm . An optical feedback loop ensured linearity . The output of the LEDs was coupled to the microscope via light guides . White light consisted of an equal quantal output of the three LEDs . 0 log intensity was 8 . 5×1015 quanta m−2 s−1 . The full-field light onset responses of the negative feedback measurements were fitted with a single exponential: ( 1 ) and if possible with a double exponential function: ( 2 ) To test which of the two models described the raw data best , an F test comparing the sum of squares of residuals of each fit was performed . Often the slow component of feedback present in control conditions was reduced by our pharmacological manipulation to such a level that the onset of the feedback current could no longer be fitted accurately by a double exponential function . In order to quantify the effect of the various compounds used on the slow component of feedback , the average amplitudes were determined for 40 ms stretches of the feedback current centered at 160 ms and 460 ms after response onset , and the difference was calculated . This difference was taken as the amplitude of the slow component of feedback . The amplitude of the slow component in the pharmacological condition ( B ) was divided by the amplitude of the slow component in the control condition ( A ) , yielding the reduction ratio . The activation curve of ICa was derived by leak subtracting the IV relation using the linear part of the IV relation between −80 and −60 mV [16] . The half activation potential was then determined by fitting a Boltzmann relation ( Eq . 3 ) through the leak subtracted IV relations: ( 3 ) where V is the membrane voltage , is the midpoint , and m is the slope factor . This was done for each cell in both control ( C ) and experimental ( E ) conditions and the shift in the half activation potential calculated as K ( E ) minus K ( C ) . Immunohistochemical procedures were similar to published methods [7] , [16] . The Panx1 antibody was raised against the zebrafish sequence and characterized by Prochnow et al . [11] . The primary antibody against NTPDase1 was raised in rabbit to the corresponding amino acid sequence 102–130 of human NTPDase1 ( GIYLTDCMERAREVIPRSQHQETPVYLGA ) . It was obtained from CHUQ ( www . ectonucleotidases-ab . com ) and characterized by Ricattie et al . [61] . The ADA ( AB176 ) and GluR2 antibodies were purchased from Chemicon International ( Temecula , CA ) . The ADA rabbit polyclonal antibody was raised against calf spleen ADA . The GluR2 ( MAB397 ) mouse monoclonal ( clone 6C4 ) antibody was raised against a recombinant fusion protein TrpE–GluR2 ( N-terminal portion , amino acids 175–430 of rat GluR2 ) . Secondary antibodies , goat–anti-mouse Alexa 488 , and goat–anti-rabbit Cy3 were purchased from the Jackson Immuno Research Lab ( West Grove , PA ) . For light microscopical ( LM ) purposes , 10-µm-thick sections were made and stored at −20°C . Sections were first preincubated in 2% Normal Goat Serum ( NGS ) for 30 min , then incubated with primary antibodies for 24–48 h , followed by 35 min of incubation with secondary antibodies at 37°C . The 0 . 25 µm optically sectioning was performed with Zeiss CLSM meta confocal microscope ( Zeiss , Germany ) . For electron microscopical ( EM ) purposes , 40-µm-thick sections were incubated with the primary antibody in PB for 48 h , then rinsed before being incubated with rabbit peroxidase antiperoxidase ( PAP ) for 2 h , rinsed , and then developed in a 2 , 2′-diaminobenzidine ( DAB ) solution containing 0 , 03% H2O2 for 4 min . Afterwards the gold substitute silver peroxidase method [62] was performed; sections were fixed in sodium cacodylate buffer ( pH 7 . 4 ) containing 1% Osmium tetra oxide and 1 . 5% potassium ferricyanide . Sections were then dehydrated and embedded in Epoxy resin , ultrathin sections made and examined with a FEI Tecnai 12 electron microscope . All data are presented as mean ± sem unless otherwise stated . Significance was tested using a two-tailed t test on paired or independent group means as appropriate . | At the first retinal synapse , specific cells—horizontal cells ( HCs ) —inhibit photoreceptors and help to organize the receptive fields of another retinal cell type , bipolar cells . This synaptic interaction is crucial for visual contrast enhancement . Here we show that horizontal cells feed back to photoreceptors via a very fast ephaptic mechanism and a relatively slow mechanism . The slow mechanism requires ATP release via Pannexin 1 ( Panx1 ) channels that are located on HC dendrites near the site where photoreceptors release the neurotransmitter glutamate to HCs and bipolar cells . The released ATP is hydrolyzed to produce AMP , phosphate groups , and protons; these phosphates and protons form a pH buffer , which acidifies the synaptic cleft . This slow acidification inhibits presynaptic calcium channels and consequently reduces the neurotransmitter release of photoreceptors . This demonstrates a new way in which ATP release can be involved in synaptic modulation . Surprisingly , the action of ATP is not purinergic but is mediated via changes in the pH buffer capacity in the synaptic cleft . Given the broad expression of Panx1 channels in the nervous system and the suggestion that Panx1 function underlies stabilization of synaptic plasticity and is needed for learning , we anticipate that this mechanism will be more widespread than just occurring at the first retinal synapse . |
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Visceral leishmaniasis ( VL ) in the Indian subcontinent is a fatal disease if left untreated . Between 1994 to 2013 , the Ministry of Health of Bangladesh reported 1 , 09 , 266 cases of VL and 329 VL related deaths in 37 endemic districts . Indoor residual spraying ( IRS ) using dichlorodiphenyltrichloroethane ( DDT ) was used by the national programme in the 1960s to control malaria . Despite findings of research trials demonstrating that the synthetic pyrethroid deltamethrin 5 WP was very effective at reducing vector densities , no national VL vector control operations took place in Bangladesh between 1999 to early 2012 . In 2012 , IRS using deltamethrin 5 WP was re-introduced by the national programme , which consisted of pre-monsoon spraying in eight highly endemic sub-districts ( upazilas ) . The present study aims to evaluate the effectiveness of IRS on VL vectors , as well as the process and performance of the spraying activities by national programme staff . Five highly endemic upazilas of Mymensingh district were purposively selected ( Fulbaria , Trishal , Mukthagacha , Gaforgaon and Bhaluka ) to conduct the present study using the WHO/TDR monitoring and evaluation tool kit . IRS operations , conducted by 136 squads/teams , and 544 spraymen , were observed using check lists and questionnaires included in the WHO/TDR monitoring and evaluation tool kit . A household ( HH ) acceptability survey of IRS was conducted in all study areas using a structured questionnaire in 600 HHs . To measure the efficacy of IRS , pre-IRS ( two weeks prior ) and post-IRS ( at one and five months after ) , vector density was measured using CDC light traps for two consecutive nights . Bioassays , using the WHO cone-method , were carried out in 80 HHs ( 40 sprayed and 40 unsprayed ) to measure the effectiveness of the insecticide on sprayed surfaces . Of the 544 spraymen interviewed pre-IRS , 60% , 3% and 37% had received training for one , two and three days respectively . During spraying activities , 64% of the spraying squads had a supervisor in 4 upazilas but only one upazila ( Mukthagacha ) achieved 100% supervision of squads . Overall , 72 . 8% of the spraying squads in the study upazilas had informed HHs members to prepare their houses prior to spraying . The required personal protective equipment was not provided by the national programme during our observations and the spraying techniques used by all sprayers were sub-standard compared to the standard procedure mentioned in the M&E toolkit . In the HH interviews , 94 . 8% of the 600 respondents said that all their living rooms and cattle sheds had been sprayed . Regarding the effectiveness measurements ( i . e . reduction of vector densities ) , a total of 4132 sand flies were trapped in three intervals , of which 3310 ( 80 . 1% ) were P . argentipes; 46 . 5% ( 1540 ) males and 53 . 5% ( 1770 ) females . At one month post-IRS , P . argentipes densities were reduced by 22 . 5% but the 5 months post-IRS reduction was only 6 . 4% for both male and female . The bioassay tests showed a mean corrected mortality of P . argentipes sand flies at one month post-IRS of 87 . 3% which dropped to 74 . 5% at 4 months post-IRS in three upazilas , which is below the WHO threshold level ( 80% ) . The national programme should conduct monitoring and evaluation activities to ensure high quality of IRS operations as a pre-condition for achieving a fast and sustained reduction in vector densities . This will continue to be important during the maintenance phase of VL elimination on the Indian subcontinent . Further research is needed to determine other suitable vector control option ( s ) when the case numbers are very low .
Visceral leishmaniasis ( VL ) , also known as kala-azar ( KA ) in the Indian subcontinent ( ISC ) , is a fatal disease if left untreated [1] . In the ISC , the disease is transmitted exclusively by the sand fly vector Phlebotomus argentipes [2] . Leishmania donovani is the protozoan causative agent . Visceral leishmaniasis is affecting marginalized communities worldwide [1 , 3] . Within the ISC , VL is highly prevalent in Bangladesh , India and Nepal; and some sporadic cases were reported from Bhutan [4] . Among the global burden , 90% of all VL cases are reported by only six countries ( India , Bangladesh , Sudan , South Sudan , Ethiopia and Brazil ) and it is estimated that 200 , 000 to 400 , 000 cases and 20 , 000 to 40 , 000 deaths occur annually on a global scal [3] . The first historical report of VL was from Jessore district , currently located in the south-western part of Bangladesh , where an epidemic outbreak killed an estimated 75 , 000 people between 1824 and 1827 [5] . In the 1960s , a comprehensive malaria eradication programme ( MEP ) was launched using dichlorodiphenyltrichloroethane ( DDT ) for indoor residual spraying ( IRS ) for malaria vector control . Visceral leishmaniasis was virtually eradicated at that time , possibly as a beneficial by-product of the MEP . However , with the discontinuation of MEP , VL re-emerged in the early 1980s [6 , 7] . DDT was later banned entirely by the Government of Bangladesh in 1998 as it was an environmental hazard [8] . In 2005 , a memorandum of understanding ( MoU ) was signed among three countries ( Bangladesh , India and Nepal ) in the ISC , with the aim to eliminate VL as a public health problem across the region by 2015 [9] . This date was later extended to 2017 with the inclusion of Bhutan and Thailand in the consortium [10] . The target was to reduce the VL incidence to less than one case per 10 , 000 of the population annually at the upazila level in Bangladesh [9] . Between 1994 to 2013 , the Directorate General of Health Services ( DGHS ) of Bangladesh reported 1 , 09 , 266 cases of VL and 329 VL related deaths from 37 endemic districts [7]; with about 50% of total VL cases being reported from five upazilas in the Mymensingh district . In the period after signing the MoU , extensive research activities were conducted to reduce VL transmission and provide treatment in coordination with the Special Programme for Research and Training in Tropical Diseases ( TDR ) at World Health Organization ( WHO ) . This lead to a slow , but continued , reduction of VL cases until the elimination target was achieved and the first steps of the maintenance phase were initiated in 2017 [11] . Integrated vector management ( IVM ) is one of the most important elements in the regional elimination strategy [9] . However , for a significant period during the attack phase of the programme , from 1999 to early 2012 , no VL vector control activities were performed in Bangladesh [7 , 12] , and the number of cases increased every year during that time . A research programme in Bangladesh , India and Nepal showed that IRS using the synthetic pyrethroid ( SP ) deltamethrin 5WP was very effective against the VL vector if performed in accordance with guidelines [13 , 14] . Based on this , a pilot IRS trial was conducted in 2011 by the national programme using deltamethrin 5 WP , which proved to be successful . In 2012 a scale-up IRS operation was undertaken in eight highly endemic upazilas in four districts [7] . The Ministry of Health and Family Welfare , Government of Bangladesh organized several training courses for spraymen and supervisors . In parallel , WHO/TDR , along with programme managers and local researchers , developed a monitoring and evaluation toolkit for IRS [15] . This comprehensive toolkit should be used in order to provide an evidence base and explain possible reasons affecting the efficacy of IRS [16] . The present study will use this toolkit to monitor and document activities of IRS operations conducted by the national programme in Bangladesh using deltamethrin 5 WP .
In 2012 , pre-monsoon ( May-June ) IRS was conducted in eight highly endemic upazilas , namely: Fulbaria , Trishal , Mukthagacha , Gaforgaon , Bhaluka of Mymensingh district ( Fig 1 ) , and Nagarpur , Madarganj , Terokhada upazilas of Tangail , Jamalpur and Khulna districts , respectively . The study was carried out from March to October of 2012 and included the following activities: All field and laboratory data was checked , verified , cleaned of obvious errors and entered into databases using Microsoft Office Access 2007 . The data analyses was performed using SPSS version 25 ( SPSS Inc , Chicago , IL ) and Stata version 12 ( Stata Corp , College Station , TX ) . Descriptive statistics were used to explore the nature of the data . Frequency and percentages were used to analyze all the datasets ( observation of spray team , household acceptability surveys , vector density measurement and bioassay ) . Due to the over-dispersed vector density measurements , the nonparametric Mann-Whitney U test was used to test whether there was a significance difference between intervention and control groups in between pre-intervention and post-intervention measurements . Wilcoxon Signed Rank test for paired sample was used to test whether there was any significance difference between baseline and follow-up . For each post-IRS timepoint , the percentage reduction in sand fly counts attributable to IRS was calculated as 100 [ ( mean post-intervention value for the intervention group-mean baseline value for the intervention group ) - ( mean post-intervention value for the control group-mean baseline value for the control group ) ]/ ( mean baseline value for the intervention group ) . To investigate the effect of vector density reduction we used the following formula: Reductionrate ( % ) = ( I¯−I¯base ) − ( C¯−C¯base ) I¯base×100 Where , I¯base = average number of baseline sand fly in intervention group; C¯base = average number of baseline sand fly in control group; I¯ = average number of post intervention sand fly in intervention group; C¯ = average number of post intervention sand fly in control group . The effect was negative and positive if the sand fly density decreased or increased post-intervention , respectively . The study protocol ( ID: BMRC/NREC/2010-2013/535 ) was approved by the ethical review committees of the Bangladesh Medical Research Council ( BMRC ) , Dhaka , Bangladesh . On behalf of the national programme , Director Disease Control , DGHS had kindly given permission to observe spraying activities and interview spraying squads . Written informed consent was obtained from the head of each study household for active participation in the study .
Each squad consists of five people: four spraymen and one squad leader . We observed IRS operations of 136 squads ( 544 spraymen and 136 squad leaders ) , in five endemic upazilas of Mymensingh district . Table 1 shows that 59 . 0% , 3 . 1% and 37 . 4% of the 544 spraymen received training for one , two and three days , respectively , before the IRS operation but all 136 squad leaders had training for three days . Only 14 . 0% of spraymen were involved in previous IRS activities . Regarding the procedures to approach houses for spraying , about 3 . 0% of squad leaders reported that there were only a few HHs who did not want to get their houses sprayed . Forty nine percent of squad leaders informed that they found some houses locked when they arrived . Almost all squads ( 98 . 5% ) reported that they washed their spray pump at the end of the day and 72 . 8% of spraymen knew how to handle left-over insecticide or did not have any left-over ( 20 . 6% ) [Table 1] . All the respondents in the 600 HHs of the five study upazilas mentioned that their houses had been sprayed with insecticide [Table-3] and 94% said that their living rooms and cattle sheds had also been sprayed . Only 36 . 2% of interviewees mentioned that they had been informed about IRS in advance . The majority of respondents ( 85 . 3% ) said that they were happy with the IRS activities . Of the 15% respondents who were unhappy , the main source of complaint ( 76% of this subgroup ) was that they did not recognize that the spraying killed any insects . Although the majority of the respondents ( 81 . 7% ) received advice to remove or cover clothes , food/utensils , children and animals prior to spraying , only 13 . 3% reported that they were instructed not to enter the house after spraying was completed . Only 7 . 2% of respondents had been informed about possible side effects ( itching , burning of skin , dizziness , cough , etc . ) of the spraying and 91 . 3% reported that they were not advised to abstain from re-plastering ( mud wall ) or re-painting ( pacca/tin house ) their houses after spraying [Table 3] . Table 4 shows the absolute numbers , species , and physiological status of sand flies collected in the households in both treatment groups ( IRS and control ) of the study . A total of 4132 sand flies were trapped in three time intervals ( at baseline , 1 month and 5 months post-intervention ) , of which 3310 ( 80 . 1% ) were P . argentipes and 822 other species [Table 4] . Of all P . argentipes , 46 . 5% ( 1540 ) were males and 53 . 5% ( 1770 ) females; including 20 . 1% ( 666 ) gravid females and only 2 blood-fed females . The average numbers of P . argentipes per household per night per trap in IRS and control arms were 4 . 36 and 8 . 31 for both ( combined ) male and female , 2 . 29 and 3 . 47 for only male , 2 . 71 and 5 . 12 for only female , 0 . 60 and 2 . 19 for gravid female respectively [Table 5] . At baseline ( pre-intervention ) , the number of P . argentipes was significantly lower in the IRS treatment group compared with the control group in both male and female ( p < 0 . 05 ) , only female ( p<0 . 05 ) , and gravid female ( p<0 . 01 ) whereas no significance difference was observed in only male ( p = 0 . 177 ) . Similarly , at 1 month post-IRS , there was a significant difference in P . argentipes densities between the treatment groups for both male and female ( p<0 . 05 ) and for only male ( p<0 . 05 ) . But no significant difference was found for only female ( p = 0 . 318 ) and gravid female ( p = 0 . 106 ) . At 5 months post-IRS , no significant difference in P . argentipes densities was observed between treatment groups for both male and female ( p = 0 . 282 ) , for only male ( p = 0 . 350 ) , for only female ( p = 0 . 261 ) and for gravid female ( p = 0 . 264 ) . This indicates that IRS is able to control the increase of only male P . argentipes density in the IRS areas up to one month post spraying ( at first follow-up ) . But it fails to control the P . argentipes density in IRS houses at five months ( second follow-up ) . Table 5 shows that one month after IRS the P . agrentipes density was reduced by 22 . 61% and 118 . 79% in both ( combined ) male and female , and only male which dropped to 6 . 37% and 53 . 94% at five months respectively . However , no reduction was found in only female and gravid female P . argentipes density . Fig 2 ( A ) shows that the average number of P . argentipes sand flies per household per night per trap increase in both IRS and control treatment groups after one month post-IRS but the increase was more pronounced in the control group for both ( combined ) male and female . Similar increases happened in female and gravid female P . argentipes sand flies as well but the increase was not pronounced in both IRS and control treatment groups . But reduction of density was observed only in male P . argentipes sand flies in IRS treatment group and was increased in the control treatment group [Fig 2 ( A ) ] . There is no significant difference between the treatment groups compared with their baseline measurements apart from male P . argentipes sand flies . At 5-months post-intervention , there was no significant difference between the two treatment groups from baseline for all categories of P . argentipes sand flies [Fig 2 ( B ) ] . On an average , one P . argentipes sand flies increased in the sprayed areas per HH per night per trap compared to baseline at first follow-up which is two in control areas for both ( combined ) male and female . In sprayed areas , only male P . argentipes sand flies density reduced on an average 0 . 5 compared to baseline , which was 2 . 5 increases in the control areas at first month of IRS whereas only female increase was 1 . 5 and about one increase for gravid female [Fig 2 ( B ) ] . Bioassays was conducted in three upazilas , namely: Fulbaria , Mukthagacha and Trishal . The mean corrected mortality of female P . argentipes sand flies was 87 . 3% ( CI = 82 . 3%–92 . 3% ) and 74 . 5% ( CI = 69 . 3%–79 . 7% ) at one and four months after spraying [Fig 3] showing that the insecticidal effect rapidly disappeared .
Spraymen and squad leaders are hired temporarily according to the requirements of the sub-district and in accordance with the micro-action plan of IRS . Spraymen training is compulsory prior to the IRS operation . The present study identified that only 37 . 5% of spraymen had received a full training in accordance with the national guidelines . As a result , the overall performance of house spraying was poor . Issues with the late release of fund , low daily wage of spraymen and squad leaders , and local pressure are hindering the timely recruitment of spraymen and squad leaders . Due to this , the national programme is facing challenges when training the spraymen . As no IRS was conducted for VL vector control from 1998 to early 2012 in the country [7 , 12] , when DDT was banned [8] , more than 85% of spraymen were never involved in IRS activities before . Though the acceptance of IRS activities was found to be very high ( 97% ) in the study areas , more than 50% spraying squads found that there was no one home while they were in the village . This points to a communication gap between the community and the service provider . Often , the quality of IRS could not be ensured by proper supervision: the present study established that less than 44% of supervisors were present in Fulbaria and Trishal upazilas during the spraying activities [7] . Over- or under-concentration of the insecticide solution depends on the correct filling of the pumps; proper mixing of the insecticide and correct spraying technique ensures the uniform distribution of insecticides on sprayed surfaces . We found these two indicators to be substandard in all upazilas and extremely poor in two particular upazilas . Personal protective equipment is key to shield a person from any occupational hazard . However , as these were not distributed , we observed that none of the squad members were wearing boots . The use of goggles and gloves were also found to be low in all sites due to weak supervision . Though adequate training and proper supervision are essential for ensuring proper spraying , we observed substandard spraying techniques ( ranges from 34 . 2%– 52 . 9% correct technique ) except for spraying from bottom to top . Marking of sprayed houses is important for their supervision and identification; however , we found poor compliance with the guidelines , apart from mentioning the team number ( about 84% ) . Despite the many limitations , the communities positively accepted the IRS operations as they had not experienced any IRS or other vector control activities for a long time [7 , 12] . Understandably , people in the endemic communities do not want to suffer from insect nuisance , particularly VL , so they welcomed IRS activities because ‘something is better than nothing’ . A previous study described how community members suffer from loss of earnings and how the symptoms of VL affect their wellbeing; therefore poor householders resorted to desperate actions , like the sale or rent of their assets , taking out loans , etc . to cover the expenditures of diagnostics and treatment [23] . The national programme should ensure that high quality IRS is delivered to get the maximal results . Substandard application of insecticides is also a potential cause of insecticide resistance since sub-lethal doses or poor quality insecticides enhance resistance development . Several studies have reported DDT resistance in P . argentipes [16 , 24 , 25] and it is essential that synthetic pyrethroids remain effective . IRS is highly effective in quickly reducing vector densities underlining the need to continue the operations in all three countries of the ISC even during the consolidation and maintenance phases of VL elimination . Based on our study results the following recommendations can be made: IRS is a huge operation where each step needs to include training of human resources as well as proper management . The experienced people involved in the malaria eradication programme carried out in the 1970s had all retired prior to the VL elimination programme so the national programme faced challenges to prepare a new cohort of spraymen and supervisors to handle such a complex operation . In Bangladesh , the Director of the Central Medical Store Deport ( CMSD ) , DGHS under the Ministry of Health and Family Welfare procured the insecticide and distributed the required quantity to the local hospitals ( Upazila Health Complexes , UHCs ) . The Director of Disease Control , DGHS is responsible for conducting the national IRS operation through district managers ( Civil Surgeon ) and sub-districts managers . Filed operations of IRS in the respective UHC are managed by Upazila Health & Family Planning Officer ( UH&FPO ) . The responsibility for supervision and monitoring of IRS is to be done by the following officers: Integrated vector management is one of the most important elements mentioned in the regional VL elimination strategy . IRS is an effective vector control tool but it is an expensive operation . Therefore , it is recommended that the national programme ensure: ( 1 ) procurement of quality insecticide , ( 2 ) proper training of human resources involved in IRS operation , ( 3 ) proper monitoring and supervision during spraying , ( 4 ) regular vector surveillance and bioassays on sprayed surfaces , ( 5 ) routine testing of vector susceptibility and ( 6 ) community sensitization . Since the launch of the VL elimination programme in 2005 , this is the first study conducted in Bangladesh where national IRS operation was monitored by a third party ( researcher ) and substantial information was generated out of it . Bangladesh , along with other countries in the region , are still heavily relying on IRS so we strongly feel this information can guide national programmes for conducting well coordinated IRS operations . A limitation of the study includes the condition that we had to comply with the micro action plan made by the sub-district managers ( UH&FPO ) for all monitoring visits as we monitored their IRS activities . In such a case , there could have been a chance of leakage about the monitoring visits to IRS squads from beforehand . To avoid such circumstances we did not disclose our detailed plan of visits to local health authorities so that the quality and validity of data were ensured . In conclusion , the national programme should routinely apply monitoring and evaluation activities to ensure high quality of IRS operations as a pre-condition for achieving a fast and sustained reduction of vector densities . This will continue to be important during the maintenance phase of VL elimination in the Indian subcontinent . Further research is needed to determine other suitable vector control option ( s ) when the case numbers are very low . | The visceral leishmaniasis ( VL ) elimination programme was launched in the Indian subcontinent ( Bangladesh , India and Nepal ) in 2005 . Although the integrated vector management ( IVM ) system is one of the important elements highlighted in the Regional VL elimination strategy , indoor residual spraying ( IRS ) is the sole intervention practice that has been implemented . In fact , in Bangladesh from 1999 to early 2012 , no VL vector control was used at all and pre-monsoon IRS was only re-introduced by the national programme in eight high endemic upazilas ( sub-districts ) in 2012 . The present study monitored IRS operation in five upazilas ( Fulbaria , Trishal , Mukthagacha , Gaforgaon and Bhaluka ) . Monitoring took place with the help of using observation check lists and questionnaires included in the WHO/TDR monitoring and evaluation tool kit . The study identified that training of spraymen was insufficient and a supervisor was not always present during spraying . The spraying techniques by all the sprayers were sub-standard . It was also found that all the required personal protective equipment was not provided by the national programme . It is recommended that the national programme should conduct monitoring and evaluation activities to ensure high quality of IRS operations in order to achieve maximum benefit . |
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Rhythmic voltage oscillations resulting from the summed activity of neuronal populations occur in many nervous systems . Contemporary observations suggest that coexistent oscillations interact and , in time , may switch in dominance . We recently reported an example of these interactions recorded from in vitro preparations of rat somatosensory cortex . We found that following an initial interval of coexistent gamma ( ∼25 ms period ) and beta2 ( ∼40 ms period ) rhythms in the superficial and deep cortical layers , respectively , a transition to a synchronous beta1 ( ∼65 ms period ) rhythm in all cortical layers occurred . We proposed that the switch to beta1 activity resulted from the novel mechanism of period concatenation of the faster rhythms: gamma period ( 25 ms ) +beta2 period ( 40 ms ) = beta1 period ( 65 ms ) . In this article , we investigate in greater detail the fundamental mechanisms of the beta1 rhythm . To do so we describe additional in vitro experiments that constrain a biologically realistic , yet simplified , computational model of the activity . We use the model to suggest that the dynamic building blocks ( or motifs ) of the gamma and beta2 rhythms combine to produce a beta1 oscillation that exhibits cross-frequency interactions . Through the combined approach of in vitro experiments and mathematical modeling we isolate the specific components that promote or destroy each rhythm . We propose that mechanisms vital to establishing the beta1 oscillation include strengthened connections between a population of deep layer intrinsically bursting cells and a transition from antidromic to orthodromic spike generation in these cells . We conclude that neural activity in the superficial and deep cortical layers may temporally combine to generate a slower oscillation .
The synchronous activity of neural populations results in voltage fluctuations observable in macroscopic ( e . g . , scalp electroencephalography ) and mesoscopic ( e . g . , local field potential or LFP ) recordings . Rhythmic voltage fluctuations—or oscillations—have been observed in the mammalian brain for over a century [1] . Although the purpose of these oscillations remains unknown , neural rhythms appear to temporally organize network activity patterns , and pathological changes in these rhythms often accompany disease [2] , [3] . What mechanisms produce these neural rhythms ? The complexity of the vertebrate brain—resulting not only from the sheer number of neurons ( approximately 109 ) and their connections ( approximately 1011 [4] ) , but also from the many different neuron classes ( e . g . , the diversity of inhibitory interneurons [5] ) —affords no simple answers . Yet , simple characteristic structural patterns appear fundamental to the brain's organization [5] , [6] . From these elementary network building blocks ( i . e . , structural and functional motifs ) more complicated structures may be generated in an efficient way [7]–[9] . Perhaps a similar strategy may be used to understand the rhythmic electrical activity of the brain . For example , the simplest ( yet biophysical ) model of the 40 Hz ( gamma ) rhythm involves only two interconnected cells ( one excitatory and the other inhibitory ) with reciprocal synaptic connections . In this “gamma –motif” the decay of inhibition paces the rhythm [10]–[12] . This motif , and its variations , may be used to construct more complicated gamma networks [13]–[15] . Here we consider how the elementary dynamic building blocks ( or dynamic motifs [16] ) of the gamma ( ∼40 Hz ) and beta2 ( ∼25 Hz ) rhythms may combine to generate a slower beta1 oscillation . To do so , we develop computational models motivated by a novel method of rhythm generation—period concatenation—as we now describe . We recently observed that application of 400 nM kainate to rat somatosensory cortex in vitro resulted , initially , in distinct rhythms in different cortical layers [17] . In the superficial cortical layers ( layers II and III , or LII and LIII ) we observed gamma frequency rhythms ( 38±3 Hz , n = 25 observations ) , and in the deep cortical layers ( layer V or LV ) nonsynaptic beta2 rhythms ( 24±2 Hz , n = 25 observations ) . The dominant mechanisms that regulate these two rhythms in secondary cortex are known; the decay time of GABAA IPSPs dictates the gamma period [12] , and an outward potassium current in LV pyramidal axons sets the beta2 period [18] . The initial interval of coexistent gamma and beta2 rhythms preceded the transition to a slower beta1 oscillation ( 15±2 Hz , n = 10 ) . This new rhythm appeared in both cortical layers upon reduction of glutamatergic excitation with 2 . 5 µM NBQX ( an AMPA receptor antagonist that also reduces kainate drive [19] ) . We note that the initial interval of kainate application—and its associated network activity at gamma and beta2 frequencies—was an essential prerequisite to the generation of the beta1 rhythm . If we bathed the slices in kainate and NBQX initially ( not kainate alone ) then we found no persistent rhythms . Both the population activity ( observed in the LFP ) and the spiking activity of individual neurons suggested that the beta1 rhythm resulted from period concatenation; the period of the slower beta1 rhythm ( ∼65 ms ) equaled the sum of the periods of the two faster rhythms ( gamma ∼25 ms and beta2 ∼40 ms ) . The ordering of the neural activity in each layer supported the period concatenation hypothesis: in the population and single unit activity of both layers we found delays consistent with individual cycles of the gamma and beta2 rhythms during a single beta1 oscillation . In particular , during the beta1 rhythm , the LII activity followed the LV activity by approximately one gamma cycle ( ≈1/ ( 40 Hz ) or 25 ms ) , and the LV activity followed the LII activity by approximately one beta2 cycle ( ≈1/ ( 25 Hz ) or 40 ms ) . To help illustrate these relationships between the cortical layers , we show a cartoon representation of the activity in Figure 1 . The in vitro observations suggest that concatenation of the faster gamma and beta2 rhythms ( through a summation of their periods ) may generate the slower beta1 oscillation . How might such a concatenation occur ? As a simple illustrative model we consider two excitable oscillators , one tuned to spike at gamma frequencies and the other to spike at beta2 frequencies . With enough excitation , both oscillators independently fire at their natural frequencies . If we reduce this excitation and connect the separate oscillators in a particular way , we can generate the slower beta1 rhythm as the concatenation of the gamma and beta2 periods . To do so , we connect the oscillators so that a spike in one resets and causes the other to fire one cycle later . For example , if the beta2 oscillator fires , it resets the gamma oscillator , which then fires 25 ms ( one gamma cycle ) later . Subsequently , the gamma oscillator spike resets the beta2 oscillator , which then fires 40 ms ( one beta2 cycle ) later . Connected in this way , both oscillators fire at beta1 frequency; the period ( 65 ms ) is the sum of the natural periods ( 25 ms and 40 ms ) of the excitable oscillators . Of course , biophysical cells are more complicated than simple excitable oscillators . In what follows , we examine the cell types , intrinsic currents , and synaptic connections that support the beta1 rhythm in vitro . In an ideal model of period concatenation , the mechanisms that support the faster rhythms combine to generate the slower oscillation . We will show that this scheme is nearly—but not quite—met in the computational model . The phenomenon of rhythm generation through period concatenation could be modeled in many different ways ( involving , for example , different cell types , ionic currents , and synaptic connections [20] . ) Here we implement the elementary dynamic building blocks of the gamma and beta2 oscillators ( i . e . , a gamma motif and beta2 motif ) and combine these to generate the beta1 rhythm . In doing so we describe how in vitro observations guide construction of the model and test its predictions . We begin by discussing the coexistent gamma and beta2 activity observed in vitro and simulated in the model . We then describe the transition to beta1 and propose that this transition requires a shift in the deep layer pyramidal cells from antidromic to orthodromic activity . Analysis of the cross-frequency interactions during the beta1 oscillation ( without access to the underlying mechanisms ) might simply suggest that the slower rhythm modulates the faster activity . As we will show , the combined approach of in vitro recordings , data analysis , and mathematical modeling reveals more detailed information about the intrinsic currents and synaptic connections that generate the beta1 rhythm through period concatenation of the faster oscillations .
During the high kainate condition in vitro , coexistent gamma and beta2 rhythms occur in the superficial and deep cortical layers , respectively [17] . To model the superficial layer activity , we implement a population of Pyramidal-Interneuron-Network-Gamma ( or PING ) type oscillators , each consisting of a basket and RS cell . We show the voltage dynamics of a single gamma oscillator ( i . e . , the gamma-motif ) in Figure 2A . The gamma activity results from the interactions between these two cell types , and the decay time of the inhibitory basket cell synapse determines the period of the gamma rhythm [12] . We also include a population of LTS interneurons in the superficial layer [21]–[23] . Under the high kainate conditions , these interneurons fire infrequently and have a disorganizing influence on the gamma oscillator , as we show in Figure 2B . But , following application of NBQX , the LTS interneurons play a vital role in establishing the beta1 motif , as we discuss below . We replicate the RS-basket-LTS circuit ( shown in Figure 2B ) twenty times and connect the RS neurons with electrical synapses to create a population of superficial layer cells , as described in the Methods section . To model the deep layer activity , we simulate a population of IB cells , each consisting of four compartments: an apical dendrite , basal dendrite , soma , and axon . We show a cartoon representation of a single IB cell model in Figure 3A . Recent experimental and modeling work has shown that the axonal M-current controls the period of the antidromic beta2 oscillation [18] . The same is true in the reduced model presented here; during beta2 activity , bursts of action potentials occur in an IB cell axon and travel antidromically to produce bursts of full action potential spikes and subthreshold “spikelets” in the soma ( Figure 3B ) . The dynamics of the M-current determine the interburst interval [18] . When the axon generates an action potential , the M-current increases . After enough sequential action potentials , the M-current becomes large enough that the outward , hyperpolarizing current prevents further spiking . The M-current then slowly decays and , after sufficient time , another burst of action potentials can begin ( Figure 3B ) . We may increase ( or decrease ) the interburst interval by decreasing ( or increasing ) the backward rate function of the M-current ( Figure 3C ) . A decrease in the backward rate function causes the M-current to decay more slowly and the interburst frequency decreases ( dotted curve ) . Conversely , an increase in the backward rate function increases the interburst frequency ( dashed curve ) . We model the dendrites of an individual deep layer IB cell with two compartments: a basal dendrite and an apical dendrite ( Figure 3A ) . This is , of course , a crude approximation to the true dendritic form . We utilize the two compartments to mimic changes in ionic currents and synaptic inputs that occur across the dendritic structure . In particular , we increase the conductance of hyperpolarization activated currents ( h-currents ) in the apical dendrite [24] , [25] , and will connect excitatory NMDA synapses from IB cell axons to IB cell basal dendrites [26] , [27] to generate the beta1 rhythm , as we describe below . Two types of inhibitory input also target the IB cell dendrites; the superficial layer LTS interneurons target the ascending apical dendrites , and a Poisson source of IPSPs target the basal dendrites ( we provide an interpretation of these random inhibitory inputs below . ) Although a crude approximation to the actual pyramidal morphology , we find that this simple model captures the essence of the observed dynamics , as we now describe . We show a cartoon representation of the entire reduced model in Figure 4A . We note that ascending excitatory synapses ( from the deep layer IB cells to the superficial inhibitory cells ) and descending inhibitory synapses ( from the LTS interneurons to the IB cell apical dendrites ) connect the two layers . In Figure 4B we show a typical simulation result for the 80 cells in the model ( twenty of each type ) . We plot the spiking activity of the three superficial layer cell types , and the spiking activity of the dendrites , somata , and axons of the IB cell population . We find that the individual IB cell dendrites generate action potentials infrequently and remain in a mostly inactive state . The IB cell axons generate bursts of action potentials at beta2 frequencies that are weakly synchronized across the population . The weakly organized beta2 activity results in a noisy synaptic input to the superficial layer inhibitory cells . The result is a disorganization of the dynamic motifs that define the gamma rhythm; the basket cells are perturbed directly by the deep layer excitatory inputs , while the the RS cells are perturbed indirectly through the deep layer excitation of the LTS interneurons . We illustrated the effects of these disorganizing deep layer inputs on the gamma rhythm in Figure 2B and will show below that removing the noisy synaptic inputs from the IB cells increases the superficial layer gamma power . We plot in Figure 4D the population average power spectra ( see Methods section ) of the RS cells ( green curve ) and IB cell axons ( red curve ) and find broad spectral peaks in the gamma range ( 40–50 Hz ) in the superficial layer and in the beta2 range ( 20–30 Hz ) in the deep layer . We also show the average cross-correlation ( see Methods section ) between the spike times of the IB cell axons and RS cells in Figure 4C . We find no obvious correlation between the activity of the two layers . Thus , although the layers interact through chemical synapses , these interactions are too weak to correlate the spike times of the two layers . In particular , the disorganizing beta2 frequency input from the IB cells to the superficial layer inhibitory cells does not phase lock ( and therefore correlate ) the rhythmic activity of the two layers . That the model generates coexistent gamma and beta2 rhythms is not surprising—we include in the model components necessary to produce these two rhythms and allow only weak interactions between the two layers . Having established that the reduced model can generate gamma and beta2 oscillations , we now determine how these model rhythms respond to a series of experimental manipulations performed in vitro . In each challenge the experimental results test and constrain the computational model . In addition , we use the model to suggest more detailed information not accessible in experiment . Our manipulations focus on the synaptic connections and intrinsic currents that support ( or disturb ) each rhythm . Analysis of in vitro slice preparations revealed a statistically significant increase in the gamma power of the superficial layers ( 215±29% , n = 6 , P<0 . 05 ) following a lesion through layer IV . We show example LFP recordings and power spectra for these in vitro results in Figure 5C . These observations suggested some functional connectivity between LV cells and superficial layer neurons involved in generating the gamma oscillation [17] . In the computational model , the superficial layer PING oscillators produce the gamma rhythm . We expect that ascending excitatory synapses from the IB cell axons perturb these superficial layer oscillators ( both directly and through activation of superficial LTS interneurons ) and therefore disturb the gamma rhythm . To test this expectation we separate the cortical layers in the model and observe the resulting activity . Specifically we remove the ascending excitatory synapses from the IB cells to the inhibitory cells , and we disconnect the apical dendrites from the IB cells ( we assume that a slice through layer IV severs the ascending dendrites of the IB cells . ) We suggest the associated parameter changes in Figure 5A . Following separation and elimination of the disorganizing inputs , the spiking activity of the superficial PING oscillators becomes more synchronized ( Figure 5B ) . This increased synchronization boosts the superficial gamma power ( Figure 5D ) , in agreement with the in vitro results . In experiment , separating the superficial and deep cortical layers requires a resection through the intervening layer IV . This physical separation necessarily destroys all interlaminar connections . In the model , we may study specific interlaminar connections to determine those that most disturb the superficial gamma rhythm . To do so we remove , one by one , the three types of interlaminar connections: ( i ) excitatory synapses from the IB axons to the basket cells , ( ii ) excitatory synapses from the IB axons to the LTS interneurons , and ( iii ) the apical dendritic compartments of the IB cells . We find that eliminating the first two connections ( but not the last ) increases the superficial gamma power ( data not illustrated ) . The excitatory input from the IB cells disturbs the gamma oscillators directly by depolarizing the basket cells , and indirectly by exciting the LTS interneurons ( which spike and hyperpolarize the RS cells . ) Without the disorganizing effects of the deep layer input , the RS cells drive the population of PING oscillators in synchrony . Inhibition plays a vital role in pacing the gamma and beta1 rhythms [12] , [17] . To determine the effect of inhibition on the beta2 oscillation , we applied 250 nM of gabazine to the in vitro slice preparation and found a statistically significant ( n = 5 observations , p<0 . 05 ) increase in the deep layer beta2 power ( Figure 6C ) consistent with previous results [18] . In the model , IPSPs ( from the superficial layer LTS interneurons and deep layer random sources ) target the IB cell dendrites . We think of the random inhibitory inputs as representing a noisy motif , perhaps important for other deep layer rhythms , but disruptive to the beta2 activity . Therefore , we expect that blocking IPSPs will eliminate these inputs and reduce the orthodromic , disruptive influence on the antidromic , beta2 rhythm . We determine the effect of blocking IPSPs in the model by setting the conductance of all inhibitory synapses to zero , as we indicate in Figure 6A . We find that elimination of IPSPs in the model reduces the orthodromic disruption of the beta2 rhythm . The IB cell dendrites now burst in response to the antidromic propagation of axonal activity . Without the disruptive inhibitory input to the IB cell dendrites , the IB cell axons burst with greater synchrony ( Figure 6B ) , and thus boost the beta2 power ( Figure 6D ) in agreement with the experimental results . We note that removing the basket cell IPSPs causes the electrically coupled RS cells to establish a positive feedback loop and fire rapidly ( Figure 6B ) . In the simple model of the gamma -motif implemented here , the RS cells spike on each cycle of the gamma rhythm . In vitro , individual RS cells spike much more sparsely during gamma activity [17] . We do not model this sparse activity here , which would require a much larger population of RS cells [12] , [13] . Therefore the model of superficial layer activity is not valid after removing the IPSPs that pace the gamma rhythm . We also use the model to investigate the type of inhibitory synapse most disruptive to the beta2 rhythm . To do so , we determine the individual effects of removing inhibitory synapses from: ( i ) the basket cells alone , ( ii ) the LTS cells alone , and ( iii ) the random sources ( to the IB dendrites ) alone . We find that , of the three , removing the latter two increases the beta2 power ( simulations not illustrated ) . Again we conclude that eliminating the disruptive activity of the IB cell dendrites ( here by removing disruptive inhibitory inputs to these compartments ) boosts the beta2 activity . The beta2 rhythm in the deep layer IB cell population originates in the axonal compartments ( in fact , the axonal M-current sets the period of this rhythm [18] ) . Input from the IB cell dendrites disturbs this rhythm , and we expect that silencing the dendritic compartments would boost the deep layer beta2 power . We test this hypothesis in the mathematical model by blocking a current important to both the dendrites and their superficial layer inputs ( the LTS interneurons ) : the h-current . We do so by setting the h-current conductance to zero in the RS cells , LTS interneurons , and IB cell dendrites . We shade the affected cells and compartments gray in Figure 7A and plot an example of the model spiking activity in Figure 7B . We find that blocking all h-currents eliminates the disruptive effect of the IB cell dendrites on the beta2 rhythm . The IB cell axons burst with increased synchrony and therefore the beta2 oscillations in the IB cell population increase in magnitude . The result is an increase in beta2 power , as we show in Figure 7D . We tested the effect of h-current block in vitro by applying 10 µM ZD-7288 to the slice preparation and found a dramatic increase in the beta2 power ( Figure 7C ) . This increase was statistically significant ( n = 5 observation , p<0 . 05 ) and verified the model prediction . The global blockade of h-current does not suggest which particular cell type ( if any ) is most important to this effect . Therefore , we consider in the model the effects of h-current block: ( i ) in the RS cells alone , ( ii ) in the LTS interneurons alone , ( iii ) in the IB cell basal dendrites alone , and iv ) in the IB cell apical dendrites alone . Of these three , only the middle two increase the beta2 power . By eliminating the h-current in the IB cell basal dendrites , we essentially silence these compartments and reduce the effects of the random inhibitory synaptic inputs to them . By eliminating the h-current in the LTS interneurons , we silence these cells and their disturbing inputs to the IB cells . We conclude that eliminating the disruptive inputs to the IB cells enhances the deep layer ( antidromic ) beta2 rhythm . In vitro , the initial interval of coexistent gamma and beta2 rhythms must precede the slower beta1 activity . Without this initial interval ( i . e . , with immediate application of NBQX to the slice preparation ) no beta1 activity occurs . We therefore expect that the coexistent fast rhythms change the network in a way that supports the slower beta1 oscillation . In addition , we know that NMDA receptor-mediated synaptic events support the beta1 activity—blocking NMDA before or after NBQX application prevents the beta1 oscillations ( as we discuss in detail below ) . To model the change in network structure that occurs during the fast rhythms , we assume a strengthening of all-to-all NMDA synapses from each IB cell axon to all IB cell basal dendrites [26] , [27] . We note that potentiation of NMDA synapses has been observed in LV pyramidal cells with bursting behavior [28] . We expect that these NMDA synapses between the IB cells strengthen gradually during the interval of coexistent gamma and beta2 activity . What effect does this gradual change have on the faster rhythms ? In vitro we observed that the patterns of field potentials remained constant from the onset of gamma and beta2 activity to immediately before application of NBQX . Whatever changes occurred in the network to support the beta1 activity had no impact on the faster rhythms . We test this in the model by strengthening the NMDA synapses between the IB cell population under the high kainate conditions . In agreement with the experimental observations , we find no change in the gamma and beta2 activity of each layer; the power spectra in the deep and superficial layers match those shown in Figure 4D ( results not shown . ) We conclude that the strengthening NMDA synapses between the IB cells do not impact the network dynamics until a reduction of excitation ( induced by NBQX ) occurs in the model , as we now describe . After an initial interval of coexistent gamma and beta2 rhythms in vitro , the transition to beta1 activity followed application of NBQX , resulting in a reduction of glutamatergic excitation via AMPA and kainate receptor subtypes . In both the deep and superficial layers , population activity ( as observed in the LFP ) oscillated at beta1 frequency and a consistent lead/lag relationship appeared between the two layers: LII activity preceded LV activity by ∼40 ms , and LV activity preceded LII activity by ∼25 ms ( see Figure 1 ) . We note that the timing of these lead / lag relationships suggests that the faster dynamics motifs ( i . e . , the gamma-motif [25 ms period] and beta2-motif [40 ms period] ) collaborate to generate the beta1 rhythm [17] . We now consider whether such collaborative interlaminar interactions can produce the beta1 oscillation in the model . We first assume that reduction of glutamatergic excitation decreases excitation in the network and inactivates the gamma and beta2 motifs [29] . We approximate this reduction by decreasing the depolarizing input current to the superficial layer cells , and the deep layer IB cell dendrites and axons . We also halt the Poisson distribution of IPSPs to the IB cell dendrites , thereby eliminating these disruptive inputs . We indicate these changes in Figure 8A by shading blue the affected cells and compartments . As described above , we also include NMDA synapses ( decay time constant 100 ms ) within the deep layer IB cell population that target the IB cell basal dendrites; we indicate these slowly decaying excitatory synapses with red lines and filled circles in Figure 8A . Producing the beta1 rhythm requires many mechanisms that support the superficial gamma and deep beta2 activity . To describe these mechanisms , we follow a cycle of the simulated oscillation as it propagates from the deep to superficial layer and back ( Figure 8B ) . We start at a burst of activity in the IB cell axons ( red dots , see Label 1 in Figure 8B ) . This burst delivers strong excitatory input to the superficial layer inhibitory cells . The basket cells spike ( blue dots , Label 2 in Figure 8B ) , thus inhibiting the RS cells and the LTS interneurons . We note that the basket cells fire and inhibit the LTS interneurons before the ascending NMDA synapses can depolarize these interneurons . This occurs in the model because we make the rise time of the NMDA synapse longer for the LTS interneurons than for the basket cells ( see Methods section ) . After receiving inhibitory input , the h-currents of the RS cells activate and depolarize these cells on a slow time scale . The RS cells recover from inhibition and spike ( green dots , Label 3 in Figure 8B ) , inducing the population of LTS interneurons to fire . Only a weak excitatory input from an RS cell is required to push an LTS interneuron past spike threshold; the slowly decaying excitatory input from the deep layer and the intrinsic h-current have steadily depolarized each LTS interneuron . Upon spiking ( purple dots , Label 4 in Figure 8B ) , the LTS interneurons inhibit the RS cells and the IB cell apical dendrites , temporarily halting the slow depolarization of the deep layer cells due to NMDA synaptic input . The slow depolarization of the dendrites continue—due to both h-currents and NMDA synapses—and the IB cells spike ( Label 5 in Figure 8B ) , inducing a synchronous burst in the deep layer population and restarting the beta1 cycle . Computing the population average power spectra of the deep and superficial layer cells , we find peaks near 13 Hz and higher order harmonics ( e . g . , 26 Hz , 39 Hz , 52 Hz , and so on; Figure 8D ) . For the RS cell , we note that the largest peak occurs near 26 Hz . The power of this peak includes two contributions: ( 1 ) harmonic power of the 13 Hz oscillation ( note 2×13 = 26 Hz ) , and ( 2 ) power resulting from the interval between an RS cell spike and the subsequent inhibitory input ( one beta2 cycle ) . The model of beta1 activity agrees with the observed rhythm in a fundamental way: this rhythm results from period concatenation . To show this we plot in Figure 8C the population average cross-correlation between the spiking activity of the RS cells and the IB cell axons . We find two intervals of increased correlation: between approximately −35 ms and −20 ms , and between approximately 45 ms and 60 ms . Thus , the deep layer population activity precedes the superficial layer activity by approximately 30 ms . The reason for this delay is that the bursting IB cell axons activate the superficial basket cells that inhibit the superficial RS cells . We also conclude that the superficial pyramidal activity precedes the deep layer activity by approximately 50 ms . The reason for this delay is that the RS cells activate the superficial LTS interneurons which inhibit the deep IB cell apical dendrites . These correlation results show that the temporal distributions of the superficial and deep layer activities in the model agree with those of the experiments . In both the model and experiments , during the beta1 rhythm the LII activity follows the LV activity by approximately one gamma cycle ( 30 ms or ≈33 Hz ) , and the LV activity follows the LII activity by approximately one beta2 cycle ( 50 ms or ≈20 Hz ) [17] . We note that the rhythmic activity of the model IB cells differ during the high kainate and low kainate drive conditions . During high kainate conditions , antidromic activity generates the beta2 rhythm . The M-current in the axons sets the period of the IB cell bursting [18] , and the dendritic activity interferes with this rhythm . During low kainate drive conditions , following potentiation of the NMDA synapses , orthodromic activity generates the beta1 rhythm . The h-current contributes to the dendritic depolarization following inhibitory input from the superficial layer and induces the axons to fire , thus continuing the beta1 oscillation . The intrinsic currents of , and synaptic inputs to , the IB cell dendrites play an important role in the beta1 activity . We illustrate the dynamics of these currents and inputs within the dendritic compartments of a single IB cell in Figure 9 . During a burst of activity in the IB cell axons ( e . g . , a strip of red dots in Figure 8B ) the slowly-decaying NMDA current ( red in Figure 9 ) activates and depolarizes the basal dendrite ( near t = 25 ms in Figure 9 ) . Approximately 30 ms later , inhibitory synaptic input from a superficial LTS interneuron hyperpolarizes the apical dendrite ( arrow , upper figure ) and activates the apical dendritic h-current ( blue ) . Both the h-current and NMDA current continue to depolarize the IB cell until a fast sodium current quickly activates ( gray ) and the neuron fires a burst of spikes near t = 100 ms . During spiking , dendritic calcium and potassium currents activate ( not shown ) , the h-current and NMDA current reset , and the beta1 cycle restarts . We note that the slowly decaying NMDA input depolarizes the basal dendrites , and that the population of dendrites enters a more active state than during the high kainate conditions ( compare the dendritic activity shown in Figures 4 and 8 ) . The synchronous bursts of activity strengthen the postsynaptic effect of the IB cells and effectively strengthen the ascending synapses from the deep to superficial layer . To illustrate the interplay of the M-current and h-current during beta1 , we plot in Figure 10 examples of the apical dendritic voltage ( yellow ) and axonal voltage ( orange ) of an IB cell , and the gating variables for the h-current ( dotted curve ) and M-current ( dashed curve ) . When the apical dendrite receives an IPSP ( from a superficial LTS interneuron ) , the h-current gating variable opens and depolarizes the dendrite , thus promoting spiking . When the IB cell generates an action potential , this gating variable closes and removes this depolarizing influence . The M-current in the axon behaves in an opposite way . When the axon bursts , this gating variable opens and hyperpolarizes the axon , thus preventing further spiking . The axon may spike again only after the M-current decays sufficiently . This example illustrates the complementary effects of the M-current and h-current in the IB cell model . The M-current acts to prevent spiking in the axon , while the h-current acts to promote spiking in the dendrite . In an ideal model of period concatenation , the mechanisms that generate the two fast rhythms would combine to produce the slow oscillation . This statement is nearly—but not quite—appropriate for the model proposed here . In the model of superficial layer activity , the inhibitory synapse from the basket cell to the RS cell paces the gamma rhythm . Increasing the decay time of this synapse slows the gamma rhythm . A population of these basket cell synapses participates in the beta1 rhythm , and if we increase the decay time of these synapses , then we also slow the beta1 oscillation . Thus , a fundamental mechanism pacing the fast gamma rhythm—namely the basket cell inhibitory synapses—also paces the beta1 rhythm . A more complicated relationship exists between the beta2 rhythm and its contribution to beta1 . During beta2 activity , M-currents in the IB cell axons pace the deep layer rhythm . But , during beta1 these M-currents have less influence on the frequency of the deep layer activity . More important are the h-currents , excitatory synaptic inputs , and inhibitory synaptic inputs to the IB cell dendrites . The transition from beta2 to beta1 involves a switch in the IB cell from antidromic to orthodromic activity . Therefore the beta2 component of the beta1 rhythm is dominated by mechanisms in the IB cell dendrites , not the IB cell axon . Thus , in the model , the concatenation depends on having two mechanisms ( an M-current and an h-current combined with excitatory and inhibitory synaptic inputs ) with the same time scale . In the model , the same cells generate the coexistent fast rhythms and combined slow oscillation , although the biophysical mechanisms important to each rhythm change in the deep layer IB cells . Simpler period concatenation models may be developed , but we do not believe that these models would agree in all ways with the in vitro data . In what follows , we compare the model with an additional set of experimental manipulations . We show that the two are consistent and verify a model prediction of the fundamental role for the h-current in vitro . We also use the model to suggest specific mechanisms important to the beta1 activity and its propagation between cortical layers . We begin with the observation that: Analysis of the in vitro data allowed us to constrain the computational model in many ways , but not completely . In the model of beta1 activity described above , we considered a strengthening of NMDA synapses between the population of deep layer IB cells . As a second model for the change in network connectivity that supports the beta1 rhythm , we consider NMDA synapses that descend from the superficial to deep layer pyramidal cells . In this case , we include slow excitatory synapses ( rise and decay times of 10 ms and 150 ms , respectively ) from each superficial layer RS cell to all IB cell apical dendrites [15] . Performing simulations identical to those described above , we find that the model produces beta1 activity with the characteristics of period concatenation . Moreover , we find that separating the cortical layers , blocking IPSPs , blocking NMDA synapses , or blocking the h-current destroys the beta1 activity . Thus , this second model—with potentiating NMDA synapses originating in the superficial layer RS cells and targeting the deep layer apical dendrites—also produces results consistent with the in vitro experiments and modeling results described above . The essential ideas of the previous model can be captured in a simpler model , but at the expense of becoming more abstract . To construct a simpler model , we do not replicate the cells and dynamic motifs to create neural populations . Instead , we employ single copies of the gamma-motif , the beta2 -motif ( a single IB cell ) , and the LTS interneuron . In addition , we represent the IB cell dendrite as a single compartment ( and do not distinguish between the apical and basal dendrites . ) In each cell and compartment we implement the same currents and synaptic connections utilized in the more detailed model ( e . g . , an M-current in the IB cell axon and ascending excitatory synaptic connections from the IB cell to the superficial inhibitory cells . ) The goal of creating such a simple model is to understand the fundamental mechanisms that support the beta1 oscillation . We show a cartoon representation of the simple model in Figure 12A . In the superficial layer , the RS and basket cells form a single ( PING ) oscillator that generates gamma activity , and in the deep layer a single IB cell axon generates the beta2 rhythm . The mechanisms that support the gamma- and beta2-motifs are identical to those in the more detailed model . We simulate the transition to beta1 activity in two ways . First , we decrease the excitability of the network by reducing the depolarizing input currents to all cells and compartments except the IB cell dendrite and soma; we indicate these hyperpolarizations by shading the affected cells and compartment blue in Figure 12B . Second , we depolarize the IB cell dendrite ( yellow ) and strengthen the ascending synapses from the IB cell to superficial inhibitory cells ( thick black lines , Figure 12B ) . Both changes mimic effects in the more detailed model . For the population of cells described above we included excitatory NMDA synapses between the IB cells . These synapses act to depolarize the IB cell basal dendrites and help synchronize the IB cell bursting , thus effectively strengthening the ascending synapses . In the simple model , we approximate the effect of increased IB cell synchronization as increased ascending synaptic input . The result is stronger synapses from excitatory to inhibitory cells , which we might also interpret as potentiation of these synapses between the two cell populations . We find that the simple model can reproduce all of the in vitro observations ( e . g . , blocking the h-current boosts beta2 activity and eliminates beta1 activity; data not shown ) . We suggest that this greatly reduced model reveals the fundamental mechanisms of the rhythms and period concatenation with four ( rather than 80 ) cells .
We have constructed a computational model , consisting of four cell types , and compared it with in vitro observations from rat somatosensory cortex [17] . In those recordings , we observed a transition from coexistent gamma and beta2 rhythms in the superficial and deep cortical layers , respectively , to a common beta1 oscillation in both cortical layers . We proposed that the slower rhythm resulted from the concatenation of periods of the two faster rhythms: gamma period ( 25 ms ) +beta2 period ( 40 ms ) = beta1 period ( 65 ms ) . Both the fast and slow rhythms were sensitive to numerous experimental manipulations ( e . g . , separating the cortical layers boosted the gamma power under high kainate conditions and eliminated the beta1 power under conditions of low kainate drive . ) In this manuscript , we showed that a biologically realistic , yet simplified , computational model could reproduce the coexistent gamma and beta2 rhythms , the transition to beta1 , and numerous experimental manipulations . Moreover , we used the model to suggest the specific mechanisms important for the generation and modification of each rhythm . In the superficial layer , reciprocal synaptic connections between an RS and basket cell created the inhibition-based gamma rhythm; the decay time of the basket cell inhibitory synapse determined the period of this oscillation . In the deep layer , the M-current in the IB cell axons paced the beta2 rhythm . We proposed in the model that these dynamic motifs combined to create the slower beta1 oscillation . During beta1 activity , the LTS interneurons ( initially disruptive to the coexistent gamma and beta2 oscillations ) served a vital role , and the rhythm propagated between the cortical layers . Thus , unlike the coexistent gamma and beta2 rhythms , the beta1 rhythm represented a state in which activity in both deep and superficial layers of neocortex temporally combined to produce oscillations in which interlaminar interactions were vital . We considered four manipulations of the model and in vitro preparation during beta1 activity . The outcome of each manipulation—separating the cortical layers , blocking the h-current , blocking IPSPs , or blocking the NMDA synapses between IB cells—was the same: elimination of the beta1 rhythm . We noted that the loss of beta1 activity occurred when the rhythm could not propagate between the cortical layers; thus , connections between layers were essential to the beta1 rhythm . We also noted that the beta1 rhythm is not quite a concatenation of the gamma and beta2 rhythms . Instead , we found that two different mechanisms support the beta2 and beta1 oscillations . In the high kainate condition , the beta2 rhythm resulted from antidromic activity in the IB cell axons . In the subsequent low kainate drive condition , the beta1 rhythm resulted from orthodromic activity in the IB cell dendrites . We noted that the initial interval of coexistent gamma and beta2 activity must precede the beta1 rhythm . If we start the in vitro slice preparation in the low kainate drive condition ( i . e . , with kainate+NBQX ) we find no oscillatory activity . Therefore , the initial interval of fast rhythms must change the network to support the slower beta1 oscillation . In the model , we assumed that the coexistent fast rhythms facilitated a potentiation of NMDA synapses between the population of IB cells [28] . In developing the computational model , we also proposed two additional scenarios for the change in network connectivity that supports the beta1 rhythm . First , we suggested strengthening the descending synapses from the superficial RS cells to the deep layer IB cells . Second , for the reduced model , we strengthened ascending synapses from the deep layer IB cells to the superficial inhibitory neurons . Each model was sufficient to generate the beta1 activity through period concatenation and agreed with the observational data . In vitro the transition to beta1 may perhaps incorporate aspects from all three scenarios , and future studies may suggest more general conditions for period concatenation incorporating all three models . In the population models proposed in this work , we considered the activity generated in a single cortical column of somatosensory cortex . An improved model would describe the activity of multiple , interacting cortical columns . Including interactions between columns would permit synaptic connections not present in the single-column model ( for example , excitatory synapses from deep layer IB cells to superficial RS cells observed in rat motor cortex [30] ) . Multi-column models incorporating adjacent cortices ( and their region-specific oscillatory circuits and agonists [31] ) may reveal even richer phenomena . What computational roles might the separate gamma and beta2 rhythms , and the transition to a slower beta1 oscillation , serve ? In the in vitro observations and computational models considered here , the initial faster rhythms coexisted in different cortical layers . The transition to the slower beta1 oscillation established a common rhythm that propagated between both layers . We might therefore interpret the transition to the beta1 oscillation as binding the superficial and deep layers of a cortical column . This binding organizes subnetworks of neurons within the input ( superficial ) and output ( deep ) layers of a cortical column [32] . Transitions between gamma , beta2 , and beta1 oscillations are also observed in vivo [33] , [34] although the role of rhythms in these cognitive functions remains unknown . However , beta oscillations ( 12–29 Hz ) have been associated with long-range synchronization [35] , which may be relevant to the interlaminar beta1 oscillation analyzed here . Recent observations suggest that rhythms within distinct frequency intervals interact , and that these interactions may occur in different ways [36]–[45] . Given the increasing amount of evidence supporting cross-frequency interactions , the mechanisms involved in generating multi-frequency oscillatory states at the cellular and network level become fundamental to understanding brain rhythms . In this work , we described these mechanisms for a particular example of rhythm generation through period concatenation . Because the slow rhythm ( beta1 ) resulted from the concatenation of two faster rhythms ( beta2 and gamma ) , the fast activity was necessarily locked to a specific phase of the beta1 rhythm . For example , the interval between the basket cell firing and the RS ( or LTS ) cell firing defines ( approximately ) one gamma cycle . This fast activity is locked to a particular phase of the IB cell population bursts; this locking is apparent in Figure 8B . Analysis of these data alone suggests that the slow rhythm ( beta1 ) modulates the faster rhythm ( gamma ) . One might therefore conclude that the slow oscillation drives the faster rhythms . This conclusion is only partially correct; in the model the faster rhythms combine to create the slower oscillation . Data analysis alone cannot distinguish the mechanisms producing each rhythm . Instead , a combined approach of data analysis and biophysical modeling is required . Using this combined approach we suggest that faster rhythms , of different coexistent frequencies in different cortical laminae , may be present to maintain independent temporal processing of cortical input in each lamina . If synaptic potentiation occurs then these faster rhythms may concatenate , producing a slower rhythm representing the temporally correlated activity patterns in both deep and superficial laminae—thus uniting previously independent , lamina-specific activity patterns into a single cortical dynamic process .
Slices of parietal neocortex ( 450 µm thick ) , were prepared from adult male Wistar rats and maintained in an interface chamber . Details of artificial cerebrospinal fluid composition and recording techniques are in [18] . Spectra were all calculated from 60 s epochs of LFP data using MATLAB . All drugs were applied directly to the slice perfusate . All procedures were performed in accordance with the UK animals ( scientific Procedures ) act . The model consists of four cell types in two cortical layers . We describe the cells , synapses , and analysis methods in this section ( detailed equations may be found in the Text S1 . The model cells are reductions [46] of detailed multi-compartment representations of rat cortical neurons [15] . To construct the reduced model , we first include explicitly the mechanisms essential for gamma and beta2 rhythm generation observed in the superficial and deep cortical layers , respectively . We then include an additional cell type observed in vitro and chemical synapses to connect the two cortical layers . We begin with a description of the superficial layer model . A simple model of the superficial layer gamma rhythm consists of two neurons—one excitatory and one inhibitory—interacting through reciprocal synapses ( the so-called Pyramidal-Interneuron-Network-Gamma or PING rhythm [12] . ) For the excitatory neuron , we implement a reduction of a superficial regular spiking ( RS ) pyramidal cell [15] . The reduced model consists of one compartment with four intrinsic membrane currents: a leak current , a transient inactivating sodium current ( NaF current ) , a delayed rectifier potassium current ( KDR current ) , and a hyperpolarization activated ( or anomalous rectifier ) current ( h-current ) . The first three currents facilitate the generation of action potentials ( or spiking ) in the model [47] . The last current slowly depolarizes the RS cell following inhibitory input [48] and serves a vital role in the beta1 oscillation , as we describe in the Results section . The form of these intrinsic membrane currents , the reversal potentials , and the dynamics of the gating variables follow [49] . For the inhibitory neuron , we implement a superficial basket cell [15] . The reduced model consists of one compartment with three intrinsic membrane currents: a NaF current , a KDR current , and a leak current . The dynamics and reversal potentials for these currents follow the intrinsic interneuron properties stated in [50] . We note that the traditional spiking currents ( the NaF , KDR , and leak currents ) are similar ( but not identical ) for the RS cell and basket cell models . We connect the RS and basket cells with reciprocal synapses . The equations governing the synaptic dynamics are similar to those described in [35] and [51] . The rise and decay times of the fast excitatory ( AMPA ) synapse are 0 . 25 ms and 1 ms , respectively . The rise and decay times of the fast inhibitory ( GABAA ) synapse are 0 . 5 ms and 5 ms , respectively . We also include a fast inhibitory autapse on the basket cell with the same GABAA dynamics . We did not include fast rhythmic bursting ( FRB ) neurons in the reduced gamma model . Recent experimental and modeling results suggest that these cells provide excitation via axonal plexus activity to drive neocortical gamma rhythms [50] . To avoid the complexity of such a system ( which requires a large cell population with axonally interconnected principal cells ) while maintaining sufficient phasic drive to generate gamma activity , we include tonic input currents injected to both cells . Each cell also receives a weak stochastic input with normal distribution that results in fluctuation of ±0 . 25 mV . The final cell type we include in the superficial layer is a low threshold spiking ( LTS ) interneuron [21]–[23] . Our reduced model of this cell consists of a single compartment with four currents: a NaF current , a KDR current , a leak current , and an h-current . The dynamics and reversal potentials for these currents follow the intrinsic interneuron properties stated in [50] . We form reciprocal synaptic connections between the LTS interneuron and RS cell . The rise and decay times of the excitatory synapse are 2 . 5 ms and 1 . 0 ms , respectively , and the rise and decay times of the inhibitory synapse are 0 . 5 ms and 20 ms , respectively . We also include an inhibitory synapse from the basket cell to the LTS interneuron ( rise time 0 . 5 ms and decay time 6 . 0 ms ) and an inhibitory autapse on the LTS interneuron ( rise time 0 . 5 ms and decay time 20 ms ) . A weak stochastic input ( normally distributed ) perturbs the voltage and results in fluctuation of ±0 . 25 mV . To establish a population model of the superficial layer gamma activity , we create twenty replications of the RS-basket-LTS cell circuit described above and shown in Figure 2B . We make all parameters identical for each cell type of the population except for the depolarizing input currents which we independently vary for each cell of each triad . This heterogeneity causes the twenty RS cells to spike at different frequencies , ranging from 30 Hz and 50 Hz . We connect the RS-basket-LTS cell triads with a single type of connection: all-to-all electrical coupling between the RS cells [15] . To model the deep layer beta2 rhythm , we implement a reduction of the LV tufted intrinsically bursting ( IB ) pyramidal cell [15] . This cell type appears to dominate the beta2 rhythm in vitro [18] . Our reduced model of the single IB cell consists of four compartments: an axon , soma , apical dendrite , and basal dendrite . Each compartment contains the intrinsic membrane spiking currents: a NaF current , a KDR current , and a leak current . In addition , we include in the axon a muscarinic receptor suppressed potassium current ( M-current [52] , [53] ) , and in the dendrites we include a h-current , a M-current , and a high-threshold noninactivating calcium current ( CaH current ) . The maximum conductance of the h-current in the apical dendrites exceeds that in the basal dendrites ( to crudely mimic the increase in h-current observed with distance from the soma in apical dendrites [24] , [25] ) . Both dendritic compartments also receive inhibitory synaptic input—the basal dendrite from a Poisson source of IPSPs and the apical dendrite from the superficial layer , as we describe below . We connect the dendritic and axonal compartments to the soma through electrotonic coupling . The coupling conductances between the axon and soma were identical for both compartments . We set the coupling conductance from the dendrites to soma to exceed the coupling conductance from the soma to dendrites ( so that the soma voltage has a weaker effect on the dendritic compartment's dynamics [46] , [54] . ) Each compartment receives a tonic drive , and the axon and dendrites receive weak stochastic inputs ( normal distribution , ±0 . 1 mV fluctuations ) . To establish a population model of the deep layer activity we create twenty replications of the IB cell . Each member of the IB cell population consists of the same ( four ) compartments and currents described above . We make all parameters the same in each cell , except for the depolarizing currents to each compartment . We vary these inputs from cell to cell to establish heterogeneous bursting activity in the cell population; the interburst frequency of the twenty IB cells ranges from 20 Hz to 30 Hz . In the model , consisting of only twenty IB cells , we cannot represent a sparsely connected axon plexus which involves a large number of cells [15] . Therefore , we simply connect the IB cell population with all-to-all axonal gap junctions . From the superficial to deep layer we include a synapse from a single LTS interneuron to a unique apical dendrite of an IB cell ( we note that the axons of superficial LTS interneurons extend up to layer I—a lamina rich in LV pyramidal cell apical dendrites [22] . ) Explicitly , if we number the population of LTS interneurons and IB cell apical dendrites 1 through 20 , then LTS interneuron 1 forms a synapse on IB cell 1 , LTS interneuron 2 forms a synapse on IB cell 2 , and so on . The rise time and decay time of these synapses are 0 . 5 ms and 20 ms , respectively [15] . From the deep to superficial layer , we include synapses from each IB cell axon to all basket cells and LTS interneurons [55] , [56] . For the basket cell postsynaptic target , the rise and decay times are 0 . 25 ms and 1 . 0 ms , respectively , and for the LTS interneuron postsynaptic target the rise and decay times are 2 . 5 ms and 50 ms , respectively . We note that the differences in the synaptic rise times may result from differences in axonal conduction times or dendritic delays ( resulting perhaps from differences in dendritic morphologies or differences in locations of synaptic contacts [27] , [57] . ) We do not include ascending synapses from deep layer IB cells to superficial pyramidal cells because anatomical studies indicate few synapses between these populations within a cortical column [15] , [56] , [58] . In addition to these three types of permanent synapses , we include a fourth potentiating synapse: a slow ( NMDA ) excitatory synapse extending from each IB cell axon to all IB cell basal dendrites [26] , [27] . The rise and decay times for this synapse are 0 . 5 ms and 100 ms , respectively . We employ the latter synapse to generate the beta1 rhythm , as we show in the Results section . We use the Interactive Data Language ( IDL ) to compute numerical solutions to the model equations . We implement a second-order difference method with a time step of 0 . 01 ms , and follow the Euler-Maruyama algorithm to include stochastic inputs . Readers may obtain the simulation code by contacting the authors . To analyze the simulation results we compute two measures: the power spectrum and the cross-correlation . We briefly describe each measure here ( detailed discussions of these measure may be found in the literature , for example [59] ) . To compute a power spectrum , we first compute ten realizations of the model , each with a different set of random tonic input currents to the cells and compartments , and each lasting 500 ms . Then for each realization we compute the population average voltage of the cell type of interest by summing the voltages of all ( twenty ) cells and dividing by the total number of cells . We then compute the power spectra of the population average voltages , and average these spectra across the ten realizations . To compute the cross-correlation between the spike times of the RS cells and IB cell axons , we first simulate the model to create 1 s of data . We then locate the spike times and create new “binary” time series for each ( of the twenty ) RS cell and IB cell axon . We make the binary times series 0 everywhere except at the spike times where we set the time series to 1 . We then compute the cross-correlation between the binary time series of each IB cell axon and the corresponding RS cell . By corresponding , we mean the RS cell in the unique triad whose LTS interneuron forms a synapse on a particular IB cell dendrite . We compute the cross-correlation in this way to avoid the effects of correlations in the subthreshold membrane potentials ( e . g . , correlations in hyperpolarizations of two cells . ) We compute these cross-correlations for all twenty pairs of IB and RS cells , and average the results over the twenty cell pairs . | Since the late 19th century , rhythmic electrical activity has been observed in the mammalian brain . Although subject to intense scrutiny , only a handful of these rhythms are understood in terms of the biophysical elements that produce the oscillations . Even less understood are the mechanisms that underlie interactions between rhythms; how do rhythms of different frequencies coexist and affect one another in the dynamic environment of the brain ? In this article , we consider a recent proposal for a novel mechanism of cortical rhythm generation: period concatenation , in which the periods of faster rhythms sum to produce a slower oscillation . To model this phenomenon , we implement simple—yet biophysical—computational models of the individual neurons that produce the brain's voltage activity . We utilize established models for the faster rhythms , and unite these in a particular way to generate a slower oscillation . Through the combined approach of experimental recordings ( from thin sections of rat cortex ) and mathematical modeling , we identify the cell types , synaptic connections , and ionic currents involved in rhythm generation through period concatenation . In this way the brain may generate new activity through the combination of preexisting elements . |
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Despite free of charge biomedical treatment , the cost burden of Buruli ulcer disease ( Bu ) hospitalisation in Central Cameroon accounts for 25% of households' yearly earnings , surpassing the threshold of 10% , which is generally considered catastrophic for the household economy , and calling into question the sustainability of current Bu programmes . The high non-medical costs and productivity loss for Bu patients and their households make household involvement in the healing process unsustainable . 63% of households cease providing social and financial support for patients as a coping strategy , resulting in the patient's isolation at the hospital . Social isolation itself was cited by in-patients as the principal cause for abandonment of biomedical treatment . These findings demonstrate that further research and investment in Bu are urgently needed to evaluate new intervention strategies that are socially acceptable and appropriate in the local context .
Buruli ulcer disease ( henceforth , Bu ) is the third most common mycobacterial disease in humans , after tuberculosis and leprosy [1]; nonetheless , despite the dramatic increase in incidence rates in West Africa during the last decade , it remains largely neglected [2] . Its causative agent , Mycobacterium ulcerans , is an environmental mycobacterium endemic to restricted foci throughout the tropics and directly related to stagnant or slow-flowing water [3] . Inoculation of Mycobacterium ulcerans into the subcutaneous tissues likely occurs through penetrating skin trauma , though the mode of transmission is not entirely clear . The agent produces a potent toxin known as mycolactone which destroys cells in the subcutis leading to the development of large skin ulcers [4] . Major advances have been made in the management of the disease with the introduction of rational antibiotic therapy [4] , however , successful clinical outcomes require that patients initiate treatment promptly and adhere to treatment regimens [5] , [6] , [7] . Furthermore , because Bu often leads to irreversible physical disabilities , the disease takes a significant toll on affected patients and their households [7] . In Cameroon , Bu was first documented in 1969 in 47 cases , all originating from the well-circumscribed rural area of Ayos and Akonolinga in Central Cameroon . The endemic region was later identified as an area stretching along the Nyong River and some of its tributaries for approximately 100 km in length and 10–30 km in width with a population of 98 , 500 people [3] . A study by Noeske and others [3] conducted in 2001 identified an overall prevalence rate of 0 . 44% constituting active and inactive Bu in the surveyed area . The highest prevalence of active cases found in a particular settlement was 8% . Disease prevalence was higher in villages closer to the Nyong river . Bu occurs principally among impoverished rural people with limited geographic and economic access to health facilities . Recently , a survey carried out by Um Boock in 2004 [8] detected new foci of Bu outside of the previously established endemic region , particularly in other areas of the Central Province and in the provinces of the East and Southwest . At the national level 930 cases were detected [8] . Biomedical treatment Bu in the endemic region is provided at the Ayos and Akonolinga Hospitals which have specialised Bu programmes sponsored by Aide aux Lépreux Emmaus Suisse and Médicins Sans Frontières respectively . Both offer free of charge medical treatment and supplementary aid . These services include free of charge medication and in-patient treatment; free meals served once or twice a day ( depending on the institution ) ; complementary accommodations for in-patients and their caretakers for the duration of their stays; extra schooling ( at Ayos Hospital ) ; and , the free provision of basic materials for everyday needs such as soap and bandages , sheets , etc . However , the provision of these materials is often irregular as stock-outs are common , directly affecting patients' hospitalisation costs . The main objective of this article is to evaluate the economic and social impact of hospital treatment for Bu disease on the patient and the household in a setting where medical costs for hospital treatment and supplementary aid for everyday needs are subsidised by the local health care system and/or through foreign aid .
The present study was conducted in the above-mentioned endemic region of Ayos and Akonolinga . It was an a focused ethnographic study using both qualitative and quantitative research methods . Field work was conducted in both community and clinical settings for a period of four months , three of which were spent at the Ayos and Akonolinga hospitals and one in the selected endemic communities of Eyess , Edou , Ebanda and Ngoulemakong , all belonging to the catchment areas of the respective hospitals . Considering the focalised character of Bu infection rates , [7] restricted local and geographical units were selected for analysis . During field work the following techniques were used: ( i ) Participant observation . Participant observation , or the observation of people's behaviour in its natural setting , is a fundamental and often neglected part of qualitative research . This technique consists of participating in everyday activities , working in the native language ( in this case the official language and lingua franca , French ) and observing events in their everyday context . Gaining patients' and household members' confidence through participant observation methods increases the validity of ethnographic data . In this study , participant observation was particularly important in establishing costs since patients and household members are unlikely to accurately recall all costs associated with this long-term illness . Participant observation also enabled us to contrast reported adherence to treatment , expenditures , frequency of visits to hospitalised Bu patients , and other behaviours with actual observed patterns . ( ii ) In-depth interviews . During field work , in-depth interviews were carried out with Bu patients and their households , extended family members , local traditional healers , community leaders and key informants ( such as teachers , school directors , priests , etc ) . One or more in depth interviews were carried out with Bu patients and their households . All interviews were conducted by the authors , personally , and were not mediated by hospital , public health or other biomedical staff for fear that their presence would guide responses ( e . g . downplay negative opinions of current health provisions ) . ( iii ) Focus group discussions . Focus group discussions were held with medical staff at the Ayos and at the Akonolinga Hospitals and with extended family groupings in the various communities . Treatment , costs and health seeking behaviour ( including perceived aetiology of the illness , traditional healing and delay ) were among the primarily topics during these sessions . Insights and established categories from an initial phase of qualitative research were used to assess preliminary data , and construct cost categories , which facilitated further systemisation and eliciting of costs from respondents , and were useful as secondary indicators to evaluate people's stated costs and economic coping strategies . After this stage , data were more systematically gathered and standardised through ( iv ) a quantifiable half-open structured questionnaire realised with all patients at Ayos and Akonolinga Hospitals and carried out in the local communities . At the Ayos and Akonolinga Hospitals , 79 clinically confirmed hospitalised Bu patients were included in the sample , representing all patients in treatment during the four month period of the study from November 2005 to February 2006 . The costs and cost burden presented in this article apply exclusively to the hospitalised patients . However , to gain a better understanding of Bu patients' and households' health seeking behaviour , the social and economic burden of the disease , and the relationship between local communities and the hospital setting , 73 patients and their households were further included for qualitative analysis at the community level , representing all active and inactive Bu cases living in the selected communities at the time of study . The costs accrued by the latter are outside the scope of this article . For the definition and categorisation of costs , the conceptual framework proposed by Russell [9] was employed to ensure clarity and precision and to allow for further theory building and comparability of the economic burden of the illness across settings ( Figure 1; Table 1 ) . In the present context , the household was operationally defined as a group of people who live together in a common residence , forming a unit of economic cooperation , and who are responsible for the socialisation of the children born of its members . The household was the preferred unit of analysis for assessing the economic costs and consequences of illness for two reasons . Firstly , local kinship relations are traditionally based both on patrilineal filiation ( kinship through the father's line ) and patrilocal residence patterns ( adoption of the father's place of residence in marriage ) . Communities are consequently divided into different settlements of patrilocally nucleated extended family clusters ( or patrikin groups ) , consisting of various related households that nevertheless function independently . Despite the proximity of patrikin , the household is the basic economic unit when coping with the illness costs of its members . Secondly , because decisions about treatment are based on negotiations within the household ( though not necessarily from an equal bargaining position ) , since the costs of illness reach beyond the sick to involve other household members who care for and accompany them to treatment and who ultimately are affected by the costs of illness which fall on the household budget and diminish the resources available for other household members [9] . Household subsistence strategies at the local level consist of a combination and alternation of revenues . Of primary importance is slash and burn agriculture , characterised by the simultaneous exploitation of several plots with a variety of intercropped products ( such as plantains , macabos , maniocs and peanuts ) that are harvested at different periods and can be more intensively exploited according to the necessities of general household spending . Earnings from slash and burn agriculture are often supplemented by earnings from cacao and/or café plantations ( a heritage of colonial times ) , which require different agricultural techniques and can only be harvested at specific intervals each year . Furthermore , the proximity of the Nyong and its tributaries provides communities with fish while the tropical rainforest offers possibilities for hunting . Both resources are used for consumption and sale . Occasional formal and salaried employment and migration to urban centres or abroad represent additional complementary subsistence strategies , although the latter signifies the separation of the household . Earnings were compiled through in-depth interviews with the household providers and was calculated based on the combined earnings of all household members for the period of one calendar year . For most cases , households were entirely dependent on subsistence farming . For slash and burn farming , activities were systemised in an agricultural calendar specifying products' harvest times or intervals , product prices and the subsequent estimation of earnings . Earnings from fishing were calculated according to an activity calendar , incorporating the amount of fish , fish prices and number of days worked . Hunting is a more irregular activity and meat is consumed more often then sold . Nevertheless , extra earnings of sold bush meat were also incorporated into calculations . In cases where one of the household members had formal employment , his/her yearly earnings were calculated according to the official salary . Usually this person further participates in the household's slash and burn agriculture , the revenues of which contribute to the general household earnings . The yearly revenues for informal urban and semi-urban employment ( i . e . undocumented , unofficial and irregular employment such as construction work , etc . ) were also included in calculations for the patient and/or other household members . The study was approved by the ethical committee of the Ministry of Health , Cameroon ( No . 0123/ARRO/MSP/DPSPL ) . For the field work , all interviewers were requested to follow the Code of Ethics of the American Anthropological Association ( AAA ) [10] . As proposed by the AAA , all interviewees were informed before the start of the interview about project goals , the topic and type of questions , their right to reject being interviewed , to interrupt the conversation at any time , and to withdraw any given information during or after the interview , and the intended use of results for scientific publications and reports to health authorities . Anonymity was guaranteed and confidentiality of interviewees was assured by assigning a number to each informant . The interviewers sought oral consent from all interviewees . Oral consent was preferred , since the interviewees were not put at any risk of being harmed in their safety or psychological well-being and the act of signing one's name when providing economic data was considered a potential reason for mistrust ( see also AAA which states that “It is often not appropriate to obtain consent through a signed form-for example ( … ) where the act of signing one's name converts a friendly discussion into a hostile circumstance” [11] .
During the study period , 34% of all patients registered at the Ayos and Akonolinga Hospitals since 2003 were classified as “healed” with a median hospitalisation time of 157 days ( range 47–645 days ) . 39% of hospital patients were female and 61% male . 56% were children and adolescents ( <20 ) while 44% were adults or elderly . Monthly household earnings of Bu hospital patients previous to their illness were calculated for all patients and households . The median value was €40 , 7 ( 26 . 400 FCFA ) . This was slightly higher than the median earnings for subsistence farming in the studied endemic communities , which was estimated during fieldwork at €33 , 8 ( 22 . 000 FCFA ) . These median values correspond with those of the World Bank [12] and UNDP [13] , which state that 51% of Cameroon's population lives below the $2/day mark while 17% earn less then $1/day . During in-patient treatment , the most common coping strategies included the use of savings ( 39% ) ; making claims from social networks ( 75% ) ; borrowing ( 53% ) ; reducing consumption of non-essentials ( 100% ) and essentials ( 69% ) ; patient informal employment ( 36% ) ; informal employment of the caretaker ( 43% ) ; the supplying of provisions from family relations in nearby villages ( 56% ) ; and , the social isolation of the patient ( 63% ) . The coping strategies employed were generally cost prevention strategies , similar to those used for other long-term and chronic illnesses , examples of which are found in studies of TB illness costs where households engage in either cost prevention strategies ( do not seek treatment or abandon treatment ) or asset strategies to mobilise substantial sums of money [9] , [14] , [15] . The median total costs for household members' involvement for a patient that was not isolated totalled €105 , 9 ( 68 . 848 FCFA ) while for an isolated patient the costs decreased dramatically to only €12 , 4 ( 8 . 051 , 87 FCFA ) . These numbers signify that for patients who were not isolated the costs for household involvement during the healing process was 8 , 6 times higher than for isolated patients . Subsequently 63% of households did avoid these costly direct expenses , leading to the social isolation of the in-patients . In the words of isolated patients:
Growing awareness of the connection between ill health and impoverishment has placed health at the centre of development agencies' poverty reduction targets and strategies and strengthened arguments for a substantial increase in health sector investment . Subsequent research , however , has shown the difficulty in alleviating the cost burden of illness for poor households due to the presence of hidden costs . Our research found that despite the availability of free of charge medical care , households of Bu patients in the study area faced an unbearable cost burden . The majority of households responded by withdrawing involvement in care , leaving patients socially isolated . This reality raises questions of how to increase accessibility to biomedical treatment and how to proceed with future programs . The median cost burden of Bu hospital treatment was estimated at 25% for costs directly covered both by the household and by the patient . In most illness studies mean direct costs are estimated between 2 , 5% and 7 , 0% of household earnings . However , many chronic illnesses , such as TB in Malawi , account for cost burdens between 8–20% of annual earnings; and in sub-Saharan Africa and Thailand , AIDS related treatment absorbed 50% or more of annual income [9] ( median values were not presented in the referenced articles ) . With its 25% cost burden , Bu surpasses the cost burden threshold of 10% , which has shown to be catastrophic for the household economy [9] , [16] , [17] and likely leads to further impoverishment . Households , therefore , recur to a variety of coping strategies to manage these expenses and indirect losses . A common coping strategy against the accumulation of unmanageable costs is manifest in the breaking of ties with the individuals most taxing on the household economy; in this case the patients . As stated in the results , of the total direct cost , only 43% are expenses directly related to the patient and his or her treatment , while 57% are due to household involvement during the illness period . However , the 57% of direct expenses can -and often is- avoided by households . This circumvention of costs was evident for 63% of in-patient children and 71% of the adults who had no caretaker present . These numbers signify that for patients who are not isolated the direct costs for their households during the healing process is 8 , 6 times higher than for isolated patients . Furthermore , qualitative research affirmed that the caretaker during hospital treatment is not a stable fixture; rather , a considerable percentage of caretakers leave during treatment , especially when they perceive the illness to no longer be ‘serious’ . Subsequently , 63% of in-patients were socially isolated at the hospital setting . Furthermore , initial qualitative data showed that , according to hospital patients , social isolation is the principal cause for abandonment of in-patient treatment . In endemic communities , it was further stated that fear of social isolation is one of the major reasons for postponing or avoiding hospital treatment and why traditional healing is often preferred since it is usually locally provided or provided in the vicinity of the patient's community . With respect to the cost burden equation , the weight of certain variables such as transportation costs , feeding costs and productivity loss of caretaker ( s ) are proportionate to the distance from the community to the treatment centre: ( 1 ) Transportation costs . While transportation costs are an additional cost for the patient , they represent a debilitating burden for caretakers since they continue to have social obligations to other household members ( i . e . other children ) , signifying repeated visits to the hospitalised patient . Accordingly , transportation costs account for 29% of median direct costs . ( 2 ) Feeding costs . The costs of food for the patient at the hospital setting can be minimised through the provision of agricultural products from the household's slash and burn cultivation . However , this coping strategy is only feasible in cases when regular visits are also feasible , such as when treatment is carried out in the general vicinity of the patient's community . This avoids extra expenses for paid meals for patients and caretakers ( feeding costs during hospitalisation represent 25% of median direct costs ) . ( 3 ) Productivity loss . Though the productivity loss of Bu patients could arguably be comparable in the hospital and community settings , that of the caregivers is greatly affected by the location of patient's treatment . When the patient receives treatment in the community or within the vicinity the caregiver can combine his/her daily economic activities with the patient's care without a serious impact on productive activities ( median indirect costs for caretakers represent one of the most taxing costs ) . The importance of the above-mentioned factors in relation to the economic and social impact of Buruli ulcer disease was also evident in patients' health seeking behaviour during field work . The fact that traditional healing minimised or largely avoided such costs was cited by respondents as a major reason why traditional treatment was often preferred to biomedical treatment . In this sense , decentralisation is expected to sidestep or drastically reduce the mentioned costs and to increase the sustainability of households' involvement during the patient's illness period , reducing patients' social isolation and create possibilities to increase adherence . From a socio-economic perspective , it can , therefore , be concluded that a decentralised system of treatment with minimal hospital stays could limit household impoverishment as the long term nature of the illness makes it impossible for a household to indefinitely sustain its involvement with the hospitalised patient . A multidisciplinary study to evaluate a decentralised system of care with minimal hospital stays is , therefore , essential . | The cost burden of free of charge Buruli ulcer disease ( Bu ) hospital treatment is not sustainable for a majority of patients and their families in Central Cameroon . The long term nature of Bu taxes the patients' and their families' resources often to a breaking point , consequently often leading to the abandonment of patients by the family . In the study area , 62% of families ceased providing social and financial support to the patient , which resulted in the patient's isolation at the hospital . Significantly , social isolation was cited by in-patients as the principal cause for abandonment of biomedical treatment . Paradoxically , this phenomenon was observed in settings where hospital in-patient treatment , room and board were provided free of charge for the patient and caretaker . These findings show that despite the significant reduction in costs for medical care , in its current form , hospital treatment for Buruli ulcer often remains financially and socially unsustainable for patients and their households , leading to the abandonment of biomedical treatment or altogether avoiding it . Further investment and research are urgently needed to evaluate new intervention strategies that are both socially and financially acceptable and appropriate in local settings . |
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Helminths express various carbohydrate-containing glycoconjugates on their surface , and they release glycan-rich excretion/secretion products that can be very important in their life cycles , infection and pathology . Recent evidence suggests that parasite glycoconjugates could play a role in the evasion of the immune response , leading to a modified Th2-polarized immune response that favors parasite survival in the host . Nevertheless , there is limited information about the nature or function of glycans produced by the trematode Fasciola hepatica , the causative agent of fasciolosis . In this paper , we investigate whether glycosylated molecules from F . hepatica participate in the modulation of host immunity . We also focus on dendritic cells , since they are an important target of immune-modulation by helminths , affecting their activity or function . Our results indicate that glycans from F . hepatica promote the production of IL-4 and IL-10 , suppressing IFNγ production . During infection , this parasite is able to induce a semi-mature phenotype of DCs expressing low levels of MHCII and secrete IL-10 . Furthermore , we show that parasite glycoconjugates mediate the modulation of LPS-induced maturation of DCs since their oxidation restores the capacity of LPS-treated DCs to secrete high levels of the pro-inflammatory cytokines IL-6 and IL-12/23p40 and low levels of the anti-inflammatory cytokine IL-10 . Inhibition assays using carbohydrates suggest that the immune-modulation is mediated , at least in part , by the recognition of a mannose specific-CLR that signals by recruiting the phosphatase Php2 . The results presented here contribute to the understanding of the role of parasite glycosylated molecules in the modulation of the host immunity and might be useful in the design of vaccines against fasciolosis .
Fasciolosis is a major parasitic disease of livestock that causes significant economic losses worldwide [1–2] . Currently , fasciolosis is also considered an emerging zoonosis with an increasing number of human infections globally [1] . In temperate regions this disease is caused by the liver fluke Fasciola hepatica . During infection , this pathogen can modulate the host immune response by different cellular and molecular mechanisms that include the production of immune-suppressive cytokines by the host [3] , the increase of regulatory T cells [4] , the alternative activation of macrophages [3] or the modulation of maturation and function of dendritic cells ( DCs ) [5–7] . Helminths express various carbohydrate-containing glycoconjugates on their surface and they release glycan-rich excretion/secretion products that can be very important in their life cycles and pathology , since they can participate in immune escape [8] . Carbohydrate-signatures from parasites are decoded by the immune system through the interaction of several immune receptors . In particular , receptors of innate immunity that recognize glycan motifs consist of soluble or membrane-associated lectins , siglecs and scavenger receptors , among others . Notably , C-type lectin receptors ( CLRs ) have been described to mediate internalization of parasite glycosylated molecules as well as cell-surface signaling , modulating the host immune response [9] . For instance , Schistosoma mansoni , through a glycosylated RNAse , impairs protein synthesis of IL-12 . The glycans on this enzyme are essential to allow its uptake by DCs where it degrades both ribosomal and messenger RNA , leading to a Th2-polorized T-cell response [10] . On the other hand , glycans from the nematode Brugia malayi were reported to participate in the induction of the specific Th2 immune response , since sodium periodate-treated soluble extracts from this parasite induced lower levels of IL-4 by specific lymph node cells [11] . Evidence demonstrating that helminths can mediate the modulation of the activity or function of DCs has also been reported [5–7] . DCs are potent antigen presenting cells that possess the ability to stimulate naive T cells . In response to infectious agents DCs undergo a maturation process during which they migrate to secondary lymphoid organs where they present captured antigens to naive T cells , for the triggering of specific immunity . This process is associated to an up-regulation of the expression of MHC molecules , adhesion molecules and co-stimulatory molecules ( CD40 , CD80 or CD86 ) as well as a down-regulation of their endocytic capacity [12] . However in the presence of helminth antigens mature DCs express reduced levels of co-stimulatory markers and MHC class II molecules , as compared to DCs matured with Toll-like receptor ( TLR ) ligands such as lipopolysaccharide ( LPS ) [13] . Also , these DCs are not capable of producing high levels of pro-inflammatory cytokines ( IL-12 , IL-6 or TNFα ) [13] . In this sense , independent in vitro studies have reported that different F . hepatica components modulate TLR-initiated DC maturation and their stimulatory function [5–7] . Although under investigation , the identity of the molecular components from helminths that mediate DC immune-modulation is limited . Nevertheless , growing evidence suggests that parasite glycoconjugates could play a role in the modulation of DC-maturation [14] . Indeed , a recent report described that glycosylated components from the whipworm Trichuris suis mediate the suppression of TNFα production by DCs stimulated with LPS , through the recognition of mannose ( Man ) residues or terminal N-acetyl-Galactosamine ( GalNAc ) by specific CLRs [15] . Interestingly , it has been recently reported that fucose-carrying helminth components can trigger a DC-SIGN specific signaling pathway on DCs that directs differentiation of T cells into follicular helper T cells [16] . Little is known about the glycans produced by F . hepatica , with only two recent reports describing lectin reactivity in the miracidial surface [17] or in the gut of adult flukes [18–19] , that suggest the presence of Man and glucose ( Glc ) residues . Another independent work from Wuhrer and collaborators described Galβ1-6Gal-terminating glycolipids by using mass spectrometry [20] . Finally , our group has previously described the expression of the GalNAc-O-Ser/Thr structure ( known as Tn antigen ) [21] . As for their structure , the immune-modulatory roles of F . hepatica glycans have also barely been investigated . Their role in alternative activation of macrophages has been reported by treating glycans with periodate [3] or by inhibiting macrophage binding and function using antibodies specific for CLRs [22–23] . Nevertheless , the evidence available about the function of F . hepatica carbohydrate in the regulation of parasite immunity or DC function is still poor . In this work , we show that glycoconjugates from F . hepatica are involved in the modulation of host immunity , promoting the production of IL-4 and IL-10 , and suppressing IFNγ production . During infection this parasite is able to induce a semi-mature phenotype of DCs which express low levels of MHCII and secrete IL-10 . Furthermore , we show that parasite glycosylated molecules mediate the modulation of LPS-induced maturation of DCs since their oxidation restores the capacity of LPS-treated DCs to secrete high levels of the pro-inflammatory cytokines IL-6 and IL-12/23p40 and low levels of the anti-inflammatory cytokine IL-10 . Inhibition assays using carbohydrates suggest that the immune-modulation is mediated by the recognition of a Man specific-CLR that signals by recruiting the phosphatase Php2 . The results presented here contribute to the understanding of the role of parasite glycoconjugates in the modulation of the host immunity and might be useful in the design of vaccines against fasciolosis .
Mouse experiments were carried out in accordance with strict guidelines from the National Committee on Animal Research ( Comisión Nacional de Experimentación Animal , CNEA , National Law 18 . 611 , Uruguay ) . Adult worms were collected during the routine work of a local abattoir ( Frigorífico Carrasco ) in Montevideo ( Uruguay ) . All procedures involving animals were approved by the Universidad de la República's Committee on Animal Research ( Comisión Honoraria de Experimentación Animal , CHEA Protocol Numbers: 071140-001822-11 and 071140-000143-12 ) . Six- to 8-week-old female BALB/c mice were obtained from DILAVE Laboratories ( Uruguay ) . Animals were kept in the animal house ( URBE , Facultad de Medicina , UdelaR , Uruguay ) with water and food supplied ad libitum , and handled in accordance with institutional guidelines for animal welfare by the Committee on Animal Research ( CHEA , Uruguay ) . Live adult worms of F . hepatica were obtained from the bile ducts of bovine livers , washed in phosphate buffered saline ( PBS ) pH 7 . 4 , then mechanically disrupted and sonicated . After centrifugation at 40 , 000 × g for 60 min supernatants were collected and dialyzed against PBS . The obtained lysate ( FhTE ) was resuspended on PBS containing a cocktail of protein inhibitors ( Sigma-Aldrich , St . Louis , MO ) and dialyzed against PBS for 24 h . Carbohydrate glycol groups present in FhTE were oxidized with sodium periodate ( 10 mM ) . The oxidation was performed at room temperature for 45 min in the dark , followed by the reduction with sodium borohydride ( 50 mM ) of the reactive aldehyde groups . The resulting oxidized lysate is referred as FhmPox . In order to perform control experiments , the following control extracts were prepared: FhCB , consisted of FhTE subjected to the whole treatment excepting for the incubation with sodium periodate; and CmPox , consisting of PBS subjected to the entire treatment . Lysates were dialyzed against PBS and their protein concentration was measured using the bicinchoninic acid assay ( Sigma-Aldrich , St . Louis , MO ) . To remove endotoxin contamination , the lysates were applied to a column containing endotoxin-removing gel ( detoxi-gel , Pierce Biotechnology ) . The endotoxin levels were determined by using the Limulus Amebocyte Lysate kit Pyrochrome ( Associates of Cape Cod ) . Protein preparations showed very low levels of endotoxins and were not able to induce DC maturation ( as IL-12 read out ) on their own . The concentration of all F . hepatica extracts described here and used in culture experiments did not modify cell viability evaluated by MTT ( 2-[4 , 5-dimethyl-2-thiazolyl]-3 , 5-diphenyl-2H-tetrazolium bromide ) assay . The lysates were analyzed by electrophoresis and western blotting using the anti-Tn mAb 83D4 ( kindly provided by E . Osinaga , Uruguay ) and a polyclonal antibody specific for the Cathepsin-L1 from F . hepatica ( FhCL1 , kindly provided by P . Berasain , Uruguay ) . Proteins were separated in a 15% SDS-PAGE and transferred to nitrocellulose sheets ( Amersham , Saclay , France ) at 45 V overnight in 20 mM Tris–HCl , pH 8 . 3 , 192 mM glycine and 10% ethanol . Residual protein-binding sites were blocked by incubation with 1% bovine serum albumin ( BSA ) in PBS at 37°C for 1 h . The nitrocellulose was then incubated for 2 h at room temperature with either the anti-Tn mAb 83D4 or the anti-FhCL . After three washes with PBS containing 0 . 1% Tween-20 , the membrane was incubated for 1 h at room temperature with an anti-mouse or anti rabbit immunoglobulins , conjugated to peroxidase ( Dako , CA , USA ) diluted in PBS containing 0 . 1% Tween-20 and 0 , 5% BSA and reactions were developed with enhanced chemiluminiscence ( ECL ) ( Amersham , Saclay , France ) . The same procedure was performed omitting the primary antibodies as a negative control . BALB/c mice of 8 weeks old ( 5 per group ) were orally infected with 10 uncapped F . hepatica metacercariae ( Baldwin Aquatics , USA ) per animal . After 1 , 2 or 3 weeks of infection spleens , hepatic draining lymph nodes ( HLN ) and peritoneal exudates cells ( PECs ) were removed . PECs were harvested by washing the peritoneal cavity with 10 mL of cold PBS . Splenocytes , HLN , PECs ( 0 . 5–1 × 106 cells/mL ) or purified CD4+ T cells ( 0 . 2 × 106 cells/mL ) were cultured in complete medium consisting of RPMI-1640 with glutamine ( PAA Laboratories , Austria ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , 50 μM 2-mercaptoethanol , 100 U/ml penicillin and 100 mg/ml streptomycin ( Sigma-Aldrich , St . Louis , MO ) , in the presence or absence of FhTE ( 75 μg/ml ) , Concavalin-A ( ConA ) ( 5 μg/ml ) , FhmPox ( 75 μg/ml ) or the controls FhCB and CmPox , for 72 h at 37°C and 5% CO2 . IFNγ , IL-4 , IL-5 and IL-10 levels were evaluated by specific ELISAs or quantitative RT-PCR ( qRT-PCR ) . Uninfected naive animals were used as a control group . Proliferation with the control CmPox always provided background levels , such as with medium alone . Infections were also carried with 5 , 10 or 15 metacercariae/mouse and mice were sacrificed at 3 weeks after the infection . Alanine transaminase activity was measured in sera from infected and non-infected animals using a commercial kit ( Spinreact , Spain ) , according to the manufacturer’s instructions . The presence of flukes in livers from infected animals was analyzed so as to calculate fluke burden . Livers from infected mice presented macroscopic damage and/or necrosis . They were also histologically analyzed and found to present inflammatory infiltration and flukes , in some cases . IFNγ , IL-4 , IL-5 , IL-6 , IL-10 and IL-12p40/23 levels on culture supernatants were quantified by interleukin-specific sandwich ELISA assays ( BD Bioscience , NJ , USA ) . MIP-1α and MIP-2 were also detected in the culture media using sandwich ELISA assays , according to the instructions of the manufacturer ( RayBiotech , Inc . , GA , USA ) . In some cases , cytokines were detected by qRT-PCR using a Corbett Rotor Gene 6000 Real-Time PCR Machine and the SYBR Green 1 dye ( Applied Biosystem ) . Standard amplification conditions were 3 min at 95°C and 40 cycles of 10 s at 95°C , 30 s at 60°C , and 30 s at 72°C . For detection of cytokines the following primers were used: IFNγ: F: 5′-GGAGGAACTGGCAAAAGGATGGTGA-3′ and R: 5′-GCGCTGGACCT-GTGGGTTGT-3′; IL-4: F: 5′-AGGTCACAGGAGAAGGGACGCC-3′ and R: 5′-TGC-GAAGCACCTTGGAAGCCC-3′; IL-10: F: 5′-TTCCCAGTCGGCCAGAGCCA and R: 5′-GGGGAGAAATCGATGACAGCGCC-3′ . Results were expressed as the ratio between each evaluated cytokine and GAPDH expression . For GAPDH detection , sense and antisense primers were 5′-TCGGAGTCAACGGATTG-3′ and 5′-CCTGGAAGAT-GGTGATGG-3′ , respectively . The reactivity of polyclonal antibodies from infected or non-infected animals was evaluated by ELISA . Briefly , ninety-six-well microtiter plates ( Nunc , Roskilde , Denmark ) were coated overnight at 4°C with 1 μg/well of FhTE in 50 mM carbonate buffer ( pH 9 . 6 ) . After blocking with 1% BSA in PBS , three washes with PBS containing 0 . 1% Tween-20 were performed . For the oxidation of the glycan moieties of FhTE , wells were treated with 10 mM of sodium meta-periodate in 50 mM sodium acetate buffer pH 4 . 5 for 30 min at room temperature in the dark , washed with 50 mM sodium acetate buffer and subsequently incubated for 1 h with glycine 1% at room temperature . As controls ( Fhmock ) , wells were subjected to the same treatment except for the incubation with sodium meta-periodate . Serially diluted sera in buffer ( PBS containing 0 . 1% Tween-20 and 0 . 5% BSA ) were added to the wells for 1 h at 37°C . Following three washes , wells were treated 1 h at 37°C using goat anti-mouse polyvalent peroxidase-conjugate ( Sigma-Aldrich , St . Louis , MO ) and o-phenylenediamine-H2O2 was then added as substrate . Plates were read photometrically at 492 nm in an ELISA auto-reader ( Labsystems Multiskan MS , Finland ) . The lectin-reactivity on FhTE , FhmPox or FhCB lysates was evaluated by an ELISA-type assay . Briefly , Nunc microtiter plates were coated with 2 . 5 μg/well of parasite lysates and blocked with 1% BSA in PBS for 1 h at 37°C . Then , different concentrations of biotin coupled lectins were added and incubated for 1 h at 37°C . For inhibition assays , the lectins were pre-incubated for 30 min at 37°C with 50 mM of the indicated monosaccharide . After three washes , streptavidin conjugated to DyLight 800 was added to each well for 30 min at 37°C . Plates were then washed and analyzed with an Odyssey Infrared Imaging System ( LI-COR Biosciences , NE , USA ) . Lectins from Vicia villosa ( VV: GalNAc , Tn antigen ) , Triticum vulgaris ( WGA: ( GlcNAc ) 2 ) , Canavalia ensiformis ( ConA: αMan>αGlc ) , Arachis hpogaea ( PNA: βGal ( 1 , 3 ) GalNAc ) , Ulex europaeus ( UEA: Fucα ( 1 , 2 ) Gal ) , Erythrina cristagalli ( ECA: βGal ( 1–4 ) GlcNAc ) , Sambucus nigra ( SNA: αNeuAc ( 2 , 6 ) Gal ) and Helix pomatia ( HPM: GalNAc ) were used in this study . Splenocytes or PECs from infected and non-infected mice were washed twice with PBS containing 2% FBS and 0 . 1% sodium azide . Cells were then stained with different antibody mixes to identify DCs or macrophages . First , CD3+ cells were excluded from the gatings . DCs were defined as CD11chi F4/80- CD3- cells . Macrophages were identified as F4/80+ CD11c- CD3- cells . The following antibodies were used in these experiments: anti-CD3 ( 17A2 ) , CD11c ( N418 ) , CD40 ( HM40-3 ) , I-A/I-E ( 2G9 ) , F4/80 ( BM8 ) , CD80 ( 16-10A1 ) , CD86 ( GL1 ) . Cells were then washed twice with PBS containing 2% FBS and 0 . 1% sodium azide and fixed with 1% formaldehyde . Cell populations were analyzed using a CyAn ADP Analyzer ( Beckman Coulter ) . Antibodies were obtained from Affymetrix ( CA , USA ) or from BD-Biosciences ( CA , USA ) . IL-10 and IL-12/IL23p40 in vivo production by DCs or macrophages was analyzed by intracellular staining . Splenocytes and PECs from infected and non-infected mice were cultured for 6 h with GolgiPlug ( BD Biosciences ) , washed , stained with CD11c , F4/80 and CD3 , and then fixed and permeabilized using the Cytofix/Cytoperm kit ( BD Biosciences ) and subsequently stained with Abs specific for IL-12/23p40 or IL-10 ( Biolegend , CA , USA ) . Bone Marrow-derived Dendritic Cells ( BMDCs ) were generated from bone marrow precursors from BALB/c mice . Briefly , bone marrow precursor cells were harvested and plated at a density of 2–5 × 105 cells/ml in complete culture medium supplemented with GM-CSF-containing supernatant . After 3 days of culture at 37°C , the medium was replaced . Cells were recovered on day 8 and analyzed for the expression of CD11c by flow cytometry . To analyze DC-maturation , BMDCs ( 2 . 5 × 105/well ) were incubated at 37°C and 5% CO2 in 96-well plates with FhTE , FhPox , FhCB ( 75 μg/ml ) or medium alone in the presence or absence of LPS ( Escherichia coli 0111:B4 , 0 . 5–1 μg/ml ) overnight at 37°C . Alternatively , cells were pre incubated for 45 min . at 37°C with 10 mM of monosaccharides ( Man , GalNAc or arabinose ) or 10 μM of specific signaling inhibitors ( PHPS1; GW5074; and ER27319 ) . Cells were then centrifuged at 1 , 500 rpm for 5 min at 4°C and supernatants were then collected . Cytokine ( IL-12/23p40 , IL-10 and IL-6 ) levels were tested on culture supernatants by interleukin specific sandwich ELISA assays ( BD Bioscience , NJ , USA ) . The in vitro internalization and binding of the lysates were analyzed by flow cytometry . BMDCs were incubated ( 2 . 5 x 105/well ) with Alexa 647-labeled Ag for 1 h at 37°C in complete medium ( to assess uptake ) , or at 4°C in complete medium ( to assess binding ) . Cells were then washed twice and analyzed by FACS . For inhibition assays , cells were preincubated with 5 mM EDTA or 50 mM of different carbohydrates for 30 min at 37°C . The Student t test was used for statistical comparisons; p values <0 . 01 or <0 . 05 were considered to be statistically significant , depending on the experiment .
One of the objectives of this work was to evaluate the role of F . hepatica glycoconjugates structures in the induction of the host immune response . Indeed , the parasite induces a Th2 immune response with a regulatory component [3] . However , the parasite molecules involved in this immune-regulation are still unknown . Thus , we first evaluated different parameters of the immunity against F . hepatica in our experimental model and correlated them with the course of the infection and the level of liver damage . To this end , BALB/c mice were infected with 10 metacercariae and the production of IL-4 , IL-5 IL-10 and IFNγ was evaluated on splenocytes from infected animals at 1 , 2 and 3 weeks post-infection ( wpi ) . Splenocytes removed from infected animals and stimulated in vitro with a total parasite extract ( FhTE ) produced high levels of IL-4 , IL-5 and IL-10 , with significant higher levels of IL-4 and IL-10 as soon as the first wpi , reaching a plateau at week 2 after infection . On the other hand , IFNγ was slightly increased , although at very low levels ( ≤200 pg/ml ) , compared to the 30-fold increase of IL-10 ( around 6 ng/ml ) ( Fig 1A ) . The increase of IL-4 and IL-5 as well as the regulatory cytokine IL-10 coincided with the detection of liver damage evaluated by the alanine aminotransferase ( ALT ) activity in serum , a common marker to detect hepatic dysfunction [24] . Indeed , the ALT activity augmented 5-fold at 2 wpi , while it increased more than 20 fold at 3 wpi compared to levels detected in naïve/control animals ( Fig 1B ) . ALT activity also augmented with parasite dose of infection , confirming its usefulness for detecting liver damage and monitoring F . hepatica infection ( S1A Fig ) . The strong production of IL-4 and IL-10 at 3 wpi was confirmed by qRT-PCR , revealing around a 100-fold increase of IL-10 and only a 3-fold increase of IFNγ expression , with respect to uninfected animals ( Fig 1C ) . The strong modified Th2 polarization observed was also evidenced with a polyclonal stimulus , such as ConA , on splenocytes from infected animals . They produced higher levels of IL-4 , IL-5 and IL-10 , while their capacity to produce IFNγ was significantly diminished ( a 3-fold decrease ) , comparing to splenocytes from naïve animals stimulated in the same conditions ( Fig 1D ) . These results are in agreement with previous work describing the Th2 polarization induced by F . hepatica during infection [3] . Carbohydrate structures produced by parasites participate in critical processes such as infection or invasion [25–26] . Although much advance in the area of glycomics has been gained in recent years , the knowledge about the structure and function of F . hepatica glycans is still poor . In order to identify the carbohydrates present in the FhTE used in this study we carried out lectin-reactivity assays with a panel of different vegetal lectins . Glycoconjugates from FhTE strongly reacted with lectins from Vicia Villosa ( VV ) , Triticum vulgaris ( WGA ) , Canavalia ensiformis ( ConA ) , Arachis hpogaea ( PNA ) and Ulex europaeus ( UEA ) ( Fig 2A ) , revealing the presence of N-acetil-galactosamine-Ser/Thr ( GalNAc-Ser/Thr ) , N-acetyl-glucosamine ( GlcNAc ) 2 , mannose ( Man ) or glucose ( Glc ) , galactose ( Gal ) in ( βGal ( 1–3 ) GalNAc and fucose ( Fuc ) in Fucα ( 1–2 ) Gal , respectively . We also performed inhibition assays with specific monocarbohydrates and with non specific carbohydrates as negative control . More than the 70% of the lectin reactivity was lost when incubating with GalNAc , GlcNAc or Man and VV , WGA or ConA , respectively , confirming the carbohydrate specificity by these lectins ( Fig 2B ) . Finally , we carried out the assays on oxidized FhTE ( FhmPox ) . Mild periodate oxidation of glycans is usually used to evaluate the functional roles of glycoconjugates [11 , 15] . During this process the glycol groups in carbohydrates are oxidized to reactive aldehyde groups , which are in turn reduced with sodium borohydride . Thus , the structure of carbohydrates is lost , as well as the possible biological activity that they can mediate . The recognition of the specific carbohydrates on FhTE by most reactive lectins was completely abrogated with meta-periodate oxidation ( FhmPox ) ( Fig 2B ) . Importantly , the integrity of oxidized parasite glycoconjugates ( FhmPox ) remained unchanged since the electrophoretic mobility of FhTE molecular components was similar to that of FhmPox ( Fig 2C ) . Furthermore , oxidation with meta-periodate totally abolished the recognition of a monoclonal antibody specific for the GalNAc-O-Ser/Thr carbohydrate structure that is reactive to FhTE [27] , while it did not modify the recognition of cathepsin-L1 by a specific polyclonal antibody ( Fig 2D ) . Thus , the chemical oxidation of terminal carbohydrates abrogates recognition of glycans by carbohydrate binding proteins , resulting in an adequate strategy to study their recognition by proteins or receptors and could be useful to study their biological functions . To determine whether F . hepatica glycosylated molecules participate in the induction of high levels of the Th2-type cytokines and the suppression of IFNγ we cultured splenocytes from infected animals with oxidized parasite components . Thus , splenocytes removed from infected animals at 3 wpi were in vitro stimulated with FhTE , and the production of cytokines in the culture supernatant was determined and compared to those incubated with oxidized parasite components ( FhmPox ) . Splenocytes stimulated with FhmPox produced lower levels of IL-4 and IL-10 than cells incubated with FhTE . Surprisingly , in these conditions , spleen cells produced significant higher levels of IFNγ ( Fig 3A ) . On the other hand , the levels of IL-5 remained unchanged . Splenocytes incubated with the control FhCB , consisting in FhTE subjected to the whole treatment excepting for the incubation with sodium periodate , behaved essentially as cells in presence of FhTE , as expected . The cytokine production was specific of parasite components since stimulated spleen cells from non-infected animals did not produce any of the evaluated cytokines . The oxidation-dependent decrease of IL-4 and IL-10 by splenocytes from infected animals was also confirmed by qRT-PCR , although no significant difference was found between the production levels of IFNγ by FhmPox-stimulated splenocytes ( Fig 3B ) . Cells from the hepatic draining lymph nodes of infected animals also produced decreased levels of IL-10 when stimulated with oxidized parasite components , with no changes in the production of IL-5 ( Fig 3C ) . Finally , in order to establish a possible role of glyans in the recognition of parasite molecules by the humoral immune response , we evaluated the recognition of FhmPox by sera from infected animals . IgG antibodies from infected animals recognized in a similar manner both FhTE and FhmPox , suggesting that oxidation of terminal glycans does not modify the recognition of parasite components by antibodies induced during infection ( Fig 3D ) . Evidence demonstrating that F . hepatica components can modulate DC-maturation and function in vitro has been previously reported [5–6 , 10 , 28] . Also , the phenotype of DCs has been evaluated suggesting that the parasite immune-modulates DCs upon infection [13] . Nevertheless , an exhaustive study of DC immune-modulation by the parasite has not been carried out . Thus , in order to deeply evaluate their role in the host immune-modulation by F . hepatica , we sought to evaluate DCs in vivo both in the spleen and in the peritoneum of infected animals . Upon infection , we observed a marked recruitment of cells in the spleen and in the peritoneal cavity of infected animals , which increased with time of infection ( Fig 4A and 4B , respectively ) . Among these cells , DCs ( defined as CD11chi F4/80- cells ) were recruited both at the spleen and the peritoneum since the first wpi , and their number augmented with the course of infection ( Fig 4A and 4B ) . In spite of the fact that all DCs were MHC class II positive , they presented remarkable decreased levels of MHCII expression ( Fig 4C–4E ) . Indeed , splenic DCs presented around 50% reduction of MHCII expression since the first wpi together with lower levels of CD40 on their surface ( S1B Fig ) . MHC class II- or CD40-decreased expression on DC surface was not modified when infecting animals with higher parasite dose ( S1B Fig ) . Moreover , from the second wpi , an increase of IL-10 secreting splenic DCs was evidenced ( Figs 4D and S2 ) . Strikingly , IL-12+ DCs also augmented in the spleen ( Fig 4D ) . DCs recruited into the peritoneum also reduced the expression of MHC class II ( Fig 4E ) while the expression of CD80 and CD86 was increased ( S1C Fig ) . There was also an increase of IL-10 secreting DCs in the peritoneal cavity , while IL-12 secreting DCs remained very low in the peritoneum ( Figs 4E and S2 ) . Macrophages , defined as F4/80+ CD11c- cells were also recruited to the spleen and peritoneum upon infection ( S3 Fig ) . In spleen , the recruitment of IL-10+ macrophages was favored while IL-12 secreting macrophages considerably decreased ( S2A and S3C Figs ) . On the other hand , macrophages from the peritoneal cavity increased the expression of surface MHC class II from the first wpi . IL-10+ or IL-12+ macrophages were also increased in the peritoneum ( S2 and S3D Figs ) . In order to determine whether parasite glycan structures can also modulate the function of DCs , we evaluated the in vitro activation of splenocytes ( Fig 5B ) and purified CD4+ T cells ( Fig 5C ) from infected animals by DCs . To this end , bone marrow derived DCs ( BMDCs ) were loaded with parasite-derived components ( FhTE ) or with oxidized total lysate ( FhmPox ) . A control consisting on FhTE subjected to the chemical process in absence of meta-periodate was also included ( FhCB ) . Importantly , the viability of loaded-BMDC was not affected by the treatment with different parasite lysates ( Fig 5A ) . Then , loaded BMDCs were washed and subsequently incubated with splenocytes or purified CD4+ T cells from infected animals . As seen in Fig 5B and 5C , when incubated with FhmPox-loaded BMDCs , splenocytes and purified CD4+ T cells produced lower levels of IL-4 and IL-10 than cells stimulated with FhTE-loaded BMDCs , while IL-5 and IFNγ production remained unchanged . No cytokine production was detected when loaded BMDCs were incubated with splenocytes or purified CD4+ T cells from uninfected animals ( naïve ) ( Fig 5B and 5C ) . These results strongly suggest that glycoconjugates modulate DC-stimulatory capacity by increasing the production of IL-4 and IL-10 by CD4+ T cells during infection . Glycans can modulate DC function through a variety of mechanisms . For instance , they can interact with lectin receptors expressed on the surface of DCs that can endocytose the glycan components and/or signal through kinase-dependent cascades [29] . Thus , we evaluated whether F . hepatica glycoconjugates could interact with the DC surface , or be internalized by DCs . To this end , Atto-647-labeled FhTE was incubated with DCs both at 4°C ( to evaluate binding ) or at 37°C ( to test internalization ) and the fluorescence intensity was determined by flow cytometry . As shown in Fig 6A and 6B , FhTE both interacted with and was internalized by DCs . To determine whether this binding or uptake was dependent on C-type lectin receptors , requiring Ca2+ for binding , we incubated DCs with FhTE in presence of EDTA , a chelating agent . We observed that around 70% of the FhTE internalization was abrogated with EDTA incubation , indicating that the internalization process was mediated by Ca2+-dependent lectin receptors ( Fig 6A ) , On the contrary , only 25% of the binding was inhibited in presence of EDTA ( Fig 6A ) , suggesting that CLR-dependent recognition of glycosylated molecules is less relevant in this process . In order to confirm the participation of glycoconjugates in the internalization or binding of parasite components to DCs , FhCB and FhmPox were also stained with Atto-647 and further incubated with DCs in the same conditions as FhTE . Oxidation of parasite glycans resulted in a decrease of around 50% of FhTE internalization , while only 25% of reduction in the binding was observed ( Fig 6B ) , indicating that they partially mediate binding and internalization of parasite components by DCs . As expected , no significant difference between FhTE and FhCB ( control ) binding or internalization was observed ( Fig 6B ) . Finally , we carried out inhibition assays with several carbohydrates , as well with laminarin , a ligand of the CLR Dectin-1 [30] . Laminarin was included in these assays since Dectin-1 seems to mediate the binding of F . hepatica glycoconjugates by macrophages [22] . As shown in Fig 6C , the binding of FhTE was inhibited by both Man and GalNAc , while only Man was capable of inhibiting FhTE uptake by DCs . Incubation with laminarin did not significantly modify FhTE binding or internalization by DCs , indicating that Dectin-1 is not involved in this process . In order to evaluate whether parasite glycosylated molecules are able to influence DC-maturation we incubated BMDCs with parasite components or oxidized-FhTE in absence or presence of a maturation stimulus ( LPS ) . Furthermore , the control FhCB ( consisting in FhTE subjected to the whole treatment excepting for the incubation with sodium periodate ) was also included . Then , we evaluated the production of several cytokines by DCs . BMDCs incubated with FhTE in presence of LPS produced higher levels of IL-10 than DCs incubated only with LPS . Interestingly , when oxidized-parasite glycans ( FhmPox ) where incubated with BMDCs , they produced similar levels of IL-10 than those produced by cells incubated with LPS alone , indicating that oxidation of glycans abrogates the immunomodulatory activity of FhTE on DCs ( Fig 7A ) . On the other hand , the opposite situation was observed with the pro-inflammatory cytokines IL-6 and IL-12/23p40 . Indeed , FhTE incubated with BMDCs in presence of LPS decreased the production of IL-6 and IL-12/23p40 induced by LPS , while FhTE oxidation restored the levels of both cytokines ( Fig 7A ) . As expected , the control FhCB/LPS behaved essentially in the same way as FhTE/LPS , while the CmPox/LPS condition induced the same levels of cytokine production than cells incubated only with LPS . Altogether , these results indicate that glycoconjugates from F . hepatica modulate LPS-induced maturation of DCs by augmenting anti-inflammatory cytokines and decreasing pro-inflammatory cytokines . Next , we carried out inhibition assays of the immunomodulatory capacity of FhTE with the carbohydrates Man and GalNAc since they were capable of inhibiting binding or internalization of FhTE . An irrelevant carbohydrate , arabinose ( Ara ) was used . As shown in Fig 7B , only Man could inhibit the immune-modulation of FhTE on DC-maturation . Indeed , incubation with Man restored the levels of IL-10 and IL-12/23p40 production induced by LPS alone on DCs . Nevertheless , the levels of IL-6 were not restored by inhibition assays with Man , suggesting that the production of this cytokine is triggered by a Man-independent signaling process . Incubation of DCs with FhTE in presence of GalNAc or Ara did not modify the levels of the evaluated cytokines ( Fig 7B ) . The production of the inflammatory chemokines MIP-1α and MIP-2 by DCs was also investigated . These chemokines regulate the influx of inflammatory cells , and can be produced by DCs under pathogenic conditions . MIP-1α is a ligand for CCR5 , a chemokine receptor expressed on Th1 cells , while MIP-2 selectively chemoattracts Th2 cells [31] . DCs stimulated with LPS produced both chemokines . When incubated with FhTE the production of MIP-1α significantly decreased , while MIP-2 increased ( Fig 7C ) . However , inhibition assays with Man did not modify the production of either chemokines induced by LPS/FhTE , suggesting that the production of these chemokines does not depend on Man-specific receptors on DCs . Finally , in order to provide evidence about the possible CLR implicated in the recognition of Man-containing glycans from F . hepatica , we performed cell cultures in presence of chemical inhibitors of different signaling pathways: GW5074 , ER27319 and PHPS1 that inhibit pathways mediated by Raf-1 , Syk and Shp2 , respectively . This inhibitors were chosen since a group of CLRs that recognize Man residues from pathogens , such as Dectin-1 , Man Receptor , SIGNR or DCIR , are expressed on BMDCs and signal through these molecular mediators [29] . Thus , after incubation of these molecules , we evaluated the production of IL-12/23p40 . Either GW5074 or ER27319 did not modify the decrease of IL-12/23p40 production induced by FhTE in presence of LPS . Nevertheless , incubation of DCs with PHPS1 in presence of FhTE/LPS completely abrogated the immunomodulatory effect of FhTE on DCs , leading to the production of similar levels of IL-12/23p40 as those obtained with LPS ( Fig 7D ) .
Carbohydrate structures can exert different biological functions , ranging from cell growth or development to tumor growth or metastasis . Moreover , glycans participate in diverse processes such as coagulation , induction of immunity , cell-cell communication or microbial pathogenesis [32–33] . In this context , accumulating evidence demonstrates that glycoconjugates produced by helminths favor parasite survival by influencing the host immune response [34–35] . In this work we show that F . hepatica glycoconjugates are involved in the induction of high levels of IL-10 and IL-4 as well as in the reduction of IFNγ production by splenocytes during infection . In this sense , different previous reports used meta-periodate treatment of glycans to identify the role of glycoconjugates in the regulation of host immunity . Sodium meta-periodate treatment at low concentration does not remove glycans or compromise the integrity of glycoproteins , but instead opens up the “chair” structure altering molecular conformation of the glycans . Thus , when F . hepatica components were treated with meta-periodate and used to stimulate spleen cells from infected animals , they reduced their capacity to produce the Th2 cytokine IL-4 as well as the regulatory cytokine IL-10 , suggesting a role of glycoconjugates in the induction of Th2/regulatory T cell immune response . Interestingly , stimulation of splenocytes from infected mice with oxidized parasite components also increased IFNγ production compared to the IFNγ levels obtained with F . hepatica total lysate containing non-modified glycans , indicating that they could suppress specific Th1 responses . Glycoconjugates from other helminths such as Schistosoma mansoni [36] and Brugia malayi [11] have also been reported to regulate the host immune shift toward a Th2 response , or inducing regulatory responses via induction of IL-10 [11 , 37–38] . Indeed , S . mansoni produces the glycan structure Lewisx ( Galβ1-4 ( Fucα1–3 ) GlcNAc-terminal structure ) that possesses immune-regulatory properties and accounts for induction of host IL-10 induced by the parasite [36] . Fasciola hepatica , however , does not express this glycan structure [39] . Thus , the immune-regulatory glycan structures from this parasite remain unknown . Although carbohydrate moieties from parasites such as Echinococcus [40–42] , S . mansoni [20 , 43–45] , among others [35 , 46] , have been well determined , glycoconjugates produced by F . hepatica still remained poorly characterized . Previous works have identified the presence of Gal ( β1–6 ) Gal and GlcNAc ( α1-HPO3-6 ) Gal terminating glycolipids by mass spectrometry [20 , 47] , as well as glycans carrying Man , Glc , GlcNAc or GalNAc by lectin reactivity [17–19 , 48] both in tegument and tissues throughout the parasite . Here , we identified the presence of glycan structures containing Man/Glc , GalNAc or GlcNAc in the parasite lysate used in this study , confirming the strong binding of Man/Glc , GalNAc and GlcNAc-reactive lectins ( ConA , WGA and VV , respectively ) . It is worth noting that , in our case , the lectin reactivity was abrogated when incubating with specific sugars , confirming the specificity for their respective carbohydrates . Our results also showed a slight recognition of parasite glycans by the Ulex europeus agluttinin ( UEA-1 ) specific for terminal Fuc residues . However , it should be noted that the reactivity was lower than that observed with the other lectins , and that inhibition of lectin binding with Fucose did not completely abolish lectin recognition . Nevertheless , several helminths , including F . hepatica , are reactive to the Lotus tetragonolobus agglutinin , which binds Fucα1-3GlcNAc , confirming the expression of α1 , 3-fucosylated glycans [39] . In all cases , lectin reactivity was also abolished with meta-periodate oxidation of parasite glycans , demonstrating that this procedure suppressed carbohydrate specific recognition by lectins and is suitable for studying the biological role of glycans . To evaluate possible mechanisms that could explain the modulation of host immunity by F . hepatica glycoconjugates we focused on DCs , the most effective antigen presenting cells that possess the ability to stimulate naive T cells , inducing a specific Th polarization [12] . In order to guarantee the identity of selected DCs , we excluded both macrophages and CD3+ cells and then selected CD11chi cells by flow cytometry analyses . Thus , DCs ( CD3- F4/80- CD11chi cells ) from infected animals presented a different phenotype than DCs from naïve animals , characterized by a profound decrease of MHC class II expression on DC-surface , which was not found in macrophages ( CD3- F4/80+ CD11c- cells ) , probably due to a different interaction between these cells and parasite molecules . DCs recruited to the peritoneum also showed an up-regulation of the co-stimulatory molecules CD80 and CD86 , suggesting that these DCs acquire a semi-mature phenotype upon parasite infection . The expression of MHC class II-peptide complexes on the surface of DCs is essential for their ability to activate CD4+ T cells efficiently . Apparently , F . hepatica would restrict the capacity of DCs to present antigens to CD4+ T cells as well as to prime specific CD4+ T cells , an effect that increases with persistence of infection . It has been previously shown that ubiquitination of MHCII-peptide complexes regulates their surface expression , retention and degradation in DCs [49–51] , and that certain pathogens , such as Salmonella typhimurium , induce polyubiquitination of HLA-DR , resulting in a reduced surface expression of all MHC class II isotypes [52] . On the other hand , there are evidences reporting that Mycobacterium tuberculosis diminishes MHC-II synthesis by macrophages [53] in a process dependent on TLR2 ligation [54] , limiting antigen presentation . It would be interesting to evaluate whether any of these molecular mechanisms underlie the reduced expression of MHC class II on the surface of DCs from F . hepatica-infected animals . There are also pieces of evidence highlighting the role of IL-10 in inducing immune anergy by reducing expression of MHC class II on the surface of antigen presenting cells [55] . Thus , the possibility that IL-10 secreted by CD4+ T cells during infection is responsible of MHC class II decrease on DCs cannot be ruled out . To evaluate the influence of glycoconjugates parasites in the specific stimulatory capacity of DCs conditioned with F . hepatica components , we co-cultured FhTE-pulsed BMDCs with CD4+ T cells from infected and non-infected animals . Cultures from infected animals produced high levels of IL-4 , IL-5 and IL-10 with low levels of IFNγ . However , when parasite oxidized components were used for BMDC loading , CD4+ T cells from infected animals reduced their capacity to produce IL-4 and IL-10 , indicating that parasite glycoconjugates are involved in the DC-triggered production of IL-4 and IL-10 by T cells . Thus , our work demonstrates for the first time the role of F . hepatica glycoconjugates in immunomodulating the host immune response during infection and , in particular , by modulating T-cell stimulatory capacity of DCs . Regulatory DCs can exert their functions through different mechanisms , such as by decreasing their capacity of antigen presentation or by inducing regulatory T cells able to suppress inflammatory Th1/Th17 responses [56] . In this context , it is interesting to remark that antigen presenting cells from the peritoneal cavity of infected animals , but not CD11c+ spleen cells , have limited capacity to induce effector T cell response [4] , although their ability to directly induce IL-10 secreting regulatory T cells has not been evaluated . In an attempt to gain more insight in the process of glycan-mediated immune-modulation of DCs , we focused on the study of the effects of glycoconjugates on DC-maturation in vitro . Importantly , previous reports demonstrate the capacity of F . hepatica components to modulate in vitro TLR-induced maturation of DCs and/or their stimulatory function [5–7 , 16] . However , the role of parasite glycans in this immune-modulation had not been previously addressed . When BMDCs were matured in the presence of parasite components they secreted higher levels of IL-10 and lower levels of IL-6 and IL-12/23p40 than cells stimulated only with LPS . On the other hand , when DCs were matured with LPS in presence of oxidized parasite components the production levels of IL-6 , IL-10 and IL12/23p40 were restored , indicating that glycoconjugates from F . hepatica mediate modulation of TLR-induced maturation of DCs . One possible mechanism that can account for this immune-modulation is by triggering carbohydrate specific receptors , such as CLRs , that can cross-talk with TLR-stimulation . Indeed , we found that both binding and uptake of parasite components by DCs were inhibited with EDTA or with Man , suggesting a CLR mediated process of recognition and uptake of parasite glycoconjugates through Man-containing glycans . On the other hand , GalNAc inhibited binding but not uptake by DCs , indicating that GalNAc-residues on parasite components can interact with receptors on the surface of DCs , but do not mediate antigen internalization . Strikingly , incubation with laminarin , a ligand of Dectin-1 , did not inhibit either the binding or the uptake of parasite components present on FhTE by BMDCs . It has been recently published that Dectin-1 is involved in the induction of CD4+ T cell anergy by macrophages [23] . However , it should be noted that in this case excretory-secretory products from F . hepatica were used . Since the immunomodulatory properties of these parasite products on macrophages were found to depend on Dectin-1 signaling , it is likely that the different carbohydrate composition and/or nature of molecules present on FhTE ( total lysate ) and excretory-secretory parasite products determine the different CLR-mediated signaling triggered by F . hepatica components . In this sense , in previous elegant studies using Helicobacter pylori variants which differ in their expression of Lewis glycan antigens , only the variants expressing the Lewis antigen were capable to bind to DC-SIGN and block the induction of a specific Th1 response , while Lewis-negative H . pylori variants were not [57] . Eventual studies on the elucidation of the carbohydrate moieties present on F . hepatica total lysate and excretory-secretory products will be decisive to explain their different immunomodulatory properties . By carrying out DC-maturation assays in presence of Man , we provide evidence that a Man-specific CLR expressed on DC surface mediates the immunomodulatory effects of F . hepatica components . Indeed , Man incubation restored the production levels of IL-10 and IL-12/23p40 , but not those of IL-6 , MIP-1α or MIP-2 . Several CLRs have been reported to cross-talk with TLR-signaling on DCs as well as on other myeloid cells , inducing an increase of IL-10 and a decrease of pro-inflammatory cytokines [29 , 58] . Furthermore , glycans from helminths can interact with CLRs on DCs and regulate their maturation . For instance , S . mansoni , glycans are recognized by the Man receptor resulting in a Th2-polarized cell response [10] . Also , the glycosylated molecules of the whipworm T . suis interact with the Man receptor , DC-SIGN and MGL , which recognize Man and terminal GalNAc residues , respectively , and suppress TNFα production by DCs stimulated with LPS [15] . According to our experimental results obtained with different inhibitors of molecules that participate in Man-specific CLR signaling , the phosphatase SHP2 would participate in the Man-triggered signaling . This indicates a possible role of SHP2-dependent CLR signaling in the recognition of F . hepatica glycans and cross-talk with TLR4-tiggering . One possible candidate is DCIR , a CLR identified on mouse BMDCs [59] and other myeloid cells [29] , that can modulate TLR-induced gene expression at the transcriptional or post-transcriptional level . However , DCIR does not induce gene expression in the absence of other PRR signaling [29] . DCIR-triggering induces the phosphorylation of its ITIM ( Immunoreceptor Tyrosine-based Inhibitory Motif ) , which recruits the phosphatases SHP1 or SHP2 to its cytoplasmic domain [60–61] , and results in the inhibition of TLR8-mediated IL-12 and TNFα production or TLR9-induced IFNα and TNFα production by DCs [62–63] . Experiments are on their way to determine the role of DCIR in the immune-modulation induced by F . hepatica components , by specifically silencing DCIR expression on BMDCs . The possible role of DCIR in the recognition of Man residues from F . hepatica and in mediating the cross-talk with TLR4 signaling could also explain the fact that both IL-10+ DCs and IL-12+ DCs were identified in the spleen from infected animals . Indeed , splenic DC subsets differentially express membrane molecules , some of which are CLRs [64–65] . In mouse spleen two different DC-subsets can be identified , CD11b+ DCs and CD8α+ CD11b- DCs , which differ in antigen presentation and T-cell stimulatory capacity [64] . In fact , CD8α+ splenic DCs are the major subset responsible for cross-presenting cellular antigens [66] . On the other hand , CD8α− DCs preferentially present antigen to CD4+ T cells [67] . These DC subsets also express different sets of surface molecules , including distinct CLRs . Interestingly splenic CD8α- DCs have been reported to express DCIR , while CD8α+ DCs do not [67] . Thus , taking these findings into account , one could speculate that CD8α- DCs expressing DCIR are the main target of immune regulation between different DCs found in mouse spleen by F . hepatica Man-containing glycans . Furthermore , a possible DCIR signaling could explain the immunomodulatory effects of F . hepatica glycoconjugates on splenocytes , since splenic B cells also express DCIR [60] . Additional experiments are needed to support this hypothesis . In spite of the hypothesized role of DCIR in mediating immune-modulation by F . hepatica Man-glycans , inhibition culture assays with Man did not restore IL-6 , MIP-1α or MIP-2 production . Thus , it is likely that another receptor or glycan structure is participating in the immune-modulation process that could not be detected with our assays . It has recently been reported that F . hepatica glycans interact with the DC-SIGN receptor on human DCs . Interestingly when DC-SIGN interacts with Man-rich glycans , modulates the production of pro-inflammatory TLR-induced cytokines by a pathway that depends on the activation of Raf-1 [68] . In fact , the interaction of F . hepatica glycans and DC-SIGN on human DCs , in presence of LPS , induces follicular T helper cell differentiation via IL-27 [16] . However , although the authors assume that this process is Fuc-dependent , the identity of the immunomodulatory glycoconjugates from F . hepatica was not investigated . In mice , however , there are eight DC-SIGN homologs that do not recognize exactly the same glycans as DC-SIGN [69] . According to their glycan specificity , SIGNR1 and SIGNR3 are the closest candidates to fulfill DC-SIGN function in mice [69] . Interestingly , SIGNR3 has been shown to modulate M . tuberculosis immune responses , by a signaling dependent on the tyrosine kinase Syk [70] . Indeed , resistance to M . tuberculosis is impaired in SIGNR3-deficient animals [70] . The fact that our results demonstrate that modulation of TLR-induced maturation of DCs by F . hepatica components does not depend on either Raf-1- or Syk-mediated signaling would exclude the participation of these receptors in this process . Nevertheless , additional studies are needed to elucidate a possible role of other SIGNR receptors in the immune modulation by F . hepatica on mice DCs . In conclusion , the results reported here demonstrate that glycoconjugates from F . hepatica are involved in the induction of high levels of IL-10 and IL-4 by the parasite and are in agreement with the increasing evidence supporting a role for helminth glycans in regulation of the host immune shift toward a Th2/regulatory response via induction of IL-10 . Furthermore , we show that F . hepatica glycoconjugates interact with DCs and modulate DC-function and -maturation by a SHP2-dependent CLR that is inhibited by Man residues . We are currently working on the identification of these glycans and the C-type lectin receptors on DCs that participate in their recognition . These results contribute to the understanding of the role of parasite glycans in the modulation of the host immunity and might be useful in the design of vaccines against fasciolosis . | Fasciola hepatica is a helminth that infects mainly ruminants , causing great economic losses worldwide . Importantly , fasciolosis is also considered an emerging zoonosis with an increasing number of human infections globally . As other helminths , F . hepatica is able to regulate the host immune response favoring parasite survival in the host . In this work we investigated whether glycoconjugates produced by this parasite play a role in the host immune-regulation . Glycans , composed by carbohydrate chains , participate in important biological processes , but their role during Fasciola infection has not been previously addressed . We found that glycoconjugates are involved in the production of the regulatory cytokine IL-10 and in the production of the Th2-like cytokines IL-4 . Furthermore , we found that they are also involved in the modulation of dendritic cell maturation , the most efficient antigen presenting cells . Indeed , the parasite is able to inhibit the maturation of dendritic cells in a process that is glycan-mediated and dependent on a mannose-specific receptor . In conclusion , our results highlight the importance of parasite glycoconjugates in the modulation of host immunity and might be applied in the design of vaccine strategies to prevent infection . |
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The grey mould fungus Botrytis cinerea causes losses of commercially important fruits , vegetables and ornamentals worldwide . Fungicide treatments are effective for disease control , but bear the risk of resistance development . The major resistance mechanism in fungi is target protein modification resulting in reduced drug binding . Multiple drug resistance ( MDR ) caused by increased efflux activity is common in human pathogenic microbes , but rarely described for plant pathogens . Annual monitoring for fungicide resistance in field isolates from fungicide-treated vineyards in France and Germany revealed a rapidly increasing appearance of B . cinerea field populations with three distinct MDR phenotypes . All MDR strains showed increased fungicide efflux activity and overexpression of efflux transporter genes . Similar to clinical MDR isolates of Candida yeasts that are due to transcription factor mutations , all MDR1 strains were shown to harbor activating mutations in a transcription factor ( Mrr1 ) that controls the gene encoding ABC transporter AtrB . MDR2 strains had undergone a unique rearrangement in the promoter region of the major facilitator superfamily transporter gene mfsM2 , induced by insertion of a retrotransposon-derived sequence . MDR2 strains carrying the same rearranged mfsM2 allele have probably migrated from French to German wine-growing regions . The roles of atrB , mrr1 and mfsM2 were proven by the phenotypes of knock-out and overexpression mutants . As confirmed by sexual crosses , combinations of mrr1 and mfsM2 mutations lead to MDR3 strains with higher broad-spectrum resistance . An MDR3 strain was shown in field experiments to be selected against sensitive strains by fungicide treatments . Our data document for the first time the rising prevalence , spread and molecular basis of MDR populations in a major plant pathogen in agricultural environments . These populations will increase the risk of grey mould rot and hamper the effectiveness of current strategies for fungicide resistance management .
Synthetic fungicides are used worldwide that provide protection of major crops from destruction by fungal plant pathogens [1] . As a result of repeated fungicide treatments , however , resistant strains of the pathogens are being selected [2] . Different resistance mechanisms have been reported that reduce fungicide effectiveness in field and greenhouse environments . Mutations leading to changes in the target proteins that are still functional but less sensitive to the drugs are most common in plant pathogenic fungi . For example , rapid accumulation of mutations in the gene encoding ß-tubulin have been observed in a variety of plant pathogens after introduction of the benzimidazole fungicides , leading to resistance against these fungicides [3] . Other mechanisms , such as overexpression of the gene encoding the target site or increased fungicide metabolism have also been described [3]–[6] . Multidrug resistance ( MDR ) , an important resistance mechanism in human pathogenic microbes and cancer cells , has often been correlated with the activity of energy dependent plasma membrane efflux transporters with low substrate specificity . Mutations leading to overexpression of individual transporters can result in increased export and thereby reduced sensitivity to a variety of drug molecules [7]–[9] . In fungi , the major types of drug efflux proteins are ATP binding cassette ( ABC ) and major facilitator superfamily ( MFS ) transporters [10]–[12] . Constitutive overexpression of the ABC transporters CDR1 and CDR2 , or the MFS transporter MDR1 has been observed in Candida spp . with MDR phenotypes that have been selected by prolonged fluconazole treatments in humans [13] . In filamentous fungi , the role of ABC and MFS transporters in the efflux of natural and synthetic toxicants is well known [14] . For example , analysis of knock-out mutants revealed that the ABC transporter AtrB from Aspergillus nidulans and its orthologue AtrB from Botrytis cinerea transport a wide variety of fungicides as well as toxins of plant and microbial origins [14]–[16] . Similarly , the MFS transporter Mfs1 of B . cinerea was found to mediate efflux of several fungicides as well as plant derived and microbial toxins [17] . Several efflux transporter genes have been shown to be rapidly induced by fungicides or natural toxins , such as Mycosphaerella graminicola Atr1 and Atr2 , or B . cinerea atrD and atrB [16] , [18] . Some ABC transporters have been shown to be involved in plant pathogenesis [19]–[22] . This is probably mainly due to the export of plant defence compounds , for example the Arabidopsis phytoalexin camalexin in the case of B . cinerea AtrB [22] . For the ABC1 transporter of Magnaporthe grisea , evidence was provided that it is required for tolerance to oxidative stress during appressorial penetration [19] . Despite some reports of MDR phenotypes in laboratory mutants of B . cinerea [16] , [18] and field strains of Penicillium digitatum and Mycosphaerella graminicola [23] , [24] , a significant role of MDR in agricultural environments has not yet been described for plant pathogens . However , a long term monitoring for fungicide resistance of B . cinerea initiated in French wine-growing regions has revealed , in addition to drug specific resistance mechanisms , the appearance of strains with cross resistance to chemically unrelated fungicides in the Champagne [4] , [25] , [26] . In this report , we have investigated these strains in further detail and describe the increasing prevalence of three different MDR populations in commercial vineyards . We show that their phenotypes are caused by mutations leading to overexpression of efflux transporters , and present evidence for long-distance migration of MDR strains from France to German wine-growing regions .
In 1994 , strains with two different MDR phenotypes , formerly designated AniR2 ( here MDR1 ) and AniR3 ( here MDR2 ) , because of their reduced sensitivity to anilinopyrimidine fungicides , have been identified for the first time in the Champagne ( Fig . 1A ) [26] . A third MDR phenotype ( MDR3 ) was first detected in 2001 . Since then , the frequency of MDR strains in the Champagne steadily increased until 2008 , when the three MDR phenotypes together represented 55% of the total population ( Fig . 1A ) . In vineyards of the German Wine Road region , a similar survey of B . cinerea isolates for fungicide sensitivity was performed for three years . Between 2006 and 2008 , increasing MDR populations were also observed , but in contrast to the Champagne the MDR1 phenotype was clearly dominating ( Fig . 1B ) . As previously described [26] , MDR1 and MDR2 strains had overlapping but distinct profiles of increased tolerance to a number of different classes of fungicides and other drugs ( Table 1 ) . Although the levels of tolerance observed were not as high as specific resistance mechanisms ( e . g . target site mutations ) , they were clearly genetically based and heritable and therefore called resistance throughout this paper . While MDR1 strains showed considerable resistance levels mainly towards fludioxonil , cyprodinil and tolnaftate , MDR2 strains were characterized by increased resistance to fenhexamid , tolnaftate , cycloheximide and cyprodinil . MDR3 strains showed the highest levels and broadest spectrum of resistance against most fungicides tested ( Table 1 ) . MDR phenotypes in fungi are usually correlated with increased drug efflux [8] . When the B . cinerea MDR strains were tested , they showed indeed lower fungicide accumulation than sensitive strains , indicating increased efflux activity . As previously reported [16] , sensitive strains show a transient accumulation of 14C-fludioxonil , followed by efflux of the drug after approximately 30 min , due to activation of efflux transporters ( Fig . 2A ) . In contrast , two MDR1 strains showed only low initial fludioxonil accumulation . After addition of the uncoupler CCCP , rapid influx was observed for all strains indicating the presence of energy-dependent efflux systems ( Fig . 2A ) [16] . Similarly , MDR3 strains accumulated little fludioxonil , while MDR2 strains behaved similar to sensitive strains ( Fig . 2B ) . These data indicated the presence of a constitutive efflux system in MDR1 and MDR3 strains . With 14C-bitertanol , all MDR strains showed reduced initial accumulation levels compared to sensitive strains , although this effect was less pronounced in MDR1 than in MDR2 and MDR3 strains ( Fig . 2B ) . In accordance with these data , lower accumulation of tebuconazole and triadimenol by a B . cinerea strain with MDR2 phenotype has been described previously [25] . The specificity of the uptake experiments was confirmed by experiments with heat-inactivated germlings , which displayed very high , non-transient fungicide accumulation ( Fig . 2C ) . The phenotype of MDR1 strains , including the low initial fludioxonil accumulation , was similar to that of a B . cinerea laboratory mutant which showed overexpression of the ABC transporter AtrB [16] . AtrB and its orthologs in other filamentous fungi , including Aspergillus nidulans and Penicillium digitatum , is a conserved efflux pump that contributes tolerance to various fungicides and natural antifungal compounds [14]–[16] , [21] , [22] , [27] , [28] . Indeed , atrB was constitutively upregulated in MDR1 and MDR3 strains , but not in MDR2 strains , showing 50–150 fold overexpression relative to sensitive strains . As previously reported , high levels of atrB expression were observed in sensitive strains after 30 min treatment with fludioxonil ( Fig . 3 ) [16] . Two other genes encoding ABC transporters , atrK and BMR3 , were also upregulated in the absence of drug induction in MDR1 and MDR3 strains when compared to sensitive strains , but only 2 . 5–5 fold ( Fig . 3B ) . To identify efflux transporters that are specifically upregulated in MDR2 strains , microarray hybridizations with B . cinerea whole genome chips were performed ( data not shown ) . These experiments revealed that mfsM2 ( Major facilitator superfamily transporter involved in MDR2 ) , which showed very weak expression in sensitive and MDR1 strains , was more than 600 fold overexpressed in MDR2 and MDR3 strains ( Fig . 3 ) . To confirm a causal relationship between overexpression of efflux transporters and MDR phenotypes , atrB and mfsM2 mutants were generated . As described further below , MDR1 strains with atrB deletions had lost the MDR phenotype . Two MDR2 strains with mfsM2 deletions had lost increased efflux activity for 14C-bitertanol ( Fig . 4A ) . Furthermore , the mfsM2 deletion mutants had lost the reduced sensitivity to various fungicides , showing levels similar to sensitive strains ( Fig . 4B , C ) . In contrast , when a sensitive strain was transformed with a construct providing constitutive overexpression of mfsM2 ( mfsM2ox ) , which led to 1481 ( ±309 ) -fold upregulation of mfsM2 relative to the parent strain , it acquired drug resistance levels similar to MDR2 strains ( Fig . 4B , C ) . Overexpression of atrB and mfsM2 is therefore necessary and probably sufficient to generate MDR1 and MDR2 phenotypes , respectively , in B . cinerea field strains . Apart from its role in MDR , we found only slight growth differences of the mfsM2 deletion mutants . When tested for pathogenicity , the mfsM2 deletion mutants showed no significant differences compared to their parent strains ( data not shown ) . Mutations leading to changes in gene expression are often located either in the promoters of these genes , or in regulatory genes . Sequencing of the atrB promoter regions from several sensitive , MDR1 and MDR3 strains did not reveal any MDR1-specific mutations ( not shown ) . Since other ABC-transporter genes besides atrB were also found to be upregulated in MDR1 and MDR3 strains ( Fig . 3B ) , we assumed that the MDR1 phenotype might have been generated by mutations in a regulatory gene . In order to locate the suspected MDR1-specific regulator gene , a map-based cloning approach was performed . When F1 progeny isolates of several crosses with MDR strains were analyzed , the segregation data confirmed that MDR1 and MDR2 phenotypes are determined by just one genetic locus each , and that strains with MDR3 phenotype can originate from recombination between MDR1 and MDR2 strains ( Table S1 ) [26] . By identifying polymorphic molecular markers that cosegregate with MDR1 and MDR3 phenotypes in the F1 progeny , it was possible to localize and identify mrr1 ( multidrug resistance regulator 1 ) encoding a putative Zn ( II ) 2Cys6 zinc cluster transcription factor ( TF; Table S2 ) [29] . The genetic marker closest to mrr1 , BC63-17 , located 1 . 8 kb away from mrr1 , showed 100 percent cosegregation with MDR1 phenotypes . Similarly , using crosses with MDR2 strains , a marker located just 1 . 6 kb away from the efflux transporter gene mfsM2 was found completely cosegregate with MDR2 phenotypes , indicating that they are caused by mutations in mfsM2 ( Table S3 ) . Sequencing of mrr1 from eight sensitive field strains revealed no or only silent nucleotide changes when compared to mrr1 of the sequenced reference strains T4 and B05 . 10 ( data not shown ) . In contrast , all MDR1 ( n = 15 ) and MDR3 ( n = 5 ) strains analyzed showed at least one point mutation leading to amino acid changes in Mrr1 . In total , at least eight different MDR1-related mutations were identified ( Fig . 5; Table S4 ) . Their role in generation of the MDR1 phenotype was supported by the observation that five of these mutations had occurred in more than one MDR1 or MDR3 strain . To confirm that mrr1 encodes the TF responsible for MDR1-related atrB overexpression , mrr1 and atrB deletions were generated in MDR1 strains and a sensitive strain . Consistent with the expected phenotype of a regulatory mutant , mrr1 mutants showed very low levels of atrB expression , not inducible by fludioxonil . Furthermore , they showed reduced expression of the ABC transporter genes BMR3 and atrK that are also upregulated in MDR1 strains ( Fig . 6A ) . Both the mrr1 and atrB mutants of the MDR1 strain showed increased fludioxonil uptake , indicating loss of AtrB-mediated efflux activity ( Fig . 6B ) . With regard to their drug sensitivity , the mrr1 and atrB mutants of MDR1 strains had completely lost their MDR1 phenotypes and were slightly hypersensitive to fludioxonil , similar to previously described atrB mutants ( Fig . 6C , D ) [16] . The MDR1 strain D06 . 7-27 showed an unusually high cyprodinil resistance and a rather low tolnaftate resistance , compared to other MDR1 strains , for unknown reasons . In the D06 . 7-27 ( Δmrr1 ) mutant , the cyprodinil resistance was significantly reduced , but still higher than in sensitive strains ( Fig . 6C ) . To confirm that single mrr1 mutations are sufficient for generation of the MDR1 phenotype , a sensitive strain was transformed with the mrr1V575M allele from MDR1 strain D06 . 7-27 . The transformants , expressing both wild type mrr1 and mrr1V575M , showed constitutive upregulation of atrB and , to a lower extent , atrK and BMR3 ( Fig . 6A ) , as well as a drug resistance phenotype similar to MDR1 strains ( Fig . 6C , D ) . The atrB and mrr1 mutants showed only little changes in sensitivity to fenhexamid , a substrate for MfsM2 but not AtrB , which confirmed that the functions of AtrB and Mrr1 are fungicide specific . These data confirmed that Mrr1 is the main transcriptional activator of atrB , and that activating mutations of mrr1 lead to overexpression of atrB and thus to MDR1 phenotypes . There are interesting parallels to the yeasts S . cerevisiae and C . albicans , in which MDR-related efflux transporter genes are regulated by Zn ( II ) 2Cys6 TFs as well . C . albicans MDR strains selected by fluconazole treatments in humans also resulted from gain-of-function TF mutations , leading to overexpression of ABC- and MFS-type MDR transporters [13] , [30] . Focusing on the search for mutations that are responsible for mfsM2 upregulation in MDR2 and MDR3 strains , we found a rearrangement in the mfsM2 upstream region , caused by insertion of a foreign gene fragment and concurrent deletion of a portion of the putative mfsM2 promoter . The inserted DNA , 1326 bp in length , is probably derived from an as yet unknown fungal long-terminal-repeat ( LTR ) retrotransposon ( Fig . 7A ) [31] . Surprisingly , this sequence is not present in the published genome sequences of the two B . cinerea strains B05 . 10 and T4 . It encodes truncated portions of a putative enzyme with domains of reverse transcriptase and RNase H , with closest homologs to sequences in the REAL retrotransposon of the plant pathogenic fungus Alternaria alternata [32] and in the Boty retroelement of B . cinerea ( Fig . S1 ) [33] . Out of 17 MDR2 and MDR3 strains analyzed from the Champagne ( 9 strains ) and from the German Wine Road ( 8 strains ) by sequencing or PCR analysis , all revealed the identical mfsM2 promoter rearrangement . In contrast , in 8 sensitive and 15 MDR1 strains no such rearrangement was found ( Table S5; data not shown ) . This observation strongly suggests that the mfsM2 alleles of these MDR2 and MDR3 strains have a common progenitor . That the promoter rearrangement is responsible for mfsM2 overexpression , was supported by the already described MDR2-like phenotype of a sensitive strain transformed with an mfsM2 overexpression construct . This was further confirmed by creating B . cinerea strains expressing mfsM2::uidA reporter gene fusion constructs . Only with the mfsM2 promoter fragment from an MDR2 strain , but not with a fragment from a sensitive strain , strong expression of ß-glucuronidase was observed ( Fig . 7B ) . The rapidly increasing MDR populations in French and German wine-growing regions indicate that strong selection for MDR phenotypes occurs by fungicide treatments . This was confirmed by two field experiments , in which mixtures of an MDR3 strain and a sensitive strain were introduced into two vineyards . A single treatment with a commercial fungicide mixture ( fludioxonil and cyprodinil ) during early berry development led to a significantly increased recovery of the MDR3 strain relative to the sensitive strain during grape harvest ( Fig . 8 ) . The recovery rates of the introduced MDR3 strain were 16% ( untreated vineyard ) and 62% ( treated vineyard ) in 2007 , and 34% ( untreated ) and 55% ( treated ) in 2008 , while the recovery rates of the introduced sensitive strain were 26% ( untreated ) and 14% ( treated ) in 2007 , and 28% ( untreated ) and 15% ( treated ) in 2008 . In addition , the MDR3 strain showed high survival rates after the following winter periods in the absence of fungicide treatments ( 40% in spring 2008 , 62% in spring 2009 ) . These data indicate also that the mutations in mrr1 and mfsM2 do not impair the fitness of MDR strains to a major extent .
Since the introduction of modern fungicides with specific modes of action , resistance development in fungal field populations has been observed , notably in B . cinerea which is considered to be a high risk pathogen [2] . Therefore , rules for fungicide resistance management have been established that include a recommendation to avoid the repetitive use of fungicides with similar targets within one growing season [2] , [5] . For Botrytis control in commercial European vineyards , two or three treatments with different mode-of-action fungicides are common . The increasing and widespread prevalence of MDR strains indicates that they have been selected under these conditions within the last decade . The three MDR phenotypes found in French and German vineyards were clearly correlated with increased drug efflux activity . Increased fludioxonil efflux was observed for MDR1 and MDR3 strains but not for MDR2 strains , while bitertanol efflux was observed for all MDR phenotypes , although more weakly for MDR1 . Furthermore , all MDR strains showed strong constitutive overexpression of one ( MDR1 , MDR2 ) or two ( MDR3 ) drug efflux transporter genes . In addition , weak overexpression of two other ABC transporter genes was observed in MDR1 and MDR3 strains . A causal correlation of MDR1 phenotypes with overexpression of the ABC transporter atrB , and of MDR2 phenotypes with MFS transporter mfsM2 overexpression was confirmed by the analysis of deletion and overexpression mutants . Two MDR1 strains with an atrB mutation had lost the MDR1 phenotype , and two MDR2 strains with an mfsM2 mutation had lost the MDR2 phenotypes . In addition , a sensitive strain which artificially overexpressed mfsM2 showed an MDR2-like phenotype . Thus , overexpression of atrB and mfsM2 are likely to be sufficient for the observed MDR1 and MDR2 phenotypes in B . cinerea field strains . While the role of atrB in the export of multiple natural and synthetic toxicants has been described in detail [14] , [28] , the function of mfsM2 in sensitive strains remains unknown . The protein is not highly conserved in other fungi , and even in the genome sequence of the closely related Sclerotinia sclerotiorum , no apparent orthologue to B . cinerea mfsM2 could be identified . In sensitive strains mfsM2 expression is very low , and up to now we did not find any fungicide or other compounds that induce mfsM2 ( data not shown ) . In MDR1 strains , the mutations leading to atrB overexpression were found to be located not in atrB itself , but in the transcription factor gene mrr1 . Out of 20 MDR1 and MDR3 strains analyzed , all carried mutations in the coding region of mrr1 , and several strains with different geographical origin or collected in different years showed identical mutations . Because mrr1 mutants failed to express atrB to significant levels , and because sensitive strains expressing an activated version of Mrr1 ( Mrr1V575M ) showed an MDR1-like phenotype , Mrr1 was confirmed to be a transcriptional activator of atrB . The mrr1 mutants also showed reduced expression of atrK and BMR3 , when compared to MDR1 strains , indicating that Mrr1 also plays a role in activation of other efflux transporter genes . However , since the drug sensitivity phenotypes of MDR1 strains with either atrB or mrr1 mutations were indistinguishable from each other , and since MDR1 atrB mutants have completely lost their MDR1 phenotypes , the weak overexpression of atrK and BMR3 does not seem to contribute significantly to the phenotype of MDR1 strains . While our data indicate that the main physiological role of Mrr1 is regulation of atrB , the whole set of genes controlled by Mrr1 in the genome of B . cinerea remains to be determined . Interestingly , while structurally and also functionally conserved orthologues of B . cinerea AtrB occur in other ascomycetous fungi , e . g . in A . nidulans , P . digitatum [15] , [22] , no clear orthologues ( best bidirectional hits ) of B . cinerea Mrr1 could be identified in other fungi . This indicates that the regulation of ABC transporters in filamentous fungi might be not highly conserved . Similar observations have been made for yeasts , because the major efflux transporter in S . cerevisiae , PDR5 , and its orthologue pair CDR1/CDR2 in C . albicans , are under control of different transcription factors [34] . With regard to fungicide resistance , efflux activities , efflux transporter gene overexpression , and genetic data , MDR3 strains were clearly identified as recombinants carrying both MDR1-specific mutations in mrr1 and MDR2-specific mutations in mfsM2 . Collectively , all data indicate that the three MDR phenotypes in B . cinerea have originated by mutations in just two genes . Obviously , the different mrr1 point mutations leading to MDR1 have occurred repeatedly . Thus MDR1 phenotypes could appear ( and might have already appeared ) in different agricultural environments in which selective conditions for these phenotypes prevail . Similarly , a variety of gain-of-function mutations have been found in transcription factor genes TAC1 and MRR1 of clinical MDR isolates of C . albicans , leading to overexpression of the efflux transporter genes MDR1 and CDR1 or CDR2 , respectively [13] , [30] , [35] . In contrast , the rearrangement in the mfsM2 promoter appeared to be a unique event , found in all MDR2 and MDR3 strains analyzed so far . Based on the time course of appearance of these strains in the Champagne , and their lower frequency compared to MDR1 strains in Germany , we assume that the rearrangement in mfsM2 originated once in the Champagne , possibly in the early 1990s . The rearranged mfsM2 allele later spread into the German Wine Road region , 250 km east of the Champagne , possibly by air currents . Because of the small size of the retrotransposon-derived gene fragment and of the lack of any remaining LTR sequences , the origin of the sequence inserted into mfsM2 and the mechanism of the insertion-deletion rearrangement remain obscure . The absence of the integrated sequence in the published genomes of strains B05 . 10 and T4 indicates that it occurs only in subpopulations of B . cinerea . A search for the presence of the sequence in a variety of field strains by using PCR , Southern hybridization and sequencing revealed that similar but non-identical sequences are present in some sensitive strains ( data not shown ) . The mfsM2 mutation is reminiscent of a transposable element insertion into the promoter of a gene for a cytochrome P450 monooxygenase involved in insecticide detoxification and resistance in Drosophila , leading to overexpression and global spread of the mutated gene [36] . A summarizing model of the data in this paper is shown in Fig . 9 . It is assumed that MDR strains in the Champagne have appeared due to selection pressure in fungicide treated vineyards , and to mutations leading to the appearance of MDR1 and MDR2 strains , and a few years later also to the appearance of MDR3 strains . Because a repeated occurrence of the unusual rearrangement found in the mfsM2 promoter appears to be highly unlikely , we assume that MDR2 strains carrying this rearrangement have migrated from France to Germany , probably in the last decade . To support this hypothesis , population genetic studies with MDR strains from different geographical origins are currently performed . In addition , we are searching for MDR strains in other regions with different crop cultures and different fungicide treatment schedules , in order to achieve a better understanding of the distribution of MDR strains , and the factors leading to their appearance and selection . A field experiment has clearly demonstrated selection of an artificially introduced MDR3 strain by a standard fungicide treatment . This confirms that the MDR3 phenotype confers selective advantage to B . cinerea in fungicide-treated vineyards , and that this advantage outweighs possible fitness defects . Furthermore , the MDR3 strain was recovered with high albeit varying frequencies after overwintering periods , in the absence of fungicide selection pressure . These data indicate that the general fitness of strains showing atrB and/or mfsM2 overexpression can be rather high in field environments , but this needs further studies . We are currently testing various fitness parameters in isogenic atrB , mrr1 and mfsM2 knock-out and overexpression strains in order to estimate the performance of these strains . Because the natural role of MDR-related efflux transporters seems to be the protection against various biotic toxic compounds [14] , [26] , [28] , it is possible that the MDR strains have acquired properties that increase their fitness in natural environments even in the absence of fungicides . Our work is the first documented case of a massive appearance of MDR populations in a major plant pathogen in fungicide-treated agricultural environments . To what extent the effectiveness of fungicide treatments against MDR strains is reduced in comparison to sensitive strains needs to be investigated . Nevertheless , the possibilities of a further rise of MDR3 strains and of additional mutations leading to higher levels of broad-spectrum fungicide resistance are expected to be a major threat for chemical control of grey mould disease in the near future .
Strains were isolated from commercial vineyards in the Champagne and the German Wine Road ( Palatinate ) . In the Champagne , samples were collected from vineyards located around Moulins , Hautvillers , Vandières and Courteron , Moulins being the northernmost ( 49°34′N/03°28′E ) and Courteron the southernmost ( 48°01′N/04°26′E ) town , 167 km apart from each other . Samples were collected from approximately 200 locations each year . Each sample represented a bulk population consisting of spores of at least 20 infected berries within the chosen plot . The spores from each bulk sample were spread onto agar media containing different fungicides , and analyzed for different phenotypes as described [25] , [37] . In the German Palatinate , the same six vineyards were used for sampling each year . The plots are located along the German Wine Road , between Dackenheim ( northernmost: 49°53′N/8°19′E ) and Walsheim ( southernmost: 49°23′N/8°13′E ) , 32 km apart from each other . Thirty isolates were obtained per vineyard from single infected berries , resulting in about 180 isolates per year . From each sample , HA ( 1% ( w/v ) malt extract , 0 . 4% ( w/v ) yeast extract , 0 . 4% ( w/v ) glucose , pH 5 . 5 ) cultures were grown , and terminal mycelial fragments cut off for subculture of isolates . Conidia were used for fungicide tests . A list of B . cinerea strains is shown in Table S5 . Fludioxonil , cyprodinil ( Syngenta-Agro , Maintal , Germany ) , fenhexamid , tebuconazole ( Bayer Crop Sciences , Monheim , Germany ) , boscalid , iprodione ( BASF , Ludwigshafen , Germany ) , were kindly provided by the companies , carbendazim , tolnaftate and cycloheximide were purchased from Sigma-Aldrich ( St . Louis , USA ) . The drugs were dissolved in 100% ethanol or 100% DMSO ( carbendazim ) , and added to the required concentrations to the assays . For dilution series , fungicide stock solutions were adjusted to keep the final solvent concentrations between 0 . 2 and 1 . 5% ( v/v ) for ethanol and between 0 . 3 and 1 . 0% ( v/v ) for DMSO . Control assays revealed no significant differences in growth of the strains at these concentrations relative to no-solvent controls ( not shown ) . For each isolate tested , 2×105 conidia were pre-incubated for 1 . 5 hours in 1 ml malt extract broth ( pH 5 . 5; Difco ) before use . Effective inhibitory drug concentrations ( EC50; mg/l ) were determined with 1000 spores in 0 . 1 ml 96-microplate cultures , using threefold drug dilution series . Tests were performed in malt extract broth , except for cyprodinil ( Gamborg B5 minimal medium supplemented with 10mM KH2PO4 , 50mM glucose; pH 5 . 5 ) , and boscalid [38] . After 48 h ( boscalid: 96 h ) incubation at 20°C , A600 was determined . The assays were repeated at least 3 times . Mean data , with standard deviations are presented . For calculation of EC50 values , the Origin6 . 0 software package ( Origin Lab Cooperation , USA ) was used . Fungicide accumulation assays with 14C-labeled fludioxonil and bitertanol were performed with 14 h old germlings germinated as described previously [27] . Experiments were initiated by adding the labeled fungicide to final concentrations of 6 µM ( 10 Bq/nmol ) fludioxonil , or 10 µM ( 10 Bq/nmol ) bitertanol . The uncoupler CCCP was added at a final concentration of 10 µM . Three 5 ml samples each were taken 10 and 60 min after adding the fungicide . Heat inactivation of germlings for control experiments was performed for 10 min at 60°C . Experiments were done in triplicates and repeated at least three times . DNA isolation and manipulation was performed according to established protocols . For transcript studies , B . cinerea conidia ( 2×106 ) were germinated for 15 h in polystyrene Petri dishes coated with apple wax ( 0 . 01 mg/cm2 ) using Gamborg B5 medium supplemented with 10 mM fructose and 10 mM KH2PO4 ( pH 5 . 5 ) . The germlings were incubated for further 30 min either without or with 1 mg/l fludioxonil . For RNA isolation , the wax with the embedded germlings was scraped from the surfaces with a tissue cell scraper ( TPP AG , Trasadingen , Switzerland ) , centrifuged for 5 min at 4000 rpm at 4°C , washed with 20 ml of ice-cold water and centrifuged once more . The pellet was transferred into a mortar containing liquid nitrogen and sea sand for grinding . Total fungal RNA was isolated using the RNeasy Plant Mini Kit ( Qiagen , Hilden , Germany ) , and reverse transcribed into cDNA with oligo ( dT ) primers ( Verso cDNA Kit; Thermo Fisher Scientific , Surrey , United Kingdom ) . Northern hybridization and quantitative RT-PCR were performed according to standard protocols . Expression of the genes was calculated according to Pfaffel [39] . Transcript levels were normalised against the expression levels of housekeeping genes encoding elongation factor 1α ( BC1G_09492 . 1 ) and actin ( BC1G_08198 . 1 ) , and shown as normalized fold-expression relative to expression levels of non-induced germlings from sensitive strains . Means of at least two biological replicates , with three strains of each phenotype , are shown . The following efflux transporter genes were analyzed: ( Bc ) atrB [16] , ( Bc ) atrD [18] , ( Bc ) atrA [40] , ( Bc ) atrF ( BC1G_01454 . 1 ) , ( Bc ) atrK/BMR1 [16] , BMR3 ( BAC67160; BC1G_02799 ) [41] , ( Bc ) mfsM2 ( BofuT4_P024110 . 1 ) . For identification of mutations in the mrr1 alleles of sensitive , MDR1 and MDR3 strains , mrr1 fragments were amplified from total DNA by PCR , using primers mrr1_TF1-1 and mrr1_TF1-4 , and sequenced . For identification of the mfsM2 alleles in sensitive and MDR2/MDR3 strains , the mfsM2 upstream region was amplified from genomic DNA by primers mfsM2-pfor/mfsM2-prev , yielding a 1625 bp fragment with sensitive strains , and a 2273 bp fragment with MDR2 and MDR3 strains . For confirmation of their identity , the insertions of 6 MDR2 strains were sequenced . For atrB mutagenesis , the construct described by Vermeulen et al . [16] was used . For mrr1 deletion , a genomic B . cinerea mrr1 fragment was amplified ( primers mrr1-for1/mrr1-rev2 ) , digested ( ApaI/SacII ) and cloned into pBSKS ( + ) . Inverse PCR was performed ( primers mrr1-rev1/mrr1-for2 ) , the product digested ( EcoRV/XmaI ) and ligated with a hygromycin cassette [42] . After transformation into B . cinerea [43] , mutants were identified with primers mrr1-for1/tubB-inv ( yielding a 1304 bp product in k . o . mutants ) . For mfsM2 deletion , two mfsM2 flanking fragments were amplified ( 1: mfsM2-KO1/mfsM2-KO2; 2: mfsM2-KO3/mfsM2-KO4 ) , digested ( 1: XbaI/EcoRI; 2: KpnI/XhoI ) , and successively cloned into pBSKS ( + ) . The hygromycin cassette from pLOB1 ( AJ439603 ) was inserted between the fragments via EcoRI and XhoI . The construct was amplified ( primers mfsM2KO/mfsM2-KO4 ) and transformed into MDR2 strains D06 . 6-5 and D06 . 2-6 . The ΔmfsM2 mutants were confirmed by PCR ( primers mfsM2-KO1/oliC-Sma-Rev ) , yielding a 1605 bp product in k . o . mutants . To construct strains expressing an mrr1 allele conferring MDR1 , mrr1V575M of strain D06 . 7-27 was amplified ( primers mrr1-atg/mrr1-uaa ) , digested ( XmaI/PvuII ) and cloned into pBSKS ( + ) carrying a 5′-fragment of the hygromycin resistance gene driven by the oliC promoter [44] . Additional 1578 bp of the mrr1 upstream region were amplified ( primers mrr1-pro1/mrr1-rev1 ) , digested ( BamHI/XmaI ) and ligated next to mrr1V575M . The resulting plasmid was linearized ( KpnI ) and transformed into B . cinerea B05 . Hyg-3 [44] . To generate mfsM2 overexpression strains ( mfsM2ox ) , mfsM2 was fused to the oliC promoter . The mfsM2 coding sequence was amplified ( primers mfsM2-ATG-SmaI/mfsM2-TAG-EcoRI ) , digested ( SmaI/EcoRI ) and ligated into poliGUS-Hyg5 , replacing uidA . poliGUS-Hyg5 was constructed by fusing an oliC promoter fragment from pLOB1 ( primers KO-Hyg1-BamHI/oliC-Sma-Rev ) , an uidA coding sequence from p35S-GUS [45] ( primers 35S-gus-for-Sma/35S-gus-rev-Eco ) , the B . cinerea niaD terminator ( primers niaDTerm-for-Eco/niaDTerm-rev-Hind ) and the 5′ part of a splitted hygromycin resistance cassette from pBS . Hyg-5 [44] ( HindIII/XhoI ) into pBSKS ( + ) . The resulting plasmid was linearized ( KpnI ) and transformed into strain B05 . Hyg-3 , yielding B05 . Hyg-3 ( mfsM2ox ) . To construct mfsM2 promoter-reporter fusions , the oliC promoter fragment in poliGUS-Hyg5 was replaced either by a 1501 bp mfsM2 upstream fragment from strain B05 . 10 , or by a 2149 bp fragment from MDR2 strain D08 . 2-12 including the 1326 bp retrotransposon-derived fragment and the remaining mfsM2 upstream region ( primers mfsM2-pfor-Not/mfsM2-prev-Sma ) , before transformation into B05 . Hyg-3 . B . cinerea B05 . Hyg-3 transformants were analyzed for correct genomic integration of the constructs by PCR . GUS staining of transformants was performed as described [46] . MDR3 strain D06 . 7-33 and the sensitive ( BenR ) strain D06 . 5-25 were chosen for a mixed-inoculation experiment , performed twice in 2007 and 2008 in experimental vineyards at the German Wine Road ( Neustadt an der Weinstrasse ) . In June ( berry stage BBCH-77 ) the vineyards received a standard treatment with Teldor . In summer , immature grapes ( stage BBCH-81 ) were inoculated , using hand spraying bottles , with a 1∶1 strain mixture ( 2×104 conidia/ml per strain ) in water until runoff . The vineyards were randomly divided into three fungicide-treated and three untreated plots . The first inoculation was one day before , the second one day after standard treatment with Switch in the treated plots , and in the same way in non-treated plots . Before grape harvest at late September , 50 ( 2007 ) and 90 ( 2008 ) isolates per plot were recovered from moulded berries from the inoculated plots and from a non-inoculated , untreated control plot nearby . The introduced isolates were identified by fungicide tests , using HA plates containing 0 . 2 mg/l fludioxonil , 5 mg/l iprodione , or 5 mg/l carbendazim . In the control plot , MDR3 strains were never detected , while BenR strains were found with frequencies of 12% ( 2007 ) and 6% ( 2008 ) . Their genetic identity was further confirmed by IGS-AFLP markers [31] . For analysis of the overwintered populations , B . cinerea isolates were recovered in the following spring from bark fragments of inoculated grapevines ( 2008: 137; 2009: 313 isolates ) , by incubation on a selection medium for B . cinerea [47] , followed by fungicide tests . Experiments were performed at least three times , unless indicated otherwise . Statistical differences of data were checked by unpaired , two-tailed t tests , and labeled as follows: n . s . : not significant; * p<0 . 05; ** p<0 . 01; *** p<0 . 001 . In the graphs , standard deviations are indicated . The DNA sequence reported in this paper has been deposited in GenBank , under accession number GQ292709 ( RE-like gene fragment inserted in mfsM2 ) . Further accession numbers: mfsM2: BofuT4_P024110 . 1; mrr1: BofuT4_P063510 . 1 ( http://urgi . versailles . inra . fr/projects/Botrytis/ ) . | Bacterial and fungal pathogens cause diseases in humans and plants alike . Antibiotics and fungicides are used for disease control , but the microbes are able to adapt quickly to these drugs by mutation . Multiple drug resistance ( MDR ) is well investigated in human pathogens and causes increasing problems with antibiotic therapy . Driven by the continuous use of fungicides in commercial vineyards , three types of rapidly increasing multidrug resistant populations of the grey mould fungus Botrytis cinerea have appeared in French vineyards since the mid 1990s . Using a combination of physiological , molecular and genetic techniques , we demonstrate that these MDR phenotypes are correlated with increased drug efflux activity and overexpression of two efflux transporters . Just two types of mutations , one in a regulatory protein that controls drug efflux , and the other in the gene for an efflux transporter itself , are sufficient to explain the three MDR phenotypes . We also provide evidence that a subpopulation of the French MDR strains has migrated eastward into German wine-growing regions . We anticipate that by continuous selection of multi-resistant strains , chemical control of grey mould in the field will become increasingly difficult . |
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Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons . In vivo , waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation . In vitro , coordinated spontaneous spiking activity , or network bursts ( NBs ) , interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures . Several studies have investigated this type of activity and its dynamics , but how a neuronal system generates these coordinated events remains unclear . Here , we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling . Our analysis reveals that NBs are generated in specialized regions of the network ( functional neuronal communities ) that feature neuronal links with high cross-correlation peak values , sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions . We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs . Through the analysis of simulated and experimental data , we also show that AMPA currents drive the coordinated activity , while NMDA and GABA currents are only involved in shaping the dynamics of NBs . Overall , our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events . As suggested by the rules used for implementing our computational model , such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity .
Neuronal systems , including brain circuits’ in vivo and cultured networks , intrinsically generate coordinated and spatiotemporally propagating spontaneous spiking activity during their development [1] . This type of spontaneous activity ( or “waves” ) has been studied in several brain circuits , including the cerebellum [2] , the hippocampus [3 , 4] , the thalamus [5] and in sensory systems such as the retina [6] . These studies have shown that coordinated spontaneous waves of spikes transiently characterize the firing regime of early developing brain circuits and are associated with activity-dependent circuit formation [7 , 8] . For instance , in the visual sensory system , waves of spikes are required for the normal development of sensory representations before visual experience formation , and several studies have investigated the underlying cellular and molecular mechanisms that can change the dynamics of these spontaneous waves [6 , 9] . Indeed , as brain circuits form , the spatiotemporal dynamics of the coordinated spiking activity changes and characterizes different stages of development . Successively , through the effect of neuromodulation , the spiking activity switches from a coordinated firing regime to a regime characterized by asynchronous spiking neurons [1 , 7] . Interestingly , neurons in vitro can also self-organize and rewire to form networks [10 , 11] that spontaneously express a rich repertoire of coordinated spiking activity after a few weeks of growth . The network activity of these isolated neuronal systems does not switch to a sparse spiking regime as occurs in vivo , and collective spiking patterns can rather persist over time , lasting up to a few months [12 , 13] . During collective spiking events , referred to as network bursts ( NBs ) [14] , most neurons in the network fire together [15 , 16] . As clearly unveiled only recently with high-resolution electrical recordings [17 , 18] or Ca2+ functional imaging [19] , cultured networks also propagate waves of spikes . Even though the specific dynamic of these spiking waves varies depending on the neuronal system and point in development , all of these spontaneous coordinated events share remarkably similar macroscopic properties . Indeed , these events typically last up to a few hundreds of milliseconds , spatiotemporally propagate through the network and occur with an interval in the order of minutes . Additionally , the number of spatiotemporal patterns tends to be restricted to a few classes , each one having distinct regions that initiate these propagating events . However , despite the large interest in the dynamics of this type of activity , the mechanism by which a neuronal system can generate coordinated spiking events and whether the regions initiating these events require specific cellular or connectivity properties remain unclear . Previous works have suggested that the presence of hub neurons [20] and the local cellular properties of sub-populations of neurons [21] might underlie the sub-networks that act as nucleation centres of coordinated spiking activity events . Here , we investigate the generation of NBs in primary neuronal cultures by combining high-resolution electrical recordings and computational modelling . Neurons in culture form isolated networks that can intrinsically generate coordinated spiking activity interleaved with phases of asynchronous spikes and offer several advantages for our study . Indeed , while coordinated spiking activity in developing brain circuits has been suggested to depend on the specific topological properties of the circuit ( hub neurons in the hippocampus or short connections between Purkinje cells in the cerebellum ) [20] , cultured networks can display this type of spontaneous activity without a clear physiological organization of the cellular network topology . Additionally , neuronal cultures are the neuronal system that currently offers the lowest undersampling of firing neurons when recorded with high-resolution multielectrode arrays ( MEAs ) [22] that consist of complementary metal-oxide-semiconductor ( CMOS ) devices [17 , 23] . The detailed and simultaneous access to the spiking activity of several thousands of neurons provided by 4096-electrode CMOS-MEAs allows for the fine quantification of mean activity parameters [22 , 24] , the tracking of spatiotemporal propagations and the localization of the sites generating the NB events [18] . To evaluate the potential contribution to the generation of NBs of different and experimentally hidden structural and functional variables , we combined the analysis of high-resolution electrical recordings with similar analyses of simulated data . The computational network model developed in this work consists of 4096 conductance-based point-process neurons [25] connecting near neurons with a higher probability than far neurons . This rule was found to enable our model to both quantitatively and qualitatively mimic the experimental spontaneous spiking activity of cultured neuronal networks . The model was also further assessed by comparing simulated and experimental data under conditions of pharmacological manipulation of synaptic transmission , thus also allowing for the verification of the role of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) , γ-aminobutyric acid ( GABA ) and N-methyl-D-aspartic acid ( NMDA ) currents in the generation of NBs and in shaping their dynamics . We then used the experimental data and the full details of our model to investigate the connectivity properties of the network regions associated with the generation of NBs ( or ignition sites , ISs ) . This allowed us to identify functional communities ( fCOMs ) of neurons associated with the regions initiating NBs , to characterize their structural and functional properties and to suggest a possible mechanism for the local initiation of NBs that relies on the connectivity properties of the fCOMs . Finally , as previously suggested in [21] , by analysing the temporal motifs of the spike patterns , we investigated whether the spiking activity preceding an NB can be predictive of the following NB .
The spontaneous electrophysiological activity of 15 hippocampal neuronal cultures ( 19–21 days in vitro , DIVs ) was recorded at high resolution with the 4096 electrodes of CMOS-MEAs ( Fig 1A ) . The recordings show two alternating firing regimes consisting of long epochs of sparse asynchronous spiking lasting several seconds interleaved with short periods of NB events , see Fig 1B . During an NB , a large fraction of neurons in the network fired action potentials at a high rate ( ≃ 100 Hz ) for a short time ( <100 ms ) . In contrast , before and after an NB , neurons rarely fired action potentials and showed a low mean firing rate ( MFR ) ( ≃ 0 . 1Hz ) . As shown in Fig 1C , NBs comprised the sequential activation of neighbouring neurons recruited to propagate the spiking activity throughout the entire network . The origin and the propagation trajectory of each NB was estimated by computing the average position of the spiking activity over time using a centre-of-activity trajectory ( CAT ) analysis [18] . The results of this analysis are reported in Fig 1D for the two NBs shown in Fig 1C . Based on the CAT analysis [26] , NBs could be sorted into a few classes ( i . e . , < 10 ) of spatiotemporal patterns , with each class including events sharing a similar average propagation trajectory and origin . In accordance with previously reported data [15] , the cross-correlation matrix of NBs ( Fig 1E and 1F ) computed on our high-resolution recordings shows that NB classes are preserved along the whole recording duration and that the temporal sequence of these different NBs does not show any recognizable time-periodicity or class-related structure [15 , 18] . We developed a computational network model to investigate experimentally hidden network variables based on the recorded experimental activity . Briefly , this network model ( see the Materials and Methods section for more details ) consisted of 4096 conductance-based point-process neurons [25] with a connectivity determined using a Gaussian radial basis function . Other connectivity rules , such as the random and radius graphs , were discarded because they were unable to reproduce the propagation patterns and velocities of the experimental NBs ( see S1 Appendix for a comparison with other network topologies ) . In the model , each neuron receives independent Poisson inputs that generate fluctuations in the membrane potential and lead to asynchronous spiking activity in the network . The simulated spontaneous network activity was qualitatively comparable to the activity of our experimental recordings ( compare Fig 2A with Fig 1B ) . In addition , the adopted connectivity rule enabled the generation and propagation of NBs throughout the entire network ( Fig 2B and 2C ) and mimicked our experimental recordings ( Fig 1B and 1C ) . As shown in the raster plots , different neurons involved in an NB fired with different timings depending on their recruitment in the NB propagation . As observed in the experimental data , the simulated NBs also did not show any recurrent or periodic organization ( Fig 2D ) . Simulated NBs could be clustered in a few classes of spatiotemporal patterns ( Fig 2E and 2F ) based on the CAT analysis as in the previous experiments and in [18] . Finally , the statistical results of the simulated network activities were in accordance with those obtained from the experimental data ( Fig 2G ) , including the MFR , the mean bursting rate ( MBR ) , mean firing intra-burst ( MFIB ) and mean burst duration ( MBD ) ( see the Materials and Methods section ) . These results show that our computational network model can spontaneously generate NBs as expressed by neuronal cultures in our recordings and similar to NBs reported in previous studies [12 , 27 , 28] . To further validate our model , we assessed its performance in reproducing pharmacologically manipulated network activity . Given that synaptic signalling is a fundamental component in the relay of information among neurons in a network , we investigated its role in the dynamics of NBs by exploring the contribution of three main excitatory and inhibitory currents ( i . e . , AMPA , NMDA and GABA ) . In accordance with previous works [29 , 30] , we observed that our computational network model endowed with AMPA and GABA conductance ( or AG-networks ) could trigger NBs . Experimentally , impairing excitatory synaptic transmission with CNQX ( a selective blocker of AMPA receptors ) caused the silencing of NBs [31 , 32 , 33] . To understand the extent to which manipulations of AMPA can affect NBs , we tested the response of the AG-network by decreasing AMPA conductance from 48 to 30 μS , leaving GABA inhibition unaltered . Our simulation results show that a decrease in AMPA induced a reduction in spontaneous spiking activity , as quantified in Fig 3A with the MFR . For low values of AMPA conductance ( < 38 μS ) , the network stopped firing NBs ( MBR was close to zero; Fig 3B , black dashed lines ) , and the percentage of random spikes ( not belonging to a burst ) were predominant ( Fig 3B , red dashed lines ) . Under this low-AMPA condition , the addition of NMDA currents ( or AGN-network ) partially compensated for the reduction in AMPA excitation ( Fig 3A and 3B , solid lines ) . Indeed , we observed that for a 30% decrease in AMPA conductance , the spiking activity of the AGN-networks became very sparse , with inter spike intervals ( ISIs ) of several seconds ( Fig 3C , red distribution ) . Conversely , in AGN-networks in the control condition ( 0% decrease in AMPA conductance ) , the ISI distribution ( Fig 3C , blue line ) showed both short ( < ~100 ms ) and long ( > ~500 ms ) ISIs , indicating the presence of NBs interleaved with asynchronous spikes . Notably , previous experimental observations [34] reported that homeostatic changes in AMPA currents comparable to those of our results are required to drive the network from uncoordinated to coordinated firing regimes . In addition to the effects of AMPA , we investigated those of GABA inhibition on NBs , both experimentally by blocking GABA-receptors with bicuculline ( n = 4 recordings , 30 μM ) and with simulations ( n = 5 ) by setting a null GABA conductance in the model . The blockade of GABA currents has previously been shown to not prevent the firing of NBs in cell culture networks [35 , 36 , 37] . Compared with the results in control conditions ( i . e . , unblocked GABA; “GABA-ON” ) , the blockade of inhibitory synapses in the model ( GABA-OFF ) caused an increase in the MFR , MBR , MFIB and a decrease in the MBD , both in our recordings and in simulations ( Fig 3D; for further details see S5 Appendix ) . We finally investigated the impact of NMDA on the dynamics of the network activity . Experimentally , the main consequence of blocking NMDA currents ( 50 μM of APV , n = 3 ) was the suppression of their superburst firing regime [12] ( sequences of NBs interleaved by ~ 200 ms ) in favour of the single-NB firing regime ( Fig 3E ) . Interestingly , we could mimic the superburst firing regime by including NMDA conductance in our model ( Fig 3E , n = 10 simulations , AGN-networks ) . In the full model with the NMDA current , the ISI distribution of single spiking neurons was tri-modal ( Fig 3F , black , n = 10 AGN-networks ) , whereas without NMDA currents ( Fig 3F red , n = 10 , AG-networks ) it was bi-modal . The first peak ( P1 ) corresponded to the mean firing within an NB and the second peak ( P2 ) to the mean firing between consecutive NBs . The third peak was only obtained in AGN-networks ( Fig 3F , highlighted with an arrow ) and accounted for the time intervals between consecutive sequences of NBs in superbursts ( see S6 Appendix for a detailed analysis of superbursts ) . Overall , our results show that our computational model can express propagating NBs , as well as express different dynamics resulting from the manipulation of the main excitatory and inhibitory synaptic currents . Consistent with other previous studies [31 , 32 , 35 , 36 , 37] , we found that AMPA currents drive NBs , while NMDA and GABA currents are only involved in shaping the dynamics of these coordinated spiking activity events . Following these results , the simulations in the next sections were performed with AG-networks endowed with AMPA and GABA conductance only . NMDA was not included because we found that its build-up during the initiation phase of an NB is much slower than the kinetics of AMPA ( see Fig 3G ) . Here , we investigated the properties of the network that might underlie the generation of NBs . To do so , we quantified the correlated spiking activity among pairs of neurons in both experimental and simulated data . Based on the obtained cross-correlation matrices , we built functional graphs , whose functional links connect highly correlated neurons ( see Material and Methods ) . Interestingly , even though the cross-correlation does not explicitly takes into account the position of neurons ( or of the electrodes ) , the functional connectivity analysis of both simulated and experimental data shows that these functional links tend to cluster into a few regions of the network ( Fig 4A and 4B , see S7 Appendix for additional example ) . A further post-processing of the functional graph ( i . e . , with the Infomap algorithm ) confirmed that these links clustered in a few ( <10 ) spatially segregated regions of the network . In the following sections , we will refer to these regions of the network as functional communities ( fCOMs ) . Since in each dataset we have found a number of fCOMs regions that is similar to the one of the initiation sites ( ISs ) of NBs , we wondered whether the fCOMs regions might be associated with the initiation sites ( ISs ) of the NBs . As quantified in Fig 4C and 4D , we found that the position of the fCOMs co-localized with the ISs of the NBs and that the fCOMs covered a small area of the networks ( Fig 4E ) . This overlap exceeded the 95% bootstrap threshold ( see the Materials and Methods section ) in 18 out of 20 simulated networks and in 10 out of 15 experimental recordings . We also observed the presence of fCOMs without any corresponding ISs ( Fig 4B bottom-right ) , but all ISs of a cluster of NBs were always associated with a distinct fCOM . Therefore , these results indicate that the analysis of the fCOMs can be used to identify the potential regions of a network that initiate NBs . Indeed , the functional connections among neurons belonging to the same fCOM showed high cross-correlation peak values ( 0 . 52±0 . 06 and 0 . 19±0 . 04 for simulated and experimental data , respectively ) and small time lags ( less than 1ms , i . e . , the neurons belonging to an fCOM fire almost synchronously ) . These findings reveal that the fCOMs are associated with the ISs of the NBs and that the fCOMs show particular functional connectivity properties . This latter result suggests that the fCOMs ( and , consequently , the ISs ) may be formed by neurons with a particular local structural connectivity . Given that our simulation results have shown a good similarity with respect to experimental results ( spontaneous activity ( Fig 2G ) , pharmacological manipulations ( Fig 3D and 3E ) and on the fCOMs-ISs correspondence ) , thus validating the goodness of our model , we further exploited this network model to access experimentally hidden quantities . Specifically , we exploited the model to investigate the structural connectivity underlying the fCOMs . To do so , we quantified the occurrence of small template subgraphs , or structural motifs [38] ( all possible non-isomorphic graphs up to six nodes , see the Materials and Methods section ) , in the structural connectivity underlying the fCOMs . In particular , we found a clear positive correlation ( Fig 5A , gray line ) between the clustering coefficient and relative abundance of motifs between the structural connectivity of fCOMs and rndCOMs ( i . e . random graphs with the same number of edges , see Materials and Methods ) . To further test whether the clustering coefficient could be a key feature underlying the fCOMs in the anatomical network , we repeated the same quantification with respect to the rCOMs and sCOMs ( i . e . graphs derived from the same network that generated the fCOMs but characterized by a high clustering coefficient , see Materials and Methods ) . Interestingly , although the clustering coefficient and the abundance of motifs were not anymore positively correlated in both rCOMs ( Fig 5B , gray ) and sCOMs ( Fig 5C , gray ) , we found that a subset of all tested templates ( Fig 5D ) occurred preferably in the fCOMs . Among these motifs , the correlation between abundance and clustering coefficient ( Fig 5A–5C , red dots ) , was always significantly positive ( red lines , Fig 5A–5C ) , independently from the compared null model . In particular , the overabundant motifs in the fCOMs were characterized by a significantly higher number of recurrent connections ( average clustering coefficient of 0 . 56±0 . 30 , Fig 5D positive indexes ) , whereas the motifs underrepresented in the fCOMs were mainly composed of simple connectivity paths ( average clustering coefficient of 0 . 19±0 . 27 , Fig 5D negative indexes ) . This result was robust across different simulated networks ( n = 20 ) , suggesting that the high recurrence in the structural connectivity is a fundamental property of the regions eliciting NBs . Next , we investigated possible mechanisms of NB generation associated with the structural properties of the fCOMs . Indeed , these regions may act as local amplifiers of spontaneously elicited spikes ( i . e . , Poisson spikes ) or fading activities deriving from previous activation of the network . We tested this hypothesis of local amplification in our model by perturbing regions of the network with mild subthreshold stimulations ( Fig 6A ) . Importantly , to assess differences among different network regions , we used a mild perturbation that did not ensure the generation of an NB , but rather , could be ineffective ( Fig 6B . 1 ) or effective ( Fig 6B . 2 ) at generating an NB . As shown in Fig 6B . 3 , the selected stimulation amplitude gave rise either to a small or large fraction of spiking neurons depending on the effective initiation of an NB but independent of whether the region was an fCOM . In a representative case ( see Fig 6C ) , we stimulated seven regions of the network described in Fig 4A . These regions consisted of four fCOMs ( i . e . , I , II , III and IV ) and three reference regions ( RI , RII , PII ) . The latter reference regions were chosen to test the effectiveness of a perturbation when delivered to the center , the border of the network ( RI versus RII ) or to a shifted position in an fCOM ( PII versus II ) . Each protocol of stimulation consisted in the perturbation of four target regions ( I-II-III-IV ) , ( I-II-IV-RII ) , ( I-IV-PII-RI ) and ( I-IV-PII-RII ) as depicted in Fig 6D . The simulation results show that the subthreshold stimulation delivered to the fCOMs had a significantly higher probability of evoking NBs than stimulations delivered to reference regions of the network ( Fig 6D ) . Moreover , as shown in the figure , the results indicate that to evoke NBs reliably , the stimulation has to be focused in the centre of the fCOMs ( the probability of evoking an NB by confining the stimulation to II is significantly higher than by delivering it to PII , RI and RII ) . Therefore , fCOMs are spatially selective to subthreshold stimulations . On the other hand , the number of reference regions probed can enhance the probability of evoking NBs in fCOMs ( c . f . r . I and IV across different paradigms of stimulation ) . Altogether , these results indicate that fCOMs are specialized regions of the network that can initiate NBs by locally amplifying background spiking activity . Finally , we investigated whether the spiking activity preceding an NB ( or pre-NB spikes ) could be predictive of the following NB , as previously suggested [39] . To compare the pre-NB spiking activity of different coordinated events , we isolated the largest spiking pattern ( see the Materials and Methods section ) shared by the pre-NB activity of each pair of NBs ( Fig 7A , number of shared spikes M = 12 ) . Illustrative examples of these temporal motifs shared by the pre-NB spiking activity of events in an NB cluster are reported in Fig 7B ( see S8 Appendix for additional examples ) . We found that the similarity measure of the temporal motifs ( see the Materials and Methods section ) computed for the pre-NB activity of an NB cluster was significantly higher than for the pre-NB activity of NBs belonging to different clusters ( Fig 7C ) . The similarity measure increased with the maximal size of the temporal motifs and reached a plateau at approximately M = 50 spikes ( Fig 7D ) . While most of the pre-NBs activities shared only from M = 6 to M = 20 spikes , some of them shared more than M = 40–50 spikes , suggesting that NBs might need the coordination of a different number of spiking neurons for their generation . Given the similarity of the pre-NB activities of events belonging to the same NB cluster , we used this measure of similarity to cluster the NBs rather than using the analysis of the CATs . We found that upon reordering the NB events with a hierarchical clustering algorithm and considering pre-NB activities ( Fig 7E ) , the similarity matrix exhibited a block structure similar to the one obtained with the clustering of the CATs ( c . f . Fig 2E ) . Next , to verify these computational findings in biological neuronal networks , we investigated whether stereotyped pre-NBs activities also characterize the initiation phase of NBs in our recordings . Consequently , we computed the similarity matrix of pre-NB activities on experimental recordings and , although less pronounced than in simulations ( Fig 7F ) , we also obtained a block structure similar to that obtained from the simulated activity ( Fig 7E ) . In summary , these results , obtained by simulation and verified experimentally , suggest that NBs are not induced by random spiking activity patterns in the fCOMs , but are rather induced by similar patterns of pre-NB spikes .
While changes in the dynamics of coordinated , spontaneous spiking activity have been investigated in different in vivo and in vitro neuronal systems , the mechanisms underlying the generation of these propagating waves of spikes still remain unclear . In this work , we investigated the generation of these events , or NBs , in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings , computational network modelling and the reduced complexity of these isolated 2D network models . As shown in our experimental results and in accordance with previous studies [15 , 18 , 37] , a cultured network expresses only a few classes of NBs , each one having a distinct and local IS . We found that the initiation sites of NBs correspond to the fCOMs of neurons where the strongest neuronal functional links of the network are clustered . The analysis of the structural connectivity in the computational model reveals that these regions initiating NBs in the network share similar connectivity motifs and have a higher level of recurrent local connections with respect to other similar regions of the network . We also show that the local connectivity properties of the fCOMs may enable these regions to act as amplifiers of the pre-NB spiking activity and that this property is most likely the underlying mechanism generating NBs . Indeed , when stimulated with sub-threshold stimuli , the fCOMs show a higher probability of generating an NB than any other equivalent sub-region of the network . Consequently , the fCOMs may initiate NBs by amplifying local asynchronous spiking activity , a concept similar to the ‘noise-focusing’ proposed in [19] . Interestingly , previous works [21 , 39] have indicated that NBs are preceded by the activation of a subset of overactive electrodes . As suggested by our simulations and confirmed with experimental data , our analysis of the pre-NB activity shows that NBs of the same class ( sharing a similar propagation trajectory and initiation site ) are indeed triggered by pre-NBs spiking patterns sharing similar temporal motifs . These patterns of spiking activity preceding NBs of the same class are not only spatially confined to the surround of the sites initiating an NB but also determine the following NB propagation . To perform this study , we developed a computational network model able to both qualitatively and quantitatively replicate the spontaneous network activity recorded in our experiments . This allowed us to study the properties of the regions initiating NBs with full access to the structural and functional neuronal connectivity . Previous computational modelling studies on cultured networks have shown that NBs can be robustly replicated using different cellular , synaptic and network settings [14 , 30 , 40 , 41 , 42 , 43] . However , none of these computational network models displayed spontaneously propagating NBs as obtained in our model . In our work , the simulated spontaneous activity of each neuron is induced by independent subthreshold synaptic inputs whose occurrence is modelled by a Poisson process . However , this process gives rise only to sparse single-neuron spikes in the network and it is not sufficient for the generation of coordinated spiking events . Here , we have exploited our model to investigate what allows a network to turn this sparse activity into travelling waves of spikes and to explain why NBs are always elicited from specific regions of the network as observed experimentally . Notably , by adopting rather simple and biologically plausible connectivity rules ( i . e . , the connectivity probability among neuron pairs decays with the relative distance ) , we found that the model is able to: i ) spontaneously express a few classes of propagating NBs as observed in in vitro experiments , ii ) fire asynchronous and synchronous spiking activities with statistical properties close to those of experimental recordings , and iii ) recapitulate previously reported experimental findings on cultures manipulated with AMPA , GABA and NMDA pharmacological blockers . The validation of the model against pharmacological manipulations also allowed us to evaluate the contribution of AMPA , GABA and NMDA synaptic currents in shaping the NB dynamics . In accordance with previously reported results , our data show that AMPA is required to drive the network from uncoordinated to coordinated firing regimes , while NMDA and GABA synaptic currents are mainly involved in shaping the NB dynamics . The inhibitory GABA current mainly regulates the duration , strength and frequency of NB occurrences . Interestingly , the shorter NB duration in the GABA-OFF condition , observed in both the in vitro experiments [17] and in the model , suggests that inhibition keeps the firing rate low and prevents a strong depression of the excitatory synapses . The excitatory NMDA , in turn , is a key player for the insurgence of the superburst firing regime . Our model suggests that the slow dynamics of the NMDA currents counteract synaptic depression and neuronal adaptation , indicating that the closure of a superburst event occurs when the latter two mechanisms prevail over the NMDA current . These results show , similar to previously reported in vivo [44] and in vitro [45] experimental data , that the NMDA current can sustain persistent activities in the network . Overall , our results suggest that the generation of events of coordinated spontaneous activity in a neuronal system might be related to the presence of fCOMs rising from the local and inhomogeneous connectivity of neurons in the network . This finding suggests that for a developing neural system that is able to express coordinated spontaneous activity , parallel to reshaping the cellular and synaptic properties [46] , forming regions in the network with local heterogeneities ( such as neuronal density or neuronal processes density ) that can establish a high degree of local recurrent structural connections might be very important . However , our considerations are derived from the study of in vitro neuronal cultures that certainly show a simpler organization compared to that of brain circuits developing in 3D . Given that 3D neuronal cultures have been shown to display spontaneous NBs [47 , 48] and considering that a 3D implementation of our model has been shown to allow for the interpretation of distinct spiking activity regimes characterizing 2D and 3D experimental preparations [47] , the generation of NBs in a 3D neuronal network might be investigated in future works . The results of the present study allow us to suggest that fCOMs of neurons may naturally emerge by following simple constraints of distance-based connectivity . However , to address this hypothesis with simulations , a probabilistic growth model [49] describing the critical parameters during the process of network formation should be developed in future work . Finally , the presented network model might be directly applicable to the interpretation of experimental recordings of spontaneous activity changes induced by neuroactive compounds or may provide complementary information on the integration at the cellular scale of electrical [50] or optogenetic [51] external stimuli .
All procedures involving experimental animals were approved by the institutional IIT Ethic Committee and by the Italian Ministry of Health and Animal Care ( Authorization number 110/2014-PR , December 19 , 2014 ) . Previous studies highlighted that the coordinated activities in cell cultures are determined by the chemical synapses and not by gap junctions or extracellular substances [56] . Therefore , we modelled the dynamics of excitatory AMPA and NMDA as well as inhibitory GABA chemical synapses . Synaptic transmission was delayed by a fixed time ( 0 . 5 ms ) to account for synapse activation and a variable delay ( maximum 1 . 5 ms ) to account for the propagation of the pre-synaptic spike . Each type of synapse contributed with a current Isyn modelled as follows: {Isyn=gsyn ( v−Erev ) τsyndgsyndt=−gsyn where gsyn is the synaptic conductance and Erev is its reversal potential . The synaptic conductance has a bi-exponential profile ( parameters in S3 Table ) : {τsyndgsyndt=−gsynτrisedgrisedt=−griseIsyn= ( gsyn−grise ) ( v−Erev ) Each time an action potential is delivered to a target neuron ( i . e . , a time tsp ) , the conductance parameters gsyn and grise are increased by g¯×y , where g¯ is the maximum value for the synaptic conductance and y is the fraction of the active resources ( i . e . , released neurotransmitters ) . The synaptic current exhibits short-term depression modelled under the assumption of finite synaptic resources [57]: {dxdt=zτrec−u⋅x⋅δ ( t−tsp ) dydt=−yτ1+u⋅x⋅δ ( t−tsp ) dzdt=yτ1−zτrec where x , y and z represent the fraction of available , active and recovered resources , respectively . The time constant τ1 regulates the transition between the available and active state , τrec is the recovery time constant and u represents the fraction of available resources transferred to the active ones , when the synapse is activated . The NMDA current was modelled similarly to the AMPA and GABA currents , with an additional magnesium block mechanism [58]: INMDA=gNMDA⋅ ( v−Erev ) ⋅1/ ( 1+exp ( − ( v−v0 ) /k0 ) ) where k0 = 6 mV ( steepness of voltage dependence ) and v0 = −40 mV ( half-activation potential ) . The maximum NMDA conductance ( g¯NMDA ) is written in terms of the AMPA conductance: g¯NMDA=KNMDA⋅g¯AMPA such that in basal/standard conditions KNMDA = 0 . 09 ( i . e . , g¯NMDA = 4 . 32 nS ) , and while under APV application , an NMDA antagonist , KNMDA = 0 . All parameter values are reported in S3 Table . To mimic the effects of the NMDA and GABA synaptic blockers ( APV and BIC ) , the conductance of the target receptor was set to zero . Networks with only the AMPA and GABA synapses are called AG-networks in the text , and networks with in addition the NMDA current are called AGN-networks . From its earliest days in vitro , cultured neuronal networks display random spontaneous spiking activity . In the model , this activity was mimicked by injecting sub-threshold synaptic noise ( i . e . , miniature events [56] ) modelled as independent Poisson processes at a mean frequency of 25 Hz . The summation of the synaptic noise occasionally brought the neurons to fire in the uncoupled network and it determined a background spiking activity of 0 . 010 ± 0 . 007 Hz ( close to the reduction of spiking activity found in experiments when AMPA receptors are blocked with CNQX [31 , 33] ) . Although network topology has been recognized to play an important role in determining network activity , many works have neglected the spatial constraints derived from the location of neurons in the network [42 , 43] ( see also S1 Appendix ) . To be comparable with our experimental recordings , 4096 neurons were uniformly distributed on a unit square , and the connectivity probability among the neurons depended on the distance according to a radial Gaussian function . The distance-based connectivity rule allowed for the creation of biologically inspired networks whose graph properties ( i . e . , clustering coefficient or the presence of shortcuts ) were not imposed but were rather inherited from the imposed spatial organization of neurons . Only graphs without any isolated component were used in the simulations . It is important to highlight that the random arrangement of neurons , the distance-based connectivity rule and the sparseness of the connections used to establish the network topology gives rise to an inhomogeneously connected network that includes clusters of nodes with denser connections ( see S2 Appendix for additional details ) . As a consequence , although the node degree is quite comparable between the Gauss and random graph , the clustering coefficient of the former is significantly higher than in the latter one , ( see S3 Appendix for additional details on clustering coefficient , link length and shortest path length [38] ) . The directionality of the synaptic connections between pairs of neurons was assigned with equal probability . Although bidirectional connections are quite common in the brain , computer simulations have shown that networks with only depressing synapses ( as assumed in this work ) tend to evolve unidirectional connections [59] . Regarding the connectivity of the network , in 2D cell cultures , each neuron receives somewhere from 150 to 400 synapses [60] . Because each neuron is contacted by eight synapses from the same neuron on average [61] , the actual effective synapses in the model were decreased to 41 . 6 ± 6 . 4 synapses per neuron . S1 Table summarizes the graph properties ( e . g . , clustering coefficient , mean path length , degree [62] ) of the simulated network . Note that neurons at the border of the domain were treated exactly like the other nodes and were consequently connected to a smaller number of neurons . The network model is available at doi:10 . 5061/dryad . 5k67r . A stimulation protocol was designed to probe the sensitivity of the modelled network to respond to local stimulations . To this aim , mild sub-threshold stimuli were delivered to specific sub-regions of the network ( e . g . , RI Fig 6C ) composed of ≃ 40 neurons . The stimulation consisted of Poisson spike trains at 1 Hz ( per neuron ) that activated currents with the same time course of AMPA receptors . In addition , to ensure that the stimulation was effectively confined to local sub-regions of the network , the conductance was imposed to decay from the centre of the sub-regions . That is , for d≤0 . 08 , ( 1− ( d20 . 08 ) ) gSTwithgST=45nS and for d > 0 . 08 , the conductance was gST = 0 nS . As depicted in Fig 6D , each stimulation protocol involved four different sub-regions of the network ( 100 stimuli per sub-region , maximum two non-fCOMs sub-regions ) , which were alternated with a pseudo-random sequence to minimize interferences among subsequent stimuli . The rationale for this choice was to probe reference regions of the network while continuously monitoring the success rate of eliciting NBs by stimulating the fCOMs . NBs occurring later than 150 ms from the stimulation were counted as spontaneous activity . To facilitate the comparison with the experimental data , the simulated spike trains were subjected to the same filtering criteria used in the experiments . Thus , only neurons whose firing rate ( i . e . , average number of spikes per unit time ) fell in the interval [0 . 1–15] Hz were considered for all subsequent analysis . In the manuscript , significant differences among normally distributed groups were evaluated through an independent or paired t-test , while a Mann-Whitney’s U test and Kolmogorov-Smirnov test were used for non-normally distributed samples . In the text and figures , the reported error bars are standard deviations . Emergent network activity can be explained to a large extent by the anatomical connectivity [38] . However , such activity can also be determined by the particular dynamical state of the network . Thus , an analysis of the statistical relationships between firing neurons can be informative of the information flow in the network . To determine the strength of the functional connections in the network , we performed a cross-correlation analysis [30] . Functional links were selected to meet two requirements . First , we considered the pairs of neurons whose cross-correlation peak was above the 95th percentile of all the computed cross-correlation values . Second , for each selected pair , we assigned a functional link every time that their cross-correlation peak was ranked in the top of the ten strongest correlation peak values for both neurons , thus determining a bi-directional relationship . The first condition avoids the inclusion of spurious functional connections in the analysis . The second condition reveals potential structural network motifs that determine synchronous activities in the network . The bi-directional functional connections clearly do not correspond to structural ones ( that are unidirectional , see Section Network Topology in the Materials and Methods section ) . Indeed , the functional graphs shared only 4 . 9 ± 1 . 8% of connections with the anatomical graph . However , these conditions allow for the determination of pathways of activity of neuronal pairs that receive similar inputs , either direct or indirect , from common firing neurons . The functional links with the longest connection ( longer than 0 . 2 , which roughly corresponds to 550 μm on the CMOS-MEA ) were discarded from the analysis . | Coordinated spontaneous spiking activity is fundamental for the normal formation of brain circuits during development . However , how ensembles of neurons generate these events remains unclear . To address this question , in the present study , we investigated the network properties that might be required to a neuronal system for the generation of these spontaneous waves of spikes . We performed our study on spontaneously active neuronal cell cultures using high-resolution electrical recordings and a computational network model developed to reproduce our experimental data both quantitatively and qualitatively . Through the analysis of both experimental and simulated data , we found that network bursts are initiated in regions of the network , or “functional communities” , characterized by particular local connectivity properties . We also found that these regions can amplify the background asynchronous spiking activity preceding a network burst and , in this way , can give rise to coordinated spiking events . As a whole , our results suggest the presence of functional communities of neurons in a developing neuronal system that might naturally emerge by following simple constraints on distance-based connectivity . These regions are most likely required for the generation of the spontaneous coordinated activity that can drive activity-dependent circuit formation . |
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Trypanosoma cruzi , the etiological agent of Chagas' disease , presents nutritional requirements for several metabolites . It requires heme for the biosynthesis of several heme-proteins involved in essential metabolic pathways like mitochondrial cytochromes and respiratory complexes , as well as enzymes involved in the biosynthesis of sterols and unsaturated fatty acids . However , this parasite lacks a complete route for its synthesis . In view of these facts , T . cruzi has to incorporate heme from the environment during its life cycle . In other words , their hosts must supply the heme for heme-protein synthesis . Although the acquisition of heme is a fundamental issue for the parasite’s replication and survival , how this cofactor is imported and distributed is poorly understood . In this work , we used different fluorescent heme analogs to explore heme uptake along the different life-cycle stages of T . cruzi , showing that this parasite imports it during its replicative stages: the epimastigote in the insect vector and the intracellular amastigote in the mammalian host . Also , we identified and characterized a T . cruzi protein ( TcHTE ) with 55% of sequence similarity to LHR1 ( protein involved in L . amazonensis heme transport ) , which is located in the flagellar pocket , where the transport of nutrients proceeds in trypanosomatids . We postulate TcHTE as a protein involved in improving the efficiency of the heme uptake or trafficking in T . cruzi .
Trypanosoma cruzi is the etiological agent of Chagas' disease or American trypanosomiasis . It is estimated that about 6 to 7 million people are infected worldwide , mostly in Latin America and southern states of USA , where Chagas’ disease is considered endemic . It is also becoming relevant in non-endemic regions due to migrations and the absence of control in blood banks and organ transplantation ( http://www . who . int/mediacentre/factsheets/fs340/en/ ) [1 , 2] . T . cruzi has a complex life cycle alternating between two hosts and displaying at least four developmental stages . In the mammalian host , it is present as intracellular forms , mainly amastigotes ( replicative stage ) or transiently as intracellular epimastigotes , and as bloodstream trypomastigotes ( non replicative , infective stage ) . In the insect vector , it is present as epimastigotes ( replicative stage ) and metacyclic trypomastigotes ( non-replicative , infective stage ) . During the different life-cycle stages , the parasite faces different environments and has to adapt its metabolism to the nutritional availability through the different hosts [3 , 4] . T . cruzi presents nutritional requirements for several nutrients and cofactors , and heme is included in this list . As other trypanosomatids responsible for human diseases ( T . brucei and Leishmania spp . ) , it lacks the enzymes for heme biosynthesis [5 , 6] . However these trypanosomatids present several heme-proteins involved in essential metabolic pathways like the biosynthesis of ergosterol , unsaturated fatty acids and mitochondrial cytochromes in the respiratory chain [7] . In this context , it is reasonable to hypothesize that T . cruzi must acquire heme from the extracellular environments along its life cycle . Several mechanisms have been proposed for heme transport in different organisms . In gram-positive and gram-negative bacteria , direct heme uptake , bipartite heme receptors or hemophore-heme uptake systems were described [8 , 9] . In eukaryotic organisms some proteins were found playing a role as heme transporters , but none of them showed to be homologous to those of prokaryotes . The complete characterization of heme transport and distribution in these organisms is a long way from being completely understood yet [10 , 11] . As mentioned before , only a small number of proteins were identified and characterized as heme transporters . It is worth mentioning CeHRG-1 and CeHRG-4 , both having a highly conserved function in modulating heme homeostasis in Caenorhabditis elegans [11–13] . CeHRG-4 was suggested to be involved in heme uptake by C . elegans intestinal cells and CeHRG-1 seems to mediate intracellular compartment heme delivery . Additionally , proteins involved in heme uptake have been identified in Leishmania spp . These proteins are the ABC transporter , ABCG5 , responsible for intracellular heme traffic derived from the hemoglobin breakdown [14] and LHR1 postulated as a plasma membrane heme transporter [15] . Besides , it was shown that T . cruzi epimastigotes’ proliferation depends on heme in a dose dependent manner and that the heme uptake might occur via a specific porphyrin transporter , possibly a member of the ABC-transporter family [16 , 17] . In the present work , we show for the first time that heme transport takes place in the replicative life-cycle stages: the epimastigotes occurring in the midgut of the insect vector and the intracellular amastigotes occurring in mammalian host-cells . Notably , heme uptake does not occur in the infective non-replicative stage trypomastigotes . In addition , we characterize a T . cruzi protein ( TcHTE ) , which promotes heme uptake when expressed in yeasts and improves the heme uptake/trafficking in T . cruzi epimastigotes . Remarkably , TcHTE is located in the flagellar pocket region , where it is postulated that nutrient transport takes place in trypanosomatids .
Dulbecco's Modified Eagle Medium ( DMEM ) was obtained from Life Technologies . Fetal Bovine Serum ( FBS ) was purchased from Natocor SA ( Córdoba , Argentina ) . δ-Aminolevulonic acid ( δ-ALA ) , Hemin ( Fe ( III ) protoporphyrin IX ) , Zn ( II ) mesoporphyrin IX ( ZnMP ) , Zn ( II ) protoporphyrin IX ( ZnPP ) , Sn ( IV ) mesoporphyrin IX ( SnMP ) and Ga ( III ) protoporphyrin IX ( GaPP ) were obtained from Frontier Scientific ( Logan , UT , USA ) . Hemin was dissolved in 50% ( v/v ) EtOH , 0 . 01 M NaOH at a final concentration of 1 mM , filtered using a 22 μm syringe disposable filter and it was stored at -80°C . ZnMP , ZnPP , SnMP and GaPP were dissolved in DMSO at a final concentration of 10–20 mM and were stored at -80°C . The working solutions were prepared dissolving the stock solution in 0 . 01 M NaOH at a final concentration of 1 mM . Escherichia coli strains used for all cloning procedures were grown at 37°C in Luria–Bertani medium supplemented with ampicillin ( 100 μg/mL ) or kanamycin ( 50 μg/mL ) as necessary . The yeast Saccharomyces cerevisiae cells ( hem1Δ ) were grown either in a rich medium ( YP , 1% yeast extract , 2% peptone ) , or in a synthetic complete medium ( SC ) lacking the appropriate nutrients for plasmid selection , supplemented with hemin or δ-ALA when it was necessary . In both cases , 2% Glucose ( Glc ) or 3% Glycerol–2% Ethanol ( Gly–EtOH ) were used as carbon sources . S . cerevisiae hem1Δ , MATα , ura3-52 , leu2-3 , 112 , trp1-1 , his3-11 , ade6 , can1-100 ( oc ) , heme1::HIS3 , used for heterologous complementation assays , was generously provided by Dennis R . Winge , University of Utah . The yeast cells were transformed using the lithium acetate method [18] . The Vero cell line ( ATCC CCL-81 , already available in our laboratory ) was routinely maintained in DMEM medium supplemented with 0 . 15% ( w/v ) NaHCO3 , 100 U/mL penicillin , 100 μg/mL streptomycin and 10% heat inactivated Fetal Bovine Serum ( DMEM 10% FBS ) at 37°C in a humid atmosphere containing 5% CO2 . During the T . cruzi infections , Vero cells were incubated in DMEM medium supplemented with 0 . 15% ( w/v ) NaHCO3 , 100 U/mL penicillin , 100 μg/mL streptomycin and 2% heat inactivated Fetal Bovine Serum ( DMEM 2% FBS ) at 37°C in a humid atmosphere containing 5% CO2 . The epimastigotes of T . cruzi ( Dm28c strain ) were maintained in mid-log phase by successive dilutions through Liver Infusion Tryptose ( LIT ) medium supplemented with 10% heat inactivated FBS ( LIT 10% FBS ) and 20 μM hemin [19] , at 28°C . Metacyclic trypomastigotes were obtained by spontaneous differentiation of Dm28c epimastigotes at 28°C . Cell-derived trypomastigotes were obtained after the infection of the monolayered Vero cells with metacyclic trypomastigotes . After two rounds of infections , the cell-derived trypomastigotes were used to perform analogs uptake assays or to infect Vero cells to obtain amastigotes . Intracellular amastigotes obtained 48–72 hours after the infection of the monolayered Vero cells were used for analogs uptake assay . The purity of all the obtained forms as well as their viability was evaluated by microscopic observation . The orthologous sequences of LHR1 in other trypanosomatids were obtained from TriTrypDB ( http://tritrypdb . org/tritrypdb/ ) [24] using LmjF . 24 . 2230 from Leishmania major as seed . Amino acids multiple sequence alignments were carried out using the version 2 . 0 of Clustal W and Clustal X software ( http://www . ebi . ac . uk/Tools/msa/clustalw2 , [25] ) . Transmembrane-domains were predicted with TMHMM software , accessible from ExPASy Bionformatics Resource Portal ( http://www . cbs . dtu . dk/services/TMHMM-2 . 0 , [26] ) . The epimastigotes were fixed with 500 μL of formaldehyde 3 . 7% ( w/v ) in PBS . Then they were washed twice with PBS , suspended in 40 μL of PBS , settled on polylysine-coated coverslips and mounted with VectaShield reagent . Also the trypomastigotes incubated with HAs were settled on polylysine-coated coverslips and mounted with VectaShield reagent . Additionally , the infected and non-infected Vero cells treated with HAs were mounted with VectaShield reagent . All the images were acquired with a confocal Nikon Eclipse TE-2000-E2 microscope containing the following fluorescent filter cubes: 99748-C55027 and 99749-C87049 , and using Nikon EZ-C1 software . The emission of the samples displaying red fluorescence ( treated with HAs or treated with secondary antibody conjugated with Alexa Fluor 555 ) was registered at 605/75nm . The emission of the samples containing GFP fusion proteins was registered at 515/30 nm . The emission of the samples treated with DAPI was registered at 450/30 nm . All the images were processed using the ImageJ software [21 , 22] . The heme content was quantified by the alkaline pyridine method [23] . 50–250 x 106 cells of T . cruzi epimastigotes were collected and washed twice with PBS . Samples were suspended in 500 μL with PBS and then combined in a 1 mL cuvette with 500 μL of a stock solution containing 0 . 2 N NaOH and 40% ( v/v ) pyridine . The oxidized and reduced ( by addition of sodium dithionite ) spectra were recorded between 450 nm and 700 nm . The 557 nm peak was identified in the differential -reduced minus oxidized- spectrum . The heme concentration was estimated using the molar extinction coefficient 23 . 98 mM−1 cm−1 . The experiments were performed by triplicate , and the data is presented as the mean ± SD ( standard deviation ) . All the assays were independently reproduced at least 2–4 times . Statistically significant differences between groups ( p values less than 0 . 05 ) were assessed using Mann Whitney U test ( intracellular heme content ) or Kruskal-Wallis one-way analysis of variance by ranks followed by Dunn's multiple comparison post-test ( HAs cytotoxicity effect on Vero cells ) , as appropriate ( GraphPad , version 3 . 0 ) .
As previously reported , the 10 minutes treatment with heme analogs ( SnPP , PdMP and ZnMP ) prevented heme incorporation by epimastigotes and competed with heme for its transport [17] . Therefore , it is rational to use this type of compounds to trace the heme transport in other life-cycle stages than epimastigotes . In this work we used the derivatives of protoporphyrin IX ZnPP ( Zn ( II ) protoporphyrin IX ) and GaPP ( Ga ( III ) protoporphyrin IX ) and the derivatives of mesoporphyrin IX ZnMP ( Zn ( II ) mesoporphyrin IX ) and SnMP ( Sn ( IV ) mesoporphyrin IX ) to trace heme transport along the different T . cruzi life-cycle stages . All these heme analogs ( HAs ) are fluorescent and mimic heme in structure ( S1 Fig ) . First , we confirmed HAs transport in the epimastigote stage , where exponential growth phase epimastigotes were incubated with the different HAs ( 100 μM in PBS ) for 5 minutes and HAs incorporation was analyzed by confocal microscopy ( Fig 1A ) . The confocal images revealed that ZnPP and ZnMP were clearly imported and maintained by the parasite while samples treated with GaPP and SnMP did not show the expected signal . To confirm this observation a fraction of treated epimastigotes were lysed and the soluble fraction ( total cell-free extracts ) was analyzed by direct FI measurements ( S1 Text and S2 Fig ) . Parasites treated with ZnPP , ZnMP and less extent with GaPP displayed a fluorescent signal , while those treated with SnMP did not show any ( Figs 1A and S2A ) . The viability of the treated epimastigotes was also evaluated , therefore aliquots of treated parasites were taken , washed and incubated in LIT 10% FSB plus hemin , and the cell growth was followed up for four days . We did not detect irreversible toxicity as consequence of the 5 minutes treatment with these HAs ( S2B Fig ) . As mentioned above , the fluorescent signal of the epimastigotes incubated with GaPP was weaker than those of ZnPP and ZnMP ( S2 Fig ) and was not detected by confocal microscopy ( Fig 1A ) . This fact could be due to three possibilities: 1 ) the analog could have been degraded , 2 ) the analog could have been exported back to the extracellular medium or 3 ) the GaPP transport was less efficient than the other HAs transport . To determine the possible GaPP degradation by any enzymatic activity ( like heme oxygenase activity ) , we evaluated the stability of this compound in presence of total cell-free extract of epimastigotes . The analog specific absorbance was followed up during 5 hours and no significant change on the acquired spectra was observed , ruling out the degradation of GaPP by intracellular components . Alternatively , it was possible to postulate that the observed low FI signal could be the result of an inefficient import process or a rapid export response , determining a stationary state with low intracellular concentrations of GaPP . On the other hand , it remains to be elucidated why SnMP is not accumulated into the cell at detectable level as other HAs . To explore if T . cruzi is able to import heme in other life-cycle stages , we evaluated the transport of these compounds by trypomastigotes using similar assays to those used for epimastigotes . In this case , cell-derived trypomastigotes were incubated 30 minutes with 100 μM of HAs and then analyzed by confocal microscopy . Surprisingly , a fluorescent signal was not observed in samples treated with any of the used HAs ( Fig 1B ) , suggesting that trypomastigotes did not import heme , at least under these experimental conditions . We were also interested in evaluating the heme transport in the intracellular T . cruzi amastigotes , the replicative life-cycle stage during the mammalian host infection . To accomplish this , we first evaluated the toxicity and transport of the HAs into the mammalian host cells . The Vero cells were treated with 100 μM of the analogs ( ZnPP , GaPP , ZnMP and SnMP ) and after 2 hours their viability and the HAs uptake were evaluated . No toxicity was detected in these experimental conditions ( S3 Fig ) , and 30 minutes of incubation were enough to allow the compounds to get inside and become available for the amastigotes ( Fig 2A ) . After that , non-infected and infected cells ( 2–3 days post infection ) were treated with 100 μM analogs for 30 minutes and analyzed by confocal microscopy ( Fig 2A and 2B ) . All HAs–including SnMP- produced a homogeneously distributed fluorescent signal inside the non-infected cells , showing that they were imported and maintained in the intracellular environment . However , when infected Vero cells were incubated with HAs , a completely different fluorescent pattern was observed ( Fig 2B ) . After the treatment with ZnPP , ZnMP and GaPP , they showed a punctuated fluorescent signal overlapping with the location of intracellular amastigotes . Interestingly , infected cells treated with SnMP showed the same fluorescent pattern as non-infected cells . These results strongly suggest that the HAs are being taken up by the cells and , except for SnMP , efficiently imported by T . cruzi amastigotes . Remarkably , intracellular amastigotes could discriminate between the assayed HAs , as previously observed for epimastigotes . After searching in T . cruzi databases , we identified a gene ( AYLP01000162 from Dm28c genomic sequence and TcCLB . 504037 . 10 and TcCLB . 511071 . 190 from CL Brener genomic sequence ) that encodes for a protein with a 55% sequence similarity to LHR1 , a heme transporter identified in L . amazonensis [15] and 38% with CeHRG-4 ( C . elegans protein [12] ) . The T . cruzi protein sequence was scanned in silico for transmembrane spanners in order to predict its secondary structure and our results indicated the presence of four probable transmembrane domains ( TMD ) as it was also predicted for LHR1 and CeHRG-4 . Besides their similarities–in sequence and predicted topology- two essential tyrosine residues present in LHR1 ( Y18 and Y129 [32] ) are conserved in the T . cruzi protein , which correspond to Y16 and Y127 following the T . cruzi protein numbering . However , another relevant tyrosine ( Y80 in LHR1 ) is substituted by a phenylalanine , F78 , in the T . cruzi protein . Nevertheless , none of these residues are conserved in the C . elegans protein CeHRG-4 ( S4 Fig ) . In the same way , other residues or motifs relevant for CeHRG-4 activity were not conserved in the T . cruzi protein . This evidence pointed out that the T . cruzi putative protein could play a role in T . cruzi heme transport therefore we decided to clone its gene for further characterization . The complete ORF was amplified , sequenced and cloned upstream of a sequence encoding for a C-terminal His-tag or a His-GFP-tag . To find the possible participation of this T . cruzi putative protein in heme transport we tested its activity in hem1Δ yeasts ( deficient in heme synthesis , [33] ) . Both constructs were cloned into a yeast expression vector , which were used to transform hem1Δ cells . The transformed yeasts were exposed to a selective medium supplemented with different hemin concentrations . The yeast cells expressing the T . cruzi gene ( both versions ) were able to grow at a lower hemin concentration compared to the control ( Fig 3 ) , indicating that , similarly to LHR1 [15 , 32] , the T . cruzi protein promoted heme transport by hem1Δ yeast cells ( Fig 3A ) and also allowed the recovery of the respiratory growth when a non-fermentable carbon source such as glycerol-ethanol was used ( Fig 3B ) . The presence of the T . cruzi protein ( 6His-GFP version ) in total cell extracts was verified by western blot analysis ( Fig 3C ) . Taking into account that heme uptake has been improved by the expression of the T . cruzi gene , we named its product TcHTE for T . cruzi Heme Transport Enhancer protein . The yeast cells expressing a functional TcHTE . 6His-GFP were used to analyze the protein location by confocal microscopy ( Fig 3D ) . The images confirmed that TcHTE . 6His-GFP is present in the plasma membrane , as it was expected for proteins involved in transport processes . Considering that the addition of 15–30 μM hemin is an essential requirement to maintain epimastigotes of T . cruzi in all axenic media used up to now , first we evaluated the parasite’s growth when the medium was supplemented with different concentrations of hemin . We did not observe differences in its growth ( S5 Fig ) or in the intracellular heme content when the epimastigotes were maintained with 5 or 20 μM hemin ( 3 . 572 ± 0 . 012 and 3 . 667 ± 0 . 129 nmol heme/109 parasites respectively , p>0 . 05 ) . After that , to unveil the role of TcHTE in T . cruzi , we analyzed the epimastigotes’ growth and heme content in parasites over-expressing the TcHTE gene . In this case , Dm28c . pLEW13 epimastigotes were transfected with the TcHTE6HIS-GFP versions of the gene cloned in the inducible T . cruzi expression vector pTcINDEX-GW or with the empty vector as control . After selection , the parasites containing the TcHTE . 6HIS-GFP gene or the empty vector were used to test their growth under different conditions of heme availability . First , these transfected parasites were kept for 72 hours in the absence of hemin , and then the cultures were either maintained without the addition ( 0 μM hemin ) or supplemented with 5 and 20 μM hemin . In all cases 0 . 5 μg/mL of tetracycline was added to induce the gene expression and the growth was monitored . We did not observe significant differences when these parasites were maintained in a medium supplemented with 5 μM or without hemin , suggesting that the induction of TcHTE . 6HIS-GFP gene expression did not alter the parasite growth in these conditions ( Fig 4A and 4B ) . Surprisingly , when parasite cultures were supplemented with 20 μM hemin , the growth was moderately impaired in epimastigotes expressing the TcHTE . 6HIS-GFP gene compared to the control as it is shown in Fig 4B . Also the intracellular heme content was quantified in epimastigotes maintained in 20 μM hemin , and it significantly increased from 3 . 1 ± 0 . 2 in the control to 5 . 3 ± 0 . 5 nmol heme/109 parasites in the epimastigotes expressing the TcHTE . 6HIS-GFP gene ( p<0 . 05 ) . Finally , we evaluated the location of the TcHTE . 6His-GFP protein . In that case , transfected parasites expressing TcHTE . 6HIS-GFP were analyzed by confocal microscopy . Most of GFP fluorescent signals were punctually located in a region corresponding to the flagellar pocket close to the flagellum emerging site , where it is postulated that metabolite transport processes take place in the T . cruzi ( Fig 5A ) . The TcHTE . 6His-GFP location was verified by the flagellar complex isolation assay where the GFP signal was observed next to the flagellar complex ( Fig 5B ) .
Trypanosoma cruzi requires a supply of heme like other trypanosomatids , which are auxotrophic for this cofactor . In view of these facts , it is postulated that T . cruzi must import it from the different hosts along its different life-cycle stages [7] . Since heme is a toxic molecule , it is accepted that these organisms must possess an essential and controlled pathway for its uptake and distribution [6 , 34] . Recently , several proteins were described as heme transporters or involved in heme transport in trypanosomatids [14 , 15] and other organisms with heme auxotrophy like C . elegans [35] . Among them , CeHRG-4 and CeHRG-1 proteins from C . elegans [12 , 13] and LHR1 from L . amazonensis , were described as proteins involved in heme transport or playing a role as a heme transporter . Also , LHR1 activity was relevant for the infectivity of L . amazonensis [32 , 36] . In addition , the activity of an ABC transporter–ABCG5- was reported as a heme transporter involved in the intracellular heme distribution in L . donovani [14] . Moreover , it was demonstrated that heme is relevant for T . cruzi epimastigote proliferation and that its uptake was inhibited by the treatment with ABC transporters’ inhibitors , indicating that heme transport should be dependent on an active transporter [16 , 17] . Nevertheless , up to date , the identity of the T . cruzi heme transporter was not unraveled . In this work we used different fluorescent heme analogs ( ZnPP , GaPP , ZnMP and SnMP ) to study heme transport along the different T . cruzi life-cycle stages . Only ZnPP , ZnMP , and to a lesser extent GaPP ( only detected by direct fluorescent measurements ) were accumulated inside the epimastigotes . The weak fluorescent signals detected in parasites incubated with GaPP could be a consequence of its slower uptake , its faster export , its degradation , or any combination of them . Since we were not able to detect GaPP degradation in the presence of total cell-free extracts we exclude this possibility . This reinforces the idea that the reduced intracellular concentration of GaPP might be a consequence of an unfavorable relationship between influx and efflux when compared to ZnMP and ZnPP . On the other hand , SnMP was not accumulated at detectable levels in epimastigotes . These results suggest that the HAs were selectively taken up by the T . cruzi epimastigotes . In spite of its relevance , no information was found , up to now , about heme transport in other life-cycle stages besides epimastigotes . Our results reveal that only replicative stages ( epimastigotes and amastigotes ) , but not infective stages ( trypomastigotes ) , were able to uptake heme . Moreover , based on our observations , we postulate that T . cruzi amastigotes are able to selectively withdraw most of the HAs ( ZnPP , ZnMP and GaPP ) available from their environment , the host cell cytosol . Apparently , SnMP did not accumulate in intracellular amastigotes as it was observed in epimastigotes . These results reinforce the hypothesis that T . cruzi presents an exclusive heme transporter that might discriminate between structurally related compounds . Remarkably , the heme uptake regulation during the life cycle together with the proposed selectivity of the heme transporter points that this process is critical , being very specific and tightly regulated . Recently , LHR1 was identified as a protein relevant for heme transport in L . amazonensis [32] . Therefore , we described a T . cruzi protein -herein named TcHTE- that shows sequence similarity to LHR1 and also with the C . elegans protein CeHRG-4 [12] . In spite of their sequence similarity and the conserved predicted topology , TcHTE , as well as LHR1 and CeHRG-4 , does not show any recognized domains or motifs that allow its classification as an active or ABC transporter [37 , 38] , as it was expected based on previous reported results [17] . Nevertheless , the hypothetical T . cruzi protein was characterized . TcHTE activity was first evaluated in the yeast hem1Δ ( HEM1 gene encodes for the 5-aminolevulenic acid synthase , the first enzyme in heme biosynthesis ) as it was previously used to assess the heme transport activity of CeHRG-4 and LHR1 proteins [13 , 15] . Since yeast exhibit a low affinity transport activity , hem1Δ cells can grow in a medium supplemented with high concentrations of hemin . The expression of the TcHTE gene enhanced heme transport and/or distribution in these yeast cells allowing their growth at lower hemin concentrations and also the recovery of the respiratory growth . Moreover , TcHTE was located on the yeast's plasma membrane as it was expected for a transporter protein , the same was observed with LHR1 [14 , 35] and Ce-HRG4 [13] . Nevertheless the results presented here do not allow us to discriminate if TcHTE is itself the heme transporter or if it is an essential protein that promotes/enhances the heme transport ( or trafficking ) activity . Finally , we explored the role of TcHTE in T . cruzi epimastigotes . We did not observe significant differences when epimastigotes overexpressing the TcHTE gene were grown in a medium supplemented with low hemin concentrations . However , when these epimastigotes were exposed to 20 μM hemin–the standard hemin concentration used in axenic medium- the presence of extra TcHTE turned out to be a disadvantage to maintain the optimal growth and an increment in the intracellular heme was observed . In the later case , the extra TcHTE could push on the heme uptake rendering more intracellular heme than the parasite is able to metabolize causing a mild negative effect on growth rates . These observations strongly suggest that TcHTE displays a role enhancing heme transport or trafficking . Additionally , epimastigotes transfected with a plasmid containing TcHTE . 6HIS-GFP were analyzed by confocal microscopy . The GFP fluorescent signal was located on the flagellar pocket region , similarly to the reported hemoglobin receptor of Leishmania donovani [39] . Surprisingly , TcHTE was not homogenously distributed along the plasma membrane or in internal vesicles as it was observed for LHR1 on L . amazonensis promastigotes [15] . These observations might indicate differences in heme transport between these trypanosomatids , which could be mediated by different transporters and/or involving different mechanisms . Although many transporters have been biochemically characterized in T . cruzi , their location remains unknown [4 , 40 , 41]; however it is established that the trypanosomatids’ transport proceeds through the flagellar pocket [39 , 42] . Two different mechanisms ( or pathways ) were proposed for heme acquisition by T . cruzi epimastigotes . One of them , probably via the endocytosis , is responsible for the hemoglobin import , and the other , a faster pathway ( few seconds ) , allows the transport of free heme ( followed by the fluorescent heme analog PdMP ) depending on an active transporter [16] . In both cases the fluorescent signal ( from the heme analog PdMP or hemoglobin-rhodamine ) was initially observed in the anterior part of the parasite ( on the flagellar pocket region ) , which corresponds with the cellular location for TcHTE protein . Although the TcHTE protein itself does not exhibit any distinctive signature allowing its classification as an active transporter , the results presented here allowed us to postulate that TcHTE plays a relevant role in promoting/enhancing the heme transport in order to regulate this activity . However the identity of the heme transporter still remains unknown . In summary , the results presented here show , for the first time , that T . cruzi transports heme along its replicative life-cycle stages , the epimastigote in the midgut of the insect vector and the intracellular amastigote in the mammalian host , but not in the infective form , the trypomastigote stage . Our findings revealed the existence of a protein transporter that might discriminate between structurally related compounds . Besides , we presented the first characterization of a T . cruzi protein , named TcHTE , which is conserved in trypanosomatids . We postulate that TcHTE plays a relevant role in T . cruzi heme transport/trafficking enhancement regulating this activity , without excluding it as part of or associated with the transporter complex . Considering the critical role of heme as an essential cofactor in T . cruzi , and the fact that its uptake inhibition results deleterious for the parasite , new approaches will be required for the complete identification of the molecular partners involved in its transport and distribution . Their identification and characterization might result in new molecular targets to inhibit parasite proliferation . | Trypanosoma cruzi is a protozoan parasite that causes Chagas’ disease , the most common parasitic disease in the Americas , but due to migration movements it has become relevant in non-endemic countries . T . cruzi , as other trypanosomatids responsible for human diseases ( T . brucei and Leishmania spp . ) , lacks the enzymes for heme biosynthesis . However , it presents several heme-proteins involved in essential metabolic pathways , like biosynthesis of ergosterol and unsaturated fatty acids and mitochondrial cytochromes in its respiratory chain . In this work , we used different fluorescent compounds , which are structurally similar to heme ( heme analogs ) , to explore the heme uptake in T . cruzi . The treatment of the different forms of T . cruzi with these compounds shows that this parasite is able to import heme during the replicative stages: epimastigote in the insect vector and intracellular amastigote in the mammalian host . We also characterized a T . cruzi protein , TcHTE , located in the flagellar pocket , where the transport of nutrients takes place in trypanosomatids . We propose TcHTE as a protein that improves the efficiency of the heme uptake . Considering the critical role of heme as an essential cofactor in T . cruzi , the identification of proteins involved in heme transport and distribution might result in the discovery of new molecular targets that may inhibit parasite proliferation . |
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Pheromones are secreted molecules that mediate animal communications . These olfactory signals can have substantial effects on physiology and likely play important roles in organismal survival in natural habitats . Here we show that a blend of two ascaroside pheromones produced by C . elegans males primes the female reproductive system in part by improving sperm guidance toward oocytes . Worms have different physiological responses to different ratios of the same two molecules , revealing an efficient mechanism for increasing coding potential of a limited repertoire of molecular signals . The endogenous function of the male sex pheromones has an important side benefit . It substantially ameliorates the detrimental effects of prolonged heat stress on hermaphrodite reproduction because it increases the effectiveness with which surviving gametes are used following stress . Hermaphroditic species are expected to lose female-specific traits in the course of evolution . Our results suggest that some of these traits could have serendipitous utility due to their ability to counter the effects of stress . We propose that this is a general mechanism by which some mating-related functions could be retained in hermaphroditic species , despite their expected decay .
Comprehensive understanding of any organism requires bridging the knowledge of laboratory biology and natural history [1] . These two approaches to the study of life are complementary and mutually reinforcing , since one offers a powerful methodology for detailed mechanistic investigation , while the other illuminates problems relevant in native evolutionary and ecological contexts . The two aspects of natural history that are often underemphasized in laboratory studies are variation in the environment and interactions among organisms . A paradigmatic example of a mechanistic study informed by the considerations of natural history is the analysis of dauer formation in C . elegans [2] . In response to high population density , paucity of food , and other noxious stimuli , L1-stage larvae can enter a morphologically distinct quiescent state that is alternative to reproductive development and is protective against harsh environments . Dozens of genes that control dauer decision have been identified; they connect several signaling pathways and other conserved molecular processes with a flexible reaction to changing environmental conditions . Pheromones , secreted substances that potentiate behavioral or physiological responses , play prominent roles in dauer decision as they communicate information about the number and possibly the status of other worms in the population [3 , 4] . We believe that detailed analyses of the physiological and molecular mechanisms that allow worms to cope with other stresses could similarly uncover important biological phenomena . In their native habitats , C . elegans likely experience frequent and substantial temperature fluctuations [5 , 6] . We have studied the mechanisms used by worms to thrive under these conditions [7 , 8] . Here we specifically focus on the role of pheromone-mediated communication among animals exposed to prolonged heat stress .
C . elegans hermaphrodites can recover reproductive ability following even a prolonged exposure to temperatures at which egg laying stops and none of the offspring survive [8] . Importantly , because C . elegans hermaphrodites are self-fertile , recovery of a single individual could be sufficient to re-establish a population . To explore this process further and to avoid the potentially confounding effects of population density [9–11] we tested the ability of singled hermaphrodites to recover ( Fig 1A ) . Only a small fraction of individuals recovered when kept alone ( Fig 1B ) . In contrast , when one hermaphrodite and one male experienced stress together , the probability of recovery increased more than two-fold . We have previously demonstrated that because the primary cause of fecundity loss post heat stress is sperm death , hermaphrodites recovered much better after mating with unstressed males [8] . In the experiments reported here , matings did indeed occur when males and hermaphrodites were stressed together or when unstressed males were added ( S1 Fig ) . Yet , broods of many of the recovered hermaphrodites lacked males , which are expected if male-supplied sperm yielded extra progeny . This suggested to us that in addition to male sperm , other causes contributed to the better reproductive recovery of hermaphrodites in the presence of males . Importantly , the physical presence of a male was not required to increase the probability of recovery–scent left on a plate was sufficient , that is , hermaphrodites recovered approximately twice as frequently when they experienced stress and recovery on male-scented plates . Hermaphrodite scent may also improve recovery , but only marginally ( Fig 1B ) . These results suggest that a secreted substance produced primarily by adult ( S2 Fig ) males conveys a signal that improves reproductive recovery from prolonged heat stress . We tested whether exposure to male scent only during stress or recovery had the same effect as continuous incubation on scented plates . Intermediate recovery levels under each of these treatments suggested that their effects were somewhat cumulative ( S3 Fig ) Ascaroside pheromones [3 , 4 , 12 , 13] are likely candidates to mediate this scent-improved recovery . No pheromones produced exclusively by individuals of either sex have been reported in C . elegans so far . However , two ascarosides , ascr#3 ( asc-ΔC9 ) and ascr#10 , are produced in different ratios by the two sexes– 4:15 in males and 7:2 in hermaphrodites , respectively [14 , 15] . Thus , sex-specific pheromone cocktails , rather than particular molecules , likely constitute the male and hermaphrodite scents in C . elegans . Genetic evidence suggests that sex-specific ascaroside signals are involved in reproductive recovery from heat stress . Loss-of-function of daf-22 , a gene encoding a β-ketoacyl-CoA thiolase [16] , abrogates excretion of shorter chain ascarosides , such as ascr#3 and ascr#10 [14 , 15 , 17] . The scent of daf-22 ( m130 ) mutant males was unable to improve recovery as much as the scent of wild type males ( Fig 1B ) . In contrast , loss of an acyl-CoA oxidase ACOX-1 inverts the ratio of ascr#10 to ascr#3 production in hermaphrodites to make ascr#10 even more prevalent than in males [14 , 15 , 18] . As expected , the scent of acox-1 ( ok2257 ) hermaphrodites was as potent as that of the wild type males in improving recovery ( Fig 1B ) . Because male scent improved recovery and ascr#10 is predominantly found in males , we expected that exposure of hermaphrodites to this molecule would increase the probability of fecundity recovery following heat stress . Surprisingly , hermaphrodites on plates scented with purified ascr#10 recovered no better than on control plates ( Fig 2 ) . In contrast , ascr#3 , the hermaphrodite-enriched pheromone , improved recovery significantly better , to a level comparable to that induced by the complete hermaphrodite scent . Because both males and hermaphrodites produce ascr#3 and ascr#10 , albeit in different quantities , we conducted recovery experiments using cocktails of these molecules . These were concocted to have ascaroside concentrations that matched those produced by a single live animal ( male or hermaphrodite ) in 24 hours , as reported by Izrayelit et al . [14] , because in our experiments single live animals were used to scent plates for 24 hours . Remarkably , simple mixtures of two ascarosides recapitulated the salutary activity of the complete male and hermaphrodite scents , respectively ( Figs 1B and 2 ) . The significant difference between the effects of “male” and “hermaphrodite” cocktails suggests that worms can discriminate ratios of ascr#3 and ascr#10 . Reinforcing this conclusion , we found that an equal mixture of these two ascarosides , an intermediate between the “male” and “hermaphrodite” cocktails , was no more potent than the “hermaphrodite” cocktail ( S4 Fig ) . The male scent must be quite strong since a single male can produce enough of it in as little as 16 hours ( S5 Fig ) . Although the amounts of ascarosides produced by individual animals were reported previously [14] , we examined the effects of a range of concentrations of these molecules on reproductive recovery . ascr#10 had no detectable effect on recovery even at concentrations ~100 times higher than physiological ( S6 Fig ) . In contrast , activity of ascr#3 was dependent on the concentration ( S6 Fig ) . At 2 fmol , the amount produced by a single male in 24 hours [14] , it had no discernable effect in our assay , whereas the effect of 10 fmol of ascr#3 ( with no ascr#10 added ) was indistinguishable from that of the hermaphrodite cocktail . Remarkably , when 2 fmol of ascr#3 was mixed with 7 . 2 fmol of ascr#10 , which alone did not improve recovery ( Fig 2 ) , this cocktail was as potent as the complete male scent ( Fig 2 ) . Why do C . elegans males produce a signal that promotes self-reproduction ( evident as improved recovery from heat stress ) , which presents a direct competition to their own reproductive success ? Why does the efficient recovery of hermaphrodite reproduction from stress rely on a signal produced by males , which comprise only ~0 . 1–0 . 5% of C . elegans populations in the wild [6 , 19] , likely making encounters between the sexes infrequent ? We hypothesized that these apparent paradoxes may have an explanation in the evolutionary origin of the C . elegans mode of reproduction . The few extant hermaphroditic species in the genus Caenorhabditis arose from gonochoristic ( male-female ) ancestors relatively recently [5] . It is reasonable to assume that in a species in which mating is essential for reproduction , a male-produced signal that alerts the female reproductive system and increases mating efficiency would be advantageous . Recently arisen hermaphroditic species , such as C . elegans , may have retained elements of this mechanism . We therefore tested whether the male-produced pheromone signal is species-specific . Consistent with the previous finding that ascaroside signaling is broadly conserved in nematodes [20] , we found that the scent of males of three species from the genus Caenorhabditis ( two gonochoristic and one hermaphroditic ) could rescue the reproductive performance of heat-stressed selfing C . elegans hermaphrodites as well as the scent of the conspecific males ( Fig 3 ) . We concluded that despite changes in the reproductive mode , C . elegans retained the male-specific pheromone signaling system that arose in its gonochoristic ancestor–males still produce the pheromone , while hermaphrodites can respond to it . We thought that a likely ancestral function of male pheromones was to facilitate reproduction following mating . For this reason , we compared brood sizes produced by females of a gonochoristic species C . remanei that were either exposed to male scent or maintained on control plates . Reports of the time for sperm transfer during mating in C . elegans vary from 4 seconds [21] to 90 seconds [22] with the majority of sperm transferred at the beginning of ejaculation . In addition to measuring the time of sperm transfer during mating ( about 17 seconds ) , Lebouef et al . [23] studied the refractory period in C . elegans males . They found that males fell into two classes–those with a relatively short refractory period between matings ( less than 9 minutes ) and those with a longer refractory period between matings ( greater than 9 minutes ) . Males that exhibited a short refractory time were unlikely to produce many progeny from a second mating because they could not produce fresh sperm during the shorter refractory period . We considered these facts and decided that a ten-minute mating period made it most likely that only one successful mating could take place . Incidentally , this period is approximately 1% of the time that is required for male scent to have an effect on the probability of hermaphrodite’s recovery ( S5 Fig ) . At the end of the ten minutes , if the mating pair was still together , the female was gently touched with a platinum wire . If the mating pair did not separate–indicating that mating was ongoing , that female was excluded from the data . As expected , we found that brood sizes of C . remanei females that had been exposed to male scent prior to mating were significantly greater than the brood sizes of mated naïve females ( Fig 3B ) . This result strongly argues that the function of the male-specific pheromones in the gonochoristic ancestors of C . elegans was to alert the female reproductive system and induce appropriate physiological changes that resulted in substantially increased reproductive efficiency upon mating . We tested whether the male scent affects brood sizes of C . elegans hermaphrodites reproducing via self-fertilization at non-stressful conditions ( 20°C ) , but found no detectable differences between control and male-scented plates ( Fig 4A ) . Next we wondered whether the two-fold more likely recovery from heat stress in the presence of male scent ( Fig 1B ) was accompanied by an increase in brood sizes . It was not–brood sizes of C . elegans recovering via selfing on male-scented plates were not significantly different from those of animals recovering on control plates ( Fig 4B and S7 Fig ) . We considered several possible physiological mechanisms by which pheromone signaling could have improved reproductive recovery following heat stress . Because hermaphrodites were exposed to the 29°C stress after gamete production irreversibly shifted from spermatogenesis to oogenesis , improved recovery in the presence of male scent could in principle be due to a ) improved sperm survival/retention of function , b ) better preservation of the “female” aspects of the reproductive system including oocytes or gonads , or c ) higher post-zygotic survival . Although the catastrophic reduction of brood sizes in hermaphrodites recovering alone from heat stress is primarily due to sperm loss [8 , 24 , 25] , the similar brood sizes of worms recovering on scented and control plates suggested that the increase in the probability of recovery induced by the male scent was due to a more efficient gamete utilization , not their improved survival or post-zygotic mechanisms . In addition to the catastrophic sperm loss , other elements of the reproductive system also suffered considerable damage during stress . For example , large concretions consisting of oocytes and embryos formed in the uterus [8] . Recovery required purging of these obstacles to allow live fertilized oocytes to pass . This and possibly recovery of the gonad itself required time . Both self recovery and recovery following mating with unstressed males did not yield live L1-stage progeny until ~72 hours post-stress , suggesting that first productive fertilizations occurred ~48–60 hours after the return from 29°C to 20°C ( S8 Fig ) . We have previously shown that C . elegans hermaphrodites are able to suppress ovulation when the temperature reaches 31°C and that this improves recovery when the worms are shifted to a more permissive temperature [8] . Therefore , we tested whether male scent suppressed ovulation at 29°C . The numbers of oocytes in the gonad and embryos in the uterus were likewise not appreciably different between these treatments ( S9 Fig ) . We concluded that other mechanisms were likely responsible for the improved recovery . One phenomenon related to gamete utilization that is modulated by ascarosides has been recently described in C . elegans [26–28] . Oocytes were shown to secrete a prostaglandin signal that improves sperm targeting to the spermatheca , an organ where fertilization occurs ( Fig 5A and 5B ) . We therefore tested whether sperm guidance was improved on male-scented plates . Using a strain in which sperm were marked with an mCherry reporter gene [29] ( recovery of this strain does not appear to be different from that of N2; S10 Fig ) , we measured the distribution of this fluorescent label in the reproductive tracts of hermaphrodites following heat stress . In worms recovering on control plates most sperm were trapped in the uterus ( Fig 5C ) , likely because continued ovulation under these conditions ( [8] and S9 Fig ) displaced sperm from the proximal gonad ( all spermatids remained in the gonad in the absence of ovulation at 31°C; see S11 Fig ) . In contrast , we saw evidence of significantly higher localization of sperm in the spermatheca and the proximal gonad in worms recovering on male-scented plates ( Fig 5E and 5G ) . In unstressed animals male scent did not appear to have a detectable effect on sperm guidance ( Fig 5D , 5F and 5H ) . We inferred that under these conditions , guidance of self sperm toward oocytes is sufficiently high and cannot be substantially improved by the pheromone signal . To confirm that the improved sperm guidance caused by the male scent was mediated by ascarosides , we tested sperm guidance on plates containing ascr#10 , ascr#3 , and their mixtures . Sperm guidance was significantly improved on male- but not hermaphrodite-specific cocktail , an effect that appears to be mediated by ascr#10 alone ( Fig 6A ) . Because ascr#3 did not have any discernable effect on sperm guidance and yet somewhat improved recovery ( Fig 2 ) , we wondered about its possible mode of action that was distinct from ascr#10 . To explore whether it had an effect on the female aspects of the reproductive system , we examined the rates of ovulation and egg-laying , but did not observe any difference between control animals and those on plates containing either ascaroside . We did , however , find that in the presence of ascr#3 many fewer animals had large concretions in the uterus during recovery than did animals on ascr#10 ( Fig 6B ) . Clearing of the reproductive tract is important for the passage of fertilized oocytes , suggesting that ascr#3 contributes to an improved recovery of fecundity via a mechanism distinct from that of ascr#10 . The DAF-7 ( a TGF-β-like ligand ) function in ASI neurons mediates multiple aspects of C . elegans response to the environment , including pheromones [30 , 31] . In particular , it plays an important role in connecting sperm guidance to environmental conditions [27 , 28] . daf-7 loss-of-function or high concentrations of dauer pheromones ascr#2 and ascr#3 disrupt sperm guidance [28] . We therefore explored the role of DAF-7 signaling on reproductive recovery from heat stress . As expected , we found that daf-7 ( e1372 ) mutants recovered no better in the presence of male scent than they did on control plates at the frequency comparable to that of N2 on control plates ( Fig 7 ) . When the daf-7 mutation was rescued by constitutively expressing DAF-7 in ASI neurons , recovery was significantly improved , although it still was insensitive to male scent . We interpret these results to mean that DAF-7 signaling is both necessary and sufficient for improved recovery and , in particular , DAF-7 inducibility is required for the response to male scent . A peculiar feature of the set up of this experiment offered us an additional insight into the complex nature of environmental influences on the reproductive system . The daf-7 ( e1372 ) allele is temperature-sensitive and worms have to be reared at the permissive temperature of 16°C past the L2-larval stage to avoid the constitutive dauer phenotype [30] . We suspected that this treatment–16°C until late L2 and 20°C until young adulthood–improved the probability of recovery from the 29°C stress ( compare daf-7 ( e1372 ) in Fig 7 and N2 on control plates in Fig 1B ) . We confirmed that the temperature treatment and not the genotype of the animals was responsible for the observed differences . The N2 worms reared under this regime recovered better than the animals grown constantly at 20°C , whereas daf-7 ( e1372 ) ; gpa-4p::daf-7 grown at 20°C recovered less well than when grown at 16°C until late L2 and at 20°C thereafter ( S12 Fig ) . Cooler temperatures during the first two larval stages evidently make some aspect ( s ) of the reproductive system more apt to recover from heat stress .
Our results support six conclusions . First , secreted compounds produced primarily by males improve female reproductive performance in a variety of species [32–34] . We showed that this is the case in Caenorhabditis nematodes–male scent potently affected different aspects of female/hermaphrodite reproductive physiology . In a gonochoristic species C . remanei this resulted in larger brood sizes . In a hermaphroditic C . elegans it conferred salubrious effects after heat stress . Whereas animals of both sexes produce pheromone cocktails containing multiple distinct molecules [13 , 14] , we showed that ascr#3 and ascr#10 promote efficient uterine clearing and improve sperm guidance , respectively . Second , the study of McKnight et al . [28] , which demonstrated the effects of environmental cues on sperm guidance , reported that “pheromones” inhibited proper targeting . Here we report an ostensibly opposite result of “pheromones” promoting sperm guidance . We think that two factors could readily explain this apparent contradiction . The pheromones used by McKnight et al . [28] were ascr#2 and ascr#3 ( also known as asc-C6-MK and asc–ΔC9 , respectively ) , two main components of the “dauer pheromone” [4 , 12] . In contrast , we tested ascr#10 and ascr#3 . The amounts of applied pheromones were also different . Whereas we tested concentrations as low as ~2–10 fmol , roughly corresponding to daily production of single animals [14] , McKnight et al . [28] reported results using 10 μmol . Taken together , these results suggest a model in which low , male-specific concentrations of ascarosides manipulate the hermaphrodite reproductive system in a way that potentiates improved sperm guidance , whereas high concentrations of hermaphrodite pheromones ( reflecting overcrowding ) disrupt sperm guidance . Third , the importance of relative concentrations of ascarosides is highlighted by the curious mode of action of ascr#3 and ascr#10 . In previously described synergistic interactions between ascarosides , each molecule had some effect , while the combined effect was greater [12 , 35 , 36] . The male-enriched ascr#10 alone has no discernable effect on the reproductive recovery from stress , whereas the hermaphrodite-enriched ascr#3 has a modest effect . This is all the more remarkable considering that the two molecules are nearly identical , the only difference between them being one double bond [14] . The synergy between these two molecules depends on their relative concentrations–a “male-like” ratio of ~15:4 ( ascr#10:ascr#3 ) greatly potentiates recovery , whereas a “hermaphrodite-like” ratio of ~2:7 ( ascr#10:ascr#3 ) is indistinguishable from the effects of ascr#3 alone . This implies that worms could discriminate not only the presence of specific pheromone molecules , but their ratios as well . This synergy appears to be mediated by the complementary action of two distinct mechanisms–clearing of the reproductive tract ( by ascr#3 ) and sperm guidance ( by ascr#10 ) . The phenotypic effects of other pheromone mixtures may be similarly complex . The relationships between concentration and activity may also be different for different functions mediated by ascr#3 and ascr#10 [4 , 10 , 20] . Still , for example , ascr#10 was active at concentration ~10 fmol in both mate holding [14] and increasing sperm guidance ( Fig 6A ) . Our findings expand the universe of functions previously ascribed to ascr#3 –in dauer formation [4] , in attracting males and repelling hermaphrodites [12] and ascr#10 –in attracting hermaphrodites and holding them in place [14] . Fourth , the results of McKnight et al . [28] and our data ( Fig 7 ) suggest that the DAF-7 TGF-β-like ligand in ASI neurons plays a critical role in conveying the signal to the reproductive system of , respectively , the dauer ascarosides ( ascr#2 and ascr#3 ) and male-specific cocktail of ascr#10 and ascr#3 . Previous studies documented DAF-7 functions in mediating response to dauer pheromone [30 , 31] and male sexual attraction to hermaphrodite pheromones [37] . Together these studies implicate DAF-7 in ASI neurons in mediating multiple aspects of pheromone-influenced behaviors . Fifth , C . elegans researchers are well familiar with the fact that cultivation conditions could have profound effects on animal physiology . Dauer formation in response to high density or paucity of food [2] and more subtle consequences of being raised in isolation [9] are prime examples of this . Our finding that male pheromones , even at femtomole concentrations , can have substantial effects on hermaphrodite reproduction raises a note of practical caution–the presence of males could change multiple aspects of hermaphrodite behavior and should thus be considered in experimental design . Finally , our results suggest a simple scenario for the evolution of this pheromone signaling system . Its original function in the ancestral gonochoristic species was to communicate the proximity of males to females , which facilitated reproductive success following mating ( Fig 3 ) . Transition to a self-fertile hermaphroditic mode of reproduction is expected to have changed selection pressures on sex-specific traits [38] . In particular , hermaphrodites do not need to locate mates for reproduction . Whereas Caenorhabditis hermaphrodites lost several “female” functions [39–41] , why did they retain the ability to respond to male-specific pheromones , which do not increase brood sizes produced by selfing , a predominant mode of reproduction in the wild [6 , 19] ? Temperature fluctuations likely routinely expose C . elegans to chronic heat stress in its natural habitats [6] . This results in drastically reduced brood sizes , but also increased incidence of males among the recovered offspring [42–44] . Hermaphrodites that retained the ability to respond to male pheromones would therefore gain a substantial advantage because of a greatly increased probability of reproductive recovery . Other female-specific functions in recently evolved hermaphroditic species could also have been preserved by co-option due to their serendipitous ability to counteract the effects of stress .
N2 C . elegans WT , DR476 daf-22 ( m130 ) II , VC1785 acox-1 ( ok2257 ) I , EG4883 oxIs318[pCFJ167 ( Pspe-11::mCherry::histone–Cbr-unc-119 ( + ) ) ] II unc-119 ( ed3 ) II , CB1372 daf-7 ( e1372 ) III , DA2202 daf-7 ( e1372 ) III; adEx2202[gpa-4::daf-7 + rol-6p::GFP] , AF16 C . briggsae WT , EM464 C . remanei WT , PB2801 C . brenneri WT . All strains were maintained at 20°C under standard conditions [45] , except daf-7 ( e1372 ) and DA2202 , which were maintained at both 16°C and 20°C for different experiments . Synchronized cultures of L1 larvae were prepared by hypochlorite treatment of gravid hermaphrodites [46] . The liberated eggs were allowed to hatch in M9 Buffer overnight and the arrested L1 larvae were plated the next morning . The time that L1 worms were deposited on plates was noted as “0 hours post L1 arrest” . Between 30 and 50 L1 larvae were transferred to each lawn plate of E . coli OP50 and hermaphrodites were kept on these small population plates until just before young adulthood ( generally , 48 hours post L1 arrest at 20°C ) . In a typical experiment , to assess one condition , 25 or 50 hermaphrodites were singled onto plates just after the L4/adult molt ( around 46 hours post L1 arrest ) for each condition being assayed . Experiments contained multiple conditions ( including the control ) that were tested at the same time for a total of 100 to 125 plates–each containing a single hermaphrodite . These plates were banded together in stacks of five , placed in shoeboxes , and shifted at 48 hours post L1 arrest to 29°C for 24 hours . When the experiment called for male and hermaphrodite worms to be stressed together , age-matched pairs of males and hermaphrodites were used . After 24 hours of heat stress , the worms were returned to 20°C and allowed to recover . The numbers of eggs , both fertilized and unfertilized , and larvae produced by each worm were recorded daily . Worms were deemed to have recovered fecundity if they produced live progeny in the first 120 hours of recovery . See S1 Table for raw experimental data including numbers of independent trials and worms tested in each trial . C . remanei and C . brenneri produce large numbers of males . In other strains , males were generated by subjecting mid-L4 hermaphrodites to heat stress at 31°C for 3 hours and subsequently maintained by mating . To scent plates , males were segregated from hermaphrodites as L4 larvae and singled as one-day-old virgin adults . Unless otherwise noted , males were left on plates for 24 hours to deposit a scent and subsequently removed . Wild type and acox-1 hermaphrodites aged 24 hours post L1 arrest were used to condition plates until 52 hours post L1 arrest . This time span corresponds to the developmental stages from mid L3 larvae to young adult [47] . Purified ascr#3 and ascr#10 were a generous gift from Frank C . Schroeder ( Cornell University ) . For recovery of fecundity experiments , ascarosides were diluted in 10% ethanol and applied to Noble agar ( US Biological ) NGM plates in a total volume of 100 uL . The concentrations listed represent the total amount of ascaroside applied to each plate . A 10% solution of ethanol alone was used as the control . The diluted ascarosides were spread on the plate with a glass rod and allowed to absorb into the agar overnight at 20°C . The next day , these plates were seeded with a 1:100 dilution of an overnight culture of OP50 bacteria and incubated at 20°C . The plates were used the following morning in heat stress and recovery experiments as described above . CB1372 and DA2202 strains were treated in the same manner . A one-hour egg lay was used to produce synchronous populations that were maintained at 16°C for 54 hours–until the worms were in the mid L3 larval stage . They were next transferred to 20°C for 16 hours until the young adult stage equivalent to that of N2 worms ( raised at 20°C ) at 48 hours post L1 arrest . This was confirmed by counting oocytes in the gonad [8] . Supplemental experiments used only DA2202 raised until young adulthood at 20°C ( S12 Fig ) . For experiments using total male scent , a synchronized population of EG4883 hermaphrodites [29] was raised on small population plates at 20°C until they were 48 hours post L1 arrest . Worms were transferred to either control plates or plates conditioned with four young males . For heat stress experiments , small population plates were shifted to 29°C for 24 hours . For sperm guidance experiments with single ascarosides and cocktails of two ascarosides , the ascarosides were diluted in water and hermaphrodites were singled onto prepared plates in the same manner as heat stress and recovery experiments . These were single-blind experiments . In all sperm guidance experiments , worms were immobilized with sodium azide and mounted on 2% agarose pads . Images were taken with a Retiga 2000R camera mounted on a Leica DM5000B compound microscope and analyzed using ImageJ software . Individual images were stitched together using the MosaicJ plug-in [48] . Analyze Particles was used to count fluorescent sperm . An automatic thresholding program ( MaxEntropy ) [49] was used to determine image thresholds . L4 C . remanei males were isolated from a mixed population and allowed to develop at 20°C overnight , after which they were used to condition half of the prepared mating plates ( NGM plates with 5uL of a 1:100 dilution of an overnight culture of OP50 kept at 20°C overnight ) for 24 hours . From a synchronous population ( ~40 hours post L1 arrest ) , females were moved to separate plates in groups of 20–30 . At 48 hours post L1 arrest , they were singled onto either control or male-conditioned plates . Matings started 16 hours later , to ensure that both males and females were receptive . A single male was placed on top of an age-matched female in the shape of an X . Matings were short ( 10 minutes ) to make multiple matings unlikely [23] and to ensure that males did not have a chance to deposit much scent on plates ( S5 Fig ) . After 10 minutes , males were removed and the females were examined for the presence of a copulatory plug [50 , 51] . All females , whether or not a copulatory plug was detected , were kept at 20°C and transferred to fresh , seeded NGM plates daily; fertilized eggs and larvae were counted . | The Caenorhabditis elegans metabolome contains over a hundred ascaroside molecules . Most of them have no known function , or no function at all , but some act as pheromones . Two of these molecules , ascr#10 and ascr#3 , are produced in different proportions by males and hermaphrodites . We report that when a hermaphrodite senses a male-specific mixture of these molecules , it changes several aspects of its reproductive physiology , including signaling that guides sperm toward oocytes . During evolution from an ancestor that had both males and females , C . elegans hermaphrodites lost several female-specific traits , but their reproductive system retained the ability to respond to male pheromones . This greatly aids them during recovery from heat stress . We suggest that serendipitous side benefits of female-specific traits could be a general cause of their retention during evolution . |
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How organisms determine particular organ numbers is a fundamental key to the development of precise body structures; however , the developmental mechanisms underlying organ-number determination are unclear . In many eudicot plants , the primordia of sepals and petals ( the floral organs ) first arise sequentially at the edge of a circular , undifferentiated region called the floral meristem , and later transition into a concentric arrangement called a whorl , which includes four or five organs . The properties controlling the transition to whorls comprising particular numbers of organs is little explored . We propose a development-based model of floral organ-number determination , improving upon earlier models of plant phyllotaxis that assumed two developmental processes: the sequential initiation of primordia in the least crowded space around the meristem and the constant growth of the tip of the stem . By introducing mutual repulsion among primordia into the growth process , we numerically and analytically show that the whorled arrangement emerges spontaneously from the sequential initiation of primordia . Moreover , by allowing the strength of the inhibition exerted by each primordium to decrease as the primordium ages , we show that pentamerous whorls , in which the angular and radial positions of the primordia are consistent with those observed in sepal and petal primordia in Silene coeli-rosa , Caryophyllaceae , become the dominant arrangement . The organ number within the outmost whorl , corresponding to the sepals , takes a value of four or five in a much wider parameter space than that in which it takes a value of six or seven . These results suggest that mutual repulsion among primordia during growth and a temporal decrease in the strength of the inhibition during initiation are required for the development of the tetramerous and pentamerous whorls common in eudicots .
How to determine the numbers of body parts is a fundamental problem for the development of complete body structures in multicellular organisms . Digit numbers in vertebrates are evolutionarily optimized for the specific demands of the organism [1]; the body-segment number in insects is constant despite the evolutionarily diversified gene regulation in each segment [2–4]; and five petals are indispensable to forming the butterfly-like shape that is unique to legume flowers [5] . Studies of animal structures , such as vertebrate limbs and insect segments , strongly suggest that crosstalk between pre-patterns ( e . g . , morphogen gradients ) and self-organizing patterns underlies the developmental process of organ-number determination [6–13] . In plant development , a self-organization based on the polar transport of the phytohormone auxin [14–16] is conserved among seed plants [17] and seems to be the main regulator of the development of a hierarchal body plan , called a shoot , consisting of a stem and lateral organs such as leaves . The number of concentration peaks in most self-organizing patterns , such as Turing pattern and the mechanisms proposed for plant-pattern formation , is proportional to the field size [15 , 18 , 19] . Despite having a diversified field size for floral-organ patterning , the eudicots , the most diverged clade among plants , commonly have pentamerous or tetramerous flowers containing five or four sepals and petals ( the outer floral organs ) , respectively , and rarely have other numbers of organs [20 , 21] . Here , we focus on the developmental properties that so precisely and universally determine the floral organ numbers through self-organizing processes . Phyllotaxis , the arrangement of leaves around the stem , provides insight into floral development , because studies of floral organ-identity determination [22] have verified Goethe’s foliar theory , which insists that a flower is a short shoot with specialized leaves [23] . Phyllotaxis is mainly classified into two types: spiral phyllotaxis , which has a constant divergence angle and internode length , and whorled phyllotaxis , which has several leaves at the same level of a stem [24] . For spiral phyllotaxis , Hofmeister described a hypothesis of pattern formation in 1868 [24] , which we summarize in three basic rules: the time periodicity of primordia initiation , the initiation of a primordium at the largest available space at the edge of the meristem ( the undifferentiated stem-cell region ) , and the relative movement of primordia in a centrifugal direction from the apex due to the growth of the stem tip . Following that hypothesis , numerous mathematical models incorporating contact pressure [25 , 26] , inhibitor diffusion [27] , reaction-diffusion [18 , 28] , and mechanical buckling of the epidermis [29 , 30] were proposed to explain the observed phyllotactic patterns . Over the past ten years , these mathematical models were tested and interpreted in light of modern molecular biology . Several studies have suggested that the competitive polar transport of the auxin accounts for two of Hofmeister’s rules , the periodicity of initiation and the initiation at the largest space , which together are capable of reproducing both spiral phyllotaxis and whorled phyllotaxis [15 , 16 , 31] . Despite their simple rules and uncertain molecular basis , the phyllotaxis models can account for several of the quantitative properties observed in organ patterning . For example , one model showed that the divergence angle between successive leaves is 180 degrees for the first and second leaves , 90 degrees for the second and third leaves , and oscillating thereafter , converging to the golden angle , 137 . 5 degrees , which agrees with the phyllotaxis of true leaves in Arabidopsis thaliana after the two cotyledons [32 , 33] . Similar oscillatory convergence to a particular divergence angle occurs in the sepal primordia of the pentamerous flower of Silene coeli-rosa , Caryophyllaceae . In S . coeli-rosa , the divergence angle is 156 degrees at first , and then it oscillates , converging on 144 degrees [34] . The golden angle also appears in the floral organs of several Ranunculaceae species [35 , 36] . The agreements between the phyllotaxis models and actual floral development suggest that mathematical models can give useful clues to the underlying mechanisms of not only phyllotaxis but also floral organ patterning . There are at least three fundamental differences , however , between real floral development and the phyllotaxis models . The first difference is the assumption of constant primordium displacement during tip growth , which comes from Hofmeister’s hypothesis and has been incorporated into most phyllotaxis models . Although the helical initiation has been thought to always result in spiral phyllotaxis , many eudicots form the whorled-type sepal arrangements in their blooming flowers subsequent to helical initiation [37] ( Fig 1; e . g . , Caryophyllaceae [34] , Solanaceae [38] , Nitrariaceae [39] , and Rosaceae [40] ) . The remnants of helical initiation are more obvious in the pseudo-whorls ( e . g . , Ranunculaceae [41] ) , where the distance between each organ primordium and the floral center varies slightly even in the whorls of mature flowers , which usually have more varied floral organ numbers [20 , 35] , suggesting that post-meristematic modifications of primordia positions [42] play an essential role in generating the whorled arrangement and determining the floral organ number during floral development . In contrast , most phyllotaxis models have assumed constant growth of the primordia , so that the whorls appear only after the simultaneous initiation of several primordia [19] . The second difference comes from the fact that floral development is a transient process , whereas most phyllotaxis models have focused on the steady state of the divergence angle . Although the golden angle ( 137 . 5 degrees ) is quite close to the inner angle of regular pentagon ( 144 degrees ) , the developmental convergence from 180 degrees ( cotyledon ) to 137–144 degrees in phyllotaxis requires the initiation of more than five primordia , both in A . thaliana leaves and in the mathematical models [16 , 33] . In contrast , the divergence angle between the second and third sepal primordia in pentamerous eudicot flower development is already close to 144 degrees [34] . The third difference comes from the accuracy of the floral organ number in many eudicots . Although the polar auxin-transport model reproduced both wild-type and mutant A . thaliana floral organ positioning [43] , the organ number in the model was more variable , even with an identical parameter set ( Fig 3 in [43] ) , than that in experimental observations ( Table 1 in [44] ) . Moreover , among eudicot species , the appearance of pentamerous flowers is robust , despite the diversity of the meristem size and the outer structures , including the number and position of outside organs such as bracts [20] . Together , the differences between real floral development and previous phyllotaxis models indicate that floral development requires additional mechanisms to determine the particular organ number . To resolve the inconsistencies between the earlier models and actual floral development , we set out a simple modeling framework , integrating Hofmeister’s rules with two additional assumptions , namely , the repulsion between primordia that can repress primordium growth and the temporal decrease in initiation inhibition of new primordium , which were proposed independently in the contact pressure model [25 , 45 , 46] and the inhibitory field model [33 , 47 , 48] , respectively , for phyllotaxis . First , when we incorporated mutual repulsion among primordia into the growth process , a whorled-type pattern emerged spontaneously following the sequential initiation of primordia . The mutual repulsion obstructed the radial movement of a new primordium after a specific number of primordia arose , causing a new whorl to emerge . The number of primordia in the first whorl tended to be four or eight . Second , when we assumed that older primordia have less influence on the initiation of a new primordium , the pentamerous whorl arrangement , which is the most common arrangement in eudicot flowers , became dominant . We analytically show the conditions for the development of tetramerous and pentamerous whorls , and we predict possible molecular and physiological underpinnings .
Following the earlier models [49] , we represented the meristem as a circular disc with radius R0 and the primordia as points ( Fig 2A ) . A new primordium arises at the point along the edge of the meristem ( R0 , θ ) , in polar coordinate with the origin at the meristem center , where θ gives the minimum value of the inhibition potential Uini . As one of the simplest setups for sequential initiation [37] , we followed the assumption of earlier models for spiral phyllotaxis [49] , which state that new primordia arise sequentially with time intervals τ , as opposed to the simultaneous initiation studied previously for whorled phyllotaxis [19] ( Fig 1 ) . Although the structures outside of the flower , such as bracts and other flowers , as well as the position of the inflorescence axis , may affect the position of organ primordia , the pentamerous whorls appear despite their various arrangement [20] . Therefore , as the first step of modelling of floral organ arrangement , we assumed that whorl formation is independent of any positional information from structures outside of the flower . Thus , we calculated the inhibition potential only from floral organ primordia which are derived from a single floral meristem . The potential functions for the initiation inhibition by preexisting primordia have been extensively analyzed in phyllotaxis models [16 , 47 , 49] . The potential decreases with increasing distance between an initiating primordium and the preexisting primordia account for the diffusion of inhibitors secreted by the preexisting primordia [27 , 50] , and the polar auxin transport in the epidermal layer , as proposed in previous models of phyllotaxis [15 , 16 , 31] and the flowers [43] . We employed an exponential function exp ( −dij/λini ) as a function of θ , where dij denotes the distance between a new primordium i and a preexisting primordium j at ( rj , θj ) as d i j = R 0 2 + r j 2 - 2 R 0 r j cos ( θ - θ j ) ⋅ ( 1 ) The function decreases spatially through the decay length λini exponentially , induced by a mechanism proposed for the polar auxin transport , i . e . , the up-the-gradient model [15 , 16] . Up-the-gradient positive feedback amplifies local auxin concentration maxima and depletes auxin from the surrounding epidermis , causing spatially periodic concentration peaks to self-organize [15 , 16] and thus determine the initiation position of the primordia [51] . The amplification and depletion work as short-range activation and long-range inhibition , respectively [52] , which are common to Turing patterns of reaction-diffusion systems [18] . Since the interaction of local maxima in the reaction-diffusion systems follows the exponential potential [53 , 54] , the up-the-gradient model likely explains the exponential potential between the auxin maxima , while the rigorous derivation requires further research . The decay length λini depends not only on the ratio of the auxin diffusion constant and the polar auxin-transport rate [15] but also on other biochemical parameters for polar transport and the underlying intracellular PIN1 cycling [55] . Another mechanism , referred to as the with-the-flux model [56 , 57] , has been proposed for the polar auxin transport . Although with-the-flux positive feedback can also produce spatial periodicity , the primordia position corresponds to auxin minima [57] , which is inconsistent with observations [51] . On the other hand , the with-the-flux mechanism can explain auxin drain from the epidermal layer of the primordia to internal tissue [58] . Since the drain gets stronger as the primordia mature [58 , 59] , the auxin drain could cause decay of the potential depending on the primordia age . The auxin decrease in maturing organs can also be caused by controlling auxin biosynthesis [60 , 61] . Therefore , we integrated another assumption , namely that the inhibition potential decreases exponentially with the primordia age at the decay rate α ( Fig 2B ) . Temporally decaying inhibition was proposed previously to represent the degradation of some inhibitors [47 , 48] and account for various types of phyllotaxis by simple extension of the inhibitory field model [33] . Taken together , the potential at the initiation of the i-th primordium is given by U i n i ( θ ) = ∑ j = 1 i - 1 exp ( - α ( i - j - 1 ) ) exp ( - d i j λ i n i ) ⋅ ( 2 ) Most phyllotaxis models have assumed , based on Hofmeister’s hypothesis , that the primordia move outward at a constant radial drift depending only on the distance from the floral center without angular displacement , which makes helical initiation result in spiral phyllotaxis [49] . Here , we assumed instead that all primordia repel each other , even after the initiation , except for movement into the meristematic zone ( Fig 2C ) following observation of the absence of auxin ( DR5 expression ) maxima at the center of the floral bud ( e . g . , [62] ) . Even at the peripheral zone away from the meristem , the growth is not limited . Hence there is no upper limit for the distance between primordia and the center . The repulsion exerted on the k-th primordium is represented by another exponentially decaying potential when there are i primordia ( 1 ≦ k ≦ i ) : U g , k ( r , θ ) = ∑ j = 1 , j ≠ k i exp ( - d k j λ g ) , ( 3 ) where the decay length , introduced as λg , can differ from λini . The primordia descend along the gradient of potential Ug to find a location with weaker repulsion . The continuous repulsion can account for post-meristematic events such as the mechanical stress on epidermal cells caused by the enlargement of primordia [63 , 64] or the gene expression that regulates the primordial boundary [42] . The present formulation ( Eq 3 ) is similar to the contact pressure model , which has been proposed for re-correcting the divergence angle after initiation [25 , 45 , 46] . Another type of post-initiation angular rearrangement has been modeled as a function of the primordia age employed as i −j −1 in the present model ( Eq 2 ) and the distance between primordia with some stochasticity [65] . Eq 3 accounts for not only the angular rearrangement but also the radial rearrangement with stochasticity in both directions as will be described in the next subsection . We modeled the initiation process numerically by calculating the potential Uini ( Eq 2 ) for angular position θ incremented by 0 . 1 degree on the edge of the circular meristem . We introduced a new primordium at the position where the value of Uini took the minimum , provided that the first primordium is initiated at θ = 0 . We modeled the growth process by using a Monte Carlo method [66] to calculate the movement of primordia in the outside of the meristem depending on the potential Ug , k ( Eq 3 , Fig 2C ) . After the introduction of a new primordium , we randomly chose one primordium indexed by k from among the existing primordia and virtually moved its position ( rk , θk ) to a new position ( r k ′ , θ k ′ ) in the outer meristem ( r k , r k ′ ≥ R 0 ) . The new radius r k ′ and the angle θ k ′ were chosen randomly following a two-dimensional Gaussian distribution whose mean and standard deviation were given by the previous position ( rk , θk ) and by two independent parameters , ( σr , σθ/rk ) , respectively . Whether or not the k-th primordium moved to the new position was determined by the Metropolis algorithm [66]; the primordium moved if the growth potential ( Eq 3 ) of the new position was lower than that of the previous position ( i . e . , U g , k ( r k ′ , θ k ′ ) < U g , k ( r k , θ k ) ) . Otherwise , it moved with the probability given by P M P = exp ( - β Δ U g ) , ( 4 ) where Δ U g = U g , k ( r k ′ , θ k ′ ) − U g , k ( r k , θ k ) and β is a parameter for stochasticity . This stochasticity represents a random walk biased by the repulsion potential . A case PMP = 0 represents that primordia movement always follows the potential ( ΔUg < 0 ) . The first primordium stays at the meristem edge r = R0 until the second one arises when PMP = 0 because the growth potential is absent , while it can move randomly outside of the meristem when PMP ≠ 0 . To maintain the physical time interval of the initiation process at τ steps for each primordium , the number of iteration steps in the Monte Carlo simulation during each initiation interval was set to iτ , where i denotes the number of the primordia . We also studied the movement following Ug by numerical integration ( fourth-order Runge-Kutta method ) of ordinary differential equations to confirm the independence of the numerical methods ( S1 Fig ) . All our programs were written in the C programming language and used the Mersenne Twister pseudo-random number generator ( http://www . math . sci . hiroshima-u . ac . jp/m-mat/MT/emt . html ) [67] . Because the initiation time interval is constant , one possible scenario for forming a whorled pattern should involve decreasing or arresting the radial displacement of primordia ( Fig 1 , forth row ) . Therefore , we focused on the change in radial position and velocity to find the whorled arrangement , while angular positions were not taken into account in the present manuscript .
Numerical simulations showed that several whorls self-organized following the sequential initiation of primordia . Although several previous phyllotaxis models showed the transition between a spiral arrangement following sequential initiation and a whorled arrangement following simultaneous initiation [15 , 16 , 19] , they were not able to reproduce the emergence of a whorled arrangement following sequential initiation , which is the situation observed in many eudicot flowers ( Fig 1 ) [34 , 37 , 38 , 40 , 41] . In the present model , a tetramerous whorl appeared spontaneously that exhibited four primordia almost equidistant from the meristem center ( Fig 2D , left and middle ) , by arresting radial movement of the fifth primordium at the meristem edge until the seventh primordium arose ( arrowhead in Fig 2D , right ) . Likewise , subsequent primordia produced the same gap in radial distance for every four primordia ( Fig 2D , middle and right ) , leading to several whorls comprising an identical number of primordia ( Fig 2D ) . The radial positions of all primordia were highly reproducible despite stochasticity in the growth process ( error bars in Fig 2D–2F , middle and right ) . Therefore , we identified the whorled arrangement by radial displacement arrest ( arrowhead in Fig 2D , right ) . The initiation order and angle of the first tetramerous whorl in the model reproduced those observed in A . thaliana sepals [68] ( S2A Fig ) . The first primordium scarcely moved from the initiation point until the second primordium arose because growth repulsion was absent . The second primordium arose opposite the first , whereas the third and fourth primordia arose perpendicular to the preceding two . The angular position of the primordia did not change once the whorl was established because the primordia within a whorl blocked the angular displacement by the growth potential Ug ( S3 Fig ) . Introducing mutual repulsion among the primordia throughout the growth process caused the whorled arrangement to spontaneously emerge ( Fig 2D ) . This was in contrast to the model of constant growth in which all primordia move away depending only on the distance from the floral apex [49] . A study of post-meristematic regulation by the organ-boundary gene CUP-SHAPED COTYLEDON2 ( CUC2 ) showed that A . thaliana plants up-regulating CUC2 gene have an enlarged primordial margin and have whorled-like phyllotaxis following the normal helical initiation of primordia [42] , suggesting that repulsive interactions among primordia after initiation are responsible for the formation of the floral whorls . In the present model , the meristem size R0 controls the transition from non-whorled ( Fig 2E ) to whorled arrangement ( Fig 2D ) . Radial spacing of the primordia was regular when R0 was small ( Fig 2E , middle ) because the older primordia pushed any new primordium across the meristem ( Fig 2E , left ) , causing continuous movement at the same rate ( Fig 2E , right ) . Above a threshold meristem size R0 , a tetramerous whorl appeared spontaneously . The primordium number within each whorl increased up to eight with increasing R0 , but the number tended to be more variable ( S2B Fig ) . In the A . thaliana mutant wuschel , which has a decreased meristem size , the pattern of four sepals does not have square positions at the stage when the wild-type plant forms a tetramerous sepal whorl [69] . Conversely , the clavata mutant , which has an increased meristem size , has excessive floral organs with larger variation [69] . Our model consistently reproduced not only the transition from the non-whorled arrangement ( Fig 2E ) to the tetramerous whorled arrangement ( Fig 2D ) but also the variable increase in the primordia number within a whorl as the meristem size R0 increased . The pentamerous whorl stably appeared in the presence of temporal decay of initiation inhibition ( α > 0 in Eq 2 ) . The whorls comprising five primordia appeared in the same manner as the tetramerous whorls , namely , via the locking of the sixth primordium at the initiation site ( Fig 2F , right; S2C Fig ) . In order to study the organ number within each whorl extensively , known as the merosity [70] , we counted the number of primordia existing prior to the arrest of primordium displacement , which corresponds to the merosity of the first whorl ( arrowheads in Fig 2D and 2F , right ) . We defined arrest of primordium displacement as occurring when the ratio of the initial radial velocity of a new primordium immediately after initiation to that of the previous primordium was lower than 0 . 2 . The definition does not affect the following results as long as the ratio is between 0 . 1 and 0 . 6 . We found that the key parameter for merosity is the relative value of R0 normalized by the average radial velocity V = σ r / 2 π ( see S1 Text ) and the initiation time interval τ ( Fig 3 ) . The arrest of radial displacement did not occur below a threshold of R0/Vτ ( the left region colored red in Fig 3A ) , whereas the whorled arrangement appeared above the threshold value of R0/Vτ . As R0/Vτ increased further , tetramery , pentamery , hexamery , heptamery , and octamery appeared , successively ( Fig 3A ) . The present model showed dominance of special merosity , i . e . , tetramery and octamery in the absence of temporal decay of inhibition ( α = 0 in Eq 2; Fig 3A ) ; pentamery in the presence of temporal decay ( α > 0; Fig 3B and 3C ) , in contrast to previous phyllotaxis models for whorled arrangement in which the parameter region leading to each level of merosity decreased monotonically with increasing merosity [19] . The major difference between α = 0 and α > 0 was that θ3 , the angular position of the third primordium , took an average value of 90 degrees when α = 0 ( arrowhead in Fig 3A bottom magenta panel ) and decreased significantly as α increased ( arrowhead in Fig 3A bottom cyan panel ) . In a pentamerous flower Silene coeli-rosa , the third primordium is located closer to the first primordium than the second one [34] . This is consistent with the third primordium position at α > 0 , indicating the necessity of α , as we will discuss in the next section . The parameter region R0/Vτ for pentamery expanded with increasing α , whereas the border between the whorled and non-whorled arrangements was weakly dependent on α ( Fig 3C ) . The tetramery , pentamery , and octamery arrangements were more robust to R0/Vτ and α than the hexamery and heptamery arrangements . Dominance of the particular number also appears in the ray-florets within a head inflorescence of Asteraceae [71] , in which radial positions show the whorled-type arrangement [72] . Meanwhile , the leaf number in a single vegetative pseudo-whorl transits between two to six by hormonal control without any preference [73] . Moreover , the transition between the different merosities occurred directly , without the transient appearance of the non-whorled arrangement . This is in contrast to an earlier model [19] in which the transition between different merosity always involved transient spiral phyllotaxis . The fact that the merosity can change while keeping its whorled nature in flowers ( e . g . , the flowers of Trientalis europaea[74] ) supports our results . To our knowledge , ours is the first model showing direct transitions between whorled patterns with different merosities as well as preferences for tetramery and pentamery , the most common merosities in eudicot flowers . To further validate our model of the pentamerous whorl arrangement , we quantitatively compared its results with the radial distances and divergence angles in eudicot flowers . Here we focus on a Scanning Electron Microscope ( SEM ) image of the floral meristem of S . coeli-rosa , Caryophyllaceae ( Fig 4A–4C ) [34] , because S . coeli-rosa exhibits not only five sepals and five petals in alternate positions , which is the most common arrangement in eudicots , but also the helical initiation of these primordia , which we targeted in the present model . In addition , to our knowledge , this report by Lyndon is the only publication showing a developmental sequence for both the divergence angle Δθk , k+1 = θk+1 −θk ( 0 ≤ Δθk , k+1 < 360 ) and the ratio of the radial position , rk/rk+1 , referred to as the plastochron ratio [75] , in eudicot floral organs . Reconstructing such developmental sequences of both radial and angular positions is an unprecedented theoretical challenge , while those which describe the angular position alone for the ontogeny of spiral phyllotaxis ( 180 degree , 90 degree and finally convergence to 137 degree [16 , 33]; the ‘M-shaped’ motif , i . e . , 137 , 275 , 225 , 275 and 137 degrees [76 , 77] ) have been reproduced numerically . By substituting the initial divergence angle between the first and second sepals of S . coeli-rosa into Δθ1 , 2 = 156 but not any plastochron data into the simulation ( θ1 = 0 and θ2 = 156 degree ) , we numerically calculated the positions of the subsequent organs ( Fig 4D ) . The observed divergence angle Δθ2 , 3 = 132 degree indicates α > 0 , because Δθ2 , 3 = Δθ1 , 3 = ( 360−156 ) /2 = 102 degree at α = 0 , in the present model setting r1 ≅ r2 . Even when r1 > r2 , the divergence angle was calculated as Δθ2 , 3 = 113 degree ( r1 = R0+2Vτ , r2 = R0+Vτ , R0 = 1 , Vτ = 0 . 14 , and λini = 0 . 05 estimated from the S . coeli-rosa SEM image [34]; see S4 Fig for detail ) , which is still less than the observed value . As α became larger , the inhibition from the second primordium became stronger than that from the first one , making Δθ2 , 3 consistent with the observed value in S . coeli-rosa ( Fig 4E , top ) . For the subsequent sepals and petals , the model faithfully reproduced the period-five oscillation of the divergence angle and the plastochron ratio until the ninth primordium ( Fig 4E ) , notably in the deviation of the divergence angle from regular pentagon ( 144 degree ) and the increase of plastochron ratio at the boundary between the sepal and petal whorls . Moreover , a similar increase in the plastochron ratio occurred weakly between the second and third primordia in the first whorl ( closed arrowhead in Fig 4E ) , indicating a hierarchically whorled arrangement ( i . e . , whorls within a whorl ) . Such weak separation of the two outer primordia from the three inner ones within a whorl is consistent with the quincuncial pattern of sepal aestivation that reflects spiral initiation in many of eudicots with pentamerous flowers ( e . g . , Fig 2D–E in [21] ) . Even with an identical set of parameters , the order of initiation in the first pentamerous whorl can vary depending on the stochasticity in the growth process . The variations of the initiation order in simulations may be caused by the absence of the outer structure , because the axillary bud seems to act as a positional information for the first primordia in S . coeli-rosa floral development ( Fig 4B ) . The positioning of the five primordia in the first whorl was reproducible in 70% of the numerical replicates , within less than 20 degrees of that in S . coeli-rosa or that of the angles in a regular pentagon . Mismatches in the inner structure ( from the tenth primordium , i . e . , the last primordium in petal whorl ) might be due to an increase in the rate of successive primordia initiation later in development [35] , which we did not assume in our model . The agreements between our model and actual S . coeli-rosa development of sepals and petals in both the angular and the radial positions suggests that the S . coeli-rosa pentamerous whorls are caused by decreasing inhibition from older primordia . A possible mechanism to arrest the radial displacement of a new primordium , a key process for whorl formation ( arrowheads in Fig 2D and 2F ) , involves an inward-directed gradient of the growth potential Ug , k ( Eq 3 ) of a new primordium so that its radial movement is prevented . To confirm this for tetramerous whorl formation ( Fig 3A ) , we analytically derived the parameter region such that the radial gradient of the growth potential at the angle of the fifth primordium Ug , 5 ( Eq 3 ) , which is determined by the positions of the preceding four primordia , is inward-directed . For ease in the analytical calculation , we set α = 0 and PMP = 0 . The first four primordia positions were intuitively estimated ( see S2 Text ) as r 1 = R 0 + 3 τ V , θ 1 = 0 r 2 = R 0 + 3 τ V , θ 2 = 180 r 3 = R 0 + 2 τ V , θ 3 = 90 r 4 = R 0 + τ V , θ 4 = 270 , ( 5 ) which agreed with the numerical results with an error of less than several percent regardless of the parameter spaces . Hereafter we demonstrate a case Vτ = 6 . 0 . The position of the fifth primordium derived from the positions of four existing primordia ( Eq 5 ) becomes θ5 = 90 when R0 ≤ 2 , whereas θ5 ∼ 135 when R0 > 2 ( S5 Fig ) . Next , we calculated the potential for the fifth primordium in radial direction by substituting Eq 5 and the position of the fifth primordium θ5 into Eq 3 . The function becomes U g , 5 ( r , θ 5 ) = ∑ j = 1 4 exp ( - d 5 j λ g ) = ∑ j = 1 4 exp ( - r j 2 + r 2 - 2 r j r cos ( θ j - θ 5 ) λ g ) ⋅ ( 6 ) The potential exhibits a unimodal ( 2 < R0 < 10; Fig 5A ) or bi-modal ( R0 < 2 , R0 > 10; Fig 5B and 5C ) shape . At R0 < 10 , the potential gradient at the initiation position of the fifth primordium ∂Ug , 5 ( r , θ5 ) /∂r∣r = R0 is outward-directed ( Fig 5A ) , providing almost constant growth resulting a non-whorled arrangement in the simulations ( Fig 3A , red region ) . At R0 > 10 , we defined the radial position of the local maximum closest to the fifth primordium as rmax ( open arrowhead in Fig 5B and 5C; red squares in the upper half of Fig 5D ) and the local minimum as rmin ( blue circles in Fig 5D; 0 < rmin < rmax ) . The potential gradient ∂Ug , 5 ( r , θ5 ) /∂r∣r = R0 has a negative value when R0 < rmin or rmax < R0 ( Fig 5C ) , causing the fifth primordium to constantly move outward . On the other hand , the potential gradient is positive , i . e . , directed inward ( Fig 5B ) , when rmin < R0 < rmax ( between the two solid arrowheads in Fig 5D ) , causing the arrest of radial movement of the fifth primordium . The values of rmin and rmax , analytically calculated as function of R0 and τ ( solid black line in Fig 5E ) , were faithfully consistent with the parameter boundaries between the non-whorled pattern and the tetramerous-whorled pattern and between the tetramerous-whorled and pentamerous-whorled patterns , respectively , in the numerical simulations ( Fig 5E ) . The assumption r1 = r2 ( Eq 5 ) according to our numerical results ( Fig 2D ) , which is a similar setup to co-initiation of two primordia , is not a necessary condition for consistency ( S6 Fig ) . Thus the inward-directed gradient of the growth potential ( Eq 3 ) , which works as a barrier to arrest the outward displacement of the fifth primordium , causes the formation of tetramerous whorl . The inward radial gradient of the potential Ug , k ( Eq 3 ) also accounted for the emergence of pentamerous whorls at α > 0 . Unlike the case of α = 0 , the angular position of the third primordium θ3 at the global minimum of Uini decreases from 90 degrees as α increases ( Fig 6A ) . For example , the recursive calculations for the minimum of Uini gave the angular positions of the two subsequent primordia , θ3 ≅ 62 and θ4 ≅ 267 , respectively , at α = 2 . 0 ( Vτ = 6 . 0 , R0 = 20 . 0 , and PMP = 0 ) . Those angular positions were consistent with the numerical results ( e . g . , Fig 2F and S2B Fig ) . The gradient of the growth potential ∂Ug , 5 ( r , θ5 ) /∂r at the edge of the meristem for the fifth primordium that arises at θ5 ≅ 129 is negative ( Fig 6B ) . Therefore , the fifth primordium moves outward at constant velocity so that the tetramerous whorl is unlikely to emerge . The inward-directed potential at the position of the new primordium first appears when the sixth primordium arises around 343 degrees , which was derived by the recursive calculation ( Fig 6C ) . The first primordium ( the rightmost potential peak in Fig 6C ) prevents the outward movement of the sixth primordium ( red circle in Fig 6C ) . Arrest of radial displacement of the sixth primordium is maintained until the seventh primordium arises to allow the radial gap between these primordia to appear ( i . e . , a pentamerous whorl emerged ) . After the appearance , the growth potential gradients of the sixth and the seventh primordia become outward-directed , providing their constant growth with keeping the radial gap to the first whorl . Likewise , the other merosities can be explained by similar recursive calculations of the angular position from the initiation potential ( Eq 2 ) and the radial gradient of the growth potential ( Eq 3 ) . Based on these analytical results ( Figs 5 and 6 ) and the dimensionless parameter G = τV/R0 , which represents the natural logarithm of the average plastochron ratio [49 , 75] , we quantitatively compared the present model against previous phyllotaxis models assuming simultaneous initiation based on the initiation potential [19] . The tetramerous and pentamerous whorls appeared in , at most , 1 . 3-fold and 1 . 2-fold ranges of G , respectively , in the earlier study ( Fig 4D in [19] ) ; however , they appeared in much wider ranges in our model ( i . e . , 3-fold to 5-fold and 1 . 2-fold to 5-fold ranges of G , respectively; Fig 3C ) . Here , another key parameter is the temporal decay rate of the initiation inhibition α that shorten the transient process approaching to the golden angle ( Fig 6A ) than those of spiral phyllotaxis [32 , 33] . λini , representing the gradient of the initiation potential ( Eq 2 ) , little affected the border between the whorled and non-whorled arrangements at α = 0 ( Fig 7A and 7B ) ; λini affected the border only when α ≠ 0 ( Fig 7C and 7D ) . The independency of λini at α = 0 is consistent with the result shown by the previous model , which did not incorporate temporal decay of the potential and indicated that the phyllotactic pattern depends little on the functional type of initiation potential [49] . On the other hand , the gradient of the growth potential ( Eq 3 ) regulated by λg caused a drastic transition between the whorled and non-whorled arrangements ( Fig 7E and 7F ) . Unlike G , λini , and α ( Fig 6A ) , λg hardly affects the angular position , as demonstrated in the previous sections , but it controls how far the growth potential works as a barrier to determine the merosities of the whorls ( Fig 7E and 7F ) . Thus , λg , α , and G differentially regulate phyllotaxis of the floral organs , suggesting the involvement of distinct molecular or physiological underpinnings . We have seen that both the mutual repulsion of growth regulated by λg and the temporal decay of initiation inhibition controlled by α are responsible for the formation of tetramerous and pentamerous whorls following sequential initiation . These mechanisms can be experimentally verified by tuning λg and α . Here , we discuss several candidates for the molecular and physiological underpinnings . Future studies should also clarify the limits and applicability of the common developmental principle elucidated here by exploring more complex development in a wide variety of flowers . Because our model assumes sequential initiation of the primordia , it does not cover the floral development of all eudicots; sepal primordia arise simultaneously in some eudicot clades ( Fig 1; e . g . , mimosoid legume [94] ) . Likewise , in later development , several primordia arise at once in the stamen and carpel whorls ( e . g . , Ranunculaceae [35] ) . The transitions between simultaneous and sequential development have two additional intriguing implications for evolutionary developmental biology . First , the initiation types may affect the stochastic variation of floral organ numbers , possibly caused by the absence or presence of pseudo-whorls ( Fig 1 ) and the noisy expression domain of homeotic genes [95] . Second , such transitions occur even in animal body segmentation [3 , 4] , possibly caused by evolution of both gene regulatory network topologies and embryonic growth [7 , 9–11] . The limitations of the model can be reduced by introducing initiation whenever and wherever the potential ( Eq 2 ) is below a threshold , allowing simultaneous as well as sequential initiation [19] . The threshold model exhibiting both types of initiation does not by itself result in the dominance of particular merosities [19] . Incorporating two mechanisms , mutual growth repulsion and temporally decreasing inhibition at the point of initiation , into the threshold model could explain the dominance of particular merosities following both the sequential and the simultaneous initiation of floral organ primordia ( Fig 1 ) . Another problem is the absence of trimerous whorls in the present model ( Fig 3 ) . The transition between the trimery and tetramery or pentamery , and vice versa , occurred multiple times during the evolution of angiosperms . Therefore , trimerous flowers are scattered across the basal angiosperms , monocots , and a few families of eudicots [96 , 97] . Elucidating the developmental mechanisms underlying the transitions between the different merosities , as well as those between sequential and simultaneous initiation , will be an important avenue for future studies . One problem in determining floral organ number is how to generate whorls comprised of a specific number of organs . By introducing a growth assumption ( i . e . , continuous repulsion among primordia throughout development , which was originally proposed as the contact pressure model [25 , 45 , 46] and is supported by experimental observations [42] ) into a dynamical model of phyllotaxis [49] , we showed that the whorled arrangement arises spontaneously from sequential initiation . Moreover , when we allowed the inhibition to decay over time [33 , 47 , 48] , pentamerous whorls became the dominant pattern . The merosity tended to be four or five in much larger parameter spaces than those in which it tended to be six or seven . The emergence of tetramerous and pentamerous whorls could be verified experimentally by tuning the two parameters α and λg . | Why do most eudicot flowers have either four or five petals ? This fundamental and attractive problem in botany has been little investigated . Here , we identify the properties responsible for organ-number determination in floral development using mathematical modeling . Earlier experimental and theoretical studies showed that the arrangements of preexisting organs determine where a new organ will arise . Expanding upon those studies , we integrated two interactions between floral organs: ( 1 ) spatially and temporally decreased inhibition of new organ initiation by preexisting organs , and ( 2 ) mutual repulsion among organs such that they are “pushed around” during floral development . In computer simulations incorporating such initiation inhibition and mutual repulsion , the floral organs spontaneously formed several circles , consistent with the concentric circular arrangement of sepals and petals in eudicot flowers . Each circle tended to contain four or five organs arranged in positions that agreed quantitatively with the organ positions in the pentamerous flower , Silene coeli-rosa , Caryophyllaceae . These results suggest that the temporal decay of initiation inhibition and the mutual repulsion among growing organs determine the particular organ number during eudicot floral development . |
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Alpha-methylacyl-coenzyme A racemase ( AMACR ) regulates peroxisomal β-oxidation of phytol-derived , branched-chain fatty acids from red meat and dairy products — suspected risk factors for colon carcinoma ( CCa ) . AMACR was first found overexpressed in prostate cancer but not in benign glands and is now an established diagnostic marker for prostate cancer . Aberrant expression of AMACR was recently reported in Cca; however , little is known about how this gene is abnormally activated in cancer . By using a panel of immunostained-laser-capture-microdissected clinical samples comprising the entire colon adenoma–carcinoma sequence , we show that deregulation of AMACR during colon carcinogenesis involves two nonrandom events , resulting in the mutually exclusive existence of double-deletion at CG3 and CG10 and deletion of CG12-16 in a newly identified CpG island within the core promoter of AMACR . The double-deletion at CG3 and CG10 was found to be a somatic lesion . It existed in histologically normal colonic glands and tubular adenomas with low AMACR expression and was absent in villous adenomas and all CCas expressing variable levels of AMACR . In contrast , deletion of CG12-16 was shown to be a constitutional allele with a frequency of 43% in a general population . Its prevalence reached 89% in moderately differentiated CCas strongly expressing AMACR but only existed at 14% in poorly differentiated CCas expressing little or no AMACR . The DNA sequences housing these deletions were found to be putative cis-regulatory elements for Sp1 at CG3 and CG10 , and ZNF202 at CG12-16 . Chromatin immunoprecipitation , siRNA knockdown , gel shift assay , ectopic expression , and promoter analyses supported the regulation by Sp1 and ZNF202 of AMACR gene expression in an opposite manner . Our findings identified key in vivo events and novel transcription factors responsible for AMACR regulation in CCas and suggested these AMACR deletions may have diagnostic/prognostic value for colon carcinogenesis .
Alpha-methylacyl-CoA racemase ( AMACR ) is a peroxisomal and mitochondrial enzyme that is indispensable in the catabolism of phytol-derived , 2-methyl-branched-chain fatty acids and the synthesis of bile acids [1] . In hepatocytes , AMACR catalyzes the conversion of pristanoyl-CoA and C27-bile acyl-CoAs from R- to S-stereoisomers , which are the only stereoisomers that can undergo β-oxidation . Bile acid intermediates undergo one round of β-oxidation in the peroxisomes and are secreted . In contrast , branched-chain fatty acid derivatives are transported to mitochondria , where they are further degraded to generate biological energy . Since most malignancies increase fatty acid utilization as an energy source to fuel growth [2] , it has been suggested that increased β-oxidation of branched-chain fatty acids provides transformed cells with a unique metabolic advantage [3] . This idea is supported by recent findings that knockdown of AMACR transcripts or inhibition of the racemase activity effectively blocked growth of prostate cancer ( PCa ) cells [4] , [5] . In humans , the major sources of phytol-derived , 2-methyl-branched fatty acids are dietary ruminant fats , meat , and dairy products . Increased consumption of these foods are known risk factors for prostate and colon carcinoma ( CCa ) [6] , [7] . Aberrant expression of AMACR was first reported in PCa and high-grade prostatic intraepithelial neoplasia but not in benign hyperplastic lesions or normal epithelia [8] , [9] . These findings quickly led to the establishment of AMACR as a reliable diagnostic marker for PCa [10]–[13] . More recently , overexpression of AMACR also was reported in CCa [14]–[18] , with a prevalence between 45% and 75% [19]–[21] . However , the relationship between levels of AMACR expression and the sequence of adenoma-carcinoma progression in the colon [22] has not been fully characterized . Except for a report that identified a non-canonical CCAAT enhancer element in the AMACR promoter [5] and a lack of regulation of this gene by androgen [16] , [23] , no information is available regarding how the AMACR gene is regulated . Furthermore , although recent studies have identified a few AMACR gene variants to be associated with PCa [24] , [25] or CCa [26] risks , a sequence polymorphism in the promoter region of AMACR has not been reported . Given the potential significance of AMACR in CCa , our objectives in this study were to determine the mechanisms of AMACR gene regulation in vivo during neoplastic transformation of the colon epithelium . Through the use of a comprehensive panel of immunostained-laser-capture-microdissected ( iLCM ) clinical samples comprising the entire colon adenoma-carcinoma sequence , we now report that the deregulation of AMACR during colon carcinogenesis involves non-random events , resulting in a double-deletion at CG3 and CG10 , and alterations in the frequencies of deletion of CG12-16 in a newly identified CpG island ( CGI ) located within the core promoter of AMACR . We also identified deletion of CG12-16 as a putative regulatory polymorphism and the double-deletion at CG3 and 10 as a somatic lesion . The DNA sequences housing these deletions were indicated to be a cis-regulatory element for Sp1 and a putative ZNF202-binding site , respectively , and to exert opposite effects on AMACR transcription .
We first provided a detailed description of the relationship between AMACR expression levels and the sequence of adenoma-carcinoma progression in the colon . The levels of AMACR in 55 foci representing seven normal , premalignant and malignant histological entities in 35 colon specimens were semiquantified in immunostained slides ( Figure 1A to 1H ) . These foci were subsequently microdissected for AMACR promoter studies . In general , AMACR immunostaining was negative to weak in normal cryptal ( Figure 1A ) and apical ( Figure 1B ) epithelia , as well as in tubular adenomas ( TAs ) with mild dysplasia ( Figure 1C ) . In contrast , villous adenomas ( VAs ) ( Figure 1D ) , well- ( Figure 1E and 1F ) and moderately ( Figure 1G ) differentiated adenocarcinomas expressed high levels of AMACR . AMACR immunostaining was almost absent to negligible in poorly differentiated carcinomas ( Figure 1H ) . Compared with the expression in normal crypt , levels of AMACR expression , represented as a score of 0 to 4 , were significantly increased in VAs and in well- and moderately differentiated carcinoma but not in normal apex , TAs , and poorly differentiated carcinoma ( Figure 1I ) . Because virtually no information is available on how AMACR is regulated in vivo , we initially were interested in determining if changes in DNA methylation status of the AMACR 5′ flanking promoter region play a role in gene regulation . In silico analysis revealed that AMACR transcripts share the same first exon with an 88-bp 5′ untranslated region ( 5′ UTR ) , suggesting that the gene is controlled by one promoter . Two CGIs were identified flanking the transcription start site ( Figure 2A ) . The first is a novel CGI located upstream of the ATG site ( −230 to −60; the position of the translation start site was set as +1 ) with 18 CG dinucleotides , whereas the second CGI downstream of the ATG site ( 48 to 357 , not shown in Figure 2A ) has been reported and shown to not be involved in gene regulation in PCa cells [5] . In concordance , our pilot studies indicated that the downstream CGI exhibited no differences in methylation/deletion/mutation status among the histological entities of the colon ( data not shown ) . Hence , subsequent studies were focused on analyses of the previously not reported proximal CGI in the AMACR promoter region ( the AMACR promoter CGI ) . Our bisulfite sequencing data did not support the involvement of DNA methylation of this newly identified CGI in AMACR gene regulation in vivo , since the promoter is largely unmethylated in all 55 iLCM samples ( next section ) . However , in silico analyses identified two putative Sp1 binding sites at CG3 and CG10 and a non-canonical ZNF202 [27] cis-element at CG12-16 of this CGI ( Figure 2B ) . Variable frequencies of deletions were found at these sites and later shown to be involved in gene regulation ( next section ) . A previously reported non-canonical CCAAT enhancer element [5] was aligned to CG5 . Two direct repeat sequences , 7 bp in length , were noted to flank the transcription start site . We later proposed that these two repeated sequences are involved in the generation of the CG12-16 deletion ( dotted lines; see Discussion below ) . A 222-bp region encompassing all 18 CG sites in the newly identified AMACR promoter CGI ( Figure 2 ) was analyzed for methylation , deletion , and mutation changes using DNA obtained from LCM samples and CCa cell lines . Bisulfite sequencing analyses of 239 alleles from 55 foci and regular DNA sequencing of 37 alleles from 9 foci as the control ( also see next section ) showed that most of the CG sites were unmethylated ( Table 1 ) . However , variable frequencies of deletions , methylation , and mutations were found to occur almost invariably at CG3 , CG10 , and CG12-16 , with deletions as the predominant lesion among all aberrations . The sequences of these deletion and mutation variants were deposited to Genbank with the accession number from EF636492 to EF636496 . Cluster analyses demonstrated that deletion of CG12-16 was the most common co-occurrence , followed by deletion at CG3 and CG10 ( double-deletion at CG3 and CG10 ) ( Figure 3A ) . Cluster analyses data for methylation ( Figure 3A ) , mutations ( Figure S1 ) , and all aberrations ( Figure S1 ) were also obtained . The number of deleted nucleotides ( nts ) was 2 to 8 nts at CG3 and 2 nts at CG10 ( Figure 3B ) . Deletion at CG12-16 was found to be precisely 20 nts . Among the four CCa cell lines examined , CG12-16 deletions were found in SW480 and SW620; no double-deletion of CG3 and 10 was detected in any of these cell lines . Thus , while methylation of this novel CGI does not appear to play a role in gene regulation , deletions of specific sequences or deletion hotspots within this sequence were identified and might play critical roles in the regulation of gene expression and/or the adenoma-carcinoma progression . As our focus on promoter assay will be based on the above sequencing results , we herein provide several pieces of data to ensure that the deletions were not artifacts of bisulfite-treatment of the DNA , PCR or sequencing . First , bisulfite modification reduced the GC content to ∼41% in the 222-bp AMACR promoter CGI , which made the sequencing easier to read; second , visual examination of sequencing chromatogram files showed clean and discrete peaks in the CGI region , indicating that the deletions we observed in bisulfite sequencing were not due to a GC compression artifact ( Figure S2A ) . In addition , as an internal control to ensure complete bisulfite modification , we routinely examined and found that almost 100% of the non-CpG cytosines in this region were converted to T , indicating complete bisulfite modification . To demonstrate that the CG12-16 deletion was not due to the PCR artifact , we used sequencing-verified plasmids with or without CG12-16 deletion as PCR templates , the PCR products showed expected size with different positions in 3% agarose gel ( Figure S2B , left panel ) . Additionally , we used unmodified ( not shown ) and bisulfite-treated genomic DNA , with or without the CG12-16 deletion , as templates and performed multiple PCRs on the same two samples ( Figure S2B , right panel ) . Results demonstrated the sizes of the amplicons derived from wild-type and deletion-variant templates were consistent , indicating that the deletion of CG12-16 was neither a PCR artifact nor a result of bisulfite-treatment . Blast searches provide additional evidence that the CG12-16 deletion exists in the human genome , as of the two genome sequences , one is the reference assembly that corresponds to the sequence ( NT_006576 . 15 ) without CG12-16 deletion and the other is the Celera assembly ( NW_922562 . 1 ) exhibiting the deletion , which exactly matches what we discovered in the AMACR promoter in clinical samples ( Figure S2C ) . Finally , we conducted parallel bisulfite and regular sequencing on DNA isolated from LCM-captured normal or malignant colon epithelial cells from 9 colon specimens . Identical sequence results were obtained with the two methods ( data not shown ) . Thus , in conclusion , these control experiments and in silico analyses demonstrate that the observed deletion hotspots in this CGI exist in colon tissues and are not results of artifacts generated from bisulfite-treatment , PCR or sequencing . We then investigated the relationship between deletion patterns in the AMACR promoter CGI and levels of AMACR expression ( Table 2 , left ) in 55 iLCM samples to gain insight into how these deletions might affect gene expression in vivo . CG3-only deletions were rather common ( 13–41% ) but were not correlated with AMACR expression , and CG10-only deletions were rare ( 0–5% ) . However , double CG3 and 10 deletions occurred at higher frequencies and invariably only in foci with no or little AMACR expression ( 17–28% , scores 0 and 1 ) . In contrast , CG12-16 deletions were common ( 21–67% ) and showed a positive correlation with the AMACR expression score . In total , foci with moderate and high AMACR expression ( scores 2–4 ) had a high frequency of CG12-16 deletions ( 53–67% ) and no double CG3 and 10 deletions . Next , we examined the type of deletions found in the six histological entities ( Table 2 , right ) to determine their relationship to the adenoma-carcinoma progression paradigm . CG3-only deletions were commonly found among normal and CCa foci . In most cases , GC10 deletions occurred as double CG3 and 10 deletions found in normal epithelium and TA ( 24–25% ) . In contrast , the double-deletion was not identified in VA or in CCa of any grade . CG12-16 deletions were found in all six histological entities; however , their frequency markedly increased in well- ( 56% ) and moderately ( 89% ) differentiated cancers and correlated with high AMACR expression in these lesions ( mean expression score ∼3; Figure 1E–G ) . It is of interest that the frequency of deletions of all kinds was low in poorly differentiated cancers; 72% of these foci have no lesions in the AMACR promoter CGI . Like all other CCas , they lack the deletion of CG3 and 10; the frequency of CG12-16 deletion in these CCas was low ( 14% ) , which correlates with negligible to low levels of AMACR expression in these lesions ( mean expression score ∼0; Figure 1H ) . Compared with the CG12-16 deletion in the moderately differentiated group that has the highest deletion rate , statistic analysis indicated the deletion was significantly changed in the normal , TA , VA and poorly differentiated groups but not in the well differentiated group . Together these data showed an intriguing in vivo phenomenon . Consistently , in all the samples analyzed , deletion of CG12-16 are not co-existed with double CG3 and 10 deletions ( frequency = 0; Table 2 ) . Additionally , double-deletions at CG3 and 10 are found only in normal epithelium and TA and are not observed in VA and CCa of all grades . In contrast , CG12-16 deletions are associated with moderate and well differentiated CCa that express high AMACR but not in poorly differentiated cancers that show negligible AMACR expression . These findings provide the impetus for a study of the effects of these deletions on AMACR transcription an in vitro system ( the HCT 116; see below ) . To better understand the relevance of these deletions to colon carcinogenesis , we must ask if these deletions are results of genetic events occurring in somatic cells of the colon or are constitutional alleles exist in the general population . Before this study , the only information available is that a sequence ( NW_922562 . 1 ) harboring the CG12-16 deletion in the Celera assembly ( Figure S2C ) . No AMACR sequences with deletion at CG3 or CG10 , or at both sites have been reported in genomic databases . We used randomly sampled genomic DNA isolated from whole blood of 96 individuals ( 48 males and 48 females ) from a relatively homogeneous Caucasian population of northern German for our study [28] . A 173 bp region encompassing all 18 CG sites within the AMACR promoter CGI were analyzed by regular and bisulfite sequencing ( Figure 2B ) . The CG12-16 deletion was found to be a sequence variant with an allele frequency of 43% in the population ( Table 3 and 4 ) . The observed genotype frequencies conform to the expectations of Hardy-Weinberg proportions ( Table 3 , p>0 . 05 ) . Between male and female samples , chi-square test for the genotype difference and allele frequency differences are not statistically significant ( p>0 . 05 ) . In contrast , in these blood DNA samples , no other deletions or mutations were found at any of the other CG sites in this region of the AMACR CGI , including CG3 and CG10 . Interestingly , although deletions/mutations at CG3 and/or CG10 were not found by normal sequencing , bisulfite sequencing demonstrated that the two CG sites are methylation hotspots in blood DNA samples , exhibiting a prevalence of 16 . 7% and 11 . 1% , respectively ( Table 4 ) . These frequencies were higher than those observed in tissue samples in which deletion is the predominant type of lesion at these two sites ( Table 1 ) . The fact that both single and double deletions at CG3 and CG10 are completely absent in blood samples but occur at frequencies between 13–30% in colon tissue DNA indicates that they are somatic lesions . To determine whether the in vivo deletions affect AMACR gene transcription , we first established that the human CCa cell line HCT 116 is a suitable model for AMACR promoter study in vitro . These cells express AMACR transcripts , have an intact promoter sequence with an unmethylated CGI ( data not shown ) , and therefore should have an intact “transcriptional machinery , ” including transcription factors for AMACR expression . Real-time RT-PCR showed that this cell line expresses both Sp1 and ZNF202 at significant levels . We cloned a long ( 1 , 818 bp; −1821/−4 ) and a short ( 599 bp; −602/−4 ) 5′ AMACR promoter sequence , both containing the newly identified CGI , into pGL3b reporter vector ( Figure 4A ) . The two sequences showed comparable promoter activities in HCT 116 cells . These data suggest the localization of core promoter elements within the 599-bp sequence ( AMACR599 ) , which was used to derive all other mutants in this study . To directly demonstrate that the deletion hotspots affect gene transcription , we generated deletion and/or mutation mutants of AMACR599 by targeting single or multiple sites ( Figure 4B ) . Since a previous study reported gene-regulatory activity of the CCAAT enhancer aligned to CG5 [5] , we also included deletion mutants targeting this sequence in our study . Reporter assays performed in HCT 116 cells showed that deletion of the CCAAT enhancer sequence at CG5 led to a marked reduction in promoter activity ( ∼60% ) regardless the integrity of CG3 , CG10 , or CG12-16 ( Figure 4C ) . However , in the presence of an intact CCAAT enhancer , deletion of CG12-16 , in the absence or presence of CG3 , CG10 , or double CG3 and 10 deletions , resulted in augmentation of promoter activity ( ∼100% ) . In contrast , deletion of CG3 and 10 , but not a single deletion of either CG3 or CG10 , caused a significant loss of promoter activity ( ∼60% ) . These findings indicate that deletion of CG12-16 and double-deletion of CG3 and 10 exert opposite actions on AMACR transcription . To demonstrate that these regulatory mechanisms are not limited to CCas , we transfected these mutants into two PCa cell lines ( PC-3 and LNCaP ) and similar data were obtained ( data not shown ) . We next sought to understand how these deletions affect AMACR gene transcription . In silico analyses suggest the localization of Sp1 binding sites at CG3 and CG10 and a non-canonical ZNF202 binding site within the CG12-16 region ( Figure 2B ) . However , it should be noted that in silico-based prediction requires experimental confirmation since recent ChIP-chip results have demonstrated a weak match between many consensus sequences and in vivo binding sites for specific transcription factors ( TFs ) [29] , [30] . Poor correlations could be due a high degree of degeneracy for some motifs and/or the participation of other proteins at the binding sites . A series of confirmation studies were therefore performed to support our in silico-based predictions . We predict that deletion at CG3 or CG10 affects one of the two putative Sp1 binding sites , and deletion at CG12-16 impede occupancy of a ZNF202 protein to its cis-element located between CG12-16 ( Figure 2B ) . Using nuclear extracts from HCT116 cells , chromatin immunoprecipitation ( ChIP ) experiments were performed . Sp1 was found binding to a 174-bp sequence ( −234/−60 ) that contains the two putative Sp1-sites at CG3 and CG 10 ( Figure 5A , upper panel ) but not to a 169-bp sequence ( 19553/19721 ) located in the last exon of AMACR ( Figure 5B , lower panel ) . Small interfering ( si ) RNA-mediated Sp1 knockdown decreased AMACR mRNA expression at the second-round of transfection ( Figure 5B ) but did not reduce transcript levels of glucuronidase β ( GUSB ) or cyclophilin A ( PPIA ) , two unrelated genes ( data not shown ) , in HCT 116 cells . Since there is no commercially available ZNF202 antibody for ChIP , gel shift assays were performed to assess HCT 116 nuclear protein binding to the putative ZNF202 binding site located within CG12-16 of the AMACR CGI . As can be seen in Figure 6A , one specific protein–DNA complex ( arrow ) was formed on the 45-bp 32P-labeled double-stranded oligonucleotide ( ODN ) encompassing CG12-16 and its flanking sequences ( Probe WT ) . The formation of this complex could be impeded by 100-fold excess of unlabeled WT or a 26-bp ZNF202 consensus sequence ( GnT; [27] ) . However , it is resistant to competition by 100-fold excess of a 45-bp mutant with the ZNF202 core sequence [31] mutated ( Mut ) or a 32-bp ODN devoid of CG12-16 ( Del ) . Interestingly , protein-DNA complex formation patterns on labeled WT and Del were different with notable absence of the lower band that could be competed off by excess cold WT or GnT ( Figure 6B ) . Finally , ectopic expression of ZNF202 induced a dose-dependent reduction of AMACR599 promoter activity and concordant lower levels of AMACR mRNA ( Figure 6C ) . In sum , these findings provide evidence in support of CG3 and CG10 as Sp1 binding sites and CG12-16 as a ZNF202 cis-element . Sp1 and ZNF202 appear to regulate AMACR expression in an opposite manner .
The main objective of this study was to elucidate the regulatory mechanism underpinning AMACR gene expression in relation to CCa development . We identified a novel CGI upstream the translation start site in the proximal core promoter of AMACR . Although aberrant methylation of promoter CGIs is a common cause of transcriptional deregulation of genes involved in tumorigenesis [32] , we found that AMACR activation did not occur by this mechanism during colon carcinogenesis . Instead , we found that two non-random , mutually exclusive in vivo events , involving a double-deletion at CG3 and 10 and the deletion of CG12-16 , play essential but opposite roles in the process . Additionally , we discovered the differential “origins” of these two in vivo deletions by comparing sequencing data from blood DNA in a general population and those from LCM-microdissected colon samples . The deletion of CG12-16 in the AMACR 5′ CGI was found to be a constitutional allele with a frequency of 43% in a general population . In contrast , deletions at CG3 and/or CG10 were not observed in the blood samples indicating that these are genetic events occurring in somatic cells of the colon . We observed a strong positive correlation between AMACR expression and the sequence of adenoma-carcinoma progression , suggesting a promotional function of AMACR in colon carcinogenesis . This postulate agrees with recent studies reporting that siRNA-mediated knockdown of AMACR mRNA or inhibition of the enzyme activity effectively curbed the growth of PCa cells [3] , [4] . Intriguingly , both gene expression and CCa progression were closely correlated with the status of two mutually exclusive deletions found in the iLCM samples . Specifically , the double CG3 and 10 deletion was found only in histologically normal colonic glands and TAs that had negligible to absent AMACR expression and was absent in VA or CCas of all grades that had variable levels of AMACR expression . More important , the simultaneous deletion of these two sites effectively negated AMACR transactivation in HCT 116 . We therefore propose that deletion at CG3 and 10 may effectively obviate colon carcinogenesis , possibly by impeding AMACR expression in vivo . In this regard , adenomas harboring double-deletions of CG3 and 10 might have a low likelihood of development to CCas; its potential diagnostic value merit further study . In contrast to CG3 and 10 double-deletions , deletions of CG12-16 were highly prevalent in well- and moderately differentiated CCas that strongly expressed AMACR . This finding is impressive because it stands in stark contrast with the classical view that deletions cause functional inactivation of genes . In this instance , the CG12-16 deletion in the AMACR promoter CGI behaves like a “gain-of-function” deletion . When viewed in this context , the CG12-16 deletion may be part of the sequential genetic changes that occur in tumor suppressors , DNA repair genes , and oncogenes during the development of CCas from adenomatous lesions [33] . Unlike the better differentiated CCas , most poorly differentiated cancers had a low percentage of deletions at CG12-16 and lacked AMACR expression . These cancers also had very few other aberrations including the double-deletion at CG3 and 10 , in their AMACR promoter CGI . Because the gene is not silenced by DNA methylation , or by irreversible genetic events such as deletions , we have to consider the possibility that these cancers may have a clonal origin different from that of the better differentiated carcinomas . Alternatively , during their evolution , these cancers may acquire a metabolic phenotype that is independent of AMACR overexpression . Much could be learned about the relationship between the CG12-16 deletion polymorphism and CCa risk by comparing the allelic frequency of this sequence variant in blood samples ( Table 3 & 4 ) to those found in LCM-captured histological entities of the colon ( Table 1 ) . The overall allelic frequency of this deletion amongst the 239 alleles from the various histological entities of the colon was found to be ∼42% ( Table 1 , row 1 ) , which matches the allele frequency observed in the blood samples is ∼43% . This suggests that there may not be additional somatic events altering the frequency of this constitutional sequence in the colon . Yet , the prevalence of this lesion reaches 89% in the moderately differentiated CCas , which showed significant difference ( p<0 . 05 ) compared to the normal , TA , VA and poorly differentiated but not the well differentiated samples . Collectively , these data adds up to the hypothesis that individuals with the CG12-16 deletion variant are more likely to develop CCas that are well or moderately differentiated . Conversely , those carrying the wild type variant may be more prone to develop poorly differentiated CCas . Clearly , such a provocative hypothesis would have to await a well-designed population study for confirmation . Contributing significantly to our understanding of how AMACR is regulated , we here provide the first evidence that deletion hotspots in the AMACR promoter CGI correspond to cis-elements for Sp1 and that this transcription factor regulates AMACR expression . Our data support the regulation of AMACR by Sp1 , as ChIP assays showed Sp1 binding to a region of the AMACR promoter CGI containing the predicted sites and siRNA-mediated Sp1 knockdown decreased AMACR mRNA levels in HCT 116 . Reporter assays revealed that single deletion at either CG3 or CG10 did not affect AMACR transcription , whereas the double-deletion significantly abrogated the promoter activity , suggesting that the integrity of one site at either CG3 or CG10 is sufficient to maintain the promoter activity . In contrast , deletion of CG12-16 enhanced AMACR transcription , signifying the likely presence of a repressor binding site in this region . We did not perform a ChIP assay for ZNF202 because no antibody was commercially available for the immunoprecipitation . However , results from gel shift assays were highly suggestive of the existence of a non-canonical ZNF202 binding site within this sequence . Our finding that ectopic overexpression of ZNF202 reduced AMACR promoter activity lends credence to this notion . However , we are aware of the fact that these data did not provide the definitive evidence that the CG12-16 sequence contains a ZNF202 cis-element , which still awaits a formal demonstration in future investigations . It is always possible that some unknown transcription factors other than ZNF202 could be involved in this regulation . Intriguingly , ZNF202 is a transcriptional repressor for genes affecting the vascular endothelium as well as lipid metabolism . We have examined the promoters of five other ZNF202 target genes [27] , [34] , [35] ( apoA4 , apoE , lecithin cholesterol acyltransferase , lipoprotein lipase , and phospholipid transfer protein ) and did not find deletions or other aberrations in their ZNF202 cis-element in colon and prostate cancer cells ( unpublished data ) . Thus , the activation of AMACR via deletion of a ZNF202 cis-element would be a phenomenon unique to AMACR gene regulation , if the CG12-16 sequence was shown to house this element . Several epidemiologic and animal studies have observed associations between the risk of metabolic syndromes/coronary heart diseases and the prevalence of colon adenomas/carcinomas [36]–[38] . Perhaps the loss of ZNF202-mediated repression of specific target genes , including AMACR , is a common cause of these diseases . Apropos of this view , carcinogenesis is being recognized increasingly as a metabolic disorder characterized by a shift from glycolysis to fatty acid utilization as the energy source fueling cell growth [2] . Finally , deletion of the CCAAT enhancer resulted in the loss of promoter activity regardless of the status of other elements , indicating that CCAAT enhancers are part of the basal transcriptional complex for AMACR . However , we did not find alterations in this cis-element in the iLCM samples , suggesting that such alterations do not contribute to aberrant expression of AMACR during colon carcinogenesis . At present , it is unclear how the deletions at in the AMACR promoter arise . However , first , we noticed that deletion hotspots at CG3 and CG10 are also methylation hotspots ( Table 1 and Table 4 ) . It has been reported that methylated CG sites are mutation hotspots [39] as suggested in Figure S3A . Second , scrutiny of the CGI sequence revealed two 7 nt direct repeats ( Figure 2B ) . We postulated that forward slipped-strand mispairing [40] , [41] of the repeats , may result in the CG12-16 deletion during DNA replication ( Figure S3B ) . If this mispairing happens , such slippage will cause the exact 20 bp deletion found in AMACR promoter . These proposed mechanisms speculated to be responsible for these deletions will of course have to await future experiments for corroboration . Collectively , we identified two major types of in vivo deletions in the AMACR promoter that appear to modulate gene expression and may play contrasting roles in carcinogenesis . In essence , a double-deletion at CG3 and 10 prevents AMACR overexpression and may impede colon carcinogenesis . In contrast , carriers of sequence variants with or without the CG12-16 deletion may have different propensity to develop well/moderately differentiated CCas versus the poorly differentiated cancers . Finally , our data suggest that these deletion hotspots are cis-elements for Sp1 at CG3 or CG10 and for ZNF202 at CG12-16 . The proposed mechanisms for AMACR promoter regulation and the deletion hotspots provided important platforms for the further study of AMACR gene deregulation during carcinogenesis .
Archival specimens were obtained from the Department of Pathology at the University of Massachusetts Medical School . Specimens from 35 cases were immunostained and microdissected to obtain the 55 iLCM samples: 11 TAs with mild dysplasia , 8 VAs , 6 well differentiated carcinomas , 6 moderately differentiated carcinomas , 7 poorly differentiated carcinomas , and 17 histologically normal colon tissues with 9 normal crypt and 8 apical surface epithelial samples . For the TAs , pronounced dysplastic changes , which often linked to positive AMACR , were uncommon . Most of the foci had mild dysplastic changes , and we focused our study of TAs on this type of sample . These samples were used for bisulfite sequencing analysis . Specimens for nine additional cases were obtained from the Pathology Department of the University of Cincinnati Medical Center and used to obtain nine LCM samples of normal epithelial , adenomatous , and carcinomatous cells for a regular DNA sequencing for comparison with bisulfite sequencing . Blood samples for polymorphism assay were from a relatively homogeneous Caucasian population of northern German [28] . The use of these samples was reviewed and approved by the respective institutional review boards at the two institutions . Multiple sections were cut from each case specimen . One section was stained with hematoxylin and eosin ( H&E ) and used for identification of histologic entities . The others were immunostained for AMACR with the P504S antibody ( Dako Cytomation , Carpinteria , CA ) and lightly counterstained with hematoxylin as previously described [8] , [42] . Areas representative of the histologic features and the overall intensity of AMACR expression found in a given case were identified in immunostained sections . These areas were then located in the replicate . The coverslips were then removed , H&E-stained , and microdissected as previously described [43] . Each of microdissected foci was given a score ( 0–4 ) reflective of the level of AMACR expression . When uniformly intense immunostaining was observed in at least 95% of cells in the section , the level of AMACR expression was designated as very strong ( score = 4 ) . If staining was less intense , not uniform throughout the section , and in fewer than 95% of the cells , the level of expression was designated as strong ( score = 3 ) . If the intensity of stain was weak , not uniform , and in 50% or fewer the cells , the section was graded as medium ( score = 2 ) or weak ( score = 1 ) . Cases were scored as negative ( score = 0 ) when the section showed no staining . Genomic DNA was extracted from the LCM samples by DNeasy Blood & Tissue Kit ( Qiagen , Valencia , CA ) with 20 µg of yeast tRNA added as a carrier . DNA was bisulfite-modified with the CGenome DNA Modification Kit ( Millipore , Billerica , MA ) . Sequencing service was provided by Macrogen ( Seoul , Korea ) with BigDye terminator used in a 96-capillary 3730xl DNA analyzer . Bisulfite-sequencing PCR-targeting AMACR promoter CGI was conducted by nested PCR . Primers AM-bisF1/AM-bisR1 and AM-bisF2/AM-bisR2 ( Table 5 and Figure 2 ) were used in the first round and nested PCR , respectively . The targeting region was from −276 to −55 , with the translation start site designated as +1 . PCR was performed with platinum Taq ( ABI/Invitrogen , Carlsbad , CA ) for 38 cycles with the annealing temperature at 56°C and 57°C in the first and nested PCR , respectively . Amplified fragments were purified in 1% agarose gel , TA-cloned , and about five colonies were picked from each sample for sequencing . Regular sequencing of the same CGI flanking region was performed in parallel using unmodified DNA samples and the regular primers AM-F1/AM-R1 and AM-F2/AM-R2 ( Table 5 ) . Proper controls were included in all experiments to ensure that the findings were not confounded by incomplete bisulfite modification , PCR artifact , or sequencing errors . Blood genomic DNA for the polymorphism study was extracted by DNeasy Blood & Tissue Kit . Using 50 ng genomic DNA as template , the PCR was performed for 40 cycles in the presence of 5% DMSO by platinum Taq with PolyF/PolyR as the primers ( Table 5 and Figure 2 ) . The annealing temperature was set at 58°C . The expected PCR product encompassing CpG sites 1–18 without CG12-16 deletion is 173 bp in length . After gel purification , the PCR products were TA cloned and the plasmids in colonies were directly amplified for sequencing by the Rolling Circle Amplification Kit ( GE Health Care , Piscataway , NJ ) . PCR products from alleles with the deletion of CG12-16 could also be visualized by a size difference from amplicons derived from wild type alleles in a 3% agarose gel . To determine the prevalence of methylation in this region of the AMACR promoter , aliquots of the extracted genomic DNA was subjected to bisulfite sequencing . The AMACR promoter region immediately upstream of the translation start site was amplified from genomic DNA of HCT116 cells . With forward primer pAM-F1 ( Table 5 , XhoI site underlined ) and reverse primer pAM-R0 ( HindIII site underlined ) used in PCR , the resulting 1818-bp AMACR promoter ( from −1821 to −4 ) was cloned into luciferase reporter vector pGL3b ( Promega , Madison , WI ) and designated as AMACR1818 . The promoter sequence was verified by sequencing . A 5′ truncated promoter ( designated as AMACR599 , from −602 to −4 ) was generated by nested PCR with PAM-F2/PAM-R0 as the primers . Promoter site-specific deletion variants were obtained by using the Genetailor site-directed mutagenesis kit ( Invitrogen ) . After sequencing , the promoter variants were released from the cloning vector and recloned into pGL3b . All reagents used for cell cultures , including heat-inactivated FBS , were obtained from Invitrogen . Human CCa cell lines HCT 116 , SW480 , SW620 , and DLD-1 were obtained from the American Type Culture Collection ( Manassas , VA ) . The cells were maintained in the same condition as HCT 116 cells , which are cultured according to the provider's recommendations . Unless specified , 6×104 HCT 116 cells were plated one day before transfection in each well of the 24-well plate . The cells were transfected with a total of 0 . 2 µg of DNA , including 10 ng of cotransfected CMV promoter-driven LacZ gene ( CMV-LacZ ) as the internal control . Plasmids for transfection were purified with the EndoFree Plasmid Maxi Kit from Qiagen . Two microliters of Plus and 1 µl of Lipofectamine ( Invitrogen ) were used in the transfection according to the protocol . The promoter activity was analyzed as previously described [44] . The ChIP assay was performed with the EZ ChIP Kit from Millipore according to the manufacture's instruction . A total of 7 . 5 µg of anti-Sp1 rabbit polyclonal IgG ( cat . no . 07-645 , Upstate/Millipore ) was used in each IP . Primers Sp1-IPf/Sp1-IPr targeting −234 to −60 CGI ( Table 5 ) were used in PCR with platinum Taq in the presence of 5% DMSO with an initial denaturation at 94°C for 1 min , followed by 36 cycles of 94°C for 30 sec , 58°C for 30 sec , and 72°C for 15 sec . As a negative control for DNA IP , primers ChIPnegF/ChIPnegR targeting the gene's last exon were used in PCR ( Table 5 ) . RNA extraction , reverse transcription , and real-time PCR , together with the primers for GAPDH and 18S rRNA , were described previously [44] . The tested primers used to detect AMACR and Sp1 transcripts were AMf/AMr and Sp1f/Sp1r , respectively ( Table 5 ) . As the siRNA control , primers for GUSB and PP1A gene were used in the real-time RT-PCR and listed in the Table 5 . The relative level of gene expression was calculated by the 2−ΔΔCt method as described in detail in our previous studies [44] , [45] . 1 . 5×105 HCT 116 cells were seeded at day -1 before transfection in each well of the 6-well plate . At day 0 , transfection was performed with 5 µl of Lipofectamine 2000 ( Invitrogen/ABI ) and 7 . 5 µl of 20 µM siRNA per well according to the protocol . siSp1 ( ON-TARGETplus SMARTpool , cat . no . L-026959-00 , Dharmacon , Lafayette , CO ) was used to knockdown Sp1 expression with Non-Targeting siRNA ( cat . No . D-001210-01-05 ) as the control . At day 3 , the cells either were collected for real-time RT-PCR analysis or were split at 1 . 5×105 cells per well . The second round of siRNA was performed on day 4 and analyzed on day 7 . To demonstrate the specificity of siRNA knockdown effects , in parallel , the expression of two unrelated genes of GUSB and PP1A were analyzed . Full-length coding sequence of ZNF202 m1 transcript [27] was amplified by primers NotIZ202 and Z202ApaI ( Table 5 ) from LNCaP cDNA . The sequencing-verified fragment was subcloned into pcDNA4/His/Max A expression vector ( Invitrogen ) . For real-time RT-PCR , the expression plasmid was transfected into HCT 116 in the 6-well plate with the Nucleofector Kit and Nucleofector II device from Amaxa ( Gaithersburg , MD ) . Probe sequences were shown in Table 5 . Complementary single-strand DNA oligos were annealed in 1×PCR buffer ( 20 mM Tris-HCl , 50 mM KCl , pH 8 . 4 ) in a water-filled heat block . The annealing mixture was heated at 95°C for 3 min and cooled to below 30°C in 1 hr to generate 50 µM double-strand oligo . The double-strand oligos showed a single and stronger band in 3% agarose gel , and located at a different position than the single-strand oligos ( photos not shown ) . HCT 116 cells nuclear extract was prepared by Nuclear Extract Kit ( Active Motif , Carlsbad , CA ) according to the manufacturer's instructions . Three µg of nuclear extract ( 1 µl ) was used in each binding assay at 18°C in 10 µl . The assays were carried out according to the protocol described in the Gel Shift Assay System ( Promega ) with the following modifications: Probe labeling was performed with 10 U T4 polynucleotide kinase ( New England Biolabs , Ipswich , MA ) and 2 µl [γ-32P]ATP ( 3 , 000 Ci/mmol at 10 mCi/ml , Perkin Elmer , Waltham , MA ) in a total volume of 10 µl at 37°C for 20 min . Electrophoresis of DNA-protein complexes was resolved in 6% DNA Retardation gel ( Invitrogen ) using 4°C 0 . 5× TBE buffer at 250 V for ∼35 min . Dried gels were exposed to X-ray film at −80°C for ∼1 hr and the images were captured by a digital camera . Five newly identified sequences of AMACR promoter variants with deletion/mutation at CpG hotspots were deposited into the Genbank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) . The accession numbers for these variants are from EF636492 to EF636496 , which represent a CG3 deletion , a CG3 mutation , a CG10 deletion , CG3 and 10 double-deletions , and a CG12-16 deletion , respectively . In addition , the accession number for the AMACR promoter from the Genbank reference assembly and Celera assembly are NT_006576 . 15 and NW_922562 . 1 , respectively . The transcript reference sequences are NM_014324 . 4 and NM_203382 . 1 for AMACR , NM_003455 for ZNF202 m1 , NM_138473 . 2 for Sp1 , NM_000181 . 2 for GUSB , and NM_021130 . 3 for PP1A . Extensive gene analyses were carried out with GeneCards ( www . genecards . org ) . BLAST ( www . ncbi . nlm . nih . gov/BLAST/ ) was used to compare the sequence against Genbank . CGI was identified by MethPrimer at http://www . urogene . org/methprimer/index . html . Gene exon and intron information was obtained from Blat ( http://genome . ucsc . edu/ ) . PCR primers , except for real-time RT-PCR negative control ( Real Time Primers , Elkins Park , PA ) and bisulfite PCR , were designed by Primer3 [46] at http://frodo . wi . mit . edu/cgi-bin/primer3/primer3_www . cgi . The sequencing data were analyzed by ClustalW at http://www . ebi . ac . uk/clustalw . Putative TF binding sites in AMACR promoter deletion hotspots were scanned by MatInspector [31] . MatInspector utilizes transcription factor knowledge base to locate putative TF binding sites in sequence and minimize the number of false positive hits , but requires further confirmation through wet-bench works . It defines the “core sequence” ( Table 5 and Figure 2B ) of a putative binding site as the consecutive highest conserved positions in the DNA binding site . The hierarchical cluster analysis was based on the average linkage principle , and the absolute number of co-occurrences of different CG deletions was based on the similarity measure . The differences in AMACR expression ( Figure 1 ) in the microdissected foci were compared among the different histologic categories using a one-way analysis of variance ( nonparametric ) , followed by Tukey's HSD post hoc test for comparisons of all classes of lesions against normal cryptal cells . The analysis of CG12-16 deletion among the different histologic categories ( Table 2 ) was carried out by SAS Proc Genmod software that assuming a log link and robust standard error estimation . The program estimates and tests differences between groups with respect to the proportion of deletion . A generalized linear model of binomial proportions was analyzed to detect differences . In other experiments , a two-tailed , unpaired t-test was performed between two groups . Except else where mentioned , the columns with error bars in the figures represent mean±95% confidence interval . For the CG12-16 deletion polymorphism study , Hardy-Weinberg equilibrium was used to test if specific disturbing influences are introduced to the samples , and chi-square test was used to exam genotypic and allelic differences between male and female . In all the analyses in this paper , unless otherwise stated , p<0 . 05 was considered as statistically significant . | Men consuming high amounts of red meat and dairy products are at a higher risk of developing colon and prostate cancer . Alpha-methylacyl-coenzyme A racemase ( AMACR ) is an enzyme that helps to break down fat from these foods to produce energy . An increase in the utilization of energy from fat is a hallmark of many cancers including colon and prostate cancers . Indeed , the AMACR gene was first found to be abnormally active in prostate cancers , and its abnormal expression has become a diagnostic marker for the cancer . However , little is known about how AMACR becomes activated in cancer cells . Here , we show that AMACR is also highly expressed in certain stages of colon cancer , though not all stages . A close examination of the AMACR gene in a panel of normal and progressively malignant colon tissues reveals that deletions of specific sequences in the AMACR gene may trigger its abnormal expression during the evolution of colon cancer . We also identify unique proteins known as “transcription factors” that normally bind to these deleted sequences to maintain normal expression of the gene . Finally , we report a new deletion variant of the AMACR gene in the general population that may influence the course of colon carcinogenesis . |
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DNA methylation changes dynamically during development and is essential for embryogenesis in mammals . However , how DNA methylation affects developmental gene expression and cell differentiation remains elusive . During embryogenesis , many key transcription factors are used repeatedly , triggering different outcomes depending on the cell type and developmental stage . Here , we report that DNA methylation modulates transcription-factor output in the context of cell differentiation . Using a drug-inducible Gata4 system and a mouse embryonic stem ( ES ) cell model of mesoderm differentiation , we examined the cellular response to Gata4 in ES and mesoderm cells . The activation of Gata4 in ES cells is known to drive their differentiation to endoderm . We show that the differentiation of wild-type ES cells into mesoderm blocks their Gata4-induced endoderm differentiation , while mesoderm cells derived from ES cells that are deficient in the DNA methyltransferases Dnmt3a and Dnmt3b can retain their response to Gata4 , allowing lineage conversion from mesoderm cells to endoderm . Transcriptome analysis of the cells' response to Gata4 over time revealed groups of endoderm and mesoderm developmental genes whose expression was induced by Gata4 only when DNA methylation was lost , suggesting that DNA methylation restricts the ability of these genes to respond to Gata4 , rather than controlling their transcription per se . Gata4-binding-site profiles and DNA methylation analyses suggested that DNA methylation modulates the Gata4 response through diverse mechanisms . Our data indicate that epigenetic regulation by DNA methylation functions as a heritable safeguard to prevent transcription factors from activating inappropriate downstream genes , thereby contributing to the restriction of the differentiation potential of somatic cells .
Development is based on a series of cell-fate decisions and commitments . Transcription factors and epigenetic mechanisms coordinately regulate these processes [1] , [2] . Transcription factors play dominant roles in instructing lineage determination and cell reprogramming [3] , [4] . Transcription factor and co-factor networks regulate cell-specific gene programs , allowing a given transcription factor to be used repeatedly in different cellular and developmental contexts [5] . In addition , epigenetic mechanisms , which establish and maintain cell-specific chromatin states ( or epigenomes ) during differentiation and development [6] , modulate the functions of transcription factors in cell-type-dependent manners [7] , [8] . Alterations of chromatin states can increase the efficiency of transcription factor-induced cell reprogramming [9] , [10] and lineage conversion in vivo [11] , [12] . However , how epigenetic mechanisms and transcription factor networks coordinately regulate cell differentiation remains elusive . DNA methylation at cytosine-guanine ( CpG ) sites is a heritable genome-marking mechanism for epigenetic regulation , modulating gene expression through chromatin regulation [13] . Genome-wide DNA methylation profiles have revealed that the methylated CpG in the mammalian genome is specifically distributed in a cell-type-dependent manner [14]–[16] , and the methylated CpG sites are dynamically reprogrammed during embryogenesis and gametogenesis [17]–[19] . The DNA methylation profile is established and maintained by three DNA methyltransferases ( DNMTs ) , Dnmt1 , Dnmt3a , and Dnmt3b [20] , together with DNA demethylation mechanisms [21] . Dnmt1 is required for the maintenance of DNA methylation profiles , whereas Dnmt3a and Dnmt3b are required to establish them . The inactivation of Dnmt1 or both Dnmt3a and Dnmt3b in mice leads to early embryonic lethality , showing that DNA methylation has essential roles in mammalian embryogenesis [22]–[24] . DNA methylation is involved in various cell-differentiation processes , and several studies have identified the underlying mechanisms for specific cases [25]–[29] . However , the roles of DNA methylation in differentiation and development remain largely unexplored . The evolutionarily conserved zinc-finger transcription factor GATA family controls tissue-specific gene expression and cell-fate determination in many cell types [30] . Gata4 is broadly expressed in endoderm- and mesoderm-derived tissues as well as in pre-implantation embryos . Gata4 functions in endoderm formation , cardiac morphogenesis , the establishment of regional identities in the small intestine , and tissue-specific gene expression in the liver and osteoblasts [31]–[36] . Even though Gata4 has a broad expression profile , it still has cell-specific functions , which are determined largely by transcription factor and co-factor networks . Unique interactions with cardiogenic transcription factors and co-factors allow Gata4 to regulate cardiac gene expression specifically in cardiac progenitor cells and their derivatives [37] . In contrast , the overexpression of Gata4 alone causes mouse ES cells to differentiate into extra-embryonic primitive endoderm cells [38] , indicating that Gata4 functions as a master regulatory transcription factor for endoderm specification in ES cells . It is likely that Gata4 is unable to activate a cardiac gene program in ES cells , because of the lack of cardiac transcription factors and co-factors . However , it remains unclear how the endoderm-instructive function of Gata4 is suppressed in non-endoderm tissues , such as mesoderm . Epigenetic mechanisms such as DNA methylation may modulate the cell-specific functions of Gata4 . Here , we have established an in vitro experimental system to test the downstream output of Gata4 in two defined cell types , ES and mesoderm progenitor cells , using a drug-inducible Gata4 and an ES-cell differentiation protocol . Using this experimental system , we examined the effect of DNA methylation on Gata4-induced endoderm differentiation and developmental gene regulation during mesoderm-lineage commitment . Our findings suggest that DNA methylation restricts the endoderm-differentiation potential in mesoderm cells and controls the responsiveness of developmental genes to Gata4 .
To explore the role of DNA methylation in the context-dependent function of transcription factors , we focused on Gata4 as a model . Gata4 instructs the primitive endoderm fate in ES cells [38] , while it regulates various endoderm and mesoderm tissue-specific genes in somatic cells [30] . In this study , we took advantage of a drug-inducible Gata4 construct where the Gata4 coding region is fused with the ligand-binding domain of the human glucocorticoid receptor ( Gata4GR ) [39] . The activation of Gata4GR by adding dexamethasone ( Dex ) , a glucocorticoid receptor ligand , drove the differentiation of wild-type ( WT ) ES cells into the primitive endoderm lineage , in which all the cells were positive for the primitive endoderm marker Dab2 ( Figure S1A–S1D , LIF ( + ) condition ) . However , when the ES cells were first differentiated for 3 days by withdrawing leukemia inhibitory factor ( LIF ) from the ES maintenance medium , the cells became resistant to the Gata4-induced endoderm differentiation ( Figure S1A–S1D , LIF ( − ) condition ) , showing that the endoderm-instructive function of Gata4 is suppressed after somatic cell differentiation . To investigate the Gata4 response in a defined somatic cell population , we employed a mesoderm differentiation protocol , in which ES cells were co-cultured with OP9 stroma cells [40] without LIF for 4 days and then sorted to isolate the Flk1 ( also known as VEGFR2 or KDR ) -positive ( + ) population [41] ( Figure 1A ) . Flk1 ( + ) cells derived from ES cells are considered to be equivalent to a mixture of primitive and lateral mesoderm [41] , and these cells can differentiate into several mesoderm derived lineages . To eliminate less differentiated cells ( including mesendoderm ) and ensure their mesoderm commitment , we isolated the Flk1 ( + ) /E-cadherin ( − ) population by flow cytometry [42] ( Figure 1B ) . Flk1 ( + ) mesoderm cells derived from WT ES cells can efficiently differentiate into vascular mural cells , which express alpha-smooth muscle actin ( SMA ) , when cultured on type IV collagen [43] ( Figure 1A , 1C , 1D , WT Dex− ) . When we activated Gata4GR by adding Dex , the WT Flk1 ( + ) mesoderm cells also differentiated into mural cells , and the entire cell population was positive for SMA staining at an intensity similar to that of cells without Gata4 induction , although some of the Gata4-induced cells were noticeably smaller in size ( Figure 1C , 1D , WT Dex+ ) . We found no round , Dab2-positive cells among the WT Flk1 ( + ) cells that differentiated with Gata4 induction ( data not shown ) . These results indicated that the endoderm-instructive function of Gata4 was suppressed after the differentiation of ES cells into Flk1 ( + ) mesoderm . We next examined whether DNA methylation was involved in the suppression of the endoderm-instructive function of Gata4 in Flk1 ( + ) mesoderm cells . For this , we used Dnmt3a−/−Dnmt3b−/− double-knockout ( DKO ) mouse ES cells , which have no de novo methylation activity and low DNA methylation levels at many loci [24] , [44] . DKO ES cells expressing Gata4GR differentiated efficiently from ES cells into primitive endoderm in the presence of Dex , similar to WT ES cells ( Figure S2 ) . We obtained the DKO Flk1 ( + ) mesoderm population at the same high efficiency as the WT Flk1 ( + ) cells ( Figure 1B ) , and the DKO Flk1 ( + ) cells differentiated into SMA ( + ) mural cells with a similar efficiency to WT Flk1 ( + ) cells ( Figure 1C , 1D , DKO Dex− ) , indicating that DNA hypomethylation does not by itself inhibit ES-cell differentiation into Flk1 ( + ) mesoderm and SMA ( + ) mural cells . We then tested the response of the DKO Flk1 ( + ) mesodermal cells to Gata4 activation ( Figure 1A ) . After 4 days of induction with Gata4 , most of the differentiated DKO Flk1 ( + ) cells stained weakly for SMA , although the intensity was somewhat variable ( Figure 1D , DKO Dex+ ) . Strikingly , a small number of cells in the population ( about 1% ) had a round , endoderm-like morphology and were positive for the primitive endoderm marker Dab2 ( Figure 1C , 1E , DKO Dex+ ) . Morphologies of the SMA-positive cells ( flat and large cytoplasm ) and the Dab2-positive cells ( round and small cytoplasm ) were distinct ( Figure 1C–1E ) . These results indicated that some DKO Flk1 ( + ) mesoderm cells were converted to endodermal identity in response to Gata4 . Dnmt3a and Dnmt3b have transcriptional repression activities that are independent of their enzymatic activities [45] , [46] . To examine whether this lineage conversion was DNA methylation-dependent , we prepared Flk1 ( + ) mesoderm cells from Dnmt1−/− ( KO ) ES cells [23] expressing Gata4GR ( Figure S3A ) , in which DNA methylation in the genome is extensively decreased due to the loss of maintenance methylation activity . Although overall tendencies for low growth and survival were observed , the Dnmt1−/− Flk1 ( + ) cells efficiently differentiated into SMA ( + ) cells without Gata4 induction , whereas Dab2 ( + ) primitive endoderm-like cells emerged when Gata4 was induced ( Figure S3B , S3C ) . These results indicated that it was the loss of DNA methylation that promoted the Flk1 ( + ) mesoderm cells to convert their lineage to endoderm in response to Gata4 . These results also exclude the contribution of clonal effects caused by genetic or epigenetic changes associated with individual cell lines unrelated to DNMT functions . We observed similar results using DKO Flk1 ( + ) mesoderm cells obtained using a different mesoderm differentiation condition , in which ES cells were cultured on type IV collagen-coated dishes [41] ( Figure S4A ) . Although the recovery of the Flk1 ( + ) population from DKO ES cells was low ( 3% ) under this condition ( Figure S4B ) , the DKO Flk1 ( + ) cells differentiated into SMA-positive mural cells with an efficiency similar to that of the WT Flk1 ( + ) cells ( Figure S4C , D , Dex− ) , confirming the commitment of the DKO Flk1 ( + ) cells to the mesoderm fate . Using this condition , we observed that Gata4 reproducibly induced highly efficient differentiation of DKO Flk1 ( + ) mesoderm cells into the endoderm , in which most cells were Dab2-positive with an endoderm-like , round cell morphology , while no such cells were observed in the WT Flk1 ( + ) cell cultures ( Figure S4C , S4D , WT DKO Dex+ ) . We used RT-PCR and microarray analysis to obtain gene expression profiles of these cell populations , and found that when DKO Flk1 ( + ) mesoderm cells were cultured for 4 days with Gata4 activation ( DKO Flk1+Dex+ ) , their gene expression profile was similar to that of primitive endoderm cells that were derived directly from ES cells ( Figure S4E–S4G ) . However , we found that this culture condition was not stable; although it initially gave more efficient Gata4-induced differentiation of DKO Flk1 ( + ) mesoderm cells into the endoderm ( Figure S4C , S4D , DKO Dex+ ) compared to the OP9 co-culture condition ( Figure 1C–1E , DKO Dex+ ) , later , the efficiency of the both conditions became similar . We speculated that stroma-cell-free culture systems , such as the type IV collagen condition , may be more sensitive to factors such as the serum lot used in the culture medium . Because of the consistent differentiation properties and the high recoveries of Flk1 ( + ) mesoderm cells , we used the OP9 co-culture condition for mesoderm differentiation in the remaining analyses . The above results indicated that DNA methylation is involved in the suppression of the endoderm-instructive function of Gata4 in mesoderm cells . Thus , we wondered how the DNA-hypomethylated mesoderm cells reprogrammed their transcription profiles from mesoderm to endoderm in response to Gata4 . Previous studies in other cell-differentiation models suggested two possibilities: ( 1 ) the loss of DNA methylation together with Gata4 activity de-represses a few gatekeeper genes for endoderm differentiation , and these gatekeepers then activate the endoderm transcriptional program [27] , [29]; ( 2 ) alternatively , the loss of DNA methylation allows Gata4 to directly activate its endoderm downstream genes [26] . To address these possibilities , we dissected the temporal changes of the transcriptome in response to Gata4 in Flk1 ( + ) mesoderm cells ( Figure 2A ) . RNA was isolated at several time points ( up to 72 hr ) from WT or DKO Flk1 ( + ) cells cultured with or without Gata4 activation , and their genome-wide transcriptional profiles were analyzed using microarrays ( Figure 2A , Flk1+ mesoderm ) . For comparison , we also obtained the temporal transcriptome of ES cells in response to Gata4 at similar time points ( Figure 2A , ES ) . We first examined how many genes were differentially expressed as a result of the loss of Dnmt3a/Dnmt3b or Gata4 activation at 72 hr ( Figure 2B ) . In total , 941 genes were expressed at a more than fourfold higher level in DKO Flk1 ( + ) mesoderm cells cultured without Gata4 activation , compared to WT cells ( Figure 2B , WT Dex− vs . DKO Dex− , up ) , which may represent genes directly repressed by DNA methylation . The gene ontology ( GO ) terms related to immune response , meiosis , and gametogenesis were significantly enriched in this gene set ( Table S1 ) , which is consistent with a previous report showing that promoters of germline- or inflammation-associated genes are methylated de novo during mouse embryogenesis in a Dnmt3-dependent manner [47] . Because DKO Flk1 ( + ) cells without Gata4 activation properly differentiated into mural cells with a cell morphology indistinguishable from that of WT Flk1 ( + ) cells ( Figure 1C , 1D ) , the upregulation of these 941 genes seemed to have little impact on the cellular phenotype of mesodermal differentiation . In contrast , Gata4 activation in both the WT and DKO Flk1 ( + ) cells resulted in differential expression of hundreds of genes , for which the GO terms related to various developmental processes were enriched ( Figure 2B , Table S1 ) . We then extracted the genes that responded to Gata4 preferentially in DKO cells with hypomethylated DNA ( Figure 2C–2E , Figure S5 ) . The overlap between ( i ) the genes expressed more highly in DKO cells than in WT cells with Gata4 induction ( WT Dex+<DKO Dex+ , >2-fold change ) and ( ii ) the genes expressed more highly in DKO cells with Gata4 induction than in the same cells without Gata4 induction ( DKO Dex−<DKO Dex+ , >2-fold change ) separated the Gata4-responsive genes ( 710 genes ) from the DNA methylation-sensitive genes ( 974 genes ) ( Figure 2C , Table S2 ) . Based on the further overlap with ( iii ) the genes expressed at higher levels in DKO cells after 72 h of mesodermal differentiation with Gata4 induction than in the cells immediately after mesodermal differentiation ( DKO 0 hr<DKO Dex+ , >2-fold change ) , we identified 320 genes that responded to Gata4 at the 72 hr time point preferentially on the DKO background in Flk1 ( + ) mesoderm cells ( Figure 2D ) . To extract early response genes to Gata4 , we identified the same overlaps in gene sets differentially expressed at 24 hr ( 146 genes , Figure S5 ) . Based on the overlap of these 146 genes with the Gata4 responsive genes at 72 hr , we identified 94 genes that responded to Gata4 within 24 hr and lasted for at least 72 hr , preferentially on the DKO background , in Flk1 ( + ) mesoderm cells ( Figure 2E , Table S3 ) . These results indicated that a significant number of genes became hyper-responsive to Gata4 in DNA-hypomethylated DKO mesoderm cells . We then examined the time course of the expression profiles of these Gata4-hyper-responsive genes in Flk1 ( + ) mesoderm and ES cells with or without Gata4 induction ( Figure 3A , Figure S6 ) . These Gata4-hyper-responsive genes were divided into two groups by hierarchical clustering ( Table S3 ) : group 1 genes responded to Gata4 in ES cells ( upper part , Figure 3A , Figure S6 ) , whereas group 2 genes did not ( lower part , Figure 3A , Figure S6 ) . Consistent with the endoderm-instructive function of Gata4 in ES cells [38] , group 1 contained many genes expressed in endoderm-derived tissues , such as the liver , intestine , and stomach ( BioGPS , http://biogps . org/ ) [48] . Endoderm transcription factor genes ( Sox17 , Foxa2 , Elf3 , Foxq1 ) as well as endoderm lineage-specific genes ( Aqp8 , Sord , Akr1b8 , Pga5 ) were upregulated in response to Gata4 in the DKO Flk1 ( + ) mesoderm cells to the same extent as in ES cells ( Figure 3B , Figure S7A ) . In addition , several genes whose expression was not restricted to endoderm-derived tissues ( as listed in BioGPS ) showed a similar expression profile ( Figure S7B ) . It should be noted that the group 1 genes showed almost no response to Gata4 in WT Flk1 ( + ) mesoderm cells ( Flk1+ WT Dex− , Figure 3A , Figure S6 ) . These results suggest that the upregulation of group 1 genes represents the ectopic activation of the endoderm genetic program in the DNA-hypomethylated DKO mesoderm in response to Gata4 . Endoderm transcription factor genes Sox7 and endogenous Gata4 , which also have mesoderm functions , were expressed in the Flk1 ( + ) mesoderm cells before Gata4 activation , but their expression remained only in the DKO Flk1 ( + ) mesoderm cells in response to Gata4 ( Figure S7D ) . Interestingly , several primitive endoderm genes ( Hnf4a , Fgfr4 , Amn , S100g ) responded to Gata4 specifically in the DKO mesoderm , but the extent of their response was modest compared to that in the ES cells ( Figure S7C ) . No detectable response of other primitive endoderm genes , such as Gata6 and Snai1 , to Gata4 was observed in the DKO mesoderm ( Figure S7E ) . Group 2 contained many genes involved in heart development and function . Cardiac transcription factor ( Nkx2-5 , Myocd , Tbx18 , Lbh , Tbx5 ) and heart-specific genes ( Ednra , Tnnt2 , Fhod3 , Acaa2 , Ryr2 , Kcnj5 ) were upregulated in response to Gata4 preferentially in the DKO Flk1 ( + ) mesoderm within 24 hr ( Figure 3C , Figure S7F ) . In addition , several genes that are highly expressed in skeletal muscles or osteoblasts ( Fbxo32 , Thbs4 , Gyg , Pdlim3 , Leprel4 ) showed a similar response in the DKO Flk1 ( + ) mesoderm cells ( Figure S7G ) . These results are consistent with the cardiac and other mesodermal functions of Gata4 [36] , [37] . Because Gata4 regulates cardiac genes through its cooperation with other cardiac transcription factors and co-factors [37] , it is likely that the cardiac genes did not respond to Gata4 in ES cells because of the lack of such co-factors . In contrast , since Flk1 ( + ) mesoderm includes cardiac progenitors [49] , Flk1 ( + ) cells may be competent to activate the expression of cardiac genes . Consistent with this idea , unlike the group 1 genes , the group 2 genes , including the cardiac genes , responded weakly to Gata4 in WT Flk1 ( + ) mesoderm cells ( Flk1+ , WT Dex+ , Figure 3A , Figure S6 ) . These results suggested that the response of the cardiac genes in group 2 represents the precocious expression of the cardiac gene program in DNA-hypomethylated DKO Flk1 ( + ) mesoderm cells . In addition , group 2 contained genes highly expressed in endoderm-derived tissues ( Tspan8 , Aldh1a1 , Psen2; Figure S7H ) , which may represent the ectopic activation of the definitive endoderm program . To determine whether the loss of Dnmt3a/Dnmt3b permits Gata4 to directly activate downstream target genes , we examined the immediate response to Gata4 activation by analyzing transcriptome changes occurring within 3 hr . Within the entire transcriptome , the expression of 64 genes were significantly increased , by more than 2-fold , 3 hr after Gata4 activation in DKO Flk1 ( + ) mesoderm cells . Fifteen of these genes overlapped with the Gata4-hyper-responsive genes identified in Figures 2D and S5 ( data not shown ) . Among them , both group 1 genes ( Aqp8 , Akr1b8 , Elovl7 , Pga5 , Figure 4A ) and group 2 genes ( Nkx2-5 , Myocd , Ednra , Lbh , Thbs4 , Mrap , Figure 4B ) immediately responded to Gata4 in the DNA-hypomethylated DKO mesoderm cells , but not in the WT cells . We also confirmed these results by RT-qPCR ( Figure S8 ) . These results suggested that DNA methylation contributes to suppress Gata4 from directly activating these genes . To gain insight into how DNA methylation modulates Gata4 activation , we examined the Gata4-binding sites in WT and DKO Flk1+ mesoderm cells by ChIP and high-throughput sequencing ( ChIP-seq ) , using anti-Gata4 antibodies . To obtain a large number of cells for the ChIP-seq experiment , WT or DKO ES cells expressing Gata4GR were differentiated in a large-scale culture on OP9 stroma cells , and the Flk1 ( + ) cells were purified by magnetic-activated cell sorting . Gata4 was activated for 3 hr before the cell purification by adding Dex . As controls , WT and DKO ES cells expressing Gata4GR but without Dex treatment were subjected to the analysis . We identified 20 , 410 peaks for WT Gata4-activatd Flk1 ( + ) cells and 22 , 733 peaks for DKO Gata4-activated Flk1 ( + ) cells using the DNAnexus software tools . To validate the Gata4-ChIP-seq peaks , we performed two independent motif analyses in the MEME Suite software package ( http://meme . nbcr . net/ ) [50] . Using the JASPAR CORE vertebrate motifs ( http://jaspar . genereg . net/ ) [51] and UniPROBE mouse transcription factor motifs ( http://thebrain . bwh . harvard . edu/uniprobe/ ) [52] , ab initio motif discovery analysis by DREME [53] identified the most highly enriched motifs in the Gata4-ChIP-seq peak regions from both WT and DKO Flk1 ( + ) cells with Gata4 activation as Gata factor-binding motifs ( Figure 5A ) . Similarly , central motif enrichment analysis by CentriMo [54] , which assumes that the direct DNA-binding sites tend toward the center of the ChIP-seq peak region , identified the three most highly ‘centrally enriched’ motifs as Gata-factor-binding motifs , using the same motif databases ( Figure S9 ) . These results indicated that Gata4-binding sites were highly enriched in the Gata4-ChIP-seq peaks . We next examined the Gata4 peaks and DNA methylation states of individual Gata4-response genes . Among 146 genes that transcriptionally responded to Gata4 within 24 hours specifically in DKO Flk1 ( + ) mesoderm cells ( Figure S5 ) , 70 were associated with the Gata4 peaks in DKO Flk1 ( + ) mesoderm cells , and 52 were associated with DKO-specific Gata4 peaks within a 5-kb distance ( data not shown ) . We then searched for the genes in which either promoter regions [55] or Gata4 peak regions were differentially methylated between WT and DKO mesoderm cells and/or between ES and mesoderm cells , by bisulfite sequencing analysis . Aqp8 has a low-CpG promoter , and a DKO-specific Gata4 peak was observed in its intronic region in DKO-mesoderm cells ( Figure 5B ) . Gata4 also bound to the same intronic region in WT ES cells in response to Gata4 activation as revealed by ChIP-qPCR ( Figure S10 ) . Both regions were highly methylated in WT but not in DKO mesoderm cells . Thus , the DNA methylation of these regions might affect their Gata4-binding ability or the downstream response of Gata4 . However , these regions were moderately or highly methylated in WT ES cells , in which Aqp8 responded to Gata4 ( Figure S7 ) . Thus , the DNA methylation in these regions may have different functions between ES and differentiated somatic cells , as observed in the retrotransposon IAP [56] . Sox7 has a high-CpG promoter , and DKO-specific Gata4 peaks were observed at this promoter region ( Figure 5C ) . While the 5′-upstream and promoter region of Sox7 was unmethylated at the undifferentiated ES cell stage , this region was de novo methylated , highly at the distal part , during mesoderm differentiation . Similar de novo methylation during mesoderm commitment and inverse correlation with Gata4 peaks were observed at the Cldn7 locus ( data not shown ) . The Gata4-responsive endoderm gene Lgmn was associated with two DKO-specific Gata4 peak regions that were highly methylated in Flk1 ( + ) mesoderm cells ( Figure S11A ) . One of the Gata4 peaks , located in the 3′ region of the neighboring gene Rin3 , was methylated de novo during mesoderm differentiation . Since Rin3 itself did not respond transcriptionally to Gata4 , the Gata4 peak located at Rin3 may contribute to Lgmn's transcription . Mrap and Thbs4 , which have high-CpG promoters , were associated with Gata4 peaks within the gene or the neighboring gene in both WT and DKO mesoderm cells ( Figure S11B , S11C ) . The promoter of Mrap was heavily methylated in a Dnmt3-dependent manner , consistent with a previous study [16] , while the promoter of Thbs4 was de novo methylated during mesoderm differentiation . These promoter methylations may modulate downstream response of these genes in response to Gata4 binding . We also confirmed by luciferase reporter assay that at least some short fragments associated with Gata4 peaks had enhancer activities in response to Gata4 activation ( Figure S12 ) . Note that we also observed DKO-specific Gata4 peak regions that remained locally unmethylated in WT mesoderm cells ( data not shown ) . These Gata4 peaks may represent cooperative binding with other Gata4 molecules or co-factors , or higher-order chromatin state changes induced by a decrease in DNA methylation . Taken together , the Gata4-peak and DNA-methylation profiles suggested that DNA methylation modulates cellular responses to Gata4 through diverse mechanisms . Collectively , our results show that a significant number of developmental genes , including transcription factors and terminal differentiation genes , were promptly and simultaneously activated in DKO mesoderm cells by Gata4 . This finding supports the model in which DNA methylation globally restricts the responsiveness of downstream genes to Gata4 , rather than controlling a few gatekeeper genes .
In this study , we characterized the role of DNA methylation in the output of the single transcription factor Gata4 in defined cell types using an ES-cell differentiation method . Mesoderm cells derived from Dnmt3a/Dnmt3b-deficient ES cells were hyper-responsive to Gata4 and activated inappropriate developmental programs . Gata4 induced ectopic expression of endoderm downstream genes and precocious activation of cardiac and other downstream genes in DNA-hypomethylated mesoderm cells; these genes do not respond or respond only weakly to Gata4 in WT cells , suggesting that inappropriate Gata4 target genes such as endoderm genes are repressed in a DNA methylation-dependent manner . Our results indicate that epigenetic regulation by DNA methylation ensures the proper spatial and temporal developmental gene regulation by Gata4 and stabilizes differentiated cellular traits against the possible influences of natural fluctuation or environmental perturbations . We showed that a fraction of Dnmt3a/Dnmt3b-deficient mesoderm cells , but not WT cells , can convert to endoderm cells in response to Gata4 . This effect could be attributable to de-differentiation or the response of a small population of immature cells . However , our data suggest that these possibilities are unlikely . First , we purified the Flk1 ( + ) /E-cadherin ( − ) cells by flow cytometry , which removes the immature mesendoderm population [42] . Second , Gata4 globally induced endoderm genes in Dnmt3a/Dnmt3b-deficient mesoderm cells , and some genes were expressed at the same level as in Gata4-activated ES cells , the whole population of which differentiates to endoderm . Third , many endoderm genes responded to Gata4 within 12 hr in Dnmt3a/Dnmt3b-deficient mesoderm , and some even responded within 3 hr . These results suggested that Gata4 globally and promptly activates an endoderm gene program in a large population of Flk1 ( + ) mesoderm cells on the Dnmt3a/Dnmt3b-deficient background , but not in WT cells . Thus , it is unlikely that relatively slow processes such as de-differentiation or reversion to pluripotent states [57] are involved in this endoderm differentiation . Using the mesoderm cells differentiated with OP9 stroma cell co-culture , we found that only a small fraction of the cultured mesoderm cells was differentiated into endoderm based on Dab2 staining ( ∼1% ) , even though the expression of endodermal genes were significantly increased ( Figure 3 , Figure S6 , Figure S7 ) . This implies that some cells retaining the mesodermal phenotype express both endoderm and mesoderm genes . During transcription factor induced-somatic cell reprogramming to pluripotent cells , partially reprogrammed cell clones express both stem cell-related genes and lineage-specific genes together [10] . We suggest a model in which an endoderm gene program is activated in a large mesoderm population , priming it for endoderm differentiation , but only a small subset of this population accomplishes the primitive endoderm differentiation . We showed that the loss of DNA methylation allowed mesoderm cells being converted to endoderm cells in response to Gata4 using both Dnmt1-deficient and Dnmt3a/Dnmt3b-deficient mesoderm cells . However , the low efficiency and incomplete reprogramming of the conversion suggest that additional mechanisms such as cell-specific trans-factors or other epigenetic signatures [58] , [59] may also restrict the Gata4-induced endoderm differentiation in Flk1 ( + ) mesoderm cells . These other mechanisms may be coordinately regulated or maintained by DNA methylation . We obtained variable ratios of endoderm differentiation from Gata4-activated Dnmt3a/Dnmt3b-deficient Flk1 ( + ) mesoderm cells when we used the stroma cell-free condition with type IV collagen-coated dishes for mesoderm cell formation ( Figure S4 and data not shown ) . This variation in differentiation efficiency may be due to effects of such restriction mechanisms other than DNA methylation . It is possible that differentiation conditions without stroma cells are more sensitive to various factors in cell culture , which may affect gene expression or epigenetic signatures of the Flk1 ( + ) mesoderm cells differentiated with this condition . DNA methylation restricts cell differentiation potential during development . Trophectoderm differentiation is restricted in mouse ES cells , and DNA methylation is involved in this process through the DNA methylation-dependent silencing of trophectoderm transcription factor Elf5 [27] . Pancreatic β cell identity is maintained by DNA methylation-dependent silencing of the lineage-determining transcription factor Arx [29] . In these cases , DNA methylation suppresses a limited number of gatekeeper transcription factors . The loss of DNA methylation de-represses these transcription factors , which subsequently activate their downstream transcriptional programs . In contrast , we showed that , in our cellular models , DNA methylation stabilizes mesoderm identity during cell differentiation by restricting the responsiveness of downstream genes to the transcription factor Gata4 . We found that the induction of Gata4 together with the loss of DNMTs , but not Gata4 alone , activates the endoderm gene program in mesoderm cells and promotes endoderm differentiation . During this process , Gata4 promptly activates many endoderm genes , including both transcription factors and terminal differentiation genes with a similar time frame , suggesting that DNA hypomethylation allows Gata4 to activate endodermal target genes directly in mesoderm cells . However , we cannot exclude the possibility that endoderm transcription factors such as Sox17 and Foxa2 , which respond to Gata4 early , may contribute more than other proteins to the endoderm differentiation phenotype . Our results also showed that the loss of DNA methylation alone does not induce endoderm differentiation , showing that the role of DNA methylation is permissive , not instructive , in this differentiation . This is consistent with a previous study of astrocyte differentiation showing that DNA methylation suppresses the responsiveness of embryonic neuroepithelial cells to the gliogenic LIF signal during mouse embryogenesis [26] . The binding of the transcription factor STAT3 to the promoter region of the astrocyte gene Gfap is suppressed in a DNA methylation-dependent manner . Similarly , DNA methylation restricts the responsiveness of neuroepithelial cells to Notch signaling by suppressing the binding of the transcription factor RBP-J to the Hes5 gene promoter [60] . These reports suggest that regulation of the responsiveness of downstream genes to transcription factors is likely to be a broadly used mechanism of DNA methylation-dependent gene regulation . In hematopoietic stem cells ( HSCs ) , a deficiency of Dnmt1 results in impaired self-renewal and skewed myeloid/lymphoid differentiation [61] , [62] , whereas the inactivation of Dnmt3a leads to an increase in self-renewal associated with the incomplete repression of HSC genes such as Runx1 [63] . Thus , it is likely that DNA methylation modulates cellular differentiation by multiple pathways and mechanisms . Several transdifferentiation studies have shown that one or a few transcription factors are sufficient to convert somatic cell fate within several days [3] , [64]–[67] . It is likely that there are many genes downstream of transcription factors initiating trans-differentiation , but this aspect has yet to be analyzed in depth . Our study focused on the initial processes during cell fate conversion , and uncovered the contribution of DNA methylation in restricting the global response to the single transcription factor Gata4 . Several studies have also suggested a link between DNA methylation and transcription factor-induced cell reprogramming to pluripotency ( i . e . iPS cells ) [4] . DNA methylation and de-methylation are closely correlated with the epigenetic memory of the original donor cells , and this may contribute to the variable differentiation propensity of iPS cells [68]–[71] . In addition , overall efficiency of the reprogramming process can be improved when somatic cells are treated with DNMT inhibitors [10] . Although , the reprogramming to pluripotency is different from Gata4-induced transdifferentiation in that it requires a much longer period of time , DNA methylation-dependent mechanisms similar to those described here may be involved in the reprogramming process . DNA methylation regulates gene expression by various mechanisms [72] . The promoter regions of germ-cell-specific genes , inflammation-response genes , and some tissue-specific genes are methylated de novo by Dnmt3a/Dnmt3b around the implantation stage of mouse embryogenesis . The expression of these genes is increased by the loss of DNA methylation , indicating that DNA methylation directly represses their transcription [47] . In contrast , in neuronal progenitor cells , the gene body region of neural genes is methylated in a Dnmt3-dependent manner , and the expression of these genes is decreased by the loss of DNA methylation , suggesting that DNA methylation is required to maintain the expression of these genes [73] . Whole-genome DNA methylation analysis showed that the DNA methylation state of distal regulatory enhancers changes dynamically and is linked to changes in the expression of adjacent genes [16] , and that the DNA methylation changes of enhancers are driven by transcription-factor binding [16] , [74] . In this study , we showed that groups of developmental genes downstream of Gata4 become hyper-responsive to this transcription factor on a Dnmt3a/Dnmt3b-deficient background . The loss of DNA methylation together with Gata4 activation induces the expression of these genes , but the loss of DNA methylation alone does not alter their expression , indicating that DNA methylation does not directly regulate the transcription of these genes . This finding implies that the transcriptome for a given methylome depends on the composition of the transcriptional regulators in a cell . This notion is consistent with previous reports that genome-wide DNA methylation profiles are not well correlated with gene expression [15] , [63] . Further mechanistic studies will be necessary to connect the DNA methylome to cellular phenotypes . In conclusion , our results extend our understanding of the role of DNA methylation in cell differentiation and the stabilization of cellular traits . Together with its feature of clonal inheritance [75] , DNA methylation is likely to function as a memory of a cell's developmental history . Elucidation of the mechanisms of DNA methylation targeting and its interaction with chromatin may provide insight into the role of epigenetic regulation in development and cellular reprogramming .
Dnmt3a−/−Dnmt3b−/− DKO ES cells ( clone 16aabb ) , Dnmt1−/− ES cells ( clone 36 ) , and WT J1 ES cells were described previously [23] , [24] . WT , DKO , and Dnmt1−/− ES cell clones stably expressing dexamethasone ( Dex ) -inducible Gata4 were generated by introducing by electroporation an expression plasmid for Gata4 fused with the ligand-binding domain of the human glucocorticoid receptor ( Gata4GR ) driven by the CAG promoter [39] , followed by selection with L-histidinol dihydrochloride ( HisD ) ( clones J1G4 . 211 , 16G4 . 3 , and 36G4 . 3 , respectively ) . ES cells were maintained on gelatinized culture dishes in either ES medium , consisting of Glasgow Minimum Essential Medium ( GMEM , Sigma ) supplemented with 10% fetal calf serum ( FCS ) , 0 . 1 mM nonessential amino acids ( Invitrogen ) , 1 mM sodium pyruvate , 0 . 1 mM 2-mercaptoethanol , and 2000 U/ml LIF , or the same medium except for the replacement of 10% FCS with 10% Knockout Serum Replacement ( KSR , Invitrogen ) and 0 . 5% FCS . OP9 stromal cells , kindly provided by Dr . Shin-ichi Nishikawa , were maintained in α-Minimum Essential Medium ( α-MEM , Invitrogen ) supplemented with 20% FCS . For in vitro differentiation by LIF withdrawal , ES cells were cultured overnight in ES medium containing 10% FCS , then differentiation was induced by replacing the medium with medium lacking LIF , after a wash with phosphate-buffered saline ( PBS ) . For primitive endoderm ( PE ) differentiation , 100 nM Dex was added to ES cells stably expressing Gata4GR , in ES medium containing 10% FCS for 4 days [39] . For the time-course analysis , the cells were recovered by trypsinization at the indicated times after the addition of Dex , and RNA was isolated . For Flk1 ( + ) mesoderm differentiation , ES cells were cultured either on type IV collagen-coated dishes or on OP9 stromal cells [40] , [41] . For the type IV collagen-coated dish method , 1×105 WT ES cells or 5×105 DKO ES cells were plated on a type IV collagen-coated 10-cm dish ( BioCoat , BD Biosciences ) in differentiation medium ( α-MEM supplemented with 10% FCS and 50 µM 2-mercaptoethanol ) and cultured for 4 days . The cultured cells were then collected using 0 . 25% trypsin-EDTA , and single-cell suspensions were stained using an allophycocyanin ( APC ) -conjugated anti-Flk1 antibody ( AVAS12 , eBioscience ) , a biotinylated anti-PDGFRα antibody ( APA5 , eBioscience ) , and phycoerythrin-conjugated streptavidin ( eBioscience ) . For the OP9 stroma co-culture method , 2×105 WT ES cells or 2 . 4×105 DKO or Dnmt1−/− ES cells were plated on a 10-cm dish with confluent OP9 stromal cells in the differentiation medium for 4 days . The cultured cells were collected using 0 . 25% trypsin-EDTA , and single-cell suspensions were stained using an APC-conjugated anti-Flk1 antibody , a biotinylated anti-E-cadherin antibody ( Eccd2 , TaKaRa Bio or DECMA-1 , eBioscience ) , and phycoerythrin-conjugated streptavidin . Flk1 ( + ) , Flk1 ( + ) /PDGFRα ( + ) , and Flk1 ( + ) /E-cadherin ( − ) cells were sorted by a FACSAria ( BD Biosciences ) , and the flow cytometry profiles were visualized with FlowJo software ( Tree Star ) . The sorted cells were further cultured on type IV collagen-coated dishes in differentiation medium in the absence or presence of 100 nM Dex for 4 days or the indicated times . For the short-term Gata4-response experiment , ES cells were plated on a 10-cm dish with confluent OP9 stroma cells and cultured for 4 days as described above . One , two , or three hours before cell collection , 100 nM Dex was added to the cell culture . The cells were collected by trypsinization , and the Flk1 ( + ) /E-cadherin ( − ) cells were sorted as described above . The sorted cells were directly used for RNA isolation . Cells grown on gelatin- or type IV collagen-coated dishes were washed in PBS , fixed with 4% paraformaldehyde for 10 min at room temperature , and permeabilized with 0 . 5% Triton X-100 for 10 min . After being blocked in 4× saline-sodium citrate ( SSC ) containing 3% BSA and 0 . 2% Tween 20 for 30 min at 37°C , the cells were incubated with primary antibodies in detection buffer ( 4× SSC containing 1% BSA and 0 . 2% Tween 20 ) for 1 hr at 37°C , washed twice with 4× SSC , and incubated for 1 hr at 37°C with secondary antibodies conjugated with Alexa Fluor 488 or Alexa Fluor 555 . For DNA staining , fixed cells were incubated with 0 . 2 µg/mL 4′ , 6-diamidino-2-phenylindole dihydrochloride ( DAPI ) or 1 µg/mL Hoechst 33342 and then washed in 4× SSC . The following antibodies were used: anti-Disabled-2/p96 ( Dab2 ) mouse monoclonal antibody ( clone 52 , BD Biosciences , 610464 ) , anti-Gata4 rabbit polyclonal antibody ( Santa Cruz Biotechnology , sc-9053 ) , anti-alpha-SMA mouse monoclonal antibody ( clone 1A4 , Sigma , A5228 ) , anti-Sox17 polyclonal goat antibody ( R&D Systems , AF1924 ) , goat anti-mouse IgG conjugated with Alexa Fluor 488 ( Invitrogen , A11017 ) , goat anti-rabbit IgG conjugated with Alexa Fluor 488 or Alexa Fluor 555 ( Invitrogen , A11070 , A21430 ) , and rabbit anti-goat IgG conjugated with Alexa Fluor 488 ( Invitrogen , A21222 ) . For RT-PCR , total RNA was isolated with TRIzol Reagent ( Invitrogen ) , and the first-strand cDNA was synthesized from 1–5 µg of total RNA with random hexamer primers and SuperScript II reverse transcriptase ( Invitrogen ) , according to the manufacturer's protocol . Primer sequences and PCR conditions are listed in Table S4 . For RT-qPCR , cytoplasmic RNA was isolated with the RNeasy Mini Kit ( Qiagen ) according to the manufacturer's cytoplasmic RNA protocol with the DNase digestion option . The first-strand cDNA was synthesized from 400 ng of total RNA with SuperScript VILO cDNA synthesis kit ( Invitrogen ) , according to the manufacturer's protocol . Expression levels of genes of interest in cDNA samples were quantitated by real-time PCR using FastStart SYBR Green Master ( Roche Applied Science ) , based on a standard curve using genomic DNA . The RT-qPCR data were normalized by values of three housekeeping genes , Gapdh , Rps21 and Rps27a as internal control genes . Primer sequences and PCR conditions are listed in Table S4 . For the Affymetrix microarray analysis , total RNA was isolated with TRIzol reagent and purified using an RNeasy Mini Column ( Qiagen ) . The cRNA probe was prepared using a two-cycle target-labeling assay and hybridized to Affymetrix MOE430v2 oligonucleotide arrays as recommended by the manufacturer ( Affymetrix ) . RNA from two independent experiments were used as duplicates for each experimental condition . The microarray data were analyzed using the Affy package [76] of the Bioconductor suite of programs [77] in combination with the eXintegrator system [78] ( http://www . cdb . riken . jp/scb/documentation/ ) . Data from the raw . CEL files were used either to calculate expression values using RMA expression values or as input to the eXintegrator system . A Principal Component Analysis ( PCA ) was carried out using the R “prcomp” function [79] , and differentially expressed genes were identified using the SAM algorithm [80] . For Agilent microarray analysis , total RNA was isolated with an RNeasy Plus Micro Kit with a gDNA Eliminator column ( Qiagen ) in most cases . For the short-term Gata4-response experiment ( 0–3 hr ) , cytoplasmic RNA was isolated with the RNeasy Mini Kit ( Qiagen ) according to the manufacturer's cytoplasmic RNA protocol with the DNase digestion option . RNA quality was checked by electrophoresis on an Agilent 2100 Bioanalyzer ( Agilent Technologies ) . Fifty nanograms of total RNA was labeled by a Low Input Quick Amp Labeling Kit ( Agilent Technologies ) and hybridized to a Mouse Gene Expression 8x60k Microarray ( Agilent Technologies ) according to the manufacturer's instructions . For 72 hr-time-course analysis , each RNA was labeled in triplicate ( Flk1 ( + ) cells at 0 hr and at 12 , 24 , 36 , 48 and 72 hr in the presence of Dex ) or in duplicate ( others ) . For 3 hr-time-course analysis , RNA from two independent experiments for WT or DKO cells expressing Gata4GR transgene ( WT+Gata4GR and DKO+Gata4GR ) were labeled and used as duplicate for each time point , while each RNA from one experiment was labeled in duplicate for DKO cells without the Gata4GR transgene ( DKO ) . The microarray data were quantile-normalized and analyzed using custom R scripts with the Limma package [81] , the Bioconductor package suite [77] , and custom Perl scripts . Probe values from the same gene were merged into the mean values to calculate the gene expression values , and the analyses described below were performed only for genes that had a GeneSymbol . To identify differentially expressed genes , we used empirical Bayes methods [82] . Genes that had a p value <0 . 01 and fold change >2 or 4 were selected . Unsupervised hierarchical clustering was performed in the clustering module for Perl [83] with 1 - ( Pearson correlation coefficient ) as a distance and average linkage . The clusters were visualized by Java Treeview ( http://jtreeview . sourceforge . net/ ) [84] and custom Perl scripts . Venn diagrams were generated using the BioVenn web application ( http://www . cmbi . ru . nl/cdd/biovenn/ ) [85] . Gene ontology analysis at Biological Process level 4 ( BP4 ) was performed using DAVID ( http://david . abcc . ncifcrf . gov/ ) [86] version 6 . 7 with default parameters . The microarray data have been deposited into the Gene Expression Omnibus ( GEO ) database ( accession number GSE36814 for the experiment using the type IV collagen-coating condition by Affymetrix microarray analysis and GSE36313 for the experiment using the OP9 co-culture condition by Agilent microarray analysis ) . ChIP was performed using the ChIP-IT Express chromatin immunoprecipitation kit ( Active Motif , Rixensart , Belgium ) according to the manufacturer's instructions . Briefly , WT or DKO ES cells differentiated on 15-cm dishes for 4 . 5 days using OP9-co-culture were treated with Dex for 3 hours and then collected by trypsinization . The differentiated cells were stained with a biotin-conjugated anti-Flk1 antibody ( AVAS12 , eBioscience ) followed by streptavidin microbeads ( Miltenyi Biotec ) , and then sorted with a magnetic cell separation system ( MACS , Miltenyi Biotec ) . The isolated mesoderm cells were crosslinked with 1% formaldehyde for 10 min at room temperature , then the formaldehyde was quenched by adding glycine to a final concentration of 0 . 125 M . Chromatin was sonicated to an average size of 0 . 3–0 . 5 kb using Covaris shearing technology ( Covaris , Massachusetts , USA ) . A mixture of equal amounts of three anti-Gata4 antibodies ( sc-1237 and sc-25310 , Santa Cruz Biotechnology and L97-56 , BD Biosciences ) , bound to magnetic beads ( Active Motif ) , was added to the sonicated chromatin , and the mixture was incubated for 4 hours at room temperature . After the beads were washed , the chromatin was eluted , and the crosslinking was then reversed . The DNA was purified with a MinElute DNA purification kit ( Qiagen ) . The resultant ChIP DNA was quantified using a Bioanalyzer ( Agilent Technologies ) and a Quant-it dsDNA assay kit ( Invitrogen ) . Undifferentiated WT or DKO ES cells without Dex treatment were used as controls for ChIP . Libraries for high-throughput sequencing were prepared with the Illumina ChIP-seq DNA Sample Prep Kit according to the manufacturer's instructions . High-throughput sequencing using an Illumina Hiseq and mapping of the resulting reads were performed by Hokkaido System Science Co . , Ltd . Japan . Data analysis and visualization of the sequence reads were performed with DNAnexus software tools ( https://dnanexus . com/ ) . The ChIP-seq raw data have been deposited into the GEO database ( accession code GSE41361 ) . Of the initial 250 , 590 , 286 reads for WT Flk1+ cells and 392 , 760 , 766 reads for DKO Flk1+ cells obtained in the Gata4-ChIP-seq experiment , 209 , 514 , 577 ( 83 . 61% ) and 368 , 177 , 514 ( 93 . 74% ) were mapped to the mouse reference genome ( NCBI v37 , mm9 ) , respectively . The ChIP-seq peaks for Gata4 were determined with DNAnexus software tools using the following settings: KDE ( kernel density estimation ) bandwidth = 30 , ChIP candidate threshold = 5 . 0 , Experiment to background enrichment = 3 . 0 , Minimum ratio of confident to repetitive mapping in region = 3 . 0 . Peak calling with DNAnexus software tools identified 20 , 410 peaks for WT Flk1+ cells and 22 , 733 peaks for DKO Flk1+ cells using WT or DKO ES ChIP-seq reads , respectively , as background controls . For DKO-specific Gata4 peak calling , 10 , 636-enriched peaks were identified from the comparison between WT and DKO Flk1+ cells . The nearest Refseq genes ( within 5 kb ) were identified from the Gata4 peaks . Transcription-factor binding-site motif analysis for the Gata4 peak sequences was performed using the MEME-ChIP suit ( http://meme . nbcr . net/ ) [50] . For ChIP-qPCR , 100 nM Dex was added to WT ES cells stably expressing Gata4GR cultured on 15-cm dishes in ES medium containing 10% FCS . At 3 hours after the addition of Dex , the cells were crosslinked with 1% formaldehyde for 10 min at room temperature on the dishes; then the formaldehyde was quenched by adding glycine to a final concentration of 0 . 125 M . The fixed cells were recovered by scraping with a rubber policeman in ice-cold PBS and collected by centrifugation ( Dex+ ) . WT ES cells without Dex treatment were used as controls ( Dex− ) . Chromatin sonication , ChIP with the mixture of three anti-Gata4 antibodies , and purification of ChIP DNA were performed as described for ChIP-seq , except for using Protein G FG beads ( Tamagawa seiki ) instead of magnetic beads in the kit . Relative abundance of regions of interest in precipitated DNA to input DNA was quantitaed by real-time PCR using Thunderbird SYBR qPCR Mix ( TOYOBO ) with the comparative CT method . Gata4 enrichment was calculated as fold change of relative abundance for Dex+ to that for Dex− . Primer sequences and PCR conditions are listed in Table S4 . Genomic DNA was isolated from ES cells or Flk1 ( + ) mesoderm cells with a QIAamp DNA micro kit ( Qiagen ) and subjected to bisulfite conversion with an EpiTect Bisulfite kit ( Qiagen ) , according to the manufacturer's instructions with a slight modification [87] . Target sequences of the bisulfite-converted DNA were amplified by PCR , and 24 clones for each sample were sequenced . The primers for bisulfite sequencing were designed using MethPrimer ( http://www . urogene . org/methprimer/ ) [88] . The bisulfite sequencing data were analyzed using QUMA ( http://quma . cdb . riken . jp/ ) [89] . Primer sequences and PCR conditions are listed in Table S4 . Constructs used for the luciferase reporter assays of Gata4-ChIP target sequences were based on the pFgf3_1 . 7k-luc vector [39] , in which a fragment of DNA encompassing 1 . 7 kb of sequence immediately 5′ of the Fgf3 coding region containing GATA binding sites [90] was inserted into pGL4 . 10 ( Promega ) . We generated the pFGF3_0 . 8k-luc vector by removing the 5′-half ( 0 . 9 kb ) of FGF3 1 . 7 kb fragment from the pFgf3_1 . 7k-luc vector with digestion of 5′-end XhoI site on GL4 . 10 vector and internal AflII site . Luciferase reporter vectors containing Gata4-ChIP target sequences were constructed by inserting a 0 . 2–0 . 3 kb fragments centered around Gata4-ChIP-seq peaks amplified with HotStarTaq DNA polymerase ( Qiagen ) using primers containing XhoI site ( forward ) and AflII site ( reverse ) sites , between the XhoI and AflII sites of the pFgf3_0 . 8k vector ( See also Figure S12A ) . The following Gata4-ChIP peak regions were used for the construction and luciferase reporter assay; Aqp8 ( intron ) , Grk5 ( promoter ) , Sord ( promoter ) , Sox7 ( promoter ) , Lgmn ( intron ) , Myocd ( intron ) , and Spon1 ( 3′UTR ) . Primer sequences are listed in Table S4 . For transfection of reporter plasmids , 1×104 cells were seeded in each well of a 96-well plate in ES medium containing 10% FCS , and incubated with 330 ng reporter plasmid and 8 ng of the internal control plasmid pRL-CMV ( Promega ) , together with Lipofectamine 2000 ( Invitrogen ) , following the manufacturer's protocol . At 3 hr after the transfection , 100 nM Dex was added . Luciferase assays were performed at 30 hr after the addition of Dex using a Dual-luciferase assay kit ( Promega ) . | Animal bodies are constructed from many different specialized cell types that are generated during embryogenesis from a single fertilized egg , and acquire their specific characteristics through a series of differentiation steps . After being committed to a specific cell type , it is generally difficult for differentiated cells to convert to other cell types , at least partly because the cells maintain some memory or mark of their developmental history . Such cellular memory is mediated by “epigenetic” mechanisms , which function to stabilize the cell state . DNA methylation , a chemical modification of genomic cytosine residues , is one such mechanism . Genomic DNA methylation patterns in early embryonic cells are established in a cell-type-dependent manner , and these specific patterns are propagated through cell divisions in a clonal manner . However , our understanding of how DNA methylation controls cell differentiation and developmental gene regulation is limited . In this study , using an in vitro model of differentiation , we obtained evidence that DNA methylation modulates the cell's response to DNA-binding transcription factors in a cell-type-dependent manner . These findings extend our understanding of how cellular traits are stabilized within specific lineages during development , and may contribute to advances in cellular engineering . |
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Chagas disease , caused by the vector-borne protozoan Trypanosoma cruzi , is increasingly recognized in the southern U . S . Government-owned working dogs along the Texas-Mexico border could be at heightened risk due to prolonged exposure outdoors in habitats with high densities of vectors . We quantified working dog exposure to T . cruzi , characterized parasite strains , and analyzed associated triatomine vectors along the Texas-Mexico border . In 2015–2016 , we sampled government working dogs in five management areas plus a training center in Texas and collected triatomine vectors from canine environments . Canine serum was tested for anti-T . cruzi antibodies with up to three serological tests including two immunochromatographic assays ( Stat-Pak and Trypanosoma Detect ) and indirect fluorescent antibody ( IFA ) test . The buffy coat fraction of blood and vector hindguts were tested for T . cruzi DNA and parasite discrete typing unit was determined . Overall seroprevalence was 7 . 4 and 18 . 9% ( n = 528 ) in a conservative versus inclusive analysis , respectively , based on classifying weakly reactive samples as negative versus positive . Canines in two western management areas had 2 . 6–2 . 8 ( 95% CI: 1 . 0–6 . 8 p = 0 . 02–0 . 04 ) times greater odds of seropositivity compared to the training center . Parasite DNA was detected in three dogs ( 0 . 6% ) , including TcI and TcI/TcIV mix . Nine of 20 ( 45% ) T . gerstaeckeri and T . rubida were infected with TcI and TcIV; insects analyzed for bloodmeals ( n = 11 ) fed primarily on canine ( 54 . 5% ) . Government working dogs have widespread exposure to T . cruzi across the Texas-Mexico border . Interpretation of sample serostatus was challenged by discordant results across testing platforms and very faint serological bands . In the absence of gold standard methodologies , epidemiological studies will benefit from presenting a range of results based on different tests/interpretation criteria to encompass uncertainty . Working dogs are highly trained in security functions and potential loss of duty from the clinical outcomes of infection could affect the work force and have broad consequences .
Chagas disease , a potentially deadly cardiac disease of humans and dogs , is caused by the flagellated protozoan parasite Trypanosoma cruzi . The parasite is transmitted by infected hematophagous triatomine insects , commonly known as ‘kissing bugs’ . Chagas disease is estimated to infect nearly 6 million people throughout Latin America , and occurs across the southern US in enzootic cycles [1 , 2] , where raccoons and other wildlife serve as reservoirs [2 , 3] . In many areas of Latin America , such as in the Gran Chaco ecosystem , domestic dogs are an important reservoir of T . cruzi and domestic vectors that fed on dogs showed higher infection prevalence than vectors that fed on other domestic hosts [4 , 5] . The importance of canines in the T . cruzi transmission cycle in the US is not yet understood . The occurrence of T . cruzi infected canines in the USA is especially high in the state of Texas [1 , 6 , 7] , where 439 cases were reported across 58 counties between 2013–2015 when there was mandatory reporting of T . cruzi infected dogs [8] . Texas harbors at least seven established species of triatomine vectors capable of transmitting T . cruzi [3] and infected wildlife are widespread [1] . The high frequency of canines infected with T . cruzi likely reflects robust enzootic transmission in the state . Outside of Texas , dogs infected with T . cruzi have been reported in Louisiana [9 , 10] , Oklahoma [11 , 12] , Tennessee [13] and Virginia [14] . Across the studied populations , apparent seroprevalence ranged from 3 . 6–57 . 6% and predispositions of infection status with certain breeds or types of dogs do not appear to be strong , with hunting dogs , working dogs , household pets , shelter and stray dogs all impacted [6 , 7 , 9 , 12 , 14 , 15] . T . cruzi infection can occur by vector-mediated transmission through the introduction of infected bug feces into the bite site or mucous membrane or through the ingestion of infected bugs or their feces [5] . Additionally , congenital transmission may occur [3] . Dogs are more likely to become infected than humans [16 , 17] , which could be from dog’s affinity to consume bugs [12 , 18–21] . T . cruzi-infected dogs may be asymptomatic or may develop debilitating acute or chronic cardiac disease , characterized by myocarditis , hepatomegaly , ascites , cardiac dilatation , or sudden death [22] . There are currently no vaccinations or approved anti-parasitic treatments for T . cruzi infections in dogs in the US , and infected dogs are treated symptomatically . The Department of Homeland Security ( DHS ) of the US government manages over 3 , 000 working dogs in various capacities including the Transportation Security Authority , Coast Guard , Secret Service , Federal Protective Services , Customs and Border Protection , and Federal Operations . These dogs are highly trained in working duties performed in the indoor and outdoor environment including search and rescue functions as well as detection of concealed persons , narcotics , or explosives . DHS working dogs may be at increased risk for contact with vector species from working and sleeping outdoors . Some of the working dogs are kept in group kennels , which have previously been shown to be a risk factor for T . cruzi infection [7] . Their working environment could further be an attractant to the vector , where there is high vehicle traffic emitting CO2- a known attractant [23] , bright lights at night , and concentrations of animals and people in otherwise rural areas . In order to provide a baseline for conducting clinical assessments and developing disease management strategies , we conducted a seroepidemiological investigation to quantify the prevalence of T . cruzi infection in populations of working dogs along the Texas-Mexico border . Additionally , we aimed to determine the infection status and feeding patterns of triatomine vectors in the environments where these dogs work and are kenneled .
All canine samples were collected in adherence with animal use protocols approved by Texas A&M University’s Institutional Animal Care and Use Committee on 08/17/2015 under the number 2015–0289 . Written consent was received for each canine sampled from DHS personnel . Sampled DHS working dog breeds were predominantly Belgian Malinois and German Shepherds . Most dogs were bred in Europe , and less commonly dogs came from vendors within Texas or other parts of the US . Dogs receive over 6 months of training at either a training facility in El Paso , Texas , or Front Royal , Virginia , and specialize in various jobs such as track and trail , detection of humans , narcotics , currency , or agricultural products , and search and rescue . After training , dogs are typically assigned to a specific management area and have limited travel . The dogs in our study perform working duties either immediately adjacent to the geopolitical border ( ports of entry ) or north of the border ( checkpoints ) . Off-duty canines are either kenneled individually at their handler’s residence or in a group kennel . Residential kennels are indoor-outdoor metal kennels raised 2 feet from the ground , giving the dog the option of sleeping inside or outside . Group kennels are indoor-outdoor , concrete kennels , and dogs are confined inside during the night . We used a cross sectional study design to collect blood samples from DHS working dogs during November 2015 and April 2016 . Working dogs were sampled from all 5 management areas , with a goal of sampling at least 60% of the dogs that occurred within each management area . Additionally , we sampled DHS canines that were in training at a training facility in management area #1 ( Fig 1 ) . Sample criteria included dogs over 6 months in age and on active duty or in training . Demographic information was collected on all dogs sampled including age , sex , breed , canine job , sleeping location and station of duty . A minimum of 1 ml of blood was collected by venipuncture and aliquoted into serum and EDTA tubes . Samples were screened for anti-T . cruzi antibodies by Chagas Stat-Pak rapid immunochromatographic test ( ChemBio , NY ) which was designed for use in humans and has been validated in dogs [9] . Stat-Pak assay uses three T . cruzi recombinant antigens that are bound to the assay membrane solid phase . Serum or plasma samples were tested according to manufacturer’s protocol and read for result determination after 15 minutes . Tests were considered negative when no color developed and positive when a clear line developed . Additionally , very faint bands that were not perceptible enough to be consider a clear positive , yet with some low level of color development to differentiate them from negative , were tracked as ‘inconclusive’ and subjected to additional testing . All positive or inconclusive samples as determined by Stat-Pak plus 10% of the negatives were tested by both indirect fluorescent antibody ( IFA ) test and Trypanosoma Detect ( InBios , International , Inc . , Seattle , WA ) . IFA detects anti-T . cruzi IgG antibodies and was performed by the Texas Veterinary Medical Diagnostic Laboratory ( TVMDL , College Station , TX ) on serum or plasma samples . Titer values of 20 or higher were considered positive per TVMDL standard protocol; this titer value cutoff has also been used in human medicine [24] . IFA readers were blinded to previous serologic results . Trypanosoma Detect is a rapid immunochromatographic dipstick assay that employs a multi-epitope recombinant antigen for the detection of anti-T . cruzi antibodies . The Trypanosoma Detect test was designed for use in humans but has been found to have high sensitivity and specificity for use in dogs [25] . Serum or plasma were tested according to manufacturer's protocol and read for result determination after 20 minutes . Test results were scored as positive , inconclusive , or negative using the same criteria as described above for the Chagas Stat-Pak . Serological positive status was assigned to samples that tested positive on at least two independent tests . Amplification of parasite DNA from blood samples by real time PCR was performed on all sampled dogs . DNA was isolated from 250 uL of buffy coat by using E . Z . N . A . Tissue DNA kit ( Omega Bio-Tek , Norcross , GA ) . Negative controls ( phosphate buffered saline or water template ) were included in the DNA extractions and the PCR . To determine if analysis of clot rather than buffy coat may result in a greater ability to detect parasite DNA , we conducted additional work with a subset of samples as follows . From 12 dog samples , we extracted DNA from 1 mL of clot for PCR analysis . These 12 dogs comprised 10 that were seropositive and PCR negative based on buffy coat; 1 that was seropositive and PCR positive based on buffy coat; and 1 that was seronegative and PCR positive based on buffy coat analysis . Samples were first screened for presence of T . cruzi satellite DNA using the Cruzi 1/2 primer set and Cruzi 3 probe in a real-time assay to amplify a 166-bp segment of a repetitive nuclear DNA [26 , 27] . Reactions consisted of five microliters of extracted DNA , primers I and II each at a concentration of 0 . 75 μM , 0 . 25 μM of probe , and iTaq University Probes Supermix ( BioRad Laboratories , Hercules , CA ) , in a 20 μL reaction volume . Previously published thermocycling parameters were followed except with a 3-minute initial denaturation using a Stratagene MxPro3000 ( Agilent Technologies , Santa Clara , CA ) . T . cruzi DNA extracted from isolate Sylvio X10 CL4 ( ATCC 50800 , American Type Culture Collection [ATCC] ) was used for a positive control . Machine-calculated thresholds and reaction curves were visually checked for quality . Samples with Ct values less than 34 were considered suspect positive and subjected to further testing . Suspect positive samples by qPCR were run on a second , independent PCR using T . cruzi 121/122 primers to amplify a 330-bp region of kinetoplast DNA [28 , 29] . Reactions included 1μL template DNA , primers at final concentrations of 0 . 75 μM each , and FailSafe PCR Enzyme Mix with PreMix E ( Epicentre , Madison , WI ) in a final reaction volume of 15 μL . Amplicons were visualized on 1 . 5% agarose gels stained with GreenGlo safe DNA dye ( Denville Scientific Inc . , Metuchen , NJ ) . Samples that yielded a band of the appropriate size were interpreted as positive in this assay . Parasite positive dogs were defined as those that tested positive on both the rt-PCR screening and the secondary PCR assays . We used a multiplex quantitative , real time PCR to determine T . cruzi discrete taxonomic unit ( DTU ) of samples that were positive or suspect positive on the screening assay based on amplification of the nuclear spliced leader intergenic region ( SL-IR ) [30] . Using a QIAGEN Multiplex PCR Kit ( QIAGEN , USA ) reactions were performed using 2μL template DNA in a final volume of 20 μl and run on a BioRad CFX96 ( Hercules , CA , USA ) . The only deviation from the previously described protocol was the extension of cycles from 40 to 45 and substitution of dyes as previously described [7] . Positive controls consisted of DNA from triatomines collected across Texas that were previously characterized as infected with TcI or TcIV based on amplification and sequencing of the TcSC5D gene [31] . Samples with Ct values less than 34 were considered positive , and fluorescence signal determined the strain type . Triatomine bugs were opportunistically collected by dog handlers in summer 2016 from group kennels , outside handler’s residence around canine housing , and at stations where dogs worked . To encourage collections , outreach materials with photos of triatomines and look-alike species were disseminated by email and in printed format to dog handlers prior to the summer peak of adult triatomine activity . Bugs were identified to species using morphologic features [32] and sexed . After bugs were washed in 10% bleach solution and rinsed in distilled water , sterile instruments were used to dissect the bugs , isolate hindgut material and evidence of a recent bloodmeal was noted . DNA was extracted from hindguts and tested for T . cruzi DNA and determination of T . cruzi DTU using the same methods as the above testing of dog samples . In order to determine the source of recent bloodmeals , hindgut DNA was subjected to PCR amplification of vertebrate cytochrome B sequences using previously published primers and cycling conditions [33 , 34] . Reactions included 3 μL template DNA , primers at final concentrations of 0 . 66 μM each , and FailSafe PCR Enzyme Mix with PreMix E ( Epicentre , Madison , WI ) in a final reaction volume of 50 μL . Amplicons were visualized on 1 . 5% agarose gel , prepared for sequencing using ExoSAP-IT ( Affymetrix , Santa Clara , CA , USA ) , and Sanger sequencing was performed ( Eton Bioscience Inc . , San Diego , CA , USA ) . Resulting sequences were compared to existing sequences using Basic Local Alignment Search Tool ( National Center for Biotechnology Information , US National Library of Medicine ) . In recognition of the potential for contamination from the environment , samples that aligned to human were re-run on another PCR assay to provide a secondary line of evidence . Due to the uncertainty of sample serostatus associated with the inconclusive band development , antibody-positive dogs were defined using two methods; a ) in the conservative method , inconclusive band development was interpreted as negative , and b ) in the inclusive method , inconclusive band development was interpreted as positive . In the absence of gold standard serological methodology , these two different criteria of positivity ( method A and B ) were analyzed separately to provide a range of results . To evaluate the relationship between potential risk factors and the serostatus of canines , data were imported into R software [35] for analysis . Assessed variables were dog age ( young = 6 months to <3 years , middle age = ≥ 3 years to <6 years , senior = ≥ 6 years ) , sex , breed , sleeping location ( individual kennel at handler’s residence or group kennel ) and management area ( locations 1–5 or training center ) . Due to the small sample size of dogs in some jobs , canine job was dichotomized based on type of detection . Bivariable analysis using the chi-squared or Fisher’s exact was used to identify putative risk factors . Factors with a p≤ 0 . 25 from the initial screening were used in a logistic regression model , while controlling for management area as a random effect . Generalized linear mixed models were calculated and factors with values of p < 0 . 05 were considered significant . Odds ratios and 95% confidence intervals were calculated . To determine variation in seroprevalence across management areas , a logistic regression model was used in which the training center served as the referent to which all five management areas were compared . Kappa index was used to test the agreement between each pairwise combination of the results of the three serological assays for the samples that were tested on all three assays; this sample set was biased toward Stat-Pak positive samples .
In considering inconclusive bands on immunochromatographic tests as negative , 39 of 528 ( 7 . 4% ) of dogs were seropositive for antibodies to T . cruzi on at least 2 assays . Across management areas and the training center , seroprevalence ranged from 4 . 3% to 10% ( Fig 1 ) . In the bivariable analysis , T . cruzi seroprevalence was significantly different across dog breed ( p = 0 . 03 ) , with seroprevalence of German Shepherds being lowest ( 3 . 7% ) and ‘other’ breeds being highest ( 14 . 3%; Table 1 ) . Dogs that spent off-duty time in residential kennels had a significantly higher seroprevalence ( 29/295 , 9 . 8% , p = 0 . 02 ) than those that were group-kenneled ( 10/233 , 4 . 3% ) . Seroprevalence was significantly different among age groups ( p = 0 . 04 ) , where senior dogs had a seroprevalence of 10 . 4% , middle age dogs a seroprevalence of 7 . 9% and young dogs 3 . 2% . Seroprevalence did not vary significantly by sex or canine job . Multivariable logistic regression analysis showed a significant association ( odds ratio [OR] 0 . 41 , 95% CI 0 . 17–0 . 99 , p = 0 . 047 , Table 2 ) between breed and seropositive dogs , after controlling for management areas as a random effect ( Table 2 ) , in which German Shepherds were associated with a significantly lower seroprevalence ( 3 . 7% ) than Belgian Malinois ( 8 . 6% ) . No significant association was found between age , job , or sleeping location and seroprevalence . In considering inconclusive bands on serologic tests as positive , 100 of 528 ( 18 . 9% ) of dogs were seropositive for antibodies to T . cruzi on at least 2 assays . Seroprevalence ranged from 11 . 6% to 26 . 7% across management areas and the training center ( Fig 1 ) . When running bivariable analysis , dogs that spent off-duty time in residential kennels ( 65/295 , 22% ) were marginally ( p = 0 . 09 , Table 2 ) more likely to be seropositive than dogs sleeping at a group kennel ( 36/233 , 15 . 4% ) . Seroprevalence did not vary significantly by age , breed , sex or canine job . Multivariable logistic regression analysis showed that there was no association between age , job , or sleeping location and seroprevalence . Backwards elimination was performed and when only age was included in the model there was a marginal association in which old dogs had a higher seroprevalence ( 39/182 , 21 . 4% ) than young dogs ( 22/156 , 14 . 1%; p = 0 . 09 ) , after controlling for management areas as a random effect . While seroprevalence did not significantly differ across management areas and the training center when positivity was defined according to Method A , dogs from management area #2 ( OR 2 . 6 , 95% CI 1 . 0–6 . 7 , p = 0 . 04 ) and #3 ( OR 2 . 8 , 95% CI 1 . 2–6 . 8 , p = 0 . 02 ) had significantly higher seroprevalence compared to the training center when seropositivity was determined according to Method B ( Table 3 ) . This indicates that area #2 and #3 were both associated with many samples that produced very faint ( inconclusive ) bands on the immunochromatographic tests . In comparing the results across all three serological testing platforms ( Table 4 ) , all IFA positive samples are positive on Trypanosoma Detect , and all but two samples are Stat-Pak positive-both of these samples having a titer of 20 . When comparing the IFA negative samples 71 . 3% are positive or inconclusive on Stat-Pak and 48 . 4% are positive or inconclusive on Trypanosoma Detect . From the 528 dog samples in the study , 215 samples were tested on all three serology assays . Overall test agreement ranged from slight to moderate agreement based on the Kappa Indices ( Table 5 ) , with agreement between tests being better when interpreting immunochromatographic test results using the conservative method A ( kappa range 0 . 37–0 . 48 ) compared to inclusive method B ( kappa range 0 . 05–027 ) . The best agreement was using method A between Stat- Pak and Trypanosoma Detect , with a Kappa index of 0 . 48 ( moderate agreement ) . Of the 57 randomly-selected Stat-Pak negative samples that were subjected to additional serologic testing , one was positive on both IFA ( titer 20 ) and Trypanosoma Detect; this sample was counted as positive in the seroprevalence estimates . Nine ( 15 . 8% ) samples that were both Stat-Pak and IFA negative were positive on Trypanosoma Detect; these dogs were counted as negative in the seroprevalence estimates , but could be false negatives . When applying this prevalence of potential false negatives to the total number of dogs that were negative by Stat-Pak , an additional 49 dogs are extrapolated to be potential false negatives; including these samples as positive would increase seroprevalence to 15 . 9% ( 84 dogs total ) by conservative method A , and 25 . 4% ( 149 dogs total ) by inclusive method B . Inconclusive bands were reported from 108 ( 20 . 5% ) samples screened on Chagas Stat-Pak . When tested on IFA only 1 ( 0 . 9% ) inconclusive tested positive with a titer of 20 . When inconclusive samples were run on Trypanosoma Detect , 37 ( 29 . 6% ) had inconclusive bands on Trypanosoma Detect , 20 ( 18 . 5% ) were positive , and 51 ( 47 . 2% ) were negative . T . cruzi DNA was detected in the buffy coat fraction of the blood in three of 528 ( 0 . 6% ) dog samples according to our diagnostic method which included amplification in both a screening and confirmatory assay . The first PCR-positive dog was sampled from area # 5 in November and was positive for antibodies by all three serology assays with a relatively high titer ( 640 ) on IFA . Using the multiplex real time PCR to determine T . cruzi DTUs , we found that this dog harbored DTU TcIV . The second PCR-positive dog was from the canine training center , sampled in April , positive on all serology assays with a titer of 320 and harbored a mix TcI/TcIV . The third dog was from area # 2 , sampled in April , was negative by all serological assays , and strain type could not be determined . When this PCR positive yet serologically-negative dog was included in binomial analysis of risk factors and the logistical regression model , no difference was found in significant associations . The subset of 12 samples that were subjected to an additional DNA extraction from 1mL of clot produced PCR results that were identical to the results obtained from the 250 uL buffy coat extractions with the exception of the sample from the seronegative , buffy coat-positive sample . This sample was negative based on clot analysis . In the summer of 2016 , a total of 20 adult triatomine bugs of two species ( 18 Triatoma gerstaeckeri and 2 T . rubida ) were opportunistically collected by canine handlers from three management areas ( Table 6 ) . Kissing bugs were collected from stations where dogs and handlers work ( n = 6 ) , handler’s residence near canine housing ( n = 7 ) , group kennels ( n = 4 ) , from the field ( n = 2 ) and 1 bug was removed from a dog while working . Nine ( 45% ) triatomines were positive for T . cruzi including half of the T . gerstaeckeri specimens but neither of the two T . rubida specimens . Of the 9 positive bugs , parasite strain typing revealed DTU TcI in 6 , TcIV in 1 , and a mixed TcI/TcIV coinfection in 2 . From dissection , 13 of the 20 bugs had evidence of a recent blood meal in their hind gut , and 11 of these yielded results after the blood meal analysis protocols , revealing human , canine , coastal-plain toad ( Bufo nebulifer ) and rat ( Rattus rattus ) DNA ( Table 6 ) .
We found widespread T . cruzi infection in government working dogs along the Texas-Mexico border . DHS working dogs play an important role in detection and security functions in the Unites States and the clinical manifestation of infection may be associated with significant future economic and security consequences . We are aware of only two prior epidemiological investigations of T . cruzi infection in working dogs in the US . In 2007 , a serological survey was conducted on military working dogs ( MWD ) in San Antonio , TX , after veterinarians noted an increase in Chagas disease diagnoses , revealing 8% of the kenneled dogs were positive by IFA [36] . Such findings are of utmost importance in these dogs; in 2009 , MWDs deployed in Iraq were evacuated due to cardiac symptoms and diagnosed with T . cruzi infection leaving troops vulnerable without explosive detection dogs [36] . Recently , populations of working hound dogs in south central Texas that are used for scent detection and track/trail were characterized with an extremely high seroprevalence of 57 . 6% ( n = 85 ) in which positive dogs were reactive on both Stat-Pak and IFA [7] . The study population also included many dogs with parasite DNA in the blood and other organs , and infected triatomines collected from the dog kennels were determined to have fed on dogs , allowing the authors to conclude that multi-dog kennels can be high risk environments of T . cruzi transmission [7] . Exposed dogs were present in all five management areas and the canine training school , with an overall apparent seroprevalence of 7 . 4–18 . 9% . This seroprevalence is similar to that reported from dogs in Chagas-endemic areas in Latin America including populations in Peru ( 12 . 3% ) [37] , Argentina ( 45 . 6% ) [38] , Panama ( 11 . 1% ) [39] , Costa Rica ( 27 . 7% ) [19] , Yucatan State , Mexico ( 9 . 8%-14 . 4% ) [40] and Mexico State , Mexico ( 10%-15 . 8% ) [41] . Previous epidemiological investigations of T . cruzi in canines in the US are limited , and most have focused on stray dogs or those sampled from animal shelters , which may be considered as high risk populations due to outdoor activity . A serosurvey of high risk kenneled dogs in southern Louisiana found that 22 . 1% [9] of dogs tested positive for T . cruzi antibodies using the same three serology assays performed in this study . A study in Oklahoma sampling shelter dogs and pet dogs concluded that 3 . 6% dogs were seropositive when testing by radioimmunoprecipitation assay ( RIPA ) [12] . Earlier studies in southern Texas stray dogs 375 dogs were tested and 7 . 5% were positive by indirect immunofluorescence [15] . Similarly , across Texas shelter dogs had a seroprevalence of 8 . 8% when testing dogs on Chagas Stat-Pak [6] . These studies and ours suggest that despite the regular veterinary care , quality food and shelter , highly-valued working dogs can have similar or greater T . cruzi infection than stray and shelter dogs in the US and free roaming or pet dogs in endemic countries . Both population-level and individual-level T . cruzi studies of naturally-infected hosts suffer from a lack of gold standard tests or diagnostic recommendations . Discordance among tests results is prevalent in human and veterinary Chagas diagnostics . For example , a study looking at seroprevalence in people from Veracruz , Mexico used 5 assays and found that test agreements ranged from 0 . 038–0 . 798 on the Kappa index [42] . Similarly , using the Kappa index we found a high discordance among serology assays used , with agreement ranging from slight to moderate depending on the interpretation method . Assay discordance could be affected by the single freeze-thaw cycle , or the age of the sample . These diagnostic challenges make it difficult to directly compare seroprevalence across populations and diagnostic methods , and presents a challenge in clinical settings for diagnosis . Two of the three serological tests we used are only available for research use for dogs in the US , and a limited number of commercial laboratories offer canine T . cruzi diagnostic test services . As in most diagnostic tests , there is some subjectivity in the interpretation of results , and the development of very faint ‘equivocal’ serological bands on both Chagas Stat-Pak and Trypanosoma Detect posed particular complexities in our analysis . The Stat-Pak and Trypanosoma Detect instructions state that band intensity will vary , but faint bands should be interpreted as positive [43 , 44] and that variation is dependent on the concentration of antibodies present [44] . However , some previous canine studies have counted faint bands as negative [6 , 9] while others have interpreted them as positive for analysis [45] . Our presentation of a seroprevalence calculated both conservatively ( very faint bands interpreted as negative ) and inclusively ( very faint bands interpreted as positive ) is an effort to account for imperfect diagnostics . Until refined T . cruzi diagnostic tools are available , we encourage transparency in presenting results on single vs . multiple tests across all strengths of test response . The discordance between test results and the within assay variation could be caused by parasite heterogeneity [45] . T . cruzi is notably heterogeneous with seven major genotypes or discrete typing units ( DTUs ) described as TcI-TcVI and TcBat which vary be region [46 , 47] . Additionally , a notable intra-DTU variability has been found [47 , 48] . Previous research has found that assay reactivity varies by geographic origin of the patient [49] . O’Connor and others found that strain TcI clusters geographic between North and South America [50] . The Chagas Stat-Pak was validated with human sera from Central America to detect strains circulation in that region [51] and may not be optimized for T . cruzi clones from Texas . When very faint bands were interpreted as positive ( method B ) , seroprevalence was significantly higher in two western management areas ( OR 2 . 6–2 . 8 , 95% CI: 1 . 0–6 . 8 p = 0 . 02–0 . 04 ) compared to the training center ( Table 3 ) , whereas this difference was not evident when very faint bands were interpreted as negative ( method A ) . The disproportionate abundance of very faint bands in this geographic area may be driven by differences in the locally-circulating T . cruzi clones . Diosque et al . performed a genetic survey of T . cruzi isolates within a restricted geographical area ( ~300 km2 ) and found five different clones circulating [52]; such findings are clinically and diagnostically relevant because parasite heterogeneity has been shown to cause varying infectivity and immune response [52–54] . In addition , host biological factors ( exposure history , coinfection , genetic makeup ) could also cause reaction variability within and across serology assays . Sleeping location ( group housed indoors vs . individually housed outdoors ) appeared to be independently associated with T . cruzi status with a higher seroprevalence in dogs sleeping outdoors than indoors by method A ( p = 0 . 02 ) , and marginally significant by method B ( p = 0 . 09 ) in bivariable analysis . Previous studies have indicated dogs housed outdoors where vector contact is more likely to be at a higher risk for exposure [6 , 9 , 39] . Dogs in Tennessee spending 100% of their time outdoors were significantly more likely to be seropositive for T . cruzi than dogs spend ≤50% of their time outdoors [13] . Seroprevalence did increase with age in both method A and B , but was only significant in bivariable analysis in method A , where senior ( >6 years ) and middle age ( ≥ 3 years to <6 years ) , were more likely to be seropositive than young dogs ( <3 years old ) ( Table 1 ) . This is anticipated in infectious disease since exposure increases with age and has been found in previous studies [7 , 13 , 17 , 55] . We found that German Shepherds were associated with a significantly lower seroprevalence ( 3 . 7% ) than Belgian Malinois ( 8 . 6% ) in our study; although the driving factors for this difference are currently unknown , it may relate to host behavior , differences in host immune response , or a physical characteristic . We found three dogs ( 0 . 6% ) harbored parasite PCR in their blood , suggesting that these dogs are parasitemic . While two of the three PCR-positive dogs also harbored detectable anti T-cruzi antibodies , one did not , suggesting this dog may have been in the acute stage of infection [56] . The two dogs with successfully typed infections harbored DTUs TcIV and a TcI/TcIV mix , consistent with previous studies on dogs in the US [57 , 58] . Both strain types infect a variety of hosts and vectors in the southern US [3] . DTU TcI is an ancient strain found throughout South and Central America and the predominant strain infecting humans in the US [3] , where it is also associated with wildlife reservoirs including opossums ( Didelphis virginiana ) [57] . TcIV is also associated with wildlife , especially raccoons ( Procyon lotor ) [3] and to our knowledge has not been implicated in the small number of typed human infections in the US . This study found a lower prevalence of dogs PCR positive then previous studies , which likely reflects the time of sampling ( November and April ) when the vector is less active and dogs in Texas are less likely to come in contact with the kissing bug [58] . In recognition of other datasets that have shown that analysis of clot , rather than buffy coat , may afford a greater the chance of detecting parasite DNA [59] , we subjected 12 clot samples to PCR and compared results to previous results from analysis of buffy coat . We found that buffy coat and clot results were identical across this subset with the exception of a sample from a single seronegative dog which was positive from buffy coat and negative from clot . Based on this small comparison trial , we suggest that the low frequency of encountering PCR-positive dogs in our study was not due to the blood fraction used in the analysis . We found an infection prevalence of 45% in the kissing bugs collected from areas where the working dogs frequent , including kennels , stations and handler’s residence , including DTUs TcI , TcIV , and TcI/IV mix . This infection is slightly lower than previous estimates across the state of Texas of 63% and 51% [59 , 60] . Bloodmeal analysis revealed canine , human , and wildlife DNA within the hindguts of these insects , underscoring the generalist feeding strategies of triatomines that often use the most locally abundant hosts . Strict protocols were used to reduce the risk of contamination of samples by exogenous DNA ( i . e . , human DNA ) , including surface sterilization of vectors and dissection of the hindguts . It is biologically plausible that the insects associated with suspected human blood feeding encountered humans at their residence or station or work . A study in California and Arizona that collected bugs by light traps found that 5 of 13 bugs ( 38% ) bugs were positive for a human blood meal , 4 fed on canine and 1 each for rat , pig , chicken and mouse [61] . In Texas , Gorchakov et al . found 65% ( n = 62 ) of bugs positive for human bloodmeal and 32% for canid bloodmeal [62]; in contrast , Kjos et al . found only 1% of vectors ( n = 96 ) collected from residential settings had fed on a human , and 20% on dogs [63] . Larger sample sizes of engorged vectors from the working dog environments will assist in learning the local vector-host interactions that sculpt disease risk . Using dogs as sentinels has been suggested for targeted vector control programs endemic areas such as Peru [37] and to monitor transmission in Argentina [55] . However , the relative importance of dogs as reservoirs , and whether or not they can be a sentinel species for human disease risk in the US , is unknown . Further , because the triatomines in the US tend not to be colonized within homes , dogs are less likely to be useful sentinels at the household level . Nonetheless , given these infected working dogs signal the presence of infected vectors in the environment , there are public health implications of these findings especially with respect to the human handlers who are exposed to the same environments . Because not all T . cruzi-infected dogs will develop disease [21] , the prognosis and clinical implications of the widespread presence of T . cruzi-infected government working dogs along the US-Mexico border is unknown . Nonetheless , the potential loss of duty days resulting in an inadequate canine workforce must be considered . Additionally , given that the canine training school in west Texas ( Fig 1 ) occurs in an area where triatomines are endemic , vector and canine surveillance must be conducted to determine if young dogs may be exposed to the parasite while in training , which would not only have implications for the health of the dog but also potentially afford dispersal of the parasite to the new areas across the US where these dogs are stationed . Understanding the epidemiology of T . cruzi infection is the first step toward implementing control measures to protect the health of these high-value working dogs . | Chagas disease , a potentially deadly cardiac disease of humans , canines and other mammals is caused by the parasite Trypanosoma cruzi . The parasite is primarily transmitted to dogs by ingestion of infected triatomine ‘kissing bug’ vectors or through contact with the insect’s feces . Previous studies concluded that stray and shelter dogs are at high risk of infection in the southern U . S . We proposed that high-value U . S . government working dogs along the Texas-Mexico border may also be at high risk because of their activities in regions with established , infected vector populations . We sampled 528 working dogs along the Texas-Mexico border , and found that 7 . 4–18 . 9% of dogs were positive for T . cruzi antibodies and a small proportion ( 0 . 6% ) also had parasite circulating in the blood . We collected two species of kissing bugs from the canine environments and used molecular approaches to determine that 45% were positive for T . cruzi and the majority had recently fed on canines . We highlight the need for better diagnostic tools for canine Chagas disease research and diagnosis . The widespread burden of T . cruzi infection in the government working dogs could be associated with far-reaching consequences for both animal and human well-being . |
You are an expert at summarizing long articles. Proceed to summarize the following text:
The current Ebola outbreak poses a threat to individual and global public health . Although the disease has been of interest to the scientific community since 1976 , an effective vaccination approach is still lacking . This fact questions past global public health strategies , which have not foreseen the possible impact of this infectious disease . To quantify the global research activity in this field , a scientometric investigation was conducted . We analyzed the research output of countries , individual institutions and their collaborative networks . The resulting research architecture indicated that American and European countries played a leading role regarding output activity , citations and multi- and bilateral cooperations . When related to population numbers , African countries , which usually do not dominate the global research in other medical fields , were among the most prolific nations . We conclude that the field of Ebola research is constantly progressing , and the research landscape is influenced by economical and infrastructural factors as well as historical relations between countries and outbreak events .
No other infectious disease event has captured the attention of the international health community in recent years like the Ebola outbreak . The current epidemic started in December 2013 leading to over 26 . 000 infected patients and more than 10 . 000 deaths [1] The outbreak reached global dimension as hospitals in the United States of America ( US ) and Europe are now treating patients returning from health missions in Ebola affected countries [2] . 28 outbreaks were documented since 1976 , which all , except the recent one , occurred in isolated regions . During the first epidemic in the Democratic Republic of Congo and the Sudan the Ebola virus was identified as a non-segmented , negative-strand RNA virus and placed within the Filoviridae family . Since then five distinct virus species were distinguished . Although the pathogenesis of the Ebola virus is intensively investigated worldwide , the undisputed identification of the natural reservoir has not been successful yet . Bats were implicated as a possible host for the Ebola as well as the related Marburg virus [3] . Despite the short time span since the discovery of the Ebola virus , scientists have authored a substantial body of related scientific literature worldwide . Nevertheless , it should be an ethical obligation of all industrialized countries to invest future capacities in research of this life-threatening disease and vaccination strategies as one of the most effective means to fight infectious diseases [4 , 5] . In order to cast a first light on the question of global research activity in this field since 1976 , we present a combined scientometric and density equalizing study . It encompasses scientometric tools and advanced visualizing techniques such as density equalizing mapping [6] and draws a sketch of the global Ebola research architecture over the past 40 years . Scientometric analysis of the scientific output of individuals , institutions and countries is represented in the number of publications as well as citations and their bibliometric parameters . Density equalizing map projections ( DEMP ) are used as a state of the art technique to demonstrate the global architecture on the research output via distorted maps .
The data was analyzed using scientometric methods developed in the NewQIS project as previously described [7] . The analyzed data was retrieved from the Thomson Reuters Web of Science database ( WoS ) using the search term “Ebola” in the Science Citation Index Expanded ( SCIE ) and the Social Sciences Citation Index ( SSCI ) ( time frame between the first description of the virus in 1976 and 2014 ) . To limit our search to the original research articles , we used the WoS´s option for selecting the document type and included only “articles” in the analysis . Data was processed as previously described using a combination of scientometric tools with density equalizing mapping [8] . For the generation of density equalizing mapping , the Gastner and Newman’s algorithm was employed [6] . As parameters , citation quantities were determined using the “citation report” function ( number of citations per article , the citation rate of countries , and authors ) , H-indices [9] along with other general operating figures ( year of publication , country of publication , co-operations between different countries , language of publication , document types , subject areas , and journals ) . Also , author analysis , subheading-terms , and individual subject areas were examined . To evaluate the quality of a country’s publication output , we assessed the citation rate and the modified H-index [9] .
The total numbers of publications in the database added up to 2482 ( search term “Ebola” only as title word ) and 3081 ( search term “Ebola” also in keywords and abstract ) starting from 1976 with a steady yearly increase in publication activity until 2014 . Ebola research originated from 78 countries . Research groups based in the USA published most research with 1367 articles ( 44 , 4% of all determined articles ) , followed by groups from Germany ( 272 articles , 8 . 8% ) , Canada ( 202 articles , 6 . 6% ) , France ( 179 articles , 5 . 8% ) , United Kingdom ( 167 articles , 5 . 4% ) , Japan ( 157 articles , 5 . 1% ) , Russia ( 84 articles , 2 . 7% ) , Gabon ( 70 articles , 2 . 3% ) , Belgium ( 58 articles , 1 . 9% ) and Switzerland ( 51 articles , 1 . 7% ) . Each percent value stands for the part of the overall Ebola research output that was retrieved via the WoS . The rest of articles originated from the remaining 68 countries that are involved in Ebola research . Overall , North American and European countries took a lead position . The density equalizing mapping of the world shows that research activity translated into a distorted global architecture ( Fig 1 ) . African countries affected by Ebola cases exhibited a relatively low activity but were present . The continents South America , and Asia almost disappeared from the cartogram . When relating these operating figures to population numbers ( Fig 2 ) , we found that—besides the most active nations ( US , European ) —smaller African countries such as Gabon , Republic of Congo , Central African Republic and Cameroon gained importance . Uganda , Republic of Congo , Gabon and South Africa reached increased ratios regarding their research activity adjusted to Gross Domestic Product ( GDP , Fig 3 ) . We did not find any association between the death rate and the research output on a regional scale . With reference to the citation rate ( CR = average ratio of citations per publications ) , Gabon was ranked first ( CR 43 . 3 ) followed by western countries ( Switzerland = CR 34 . 4; Germany = CR 33 . 2; France = CR 32 . 9; US = CR 32 . 6; and UK = CR 30 . 1 ) ( Fig 4 ) . Focusing on the modified h-index ( country specific and thematically related ) , the US exhibited the highest h-index ( 102 ) followed by Germany ( 55 ) , France ( 46 ) , Canada ( 41 ) , UK ( 37 ) , Japan ( 32 ) , Gabon ( 26 ) , Switzerland ( 24 ) , Belgium ( 23 ) and Russia ( 20 ) ( Fig 5 ) . Ebola research has been characterized by strong international cooperations since its beginnings . Although most global research involved cooperation with US-American research groups ( 449 collaborative articles ) , the overall rate of US collaborative publications in relation to the total US output activity with 1367 articles in total ( 33% ) was relatively low compared to other countries ( Fig 6 ) . Most frequent research partners of the USA were Canada ( 121 US-Canadian collaborative articles ) , Japan ( 99 US-Japan collaborative articles ) and Germany ( 88 US-German collaborative articles ) . Canada also played an important role with 148 collaborative articles altogether conducted with a total of 11 countries ( 73% of all Canadian articles ) , followed by Japan with 121 collaborative articles in total ( 77% of all Japanese articles ) , Germany with a total of 175 collaborative articles ( 64% ) , France with 123 collaborative article all in all ( 69% from all French articles ) , the UK with 87 collaborative articles in total ( 52% of all UK articles ) and Belgium with 50 collaborative articles ( 71% of all Belgian articles ) . Due to the fact that most Ebola outbreaks were geographically defined to the African continent we found particularly strong cooperations of France with Gabon with 38 collaborative articles ( USA and Gabon have 19 common articles; Germany and Gabon have 14 common articles ) . Gabon has worked out 87% of the overall publication output together with other countries . The collaboration between the USA and Uganda followed with 21 collaborative articles . Uganda has written 26 together with other countries ( 72% of all Ugandan articles ) . South Africa published 94% of the overall research output as collaborative articles and Congo nearly 97% . On an institutional level most cooperate publications were produced between the Canadian Science Center of Human and Animal Health and the University of Manitoba . Both located in Winnipeg and have an overlap of staff working in both facilities . Also , numerous cooperations were found between research institutions within the US . Laboratories with the highest biosafety level clearance were available in 13 of the leading 33 cooperating institutions ( Fig 7 ) . Subject categories are defined classes of themes indicating a general area of science . For the field of Ebola research , they were determined and depicted in four year intervals ( Fig 8 ) . The research interest shifted from a predominant focus on general and internal medicine to a much more diversified picture covering subject categories in immunology , cell biology , pharmacology , experimental medicine , biochemistry and molecular medicine . We did not find a remarkable attribution to the category of public health .
In order to give an overview over the global Ebola research architecture , the current study employed the Web of Science and conducted the first analysis of the research output for the entire field since 1976 . Other than the majority of infectious diseases that have been discussed in the scientific literature for more than a century ( e . g . yellow fever or dengue [10 , 11] ) the advent of Ebola research is recent and started from its first description in 1976 [12] . Therefore , Ebola research offers a rare opportunity to observe the entire development of a scientific field since all related publications are achieved in electronically accessible scientific databases . Overall , we found that the number of publications was constantly low in the years after 1976 and remained on a level of up to 20 publications per year . This is surprisingly low for a dangerous infectious disease but understandable since a biosafety level 4 laboratory is needed to carry out the research . Coinciding with the reemergence of the virus in Kikwit ( located in the Democratic Republic of Congo ) [13] , research activity was evidently enforce dafter 1995 . With regard to the landscape of Ebola research analyzed in this study , US-American institutions contributed the largest amount of international research . This finding again demonstrates the leading role of the USA in science , as it is present in almost all other areas of biomedical research [14] . We also showed a particular strong involvement of German institutions in Ebola research . This might be explained by the local availability of numerous suitable research facilities that were established after the first European outbreak of the Marburg virus . This virus is termed the “forgotten cousin” of Ebola and also causes life threatening hemorrhagic fever [15] . The application of the number of the total population and socio-economic parameters changed the ranking of the nations: Whereas the apparent superiority of the USA research was put in perspective , the African countries that are traditionally most affected by the disease such as Gabon , Republic of Congo , Central African Republic , Cameroon and Uganda increased their ranking . Only a few studies evaluated the worldwide research efforts in the field of tropical diseases . These also found a predominance of North American and European countries regarding overall research activity . As examples of countries where particular diseases play a significant role for the health of the public , only Brazil , where yellow fever is endemic [16] , as well as Brazil and India , which are affected by Leishmaniasis , were among the 10 most prolific countries [17] . Even high prevalent infections such as malaria have not lead to a increased global impact of research originating from African countries [18] . From its first appearance in scientific databases Ebola research was characterized by a high percentage of collaborative studies as demonstrated in our study . It has been shown numerously that involved scientists benefits from the international cooperation [19] . Although the US is the favorite global cooperation partner for scientists from other countries , it had the lowest cooperation ratio in regard to its own overall publication activity . This might be caused by the fact that American scientists cooperate to a large extend with national colleagues due to the efficient and well-funded academic structure that is present in the US . When focusing on the collaboration of institutions committed to Ebola research , we found a network that favors only a limited amount of institutions . This might be explained by the necessary prerequisite of Ebola research . The risks involved in handling the virus require the maximum biosafety level [20] . Research facilities that provide these resources are sparsely distributed throughout the world and its highest numbers are found in the US–the leading country of Ebola research output . Subject categories in health research represent the interest of scientist in different aspects of a disease . In the beginning of Ebola research , publications dominated that were attributed to the categories of microbiology , internal medicine and virology . Then the field became more diverse including other subjects such as immunology , cell biology , pharmacology , and biochemistry . Research in the field of public health encompasses society-based measures to combat diseases . We want to point out that a lack of publication output regarding public health topics is apparent in the field of Ebola research , which is in sharp contrast to other tropical diseases [16] . In conclusion , we here present a first detailed analysis of the global Ebola research landscape . The collected data indicated that the efforts in scientific research have been constantly increasing since the time of discovery in 1976 . The USA was identified as being the leading country and a total of more than 3000 publications . However , the danger of the virus , the change in pattern of distribution and the neglect to put more emphasis on the development vaccines before the outbreak of 2013–2015 clearly point to the need that 1 ) research in the field of hemorrhagic fevers needs to be strengthened , 2 ) vaccine development should also be enforced for other neglected tropical diseases in order to prevent similar catastrophes in the future , and 3 ) research endeavors should be focused on the area of public health since we could identify a neglect in Ebola related public health research efforts . | For the first time in the history of the disease , the Ebola virus left its local setting and affected people not only in isolated rural areas , but reached larger towns and cities leading to worldwide repercussions . This development prompted a joint global response to this health threat . This encompassed not only immediate relief efforts , but also an up search in global research work . In this study , the scientific output in Ebola research available in one of the mayor medical search platforms was characterized . We studied among others the origin of research , the collaboration between countries and the research topics . Partly , the obtained data was weighted against economic parameters . We attained a detailed map of the research activities from the discovery of Ebola in 1976 up to today . Our research provides the first overview of the worldwide Ebola research output . It might help stakeholders in Ebola research to better plan investigations with a global perspective . |
You are an expert at summarizing long articles. Proceed to summarize the following text:
As a result of poor economic opportunities and an increasing shortage of affordable housing , much of the spatial growth in many of the world's fastest-growing cities is a result of the expansion of informal settlements where residents live without security of tenure and with limited access to basic infrastructure . Although inadequate water and sanitation facilities , crowding and other poor living conditions can have a significant impact on the spread of infectious diseases , analyses relating these diseases to ongoing global urbanization , especially at the neighborhood and household level in informal settlements , have been infrequent . To begin to address this deficiency , we analyzed urban environmental data and the burden of cholera in Dar es Salaam , Tanzania . Cholera incidence was examined in relation to the percentage of a ward's residents who were informal , the percentage of a ward's informal residents without an improved water source , the percentage of a ward's informal residents without improved sanitation , distance to the nearest cholera treatment facility , population density , median asset index score in informal areas , and presence or absence of major roads . We found that cholera incidence was most closely associated with informal housing , population density , and the income level of informal residents . Using data available in this study , our model would suggest nearly a one percent increase in cholera incidence for every percentage point increase in informal residents , approximately a two percent increase in cholera incidence for every increase in population density of 1000 people per km2 in Dar es Salaam in 2006 , and close to a fifty percent decrease in cholera incidence in wards where informal residents had minimally improved income levels , as measured by ownership of a radio or CD player on average , in comparison to wards where informal residents did not own any items about which they were asked . In this study , the range of access to improved sanitation and improved water sources was quite narrow at the ward level , limiting our ability to discern relationships between these variables and cholera incidence . Analysis at the individual household level for these variables would be of interest . Our results suggest that ongoing global urbanization coupled with urban poverty will be associated with increased risks for certain infectious diseases , such as cholera , underscoring the need for improved infrastructure and planning as the world's urban population continues to expand .
In 2008 , for the first time in human history , more than half of the world's population was living in urban areas , and this proportion is expected to increase . Much of the future growth in urban areas is expected to take place in developing countries , with Africa and Asia predicted to have nearly seven out of every ten urban inhabitants in the world by 2030 [1] . Unfortunately , due in part to limited economic opportunities and an increasing shortage of affordable housing , the majority of urban growth in many of the developing world's fastest growing cities is a result of the expansion of informal settlements , often referred to as slums [2] . UN-HABITAT defines slums as urban areas where households lack one or more of the following conditions: access to an improved drinking water source , access to improved sanitation facilities , sufficient living area , durable housing in a non-hazardous location , and security of tenure [2] . As one might expect , these conditions can have severe consequences for human health and are of particular concern when considering their potential impact on the spread and burden of infectious diseases [3] , [4] . For example , tuberculosis , influenza , meningitis , typhus , plague , typhoid and cholera are among many infectious diseases historically associated with conditions now common in urban informal settlements . In spite of this , few modern data are available assessing the current association of infectious diseases with ongoing global urbanization , especially at the neighborhood and household level in informal settlements [5] , [6] . To begin to address this , we therefore analyzed urban environmental data and the burden of cholera in Dar es Salaam , Tanzania , a rapidly expanding African city . Dar es Salaam , Tanzania's largest city , covers an area of approximately 1800 km2 situated alongside the Indian Ocean . The city is comprised of three municipalities – Kinondoni , Ilala , and Temeke – which are divided into 73 wards ( Figure 1 ) . The city grew from a population of 76 , 000 in 1950 to a population of 3 . 31 million in 2008 [7] . With a current estimated growth rate of 4 . 3% per year [8] , Dar es Salaam contains 29% of the country's urban population , though this percentage is expected to nearly double by 2010 [7] . Most of the city's growth has occurred along the coastline and along four main arterial roads [9] ( Figure 2 ) . Despite increasing efforts on the part of municipal and city governments to address informal settlement expansion , the speed of the city's growth and the inability to invest adequately in housing and infrastructure have led to the growth of existing settlements and to the development of new ones . As a result , Dar es Salaam now has one of the highest proportions of informal-settlement households in East Africa , with 65% of households living in informal areas [7] . Investigating the relationship between cholera incidence and the size of and conditions in informal settlements may help to shed light on some of the health consequences of settlement expansion . Vibrio cholerae is a water- and food-borne Gram-negative bacterium and the cause of cholera , a severe , dehydrating diarrhea in humans . V . cholerae is unique among the diarrheal pathogens because of its ability to cause global pandemics . The disease has a short incubation period of 18 hours to five days [10] , and can thus spread rapidly through a population . Cholera may cause explosive outbreaks in crowded conditions , such as occurred in Goma in 1994 [11] and in Zimbabwe in 2009 [12] . In addition to causing epidemic disease , cholera is also endemic in many areas of the world , especially sub-Saharan Africa and South Asia . If untreated , mortality rates due to cholera can be as high as 50% [13] , though nearly all deaths can be avoided if replacement fluids are promptly administered . Antibiotics may also be used to shorten the recovery period for severe cases [14] . Strains of V . cholerae can be differentiated serologically by the O side chain of the lipopolysaccharide ( LPS ) component of the outer membrane , and the strains that produce epidemic cholera belong to serogroup O1 or O139 . V . cholerae O1 itself is classified into two biotypes , classical and El Tor , which differ clinically and biochemically . V . cholerae O1 biotype El Tor is responsible for the current seventh pandemic of cholera , and is the current cause of cholera in Dar es Salaam [15] . Although reporting may be incomplete , cholera is one of the few diseases that require reporting to the WHO under the International Health Regulations . Though a cholera epidemic was first reported in East Africa in 1836 , no cases were reported across Africa between 1870 and 1970 [16] , when cholera returned to the continent as part of the seventh cholera pandemic , which started in Asia in 1961 [10] . Cholera cases associated with this most recent pandemic were reported for the first time in Tanzania in 1974 , and have been reported each year since 1977 . The first major outbreak in Tanzania occurred in 1992 , although the largest country-wide epidemic occurred in 1997 , with more than 40 , 000 cases reported . This epidemic was reported to have started in Dar es Salaam [15] . Of all regions in Tanzania , Dar es Salaam has had the most cholera cases since 2002 . In an outbreak in 2006 , Dar es Salaam was the most affected region , accounting for 63% of the total cases ( 14 , 297 ) and 40% of the total deaths ( 254 ) [15] . Cholera is most commonly caused by ingestion of water or food contaminated with fecal matter , and due to this mode of transmission , risk factors for cholera include lack of safe drinking water , poor sanitation , high population density , crowding , and lack of previous exposure , all of which are often common features in urban slum areas [17] , [18] . In a number of countries , cholera incidence has been shown to be highest in highly urbanized areas [19]–[21] . We , therefore , chose to focus our analysis on the associations between cholera incidence and the nature of informal settlements in Dar es Salaam .
In order to gain a sense of the general pattern in cholera cases in Dar es Salaam and to identify a time period that might be particularly useful for analysis , weekly suspected cholera case reports from 2006 through June 2008 were gathered from the City Health Officer and analyzed over time and by municipality . Suspected cases included all those patients assessed at one of the city's three cholera camps ( described in detail below ) whose symptoms were deemed to meet the WHO case definition for cholera in endemic areas: “a patient older than 5 years who develops severe dehydration from acute watery diarrhea ( usually with vomiting ) ; or any patient above the age of 2 years with acute watery diarrhea in areas where there is an outbreak of cholera” [13] . Suspected cases of cholera among children younger than 2 years of age were also included in analyses if confirmatory laboratory results were available . However , though ages of suspected cholera cases were available in Kinondoni , they were not available for the other two municipalities , and it was not possible to link suspected cases to confirmatory laboratory results for these areas . Based on data from Kinondoni , it was estimated that less than 2% of the sample was comprised of children under the age of 2 years . Daily reports of suspected cases were collected from the District Medical Officer in each municipality , including information on the ward of residence , month and year . Where possible , information on the age and gender of suspected cases was also collected . Rectal swab results from those suspected cholera patients who were tested were also obtained from Amana Hospital in Ilala , from Temeke Hospital in Temeke , and from Mwananyamala Hospital in Kinondoni from 2006 through June 2008 in order to confirm the presence of V . cholerae O1 during months when suspected cases were being reported . Rectal swabs were collected at the discretion of the attending health provider as part of routine diagnostic assessments and were not collected for research purposes . Cholera incidence was calculated by ward for each year , using the available cholera count data and population projections for each ward based on the 2002 Census [22] . In comparison to the year 2006 , in which 8 , 753 cases of cholera were reported in Dar es Salaam , only 395 cases were reported in 2007 and only 216 cases had been reported by the end of June in 2008 . Due to the high volume of cholera cases in 2006 , we focused our analysis on this year alone . Though the number of cases by municipality was available for all months in 2006 , the location of cases by ward for the months of June through September was unable to be retrieved . However , 89 . 2% of the year's cases occurred in other months , including the months with the highest number of cases . Therefore , our analysis focused on these eight months . In addition , by using data from the Unplanned Land Property Register Project , analysis was limited to the 45 of the city's 73 wards included in the project; included areas were predicted to contain 84% of the city's population ( Figures 1 and 2 ) . Because the relationship between cholera incidence and predictors was nonlinear , cholera incidence was transformed into a natural logarithmic scale . All potential cholera cases were sent to one of three cholera camps located in the city for further assessment and , if needed , treatment . These camps were located at Buguruni Health Center in Ilala , Mburahati Dispensary in Kinondoni , and at Temeke Hospital between Azimio and Tandika wards in Temeke . The geographic coordinates for these camps were obtained from the Tanzania Service Availability Mapping 2005–2006 [23] . In order to calculate population density and distance to the nearest cholera camp , all data were mapped in ArcMap 9 . 2 [24] , utilizing a map file of wards in Dar es Salaam obtained from the International Livestock Research Institute [25] . Since we were not able to acquire individual addresses of suspected cholera cases , the distance to the closest cholera clinic was calculated , utilizing the center of the ward as the spatial reference . In addition , the area of each ward was calculated in square kilometers , and population density estimated based on the 2006 population estimates by ward . A shapefile of roads in Tanzania was acquired from the Food and Agriculture Organization's Multipurpose Africover Database [26] , and the presence or absence of a major road in each ward was recorded as a categorical variable . Between 2004 and 2007 , the Ministry of Lands and Human Settlements Development conducted a survey in informal settlements located in 45 wards as part of the Dar es Salaam Unplanned Land Property Register Project . The data were collected for administrative purposes . Data on 225 , 911 informal plots were gathered , mostly collected in 2005 . Questionnaires were distributed to owners or close relatives , or , in the case of residences occupied by tenants only , tenants were questioned and the homeowner was notified in order to verify the information provided . Information was collected about plot use ( residential , commercial , industrial , agricultural , other ) , the type and quantity of occupants , road access , source of water supply , type of sanitation , electricity , phone , building materials , length of time the occupants had lived in the area , ownership of household items and livestock , household income and type of employment , and garbage collection , among other characteristics . Of 207 , 085 plots whose use was known , 98 . 2% contained residences . Of 203 , 289 residential plots , 186 , 631 were occupied at the time of the survey , 23 . 9% of which were in Ilala , 37 . 4% of which were in Kinondoni , and 38 . 8% of which were in Temeke . Only records for occupied residential plots were included for analysis . Among these records , 97 . 5% ( 181 , 896 records ) contained complete information about a water source , while 61 . 4% ( 114 , 593 records ) contained complete information about sanitation . The number of informal residents in each ward was calculated by aggregating the number of occupants in each residential plot at the ward level . The percentage of informal residents in each ward was then calculated based on the 2006 population estimates . For three wards , estimates of the percent of the population that is informal were greater than 100 . This is likely a result of rapid urban expansion that was underestimated by the population estimates . As a result , for these three wards , the maximum informal percentage obtained from other wards , which was 95 . 8% , was used . The percentage of informal residents in each ward was used as a proxy for the percent of residents in each area that would likely be living in areas with poorer environmental conditions . Access to improved drinking water and access to improved sanitation were assessed based on guidelines from the WHO Joint Monitoring Program for Water Supply and Sanitation [27] . Thus , improved water sources included household connections , a neighbor's on-plot connection , and community pumps . Deep wells were also considered improved sources while shallow wells were not . The survey did not ask specifically about whether wells were protected , but due to the fact that shallow wells have a higher potential for contamination , particularly if they are uncovered , shallow wells were considered unimproved sources . Only sanitation facilities connected to the sewer system or to a septic tank were considered improved , as the type of pit latrine was not specified . Due to the lack of a reliable measure of income among informal residents , an asset index was constructed in order to serve as a proxy for long-term economic status and to provide a more sensitive means of differentiating between poor households [28] . Following the example of Filmer and Pritchett [29] , principle components analysis was used to generate weights for an index based on 22 asset indicators , including household ownership of consumer goods ( air conditioner , bicycle , car , computer , DVD player , fan , furniture , iron , motorcycle , radio or CD player , refrigerator , sewing machine , stove , TV or video player , tools , truck ) and household ownership of animals or livestock ( goat or sheep , chicken , cow , pig , dog or cat , other animals ) . Only the first principal component was used , which explained 16 . 9% of the variance . The index was designed so that a score of zero would indicate possession of no assets , while the score would increase with the possession of increasing numbers of assets . Data analysis was conducted using only de-identified data , which were analyzed at the ward level . As a way to assess whether environmental conditions in informal settlements had an effect on the city's pattern of cholera incidence , correlations were first calculated between the natural log of cholera incidence in 2006 and the percentage of a ward's residents who were informal , the percentage of a ward's informal residents without an improved water source , the percentage of a ward's informal residents without improved sanitation , distance to the nearest cholera camp , population density , median asset index score in informal areas , and presence or absence of a major road . Including as predictors only those variables significantly correlated at the 95% confidence level with the natural log of cholera incidence , data on the number of cholera cases per ward in 2006 were used to estimate cholera incidence rate ratios associated with the percentage of a ward's residents who were informal , population density , the percentage of a ward's informal residents without improved sanitation , and median asset index score in informal areas . Since we sought to model the number of cholera cases , and considering that overdispersion was observed ( i . e . the variance in the number of cholera cases exceeded the mean ) , a negative bionomial regression model was used for analysis , with the logarithm of a ward's population size included as an offset variable .
In 2006 , 8 , 753 cases of cholera were reported in Dar es Salaam , of which 42 . 8% were in Ilala , 32 . 5% in Kinondoni , and 24 . 7% in Temeke . The outbreak displayed two peaks – one in April and one in October ( Figure 3 ) . In any given month , V . cholerae O1 was identified by culture from rectal swabs taken from suspected cases in at least a third of those tested . The median incidence of cholera per 10 , 000 people in Dar es Salaam in 2006 was 15 . 8 , with 25% percent of wards reporting an incidence of 7 . 8 cases or fewer per 10 , 000 people , and 25% of the wards reporting an incidence of 36 . 6 cases or higher per 10 , 000 people . The maximum incidence reported was 99 . 6 , while one ward reported no cases ( Figure 4 ) . Since the Unplanned Land Property Register Project was restricted to 45 wards , further analysis was restricted to this area . These wards contain the majority of urban areas in Dar es Salaam , and account for 84% of the city's population . For the 45 wards analyzed , the percentage of informal residents without access to improved drinking water ranged from 37 . 8% to 90 . 0% , with a mean of 71 . 8% . The percentage of informal residents lacking improved sanitation ranged from 71 . 7% to 97 . 3% , with a mean of 92 . 4% . The average percentage of informal residents per ward was 60 . 1 , ranging from 5 . 4 to 95 . 8 . The mean population density for these wards was 14 , 874 people per km2 , ranging from a low of 334 people per km2 to a high of 48 , 257 people per km2 . Distance to the nearest cholera clinic ranged from 0 . 4 km to 17 . 4 km , with a mean of 3 . 8 km . The median asset index score overall ranged from 0 in 26 wards to 0 . 7 in one ward . Among occupied residential households , 56 . 3% did not report ownership of any of the items about which they were asked ( Table 1 ) . Major roads crossed 27 of the 45 wards analyzed . Of all the variables considered ( Figure 5 ) , median asset index score was the most highly correlated with the natural log of cholera incidence ( r = −0 . 53 , p<0 . 001 ) . The percentage of a ward's informal residents without access to improved sanitation , the percentage of informal residents in a ward and population density had moderate and positive effects on cholera incidence ( r = 0 . 49 , p<0 . 001 , r = 0 . 42 , p = 0 . 004 and r = 0 . 35 , p = 0 . 02 , respectively ) . In comparison , major road presence , distance to the nearest cholera clinic , and percentage of informal residents without access to improved water sources were not found to be significantly correlated with cholera incidence . Results of the negative binomial regression model indicated that when all four predictors were included , the effects of percent of informal residents , population density , and median asset index score on cholera incidence were found to be significant , while the effect of percentage of informal residents without access to improved sanitation was not found to be significant ( Table 2 ) . Although additional data would strengthen any potential explanatory model , our analysis of available data and interpretation of coefficients of the negative binomial model suggested nearly a one percent increase in cholera incidence for every percentage point increase in informal residents , and approximately a two percent increase in cholera incidence for every increase in population density by 1 , 000 people per km2 in Dar es Salaam in 2006 . At the same time , the model suggested nearly a fifty percent lower cholera incidence in a ward with a median asset index score of 0 . 32 , which is equivalent to informal residents owning a radio or CD player , compared to a ward where the median asset index score was zero .
Through an analysis of the spatial patterns of cholera in Dar es Salaam , we found in this study that the extent of informal occupancy , population density , and income level had significant effects on cholera incidence in the city in 2006 . Interestingly , in our analysis , we did not find an association between cholera incidence and access to improved water sources or improved sanitation at the ward level , despite the fact that cholera is transmitted fecal-orally . This may be due to the fact that , as shown in Figure 5 , the range of access to improved sanitation and improved water sources was quite narrow at the ward level in Dar es Salaam in 2006 , limiting our ability to discern relationships between these variables and cholera incidence . Analysis at the individual household level for these variables would be of interest . Poverty has , however , been closely linked to inadequate water and sanitation in the past [30] , and our detection of an association between median asset index score and cholera incidence may reflect in part the level of access to improved water and sanitation in this study . Underscoring the relationship between cholera and extreme poverty , our study suggests that relatively minor increases in income can correlate with a marked decrease in the risk of cholera . Our observed association of cholera incidence with population density and the extent of informal settlement may also reflect in part the increased risk of acquiring V . cholerae recently passed by another human in a densely populated urban slum area , compared to the risk of acquiring V . cholerae from ecological reservoirs not recently contaminated by humans . V . cholerae can exist in environmental water sources independent from humans , especially in association with zooplankton and phytoplankton , and these organisms may serve as an important reservoir for infection [10] , [31] . However , the passage of V . cholerae through a human intestine leads to a transient hyperinfectious bacterial state that can persist for up to 24 hours in environmental reservoirs , and such hyperinfectiousness may contribute significantly to human-to-human transmission [32]–[36] . Individuals in densely populated urban slums characterized by poor sanitary conditions may be at particular risk of ingesting such hyperinfectious V . cholerae , of therefore becoming involved in explosive outbreaks and epidemics , and of contributing to ongoing human-to-human transmission . Interestingly , recent data suggest that V . cholerae loses a significant degree of its hyperinfectiousness within hours of passage from a human intestine , suggesting that even in densely populated urban slums , relatively minor modifications in water usage patterns ( such as securely storing water for perhaps as short as a day prior to ingestion to allow V . cholerae to revert to its non-hyperinfectious state ) could possibly have significant impact on the burden of cholera in a slum area [37] . By suggesting that variations in the economic and physical conditions between and within informal settlements may translate into variations in disease patterns , this study validates previous findings that statistical analysis at the city level may mask important intra-urban differences . For example , infant and child mortality rates among Nairobi's slum populations were found to be three to four times higher than the city's average , and higher even than the average for rural areas in Kenya [38] . In Accra , Ghana , the relative risk of infectious and parasitic diseases was also found to be twice as high in areas of the city with the worst social and physical environmental conditions compared to the burden in the best areas [39] . Furthermore , this study adds to the growing body of research demonstrating that even within and between informal settlement areas , relatively small variations in social and physical conditions can result in markedly different rates of infection [18] , [40] , [41] . This pattern resembles the conditions resulting from rapid growth of European cities in the eighteenth and nineteenth centuries , which often led to a poorer health environment in urban areas [42] . However , the rate at which urban growth has been and is occurring in developing countries is unprecedented , and the resources available to address the myriad issues coupled with this growth are often extremely limited [4] , [43] . Although urban-associated infectious disease burden will predominantly be borne by the poorest in many of the world's fastest growing cities , the increased movement of people within and between population centers and countries will also facilitate the spread of these diseases between poor urban centers and other areas and populations [3] . Again , using cholera as an example , between 2006 and 2007 , more than 82 , 000 cases of cholera and 3 , 000 deaths were reported in Angola , the worst outbreak ever reported in that country [44] . The epidemic was reported to have started in one of the poorest and most overcrowded informal settlements in the capital city of Luanda and , over the course of the epidemic , spread to 16 of the country's 18 provinces [45] . Our study has a number of limitations . We analyzed cholera data collected during an epidemic , and patterns of endemic disease and factors involved in its transmission during endemic periods might be different than those we assessed . However , our use of data during the selected period allowed us to analyze a sufficient cholera case burden in the context of our measured environmental parameters . We used extant data sets reporting cholera burden that themselves were largely based on a syndromic classification system using WHO criteria . It is possible that cases classified as cholera where not caused by V . cholerae infection , and that other actual cases of cholera were not captured in the municipal reporting system; however , all cases included in this analysis met WHO criteria , microbiologic data were highly supportive when performed , and reporting criteria did not change during the period of data collection . In addition , though the negative binomial model used in this analysis accounted for high variability in the number of cholera cases , it did not account for any spatial structure that might have been present in the data . Given that our model tended to underestimate the number of cholera cases in the center of the city while overestimating the number of cases farther from the center , a spatial model might have provided additional insight , but was not able to be used given the small number of wards for which data were available . By using an extant data set , we were also limited by incomplete survey information; however , we felt that the ability to analyze an urban environmental survey undertaken immediately prior to an urban cholera epidemic provided a unique opportunity for analysis and , at least in part , mitigated this limitation . Similarly , while we may have obtained a more robust understanding of the risk of cholera had we analyzed data at the household or individual level ( as opposed to the ward level ) , such data were not available . Such an analysis may have shown that access to specific water sources or certain types of sanitary conditions or behaviors may affect the risk for cholera within a household . For instance , in a randomized control trial in low-income squatter settlements with poor water and sanitation in urban Pakistan , children in households encouraged to wash their hands with soap had a 53% lower incidence of diarrhea than in control households [46] . It should also be mentioned that we made the assumption in this analysis that people who became ill with cholera did so due to conditions in their area of residence , while it may also be the case that where people work and the types of activities they engage in away from home may have an effect on their risk of diarrheal illness . For example , consumption of food prepared away from the home is increasing in many developing countries , and was found in Nairobi to be associated with the distance one worked from the home . Preparation of these foods often involves questionable hygienic practices , which may increase the risk of diarrheal disease for those who consume them [47] . However , during an outbreak such as the one in 2006 in Dar es Salaam , the home environment may well play an important role in the spread of the disease . In order to determine whether the relationship between environmental conditions and cholera in Dar es Salaam is unique or indicative of a more general relationship between environmental conditions and the risk for infectious disease , it would also be useful to compare the pattern of cholera incidence with patterns of other diseases across the city to see whether the nature and spatial pattern of risks are similar . For instance , one might predict that the risk for other enteric diseases such as shigellosis might similarly correlate with population density and sanitation , while tuberculosis and influenza might correlate with crowding , and vector-borne diseases such as arboviral infections and malaria might correlate more with water sources , vector biting habits , and human behavior . Equally important , as the municipalities have taken important steps in recent years to address conditions in informal areas , evaluating the impact of these efforts on the incidence of disease should be a crucial part of determining their effectiveness . For instance , in at least one example of informal settlement upgrading , improvements in health have already been noted . Specifically , the upgrading of an informal settlement in Hanna Nassif ward in Dar es Salaam from 1994 to 1998 , which focused on the installation of water vending kiosks , the management of solid waste collectors , and construction of improved access roads and storm water drainage channels , led to a 39% reported decline in waterborne diseases from the start to the finish of the project [48] . In every developing region of the world excluding North Africa , slum growth is expected to closely match the rate of city growth [2] . In sub-Saharan Africa , 59 cities are expected to exceed one million inhabitants by 2015 , with 43% of urban inhabitants living below the poverty line [7] . Our analysis suggests that the ongoing growth of many of the world's cities and expansion of informal settlements will be associated with increased risks to human health , including cholera and possibly other infectious diseases , and underscores the importance of urban planning , resource allocation , and infrastructure placement and management as the rapidly progressive trend of global urbanization proceeds . | In 2008 , for the first time in human history , more than half of the world's population was living in urban areas , and this proportion is expected to increase . As a result of poor economic opportunities and an increasing shortage of affordable housing , much of the spatial growth in many of the world's fastest growing cities is a result of the expansion of informal settlements where residents live without security of tenure and with limited access to basic infrastructure . Although inadequate water and sanitation facilities , crowding , and other poor living conditions can have a significant impact on the spread of infectious diseases , analyses relating these diseases to ongoing global urbanization , especially at the neighborhood and household level in informal settlements , have been infrequent . To begin to address this deficiency , we analyzed urban environmental data and the burden of cholera in Dar es Salaam , Tanzania . We found that cholera incidence was most closely associated with informal housing , population density , and the income level of informal residents . Our analysis suggests that the current growth of many cities in developing countries and expansion of informal settlements will be associated with increased risks to human health , including cholera and other infectious diseases , and underscores the importance of urban planning , resource allocation , and infrastructure placement and management , as the rapidly progressive trend of global urbanization proceeds . |
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The cerebral cortex of mammals exhibits intricate interareal wiring . Moreover , mammalian cortices differ vastly in size , cytological composition , and phylogenetic distance . Given such complexity and pronounced species differences , it is a considerable challenge to decipher organizational principles of mammalian connectomes . Here , we demonstrate species-specific and species-general unifying principles linking the physical , cytological , and connectional dimensions of architecture in the mouse , cat , marmoset , and macaque monkey . The existence of connections is related to the cytology of cortical areas , in addition to the role of physical distance , but this relation is attenuated in mice and marmoset monkeys . The cytoarchitectonic cortical gradients , and not the rostrocaudal axis of the cortex , are closely linked to the laminar origin of connections , a principle that allows the extrapolation of this connectional feature to humans . Lastly , a network core , with a central role under different modes of network communication , characterizes all cortical connectomes . We observe a displacement of the network core in mammals , with a shift of the core of cats and macaque monkeys toward the less neuronally dense areas of the cerebral cortex . This displacement has functional ramifications but also entails a potential increased degree of vulnerability to pathology . In sum , our results sketch out a blueprint of mammalian connectomes consisting of species-specific and species-general links between the connectional , physical , and cytological dimensions of the cerebral cortex , possibly reflecting variations and persistence of evolutionarily conserved mechanisms and cellular phenomena . Our framework elucidates organizational principles that encompass but also extend beyond the wiring economy principle imposed by the physical embedding of the cerebral cortex .
Mapping and understanding the wiring of the cerebral cortex at the micro- , meso- , and macroscale level is a central challenge in neuroscience [1–9] . Extensive studies have mapped the structural connections among cortical areas—that is , the macroscale connectional architecture—in different mammals , such as cats [3] , mice [6 , 7] , and macaque and marmoset monkeys [8 , 10] . These studies have uncovered a characteristic pattern of cortico-cortical connections among cortical areas , providing the structural scaffold for the communication of cortical areas , which is essential for cognition and behavior [11–13] . Moreover , invasive tract-tracing studies in mammals have uncovered a graded variation in the laminar origin of connections [14–16] , a connectional feature related to physiological properties of long-range connections [12] , central to contemporary theories of brain structure and function [17 , 18] and the basis of the so-called hierarchical arrangement of the areas of the cerebral cortex [19] . From a network topology standpoint—that is , the arrangement of connections between the distinct areas of the cortex—cortical connectomes possess a tightly interconnected structural core [20 , 21] , a network topology that is considered important for flexible behavior and large-scale functional integration [22] . Moreover , mammalian species exhibit a divergent evolutionary history of millions of years , as well as pronounced differences with respect to , for instance , brain size , number of neurons , and duration of neurogenesis [23–28] ( Fig 1 ) . Given the intricate wiring configuration of the cortex and such pronounced differences , is it possible to decipher unifying principles that link the connectional architecture with other dimensions of cortical architecture and thus sketch out a blueprint of the cortical organization of mammals ? A principle related to the existence of connections is the wiring cost principle . Specifically , nearby areas are more likely to be connected than remote areas [21 , 29–31] . However , wiring cost , reflected in the physical distance between cortical areas , does not fully explain the existence of connections [21 , 32 , 33] . Qualitative observations in the macaque monkey cortex suggest that the existence of connections is closely related to the gradients of cytoarchitectonic differentiation of the cerebral cortex , specifically to the similarity of the degree of cytoarchitectonic differentiation of cortical areas [34] . Gradients of cytoarchitectonic differentiation are formed by spatially ordered changes in the cytological composition of areas , including the appearance of the granular layer ( layer IV ) and the increase of its neuronal density and width , as well as the successive distinguishability and increase of the neuronal density of upper ( supragranular ) layers compared to lower ( infragranular ) layers [16 , 27 , 35–38] . Systematic studies in different mammalian species have demonstrated that the similarity of cytoarchitectonic differentiation of cortical areas , above and beyond their physical distance , is closely related to the existence of connections , suggesting a common wiring principle of mammalian cortices [30 , 31 , 33 , 39] . It is , however , unknown if this wiring principle is manifested in a species-specific manner and how general it is across the mammalian phylogeny . With respect to the systematic shifts of the laminar origin of connections , two main explanations have been put forward . On the one hand , a framework postulates that the graded shift of the laminar origin of connections , from predominantly infragranular to predominantly supragranular , giving rise to “feedback” and “feedforward” type of connections , respectively , is manifested across the rostrocaudal axis of the brain [14 , 40–42] . On the other hand , a cytoarchitecture-based framework has emphasized the central role of cortical cytoarchitectonic gradients in relation to the gradual shifts of the laminar origin of connections [15 , 30 , 33 , 39 , 43] . Therefore , a conjoint examination of these alternative frameworks in different mammalian cortices is needed for deciphering the central dimension of cortical organization that is related to the graded shifts of the laminar origin of connections . From a network topology standpoint—that is , the arrangement of connections between the different areas of the cortex—a core–periphery structure characterizes the mouse and macaque monkey cortex [20 , 21] . The core–periphery structure corresponds to two sets of areas , a tightly interconnected set of areas constituting the core and the rest of the areas constituting the periphery . This network configuration is central to theories of animal cognition [22] . In the macaque monkey , the core–periphery division is also reflected in the cytoarchitecture of the cortex , thus offering a unifying principle linking network topology and cytology by elucidating the cellular composition of topologically central cortical areas and the potential neurodevelopmental mechanisms leading to their central role in the cortical connectome [33] . Therefore , it is important to elucidate the species-general or species-specific nature of the relation of the core–periphery structure to the cytology of the cortex across different mammals . Here , we examine the connectomes of the mouse , cat , and macaque and marmoset monkey . We relate the connectional , cytoarchitectonic , and physical dimensions of the cerebral cortex , thus highlighting unifying principles that link the different dimensions of cortical architecture . These principles are manifested in a species-general but also species-specific manner , distinguishing the mouse and the marmoset monkey from the cat and the macaque monkey cortex . Commonalities allow the extrapolation of connectional features to unexamined species , such as humans , whereas the species-specific principles point at potential functional differences across species and indicate varied degrees of vulnerability to pathology . The observed unifying principles may reflect variations of evolutionary conserved neurodevelopmental mechanisms .
Extensive cytoarchitectonic and connectome data as well as information on the physical distance between cortical areas of mouse , cat , and marmoset and macaque monkey cortices were used in the analyses [8 , 10 , 30 , 31 , 33 , 44 , 45] . For the mouse and cat cortex , the cytoarchitectonic differentiation of areas was assessed qualitatively by defining an ordinal scale of cortical types based on Nissl-stained sections [30 , 31] . Cortical types reflect a multidimensional characterization of the cytoarchitectonic differentiation of cortical areas , based primarily on the density of neurons in the different cortical layers , as well as the appearance , neuronal density , and thickness of layer IV [39] . Low cortical types—that is , less differentiated and overall less neuronally dense areas—are not clearly laminated , and layer IV is absent or only weakly present . By contrast , high cortical types—that is , more differentiated and overall more neuronally dense areas—are clearly laminated , with a clearly defined layer IV . By these criteria , the highest cortical type corresponds to areas such as the primary visual cortex [30 , 39] . Therefore , cortical areas constitute a cortical spectrum of cytoarchitectonic differentiation , ranging from less to more differentiated and , thus , overall neuronally dense cortical areas ( Fig 1 ) . The cytoarchitectonic differentiation of the marmoset and macaque monkey cortical areas was assessed quantitatively by their overall neuronal density—that is , the number of neurons per mm3 . Neuronal density constitutes a fingerprint of the cytoarchitectonic status of cortical areas [33 , 36 , 37] . For the macaque monkey , Nissl- and NeuN-stained material was used [33] . For the marmoset monkey , NeuN-stained material was used [44] . Qualitative assessment of cytoarchitectonic differentiation of the areas of the macaque monkey , based on cortical types , was used as a control analysis . We used the most-comprehensive available cortical connectomes of the mouse [7 , 21] , cat [3] , and marmoset [10] and macaque monkey [8] . We used the geodesic or Euclidean distance between the barycenters of the cortical areas as a measure of their physical distance [8 , 10 , 21 , 31] . In the absence of a stereotaxic atlas with the parcellation scheme of Scannell and colleagues [3] for the cat cortex , physical distance between areas of the cat cortex was defined as the number of areas separating a pair of areas [30] . The presence or absence of a connection was viewed against two dimensions of cortical organization—that is , the cytoarchitectonic and physical dimension ( Fig 2 ) . We verified that connections that are present span shorter distances than absent connections ( statistical energy test: 0 . 17 , 0 . 74 , 0 . 03 , 0 . 33 for the mouse , cat , and marmoset and macaque monkey , respectively; all p < 0 . 001 ) . Moreover , connections that are present involve pairs of areas with more-similar cytoarchitecture than areas that are not connected ( statistical energy test: 0 . 32 , 0 . 29 , 0 . 16 , 0 . 23 for the mouse , cat , and marmoset and macaque monkey , respectively; all p < 0 . 001 ) . Similarity of cytoarchitecture was assessed as the absolute difference of the cortical type or neuronal density of a pair of areas . Fig 2 summarizes these findings , demonstrating that cytoarchitectonic similarity of cortical areas and their physical distance relates to the existence of connections . Using an alternative dataset for mouse connectivity [21] and qualitative assessment of the cytoarchitectonic status of the areas for the macaque monkey cortex led to similar qualitative results ( S1 Fig ) . Conjoint examination of the role of cytoarchitectonic similarity and physical distance to existence of connections with multivariate logistic regression revealed a statistically significant contribution of both factors in all species with the exception of the marmoset monkey , in which cytoarchitectonic similarity did not reach statistical significance ( S2 Fig ) . Thus , in the marmoset monkey , cytoarchitectonic similarity does not relate , above and beyond physical distance , to the pattern of existence of connections among cortical areas . This discrepancy constitutes the first species-specific manifestation of the relation of cytoarchitecture and connectivity . Control analyses for the species for which an ordinal scale was used in assessing the cytoarchitectonic status of cortical areas—that is , the cat and mouse—revealed that the relation of cytoarchitectonic similarity and existence of connections was robust to the exact assignments of cortical types to areas , as well as the exact range of the ordinal scale used for the qualitative evaluation of cytoarchitectonic differentiation in these species ( S3 Fig ) . To further investigate species-specific relations of cytoarchitecture and connectivity , we performed a logistic regression analysis for the mammals that showed a significant relation between cytoarchitecture and connectivity in the multivariate logistic regression analysis—that is , the mouse , cat , and macaque monkey . For each pair of these mammals , a model was estimated with existence of connections as the binary dependent variable and cytoarchitectonic similarity and distance as regressors . For investigating species-specific effects , a further regressor coding for the different species and their interaction with cytoarchitectonic similarity was added . Coefficients from the logistic regression denote the impact of each regressor on the probability of finding a connection between a pair of areas , as well as the dependence of such an effect on the interaction of the regressors . It should be noted that our approach does not require the establishment of area homologies across species , since the cross-species analysis relies on pairs of cortical areas and examines the factors related to the presence or absence of a connection between each pair of areas , irrespective of potential homologies or absence thereof . These analyses showed that the effect of cytoarchitectonic similarity on the existence of connections was significant in all cases , but its role was different when the mouse was compared with the cat and macaque monkey . For the mouse versus macaque monkey analysis , the coefficients were distance = −2 . 74 , cytoarchitectonic similarity = −0 . 80 , and species by cytoarchitectonic similarity = −2 . 47 ( all p < 0 . 0001 ) . The inclusion of the species by cytoarchitectonic similarity interaction significantly improved the model fit , as indicated by a likelihood ratio ( LR ) test ( LR = 16 . 95 , p < 0 . 001 ) . For the mouse versus cat analysis , the coefficients were distance = −1 . 67 , cytoarchitectonic similarity = −0 . 90 , and species by cytoarchitectonic similarity = −1 . 77 ( all p < 0 . 0001 ) . The inclusion of the species by cytoarchitectonic similarity interaction significantly improved the model fit , as indicated by an LR test ( LR = 20 . 55 , p < 0 . 001 ) . For the cat versus macaque monkey analysis , the coefficients were distance = −2 . 70 and cytoarchitectonic similarity = −2 . 76 , ( both p < 0 . 001 ) , but the interaction between the species and cytoarchitectonic similarity regressors was not significant ( species by cytoarchitectonic similarity = −0 . 53 , p > 0 . 1 ) . The negative coefficients for the interaction between species and cytoarchitectonic similarity for the mouse–cat and mouse–macaque monkey analyses were significant . This indicates that the impact of the decrease of the cytoarchitectonic similarity on the decrease of the probability of the existence of a connection is higher in the cat and macaque monkey when compared to the mouse ( see also the Materials and Methods section ) . For a better understanding of this effect , we visualized the impact of cytoarchitectonic similarity on the probability of the existence of a connection for different physical distance values for all pair-wise species analyses ( Fig 3 ) . For an equal decrease of cytoarchitectonic similarity , the probability of the existence of a connection decreased more slowly for the mouse when compared to the cat and macaque monkey ( Fig 3 ) . A control analysis using the qualitative cytoarchitectonic status of the macaque monkey cortical areas and a different dataset for the mouse cortico-cortical connectivity [21] led to the same qualitative results . Specifically , the coefficients for the mouse versus cat analysis were distance = −2 . 21 , cytoarchitectonic similarity = −0 . 84 , and species by cytoarchitectonic similarity = −1 . 87 ( all p < 0 . 001 ) . The inclusion of the interaction of species and cytoarchitectonic similarity significantly improved the model fit , as indicated by an LR test ( LR = 19 . 49 , p < 0 . 001 ) . For the mouse versus macaque monkey analysis , the coefficients were distance = −3 . 21 , cytoarchitectonic similarity = −0 . 77 , and species by cytoarchitectonic similarity = −2 . 07 ( all p < 0 . 01 ) . Also in this case , the inclusion of the interaction of species by cytoarchitectonic similarity significantly improved the model fit , as indicated by an LR test ( LR = 19 . 23 , p < 0 . 001 ) . Finally , for the cat versus macaque monkey analysis , the coefficients were distance = −3 . 27 and cytoarchitectonic similarity = −2 . 83 ( both p < 0 . 001 ) , but the interaction of the species and cytoarchitectonic similarity regressors was not significant ( species by cytoarchitectonic similarity = 0 . 01 , p > 0 . 1 ) . A visual depiction of the different effect of cytoarchitectonic similarity on the existence of connections in the mouse compared to the cat and macaque monkey for this control analysis is provided in ( S4 Fig ) . In summary , cytoarchitectonic similarity relates to the existence of connections in mammalian cortices in a species-specific manner , differentiating the mouse and marmoset monkey from the cat and macaque monkey . Next , we aimed at deciphering the central dimension of cortical organization related to the graded shifts of the laminar origin of connections across the different cortical areas . This analysis focused on the cat and macaque monkey , for which quantitative data on the laminar origin of connections were available [45 , 46] . A cytoarchitecture-based model was used , based on evidence from the prefrontal [15] and visual cortex [33 , 39] of the macaque monkey , highlighting the cytoarchitectonic status of the interconnected areas as predictive of the laminar origin of the connections . Here , we used quantitative information—that is , the percentage of supragranular labeled neurons ( NSG% ) [45] ( Fig 4A ) —that extends beyond the visual system of the macaque monkey and encompasses the rest of the cortex . We examined the cytoarchitecture-based model [15 , 43] conjointly with a rostrocaudal-based model that corresponds to suggestions that the rostrocaudal axis of the cortex is a central predictive factor of the laminar origin of connections [14 , 40–42] . We used support vector regression and partial Spearman's rank correlations . The conjoint examination of the relation of the laminar origin of connections to the rostrocaudal axis and cytoarchitectonic gradients was also performed with data from the cat cortex ( see Materials and Methods ) . In the macaque monkey , the cytoarchitecture-based model explained significantly more variance of the NSG% values than the rostrocaudal-based model ( Fig 4B and 4C ) . The addition of the rostrocaudal distances as a predictor to the cytoarchitecture-based model did not lead to statistically better NSG% predictions , compared to the model based solely on the cytoarchitecture of areas ( p > 0 . 1 ) ( Fig 4B and 4C ) . Specifically , the Spearman's rank correlation between the actual and predicted NSG% values for the cytoarchitecture-based model was rho = 0 . 36 , for the rostrocaudal-based model rho = 0 . 26 , and for the combination of cytoarchitecture and rostrocaudal distances rho = 0 . 37 ( all p < 0 . 0001 ) . The cytoarchitecture-based model explained more variance than the rostrocaudal-based model when partial Spearman's rank correlations were estimated: the correlation between NSG% and cytoarchitecture , when partialing out the rostrocaudal distances , was rho = 0 . 27 ( p < 0 . 0001 ) , and the correlation between NSG% and rostrocaudal distances when partialing out the cytoarchitectonic status of cortical areas was rho = 0 . 10 ( p < 0 . 05 ) . The same qualitative results were obtained when using the qualitative scale for assessing the cytoarchitecture of the macaque monkey cortex ( S5 Fig ) . Moreover , when computing partial Spearman's rank correlations for this control analysis , the cytoarchitecture-based model explained more variance than the rostrocaudal-based model: the correlation between NSG% and cytoarchitecture when partialing out the rostrocaudal distances was rho = 0 . 45 ( p < 0 . 0001 ) , and the correlation between NSG% and rostrocaudal distances when partialing out the cytoarchitectonic status of cortical areas was rho = 0 . 08 ( p < 0 . 05 ) . Moreover , recent studies advocating the importance of the rostrocaudal axis in predicting the laminar origin of connections in the macaque monkey cortex exclude the less differentiated areas of the cingulate and insular cortex [42] . Thus , we also performed the NSG% predictions while excluding these less differentiated cortical areas . This control analysis led to the same qualitative results—that is , the cytoarchitecture-based model yielded the highest NSG% predictions—and the addition of the rostrocaudal distances did not carry any additional information ( S6 Fig ) . The cytoarchitecture-based model also resulted in the best predictions of the NSG% values for the cat cortex ( Fig 5A ) . The same pattern of results as for the macaque monkey cortex was observed; namely , the cytoarchitecture-based model explained significantly more variance of NSG% values when compared to the rostrocaudal-based model ( Fig 5B and 5C ) . The addition of the rostrocaudal distances as an extra predictor to the cytoarchitecture-based model did not lead to statistically better NSG% predictions compared to the model based solely on the cytoarchitecture of areas ( p > 0 . 1 ) ( Fig 5B and 5C ) . Specifically , the Spearman's rank correlation between the actual and predicted NSG% values for the cytoarchitecture-based model was rho = 0 . 80 , for the rostrocaudal-based model rho = 0 . 21 , and for the combination of cytoarchitecture and rostrocaudal distances rho = 0 . 79 . The same conclusions were obtained for the partial Spearman's rank correlations: the correlation between NSG% and cytoarchitecture when partialing out the rostrocaudal distances was rho = 0 . 79 ( p < 0 . 0001 ) , and the correlation between NSG% and rostrocaudal distances when partialing out the cytoarchitectonic status of cortical areas was rho = 0 . 05 ( p > 0 . 1 ) . These conclusions are further supported by an analysis of an additional dataset with categorical data on the laminar patterns of the connections in the cat cortex ( S7 Fig ) . Our results highlight the cytoarchitectonic gradients of the cerebral cortex as a central axis of organization related to the graded shifts in the laminar origin of connections across the cortical sheet . The implications of these findings are 2-fold . First , they offer a guiding thread for deciphering the cellular phenomena or mechanisms responsible for such a close systematic relation between cytoarchitecture and laminar origin of connections ( see Discussion ) . Second , a cytoarchitecture-based model built on macaque monkey data can predict the laminar origin of connections in the human cortex , since such connectional data cannot currently be obtained by in vivo experiments ( Fig 6 ) . Such extrapolation of connectional features renders possible novel structure–function relations to be examined at a whole-cortex level , e . g . , relating interareal functional communication of cortical areas and the underlying laminar origin of the connections between them [47] , without the necessity of establishing macaque–human cortical area homologies . It is known that the mouse and macaque monkey cortex possesses nonrandom topological connectivity features , such as a core–periphery structure [20 , 21] . A core is a set of areas that are highly interconnected , with the remaining noncore areas constituting the periphery . A core–periphery network topology characterizes not only cortico-cortical networks but also other biological and technological networks , providing properties such as high topological efficiency [22 , 49] . We aimed to map this topological structure and investigate its association with the cytoarchitectonic gradients of the cerebral cortex of the different mammals . The core–periphery structure has already been mapped in the mouse and macaque monkey cortex by uncovering the largest cliques of the cortical connectome and forming the core as the union of areas participating in these cliques [20 , 21] ( Fig 7A and 7D ) . Here , to enable a comparative examination , we mapped the core–periphery structure with the same method in the cat and marmoset monkey . We found that the cat exhibits a core that consists of the union of two cliques of size 9 ( S3 Table ) , whereas the marmoset monkey core is composed of the union of 12 cliques of size 16 ( S4 Table ) . For both the cat and marmoset monkey , the size of the largest cliques forming the core was significantly different from the size of the largest cliques observed in random networks matched for degree distribution , number of nodes , and number of edges ( p < 0 . 001 for both the cat and marmoset monkey , 1 , 000 null networks ) . The cat core consists of areas that have a visuomotor and multisensory integration functional signature [3] ( Fig 7C ) . The marmoset core consists of “association” and multimodal areas of the frontal , parietal , and temporal lobe ( Fig 7B ) . We next related the core–periphery topology with the cytoarchitecture of the cerebral cortex . These analyses distinguished the mouse and marmoset monkey from the cat and macaque monkey . Specifically , in the mouse and marmoset monkey , the core areas did not significantly differ from the periphery areas in terms of cytoarchitectonic differentiation ( Fig 7A and 7B ) . On the contrary , in the cat and macaque monkey cortex , significant differences were observed between the core and periphery areas , with core areas exhibiting lower neuronal densities and degree of cytoarchitectonic differentiation than the periphery areas ( Fig 7C and 7D ) . Thus , although a structural network core characterizes the cortico-cortical network of all examined species , this topological structure is related differently to the cytology of the cerebral cortex , with a shift of the network core to less neuronally dense and differentiated areas of the cortex as the arbiter between the examined species . We subsequently proceeded to the explicit elucidation of the role of the network core in the communication among cortical areas . To this end , we examined the efficiency of the core areas , under two diametrically opposite and recently suggested scenarios of network communication [50] . Specifically , we assessed if the core exhibited higher efficiency under the scenario that communication in the cortical network takes place via the shortest paths or as passive diffusion ( corresponding to random walks ) [50] . For all species and both modes of communication ( shortest path or random walk ) , core areas exhibited higher incoming efficiency than the periphery areas ( Fig 8 ) . Thus , the core , compared to the periphery , can be reached faster from cortical areas under both modes of communication . Moreover , for all species , the core areas also exhibited higher outgoing efficiency for shortest paths but not for the random walk mode of communication ( Fig 8 ) . Thus , under the random walk mode of communication , core areas , compared to the periphery , are not topologically privileged for fast access to other cortical areas . Hence , in the context of the two aforementioned modes of network communication , the structural core must adhere to a mode of communication geared toward shortest paths in order to achieve fast access to the areas of the cortical network . In sum , the structural core , which is central for network communication , constitutes a common network topology of diverse mammals . The core–periphery topology is related to the cytology of the cortex in a species-specific manner . Specifically , a displacement takes place toward the less differentiated and overall neuronally dense areas of the cerebral cortex when transitioning from the mouse and marmoset monkey to the cat and macaque monkey .
The premise that neuronal systems are wired in such a way that minimizes the physical distance between the interconnected elements explains part of the characteristic pattern of presence and absence of connections between cortical areas in different mammalian species [9 , 20 , 21 , 29–31] . The current examination demonstrated that wiring cost also constrains the cortical connectome of a New World monkey—that is , the marmoset monkey . The current comparative framework allowed us to gain deeper insights into how the relation of cytoarchitectonic similarity to the existence of connections manifests across species . Our results reveal that cytoarchitectonic similarity has an attenuated impact on the probability of the existence of connections in the mouse when compared to the cat and macaque monkey and is statistically absent in the marmoset monkey . Thus , unifying wiring principles linking connectivity and cytoarchitecture not only distinguish rodents from primates but also point out differences within the primate order . Primates are distinguished from rodents with respect to the scaling of the size of the brain and the number of neurons it contains [55] . Our study offers further comparative insights by relating the cytology of the cortex to its macroscale connectivity and assessing how this relation is manifested in different mammals . The species-specific manifestation of the relation of connectivity and cytoarchitecture is systematic and highlights the trajectory of this relation across the mammalian spectrum . Specifically , our framework predicts that , on average , the relation of cytoarchitectonic similarity and the existence of connections in smaller mammalian cortices ( e . g . , hamster , treeshrew ) might be attenuated or even absent when compared to larger mammalian cortices ( e . g . , great apes , humans ) . Modifications of evolutionarily conserved developmental mechanisms may be the cause for this species-specific relation of cytology and connectivity ( Fig 9 ) ( see “Heterochronous , graded neurogenesis and pyramidal cell size heterogeneity” ) . The current results complement and resonate well with recent findings that show a common , but also species-specific manifestation , of the role of physical distance in the wiring of the mouse and macaque monkey cortex [21] . The current quantitative cross-species examination also allows to decipher the central cortical dimension that relates to the graded shifts of the laminar origin of cortico-cortical connections . In both the cat and macaque monkey , the laminar origin of the connections is dictated by the cytoarchitectonic status of the interconnected areas and not the orientation of a connection along the rostrocaudal spatial axis . Thus , the current investigation , conjointly with previous results [33 , 39 , 56] , highlight the close relation of cortical cytoarchitectonic gradients to the systematic shifts of the laminar origin of connections . The demonstration that cytoarchitectonic differentiation is a central cortical dimension related to the laminar origin of connections offers the ground for deciphering the concrete cellular phenomena across the cortical sheet that might be responsible for the shifts of the laminar origin of cortico-cortical connections ( see “Heterochronous , graded neurogenesis and pyramidal cell size heterogeneity” ) . In addition , the close relation of cytoarchitectonic gradients and laminar origin of connections can be used for extrapolating this connectional feature to the human cortex ( see “Unifying wiring principles allow cross-species predictions” ) . We should also note that the cytoarchitectonic gradients of the cerebral cortex explain a substantial part of the variance of the shifts of the laminar origin of connections , but not the total variance; thus , it is important to uncover additional factors that may shape the laminar shifts of connections across the cortical sheet of mammals . The species-general finding that cytoarchitectonic gradients constitute a central cortical dimension related to the laminar origin of connections not only offers neurobiological insights but also allows the extrapolation of this connectional feature from macaque monkeys , the closest primate to humans that can be invasively examined , to the human cerebral cortex . Such extrapolation allows novel structure–function examinations . For instance , frequency-dependent communication between areas of the human visual system obeys the same structure–function principles observed in the macaque monkey [12 , 47] . Our results allow extrapolation of the laminar origin of connections to humans , thus allowing such structure–function examinations to be performed at the global , whole-cortex level without the need for establishing homologies between the cortical areas of the two species . In sum , our results , conjointly with recent efforts [57 , 58] , demonstrate the value of uncovering unifying principles that can be used to link a wealth of data on the macaque monkey cerebral cortex to the human cerebral cortex . Our results highlight a structural network core in the mammalian cerebral cortex , important for the communication of cortical areas . Our comparative analysis demonstrates the displacement of the structural network core in the mammalian phylogeny . This displacement is manifested as a species-specific relation of the network core to the cytology of the cortex , leading to the neuronal sparsification of the core in cats and macaque monkeys—that is , the displacement of the network core toward the least neuronally dense parts of the cerebral cortex . We have demonstrated that across mammalian species , the structural core , compared to the periphery , is reached faster from cortical areas under two modes of network communication—that is , communication based on passive diffusion or shortest paths [50] . Passive diffusion is not costly from an information point of view , since navigating the network relies on random transitions from area to area , with no “knowledge” about the topology of the network . Shortest paths , on the other hand , require the channeling of communication between cortical areas through the shortest routes of the cortical network , and thus this mode of communication is considered information-costly [50] . Passive diffusion traps signals inside the densely interconnected core; hence , the structural core can reach faster , compared to the periphery , other areas only under the adoption of a more information-costly mode of communication that is geared toward shortest paths . Topologically central parts of the brain also exhibit a high energetic cost [59 , 60] , and thus a substantial part of this energetic cost , or overall energy consumption of the mammalian brain , might be attributable to the need of the areas of the core to adopt an information-costly mode of communication in order to achieve fast communication with other cortical areas . In sum , we highlight the importance of the structural network core in the communication of cortical areas . These findings provide an empirical foundation to theoretical frameworks [22] and extend previous results [9] by situating the structural network core in a comparative context and elucidating its role in light of recently suggested taxonomies of network communication processes . The displacement of the network core across the cortical gradients , leading to its neuronal sparsification in cats and macaque monkeys , highlights three points with potential functional ramifications and suggests varied degrees of vulnerability to pathologies . First , less cytoarchitectonically differentiated—and thus overall less neuronally dense—areas in mammalian cortices , such as the areas of the insular and cingulate cortex , are also overall less myelinated when compared to more differentiated areas , such as primary sensory-motor areas [35 , 61–63] . Both in vivo and in vitro studies in mammals demonstrate that high degree of myelination suppresses synaptic plasticity and axonal growth [64–66] . Thus , less myelinated areas are more flexible than more myelinated areas [67] . Therefore , the cat and macaque monkey network core , in contradistinction to the core of the mouse and , to a certain extent , marmoset monkey , seems to include the most-flexible areas of the cerebral cortex , bestowing the network core in these species with higher degrees of adaptability . Second , in the macaque monkey , spine densities vary across cortical areas , with less differentiated and overall neuronally dense areas exhibiting high spine densities . In mice , differences of spine densities across cortical areas are very attenuated [68 , 69] . Computational modeling employing the heterogeneity of spine densities across areas as a proxy for the strength of excitatory input to pyramidal cells demonstrates that spine density heterogeneity is important for the generation of temporal receptive windows across cortical areas , bestowing the less differentiated parts of the cortex with more-prolonged time windows that are ideal for integration of signals over longer time periods [45] . Thus , the above computational evidence and interspecies differences with respect to spine density heterogeneity across the cortical gradients indicate that in macaque monkeys , contrary to mice , the alignment of the network core with areas exhibiting high spine densities constitutes a synergy of connectional and microcytological features that may enhance the functional integration capacity of the core in macaque monkeys . Third , although the neuronal sparsification of the network core might entail differences in terms of integration and plasticity , it might also entail an increased vulnerability . Less neuronally dense areas also exhibit high metabolism and cellular stress [67] . Highly connected areas , like the areas of the network core that we have currently highlighted , are more affected in diverse pathologies [70] . Thus , a cortex characterized by a synergy between highly connected and neuronally sparse areas can also entail an increased vulnerability to pathologies . In sum , our results highlight the relation of the network core to the cytology of the cortex across different mammals and the functional ramifications of such relation , thus situating the topology of the mammalian connectome in a comparative and neurobiologically interpretable context . Uncovering unifying wiring principles of the cerebral cortex harnesses the complexity of cortical wiring and renders possible a glimpse into the neurodevelopmental mechanisms suggested by these principles . The more pronounced relation of cytoarchitectonic similarity to the existence of connections observed in cats and macaque monkeys in relation to mice and marmoset monkeys may be rooted in the spatiotemporal structure of neurogenetic gradients during development [16 , 36 , 53 , 71 , 72] . Specifically , the spatially ordered cytoarchitecture of cortical areas might reflect the spatially ordered heterochronous neurogenesis and subsequent migration of neurons across the developing pallium . Hence , areas with similar cytoarchitecture might also exhibit similar developmental time courses [27 , 36 , 73] . Therefore , areas with similar cytoarchitecture in the adult cortex might be more likely to be connected , since during development they host neurons that constitute more-readily available connection partners , following a “what develops together , wires together” principle [39] . This mechanistic explanation assigns a central role to the heterochronicity of neurodevelopmental events in the formation of intricate wiring configurations . Such a mechanism is directly supported by empirical studies investigating the neurogenesis and connections of the olfactory bulb and the primary olfactory cortex in rats [72] and computational modeling [74 , 75] . The duration of neurogenesis is shorter in mice compared to macaque monkeys [76] and possibly arises from a common evolutionarily conserved mechanism [54] . Less distinct time windows and overall shorter neurogenesis in the mouse and the marmoset monkey , when compared to the cat and macaque monkey , may result in the currently observed species-specific manifestation of the relation of existence of connections and the cytological composition of cortical areas ( Fig 9 ) . In addition , computational modeling [77] and empirical evidence from Caenorhabditis elegans suggest that heterochronicity in neurogenesis might also partially explain the formation of a structural core; that is , neurons that constitute a tightly interconnected core in the adult worm are born earlier than noncore neurons [78] . A similar “early neurogenesis advantage” , in addition to spatial constraints imposed by the geometry of the cortex [20] or the distinct molecular signature of core areas [79] , might constitute factors that lead to the formation of a network core in the mammalian cortex . Our results demonstrate a tight relation of laminar origin of connections and cytoarchitecture . Why do less differentiated cortical areas elicit connections predominantly from infragranular layers , whereas more-differentiated areas elicit connections from predominantly supragranular layers ? Part of the answer might lie in the phenomenon of externopyramidization ( Externopyramidisierung ) [38 , 80 , 81] . This structural organization principle of the cerebral cortex , observed in diverse species , including humans [38 , 61 , 80 , 82] , describes the rate of change of the ratio of the soma size of pyramidal neurons located in upper ( supragranular ) layers versus lower ( infragranular ) layers across the cortical sheet . In mammalian cortices ( for instance , cat and monkey cortices ) , the progressive differentiation of areas is accompanied by an increase of the soma size of supragranular pyramidal cells relative to the soma size of the infragranular pyramidal cells [81] . A larger soma size of pyramidal cells entails larger axon diameters , higher conduction velocities , and larger boutons that contain a higher number of vesicles , leading to higher probabilities and larger amounts of neurotransmitter release [81] . Therefore , the soma size of pyramidal neurons entails ultrastructural and functional properties of the corresponding axons , possibly rendering connections originating from pyramidal neurons with large soma more suitable for high-throughput long-range communication . Consequently , the phenomenon of externopyramidization might partially explain why less differentiated areas can establish and maintain long-distant connections primarily from infragranular layers , whereas more differentiated areas primarily from supragranular layers [81] . The phenomenon of externopyramidization is manifested with a varied degree of prominence across the mammalian spectrum , thus allowing the prediction of the laminar origin of connections in not-yet-examined species [81] . Specifically , in species with an attenuated manifestation of the phenomenon of externopyramidization , like mice , less pronounced shifts of the laminar origin of connections across the cortical sheet will be observed , whereas in species with a more prominent manifestation of the phenomenon of externopyramidization , like gorillas and humans , more pronounced shifts of the laminar origin of connections will be observed , with a gradual emphasis on supragranular layers [81] . In sum , our results highlight specific neurogenetic and cellular phenomena giving rise to unifying principles linking the cytoarchitectonic and connectional organization of the adult mammalian cortex . Gradients of cortical differentiation entail changes of multiple cortical features , such as myelin and density of different receptors and interneuron subtypes [16 , 80 , 83] . Thus , apart from obtaining more comprehensive quantitative cytoarchitectonic data , future studies in mammals should also elucidate how macroscale connectivity relates to other dimensions of cortical architecture . Moreover , changes across cortical gradients are layer-specific [27] . Therefore , in order to reveal a more fine-grained picture of cortical architecture , quantitative measurements should ideally also be obtained in a layer-wise manner . Furthermore , additional features such as the strength heterogeneity of connections , as well as new results from invasive tract-tracing studies [84] , should be examined . In the macaque monkey , connectivity strength heterogeneity is related not only to the physical embedding of the cortex but also to the homophily principle—that is , the connectional similarity of cortical areas [85] . Thus , we predict that the homophily principle will help explain the strength heterogeneity of cortico-cortical connections in other mammals . Lastly , in phylogenetically close species , such as monkeys and humans , common long-range fiber systems can be discerned and used for the examination of species-general and species-specific organizational principles [86] . Such an approach , in conjunction with the approach that we have adopted , increases the tools for quantitative cross-species examinations and hopefully will further pave the way for additional insights into the organization of mammalian cortices . Our results sketch out a connectional blueprint for the mammalian cerebral cortex by demonstrating species-general and systematic species-specific unifying principles linking the connectional , cytological , and physical dimensions of the cerebral cortex . The common principles allow the extrapolation of connectional features to not-yet-examined mammalian species , whereas the species-specific variations highlight unique aspects of cortical organization across the mammalian spectrum with potential function ramifications . Commonalities and differences of cortical organization may stem from variations and persistence of evolutionarily conserved neurodevelopmental mechanisms and cellular phenomena .
We used binary logistic regression for the prediction of the existence of connections across species . In order to render cross-species predictions feasible , the predictors ( physical distance and cytoarchitectonic similarity ) were linearly normalized to the 0–1 interval separately for each species . Note that this normalization does not artificially expand or shrink the levels of differentiation or size of each species , since the relative changes indicated by these regressors are of importance . Subsequently , a model was built with the existence of connections as a binary dependent variable and the physical distance and cytoarchitectonic similarity as predictors . We were interested in investigating if the cytoarchitectonic similarity of cortical areas relates to the existence of connections in a species-specific manner . Therefore , a categorical predictor coding for the different species was added to the model as well as the interaction of this predictor and the cytoarchitectonic similarity predictor . The improvement of the model fit , when the interaction of species and cytoarchitectonic similarity was included , was assessed with the LR test . For predicting the laminar origin of connections , an out-of-sample classification approach was adopted . We used support vector regression with a regularization parameter C = 1 . A cytoarchitecture-based model , quantifying the difference of the cytoarchitecture of the cortical area of projection origin versus the cytoarchitecture of the cortical area of projection termination , was built on 70% of the data and tested on the remaining data . The predictions were computed 1 , 000 times , each time using 70% of the available data to build the model ( drawing without replacement ) . The quality of the predictions was assessed by computing the Spearman's rank correlation between actual and predicted NSG% values . In the same fashion , a rostrocaudal-based model was built and tested . The coordinates of the barycenters of the cortical areas along the rostrocaudal axis were normalized to the 0–1 interval , with 0 denoting the most caudal area and 1 the most rostral area . Subsequently , for each connection the rostrocaudal coordinate of the connection origin was subtracted from the rostrocaudal coordinate of the connection termination . Hence , increasingly positive ( negative ) values of this rostrocaudal distance metric denote increasing rostral-to-caudal ( caudal-to-rostral ) distances . A 3D stereotaxic atlas was used for the macaque monkey [8] . For the cat cortex , in the absence of a 3D stereotaxic atlas , we used the 2D atlas of Scannell and colleagues [3] . The map was digitally reproduced , each cortical area was color-coded with a unique color , and the map was imported in MATLAB . Each area was assigned to a position along the rostrocaudal axis in this native coordinate system by computing the mean coordinate in the y-axis of all the pixels belonging to each area , and subsequently rostrocaudal distances were computed as described for the macaque monkey cortex . For estimating the unique variance explained by each predictor , we additionally computed partial Spearman's rank correlations between the NSG% values and the rostrocaudal distance and the cytoarchitectonic difference of the connection origin and termination . For the cat cortex , the laminar origins of the connections were available either as quantitative NSG% values or as a binary category—that is , “feedforward” or “feedback . ” For the NSG% values , the Spearman's rank correlation between the predicted and actual NSG% values was used for assessing the quality of the predictions . For the binary case , the quality of the predictions was assessed by computing the corresponding area under the curve of the receiver operating characteristic curves . Null predictions and significance levels were obtained by training the model 100 times on shuffled NSG% values or binary labels . For detecting the core in the cortico-cortical network , we followed the approach described in [20] . We used a MATLAB implementation of the Bron-Kerbosch algorithm with pivoting and degeneracy ordering ( https://de . mathworks . com/matlabcentral/fileexchange/47524-find-maximal-cliques-for-large—sparse-network ) to detect the largest cliques ( that is , sets of fully connected areas ) . The core was defined as the union of areas participating in the largest cliques , and the rest of the areas were assigned to the periphery . Applying this algorithm to the macaque monkey and mouse data resulted in the exact same core areas as the ones reported in [20] and [21] . We applied the same algorithm for detecting the core areas of the cat and marmoset monkey connectome . To test if the core observed in the empirical networks was not solely the result of the degree distribution heterogeneity of the networks , we applied the core–periphery algorithm as described above to 1 , 000 surrogate networks , matched for degree distribution , nodes , and edges to the empirical networks . The statistical significance of the core was computed by examining if the size of the largest cliques ( constituting the core ) in the surrogate networks exceeded the size of the largest cliques in the empirical networks . We used the Brain Connectivity Toolbox ( https://sites . google . com/site/bctnet/ ) [91] for estimating the in- and out-efficiency ( based on shortest paths or random walks ) of cortical areas . Because of an absence of quantitative information on the strength of connections for the mouse and cat connectomes , these measures were computed in binary connectomes . We used permutation tests for comparing the in- and out-efficiency ( based on shortest paths or random walks ) and cytoarchitectonic differentiation of the core and periphery areas . The labels of the areas denoting if they belong to the core or the periphery were permuted , and the core–periphery differences were estimated with the Kolmogorov-Smirnov test or the statical energy test , a nonparametric test for comparing two distributions [92] ( https://github . com/brian-lau/multdist/blob/master/minentest . m ) . The procedure was repeated 1 , 000 times , and the obtained null values were compared to the values obtained with the original core–periphery assignments . | The cerebral cortex is wired in a highly intricate manner and exhibits striking differences across mammals—for instance , in overall size and number of neurons . Here , we uncover common , but also species-specific , principles that link the physical , cellular , and connectional architecture of mouse , cat , and monkey brains . Commonalities allow the extrapolation of features to further , unexamined species , such as humans , whereas the species-specific principles point at potential functional differences , but also varied degrees of vulnerability , of mammalian brains . The observed unifying principles may reflect variations of evolutionarily conserved neurodevelopmental mechanisms . In sum , we sketch out a blueprint of mammalian cortical organization that elucidates the links between the physical , cytological , and connectional architecture . Our results indicate that caution is warranted when translating findings from one mammalian species to another , since some , but not all , cortical organizational properties are common across the mammalian spectrum . |
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Transmission of plant pathogens by insect vectors is a complex biological process involving interactions between the plant , insect , and pathogen . Pathogen-induced plant responses can include changes in volatile and nonvolatile secondary metabolites as well as major plant nutrients . Experiments were conducted to understand how a plant pathogenic bacterium , Candidatus Liberibacter asiaticus ( Las ) , affects host preference behavior of its psyllid ( Diaphorina citri Kuwayama ) vector . D . citri were attracted to volatiles from pathogen-infected plants more than to those from non-infected counterparts . Las-infected plants were more attractive to D . citri adults than non-infected plants initially; however after feeding , psyllids subsequently dispersed to non-infected rather than infected plants as their preferred settling point . Experiments with Las-infected and non-infected plants under complete darkness yielded similar results to those recorded under light . The behavior of psyllids in response to infected versus non-infected plants was not influenced by whether or not they were carriers of the pathogen . Quantification of volatile release from non-infected and infected plants supported the hypothesis that odorants mediate psyllid preference . Significantly more methyl salicylate , yet less methyl anthranilate and D-limonene , was released by infected than non-infected plants . Methyl salicylate was attractive to psyllids , while methyl anthranilate did not affect their behavior . Feeding on citrus by D . citri adults also induced release of methyl salicylate , suggesting that it may be a cue revealing location of conspecifics on host plants . Infected plants were characterized by lower levels of nitrogen , phosphorus , sulfur , zinc , and iron , as well as , higher levels of potassium and boron than non-infected plants . Collectively , our results suggest that host selection behavior of D . citri may be modified by bacterial infection of plants , which alters release of specific headspace volatiles and plant nutritional contents . Furthermore , we show in a laboratory setting that this apparent pathogen-mediated manipulation of vector behavior may facilitate pathogen spread .
Transmission of plant pathogens by insect vectors is a complex biological process involving interactions between the plant , insect , and pathogen [1]–[2] . Pathogens can induce changes in the traits of their primary hosts as well as their vectors to affect the frequency and nature of interactions between hosts and vectors [3]–[13] . Plant morphology , as well as , primary and secondary plant compounds , including emitted volatiles and plant nutrients , are some of the traits that can be altered by pathogen infection of plants [14]–[16] . Fecundity , survival , and behavior are primary traits altered in insect vectors due to such infection [7]–[10] , [12]–[13] , [17]–[21] . Plant pathogen infection may alter both plant morphology and chemistry; therefore , research efforts have focused on the vector's response to such changes in their plant host [7]–[12] , [18]–[21] . Candidatus Liberibacter asiaticus ( Las ) is a gram-negative , fastidious , phloem-limited bacterium that causes huanglongbing ( HLB ) disease in citrus [20] . Based on their 16S rRNA gene sequence , three species of the pathogen have been identified to date: 1 ) Candidatus Liberibacter asiaticus ( Las ) , 2 ) Candidatus Liberibacter africanus ( Laf ) , and 3 ) Candidatus Liberibacter americanus ( Lam ) [22]–[26] . Each species can be graft-inoculated into plants with infected plant tissue [22]–[23] , [27]–[29] . Las and Lam are transmitted by the Asian citrus psyllid , Diaphorina citri Kuwayama ( Hemiptera: Psyllidae ) ; whereas , Laf is transmitted by the African citrus psyllid , Trioza erytreae ( Del Guercio ) ( Hemiptera: Psyllidae ) . Both D . citri adults and nymphs are capable of transmitting Las after feeding on an infected plant for 30 minutes or longer [19] , [30]–[32] . If the pathogen is acquired by psyllid nymphs , adults are capable of transmitting the bacteria immediately after emergence [19] , [30] , [33] . The bacteria presumably multiply within the vector and are retained throughout the entire life cycle of the vector [33]–[34] . HLB , otherwise known as citrus greening disease , is one of the most devastating diseases of citrus worldwide [22] , [28] , [35] . HLB affects plant phloem , causing yellow shoots , mottling , chlorosis , and twig die back that result in rapid tree decline and ultimately tree death . In areas of the world where HLB is endemic , citrus trees decline and die within a few years and may never produce usable fruit [28] . All commercial citrus cultivars are susceptible to HLB and there is no cure currently . HLB spread is dependent upon the density and movement of D . citri from infected trees to uninfected trees [36] . Mating and oviposition in D . citri occurs exclusively on the new and tender flush of its host plants [37]–[38] . In addition , D . citri is known to use combined olfactory and visual cues to locate citrus host plants [39]–[40] . Many plant species emit volatiles in response to arthropod feeding or pathogen attack [41]–[43] . Plants can defend themselves by producing infochemicals or toxins that act directly against herbivores or indirectly by producing chemicals , which attract herbivore natural enemies [44]–[49] . Several metabolic pathways are activated in plants when pathogens invade [49]–[50] . Some metabolites accumulate in leaves , whereas others are only produced in a particular type of induction [50]–[53] . For example , biosynthesis of the phenolic defense hormone , salicylic acid ( SA ) , and its conversion to derivatives such as salicylic acid β-glucoside , salicylic acid glucose ester , and methyl salicylate ( MeSA ) , are major and general metabolic events in pathogen-infected plants [50] , [54]–[55] . Such plant hormone-mediated responses affect the interactions between pathogens and herbivores on plants in many ways [56] . Pathogens can also induce the release of chemicals from plants that benefit their survival and spread or suppress plants' defense responses [16] , [57] . For example , infection of apple with Candidatus Phytoplasma mali , ( causal agent of apple proliferation ) induces release of β-caryophyllene , which attracts apple psyllids ( Cacopsylla picta ) to infected trees and facilitates pathogen spread [16] . Similarly , cucumber mosaic virus ( CMV ) - , potato leaf roll virus ( PLRV ) - and barley yellow dwarf virus ( BYDV ) -infected plants are more attractive to their aphid vectors , Aphis gossypii , Myzus persicae and Rhopalosiphum padi or Schizaphis graminum , than non-infected plants due to induced production of volatiles as a result of infection [3]–[4] , [58]–[59] . CMV was reported to reduce the quality of squash plants for M . persicae and A . gossypii [9] . Similarly , BYDV reduced the quality of wheat plants for R . padi [12] . In contrast , PLRV improved the quality of potato plants as hosts to its vector , M . persicae [13] . Although most investigations of plant-pathogen interactions have focused on individual aspects of pathogen-induced plant defense [50] , [54]–[55] or the response of insect vectors to infected versus non-infected plants [9] , [15]–[16] , studies are emerging which highlight multiple interactions between herbivores and phytopathogens [9] , [47] , [56] , [60] . The goal of current research was to understand the interactions between the pathogen ( Las ) , the vector ( D . citri ) , and the host plant ( citrus ) to determine whether pathogen-induced plant response may facilitate spread of the HLB pathogen . The specific objectives were to determine whether pathogen infection alters the attractiveness of host plants to the vector and to determine how infection of the vector with the pathogen affects its behavioral response . To assess the effects of Las infection on vector-plant interactions , we conducted olfactometer and gas chromatography-mass spectroscopy ( GC-MS ) assays to identify specific olfactory cues attractive to psyllid vectors . In addition , we evaluated the effect of olfactory cues on the settling and dispersal behaviors of psyllids . Our results indicate apparent pathogen-mediated manipulation of vector behavior , which may promote pathogen spread .
The majority ( >80% ) of those D . citri tested responded to the odors of either non-infected or Las-infected citrus plants . Significantly more D . citri males ( χ2 = 4 . 32 , df = 1 , P = 0 . 04 ) and females ( χ2 = 6 . 53 , df = 1 , P = 0 . 01 ) were attracted to the odors from Las-infected plants than non-infected plants ( Fig . 1 ) . Las-infected male ( χ2 = 5 . 24 , df = 1 , P = 0 . 02 ) and female ( χ2 = 4 . 37 , df = 1 , P = 0 . 04 ) psyllids were also more attracted to the odors from infected than non-infected plants ( Fig . 1 ) . No significant differences were detected when D . citri adults were exposed to non-infected grafted ( sham grafted ) vs . non-grafted-non-infected plants ( χ2 = 1 . 51 , df = 1 , P = 0 . 22 ) . Movement of non-infected or Las-infected D . citri from Las-infected to non-infected plants was greater than observed for any other treatment combination ( F = 24 . 95 , df = 3 , 24 , P<0 . 0001 ) ( Fig . 3 ) . More Las-infected D . citri tended to move than uninfected counterparts after initial settling , although this difference was not statistically significant ( F = 3 . 70 , df = 1 , 24 , P = 0 . 07 ) . Las infection status of psyllids did not interact significantly with the direction of movement between plant treatments ( F = 0 . 91 , df = 3 , 24 , P = 0 . 45 ) . Approximately 5% of non-infected psyllids moving from infected to non-infected plants acquired the Las bacterium ( Table 1 ) . Four months after the termination of the experiment , all of the non-infected plants that had been inserted into cages with Las-positive plants tested positive for Las ( Table 1 ) . There were marked differences between infected and non-infected plants with respect to nutrient content ( Table 2 ) . Las-infected plants were deficient in nitrogen , phosphorus , magnesium , zinc , and iron as compared with non-infected plants ( Table 2 ) . However , Las-infected plants had higher potassium and boron contents compared with non-infected plants ( Table 2 ) . There were no differences between Las-infected and non-infected plants for calcium , sulfur , silicon , manganese , sodium , molybdenum , aluminum , and chlorine . The number of honey dew droplets produced by psyllid feeding , a surrogate measure of feeding efficiency , was significantly affected by the infection status of plants ( F = 65 . 99 , df = 1 , 39 , P<0 . 0001 ) , feeding exposure time ( F = 81 . 81 , df = 3 , 24 , P<0 . 0001 ) , and the interaction between the two factors ( F = 7 . 11 , df = 3 , 28 , P = 0 . 0011 ) . There was no significant difference in the amount of feeding on non-infected versus infected plants after 24 h of feeding by psyllids ( Fig . 4 ) . However , psyllids fed significantly more on non-infected than on Las-infected plants after 48 , 72 and 96 h , respectively . There were significant qualitative and quantitative ( Table 3 , Fig . 5 ) differences between the headspace volatiles of non-infected and Las-infected plants ( Table 3 ) . Las-infected plants released significantly more MeSA than non-infected plants ( Table 3 , Fig . 5 ) , while non-infected plants released more methyl anthranilate ( MeAN ) and D-limonene than infected plants ( Table 3 ) . Using a Random Forests algorithm , a minimum of two compounds , MeAN and MeSA , were identified that discriminated between the infected and non-infected volatile organic compound ( VOC ) signatures of these treatments , with an estimate prediction error of 0 . 15 and a ‘leave-one-out’ bootstrap error of 0 . 20 . A significant amount of MeSA was detected in headspace volatiles from plants after psyllids were introduced and allowed to feed , as compared with headspace volatiles from plants prior to psyllid feeding ( t = −2 . 97 , df = 5 , P = 0 . 03 ) . MeSA was detected in headspace profiles from plants infested with female as well as male psyllids; however , there was no significant difference in the amount of MeSA released in response to male verses female feeding ( t = 0 . 27 , df = 4 , P = 0 . 80 ) . Analysis of the volatile profiles of plants prior to the introduction of psyllids of either sex or from negative control plants yielded no MeSA . The amount of MeSA released ( mean ± SE ) by plants exposed to psyllid feeding ( 10 . 3±3 . 00 ng/plant/24 hr ) was similar to the amount released by Las-infected plants ( 12 . 87±4 . 78 ng/plant/24 hr ) , and both of these amounts were similar to the quantity of synthetic MeSA found to be attractive to psyllids in behavioral bioassays ( Table 4 ) . Behavioral bioassays with synthetic chemicals identified from Las-infected and non-infected plants revealed that D . citri were significantly attracted to MeSA at the 0 . 01 µg dosage , but that they were repelled by this chemical at the 100 µg dosage ( Table 4 ) . D . citri were attracted to D-limonene only at the 100 µg dosage , while MeAN did not attract or repel D . citri at any of the dosages tested . The positive control ( β-ocimene ) was attractive at 1–100 µg dosages ( Table 4 ) .
Our results indicate that Las-infected plants were initially more attractive to D . citri adults than non-infected plants; however , psyllids dispersed subsequently to non-infected plants after initially settling on infected plants . Similar results obtained with the behavioral responses of D . citri under both light and dark conditions suggest that initial movement of psyllids to Las-infected plants is likely mediated by volatile cues . Infection of citrus with this plant pathogen induced release of MeSA , which attracted D . citri . A similar amount of MeSA was also released by similar trees in response to D . citri infestation , suggesting that the same cue exploited by the pathogen to attract its vector may be used by the vector to locate congregations of conspecifics feeding on non-infected plants . Following initial chemically mediated attraction to infected plants , psyllids tended to subsequently disperse to non-infected plants , making them their preferred settling choice . We speculate that this may have been due to the sub-optimal quality of infected plants as compared with non-infected plants , as evidenced by lower feeding efficiency on infected than non-infected leaves . Importantly , this pathogen-manipulated behavioral sequence facilitated pathogen spread by the vector in a controlled laboratory experiment . Our results suggest that an insect-transmitted bacterial plant pathogen alters plant traits so as to induce odor-mediated behavior from the vector that may ultimately benefit the pathogen . While increased preference of insect vectors to virus-infected versus non-infected host plants has been demonstrated for both persistently and non-persistently transmitted viruses [3] , [4] , [9] , little is known about similar interactions involving pathogenic bacteria . Furthermore , most previous studies on pathogen-vector interactions have focused on single mechanisms ( olfactory , visual or nutritional ) [15]–[16] , [61]–[64] . In the current investigation , we sought to determine several underlying mechanisms ( chemical , visual , and nutritional ) that affect the behavior of an insect ( D . citri ) in response to plant infection by the pathogen ( Candidatus Liberibacter asiaticus ) that it transmits . Volatiles from infected and non-infected citrus are attractive to D . citri as confirmed by the current data and previous studies [40] . D . citri were attracted to common volatiles released by citrus such as β-ocimene and D-limonene , implicating these as general host selection cues . Both β-ocimene and D-limonene are predominant citrus volatiles released by citrus flush , foliage , and fruit [40] , [65]–[67] . However , pathogen infection induced release of certain novel components or a quantitative increase in release of certain compounds that were released in lower quantities by non-infected plants . Among the novel chemicals identified that distinguished Las-infected from non-infected plants , only MeSA elicited attraction from D . citri and may explain the enhanced attractiveness of infected plants . However , the effect of MeSA on D . citri behavior , as part of a complex citrus volatile blend , requires further investigation . Specific blends of plant volatiles are important for attraction of herbivores to their host plants; however , individual components of such blends can act as repellents when not perceived within the context of the entire blend [68]–[69] . Movement of psyllids from infected to non-infected plants after initial selection of infected plants suggests that their initial response to olfactory cues may not be directly linked to the most beneficial host plant . This was also confirmed by feeding assays measured by honeydew excretion . These results suggest that psyllids must feed on infected plants in order to discern poor quality of the host . Therefore , volatiles appear to be involved in host finding , but not in arrestment on the host . Feeding is required for insects to differentiate between non-infected and infected plants and gustatory cues are involved in host acceptance [57] , [70]–[71] . Insect herbivory can change the volatile profile of host plants . Insects that use a piercing/sucking mode of feeding have long-lasting interactions with plants cells and/or phloem [44] . It may not be surprising that plant responses to phloem-feeding are distinct from those occurring in response to chewing insects and that phloem-feeders often induce salicylate-dependent defense pathways commonly activated by bacterial , fungal , and viral pathogens [44] , [56] , [72]–[73] . A salicylate-dependent association is implicated in the current investigation given detection of MeSA release from plants infected with the Las pathogen . Production of MeSA is associated with the phytohormone , salicylic acid ( SA ) , and its role in signaling plant defensive pathways , which regulate systemic acquired resistance against a wide variety of invading pathogens [74]–[75] . Zarate et al . [76] demonstrated that the whitefly , Bemisia tabaci , induces SA-signaling pathways , which suppress basal defenses constraining nymph development . Thus , attraction to plants with high levels of SA-signaling may benefit the herbivore . Furthermore , suppression of defenses via SA may be consistent with a number of strategies employed by phloem-feeding insects to avoid such defenses ( see Walling [77] ) . We investigated whether release of MeSA may be induced by D . citri feeding as a possible cue used by psyllids for locating established conspecifics or plants that have suppressed basal defenses . Volatile collections from psyllid-infested plants detected induced release of MeSA , suggesting a coincidental convergence on a single cue that may simultaneously benefit the pathogen by deceptively attracting its vector , which also uses this cue to locate conspecifics or identify vulnerable hosts . Broadly , these results are consistent with the “deceptive host phenotype” hypothesis proposed by Mauck et al . [9] , given that release of an existing cue that attracts the vector ( MeSA ) to its host plant was also induced by pathogen infection , thus deceptively attracting the vector to nutritionally sub-optimal hosts . Pathogen-induced release of plant volatiles has been previously established following virus infection . In these cases , CMV , PLRV , and BYDV induced release of volatiles that rendered infected plants more attractive to their aphid vectors than non-infected plants . Both PLRV and BYDV infections improved the quality of the host plants to their respective vectors [3] , [4] , [78]–[79] , resulting in preferential arrestment on the infected plants due to contact and gustatory cues [3] , [4] , [58] . Our results are similar to the findings of Mauck et al [9] , where squash plants infected with CMV released volatiles that initially attracted the aphid vector , but were poor hosts for their vectors , causing subsequent movement from infected to non-infected plants . Possible pathogen-mediated manipulation of vector behavior may depend on how pathogens are transmitted . Persistent pathogens , which require longer feeding durations for acquisition , benefit from increased arrestment of their vector , while non-persistent or semi-persistent pathogens may induce changes in plant phenotype that cause vectors to quickly disperse following acquisition [9] . In our experiments , psyllids were initially attracted to infected plants , but later preferred to settle on non-infected hosts . Therefore , our results with a bacterial plant pathogen appear to share similarities with previous results obtained with both non-persistent and persistent plant viruses . Although D . citri were initially attracted to infected plants , which was likely mediated by a volatile cue ( consistent with both persistent and non-persistent virus examples [3] , [9] ) , psyllids preferentially dispersed to non-infected plants after initial feeding on infected ones ( consistent with non-persistent virus example [9] ) . Pathogen spread may be favored in an environment with a low frequency of infected plants , when vectors prefer infected plants [80] . However , in an environment with a high frequency of infected plants , pathogen spread may be favored when vectors prefer non-infected plants [80] . More recent mathematical modeling similarly suggests that when vectors prefer to orient to infected plants , pathogen spread is rapid when the frequency of plant infection is low , but pathogen spread is slow when most plants are infected [57] . Moreover , vector preference for infected versus non-infected plants is partitioned into orientation preference and feeding preference and these two distinct suites of behavior affect pathogen spread differently , depending on whether vectors prefer infected versus non-infected plants [57] . While feeding preference for infected plants may decrease pathogen spread , orientation preference for infected plants may lead to rapid pathogen spread [57] . Acquisition of Las by D . citri can occur after 30 minutes to 24 hr of feeding [30] , [33] . Therefore , initial landing by psyllids on infected plants for 24 hr or longer is sufficient for acquisition of the pathogen . Subsequent movement of psyllids from infected to non-infected plants will result in inoculation of new plants as demonstrated by the current results . Therefore , possible manipulation of D . citri behavior due to Las infection of plants may increase pathogen spread in the field; however , this will likely depend on the frequency of plant infection [57] . The effect of vector preference on pathogen spread depends on several other factors , including the mode of pathogen transmission , latency period , vector movement behavior , and the combined dynamic spatial patterns of the plant , pathogen , and vector [57] , [80] . Las-infected plants were deficient in nitrogen , phosphorus , magnesium , zinc , and iron , but were characterized by higher concentrations of potassium and boron . It is possible that infected plants are a sub-optimal host for D . citri because of a nutritional imbalance caused by Las infection . Phloem-feeding plant hoppers ( Prokelisia dolus ) disperse to higher quality Spartina plants when reared on nitrogen and phosphorus deficient plants [81] . Also , increased nitrogen content improves the performance of leaf-feeding aphids ( Metopolophium dirhodum ) on wheat and barley [81]–[83] . Physiologically , nitrogen is essential for insect growth , survival , and reproduction due to its fundamental role in protein synthesis [84]–[88] . Phosphorus acts against viral disease by promoting plant maturity , thus restricting pathological effects of virus infection [89] . However , plant nutrient deficiencies can have a negative or positive effect on population dynamics of phloem feeding insects . For example , potassium deficiency in soybean increases fecundity and population growth of soybean aphid [90]–[92] . The nutrient analyses described in the current study only addressed the elemental composition of leaf tissues . There may be several other unidentified factors including sugars , amino acids , and proteins that could have contributed to the movement of D . citri from infected to non-infected plants . D . citri feed specifically within phloem cells , obtaining nutrition from free amino acids , proteins and sugars [28] , which are known to affect vector growth [2] , [71] , [93]–[94] . Further comparisons of the elemental composition of psyllids and phloem tissues should help elucidate how host quality affects movement of D . citri . Las-infected leaves have been reported to accumulate up to 7 . 9-fold more starch than non-infected leaves , resulting in blockage of the phloem vessels [95]; however , the effect of starch accumulation on D . citri nutrition is still unknown . Starch accumulation and callose formation in rice leaves is reported to increase plant resistance to the brown plant hopper , Nilaparvata lugens , by preventing phloem ingestion [96] . In a similar experiment , plants infected with Zucchini yellow mosaic virus ( ZYMV ) were characterized by lower total protein and sugar content than non-infected plants [71] . However , both ZYMV-infected and non-infected plants had identical total amino acid contents and Aphis gossypii lived longer and produced more offspring on infected than on non-infected plants [71] . In summary , Las-infected plants were initially more attractive to D . citri adults than non-infected plants; however , psyllids dispersed subsequently to non-infected plants to make them their preferred location of settling rather than infected plants . Consistent with the “deceptive host phenotype” hypothesis [9] , vectors were ‘deceptively’ attracted to the nutritionally suboptimal infected host plants during the initial settling period , which was of sufficient duration for D . citri to acquire the Las pathogen . Thus , the pathogen may be indirectly modifying the behavior of the vector by inducing changes in the attractiveness of the host plant through olfactory cues . This scenario suggests a mechanism for spread of the pathogen in the field because initial attraction and feeding of D . citri on infected host plants should facilitate acquisition of the pathogen , while subsequent movement away from potentially sub-optimal infected plants should facilitate inoculation of non-infected plants . Our results indicate that MeSA may be the specific chemical cue mediating initial psyllid attraction to Las-infected plants . It is possible that changes in nutritional content of plants stimulate subsequent dispersal of psyllids from infected to uninfected plants; however , this will require further investigation .
Non-infected adult D . citri used in behavioral bioassays were obtained from a laboratory culture at the University of Florida , Citrus Research and Education Center ( Lake Alfred , USA ) . The culture was established in 2000 from field populations in Polk Co . , FL , USA ( 28 . 0′N , 81 . 9′W ) prior to the discovery of HLB in FL . The culture is maintained without exposure to insecticides on sour orange ( Citrus aurantium L . ) and ‘Hamlin’ orange [C . sinensis ( L . ) Osb . ] . Monthly testing of randomly sampled D . citri nymphs , adults , and plants by quantitative real-time polymerase chain reaction ( qPCR ) assays is conducted to confirm that psyllids and plants in this culture are free of Las . Las-infected D . citri were obtained from Las-infected C . aurantium and C . sinensis plants maintained in a secure quarantine facility at the University of Florida , Citrus Research and Education Center . Routine sampling indicated that about 70% of D . citri obtained from this colony were positive for Las when tested in qPCR assays . Both colonies were maintained at 27±1°C , 63±2% RH , and under a L14∶D10 hour photoperiod . Non-infected and Las-infected D . citri cultures were maintained in double-screened , 3 . 7×4 . 6 m secure enclosures located in separated buildings and with minimal risk of cross contamination . Las infection in host plants was maintained by graft-inoculation of non-infected C . sinensis with Las-infected key lime ( Citrus aurantifolia ) budwood collected from citrus groves in Immokalee , FL , USA . Grafted plants were tested for Las infection by qPCR four months after grafting . Plants that tested positive for Las were used in experiments and for maintenance of Las cultures . Cultures of Las-infected plants were maintained through graft-inoculations because of low transmission efficiency of D . citri adults [19] . Because Las is not seed transmissible [97] , Las-free host plants used in experiments were cultivated from C . sinensis seed or obtained as potted seedlings from an HLB-free commercial nursery to minimize the risk of undetectable latent infection of Las in grafted plants . The nursery-obtained plants were confirmed negative for Las infection by qPCR . All infected plants used for experiments exhibited minor or no symptoms , ranging from 0 to 1 on a graded symptom scale of 1 to 10 . Non-infected and Las-infected plants were maintained in separate secure enclosures with minimal risk of cross contamination as described above . Dual-labeled probes were used to detect Las in D . citri and citrus plants using an ABI 7500 system ( Applied Biosystems , Foster City , CA ) in a multiplex TaqMan qPCR assay described in [19] , [98] . DNA from insect and plant samples was isolated using the DNeasy blood and tissue or DNeasy plant kits ( Qiagen Inc , Valencia , CA ) , respectively . Las-specific 16S rDNA from psyllid and plant extracts was amplified using probe-primer sets targeting internal control sequences specific to D . citri [insect wingless] or plant [cytochrome oxidase] gene regions [19] , [98]–[99] . DNA amplifications were conducted in 96-well MicroAmp reaction plates ( Applied Biosystems ) . Quantitative PCR reactions consisted of an initial denaturation step of 95°C for 10 min followed by 40 cycles of 95°C for 15 s and 60°C for 60 s . Each 96-well plate containing D . citri samples included a no template control , a positive control ( Las DNA in DNA extractions from D . citri ) , and a negative control ( no Las DNA in DNA extractions from D . citri ) . Likewise , plates containing plant samples included a no template control , a positive control ( Las DNA in DNA extractions from plant ) , and a negative control ( no Las DNA in DNA extractions from plant ) . Reactions were considered positive for either target sequence if the cycle quantification ( Cq ) value , determined by the ABI 7500 Real-Time software ( version 1 . 4 , Applied Biosystems ) , was ≤32 [19] . A custom designed two-port divided T-olfactometer [Analytical Research Systems ( ARS ) , Inc . Gainesville , FL] that has been thoroughly described in [100] was used to evaluate the behavioral response of D . citri . Briefly , the olfactometer consisted of a 30 cm long glass tube with 3 . 5 cm internal diameter that is bifurcated into two equal halves with a Teflon strip forming a T-maze . Each half served as an arm of the olfactometer enabling the D . citri to make a choice between two potential odor fields . The olfactometer arms were connected to odor sources placed in 35 cm tall×15 cm wide dome shaped guillotine volatile collection chambers ( GVCC ) ( ARS , Gainesville , FL ) through Teflon-glass tube connectors . The odor sources comprised 14–16 week old Las-infected or non-infected C . sinensis plants placed into the GVCC . Purified and humidified air was pushed through the GVCC via two pumps connected to an air delivery system ( ARS , Gainesville , FL ) . A constant airflow of 0 . 1 L/min was maintained through both arms of the olfactometer . The olfactometer was housed within a temperature-controlled room and positioned vertically under a fluorescent 900-lux light bulb within a fiberboard box for uniform light diffusion . This positioning took advantage of the negative geotactic and positive phototactic response of D . citri [100] . D . citri adults were released individually into the inlet adapter at the base of the olfactometer . An odor source was randomly assigned to one of the arms of the olfactometer at the beginning of each bioassay and was reversed after every 30 insects to eliminate positional bias . Initially , D . citri adults were exposed to clean air vs . clean air in the olfactometer to verify the absence of positional bias . Thereafter , the behavioral response of non-infected or Las-infected D . citri was tested against non-infected versus Las-infected plants . D . citri adults were also exposed to non-infected grafted ( sham grafted ) vs . non-grafted-non-infected plants to determine the effects of grafting only on D . citri behavior . For each treatment , C . sinensis plants were grafted with C . aurantifolia budwood four months prior to initiating behavioral experiments . A minimum of 150 male or female D . citri adults was examined per treatment combination ( five replications of 30 D . citri ) . Each D . citri that responded to odors was individually subjected to qPCR to determine Las infection status using the procedures described above . Chi square ( χ2 ) tests were used to compare between the numbers of D . citri entering the treatment or control arm in the T-maze olfactometer . Given that initial orientation of insect vectors to plants could be different from their final settling choices [57] and both could affect pathogen spread , we evaluated movement of D . citri between the Las-infected and non-infected plants . A non-infected or Las-infected plant was inserted at random into an above described observation cage . Thereafter , 60 D . citri adults were confined on this plant using a mesh sleeve for forced settling . Once all psyllids settled on the plant , an additional , randomly selected non-infected or Las-infected plant was inserted into the cage and placed 15 cm away from the initial plant . Immediately thereafter , the mesh sleeve was removed from the initial plant to allow psyllid movement between the two plants . The number of D . citri moving from the initial plant on which they settled and onto the inserted plant was counted seven days after release . Four combinations of plant pairs were examined using either non-infected or Las-infected D . citri for a total of eight treatments . The plant combinations tested were: 1 ) Las-infected settling plant with a non-infected plant introduction , 2 ) non-infected settling plant with a Las-infected plant introduction , 3 ) non-infected settling plant with a non-infected plant introduction , and 4 ) Las-infected settling plant with a Las-infected plant introduction . The cages were housed in a temperature-controlled room at 27±1°C , 63±2% RH , and under a L14∶D10 h photoperiod . Each combination was replicated four times . Non-infected D . citri adults found moving from infected to non-infected plants were individually analyzed for Las infection by qPCR as described above . Thereafter , the inserted plants were subsequently tested for nutritional status and possible Las infection due to movement of infected psyllids . Only female psyllids were used in these experiments , because no differences were observed in behavioral responses between the sexes in preliminary tests . A two-way ANOVA was used to analyze the number of D . citri adults moving between plants , with psyllid status ( infected or non-infected ) and plant treatment pair serving as factors . Means were subsequently separated using Tukey's HSD test . D . citri adults that did not make a choice were excluded from statistical analyses . The nutritional status of Las-infected and non-infected citrus plants was analyzed by a commercial laboratory ( Waters Agricultural Lab , Inc , Georgia ) . Leaf samples from 16 qPCR-confirmed Las-infected or non-infected plants were washed , dried and ground to pass a 0 . 38-mm sieve . An individual leaf sample comprised about three g of dry leaf biomass obtained from three-month-old leaves . Leaf phosphorus , potassium , calcium , magnesium , sulfur , manganese , iron , copper , zinc , boron , silicon , sodium , molybdenum , nitrate , aluminum , and chlorine concentrations were determined by inductively coupled plasma atomic emission spectroscopy . The nutrient data obtained from Las-infected versus non-infected plants were analyzed using two-sample t-tests . The objective of these experiments was to compare feeding efficiency of D . citri adults on Las-infected versus non-infected plants by quantifying their honeydew excretion . Four sets of experiments were conducted to quantify honeydew excretion of psyllids after 24 , 48 , 72 or 96 hr of feeding . Each experiment was replicated five times . Citrus leaf discs obtained from Las-infected or non-infected plants were placed individually on 1 . 5% agar beds within 60 mm plastic disposable Petri dishes . Thereafter , ten D . citri adults of mixed age and sex ( ∼1∶1 ) were released into each dish . Each Petri dish was sealed with lids lined with 60 mm Whatman filter paper ( Whatman International Ltd , Kent , UK ) . Petri dishes were inverted to collect honeydew droplets onto the filter paper . Dishes were maintained at 25±1°C , 50±5% RH , and L14∶D10 h photoperiod in an incubator . Collected filter papers were subjected to a ninhydrin ( Sigma-Aldrich , St Louis , MO ) test to facilitate counting of honeydew droplets [102]–[103] . Significant differences in the number of honeydew droplets excreted on Las-infected versus non-infected leaves were determined by a two-way ANOVA with Las infection status of leaves and D . citri exposure period as independent factors . Means were compared using Tukey's HSD tests . This technique , developed by Auclair [104] , has been successfully used to quantify honeydew excretion by psyllids , whiteflies , aphids and plant hoppers [102]–[103] , [105]–[106] . Infected or non-infected plants , as described above , were confined within a GVCC ( ARS , Gainesville , FL ) . Charcoal-purified and humidified air was drawn over plants and pulled out at a rate of 300 mL min-1 through a trap containing 50 mg of Super-Q adsorbent , 800–1000 mesh ( Alltech Assoc . , Deerfield , Illinois , USA ) for 24 hrs . Thereafter , Super-Q traps were rinsed with 150 µl of dichloromethane into individual 2 . 0 ml clear glass vials ( Varian , Palo Alto , CA , USA , part number: 392611549 equipped with 500 µl glass inserts ) . Volatiles were sampled from two plants simultaneously ( one Las-infected and the second non-infected ) . A total of five plants that were previously identified as infected and five similar plants identified as non-infected were sampled . An internal standard of n-octane ( 300 ng ) was added to each sample prior to GC-MS analysis . A one µl aliquot of each extract was injected onto a gas chromatograph ( HP 6890 ) equipped with 30 m×0 . 25-mm-ID , 0 . 25 µm film thickness DB-5 capillary column ( Quadrex , New Haven , CT , USA ) , interfaced to a 5973 Mass Selective Detector ( Agilent , Palo Alto , CA , USA ) , in both electron impact ( EI ) and chemical ionization ( CI ) modes . Helium was used as the carrier gas in the constant flow mode of 30 cm/sec . The injector was maintained at 260°C . The oven was programmed from 40 to 260°C at 7°C/min . Isobutane was used as the reagent gas for CI , and the ion source temperature was set at 250°C in CI and 220°C in EI . The mass spectra were matched with NIST 2005 version 2 . 0 standard spectra ( NIST , Gaithersburg , MD ) . The compounds with spectral fit values equal to or greater than 90 and appropriate LRI values were considered positive identifications . When available , mass spectra and retention times were compared to those of authentic standards . Compounds were quantified as equivalents of the total amount of n-octane within each analyzed volatile collection sample . A more accurate quantification of MeSA was made by comparing the total ion chromatograph ( TIC ) EI response for standard solutions with known quantities of n-octane and MeSA . A response factor of 1 . 2 was then established for MeSA relative to the n-octane based values . The resulting volatile profiles were standardized as equivalents of n-octane within each sample analyzed . The characteristic set of variables that defined a particular group ( e . g . non-infected versus infected plant ) was found using the ‘varSelRFBoot’ function of the package ‘varSelRF’ for the ‘randomForest’ analysis ( R software version 2 . 9 . 0 , R Development Core Team 2009 ) . We used the varSelRF algorithm with Random Forests to select the minimum set of VOCs that were characteristic of differences between infected and non-infected plants . The tree-based Random Forests algorithm performs hierarchical clustering via multi-scale and combinatorial bootstrap resampling and is most appropriate for data where the variables ( VOCs in this case ) outnumber the samples , and where the variables are auto correlated , which is a typical problem of conventional multivariate analysis of such data . Consequently , this type of analysis is common in bioinformatics , chemoinformatics and similar data-rich fields . We employed 200 bootstrapping iterations of the Random Forests algorithm to arrive at a minimal set of VOCs that could differentiate between infected and non-infected plants . We also calculated the mean decrease in accuracy ( MDA ) when individual VOCs are removed from the analysis . MDA values indicate the importance value of particular VOCs for the discrimination between treatments . Volatiles were collected to determine whether release of MeSA was induced by psyllid feeding . Plants were placed within a GVCC with identical flow rates and collection procedures as described above . Volatiles were collected simultaneously from three undamaged plants ( no psyllid feeding ) for 24 hrs , after which the adsorbent traps were rinsed into vials for gas chromatography–mass spectrometry ( GC-MS ) analysis as described above for the collection of volatiles from infected and uninfected plants . Psyllids were divided by sex and randomly introduced into two of the three plant chambers . Each infested plant received 300 psyllids ( one plant receiving only males and another plant receiving only females ) . The third plant was left uninfested as a control . Volatiles were collected from plants for 24 hr , after which traps were rinsed into vials for GC-MS analysis . Volatiles were collected in this manner on three separate dates with six plants evaluated before and after D . citri introduction and three plants serving as simultaneous undamaged controls . VOCs that were characteristic of differences between plants before and after psyllid feeding were determined as described above . Treatment differences between individual volatiles collected from headspace of infected and non-infected plants were compared using two-sample t-tests . MeSA was released in greater quantities from Las-infected plants than non-infected plants , while MeAN and D-limonene were released in greater quantities by non-infected than infected plants ( See results ) . Therefore , the behavioral response of D . citri to these synthetic chemicals was tested . A known D . citri attractant , β-ocimene [40] , was used as a positive control . All chemicals were obtained from Sigma Aldrich ( St Louis , MO ) with purities ranging between 97 and 99% . Purity of β-ocimene was ≥90% and contained a mixture of isomers comprising 20–25% limonene . Each treatment was dissolved in 100 µl of dichloromethane and pipetted onto a 2 cm Richmond cotton wick ( Petty John Packaging , Inc . Concord , NC ) at 0 . 001 , 0 . 01 , 0 . 1 , 1 . 0 , 10 . 0 and 100 . 0 µg dosages . The control treatment consisted of a cotton wick impregnated with solvent only . The solvent from both treatments was allowed to evaporate within a fume hood for 30 min prior to assays . The bioassay procedures with synthetic chemicals were identical to those described earlier for plant samples . However , in this case , treatments ( odor sources ) were placed into solid phase micro extraction chambers ( SPMEC ) ( ARS , Gainesville , FL ) instead of the GVCC described above . The SPMEC consists of a straight glass tube ( 17 . 5 cm long×2 . 5 cm internal diameter ) supported with an inlet and outlet valve for incoming and outgoing air streams , respectively [102] . The treated and control cotton wicks were wrapped in laboratory tissue ( Kim wipes , Kimberly-Clark , Roswell , GA ) to minimize contamination and placed randomly into one of the two SPMEC enclosures that were connected to the olfactometer and the air delivery system through Teflon-glass tube connectors . Only non-infected female psyllids were assayed in these experiments , because no differences in behavioral responses were observed between the sexes or between infected and non-infected psyllids in preliminary tests . At least 120 non-infected female adults were examined per treatment combination . The treatment combinations evaluated in this set of experiment were 1 ) MeAN vs . clean air , 2 ) MeSA vs . clean air , 3 ) D-limonene vs . clean air , and 4 ) β-ocimene vs . clean air . Chi square ( χ2 ) tests were used to compare between the numbers of D . citri entering the treatment or control arm in the T-maze olfactometer . | In this investigation , we experimentally demonstrate specific mechanisms through which a bacterial plant pathogen induces plant responses that modify behavior of its insect vector . Candidatus Liberibacter asiaticus , a fastidious , phloem-limited bacterium responsible for causing huanglongbing disease of citrus , induced release of a specific volatile chemical , methyl salicylate , which increased attractiveness of infected plants to its insect vector , Diaphorina citri , and caused vectors to initially prefer infected plants . However , the insect vectors subsequently dispersed to non-infected plants as their preferred location of prolonged settling because of likely sub-optimal nutritional content of infected plants . The duration of initial feeding on infected plants was sufficiently long for the vectors to acquire the pathogen before they dispersed to non-infected plants , suggesting that the bacterial pathogen manipulates behavior of its insect vector to promote its own proliferation . The behavior of psyllids in response to infected versus non-infected plants was not influenced by whether or not they were carriers of the pathogen and was similar under both light and dark conditions . Feeding on citrus by D . citri adults also induced the release of methyl salicylate , suggesting that it may be a cue revealing location of conspecifics on host plants . |
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Because cohesion prevents sister-chromatid separation and spindle elongation , cohesion dissolution may trigger these two events simultaneously . However , the relatively normal spindle elongation kinetics in yeast cohesin mutants indicates an additional mechanism for the temporal control of spindle elongation . Here we show evidence indicating that S-phase CDK ( cyclin dependent kinase ) negatively regulates spindle elongation . In contrast , mitotic CDK promotes spindle elongation by activating Cdc14 phosphatase , which reverses the protein phosphorylation imposed by S-phase CDK . Our data suggest that S-phase CDK negatively regulates spindle elongation partly through its phosphorylation of a spindle pole body ( SPB ) protein Spc110 . We also show that hyperactive S-phase CDK compromises the microtubule localization of Stu2 , a processive microtubule polymerase essential for spindle elongation . Strikingly , we found that hyperactive mitotic CDK induces uncoupled spindle elongation and sister-chromatid separation in securin mutants ( pds1Δ ) , and we speculate that asynchronous chromosome segregation in pds1Δ cells contributes to this phenotype . Therefore , the tight temporal control of spindle elongation and cohesin cleavage assure orchestrated chromosome separation and spindle elongation .
In eukaryotic cells , spindle elongation and sister-chromatid separation are two critical mitotic events , and the coordination of these two events is essential for the fidelity of chromosome segregation . During mitosis , the cleavage of sister chromatid cohesion allows the sister chromatids to move toward the respective spindle pole , then the spindle elongates further to pull sister chromatids into two daughter cells [1] . Because sister-chromatid cohesion prevents sister chromatid separation and spindle elongation , these two events could be coupled by the cleavage of cohesin . Before anaphase entry , cohesin cleavage is prohibited by the presence of securin ( Pds1 ) , which binds to and inhibits separase ( Esp1 ) that cleaves cohesin Scc1/Mcd1 [2] , [3] . The degradation of Pds1 before anaphase entry alleviates the inhibition of Esp1 , resulting in the robust cohesin cleavage and simultaneous sister-chromatid separation [2] , [4] , [5] . However , yeast cells lacking either Pds1 or cohesin do not show premature spindle elongation , indicating that a cohesion-independent mechanism controls the timing of spindle elongation [6] . CDKs are the key driving force for cell cycle progression . In budding yeast , S-phase cyclins Clb5 and Clb6 appear during S-phase , which is consistent with their function in DNA synthesis . Compared to mitotic CDK Clb2-Cdk1 , Clb5-Cdk1 shows stronger kinase activity toward a subset of CDK substrates [7] . In addition to proteins involved in DNA synthesis , other Clb5-specific substrates , such as Sli15 , Fin1 , Ase1 , and Spc110 , associate with the SPB or microtubules , indicating that S-phase CDK may also regulate spindle dynamics [8]–[10] . The tightly regulated activity of CDK and phosphatase enables unique temporal phosphorylation kinetics of each CDK substrate during the cell cycle . The periodical expression of cyclins controls the activity and substrate specificity of the CDK , while a conserved protein phosphatase Cdc14 reverses the phosphorylation of these CDK substrates [11] . In budding yeast , the regulation of Cdc14 activity is achieved through its subcellular localization . Before anaphase entry , Cdc14 is sequestered within the nucleolus by binding to a nucleolar protein Net1 [12] , [13] . The dephosphorylation of Net1 by PP2ACdc55 ensures a strong Net1-Cdc14 interaction , while during early anaphase the phosphorylation of Net1 by mitotic CDK , Clb2-Cdk1 , triggers the release of Cdc14 from the nucleolus [14]–[17] . The degradation of Pds1 frees separase Esp1 , which not only cleaves cohesin rings but also down-regulates PP2ACdc55 with the assistance of other FEAR components [15] . The increased ratio of Clb2-Cdk1/PP2ACdc55 leads to Net1 phosphorylation and the subsequent Cdc14 release . Because S-phase cyclin Clb5 is also degraded before anaphase entry [18] , the combination of increased Cdc14 and decreased Clb5-Cdk1 activity during early anaphase results in the dephosphorylation of Clb5-specific substrates [19] , [20] . Therefore , mitotic CDK may promote mitotic progression by reversing the phosphorylation imposed by S-phase CDK . Recent work in the Amon lab has shown that the loss of function of both mitotic cyclins Clb1 and Clb2 in clb1Δ clb2-IV mutant cells prevents spindle elongation , indicating the essential role of mitotic CDK in this process . Moreover , neither the loss of sister-chromatid cohesion nor deletion of securin Pds1 is able to rescue the spindle elongation defect in clb1Δ clb2-IV mutant cells , further supporting the direct role of mitotic cyclins in spindle elongation , but the CDK substrates involved in this process remain unknown [21] . If mitotic CDK promote spindle elongation , cells overexpressing these cyclins are expected to show premature spindle elongation , but these cells exhibit relatively normal spindle elongation , although defect in mitotic exit was noticed [22] , [23] . The presence of the CDK inhibitory kinase Swe1 may prevent hyper-activation of mitotic CDK when CLB2 is overexpressed , as Swe1 specifically inhibits mitotic CDK [24]–[27] . Here we show evidence indicating that overexpression of mitotic cyclin CLB2 induces premature spindle elongation in swe1Δ mutant cells . We further found that FEAR mutants suppress this premature spindle elongation , suggesting that Clb2 induces spindle elongation through FEAR that facilitates Cdc14 release during early anaphase . In contrast to mitotic cyclins , we found that S phase cyclin Clb5 plays a negative role in spindle elongation . Therefore , mitotic CDK likely activates the FEAR pathway to alleviate the inhibitory effect of S-phase CDK on spindle elongation . Our data further suggest that the phosphorylation of a SPB protein Spc110 by S-phase CDK contributes at least partially to the inhibition of spindle elongation . Moreover , high levels of S-phase CDK prevent microtubule localization of Stu2 , a microtubule-plus-end tracking protein essential for spindle elongation . Strikingly , overexpression of CLB2 in securin mutants pds1Δ leads to uncoupled sister-chromatid separation and spindle elongation . Given the established role of securin Pds1 in the synchronous chromosome segregation [5] , our data support the possibility that this securin-dependent synchrony and the temporal control of spindle elongation by the balance of mitotic versus S-phase CDK ensure the sequential order of sister-chromatid separation and spindle elongation , which is critical for faithful chromosome segregation .
Clb2 is the major mitotic cyclin in budding yeast , but its overexpression from a galactose-inducible promoter does not cause obvious premature mitosis in wild-type ( WT ) cells . Because Swe1 kinase phosphorylates and inhibits mitotic CDK , it is possible that the presence of Swe1 prevents the hyper-activation of mitotic CDK after CLB2 overexpression . Therefore , we overexpressed CLB2 from a galactose-inducible promoter in swe1Δ mutant cells and examined the cell growth . The Western blotting result confirmed the high level expression of Clb2 protein after galactose induction ( Figure S1A ) . Compared to the control cells , swe1Δ mutants with a PGALCLB2 plasmid showed obvious growth defect on galactose plates . Overexpression of Clb1 , which is closely related to Clb2 , also caused sick growth phenotype in swe1Δ cells , but overexpression of Clb3 , Clb4 , or S phase cyclin Clb5 , Clb6 was not toxic ( Figure 1A and S1B ) . Therefore overexpression of mitotic cyclins Clb1 and Clb2 is toxic to swe1Δ cells . To confirm that mitotic CDK is hyperactive in swe1Δ cells after CLB2 overexpression , we compared the phosphorylation kinetics of Pol12 in synchronized WT and swe1Δ cells overexpressing CLB2 , as Pol12 is a known substrate of mitotic CDK required for DNA replication [24] . Our results showed that CLB2 overexpression induces premature Pol12 phosphorylation in both WT and swe1Δ cells based on the band-shift , and the phosphorylation became more significant in swe1Δ cells as indicated by the increased slow migrating band ( Figure S1C ) . Therefore , the absence of Swe1 indeed causes hyper-activation of mitotic CDK after CLB2 overexpression . To understand the cause of this sick growth phenotype , G1-arrested WT and swe1Δ cells with a control vector or a PGALCLB2 plasmid were released into galactose medium to induce CLB2 overexpression and we compared the cell cycle progression in these cells . Both WT and swe1Δ cells showed almost identical budding index and DNA synthesis kinetics either with or without CLB2 overexpression ( Figure S2A and S2B ) . These cells also exhibited similar cell cycle regulated fluctuation of Pds1 protein levels ( Figure S2C ) . However , we noticed premature spindle elongation in some small-budded and unbudded swe1Δ cells overexpressing CLB2 , but this phenotype is much less significant in WT cells ( Figure 1B ) . After G1 release into galactose medium for 120 min , 14% of WT cells with a PGALCLB2 plasmid had elongated spindles ( >3 µM ) , while 36% of swe1Δ cells showed elongated spindles . Interestingly , about 8% swe1Δ cells became binucleate after G1 release for 160 min , i . e . two nuclei were observed in a single cell body ( the arrow in Figure 1B ) . Among them , half were small-budded , while the others were unbudded . Previous data indicate that overexpression of a single copy of a Clb2 destruction box mutant prevents bud formation and results in binucleate cells [28] . Indeed , we found that some cells overexpressing CLB2 remained unbudded after G1 release ( Figure S2A ) . To further define the role of the inhibition of budding and premature spindle elongation in the formation of binucleate cells , we performed live-cell imaging to examine the dynamics of spindle elongation . G1-arrested swe1Δ cells with a control vector or a PGALCLB2 plasmid were released into galactose medium . The spindle elongation in swe1Δ ( PGALCLB2 ) cells initiated ∼20 min earlier compared to the cells with a control vector . Interestingly , we observed premature spindle elongation in both small-budded and unbudded swe1Δ cells ( Figure 1C ) , indicating that both budding inhibition and premature spindle elongation may contribute to the formation of binucleate cells . Because we also observed the binucleate phenotype in both unbudded and small budded cells after Clb2 overproduction ( Figure 1B ) , we reason that the inhibition of budding is not essential for the formation of binucleate cells . If a cell elongates spindle when cohesion is still present , some sister chromatids may remain together after spindle elongation . However , all the swe1Δ cells overexpressing CLB2 with an elongated spindle showed separated sister chromatids ( Figure S3A ) , indicating that spindle elongation did not occur prior to cohesion dissolution . Alternatively , hyperactive mitotic CDK may promote cohesin cleavage . Thus , we examined Scc1 proteins in WT and swe1Δ cells with and without CLB2 overexpression , but all these cells exhibited similar Scc1 cleavage kinetics based on the appearance of the short Scc1 fragments ( Figure S3B ) , arguing against the possibility that Clb2 induces spindle elongation through cohesin cleavage . We speculate that both hyperactive mitotic CDK and cohesion dissolution are essential for spindle elongation . If that is the case , overexpression of CLB2 may cause more dramatic premature spindle elongation in cohesin mutant cells . We first found that scc1-73 mutant cells with PGALCLB2 plasmids grew more slowly on galactose plates at 25°C compared to control cells ( Figure S4A ) . Moreover , after G1 release , CLB2 overexpression caused premature spindle elongation in scc1-73 cells ( Figure S4B ) , and this phenotype became more pronounced in swe1Δ scc1-73 cells ( Figure S4C ) . Therefore , hyperactive mitotic CDK cause more dramatic premature spindle elongation in cells with compromised sister chromatid cohesion . One of the substrates of mitotic CDK is the nucleolar Cdc14-binding protein Net1 , whose phosphorylation triggers the dissociation of Cdc14 from Net1 and the release of Cdc14 from the nucleolus . It is possible that hyperactive mitotic CDK stimulates spindle elongation by activating FEAR . Because the replacement of 6 CDK phosphorylation sites in Net1 with alanine generates net1-6Cdk mutant , which prevents FEAR activation [14] , we first compared the growth of swe1Δ and swe1Δ net1-6Cdk cells after CLB2 overexpression . The swe1Δ net1-6Cdk cells grew much better than the single mutant cells after CLB2 overexpression . Another FEAR mutant spo12Δ showed an even stronger suppression of the sick growth phenotype of swe1Δ ( Figure 2A ) . The nuclear morphology was also compared in swe1Δ , swe1Δ spo12Δ and swe1Δ net1-6Cdk mutant cells overexpressing CLB2 , both spo12Δ and net1-6Cdk mutants suppressed the formation of binucleate cells ( Figure 2B ) , suggesting that the activation of FEAR pathway contributes to the growth defect in swe1Δ cells overexpressing CLB2 . To directly determine if the toxicity of CLB2 overexpression to swe1Δ cells is due to hyperactive Cdc14 , we examined the growth of swe1Δ cdc14-1 cells overexpressing CLB2 . We found that cdc14-1 swe1Δ cells with PGALCLB2 plasmids grew better than swe1Δ cells on galactose plates at 25°C ( Figure 2A ) . In addition , the cdc14-1 mutant partially suppressed binucleate phenotype of swe1Δ cells ( Figure 2C ) . We reason that the incomplete suppression is due to the presence of partial functional Cdc14 in cdc14-1 mutant . Therefore , CLB2 overexpression likely induces premature spindle elongation by activating Cdc14 . Unlike the FEAR pathway , the mitotic exit network ( MEN ) induces Cdc14 release in late anaphase [22] , [29] . To further test if Clb2-Cdk1 promotes spindle elongation by activating FEAR or MEN , we examined Clb2-induced premature spindle elongation in cdc15-2 swe1Δ cells . The abolishment of MEN function in cdc15-2 mutant did not block Clb2-induced premature spindle elongation . However , the introduction of either net1-6Cdk or spo12Δ mutation in swe1Δ cdc15-2 cells abolished premature spindle elongation completely ( Figure 2D and Figure S5 ) , indicating that Clb2-Cdk1 induces this phenotype through FEAR but not MEN . To directly determine if CLB2 overexpression in swe1Δ cells causes premature FEAR activation , the localization of Cdc14 was examined after G1-arrested swe1Δ cells were released into galactose medium containing microtubule poison nocodazole that arrests cells in metaphase . Strikingly , 75% of swe1Δ cells with a PGALCLB2 plasmid showed released Cdc14 after incubation in galactose medium for 4 hrs , while only 23% of WT cells showed this phenotype . In contrast , swe1Δ cells with a control vector did not show any Cdc14 release . net1-6Cdk mutant suppressed the premature Cdc14 release in swe1Δ cells ( Figure 2E ) . All these data indicate that excess mitotic CDK likely trigger premature spindle elongation by activating Cdc14 through the FEAR pathway . However , we cannot exclude the possibility that Clb2-Cdk1 also phosphorylates other substrates to promote spindle elongation . Overexpression of CLB5 slows cell growth , suggesting that hyperactive Clb5-Cdk1 may have a negative effect on the cell cycle ( Figure 1A ) . To test if hyperactive S-phase CDK inhibits spindle elongation , we examined the spindle structure in WT cells overexpressing CLB5 . After incubation in galactose medium for 4 hrs , more yeast cells with a PGALCLB5 plasmid arrested with a large bud and a very short spindle structure ( Figure 3A ) . Because these spindles are very short , one explanation is that the failure of SPB separation contributes to the spindle elongation defect . Therefore , we examined the spindle elongation kinetics in cells overexpressing CLB5 after release from hydroxyurea ( HU ) arrest . HU blocks DNA synthesis and HU-arrested cells have a short spindle with separated SPBs [30] . After HU wash off , CLB5 overexpression also caused a clear spindle elongation delay as indicated by the accumulation of cells with a bar-like short spindle structure ( Figure 3B ) . This result suggests that the short spindle observed in cells with hyperactive S-phase CDK is not due to SPB separation defect . As we have shown that CLB5 overexpression blocks nuclear division in FEAR mutants , such as slk19Δ and spo12Δ [19] , we further examined the spindle elongation kinetics in spo12Δ mutants with and without CLB5 overexpression after HU release . Obviously , spindle elongation was largely blocked by CLB5 overexpression in spo12Δ mutant cells ( Figure 3B ) . The spindle elongation defect could be due to the activation of the DNA damage or the spindle checkpoint that prevents anaphase onset . Because the activation of these checkpoints depends on the stabilization of securin Pds1 [31] , [32] , deletion of PDS1 will abolish these checkpoints . We therefore compared the spindle elongation kinetics in WT , pds1Δ , spo12Δ , and spo12Δ pds1Δ mutant cells when CLB5 is overexpressed . Like WT and spo12Δ mutants , pds1Δ and spo12Δ pds1Δ mutants also exhibited accumulation of large-budded cells with a short spindle structure after CLB5 overexpression ( Figure 3C ) , indicating that hyperactive S-phase CDK prevents spindle elongation in a checkpoint-independent manner . Another possibility is that high levels of S-phase CDK block the cleavage of sister chromatid cohesin to prevent spindle elongation . Strikingly , cells overexpressing CLB5 showed almost identical kinetics for cohesin cleavage compared to control cells ( Figure S6A ) . Moreover , overexpression of CLB5 also caused delayed spindle elongation in cohesin mutants incubated at the non-permissive temperature ( Figure S6B ) . Therefore , we conclude that S-phase CDK negatively regulates spindle elongation , and this function is unlikely due to the inability of cohesion resolution . If S-phase CDK plays a negative role in spindle elongation , we expect that the loss of S-phase cyclins will cause premature spindle elongation . However , both clb5Δ and clb5Δ clb6Δ mutants showed normal spindle elongation kinetics . It is possible that other redundant mechanism , such as the presence of sister-chromatid cohesion , prevents premature spindle elongation in these mutant cells . To test this possibility , we need to examine the spindle elongation kinetics in clb5Δ clb6Δ mutants in the absence of cohesion . Because the spindle is unstable in cohesin mutant cells and deletion of the spindle checkpoint , such as MAD2 , suppresses the spindle instability [33] , we determined the spindle elongation kinetics in scc1-73 mad2Δ and clb5Δ clb6Δ scc1-73 mad2Δ mutants . Consistent with previous data , scc1-73 mad2Δ cells elongated spindle with kinetics similar to WT cells , but clb5Δ clb6Δ scc1-73 mad2Δ mutant cells showed premature spindle elongation ( Figure S7A ) . Therefore , the absence of S-phase cyclins leads to premature spindle elongation in the absence of cohesion . Our data indicate that S-phase and mitotic CDK may play opposing roles in spindle elongation . If that is true , we expect CLB2 overexpression to cause premature spindle elongation in the absence of S-phase cyclins . Therefore , we examined spindle elongation in clb5Δ clb6Δ mutant cells with a PGALCLB2 plasmid and control cells . As expected , clb5Δ clb6Δ cells showed premature spindle elongation when CLB2 is overexpressed and these cells grew slowly on galactose medium ( Figure S7B ) . Together , these data support the conclusion that S-phase CDK plays a negative role in spindle elongation , while mitotic CDK plays a positive role in this process . Functional FEAR is required for Clb2-induced premature spindle elongation , and the FEAR promotes Cdc14 release to dephosphorylate Clb5 substrates , such as Ase1 and Fin1 [7] , [34] , but we found that ase1Δ or fin1Δ mutant did not suppress Clb2-induced premature spindle elongation . A previous study suggests that Clb5-Cdk1-induced phosphorylation of Spc110 , one of the SPB proteins , also modulates spindle dynamics [9] . Interestingly , a phospho-mimetic spc11018D91D mutant , in which the CDK phosphorylation sites at Thr18 and Ser91 were mutated to aspartic acid , showed dramatic suppression of the binucleate phenotype in swe1Δ cells overexpressing CLB2 ( Figure 4A ) , indicating that the dephosphorylation of Spc110 might be required for Clb2-induced premature spindle elongation . We further compared the spindle elongation kinetics in WT and spc11018D91D mutant cells and found that the mutant cells did exhibit delayed spindle elongation , although the delay was not pronounced ( Figure S8 ) . Nevertheless , the spc11018D91D mutant failed to rescue the sick growth phenotype of swe1Δ cells with PGALCLB2 on galactose medium , suggesting that phosphorylation of other Clb5 substrates can prevent spindle elongation as well . Alternatively , other unidentified defects induced by Clb2 overexpression may also lead to the sick growth . The phosphorylation of Spc110 by Clb5-Cdk1 produces a band-shift on protein gels detectable by Western blotting [9] . We have demonstrated that the dephosphorylation of some Clb5-Cdk1 substrates depends on functional FEAR [19] . To test if the FEAR pathway is also required for the dephosphorylation of Spc110 , we compared the band-shift of Spc110 protein in WT and mutant cells lacking functional MEN or FEAR . Significant Spc110 phosphorylation was not observed in synchronous WT and MEN mutant cells cdc15-2 , but we noticed more obvious Spc110 phosphorylation in cdc15-2 spo12Δ mutant wherein the function of both MEN and FEAR is abolished , indicating that functional FEAR may be required for Spc110 dephosphorylation . Because mitotic CDK activates the FEAR by phosphorylating Net1 , we also examined Spc110 phosphorylation in clb1Δ clb2-VI temperature sensitive mutants . These mutant cells exhibited more Spc110 phosphorylation , which supports the notion that mitotic CDK promotes Spc110 dephosphorylation ( Figure 4B ) . We previously showed that overexpression of S-phase cyclin CLB5 is toxic to FEAR mutants and delays nuclear separation in the mutant cells [19] . If Clb5-induced phosphorylation of Spc110 contributes to this phenotype , a nonphosphorylatable spc110 mutant will suppress this phenotype . We first found that a nonphosphorylatable spc11018A91A mutant partially restored the growth of FEAR mutants ( spo12Δ ) with a PGALCLB5 plasmid on galactose medium ( Figure 4C ) . The spindle elongation dynamics was also examined in spo12Δ and spo12Δ spc11018A91A mutants overexpressing CLB5 . Clb5 overproduction in spo12Δ mutants significantly delayed spindle elongation . After G1 release for 150 min , 45% of WT cells exhibited elongated spindles , while only 23% of spo12Δ mutant cells did . However , 35% of spo12Δ spc11018A91A mutant cells displayed elongated spindle at 150 min ( Figure 4D ) , indicating that active S-phase CDK prevents spindle elongation at least partially through Spc110 phosphorylation . Consistently , more significant Spc110 phosphorylation was observed in spo12Δ cells overexpressing CLB5 ( Figure 4E ) . In these cells , the kinetics of DNA synthesis is indistinguishable with or without CLB5 overexpression based on the FACS analysis . Collectively , these data reveal the possibility of a negative role of Clb5-dependent Spc110 phosphorylation in spindle elongation . Our data suggest that the phosphorylation of SPB component Spc110 plays a role in the timing control of spindle elongation , and this phosphorylation is regulated by the balance of S-phase and mitotic CDKs . As a SPB component , however , it is likely that the phosphorylation of Spc110 regulates the spindle elongation via other microtubule-associated protein ( s ) . Stu2 is the yeast homologue of the XMAP215 protein that binds to the microtubule plus-end [35] , [36] . This protein is a processive microtubule polymerase essential for spindle elongation [37] , [38] . One possibility is that the CDK activity controls the timing of spindle elongation by regulating the activity of Stu2 . Interestingly , the temperature sensitive mutant stu2-10 dramatically suppressed the toxicity of CLB2 overexpression to swe1Δ mutant cells when incubated at 25°C ( Figure 5A ) , indicating that intact Stu2 function is required for CLB2-induced premature spindle elongation . In contrast , stu2-10 mutant cells were more sensitive to CLB5 overexpression than WT cells ( Figure 5B ) , indicating that Clb5 may negatively regulates Stu2 function . As we have showed that the phosphorylation of Spc110 by Clb5-Cdk1 plays a negative role in spindle elongation ( Figure 4C and 4D ) , we further compared the growth and spindle elongation in stu2-10 and stu2-10 spc11018A91A at 35°C . The results showed that nonphosphorylatable spc110 mutant partially suppressed the temperature sensitivity and the spindle elongation defect of stu2-10 mutant cells ( Figure 5C ) , suggesting that S-phase CDK-dependent Spc110 phosphorylation may down-regulate Stu2 function . To further test if the spindle localization of Stu2 is impaired in cells overexpressing CLB5 , live-cell imaging was performed in cells with and without CLB5 overexpression . Consistent with previous reports , we found that Stu2 localized on both spindle poles and spindle at metaphase and early anaphase [36] . At some cell cycle stages , the localization of Stu2 on the cytoplasmic microtubules was also clearly observed in the control cells ( Figure 5D ) . Although the SPB-localization of Stu2 remained similar , we noticed that overexpression of CLB5 significantly decreased the Stu2 localization on spindle and cytoplasmic microtubules . However , spc11018A91A mutant restored the microtubule-localization of Stu2 in cells overexpressing CLB5 ( Figure 5D ) , which is consistent with the result that spc11018A91A partially suppressed the temperature sensitivity of stu2-10 . Therefore , we speculate that the phosphorylation of Spc110 by S-phase CDK prevents the localization of Stu2 on spindle and cytoplasmic microtubules , presumably at the plus-ends . In contrast , Spc110 dephosphorylation likely facilitates the microtubule localization of Stu2 , which promotes spindle elongation . Our data indicate that the balance of mitotic and S-phase CDKs regulates the timing of spindle elongation . In addition , the presence of sister chromatid cohesion prevents spindle elongation . Although cohesion mutant cells ( scc1-73 ) exhibit premature spindle elongation when CLB2 is overexpressed , most of the cells have a successful mitosis because they are viable after CLB2 overexpression . We suspect that an additional mechanism also plays a role in the coordination of cohesion cleavage and spindle elongation . Securin Pds1 binds to and inhibits separase Esp1 , whose activity is required for the cleavage of cohesion Scc1/Mcd1 and the subsequent anaphase onset [2] . Thus , cell cycle-regulated Pds1 protein levels control the timing of cohesion cleavage and anaphase onset . A previous study showed that deletion of a nonessential gene CDH1 caused lethality in pds1Δ cells and expression of an extra copy of SWE1 suppressed this lethality [39] . Because CDH1 encodes an APC activator required for Clb2 degradation , it is possible that the high Clb2 levels contribute to the synthetic lethality of cdh1 pds1 mutants . Therefore , we examined the growth of pds1Δ cells overexpressing CLB2 and found that overexpression of CLB2 was very toxic to pds1Δ cells ( Figure 6A ) . One possibility is that high levels of mitotic CDK cause chromosome biorientation defects [40] , which require the intact spindle assembly checkpoint for survival . However , overexpression of CLB2 was not toxic to checkpoint mutant mad2Δ ( Figure S9 ) , excluding the possibility that the checkpoint defect in pds1Δ contributes to the lethality . After CLB2 expression for 4 hrs , 68% of pds1Δ cells lost viability ( Figure 6B ) . A FEAR mutant spo12Δ failed to suppress the growth defect of pds1Δ ( PGALCLB2 ) cells on galactose plates , but it partially suppressed the lethality of pds1Δ ( PGALCLB2 ) cells after incubation in liquid galactose media ( Figure 6B ) . Therefore , we conclude that high level of Clb2 proteins causes the lethality in pds1Δ mutant cells . The presence of Pds1 prevents the activation of separase Esp1 and the abrupt Pds1 degradation prior to anaphase onset allows a robust cohesion cleavage , resulting in synchronous dissolution of all chromosome pairs . The absence of Pds1 decreases this synchrony [5] , thus , the loss of this synchrony may cause catastrophic mitosis when CLB2 is overexpressed . To test this possibility , we first examined the spindle elongation and sister centromere separation in pds1Δ cells overexpressing CLB2 . After pds1Δ cells were released from G1-arrest into galactose medium for 3 hrs , about 12% cells exhibited an elongated spindle with a single CEN4-GFP dot , indicating the failure of chromosome IV separation after spindle elongation ( Figure 6C ) . Given that this number is only for one of the 16 chromosomes , the defect in chromosome segregation should be very dramatic and quick viability loss supports this speculation . We found that the mis-segregation of CEN4-GFP was largely suppressed by spo12Δ , which is consistent with the notion that CLB2 promotes spindle elongation through FEAR ( Figure 6C ) . We further used DAPI staining to examine the chromosome segregation in pds1Δ cells overexpressing CLB2 . Strikingly , most of the cells failed to show two clear DNA masses after spindle elongation . Instead , they exhibited lagged DNA along the elongated spindle ( Figure 6D ) , indicating a remarkable chromosome segregation defect . The observation of cells with an elongated spindle and a single CEN4-GFP dot supports this speculation . We further examined the segregation of the telomere of chromosome V ( TEL5-GFP ) in pds1Δ cells overexpressing CLB2 . After G1 release into galactose for 180 min , 23% of pds1Δ cells exhibited a single TEL5-GFP dot with two kinetochore clusters ( Nuf2-mCherry ) separated to two daughter cells , indicating the failure of telomere separation for chromosome V ( Figure 6E ) . spo12Δ also showed partial suppression of TEL5-GFP mis-segregation ( Figure 6E ) . Interestingly , the percentage of pds1Δ cells with a single TEL5-GFP dot after CLB2 overexpression is obviously higher than that with unseparated CEN4-GFP ( 23% vs . 11% ) . Our explanation is that both unseparated and partially separated chromosomes contribute to the telomere separation defect . The results support the possibility that overexpression of CLB2 in pds1Δ cells induces spindle elongation when cohesin still holds a few chromosomes or some parts of a chromosome . Nevertheless the small amount of cohesin is unable to restrain the premature spindle elongation induced by CLB2 overexpression , thereby resulting in the failure for the segregation of entire or part of a chromosome . Premature spindle elongation in pds1Δ mutant cells , therefore , induces uncoupled chromosome segregation and spindle elongation , which leads to significant chromosome mis-segregation .
The key to a successful cell division is the coordination of various cell cycle events . For efficient chromosome segregation , spindle elongation should follow the dissolution of sister-chromatid cohesion in an orderly fashion . The molecular mechanism that ensures this sequential order remains unclear . The absence of premature spindle elongation in cells lacking cohesion indicates a cohesion-independent mechanism that controls the timing of spindle elongation . Here we show that S-phase CDK negatively regulates spindle elongation , while mitotic CDK actives the FEAR pathway to trigger Cdc14 release , which reverses S-phase CDK-dependent protein phosphorylation and simulates spindle elongation . Therefore , the balance of mitotic vs . S-phase CDK activity is critical for the timing control of spindle elongation . We also show that S-phase CDK prevents spindle elongation in part by phosphorylating a SPB component Spc110 , while dephosphorylation of Spc110 by Cdc14 likely facilitates the localization of Stu2 , a plus-end tracking protein , to spindle microtubules , which may directly promotes spindle elongation by enhance microtubule polymerization [37] . Furthermore , hyperactive mitotic CDK in pds1Δ cells , where the synchrony of chromosome segregation is compromised , leads to uncoupled sister-chromatid separation and spindle elongation , resulting in chromosome mis-segregation and cell death . A model illustrating and integrating this cell cycle regulatory network is shown in Figure 7 . These findings reveal at least two mechanisms that prevent premature spindle elongation . Firstly , before anaphase onset , S-phase CDK phosphorylates several microtubule-associated proteins to prevent spindle elongation . Therefore , in addition to promoting DNA synthesis , S-phase CDK also negatively regulates mitosis to ensure the correct order of S and M phase spindle function [41] . Secondly , sister-chromatid cohesion restrains spindle elongation as well . To allow spindle elongation , both the loss of sister-chromatid cohesion and the reversion of protein phosphorylation imposed by S-phase CDK have to be achieved . Before anaphase onset , the destruction of Pds1 frees separase Esp1 that cleaves cohesin rings . Nevertheless , the reversion of S-phase CDK-dependent phosphorylation requires the destruction of S-phase cyclin Clb5 as well as the activation of phosphatase Cdc14 . Clb5 is degraded before anaphase onset along with Pds1 through APCCdc20 [18] . The release of Esp1 from the inhibition by Pds1 not only triggers cohesin cleavage but also activates FEAR to release Cdc14 [15] . Therefore , this mechanism ensures that cohesin cleavage and spindle elongation are coordinated temporally ( Figure 7 ) . The tightly regulation of the protein levels of securin Pds1 during cell cycle not only avoids the dissolution of sister-chromatid cohesion prior to anaphase entry [42] , but also ensures that all chromosome pairs disjoin almost simultaneously [5] . Loss of this switch-like mechanism in pds1Δ mutant cells results in asynchronous cohesion dissolution . Therefore , in pds1Δ mutant cells , it is possible that only a few sister chromatids are linked by cohesin to restrain spindle elongation while cells have initiated spindle elongation . Surprisingly , most pds1Δ cells are able to segregate chromosomes successfully . The temporal control mechanism described above may prevent spindle elongation when cohesion is partially dissolved in pds1Δ mutants , but deregulation of this temporal control mechanism could lead to catastrophic mitosis . Indeed , we showed that overexpression of CLB2 in pds1Δ mutants results in the failure of the separation of some sister chromatids after spindle elongation . In addition to high levels of mitotic cyclins , constitutive activation of FEAR by deletion of CDC55 or the deletion of S-phase cyclins has also been shown to be lethal to pds1Δ mutants [39] , [43] , [44] , and the induction of premature spindle elongation is likely the cause of the lethality . Because only some sister-chromatids are linked by cohesin rings when spindles elongate in these cells , the pulling force may break the kinetochore-microtubule interaction , resulting in chromosome mis-segregation . It is also possible that cohesin only exists in part of a chromosome , such as the arm or telomere regions while the spindle is elongating . Under this situation , sister centromeres segregate successfully , but the telomeres of this chromosome still stay together . Previous work validates the role of mitotic CDK in spindle elongation , as cells lacking both Clb1 and Clb2 fail to elongate the spindle . Because overexpression of Cdc14 phosphatase cannot suppress the spindle elongation defect in clb1Δ clb2-IV mutant , it was speculated that mitotic CDK promotes spindle elongation in FEAR-independent manner [21] . Our observation that FEAR mutants completely suppress Clb2-induced premature spindle elongation and toxicity to swe1Δ cells strongly supports the conclusion that mitotic CDK promotes spindle elongation through the FEAR pathway . However , we cannot exclude the possibility that additional mitotic CDK targets might also be involved in spindle elongation . An interesting question is whether cohesin cleavage and spindle elongation occur at the same time . We found that spindle elongation happens earlier in swe1Δ mutant cells overexpressing CLB2 , but the cells with elongated spindle always show separated sister chromatids . Moreover , our result indicates that CLB2 overexpression does not leads to premature cohesin cleavage . These results suggest that spindle elongation may occur later than cohesin cleavage , but hyperactive CDK eliminates this lag . Because FEAR mutants block Clb2-induced premature spindle elongation in swe1Δ mutants , we speculate that the activation of FEAR occurs later than cohesin cleavage , which contributes to the time difference . Therefore , an important function of the FEAR pathway is likely to ensure the sequential order of cohesin cleavage and spindle elongation . Although the hyperactive FEAR in cdc55Δ mutant cells did not result in dramatic mitotic defect , cdc55Δ bub2Δ double mutant cells exhibit catastrophic mitosis , wherein both FEAR and MEN are hyperactive [16] . Several microtubule-associated proteins , such as Ase1 , Fin1 , and Ask1 , are phosphorylated more efficiently by S-phase CDK and their phosphorylation regulates spindle dynamics [8] , [34] , [45] . Here we show that S-phase CDK-mediated phosphorylation of Spc110 also plays an important role in spindle elongation . We found that the phospho-mimetic spc11018D91D mutant partially abolishes Clb2-induced premature spindle elongation . In contrast , the nonphosphorylatable spc11018A91A mutant suppresses the Clb5-induced delay in spindle elongation . Although spc11018D91D mutant cells show a noticeable delay in spindle elongation , the delay is not significant . We reason that the phosphorylation of a group of proteins by Clb5-Cdk1 prevents spindle elongation , but the phospho-mimetic mutant for each single protein may not be sufficient to block spindle elongation completely . In contrast to Ase1 and Fin1 , which directly bind to microtubules and regulate microtubule dynamics , Spc110 is a SPB component , so it is likely that Spc110 phosphorylation regulates spindle elongation in an indirect way . We show that overexpression of CLB5 clearly impairs the localization of Stu2 on spindle and cytoplasmic microtubules . The dephosphorylation of Spc110 and/or other Clb5 substrates likely promotes the localization of Stu2 to the plus-end of spindle microtubules , where Stu2 triggers the polymerization and spindle elongation . Further studies are needed to understand how S-phase CDK prevents microtubule localization of Stu2 . In summary , our data reveal the molecular mechanism that coordinates chromosome segregation and spindle elongation . Before anaphase , S-phase CDK and sister chromatid cohesion prevent spindle elongation . The establishment of chromosome bipolar attachment triggers the degradation of both securin Pds1 and S-phase cyclin Clb5 . The disappearance of Pds1 activates Esp1 that cleaves cohesin and triggers Cdc14 release through FEAR . Consequently , the loss of sister chromatid cohesion and the reversion of S-phase CDK-dependent protein phosphorylation trigger spindle elongation . This mechanism becomes essential in cells with decreased synchrony of chromosome segregation , as induction of premature spindle elongation in these cells results in unseparated or partially separated chromosomes after spindle elongation . Like budding yeast Clb5 , the S-phase cyclin ( Cyclin A ) in mammalian cells also exhibits substrate specificity [46] . Moreover , the proteins involved in this regulation , such as Ase1 , Spc110 , and Stu2 , are well conserved in higher eukaryotic cells . For example , XMAP215 , the Stu2 homologue in mammalian cells , is required for microtubule polymerization and spindle elongation [47] . Therefore , the mechanism of the temporal control of spindle elongation described in budding yeast could be conserved . We represent data showing that the temporal control of spindle elongation is critical for accurate chromosome segregation , suggesting that defects in this network may contribute to aneuploidy that is associated with cancer progression .
The yeast strains used in this study are listed in Table S1 . All strains are isogenic to Y300 , a W303 derivative . Yeast cells were grown in YPD ( Yeast extract , Peptone , Dextrose ) or indicated synthetic medium . To arrest cells in G1 phase , 5 µg/ml α-factor was added into cell cultures . After 2 hr incubation , the G1-arrested cells were washed twice with water and then released into fresh medium to start the cell cycle . Nocodazole was used at 20 µg/ml in 1% DMSO . To induce cyclin overexpression , galactose was added to the medium to a final concentration of 2% . Cells with GFP , mCherry or mApple-tagged proteins were fixed with 3 . 7% formaldehyde for 5 min at room temperature , and then washed twice with 1× PBS buffer and resuspended in PBS buffer for fluorescence microscopy ( Carl Zeiss MicroImaging , Inc . ) . The spindle morphology was monitored by using TUB1-GFP , TUB1-mApple , or TUB1-mCherry strains and we count the spindles longer than 3 µm as elongated spindles . For DAPI staining , cells were fixed with 3 . 7% formaldehyde for 5 min at room temperature , and then resuspended in 100% MEOH at −20°C for 30 min . The cells were incubated in DAPI solution ( final concentration of 2 . 5 µg/ml ) for 1 min at room temperature . For each experiment , we repeated 3 times and at least 100 cells were counted for each sample . Live-cell microscopy was carried out with a Nikon Eclipse Ti imaging system ( Andor ) . We used a glass depression slide to prepare an agarose pad filled with synthetic complete medium with the addition of galactose . All live-cell images were acquired at 25°C with an ×100 objective lens . Twelve Z-sections were collected at each time point , and each optical section was set at 0 . 5 µm thick . The time-lapse interval was set at 5 min . Maximum projection from applicable time points were created using Andor IQ2 software . G1 phase-arrested cells in raffinose medium were released into galactose medium . Samples were taken at various time points and fixed in 70% EtOH overnight at 4°C . Cells were then incubated in Tris pH 7 . 8 buffer with 0 . 2 mg/ml RNase A at 37°C for 4 hrs and stained with 30 µg/ml propidium iodide at 4°C overnight . FACS analysis was performed using FACSCanto equipped with the FACSDiva software . Cell pellets from 1 . 5 ml of cell culture were resuspended in 200 µl 0 . 1 N NaOH and incubated at room temperature for 5 min . After centrifugation , the cells were resuspended in equal volume ( 30 µl ) of ddH2O and SDS protein-loading buffer . The samples were then boiled for 5 min and resolved with 8% SDS-polyacrylamide gel . Proteins were detected with ECL ( Perkin Elmer LAS , Inc . ) after probing with anti-Myc or anti-HA antibodies ( Covance Research Products , Inc . ) and HRP-conjugated secondary antibody ( Jackson ImmunoResearch , Inc . ) . | Before anaphase onset , the presence of securin Pds1 prevents the cleavage of cohesin , a protein complex that holds sister chromatids together , but the degradation of Pds1 leads to robust cohesion dissolution , resulting in synchronous separation of all chromosomes . Since sister chromatids are attached to spindle microtubules before anaphase onset , spindle elongation segregates sister chromatids to each daughter cell after cohesion dissolution . A fundamental question is how spindle elongation is temporally regulated in order to coordinate with sister chromatid separation . Cyclin-dependent kinases ( CDKs ) play a key role in the cell cycle , and it is well established that S-phase CDK promotes DNA synthesis . We found that hyperactive S-phase CDK delays spindle elongation in budding yeast , while hyperactive mitotic CDK leads to premature spindle elongation . We further provide evidence indicating that mitotic CDK promotes spindle elongation by activating a protein phosphatase that antagonizes S-phase CDK , thus the balance of S-phase and mitotic CDK controls the timing of spindle elongation . Interestingly , hyperactive mitotic CDK induces uncoupled spindle elongation and sister chromatid separation in the absence of securin , suggesting that the temporal control of spindle elongation and cohesin cleavage is essential for faithful chromosome segregation . |
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Viral replication efficiency is in large part governed by the ability of viruses to counteract pro-apoptotic signals induced by infection of host cells . For HHV-8 , viral interferon regulatory factor-1 ( vIRF-1 ) contributes to this process in part via inhibitory interactions with BH3-only protein ( BOP ) Bim , recently identified as an interaction partner of vIRF-1 . Here we recognize that the Bim-binding domain ( BBD ) of vIRF-1 resembles a region ( BH3-B ) of Bid , another BOP , which interacts intramolecularly with the functional BH3 domain of Bid to inhibit it pro-apoptotic activity . Indeed , vIRF-1 was found to target Bid in addition to Bim and to interact , via its BBD region , with the BH3 domain of each . In functional assays , BBD could substitute for BH3-B in the context of Bid , to suppress Bid-induced apoptosis in a BH3-binding-dependent manner , and vIRF-1 was able to protect transfected cells from apoptosis induced by Bid . While vIRF-1 can mediate nuclear sequestration of Bim , this was not the case for Bid , and inhibition of Bid and Bim by vIRF-1 could occur independently of nuclear localization of the viral protein . Consistent with this finding , direct BBD-dependent inactivation by vIRF-1 of Bid-induced mitochondrial permeabilization was demonstrable in vitro and isolated BBD sequences were also active in this assay . In addition to Bim and Bid BH3 domains , BH3s of BOPs Bik , Bmf , Hrk , and Noxa also were found to bind BBD , while those of both pro- and anti-apoptotic multi-BH domain Bcl-2 proteins were not . Finally , the significance of Bid to virus replication was demonstrated via Bid-depletion in HHV-8 infected cells , which enhanced virus production . Together , our data demonstrate and characterize BH3 targeting and associated inhibition of BOP pro-apoptotic activity by vIRF-1 via Bid BH3-B mimicry , identifying a novel mechanism of viral evasion from host cell defenses .
Human herpesvirus 8 ( HHV-8 ) specifies a number of proteins expressed during the lytic cycle that have demonstrated or potential abilities to promote virus productive replication via inhibition of apoptotic pathways induced by infection- or replication-induced stress . These proteins include membrane signaling receptors K1 and K15 [1]–[3] , Bcl-2 and survivin homologues encoded by open reading frames 16 and K7 [4]–[7] , viral chemokines vCCL-1 and vCCL-2 [8] , and viral G protein-coupled receptor ( vGPCR ) [9] , [10] . The viral interferon regulatory factor homologues , vIRFs 1–4 , also are believed to play important roles in blocking interferon and other stress responses to virus infection and replication . Their functions include inhibitory interactions with cellular IRFs , IRF-activating pathways , and/or IRF-recruited p300/CBP transcriptional co-activators to IRF-stimulated promoters [11]–[15] . Additionally , the vIRFs inhibit apoptosis via targeting of other cellular proteins; these include p53 ( vIRFs 1 and 3 ) [16]–[18] , p53-activating ATM kinase ( vIRF-1 ) [19] , p53-destabilizing MDM2 ( vIRF-4 ) [20] , retinoic acid/interferon-inducible protein GRIM19 ( vIRF-1 ) [21] , and TGFβ receptor-activated transcription factors Smad3 and Smad 4 ( vIRF-1 ) [22] . To date , the v-chemokines , vGPCR and vIRF-1 are the only HHV-8 proteins that have been demonstrated both to inhibit apoptosis in lytically infected cells and to promote HHV-8 productive replication , in the context of lytic reactivation in endothelial cells in the case of the vCCLs and vIRF-1 and additionally in primary effusion lymphoma ( PEL ) cells for vGPCR [23] , [8] , [10] . In addition to its other cellular binding partners , vIRF-1 also interacts with the pro-apoptotic BH3-only protein ( BOP ) Bim [23] , a protein also targeted for suppression by v-chemokine signaling and demonstrated to be both induced during lytic replication and a very powerful negative regulator of viral replication efficiency [8] . Bim , like other BOPs , functions by virtue of its BH3 domain to target anti-apoptotic members of the Bcl-2 family and to disrupt their interactions with apoptotic executioner proteins Bax and Bak , liberating them for oligomerization and mitochondrial permeabilization [24] , [25] . However , Bim can also interact with and activate Bax and Bak directly , via induced conformational changes [26]–[28] . This property of direct activation of Bax and/or Bak is shared by BOPs Bid and Puma , although other BOPs appear to act indirectly via BH3-mediated interactions with Bcl-2-family proteins [26] , [27] , [29] , [30] . Activities of several BOPs , such as Bim , Bmf and Bad , are regulated via phosphorylation , to effect activation , inactivation , or alteration of protein stability [31]–[33] . For example , Bim is activated by JNK-mediated phosphorylation of residue T56 , causing release of Bim from microtubules , inactivated by Akt phosphorylation of S87 , which allows 14-3-3 association and cytosolic sequestration , and ERK phosphorylation of S69 to effect proteasomal degradation of Bim [34] , [32] , [35] . Bid is unique among BOPs in its activation via protease cleavage , typically by death receptor-activated caspases but also by other proteases , such as granzyme B [36]–[38] . Cleavage removes N-terminal sequences ( p7 ) containing a motif , termed BH3-B , which interacts intramolecularly with the BH3 domain to inhibit Bid pro-apoptotic activity [39] , [40] . Mitochondrial membrane targeting of cleaved , truncated Bid ( tBid ) is promoted via surface exposure of hydrophobic residues and N-terminal glycine myristoylation [41] , [42] . While the nature of BH3 interactions with Bcl-2 family proteins , involving BH3 α-helix association with Bcl-2 BH1–3-comprised hydrophobic grooves , is well characterized [43] , [44] , the basis of Bid BH3∶BH3-B interaction is at present poorly understood [40] . Our previous studies of vIRF-1 interaction with Bim identified a unique mechanism of Bim regulation , via nuclear sequestration of the BOP away from mitochondria , and documented the first example of interaction between a Bcl-2 family member and an IRF homologue [23] . While the Bim-binding domain ( BBD ) of vIRF-1 was mapped , to residues 170–187 comprising a putative amphipathic α-helix , the region of Bim interacting with vIRF-1 was not determined . Subsequent comparisons of the BBD primary and predicted secondary structures with those of Bid BH3-B revealed similarities , in terms of conserved residues , α-helical structure and amphipathicity , indicating , by analogy , that BBD may target for binding and direct functional inhibition the BH3 domain of Bim ( in addition to enabling vIRF-1-mediated inactivation of Bim via nuclear sequestration ) , and indeed may be able to bind Bid BH3 also . The data presented here demonstrate that this is indeed the case , that vIRF-1 can inhibit Bid as well as Bim pro-apoptotic activity , and that BBD can also recognize the BH3 domains of certain other BOPs , dependent on residues that are common and particular to these domains . Thus , vIRF-1 BBD mediates BH3-B mimicry , to our knowledge the first example of viral usurpation of this mode of inhibition of BOP function and pro-apoptotic signaling .
We noted previously the amphipathic α-helical structure of the Bim-binding domain ( BBD , residues 170–187 ) of vIRF-1 [23] . This type of structure also is apparent in the so-called BH3-B domain of Bid , which interacts intramolecularly with the BH3 domain to effect inhibition of Bid activity [40] . Indeed , the sequences of BBD and BH3-B are similar to each other and to BH3 domains of other proteins ( Fig . 1 ) . Particularly noteworthy are the BH3-conserved hydrophobic and BH3-B-conserved basic residues of the BBD core sequence ( Fig . 1 ) . The structural similarities of BBD and BH3-B suggested the possibility that BBD might interact with Bim via its BH3 domain and that vIRF-1 might target Bid ( and possibly other BOPs ) in addition to Bim . To test whether the BBD of vIRF-1 interacted with the BH3 domain of Bim , recombinant fusion proteins were made for co-precipitation binding assays . The proteins comprised T7/DsRed-fused wild-type and Bim-binding-refractory GK179AA versions of BBD ( vIRF-1 residues 170–187 ) , and also Bid BH3-B ( BidL residues 34–51 ) , and chitin-binding domain ( CBD ) -tagged GFP-Bim BH3 ( BimEL residues 148–161 ) and GFP ( control ) . Paired T7/DsRed and GFP/CBD fusion proteins were mixed , CBD-tagged proteins precipitated with chitin beads , and precipitated material analyzed by SDS-PAGE and immunoblotting . This experiment revealed binding of BBD , but not BBD ( GK179AA ) or BH3-B , to Bim BH3 , with no detectable background binding to negative control GFP-CBD ( Fig . 2A ) . An analogous experiment using GFP/CBD-fused Bid BH3 ( residues 86–99 ) as the “bait” identified interaction with Bid BH3-B ( positive control ) and also with vIRF-1 BBD ( Fig . 2B ) , thereby identifying Bid BH3 , as well as Bim BH3 , as a target of vIRF-1 BBD interaction . To confirm interaction of full-length vIRF-1 and Bid proteins , as we had done previously for vIRF-1 and Bim [23] , appropriate expression plasmids were used to transfect HEK293T cells , and cell lysates were used for co-precipitation assays . Immunoprecipitation of Flag epitope-tagged Bid , as well as Bim ( positive control ) , enabled co-precipitation of vIRF-1 , demonstrating interaction between vIRF-1 and Bid ( Fig . 2C ) . The relationship between vIRF-1 BBD and Bid BH3-B was tested by substitution of the latter with the former in the context of full-length , uncleaved Bid ( BidL ) and testing the constructions ( Fig . 3A ) for pro-apoptotic activities in appropriately transfected cells . Apoptotic activity of Bid was measured by a GFP-based assay , in which loss of GFP fluorescence in GFP vector-cotransfected cells correlates with loss of cell viability and corresponds to rates of apoptosis , e . g . as measured by TUNEL assay [23] . Transfection of BidL and GFP expression vectors into HEK293T cells led to substantially reduced GFP fluorescence ( ∼39% ) relative to empty vector plus GFP control , set at 100% ( Fig . 3B ) , consistent with previously reported apoptotic activity of uncleaved Bid [45] , [40] . However , this activity was increased substantially by introduction of Bid-BH3 binding-abrogating mutations ( GHE41VLA [40] ) into BH3-B , suppressing GFP fluorescence to ∼20% [Fig . 3B , Bid ( mBH3-B ) ] . Importantly , BBD could substitute fully for BH3-B in this assay , inhibiting BH3-mediated Bid apoptotic activity more effectively than native BH3-B ( 75% GFP fluorescence relative to 39% ) . Increased inhibition by BBD indicates that it may bind with higher affinity than BH3-B to Bid BH3 . Although it is more likely that BBD and BH3-B mediated inhibition of BidL activity occur via intramolecular interactions with Bid BH3 , it is also possible that trans-inhibition may occur . Introduction of mutation GK179AA ( previously shown to abrogate Bim interaction [23] ) into BBD ( mBBD ) abolished its inhibition of BidL activity , leading to GFP fluorescence ( cell viability ) levels similar to those obtained upon mutation of BH3-B . Similar transfections with these constructions were undertaken to confirm apoptotic inhibitory activity of BBD in the context of BidL . Annexin V-Cy3 staining ( Fig . 3C ) and cytochrome c release assays ( Fig . 3D ) were employed to quantify apoptosis and to monitor induction of the apoptotic pathway , respectively . The results derived from annexin V-Cy3 staining mirrored those obtained from the GFP-based assay ( Fig . 3B ) , demonstrating full functional substitution of Bid BH3-B by vIRF-1 BBD . Apoptosis induced by tBid , included in this experiment , was notably higher than that of BidL , BidL-mBH3-B and BidL-mBBD , as expected because of the complete absence of the inhibitory N-terminal region of Bid and efficient mitochondrial targeting of the truncated form . Congruent results were obtained from the cytochrome c release assays , confirming the ability of BBD to substitute functionally for BH3-B in the context of BidL ( Fig . 3D ) . Combined , the data presented in Figure 3 demonstrate functional equivalence of Bid BH3-B and vIRF-1 BBD and provide further evidence , in a biologically relevant context , of BBD interaction with Bid BH3 . In HHV-8 lytically reactivated endothelial cells , Bim is found predominantly in the nucleus , and nuclear location of Bim can be induced by vIRF-1 in transfected HEK293T cells [23] . As nuclear-localized Bim is inactive in respect of apoptotic induction , its nuclear sequestration represents a mechanism of BOP inactivation . To determine the nuclear-cytoplasmic distribution of Bid during HHV-8 lytic reactivation , dual-label immunofluorescence assays ( IFA ) were undertaken to identify Bid induction and distribution in reactivated cells expressing lytic antigen ( vIRF-1 ) . Like Bim , Bid was induced during lytic reactivation in telomerase-immortalized endothelial ( TIME ) cells [46] , here engineered to express HHV-8 immediate-early protein RTA in response to doxycycline ( see Materials and Methods and Fig . S1 ) ( Fig . 4A ) . However , little or no nuclear localization of Bid was apparent , in sharp contrast to the predominant nuclear localization of Bim in lytically reactivated cultures [23] ( Fig . 4A ) . In cells transfected with BidL or tBid expression vectors together with an empty or vIRF-1 expression plasmid , the nuclear-cytoplasmic distribution of each Bid protein was refractory to vIRF-1 influence ( Fig . 4B ) . It is notable that some nuclear localization of BidL was apparent , consistent with previous reports of nuclear localization and associated activities of Bid [47]–[49] , but no nuclear staining was evident for tBid . In contrast to Bid , and consistent with previous findings [23] , Bim distribution was altered in the presence of vIRF-1 , with strong nuclear staining apparent exclusively with vIRF-1 co-expression ( Fig . 4B ) . That vIRF-1 indeed did not influence nuclear-cytoplasmic distribution of Bid was verified by using immunoblotting of cytoplasmic and nuclear fractions of transfected cells . Again , while nuclear localization of a proportion of BidL was detected , this was not detectably influenced by vIRF-1 co-expression , and tBid localization was restricted to the cytoplasm in the absence and presence of vIRF-1 ( Fig . 4C ) . Furthermore , a nuclear localization-defective vIRF-1 variant ( see below ) also did not influence the nuclear-cytoplasmic distribution of BidL , although induction of Bim nuclear localization was abolished ( Fig . 4C ) . Targeting of BH3 domains of Bim and Bid by vIRF-1 , coupled with the inability of vIRF-1 to induce significant Bid nuclear localization , suggested the possibility of direct inactivation of BOP apoptotic activity by vIRF-1 binding . To address this issue , we generated a nuclear localization-defective version of vIRF-1 for use in functional assays . Each of four potential nuclear localization signals ( NLS ) was mutated ( Fig . 5A ) , and the respective vIRF-1 proteins were tested for nuclear localization in expression vector-transfected cells . vIRF-1 ( RGRRR163AGAAA ) was found to be defective for nuclear localization , as determined by IFA ( Fig . 5B ) . This was verified using a functional assay based on p53-inhibitory activity of vIRF-1; while wild-type vIRF-1 was able to suppress reporter-detected transactivation by nuclear p53 , NLS-mutated vIRF-1 was completely inactive ( Fig . 5C ) . As mentioned above , the vIRF-1 variant was also confirmed to be unable to induce nuclear localization of Bim ( Fig . 4C ) . The wild-type and NLS-mutated versions of vIRF-1 were used in GFP-based viability assays to compare their abilities to inhibit Bim and Bid activities . Both vIRF-1 proteins were able to protect cells from Bim- and Bid-induced apoptosis , with very similar dose-activity profiles ( Fig . 5D ) . Therefore , vIRF-1 inhibition of both Bim and Bid can be mediated independently of nuclear-localization and nuclear-localized functions of vIRF-1 . To address the hypothesis that vIRF-1 may act directly at the mitochondrion to suppress BOP-induced apoptosis , we isolated mitochondria from vIRF-1 vector-transfected HEK293T cells and undertook SDS-PAGE and Western analysis for detection of vIRF-1 in the mitochondrial fraction . Both wild-type and Bim-refractory ( GK179AA ) vIRF-1 proteins were present in mitochondrial fractions , representing approximately 2% of total vIRF-1 present in the transfected cell lysates ( Fig . 6A ) . To assess whether this vIRF-1 was likely to be membrane inserted/associated or mitochondrial outer membrane ( OM ) protein associated , in vitro mitochondrial binding assays were used . These assays employed purified recombinant vIRF-1 ( T7 epitope-tagged ) added to isolated mitochondria prior to and after proteinase K treatment . Binding of vIRF-1 to mitochondria , apparent absent treatment , was completely abrogated by proteinase K pre-treatment ( Fig . 6B ) , indicating mitochondrial association of vIRF-1 via interaction with a cytoplasmic-exposed mitochondrial protein . Furthermore , endogenously-expressed vIRF-1 was present in mitochondrial fractions prepared from HHV-8+ primary effusion lymphoma ( PEL ) cells , BCBL1-TRE/RTA [50] , with or without lytic induction ( +Dox ) , and this vIRF-1 was susceptible to proteinase K digestion , demonstrating peripheral association of vIRF-1 with mitochondria in HHV-8 infected cells ( Fig . 6C ) . vIRF-1 has been reported previously to be expressed during latency in PEL cells but to be induced during lytic reactivation [51] , consistent with our detected patterns of vIRF-1 expression in BCBL1-TRE/RTA cells . Mitochondrial localization of vIRF-1 was verified by IFA in TIME-TRE/vIRF-1 cells [23] in which vIRF-1 expression is inducible upon addition of doxycycline to culture medium . These results demonstrated localization of detectable levels of vIRF-1 to a subset of loci staining positively for mitochondrial marker TOM20 ( Fig . 6D ) . In both Dox-inducible BCBL1-TRE/RTA PEL cells and HHV-8-infected TIME-TRE/RTA endothelial cells ( see Materials and Methods and Fig . S1 ) , vIRF-1 was found by immunoblotting of mitochondrial fractions to localize in part to mitochondria during productive replication ( Fig . 6E ) . The proportion of vIRF-1 localizing to mitochondria in the BCBL-1 cells was comparable with that measured in transfected cells ( Fig . 6A ) ; the level in the TIME cells ( +Dox ) was >4 times higher at 9% . For the PEL cells , which express vIRF-1 also during latency but at reduced levels , the proportion of mitochondrial-localized vIRF-1 was increased from 0 . 9% to 2% in Dox-treated , lytically-induced cultures , possibly reflecting biological significance of vIRF-1 activity at this site during productive replication . It should be noted that as only a subset of these cells support lytic reactivation , the proportion of vIRF-1 localized to mitochondria in lytically infected cells is likely to be substantially greater than the 2% level observed for the culture as a whole . Similar fractionation experiments in HHV-8+ TIME-TRE/RTA cells treated with Dox verified Bid , as well as vIRF-1 , localization to mitochondria during lytic reactivation ( Fig . 6F ) . Confocal immunofluorescence microscopy detected at least some colocalization of vIRF-1 and Bid in these cells ( Fig . S2 ) . Combined , our data provide evidence of mitochondrial association of vIRF-1 , in both transduced and infected cells , and indicate that vIRF-1 may target BOPs for inhibition at this site . Bid and Bim BH3 domain-targeting by vIRF-1 , nuclear localization-independent inhibition of BOP pro-apoptotic activity , and partial mitochondrial localization of vIRF-1 suggested the likelihood of direct BOP inactivation via BBD∶BH3 association . This was tested by using an in vitro mitochondrial permeabilization assay to assess the abilities of wild-type and a ΔBBD ( BOP-refractory ) variant of vIRF-1 to inhibit tBid-induced cytochrome c release . The vIRF-1 proteins and tBid were expressed as T7/intein/CBD and thioredoxin/His6/S-tag fusion proteins in bacteria and were subsequently purified and cleaved to release the respective T7- and S-tagged proteins ( see Materials and Methods ) . SDS-PAGE and Coomassie staining ( Fig . 7A ) verified their purity prior to use . Addition of recombinant tBid ( 1 . 5 µg/ml , 100 nM ) to mitochondrial preparations induced the release of cytochrome c into the soluble fraction of the mitochondrial suspension and led to a corresponding decrease in the level of cytochrome c in the mitochondrial pellet , as determined by Western analysis ( Fig . 7B ) . Inclusion of vIRF-1 ( 8 µg/ml , 100 nM ) blocked all detectable cytochrome c release , but vIRF-1ΔBBD was inactive in respect of tBid inhibition . These data demonstrate that BBD∶BH3 interaction alone is sufficient to inhibit tBid-induced apoptosis . Further experiments were undertaken to determine if BH3-B could substitute functionally for BBD in the context of vIRF-1 to block tBid-induced mitochondrial permeabilization and also to confirm direct inhibitory activities of the BBD and BH3-B domains , expressed as fusions with GST . The recombinant proteins were isolated and purified from bacterial extracts and their purities and concentrations checked prior to experimental use ( Fig . 7C ) . T7-fused wild-type vIRF-1 and its BBD-substituted counterpart each were able to inhibit tBid-induced cytochrome c release from mitochondrial preparations ( Fig . 7D , top ) . Similarly , both GST-BBD and GST-BH3-B were able to inhibit tBid-induced cytochrome c release in this assay ( Fig . 7D , bottom ) . These data confirm the functional equivalence of BBD and BH3-B , in the context of vIRF-1 , and the direct role of these domains and their interaction with Bid BH3 in the inhibition of Bid pro-apoptotic activity . We next investigated whether BBD could target additional BH3 domains , in particular those of other BOPs . The respective BH3 coding sequences were cloned in-frame with the GFP open reading frame in plasmid vector pTYB4 and expressed in and purified from bacteria; BBD was expressed and isolated similarly , as a GST fusion protein ( Materials and Methods ) . BH3 domains tested comprised those of BOPs Bad , Bmf , Bnip3L , Hrk and Noxa , along with Bim , Bid and “BH3-only” beclin [52] , and BH3s from multi-BH domain proteins Bcl-2 and Mcl-1 ( anti-apoptotic ) and Bak , Bax and Bok ( pro-apoptotic ) . Results from these co-precipitation assays identified Bik , Bmf , Hrk and Noxa BH3 domains as additional targets of BBD interaction ( Fig . 8A ) . Interactions between the corresponding full-length proteins and vIRF-1 were tested by co-immunoprecipitation of vIRF-1 with Flag-tagged BOPs from transfected cell lysates; all but Bik were able to co-precipitate vIRF-1 in this experiment ( Fig . 8B ) . Bcl-2 was essentially negative , with barely detectable levels of vIRF-1 in the co-precipitate , as was BOP Puma [not included in the BBD∶BH3 experiment ( Fig . 8A ) ] . Therefore , of the Bcl-2 protein family members tested , BOPs Bim , Bid , Bmf , Hrk and Noxa are demonstrably targeted by vIRF-1 , via BBD∶BH3 association , and Bik BH3 can also bind BBD . Comparisons of the BH3 domains of vIRF-1/BBD-interacting BOPs identified a single unique and conserved residue among the BBD-binding BH3 sequences , namely an alanine at position φ1+1 ( Fig . 9A , left ) . Mutagenesis of this position within the context of Bim BH3 was undertaken to determine its significance with respect to BBD binding; it was changed to each of the collinear residues of the non-binding BH3 domains . Additionally , residues φ1+1 to φ1+3 ( SEC ) of BBD-refractory Bax BH3 were changed to the equivalents ( AQE ) in closely related Bim BH3 and the reciprocal changes were made in Bim BH3 to determine if these “diverged” residues in combination could , respectively , confer and abrogate BBD binding . The various changes made are shown in Fig . 9A ( right ) . As before , these sequences were expressed as GFP fusions for use in GST-BBD-based coprecipitation assays . Other than wild-type Bim BH3 , glutathione bead-precipitated GST-BBD was able to efficiently co-precipitate only cysteine- and serine-substituted alanine φ1+1 , with weak binding apparent for the leucine-substituted BH3 ( Fig . 9B ) . The AQE substitution of Bax SEC residues was able to confer at least some BBD-binding capacity to Bax , demonstrating the contribution of these residues , either directly or via structural influence , to binding; the converse substitution in Bim abrogated binding . Interestingly , a sequence isolated from a phage-display dodecamer-peptide library using GST-BBD as bait had some resemblance to BH3 sequences in respect of conserved basic residues and , importantly , possessed an alanine residue at the equivalent of position φ1+1 . This sequence also showed some binding in the in vitro coprecipitation assay . Taken together , these data indicate the likely central importance of alanine at position φ1+1 for BBD interaction , although the residue's contribution is likely indirect and evidently context dependent , as particular collinear small side-chain residues ( serine and cysteine ) from non-binding BH3 domains can substitute for alanine in Bim BH3 and the AQE motif from Bim can confer only weak binding to the closely related BH3 domain of Bax . Previous studies from this laboratory identified Bim as a potent inhibitor of virus productive replication and the importance of vIRF-1 BBD-mediated interactions in countering lytic cycle-induced apoptosis and promoting HHV-8 production in TIME cells [23] , [8] . In view of the present findings that vIRF-1 inhibits Bid activity in a BBD-dependent fashion ( Fig . 7 ) and that Bid is induced in lytically reactivated TIME cells ( Fig . 4A ) , we wanted to test the significance of Bid in HHV-8 replication . To do this , we utilized lentiviral vector-delivered shRNAs directed to Bid mRNA sequences to deplete Bid in HHV-8+ ( latently infected ) TIME cells and compared levels of cell-released encapsidated viral genomes produced following TPA induction to those obtained from non-silencing ( NS ) shRNA-transduced control cultures . Bim shRNA-transduced TIME cells were also included to provide a positive control for the experiment . The data from this experiment ( Fig . 10A ) revealed that each of the two Bid shRNAs led to small but significant increases in virus production from TPA-treated TIME cells; as before , Bim depletion led to substantial amplification of virus titers . Similar experiments were undertaken in TIME-TRE/RTA cells to confirm the effect on replication of Bid depletion; here , lytic replication was induced by addition of doxycycline to the cultures . Again , Bid depletion led to increased virus production ( Fig . 10B ) , measured in this experiment by titration of released infectious virus in culture media via inoculation of naïve TIME cells and detection of virus infection by immunofluorescence assay for latency-associated nuclear antigen ( LANA ) . These data demonstrate that Bid , in addition to Bim , is a contributor to negative regulation of HHV-8 infection and suggest that its control by vIRF-1 is likely to be important for optimal virus productive replication . It is important to note , however , that the positive effects on virus replication of Bid and Bim depletion by shRNA transduction demonstrate also that vIRF-1 is not completely effective at suppressing the activities of these BOPs . This situation is not unexpected and is probably universal amongst such viral regulators of cellular activities .
Viruses have numerous and diverse mechanisms of apoptotic inhibition , necessitated by the pro-apoptotic signals induced upon de novo infection of a cell and by the processes associated with virus replication ( reviewed in [53]–[55] ) . These mechanisms include inhibition by various means of interferon induction and signaling and inactivation and suppression of p53 , pro-apoptotic proteins such as BOPs , and caspase mediators of apoptotic signaling . Examples are p53-inhibitory activities of simian virus 40 T-antigen and adenovirus E1B-55K [56] , [57] , caspase inhibition by HHV-8 specified K7/vIAP and gammaherpesvirus viral FLICE inhibitory ( vFLIP ) proteins [6] , [7] , [58] , [59] , and the Bcl-2 homologues specified by herpesviruses ( gammaherpesvirus vBcl-2s and human cytomegalovirus UL37x1/vMIA ) , poxviruses ( e . g . , fowlpox virus 039 ) , African swine fever virus ( A179L ) , adenovirus ( E1B-19K ) , and others [60] , [61] . These viral Bcl-2 homologues , while not necessarily readily identifiable at the amino acid sequence level , have been demonstrated or are believed to preserve the essential BH-like helical domains and overall three-dimensional hydrophobic groove structure of cellular Bcl-2 proteins to allow their interactions with and inhibition of pro-apoptotic BH3-only proteins and/or apoptotic executioners Bax and Bak . Thus , Bcl-2 “mimicry” is a commonly used mechanism of viral evasion from innate host cell defenses via apoptotic induction . However , alternative mechanisms of direct BOP inhibition by viral proteins have not previously been reported , to our knowledge . HHV-8 specifies a number of proteins that have predicted abilities to inhibit apoptosis induced by virus de novo infection and lytic replication and therefore have the potential to promote virus infection and establishment of latency and/or productive replication [62] . However , demonstration of such activities in the context of virus infection is largely lacking , although vIRF-1 , in part via BBD-dependent interactions , has been found to promote cell survival under lytic-induced stress and to enhance HHV-8 productive replication in culture [23] . Our previous studies identified Bim as a potent negative regulator of HHV-8 productive replication , mapped the binding domain ( BBD , residues 170–187 ) involved in its inhibition , and noted induced nuclear localization of Bim as a means of its inactivation by vIRF-1 to promote HHV-8 replication and cell survival [23] , [8] . However , the region of Bim interacting with vIRF-1 was not mapped and the possibility of direct inhibition of Bim activity , in addition to inhibition via vIRF-1-induced nuclear sequestration , was not considered . The present identification of BH3 as the target of BBD binding to Bim , as well as Bid and other BOPs , and finding of direct inactivation by vIRF-1 of Bid-induced mitochondrial permeabilization in vitro , Bim and Bid inhibition by nuclear localization-defective vIRF-1 , and inability of vIRF-1 to induce significant Bid ( BidL , tBid ) nuclear localization , demonstrate that vIRF-1 can inhibit BOPs independently of nuclear translocation . Indeed , in contrast to Bim , [23] , nuclear localization of Bid was not apparent in lytically infected cells ( Fig . 4 ) . The mechanism of induced nuclear translocation of Bim could comprise cytoplasmic-to-nuclear chaperoning by vIRF-1 and/or nuclear capture of Bim translocating independently or by other means . The latter would be analogous to HHV-8 latency-associated nuclear antigen ( LANA ) -induced nuclear localization of predominantly cytoplasmic GSK3β [63] , for example . The fact that Bid has been reported to localize in part to the nucleus , and indeed to function here as a component of the DNA repair machinery and as an apoptotic mediator in response to DNA damage [47]–[49] , indicates that simple “nuclear capture” by vIRF-1 is unlikely to be a mechanism of vIRF-1-induced nuclear sequestration of Bim , as this would be expected to operate for Bid also . Specificity of Bim nuclear chaperoning by vIRF-1 is , similarly , difficult to explain , as Bim and Bid interactions with vIRF-1 occur by the same means ( BH3∶BBD binding ) and these BOPs , able to move between cellular compartments , appear to be equally susceptible to translocation . It is possible that while both BOPs can enter the nucleus , independently or promoted by vIRF-1 , only Bim can , via its association with vIRF-1 , form stable interactions with other nuclear proteins to effect its sequestration in this compartment . In trying to resolve this issue , it would be informative to determine whether vIRF-1 can induce nuclear translocation of any of the other BBD-targeted BOPs or if this activity is restricted to Bim . Regardless of mechanism , however , it is apparent that vIRF-1-induced nuclear sequestration of Bim represents a mechanism of inactivation of its pro-apoptotic activity , in addition to its direct inactivation via BH3 binding . It is possible that this is necessary for biologically sufficient inhibition of this powerful negative regulator of HHV-8 productive replication . It is also conceivable that there are as-yet unrecognized nuclear functions of Bim that contribute to HHV-8 lytic replication . An important finding of the present study is that vIRF-1 can interact , via its Bid BH3-B-like BBD region , with the BH3 domains of BOPs Bid , Bik , Bmf , Hrk and Noxa in addition to Bim BH3 , while refractory to interaction with other tested Bcl-2 family members ( other BOPs and multi-BH-domain proteins ) . Thus , the “Bim-binding domain” of vIRF-1 should more appropriately be referred to as the “BOP-binding domain” . These additional interactions of BBD not only identify multiple new BOP interaction partners of vIRF-1 that could be targeted for inhibition , of likely relevance in the context of HHV-8 biology , but also provide the tools to better understand the molecular basis and specificity of BBD and BH3-B interactions with BH3 domains . Previous studies of the latter identified , via mutagenesis of murine Bid BH3-B sequences and analysis of BH3-B∶BH3 interaction and Bid activity , residues L35 ( first hydrophobic , BH3/BBD-conserved ) and G39 , R40 and/or E41 ( GHE in human Bid ) as important for BH3-B∶BH3 binding in vitro and for both physical and functional interactions , respectively [40] . Our own in silico , mutagenesis , and binding studies indicate that for BBD∶BH3 interactions , an alanine residue corresponding to BH3 position “φ1+1” ( Fig . 9A ) is both conserved and specific to all BBD-interacting BOP BH3 domains and important for BBD∶BH3 interaction . An alanine at this position was also identified in BH3-like sequences [RVADSLATLMMN ( Fig . 9B ) , SIANTIASVQFM , and IFAALDYNLGRH; φ1+1 underlined , collinear hydrophobic residues italicized ) isolated from a phage-display screen using BBD as bait . However , because of its simple methyl side chain , it is likely that this alanine residue permits adoption of the required local structure and “space” for binding via other BBD/BH3-residue interactions rather than being involved directly in binding . Thus , while binding was abrogated by mutation of this alanine to most other collinear residues of non-binding BH3s , mutation to cysteine or serine ( present in BH3s of Bok and Bax , respectively ) was compatible with binding to BBD . Also , introduction of this alanine along with adjacent glutamine and glutamate residues ( as present in Bim ) into BBD-refractory Bax BH3 was sufficient to confer BBD interaction , although the binding was weaker than that of Bid BH3 . Taken together , our data indicate that the φ1+1 alanine appears to be preferred and important for BH3 interaction , but that it is unlikely to contribute directly to binding and that other residues , in appropriate context , are directly involved in BH3 association and specificity . In regard to specificity of BH3 binding , we have established definitively that while BBD can recognize Bim and Bid BH3 domains , the former is not targeted by Bid BH3-B . Alignments of BBD and BH3-B , although revealing significant similarities , with three identical and three highly-related residues within the BH3-binding core regions , also show considerable divergence within and outside these central sequences ( Fig . 1 ) . It is noteworthy that previous di-alanine scanning mutagenesis of the 174–183 region of BBD identified residues within 174–181 as essential for interaction with Bim-BH3 [23] . Interestingly , however , combined mutation of the first two conserved hydrophobic residues , L174 ( qe ) I177 , did not abrogate binding ( unpublished data ) , although identical or related hydrophobic residues are conserved in BH3 domains and are key contributors to the amphipathicity of the predicted α-helix . Single-residue mutagenesis across BBD , to alanine or to collinear BH3-B residues would be warranted to further delineate those amino acids and associated properties contributing to interaction with BH3 domains and to Bim- and Bid-BH3 selectivity . In summary , data presented here identify similarity between vIRF-1 BBD and Bid BH3-B , corresponding inhibitory interactions of vIRF-1 BBD with the BH3-domains of Bim and Bid ( both induced during and inhibitory to HHV-8 productive replication ) , and additional BBD binding of the BH3 domains of BOPs Bik , Bmf , Hrk and Noxa . To our knowledge , this is the first example of BOP targeting and inhibition via Bid BH3-B domain mimicry and thereby our data reveal a novel mechanism of viral evasion from host cell , apoptosis-mediated defense against viral infection and replication .
Telomerase-immortalized endothelial ( TIME ) cells [46] and genetically engineered derivatives were cultured in EGM-2 MV medium ( Lonza; Walkersville , MD ) containing 5% fetal bovine serum ( FBS ) and cytokine supplements . TIME cell lines expressing vIRF-1 or RTA in doxycycline ( Dox ) -inducible fashion were generated using the Retro-X Tet-On Advanced system ( Clontech Laboratories; Mountainview , CA ) . Briefly , the pRetroX-Tet-On Advanced plasmid was transfected into Phoenix cells and the supernatant was used to transduce TIME cells which were selected in G418 ( 400 µg/ml ) to obtain TIME/Tet-On cells . The RTA coding region was derived from an existing eukaryotic expression vector as an EcoR1 restriction fragment and ligated into the EcoR1 site in the pRetroX-Tight-Pur plasmid . This was transfected into Phoenix cells , virus-containing supernatant used to infect TIME/Tet-On cultures , and transduced cells ( TIME-TRE/RTA ) selected in puromycin ( 1 µg/ml ) . Cloning discs were used to isolate individual colonies , and derived cells were screened by immunofluorescence assay for RTA expression following Dox induction . HeLa , HEK293 , and HEK293T cells were maintained in Dulbecco's modified Eagle's medium supplemented with 10% FBS and gentamicin . BCBL-1/TRE-RTA [50] cells were maintained in RPMI 1640 medium containing 15% FBS and gentamicin . HHV-8 virus stocks were derived from doxycycline induced BCBL-1/TRE-RTA cultures and used to infect TIME cells as described previously [23] . For cell viability or immunofluorescence assays , cells were transfected using Fugene 6 ( Roche Applied Science; Indianapolis , IN ) . For immunoprecipitation , cells were transfected using standard calcium-phosphate method or Lipofectamine 2000 ( Invitrogen; Carlesbad , CA ) . For reporter assays , HEK293 cells were transiently transfected with plasmids expressing vIRF-1 and p53 along with the PG13-luc reporter vector ( Addgene; Cambridge , MA ) for 24 h and then lysed with passive lysis buffer ( Promega; Madison , WI ) . Luciferase activity was measured by standard methods using D-luciferin and luminometry . For lentivirus production , HEK293T cells were transfected with virus vector and gag/pol-encoding plasmids using standard calcium-phosphate precipitation and virus was harvested after 48 h by centrifugation at 49 , 000×g . Cells were transduced with lentivirus in the presence of 5 µg/ml polybrene for 12 h and then cultured in complete media . For bacterial expression of T7-tagged proteins , coding sequences of T7 were first cloned between the NcoI and SalI sites of pTYB4 ( New England Biolabs; Ipswich , MA ) ; coding sequences of vIRF-1 or enhanced green fluorescent protein ( EGFP ) were then inserted between the SalI and SmaI sites of pTYB4-T7 . The EGFP cDNA was amplified from vector pEGFP N1 ( Clontech Laboratories ) . For bacterial expression of EGFP-fused BH3 peptides , EGFP coding sequences were inserted between the SalI and EcoRI sites of pTYB4 and BH3 sequences ( ds-oligonucleotides ) were then inserted between the NheI and SalI sites of pTYB4-EGFP . For the bacterial expression of Discosoma red fluorescent protein ( DsRed ) -fused peptides , coding sequences for DsRed were amplified from vector pDsRed2 ( Clontech Laboratories ) and inserted between the SalI and EcoRI sites of pTYB4-T7 . Coding sequences of vIRF-1 Bim-binding domain ( BBD ) or Bid BH3-B domain were inserted between the EcoRI and SmaI sites of pTYB4-T7-DsRed . The BBD sequence was also inserted between the BamHI and EcoRI sites of pGEX4T-1 ( GE Healthcare Life Sciences; Piscataway , NJ ) for expression of GST-BBD . For generation of recombinant S-tagged tBid ( see below ) , the coding sequence of tBid ( comprising codons 61–195 of BidL [64] ) was inserted between the BamHI and EcoRI sites of pET-32a ( + ) ( Novagen; Madison , WI ) . BOP and Bcl-2 cDNA sequences linked to Flag were cloned between the BamHI and EcoRI site of pcDNA3 . 1 ( Invitrogen ) for expression in transfected cells . Plasmids expressing vIRF-1 and Bim were described previously [23] . Mutagenesis was performed by a PCR-mediated method using Pfx DNA polymerase ( Invitrogen ) with oligonucleotide primers containing deletion or substitution mutations . Two short hairpin RNAs ( shRNA ) for Bid were cloned into pYNC352/puro ( a derivative of pTYB6 [65] , [66] ) using BamHI and MluI enzyme sites . The target sequences of the shRNAs correspond to 5′- GGGATGAGTGCATCACAAACC -3′ ( sh1 ) and 5′-CTTTCACACAACAGTGAATTT-3′ ( sh2 ) . Commercially obtained antibodies used in this study were as follow: T7 , Novagen ( Madison , WI ) , catalog number 65922; GFP , Bcl-2 and cytochrome c , Epitomics ( Burlingame , CA ) , catalog numbers 1533-1 , 1017-1 and 1896-1; Flag M2 and β-actin , Sigma ( St . Louis , MI ) , catalog numbers F3165 and A5441; polyclonal Flag and Bim antibodies , Cell Signaling Technology ( Beverly , MA ) , catalog numbers 2368 and 2819; Bax ( N-20 , catalog number sc-493 ) , Bid ( 5C9 , sc-56025 ) , GST ( B-14 , sc-138 ) , prohibitin ( H-80 , sc-28259 ) , TOM20 ( F-10 , sc-17764 ) , VDAC1 ( 20B12 , sc-58649 ) , lactate dehydrogenase ( H-160 , sc-33781 ) , and histone deacetylase 1 ( H-11 , sc-8410 ) antibodies were purchased from Santa Cruz Biotechnologies ( Santa Cruz , CA ) ; LANA ( LN53 ) , Advanced Biotechnologies Inc . ( Columbia , MD ) , catalog number 13-21-100 . vIRF-1 rabbit antiserum was provided by Dr . Gary Hayward . For immunoblotting , cells were lysed in lysis buffer ( 50 mM Tris-HCl [pH 8 . 0] , 150 mM NaCl , 1 mM EDTA , 1% IGEPAL CA-630 , and 0 . 25% sodium deoxycholate ) freshly supplemented with protease inhibitor cocktail ( Sigma ) for 1 h on ice . After centrifugation at 12 , 000×g for 20 min , the supernatant was used as a whole cell extract . For immunoblotting , proteins were size fractionated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) and transferred to a nitrocellulose or polyvinylidene fluoride membranes . Immunoreactive bands were detected with enhanced chemiluminescence solution ( GE Healthcare Life Sciences ) and visualized on X-ray film or digitally using a chemiluminescence imager . For immunofluorescence assays , cells were grown on a 0 . 1% gelatin-coated coverglass or a chamber slide and were fixed and permeabilized in chilled methanol . Following incubation with Superblock blocking buffer in phosphate-buffered saline ( PBS ) ( Thermo Scientific Inc . ; Rockford , IL ) , coverslips were incubated with primary antibody , washed with PBS , and then incubated with appropriate fluorescent dye-conjugated secondary antibody . The coverslips were mounted in 90% glycerol in PBS containing 10 mg/ml p-phenylenediamine , an antifade reagent . For immunoprecipitation , HEK293T cells transfected with plasmids encoding vIRF-1 or Flag epitope-tagged BH3-only proteins ( BOPs ) were lysed in lysis buffer , and cell extracts were incubated with anti-Flag M2 affinity gel ( Sigma ) for 3 h at 4°C . After washing with lysis buffer , immune-complexes were eluted with 30 µl of 3× Flag peptide ( 150 ng/µl ) , subjected to SDS-PAGE , and analyzed by immunoblotting using vIRF-1 antiserum or polyclonal Flag antibody . For in vitro binding assays , fluorescent protein-fused peptides or T7-tagged proteins expressed in E . coli from vector pTYB4 ( New England Biolabs ) were purified according to the manufacturer's protocol . Bacterially expressed glutathione-S-transferase ( GST ) -fused proteins were purified by standard methods . 0 . 5 µg of EGFP or EGFP-BH3 , fused to intein-chitin binding domain ( CBD ) and immobilized on chitin beads , was mixed with 1 µg of purified T7-DsRed-fused BBD or BH3-B peptides and incubated for 1 h at room temperature . After washing four times with Tris-buffered saline ( TBS ) supplemented with 0 . 1% Tween 20 , bead-associated proteins were size-fractionated by SDS-PAGE and analyzed by immunoblotting using T7- or GFP-specific antibodies . To screen for BBD interaction with a variety of BOP BH3 domains , 1 µg of GST or GST-BBD protein immobilized on glutathione sepharose 4B beads was mixed with 2 µg of EGFP-BH3 fusion proteins . GST-BBD and its associated proteins were eluted with 10 mM reduced glutathione in TBS , size-fractionated by SDS-PAGE , and analyzed by immunoblotting using GFP- or GST-specific antibodies . For nucleo-cytoplasmic fractionation , cells were homogenized in buffer A ( 20 mM Tricine-KOH [pH 7 . 8] , 5 mM MgCl2 , 25 mM KCl , 0 . 25 M sucrose , and protease inhibitor cocktail ) using a Dounce homogenizer . After centrifugation at 1 , 000×g for 10 min , the supernatant was used as the cytoplasmic fraction , and the pellet was subjected to 25–35% iodixanol discontinuous gradient centrifugation . Nuclei were collected from the interface between 30 and 35% iodixanol and resuspended in buffer C ( 20 mM HEPES [pH 8 . 0] , 1 . 5 mM MgCl2 , 420 mM NaCl , 0 . 2 mM EDTA , and protease inhibitor cocktail ) . For in vitro cytochrome c release assays , mitochondria were isolated by sucrose density gradient centrifugation . Briefly , HEK293T cells grown to subconfluence in a 10 cm dish were washed in ice-cold PBS and resuspended in mitochondrial isolation buffer ( MIB: 210 mM mannitol , 70 mM sucrose , 1 mM EDTA , and 10 mM HEPES [pH 7 . 5] ) supplemented with protease inhibitor cocktail . Next , cells were homogenized with 40 strokes of Dounce homogenizer and centrifuged at 2 , 000×g for 10 min . The supernatant was further centrifuged at 13 , 000×g at 4°C for 10 min . After resuspending in MIB , the resulting pellet was layered on top of a discontinuous sucrose gradient consisting of 1 . 2 M sucrose in buffer H ( 10 mM HEPES [pH 7 . 5] , 1 mM EDTA , and 0 . 1% BSA ) on top of 1 . 6 M sucrose in buffer H . Following centrifugation at 131 , 000×g for 2 h at 4°C , mitochondria were recovered at the 1 . 6–1 . 2 M sucrose interface , washed in MIB , centrifuged at 13 , 000×g at 4°C for 10 min , and resuspended in 100 µl of MIB . For other studies , mitochondria were purified using Axis-Shield OptiPrep ( Sigma ) according to the manufacturer's protocol . For proteinase K treatment , mitochondria in MIB ( 0 . 5 mg/ml ) were incubated on ice for 30 min with 20 µg/ml proteinase K; the reaction was stopped by the addition of 2 mM PMSF , and mitochondria were then washed twice in 1 ml of MIB and resuspended in MIB or MRM buffer ( 250 mM sucrose , 10 mM HEPES [pH 7 . 5] , 1 mM ATP , 5 mM sodium succinate , 80 µM ADP , 2 mM K2HPO4 ) . For mitochondrial binding assays , 1 µg of recombinant T7-EGFP or T7-vIRF-1 protein was added to the proteinase K-pretreated or untreated mitochondria in MRM buffer ( 50 µg protein/50 µl ) supplemented with PMSF and 0 . 1 mg/ml BSA . The mixtures were incubated for 1 h at 30°C and then centrifuged at 12 , 000×g for 5 min . The mitochondrial pellets were washed twice in MRM buffer , and the final washed pellets were resuspended in SDS sample buffer . Trichloroacetic acid ( TCA , 10% final concentration ) was added to the supernatants to precipitate proteins prior to SDS-PAGE and immunoblot analysis . For in vivo cytochrome c release assay , transfected HEK293T cells were subjected to Dounce homogenization ( 30 strokes ) in MIB buffer . The homogenate , an aliquot of which was used as a total cell extract , was centrifuged at 1000×g for 10 min at 4°C to remove nuclei and unbroken cells . The resulting supernatant was centrifuged at 100 , 000×g for 1 h at 4°C to yield the final soluble cytosolic fraction ( S100 ) . In vitro mitochondrial permeabilization assays based on cytochrome c release were undertaken essentially as described by Arnoult [67] and outlined below . Thioredoxin/His6/S-tagged tBid protein encoded from pET-32a ( + ) was purified using Ni-NTA His-tag affinity chromatography , and S-tBid ( 100 µg/ml ) was eluted following thrombin ( 0 . 5 unit/ml ) treatment for 2 h at room temperature . Thioredoxin and His sequences were retained on the Ni-NTA resin . S-tBid was purified away from thrombin using protein S agarose , eluted with 3 M MgCl2 , and dialyzed against dilution buffer ( 25 mM HEPES-KOH [pH 7 . 4] , 0 . 1 M KCl ) . Following preincubation of 10 nM or 100 nM of S-tBid with or without 500 nM of GST-fusion peptides or 100 nM of T7-vIRF-1 proteins ( wild-type or mutated ) in dilution buffer supplemented with 1 mg/ml of fatty acid-free bovine serum albumin ( FA-BSA ) , purified mitochondria were added ( 50 µg protein/50 µl of mitochondrial buffer: 125 mM KCl , 0 . 5 mM MgCl2 , 3 mM succinic acid , 3 mM glutamic acid , 10 mM HEPES-KOH [pH 7 . 4] , 1 mg/ml FA-BSA , and protease inhibitor cocktail ) . The reaction mixtures were incubated at 30°C for 30 min and then centrifuged at 12 , 000×g for 5 min at 4°C to pellet the mitochondria . The supernatants were quickly removed , and the pellet was resuspended in 70 µl of mitochondria lysis buffer ( 50 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl , 2 mM EDTA , 2 mM EGTA , 0 . 2% Triton X-100 , and 0 . 3% IGEPAL CA-630 ) . The amounts of cytochrome c in the supernatant and pellet fractions were determined by immunoblotting . After plasmid transfection of 293T cells on a coverslip , the cells were stained with Cy3-conjugated annexin V ( Biovision Inc . ; Mountain View , CA ) in AV binding buffer ( 10 mM HEPES [pH 7 . 4] , 140 mM NaCl , and 2 . 5 mM CaCl2 ) , fixed with 2% formaldehyde in AV binding buffer for 10 min , and mounted in glycerol medium containing DAPI . Annexin V-positive cells , fluorescent under UV , were counted from three randomly selected low-magnification microscopic fields . The Ph . D . -12 library , comprising a complexity in excess of one billion independent clones , was purchased from New England Biolabs ( Beverly , MA ) . Screening was performed by a solution-phase panning method with affinity bead capture as described by the manufacturer's protocol . In brief , a mixture of purified GST-BBD ( 500 nM ) and the library ( 2×1011 pfu ) was incubated for 20 min at room temperature and added to glutathione 4B beads pre-blocked with BSA . After incubating for 15 min and washing the mixture with Tris-buffered saline ( TBS ) containing 0 . 1% Tween 20 , phage were eluted and amplified . Negative selection using purified GST was performed prior to the second round of panning . After the third round of panning , the mixture was washed with TBS containing 0 . 5% Tween 20 , and phage were eluted and plaque-purified prior to DNA preparation and sequencing . For HHV-8 infection , TIME cells were centrifuged at 1 , 000×g for 1 h in the presence of HHV-8 virions and then cultured in fresh complete medium for 7 days to allow establishment of latency in the absence of ongoing lytic replication . After lentiviral transduction of control ( NS ) , Bid , or Bim shRNAs for 2 days into HHV-8+ TIME cells , lytic replication of HHV-8 was induced by treatment with TPA ( 20 ng/ml ) or 1 µg/ml doxycycline ( TIME-TRE/RTA cells ) . For determination of encapsidated HHV-8 genome copy number , viral DNA was isolated using standard phenol extraction and glycogen/ethanol precipitation methods following pre-treatment of virus suspensions with DNaseI for 20 min at 37°C to remove any unencapsidated DNA . For the determination of the viral genome copy number , all qPCRs were performed in a 96-well microplate using an ABI Prism 7500 detection system ( Applied Biosystems; Foster City , CA ) with SYBR green/ROX master mix ( SuperArray Bioscience Corp . ; Frederick , MD ) . For induced TIME-TRE/RTA cells , infectious virus titers were measured by application of induced culture media-derived virus to naïve TIME cells and immunofluorescence staining for HHV-8 latency-associated nuclear antigen ( LANA ) . | Viruses possess mechanisms of subverting host cell defenses against infection and virus replication; these mechanisms are essential to the virus life cycle . Here , we identify and characterize a novel mechanism of HHV-8 mediated inhibition of virus-induced programmed cell death ( apoptosis ) . This function is specified by viral interferon regulator factor homologue vIRF-1 , which binds to and directly inhibits pro-death activities of so-called BH3-only proteins ( BOPs ) , induced and activated by stress signals such as those occurring in infected cells . The BH3 domains of BOPs mediate their pro-apoptotic functions , and it is these domains that are targeted by vIRF-1 , via a region resembling a BH3-interacting and -inhibitory domain , termed BH3-B , present in one of the vIRF-1 targeted BOPs , Bid . The targeted BOP BH3 domains share characteristic and conserved features . As shown previously for Bim , depletion of Bid leads to enhanced HHV-8 productive replication , demonstrating that Bid , also , is a biologically significant negative regulator of virus replication and suggesting that its control by vIRF-1 is of functional importance . To our knowledge , this is the first report of viral targeting and inhibition of BOP activity via Bid BH3-B mimicry; our studies therefore expand the known mechanisms of viral evasion from antiviral defenses of the host . |
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Day length is a key environmental cue regulating the timing of major developmental transitions in plants . For example , in perennial plants such as the long-lived trees of the boreal forest , exposure to short days ( SD ) leads to the termination of meristem activity and bud set ( referred to as growth cessation ) . The mechanism underlying SD–mediated induction of growth cessation is poorly understood . Here we show that the AIL1-AIL4 ( AINTEGUMENTALIKE ) transcription factors of the AP2 family are the downstream targets of the SD signal in the regulation of growth cessation response in hybrid aspen trees . AIL1 is expressed in the shoot apical meristem and leaf primordia , and exposure to SD signal downregulates AIL1 expression . Downregulation of AIL gene expression by SDs is altered in transgenic hybrid aspen plants that are defective in SD perception and/or response , e . g . PHYA or FT overexpressors . Importantly , SD–mediated regulation of growth cessation response is also affected by overexpression or downregulation of AIL gene expression . AIL1 protein can interact with the promoter of the key cell cycle genes , e . g . CYCD3 . 2 , and downregulation of the expression of D-type cyclins after SD treatment is prevented by AIL1 overexpression . These data reveal that execution of SD–mediated growth cessation response requires the downregulation of AIL gene expression . Thus , while early acting components like PHYA and the CO/FT regulon are conserved in day-length regulation of flowering time and growth cessation between annual and perennial plants , signaling pathways downstream of SD perception diverge , with AIL transcription factors being novel targets of the CO/FT regulon connecting the perception of SD signal to the regulation of meristem activity .
The ability to adapt to changes in the environment is crucial to the survival of both animals and plants . Plants , unlike animals , are sessile organisms and have therefore evolved highly sophisticated mechanisms to anticipate seasonal changes and modulate their patterns of growth and development . Day length is one of the key environmental cues utilised by plants to anticipate seasonal changes and regulates several key developmental transitions associated with plant adaptation and reproduction . One of the most fascinating examples of this is provided by perennial plants , e . g . the long-lived trees of the boreal forest , in which the day length signal regulates the developmental transition from active growth to a more resilient dormant state prior to the onset of winter [1] . These perennial plants anticipate the approach of winter by detecting the reduction in day length ( i . e . the short day signal , or SD signal ) in the autumn and when the day length falls below the critical day length required for the promotion of growth , cell division in the meristems ceases [2] . The most visible indicator of short day–induced growth cessation is the formation of a bud that encloses the apical meristem and leaf primordia [3] . The importance of day length sensing for the survival of perennial plants is illustrated by the increased mortality due to delayed growth cessation in transgenic hybrid aspen plants that are unable to sense reductions in day length [4] . Intriguingly , there are numerous similarities at the regulatory level between day length mediated control of growth cessation in perennial plants and one of the most well studied developmental transitions in plants - the transition from vegetative growth to floral development . For example , key flowering time regulators such as the CONSTANS ( CO ) , FLOWERING LOCUS T ( FT ) and the group of photoreceptors known as PHYTOCHROMES ( PHYs ) that are involved in day length mediated regulation of flowering time regulation in Arabidopsis [5] , [6] , [7] , [8] , are all also involved in SD–induced growth cessation in trees [4] , [9] , [10] , [11] . In poplar species two closely related orthologs of FT ( FT1 and FT2 ) have been found and recent analysis in hybrid aspen clone 353 indicates that FT2 could be primarily involved in SD–mediated growth cessation whereas FT1 is primarily involved in flowering [11] . In hybrid aspen ( clone T89 , used in this study ) , it has been shown that short day mediated downregulation of FT gene expression ( FT1 and FT2 ) triggers the induction of growth cessation whereas overexpression of FT1 eliminates the plants' ability to respond to the SD signal and thus prevents timely growth cessation [9] . Both CO and PHYTOCHROME A ( PHYA ) act upstream of the FT genes in SD-mediated induction of growth cessation in much the same manner as in the flowering transition in Arabidopsis [9] . Subjecting aspen trees to conditions in which the peak of CO expression occurs in the dark ( e . g . under SD conditions ) brings about rapid downregulation of FT2 expression leading to the induction of growth cessation response [9] . These findings indicate evolutionary conservation of the day length response pathway between annual plants such as Arabidopsis and perennial trees such as hybrid aspen . Despite the evolutionary conservation of early acting components involved in day length regulated growth cessation and flowering time , considerable lacunae remain in our understanding of SD mediated regulation of growth cessation at the molecular level . Particularly , the factors targeted by SD signal downstream of the early acting components such as PHYA and the CO/FT regulon in regulating growth cessation responses remain unknown . The critical role of these hitherto unknown downstream targets of SD signal has become evident from the analysis of growth cessation response in hybrid poplar where they have been shown to be important for regulating the variation in timing of growth cessation responses [12] . Thus a key question that remains unanswered is; How does SD mediated downregulation of FT2 expression lead to the induction of growth cessation response ? Answering this question would require the identification of targets of SD signal downstream of the CO/FT regulon in trees and elucidating their role in the regulation of growth cessation responses . The network of genes involved in day length regulation of the floral transition is well defined , and downstream targets of the CO/FT pathway like SUPPRESSOR OF OVEREXPRESSION OF CONSTANS ( SOC1 ) and floral meristem identity genes FRUITFUL ( FUL ) and APETALA1 ( AP1 ) are known ( reviewed in [13] ) . In contrast the targets of the SD signal downstream of the CO/FT module in the growth cessation response can not simply be deduced from extrapolation of knowledge of floral transition related genes given the difference between the growth cessation process and floral transition . To identify the downstream targets of the SD signal in growth cessation we have previously analysed global transcriptional changes associated with this process [14] , [15] . One of the genes whose transcript is strongly downregulated during growth cessation is a Populus homolog of the Arabidopsis gene AINTEGUMENTA ( ANT ) [16]; the Populus homolog is henceforth referred to as AINTEGUMENTALIKE1 ( AIL1 ) . The expression data along with a proposed role of ANT in the regulation of cell cycle [17] , [18] , [19] suggested that AIL1 ( and most likely the other closely related members AIL2-AIL4 of this sub-family ) is a potential downstream target of the SD signal transduced via the CO/FT module in the regulation of growth cessation response in perennial trees . We tested this hypothesis by investigating the regulation of AIL1 expression by SD signal in transgenic hybrid aspen plants that are perturbed in the SD response . In a complementary approach we investigated the short day mediated regulation of growth cessation response in transgenic hybrid aspen plants that either maintain high levels of AIL1 or AIL3 expression even after SD treatment or have reduced expression of AIL1 . Finally we identified downstream targets of the AIL1 transcription factor in the apex . Taken together , these analyses show that AIL genes are targets of the SD signal downstream of the CO/FT module and their down-regulation is necessary for short day regulated growth cessation in hybrid aspen plants .
The Populus genome contains 13 genes belonging to the ANT-subgroup of the AP2 transcription factor family [20] . Four of these genes are here designated as AIL1-AIL4 ( AINTEGUMENTALIKE 1-4 ) as they belong to the same clade as the Arabidopsis ANT transcription factor ( Figure S1 ) . We investigated the expression of AIL1 as well as the expression of the related genes AIL2-AIL4 in the apex of hybrid aspen plants after SD treatment ( Figure 1A and Figure S2 ) . RT-PCR data indicates that AIL1 ( Figure 1A ) as well as AIL2-AIL4 expression ( Figure S2 ) are downregulated along with that of cell cycle markers CYCD3:2 and CYCD6:1 after SD treatment ( Figure 1B , panels B and C ) and this downregulation coincides with the cessation of growth and bud set in the apex of hybrid aspen T89 trees [21] . We then investigated the effect of perturbed SD perception or response on the regulation of AIL gene expression after SD treatment . For this we used transgenic hybrid aspen that are unable to respond to the SD signal due to overexpression of PHYA or FT1 cDNA as well as plants that are hypersensitive to the SD signal and undergo premature growth cessation due to the downregulation of FT expression ( FTRNAi ) [4] , [9] . Since all 4 AIL genes are highly similar and displayed similar expression pattern after SD treatment , we chose to perform detailed analysis of AIL1 regulation . RT-PCR analysis indicated that the downregulation of AIL1 expression after SD treatment is severely attenuated in the apex of PHYA and FT1 overexpressors in contrast with the wild type ( Figure 2A ) . In contrast the FTRNAi plants that respond more rapidly to SD treatment than wild type [9] display a stronger and earlier reduction in the expression of AIL1 ( Figure 2B ) . These results strongly suggest that AIL1 expression is a potential downstream target of the SD signal transduced via the CO/FT module in cessation of growth and bud set in the apex of hybrid aspen . We further investigated expression of AIL1 in different tissues and found that AIL1 is primarily expressed in the apical region of hybrid aspen ( Figure 3A ) . Since the downregulation of AIL1 gene expression was closely associated with cessation of growth and bud set , we analysed the domain of AIL1 gene expression in the apex . For this analysis we generated transgenic hybrid aspen expressing a transcriptional fusion between 2 . 5 Kb upstream sequence of AIL1 gene from Populus trichocarpa and the uidA ( b-glucoronidase/GUS ) reporter gene . In the transgenic hybrid aspen , the reporter gene expression was mostly confined to the zone of dividing cells in the apex , the provascular tissues and the leaf primordia ( Figure 3B ) . The expression pattern of pAIL1:UidA reporter construct correlates well with the previously described expression pattern of CYCA1 which serves as a marker of dividing cells in the apex of hybrid poplar plants [22] indicating that AIL1 expression is associated primarily with cell proliferation . Analysis of AIL1 gene expression suggested that its downregulation could be important for the SD mediated cessation of growth and bud set . We tested this hypothesis by generating transgenic hybrid aspen that would maintain high levels of AIL1 expression even after SD treatment in contrast with the wild type by expressing AIL1 cDNA under the control of the 35S promoter ( these transgenic lines are henceforth referred to as AIL1oe lines ) . Several independent lines were obtained and tested for high expression of AIL1 ( Figure S3A shows data for two chosen lines ) ; two lines were chosen for detailed analysis of their response to SD treatment . Unlike the wild type plants that undergo growth cessation and form an apical bud after 6 week of SD treatment , the apices of AIL1oe fail to undergo proper growth cessation and bud set after 6 weeks of SD treatment ( Figure 4A–4C ) . We also generated transgenic hybrid aspen overexpressing AIL3 ( Figure S3 ) to investigate whether other members of the gene family share the same function ( these transgenic lines are henceforth referred to as AIL3oe lines ) . AIL3oe display a similar phenotype to AIL1oe during SD treatment ( Figure 4D–4F ) . We also investigated the effect of downregulation of AIL gene expression on SD mediated bud set . The functional redundancy between AIL genes suggested by similar regulation and effect on bud set in AIL1oe and AIL3oe plants lead us to generate transgenic hybrid aspen plants in which the expression of all 4 AIL genes was targeted for downregulation using artificial microRNA ( amiRNA ) . Two amiRNA constructs ( 255 and 256 ) were expressed in hybrid aspen and two lines ( 255-6 and 256-23 ) with reduced expression of AIL1 gene expression ( Figure S4 ) were selected for further analysis of growth cessation response . The transition from active growth to bud set after SD treatment in the wild type and lines 255-6 and 256-23 was investigated using a method separating different stages of bud development [23] . Compared to the wild type , the lines 255-6 and 256-23 displayed a more rapid transition from active growth to bud set and a majority of the plants in the two transgenic lines made the transition to intermediate and late stage of bud set at least a week ( or more ) earlier than the wild type plants after SD treatment ( Figure S5 ) . This data along with the perturbed growth cessation response in AIL1oe and AIL3oe plants indicate that downregulation of AIL gene expression is necessary for SD mediated growth cessation response . The altered growth cessation responses in AIL1oe plants could either be due to the failure of these plants to perceive the short day signal or to a failure in properly responding to it . To distinguish between these two possibilities we compared the response of FT2 expression to SD treatment in the leaves of wild type and AIL1oe . Downregulation of FT2 expression in the leaves is the earliest known marker for the detection of the SD signal and recent results have implicated its downregulation in SD mediated growth cessation [9] , [11] , [12] . Our data show that both the wild type ( Figure 5A ) and AIL1oe lines ( Figure 5B ) exhibit similar decreases in their levels of FT2 transcripts following SD treatment . This result indicates that unlike FT genes , AIL1 does not act early in SD response but is rather a downstream target of the SD signal . The expression of several cell proliferation related genes , e . g . D-type cyclins , that are key cell cycle regulators [24] , [25] , [26] , [27] is downregulated in a similar manner to AIL genes during SD mediated cessation of growth in hybrid aspen [15] , Figure 1B , 1C ) . We therefore investigated whether the AIL1 transcription factor could be involved in the regulation of the D-type cyclin genes and if so whether their expression is perturbed in AIL1oe lines after SD treatment . Therefore we analysed the expression of two D-type cyclins , CYCD3:2 and CYCD6:1 after SD treatment in AIL1oe plants after 6 weeks of SD treatment . Our RT-PCR data ( Figure 6 ) showed that while the expression of CYCD3:2 and CYCD6:1 is downregulated in the wild type after 6 weeks of SD treatment , this was not the case in the AIL1oe plants . This result indicates that AIL1 could be involved in the regulation of D-type cyclins and the failure to downregulate AIL1 expression after SD treatment leads to a corresponding failure to downregulate the expression of these key cell cycle regulators . The observation that CYCD3:2 and CYCD6:1 expression after SD treatment is perturbed in AIL1oe plants prompted us to investigate whether the AIL1 transcription factor can interact with CYCD promoters from hybrid aspen using electrophoretic mobility shift assays ( EMSA ) . We expressed HA-tagged AIL1 protein in Arabidopsis protoplasts and used the extracts in gel shift assays using 3 different different fragments from a hybrid aspen CYCD3:2 promoter ( results from two fragments are shown here ) . Our data show that extracts containing AIL1 protein specifically display a gel shift with the promoter fragment consisting of 200 bp of sequence situated upstream of the start codon of CYCD3:2 ( Figure 7 ) . Together with the CYCD3:2 and CYCD6:1 gene expression data , the gel-shift analysis strongly suggests these cyclin genes might be potential downstream targets of the AIL1 transcription factor in hybrid aspen .
Short day mediated cessation of growth and budset prior to the onset of winter is a key developmental transition that is critical to the survival of perennial plants in boreal forest [1] . In this work , we identify AIL genes belonging to the AP2 transcription factor family as downstream targets of the SD signal transduced via the CO/FT module and that downregulation of their expression is necessary for cessation of growth and bud set in hybrid aspen . In poplar , there are 4 closely related AIL genes and our data indicates that all four AIL genes could have similar function at least in SD mediated growth cessation response as suggested by the similar phenotypes of AIL1 and AIL3 overexpressing plants as well as in plants in which these genes are targeted for downregulation . However we cannot exclude the possibility that there could be functional differences between the different AIL genes with respect to other biological processes as we have not been able to specifically downregulate individual genes of the AIL family and study the effect on growth and development so far . It is particularly important to note in this respect that even closely related genes can diverge both in expression profiles and at a functional level as suggested by the careful analysis of FT1 and FT2 in poplar species which indicate that FT1 could be primarily involved in reproductive growth whereas FT2 controls growth cessation [11] . Several observations suggest that the downregulation of AIL gene expression following SD treatment is necessary for the activation of growth cessation responses . AIL1 ( and most likely other genes of this family as well ) is primarily expressed in dividing and meristematic cells in hybrid aspen ( Figure 3 ) and the downregulation of their expression coincides temporally with the SD-mediated induction of growth cessation responses in hybrid aspen ( Figure 1 and Figure 4 ) , including the termination of elongation growth , bud set and the downregulation of core cell cycle genes such as the D-type cyclins ( Figure 1 ) . Furthermore , AIL1 downregulation after SD treatment is attenuated in FT or PHYA overexpressors ( Figure 2 ) that fail to respond properly to SD treatment [4] , [9] . Importantly , growth cessation response is perturbed when AIL1 or AIL3 expression is maintained at high levels even after SD treatment and earlier bud set is observed in transgenic hybrid aspen with reduced expression of AIL1 ( Figure 4 and Figure S5 ) . All of these results are consistent with the AIL genes being downstream targets of the SD signal in the control of the growth cessation response . Three hypotheses can be proposed to explain the role of AIL genes in SD mediated control of growth cessation response and why growth cessation response is perturbed when the AIL1 or AIL3 expression is maintained at high level even after SD treatment as in AIL1oe and AIL3oe lines . Firstly , the AIL genes could act upstream of FT2 , in which case the increased expression of AIL1 as in AIL1oe could counteract the downregulation of FT2 by the SD signal . However , this hypothesis is incompatible with the observation that the downregulation of FT2 subsequent to SD treatment proceeds as normal in AIL1oe lines ( Figure 5 ) . Alternatively , the AIL genes could act independently of FT2 and their increased expression in AIL1oe and AIL3oe could prevent the downregulation of the targets of FT2 following SD treatment . Alternatively , the AIL genes are the targets of SD signal downstream of CO/FT regulon leading to their downregulation after SD treatment . Our results support the latter hypothesis because SD treatment results in the downregulation of the AIL gene expression and this downregulation of AIL gene expression is severely attenuated in plants that overexpress FT1 . Further evidence for the connection between the CO/FT regulon and AIL1 expression was obtained by analysis of FTRNAi lines that respond more rapidly than the wild type to SDs and in these lines , AIL1 expression is significantly reduced compared to wild type after 2 weeks of SD treatment ( Figure 2B ) . Finally the downregulation of the AIL1 expression leads to earlier transition from active growth to bud set strongly suggesting that the AIL genes are the downstream targets of the SD signal . Thus our results suggest a mechanism in which AIL genes act downstream of the CO/FT regulon and that downregulation of AIL gene expression culminates in growth cessation and bud set after SD treatment . FT has been shown to act as a transcriptional co-regulator in Arabidopsis [28] . In poplar species , two FT genes are present of which FT2 is rapidly downregulated after SD treatment; thus FT2 could either directly regulate AIL at the transcriptional level in hybrid aspen or alternatively downstream targets of FT2 could regulate AIL gene expression . Our data supports the latter suggestion because the kinetics of downregulation of FT2 and AIL gene expression subsequent to SD treatment is not consistent with direct regulation of AIL gene expression by FT2 . While FT2 is typically downregulated within 3–7 days in the leaves after the commencement of SD treatment ( [9] , [12] , Figure 5 ) , it takes 2–3 weeks until downregulation of the AIL genes becomes apparent in the apex ( Figure 1 ) . Moreover , induction of FT2 in the leaves has little effect on expression of most of AIL genes [11] , which might again suggest an indirect regulation of AIL expression by FT2 . Thus these results suggest that there may be one or more genes that are direct targets of FT2 and act upstream of the AIL genes regulating their expression in the apex . Determining the identity of these targets of FT2 and regulators of AIL expression in the apex is an important objective for future research in this area . The downstream targets of FT in daylength mediated regulation of flowering time such as SOC1 and the floral meristem identity genes FUL and AP1 [13] are well known . However these are unlikely to be the targets of the CO/FT regulon in the regulation of the AIL genes in the apex unless the tree homologs of these genes have acquired novel functions and have been recruited to regulate meristem activity by controlling AIL gene expression . AIL1 is expressed in dividing cells ( Figure 2 ) , can potentially interact with the promoters of D-type cyclins ( Figure 7 ) and maintaining high level of AIL expression prevents the downregulation of D-type cyclin expression after SD treatment ( Figure 6 ) suggesting that AIL1 has a role in regulation of key cell cycle regulators . Indeed , data from Arabidopsis also shows that the putative AIL1 ortholog ANT can positively regulate cell division as its overexpression leads to increased duration of cell division [17] . We therefore propose that the downregulation of AIL gene expression after SD treatment leads to the downregulation of a subset of D-type cyclins such as CYCD3:2 and CYCD6:1 . The downregulation of the expression of core cell cycle regulators such as the abovementioned cyclins would then culminate in cessation of growth and bud set . However it is unlikely that the D-type cyclins are the only targets of AIL1 because the expression of several other cell cycle genes is also downregulated after SD treatment [15] . Additionally transcriptional network analysis indicates that several other cell cycle genes might be regulated by the AIL1 transcription factor [29] . Moreover , preliminary investigations suggest that altering CYCD3:2 expression alone is not sufficient to activate the growth cessation response . Substantial progress has recently been made in understanding how SD signal is perceived , and downregulation of FT2 expression after SD treatment has been identified as a key early event in the induction of growth cessation response [9] , [11] . However , the components targeted by SD signal downstream of the CO/FT regulon in the induction of growth cessation response have remained elusive , especially factors that would link the downregulation of FT2 expression to cessation of growth . Indeed analyses of hybrid poplar clones that differ in timing of bud set have suggested an important role for such factors in differential growth cessation [12] . Our finding that the AIL genes are the targets of the SD signal that is transduced via the CO/FT module in growth cessation response and bud set therefore represents an important step in elucidating the mechanism underlying this key developmental transition in perennial plants as this links the CO/FT module to the regulation of cell cycle through the AIL genes in SD mediated cessation of growth and bud set . The CO/FT module is an important component of the molecular machinery that allows plants to respond to changes in day length , and its role in day length mediated control of flowering time is well established [30] . Therefore it was not surprising that the same CO/FT module is also involved in controlling the timing of SD-mediated growth cessation in perennial trees , as this is another key developmental transition that is regulated by the day length signal . However , given that flowering and growth cessation processes are distinct morphologically , it appears unlikely that the downstream targets of this module in the regulation of flowering would be the same as those involved in the growth cessation response . Our findings suggest that the AIL transcription factors , which have the potential to regulate the expression of cell cycle genes , were co-opted at some point in evolutionary history to serve as mediators of the day length signal . This co-option would have allowed the versatile CO/FT module to regulate a novel developmental transition . These results demonstrate that an evolutionary “mix and match” strategy involving combining different regulatory modules can allow a small number of regulatory modules to control a wide range of diverse biological processes . In conclusion , our data demonstrates the divergence of the regulatory pathway downstream of the conserved CO/FT module between day length controlled floral transition and growth cessation response and identifies AIL1 as a potential regulator of cell cycle related genes and a novel target of the short day signal downstream of the CO/FT module in regulation of growth cessation in perennial trees .
Cuttings of hybrid aspen ( Populus tremula x tremuloides ) clone T89 ( wild type ) and the transgenic lines were grown in half-strength Murashige/Skoog medium ( ½ MS ) under sterile conditions for approximately 4 weeks and then transferred to soil . After four weeks in greenhouse the plants were moved to growth chambers ( 18 hour light/6 hour night , 20°C ) . After one week the chamber settings were shifted to short day conditions ( 8 hour light/16 hour night , 20°C or 14 hour light/10 hour night , 20°C ) . Growth cessation was determined by measurement of elongation growth and/or bud set . Pictures of apices to assess bud formation were taken using Canon EOS digital camera . For tissue specific expression analysis of AIL genes , samples were taken from tissue culture grown plants 4 weeks after cuttings were transferred to new media . AIL-genes were identified by blasting the Arabidopsis AINTEGUMENTA gene ( AT4G37750 ) against the Populus genome . Gene models ( http://genome . jgi-psf . org/Poptr1_1/Poptr1_1 . home . html ) were manually chosen based on intron-exon structure ( JGI protein ID for each model can be found in Figure S1 ) . Sequences were aligned and a bootstrapped phylogenetic tree generated using ClustalX [31] . The phylogenetic tree was visualised using TreeView ( http://darwin . zoology . gla . ac . uk/~rpage/treeviewx/ ) . The full length cDNA for AIL1 transcription factor was cloned into the donor vector pDONR201 ( Invitrogen . com ) before transfer into the destination vector pK2GW7 [32] . The resulting vectors were introduced into agrobacterium GV3101pmp90RK [33] followed by the transformation of hybrid aspen clone T89 [34] . The same strategy was used to generate AIL3oe lines with the exception of entry clone construction that in this case was performed using the pENTR/D-TOPO cloning kit ( Invitrogen . com ) . The AIL1 promoter was amplified using the primers: FW: CACCCGGGGAATGATAGGCTGACAA and RP:CCCAAAATCTTGCCTACTTCC and cloned into the pENTR/D-TOPO vector ( Invitrogen . com ) . The fragment was transferred into the pK2GWFS7 binary vector [32] . The construct was transformed into hybrid aspen using Agrobacterium mediated transformation as described before [34] . Apices from transgenic lines expressing the reporter gene were collected from greenhouse grown trees approx . 5 weeks after potting . The apices were incubated approx . 3 h at 37°C in GUS-solution ( 1 mm X-gluc , 1 mm K3Fe ( CN ) 6 , 1 mm K4Fe ( CN ) 6 , 50 mm sodium phosphate buffer ( pH 7 . 0 ) , and 0 . 1% ( v/v ) Triton X-100 ) . The samples were then rinsed with water , dehydrated to 50% ( v/v ) ethanol , fixed for 10 min in FAA ( 5% ( v/v ) formaldehyde , 5% ( v/v ) acetic acid , and 50% ( v/v ) ethanol ) , and cleared in 100% ( v/v ) ethanol . Once cleared , the samples were embedded in LR-White/10% PEG 400 resin in polypropylene capsules ( TAAB ) The apices were then sectioned on a Microm HM350 microtome ( Microm International GmbH , Germany ) at approx 20 µm , floated on water , heat-fixed to glass slides , mounted in Entellan neu ( Merck , Germany ) Sections were visualized with Zeiss Axioplan light microscope and captured with a digital camera , AxioCam together with the Axiovision 4 . 5 software ( Zeiss , Germany ) . Total RNA from poplar apices was extracted using the Aurum Total RNA kit ( Bio-Rad ) . Care was taken to collect tissue samples for RNA isolation at the same time of the day ( usually between 13–16 PM ) for each experiment . 100–500 ng of RNA was DNase treated with RNase free DNaseI ( Fermenta ) and used for cDNA synthesis using iScript cDNA synthesis kit ( BioRad ) or qScript cDNA synthesis kit ( Quanta BioSciences ) . Reference genes were validated using GeNorm Software [35] . The reference gene chosen was UBQ in all experiments except for the analysis of the overexpression of AIL1 and AIL3 in the AIL1oe and AIL3oe lines , where 18S rRNA was used as the reference gene . Analysis of expression in FTRNAi used two reference genes , UBQ and TIP-41 like . SYBR green ( Bio-Rad or Quanta BioSciences ) was used as non-specific probe in all reactions and relative expression values were calculated using the Δ-ct-method [9] . A complete list of primers used in RT-PCR analysis can be found in Table S1 . To downregulate the expression of AIL genes , artificial microRNAs were designed using the online tools at http://wmd . weigelworld . org/cgi-bin/mirnatools . Briefly primers ( Table S3 ) were used to generate artificial microRNAs directed against all the 4 AIL genes and cloned into the plant transformation vector pK2GW7 according to the cloning protocol at http://wmd3 . weigelworld . org/ . Two different miRNA contructs ( named 255 and 256 ) were made and transformed into hybrid aspen clone T89 as described earlier . Following transformation several hybrid aspen lines with reduced expression of AIL1 were obtained and one line each for the two constructs 255 and 256 were selected for the analysis of bud set after SD treatment ( lines 255-6 and 256-23 ) . Bud set was scored using the method described by [23] . We used a score of 3 to indicate active growth ( complete lack of bud set ) and 0 to indicate a completely closed bud and score of 2 or 1 to indicate intermediate stages . For this analysis , bud set was scored every 7 days in a minimum of 5 or more plants for a period of 7 weeks . AIL1 full length cDNA was amplified using the following primers: pttAIL1 ( EcoRI ) FW- CATGGAATTCATGAAATCTACGGGTGATAA and pttAIL1 ( SalI ) RP-CATGGTCGACTTCTCCTTTTCCTTGGTTCATGC . The resulting fragments were cloned into pRT104-3xHA [36] . Transfection into Arabidopsis protoplasts were performed as described [36] , [37] using 8 µg of purified plasmid . Cells were lysed in a lysis buffer containing 25 mM Tris-HCL ( pH 7 . 5 ) , 50 mM KCl , 1 mM EDTA , 10% Glycerol 1 mM DTT , 0 . 1% Igepal and 1X PIC ( Protease Inhibitor Cocktail ) . After centrifugation the supernatant was collected and immediately frozen in liquid nitrogen . The expression of the HA-tagged AIL1 protein was confirmed with western blot and resulting cell extracts were used for subsequent analysis . CycD3:2 promoter sequences were identified using the JGI populus genome database ( http://genome . jgi-psf . org/Poptr1_1/Poptr1_1 . home . html ) . Approx . 200 base pair fragments were amplified using primers specified in Table S2 . The fragments were gel-purified using E . Z . N . A . Gel Purification Kit ( Omega Bio-Tek ) followed by phenol-chloroform extraction and ethanol precipitation prior to use in gel-shift assays . Five pmol of purified fragments were biotin labeled using the Biotin 3′ End DNA Labeling Kit ( Pierce ) . Labeling and labeling efficiency determination was performed according to the manufacturers recommendation . The biotin-labelled promoter fragments were mixed with protoplast cell extracts containing AIL1-HA or control extracts from non-transfected protoplasts . For the binding reaction the following conditions were used: 10 µl protoplast cell extract , 0 . 5 µl biotin-labelled DNA ( 10 fmol/µl ) , 0 . 4 µl non-specific competitor ( poly ( dI:dC ) , 1 mg/ml ) , 0 . 5 µl BSA ( 20 mg/ml ) and lysis buffer to a total of 20 µl . For specific competition , 500 fmol non-labelled fragment was added to the reaction . Binding was performed on ice for 10 min followed by 30 min in room temperature . The samples were run on a non-denaturing polyacrylamide gel ( 5%-0 . 5xTBE ) and transferred to a Hybond N+ membrane ( GE Healthcare , Sweden ) . Crosslinking and detection was performed using the LightShift Chemiluminescent EMSA kit ( Pierce . com ) . | Day length is a critical and robust environmental cue utilised by plants to modulate their patterns of growth to adapt to changing seasonal conditions . In perennial plants such as long-lived trees of the boreal forest , reduction in day length ( short day signal/SD ) induces the cessation of growth prior to the advent of winter . This developmental switch is of major adaptive significance as inability to undergo growth cessation leads to mortality in these plants . Our knowledge of how SD signal induces growth cessation is rudimentary . Here we show that AIL1 ( AINTEGUMENTALIKE 1 ) , a plant-specific transcription factor , is a downstream target of the SD signal and can regulate the expression of key cell proliferation related genes . Intriguingly , the early acting components in day length–regulated processes such as flowering and growth cessation are conserved between annual and perennial plants . However our results show that the pathways downstream of short day perception diverge between these day length–controlled developmental transitions . These results have important implications for the evolution of the perennial life cycle and demonstrate how the same signal , namely day length , can regulate diverse developmental switches in annual and perennial plants . |
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Climate change has differentially affected the timing of seasonal events for interacting trophic levels , and this has often led to increased selection on seasonal timing . Yet , the environmental variables driving this selection have rarely been identified , limiting our ability to predict future ecological impacts of climate change . Using a dataset spanning 31 years from a natural population of pied flycatchers ( Ficedula hypoleuca ) , we show that directional selection on timing of reproduction intensified in the first two decades ( 1980–2000 ) but weakened during the last decade ( 2001–2010 ) . Against expectation , this pattern could not be explained by the temporal variation in the phenological mismatch with food abundance . We therefore explored an alternative hypothesis that selection on timing was affected by conditions individuals experience when arriving in spring at the breeding grounds: arriving early in cold conditions may reduce survival . First , we show that in female recruits , spring arrival date in the first breeding year correlates positively with hatch date; hence , early-hatched individuals experience colder conditions at arrival than late-hatched individuals . Second , we show that when temperatures at arrival in the recruitment year were high , early-hatched young had a higher recruitment probability than when temperatures were low . We interpret this as a potential cost of arriving early in colder years , and climate warming may have reduced this cost . We thus show that higher temperatures in the arrival year of recruits were associated with stronger selection for early reproduction in the years these birds were born . As arrival temperatures in the beginning of the study increased , but recently declined again , directional selection on timing of reproduction showed a nonlinear change . We demonstrate that environmental conditions with a lag of up to two years can alter selection on phenological traits in natural populations , something that has important implications for our understanding of how climate can alter patterns of selection in natural populations .
Global climate change has led to shifts in phenology , i . e . , the seasonal timing of life history events , such as budburst , flowering , hibernation , migration , or reproduction , in many species . Over the past three decades , plants and animals have shifted their timing , on average , three to four days per decade [1–8] . These shifts allowed them to at least partly adapt to the shifted optimal timing caused by the altered abiotic and biotic conditions . The observed shifts in seasonal timing have often not been as strong as the shift in the optimal timing , which could have negative consequences for fitness [9 , 10] . In seasonal habitats , reproductive success often increases with the temporal match at the peak of food availability [8 , 11–15] . Climate change can disrupt this phenological match between trophic levels because of differential phenological responses at different trophic levels [2 , 8 , 16] , but variation between specific systems is likely depending on the ecology of the interrelated trophic levels [17–21] . In migrant species , these differential responses could be caused by differences in the rates of temperature change in different geographical regions during the annual cycle [22] . Migratory species in particular must rely on environmental conditions experienced en route to predict the optimal time for stopover , arrival , or breeding at distant areas [23 , 24] . When in a population the timing of life cycle events shifts at a different rate than the optimal timing is shifting , directional selection on the timing will increase . Identifying the environmental variables that are the selective agents is important to predict future impacts of climate change and to be able to study the mechanisms underlying the ecological effects of climate change . However , these selective agents have rarely been identified , even outside the climate change setting , as pointed out earlier [25 , 26] . There are only a handful of studies that have identified such environmental variables [9 , 27–30] . One example is selection on timing of reproduction in great tits ( Parus major ) in the Netherlands , where increasing spring temperatures have advanced the peak date in the food for the nestlings ( i . e . , caterpillars ) more strongly than the egg-laying date of the birds [7] ( but see [28] for a United Kingdom great tit population where there has been no increase in phenological mismatch ) . As this mismatch between nestling food availability and demand affects the strength of selection on egg-laying date [9 , 12 , 27 , 28] , selection for earlier laying has increased [7] . As the timing of both the caterpillars and the birds depends on temperature , the strength of selection depended on spring temperature [9 , 27] . Here , we aim to identify the possible selective agents , including phenological mismatch , underlying the selection for egg-laying date in an insectivorous songbird , the pied flycatcher ( Ficedula hypoleuca ) . Pied flycatchers are long-distance migrant birds whose timing of reproduction is constrained by spring arrival time [31] . As for many long-distance migrants , spring arrival dates are advancing slowly , possibly because the environmental cues in the wintering grounds have limited reliability to forecast environmental conditions in the breeding areas [31] ( but see [23] ) . Pied flycatchers in our Dutch long-term study population have advanced their egg-laying date , but not sufficiently enough to keep track of the advancing caterpillar peak [32] , and selection for early breeding increased between 1980–1998 [31] . Pied flycatchers breeding in areas with early caterpillar phenology declined strongly , in contrast to areas with later phenology [33] , suggesting that the mismatch with caterpillar availability could be determining this change in selection patterns ( “phenological mismatch hypothesis” [7 , 34 , 35] ) . Despite these circumstantial data , the environmental variables causing the increased selection for earlier breeding have not been formally tested . We here aim to ( 1 ) describe ongoing changes in selection for timing of reproduction in our long-term pied flycatcher population , spanning more than three decades , using a novel statistical framework , and ( 2 ) identify the environmental variables that account for the temporal pattern of selection on timing of reproduction . To explore the environmental variables potentially underlying selection that could explain this temporal pattern , we analysed not only our primary fitness measure , the number of recruits produced , but also its two components: the number of fledglings produced and the probability that a fledgling recruits as a breeding bird . These two components could be affected by different environmental variables , and therefore analysing them separately may provide further insights in how environmental variables affect the number of recruits produced . As a way to identify the determinants of observed variation in ( directional ) selection , we assessed the effects of a number of environmental variables ( see below ) on the relationships between these fitness components and timing of reproduction . Three types of environmental variables were tested: climatic variables during the breeding season , food availability during the breeding season , and the conditions at the time the offspring returned to breed for the first time . As climatic variables , we considered seasonal patterns in temperature and the duration of rainfall ( which reduces the provisioning time for the parents ) , both during the chick feeding period and during the period just after fledging , i . e . , when offspring become independent , as these may affect fledgling survival from early and late broods differently ( e . g . , [36] ) . Next , we tested the hypothesis that the degree of temporal mismatch between caterpillar peak dates ( as one of the main food sources for offspring [37] ) and flycatcher egg-laying dates is important for explaining among-year variation in selection [35] . Finally , we tested an alternative mechanism to explain the observed variation in the annual strength of selection , based on previous observations that in years with low temperatures during spring arrival , early-arriving individuals are less likely to survive [38] . We investigate whether hatch date of an individual ( i . e . , the lay date of its mother ) is a predictor of spring arrival date when offspring return as a breeder and whether early- and late-hatched individuals’ survival upon arrival may thus be affected differentially by spring temperatures .
Pied flycatchers have advanced their annual mean egg-laying date between 1980 and 2010 by 12 d ( b = -0 . 38 ± 0 . 06 d/y , F1 , 29 = 40 . 2 , p < 0 . 001 ) . Over the same time period , directional selection in pied flycatchers for earlier egg-laying dates ( via numbers of local recruits produced ) initially increased in magnitude but waned again in the last ten years of our study period ( Fig . 1A; linear and quadratic trend of year in annual linear selection gradients: -0 . 05 ± 0 . 04 and 0 . 12 ± 0 . 06 , respectively , likelihood ratio test ( LRT ) of model with quadratic trend over model with only linear trend: Chi2 = 3 . 91 , df = 1 , p = 0 . 048 ) . Selection has therefore gone from stabilising in the first decade ( within-year analysis for the period 1980–1989: linear selection gradient = -0 . 09 ± 0 . 07 , p = 0 . 18 , quadratic selection gradient = -0 . 24 ± 0 . 08 , p = 0 . 012 ) to directional selection for earlier egg-laying dates in the second decade ( within-year analysis for the period 1990–1999: linear selection gradient = -0 . 50 ± 0 . 11 , p < 0 . 001; quadratic selection gradient = 0 . 43 ± 0 . 20 , p = 0 . 02 ) . However , in the last decade , directional selection on egg-laying date has weakened again but without a return to stabilising selection ( within-year analysis for the period 2000–2010: linear selection gradient = -0 . 24 ± 0 . 06 , p < 0 . 0001; quadratic selection gradient = 0 . 04 ± 0 . 09 , p = 0 . 70 ) . To explain this pattern in the annual strength of selection on egg-laying date ( Fig . 1A ) , we analysed how the relationships between egg-laying date and the number of recruits produced , and its two components , changed with environmental variables . Surprisingly , the strength of the seasonal decline in the number of recruits was not correlated with the mismatch between egg-laying date and the timing of the seasonal caterpillar peak ( interaction egg-laying date * mean population mismatch; Table 1 ) . In accordance with this result , we found no statistically significant relationship between mismatch and standardised linear selection gradients ( b = -0 . 02 ± 0 . 07 , Chi2 = 0 . 094 , df = 1 , p = 0 . 76 , LRT of model including mismatch as well as year and year2 versus a model including only year and year2 , see “Data analysis” in Materials and Methods for details ) ( Fig . 1B ) . Thus , the difference between the egg-laying date and the caterpillar peak ( the phenological mismatch ) does not appear to be a major driver of selection . Additionally , none of the meteorological variables during the breeding season , such as temperature or rainfall duration , explained variation in the strength of the seasonal decline in number of local recruits produced , the number of fledglings produced , or the recruitment probability of fledglings ( Table 1 ) . However , the seasonal decline in the number of fledglings produced was stronger when caterpillar peaks were lower , suggesting that the seasonal decline in reproductive output is related to the amount of caterpillars available ( interaction egg-laying date * height of the caterpillar peak; Table 1 ) . We found support for the alternative hypothesis that conditions upon spring arrival for recruiting offspring explain between-year variation in selection on egg-laying date . Thus , when arrival temperatures were warm in the year of recruitment , recruitment probability declined more steeply with the egg-laying date of the clutch the offspring hatched in than when the springs were cold ( interaction egg-laying date * arrival temperature; Table 1; Fig . 1C ) . As pied flycatcher fledglings mostly recruited in the first or second year after fledging ( see “Age at recruitment” in Materials and Methods ) , the arrival temperature was calculated as the mean minimum spring temperature averaged over the two years after hatching during the arrival period of females ( see Materials and Methods ) . In accordance with this , selection for earlier egg-laying date was stronger when arrival temperatures in the two years after fledging were higher ( b = -0 . 18 ± 0 . 08 , Chi2 = 4 . 76 , d f = 1 , p = 0 . 03; Fig . 1D , LRT of a model including arrival temperature as well as year and year2 versus a model including only year and year2 , see “Data analysis” in Materials and Methods for details ) , as in these years the early-hatched offspring have higher prospects to be recruited compared to cold years , while the recruitment probability of late-hatched offspring was not affected by arrival temperatures ( Fig . 1C ) . Note that when we fitted either of these year-specific temperatures alone or together ( rather than the temperature mean over the two years ) , we found similar effects ( Table 2 ) . To explain the initial increase and later relaxation of selection on egg-laying date ( Fig . 1A ) , arrival temperatures should have changed over time in a similar way , and , indeed , they have: arrival temperatures increased from 4°C to 8°C during the first 20 y of the study period , after which they decreased again over the last decade ( year2: b = -0 . 008 ± 0 . 003 , F1 , 28 = 9 . 23 , p = 0 . 03; Fig . 2; “broken stick” ( or segmented ) regression [39] testing for different ( linear ) trends in different periods: best fit for the two periods 1980–1996 and 1997–2010; in the first period , temperature increased ( b = 0 . 15 ± 0 . 06 , F1 , 15 = 5 . 44 , p = 0 . 03 ) , in the second period temperatures tended to decrease ( b = -0 . 15 ± 0 . 07 , F1 , 12 = 4 . 45 , p = 0 . 057 ) . This temporal pattern in arrival temperatures thus could explain the temporal change in the strength of selection on egg-laying dates ( Fig . 1A ) . We contend that spring arrival temperatures are important because these temperatures determine ecological conditions affecting survival and settlement just before or after arrival at the breeding grounds . The link with the egg-laying date of a bird’s mother is through a potential effect of hatch date on arrival date later in life . If early-hatched individuals themselves have an early arrival , they may benefit in warmer years but may pay a penalty in colder years ( c . f . , [38 , 40] ) . Indeed , the relative egg-laying date from the clutch in which an individual hatched was positively correlated with the first spring arrival date as adult breeder ( relative to the population mean ) , especially in females ( b = 0 . 22 ± 0 . 07 , F1 , 351 = 8 . 97 , p = 0 . 003 ) , but less so for males ( b = 0 . 11 ± 0 . 08 , F1 , 211 = 1 . 99 , p = 0 . 16 ) . Note that when we constrain the analysis for females to the same observation period as for males ( 2002–2012 ) , this effect remains statistically significant in females ( b = 0 . 28 ± 0 . 10 , F1 , 186 = 8 . 02 , p = 0 . 005 ) . A further indication that females hatched in early-laid clutches are themselves also arriving and laying eggs early is that egg-laying date is heritable: h2 = 0 . 33 ( 95% CI: 0 . 25–0 . 39 ) .
Between 1980 and 2010 , selection on egg-laying date changed from initially stabilising to directional selection for early laying , but this directional selection relaxed again during the last decade . We found no evidence indicating that this pattern was explained by environmental variables measured within the breeding season nor by the temporal mismatch with the food peak [35 , 41] . Instead , the annual variation in selection was correlated with spring temperatures up to two years after breeding , with stronger selection for earlier laying when spring temperatures in the arrival year for the cohort were high . In our study , the increase in spring temperatures initially led to stronger selection for early egg-laying , as offspring from early nests were more likely to recruit than offspring from late nests under warm conditions upon arrival ( Fig . 1C ) . Between 2000 and 2010 , however , arrival temperatures have decreased . This cooling has weakened the strength of directional selection on egg-laying date in this population . We thus show that changes in spring temperatures have a clear impact on selection on a key life history trait: timing of reproduction . We found no strong evidence for an effect of a phenological mismatch with the caterpillar peak on selection in this migratory species , as previously has been suggested [35 , 41] based on other systems [9–11 , 42] . This is unexpected , as synchrony between food peaks and avian timing of reproduction has often been suggested to be important [43 , 44] , and differential phenological responses across trophic levels to climate change have been a major hypothesis explaining increased selection , reduced reproduction , and population declines [45 , 46] . Also in our study population , caterpillars are an important component in the nestling diet of pied flycatchers , and in warm years , strong seasonal declines in the proportion of caterpillars in the diet were observed [37] . We did find that the height of the peak in caterpillar abundance , rather than the temporal synchrony with the caterpillar peak , affected the seasonal decline in number of fledglings ( Table 1 ) . In years with high caterpillar abundance , there was only a weak decline in the number of fledglings over the course of the season; whereas in low caterpillar peak years , this decline was much stronger . This suggests that caterpillars are indeed important as a food source affecting flycatcher fitness and that their availability per se influences selection on seasonal timing . The lack of an effect of synchrony could thus be partly obscured by variation in caterpillar peak height . Caterpillar availability strongly fluctuates between years , with a factor 20 or more between the lowest and highest caterpillar density years [47] and a cycle period of around 9–10 y [48] . Within our dataset , we had periods with peak abundances around 1996 and 2009 , and in these years , even birds that breed out of synchrony with the caterpillar peak do not have a strongly reduced offspring production , although very late birds always do poorly because of reduced food availability later in the season [49] . In line with this , the seasonal decline in clutch size is stronger in years when the birds breed late relative to the food peak [50] . Predictions of potential effects of climate change should thus not just consider temporal matching but also potential changes in food abundance [42] . Our fitness measure is the number of fledglings that locally recruit in our population , and therefore this fitness estimate does not include all offspring that survive and disperse out of the study population . Our best estimate is that about four times as many fledglings recruit elsewhere with dispersal distances up to 600 km [51] . This means that measuring fitness as the number of local recruits is potentially biased if differential dispersal exists with respect to laying date of the mother . If offspring that originate from early- or late-laid clutches differ in the likelihood that they recruit outside the study areas , this will lead to apparent selection for egg-laying date . However , correlative and experimental data in pied flycatchers do not support this idea; experimentally delayed hatching date ( during three consecutive breeding seasons ) did not affect natal dispersal in a metapopulation setup where dispersing offspring could be tracked up to 25 km [52] . Thus , we have no indication that differential dispersal with respect to timing exists in our study system . The idea of differential dispersal in response to climate change is that late-arriving individuals in warm years benefit by continuing their migration further north and thereby matching habitat phenology with their own timing [53] . Support for northwards dispersal has been found in black-winged stilts in dry springs [54] and in American redstarts that depart later from their wintering grounds [55] , but these studies could not unequivocally demonstrate that differential dispersal for a phenotypic trait was related to environmental variation . In our case , we predict that such a pattern would result in especially late-arriving individuals showing high survival in cold years , and low survival in warm years , whereas little effect is expected for early-arriving individuals . Our results , however , do not support these predictions for differential dispersal: late-born offspring recruited equally well when returning in colder or warmer springs ( Fig . 1C: comparing data points for laying dates >5 ) . Variation in selection between warm and cold years was thus not caused by differential survival of late-born offspring but rather for early-born offspring that return more in warmer than in colder years ( Fig . 1C ) . In almost any natural population , permanent dispersal is difficult to separate from mortality , especially when dispersal can occur over large distances . A key consideration in this respect is to assess whether this dispersal likelihood is correlated with the trait of interest , i . e . , egg-laying date . We cannot rule out the possibility that differential dispersal affects our fitness estimates , but available evidence suggests that differential dispersal is not a main factor explaining our results . Additionally , differential dispersal would only mediate the patterns observed in Fig . 1A if its relationship with laying date changed over time , which is unlikely . Consequently , in our opinion , the pattern depicted in Fig . 1A is most parsimoniously explained by temporal variation in the relationship between egg-laying date and fitness . As in many studies of natural populations , the correlation between trait ( here egg-laying date ) and fitness could potentially be caused by covariance with some environmental variable [56] . Further mechanistic experimental work could therefore be useful in determining whether variation in offspring phenology , and its consequences for offspring and parental fitness , is caused by parental egg-laying date and hence hatch date per se , or if it covaries with parental egg-laying date because of genetic or environmental correlations [57] . We also need more insights into why offspring from early-hatched clutches do so much better when the temperatures are high when they arrive ( Fig . 1C ) . Does an early arrival give them a competitive advantage which is offset against a higher mortality in colder years ? This could yield further insights into the evolutionary potential of these phenological traits through parameterisation of more formal models [58 , 59] of the relationships between parental and offspring phenotype and fitness . Studies have rarely demonstrated a relationship between selection on life history traits and climate change-altered environmental variables ( i . e . , selective agents [25] ) . Yet , all of these studies found immediate effects , i . e . , within the same season in which the life history trait is expressed [9 , 28 , 29] . Here , we have shown the apparent absence of such immediate effects but demonstrated an effect of conditions years after the life history trait is expressed . Such effects have been little studied in this context , but might be common . Studies of both delayed effects of environmental variables and the interaction between demographic and evolutionary processes on selection are urgently needed as selection is a key component for microevolution of life history traits , which is ultimately the only route for populations to adapt to novel environments [60] .
This study was carried out with the approval of the Animal Experimentation Committee of the Royal Dutch Academy of Sciences . We analysed data from 1980 until 2010 from the Hoge Veluwe study area ( 52°059 N , 05°509 E , the Netherlands ) , where about 400 nest boxes are supplied in a 171 ha study area of mixed deciduous and coniferous forest . We only analysed data until 2010 to get reliable estimates of the number of recruits produced since about half of the offspring is found to recruit only two years after fledging as a breeding individual ( see “Age at recruitment” in Materials and Methods ) . The broods of 1995 were excluded from all analyses since in this year almost all broods in the population were manipulated in a way that affected their breeding success [61] . Nest boxes were checked weekly during the egg-laying period , and first egg-dates ( henceforth called egg-laying dates ) were calculated on the assumption that one egg is laid per day . We included only clutches that we considered as first clutches of females: repeats of known females that failed were excluded , as were all clutches that started >30 d after the first egg-laying date in that year . During these regular checks , nest-building stages were reported . Adults were caught during chick feeding using nest box traps for identification based on their ring numbers . All nestlings were ringed with standard aluminium rings at an age of 7 to 12 d . The observed temporal pattern in the strength of selection ( Fig . 1A ) could be caused by a systematic change over the study period in how the adult recapture probabilities differed for birds hatched in late or in early-laid clutches . In order to test this possibility , we estimated survival and capture probabilities using a formal capture-mark-recapture analysis [62] using the software MARK [63] . We first fitted simple models , in which adult survival and recapture probability were constant over years and possibly only differed among the sexes . We then tested whether survival and recapture probabilities would differ among years and whether they depended on the egg-laying date of the clutch an individual was born in ( “birth clutch lay date” ) , and whether this relationship differed between years , by adding the interaction of “birth clutch lay date” with year to the model . If recapture probabilities would depend on “birth clutch lay date , ” and this relationship would differ among years , it could possibly bias our results . To test whether recapture probabilities could explain the observed selection and its temporal change , we fitted a range of capture-mark-recapture models in which recapture probability depended on sex , year ( linear and squared effect ) , and the egg-laying date of the clutch from which an individual hatched . While there was clear support for a linear temporal change in recapture probability ( Table 3 , model 1 ) , the support for a quadratic change of recapture probability over time was less clear ( Table 3 , model 2: Δ AICc = 2 . 0 and AICc weight = 0 . 27 ) . However , we found no evidence for capture probability being correlated with birth clutch lay date ( Table 3 , model 4: Δ AICc = 9 . 7 and AICc weight = 0 . 005 ) . Male arrival date was determined from 2002 to 2010 through daily observations covering the whole study area , from April to early May by one to three trained observers . In our pied flycatcher population , males greatly vary in their plumage characteristics ( i . e . , size and shape of forehead patch and darkness of dorsal feathers; see [64] ) , and about half of them had been marked in previous years with aluminium and colour rings . We made notes of these features during our daily observations to characterise males singing at each nest box , allowing ascribing arrival dates—based on the similarity of features—to individual males , which were captured during chick feeding in the nest boxes . Pied flycatcher males commonly breed in the immediate surroundings of the area where they are first observed singing [65] , and hence in most cases the description of the male repeatedly singing in a particular nest box matched the characteristics of the bird captured in it during breeding , which supports the validity of this approach to gather information on spring arrival phenology in this species ( see [65] for a similar approach ) . This procedure allowed us to assign the arrival date in 571 cases ( 397 different individuals ) in nine years , which is more than half of the males present in our study site . For female arrival date , we used the estimated start of nest building as proxy . Female flycatchers select a mate within hours upon their arrival [66 , 67] , and they start nest building immediately after . Over the entire study period , the stage of nest building was scored ( using a six-point scale , from little material to a complete nest ) during the weekly nest checks . We assumed that little material meant that nest building had started the day of the check , whereas a complete nest started six days ( i . e . , the day after the previous nest box check ) earlier ( and other stages in between ) . The validity of this proxy was tested using observational data on arrival dates of male and female pied flycatchers in a nearby population ( Dwingeloo , The Netherlands ) . The Dwingeloo study area has 100 nest boxes and approximately 50 pairs of pied flycatchers annually . The same observer ( CB ) checked arriving pied flycatcher on a mostly daily basis from April 10 to May 15 for 2007 , 2008 , 2009 , 2010 , and 2013 . Note that the years 2010 and 2013 were exceptionally cold springs , resembling what was normal during the early part of our time series ( 1980s ) , and therefore this allows us testing under the full range of environmental circumstances whether this proxy indeed can be used . Male flycatcher arrival was determined based on individual plumage characteristics ( see above ) . Female arrival date was determined as the pairing date , which was often clearly visible because males reduced or completely stopped singing after being paired , and these behavioural changes were mostly confirmed by the observation of a female with the focal male . Nest boxes were checked at least once every five days , but more often to confirm female arrival , and the contents of the box were noted on the same six-point scale as at the Hoge Veluwe . To test whether female arrival date correlated well with the start of nest building , we used 194 observations in which female arrival date and the start of nest building were known . The estimated slope was 1 . 05 ± 0 . 02 ( t1 = 66 . 84 , p < 0 . 001 ) . These data clearly show the validity of using the start of nest building as proxy for female arrival , as there is an almost 1:1 relationship . However , it must be noted that if females give up their nest and renest , their arrival dates cannot be estimated from the start of nest building . Especially late nests therefore should be treated with caution , adding noise to the data and , especially in cases where a large fraction of nests are abandoned , this proxy becomes inappropriate . In our Hoge Veluwe study population , however , this is rarely the case . We used the number of offspring produced that return as a breeding bird in the Hoge Veluwe population ( local recruit ) as a fitness measure . This number of recruits produced is the total number of recruits from first and replacement clutches ( after a failed first clutch ) within a single season . The number of recruits produced can be partitioned in two components , the number of fledglings produced and the probability for these fledglings to recruit . These two components can be affected by very different climatic variables , and hence we also analysed how these two components were affected by the selected climatic variables to gain more insight in how our overall fitness measure , the number of recruits , was related to climate variables . The number of fledglings was the number of chicks ringed minus the number of offspring found dead in the nest box after the offspring had fledged . The probability to recruit was calculated as the number of recruits per nest divided by the number of fledglings per nest ( thus excluding the nests without fledglings ) . As capture probability of a locally recruiting offspring was not correlated with the egg-laying date of the brood a recruit originated from ( see above and Discussion ) , we did not correct the number of recruiting offspring for capture probability . Note that an offspring is a recruit when it is caught at any age at the Hoge Veluwe , so even when birds are missed in their first breeding year , they are likely to be caught in their second year of breeding . Most locally-hatched pied flycatchers are caught as breeding adults for the first time either in their first or second year after fledging: 32% of all male recruits returned to breed for the first time at age one and 54% at age two; this pattern is almost exactly reversed in female recruits , of which 59% returned at age one and 32% at age two . Only 14% ( males ) and 9% ( females ) , respectively , returned at older ages to breed for the first time . Given our capture probabilities ( see above ) , the records of birds breeding for the first time two years after fledging is unlikely to be due to birds breeding but not being identified in their first year after fledging . Data on daily mean and minimum temperature ( °C ) and rain duration ( number of hours of rain in 24 hours ) from the weather station directly adjacent to the study area ( Deelen , 52°059 N , 05°509 E ) were obtained from the Royal Dutch Meteorological Institute ( KNMI; http://www . knmi . nl/klimatologie/uurgegevens/#no ) . We used daily mean temperature as the predictor variable for the nestling period ( defined per year as the period from the mean egg-laying date plus 18 d ( 6 d of egg laying plus 12 d of incubation ) to mean egg-laying date plus 30 d ( an additional 12 d of chick rearing ) and the fledging period ( defined per year as the period of the mean egg-laying date plus 31 d to mean egg-laying date plus 42 d ( 12 d of post-fledging care [64] ) . We used minimum temperature for the arrival period because we hypothesised that while nestling survival would be affected by a variety of factors better reflected by mean temperature , the survival of recruits upon arrival would depend on the severity of harsh conditions reflected by minimum temperatures . Temperatures at the time of arrival in spring were calculated during the period when 90% of all females arrived ( averaged over the 30 y ) . This “arrival period” was defined as the interval from the average annual 5% quantile ( between-year standard deviation 5 . 3 d ) until the average annual 95% quantile ( between-year standard deviation 6 . 7 d ) of female arrival dates ( using nest building as proxy ) : April 23 until May 12 . As most pied flycatchers were caught breeding for the first time either in their first or second year after fledging ( see “Age at recruitment” in Materials and Methods ) , we took the average daily minimum temperature during the arrival period one and two years after fledging as a measure of environmental conditions upon first arrival . We used temperatures averaged over a fixed period because using temperatures measured over the realised arrival period , i . e . , an annually variable period , would “reverse” causality: in that case , the behaviour of the birds would have determined the climatic variable rather than vice versa . Caterpillars form an important prey for the nestlings of many passerine bird species including pied flycatchers [37] , and caterpillar biomass has been monitored between 1985–2010 in the Hoge Veluwe-study area by collecting caterpillar droppings ( “frass” ) using special nets placed on the ground under oak trees ( Quercus robur ) . “Frass” was collected two to three times a week , dried , sorted , and weighed . Caterpillar biomass was then calculated from its weight using the formula given in [68] . See [11] for more details on study area and the described methods . We estimated annual standardised directional selection gradients following methods outlined in [69] . Briefly , we first characterised the relationship between egg-laying date and the number of local recruits produced , in each year using generalised linear models with a Poisson error structure . GLMs included linear and quadratic linear predictor scale regression terms ( to detect directional , stabilising or disruptive selection ) , except for 1981 , 1989 , 1990 , 1991 , and 2006 , where we fitted only linear regression terms because low numbers of recruits ( < 15 ) precluded fitting more complex models . Next , we used the R package gsg [69] to obtain yearly standardised directional selection gradients ( sensu [70] ) , and their standard errors and p-values using a parametric bootstrap algorithm . Because of the large statistical uncertainty associated with each annual selection gradient , we developed a method to robustly estimate the regression of selection gradients against predictors ( e . g . , year or environmental variables ) . We fitted the model βt ~ μ + b1t + b2t2 + mt + et , where βt are estimated selection gradients for year t , and mt are deviates of estimated selection gradients from their unknown true values and are assumed to be drawn from the distributions N ( 0 , SEt2 ) , where SEt are the standard errors associated with each estimated selection gradients . b1 and b2 are regression coefficients relating selection to year . When testing whether other environmental variables affected selection strength , coefficients relating selection to these environmental variables were included similarly in addition to linear and quadratic effects of year . et are residuals , and are assumed to be normally distributed with estimated variance . We fitted the model by maximum likelihood , tested regression terms with LRTs , and approximated the standard errors of regression coefficients from the information matrix . The maximum likelihood techniques provided nearly identical inferences to a complimentary Bayesian approach implemented by extending the meta-analytic approaches used in [71] . When formally analysing stabilising selection , we pooled data ( for reasons mentioned above ) , into the periods ( 1980–1989 , 1990–1999 , and 2000–2010 ) and regressed relative fitness against the linear and quadratic term of centred egg-laying date using the approach described above . The relationship of single fitness components ( number of recruits , number of fledglings and recruitment probability ) with egg-laying date and environmental variables was tested with GLMMs , with year as random effect , to account for nonindependence of data from the same year with respect to the environmental variables that were measured at an annual scale . Female identity was included as random effect to account for females breeding in more than one year . Number of fledglings and recruits were analysed using a log-link and a Poisson error-distribution . An observation-level random effect was included in the Poisson models , the commonly observed overdispersion in reproductive success [72] . The recruitment probability per brood was analysed using a logit-link and a Binomial error-distribution . The heritability of egg-laying date was calculated based on standard quantitative genetic approaches implemented in the so-called “animal model” ( [73] . To account for year-to-year variation and age effects , year and age were included as fixed factors . Female identity was included as “permanent environment” random effect to account for repeated breeding events of the same female . The additive genetic effect was included as random effect to estimate heritability as the ration of the variance explained by the additive genetic effect over the total phenotypic variance ( excluding year and age effects as these were fitted as fixed effects ) . All estimates are reported ± standard error . Data deposited in the Dryad repository: http://dx . doi . org/10 . 5061/dryad . cv24c [74] . | Pied flycatchers are long-distance migrant birds that have advanced their timing of reproduction over the past decades in response to climate change . We studied selection on egg-laying date using a 31-year-long population study and found that in the first 20 years , early-reproducing birds had increasingly higher fitness than late-reproducing birds , resulting in intensified selection on egg-laying date . However , during the last decade , selection on egg-laying date has weakened considerably , although the timing mismatch between breeding and food availability—supposedly the main determinant of selection on breeding phenology—did not change . Whereas conditions during breeding cannot explain the temporal pattern in the strength of selection , spring temperatures at the time the offspring’s first return to the breeding site , i . e . , one or two years later , do explain the annual variation in selection well . If spring temperature at arrival is high , early-hatched birds recruit to the breeding population better than late-hatched birds , while in cold years , early- and late-hatched birds recruit equally well . Because early-hatched daughters arrive early when they recruit into the population , females that lay eggs early ( and thus have early-hatched chicks ) have a strong selective advantage when the following years are warm . Arrival temperatures increased over the first two decades but then cooled again , leading to the observed pattern in selection . |
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The pattern and frequency of insertions that enable transposable elements to remain active in a population are poorly understood . The retrotransposable element R2 exclusively inserts into the 28S rRNA genes where it establishes long-term , stable relationships with its animal hosts . Previous studies with laboratory stocks of Drosophila simulans have suggested that control over R2 retrotransposition resides within the rDNA loci . In this report , we sampled 180 rDNA loci of animals collected from two natural populations of D . simulans . The two populations were found to have similar patterns of R2 activity . About half of the rDNA loci supported no or very low levels of R2 transcripts with no evidence of R2 retrotransposition . The remaining half of the rDNA loci had levels of R2 transcripts that varied in a continuous manner over almost a 100-fold range and did support new retrotransposition events . Structural analysis of the rDNA loci in 18 lines that spanned the range of R2 transcript levels in these populations revealed that R2 number and rDNA locus size varied 2-fold; however , R2 activity was not readily correlated with either of these parameters . Instead R2 activity was best correlated with the distribution of elements within the rDNA locus . Loci with no activity had larger contiguous blocks of rDNA units free of R2-insertions . These data suggest a model in which frequent recombination within the rDNA locus continually redistributes R2-inserted units resulting in changing levels of R2 activity within individual loci and persistent R2 activity within the population .
The abundant transposable elements present in eukaryotes have over the course of evolution helped shape the size , structure and expression of their genomes . Control over the spread of transposable elements has been suggested to be a consequence of the harmful effects of their insertions [1] , [2] and involves either the epigenetic regulation of heterochromatin formation to block transcription [3] or small RNA pathways to degrade their RNA transcripts [4] . In spite of these controls , in most populations new insertions occur continuously at low levels . In Drosophila melanogaster for example , nearly half of spontaneous mutations are caused by transposable element insertions [5] , and the insertion locations of most transposable elements are polymorphic in a population [1] . Multiple attempts have been made to monitor the activity of specific transposable elements in D . melanogaster and D . simulans [6]–[11] . However , it remains uncertain as to whether transposable elements maintain themselves by the inability of the control mechanisms to prevent all events , or the loss of transposable element control in a fraction of the individuals in a population . The ribosomal RNA ( rRNA ) genes of eukaryotes are composed of hundreds to thousands of tandemly repeated units ( the rDNA loci ) . Mature 18S , 5 . 8S and 28S rRNAs are processed from the single transcript of each rDNA unit . Frequent recombination ( unequal crossovers ) within these rDNA loci removes sequence variation , a process referred to as concerted evolution [12] . Concerted evolution is so efficient almost no nucleotide sequence variation exists between the different rDNA units of a locus [13] , [14] . Given this ability of the locus to rid itself of variation , it is surprising that the rDNA locus is home to many specialized transposable elements [15]–[17] . For example , R1 and R2 are non-LTR retrotransposable elements that insert specifically into the 28S rRNA genes ( Figure 1 ) . R1 and R2 are highly adept at maintaining themselves in the rDNA locus . They are found in most arthropods and appear to have been vertically inherited since the origin of the phylum [18] , [19] . Remarkably , R2 elements appear to have been inserting into the same site of the large subunit rRNA gene since near the origin of metazoans [17] . Within many insect species a large fraction of the rDNA units are inserted by R1 and R2 suggesting rapid rates of insertion [20]–[22] . Even though these many inserted units can not make functional 28S rRNA , the host does not appear unduly affected because in most organisms many more rDNA units are encoded than are needed for the production of rRNA [23] . An analysis of inbred laboratory stocks of D . simulans originally derived from a population in Paradise , CA revealed lines with no R2 activity as well as lines with extremely high rates of R2 retrotransposition [24] , [25] . The R2 retrotransposition activity was found to be under transcriptional control with the genetic differences between active and inactive stocks closely linked to the rDNA locus [26] . Minor , if any , influence on the level of R2 activity was found associated with the autosomes . Analysis of the rDNA loci from the active and inactive stocks revealed that the numbers of R2 elements in the active stocks were on average twice that found in the inactive stocks . The R2 elements in the active stocks were distributed throughout the rDNA locus suggesting a model in which a high density of R2 elements prevents the host from activating the needed number of uninserted rDNA units without also activating R2-inserted units . Because the active and inactive stocks used in these studies had been maintained in the laboratory for over 10 years , and considerable changes in the structure of the rDNA locus had occurred during that time [25] , it was unclear whether R2 activity in these laboratory stocks were an accurate reflection of the levels and pattern of R2 activity in natural populations . Here we studied R2 element activity in two populations of D . simulans immediately after their capture . A 100-fold range of R2 transcript levels was detected and correlated with R2 retrotransposition activity . However , the number of R2 elements in the rDNA loci of the active and inactive lines did not substantially differ , suggesting the previously detected accumulation of R2 in active stocks was the result rather than the cause of R2 activity . The property of the rDNA locus that best correlated with R2 activity was subtle changes in the distribution of elements within the rDNA locus .
R2 transcript levels were monitored in RNA isolated from adult females the first generation after establishing the iso-rDNA lines . To enable comparisons between different blots , RNA from line 58 , a laboratory stock previously shown to have stable high levels of R2 transcription and retrotransposition , was included in each analysis [25] , [26] . A representative RNA blot is shown in Figure 2 . Panel A is the RNA probed with R2 sequences , panel B is the same blot probed with a control gene ( alcohol dehydrogenase ) , and panel C is the ethidium bromide staining pattern of the RNAs used . The 3 , 600 nt hybridizing band detected at various intensities in panel A represents full-length R2 element transcripts [26] . Because the hybridization probe used in the blot was from the 5′ end of the R2 element ( Figure 1 , probe 1 ) , the series of lower hybridizing bands represent intermediate degradation products of full-length transcripts , rather than the transcripts from 5′ truncated R2 elements . These lower hybridizing bands between lines generally corresponded in relative intensity to the 3 , 600 nt band suggesting the lines had similar rates of R2 RNA degradation . Shown in Figure 3 is a summary of the R2 transcript levels in all 180 iso-rDNA lines . The populations from both Atlanta and San Diego had transcript levels that varied over a wide range . Both populations also had similar fractions of the lines at comparable transcript levels . About 45% of the stocks derived from the San Diego population and 60% of the stocks from the Atlanta population had no or extremely low levels of R2 transcripts ( defined here as hybridization less than 5 times the background hybridization to the filters ) . The remaining lines from each population had R2 transcript levels that varied in a continuous manner from 6 to 85 times background . Only four lines ( two from each population ) had transcript levels similar to or greater than the R2 active laboratory stock , line 58 . To determine if the R2 transcript levels rapidly changed while being maintained in the laboratory , 32 lines that spanned the full range of R2 transcript levels from the two populations were re-assayed either 2 or 3 generations after the initial screening . No line changed significantly in transcript levels . Lines with intermediate levels of transcription showed the highest levels of change between the two generations with some lines increasing and others decreasing by 20% ( data not shown ) . Nine lines from each population were selected to represent the observed range of R2 transcript levels ( lines indicated with asterisks in Figure 3 ) . Because the flies for each population were collected from one location over a period of only a few days , some of the flies could be closely related and thus not represent independent rDNA loci . To determine the relatedness of the rDNA loci from these lines , the specific R2 insertions in each of the 18 lines were compared . R2 elements generate identical 3′ junctions with the 28S gene but about one-half of the copies have variable 5′ junctions [27] . This 5′ end variation predominantly corresponds to truncations and has been suggested to arise from the R2 polymerase failing to reach the 5′ end of the R2 transcript during reverse transcription and/or from DNA repair processes associated with partial retrotransposition events [15] . The 5′ ends of the R2 elements from each line were amplified in a series of PCR reactions , and the products separated at single nucleotide resolution on high-resolution sequencing gels . These R2 5′ truncation profiles serve as a robust means to determine whether the rDNA loci of the different lines are related . For example , laboratory stocks with inactive R2 elements continue to have identical 5′ truncations after 100 generations [24] , [27] , while 60–90% of the truncations are identical after 30 generations when the R2 elements are active [25] . Shown in Figure 4 are the collections of 5′ truncated R2 insertions found in the 18 lines . The distances from the vertical lines to the 3′ end of the element represent the lengths of the different 5′ truncated elements in each line . Each line had from 13 to 28 ( mean 20 ) different length 5′ truncated copies . From 2 to 9 ( mean 6 ) of these elements were shared with one or more other lines ( identified in the figure with dotted vertical lines ) . These shared copies consisted largely of six specific truncations ( vertical dotted lines marked at the top with asterisks ) that were also shared between the Atlanta and San Diego populations . These long-term stable R2 copies are presumably located in regions of the rDNA locus , perhaps the edges , which seldom undergo recombination events . These data indicate that the rDNA loci in the 18 lines selected for further study are not closely related with their rDNA loci having undergone significant turnover of R2 elements since their common ancestor . Finally , the 5′ truncation profile of each line also differed from the rDNA loci present in the Bx line ( not shown ) , indicating no instance of recombination between the Bx marker and the rDNA locus during the establishment of the iso-rDNA lines . R2 activity ( retrotranspositions ) was next scored in the 18 representative lines . To monitor new retrotransposition events , the R2 5′ truncation profiles of 16 males from each line in the eighth generation after formation of the iso-rDNA lines were compared to the profile of the original male used to establish each line . Retrotransposition ( insertion ) events were scored as additional R2 5′ truncations observed in any of the male progeny . Because R2 deletions have also been correlated with R2 retrotransposition activity [25] , R2 copies present in the original rDNA loci but missing in the eighth generation loci were also scored as R2 activity . R2-induced deletions typically involve multiple rDNA units [25] , thus all deletions within a loci were scored as single events , independent of the number of R2 copies involved . Previous analyses have suggested that the insertion and deletion of 5′ truncated copies of R2 are similar to full-length copies , thus these 5′ truncated events represent about half of the total retrotransposition events within the locus [10] , [24] . A summary of the retrotransposition activities of the 18 lines plotted versus their relative R2 transcript levels is shown in Figure 5 . No changes in the 5′ truncation profiles were scored in the six low transcript lines . The 12 lines with readily detected levels of R2 transcripts had on average 4 events ( range 0 to 22 ) . For comparison our laboratory stocks with the highest levels of R2 retrotransposition typically generated 20–30 events in similar experiments [24] , [25] . While not all lines with high levels of transcript had measurable levels of retrotransposition , R2 transcript levels were positively correlated with R2 retrotransposition ( Spearman rank correlation test , r = 0 . 71 , p = 0 . 0005 ) . If the one line with the highest level of activity ( CS24 ) was excluded from the analysis , there still remained a positive correlation ( r = 0 . 67 , p = 0 . 001 ) . Re-examination of the high line ( A141 ) in Figure 5 with no retrotranspositions revealed one retrotransposition event . The lower levels of retrotransposition in some lines with significant R2 transcript levels suggest there may be other levels of control over R2 activity besides transcription . Because of the large number of R2 events that had accumulated in the most active line ( CS24 ) , it was possible to score retrotransposition events in this line at earlier generations . Six events were detected in the first generation after founding the iso-rDNA line , and nine events had accumulated by the third generation . Thus , as in our previous studies of inbred laboratory stocks , high retrotransposition activity can be stable over multiple generations . The total number of R2 copies in each of the 18 lines was determined by directly counting their different 5′ ends . In addition to the 5′-truncated copies shown in Figure 4 , about one-half of the R2 elements are full-length ( i . e . extend to the first nucleotide of the consensus sequence ) . Many of these full-length copies can also be individually counted in PCR assays because they too exhibit length variations associated with small deletions of the 28S gene and non-templated nucleotide additions [28] . To estimate the copy number of those elements with identical length 5′ ends , the intensity of each PCR band was calculated with a regression analysis using single copy variants as reference markers [29] . The total number of R2 copies in the 18 lines varied from a low of 33 to a high of 69 , while the number of full-length R2 elements varied from 18 to 34 ( Table 1 ) . Figure 6A plots the number of full-length and thus potentially active R2 copies versus the level of R2 transcription in each of the 18 lines . The six lines with low R2 transcript levels had on average 23 . 3 full-length copies , while the six lines with highest transcript levels had on average 26 . 8 copies . However , a Spearman rank correlation test suggested no correlation between R2 number and transcript level ( r = 0 . 28 , p = 0 . 19 ) . We next determined if R2 transcript levels correlated with the fraction of the rDNA units inserted with R2 . Because the 3′ ends of all R1 and R2 elements are identical , blots of genomic DNA digested with select restriction enzymes place the uninserted rDNA units , the 3′ ends of R1-inserted units and the 3′ ends of R2-inserted units on different sized fragments . These blots when hybridized with a segment of the 28S rRNA gene immediately downstream of the R1 insertion site reveal the percentage of the total rDNA units that are of each class [30] , [31] . To score those R2 copies that are inserted in the same rDNA unit as an R1 element , a second genomic digest was conducted which placed the 3′ ends of R2 elements in single and double inserted units on different sized restriction fragments and probed with the 3′ end of the R2 element ( probe 3 , Figure 1 ) [10] , [24] . The fraction of the rDNA locus inserted with R2 elements ( both full-length and 5′ truncated ) varied from 16% to 28% ( Table 1 ) . Plotted in Figure 6B is the fraction of the rDNA units inserted with R2 versus the R2 transcript levels . About 17% of the rDNA units in the lowest R2 transcript lines were inserted with R2 , while about 23% of the rDNA units were inserted in the highest transcript lines . While there is a positive correlation between transcript level and fraction inserted when all lines are included ( r = 0 . 61 , p = 0 . 003 ) , there was no correlation when the six low transcript lines were removed ( r = −0 . 17 , p = 0 . 30 ) . The total number of rDNA units ( locus size ) in each of the 18 lines were estimated by dividing the number of R2 elements by the fraction of the rDNA units inserted with R2 ( Figure 7 ) . The rDNA loci varied in size from 185 units to 395 units . Shown in Figure 6C is a plot of rDNA locus size versus the R2 transcript levels . While two of the lines with the lowest levels of R2 transcript did have the two largest rDNA loci , there was no significant correlation between rDNA locus size and R2 transcript level across all 18 lines ( r = −0 . 02 , p = 0 . 48 ) . These findings suggest that unlike our study of laboratory stocks , there was no increase in the number of R2 elements in those rDNA lines with high levels of R2 transcripts . The only significant correlation was a small decrease in the fraction of inserted rDNA units in those lines with essentially no R2 transcripts . Finally , it should be noted that R1-inserted units were at low levels in all stocks ( Table 1 ) and their estimated numbers or fraction of the total rDNA units inserted did not correlate with R2 transcript levels ( data not shown ) . Because neither the size nor the composition of the rDNA locus exhibited a dramatic correlation with the level of R2 transcripts , the distribution of R2 insertions within the locus was next determined . The restriction enzyme NotI cleaves near the middle of the R2 element ( Figure 1 ) , but does not cleave R1 elements or the transcribed and intergenic spacer regions of the D . simulans rDNA units . Thus the size of genomic NotI fragments detected in a line reveals the R2-to-R2 spacing within its rDNA locus; however , 3–12 highly 5′ truncated R2 copies in each line ( see Figure 4 ) will not be mapped by this approach . To determine the distribution of R2 elements in the rDNA loci of the low and high transcript lines , nuclei were isolated from 0–23 hr embryos , embedded in agarose , gently lysed and the DNA digested with NotI [26] . The DNA fragments were separated by pulsed-field electrophoresis , transferred to nitrocellulose and hybridized with a DNA fragment from the 18S rRNA gene . The six lines with the highest levels of R2 transcription as well as six lines with the lowest levels of R2 transcripts were used for this study ( Figure 8 ) . The pulse times for electrophoresis were established to optimize the separation of the largest NotI fragments generated from the rDNA loci . Under these conditions NotI fragments below 100 kb are not well separated from the buffer front moving through the gel . Thus those portions of the rDNA locus containing R2 elements separated by less than eight rDNA units ( the average rDNA unit size is about 12 kb ) are not present in the figure . These closely spaced R2-inserted units represent a small fraction of the rDNA locus ( ∼25% ) but a large fraction of the R2 elements ( ∼75% ) . The D . simulans lines with highest levels of R2 transcripts had on average smaller NotI fragments than the lines with low levels of R2 transcripts . This difference in size was significant , whether one used only the largest NotI fragment ( ∼800 kb for the low lines and 540 kb for the high lines , Mann-Whitney U test , p = 0 . 003 ) , or the combined length of the three largest fragments in each line ( ∼1850 kb for the low lines and 1200 kb for the high lines , p = 0 . 004 ) . This finding suggests that of the various properties of the rDNA locus that were measured , the distribution of R2 elements within the locus appeared to best serve as an indicator of whether R2-inserted units were transcribed . However , even this difference in the spacing of R2-inserted units is subtle in that changes in the location of only a small number of R2 elements within the locus could shift the profile from the low to high transcript patterns .
The goal of this study was to apply what had been learned about R2 activity from our studies of laboratory stocks to characterize the pattern of R2 activity in natural populations of D . simulans . In our previous analyses nuclear run-on transcription experiments indicated that control over R2 element activity was at the level of transcription , while crosses between active and inactive lines revealed that this transcriptional control mapped to the site of all R2 insertions , the rDNA locus on the X chromosome [26] . Therefore , in this study iso-rDNA locus lines were established from two natural populations and their levels of R2 transcripts determined within a few generations of isolation . In both populations , about one-half of the lines had levels of R2 transcripts that were very low to undetectable . In the remaining lines , the level of R2 transcript varied in a continuous manner over a wide range . In the 18 lines studied as representative of the R2 transcript range , a positive correlation was detected between R2 transcript level and R2 retrotransposition activity . While this correlation was not absolute , transcript level appeared to serve as a reliable indicator of potential R2 activity and thus a major means of control over R2 activity in a population . Structural analysis of the rDNA loci of the 18 lines indicated that the increased R2 transcription was not a result of the greater accumulation of R2 copies or changes in the size of the rDNA locus . The property of the rDNA locus that best correlated with the level of R2 transcripts was the size of the largest continuous stretch of units free of R2 insertions . This suggestion that it is a difference in the distribution of R2 elements within the rDNA locus that controls R2 transcription adds considerable support to our previously proposed model of R2 regulation [26] . This model was based on several known properties concerning the transcription of rDNA units and their R2 insertions [26] . First , R2 elements do not appear to encode their own promoter; rather their RNA transcripts are processed from a co-transcript with the 28S rRNA gene [26] , [32]–[35] . Second , the size of the rDNA locus is sufficiently large that only a fraction of the rDNA units encoded by an organism are transcribed at any one time [23] , [36]–[39] . Third , electron microscopic observations suggest that transcriptionally active rDNA units occur in contiguous blocks rather than as dispersed units throughout the locus [40] , [41] . Based on these observations we proposed that arthropods have evolved the ability to identify and actively transcribe one or more regions of the rDNA locus that contain the lowest frequency of R2-inserted units [26] . Thus significant transcription of R2 elements occurs when an organism is unable to identify large continuous regions of the rDNA locus free of insertions . In the laboratory stocks used in our previous study , there was on average a two-fold greater number of R2 elements in active lines compared to the inactive lines . As a consequence it was not possible to exclude the model that the greater number of R2 insertions in the rDNA loci led to higher transcription levels . In our natural lines , on the other hand , the number of R2 insertions was similar between the active and inactive lines , suggesting that it is R2 distribution , not number , that determines whether R2-inserted units are transcribed . Our study also suggests that it may be the location of only a few R2 elements that determines the level of R2 activity within a line . A repositioning of only a small number of R2 elements could change the NotI digestion pattern of the low transcript lines to that of the high transcript lines ( Figure 8 ) . In addition , our determination of transcript level was based on the level of full-length R2 transcripts , while the R2 distribution pattern was based on the presence of a NotI site near the middle of the R2 element . Thus the positioning of 5′ truncated R2 copies , that are of a length to retain the NotI site but not able to generate full-length transcripts , within the regions of lowest R2 abundance could explain why some lines low transcript lines ( e . g . CS22 ) have NotI profiles more similar to a high transcript line . Our analysis of the individual R2 elements present in the rDNA loci of each line ( Figure 4 ) revealed that each rDNA locus had different collections of R2 insertions suggesting that R2 elements are continually being gained and lost within a population . The rDNA loci should , therefore , be viewed as a continuously changing landscape with the individual loci shifting between low and high levels of R2 transcription . These continuously changing rDNA loci can explain another difference between natural and laboratory stocks . Of the 15 lines isolated from Paradise , CA and maintained in the laboratory for about 10 years , about one-fourth ( 4 lines ) had extremely high levels of R2 transcripts and retrotransposition [24] , [26] . However , of the 180 lines established from the Atlanta and San Diego populations , only 2% had transcript levels as high as that seen in the laboratory stocks , and only one of these lines had levels of retrotransposition as high as the laboratory stocks . While it is formerly possible that the Paradise population simply had higher levels of R2 activity , it appears more likely that during the prolonged period the Paradise stocks were maintained in the laboratory the inbreeding and relaxed selection conditions permitted the accumulation of the higher levels of R2 elements observed in these active laboratory stocks . Such shifts to high R2 element activity may also occur in the natural populations from Atlanta and San Diego , but such flies would be lost from the populations by natural selection . We have found that laboratory stocks with high levels of R2 activity are readily out-competed when crossed to laboratory stocks containing low levels of R2 activity ( D . Eickbush , unpublished data ) . One important feature of R2 control detected in our previous study of laboratory stocks was the dominance of expression of rDNA loci with low levels of R2 transcripts over loci with high levels of R2 transcripts [26] . In females containing one rDNA locus from a stock with high levels of R2 transcription ( R2 active locus ) and one locus from a stock with no R2 transcription ( R2 inactive locus ) , rDNA units in the R2 inactive loci were preferentially transcribed . We termed this ability to turn off an entire rDNA locus nucleolar dominance , as it appeared similar to the frequent dominance of one rDNA locus in interspecies hybrids [42] . To determine if such dominance could also be detected between our natural rDNA loci , we conducted reciprocal crosses between the most active line in this study , CS24 , to several inactive lines . In the heterozygous female offspring of both crosses the high level of R2 transcript seen from the CS24 loci was significantly turned off , consistent with the dominance of R2 inactive loci in natural populations ( data not shown ) . However , such crosses involving lines with the more typical lower levels of transcripts were often not recessive to R2 inactive stocks . Thus , while more experiments are needed , nucleolar dominance may only influence loci with extremely high levels of transcripts . Based on our survey of the two D . simulans populations we can estimate an R2 retrotransposition rate for the entire population . Comparing the rate of R2 retrotransposition with the level of transcripts ( Figure 5 ) , about 50% of the rDNA loci in the two populations are likely to undergo no retrotransposition . The remaining rDNA loci have a high probability of supporting some level of retrotransposition . For the 12 rDNA loci we sampled from this group , 46 retrotranspositions were detected when 16 chromosomes were monitored in the eighth generation giving a rate of 0 . 034 events/chromosome/generation [46 events / ( 12 lines ×7 generations ×16 chromosomes ) ] . As a lower limit , if we exclude the one extremely active line from this analysis the R2 retrotransposition rate drops to 0 . 019 events/chromosome/generation . Because only 5′ truncated R2 elements , which represent about half the total number of elements , were monitored in our assays , these rates should be multiplied by a factor of two ( range of 0 . 038–0 . 068 ) . Finally , correcting for the fraction of the rDNA loci that have detectable levels of transcription ( 50% ) the total R2 retrotransposition rate for the population can be estimated at 0 . 019–0 . 034 events/chromosome/generation . While this range is only a rough estimate , it is consistent with computer simulations conducted to mimic the types of recombination and rates of retrotransposition likely to be present in the rDNA loci of Drosophila [23] . The findings in this report address one of the major questions concerning the ability of transposable elements to maintain themselves within a species lineage . Does the control mechanism of the cell occasionally breakdown , or is this mechanism simply incapable of preventing all new insertions ? In the case of R2 our findings support the latter possibility . In most organisms , large numbers of R2 elements can be effectively prevented from transcription by expressing the rDNA units in only a small domain that does not contain R2 insertions . However , recombination events ( in particular unequal crossovers ) will expand , contract and rearrange the rDNA units in the locus meaning these domains free of R2 insertion are not permanently stable . Thus it is not a difference in the cellular control mechanism between animals in a population , but rather differences in their rDNA loci that determines whether R2 elements are being transcribed . The long-term stability of R2 elements is assured because even though all copies can be effectively silenced for long periods of time , the transcription of some copies will eventually be resurrected by recombination . Of course transcriptional control may not be the only level at which the activity of R2 elements are regulated . While retrotranspositions have never been detected in a stock with no detectable R2 transcripts , there may not be a complete correlation between the level of R2 transcripts and the rate of retrotransposition . Retrotransposition events were difficult to detect in one of the lines with the highest level of R2 transcripts ( Figure 5 ) . In one of our laboratory stocks , retrotransposition events were more frequent than expected based on R2 transcript levels [26] . Thus additional post-transcriptional steps appear to influence the rate of R2 retrotransposition in D . simulans . Further studies are needed to determine if these steps represents significant control mechanisms over R2 activity . Finally , this population study not only provides additional support for a domain model of transcription in the rDNA locus but also addresses two evolutionary questions raised by our earlier studies . First , why do the different copies of R2 generated by retrotransposition usually remain single-copy ( i . e . are not duplicated by recombination ) until they are eventually lost from the genome [24] , [27] , [35] ? Second , why in long-term studies of specific rDNA loci did the number of uninserted rDNA units change most rapidly than the inserted units [29] ? Both of these finding can be explained if recombination events in the rDNA loci predominantly occur in the insertion free region of the rDNA locus activated for transcription . Thus an understanding of R2 location and control is needed to appreciate the forces at work in the expression and evolution of the entire rDNA locus .
Isofemale stocks of D . simulans collected in San Diego , CA were generated by Peter Andolfatto . D . simulans females of Atlanta population were collected by Todd Schlenke in Atlanta , GA . The Beadex ( Bx ) marker line was obtained from Allen Orr . The single rDNA locus of D . simulans is located on the X chromosome . The rDNA locus of the isolated stocks was followed using Bx , a dominant wing phenotype marker that is the closest available marker to the rDNA locus in D . simulans . First ( Atlanta ) or second generation ( San Diego ) male progeny of collected females were crossed to Bx females . F1 females from these crosses were then backcrossed to the original male . F2 females homozygous for the wild rDNA loci ( XwXw ) were crossed to XwY F2 males to generate the individual lines . Because heterozygous Bx females ( XBxXw ) do not always express the Bx wing phenotype , three single pair crosses between an F2 female and an F2 male were performed . Only if all F3 males from a pair showed the normal wing phenotype was that line considered to contain only the wild-type rDNA locus ( iso-rDNA lines ) . One iso-rDNA locus line was derived from each female collected from the natural population . Because recombination at a low level can occur between Bx and the rDNA locus , the origin of the rDNA loci was confirmed in those lines used for more extensive studies by comparing the R2 5′ truncation profiles of multiple males with the R2 5′ truncation profiles of the original male and the Bx stock . Genomic DNA was extracted from single flies as described by Gloor et al . [43] . To compare the R2 elements between lines as well as count the total number of R2 elements in each line , the 5′ ends of all R2 insertions were analyzed by PCR amplification . The forward primer , 5′-TGCCCAGTGCTCTGAATGTC-3′ , which annealed to 28S gene sequences 80 bp upstream of the R2 insertion site , was 32P-5′-end-labeled and used with the following series of reverse primers that annealed to R2 sequences at various distances from the 5′ end of a full-length element: 3 . 6 kb ( 5′-GTATGGAAATCTATCGAAAGATACT-3′ ) , 3 . 1 kb ( 5′-GTCACCTGCGGCTTCGAATC-3′ ) , 2 . 8 kb ( 5′- CCCCTTGTAGTACGAGACTTC-3′ ) , 2 . 6 kb ( 5′-GCCGGACGCGATAACAATTC-3′ ) , 2 . 0 kb ( 5′-GATAGAAAATCCAACGTTCTGTC-3′ ) , 1 . 6 kb ( 5′-TCGAATGCCTTGCTTACATC-3′ ) , 1 . 3 kb ( 5′-GAAGACGGTTCTGGCCAGTC -3′ ) , 1 . 0 kb ( 5′-CGCTGGACGACAGCATACTGC-3′ ) , 0 . 4 kb ( 5′-CATCAAGTTCGTCTGGGTGC-3′ ) and 0 . 1 kb ( 5′-GACTTGAGTAAAGGAGAGACT-3′ ) . The labeled PCR products were separated on an 8% high voltage denaturing polyacrylamide gel , exposed to a PhorsphorImager screen , and quantitated with a Molecular Dynamics PhorsphorImager scanner using ImageQuant software . Most PCR bands ( est . 80% ) were of equal intensity and corresponded to single copies of the R2 element . To quantify the bands derived from multiple copies of R2 with identical length 5′ ends , the intensity of each PCR band was calculated with a regression analysis using the many single copy variants as reference [29] . While labor intensive , we have found this approach to be more accurate than quantitative PCR or relative hybridizations [24] , [27] , [29] , [35] . To score R2 retrotransposition and deletion events , the upstream ( forward ) primer without end-labeling was used with the above set of reverse PCR primers , separated on 8% native polyacrylamide gels and stained with ethidium bromide . Total RNA was extracted as previously described [35] from 30 adult females of each line in the third generation . Ten micrograms of RNA were separated on 1% agarose , 2 . 2 M formaldehyde gels , the RNA transferred to GeneScreen Plus , and hybridized with an anti-sense RNA probe from the 5′ end of the R2 element ( probe 1 , Figure 1 ) as previously described [26] . The relative levels of 3 , 600 nt full-length R2 transcripts were quantitated on a PhorsphorImager . As a control for RNA loading and quality , all R2 hybridization signals were standardized by monitoring the level of alcohol dehydrogenase hybridization on the same blots [26] . As a control for hybridization efficiency , an equal aliquot of RNA isolated from the previously characterized lab stock , line 58 [24] was included in each blot . Genomic DNA was extracted from 30–40 adult females from each line in the fifth generation . To determine the proportions of uninserted , R1-inserted and R2-inserted rDNA units , the genomic DNA was digested with ClaI and PstI , fractionated through a 1% agarose gel , and transferred to nitrocellulose . The blot was probed with a segment of the 28S gene ( probe 2 , Figure 1 ) located downstream of the R2 insertion site [29] . Uninserted units gave rise to a 2 . 3 kb ClaI-ClaI fragment , R1-inserted and doubly inserted units to a 1 . 5 kb PstI-ClaI fragment , and R2-inserted units to a 1 . 3 kb PstI-ClaI fragment . To determine the proportions of R1 and R2 double-inserted units , another aliquot of genomic DNA was digested with MspA1I and hybridized with a fragment from the 3′ end of R2 ( probe 3 , Figure 1 ) . The sequence and location of this probe were described by Perez-Gonzalez and Eickbush [10] . Because R2 insertions are located a short distance upstream of R1 insertions , R2-single inserted units gave rise to a 2 . 2 kb hybridizing fragment , while R2 / R1-double inserted units gave rise to a 1 . 3 kb fragment . The size of the rDNA locus was estimated as the number of R2 elements divided by the fraction of the rDNA locus containing R2 insertions . All data on the fraction of rDNA units of each insertion class represents the mean and standard error obtained from four separate blots . Nuclei isolation from 0–23 hour embryos , suspension in 1% InCert agarose , DNA purification , and NotI restriction digestion were conducted as previously described [26] . The digested nuclei plugs were subjected to pulsed-field electrophoresis in 1% agarose at 12°C on a CHEF-DRII apparatus ( Promega ) for 30 hours at 175 volts with switch times of 10 to 30 seconds . The DNA transfer to nitrocellulose and the hybridization a fragment of the 18S rRNA gene were conducted as previously described [26] . | Transposable elements are abundant selfish components of all eukaryotic genomes . Despite the elaborate mechanisms eukaryotes have evolved to control these elements , they continue to proliferate . Here , we study R2 retrotransposons , highly successful elements that only insert into a site within the 28S rRNA genes of animals . In two natural populations of Drosophila , we show that R2 activity is directly linked to the level of R2 transcripts within each fly . The level of R2 transcripts is in turn linked to properties of the tandemly arranged rRNA genes , also known as the nucleolar organizer or rDNA locus . R2 transcript levels appear to depend upon the distribution of R2-inserted units within the rDNA locus , rather than the number of R2 insertions or the total size of the locus . Our findings suggest that R2 transcripts and hence activity can be prevented only when a fly can identify one or more large regions of the rDNA locus free of R2-inserted units . Because recombinations continually change the number and position of rDNA units with R2 insertions , individual loci switch between R2 inactivity and activity , thus perpetuating the long-term survival of R2 elements . |
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Buruli ulcer ( BU ) is a chronic , indolent necrotizing disease of the skin and underlying tissues caused by Mycobacterium ulcerans , which may result in functional incapacity . In 2002 , Médecins Sans Frontières ( MSF ) opened a BU programme in Akonolinga Hospital , Cameroon , offering antibiotic treatment , surgery and general medical care . Six hundred patients have been treated in the project to date . However , due to the nature of the disease and its stigmatization , determining the exact prevalence and burden of disease is difficult and current estimates may not reflect the magnitude of the problem . The objectives of this survey were to estimate the prevalence of BU in the health district of Akonolinga , describe the geographic extension of the highly endemic area within the health district , and determine the programme coverage and its geographical distribution . We conducted a cross-sectional population survey using centric systematic area sampling ( CSAS ) . A 15×15 km grid ( quadrats of 225 km2 ) was overlaid on a map of Akonolinga district with its position chosen to maximize the area covered by the survey . Quadrats were selected if more than 50% of the quadrat was inside of the health district . The chiefdom located closest to the centre of each quadrat was selected and Buruli cases were identified using an active case finding strategy ( the sensitivity of the strategy was estimated by capture-recapture ) . WHO-case definitions were used for nodules , plaque , ulcer , oedema and sequelae . Out of a total population of 103 , 000 inhabitants , 26 , 679 were surveyed within the twenty quadrats . Sensitivity of the case finding strategy was estimated to be 84% ( 95%CI 54–97% ) . The overall prevalence was 0 . 47% ( n = 105 ) for all cases including sequelae and 0 . 25% ( n = 56 ) for active stages of the disease . Five quadrats had a high prevalence of >0 . 6% to 0 . 9% , 5 a prevalence >0 . 3% to 0 . 6% and 10 quadrats <0 . 3% . The quadrats with the high prevalence were situated along the rivers Nyong and Mfoumou . Overall coverage of the project was 18% ( 12–27% ) for all cases and 16% ( 9–18% ) for active cases , but was limited to the quadrats neighbouring Akonolinga Hospital . Prevalence was highest in the area neighbouring the Nyong River . Coverage was limited to the area close to the hospital and efforts have to be made to increase access to care in the high prevalence areas . Use of the CSAS method was particularly useful for project planning and to identify priority areas of intervention . An added benefit of the method is that the survey procedure incorporated an awareness campaign , providing information about the disease and treatment to the population .
Buruli ulcer ( BU ) is a neglected tropical disease caused by Mycobacterium ulcerans , belonging to the same family of organisms causing tuberculosis and leprosy . Awareness about the public health importance of the disease was raised in 1998 by the World Health Organisation ( WHO ) initiative [1] . BU affects predominantly children between 5 and 15 years . The clinical lesions of BU generally start as a painless subcutaneous nodule that secondarily ulcerates , presenting characteristic undermined edges . M . ulcerans produces a toxin , mycolactone , which destroys the skin and the subcutaneous tissues , induces necrosis and ulcerations . Ulcers are chronic , indolent and mainly located on the legs and arms . Some patients develop osteomyelitis and joint lesions . Natural evolution of the disease may lead to spontaneous healing but in the absence of early detection and appropriate treatment , the disease can extend , disseminate and leave functional incapacity [2] . Clinical diagnosis for the ulcerative form is straightforward for trained medical staff , although more difficult for the nodules , plaque and oedematous forms [3] . Based on some observational studies , the WHO recently recommended the use of the combination of Rifampicin/Streptomycin for BU treatment [4] . However , surgery remains important for BU treatment . In the early stages of infection , surgery is curative and highly cost effective , since it requires a simple excision followed by an immediate closure . In the disease's later stages , wide excisions , including healthy tissues , are needed to stop the infection and prevent recurrence or relapse at the same site . This is followed by skin grafting and requires long hospital stays [5] . As long as the mode of transmission is not understood , and in the absence of an effective vaccine , control strategies promoting early detection and treatment have achieved the best results in limiting morbidity and costs associated with the disease [6] . Although BU has been reported in 30 countries in Africa , Asia and the Western Pacific [7] , determining the exact prevalence and burden of disease is difficult and current estimates may not reflect the magnitude of the problem . These difficulties include un-diagnosed cases due to fears of stigmatization , little knowledge of the disease among both the population and health workers and the variability in clinical presentation of the disease . Further , BU occurs primarily in remote rural areas where the population may have limited access to health care and the disease is not notifiable in many countries [8] . Prevalence estimates are needed for appropriate resource allocation and to plan control strategies . In Cameroon , BU cases have been reported in 6 provinces , Adamaoua , Central , South , South-East , East and Extreme North . A national survey identified Akonolinga as a health district of high prevalence [9] . BU endemic areas are located along the Nyong River ( Ayos and Akonolinga health districts ) with an estimated prevalence of 0 . 44% in 2001 [10] . Recently , several risk factors were identified including swamp wading , wearing shorts , lower-body clothing while farming , living near cocoa plantation or wood and using adhesive bandages when hurt [11] . In 2002 , Médecins sans Frontières ( MSF ) opened a BU project in Akonolinga District , one of the 135 health districts of Cameroon , in collaboration with the local and national health authorities of Cameroon . The project was set up in Akonolinga Hospital , with a passive case detection strategy . To date , 600 BU patients have been treated in the MSF project , which offers antibiotic treatment , surgery and general medical care . Most patients present late to the Akonolinga Hospital , presenting mainly with ulcerative lesion ( about 80% ) and advanced stages of BU . A study conducted in 2004 in the district described stigmatisation of BU patients and reported that traditional healers were the first source of treatment [12] . In March 2007 we conducted a cross-sectional survey to: 1 ) estimate the prevalence of BU in the target population of the project; 2 ) to estimate the proportion of BU cases visiting the MSF project at least once ( coverage ) ; and 3 ) to estimate the proportion of patients visiting another service provider such as a traditional healer or peripheral health centre at least once ( health seeking behaviour ) . We also aimed to describe the spatial distribution of the prevalence as well as that of health seeking behaviour to help target the most affected areas and to address access problems for certain communities .
Akonolinga health-district is a 1-hour drive from Yaoundé in the department of Nyong and Mfoumou . The health district is at its longest distance approximately 70 km east to west , and 100 km north to south . It has a surface of approximately 4500 km2 . The district hospital is in Akonolinga , situated in the geographic centre of the district . The Akonolinga health-district has a total population of 103 , 000 inhabitants . We performed a cross-sectional survey using centric systematic area sampling ( CSAS ) . CSAS has been used successfully in past research in malnutrition and other low prevalence diseases [13] . It is particularly well suited to situations where the disease is visible , of low prevalence and where geographic distribution of prevalence and program coverage is of interest . A 15×15 km grid ( quadrats of 225 km2 ) was overlaid on a map of Akonolinga district with its position chosen to maximize the area covered by the survey . Quadrats were selected if more than 50% of the quadrat was inside of the health district , resulting in 20 quadrats identified . Our sampling unit was the chèferie ( chiefdom ) , the lowest administrative unit in Cameroon . The chèferie located closest to the centre of each quadrat was selected . Chèferies may be comprised of one to several villages . If the total population of the selected chèferie was below 1000 persons , the next closest chèferie was also included to obtain our required sample size as discussed below . Population information for the selected chèferies was obtained from the chief of each chèferie and crosschecked with the Chief's Office , Department of Nyong and Mfoumou and the Department of Development that compared the figures with the 2005 census . All inhabitants of the chèferies were invited to participate in the study . To estimate an expected prevalence of all forms of BU of 0 . 6% with 0 . 1% precision , our required sample size was18 , 742 inhabitants . Using the same prevalence estimate , and assuming 50% program coverage with 10% precision , our required sample size was 83 cases , corresponding to a population of 13 , 900 persons . We used the WHO case-definition of BU [2] , limiting the definition of an active case to nodule , plaque , oedema and ulcer ( Table 1 ) . We did not include the WHO papula stages because of the very low specificity of the clinical signs and considering that this clinical form is quite rare in West Africa . Sequelae were defined as having a history of BU and complications resulting directly from the lesion ( e . g . , restricted limb movement , amputation , organ loss ) . Disfiguring stellar scars not associated with disabilities were not considered as sequelae . Our secondary endpoints concerned program coverage , specifically attending the BU hospital , the Ministry of Health ( MOH ) Health Centre or a traditional healer for BU treatment . We defined “covered” as having visited the service at least once to seek treatment for the presenting BU lesion . BU cases were identified using a combined active case finding strategy . In a preparation phase , all selected chèferies were visited . A meeting was held in Akonolinga with the chiefs of all the selected villages , to explain the survey . Health delegates from the MOH network received two days of training in survey procedures . In each village , the survey started with a meeting , to explain the objectives of the survey and the clinical signs of BU . Villagers were informed that , on a specified day , a medical team would come and screen every suspect case of BU at a central location . Traditional healers were contacted and informed and asked to send their patients to this central screening location . They were assured that there would be no attempt to take patients away from them . Special attention was paid to ensure that key informants ( women leaders , traditional healers , village leaders ) understood the objectives of the survey and the different clinical forms of BU , making every effort to use non-medical terms . During the meeting , the villagers were also informed about the second part of the active case finding strategy which consisted of house to house visits by health delegates , identifying suspect cases in the household and informing them personally about the central screening . The chief introduced the health delegates identified for this task to the community . Health delegates had at least one week between the village meeting and the day of the central screening to perform the house to house visits . They provided information about the survey and identified suspected BU cases . They discussed the fear of stigmatisation with suspected cases , and arranged individual meetings with the medical team for suspected patients who did not want to come to the central meeting point , or arranged for transport for disabled patients . They asked for oral consent of suspected cases identified for possible inclusion in the survey . They also collected information on patients who were living in a household but at the time of the survey were admitted at Akonolinga Hospital . These patients were interviewed at the hospital . A team comprised of one doctor/nurse experienced in BU , one medical assistant and one interviewer performed the consultations and interviews at the central screening location . The lesion was inspected , measured and categorized according to clinical criteria using the clinical case definitions . A short standardized questionnaire was administered , inquiring when the first symptoms started and where the patients were seeking care . When relevant , patients were asked why they did not go to Akonolinga Hospital . Regular field visits were made to supervise the patient interviews and questionnaire procedures . For the statistical analyses , CSAS was treated as a random sample [14] . Prevalence ( P ) was computed as ( detected cases/population ) * ( 100/sensitivity ( % ) of the case finding strategy ) *100 . Coverage was calculated as the number of detected cases who had visited the healthcare provider/total number of detected cases . Capture/recapture was used to estimate the sensitivity of the case finding strategy . The estimated total number of patients ( N ) was N = {[ ( M+1 ) ( C+1 ) ]/ ( R+1 ) }−1 [15] with M representing the cases detected by central location screening , C the cases detected by the combined active case finding strategy , and R cases that were detected by both strategies . Since it is impossible to have fractions of cases , the estimated value for N was rounded up to the nearest whole number [16] , [17] . Sensitivity of the combined case finding strategy was computed as S = C/N . Answers to open questions in the questionnaire about health care seeking behaviour were noted word-for-word and coded in a content analysis . Resulting codes were grouped in categories . Authorization to conduct the survey was granted by the Ministry of Health , Department of Research . Approval from the National Ethical Committee ( 012/CNE/MP/07 ) and from the Ethical Committee of MSF was obtained . Informed written consent was asked from all ulcer patients who participated in the survey .
The overall prevalence of all BU cases was 4 . 7/1000 ( 95%CI: 4 . 1–7 . 3/1000 ) and the prevalence for active BU cases was estimated as 2 . 5/1000 ( 95%CI: 2 . 2–3 . 9/1000 ) . Prevalence estimates per quadrat were categorized as low , middle or high . For active cases , categories were: 0 to 2 cases/1000; >2/1000 to 4 /1000 and >4/1000 to 6/1000 ( Figure 1 ) . For all BU cases , categories were: 0 to 3 cases/1000; >3/1000 to 6 /1000; and >6/1000 to 9/1000 inhabitants ( Figure 2 ) . The spatial distribution of prevalence estimates per quadrat showed that quadrats with high estimates were predominantly situated along the Nyong and Mfoumou rivers ( Figures 1 and 2 ) . Of the 105 cases identified , 19 cases had visited Akonolinga Hospital resulting in an estimated coverage for the MSF BU project of 18% ( 95%CI 11–27% ) . A total of 23 patients visited the health centre at least once , leading to an estimated coverage of the peripheral MoH health centres of 22% ( 95%CI 15–31% ) . A high number of patients ( 77/105 ) consulted a traditional healer at least once , yielding a coverage of 73% ( 95%CI 64–81% ) . Coverage estimates per quadrat were classified in 5 categories . Coverage for all BU cases for the MSF project was above 60% in the quadrat including the Akonolinga Hospital; Health centre coverage was above 60% in 3 quadrats; and above 60% in 17 quadrats for traditional healers coverage ( Table 2 ) . The same trend in coverage was seen for active BU cases . Coverage of the MSF project was 16% ( 95%CI 9–28% ) and of the peripheral MOH Health centres was 23% ( 95%CI 14–36% ) , while coverage of traditional practitioners was 61% ( 95%CI 48–73% ) . The geographic distributions of the coverage of the three health care providers ( Hospital , health centres and traditional healers ) are shown in Figure 3 . The quadrate including the hospital had the highest hospital coverage and the lowest traditional healer coverage . We visited three chèferies ( 2700 inhabitants ) in the health district that were not part of the survey sample to estimate the sensitivity of the case finding strategy . We found 8 cases by central location screening ( M ) , 11 cases by combined active case finding strategy ( C ) , and 7 cases were found by both strategies ( R ) . The total estimated number of cases was 13 . The sensitivity of the active case finding method was 84 . 6% ( 95% CI: 53 . 7–97 . 3% ) . Out of 86 patients who did not present to Akonolinga Hospital , 79 answered the question on motive for non-attendance . In the content analysis , a total of 87 reasons were coded . Twenty-five ( 31 . 6% ) patients answered that they did not have enough money; 14 ( 17 . 7% ) that the hospital was too far; 22 ( 27 . 8% ) mentioned a lack of information; 13 were not aware of the services offered at the hospital; 9 didn't know it was free of charge; and 12 ( 15 . 2% ) said that they did not want surgery .
Prevalence and mapping of Buruli ulcer is one of the priority areas identified by the research subgroup at the 5th WHO Advisory Group Meeting on Buruli Ulcer held in March 2002 . Studies , like the one reported here , were noted as most likely to provide immediate direct benefit to Buruli ulcer patients in the medium term . The use of CSAS allowed for the identification of areas with relatively high prevalence and low coverage . An added benefit of the method is that the survey procedure also served as an awareness campaign , providing information about the disease and treatment to the population . As previously described ( 4 , 8 ) , cases identified were predominantly younger than 15 years and more often male than female . This underlines once again the importance of BU as a potentially severe disabling disease that occurs at a young age . Most of the active cases ( 85 . 7% ) identified presented with an ulcerative form of the disease , corresponding to what is seen at admission to the hospital ( 81 . 6% ) . The overall prevalence was 0 . 47% ( n = 105 ) for all BU cases including sequelae and 0 . 25% ( n = 56 ) for active stages of the disease in accordance with a survey conducted previously in the region ( 8 ) . Because of the lack of comparable survey in other regions it is difficult to compare our results to other estimates [18] , but they corresponds to the estimated prevalence of tuberculosis in 2006 in Cameroon [19] and are slightly higher than the 6000 cases detected in a national survey in Ghana in 1999 [8] . Quadrats with higher prevalence were situated along the Nyong and Mfoumou Rivers confirming reports that cases are often found near slow moving water . The quadrat of the area of the hospital had a high prevalence of BU , but the two other high prevalence areas were at a distance of 25 to 40 km from the hospital in the southwest of the health district . Overall coverage of the MSF project was disappointing with 18% ( 12–27% ) for all cases and 16% ( 9–18% ) for active cases and was limited to quadrats neighbouring Akonolinga hospital . If we combine the geographical distribution of prevalence and coverage , we can identify the southwest of the health district as a priority area for intervention with high prevalence and low project coverage estimates . The coverage of the MOH health centres ( 22% ) was slightly higher than the coverage of the hospital . It is important to note that this was also the case in some of the areas furthest from hospital and indicates the importance of decentralization and a functioning referral system . The high proportion of patients having visited the traditional healers , particularly in remote areas , underlines the importance of reviewing possibilities to integrate traditional healers in the project approach . Buruli ulcer represents a financial burden for the patients and the health structures [20] Reasons given for not choosing the hospital as a health care provider were mainly financial and distance to the hospital . Lack of information about the existence of the project was also mentioned . Information on free treatment and decentralization towards health centres of diagnosis , antibiotic treatment and daily dressing , could remove barriers and rapidly improve coverage , especially when first targeting areas with high prevalence and low coverage . A total of 15% of patients who had not gone to the hospital said it was because they did not want surgery . Providing information on the multidisciplinary combination of BU treatments ( antibiotics , dressing , surgery , physiotherapy and nutrition ) and giving patients the possibility to make an informed choice on which part of the treatment to accept might reduce fears and improve collaboration with traditional healers . The quadrat including Akonolinga town presented a high prevalence and a high coverage . Since this quadrat had presumably a high population density , the overall prevalence and coverage for the health district might be slightly underestimated [11] . Population estimates of the sampled chèferies were obtained from the chiefs of the villages and confirmed by the prefecture and did not vary largely among the quadrats . Although all chiefs and all health delegates completed the same training , trust among health delegates , chiefs and villagers is a social reality that might influence the sensitivity of case finding and might vary largely . This will remain a weakness of active case finding strategies that are based on existing social networks . In conclusion , this method was easy to use . It provided estimates of overall prevalence and coverage and identified high prevalence and low coverage areas for intervention . In addition the survey can be considered an information and awareness campaign in itself that also allowed to create a network of health delegates trained on Buruli ulcer that might refer patients in future . | As long as there is no strategy to prevent Buruli ulcer , the early detection and treatment of cases remains the most promising control strategy . Buruli ulcer is most common in remote rural areas where people have little contact with health structures . Information on the number of existing cases in the population and where they go to seek treatment is important for project planning and evaluation . Health structure based surveillance systems cannot provide this information , and previous prevalence surveys did not provide information on spatial distribution and coverage . We did a survey using centric systematic area sampling in a Health District in Cameroon to estimate prevalence and project coverage . We found the method was easy to use and very useful for project planning . It identified priority areas with relatively high prevalence and low coverage and provided an estimate of the number of existing cases in the population of the health district . The active case finding component of the method used served as an awareness campaign and was an integrated part of the project , creating a network of health delegates trained on Buruli ulcer . |
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The pathogenic yeast Cryptococcus neoformans causes fungal meningitis in immune-compromised patients . Cell proliferation in the budding yeast form is required for C . neoformans to infect human hosts , and virulence factors such as capsule formation and melanin production are affected by cell-cycle perturbation . Thus , understanding cell-cycle regulation is critical for a full understanding of virulence factors for disease . Our group and others have demonstrated that a large fraction of genes in Saccharomyces cerevisiae is expressed periodically during the cell cycle , and that proper regulation of this transcriptional program is important for proper cell division . Despite the evolutionary divergence of the two budding yeasts , we found that a similar percentage of all genes ( ~20% ) is periodically expressed during the cell cycle in both yeasts . However , the temporal ordering of periodic expression has diverged for some orthologous cell-cycle genes , especially those related to bud emergence and bud growth . Genes regulating DNA replication and mitosis exhibited a conserved ordering in both yeasts , suggesting that essential cell-cycle processes are conserved in periodicity and in timing of expression ( i . e . duplication before division ) . In S . cerevisiae cells , we have proposed that an interconnected network of periodic transcription factors ( TFs ) controls the bulk of the cell-cycle transcriptional program . We found that temporal ordering of orthologous network TFs was not always maintained; however , the TF network topology at cell-cycle commitment appears to be conserved in C . neoformans . During the C . neoformans cell cycle , DNA replication genes , mitosis genes , and 40 genes involved in virulence are periodically expressed . Future work toward understanding the gene regulatory network that controls cell-cycle genes is critical for developing novel antifungals to inhibit pathogen proliferation .
About 500 million years of evolution separate the fungal phyla Ascomycota and Basidiomycota [1 , 2] . The cell cycle is an essential biological process driving cell division of these distantly related yeasts , and therefore may be under strong selective pressure for conservation . Both Saccharomyces cerevisiae ( Ascomycota ) and Cryptococcus neoformans ( Basidiomycota ) can grow and divide asymmetrically in a budding yeast form . C . neoformans is a causative agent of deadly fungal meningitis , primarily in immune-compromised patients [3 , 4] . Many groups studying C . neoformans focus on virulence factors for human infection , such as the yeast’s polysaccharide capsule , melanin production , Titan cell formation , and others [5–9] . We propose that the function of cell-cycle regulators , which are essential for proliferation in the host , merit further investigation as virulence factors . Furthermore , there is evidence that virulence pathways are perturbed when cell-cycle progression is slowed , which suggests direct connections between cell-cycle regulators and virulence pathways [10 , 11] . The cell cycle is the process by which a cell duplicates its contents and faithfully divides into two genetically identical cells . In eukaryotes , a biochemical oscillator drives sequential cell-cycle events , where the cyclin-dependent kinase ( CDK ) and its variety of cyclin binding partners initiate events by phosphorylation , followed by destruction of kinase activity in mitosis by the anaphase-promoting complex ( APC ) . Another common feature of the eukaryotic cell cycle is a temporally regulated program of transcription , which has been demonstrated in S . cerevisiae , Schizosaccharomyces pombe , Arabidopsis thaliana , mouse fibroblasts , and human tissue culture cells [12–22] . These programs of periodic genes include cyclin mRNAs , DNA replication factors , APC activators , and other cellular components that are utilized at specific times during the cell cycle . Our group and others have proposed that this “just-in-time transcription” mechanism is an important aspect of energy-efficient and faithful cell divisions [23 , 24] . In S . cerevisiae , an interconnected network of periodic transcription factors ( TFs ) is capable of driving the periodic program of cell-cycle gene expression [15 , 25–27] . Aspects of this yeast TF network are conserved in human cells; for example , G2/M genes are activated by a periodic forkhead domain-containing TF in both eukaryotes [22 , 28] . The topology of cell-cycle entry is also functionally conserved , where a repressor ( S . c . WHI5 , H . s . RB1 ) is removed by G1 cyclin/CDK phosphorylation to activate a G1/S transcription factor complex ( S . c . SBF/MBF , H . s . E2F-TFDP1 ) [29] . However , the genes involved in cell-cycle entry are not conserved at the sequence level between fungi and mammals [30] , suggesting that the fungal pathway could be targeted with drugs without affecting mammalian host cells . Sequence-specific DNA-binding TFs have been identified in C . neoformans and phenotypically profiled by single gene knockouts [6 , 31 , 32] . This TF deletion collection was profiled over many virulence factor-inducing conditions to discover pathways that regulate disease and drug response genes [32] . Serial activation of TFs during capsule production has also been studied to elucidate the order in which TFs control virulence gene products [31] . However , the cell cycle has not been investigated in synchronous populations of cells to date . Although the phenotypes of some single mutant cell-cycle TFs have been examined from asynchronous populations , these studies offer limited understanding of temporal aspects of gene expression during the cell cycle . Here we investigate transcriptional dynamics of the pathogenic yeast C . neoformans using cells synchronized in the cell cycle . We compare our findings to the cell-cycle transcriptional program in S . cerevisiae . We find that a similar percentage of all genes ( ~20% ) are periodically transcribed during the cell cycle , and we present a comprehensive periodicity analysis for all expressed genes in both yeasts . We show that S-phase gene orthologs are highly conserved and temporally precede M-phase gene orthologs in both yeasts . Additionally , we find that many TFs in the cell-cycle entry pathway are conserved in sequence homology , periodicity , and timing of expression in C . neoformans , while others , notably genes involved in budding , are not . We also identify 40 virulence genes that appear to be cell-cycle-regulated , along with nearly 100 orthologous fungal genes that are periodic in the same cell-cycle phase . Taken together , these cell-cycle genes represent candidates for further study and for novel antifungal drug development .
Identifying approaches for synchronizing populations of C . neoformans has been challenging . We succeeded in synchronizing by centrifugal elutriation , a method that has been very successful for S . cerevisiae cells [15 , 27 , 33] . For C . neoformans , we isolated early G1 daughter cells by centrifugal elutriation and released the population into rich media ( YEPD ) at 30°C to monitor cell-cycle progression , as described previously [34] . This size-gradient synchrony procedure is conceptually similar to the C . neoformans synchrony procedure presented by Raclavsky and colleagues [35] . For S . cerevisiae , we isolated G1 cells by alpha-factor mating pheromone treatment [36] . We utilized this synchrony technique to isolate larger S . cerevisiae cells and to offset some loss of synchrony over time due to asymmetric cell divisions . A functional mating pheromone peptide for C . neoformans has been described but is difficult to synthesize in suitable quantities [37] . After release from synchronization , bud formation and population doubling were counted for at least 200 cells over time ( Fig 1 ) . The period of bud emergence was about 75 minutes in both budding yeasts grown in rich media , although the synchrony of bud emergence after the first bud in C . neoformans appeared to be less robust ( Fig 1A and 1B ) . Each yeast population completed more than two population doublings over the course of the experiments . Total RNA was extracted from yeast cells at each time point ( every 5 minutes for S . cerevisiae , or every 10 minutes for C . neoformans ) and multiplexed for stranded RNA-Sequencing . Between 87–92% of reads mapped uniquely to the respective yeast genomes ( S1 File ) . To identify periodic genes , we applied periodicity algorithms to the time series gene expression datasets . Four algorithms were used to determine periodicity rankings for all genes in each yeast: de Lichtenberg , JTK-CYCLE , Lomb-Scargle , and persistent homology [38–42] . Since each algorithm favors slightly different periodic curve shapes [43] , we summed the periodicity rankings from each algorithm and ranked all yeast genes by cumulative scores for S . cerevisiae and for C . neoformans ( S1 Table and S2 Table , respectively ) . By visual inspection , the top 1600 ranked genes in both yeasts appeared periodically transcribed during the cell cycle ( S1 Fig ) . There was no clear “threshold” between periodic and non-periodic genes during the cell cycle—rather , we observed a distribution of gene expression shapes and signatures over time ( S1 Fig ) . Previous work on the S . cerevisiae cell cycle has reported lists ranging from 400–1200 periodic genes . To validate our RNA-Sequencing time series dataset for the S . cerevisiae cell cycle , we compared the top-ranked 1600 periodic genes to previously published cell-cycle gene lists and found a 57–89% range of overlap with previous periodic gene lists ( S2 Fig ) [12–15 , 33 , 41 , 44 , 45] . Three filters were applied to each budding yeast dataset to estimate and compare the number of periodic genes ( S1 File ) . First , we pruned noisy , low-expression genes from each dataset , leaving 5913 expressed genes in S . cerevisiae ( S1 Table ) and 6182 expressed genes in C . neoformans ( S2 Table ) . Next , we took the top 1600 expressed genes from the cumulative ranking of the four periodicity algorithms described above . Finally , we applied a score cutoff to each list of top 1600 genes using the Lomb-Scargle algorithm ( see S1 File ) [39 , 40 , 43] . We estimated that there are 1246 periodic genes in S . cerevisiae ( ~21% expressed genes ) and 1134 periodic genes in C . neoformans ( ~18% expressed genes ) ( Fig 2 ) . We also provided multiple criteria for evaluating the cell-cycle expression patterns of individual genes in each yeast ( S1 Table , S2 Table , S1 Fig ) . Cellular processes that contribute to virulence are a major focus of work in the C . neoformans field . We took advantage of the partial C . neoformans deletion collection and genetic screens for virulence factors [6] and searched for periodic virulence genes . We found that 40 genes ( about 16% of the virulence genes characterized by the Madhani group and many previous studies ) were periodically expressed in C . neoformans during the cell cycle ( S3 Table ) . These virulence genes are periodic during normal cycles in rich media , which suggests that some virulence processes are directly cell-cycle-regulated . For example , budding and cell wall synthesis are coupled to cell-cycle progression in S . cerevisiae . A subset of 14 periodic virulence genes in C . neoformans had capsule and/or cell wall phenotypes reported in previous studies ( S3 Table ) . We then asked if the 40 periodic virulence genes might be co-regulated during the C . neoformans cell cycle ( S3 Fig ) . Over half of the periodic virulence genes clustered together and peaked in a similar cell-cycle phase ( 20–30 minutes into cycle 1 ) . 11 of the 14 capsule / cell wall genes were contained in this cluster ( S3 Fig , S3 Table ) . Next , we wanted to ask if periodicity and temporal ordering of orthologous genes is evolutionarily conserved between the two budding yeasts . We compiled the largest list to date of putative sequence orthologs between C . neoformans and S . cerevisiae from the literature , databases , and additional BLAST searches ( S1 File , S4 Table ) [32 , 46–48] . About half of the periodic genes from each yeast ( Fig 2 ) had at least one sequence ortholog in the other species . However , there were only about 230 pairs of orthologous genes that were labeled periodic in both yeasts . Those pairs of periodic orthologs have diverged in temporal ordering between C . neoformans and S . cerevisiae ( Fig 3 , S5 Table ) . These results indicated that the programs of periodic gene expression , and possibly the regulatory pathway , have diverged to some degree between the two budding yeasts . This altered temporal ordering between S . cerevisiae and C . neoformans periodic orthologous genes was likely not due to the experimental synchrony procedure . We obtained transcriptome data from two previous studies on S . cerevisiae cell-cycle-regulated transcription ( which applied a different cell-cycle synchrony procedure , used different lab strains of S . cerevisiae , and/or measured gene expression on different platforms ) , and our list of periodic S . cerevisiae genes maintained temporal ordering during the cell cycle in all three datasets ( S4 Fig ) . Cell-cycle regulated gene expression has also been investigated in a species of pathogenic Ascomycota , Candida albicans [49] . To ask about common periodic gene expression in an evolutionarily intermediate budding yeast species , we further identified putative periodic orthologous genes shared between S . cerevisiae , C . neoformans , and C . albicans . A core set of almost 100 orthologs appeared to have both conserved periodicity and temporal ordering between all three budding yeasts ( S5 Fig , S5 Table ) . This fungal gene set was enriched for functions in mitotic cell cycle and cell-cycle processes , which suggested that core cell-cycle regulators are under strong selection for conservation at the sequence level and by timing of periodic gene expression . We reasoned that some cell-cycle events must be invariable in temporal ordering between fungi ( S5 Fig ) . DNA replication ( S-phase ) should be highly conserved across organisms because duplication of genetic material is essential for successful division . Segregation of genomic content during mitosis ( M-phase ) is also essential for division , and duplication must precede division . Using annotations for S . cerevisiae [50] we identified lists of genes known to be involved in regulating events in various cell-cycle phases including bud formation and growth [51 , 52] , DNA replication [53 , 54] , and spindle formation , mitosis , and mitotic exit [55–58] . We filtered the resulting gene lists by periodicity in S . cerevisiae ( Fig 2A , S6 Table ) . We then identified orthologous genes in C . neoformans without enforcing a periodicity filter . We have previously shown that expression timing of canonical cell-cycle orthologs in S . cerevisiae and S . pombe can vary—some gene pairs shared expression patterns while others diverged [59] . To temporally align orthologous gene plots between S . cerevisiae and C . neoformans , we used the algorithmic approach described previously with S . cerevisiae and S . pombe time series transcriptome data [59] . The first , most synchronous cycle of budding data from each yeast was fit using the CLOCCS algorithm ( Fig 1 , S6 Fig ) [59 , 60] . Time points in minutes were then transformed into cell-cycle lifeline points to visualize the data ( see S1 File ) . As observed previously , S . cerevisiae genes that regulate budding , S-phase , and mitosis were largely transcribed periodically in the proper phases ( Fig 4A , 4D and 4G ) [12–15] . Cell-cycle gene expression peak time patterns were examined to quantitatively compare cell-cycle phases ( S7 Fig ) . Bud assembly and growth genes peaked throughout the cell-cycle transcription program , and the temporal ordering of these genes repeated across cell cycles ( Fig 4A , S7A and S7B Fig ) . Similarly , spindle assembly and mitosis genes peaked in the mid-to-late phases of the transcription program ( Fig 4G ) . DNA replication genes peaked in a defined window in the middle phase of the transcription program ( Fig 4D ) . We observed analogous expression patterns for C . neoformans orthologs associated with S-phase and mitosis ( Fig 4E and 4H ) , but orthologs associated with budding appeared to be expressed with less restriction to a discrete cell-cycle phase or strict temporal order ( S7 Fig ) . This budding gene pattern can be observed qualitatively where the unrestricted expression timing creates a more “speckled” appearance in the C . neoformans heatmap ( Fig 4B ) and differentially timed gene expression peaks ( Fig 4C ) . We hypothesize that bud emergence and bud growth are not as tightly coordinated with cell-cycle progression in C . neoformans cells . Unlike S . cerevisiae where bud emergence occurs primarily at the G1/S transition , C . neoformans bud emergence can occur in a broad interval from G1 to G2 phases [61 , 62] . The difference in budding transcript behaviors between S . cerevisiae and C . neoformans orthologs could therefore reflect the difference in the cell biology of bud emergence and growth ( Fig 4A and 4B ) . Only about 33% of the orthologous budding gene pairs were periodically expressed in C . neoformans , compared to 53% DNA replication and 61% mitosis orthologs ( Fig 4B , 4E and 4H ) . Furthermore , budding orthologs that were periodic in both C . neoformans and S . cerevisiae showed some divergence in expression timing ( Fig 4C ) . We also observed that bud emergence of C . neoformans cells during the time series appeared less synchronous in second and third cycles than S . cerevisiae cells ( Fig 1A and 1B ) . Bud emergence in C . neoformans could be controlled by both stress pathways and TF inputs because the first budding cycle is highly synchronous after elutriation synchrony , which causes a transient stress response in released cells ( Fig 1B ) . However , our data do not rule out a model where some budding genes in C . neoformans are controlled post-transcriptionally by localization , phosphorylation , or other periodic mechanisms . It is also possible that budding orthologs are more difficult to identify than other cell-cycle genes due to sequence divergence or that novel budding genes have evolved in the C . neoformans lineage . We have previously shown that a network of periodically expressed TFs is capable of driving the program of periodic genes during the S . cerevisiae cell cycle [15 , 27] . We hypothesized that a network of periodic TFs could also function in C . neoformans to drive a similar fraction of cell-cycle genes . Thus , the temporal re-ordering of part of the C . neoformans gene expression program ( Fig 3 ) could be explained by two models: evolutionary re-wiring of shared network TFs with S . cerevisiae or novel TF network components arising in C . neoformans to drive cell-cycle genes . First , we asked if network TFs were conserved from S . cerevisiae to C . neoformans . Indeed , a majority of network TFs and key cell-cycle regulators have putative orthology between the two yeasts ( Table 1 ) [30] . As observed for other cell-cycle genes ( Fig 4 ) , orthologs of some network TFs were expressed in the same phase in both yeasts , while others were expressed at different times ( Table 1 ) . Second , we asked if there were any novel periodic TFs in C . neoformans ( i . e . TFs with no predicted ortholog in S . cerevisiae , or TFs with an ortholog in S . cerevisiae that is not known to function in the TF network ) . We constructed a list of periodic C . neoformans TFs by filtering a previously annotated transcription factor list [32] with our list of periodic genes ( Fig 5 , S7 Table ) . Indeed , 30 novel TF genes were periodic during the C . neoformans cell cycle ( Fig 5A ) . Taken together , results from Table 1 and Fig 5 suggested that both network TF re-wiring and novel periodic TFs in C . neoformans could explain the differential ordering of periodic genes during the cell cycle ( Fig 3 ) . Putative S-phase regulators in C . neoformans exhibited transcript behaviors that were very similar in periodicity and in ordering to their S . cerevisiae orthologs ( Fig 4D–4F ) . Thus , we predicted that the network motifs and TFs controlling the transcription of periodic S-phase genes could be conserved . Orthologous genes in the G1/S topology were largely conserved in periodic expression dynamics at cell-cycle entry ( Fig 6 ) . The expression timing of some genes had shifted earlier in the C . neoformans cell cycle ( Fig 6C and 6F , Table 1 ) , but this result does not refute the hypothesis that these genes are activated and functional at G1/S phase . Therefore , the network topology of cell-cycle entry appeared largely conserved in C . neoformans both by sequence and by gene expression dynamics . The prediction of this model is that a common G1/S transcriptional network drives a common set of S-phase periodic genes . To test this model , we examined promoter sequences from TF network genes in S . cerevisiae and C . neoformans , as well as the promoters of 38 periodic DNA replication ortholog pairs , and did an unbiased search for enriched TF binding sequences . The core motif “ACGCGT” for SBF/MBF transcription factors [63–65] was identified in both S . cerevisiae and C . neoformans promoters . The motif was not enriched in randomly selected periodic gene promoters , suggesting that SBF/MBF is functionally conserved in C . neoformans to drive TF network oscillations and DNA replication gene expression ( S8 Fig ) .
Here , we present the first RNA-Sequencing dataset of transcription dynamics during the cell cycle of C . neoformans . Despite evolutionary distance between Basidiomycota and Ascomycota , S . cerevisiae and its extensive genome annotation provided an excellent analytical benchmark to compare to cell-cycle transcription in C . neoformans . RNA-Sequencing has been shown to be more quantitative than microarray technology for lowly- and highly-expressed genes using asynchronous S . cerevisiae cells due to microarray background fluorescence and saturation of fluorescence , respectively [66] . We demonstrate that 20% or more of all genes in the budding yeast genomes are periodically transcribed during the cell cycle . A ranking of periodicity for transcript dynamics in C . neoformans is provided ( S2 Table ) . For the sake of comparison , we have presented gene sets of 1100–1200 periodic genes with the highest relative periodicity scores as “cell-cycle-regulated”; however , there is a continuum of periodic gene expression dynamics during the cell cycle in both yeasts ( S1 Fig ) . The four periodicity algorithms applied here yielded a range of periodicity scores with no clear distinction between “periodic” and “non-periodic” gene sets ( S1 and S2 Tables ) . These results suggest that yeast mRNAs fluctuate in expression with various degrees of cell-cycle periodicity . We propose that the top 20% periodic genes presented in this study are directly regulated by periodic cell-cycle TFs in C . neoformans and in S . cerevisiae . We also posit that some of the remaining 80% genes are weakly cell-cycle regulated . For example , some genes could be subject to complex regulation with one regulatory input from a cell-cycle periodic TF and another input from a constitutively expressed TF . We raise two important questions about the yeast periodic gene expression programs: is periodic expression of a core set ( s ) of genes required for the fungal cell cycle , and how are periodic gene dynamics controlled in each yeast ? In both yeasts , periodic transcription is a high dimensional cell-cycle phenotype because transcriptional state reflects the phase-specific biology of the cell cycle over repeated cycles ( Fig 2 and Fig 4 ) . In other words , G1- , S- , and M-phase genes follow a defined temporal ordering pattern . S . cerevisiae cells synchronized by different methods and/or grown in different conditions display similar ordering of periodic cell-cycle genes , despite different cell-cycle period lengths ( S4 Fig ) . Here , we examined the transcriptome of cycling C . neoformans cells at 30°C . Other groups have shown that C . neoformans cells spend more time in G1 phase at 24°C [67] . We predict that future studies examining cell-cycle transcription of C . neoformans cells grown in different conditions ( i . e . non-rich media or 37°C infection temperature ) would continue to display a similar temporal ordering of cell-cycle genes . These findings provide more evidence that “just-in-time transcription” is a conserved feature of eukaryotic cell cycles [23] . We show that some orthologous periodic genes have diverged in temporal ordering during the cell cycles of S . cerevisiae and C . neoformans over evolutionary time ( Fig 3 ) . We specifically investigated genes that play a role in bud emergence and bud growth , and we find that many budding gene orthologs are not controlled in a defined temporal order during the C . neoformans cell cycle ( Figs 1A , 1B , 4A and 4B ) . On the other hand , DNA replication and mitosis genes do appear to be conserved by sequence homology , periodic expression , and temporal ordering ( Fig 4D–4I ) . Lastly , we find that a set of about 100 orthologous genes is both periodic and expressed in proper cell-cycle phase in the budding yeasts S . cerevisiae , C . neoformans , and C . albicans ( S5 Fig ) [49] . These findings suggest that there may be a conserved set of fungal cell-cycle-control genes , which represent novel therapeutic targets for fungal infections . We posit that a network of periodic transcription factors ( TFs ) could control the periodic gene expression program in C . neoformans , which has been shown in S . cerevisiae and suggested in human cells [15 , 22 , 25 , 27] . Many orthologous genes to S . cerevisiae TF network components have diverged in expression timing in C . neoformans cells ( Table 1 ) . However , we show that the G1/S network topology is likely conserved between S . cerevisiae and C . neoformans because orthologous genes display similar expression dynamics ( Fig 6 ) . Furthermore , we find that the promoters of G1/S TF network orthologs and promoters of periodic DNA replication orthologs are enriched for an “ACGCGT” sequence motif , which matches the SBF/MBF binding site consensus in S . cerevisiae ( S8 Fig ) [63–65] . Therefore , we propose that the G1/S transcriptional motif—where a co-repressor is removed by G1 cyclin/CDK phosphorylation and a TF activator complex is de-repressed—is also conserved in C . neoformans ( Fig 6B–6D and 6G ) [29 , 30] . Downstream of the G1/S activator complex , the C . neoformans TF network may also contain a common forkhead domain S-phase activator and homeobox domain G1/S repressor ( Fig 6E , Table 1 ) [14 , 68 , 69] . This partially conserved TF network model in C . neoformans explains the common G1/S topology , on-time DNA replication gene transcription , as well as differential expression of budding and other cell-cycle genes by divergent parts of the TF network . The regulation of periodic transcription and the function of a putative TF network warrant further investigation as virulence factors of fungal meningitis caused by C . neoformans . It has been previously shown that fluconazole drug treatment can affect cell ploidy in C . neoformans [70] . More recently , polyploid Titan cells were shown to produce haploid and aneuploid daughter cells during C . neoformans infection [71] . Therefore , future work on proper regulation of DNA replication and the contribution of periodic gene products could greatly benefit our understanding of genome stability in C . neoformans . The C . neoformans TF deletion collection was recently phenotyped , and the potential of targeted TF therapies was discussed [32 , 72] . We have added to the C . neoformans genotype/phenotype map by documenting the functional outputs of cell-cycle TFs over synchronized cell cycles . We also propose that a conserved G1/S topology of cell-cycle TFs may initiate the cell-cycle transcription network in C . neoformans . It is possible that a multi-drug combination targeting cell-cycle regulators and previously characterized virulence pathways could yield more successful antifungal therapies [72] . For example , a combination therapy could target TFs at the conserved G1/S topology to slow cell-cycle entry and also target fungal cell wall or capsule growth . In the circadian rhythm field , it has been shown that drugs targeting Clock Controlled Genes are most potent when administered at the time of the target gene’s peak expression [73] . Interestingly , deletion of the known SBF/MBF ortholog , Mbs1 ( CNAG_07464 ) , is viable in C . neoformans [32 , 74] . These genetic results do not match S . cerevisiae , where swi4 mbp1 double mutants are inviable [75] . In fact , deletion of the single known G1 cyclin ortholog , CNAG_06092 , is also viable in C . neoformans [10] . Mbs1 and the G1 cyclin are likely important for cell-cycle progression in C . neoformans because mutant phenotypes are highly defective in capsule formation in G1 phase , melanin production , and response to Hydroxyurea treatment during S phase [10 , 11 , 32 , 74] . However , the genetics are inconsistent with findings in S . cerevisiae and warrant further investigation to characterize the G1/S TF network topology of C . neoformans . It is possible that uncharacterized , redundant genes exist in the C . neoformans G1/S network motif . We find that 40 candidate virulence genes are periodically expressed during the C . neoformans cell cycle ( S3 Table , S3 Fig ) . An important direction for future work is to identify the mechanistic links between cell-cycle regulators and virulence pathways . 14 periodic virulence genes have annotated phenotypes in capsule formation and/or cell wall secretion . Fungal cells must secrete new cell wall and capsule during growth , and the direct links between cell cycle and these virulence factors in C . neoformans warrants further study because the cell wall and capsule are not present in host cells . The ultimate goal of this work is to identify the regulatory mechanism of periodic gene expression in C . neoformans and to find optimal drug targets and combination therapies for disrupting the fungal cell cycle .
The wild-type Saccharomyces cerevisiae strain is a derivative of BF264-15D MATa bar1 [76 , 77] . The wild-type Cryptococcus neoformans var . grubii serotype A strain is a derivative of H99F [47] . Yeast cultures were grown in standard YEP medium ( 1% yeast extract , 2% peptone , 0 . 012% adenine , 0 . 006% uracil supplemented with 2% dextrose sugar ) . For centrifugal elutriation , cultures were grown in YEP-dextrose ( YEPD ) medium at 30°C overnight . Elutriated early G1 cells were then resuspended in fresh YEPD medium at 30°C for time series experiments . For α-factor arrest , cultures were grown in YEPD medium at 30°C and incubated with 30 ng/ml α-factor for about 110 minutes . Synchronized cultures were then resuspended in fresh YEPD medium at 30°C . Aliquots were taken at each time point and subsequently assayed by RNA-Sequencing . Total RNA was isolated by acid phenol extraction as described previously [34] . Samples were submitted to the Duke Sequencing Facility ( https://www . genome . duke . edu/cores-and-services/sequencing-and-genomic-technologies ) for stranded library preparation and sequencing . mRNA was amplified and barcoded ( Illumina TruSeq Stranded mRNA Library Preparation Kit for S . cerevisiae and KAPA Stranded mRNA-Seq Library Preparation Kit for C . neoformans ) and reads were sequenced in accordance with standard Illumina HiSeq protocols . For S . cerevisiae , libraries of 50 base-pair single-end reads were prepared , and 10 samples were multiplexed and sequenced together in each single lane . For C . neoformans , libraries of 125 base-pair paired-end reads were prepared ( due to larger and more complex yeast transcriptome with introns ) , and 12 samples were multiplexed and sequenced together in each single lane . Raw FASTQ files were aligned to the respective yeast genomes using STAR [78] . Aligned reads were assembled into transcripts , quantified , and normalized using Cufflinks2 [79] . Samples from each yeast time series were normalized together using the CuffNorm feature . The normalized output FPKM gene expression levels were used in the analyses presented . A detailed description of each analysis pipeline is presented in the S1 File . RNA-Sequencing gene expression data from this manuscript have been submitted to the NCBI Gene Expression Omnibus ( GEO; https://www . ncbi . nlm . nih . gov/geo/ ) under accession number GSE80474 . | The opportunistic fungal pathogen Cryptococcus neoformans infects immune-compromised humans and causes fungal meningitis by proliferating in the central nervous system . The cell cycle has not been studied at the whole transcriptome level in C . neoformans . Here , we present the expression dynamics of all genes from a synchronous population of C . neoformans cells over multiple cell cycles . Our study shows that almost 20% of all C . neoformans genes are periodically expressed during the cell cycle . We also compare the program of cell-cycle-regulated transcription in C . neoformans to the well-studied but evolutionary distant yeast , Saccharomyces cerevisiae . We find that many orthologous cell-cycle genes are highly conserved in expression pattern ( e . g . DNA replication and mitosis genes ) , while others , notably budding genes , have diverged in expression ordering . We also identify 40 virulence genes from previous studies that are periodically expressed during the C . neoformans cell cycle in rich media . Our findings indicate that a conserved set of fungal transcription factors ( TFs ) controls the expression of conserved cell-cycle genes , while other periodic transcripts are likely controlled by species-specific TFs . |
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The interactions that occur during HIV Pr55Gag oligomerization and genomic RNA packaging are essential elements that facilitate HIV assembly . However , mechanistic details of these interactions are not clearly defined . Here , we overcome previous limitations in producing large quantities of full-length recombinant Pr55Gag that is required for isothermal titration calorimetry ( ITC ) studies , and we have revealed the thermodynamic properties of HIV assembly for the first time . Thermodynamic analysis showed that the binding between RNA and HIV Pr55Gag is an energetically favourable reaction ( ΔG<0 ) that is further enhanced by the oligomerization of Pr55Gag . The change in enthalpy ( ΔH ) widens sequentially from: ( 1 ) Pr55Gag-Psi RNA binding during HIV genome selection; to ( 2 ) Pr55Gag-Guanosine Uridine ( GU ) -containing RNA binding in cytoplasm/plasma membrane; and then to ( 3 ) Pr55Gag-Adenosine ( A ) -containing RNA binding in immature HIV . These data imply the stepwise increments of heat being released during HIV biogenesis may help to facilitate the process of viral assembly . By mimicking the interactions between A-containing RNA and oligomeric Pr55Gag in immature HIV , it was noted that a p6 domain truncated Pr50Gag Δp6 is less efficient than full-length Pr55Gag in this thermodynamic process . These data suggest a potential unknown role of p6 in Pr55Gag-Pr55Gag oligomerization and/or Pr55Gag-RNA interaction during HIV assembly . Our data provide direct evidence on how nucleic acid sequences and the oligomeric state of Pr55Gag regulate HIV assembly .
The assembly of the HIV particle is orchestrated by the HIV-1 Gag precursor protein ( Pr55Gag ) . As the major structural protein forming the virus , it is essential that Pr55Gag packs tightly with other Pr55Gag molecules to shape the virus shell while also encapsulating the genomic RNA required for its replication . Fittingly , Pr55Gag is comprised of multi-functional domains that facilitate key interactions involved in the early stages of the assembly process . The nucleocapsid ( NC ) domain is responsible for genomic RNA packaging and the capsid ( CA ) domain mediates the key interactions to promote Pr55Gag-Pr55Gag oligomerization ( for review see [1–3] ) . While both Pr55Gag-nucleic acid and Pr55Gag-Pr55Gag interactions have been identified as major contributors to the assembly process [4] , the mechanistic details of how they regulate HIV assembly are not well defined , specifically in the context of full length Pr55Gag . It is generally accepted that nucleic acid binds to Pr55Gag and acts as a scaffold for Pr55Gag oligomerization [5–7] , although short oligonucleotides ( 10 nucleotides or less ) are not sufficient to bridge multiple Pr55Gag molecules to support assembly [8 , 9] . The p6 C-terminus truncated recombinant Gag protein ( Pr50GagΔp6 ) , with the addition of nucleic acid and lipid-mimicking inositol phosphate , has previously been shown to assemble in vitro , forming virus-like particles ( VLPs ) in the absence of other viral proteins [7 , 8] . Cellular imaging and immunoprecipitation studies with virion producing cells have suggested that nucleic acid initially binds to cytosolic Pr55Gag , therefore promoting the formation of low order oligomers of Pr55Gag in the cytosol; higher order Pr55Gag oligomers only occur upon Pr55Gag binding to the plasma membrane [10–13] . However , cytoplasmic HIV-1 RNA has also been shown to traffic to the membrane by passive diffusion independent of Pr55Gag , suggesting that the viral RNA genome may bind to Pr55Gag molecules on the plasma membrane where a high local concentration of Pr55Gag has already been achieved for oligomerization [14] . At the plasma membrane , the assembly of Pr55Gag has also led to the reorganization of the lipid membrane , such as the nano-clustering of phosphatidyl inositol ( 4 , 5 ) biphosphate lipid [15] . Clearly , the relationships between nucleic acid binding and Pr55Gag oligomerization that lead to virus assembly are still unclear . Moreover , the energy requirements and thermodynamic properties that drive these two basic components to facilitate the formation of the viral particle are currently not known . In this regard , a thermodynamic analysis of the assembly process will provide critical information ( such as , the affinity and the stoichiometry between ligands and substrates , plus the energetics involved in these reactions ) to define the mechanisms of the process . Specifically , enthalpic and entropic components of the binding reaction derived from the analysis will enable us to gain insight into the mechanisms of the interactions that take place . Apart from the packaging signal ( Psi ) of the genomic RNA that is involved in the encapsidation of viral genome , the contributions from the rest of the viral RNA sequence to the assembly process is not well defined . Early studies have indicated that the mature nucleocapsid domain on its own displays preferential binding to selected nucleic acid sequence motifs in addition to the Psi packaging sequences [9 , 16 , 17] . Studies comparing Psi and non-Psi RNA binding to C-terminal truncated Pr55Gag protein reported differences in the electrostatic and non-electrostatic interactions of Pr55Gag proteins with different RNAs [18] , and we have shown how Pr55Gag selected between unspliced and spliced HIV RNA in vitro for viral assembly [19 , 20] . Recent cross-linking immunoprecipitation experiments have shown that different RNA motifs interact with Pr55Gag at different stages of virus biogenesis [21] . However , the functional significance and the mechanisms of these specific Pr55Gag-RNA interactions in regulating the assembly of Pr55Gag have yet to be shown . A key bottleneck in acquiring fundamental information about the mechanistic details of Pr55Gag assembly has been the limited availability of full-length recombinant Gag protein for analysis , as large quantities of full-length recombinant Pr55Gag are difficult to produce [8 , 22] . Here , we have produced large quantities of recombinant Pr55Gag and use in vitro systems to study the early steps of HIV assembly . Using the entire form of recombinant Pr55Gag in isothermal titration calorimetry ( ITC ) studies , we present the first thermodynamic analysis of how HIV Pr55Gag and RNA regulate assembly . More specifically , we directly demonstrate that: ( 1 ) the specific interaction between Pr55Gag and Adenosine ( A ) -containing RNA motifs; and ( 2 ) the oligomerization of Pr55Gag; are energetically favourable reactions that facilitate virion particle formation . Our findings benchmark the thermodynamic regulations of retroviral assembly , and provide a platform to interrogate , and ultimately to reveal , structural and biophysical properties between HIV Pr55Gag and host cell factors during viral replication .
The binding of RNA to Pr55Gag and the oligomerization of Pr55Gag molecules are two of the basic components of viral assembly . However , a thermodynamic analysis unpacking how these two components drive the assembly process has never been conducted in the context of full length Pr55Gag . We produced sufficient quantities of recombinant full-length Pr55Gag ( S1 Fig ) and validated its capacity to oligomerize and form VLPs in the presence of RNA in vitro , similar to previous observations with the truncated Pr50GagΔp6 [8 , 23–26] ( S2 Fig ) . Using ITC , we first characterized the binding between Pr55Gag protein ( domain arrangements of Pr55Gag proteins used schematized in Fig 1A ) and the packaging signal stem loop 3 RNA ( Psi SL3 RNA ) , a region of the viral RNA involved in genomic packaging and known to bind to NC with high affinity [17 , 27] . Representative ITC curves were shown in Fig 1B , while data in Fig 1C–1F reflect the means of all three experiments . The representative ITC curves in Fig 1B are the raw values prior to adjustments ( such as fluctuations of detected temperature of the reaction; fluctuation of cabinet temperature of the ITC chamber for each point of the measurement; and the normalization of baseline temperature with the corresponding buffer controls ) . The ITC curves also acted as quality assurance with these ITC experiments , demonstrating the reliability of the data . We first benchmarked the thermodynamics properties of HIV assembly using Pr55Gag molecules that are capable of oligomerizing ( Pr55Gag and Pr50GagΔp6 ) and Psi SL3 RNA that is the primary determinant for genomic RNA selection in the cytoplasm . Both Pr55Gag and Pr55GagΔp6 displayed a favourable binding enthalpy [ΔH ~ -17 kcal mol-1] as measured by the maximum heat energy release during ITC titrations of Psi SL3 RNA into the Pr55Gag protein ( Fig 1B and 1C ) . The reaction is also thermodynamically favourable with ΔG<0 . The detected energy release in these reactions is likely to be the sum of both Pr55Gag-RNA interaction and Pr55Gag-Pr55Gag oligomerization . To estimate the fraction of energy released that is contributed by Pr55Gag-RNA vs Pr55Gag-Pr55Gag oligomerization , we have used a mutant , Pr55Gag WM316-7AA ( or Pr55Gag WM ) [28] that is incapable of supporting the dimerization of Pr55Gag via the CA-hexamer dimerization domain . Therefore , mutant Pr55Gag WM is known to partially suppress the assembly of HIV via the dimerization defect of the CA hexamers [4] . As measured by the maximum heat energy release during ITC titrations of Psi SL3 RNA into the Gag protein , interaction of Psi SL3 RNA with Pr55Gag WM displayed a favourable binding enthalpy [ΔH ~ -10 kcal mol-1] ( Fig 1B and 1C ) , but it was only 60% of the energy release of the wild type Pr55Gag-RNA interaction ( Fig 1B and 1C ) . These data implied that the oligomerization of Pr55Gag during Pr55Gag-RNA interaction contributed to at least 40% of energy detected in the ITC experiment , and the Pr55Gag- Pr55Gag interactions increased its favourable binding enthalpy with Psi SL3 RNA . In support of this , interaction of Psi SL3 RNA with both processed NC proteins ( p15NC-SP2-p6 and p7NC that lack the CA-CA interaction domains for oligomerization ) also displayed less favourable binding enthalpies [ΔH ~ -5-6 kcal mol-1] compared to the full-length Gag proteins Pr55Gag . The interaction of Pr55Gag and Pr50Δp6Gag with Psi SL3 RNA however had a more unfavourable entropic contribution compared to that of Pr55Gag WM ( Fig 1D ) . As entropy is a measure of the disorder of the reaction [29] , the unfavourable entropy likely results from loss of conformational freedom due to Pr55Gag-Pr55Gag oligomerization . Nevertheless , the larger accompanying favourable enthalpic contributions observed with Psi SL3 binding to Pr55Gag and Pr50Δp6Gag compensates for the unfavourable entropic component to produce an overall favourable Gibbs free energy ( ΔG<0 ) of binding ( Fig 1E ) , highlighting that the Pr55Gag-RNA ( SL3 ) interaction is an energetically favourable process overall . On the other hand , reactions with processed NC proteins ( p15NC-SP2-p6 and p7NC ) were characterized by favourable entropic contributions , likely driven by displacement of ordered water molecules [30] as the result of NC—Psi RNA interaction ( Fig 1D ) . The favourable entropic component of Psi SL3 binding to p15NC-SP2-p6 and p7NC , combined with the accompanying favourable enthalpic component contributed to an overall favourable Gibbs free energy ( ΔG<0 ) of binding . Interestingly , calculated binding affinities for Pr55Gag [Kd = 0 . 081 ± 0 . 025 μM] , Pr50Δp6Gag [Kd = 0 . 098 ± 0 . 003 μM] , Pr55Gag WM316-7AA [Kd = 0 . 088 ± 0 . 035 μM] , p15NC-SP2-p6 [Kd = 0 . 057 ± 0 . 011 μM] and p7NC [Kd = 0 . 048 ± 0 . 023μM] did not differ significantly ( Fig 1F ) . These data imply that the binding between Pr55Gag and SL3 RNA for genomic RNA selection during viral assembly is independent of the oligomerization state of Pr55Gag . Our ITC results demonstrate that the binding of Psi SL3 RNA to Pr55Gag is energetically favourable ( ΔG<0 ) . Bindings of Psi SL3 to p15NC-SP2—p6 and p7NC are results of both favourable entropic and enthalpic contributions , suggesting the involvement of both polar and hydrophobic interactions [30] . By definition , these interactions must occur between the Psi SL3 RNA and the nucleocapsid protein . Reactions of Psi SL3 with full length Pr55Gag that has an increased capacity to oligomerize were driven by a larger favourable enthalpy , which helps in overcoming the unfavourable entropy brought about by conformational restrictions due to oligomerization . During the assembly of HIV , there are only 2 sets of SL3 sequences in the packaged HIV dimeric RNA genome . As a result , most of the 1500–2500 Pr55Gag molecules in the assembled virion must bind to other segments of the RNA genome . HIV Pr55Gag has been previously reported to transiently interact with different RNA sequence motifs during the viral assembly process [21] . It has been suggested that cytosolic Pr55Gag consisting of low-order Pr55Gag oligomers interact with Guanosine Uridine ( GU ) -containing RNA sequence motifs , while high-order Pr55Gag oligomers in the immature virion are more likely to associate with Adenosine ( A ) -containing RNA sequence motifs ( schematized in Fig 2A ) . However , the role these non-packaging segments of the RNA genome play in viral assembly still remains unanswered . These GU-containing RNA sequences and A-containing RNA sequences are also interspersed throughout the HIV genome and in many cellular RNA sequences ( S3 Fig ) . To investigate whether the thermodynamic relationship with Pr55Gag can provide insight into the function of these sequences , we conducted ITC analyses of Gag interaction with the top 4 RNA sequence motifs that were previously identified to interact with HIV Gag either in the cytosol ( GU-containing RNA motifs: 5’GAUGG3’ and 5’UGUGG3’ ) or within the immature virion ( A-containing RNA motifs: 5’GAGAA3’ and 5’AAGGA3’ ) [21] . Binding of the 20 mer of the GU-containing RNA motif 4x 5’-GAUGG-3’ RNA to the oligomerization-impaired Pr55Gag WM ( mimicking the low-order oligomeric form of cytosolic Gag ) resulted in an enthalpy release [ΔH ~-20 kcal mol-1] ( Figs 2B and 3A ) , which is comparable to that released in the binding to processed NC proteins ( p15NC-SP2-p6 and p7NC ) ( Figs 2B and 3A ) . In contrast , higher enthalpy was released in the binding of the same 20mer GU-containing RNA motif 4x 5’-GAUGG-3’ RNA with the oligomeric forms of Gag ( Pr55Gag and Pr50GagΔp6 ) with means of ΔH ~-45 kcal mol-1 and -35 kcal mol-1 , respectively ( Fig 3A ) ( or raw values of ΔH ~-35 kcal mol-1 and -30 kcal mol-1 , respectively , in the un-adjusted representative ITC curves , Fig 2B ) , suggesting that oligomerization contributes at least in part to the favourable enthalpy release of Pr55Gag binding with GU-containing RNA . These data are consistent with those observed with Psi SL3 RNA based Pr55Gag-RNA ITC studies ( Fig 1 ) . In support of this , more enthlapy release was also observed in the interaction between the 20 mer of A-containing RNA motif 4x 5’-GAGAA-3’ RNA and the oligomeric forms of Gag ( Pr55Gag and Pr50GagΔp6 ) , with means of ΔH ~-80 kcal mol-1 ( Fig 3A ) ( or raw values of ΔH ~-70 kcal mol-1 and -60 kcal mol-1 , respectively , in the un-adjusted representative ITC curves , Fig 2B ) , over the interaction between the 20 mer of A-containing RNA motif 4x 5’-GAGAA-3’ RNA and the oligomeric-deficient forms of Gag ( Pr55Gag WM ) [ΔH ~ -60 kcal mol-1] ( Fig 3A ) ( or raw value of -40 kcal mol-1 in the un-adjusted representative ITC curve , Fig 2B ) . Agreeably , ITC analyses repeated with the second set of GU-containing and A-containing RNA sequence motifs ( 4x 5’-UGUGG-3’ and 4x 5’-AAGGA-3’ , respectively ) produced a similar trend in binding enthalpies measured across the Gag constructs ( Figs 2C and 3C ) . Our results showed that the favourable enthalpy and Gibbs Free energy release from oligomerization can potentially be a general feature of RNA-Pr55Gag protein interaction during viral assembly . Conversely , across the Gag constructs tested independent of their size and oligomeric capacity , thermodynamic analysis also indicated that the interaction of the A-containing RNA motifs with Gag were characterized by about 2-times more favourable enthalpy compared to that of the GU-containing RNA motifs ( Fig 3A and 3C ) . This result suggests that the type of RNA sequence interacting with the Gag protein also contributes to the favourable release of enthalpy . Additionally , the type of RNA sequence may also be a determinant of how tightly packed Gag can bind . An analysis of the stoichiometry of binding showed a trend that 1 A-containing RNA molecule would bind to 5 Gag molecules ( RNA:Gag N~0 . 2 ) in comparison to 1 GU-containing RNA molecule would bind to 3 Gag molecules ( RNA:Gag N~0 . 33 ) ( Fig 4 ) . However , it is important to acknowledge that the binding stoichiometry data are related to a single type of GU-containing RNA motif and a single type of A-containing RNA motif , and these motifs are artificially presented in 4 consecutive repeats within a synthetic RNA . The potential importance of different RNA-Gag binding stoichiometry must be validated using multiple different natural A-containing RNA motifs and natural GU-containing RNA motifs within the context of HIV RNA genome . Furthermore , a larger unfavourable entropy was detected when A-containing RNA motifs were used in ITC experiments compared to GU-containing RNA motifs ( Fig 3B and 3D ) , suggesting that these designated A-containing RNA motifs might in part better support tight packing of Pr55Gag-RNA and/or Pr55Gag-Pr55Gag interaction during the biological process , leading to greater loss of conformational freedom . Nevertheless , the larger favourable binding enthalpy associated with binding of A-containing RNA motifs to Pr55Gag overcomes the unfavourable entropy to drive the reaction forward . These differential thermodynamic relationships between Pr55Gag and A-containing RNA motifs in immature virus vs Pr55Gag and GU-containing RNA motifs in cytoplasm have provided insight on how viral particle formations are regulated . The Gibb’s free energy ( ΔG ) consistently remained at ~-10 kcal mol-1 ( Fig 3E and 3F ) , indicating the process is an energetically favourable spontaneous event . We have also performed additional control experiments showing that less than 5% of materials can be pelleted from solution after our ITC experiments using Gag and RNA ( S4A Fig ) . Furthermore , similar amount of pelletable materials ( <5% ) were collected in parallel ITC experiments when nucleic acid free buffer was injected into the ITC chamber containing recombinant Gag ( S4B Fig ) . These data implied that no virus-like-particles were generated in these ITC reactions under our experimental conditions when ‘low’ concentrations of Pr55Gag and RNA were used ( S4 Fig ) . In addition to the more favourable binding enthalpy of A-containing RNA to various Gag constructs , calculated binding affinities ( kd ) from ITC A-containing and GU-containing RNA binding curves revealed that oligomeric capable Pr55Gag displayed ~3-times stronger binding affinity for the immature virus A-containing RNA motif over the cytosolic GU-containing RNA motif ( Fig 5A and 5B ) . This occurrence was not observed when oligomerization-impaired forms of Gag ( Pr55Gag WM ) were used in parallel experiments ( Fig 5A and 5B ) . Unexpectedly , Pr50Δp6Gag , which is capable of high-order Gag oligomerization , also did not display binding preference toward A-containing RNA motif ( Fig 5A and 5B ) . It is possible that the p6 domain in Pr55Gag plays a part in facilitating the binding of A-containing RNA to oligomeric forms of Pr55Gag , implying a previously unknown role of p6 late domain in the packing of Pr55Gag-Pr55Gag and/or Pr55Gag-RNA interaction at the late stage of HIV assembly . To independently assess whether cytosolic low-order Gag oligomer binding with A-containing RNA is a less energetically favourable process than the interaction between high-order Gag oligomer and A-containing RNA , three additional Gag oligomerization defective mutants were engineered for analysis . These Gag oligomierization-impaired mutants are: ( 1 ) Pr55Gag ( CA Helix 6 mutations , TTSTLQ 239–44 AASALA ) , Pr55Gag [CA Helix 6] ( Fig 6A left panel ) [28 , 31]; ( 2 ) Pr55Gag ( CA Helix 10 mutation , D329A ) , Pr55Gag [CA Helix 10] ( Fig 6A middle panel ) [28 , 31]; and ( 3 ) multiple sites Gag oligomerization mutant ( designated Pr55Gag [CA All 4] ) ( Fig 6A right panel ) that includes 4 sets of mutations at the: Pr55Gag ( CA dimerization interface WM 316–7 AA ) , Pr55Gag ( CA helix 6 TTSTLQ 239–44 AASALA ) , Pr55Gag ( CA helix 10 D329A ) , and Pr55Gag CA major homology region ( MHR K290A ) [28] . These respective mutations ( ie helix 6 , helix 10 and MHR mutations ) were chosen based on their reported inhibitory effects on viral assembly [28 , 31] , likely through their disruption of important intra-hexameric contacts within the immature CA hexamer [32] ( Fig 6A ) . ITC analyses of these three Pr55Gag oligomerization impaired mutants with the 20mer of cytosol GU-containing RNA ( 4x 5’-GAUGG-3’ ) showed minimal to no detectable binding ( Fig 6A ) . In contrast , the binding of these same mutants with the 20mer of immature HIV A-containing RNA ( 4x 5’-GAGAA-3’ ) have led to energy release in the range of [ΔH ~ -18-24 kcal mol-1] ( Fig 6A ) . These data are consistent with that of the previously described oligomerization impaired mutant , Pr55Gag WM ( Fig 2 ) , showing that oligomerization of Pr55Gag can help to increase the level of energy being released during Pr55Gag-RNA interaction in HIV assembly . Given that the C-terminus His-Tag is present in all of the recombinant Gag used in this study thus far , it is unlikely that the His-Tag would selectively interfere with the binding for one of these three groups of RNA ( Psi RNA , GU-containing RNA , and A-containing RNA ) with Gag . However , to directly rule out any potential selective bias that might be introduced by the C-terminus His-Tag , a Tobacco Etch Virus ( TEV ) protease cleavage site was engineered in between the C-terminus end of Gag sequences and the His Tag for Pr55Gag , Pr50GagΔp6 , and Pr55Gag WM . The His-Tag was removed via TEV protease digestion , and the His-Tag free recombinant Gag proteins ( Pr55Gag-TEV , Pr50GagΔp6-TEV , and Pr55Gag WM -TEV ) were subsequently column purified for analysis . ITC analyses of Pr55Gag-TEV [Fig 6B left panel] , Pr50GagΔp6-TEV [Fig 6B middle panel] , and Pr55Gag WM-TEV [Fig 6B right panel] with either GU-containing RNA ( 4x 5’-GAUGG-3’ ) or A-containing RNA ( 4x 5’-GAGAA-3’ ) resulted in energy release ranging from ΔH ~ -8 kcal mol-1 to ~ -50kcal mol-1 ( Fig 6B ) . More specifically , the ΔH are: ( 1 ) ~-10 kcal mol-1 for Pr55Gag-TEV-GU-containing RNA binding [Fig 6B left panel , dark blue]; ( 2 ) ~-10 kcal mol-1 for Pr50Gag Δp6-TEV-GU-containing RNA binding [Fig 6B middle panel , dark orange]; ( 3 ) ~-10 kcal mol-1 for Pr55Gag WM-TEV-GU-containing RNA binding [Fig 6B right panel , green]; ( 4 ) ~-45 kcal mol-1 for Pr55Gag-TEV-A-containing RNA binding [Fig 6B left panel , light blue]; ( 5 ) ~-25 kcal mol-1 for Pr50Gag Δp6-TEV-A-containing RNA binding [Fig 6B middle panel , light orange]; ( 3 ) ~-25 kcal mol-1 for Pr55Gag WM-TEV-A-containing RNA binding [Fig 6B right panel , dark yellow] . Analogous to the ITC results using the His-Tag containing Gag ( Pr55Gag , Pr50GagΔp6 , and Pr55Gag WM ) ( Fig 2 ) , Gag binding with A-containing RNA ( 4x 5’-GAGAA-3’ ) were consistently shown to be a more energetically favourable reaction than parallel analyses that used GU-containing RNA ( 4x 5’-GAUGG-3’ ) as substrates ( Fig 6B ) . Furthermore , the binding between Pr55Gag-TEV and A-containing RNA was a more thermodynamically favourable reaction than that between Pr50GagΔp6-TEV and A-containing RNA , highlighting the potential role of p6 in this process . Although the C-terminus His-Tag has no selective bias on the overall data interpretation , it is important to acknowledge that reduced levels of enthalpy ( ΔH ) were also detected with the Gag-RNA binding when the C-terminus His-Tag was removed from the recombinant Gag ( Fig 2 vs Fig 6B ) , implying that the C-terminus His-Tag has consistently increased the amounts of enthalpy release during Gag-RNA interaction . It is conceivable that the synthetic 20mer RNA with the 4 consecutive A-containing RNA repeats ( 4x 5’-GAGAA-3’ ) does not truly represent the binding between HIV Gag and RNA sequences during viral assembly . To directly examine the relationship between authentic HIV RNA sequences and HIV Pr55Gag protein during viral assembly , a 37mer RNA fragment representing the coding region of HIV reverse transcriptase ( RNA positions 2671–2707 ) was used for ITC analyses . Using CLIP-sequencing , this RNA sequence ( HIVNL4 . 3RNA2671-2707 ) , consisting 3 independent A-containing RNA motifs , has been previously identified to be important for Pr55Gag binding in immature HIV [21] . The same 37mer RNA fragment with mutations of the AGAAA RNA motifs ( HIVNL4 . 3RNA2671-2707 with AGAAA mutation ) was used as a control . ITC analyses of Pr55Gag-TEV , Pr50GagΔp6-TEV , and Pr55Gag WM-TEV with the 37 mer RNA fragment HIVNL4 . 3RNA2671-2707 resulted in energy release ranging from ΔH of ~ -30 kcal mol-1 to ~ -45kcal mol-1 ( Fig 6C ) , and the level of energy release with Pr55Gag-TEV was 50% and 28% more than when Pr50GagΔp6-TEV and Pr55Gag WM-TEV were used , respectively ( Fig 6C ) . In contrast , mutations of these identified A-containing RNA motifs within these authentic HIV RNA sequences eliminated most to all of its bindings with HIV Gag proteins ( Fig 6C ) . The distinct level of energy release between Pr55Gag-TEV and Pr50GagΔp6-TEV based ITC experiments further support a potential role of p6 in the Gag-RNA and/or Gag-Gag interaction at the late stage of HIV assembly . Overall , our ITC analyses provide direct evidence that the interaction between HIV Gag and RNA for Pr55Gag oligomerization is an energetically ( ΔG<0 ) favourable reaction , and it is associated with a favourable enthalpy ( ΔH ) and unfavourable entropy ( ΔS ) . There is a general binding preference of HIV Gag toward A-containing RNA during viral assembly . Unexpectedly , our data have revealed that the p6 domain within Pr55Gag also has a role in this Pr55Gag-A-containing RNA binding preference during virion biogenesis , which has raised a new question on a novel contribution of the p6 domain in the process of Gag-RNA interaction during viral assembly .
Our thermodynamic analyses showed that Pr55Gag-RNA binding was energetically favourable in terms of free energy exchange ( ΔG<0 ) and that both Pr55Gag-Pr55Gag interactions and the type of RNA sequence can contribute to the favourable change in enthalpy . Many studies have focused on the interaction of nucleic acids with the mature forms of the NC domain to probe the chaperone activity [16 , 33] and nucleic acid binding properties of NC during reverse transcription [34 , 35] . Our ITC studies represent the first thermodynamic characterization of the binding interaction between nucleic acids and full length Pr55Gag , allowing us to study the effects of Pr55Gag-Pr55Gag interaction and Pr55Gag-RNA interaction on energy release . In the context of Pr55Gag assembly , these ITC analyses reflect the net energy exchange when nucleic acids are injected into the Pr55Gag-Pr55Gag and Pr55Gag-RNA complexes . It is important to emphasize that some of the observed energy exchange detected via ITC would be the consequence of Pr55Gag-Pr55Gag oligomerization . Our analyses between Pr55Gag and SL3 RNA suggested that at least 40% of energy release in our ITC experiment is derived from Pr55Gag oligomerization . Our ITC study with RNA and full length Pr55Gag ( that is capable of forming higher-order Pr55Gag oligomers ) suggest that the additional release of energy from Pr55Gag-Pr55Gag interactions could drive the binding of Pr55Gag with nucleic acid and the oligomerization of Pr55Gag . This interpretation is consistent with recent in vitro membrane-bound Pr55Gag assembly data showing that the genomic RNA selection by Pr55Gag and the self-assembly of Pr55Gag are interdependent [36] . Likewise , our observation that the Pr55Gag binding with immature HIV A-containing RNA motifs has a greater ΔH than Pr55Gag binding with the GU-containing cytosolic Pr55Gag-interacting RNA motifs during HIV biogenesis . These data suggest the additional heat release ( that is associated with a greater entropic penalty ) could help drive the Pr55Gag-complex to interact preferentially with A-containing RNA sequences during assembly . Furthermore , our thermodynamic analyses indicated that the oligomeric capable Pr55Gag has 3-times greater affinity for immature virus A-containing RNA motifs compared to cytosolic GU-containing RNA sequences , but that oligomerization-impaired form of Gag ( Pr55Gag WM ) does not . Similar to Pr55Gag WM , other oligomerization-impaired forms of Gag ( Pr55Gag [CA Helix6] , Pr55Gag [CA Helix 10] and Pr55Gag [CA All 4] ) are consistent to have a less favorable [ΔH] in comparison to the wild type Pr55Gag and A-containing RNA ITC analyses . The nucleic acid sequence dependence of the binding enthalpy is likely related to the process of HIV assembly and virion genesis . Early work by Campbell and Rein [37] has shown that recombinant Pr50GagΔp6 has distinct behavior with different nucleic acids during in vitro assembly . Recent work by Kutluay et al have reported that HIV Pr55Gag would have a different preference towards distinct RNA sequence during virion genesis [21] , although the precise mechanistic details of this relationship between HIV Pr55Gag and specific RNA sequences during viral assembly will require further investigation . These data allow us to propose a model for how viral assembly steps are regulated . The unfavorable entropic reaction with A-containing RNA motif and an increased binding stoichiometry between Gag and A-containing RNA sequences are also in agreement with a much tighter packing of Pr55Gag molecules in the immature virus stage over low-order oligomeric Pr55Gag in the cytoplasm . It is known that the HIV-RNA genome has a bias towards A-containing codons [38 , 39] . While many of these A-rich sequences might be a consequence of G to A hyper mutation from APOBEC3G pressure [40 , 41] , HIV has found ways to utilise them to its own gain . Indeed , these A-rich viral sequences are already known to be important for RNA trafficking from nucleus to cytoplasm via the RRE-Rev relationship [42 , 43] , and also for supporting the synthesis of viral cDNA during reverse transcription [44] . Now , we have evidence to suggest the A-rich RNA codon ( in the form of identified A-containing RNA motifs ) [21] may also have a role in regulating the viral assembly process thermodynamically . It is important to note that HIV Pr55Gag consistently displayed more favourable enthalpy [ΔH] when it binds to A-containing RNA motifs over GU-containing RNA motifs , and it is remains to be determined why HIV Pr55Gag has a binding preference with GU-containing RNA motifs in the cytoplasm and the plasma membrane [21] . One potential explanation could be that the context of how these GU-containing RNA motifs are being presented in HIV genomes ( or cellular mRNA ) is also an important determinant for Pr55Gag-RNA binding during HIV assembly , and further investigations are needed to dissect this process . Moreover , it would be important to highlight that the preference of Pr55Gag toward A-containing RNA is in part associated with the p6 domain . This unexpected observation suggests a previous unknown role of p6 in Pr55Gag-RNA interaction , and how p6 ( and potentially in conjunction with ESCRT protein ) might achieve this mechanistically would require further evaluation . Taken together , our findings show how the virus might derive the energy required to drive the Pr55Gag-Pr55Gag assembly and the mechanism by which HIV-1 assembles . We propose a model ( Fig 5C ) wherein many Pr55Gag molecules bind to GU-containing RNA in the cytosol forming low-order oligomers . The Pr55Gag-RNA complex then traffics to the plasma membrane and acts as a nucleation site for Pr55Gag assembly; with energy released from Pr55Gag-Pr55Gag and Pr55Gag-RNA interactions driving the formation of higher order Pr55Gag oligomerization complexes . The high-order Pr55Gag complex in turn interacts preferentially with A-containing viral RNA sequences with a lower Kd , and the process is further enhanced by the additional release of heat through interaction with the A-containing viral RNA sequences to assist in the packaging of the genome , thus driving the completion of the particle formation .
Recombinant Gag proteins ( Pr55Gag , Pr50Δp6Gag , Pr55Gag WM 316–7 AA , Pr55Gag [CA Helix 6 TTSTLQ 239–44 AASALA] , Pr55Gag [CA Helix 10 D329A] , Pr55Gag [CA All 4] ) and processed NC proteins ( p15NC-SP2-p6 , p7NC ) were expressed with C-term His-tag and purified as previously described [45] . Large scale production and purification of Pr55Gag is described herein . Defined medium ( DM1 ) used for seed cultures contained per litre: KH2PO4 , 13 . 3 g; ( NH4 ) 2HPO4 , 4 . 0 g; citric acid , 1 . 7 g; glucose , 10 g; MgSO4 . 7H2O , 0 . 62 g; kanamycin , 50 mg; thiamine hydrochloride , 4 . 4 mg; and trace salts solution , 5 mL . Defined medium ( DM2 ) used in the bioreactors contained per litre: KH2PO4 , 10 . 6 g; ( NH4 ) 2HPO4 , 4 . 0 g; citric acid , 1 . 7 g; glucose , 25 g; MgSO4 . 7H2O , 1 . 23 g; kanamycin , 50 mg; thiamine hydrochloride , 4 . 4 mg; and trace salts solution , 5 mL . The trace salts solution contained per litre: CuSO4 . 5H2O , 2 . 0 g; NaCI , 0 . 08 g; MnSO4 . H2O , 3 . 0 g; Na2MoO4 . 2H2O , 0 . 2 g; boric acid , 0 . 02 g; CoCl2 . 6H2O , 0 . 5 g; ZnCl2 , 7 . 0 g; FeSO4 . 7H2O , 22 . 0 g; CaSO4 . 2H2O , 0 . 5 g and H2SO4 , 1 mL . As required , glucose , magnesium , trace salts , thiamine and kanamycin were aseptically added as concentrated stock solutions to media after sterilisation . Primary seed cultures were prepared from single colonies taken from a fresh transformation plate , and grown in 10 mL of DM1 ( in a 30 mL bottle ) . The cultures were incubated at 37°C shaking at 200 rpm for 23 h . A volume ( 0 . 5 mL ) of the primary seed culture was used to inoculate 500mL of DM1 ( in a 2 L Erlenmeyer flask ) . These secondary seed cultures were incubated at 37°C shaking at 200 rpm for 16 h . Recombinant HIV Gag proteins were produced in 2 L stirred tank bioreactors connected to a Biostat B ( Sartorius Stedim , Germany ) control system . The initial volume of medium in the bioreactor was 1 . 6 L and glucose as used as the carbon source . A volume of the secondary seed culture was added to the bioreactor to attain an initial optical density ( measured at 600 nm ) of 0 . 25 . Foaming was controlled via the automatic addition of 10% ( v/v ) polypropylene glycol 2025; 3 mL of the antifoam solution ( Sigma , Antifoam 204 ) was added prior to inoculation . The pH set-point was 7 . 0 and controlled by automatic addition of either 10% ( v/v ) H3PO4 or 10% ( v/v ) NH3 solution . The dissolved oxygen set-point was 30% of saturation and a two-step cascade control was used to maintain the dissolved oxygen above the specified set-point . The agitator speed ranged from 500 rpm to 1200 rpm and airflow ( supplemented with 5% pure O2 ) ranged from 0 . 3 L min-1 to 1 . 5 L min-1 . The ratio of air to oxygen was manually changed as required . To assist with correct folding of the Gag proteins , the medium was supplemented with 50 μM ZnSO4 added 1 . 8 h after inoculation . A high cell density fed-batch process was used , with the feed solution comprised of 400 mL of 660 g L-1 glucose solution to which 40 mL of 1 M MgSO4 . 7H2O was added . The feed flow rate was 21 mL hr-1 and commenced once the initial glucose supply was exhausted ( typically 8 to 9 hr after inoculation ) . Two hours after the fed-batch process was initiated the bioreactor temperature set-point was reduced to 18°C , with culture temperature dropping to 19°C within 30 min . After cooling , protein expression was induced via the addition of 1 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) and 0 . 02% ( w/v ) arabinose and the feed flow rate reduced to 4 mL hr-1 . Cells were harvested by centrifugation ( 12000 g , 4°C , 10 min ) 22 to 23 h after inoculation and cell pellets stored at -80°C . Cell pellets ( ~200 g ) were thawed and re-suspended in 1 . 0 L of ice cold lysis buffer ( 1 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 10 mM imidazole , 1 ( v/v ) % Tween-20 , 10% ( v/v ) glycerol , 5 mM DTT ) containing 50 , 000 units of DNase I , 5 mM benzamidine-HCl and 1 mM phenyl methyl sulfonyl fluoride ( PMSF ) . The cell suspension was homogenized ( EmulsiFlex-C5 homogenizer , Avestin ) pre-chilled to 4°C , three times at 700 bar pressure . The lysate was clarified by centrifugation 12 , 000 g , 4°C , 30 min ) , and the supernatant filtered using a 0 . 45 μm Stericup filter ( Millipore ) . The supernatant was loaded at 5 mL min-1 onto a 30 mL HisTRAP fast flow IMAC column ( consisting of 6 x 5 mL cartridges connected in series; GE Healthcare ) using a MINIPULS peristaltic pump ( Gilson ) . The column had previously been equilibrated with 5 column volumes ( CV ) binding buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 10 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5 mM DTT ) . The column was washed with 10 CV wash buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 25 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5 mM DTT ) and bound proteins eluted with 5 CV elution buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 250 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5mM DTT ) . Pr55Gag eluting from the IMAC column was concentrated to ~ 3 . 0 mg mL-1 using a centrifugal concentrator ( Amicon Ultra-15 , 10 , 000 molecular weight cut-off membrane; Millipore ) . The concentrated protein ( 5x 10 mL ) was fractionated by size exclusion chromatography ( SEC ) using a Superdex 200 26/60 column ( GE Healthcare ) previously equilibrated in SEC buffer ( 50 mM TRIS-HCl , 1 . 0 M NaCl , 5 mM DTT , pH 8 . 0 ) using an ÄKTApurifier chromatography workstation ( GE Healthcare ) . Peak fractions ( UV 280nm ) containing Pr55Gag were collected , pooled and concentrated to 1–2 mg mL-1 as described above , and snap frozen in liquid nitrogen before storage at -80°C . Purified protein containing the TEV sequence is digested with 1:25 ( w/w ) TEV protease ( produced in-house ) at 4°C for 14 hrs . Efficiency of the cleavage is assessed by sodium dodecyl sulfate—poly acrylamide gel electrophoresis ( SDS-PAGE ) . A 1ml NI-NTA column pre-equilibrated with SEC buffer and the TEV digested protein is applied to the column and the flowthrough is collected and passed over the column two more times . The cleaved His6-Tag and uncut fusion protein is eluted from the column using 5 CV elution buffer ( 1 . 0 M NaCl , 50 mM TRIS-HCl pH 8 . 0 , 5 mM MgCl2 , 250 mM imidazole , 1% ( v/v ) Tween-20 , 10% ( v/v ) glycerol , 5mM DTT ) . Protein was buffer exchanged into 1x Na/K 10mM phosphate buffer ( pH 7 . 4 ) with different NaCl concentrations and concentrated to 1 mg mL-1 . Yeast tRNA ( Sigma Aldrich ) ( 10% ( w/w ) ratio of nucleic acid to protein ) was added to the protein solution and incubated for 30 min at room temperature , followed by addition of paraformaldehyde ( PFA ) ( final concentration of 0 . 2% w/v ) . After incubating the solution for further 30 min at room temperature , the crosslinking was stopped by addition of 50 μL of 3M TRIS pH 8 . 0 . Samples were centrifuged 10 min at 10 , 000 g and supernatants were analyzed by size exclusion chromatography using a Superdex 200 10/30 column ( GE Healthcare ) , previously equilibrated in phosphate-buffered saline ( PBS ) with respective NaCl concentrations . Peak fractions were concentrated to 2 mg mL-1 as previously described . Fractions were electrophorised on a NuPAGE Novex 3–8% TRIS-Acetate protein gel under denaturing conditions before being transferred onto nitrocellulose membranes for Western analysis . Pr55Gag and Pr55GagΔp6 were concentrated to 2 . 0 mg mL-1 in 50 mM TRIS pH 8 . 0 containing 1 . 0 M NaCl and 10 mM dithiothreitol and mixed with TG30 at a nucleic acid to protein ration of 4% ( w/w ) prior to dialysis against 50 mM TRIS pH 8 . 0 containing 150 mM NaCl and 10 mM dithiothreitol overnight at 4°C . Particles were imaged using both negative stain transmission electron microscopy and cryo-electron microscopy . For negative stain imaging , the stock solution was diluted 100-fold to give a single layer of well-separated particles in most fields of view . Carbon-coated grids were glow discharged in nitrogen prior to use to facilitate sample spreading . Aliquots of approximately 4 μL were pipetted onto each grid and allowed to settle for 30 s . Excess sample was drawn off with filter paper , and the remaining sample stained with a drop of 2% aqueous phosphotungstic acid . Again , excess liquid was drawn off with filter paper . Grids were air dried until required . Samples were examined using a Tecnai 12 Transmission Electron Microscope ( FEI , Eindhoven ) at an operating voltage of 120kV . Images were recorded using a Megaview III CCD camera and AnalySIS camera control software ( Olympus . ) For cryo-electron microscopy , virus particles were prepared , processed and imaged as previously described [46] . For the nucleic acid mediated in vitro assembly , protein ( 2 . 5 mg mL-1 ) and a DNA 30-mer oligonucleotide with alternating TG motifs ( TG30; Macrogen ) at a 10% ( w/w ) ratio of nucleic acid to protein were mixed in 50 mM TRIS , 500 mM NaCl , pH8 . 0 prior to adding inositol hexaphosphate ( IP6 ) ( 10 μM ) and slowly decreasing the NaCl concentration by dialysis into 50 mM TRIS , 150 mM NaCl , pH8 . 0 buffer to initiate the assembly process . The buoyant density of the particles produced and their in vitro assembly efficiency was assessed by layering the assembly reaction mixture onto a 32 . 5 to 55% ( w/w ) linear sucrose gradient in TBS ( 150 mM NaCl , 50 mM Tris , pH 7 . 6 ) . Assembled HIV-1 Gag samples were layered onto the gradient and centrifuged for 16 h at 110 , 000 g ( SW41 rotor: Optima L-90k ultracentrifuge; Beckman ) . 750 μL fractions were collected from the top in separate 1 . 5 mL tubes after completion of the run and prepared for trichloroacetic acid ( TCA ) precipitation . 350 μL of 50% ( w/v ) TCA was added to each fraction and incubated at 4°C for 30 min . Tubes were centrifuged at 14 , 000 g for 10 min at 4°C and the pellet was resuspended in 200 μL of prechilled acetone and incubated for 5 min at 4°C . The centrifugation step was repeated and excess supernatant aspirated and tubes air dried . The dried pellet was resuspended in 20 μL of 4x NuPAGE reduced LDS Sample Buffer ( Life Technologies ) and heated for 5 min at 95°C . Samples were briefly centrifuged and 10 μL of each sample was loaded onto a NuPAGE 4–12% Bis-TRIS Midi gel and electrophoresed using NuPAGE MES SDS Running Buffer ( Life Technologies ) at 150 volts . PAGE gels were transferred to a Nitrocellulose membrane ( PerkinElmer ) using XCell II Blot Module ( 30 volts for 60 mins ) , and the membrane blocked overnight in blotto ( 5% ( w/v ) skim milk powder in TBS + 0 . 05% ( v/v ) Tween-20 ) at 4°C . Membrane was washed 3x with wash buffer ( TBS + 0 . 05% ( v/v ) Tween-20 ) for 5 min each and probed with a mouse anti-CA monoclonal antibody ( Hybridoma Clone 183-H12-5C; NIH AIDS Reagent program ) for 1 h at room temperature . Membrane was washed 3x with wash buffer , incubated with a secondary goat anti-mouse IRDye 800CW conjugate ( LI-COR; diluted1:30 , 000 in blotto ) for 1 hr at room temperature . Membrane was washed again 3x and analysed using an ODYSSEY CLx system ( LI-COR ) . Isothermal titration calorimetry ( ITC ) experiments were performed at 30°C using a Microcal Auto-ITC200 MicroCalorimeter ( Malvern ) . For each ITC experiment , the cell contained soluble Gag protein ( 6–10 μM ) in TBS with 1mM tris ( 2-carboxyethyl ) phosphine ( TCEP ) and the syringe contained 15–50 μM of the DNA 30-mer oligonucleotide with alternating TG motifs ( TG30 ) or RNA 20-mer oligonucleotides with 4 consecutive repeating GU-containing ( 4x 5’-GAUGG-3’; 4x 5’-UGUGG-3’ ) or A-containing ( 4x 5’-GAGAA-3’; 4x 5’-AAGGA-3’ ) sequence motifs . The SL3 DNA and the 20mer SL3 RNA ( 5’GGACUAGCGGAGGCUAGUCC3’ ) as well as the HIVNL4 . 3RNA2671-2707 ( 5’-CTTAGAAATAGGGCAGCATAGAACAAAAATAGAGGAA-3’ ) and the HIVNL4 . 3RNA2671-2707 ( with AGAAA mutation ) ( 5’-CTATCTTTTAGGGCAGCAATCTTCAAAAATAGTCCTT-3’ ) are based on NL4 . 3 HIV proviral RNA . DNA and RNA oligonucleotides were purchased from Macrogen and Integrated DNA Technologies , respectively , and dissolved in TBS with 1mM TCEP . The volume of the first injection for each ITC run was set to 0 . 4 μL over 0 . 8 s to minimize the experimental impact caused by dilution effects at the injection syringe tip; this initial injection was excluded from data analysis . The first injection was followed by 25 injections of 1 . 5 μL over 3 s or 38 injections of 1 μL over 2 s each with the interval between each injection set to 300 s . The reference power was set to 5 μcals-1 . The syringe stirring speed was set to 750 rpm . A baseline was drawn by linear extrapolation using the data points collected from control experiments and subtracted from the whole data set to correct for the heat of dilution . The total heat signal from each injection was determined as the area under the individual peaks and plotted against the [nucleic acid]/[Gag] molar ratio . The corrected data were analyzed to determine number of binding sites ( n ) , and molar change in enthalpy of binding ( ΔH ) in terms of a single site model derived as follows: The quantity r is defined as the moles of nucleic acid [D] bound per mole of protein [P] with an association constant ( Ka ) : r=Ka[D]1+Ka[D] ( Eq 1 ) Solving Eq 1 for Ka leads to: Ka=r ( 1+r ) [D] ( Eq 2 ) Since the total concentration of nucleic acid [D]T in the cell is known , it can be represented by Eq 3 wherein [P]T equals the total protein concentration in the cell and n the number of binding sites: [D]T=[D]+nr[P]T ( Eq 3 ) Since Eq 3 shows nr[P]T = [PD] , the fraction of sites occupied by the nucleic acid , combining Eqs 2 and 3 leads to: r2−r[[D]Tn[P]T+1nKa[P]T+1]+[D]Tn[P]T=0 ( Eq 4 ) Solving the quadratic for the fractional occupancy ( r ) gives: r=12[ ( [D]Tn[P]T+1nKa[P]T+1 ) − ( [D]Tn[P]T+1nKa[P]T+1 ) 2−4[D]Tn[P]T] ( Eq 5 ) The total heat content ( Q ) of the solution in the volume of the sample cell ( Vo; determined relative to zero for the apo-species ) at fractional saturation r is given by Eq 6 , where ΔH represents the molar heat of nucleic acid binding: Q=nr[P]TΔHV0 ( Eq 6 ) Substituting Eq 5 into Eq 6 gives: Q=nr[P]TΔHV02[ ( [D]Tn[P]T+1nKa[P]T+1 ) − ( [D]Tn[P]T+1nKa[P]T+1 ) 2−4[D]Tn[P]T] ( Eq 7 ) The total heat content , Q can be calculated as function of n , Ka , ΔH because [P]T , [D]T and Vo are known experimental parameters . The parameter Q defined in Eq 7 only applies to the known starting volume of protein solution in the sample cell ( Vo ) . In order to correct for the displaced volume ( Vi ) , the change in heat content Q ( i ) at the end of the ith injection is defined by Eq 8 to obtain the best fit for n , Ka , and ΔH by standard Marquardt methods until no further significant improvement in fit occurs with continued iteration . The Gibbs free energy ( ΔG0 ) was calculated from the fundamental equation of thermodynamics Eq 9: ΔG°=ΔH−TΔS=−RTlnKa ( Eq 9 ) All data fitting operations were performed with Origin V7 . 0 software ( OriginLab , Northampton , MA ) . Following ITC analysis , the solutions containing HIV protein and RNA complex was spun at 100 , 000 g for 1 hr ( TLA 100 . 2 rotor; optima max ultracentrifuge; Beckman ) . The supernatant was removed and the pellet was resuspended in 50μl of TBS . Protein estimation was done using UV-Vis ( A280 ) on both the supernatant and the pelletable materials ( NanoDrop1000; Thermo Scientific ) via Bradford Protein Assay [47] . | Formation of any virus particle will require energy , yet the precise biophysical properties that drive the formation of HIV particles remain undefined . Isothermal titration calorimetry ( ITC ) is a biophysical technique that is the gold standard to reveal parameters governing biochemical and biophysical reaction . However , ITC requires large amount of proteins for analysis . As large quantities of full-length recombinant HIV Pr55Gag proteins have not been available in the past 30 years due to technical limitation , a comprehensive thermodynamic analysis of full-length HIV Pr55Gag has not been possible . Here , we have generated sufficient amount of full-length recombinant HIV Pr55Gag protein for isothermal titration calorimetry analysis . Our analyses have shown that the major interactions amongst HIV proteins and RNA sequences during viral assembly are energetically favourable reactions . In other words , HIV Pr55Gag proteins and viral RNA have evolved to overcome the energy barrier for virus formation by utilising energy obtained from protein-RNA interactions in order to facilitate the viral assembly process . Furthermore , HIV also use the oligomeric states of HIV Pr55Gag proteins and the RNA sequences as means to regulate the viral assembly process . |
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An important application of the RNA interference ( RNAi ) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo . In several published experiments , RNAi has been used to inactivate components of the RNAi pathway itself , a procedure termed recursive RNAi in this report . The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating , and in practice the effectiveness of recursive RNAi in published experiments is highly variable . A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective . The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression . This efficacy is known to vary highly between different cell types , and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms . The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off–switch on experiments .
RNA interference ( RNAi ) is an RNA-mediated pathway of gene silencing mediated by small RNA molecules [1] , [2] . During RNAi , introduction of double-stranded RNA ( dsRNA ) encoding a sub-sequence of a gene leads to reduction in expression of the corresponding gene . The heart of the RNAi process involves two key steps . First , the dsRNA is cleaved into small RNA fragments by an enzyme called Dicer , and then these small fragments are used as a template by a complex called RISC which identifies matching sequences in target messages and leads to their degradation . RNAi technology has emerged as a powerful tool for artificially controlling gene expression , but it only works because cells have evolved small RNA based regulatory pathways in the first place . Natural regulatory pathways taking advantage of small RNAs include not only classical RNAi , which probably acts in host defense against viruses and transposons , but also microRNA-based ( miRNA ) regulatory pathways that regulate endogenous genes [3] . It is interesting to speculate that such pathways may have evolved in part because of unique aspects of regulation mediated by RNA . Compared to more classical regulatory networks based on transcription factors or kinases , the signal-processing properties of small RNA-based regulatory systems have not been extensively investigated at a theoretical level . One advantage of having a theoretical understanding of such pathways is that one could potentially predict the performance and response of systems that have been altered in defined ways , thus facilitating a “synthetic biology” of small RNA-mediated regulatory circuits [4] , [5] . For a more short-term application , one might hope that a predictive level of understanding of RNAi pathway behavior could allow improved design of experiments using RNAi as a tool . In this report the RNAi system is explored theoretically by considering its behavior following addition of an artificial negative feedback loop . It is well known in electronics that when the output of a circuit is fed back into one of its inputs , the resulting closed-loop circuit can have dramatically different behaviors than the open-loop circuit before the feedback loop was added . A key challenge for systems biology is to be able to predict the effect of feedback loops on biological circuits , either naturally occurring feedback or synthetic feedback produced by adding new linkages from output to input [6] . In the case of naturally occurring small RNA-mediated regulatory loops based on micro-RNAs , feedback loops are sometimes seen in which components of the RNAi/miRNA machinery such as Dicer or Argonaute are themselves targets of miRNA-mediated inhibition [7] , [8] . Being able to quantitatively or even qualitatively predict the effect of such feedback linkages would therefore seem crucial to developing a circuit theory for small RNA based signaling [9] . In the case of the RNAi pathway , synthetic feedback loops have been constructed by workers attempting to use RNAi to turn off the RNAi pathway . This is done simply by adding dsRNA molecules that target genes encoding components of the RNAi machinery . In such a situation , the feedback can be considered as arising from the output of the RNAi machinery ( that is , degradation of target message ) being applied as an input to the system in the form of message encoding RNAi components . This “recursive” RNAi has been used in genome-wide screens to discover new RNAi components [10]–[14] . In such screens , a reporter gene such as green fluorescent protein ( GFP ) or luciferase is silenced by RNAi , and then reporter activity is measured in the presence of a second dsRNA molecule targeting a candidate gene . Increased reporter expression indicates that the candidate gene is involved in the RNAi process . By using libraries of dsRNA molecules corresponding to all predicted genes in the genome , it is in principle possible to identify all components of the RNAi machinery . In order for screens of this type to be successful , the reporter activity must be significantly increased over the level seen when the reporter alone is targeted . Recursive RNAi has also been used as a way to reactivate genes previously silenced by RNAi . Such “switch-off/switch-on” experiments employ a procedure in which a dsRNA is introduced targeting a gene of interest , and then , following a period of inactivation , the RNAi is alleviated by adding a second dsRNA that targets the RNAi machinery itself [15] . This allows temporal control of gene expression during animal development , and has the advantage that it can be applied to any gene without having to engineer new inducible constructs for each experiment . In order for switch-off/switch-on experiments to work , the level of restoration of the targeted gene must be enough to restore approximately normal gene function . For strictly recessive genes this would probably require restoration to approximately half normal levels , while for haploinsufficient genes it would require a greater degree of restoration , to near wild-type levels . Recursive RNAi can thus potentially be a very powerful tool both for studying RNAi itself and also for controlling gene expression during development , provided a sufficient level of restoration can be achieved once the RNAi machinery is targeted . Despite the great potential of recursive RNAi , and the multiple published successes of the method , one cannot help but feel that the use of RNAi to inactive RNAi seems potentially self-defeating . Specifically , one might imagine that as the pathway is shut down , its ability to further shut itself down would be reduced , resulting potentially in a restoration of activity . Recursive RNAi presents the same difficulty as attempting to commit suicide by holding one's breath—even if one could hold one's breath to the point of passing out , the unconscious patient would at that point begin breathing again . The quantitative question thus arises as to whether introduction of recursive RNAi would provide a restoration of gene expression level that would be measurable or detectable relative to control levels . Indeed , in actual practice recursive RNAi doesn't always work . For instance , although some studies have reported that RNAi of genes encoding Dicer protein restores reporter gene expression [16] , other studies failed to observe significant restoration following RNAi of Dicer [11] . One possible explanation for the variability in results between different systems is the efficacy of RNAi at knocking down gene expression . Some cell types such as S2 cells can achieve extremely high levels of knockdown to a few percent of wild-type expression levels [11] while other systems such as C . elegans RNAi-by-feeding seem to produce a more moderate degree of knockdown . Might such variation make recursive RNAi possible in some systems and impossible in others ? This report investigates the conditions under which recursive RNAi can be effective , by constructing a mathematical model for recursive RNAi and predicting how its performance varies as a function of the efficacy of RNAi in a given system . The main prediction of the model is that increasing the efficacy of RNAi-mediated knockdown should make recursive RNAi less efficient and potentially impossible .
The RNAi pathway upon which the model is based is shown schematically in Figure 1 , and based on this diagram a model is presented in the Materials and Methods section below . Within the model , the steady-state behavior of the system is specified by a single parameter , γ , which determines the overall effectiveness of RNAi in a particular cell type . RNAi efficacy can be expressed in terms of the fold-knockdown achievable , that is , the ratio of expression level prior to RNAi relative to the expression level following RNAi . For instance , a gene whose expression is reduced to one half its normal level by RNAi would show a fold-knockdown of 2-fold . As derived in Materials and Methods , the fold knockdown predicted for a reporter gene such as GFP or luciferase , in the absence of any additional RNAi targeting Dicer or RISC , would be described in terms of an RNAi efficacy parameter γ according to the following equation: ( 1 ) Thus the parameter γ determines the efficacy of RNAi system , with larger γ indicating more extensive knockdown of gene expression . As described in Materials and Methods , and summarized in Table 1 , this parameter depends on all of the individual parameters of the detailed model , such as the catalytic rate constants of Dicer , the rate of mRNA degradation , etc . Many of the individual rate constants and parameters that contribute to γ may be extremely difficult to measure . In contrast , because of the simple relation between fold-knockdown and the value of the parameter γ this parameter is experimentally measurable simply by quantifying reporter level before and after RNAi . Typical values for γ are in the range 2–200 . Moreover , because the steady-state behavior of the system depends only on this one parameter γ , for many purposes it may not be critical to know the values of the detailed parameters given in Table 1 , as long as one knows the value of the aggregate RNAi efficacy parameter γ . In this paper the parameter γ is generally imagined to vary over the range 1–200 . The variations of the detailed parameters listed in Table 1 are not considered individually because their only effect on the model behavior is through their influence on the value of γ . A second model parameter β plays a role in determining the time-scale over which RNAi knocks down its targets , and is therefore also directly experimentally measurable . Because β has no effect on the steady-state level of knockdown , this parameter will not be considered except when the transient behavior of the system is analyzed . β and γ are the only two adjustable parameters of the model . Both parameters are phenomenological and easily measurable using standard methods of quantifying RNAi efficiency , but both parameters can also be defined in terms of detailed mechanistic parameters such as protein turnover rate , as described in Materials and Methods . When dsRNA is introduced to target a gene encoding a component of Dicer , the system stably attains a new steady state in which the level of the targeted Dicer-specific protein is partially reduced ( Figure 2 ) . As detailed in Materials and Methods , the model predicts that the inherent susceptibility of Dicer to knockdown by RNAi differs from that of a reporter gene , with the fold-knockdown for Dicer given by ( 2 ) The same equation is predicted to describe the susceptibility of RISC when it is targeted by recursive RNAi , indicating the two parts of the RNAi pathway have similar susceptibility to RNAi mediated knockdown . It is perhaps of interest to note that , for γ = 1 , corresponding to a two-fold knockdown of the reporter , the fold knockdown predicted for Dicer from Equation 2 is 2/ ( −1+√5 ) . This is the famous “Golden Ratio” , known since Greek antiquity to arise in situations involving self-similarity and recursion . The major biological significance of Equations 1 and 2 is that genes encoding components of the Dicer and RISC complexes are inherently less susceptible to RNAi knockdown compared to genes not involved in the RNAi pathway . This differential susceptibility raises questions about detectability of recursive RNAi . Would reporter gene expression be restored significantly if Dicer was simultaneously targeted ? As detailed in Materials and Methods , the model predicts RNAi-mediated reporter knockdown in the presence of RNAi targeting components of Dicer ( or of RISC—the equation ends up being the same ) to be: ( 3 ) Figure 3 graphs the predicted expression levels of a reporter gene targeted by RNAi in the presence ( Equation 3 ) or absence ( Equation 1 ) of recursive RNAi targeting Dicer , plotted as a function of the underlying RNAi efficacy in the system . Clearly , the level of reporter gene recovery depends on the efficiency of RNAi in the system , such that more effective RNAi predicts less recovery of reporter expression . As γ becomes large ( i . e . knockdown is very efficient ) , the reporter expression levels obtained with and without recursive RNAi gradually approach each other , making the effect potentially very hard to detect over measurement noise . These results can reconcile the apparent disagreement in the literature concerning the efficacy of recursive RNAi of Dicer , because the variation in RNAi efficacy ( as described by parameter γ ) between cell types and organisms should produce predictable variation in restoration ( Figure 3 ) . Comparison of the predicted restoration to published data reveals a remarkably good match . Bernstein et al [16] describe experiments in which a GFP reporter reduced to 15% of control levels by RNAi is restored to 40% of control levels when Dicer is simultaneously targeted . From Equation 1 , 15% knockdown implies γ = 5 . 5 , from which Equation 3 predicts restoration to 35% of control levels , consistent with the experiments . In a different cell type ( human HEK293 cells ) Schmitter et al [17] found that RNAi directed against a luciferase reporter knocked expression down to 45% of normal levels , and simultaneous targeting of Argonaute-2 restored expression to 60% of pre-RNAi levels . From Equation 1 , reporter knockdown to 45% implies γ = 1 . 2 , hence Equation 3 predicts restoration to 60% of control levels , exactly as observed . In these cases a moderately effective RNAi system yields substantial restoration during recursive RNAi , as predicted . In a contrasting example , Dorner et al . [11] describe a highly effective RNAi system in which the reporter was knocked down to 0 . 5% of control levels , corresponding to γ = 200 . Equation 3 predicts Dicer-specific RNAi should restore reporter expression only to 7% of controls , a relatively small recovery . Consistent with this prediction , Dorner et al . found that RNAi targeting a number of RNAi components such as Dicer-2 and R2D2 only increased reporter expression slightly to a few percent of control levels . A similar low level of restoration of reporter activity was reported in a separate study of RNAi of Dicer-2 in S2 cells [18] . In an even more extreme case , Hoa et al . [19] performed recursive RNAi in mosquito cells for which RNAi of luciferase knocks down the reporter 4000-fold . In this extremely efficient RNAi system , the authors found that targeting of Dicer only restored the luciferase reporter to 2% of control levels . A 4000-fold knockdown implies γ = 3999 , from which Equation 3 predicts a restoration of the reporter to 1 . 6% of control levels , again consistent with the observed level of restoration . These results suggest that poor restoration by recursive RNAi is likely to be a common feature of highly efficient RNAi systems . Dorner et al . [11] concluded in their study that most of the RNAi machinery genes tested in their experiments were not susceptible to RNAi . However , the model given here suggests the experiments were , in fact , effective , but due to the inherently self-limiting nature of recursive RNAi at high γ , the extent of recovery was simply not very large . The differences in performance between different systems are consistent with the predictions of the model for different values of gamma , but it is impossible to rule out that some of the differences could be due to differences in targeting sequences for the reporter versus for the RNAi machinery ( a point to be discussed further below ) . The predictions of this model regarding restoration achievable by recursive RNAi of Dicer only apply to experiments in which Dicer is targeted by addition of shRNA or other forms of dsRNA , and the limitation on knockdown is a result of the requirement for Dicer activity to generate siRNA against itself . If Dicer is targeted by directly by introduction of siRNA , then the model might predict a dramatically increased level of restoration since in this situation Dicer-mediated production of siRNA would no longer be required for its own knockdown . Consistent with this , experiments in which Dicer is targeted directly by exogenously introduced siRNA molecules show almost complete restoration of reporter activity [20] . On the other hand , dynamics of the system might be significantly different because while Dicer is not required to produce exogenously added siRNA , it may still be involved in loading these siRNA molecules into the RISC complex [21] . It is to be noted that different siRNA molecules can show extremely large differences in targeting efficiency [22]–[28] and unless the targeting efficiency of each construct is known , it is impossible to compare quantitative results between different constructs and systems , let alone compare a theoretical model with experimental data . Thus , the comparisons presented here should be viewed as showing a qualitative similarity in overall trends , with precise numerical equivalence being impossible to assess until targeting efficiencies are measured for each experiment . The foregoing results suggest that the effectiveness of recursive RNAi could be improved by reducing the effectiveness of RNAi , for example using mutant backgrounds with partial defects in one or more RNAi components . To optimize the design of recursive RNAi experiments , one approach is to define a figure of merit to describe restoration of reporter activity ( see Materials and Methods ) and then attempt to maximize its value . A figure of merit can be defined by the relative restoration ratio , R , which is the reporter-specific RNAi-mediated decrease in reporter level in the presence of Dicer RNAi divided by the decrease seen in the absence of Dicer RNAi . Figure 4 plots the value of R as a function of the RNAi efficacy parameter γ . It is easy to show that the restoration is maximal when γ equals 2 , which corresponds to a 3-fold reduction in reporter level . As overall RNAi efficacy increases past this point , the level of reporter gene restoration achievable by RNAi of RNAi decreases , in other words , the effect of recursive RNAi becomes more difficult to detect . An alternative figure of merit that may be more appropriate for certain types of screening experiments is the normalized absolute difference Δ between reporter levels with and without recursive RNAi of Dicer ( as described in Materials and Methods ) . As shown in Figure 4 , this figure of merit also predicts that the maximum restoration will occur for low values of γ . Thus , by either criterion , the success of recursive RNAi hinges on avoiding the use of highly efficient systems . This confirms the intuition that recursive RNAi can in fact be self-defeating . The analysis presented thus far treats only the steady-state behavior of the system . In many cases , however , experiments might be conducted before the system has achieved its final steady-state . Would the general conclusion presented above , namely that restoration decreases as RNAi efficacy increases , still hold in a transient condition ? Would restoration seen at a transient time-point be greater than that seen at steady state , or less ? To answer these questions numerical integration was used to simulate the transient response of the recursive RNAi system following induction of RNAi . Figure 5 illustrates the results of this analysis . First , as illustrated in Figure 5A , the restoration of reporter protein level is a monotonically increasing function of time , so that the restoration achievable at a transient time-point will always be less than that achievable at steady state . This plot shows that there are no unexpected transient dynamics or overshoots , and that rather the system smoothly approaches its steady state . Second , one can note in Figure 5A that the system always reaches its steady-state plateau at roughly the same time , with only a small variation in the time taken to plateau with respect to variation in gamma . This is confirmed in Figure 5B which shows that the time taken to reach a fixed percentage of final restoration depends only weakly on gamma . Indeed , the time to reach 50% or 90% of final restoration varies by less than two-fold when the RNAi efficacy parameter gamma varies by two orders of magnitude . Third , it can be seen in Figure 5A that at all time-points , systems with greater RNAi efficacy ( γ ) have lower restoration . This is confirmed in Figure 5C , which plots restoration versus gamma at a specific transient time-point defined as the time at which GFP would be knocked down to 50% of its steady-state knockdown level following induction of RNAi . At this transient time-point , the restoration clearly decreases as gamma increases , mirroring the results plotted in Figure 4 for the steady-state behavior . These results indicate that the general conclusions reached about the detectability and effectiveness of recursive RNAi obtained by analytic determination of the steady-state solution also apply to the transient case . The model described thus far assumes that the target gene ( GFP , for instance ) is targeted with the same efficiency as the RNAi component gene . It is well known that the efficacy of target degradation caused by a particular siRNA depends significantly on the precise sequence used for targeting [22]–[28] . The effects of unequal targeting of a reporter versus Dicer are derived in Materials and Methods and plotted in Figure 6 . The figure shows that as the relative targeting of the reporter is decreased compared to Dicer , the level of restoration can be increased significantly , as indicated by the difference in GFP expression levels with and without recursive RNAi . Figure 6 also shows that the effect becomes more pronounced as γ is increased . In particular , Figure 6 shows that for very efficient RNAi systems ( high γ ) , a more switch-like behavior could be obtained by recursive RNAi provided the targeting of the reporter gene is deliberately made inefficient . This is a prediction that could be tested experimentally by designing a series of dsRNA constructs targeting GFP chosen to span a range of targeting efficiencies , and then measuring the restoration achievable . Figure 6C shows that while restoration can be improved with targeting of the reporter is less efficient , when targeting of the reporter is made more efficient than targeting of the RNAi machinery restoration becomes progressively less efficient . It is thus clearly desirable to tune the relative targeting efficiencies of the two constructs using existing algorithms [22]–[28] in order to decrease the efficacy of reporter targeting relative to the RNAi component that is targeted in recursive RNAi experiments . A standard reason for employing feedback in electronic circuits is to reduce the sensitivity of the system performance to variations in the operating parameters of components . This is classically seen in operational amplifier circuits which , when connected in a negative feedback mode , produce an amplifier whose gain is almost completely insensitive to variations in the gain of the operational amplifier itself . Gene expression is an inherently noisy process [29] , leading to random variation in protein levels for any given gene product . Variation in levels of knockdown has been measured in RNAi experiments and is a significant problem for detectability in genome-wide screens [30] , [31] . Might recursive RNAi , by adding a feedback control to the RNAi system , make the system less sensitive to fluctuations in protein levels ? In order to investigate whether recursive RNAi might help make the operation of the RNAi system more tolerant to variations in its own components , the sensitivity of Dicer protein levels to variation in the rate of Dicer protein translation was analyzed . Translation of message into protein is often considered a major source of biological noise . Variation in Dicer was chosen for purely hypothetical reasons , there does not appear to be any published data on cell-to-cell variability in protein levels for RNAi components . Sensitivity is defined in this case as the change in Dicer protein level at steady-state caused by a given change in the translation rate of Dicer protein . As derived in Materials and Methods , the ratio of sensitivity in the recursive configuration to that in the open-loop ( i . e . , non-recursive ) configuration is a function of γ , given by the following equation: ( 4 ) This equation shows that any change to any parameter of the system that would increase γ will have the effect of making the system less sensitive to variation in the translation rate of Dicer . The same equation can easily be shown to hold for sensitivity to variation in the transcriptional rate of Dicer message . Feedback thus makes RNAi more robust to parameter variation , and the greater the efficacy of RNAi , the greater the improvement in robustness . This may explain why , in some cases , the Dicer gene appears to be under negative feedback control by the miRNA pathway [7] . In some systems , induction of RNAi leads to production of secondary siRNA using the targeted mRNA as a template for an RNA-directed RNA polymerase ( RdRP ) [32]–[34] . How would this amplification affect the behavior during a recursive RNAi experiment ? Figure 7 shows numerical simulation results plotting restoration of a reporter gene for different values of the efficacy of amplification ( as described by the parameter theta ) simulated at two different values of the RNAi knockdown efficiency parameter gamma . It is clear that increased amplification leads to reduced restoration . This is in keeping with the general conceptual idea that more efficient RNAi , which can be achieved either by higher knockdown efficacy or by increased amplification , leads to decreased restoration in recursive RNAi experiments . Comparing the two panels , it is clear that for any given value of the amplification parameter , lower gamma always leads to better restoration . Thus , the addition of the amplification pathway to the model has no effect on the overall qualitative conclusion that increased efficacy of RNAi leads to decreased restoration . The analysis presented thus far assumes that if a given RNAi pathway component was knocked down completely , it would result in complete loss of RNAi activity . This effect underlies the potentially self-defeating nature of recursive RNAi . However , only a few proteins of the RNAi pathway appear to be essential core components , with the rest making significant , but not essential , contributions to the process [35] . Even complete knockdown of the non-core components would thus allow some level of RNAi to continue . Would recursive RNAi of such non-core components produce restoration to a different degree than targeting a core component ? This question was addressed by modifying the model equations to add a new parameter rho that represents the degree of requirement of a given component for the process of RNAi . A value of ρ = 1 indicates the component is a core component essential for RNAi , while ρ = 0 indicates a component that is not involved in RNAi at all . Low values of rho would also apply for components encoded by multiple redundant gene copies . The expression of a reporter gene in the presence of recursive RNAi is plotted in Figure 8 ( based on equations derived in Materials and Methods ) as a function of the level of requirement ρ . The result is that recursive targeting of a non-essential component ( ρ<1 ) leads to less restoration than recursive targeting of an essential core component . This implies that variation in the degree of requirement of a given protein for RNAi could be an important source of variation in the level of restoration achievable by recursive RNAi inhibition of different components of the pathway . There are many ways to introduce dsRNA into cells to activate RNAi . In some cases , the dsRNA is added by soaking or feeding , in others it is expressed by stably integrated constructs . In other cases , however , the dsRNA is expressed as a short hairpin construct contained on a plasmid that is transiently transfected into cells . In this case , the rate of dsRNA production will not be uniform over time because the concentration of plasmid will decrease with first order decay kinetics as the plasmid becomes diluted during cell division . This situation was modeled as described in Materials and Methods , with results plotted in Figure 9 . The results show that introduction of a decay process for the dsRNA source leads to a transient knockdown that eventually returns to baseline expression of the reporter . For slow rates of decay , significant restoration can still be seen with recursive RNAi , but for very fast decay , the restoration becomes negligible . Transient transfection does not , however alter the basic conclusion that increased RNAi efficacy γ leads to decreased restoration . As plotted in Figure 9B , for all rates of decay that were modeled , after increasing γ past an optimum restoration value in the range 1–5 , further increasing γ decreases restoration . Thus the basic conclusion that increased RNAi efficacy leads to decreased effectiveness of recursive RNAi is predicted to still hold in transient transfection experiments , although the results also indicate that if the transfection is too transient , restoration might not be detectable in any case .
This report uses a mathematical model to predict the steady-state levels of reporter gene expression in recursive RNAi experiments . This model indicates that recursive RNAi is indeed possible , but that the level of restoration of a reporter gene , and therefore the ability to observe the effect of restoration , depends on the intrinsic efficacy of RNAi knockdown . Systems with more complete RNAi mediated knockdown are predicted to be less susceptible to RNAi . For screens in which the goal is simply to determine whether or not restoration has occurred in order to identify new RNAi components , the level of restoration only needs to be large enough relative to the measurement noise so that a reliable detection can be made . A much more stringent application is when recursive RNAi is used to restore expression of a gene previously inactivated by RNAi , as has been demonstrated in C . elegans [15] . For such switch-off/switch-on applications of recursive RNAi , the level of restoration needs to be sufficiently high to restore essentially wild-type levels of gene function . Restoration of the targeted gene to fully wild-type levels would correspond to a restoration ratio R = 1 , which according to Figure 4 is impossible to attain . In many cases , for example genes that are not haplo-insufficient , it may not be necessary to restore gene expression levels all the way to wild-type to rescue the phenotype . However , the results of the model suggest that in many cases , even a more moderate restoration , say to one half or one quarter normal expression levels , would also not be possible if the efficacy of RNAi-mediated knockdown in the organism is too high . One could , in such cases , conduct the experiment in a mutant background with a partial defect in one or more components of the RNAi machinery , so that the value of γ is reduced enough to allow a high level of restoration . Of course , this would entail a design tradeoff because decreased γ would lead to less repression during the switch-off phase of the experiment . In practice , the value of γ might need to be tuned quite carefully to achieve desired results . Moreover , genetic manipulation of the RNAi machinery may lead to undesirable side-effects due to alteration of endogenous small RNA mediated regulatory pathways . A preferable strategy , therefore , may be to carefully tune the relative targeting efficiency [22]–[28] of the reporter versus the RNAi component , so as to reduce the efficacy of targeting of the reporter , which as shown in Figure 6 can produce improved restoration . It is also worth pointing out that inducible systems for turning on and off production of siRNA have been demonstrated [36]–[38] . Recursive-RNAi based switch-off/switch-on has only been documented in nematodes where RNAi constructs can be easily introduced by soaking or feeding , and may be much harder in other types of animals , representing a distinct advantage for inducible systems . Overall , it remains to be seen whether switch-on experiments using recursive RNAi would have any advantages over these chemically inducible approaches . Switch-on by RNAi of RISC components might yield faster dynamics as it would not be limited by the degradation or dilution rate of the siRNA molecules . In comparing the predictions of the model to experimentally measured levels of reporter gene restoration ( see above ) , it was found that published values for the degree of restoration seen when Argonaute-2 is targeted are much higher than predicted by the model . This does not represent a discrepancy between the model and the data so much as a discrepancy between the experimentally observed behavior of Argonaute-2 and other RNAi components . Indeed , dramatically higher levels of reporter restoration have consistently been reported for Argonaute-2 compared to other RNAi components including Dcr-1 , Dcr-2 , R2D2 , Tudor-SN , FMRp , Drosha , Aubergine , and Piwi [11] , [39] , [40] . The fact that this protein seems to consistently show a distinctly different behavior in recursive RNAi experiments compared to all other known RNAi components [11] suggests that Argonaute-2 acts somehow differently from the RNAi components described within the model . Perhaps Argonaute-2 might be involved within additional control loops not included in the present model . Consistent with the notion that Ago-2 is somehow unique in its functions and interactions , it has recently been reported that Ago-2 depletion has a distinct and specific effect on RNAi competition that is not seen when other RNAi components are targeted [40] . These considerations suggest that the model used here , in its present form , must not fully represent the range of behavior of Argonaute-2 . The results of Equation 4 indicate that by some measures , the RNAi system may operate more reliably when operated in a closed-loop recursive mode . This result , together with the main result that the susceptibility of the RNAi machinery is to inhibition by RNAi , indicates that the RNAi pathway can demonstrate interesting properties when operated in a closed-loop “recursive” mode , even when represented by a fairly simple model . The favorable comparison with published levels of restoration versus efficacy suggests that the model may have predictive value . Other models of the RNAi pathway have previously been developed which model the system at varying levels of complexity [41]–[44] , and it would be interesting to see whether these different models give similar predictions when adapted to represent recursive RNAi experiments . It is also feasible to extend the approach described here to an analysis of the dynamic properties of other types of small RNA mediated control systems such as micro-RNA networks .
The RNAi pathway is represented using a model that is somewhat less complex than previous detailed but non-recursive RNAi models [41]–[44] but which encapsulates the main features of the system . The scheme of the model is given in Figure 1 and the parameters are defined in Table 1 . Both Dicer and RISC complexes are represented as single proteins even though in reality both are highly elaborate protein complexes . This representation , employed in most other RNAi models [41]–[44] is justified on the grounds that a typical recursive RNAi experiment would only target a single gene and its corresponding protein , and would not affect other proteins in the complex . Consequently , the protein levels of the other proteins can be simply treated by lumping their effect in with the other constants in the equations . In the following development only proteins specific to one complex or the other will be treated . In reality , some proteins are shared between the two but this analysis will not consider attempts to silence such shared factors by RNAi . The model will also not address the issue of partial redundancy , in which some RNAi machinery components may be present in multiple gene family members , such that complete inactivation of one member would only result in partial loss of RNAi function . Analysis of switching between different Dicer or Ago family members induced by recursive RNAi would be an interesting area for future study . To model transcription , it is assumed production of new mRNA at a constant rate rt which is approximately the same for all genes in the model . The model assumes that messenger RNA is degraded through a first-order decay with rate constant rdm . Translation of mRNA into protein is modeled assuming that protein is synthesized at a rate proportional to the concentration of message , with a rate constant rx , and is degraded with a first order decay rate constant rdp . Since the rates of mRNA production and degradation are significantly faster than the corresponding rates for proteins ( [45]–[47] and references cited in [42] ) , a quasi-steady state assumption may be invoked such that mRNA concentrations are set to their presumed steady-state value based on the rates of synthesis and degradation , ignoring the transient behavior while approaching this value . Production of siRNA by Dicer is represented by assuming that the siRNA is produced at a rate proportional to the concentration of Dicer , with an effective rate constant kcatD . The concentration of dsRNA is not explicitly represented , rather it is assumed to be lumped into kcatD , and it is taken as a constant thus assuming that dsRNA will not be degraded over time . The latter assumption is most appropriate for systems in which the dsRNA is expressed constitutively within the cell as a small hairpin construct . It is further assumed that siRNA is degraded by a first order decay with rate constant rds . In the simplest form of this model , to be described first , the production of additional dsRNA from targeted message by RNA-dependent RNA polymerase [32]–[34] , [42] is not modeled , but the effect of such an enzyme will be considered later in this report . It is assumed that an siRNA molecule is loaded onto a RISC complex according to a simple first-order binding process with an affinity described by the dissociation constant KDR . This assumption implies that the RISC complex is not saturated by siRNA during the modeled experiments . This assumption may not always hold true . It has been shown that when multiple siRNA species are added to a cell or in vitro RNAi system , they can compete with each other [48]–[50] , and this is thought to reflect a limited quantity of Ago2 that becomes saturated when too many siRNAs are present [40] . Whether or not RISC/Ago2 becomes saturated will depend on how much siRNA is used , for instance in one vitro study it was found that 100–200 fold more siRNA than normally used was required to show significant competition , suggesting that in the normal experimental regime employed by those workers , RISC was not saturated [48] . In the present model , saturation of RISC binding would imply an excess of siRNA thus rendering the system less sensitive to recursive RNAi targeting of Dicer . To model degradation of target messages by the RISC complex , it is assumed that a message targeted by an siRNA will be degraded by RISC at a rate equal to the product of the concentration of siRNA-loaded RISC and the concentration of the target message , with a rate constant kcatR . The linear dependence of RISC complex formation and activity on siRNA and RISC concentrations , including the assumptions of first order binding and lack of saturation , are in agreement with the prior modeling studies of RNAi [41]–[43] . The following analysis of the model will only keep track of proteins whose level will change during the course of an experiment . Proteins that are not affected by the addition of the dsRNAs , will be assumed to have attained their steady state value long before the beginning ( τ = 0 ) of the experiment . They will , therefore , be treated as constants of the model , just as the levels of basic transcriptional and translational machinery are assumed constant in the model . While the model explicitly treats only one protein component of the Dicer or RISC complexes at a time in the analysis , since in a typical recursive RNAi experiment only one protein would be targeted , in fact the model does not in any way place any limits on the number of proteins that may be present in the two complexes . However , the influence of the other proteins is subsumed within the other parameters of the model , and is taken as constant under the assumption that the other proteins in the Dicer and RISC complexes , apart from whichever protein might be targeted by recursive RNAi , do not vary in their expression levels . The following discussion will refer to the reporter gene as GFP , but would describe any target gene such as luciferase . The behaviors of components of the RNAi machinery , plus a reporter construct , can be represented as follows in three distinct cases: In order to simplify the equations representing the model , time , protein concentration , and siRNA concentration are rescaled as follows , representing the rescaled concentrations with capital letters:To simplify the resulting expressions , the following lumped parameters are defined as combinations of the detailed parameters of the model summarized in Table 1: First consider the relative susceptibility of Dicer and RISC proteins to downregulation by RNAi compared with a generic reporter protein that is not a component of the RNAi machinery . In other words , is recursive RNAi more or less effective compared with open-loop RNAi ? The relative susceptibilities SD and SR of Dicer and RISC , respectively , relative to the reporter gene , are defined as:it is obvious by inspection that the relative susceptibility of the two components of the RNAi machinery will decrease relative to the reporter gene as the efficacy of RNAi increases ( as judged by the parameter gamma ) . So as RNAi efficacy increases , RNAi genes become increasingly resistant to RNAi . In a typical recursive RNAi experiment , usually only the reporter protein level is measured , rather than the level of Dicer or RISC proteins . A candidate gene is scored in screens as being involved in RNAi if dsRNA directed against the gene results in a restoration of reporter gene activity back to control levels . In other words , if one monitors the reporter protein level , when it is targeted by RNAi the level will drop , and if a component of the RNAi machinery is also targeted , the level of the reporter will rise back up towards its level seen when no RNAi is performed . One way to quantify this restoration effect is to measure the ratio of recovery after recursive RNAi knockdown to the level of knockdown relative to control . This is expressed by the relative restoration ratios RD and RR which can be defined for the two cases RNAi of Dicer and RNAi of RISC , respectively , as follows: For a switch-off/switch-on experiment using Dicer , for example , one would want GTD≈G0 , which in turn would require that RD≈1 . In fact , RD is maximal when γ = 2 , and its maximum value is only 0 . 25 . It is thus not possible to restore gene expression back to fully normal levels , but only at most one quarter of the way back to normal levels from the level of maximum knockdown prior to “switch on” . As an alternative to these ratios , one may be more interested in the absolute difference in expression levels in the two conditions of knockdown versus knockdown in the presence of recursive RNAi . This difference ultimately determines the detectability of gene restoration when compared with the standard deviation of measurement of expression levels in the two states . The increase in expression levels , in units normalized to the control expression level of the reporter gene , is given by: Suppose that due to difference in targeting sequences , siRNA inhibition of GFP ( or whatever gene of interest is being knocked down ) is either more or less efficient than siRNA inhibition of Dicer in a recursive RNAi experiment . This effect can be represented in the model above as a difference in catalytic efficiency of siRNA-loaded RISC . This can be represented by a parameter ε such that if kcatR is the catalytic rate constant of RISC when acting on Dicer , the catalytic rate constant of RISC when acting on GFP would be ε*kcatR . In this case the only change to the systems described above will be to the differential equations representing the rate of change of GFP level , as follows:using this modified equation to solve for the steady-state GFP level yields:for the non-recursive case , andThese two expressions were used to plot the predicted expression during recursive RNAi with differential targeting in Figure 6 . The relative restoration ratio for the GFP target before and after recursive RNAi of Dicer is then given , as a function of the relative targeting efficiency of GFP , by the equation: | RNA interference is a gene regulatory system in which small RNA molecules turn off genes that have similar sequences to the small RNAs . This has become a powerful tool because a researcher can use RNA interference to turn off any gene of interest in order to test its function . There is great interest in identifying the genes required for the RNA interference pathway , and one approach to identifying such genes has been to use RNA interference to turn off potential RNA interference genes and to ask whether RNA interference still functions when these genes are turned off . The goal of our report is to ask how it is possible for RNA interference to turn itself off , using a mathematical model of the system . The results show that RNA interference cannot turn itself off if the RNA interference pathway is too effective to start with , so that experiments in which RNA interference acts on itself will only work in systems having a low efficiency . The results of our model suggest possible ways to improve the self-inactivation of RNA interference . |
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Promyelocytic leukemia protein ( PML ) has antiviral functions and many viruses encode gene products that disrupt PML nuclear bodies ( PML NBs ) . However , evidence of the relevance of PML NB modification for viral pathogenesis is limited and little is known about viral gene functions required for PML NB disruption in infected cells in vivo . Varicella-zoster virus ( VZV ) is a human alphaherpesvirus that causes cutaneous lesions during primary and recurrent infection . Here we show that VZV disrupts PML NBs in infected cells in human skin xenografts in SCID mice and that the disruption is achieved by open reading frame 61 ( ORF61 ) protein via its SUMO-interacting motifs ( SIMs ) . Three conserved SIMs mediated ORF61 binding to SUMO1 and were required for ORF61 association with and disruption of PML NBs . Mutation of the ORF61 SIMs in the VZV genome showed that these motifs were necessary for PML NB dispersal in VZV-infected cells in vitro . In vivo , PML NBs were highly abundant , especially in basal layer cells of uninfected skin , whereas their frequency was significantly decreased in VZV-infected cells . In contrast , mutation of the ORF61 SIMs reduced ORF61 association with PML NBs , most PML NBs remained intact and importantly , viral replication in skin was severely impaired . The ORF61 SIM mutant virus failed to cause the typical VZV lesions that penetrate across the basement membrane into the dermis and viral spread in the epidermis was limited . These experiments indicate that VZV pathogenesis in skin depends upon the ORF61-mediated disruption of PML NBs and that the ORF61 SUMO-binding function is necessary for this effect . More broadly , our study elucidates the importance of PML NBs for the innate control of a viral pathogen during infection of differentiated cells within their tissue microenvironment in vivo and the requirement for a viral protein with SUMO-binding capacity to counteract this intrinsic barrier .
Promyelocytic leukemia nuclear bodies ( PML NBs ) , also called nuclear domain 10 ( ND10 ) bodies , are spherical nuclear structures that are present in most mammalian cells [1] . PML protein is the essential major component and recruits other proteins , such as Sp100 , Daxx , SUMO1 , CBP and p53 [2] . PML NBs are associated with many cellular processes , including transcription , the DNA damage response , apoptosis and oncogenesis [1] . Under normal conditions , the number of PML NBs varies depending on cell type and differentiation status [3]-[5] . PML is also an interferon ( IFN ) -inducible protein and IFN treatment increases the number and size of PML NBs [6] . PML NBs have been shown to contribute to innate defenses against a broad range of viruses [7] . In turn , many viruses encode products that modify or eliminate PML NBs in cultured cells [7] . However , few studies have investigated the role of PML NBs in viral pathogenesis or mechanisms by which they are modified during viral infection in vivo [8] , [9] . Varicella-zoster virus ( VZV ) is the etiologic agent of varicella ( chickenpox ) and herpes zoster ( shingles ) and causes characteristic cutaneous lesions in both diseases [10] . VZV is an alphaherpesvirus closely related to herpes simplex virus ( HSV ) 1 and 2 [10] . PML is known to interfere with HSV early viral gene transcription and it is important for the antiviral effects of IFNs on HSV [11] , [12] . PML knock-down and over-expression experiments indicate that PML is also involved in restricting VZV replication [9] , [13] . In HSV-infected cells , the ICP0 protein triggers the proteasome-dependent degradation of sumoylated PML and Sp100 through ubiqutin E3 ligase activity mediated by its RING domain [14]-[17] . In contrast to HSV , VZV does not degrade PML protein [9] , [13] , although it does disrupt PML NBs , causing a reduction of approximately five-fold in PML NB frequencies in vitro [9] . Our recent work demonstrated that the PML NBs that persist in VZV infected cells in vitro and in skin and dorsal root ganglia ( DRG ) in vivo have the capacity to sequester newly formed nucleocapsids [9] . The entrapment of VZV capsids in these nuclear PML cages depended upon an interaction between PML and the ORF23 capsid protein and acted as an intrinsic antiviral host defense [9] . The VZV ortholog of HSV ICP0 is ORF61 [10] . Like ICP0 , ORF61 colocalizes with PML NBs shortly after virus entry and disperses Sp100 NBs in transfected cells if the conserved RING domain is intact [18] , [19] . The ORF61 RING domain also exhibits E3 ligase activity in vitro [19] , [20] . Besides these functions , ORF61 has been shown to act as a transactivator to regulate a number of viral and cellular promoters in transient transfection assays and contributes to the optimal expression of VZV glycoprotein E during virus replication in cultured cells [21]-[23] . ORF61 is essential for VZV replication in vitro; deleting ORF61 is not compatible with recovery of infectious virus and truncating ORF61 or severely limiting its expression by mutating promoter elements markedly impairs virus replication [22]-[24] . Although VZV is a highly human-restricted virus , its pathogenesis can be investigated using xenografts of human skin , thymus and DRG in severe combined immunodeficiency ( SCID ) mice [25] , [26] . The evaluation of VZV recombinant viruses with genetic mutations in the SCID mouse model makes it possible to analyze mechanisms of virus-host cell interactions in differentiated cells in vivo and to determine whether putative functional motifs in viral proteins or promoters contribute to the capacity of the virus to overcome intrinsic barriers . Targeted mutations in the viral genome that have little or no effect in VZV replication in cultured cells can disrupt functions that are critical for pathogenesis [27] . By evaluating ORF61 promoter mutants in the skin xenograft model , we demonstrated that ORF61 is necessary for VZV skin pathogenesis [23] , but the reason for this ORF61 requirement was not defined . In this study , our goal was to investigate the functional elements of ORF61 protein , which are required for interaction with PML NBs in differentiated cells infected in vivo and the contribution of this interaction to VZV infection in skin using the xenograft model . To identify potential ORF61 functional domains , we analyzed the ORF61 sequence and found that it has three putative small ubiquitin-like modifier ( SUMO ) -interacting motifs ( SIMs ) in addition to the conserved RING domain . SIMs have been identified in a number of proteins and have a hydrophobic core , consisting of 3-4 aliphatic residues ( V/L/I-x-V/L/I-V/L/I or V/L/I-V/L/I-x-V/L/I; ‘x’ means any amino acid ) , which are typically flanked by a stretch of negatively charged amino acids [28]-[30] . Structural studies indicate that the motif has an extended configuration and is embedded in the groove formed between the α-helix and the β-strand of SUMO [31] . PML protein contains a SIM and the binding through the SIM to sumoylated PML is considered to be the nucleation event for recruitment of other sumoylated and SIM-containing proteins [32] . In support of this model , SIMs in cellular proteins , including Daxx , RNF4 and Sizn1 and in the Kaposi's sarcoma herpesvirus ( KSHV ) LANA2 protein , are necessary for their association with or modification of PML NBs [33]-[36] . This study was designed to investigate whether the putative SIMs that we identified in ORF61 mediated ORF61 binding to SUMO and if so , whether these SIMs were important for ORF61 association with PML NBs and for the disruption of PML NB in vitro . These questions were addressed by mutagenesis of the SIMs in ORF61 plasmid constructs or in ORF61 in the context of the VZV genome . We then examined the significance of the ORF61 SUMO-binding capacity for PML NB association and dispersal in differentiated skin cells using the SCID mouse model . In summary , we have demonstrated that ORF61 has a SIM-mediated SUMO-binding capacity which is necessary for its capacity to target and disrupt PML NBs in differentiated skin cells in vivo and that this ORF61 SIM-mediated function is a critical determinant of the pathogenic potential of VZV in skin .
By sequence analysis , we observed that ORF61 has three putative SIMs; two are located in the N-terminus near the RING domain ( designated as SIM-N1 and SIM-N2 ) and a third is in the C-terminus ( designated as SIM-C ) ( Fig . 1A ) . Of interest , one or more putative SIMs are also present in the ORF61 orthologs of other alphaherpesviruses ( Table 1 ) . We first investigated whether the ORF61 SIMs had the functional capacity to mediate ORF61 binding to SUMO . In GST pull down assays , ORF61 expressed in VZV-infected cells and in transiently transfected cells bound to GST-SUMO1 and also to GST-SUMO2 , although less efficiently; non-specific binding to GST was not detected ( Fig . 1B ) . The ORF61 SIMs were then mutated by alanine substitutions in the ORF61 plasmid , resulting in mSIM-N ( both SIM-N1 and SIM-N2 disrupted ) , mSIM-C ( SIM-C disrupted ) , and mSIM-N&C ( all three sites disrupted ) . When these SIM mutants were evaluated with GST pull down assay , the SUMO1 binding capacity of mSIM-N was similar to ORF61 whereas binding of mSIM-C was reduced markedly and mSIM-N&C did not bind to SUMO1 ( Fig . 1C ) . Since ORF61 is expressed and colocalizes with PML NBs at very early times in VZV-infected cells [18] , we investigated the pattern of ORF61 association with PML NBs in transfected melanoma cells . As expected , distinct ORF61 nuclear puncta were observed in cells with low levels of expression and most colocalized with PML NBs ( Fig . 2A , panel I ) . The effect of the ORF61 SIM mutations on association with PML NBs was then evaluated . The localization of mSIM-N puncta with PML NBs was similar to ORF61 ( Fig . 2A , panel II ) ; mSIM-C nuclear puncta were found in only a few cells but also colocalized with PML NBs ( Fig . 2A , panel III ) . In contrast , mSIM-N&C nuclear puncta , also found in only a few cells , did not colocalize with PML NBs ( Fig . 2A , panel IV ) . Examination of an ORF61 mutant that lacked the previously characterized RING/E3 ligase domain [19] , [20] showed that this ΔRING mutant formed large bright nuclear puncta , most of which were associated with PML NBs ( Fig . 2A , panel V ) . Thus , in contrast to the ORF61 SIMs , the ORF61 RING domain was not required for PML NB association . Next , the capacity of ORF61 and SIM mutants to disperse PML NBs was examined . In cells with abundant ORF61 expression , punctate staining was less evident , the protein was distributed more diffusely in the nucleus and the number of PML NBs was decreased significantly compared to cells in the same monolayer that did not express ORF61 ( Fig . 2B , panel I ) . The PML NB frequency in ORF61-positive cells was 0 . 97±0 . 08 ( N = 224 ) , which was a 4 . 7-fold reduction ( p<0 . 0001 ) compared to mock-transfected cells ( 4 . 53±0 . 17 , N = 245 ) ( Fig . 2C ) . PML NBs were also obviously reduced in cells expressing abundant mSIM-N compared to untransfected cells but were better preserved in cells with abundant mSIM-C or mSIM-N&C ( Fig . 2B , panel II to IV ) . The ΔRING mutant had no obvious effect on PML NBs ( data not shown ) , which was consistent with a previous report using Sp100 as a PML NB marker [19] . As determined by quantification of PML NB frequencies in these cells , the capacity of mSIM-N to disperse PML NBs was slightly less than ORF61 , the effect of mSIM-C was reduced significantly and mSIM-N&C was completely ineffective ( Fig . 2C ) . The PML NB frequency in mSIM-N&C-expressing cells was comparable to that in cells expressing ORF61 ΔRING and mock-transfected cells ( Fig . 2C ) . Taken together , these experiments demonstrated that when expressed in the absence of other viral proteins , ORF61 targets and disrupts a majority of PML NBs and that the ORF61 SIMs were necessary and as important as the RING domain for ORF61-mediated dispersal of PML NBs . As a single motif , the C-terminal SIM was the most important for ORF61 SUMO-binding and PML NB dispersal but all three SIMs were required to achieve both the association with PML NBs and their efficient dispersal . To investigate the effect of ORF61 on PML protein levels , ORF61 was co-expressed with each of six PML isoforms in melanoma cells for 24 h and cells were then treated with the proteasome inhibitor MG132 for 6 h . As shown for PML IV , the PML protein level was unchanged between mock-treated and MG132-treated cells ( Fig . 2D , lanes 5 & 6 ) . Similar results were obtained with PML isoforms I-III , V and VI ( data not shown ) . ORF61 accumulated abundantly ( Fig . 2D ) , confirming its rapid turnover [20] . These data suggested that , unlike HSV ICP0 , ORF61 does not degrade PML protein despite its conserved RING domain and E3 ligase activity , which is consistent with the persistence of PML protein in VZV-infected cells [9] , [13] . We next investigated the potential role of the ORF61 SIMs in other ORF61 functions . ORF61 is known to regulate expression of the essential VZV glycoprotein , gE , through a RING domain-dependent mechanism [21] , [37] . However , disrupting the ORF61 SIMs did not alter activation of either the gE or the ORF61 promoters ( Fig . 3A ) . Since VZV inhibits NF-κB activation [38] , we also investigated ORF61 regulation of the NF-κB promoter . Of interest , NF-κB activation by TNF-α was suppressed by ORF61 in a dose-dependent manner and again , this function required the RING domain but not the SIMs ( Fig . 3B and 3C ) . Thus , while the RING domain is essential for all of these ORF61 functions , the SIMs are specifically needed for ORF61 association with and dispersal of PML NBs . These data also suggest that the SIM mutations did not result in aberrant folding of ORF61 protein , which would be expected interfere with these other functions . In order to assess the contributions of the ORF61 SIMs to PML NB association and dispersal in infected cells and to VZV replication , we made three VZV recombinants that contained the same ORF61 SIM mutations as in the ORF61 mutant plasmids . These mutants were designated as pOka-mSIM-N , pOka-mSIM-C and pOka-mSIM-N&C , respectively . Evaluation of the SUMO1-binding capacity of the ORF61 SIM mutant proteins produced in infected cells with the GST pull-down assay showed that interactions of mSIM-C and mSIM-N&C with SUMO1 were almost undetectable whereas mSIM-N binding was diminished only slightly ( Fig . 4A ) , confirming the SIM-mediated binding of ORF61 to SUMO1 as was observed in transfection experiments . Treating cells infected with pOka and ORF61 SIM mutants with MG132 showed that the intracellular processing of the ORF61 SIM mutant proteins was indistinguishable from ORF61 ( Fig . 4B ) , again suggesting that these mutant proteins were not likely to have major structural changes and that their reduced SUMO-binding capacity was attributable to disruption of the functional SIMs . When the effect of the SIM mutations on ORF61 association with PML NBs was examined in melanoma cells at 6 h after infection , all three ORF61 SIM mutants formed minute ORF61-positive nuclear puncta , colocalizing with PML NBs ( Fig . 5A ) . After 24 h , PML NBs were reduced substantially in cells infected with pOka-mSIM-N as well as pOka , whereas disruption of PML NBs was considerably less in pOka-mSIM-C- and pOka-mSIM-N&C-infected cells ( Fig . 5B ) . Quantitative analysis showed that cells infected with pOka-mSIM-C retained 2 . 5-fold more PML NBs compared to pOka-infected cells; the PML NB frequency in cells infected with pOka-mSIM-N&C was equivalent to that in mock-infected cells ( Fig . 5C , left panel ) . Similar patterns were observed in HELFs ( Fig . 5C , right panel ) . These experiments indicate that the capacity of ORF61 to bind to SUMO1 through its SIMs correlates with how efficiently it disrupts PML NBs in VZV-infected cells . The preservation of some colocalization of the mSIM-N&C protein with PML NBs in infected cells , which was not observed under transfection conditions , suggests that another viral factor ( s ) or a virus-induced factor ( s ) may also contribute to ORF61 association with PML NBs . Since neither pOka infection nor ORF61 expression alters the levels of endogenous PML protein ( 9 , Fig . 2D ) , it was expected that PML levels in melanoma cells and HELFs infected with the ORF61 SIM mutants would be unchanged ( Fig . 5D ) . Despite the compromised effect on PML NB dispersal , the growth kinetics of all three ORF61 SIM mutants was similar to pOka in melanoma cells , as determined by infectious focus assay ( Fig . 5E ) . Yields of ORF61 SIM mutants and pOka were also comparable in HELFs ( data not shown ) . Thus , the dispersal of PML NBs mediated through ORF61 SIMs is not required for VZV replication in permissive cells in vitro . When the ORF61 SIM mutants were evaluated in human skin xenografts in SCID mice , the infectious virus yield of pOka-mSIM-N was similar to pOka , while the replication of pOka-mSIM-C was delayed slightly , with virus yields that were lower than pOka at day 10 ( p<0 . 05 ) but had increased by day 21 ( p<0 . 05 ) ( Fig . 6A ) . Infectious virus was recovered from 5 of 6 or all 6 xenografts inoculated with pOka , pOka-mSIM-N or pOka-mSIM-C . In contrast , pOka-mSIM-N&C replication in skin was severely impaired . pOka-mSIM-N&C was recovered from only 4 of 6 inoculated xenografts at day 10 and 3 of 6 at day 21 . Titers from the xenografts that yielded pOka-mSIM-N&C were ∼30-fold lower than pOka at day 10 ( p<0 . 001 ) and ∼500-fold lower at day 21 ( p<0 . 001 ) ( Fig . 6A ) . Analysis of skin tissue sections showed that pOka formed the usual large necrotic VZV lesions that penetrate across the basal cell layer into the dermis whereas pOka-mSIM-N&C lesions were very small and restricted to the epidermis ( Fig . 6B ) . It is important to note that PML NBs were prominent in skin cells in mock-infected xenografts , with the highest frequency being found in cells of the basal layer ( Fig . 6C , upper panels ) . However , many more PML NBs were present in the uninfected cells present within skin xenografts that were infected with pOka or pOka-mSIM-N&C , compared to these mock-infected tissues ( Fig . 6C , middle and lower panels and Fig . S1 ) . Quantification of two skin xenografts that were infected with pOka and two infected with pOka-mSIM-N&C showed 3 . 8-6 . 6 fold increase in PML NB frequency in uninfected cells respectively , compared with the mock-infected xenograft ( Fig . 6D ) ; the PML NB frequencies did not differ significantly between xenografts inoculated with pOka or pOka-mSIM-N&C ( Fig . 6D ) . Since skin cells dramatically up-regulate IFN production in response to VZV infection in vivo and PML is IFN-inducible [6] , [39] , our findings suggest that the innate IFN response increases PML NB numbers in dermal and epidermal cells , reinforcing a pre-existing barrier to VZV spread . The small epidermal lesions and the failure of pOka-mSIM-N&C to create lesions that extend across the basal layer suggested that VZV spread from cell to cell in skin might depend on ORF61 SIM-mediated PML NB disruption . To investigate this possibility , we examined the pattern of ORF61/PML NB association and the PML NB frequencies in skin cells infected with pOka and the ORF61 SIM mutants . ORF61 nuclear puncta that colocalized with PML NBs were evident in the newly infected cells located at the margins of pOka skin lesions ( Fig . 7A , panel I ) . pOka-mSIM-N&C also formed distinct ORF61-positive nuclear puncta but in contrast to pOka , the pattern of their association with PML NBs was heterogenous in individual cells ( Fig . 7A , panel II-IV ) . Skin cells that had mSIM-N&C puncta could be categorized into three groups: those in which either all or some puncta colocalized with PML NBs ( Fig . 7A , panel II and III ) and those in which none colocalized with PML NBs ( Fig . 7A , panel IV ) . When these associations were quantitated in the skin cell nuclei that had ORF61 or mSIM-N&C puncta , colocalization of ORF61 with PML NBs was observed in 100% of pOka-infected cells ( N = 47 ) whereas only 35% of pOka-mSIM-N&C-infected cells ( N = 46 ) showed any colocalization of ORF61 with PML NBs ( p<0 . 0001 ) ( Fig . 7B , left panel ) . Analyzing these data based on total numbers of ORF61 positive puncta in infected cell nuclei showed that 89% of pOka ORF61 puncta ( N = 167 ) were associated with PML NBs compared to only 25% of mSIM-N&C puncta ( N = 202 ) ( p<0 . 0001 ) ( Fig . 7B , right panel ) . Next , the frequency of PML NBs in skin cells infected with pOka and the ORF61 SIM mutants was assessed . ORF23 , a VZV nucleocapsid protein , was used as a marker for infected cells as abundant ORF23 protein indicates a later stage of VZV infection [10] . Only infected cells with intact nuclear membranes were analyzed to exclude those in which VZV had induced necrosis . PML NBs in pOka-infected cells were reduced compared with uninfected cells in the same section ( Fig . 7C , left panels ) , while the frequency of PML NBs in pOka-mSIM-N&C-infected cells was similar to uninfected cells ( Fig . 7C , right panels ) . Quantitative analysis showed that more PML NBs ( 2 . 8-fold higher ) were preserved in pOka-mSIM-N&C-infected cells compared to pOka-infected cells ( pOka , 0 . 71±0 . 07 , N = 153; pOka-mSIM-N&C , 1 . 97±0 . 17 , N = 108; p<0 . 0001 ) ( Fig . 7D ) . The PML NB frequency in pOka-mSIM-C-infected cells ( N = 143 ) was equivalent to pOka-infected cells ( Fig . 7D ) . Both the detection of PML expression and the frequency of PML NBs were similar in the uninfected cells within xenografts infected either with pOka or pOka-mSIM-N&C ( Fig . 6C and 7E ) . This finding makes it unlikely that the difference in PML NB frequency in skin cells infected by these two viruses reflects a variation of PML NB frequencies between individual xenografts . Taken together , these results demonstrate that ORF61 SIMs determine the capacity of VZV to target and disrupt PML NBs in skin cells in vivo and indicate that VZV spread in skin depends on this function . Since these experiments in skin xenografts showed that pOka-mSIM-N&C had a growth deficiency in vivo which was not detectable in vitro and IFN-α , which induces PML , is present in VZV-infected xenografts in vivo but is not produced by VZV-infected cells in vitro [39] , [40] , we evaluated the replication of pOka and pOka-mSIM-N&C in cultured cells treated with IFN-α . The number of PML NBs was increased by IFN-α treatment and as expected , the PML NBs were disrupted by pOka but not by pOka-mSIM-N&C ( Fig . 8A and 8B ) . Nevertheless , IFN-α treatment had a comparable effect on reducing pOka and pOka-mSIM-N&C titers in melanoma cells and HELFs ( Fig . 8C ) . However , even with IFN treatment , PML NB frequencies in melanoma cells remained significantly lower than frequencies observed in skin cells in vivo ( Fig . 8A ) . That pOka-mSIM-N&C was not differentially inhibited in IFN-α treated cells when compared to pOka in vitro , in contrast to the severely impaired growth of pOka-mSIM-N&C in skin supports the significance of the very high frequencies of PML NBs that are present in dermal and epidermal cells for limiting the spread of VZV in vivo . Since VZV , like other viruses , replicates more efficiently in proliferating cells , another reason for the defective growth of the ORF61 SIM mutants in skin compared to cultured cells might be fewer proliferating cells in vivo . Based on the expression of Ki67 , a proliferation marker [41] , most HELFs were proliferating under standard cell culture conditions whereas only a few skin cells from mock- or VZV-infected xenografts were Ki67-positive ( Fig . 9A and 9B ) . No significant difference in Ki67 expression was observed between uninfected cells in pOka- or pOka-mSIM-N&C-infected xenografts ( Fig . 9A ) . To further investigate whether the ORF61 SIMs confer an advantage for VZV replication in non-proliferating cells , the growth kinetics of pOka and pOka-mSIM-N&C were compared in serum-starved HELFs . The percentage of Ki67-positive HELFs was reduced to a minimal level by serum starvation ( Fig . 9B ) . However , titers of pOka-mSIM-N&C in serum-starved HELFs were indistinguishable from pOka titers ( Fig . 9C ) . These results suggested that the difference in the importance of ORF61 SIMs for VZV replication in skin compared with cultured cells did not reflect an advantage for VZV replication in non-proliferating cells that depends upon the ORF61 SIMs .
This study provides the first direct evidence of the importance of PML NB disruption for the efficient replication of a viral pathogen in differentiated human cells located within their usual tissue microenvironment in vivo and of the requirement for the SUMO-binding capacity of a viral protein , VZV ORF61 , to counteract the innate antiviral control mediated by PML NBs . This function is critical in VZV skin infection because the persistence of the virus in the human population depends upon its capacity to produce cutaneous lesions that contain high concentrations of infectious virus particles for transferring to susceptible individuals [10] . Notably , we found that PML NBs were highly abundant in human skin cells , especially in the basal cell boundary between the epidermis and the dermis . In the absence of functional ORF61 SIMs , these PML NBs were preserved and VZV infection was severely impaired . Thus , our experiments indicate that VZV pathogenesis in skin requires ORF61 association with and dispersal of PML NBs and that this modification of host cell nuclear structures depends upon ORF61 SIMs . We suggest that PML NB-mediated control of VZV replication must be counteracted by ORF61 in order to produce the characteristic cutaneous lesions of varicella and herpes zoster and that ORF61 SUMO-binding capacity is necessary for this essential phase of the VZV life cycle in the human host . More generally , the finding that ORF61 has functional SIMs that modulate PML NBs provides new evidence that non-covalent SIM-SUMO interactions can alter the structure and dynamics of PML NBs [32]-[36] . Importantly , the evaluation of VZV ORF61 SIM-deficient mutants in skin xenografts shows that this SIM-dependent effect on PML NBs occurs in differentiated cells within tissues in vivo . To our knowledge , ORF61 is the first example showing that a viral RING finger protein has functional SIMs , like those that are present in cellular RING finger proteins , such as RNF4 , which is involved in PML degradation [34] . Of interest , the less conserved SIM-C motif ‘TIDL’ , which has also been identified in other SUMO-binding proteins [42] , appeared to be more critical than the other two highly conserved ORF61 SIMs . One possible explanation is the access of SIM-N1 and SIM-N2 sites to SUMO1 might be hindered since they are in a proline-rich region and proline may interrupt the β-strand conformation required for the interaction . Our analysis of ORF61 SIM-C provides additional evidence that the less conserved ‘TIDL’ motif can be a functional SIM and demonstrates its role in a viral SUMO-binding protein . In previous work , we found that VZV infection in skin causes a dramatic upregulation of IFN-α in uninfected cells surrounding VZV lesions and that this response is critical for controlling infection , as shown by enhanced VZV replication and extensive skin lesion formation when the IFN pathway is blocked [39] . We now report that PML NBs are also increased substantially from an already high baseline when skin is infected with VZV . Since PML expression is regulated by IFN , these observations are further evidence of the significant role of IFN-mediated innate immunity in skin [39] . Our data indicate that PML NBs are an important mechanism by which IFN control of VZV infection is achieved in vivo and that ORF61 SIM-mediated targeting and dispersal of PML NBs is necessary to counteract this innate response during VZV pathogenesis . These observations also help to explain why antiviral immunity was defective in a PML knockout mouse model [8] . The finding that the growth of pOka-mSIM-N&C was severely defective in skin xenografts , whereas it was indistinguishable from pOka in cultured cells , illustrates that the functional significance of the ORF61 SIMs could only be demonstrated when assessed in the context of VZV replication in differentiated host cells in vivo . The very high frequencies of PML NBs in differentiated skin cells in vivo suggests that the abundance of PML NBs determines whether the ORF61 SIM-mediated effect on PML NBs is necessary for VZV to achieve efficient replication . PML NB frequencies could not be enhanced to these levels in cultured cells even with IFN treatment in vitro . Since PML NBs are also numerous in the nuclei of both neurons and satellite cells [9] , it will be of interest to investigate the contribution of ORF61 SIM-dependent PML NB disruption in VZV neuropathogenesis . The need to define functional requirements for disrupting PML NBs in vivo was also evident from the fact that mutating SIM-C alone was sufficient to substantially affect ORF61 dispersal of PML NBs in cultured cells whereas all three SIMs were necessary for PML NB disruption in skin . Another difference between cell culture and skin cells in vivo was that the mutant ORF61 lacking all SIMs retained some capacity to associate with PML NBs in cultured cells but the association was reduced significantly in skin cells . These observations suggest another VZV protein ( s ) or VZV-activated cellular factor ( s ) may bind to ORF61 and facilitate ORF61 targeting of PML NBs in vitro but has a limited role in vivo . We speculate that the differential availability or expression of these factors in skin cells compared to cultured cells might have contributed to the differential requirement of the three ORF61 SIMs to VZV replication in vivo and in vitro . These questions warrant further investigation , particularly in the different types of human cells that are targeted during VZV pathogenesis . In HSV-infected cells , the ICP0 RING domain acts as an ubiquitin E3 ligase and triggers proteasome-dependent PML degradation and PML NB disruption [14] , [15] . Our experiments confirmed that the RING domain was also necessary for ORF61-induced PML NB dispersal but we found that it was dispensable for ORF61 association with PML NBs . In contrast , the ORF61 SIMs were required for both PML NB association and disruption , indicating that PML NB dispersal by ORF61 is a two-step process: the ORF61 SIMs first recognize sumoylated PML protein in PML NBs and the RING domain is needed to execute dispersal . As was consistent with the persistent PML protein expression in VZV-infected cells [9] , [13] , ORF61 expression as a single protein caused PML NB disruption but PML protein was not degraded; therefore , we speculate that the ORF61 RING domain has functions other than E3 ligase that are necessary for its contribution to PML NB disruption . For example , since RING domains mediate protein-protein interactions [43] , the RING domain in ORF61 may form a complex with other RING finger proteins in the nucleoplasm and thereby dislodge ORF61 SIM-bound PML proteins from PML NBs . However , during other cellular events in which ORF61 is involved , it is quite possible that the ORF61 RING domain acts as an E3 ligase and mediates its substrate degradation , since ORF61 RING domain is known to exhibit E3 ligase activity in vitro [19] , [20] . It is known that all alphaherpesviruses encode RING finger proteins related to ICP0 and ORF61 and most of them have been shown to target PML NB components [44] . Of interest , our sequence analyses indicate the presence of conserved SIM ( s ) in these RING finger proteins . Taken together , we propose that the interaction of these SIM-containing viral proteins with sumoylated PML is conserved and essential for alphaherpesviruses to target PML NB structures for dispersal whereas the RING domain in the orthologs has variable functions , and these functions determine whether , or the extent to which , PML protein is degraded . Our recent experiments in VZV-infected skin cells and neurons showed that PML protein that persists can form a novel class of PML nuclear cages with the capacity to sequester newly synthesized capsids and constitute an intrinsic anti-VZV defense [9] . Viruses like HSV have the capability to completely overcome the PML-mediated intrinsic barrier as they not only disrupt PML NBs but also efficiently degrade PML protein; therefore these viruses may have an advantage against the IFN defense that is triggered and amplified within infected tissues . We speculate that this difference in the effect on PML protein , resulting from the different functional capacities of ICP0 and ORF61 , may help to explain why HSV reactivates much more frequently than VZV in the naturally infected host [45] . Of note , the gammaherpesvirus KSHV-encoded LANA2 protein disperses PML NBs by a SIM-dependent process in cultured cells although it does not have a RING domain [36] . We propose that SIM-dependent disruption of the architecture of PML NBs might be a conserved mechanism among herpesviruses , independent of the fate of PML protein . As noted , many viruses encode gene products that have been shown to modify PML NBs in cultured cells [7] . However , to define the specific importance of PML NB modulation for viral replication and pathogenesis , viral mutants that are disabled only in this function are needed . HSV ICP0 RING domain mutant viruses do not disrupt PML NBs but the RING domain is also essential for other ICP0 functions , including viral gene expression , innate immune evasion and virion formation [46]-[48] . Therefore , it is difficult to attribute the replication defect of the HSV ICP0 RING domain mutant to a single function . Experiments with the ORF61 SIM mutants avoided these concerns because the RING domain was intact and other functions of ORF61 , including viral gene regulation and NF-κB suppression and ORF61 protein stability were preserved . These ORF61 functions , such as NF-κB regulation , are also likely to be important contributions of this multi-functional protein to VZV pathogenesis and warrant further investigation [23] . Nevertheless , since ORF61 SIMs might target many more sumoylated proteins in PML NBs or other nuclear compartments , other ORF61 SIM-mediated interactions could contribute to VZV replication in vitro and to pathogenesis in vivo in addition to their role in PML NB disruption . In summary , we have demonstrated that VZV ORF61 interacts with SUMO1 via three conserved SIMs . The interaction was essential for the association of ORF61 with PML NBs and ORF61-mediated PML NB disruption in infected skin cells in vivo . VZV replication and spread in skin tissues in vivo depended on this ORF61 SIM-mediated function . Our data support the conclusion that PML NBs provide an intrinsic host defense against VZV infection in skin , which is an essential stage in VZV pathogenesis during primary and recurrent infection of the human host and we have elucidated the ORF61 SIM-dependent mechanism that is used by VZV to counteract these antiviral nuclear structures .
The human fetal tissues for SCID xenograft studies were obtained from Advanced Bioscience Resources , Inc . ( Alameda , CA ) in accordance with state and federal regulations for tissue acquisition for biomedical research , in accordance with FDA 21 CFR Part 1271 GTP ( Good Tissue Practices ) , UAGA and NOTA . Human melanoma cells ( a tumor cell line ) and primary human embryonic lung fibroblasts ( HELF ) were derived and used as described previously [23] . Animal protocols complied with the Animal Welfare Act and were approved by the Stanford University Administrative Panel on Laboratory Animal Care . Human melanoma cells ( a tumor cell line ) and primary human embryonic lung fibroblasts ( HELF ) were derived and used as described previously [23] . The PML knock-down melanoma cell line ( siPML ) was a gift from Prof . Saul Silverstein , University of Columbia and was used as the negative control in PML western blot experiments . Proteasome inhibitor MG132 ( Calbiochem ) was diluted in DMSO and used at 10 µM; DMSO was used for mock treatment . Human tumor necrosis factor α ( TNF-α ) ( Biovision ) was used at 20 ng/mL for activation of the NF-κB pathway . Human IFN-α Hu-IFN-α2b ( PBL InterferonSource ) was used at 1000 international units ( IU ) per mL for upregulation of PML in melanoma cells and HELFs . Rabbit polyclonal antibodies against ORF61 and IE63 were kindly provided by Prof . Paul Kinchington , University of Pittsburgh; rabbit polyclonal anti-ORF23 was generated by rabbit immunization with purified protein [49] . PML antibodies were mouse monoclonal anti-PML ( PG-M3 ) and rabbit polyclonal anti-PML ( Santa Cruz Biotech ) . Other antibodies included mouse monoclonals against SUMO1 ( Zymed ) , VZV gE ( Chemicon ) , and α-Tubulin ( Sigma ) and rabbit polyclonal against Ki67 ( Abcam ) . The ORF61 expression plasmid pcDNA-ORF61 was constructed by cloning full-length ORF61 coding sequence into pcDNA3 . 1 ( + ) . ORF61 DNA was amplified by PCR with forward primer ( ORF61-F ) 5′-GATCAAGCTTATGGATACCATATTAGCGGG-3′ containing a HindIII site and reverse primer ( ORF61-R ) 5′-GCGAATTCCTAGGACTTCTTCATCTTGT-3′ containing an EcoRI site . The PCR product was digested with HindIII and EcoRI and ligated to HindIII&EcoRI-digested pcDNA3 . 1 ( + ) to generate pcDNA-ORF61 . The ORF61 ( ΔRING ) fragment , from which amino acids 1-60 were deleted , was amplified by PCR with the forward primer ( ΔRING-F ) 5′-GATCAAGCTTATGGTGCAATCCATCCTGCATAAG-3′ containing the HindIII site and the reverse primer ( ORF61-R ) , and ligated to HindIII/EcoRI-digested pcDNA3 . 1 ( + ) to generate pcDNA-ORF61 ( ΔRING ) . pcDNA constructs expressing ORF61 SIM mutants ( pcDNA-mSIM ) were constructed by substituting the four hydrophobic core residues of SIMs with alanines in the context of pcDNA-ORF61 ( SIM-N1 , IDIL to AAAA; SIM-N2 , IDLL to AAAA; SIM-C , TIDL to AAAA ) . Primers used for SIM mutagenesis are: SIM-N1: 5′-CATTTGAAGATTCCGCTGCCGCTGCACCGGGAGATG-3′ and 5′-CATCTCCCGGTGCAGCGGCAGCGGAATCTTCAAATG-3′; SIM-N2: 5′-CCGGGAGATGTCGCAGCTGCTGCGCCACCAAGCCCA-3′ and 5′-TGGGCTTGGTGGCGCAGCAGCTGCGACATCTCCCGG-3′; SIM-C: 5′-CGATGCTTAGCAGCAGCCGCGACATCTGAGTCTGA-3′ and 5′-TCAGACTCAGATGTCGCGGCTGCTGCTAAGCATCG-3′ PML mammalian expression plasmids were kind gifts from Prof . Peter Hemmerich , Leibnitz-Institute of Age Research , Germany and Prof . Annie Sittler , Universite Pierre et Marie Curie , France . The NF-κB reporter plasmid was gift from Prof . Dingxiang Liu in Institute of Molecular and Cell Biology , Singapore . The gE promoter luciferase construct was as described [50] . All constructs were confirmed by nucleotide sequencing ( Elim Biopharm , Inc . , Hayward , CA ) . ORF61-transfected cells or VZV-infected cells grown on 10 cm dishes were lysed in 100 ul high salt buffer ( 20 mM HEPES [pH 7 . 2] , 450 mM NaCl , 1 . 5 mM MgCl2 , 0 . 5% NP-40 , 20% Glycerol ) supplemented with EDTA-free protease inhibitor cocktail ( Roche ) . Insoluble proteins were removed by centrifugation . The supernantant was combined with 400 ul NaCl-free buffer ( 20 mM HEPES [pH 7 . 2] , 1 . 5 mM MgCl2 , 0 . 5% NP-40 , 20% glycerol ) and centrifuged again before GST pull down . GST or GST-SUMO1/2 proteins ( 5 µg ) ( Boston Biochem ) were added to the pre-cleared lysate and rotated at 4°C for 2 h . Glutathione Sepharose beads ( GE Healthcare ) were added subsequently and incubated at 4°C for 2 h . Beads were washed 4 times with the binding buffer ( 20 mM HEPES [pH 7 . 2] , 90 mM NaCl , 1 . 5 mM MgCl2 , 0 . 5% NP-40 , 20% glycerol ) and eluted with SDS sample buffer . The ORF61 protein that bound to the beads was analyzed by Western blot using the ORF61 antibody and the GST and GST-SUMO1/2 proteins that bound to the beads was analyzed by amido black staining . Recombinant viruses were generated using cosmids derived from pOka [51] . The entire pOka genome is covered by four overlapping cosmids designated Fsp73 ( pOka nucleotides [nt] 1 to 33128 ) , Spe14 ( pOka nt 21795 to 61868 ) , Pme2 ( pOka nt 53755 to 96035 ) , and Spe23 ( pOka nt 94055 to 125124 ) . The ORF61 coding region is located in the unique long region in the cosmid Spe23 ( pOka nt 103045-104445 ) . A 4 . 8 kb PstI-PmlI fragment containing full length ORF61 gene was subcloned into pCR4-TOPO ( Invitrogen , Inc . ) to make pCR4- ( PstI/PmlI ) , in which ORF61 SIM mutations were generated using two round PCR method . The PstI-PmlI fragments containing ORF61 SIM mutations were cloned into pLit ( ORF59-65 ) [23] . Mutant Spe23 cosmids were made by ligating the NheI-AvrII fragment from pLit ( ORF59-65 ) to Spe23 digested with NheI/AvrII . Recombinant viruses , designated pOka-mSIM-N , pOka-mSIM-C , and pOka-mSIM-N&C , were isolated by transfection of melanoma cells with the mutated Spe23 cosmid and the other three intact cosmids , Fsp73 , Spe14 , and Pme2 . Genomic DNA was extracted from virus-infected melanoma cells or HELFs with DNAzol reagent ( Invitrogen , Carlsbad , CA ) . A PCR fragment covering the mutated region was amplified from the genomic DNA using Taq polymerase ( Invitrogen ) and sequenced ( Elim Biopharm , Inc . , Hayward , CA ) to confirm mutations . Melanoma cells ( 106/well ) were seeded in a 6-well plate , infected with 1×103 PFU/well at day 0 , and cultured for 5 days . On each day , cells from one well were trypsinized , centrifuged , and resuspended in 1 mL of culture medium . The infected cells were serially diluted 10-fold , and 0 . 1 mL was added to melanoma cells in 24-well plates in triplicate . Cells were fixed in 4% paraformaldehyde and stained with polyclonal anti-VZV human immune serum and secondary anti-human biotin ( Vector Lab , Burlingame , CA ) . The staining was developed with the Fast Red substrate ( Sigma ) . Statistical analysis of growth kinetics was done by the Student's t test . Skin xenografts were made in homozygous CB-17scid/scid mice , using human fetal tissue supplied by Advanced Bioscience Resources ( ABR , Alameda , CA ) according to federal and state regulations; the methods used to engraft and infect the skin xenografts were as described previously ( 25 ) . Animal use was in accordance with the Animal Welfare Act and approved by the Stanford University Administrative Panel on Laboratory Animal Care . pOka and ORF61 SIM mutant viruses were passed three times in primary HELF and titered before inoculation . Skin xenografts were harvested at day 10 and 21 and titers were determined by infectious focus assay . DNA was extracted from skin tissues with proteinase K and phenol chloroform ( Invitrogen , Carlsbad , CA ) . PCR and sequencing were performed to confirm the expected mutations . Cells seeded on 12 mm glass cover slips were fixed with 4% paraformaldehyde for 15 min and permeablized with 0 . 5% Triton-X100 for 10 min . For dual staining of ORF61 and PML proteins , cells were incubated with ORF61 rabbit polyclonal antibody ( 1∶200 dilution ) and PML mouse monoclonal antibody PG-M3 ( 1∶50 dilution ) in blocking buffer ( PBS with 5% fetal bovine serum ) at room temperature ( RT ) for 1 h , followed by incubation with Alexflour 488 donkey anti-mouse immunoglobulin ( Invitrogen ) and Texas Red-conjugated donkey anti-rabbit immunoglobulin ( Jackson ImmunoResearch ) for 30 min . Cell nuclei were stained with 2 µg/mL Hoechst33342 for 10 min after secondary antibody incubation . All images were obtained with a SP2 Leica confocal microscope . Images were taken in a fixed setting with the 63x objective in PML NB number quantification experiments , and with the 100x objective to quantify the association between ORF61 puncta and PML NBs in infected skin cells . For skin experiments , sections ( 5 µm ) were made from formalin-fixed , paraffin-embedded skin tissues . After deparaffinization and rehydration , sections were treated with citrate-based antigen unmasking solution ( Vector labs ) and stained with specific antibodies ( anti-PML [PG-M3] , 1∶10; anti-ORF61 , 1∶25; anti-ORF23 , 1∶100 ) . Skin sections were examined for cell proliferation using Ki67 antibody ( Abcam ) . Skin sections were stained with VZV gE antibody ( Chemicon , 1∶2000 ) and IHC immunoperoxidase secondary detection system ( Chemicon ) . Staining was developed with Vector VIP substrate kit and methyl green counterstain ( Vector labs ) . Melanoma cells or HELFs were seeded at 5×105 cells/well in 6-well plates . Cells in each well were mock-treated or treated with 1000 IU of Hu-IFN-α2b ( PBL InterferonSource ) for 24 h prior to inoculation . Cells were inoculated with 500 PFU/well of either pOka or pOka-mSIM-N&C . The media was aspirated at 2 h post-infection to remove the inoculum and replaced with media with or without 1000 IU of IFN-α2b . Virus titers from 2 h to 36 h post-inoculation were determined by infectious focus assay on melanoma cells . HELFs were seeded at 5×105 cells/well in 6-well plates and at 2×105 cells/well in 2-well chamber slides . Cells were starved in serum-free culture medium for 48 hours and then inoculated with 50 PFU/cm2 of pOka or mSIM-N&C . At 2 hours post infection , the inoculum was removed and the medium was replaced with normal medium or serum-free medium . Virus titers in cells recovered from the 6-well plates at intervals of 2 h to 36 h post-inoculation were determined by infectious focus assay on melanoma cells . Cells on chamber slides were fixed with paraformaldehyde and stained with VZV gE ( Chemicon ) and Ki67 ( Abcam ) antibodies . All transfections were performed with Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) following the manufacturer's instructions . The transfected cells for immunofluorescence were fixed at 24 h post-transfection . To prepare heavily infected cells for Western blot analysis of PML proteins , melanoma cells and HELFs were inoculated with pOka or ORF61 SIM mutants at a ratio of 1 infected cell:100 uninfected cells . At 24 h post-inoculation , each cell monolayer was resuspended by trypsinization and re-plated on the same dish and left for another 24 h . The percentage of infected cells was examined by immunofluorescence with ORF23 antibody and was >90% at the time of harvest . To prepare infected cells for immunofluorescence microscopy , cells growing on glass coverslips were inoculated with pOka or ORF61 SIM mutants at a ratio of 1 infected cell: 1000 uninfected cells and infected for 24 h . Transfected or infected cells growing on 6-well plates were lysed in high salt buffer as described above . Proteins were separated on SDS-PAGE gels and transferred to polyvinylidene difluoride ( PVDF ) membrane ( Millipore , Bedford , MA ) with a semidry transfer cell ( Bio-Rad ) . The membranes were blocked for 1 h in 5% non-fat milk in PBST ( 1x PBS plus 0 . 1% Tween-20 ) , incubated with primary antibody at RT for 2 h , washed three times with PBST , incubated with horseradish peroxidase-conjugated rabbit or mouse immunoglobulin ( Amersham ) at RT for 1 h , and washed three times with PBST . Proteins were detected using the enhanced chemiluminescence plus detection system ( Amersham ) . Melanoma cells were seeded in 24-well plates one day before transfection . Three independent transfections were preformed for each experiment . In the gE and ORF61 promoter assay , 900 ng of the promoter construct was transfected to melanoma cells with 100 ng of pcDNA-ORF61 or pcDNA-mSIM or empty vector for 24 h . In NF-κB experiments , 500 ng of NF-κB reporter plasmid was cotransfected with 250 ng of pcDNA-ORF61 or pcDNA-mSIM or empty vector; at 24 h post-transfection , cells were treated with 20 ng/mL TNF-α for 6 h before cell lysis and luciferase assay . In the NF-κB dose-dependent experiment , increasing amounts of pcDNA-ORF61 or pcDNA-ORF61 ( ΔRING ) ( 10 ng , 50 ng , and 250 ng ) were used and the total DNA was brought to 850 ng with empty vector . In all transfections , 0 . 07 ng of the plasmid pRL-TK ( – ) in which TK promoter has been removed was included to normalize the transfection efficiency . Luciferase assays were performed using the dual luciferase kit ( Promega ) according to the manufacturer's recommendations . VZV ORF61: NP_040183; VZV ORF23: AAY57709 . 1; VZV IE63: Q77NN7; VZV gE: Q9J3M8 . | PML nuclear bodies ( PML NBs ) are spherical nuclear structures that are present in most human and animal cells . These bodies contribute to anti-viral defense and therefore many viruses have developed strategies to disrupt them . This interaction has been demonstrated for a number of viruses in cultured cells but little is known about these processes in differentiated cells within human tissues . Varicella-zoster virus ( VZV ) is a human alphaherpesvirus that causes chicken pox and shingle lesions in skin . Here we show that VZV disrupts PML NBs in epidermal and dermal cells in skin tissues implanted subcutaneously in immunodeficient mice . We found that PML NB dispersal is mediated by VZV ORF61 protein and is required for VZV cell to cell spread and lesion formation in skin . The ability of ORF61 to disrupt PML NBs depends on its capacity to bind to SUMO1 protein , which is conjugated to PML and other proteins within PML NBs . To our knowledge , our study provides the first evidence of PML NB modification through the SUMO-binding function of a viral protein , VZV ORF61 , and the importance of this molecular mechanism for virus-induced PML NB disruption in differentiated cells infected within their tissue microenvironment in vivo . |
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Wild bird movements and aggregations following spells of cold weather may have resulted in the spread of highly pathogenic avian influenza virus ( HPAIV ) H5N1 in Europe during the winter of 2005–2006 . Waterbirds are constrained in winter to areas where bodies of water remain unfrozen in order to feed . On the one hand , waterbirds may choose to winter as close as possible to their breeding grounds in order to conserve energy for subsequent reproduction , and may be displaced by cold fronts . On the other hand , waterbirds may choose to winter in regions where adverse weather conditions are rare , and may be slowed by cold fronts upon their journey back to the breeding grounds , which typically starts before the end of winter . Waterbirds will thus tend to aggregate along cold fronts close to the 0°C isotherm during winter , creating conditions that favour HPAIV H5N1 transmission and spread . We determined that the occurrence of outbreaks of HPAIV H5N1 infection in waterbirds in Europe during the winter of 2005–2006 was associated with temperatures close to 0°C . The analysis suggests a significant spatial and temporal association of outbreaks caused by HPAIV H5N1 in wild birds with maximum surface air temperatures of 0°C–2°C on the day of the outbreaks and the two preceding days . At locations where waterbird census data have been collected since 1990 , maximum mallard counts occurred when average and maximum surface air temperatures were 0°C and 3°C , respectively . Overall , the abundance of mallards ( Anas platyrhynchos ) and common pochards ( Aythya ferina ) was highest when surface air temperatures were lower than the mean temperatures of the region investigated . The analysis implies that waterbird movements associated with cold weather , and congregation of waterbirds along the 0°C isotherm likely contributed to the spread and geographical distribution of outbreaks of HPAIV H5N1 infection in wild birds in Europe during the winter of 2005–2006 .
Highly pathogenic avian influenza virus ( HPAIV ) H5N1 spread from Asia to Europe , the Middle East and Africa during the winter of 2005–2006 , with sporadic outbreaks reported in poultry and wild bird populations . Both trade of poultry and poultry products and wild bird movements may have contributed to the unprecedented geographical spread of the virus [1] , [2] . In Europe , most cases in wild bird populations occurred in areas where no outbreaks had previously been detected in poultry , indicating that wild birds were likely implicated in the spread of the infection [1]–[3] . Outbreaks of HPAIV H5N1 infection in Europe followed waterbird movements associated with a spell of cold weather [3]–[5] , and the route of introduction of HPAIV H5N1 in western European countries was most likely associated with movements of wild birds [1] , [2] . However , the role of wild birds in the spread of HPAIV H5N1 remains highly controversial [2] , [6] , and the association between movements of wild birds associated with cold weather and outbreaks of HPAIV H5N1 infection in Europe during the winter of 2005–2006 remains conjectural . Experimental infections of naïve waterbirds with HPAIV H5N1 further supported a possible role of wild birds in the spread of the virus . Certain species are highly susceptible to developing clinical signs , may rapidly succumb to severe disease ( e . g . , swans , Cygnus spp . , and European small diving ducks Aythya spp . ) , and will thus highlight local outbreaks without contributing significantly to long-distance transmission [7]–[9] . In contrast , other species show no visible signs , shed virus for several days , and potentially play a significant role as spreaders of HPAIV H5N1 ( e . g . , mallard , Anas platyrhynchos ) [8] . Migratory and within-winter movements of such species asymptomatically infected with HPAIV H5N1 may result in significant geographical spread of the virus [8] . Outbreaks of HPAIV H5N1 infection in wild birds may then occur and only be detected when spreader species aggregate with highly susceptible species . The migratory and wintering strategies of European waterbird species vary ( as do those of sub-populations of some species ) [10] . Waterbird species breeding at most northern latitudes are highly migratory , wintering at southern latitudes , including sub-Saharan Africa . These species include the Eurasian wigeon ( Anas Penelope ) and the northern pintail ( A . acuta ) . In contrast , waterbird species breeding both at northern latitudes and in more temperate regions of Europe are partially migratory . While sub-populations breeding at most northern latitudes migrate to southern latitudes , sub-populations breeding at more temperate latitudes remain and winter in these regions . Such species include the common teal ( A . crecca ) , the gadwall ( A . strepera ) , the mallard , the common pochard ( Aythya ferina ) and the tufted duck ( A . fuligula ) . The garganey ( Anas querquedula ) is one exception , as it is a fully migratory species , breeding in temperate regions of Europe and wintering exclusively in sub-Saharan Africa [10] . Waterbirds winter in areas where bodies of water remain unfrozen , allowing them to forage . Their wintering range is thus determined by the extent of ice cover [11] . For shallow fresh waters that dabbling ducks ( Anas spp . ) and lighter diving ducks ( Aythya spp . ) depend upon [11] , the occurrence of ice cover is tightly coupled to surface air temperatures [12] , and severe cold spells can drive massive within-winter movements of waterbirds [10] , [13] , [14] . Two wintering strategies are used by migratory waterbirds in response to cold weather . On the one hand , because cold spells can entail high energy expenditures and limit food availability , migratory waterbirds may winter at southern latitudes , where adverse weather conditions are rare [11] . On the other hand , because long migration distance from their wintering grounds to their breeding grounds impairs waterbirds' reproductive success [11] , wintering waterbirds may minimize this distance by congregating on unfrozen bodies of water located as close as possible to their breeding grounds [15] . In recent years , sub-populations of migratory waterbird species that typically leave their breeding grounds in northern Europe for more southern wintering grounds have remained sedentary during relatively mild winters [10] . Furthermore , migratory waterbirds , such as the Eurasian wigeon and the common teal , winter in most southern locations , such as Spain and northwestern Africa , only during harsh winters [16] . Together with the large number of partially migratory waterbird species listed above , it suggests that the latter strategy is common in Europe . In addition , migratory waterbirds using the former strategy and wintering in more southern latitudes start their spring migration towards the breeding grounds as early as February [10] , [17] . They may be slowed by cold weather , and constrained to stage on unfrozen bodies of water located at the forefront of the freezing front . All these conditions cause waterbirds to congregate during the winter period in suitable habitats along the freezing front where bodies of fresh water are not frozen . This will favour the transmission and spread of HPAIV H5N1 within and between species , resulting in outbreaks in wild bird populations [5] . Such transient aggregations of waterbirds close to populations of domestic poultry will increase the chance of pathogen spill-over into these hosts , which may eventually result in transmission into other domestic animals and humans . We hypothesize that the initial cases of HPAIV H5N1 infection in wild birds in Europe during the winter of 2005–2006 occurred at locations where surface air temperatures were close to 0°C , because of higher waterbird densities along the freezing front . To test this hypothesis , we analysed the spatio-temporal correlation of surface air temperatures with the initial outbreaks of HPAIV H5N1 infection in wild birds in Europe during the winter of 2005–2006 . The spatio-temporal correlation of surface air temperatures with waterbird abundance was also assessed , using publicly available data on maximum mid-January counts of mallards obtained between 1990 and 2003 across Europe ( from the French Atlantic coast to eastern Ukraine and from Spain and Greece to Denmark ) , and mid-January counts of abundant duck species obtained between 1993 and 2008 in eastern France and Switzerland . Census sites in Eastern France and Switzerland were chosen because this region , often hit by cold spells and surface air temperatures close to 0°C , is highly used by wintering mallards and diving ducks [10] . Mallards and a proportion of common pochards experimentally infected with HPAIV H5N1 remained clinically healthy yet shed high viral titers , potentially spreading the virus over long distances [8] . Analyzing the relationship between the abundance of these species and surface air temperatures at these census sites may thus provide useful indications for higher waterbird densities close to the 0°C isotherm .
A total of 52 locations in 15 European countries experienced initial outbreaks of HPAIV H5N1 infection in wild bird populations ( Table S1 ) . Initial outbreaks of HPAIV H5N1 infection in wild bird populations were defined as one or more cases of HPAIV H5N1 infection in wild birds in countries where the first reported case ( s ) occurred in wild birds , at locations with no outbreak in poultry or wild birds within ∼120 km in the preceding month . Only countries with first cases of HPAIV H5N1 infection in wild birds were selected to exclude cases resulting from introduction by and spill-back transmission from infected poultry nearby . Likewise , cases were selected at locations with no outbreak in poultry or wild birds within ∼120 km in the preceding month to exclude cases resulting from local spread independent of within-winter movement of wild birds . A perimeter of ∼120 km was used based on the typical range of dispersive movements of wild waterfowl of at least 100 km [10] . The date of initial outbreaks of HPAIV H5N1 infection in wild birds used in the analyses is the date infected birds were found moribund or dead . Initial outbreaks of HPAIV H5N1 infection in wild bird populations occurred most often when surface air temperatures were close to 0°C ( Fig . 1 ) . Of the 52 locations , 17 to 19 ( 33%–37% ) experienced maximum surface air temperatures of 0°C–2°C on the day wild birds infected with HPAIV H5N1 were found , and on the two preceding days ( day −2 to day 0; Fig . 1A ) . In contrast , only 1 ( 2% ) to 8 ( 15% ) locations experienced maximum surface air temperatures within any other 2 degree-range between day −2 and day 0 ( ANOVA test , F = 9 . 5 , p<0 . 0001; Fig . 2 ) . Furthermore , the number of locations experiencing maximum surface air temperatures of 0°C–2°C peaked to 17 to 19 on day −2 to day 0 , while only 8 ( 15% ) to 14 ( 27% ) locations experienced maximum surface air temperatures of 0–2°C on day −7 to day −3 and day +1 to day +7 ( ANOVA test , F = 15 . 1 , p = 0 . 0005; Fig . 2 ) . A similar but less pronounced trend was observed for minimum and average surface air temperatures ( Fig . 1B and 1C ) . Areas that did not experience surface air temperatures close to 0°C may have not reported HPAIV H5N1 outbreaks in wild birds due to reporting bias , for example associated with regional differences in the ornithological and bird-watching community , or national differences in resources allocated to surveillance programs . Because the intensity of surveillance of mortality events in wild birds may be linked to human population density in a region , we determined whether the reporting of initial outbreaks of HPAIV H5N1 infection in wild birds was biased towards more densely populated regions in Europe . The statistical distribution of population density of the first administrative regions that reported initial HPAIV H5N1 outbreaks in wild birds was not significantly different from that of regions across Europe ( t = 0 . 58 , p = 0 . 8; Fig . S1A ) . Because the intensity of surveillance of mortality events in wild birds may also be linked to a country's resources , we also determined whether the reporting of initial outbreaks of HPAIV H5N1 infection in wild birds was biased towards richer countries in Europe . Likewise , the statistical distribution of gross domestic product ( GDP ) per inhabitant of countries that reported initial HPAIV H5N1 outbreaks in wild birds was not statistical different from that in Europe ( t = 0 . 25 , p = 0 . 7; Fig . S1B ) . The highest counts of mallards obtained at each of 93 locations across Europe between 1990 and 2003 were typically recorded when average and maximum mid-January surface air temperatures were 0°C and 3°C , respectively . Average and maximum mid-January surface air temperatures ( but not mid-January minimum surface air temperatures ) at these locations on the years of maximum mallard counts were normally distributed , around a mean of 0 . 0°C and 2 . 9°C , with a standard deviation of 4 . 2°C and 3 . 6°C , respectively ( Shapiro-Wilk normality test , W = 0 . 98 and W = 0 . 98; p = 0 . 2 , and p = 0 . 3 , respectively; Fig . 3A ) . In contrast , average and maximum mid-January surface air temperatures at these locations were not normally distributed when averaged over the entire period 1990–2003 for each location ( Shapiro-Wilk normality test , W = 0 . 94 and W = 0 . 96; p = 0 . 0004 and p = 0 . 01 , respectively; Fig . 3B ) . Mallards , common pochards ( Aythya ferina ) , and tufted ducks ( A . fuligula ) were the most abundant waterbird species counted at 25 locations in Rhône-Alpes ( eastern France ) between 1993 and 2008 , and at 18 locations across Switzerland between 2002 and 2007 . Average mid-January temperatures at these locations had a mean of 0 . 2°C ( SD = 3 . 4°C ) . Minimum mid-January temperatures at these locations had a mean of −3 . 2°C ( SD = 4 . 4°C ) . Maximum mid-January temperatures at these locations had a mean of 3 . 3°C ( SD = 2 . 9°C ) . Standardized mid-January counts of mallards and common pochards ( but not tufted ducks ) were negatively correlated with standardized minimum , average , and maximum mid-January surface air temperatures ( Fig . 4 ) , based on generalized least square linear and non-linear models with an autocorrelation structure of first order . These models determine the linear or non-linear equations of the form ax+b and ax2+bx+c , respectively , that best fit the data ( and thus minimize the distance between points from the dataset and points generated by the models ) . An autocorrelation structure of first order was included because of the spatial correlation that exists between the data points ( surface air temperatures at one location are not independent of those at another location within the region investigated ) . The goodness-of-fit of linear models were slightly higher than that of non-linear models ( Table 1 ) . This analysis provides evidence for an overall negative relationship between mallard and common pochard abundance and surface air temperatures . However , because of the lack of temporal precision ( the exact mid-January count date was unknown and mid-January temperatures were averaged over a period of 10 days around January 15th ) , more detailed conclusion on the relationship between duck abundance and ice cover likelihood at the time of the count cannot be drawn . Interestingly , higher counts of mallards and common pochards were typically recorded when surface air temperatures were slightly below the mean temperatures of the region , and their abundance tended to decrease as surface air temperatures further decreased ( Fig . 4 ) .
In the present paper , we show an association between the timing and locations of initial outbreaks of HPAIV H5N1 infection in wild birds in Europe during the winter of 2005–2006 and the 0°C isotherm . Initial outbreaks of HPAIV H5N1 infection in wild birds occurred significantly more often at locations where maximum surface air temperatures were between 0°C and 2°C , on the day of the outbreaks and the two preceding days . When visualized on a dynamical map showing daily movements of maximum surface air temperature isotherms between January and March 2006 in Europe , most initial outbreaks of HPAIV H5N1 in wild birds occur within an area delineated by the 0°C and 2°C isotherms , and appear to closely follow the surge and ebb of the cold front ( Video S1 ) . Because surveillance of avian influenza in wild birds has been up-scaled in Member States of the European Union since 2002 , and in most of Europe following the introduction of HPAIV H5N1 in 2005 [18] , it is unlikely that many outbreaks went unnoticed outside the geographical range of the 0°C isotherm during the winter of 2005–2006 . Furthermore , the absence of significant difference between human population density and GDP per inhabitant in regions or countries where outbreaks were reported and those across Europe implies that surveillance efforts were not significantly biased towards more densely populated regions or richer countries . Therefore , the geographical distribution of reported HPAIV H5N1 outbreaks in wild birds in Europe along the 0°C isotherm is likely representative of a real and important phenomenon that should be used to refocus surveillance for future outbreaks . Waterbirds congregate in winter on unfrozen bodies of fresh water where they can forage [10] , [11] , and higher waterbird densities may occur along the freezing front . On the one hand , waterbirds may choose to keep as close as possible to their breeding grounds to maximize their reproductive success , and may be displaced by cold fronts; on the other hand , waterbirds may choose to migrate to and winter in regions with rare adverse weather conditions , and may be slowed by cold fronts upon their journey back to the breeding grounds , which can start as early as February [10] , [11] , [17] . Maximum counts of mallards across continental Europe typically occurred when average and maximum surface air temperatures were 0°C and 3°C at the census location , respectively . This suggests that temperatures slightly above 0°C are one factor favouring maximum numbers of mallards at wintering locations , notably in central and northern Europe , where such winter temperatures are frequently recorded . Overall , mallard and common pochard counts at 43 locations across eastern France and Switzerland were negatively correlated with surface air temperatures . Higher numbers of mallards and common pochards occurred when mid-January surface air temperatures were lower than mean mid-January temperatures of the region . These results strongly imply the existence of a relationship between duck abundance and surface air temperatures , despite the coarse quality of mid-January counts and mid-January surface air temperatures . Nevertheless , a number of confounding factors , such as the abundance of food resources , waterbird breeding success in the preceding spring and summer , or changes in wetland management and disturbance , likely impact on yearly duck counts in Europe and could not be accounted for due to the nature of the data . Detailed studies of the correlation of long-term daily counts of waterbirds in multiple sites across Europe in winter with daily surface air temperatures are needed to further assess whether the abundance of wintering ducks is higher when surface air temperatures are close to 0°C . Although limited data are available on the association between waterbird abundance and surface air temperature , it is interesting to note that , for instance , lower mean temperatures in the Dombes region in France in the winter of 2005–2006 were associated with unusual early arrival of common pochards [4] . Likewise , lasting cold weather was associated with higher waterbird densities in eastern and southern Germany [5] . On the other hand however , the abundance of tufted ducks was not correlated with surface air temperatures . Ice cover impacts on the distribution and abundance of dabbling and lighter diving ducks in winter , because they require shallow waters to forage [11] . While common pochards feed on both animal prey and plants , tufted ducks feed predominantly on molluscs and thus forage typically in deeper waters than common pochards [19] , possibly explaining their lower sensitivity to surface air temperatures , as found in this study . Maximum surface air temperature close to 0°C , yet on the positive side of the isotherm , may provide wintering conditions leading to maximum congregation of waterbirds , notably Anatidae , and so favour influenza virus transmission . Cold fronts and freezing temperatures are typically associated with anticyclonic conditions and ( north- ) easterly winds in Europe [20] . Maximum surface air temperatures above 0°C , despite minimum surface air temperatures below 0°C , thus may determine the absence of ice cover on inland bodies of fresh water located ( south- ) westward from the cold front . Such temperature conditions were prevalent at locations with initial outbreaks of HPAIV H5N1 infection in wild birds in Europe during the winter of 2005–2006 . Therefore , congregation of waterbirds asymptomatically infected with HPAIV H5N1 and highly susceptible waterbird species in suitable habitats along the freezing front likely promoted the onset of outbreaks during the winter of 2005–2006 in Europe . Accordingly , densities of mute swans ( Cygnus olor ) and Anatidae were three to six times higher in ponds of the Dombes region where outbreaks of HPAIV H5N1 infection occurred , than in unaffected ponds [4] . Likewise , in eastern and southern Germany , where outbreaks of HPAIV H5N1 infection occurred , high waterbird densities were recorded on bodies of fresh water that remained un- or partially frozen . In contrast , waterbird densities were lower in western Germany , which experienced milder weather conditions , and no cases of HPAIV H5N1 infection in wild birds [5] . Higher densities of waterbirds along the freezing front likely favoured increased transmission of HPAIV H5N1 . Excretion of HPAIV H5N1 in experimentally infected waterbirds starts as soon as one day following inoculation and peaks between 1 and 3 days following inoculation , typically lasting 5 days [7]–[9] . While the dabbling ducks tested did not develop clinical disease at all , diving ducks , as well as mute and whooper swans , can develop clinical disease as soon as 2 days , and die as soon as 4 days following inoculation [7] , [8] , although longer incubation period and longer time to death have been described in swans [9] . Clinical disease and death may occur early during the course of infection in free-ranging waterbirds due to contributing factors , such as poor nutritional and health status or harsh environmental conditions . Thus , there may be a short time-lag of 2 to 4 days between transmission of HPAIV H5N1 to uninfected birds , and death and report of HPAIV H5N1 outbreaks in wild waterbirds . Maximum surface air temperatures of 0°C–2°C were most frequently reported at locations of HPAIV H5N1 outbreaks up to two days before the day birds were found dead , which likely accounted for the time-lag between aggregation of wild waterbirds , transmission of HPAIV H5N1 and report of morbidity or mortality . Furthermore , because the peak of the infectious period is typically short , aggregation of waterbirds during two days along the 0°C isotherm may be sufficient to result in sustained HPAIV H5N1 transmission and detectable outbreaks . Alternatively , and potentially concomitantly , maximum surface air temperatures close to 0°C may favour the persistence of HPAIV H5N1 in the environment and enhance environmental transmission of the virus independently of waterbird density . Avian influenza viruses ( AIV ) have been experimentally shown to remain infective for several months in water at low temperatures ( below 17°C ) and low salinity levels ( fresh- or brackish water ) [21] , [22] . Environmental transmission of LPAIV is increasingly recognized as essential in maintaining LPAIV locally and from year to year [23]–[25] . However , little is known on the role environmental transmission had on HPAIV H5N1 dynamics in Europe , and several arguments in support of environmental transmission of HPAIV H5N1 can be raised . First , HPAIV H5N1 belonging to the lineage isolated in wild waterbirds in Europe remained infective for 158 days in fresh water at 17°C , and for 26 days at 28°C [26] . Second , although HPAIV H5N1 are mainly excreted from the respiratory tract of infected waterbirds , potentially favouring direct density-dependent transmission [8] , this does not preclude contamination of lake water by respiratory excretions or infected carcasses , allowing environmental transmission of the virus . Third , assuming persistence patterns similar to those of LPAIV [21] , slower loss of infectivity at lower temperatures may have contributed to the geographical distribution of HPAIV H5N1 outbreaks along the freezing front . However , persistence of more than 4 months in fresh water at 17°C strongly suggests that environmental transmission of HPAIV H5N1 would occur even in warmer water away from the 0°C isotherm . Furthermore , freezing and thawing are known to substantially decrease AIV infectivity by up to 10 fold , despite having little effect on AIV RNA [27] . Thus , LPAIV RNA has been recovered in ice from Siberian lakes , yet no infectious virus could be isolated [28] . Therefore , although environmental transmission cannot be ruled out , it would most likely result in a more uniform distribution of HPAIV H5N1 outbreaks where bodies of water remained unfrozen , and thus is unlikely to account alone for the geographical distribution of HPAIV H5N1 outbreaks at locations where maximum surface air temperatures were close to 0°C . Certain waterbird species do not develop clinical disease upon experimental infection with HPAIV H5N1 , and may play a crucial role as reservoirs or spreaders of infection [8] . Conversely , asymptomatic infection of wild waterbirds with low pathogenic avian influenza viruses is speculated to result in costs that may hinder the ability of infected birds to fly over long distances [29] , [30] . Also , because of the relatively short infectious period of avian influenza virus infection in waterbirds , infected waterbirds may not disperse avian influenza viruses over long distances [30] . Therefore , the role of migratory birds in the long-distance spread of HPAIV H5N1 outside Asia is still highly debated [2] , [6] . However , distance , duration and efforts associated with within-winter movements of wild waterbirds are smaller than that associated with spring and autumn migratory flights , and within-winter movements can frequently be undertaken by waterbirds [10] , [13] , [14] . Although the poultry trade may have introduced HPAIV H5N1 into Russia , the Middle East or eastern Europe in fall and early winter of 2005–2006 [1] , [3] , within-winter waterbird movements associated with movements of the 0°C isotherm , following the surge and ebb of a cold spell that originated in the Black Sea area [3] , [5] , were likely undertaken by waterbirds asymptomatically infected with HPAIV H5N1 . In conclusion , waterbird movements associated with cold weather and congregation of waterbirds along the 0°C isotherm likely contributed to the spread and geographical distribution of outbreaks of HPAIV H5N1 infection in wild birds in Europe during the winter of 2005–2006 . Movements of cold weather fronts , and in particular the 0°C isotherm of maximum surface air temperature , can readily be anticipated by operational weather forecasts . Therefore , increased active surveillance of HPAIV H5N1 infection in waterbirds should target populations occurring in areas where maximum surface air temperatures are close to freezing temperatures , i . e . , along the positive side of the 0°C isotherm . Such targeted surveillance should pay special attention to poultry-dense areas , as this may increase the chance of detecting early the presence of HPAIV H5N1 in both wild and domestic bird populations in Europe during winter .
All statistical analyses were performed in the R language [35] . Statistical differences are considered significant when p<0 . 5 . | Highly pathogenic avian influenza virus of the H5N1 subtype emerged more than a decade ago in poultry in South-East Asia . In 2005 , it spread outside Asia infecting both poultry and wild birds in the Middle East , Europe and Africa . Both trade of poultry and movements of wild birds were likely implicated in the spread of the infection; however , the ability of wild birds to carry the virus to novel geographical areas is still highly debated and remains obscure . In Europe , the virus mainly infected wild birds , and emergence coincided with a spell of cold weather , which is known to result in massive movements of wild waterbirds . In this paper , we demonstrate that movements of wild waterbirds associated with cold weather contributed to the spread and geographical distribution of outbreaks in Europe during the winter of 2005–2006 . Higher density of wild waterbirds on bodies of water that remain unfrozen ahead of the freezing line likely favoured transmission of the virus and resulted in distinctive distribution of outbreaks at locations where surface air temperatures were 0°C–2°C . This has important implications for surveillance , which should target areas where temperatures are close to freezing in winter , especially in poultry-dense regions close to areas where waterfowl aggregate . |
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The molecular mechanism by which non-enveloped viruses penetrate biological membranes remains enigmatic . The non-enveloped polyomavirus SV40 penetrates the endoplasmic reticulum ( ER ) membrane to reach the cytosol and cause infection . We previously demonstrated that SV40 creates its own membrane penetration structure by mobilizing select transmembrane proteins to distinct puncta in the ER membrane called foci that likely function as the cytosol entry sites . How these ER membrane proteins reorganize into the foci is unknown . B12 is a transmembrane J-protein that mobilizes into the foci to promote cytosol entry of SV40 . Here we identify two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) as B12-interaction partners . Strikingly , SV40 recruits B12 to the foci by inducing release of this J-protein from Erlin1/2 . Our data thus reveal how a non-enveloped virus promotes its own membrane translocation by triggering the release and recruitment of a critical transport factor to the membrane penetration site .
Membrane penetration represents a decisive event during virus infection . For enveloped viruses , fusion between the viral and host membranes delivers the core viral particle into the host cytosol [1 , 2] . By contrast , because a non-enveloped virus lacks a surrounding lipid bilayer , its membrane transport process must be distinct from an enveloped virus . Indeed , membrane penetration by the non-enveloped virus families is not fully understood to date [1–3] . A central enigma that challenges this field is whether a non-enveloped virus hijacks pre-existing channels in the limiting membrane in order to enter the host , or if it generates a membrane transport portal de novo and subsequently crosses this structure to reach the host cytosol . Intriguingly , recent reports suggest that the non-enveloped polyomavirus ( PyV ) creates its own membrane transport structure to enter the host cell and cause infection [4–6] , although aspects of this process remain to be clarified . PyV is a non-enveloped DNA tumor virus known to cause debilitating human diseases especially in immunocompromised individuals . For instance , the human JC PyV is responsible for the fatal demyelinating central nervous system disease progressive multifocal leukoencephalopathy , the BK PyV for BK-associated nephropathy and hemorrhagic cystitis , and the Merkel cell PyV for the aggressive skin cancer Merkel cell carcinoma [7 , 8] . Structurally , a PyV particle is composed of 72 pentamers of the coat protein VP1 , with each pentamer harboring either the internal hydrophobic protein VP2 or VP3 [9–11] . The VP1 pentamers form the outer shell of the virus which in turn encases the 5 kilobase viral DNA genome . A complex network of disulfide bonds , in concert with VP1 C-terminal arms emanating from a pentamer that invade neighboring VP1 pentamers , stabilize the overall viral architecture [10 , 11] . Because simian PyV SV40 displays both structural and genetic similarities to human PyVs such as JC and BK PyVs , studies of the SV40 infection pathway have historically illuminated human PyV infection pathways . To cause infection at the cellular level , SV40 binds to the ganglioside GM1 receptor on the plasma membrane [12 , 13] , entering the host cell via receptor-mediated endocytosis . Upon entry , the virus is initially sorted to endolyososomes [14 , 15] before being routed to the endoplasmic reticulum ( ER ) [13 , 16 , 17] . Here the virus undergoes conformational changes imparted by ER-resident protein disulfide isomerase ( PDI ) family members [18 , 19] . These reactions expose VP2 and VP3 , generating a hydrophobic particle that physically binds to and integrates into the ER membrane [5] . The membrane-embedded virus is initially recognized by the ER membrane protein BAP31 [5] , transiently stabilized by the transmembrane protein EMC1 [20] , and eventually ejected into the cytosol by the Hsc70-SGTA-Hsp105 cytosolic extraction machinery [6 , 21] . This machinery is associated with the cytosolic side of the ER membrane via interaction with three different ER membrane J-proteins called DNAJB12 ( B12 ) , DNAJB14 ( B14 ) and DNAJC18 ( C18 ) [4 , 6 , 21 , 22] . A J-protein typically binds to Hsp70 family proteins and stimulates their ATPase activity , enabling the Hsp70 proteins to efficiently capture their clients [23] . From the cytosol , the virus is further transported to the nucleus to cause lytic infection or cell transformation . Strikingly , prior to ER-to-cytosol membrane penetration , SV40 dynamically recruits B12 , B14 , and C18 ( as well as other membrane components ) into discrete puncta in the ER membrane called foci [4 , 6 , 21] . The kinesin-1 motor promotes maturation of the foci [24] . Because the foci structures are postulated to function as the viral cytosol entry sites [5 , 6 , 21] , SV40-triggered foci formation represents the first example of a non-enveloped virus creating its own membrane transport portal . What remains a major conundrum is how SV40 induces the reorganization of these membrane J-proteins into the foci . Using an unbiased proteomics approach , we identify two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) , which preferentially localize in detergent-resistant membranes and hetero-oligomerize to form a massive protein complex [25–28] , as B12-interacting partners . Erlin1/2 support SV40-induced foci formation , and promote virus ER-to-cytosol transport , and infection . However , Erlin1/2 themselves do not mobilize into the foci . Instead , our results indicate that these ER membrane proteins bind to and likely anchor B12 in the ER , releasing B12 into the foci upon SV40 infection . Our study thus pinpoints Erlin1/2-dependent release as a key regulatory event enabling B12 to efficiently reorganize into the foci , where this J-protein facilitates virus ER membrane penetration leading to successful infection .
Three closely related ER membrane J-proteins called B12 , B14 and C18 play critical yet non-overlapping functions in promoting ER-to-cytosol transport and infection of SV40 [4] . We previously identified novel B14- and C18-binding factors that execute distinct functions in supporting this viral membrane transport process [6 , 20 , 21] . However , potential B12-interacting proteins that may also play roles during SV40 ER membrane penetration have yet to be identified . To identify possible B12-interacting components , we used Flp-in 293 T-REx cells and generated a cell line stably expressing B12 that is tagged with 3xFLAG at the amino-terminus ( 3xFLAG-B12 stable ) ; the parental Flp-in 293 T-REx cells not expressing 3xFLAG-B12 were used as the control . When cell extracts derived from the parental Flp-in 293 T-REx and 3xFLAG-B12 stable cells were analyzed by SDS-PAGE , immunoblotting revealed that a lower level of 3xFLAG-B12 was expressed when compared to the endogenous B12 level ( Fig 1A , compare lanes 1 and 2 ) . The cell extracts were then subjected to FLAG affinity purification using the schematic depicted in Fig 1B . The FLAG peptide-eluted materials were either analyzed by silver staining or subjected to “shot gun” mass spectrometry . By silver staining , we found the appearance of many distinct bands in the sample derived from the 3xFLAG-B12-expressing cells , while fewer bands of lesser intensity were observed in the sample derived from the parental control cell ( Fig 1C ) . By mass spectrometry , peptides corresponding to more than 1 , 500 proteins were identified in samples derived from the 3xFLAG-B12 cells , but with only 58 of these proteins having at least 20 peptides ( Table 1 ) . Of the 58 proteins , the ER membrane proteins BAP31 , EMC1 , and Sel1L were present in this list . Importantly , these proteins were previously demonstrated to participate in ER-to-cytosol membrane penetration of SV40 [5 , 19 , 20] . Also included in this list are Erlin1 and Erlin2 ( Erlin1/2 ) , two closely related ER membrane proteins that hetero-oligomerize to form a massive protein complex [25 , 26 , 28] . Because Erlin1/2 have been implicated in an ER-to-cytosol transport-dependent protein quality control process called ER-associated degradation [27 , 29 , 30] , we decided to focus on these proteins since SV40 similarly undergoes ER-to-cytosol transport . To validate the mass spectrometry data and to assess the specificity of the B12-Erlin1/2 interactions , extracts derived from cells stably expressing 3xFLAG-B12 or 3xFLAG-B14 , along with the parental cells , were subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads . The precipitated material was subjected to SDS-PAGE followed by immunoblotting . Using this approach , we found that precipitating 3xFLAG-B12 but not 3xFLAG-B14 pulled down endogenous Erlin1/2 ( Fig 1D , first and second panels , compare lane 5 to 6 ) . As expected , Hsc70 co-precipitated with 3xFLAG-B12 and 3xFLAG-B14 ( Fig 1D , third panel , lanes 5 and 6 ) . These results demonstrate that Erlin1/2 interact specifically with 3xFLAG-B12 , in agreement with the mass spectrometry data . We next examined if Erlin1/2 play roles during SV40 infection . Simian CV-1 cells , which are typically used to study SV40 infection , were transfected with siRNA targeting either Erlin1 , Erlin2 , or both ( PAN-Erlin ) . The resulting cell extracts were subjected to SDS-PAGE followed by immunoblotting with antibodies against Erlin1 and Erlin2 . We found that Erlin1 siRNA downregulated Erlin1 expression ( Fig 2A , first panel , compare lane 2 to 1 ) without affecting Erlin2 expression ( Fig 2A , second panel , compare lane 2 to 1 ) . Erlin2 siRNA silenced Erlin2 expression ( Fig 2A , second panel , compare lane 3 to 1 ) while also moderately reducing Erlin1 expression ( Fig 2A , top panel , compare lane 3 to lane 1 ) , suggesting that this siRNA may target Erlin1 to some extent . As expected , the PAN-Erlin siRNA decreased both Erlin1 and Erlin2 levels ( Fig 2A , first and second panels , compare lane 4 to 1 ) . To determine if severe ER stress is induced upon depletion of the Erlins , we monitored splicing of the XBP1 mRNA by RT-PCR analysis . XBP1 splicing typically ensues when misfolded proteins massively accumulate in the ER , and is considered the most sensitive indicator of ER stress induction [31] . We found that when compared to cells treated with the chemical ER stress inducer dithiothreitol ( DTT ) , neither the Erlin1 , Erlin2 , nor PAN-Erlin siRNA triggered significant ER stress ( Fig 2B , compare lane 5 to lanes 1–4 ) . Under these conditions , we assessed SV40 infection by scoring cells for expression of the virally encoded large T antigen , a hallmark for SV40 infection , in the host nucleus using an immunofluorescence-based approach . While depleting either Erlin1 or Erlin2 individually blocked infection moderately ( Fig 2C , compare second and third to first bar ) , simultaneous knockdown of Erlin1/2 potently reduced SV40 infection ( Fig 2C , compare fourth to first bar ) . To verify that this decreased infection is indeed caused by the knockdown of Erlin1 and Erlin2 and not due to unexpected off-target effects , we employed a rescue experiment by transfecting siRNA-resistant FLAG-tagged Erlin1 ( Erlin1*-FLAG ) , Erlin2 ( Erlin2*-FLAG ) , or the control GFP-FLAG construct in cells simultaneously depleted of Erlin1/2 . Only cells expressing the FLAG protein were scored . Using this approach , we found that add-back of either Erlin1*-FLAG or Erlin2*-FLAG fully restored the block in SV40 infection imposed by Erlin1/2 depletion ( Fig 2D , compare third and fourth to first and second bars ) . These findings demonstrate that the robust decrease in SV40 infection due to the PAN-Erlin1/2 siRNA results from silencing Erlin1/2 , thus unambiguously revealing a critical function of Erlin1/2 in SV40 infection . Importantly , as reconstituting either Erlin1 or Erlin2 fully restored the infection block caused by silencing Erlin1/2 , these related membrane proteins likely execute overlapping functions during SV40 infection . Because ER-to-cytosol transport is essential for successful SV40 infection , we asked if Erlin1/2 promote this membrane transport process using a cell-based , semi-permeabilized transport assay that we previously developed [32] . Briefly , cells are treated with a low concentration of digitonin that selectively permeabilizes the plasma membrane without damaging internal membranes . The sample is then centrifuged to generate two fractions , a supernatant fraction that contains cytosolic proteins and SV40 that has penetrated the ER membrane to reach the cytosol ( cytosol ) , and a pellet fraction that contains membranous organelles , including the ER , as well as any ER-localized virus ( membrane ) . Cells transfected with scrambled , Erlin1 , Erlin2 or PAN-Erlin siRNA were infected with SV40 , and subjected to this fractionation procedure , with the resulting cytosol and membrane fractions analyzed by immunoblotting . We found that while the majority of the cytosolic marker Hsp90 was detected in the cytosol fraction ( Fig 3A , compare second to fifth panel ) , the ER marker BiP was found only in the membrane fraction ( Fig 3A , compare sixth to third panel ) , thus verifying the integrity of the fractionation procedure . Importantly , under this condition , we found that although silencing either Erlin1 or Erlin2 moderately decreased the SV40 VP1 level in the cytosol fraction ( Fig 3A , top panel , compare lanes 2 and 3 to 1; the VP1 level is quantified in Fig 3B ) , simultaneous knockdown of Erlin1/2 potently impaired VP1 appearance in the cytosol fraction ( Fig 3A , top panel , compare lane 4 to 1; quantified in Fig 3B ) . These findings strongly suggest that Erlin1/2 support SV40’s ER membrane transport event . We next used a biochemical fractionation protocol based on Triton X-100 extraction to isolate ER-localized SV40 from the membrane fraction [32] ( see also Materials and Methods ) . We found that the level of ER-localized SV40 did not decrease by depleting Erlin1 , Erlin2 , or both Erlin1/2 ( Fig 3C , third panel , compare lanes 2–4 to 1 ) , whereas it did significantly decrease when cells were treated with brefeldin A ( BFA ) which blocks SV40 trafficking from the plasma membrane to the ER ( Fig 3C , third panel , compare lane 5 to 1 ) , as expected . In fact , knockdown of Erlin1/2 moderately increased the level of ER-localized SV40 when compared to the control condition ( Fig 3C , third panel , compare lane 4 to 1 ) , suggesting SV40 is trapped in the ER in the absence of Erlin1/2 . This modest increase was again evident when a lesser amount of the samples was analyzed and quantified ( Fig 3D , top panel , compare lane 2 to 1; quantified in Fig 3E ) . Because only a small fraction of total ER-localized SV40 reaches the cytosol ( see Fig 3A , compare VP1 level in first versus fourth panels ) , the virus level that accumulates in the ER due to a block in cytosol entry is expected to be modest relative to the total amount of virus in the ER . Thus , the decreased VP1 level in the cytosol fractions observed when Erlin1 , Erlin2 , or Erlin1/2 were depleted is not due to defective ER arrival of SV40 from the cell surface . These data establish Erlin1/2 as being critical for supporting SV40 ER-to-cytosol transport , consistent with the importance of these factors during SV40 infection ( Fig 2 ) . During infection , SV40 triggers mobilization of select ER membrane proteins including B12 , B14 , C18 , and BAP31 into distinct puncta on the ER referred to as foci . The virus-induced foci structures have been proposed to function as SV40’s cytosol entry sites from the ER [4–6 , 21] . To begin to address the molecular basis by which Erlin1/2 promote ER membrane penetration of SV40 , we asked if these ER membrane proteins support foci formation . Accordingly , cells transfected with scrambled or PAN-Erlin siRNA were infected with SV40 , and subjected to immunofluorescence using antibodies against B12 and BAP31 to score for appearance of B12 and BAP31 double-positive foci . Our epifluorescence microscopy-based analyses revealed that silencing Erlin1/2 decreased formation of SV40-induced , B12/BAP31-containing foci by approximately 60% ( Fig 3F , compare top and bottom panels; the level of B12/BAP31-containing foci was quantified in Fig 3G ) . We conclude that Erlin1/2 play a pivotal function during SV40-triggered foci formation–this likely explains why Erlin1/2 are crucial for SV40’s ER-to-cytosol membrane transport ( Fig 3A and 3B ) leading to successful infection ( Fig 2 ) . Since Erlin1/2 were initially identified as B12-interacting partners , we asked if B12’s ability to interact with Erin1/2 enables B12 to mobilize into the foci in order to support viral infection . To evaluate this idea , we first sought to identify a B12 mutant that cannot bind to Erlin1/2 . Since Erlin1/2 have a short cytosolic tail followed by single transmembrane domain and an extended ER luminal region , the B12-Erlin1/2 interaction is likely mediated in the ER . In addition , the C-terminal luminal domains of B12 and B14 display a high degree of sequence variations . Because B12 but not B14 interacts with Erlin1/2 ( Fig 1D ) , we reasoned that B12’s C-terminal domain is likely responsible for engaging Erlin1/2 . To test this , we constructed WT B12 and a B12 mutant lacking the last 20 amino acids of its C-terminus , with both constructs containing a single FLAG tag appended at the N-terminus ( FLAG-B12 [WT] and FLAG-B12 [1–355]; Fig 4A ) . As control constructs , in addition to GFP-FLAG , we generated a B12 point mutant in which a mutation is introduced within its cytosolic J-domain ( FLAG-B12 [H138Q]; Fig 4A ) ; while this mutation renders the protein incapable of binding to cytosolic Hsc70 , its luminal C-terminus should remain intact . In cells expressing either GFP-FLAG , FLAG-B12 [WT] , FLAG-B12 [H138Q] , or FLAG-B12 [1–355] , we found that only pull down of FLAG-B12 [WT] or FLAG-B12 [H138Q] but not GFP-FLAG or FLAG-B12 [1–355] co-precipitated endogenous Erlin1/2 ( Fig 4B , first and second panels , compare lanes 2 and 3 to lanes 1 and 4 ) . These findings demonstrate that FLAG-B12 [1–355] cannot interact with Erlin1/2 , indicating that B12’s last 20 amino acids are required for binding to Erlin1/2 . Importantly , in contrast to FLAG-B12 [WT] , FLAG-B12 [1–355] inefficiently mobilizes into the BAP31-containing foci during SV40 infection ( Fig 4C , compare top to bottom row; the level of BAP31 and FLAG double-positive foci is quantified in Fig 4D ) . These data suggest that B12’s interactions with Erlin1/2 are important for the efficient mobilization of B12 into the foci during virus infection . To probe if FLAG-B12 [1–355]’s inability to mobilize into the foci affects virus infection , we generated a B12 CRISPR knockout ( KO ) cell line ( Fig 4E , top panel , compare lane 2 to 1 ) , which was sequenced to confirm the mutation ( S1 Fig ) . SV40 was then incubated with the parental cell line transfected with GFP-FLAG , or the CRISPR B12 KO cell line transfected with either GFP-FLAG , FLAG-B12 [WT] , or FLAG-B12 [1–355] . Virus infection was assessed by scoring for the expression of large T antigen in only FLAG-expressing cells . Using this strategy , we found that SV40 infection was potently blocked in the CRISPR B12 KO cell line when compared to the parental control cells ( Fig 4F , compare second to first bar ) , consistent with our previous studies using an RNAi-mediated knockdown strategy [4 , 22] . Strikingly , addback of FLAG-B12 [WT] but not FLAG-B12 [1–355] into the B12 KO cells fully restored infection ( Fig 4F , compare third to fourth bar ) , indicating that FLAG-B12 [1–355] cannot support SV40 infection . Thus , as a mutant B12 that cannot bind to Erlin1/2 nor efficiently mobilize into the foci also fails to restore infection , these data strongly suggest that B12’s interaction with Erlin1/2 is essential for its reorganization into the foci where B12 facilitates SV40 ER membrane penetration leading to successful infection . The observation that B12’s association with Erlin1/2 is critical for B12 to reorganize into the foci raises the possibility that Erlin1/2 might also mobilize into the foci . However , we found that neither endogenous Erlin1 ( Fig 5A ) nor transfected Erlin2*-FLAG ( Fig 5B ) mobilized into the BAP31-positive foci during SV40 infection by epifluorescence microscopy . Confocal microscopy analyses similarly revealed that neither transfected Erlin1*-FLAG nor Erlin2*-FLAG reorganize into the SV40-induced BAP31-positive foci ( S2 Fig ) . These results demonstrate that , despite the importance of the B12-Erlin1/2 interaction in supporting B12’s mobilization into the foci , Erlin1/2 themselves do not reorganize into these structures . As B12 reorganizes into the foci during SV40 infection , we reasoned that this J-protein must move laterally along the ER lipid bilayer to reach the foci during virus infection . Because Erlin1/2 do not reorganize into the foci , we hypothesize that B12’s mobilization is initiated when it is released from Erlin1/2 . To test this , we used a co-immunoprecipitation approach . Cells transfected with GFP-FLAG or Erlin2*-FLAG were mock-infected or infected with SV40 . The resulting whole cell extracts were subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads , and the precipitated proteins analyzed by SDS-PAGE and immunoblotting . We found that Erlin2*-FLAG but not GFP-FLAG co-precipitated endogenous B12 in mock-infected cells ( Fig 6A , top panel , compare lane 2 to 1 ) , as expected ( Fig 1 ) . However , SV40 infection significantly reduced the B12-Erlin2*-FLAG interaction ( Fig 6A , top panel , compare lane 3 to 2; quantified in Fig 6D ) , suggesting that the virus triggers B12 release from Erlin2 . SV40’s ability to reduce this interaction requires its presence in the ER because blocking virus trafficking to the ER ( by incubating cells with BFA ) prevented SV40 from releasing B12 from Erlin2 ( Fig 6A , top panel , compare lane 4 to 3; quantified in Fig 6D ) . SV40 regulates the B12-Erlin1-FLAG interaction in a similar manner ( Fig 6B; quantified in Fig 6E ) . Virus-induced release of B12 from Erlin1/2 is specific because SV40 does not affect binding of Erlin2*-FLAG to TMUB1 ( Fig 6C , top panel , compare lane 2 to 1; quantified in Fig 6F ) , an established ER membrane partner of Erlin2 [33] . Our binding analyses thus demonstrate that SV40 specifically promotes release of B12 from Erlin1/2 , likely to initiate B12’s mobilization and recruitment to the foci where it supports virus ER membrane penetration . We previously found that an SV40 mutant lacking VP3 ( SV40 ( ΔVP3 ) , but not VP2 , can traffic from the cell surface to the ER [32] , although this mutant virus cannot induce foci formation [4] nor penetrate the ER membrane [32] . To test if SV40 ( ΔVP3 ) induces release of B12 from Erlin2 , cells transfected with Erlin2*-FLAG were mock-infected , infected with WT SV40 , or infected with SV40 ( ΔVP3 ) ( Fig 6G ) . Cells were then harvested 16 hpi , and the resulting whole cell lysates subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads . We found that while WT SV40 markedly induced dissociation of endogenous B12 from Erlin2-FLAG , SV40 ( ΔVP3 ) did so more modestly ( Fig 6H , top panel , compare lanes 2 and 3 to 1 ) , raising the possibility that VP3 may be involved in triggering B12 release from Erlin2 . To unambiguously demonstrate that B12 release from the Erlins is necessary for B12 to reorganize into the foci to support SV40 infection , we employed a chemical-induced dimerization strategy to trap the B12-Erlin interaction in the presence of SV40 . As depicted in Fig 7A , we generated a WT B12 construct containing an FRB domain and an S-tag at its C-terminus ( B12 [WT]-FRB-S ) , and an Erlin2 construct containing an FKBP domain and a FLAG-tag at its C-terminus ( Erlin2-FKBP-FLAG ) . As the rapamycin-1 analogue ( rapalog-1 ) dimerizes FRB and FKBP [34] , this drug should induce dimerization of B12 [WT]-FRB-S and Erlin2-FKBP-FLAG , thereby forcibly trapping B12 to Erlin2 . To verify the integrity of this system , B12 CRISPR KO cells transfected with B12 [WT]-FRB-S with or without Erlin2-FKBP-FLAG were infected with SV40 or mock-infected in the absence or presence of rapalog-1 . The resulting cell lysates were subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads . Consistent with the endogenous B12-Erlin2-FLAG interaction ( Fig 6A ) , precipitating Erlin2-FKBP-FLAG specifically pulled down B12 [WT]-FRB-S ( Fig 7B , top panel , compare lane 1 to 4 ) . SV40 infection decreased the B12 [WT]-FRB-S- Erlin2-FKBP-FLAG interaction ( Fig 7B , top panel , compare lane 2 to 1 ) . Importantly , adding rapalog-1 prevented the virus-induced decrease in interaction between these two proteins and appeared to have strengthened it ( Fig 7B , top panel , compare lane 3 to 1 and 2 ) . These results demonstrate that addition of rapalog-1 indeed induced hetero-dimerization of B12 [WT]-FRB-S and Erlin2-FKBP-FLAG , as described in Fig 7A . We reasoned that if SV40-dependent B12 release from Erlin2 is required for B12’s reorganization to the foci to support virus ER membrane penetration leading to infection , artificially force-trapping B12 to Erlin2 should prevent B12 from mobilizing into the foci and thus preclude successful infection . To test this , B12 CRISPR KO and its parental CV-1 cells were transfected with Erlin2-FKBP-FLAG and either the control GFP-S , B12 [WT]-S , or B12 [WT]-FRB-S construct , and the experiments conducted with or without adding rapalog-1 . Only cells expressing the S-tagged construct was scored for the expression of large T antigen . As expected ( Fig 4F ) , B12 knockout significantly blocked SV40 infection ( Fig 7C , compare second to first bar ) . Importantly , although expressing B12 [WT]-S completely restored infection regardless of the presence of rapalog-1 ( Fig 7C , compare third and fourth to second bar ) , expressing B12 [WT]-FRB-S can only restore infection in rapalog-1’s absence ( Fig 7C , compare fifth to sixth bar ) . Thus , an exogenously expressed B12 cannot rescue SV40 infection in B12 knockout cells if the B12 protein is force-trapped to Erlin2 . These results predict that exogenously expressed B12 [WT]-FRB-S , in the presence of the Erlin2-FKBP-FLAG binding partner , would be restricted from mobilizing into the foci upon addition of rapalog-1 during SV40 infection . Indeed , B12 [WT]-FRB-S can largely mobilize to the BAP31-positive foci during SV40 infection in the absence of rapalog-1 , similar to B12 [WT]-S ( Fig 7D and S3 Fig , compare first rows in both Figs; quantified in Fig 7E , compare third to first bar ) . By contrast , whereas B12 [WT]-S can reorganize into the virus-induced foci even in the presence of rapalog-1 ( S3 Fig , second row; quantified in Fig 7E , second bar ) , B12 [WT]-FRB-S inefficiently mobilizes into this structure in the presence of rapalog-1 ( Fig 7D , second row; quantified in Fig 7E , fourth bar ) . Hence , artificially entrapping B12 to Erlin2 prevents B12 from moving into the foci structure . These data are in agreement with the infection experiments , further supporting the hypothesis that SV40-induced release of B12 into the foci is essential for promoting virus ER membrane penetration leading to successful infection . Because B12’s release from Erlin1/2 is critical during SV40 ER membrane transport , we hypothesize that part of Erlin1/2’s function is to position B12 in the ER so that B12 can be properly released upon SV40 infection . Accordingly , we probed the fate of endogenous B12 in the absence of Erlin1/2 by epifluorescence microscopy and found that a fraction of B12 mislocalizes to small aggregates that co-localize with the nucleus ( Fig 8A , compare top and bottom panels; quantified in Fig 8B ) . These small B12 aggregates were not artifacts caused by epifluorescence microscopy because confocal microscopy analyses demonstrated that B12 does not normally form such small aggregates ( S4 Fig ) . These findings suggest that Erlin1/2 normally anchor B12 in the ER . In fact , when SV40 infection was evaluated in PAN-Erlin siRNA-transfected cells where B12 displays an obvious nuclear aggregation phenotype ( as oppose to total cells transfected with the PAN-Erlin siRNA ) , the infection block was more severe ( Fig 8C ) . Thus , when Erlin1/2 depletion can lead to mislocalization of B12 to the nucleus , SV40 infection is severely compromised . The idea that B12 requires binding partners to anchor it in the ER is supported by a previous report demonstrating that overexpressed WT B12 localizes to the nucleus [35] , presumably because the overexpressed B12 lacks appropriate levels of corresponding binding partners such as Erlin1/2 that would normally restrict it in the ER . To further support this view , we used a truncated CMV promoter that retains a very low transcriptional activity [36] to drive the expression of either FLAG-B12 [WT] or FLAG-B12 [1–355] . Our results revealed that FLAG-B12 [1–355] displayed an increased mislocalization to the nucleus when compared to FLAG-B12 [WT] ( Fig 8D ) , likely because FLAG-B12 [1–355] is inefficiently anchored in the ER since it cannot bind to Erlin1/2 ( Fig 4 ) .
Membrane penetration by non-enveloped viruses remains an enigmatic process . One key question is whether the viral particle hijacks pre-existing protein-conducting channels embedded in the limiting membrane that would support the transport process , or if the virus creates de-novo membrane transport structures that facilitate membrane translocation . In the case of the non-enveloped SV40 , where translocation across the ER membrane represents the decisive infection step , increasing evidence suggest that this virus does not exploit a pre-existing channel such as the ER membrane-bound E3 ubiquitin ligase Hrd1 [5 , 37 , 38] . Hrd1 is the central component of the retro-translocation channel that translocates misfolded ER proteins to the cytosol for proteasomal degradation in a pathway called ER-associated degradation ( ERAD; [39] ) . Instead , SV40 induces reorganization of select ER membrane components into distinct puncta in the ER membrane called foci that are postulated to serve as the cytosol entry sites [4–6 , 21] . This represents the only example of a non-enveloped virus constructing its own membrane transport portal during entry . The J-protein B12 is one of the ER membrane proteins that mobilizes into the foci during SV40 infection [6] . In the foci , B12 assists in the recruitment of a cytosolic chaperone complex that ejects the virus into the cytosol in coordination with the two other transmembrane J-proteins B14 and C18 [4 , 6 , 21] . However , the molecular basis by which B12 mobilizes into the foci and the host factors co-opted to regulate this process remain unknown . In this study , we identified two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) as cellular components that bind to B12 and facilitate B12’s reorganization into the SV40-induced foci . As proposed in a model depicted in Fig 9 , we envision that Erlin1/2 , which normally hetero-oligomerize , restrict B12 in the ER membrane by associating with B12 . Upon arrival of SV40 to the ER lumen from the cell surface , it induces the release of B12 from the Erlin1/2 complex ( step 1 ) . The disengaged B12 in turn reorganizes into the foci in order to support ER-to-cytosol transport of SV40 ( step 2 ) . At present , how SV40 triggers B12’s release from Erlins is unclear . It is possible that SV40 directly interacts with and induces conformational changes to either B12 or Erlin1/2 , causing these components to lose affinity for each other . Alternatively , during SV40 infection , B12 might attract other ER or cytosolic factors that competitively interfere with binding to the Erlin proteins . Future experiments are needed to clarify this important point . Regardless of the mechanism , the release step is required for B12’s mobilization into the foci where it promotes virus infection as artificially locking B12 to Erlin2 prevents these downstream events from occurring . When Erlin1/2 is depleted , we found that endogenous B12 can mislocalize to the nucleus ( Fig 9 ) , suggesting that Erlins act as anchors , restricting B12 in the ER . Because Erlin1/2 have been shown to hetero-dimerize and form massive megadalton protein complexes [26–28] , this intrinsic property may enable them to function as efficient anchoring factors . Consistent with this , another group demonstrated that overexpressed WT B12 similarly mislocalizes to the nucleus and forms nuclear globular structures termed DJANGOS [35] , presumably due to insufficient levels of Erlins that are required to restrict the overexpressed proteins in the ER . The observation that an ER membrane protein such as B12 can localize incorrectly to the nucleus is not surprising since the ER membrane is topologically contiguous with the outer nuclear membrane . B14 and C18 also promote SV40 ER membrane penetration and infection [20 , 22] . Intriguingly , both of these proteins reorganize into the foci in a virus-dependent manner [4 , 6] . Because B14 does not bind to Erlin1/2 , the mechanism by which it is recruited into the foci must be different than B12 . However , as we previously demonstrated that C18 engages Erlin1/2 [20] , C18’s mechanism of release/recruitment into the foci might be similar to B12 . Erlin1/2-dependent restriction of B12 in the ER is highly reminiscent of the mechanism by which Erlin1/2 restricts the ER membrane protein sterol-regulated element binding protein ( SREBP ) in the ER through a direct interaction with the SREBP-SCAP complex [40] . SREBP is normally kept in the ER but must mobilize to the Golgi when the cholesterol level is low . In the Golgi , SREBP is cleaved to generate an active transcription factor , which in turn moves to the nucleus to upregulate genes responsible for cholesterol synthesis [41] . The parallel finding that Erlins anchor yet another transmembrane protein in the ER suggests that Erlins might function as a general ER-anchoring factor . Moreover , given that Erlins have been shown to play a poorly-defined role in ERAD [39] , the possibility that Erlins might act to restrict various ERAD membrane components in the ER should now be explored .
To construct a transfer vector in order to introduce 3xFLAG-B12 into Flp-in 293 T-REx cells , the 3xFLAG tag sequence was attached to the B12 cDNA by PCR using oligos containing the corresponding sequence and the resulting PCR product inserted into pcDNA5 FRT/TO . The human Erlin1 cDNA was amplified from the HEK 293T cDNA library and the human Erlin2 cDNA was a generous gift from Dr . R . DeBose-Boyd ( University of Texas Southwestern Medical Center ) . To generate siRNA-resistant Erlin1 and Erlin2 constructs , the following silent mutations were introduced into each cDNA using an overlapping PCR method: Erlin1: 248 GGG-GTT-ATG-ATT-TAC-ATC-GAT-AGG 272 , and Erlin2: 241 GGT-GTG-ATG-ATT-TAC-TTC-GAT-CGG 265 , where the underlines denote the introduced silent mutation . The resulting PCR products were inserted into pCDNA3 . 1 ( - ) in frame with the FLAG tag sequence by standard cloning methods . FLAG-B12 [WT] and FLAG-B12 [H138Q] were previously described [22 , 25] and FLAG-B12 [1–355] was constructed by amplifying the corresponding DNA sequence and sub-cloning it into pcDNA3 . 1 ( - ) . For the S-tagged constructs used in this study , PCR-amplified cDNA sequences were inserted into pcDNA3 . 1 ( - ) in frame with the S tag sequence by standard cloning methods . To express low level of B12 constructs , the enhancer region of the full length CMV promoter in pcDNA3 . 1 ( - ) ( 234–773 ) was deleted by an inverse PCR method [36] . To fuse FKBP to Erlin2-FLAG , a PCR-amplified FKBP sequence was introduced between the Erlin2 and FLAG-tag DNA sequences using standard cloning methods . Similarly , a PCR-amplified FRB sequence was introduced between the B12 and S-tag DNA sequences . To construct vectors co-expressing CMV-driven Erlin2-FKBP-FLAG and either truncated CMV-driven GFP-S , B12 [WT]-S , or B12 [WT]-FRB-S , an expression cassette containing the truncated CMV promoter , the S-tagged gene , and the polyA sequences ( with BglII and MluI sites attached at 5’ and 3’ ends , respectively ) was amplified by PCR and inserted into BglII- and MluI-digested pcDNA/Erlin2-FKBP-FLAG . Flp-in 293 T-REx cell stably expressing 3xFLAG-B12 were harvested and semi-permeabilized in a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 0 . 02% digitonin , and 1 mM PMSF at 4°C for 10 min . Following centrifugation at 16 , 100x g for 10 min at 4°C , the resulting pellet fraction was further lysed in a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 1% deoxy Big CHAP and 1 mM PMSF at 4°C for 10 min and centrifuged at 16 , 100x g for 10 min at 4°C . The resulting supernatant fraction was further centrifuged at 50 , 000 rpm for 10 min at 4°C in a TLA-100 . 3 rotor ( Beckman Coulter , Brea , CA ) to remove residual detergent-insoluble materials . The cleared lysates were incubated with anti-FLAG M2 antibody for 2 h at 4°C , mixed with Protein G magnetic beads , and further incubated for 2 h at 4°C . After the magnetic beads were extensively washed with a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , and 0 . 1% deoxy Big CHAP , bound materials were eluted with 0 . 1 mg/ml 3xFLAG peptide or SDS sample buffer . SV40 was prepared as described previously in Inoue and Tsai , 2011 [32] . 24–48 h post siRNA transfection , CV-1 cells were incubated with SV40 ( MOI ~20 ) at 4°C for 1 h , washed , and incubated at 37°C for 12 h . Cells were harvested with a scraper , semi-permeabilized with a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 0 . 1% digitonin , and 1 mM PMSF at 4°C for 10 min , and centrifuged at 16 , 100x g for 10 min . The resulting supernatant is referred to as the ‘cytosol’ fraction , while the resulting pellet ( resuspended in SDS sample buffer ) is referred to as ‘membrane’ fraction . Where indicated , the membrane fraction was further treated with a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 1% Triton X-100 , and 1 mM PMSF at 4°C for 10 min , and centrifuged at 16 , 100x g for 10 min . This second supernatant is the Triton X-100-soluble membrane fraction harboring ER-localized SV40 . Approximately 1x106 HEK 293T cells were plated on a 6-cm plate , incubated for 24 h , and transfected with the indicated constructs using PEI . 24 h after DNA transfection , cells were harvested and lysed in a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 1% Triton X-100 , and 1 mM PMSF . Following centrifugation at 16 , 100x g for 10 min , the resulting supernatant fractions were incubated with FLAG antibody-conjugated agarose beads at 4°C for 2 h . After the beads were extensively washed with lysis buffer , bound proteins were eluted from the beads with SDS sample buffer and subjected to SDS-PAGE followed by immunoblotting with the indicated antibodies . To analyze SV40-induced B12 release from Erlin1/2 , approximately 3x105 CV-1 cells were plated on a 6-cm plate , incubated for 24 h , and transfected with the indicated DNA constructs using Fugene HD . 24 h after DNA transfection , cells were infected with SV40 ( MOI~100 ) for 16 h in the absence or presence of BFA , harvested , and analyzed as above . The B12 [WT]-FRB-S-Erlin2-FKBP-FLAG interaction was analyzed similarly . Briefly , approximately 3x105 CRISPR B12 KO CV-1 cells were plated on a 6-cm plate , incubated for 24 h , and transfected with the indicated DNA constructs using PEI . 24 h after DNA transfection , cells were infected with mock or SV40 ( MOI~100 ) for 6 h and further incubated in the absence or presence of 0 . 5 μM rapalog-1 heterodimerizer for 10 h , harvested , and analyzed as above . The target sequences of the siRNAs and their final concentrations used in this study are as follows: Erlin 1 siRNA: Thermo Fisher Predesigned Silencer siRNA , ID# 1473100 ( 50 μM ) , Erlin 2 siRNA: GAACUAUACUGCUGACUAU[dT][dT] ( 10 μM ) , PAN-Erlin siRNA: Thermo Fisher Predesigned Stealth RNAi siRNA , ID# HSS116356 ( 50 μM ) . Allstar negative control siRNA ( Qiagen , Hilden , Germany ) was used as a scrambled control siRNA . CV-1 cells were reverse-transfected with each siRNA at the indicated concentration using Lipofectamine RNAi MAX and incubated for 24 h ( Erlin1 and PAN-Erlin siRNAs ) or 48 h ( Erlin 2 siRNA ) prior to SV40 infection . Evaluation of XBP1 splicing was described previously in Ravindran et al . , 2015 [21] . CV-1 cells transfected with siRNAs were infected with SV40 ( MOI~0 . 3 ) for 24 h and fixed with 1% paraformaldehyde . Following permeabilization with 0 . 2% Triton X-100 , fixed cells were stained with a mouse monoclonal SV40 large T antigen antibody , followed by Alexa Fluor 488-conjugated secondary antibodies . In each experiment , approximately 1 , 000 cells were counted to assess the extent of large T antigen expression . For the Erlin1/2 rescue experiments , cells were initially transfected with siRNA , incubated for 24 h , and further transfected with FLAG-tagged constructs using Fugene HD . 24 h post DNA transfection , cells were infected with SV40 , fixed , and permeabilized as described above . Fixed cells were stained with a mouse monoclonal SV40 large T antigen antibody and a rabbit monoclonal FLAG antibody followed by Alexa Fluor 594-conjugated anti-mouse IgG and Alexa Fluor 488-conjugated anti-rabbit secondary antibodies . Large T antigen expression was evaluated in cells expressing the FLAG-tagged protein . At least 100 cells for each sample were counted to assess the extent of large T antigen expression . B12 rescue experiments were similarly performed , except that parental CV-1 or B12 CRISPR KO cells were used without siRNA transfection . As indicated in the result and discussion sections , overexpressed B12 often mislocalized in the nucleus . To avoid artifacts caused by B12 mislocaliztion , only cells expressing FLAG-B12 [WT] or [1–355] that display a clear ER-localization patterns were counted as “FLAG-B12 [WT] or [1–355]-expressing” cells , unless otherwise noted . CV-1 cells transfected with the indicated siRNAs or DNA constructs were infected with SV40 ( MOI~30–50 ) for the indicated time . Cells were fixed and subjected to an immunofluorescence-based method as described in the SV40 infection assay , except that a rat monoclonal BAP31 antibody , the indicated primary antibodies , Alexa Fluor 594-conjugated anti-mouse IgG , and Alexa Fluor 488-conjugated anti-rabbit IgG were used . For triple staining , a rat monoclonal BAP31 antibody , a rabbit polyclonal B12 antibody , and a monoclonal mouse FLAG antibody were used as primary antibodies , while Alexa Fluor 594-conjugated anti-rat IgG , Alexa Fluor 488-conjugated anti-rabbit IgG , and Alexa Fluo-350-conjugated anti-mouse IgG secondary antibodies were used as secondary antibodies . Parental and B12 CRISPR KO CV-1 cells were plated on coverslips in a 24-well plate at a density of approximately 2 . 5x104 cells/well , incubated for 24 h , and transfected with DNA constructs using Fugene HD . 24 h after DNA transfection , cells were infected with SV40 ( MOI~0 . 3 ) for 6 h and further incubated in the absence or presence of 0 . 5 μM rapalog-1 heterodimerizer for 18 h . 24 hpi , cells were fixed and subjected to an immunofluorescence method using a mouse monoclonal SV40 large T antigen antibody and a rabbit polyclonal S-tag antibody as described above . Fixed cell samples were imaged using Nikon Eclipse TE2000-E equipped with a 40x objective and a 12-bit CCD camera . To avoid potential overexpression-induced artifacts , cells were counted and examined for the large T antigen expression when expression of B12 [WT]-S or B12 [WT]-FRB-S was localized in the ER without forming nuclear aggregates and that their signal values range from 200 to 1000 under 12-bit resolution . At least 100 cells were counted to assess the extent of large T antigen expression . For SV40-induced foci formation assay , cells were transfected in the same manner , infected with SV40 ( MOI~30–50 ) for 6 h and further incubated in the absence or presence of 0 . 5 μM rapalog-1 heterodimerizer for 10 h . Following the immunofluorescence procedure using anti S-tag and BAP31 antibodies , cell images were taken as in the infection assay , except that instead of assessing the large T antigen expression , BAP31 images were captured . At least 100 cells were counted to assess the extent of BAP31 and S double-positive foci formation in each experiment . Cells transfected with truncated CMV promoter-driven FLAG-B12 [WT] or FLAG-B12 [1–355] were fixed , subjected to the above-mentioned immunofluorescence method using a rabbit monoclonal FLAG antibody and Alexa Fluor 488-conjugated anti-rabbit IgG , and the samples were imaged as described above . To compare the expression level of FLAG-B12 [WT] and FLAG-B12 [1–355] , all images were taken using the same exposure time . Due to the varied expression level of FLAG-tagged proteins among cells , cells were counted and assessed for mislocalization of FLAG-tagged B12 constructs only when the signal values of the ER-localized FLAG-tagged B12 constructs ranged from 500 to 1500 under 12-bit resolution and/or the FLAG-tagged B12 constructs formed small nuclear aggregate structures . By contrast , cells were considered to have overexpressed exogenous B12 and removed from analysis when the signal values of ER-localized FLAG-tagged B12 constructs were above 1500 under 12-bit resolution and/or the FLAG-tagged B12 constructs formed massive nuclear aggregated structures . At least 100 cells were counted to assess mislocalization of FLAG-tagged B12 constructs in each experiment . Flp-In 293 T-REx cells ( Thermo Fisher Sciences ) were co-transfected with pOG44 ( Thermo Fisher Sciences ) and pcDNA5 FRT/TO encoding 3xFLAG-B12 [WT] using Lipofectamine 2000 . 24 h post transfection , cells were split and cultured in DMEM medium containing 100 μg/ml hygromycin and 5 μg/ml blasticidin for 10–15 days . Hygromycin-resistant colonies were cloned . PX330 and PX459 used for generating B12 KO cells were gifts from Dr . F . Zhang ( Addgene plasmid # 42230 and 62988 ) [42] . The following two oligonucleotides containing the +325 to +344 sequence from the transcription start site of the human DNAJB12 ( GI: 194306639 ) were annealed and inserted into PX459 . Fw: CACCGAG GAAGCGGAGCG CCCGGTC . Rv: AAACGACCGGGCGCTCCGCTTCCTC . The PX459 encoding the guide RNA or control PX330 was transfected into CV-1 cells using Fugne HD and 24 h post transfection , the medium was replaced with fresh medium containing 6 μg/ml puromycin . After most control cells have died off , the surviving cells transfected with the PX459 construct were re-plated for single colony isolation . When single colonies were fully grown and became visible , they were isolated with cloning cylinders . One of the established cell lines was further grown , and the resulting whole cell lysate was subjected to immunoblotting using anti-B12 antibodies . The genome was also isolated from this cell line and served as a template to amplify a region corresponding to the +273 to +619 sequence from the transcriptional start site of the human DNAJB12 ( GI: 194306639 ) using the PCR primers: Forward: CTCACACTCA CCGCGAACTC and Reverse: GCACCATTATTGACACCTAC . The amplified PCR product was subjected to agarose gel electrophoresis , and the corresponding band was excised from the gel and purified . The isolated product was analyzed by Sanger sequencing using the following primer: 5’-GACATACGGTAGCACTTCCAC-3’ . Because only one sharp peak was observed by Sanger analysis in which an “A” insertion was detected between the +341 and +342 positions , we concluded that both alleles have the same mutation . | Polyomavirus ( PyV ) is a non-enveloped DNA tumor virus that causes debilitating human diseases especially in immunocompromised individuals . At the cellular level , PyVs such as the simian PyV SV40 must enter a host cell and penetrate the ER membrane to reach the cytosol in order to cause infection . Prior to ER membrane transport , SV40 reorganizes select ER membrane proteins including the J-protein B12 to potential membrane penetration sites on the ER membrane called foci where B12 facilitates virus extraction into the cytosol . How B12 reorganizes into the foci is unclear . Here we find that two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) bind to B12 . During infection , SV40 induces release of this J-protein from Erlin1/2 to enable B12 to reorganize into the foci . Our data reveal how a non-enveloped virus mobilizes a specific ER membrane component to a membrane penetration structure to promote its own membrane transport . |
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Membrane fusion is essential for entry of the biomedically-important paramyxoviruses into their host cells ( viral-cell fusion ) , and for syncytia formation ( cell-cell fusion ) , often induced by paramyxoviral infections [e . g . those of the deadly Nipah virus ( NiV ) ] . For most paramyxoviruses , membrane fusion requires two viral glycoproteins . Upon receptor binding , the attachment glycoprotein ( HN/H/G ) triggers the fusion glycoprotein ( F ) to undergo conformational changes that merge viral and/or cell membranes . However , a significant knowledge gap remains on how HN/H/G couples cell receptor binding to F-triggering . Via interdisciplinary approaches we report the first comprehensive mechanism of NiV membrane fusion triggering , involving three spatiotemporally sequential cell receptor-induced conformational steps in NiV-G: two in the head and one in the stalk . Interestingly , a headless NiV-G mutant was able to trigger NiV-F , and the two head conformational steps were required for the exposure of the stalk domain . Moreover , the headless NiV-G prematurely triggered NiV-F on virions , indicating that the NiV-G head prevents premature triggering of NiV-F on virions by concealing a F-triggering stalk domain until the correct time and place: receptor-binding . Based on these and recent paramyxovirus findings , we present a comprehensive and fundamentally conserved mechanistic model of paramyxovirus membrane fusion triggering and cell entry .
The Paramyxoviridae is a medically-important negative-sense single-stranded RNA enveloped virus family that includes measles ( MeV ) , mumps ( MuV ) , parainfluenza ( PIV ) , respiratory syncytial ( RSV ) , Newcastle disease ( NDV ) , human metapneumo- ( HMPV ) , and the henipa-viruses Nipah ( NiV ) and Hendra ( HeV ) . NiV and HeV cause high mortality rates in humans , approaching 75% in recent NiV outbreaks [1]; death is associated with syncytium formation , vasculitis , pneumonia , and encephalitis . These biosafety level 4 ( BSL4 ) pathogens possess a broad mammalian host range [2] , animal-to-human , and human-to-human transmission [1] , [3] , and pose bio- and agro-terrorism threats to global health and economy . Thus , NiV is classified as a category C priority pathogen in the USA NIH/NIAID research agenda . Paramyxoviruses are generally thought to enter host cells by direct fusion of the viral and host cell membranes at physiological pH without viral endocytosis; however , recent reports for NiV and RSV suggest that they might also enter cells via macropinocytosis [4] , [5] . Viral-cell membrane fusion allows release of the viral ribonucleoprotein complex into the target cell to initiate infection [6] , [7] . Additionally , membrane fusion is essential for syncytium formation ( cell-cell fusion ) , a pathological hallmark of paramyxoviral infections such as that of NiV and HeV [8] and a mechanism of cell-to-cell viral spread [3] , [9] , [10] . Paramyxoviral membrane fusion requires the concerted efforts of two viral proteins: the attachment ( HN , H , or G ) and fusion ( F ) glycoproteins [7] . Upon binding to its host cell surface receptor , HN/H/G triggers F to undergo a conformational cascade that merges viral and/or cell membranes . However , there is a significant knowledge gap on the mechanism ( s ) by which HN/H/G couples receptor binding to F-triggering [3] , [9] , [10] . Paramyxovirus HN/H/G and F are fairly conserved structurally . HN/H/G has a receptor-binding globular head domain comprised of a six-bladed β-barrel typical of sialidases , as shown by X-ray crystallography [11] . The HN/H/G globular head is connected to its transmembrane anchor and short cytoplasmic tail via a stalk domain . F has canonical structural/functional features of class I fusion proteins , such as an ectodomain with a hydrophobic fusion peptide and two heptad repeat regions . Upon F-triggering , F's fusion peptide is exposed and inserted into the target cell membrane to form a pre-hairpin intermediate ( PHI ) . Subsequently , the two heptad repeats in the PHI bind each other to form a six-helix bundle ( 6HB ) , executing membrane fusion [10] , [12] . Despite conservation , important differences exist among paramyxovirus attachment glycoproteins . Those of the pneumovirinae subfamily , e . g . RSV-G and HMPV-G , are significantly smaller than those of the paramyxoviridae subfamily ( e . g . NiV-G or PIV5-HN ) . Importantly , with some exceptions ( such as PIV5-HN and RSV-G only enhancing fusion , or HMPV-G not even enhancing fusion ) , the presence of the attachment glycoprotein is required for paramyxovirus F-triggering ( reviewed in [10] , [13] ) . HN , H , and G differ in the types of host cell receptors they recognize . While HN binds sialic acid , H and G bind protein receptors [e . g . MeV-H binds CD46 , SLAM or nectin-4 [11] , and NiV-G binds ephrinB2 or ephrinB3 [14] , [15] , [16]] , although this is unknown for RSV-G and HMPV-G [13] . Interestingly , receptor type appears to determine how HN/H/G-F interactions modulate fusion . While sialic acid receptor binding appears to induce HN-F association , protein receptor binding appears to induce H-F or G-F dissociation of previously associated H-F or G-F complexes , suggesting that receptor type determines the mechanism of modulation of membrane fusion triggering [17] , [18] . Recent studies suggest that paramyxovirus HN/H/G stalk domains play important roles in interacting with and/or triggering F to execute membrane fusion [11] . Recent crystal structural reports for the NDV HN ectodomain and the PIV5 HN stalk domain revealed parallel tetrameric coiled-coil stalk helical bundles ( 4HB ) [19] , [20] . Consistent with these structural data , NiV-G tetramers are important for fusion triggering , as mutation of stalk cysteine residues critical for tetramer stabilization abrogate fusion [21] . In addition , a headless ( receptor-binding incapable ) PIV5 HN can trigger cell-cell fusion [22] , indicating that the PIV5 HN stalk is capable of triggering F . However , because PIV5 F alone can execute cell-cell fusion independent of HN [22] , [23] , it is uncertain whether the mechanism of PIV5 F-triggering applies to most paramyxoviruses , and particularly to those that bind protein receptors . We found that for the protein receptor binding NiV , a headless NiV-G was sufficient to trigger NiV-F to execute cell-cell fusion without requiring receptor binding . Furthermore , molecular , biophysical , and biochemical approaches revealed that the G head prevents premature F-triggering on virions until receptor binding occurs , by concealing an F-triggering NiV-G stalk domain . Moreover , we uncovered two spatiotemporally sequential conformational steps in the NiV-G head crucial for exposure of the F-triggering stalk domain . Our results reveal a three-step receptor-induced mechanism of NiV membrane fusion triggering , and provide a comprehensive picture of the role of NiV-G in regulating such a mechanism . Combined with recent paramyxovirus findings , our data leads to a mechanistic model of F-triggering fundamentally conserved throughout the Paramyxoviridae family .
Similarly to most paramyxoviruses , the presence of NiV-G and the ability of its head to bind a cell receptor ( ephrinB2/B3 ) are required for triggering membrane fusion and viral entry [3] . To analyze the role of the NiV-G stalk in F-triggering in a biological context , we constructed seven NiV-G truncation mutants lacking the previously crystallized NiV-G head [24] , [25] . The stalk-head transition is estimated to localize to residues 177–187 . Our headless mutants 164 , 167 , 173 , 176 , 180 , 184 , and 187 , named after their most C-terminal residue , include the entire cytoplasmic tail and transmembrane domains and either most or all of the stalk domain ( Fig . 1A ) . We first determined the cell surface expression ( CSE ) of these mutants . We tested recognition of the headless mutants by eight different rabbit antisera raised against full-length wild-type ( wt ) NiV-G or its entire ectodomain . None of these antisera reacted with the headless mutants by flow cytometry ( Fig . 1B ) or Western blot analysis ( Fig . 1C ) ( example shown is antiserum 806 [26] ) . This suggests low antigenicity of the NiV-G stalk in the context of the full NiV-G ectodomain . However , rabbit antiserum ( Ab167 ) raised against headless mutant 167 ( Fig . 1A ) reacted well with all headless mutants at the cell surface , but less efficiently with wt NiV-G by flow cytometry ( Fig . 1B ) . Thus , it appears that the NiV-G stalk epitope ( s ) that Ab167 recognizes is ( are ) likely either directly sequestered or structurally altered by the presence of the head in full-length NiV-G . Most importantly , with one clear exception , co-expression of wt NiV-F ( F ) with most mutants induced very low levels of 293T cell-cell fusion . NiV-F co-expression with mutant 167 resulted in cell-cell fusion levels similar to those induced by wt NiV-G/F ( Fig . 1B and 1D ) . These data indicate that a headless NiV-G protein can trigger NiV-F efficiently without requiring the NiV-G head and suggest that a motif ( s ) in the NiV-G stalk is important and sufficient to trigger NiV-F . Since the NiV-G head is known to mediate binding of NiV and HeV to their host cell receptors ephrinB2/B3 [15] , [27] , [28] and that receptor binding is required for fusion triggering by wt NiV-G , we tested the possibility that headless NiV-G proteins may somehow retain some ephrinB2/B3 binding ability . However , unlike wt NiV-G , none of the truncation mutants bound soluble ephrinB2 ( Fig . 2A ) . Moreover , increasing concentrations of soluble EphB3 , a natural ligand of ephrinB2/B3 , inhibited wt NiV-G-induced , but not mutant 167-induced , cell-cell fusion ( Fig . 2B ) . Lastly , when co-expressed with a previously reported hyperfusogenic NiV-F mutant ( F3F5 , designated Fhyper [29] ) mutant 167 induced 4–5-fold more cell-cell fusion in PK13 cells ( which express nearly undetectable levels of ephrinB2/B3 receptors [30] ) than wt NiV-G ( Fig . 2C–D ) . Altogether , these data confirm that headless NiV-G mutant 167 can trigger cell-cell fusion robustly without requiring receptor binding . Since receptor binding is required for fusion triggering by NiV-G in the presence of the head , but not in its absence , it seems plausible that receptor binding by the head may trigger fusion by exposing a previously sequestered F-triggering stalk domain . We used anti-NiV-G stalk polyclonal antiserum Ab167 to test this hypothesis . Indeed , binding of soluble ephrinB2 to wt NiV-G enhanced the binding of Ab167 to NiV-G's stalk by about 70% ( Fig . 3A ) . Additionally , the fact that Ab167 binds NiV-G to some extent in the absence of ephrinB2 binding suggests that either a portion of the NiV-G molecules on the cell surface are in the Ab167-binding ( receptor-activated ) conformation or that antiserum Ab167 binds more than one epitope in the NiV-G stalk , and that receptor ephrinB2 binding enhances exposure of only a portion of such epitopes ( e . g . one epitope ) . In any event , these data indicate that ephrinB2 binding to the NiV-G head causes the exposure of at least one NiV-G stalk epitope . We next asked whether headless mutant 167 would more readily trigger cell-cell fusion promoted by two NiV-F cytoplasmic tail hypofusogenic mutants than wt NiV-G . We previously reported that mutant K2A delays an early step , and mutant R3A a late step , in the NiV-F conformational cascade [31] , [32] . We hypothesized that the loss of the head in mutant 167 would allow it to more readily trigger F , as less conformational changes would be required for mutant 167 than for wt NiV-G to trigger F , and some of these conformational changes may be energy dependent . Indeed , mutant 167 yielded 4 to 5-fold higher levels of cell-cell fusion with NiV-F hypofusogenic mutants K2A and R3A than that induced by wt NiV-G ( Fig . 3B ) . In fact , mutant 167 rescued the levels of cell-cell fusion of mutant R3A to wt NiV-G/F levels ( Fig . 3B ) . As our previous studies suggested that K2A and R3A need to overcome higher activation energies than wt NiV-F to undergo the conformational changes necessary to execute membrane fusion [31] , [32] , and that mutant R3A displays a distinct ectodomain overall conformation relative to wt NiV-F [32] , the ability of mutant 167 to rescue the K2A and R3A hypofusogenic phenotypes is consistent with mutant 167 requiring less substantial conformational changes than wt NiV-G in order to trigger NiV-F . In other words , our data suggest that the loss of the NiV-G head increased the ability of the headless NiV-G to convert pre-fusion to post-fusion F , and that there likely is an energy requirement to move the head “out of the way” in order for a stalk motif to trigger F . We next confirmed that headless mutant 167 interacts with NiV-F via a co-immunoprecipitation ( co-IP ) assay between wt NiV-F and wt or mutant 167 NiV-G ( Fig . S1A in Text S1 ) . These data indicated that headless NiV-G is sufficient to interact with NiV-F , but does not rule out the possibility that the NiV-G head also makes contact with NiV-F . To test whether headless NiV-G can trigger NiV-F on virions to induce viral entry ( viral-cell fusion ) , we produced NiV/vesicular stomatitis virus ( NiV/VSV ) pseudotyped virions expressing a Renilla luciferase reporter gene . We previously established a quantitative , biosafety level 2 ( BSL2 ) NiV-G/F-mediated viral entry system [26] , [29] , [32] . When compared at equal numbers of VSV viral genome copies over several logs of viral input , wt NiV/VSV virions entered fusion-permissive Vero cells at levels about three orders of magnitude higher than negative control bald , NiV-G-only , or NiV-F-only VSV virions . 167 virions ( containing mutant 167 and wt NiV-F ) did not enter cells even if spinoculation was used during infection to “force” virions into close proximity to the cell surface ( Fig . 3C ) . Importantly , these results were not due to a lack of mutant 167 protein incorporation into NiV/VSV virions ( Fig . 3D ) . The sharp contrast between cell-cell fusion ( Fig . 1B , 1D ) and viral entry ( Fig . 3C ) results for mutant 167 is consistent with a mechanism in which the NiV-G head prevents premature triggering of NiV-F by the NiV-G stalk until receptor-binding occurs . To directly test whether NiV-F in mutant 167 virions is prematurely triggered , we used a confocal micro-Raman spectroscopy technique we recently developed to determine receptor-induced triggering of NiV-F embedded on the surfaces of either NiV/VSV pseudotyped virions or NiV viral-like particles expressing NiV-M/NiV-G/NiV-F [33] . In our previous study , we identified specific NiV-M , F , or G Raman spectroscopic signals , and ephrinB2 receptor binding-induced conformational changes in NiV-F detected by a change in a Raman signal at the wavenumber of 1409 cm−1 . As shown previously , analysis of wt NiV/VSV virions pre-bound to ephrinB2 at 4°C and then incubated at 25°C for 10 min ( sufficient energy and time to detect an early NiV F-triggering step ) yielded a downward shift in the 1409 cm−1 peak , indicating a conformational change in NiV-F . Also , this NiV-F peak shift was not observed in control virions that contained only NiV-F but not NiV-G ( Fig . 3E ) [33] . Interestingly , mutant 167 virions unexposed to ephrinB2 exhibited higher levels of F-triggering than wt virions pre-bound to ephrinB2 ( Fig . 3E ) , as opposed to negative control mutant 176 virions . These data indicated that independently of receptor binding , at least a subset of NiV-F molecules in mutant 167 virions is prematurely triggered . Based on the idea that receptor binding induces the exposure of an F-triggering domain in the NiV-G stalk , we explored the changes in the head that must take place to link these two events . We previously identified a receptor-induced conformational change in NiV-G , marked by enhanced binding of anti-NiV-G monoclonal antibody 45 ( Mab45 ) and by secondary structural changes in NiV-G [34] . Here , we analyzed the ability of several neutralizing anti-NiV-G Mabs ( Fig . 4A ) to detect conformational changes in NiV-G . We observed receptor binding enhanced Mab45 binding to NiV-G ( as expected ) , but decreased binding of Mab213 and Mab26 to NiV-G , in a receptor concentration-dependent manner ( Fig . 4B ) . These data suggest that either Mab213 and Mab26 both detect a receptor induced conformational change in NiV-G , or they compete for binding to NiV-G with soluble ephrinB2 . We tested the latter scenario and observed that neither Mab213 nor Mab26 ( up to [0 . 1 mg/ml] ) blocked binding of NiV-G to ephrinB2 ( Fig . 4C ) . Altogether , these data indicate that ephrinB2 binding to NiV-G induces a conformational change in NiV-G that Mab213 and Mab26 can detect [ostensibly the hiding of an epitope ( s ) ] . We then attempted to map the binding epitopes of Mab45 , Mab213 and Mab26 in NiV-G . We first used a series of well-expressed NiV-G deletion mutants [15] , [34] . While deletion of region 4 ( residues 195–211 , mutant Δ4 ) in the NiV-G head abolished binding of Mab45 to NiV-G , deletion of region 9 ( residues 371–392 , mutant Δ9 ) abolished binding of Mab213 and Mab26 to NiV-G ( Fig . 4D ) . The locations of regions 4 and 9 in NiV-G relative to the ephrinB2 cell-receptor binding site are shown ( Fig . S3 ) . We previously showed via triple alanine ( Ala ) scan mutagenesis that Mab45 does not directly bind region 4 [34] . Thus , to map the binding epitope of Mab45 in NiV-G , we mutated and analyzed a region C-terminal from the stalk domain and N-terminal from region 4 ( region 4N , residues 177–194 ) , by Ala scan mutagenesis using a C-terminal HA-tagged version of NiV-G . We similarly analyzed region 9 to map the binding epitope ( s ) of Mab213 and Mab26 ( Fig . S2 in Text S1 & 4E–F ) . We previously showed that HA-tagged NiV-G functions and binds ephrinB2 well [15] , [34] . We then compared by flow cytometry the binding levels of Mab45 and Mab213 to each Ala scan mutant in region 4N normalized to each mutant's CSE levels , as measured by anti-HA-tag antibody binding ( Fig . 4E ) . Although mutant V182A bound Mab213 at higher than wt levels , it bound Mab45 hardly at all , indicating that residue V182 in NiV-G is important for Mab45 binding . Interestingly , three mutants in region 4N: V178A , L181A , G183A , and C189A displayed enhanced binding levels to Mab45 , a phenotype we previously showed corresponds to a NiV-G “post-receptor-binding” conformation [21] . Similar analysis of region 9 revealed that residues K386 and Q388 in NiV-G are important for NiV-G binding to Mab213 , while residue C382 is important for its binding to Mab26 ( Fig . 4F ) . Thus , regions 4N and 9 contain binding residues for antibodies Mab45 and Mab213/Mab26 , respectively , and Mab45 binds a different NiV-G epitope than Mab213/Mab26 , making it likely that Mab213 and Mab26 detect a distinct conformational change from that detected by Mab45 . As Mab45 , Mab213 , and Mab26 neutralize NiV-G ( Fig . 4A ) without blocking receptor binding ( Fig . 4C ) , we tested whether these Mabs bind NiV-G epitopes in regions 4N and 9 important for membrane fusion modulation . Normalization of each point mutant's levels of cell-cell fusion and CSE to wt NiV-G levels , and calculation of fusion indexes as normalized fusion/normalized CSE ( fusion index for wt NiV-G = 1 , Fig . S2 in Text S1 ) , revealed that region 4N mutants V178A , L181A , and S194A yielded hypofusogenic phenotypes ( fusion indexes <0 . 5 ) , while mutant V182A and to a lesser extent mutant L184A yielded hyperfusogenic phenotypes ( fusion indexes >1 . 5 , Fig . 5A & S2 in Text S1 ) . Additionally , region 9 mutants E374A , C382A , C387A , Q388A , and P392A yielded hypofusogenic phenotypes ( Fig . 5B & S2 in Text S1 ) . Mutations in regions 4N and 9 that yielded hyper- ( red ) or hypo- ( blue ) -fusogenic phenotypes are shown in the NiV-G head structure , respectively ( Fig . 5C–D ) . These results indicate that , as hypothesized , regions 4N and 9 modulate membrane fusion . We then analyzed the spatiotemporal relationship between the two receptor-induced conformational changes detected in the NiV-G head by Mab45 and Mab213/Mab26 , using Mab45 and Mab213 F ( ab ) 2 fragments undetectable by the secondary antibodies that detect their full-length counterparts . We tested whether pre-binding region 9 with Mab213 F ( ab ) 2 would inhibit the receptor-induced conformational change in region 4N detected by Mab45 , and vice-versa . While Mab213 F ( ab ) 2 reduced the receptor-induced conformational change detected in region 4N ( enhanced binding of Mab45 decreased , Fig . 6A ) , Mab45 F ( ab ) 2 did not inhibit the conformational change detected in region 9 by Mab213 ( decrease in Mab213 binding remained unchanged , Fig . 6B ) . These data are consistent with: 1 ) Mab45 and Mab213/Mab26 binding distinct epitopes in NiV-G , 2 ) the conformational change in region 9 occurring spatiotemporally prior to that in region 4N ( our theorem ) , and 3 ) region 9 being relatively closer to the receptor-binding site in NiV-G ( Fig . S3 in Text S1 ) . To further test our theorem , we analyzed highly hyper- or hypo-fusogenic mutants in regions 4N and 9 for their abilities to undergo the conformational changes in regions 9 and 4N detected by Mab213 and Mab45 , respectively . Additionally , to fully disrupt the presumptive upstream conformational change in region 9 , we constructed and tested a region 9 mutant with an insertion of a random 19-residue peptide at K376 [K376 ( 19 ) ] ( Fig . S2 in Text S1 ) . As opposed to region 9 mutant P392A , hypofusogenic mutants C387A and K376A ( 19 ) had reduced conformational changes in region 9 ( Fig . 6C ) . Consistent with our theorem , both C387A and K376 ( 19 ) , but not P392A , also had reduced enhancement of receptor-induced Mab45 binding in region 4N ( Fig . 6D ) . Additionally , two region 4N hypofusogenic mutants ( V178A and L181A ) displayed little to no conformational changes in region 4N , as opposed to wt NiV-G or hyperfusogenic mutant V182A ( Fig . 6E ) . However , consistent with our theorem , no region 4N mutants exhibited a decrease in the conformational change detected in region 9 , as detected by Mab213 binding ( Fig . 6F ) . In summary , our data are consistent with the receptor-induced conformational change in region 9 occurring prior to that in region 4N . Next we tested whether regions 4N and 9 affect the downstream exposure of the NiV-G stalk domain by testing the ability of the most hyper- or hypo-fusogenic mutants in regions 4N and 9 to undergo receptor-induced exposure of the Ab167 G stalk domain . Fusogenicity of the region 4N and 9 mutants directly correlated with their ability to undergo receptor-induced exposure of the NiV-G stalk domain ( Fig . 6G–H ) , consistent with NiV-G stalk domain exposure-induced F-triggering occurring relatively downstream of changes in the NiV-G head . Last , we tested whether any of the phenotypes observed were due to altered abilities to bind the cell receptor ephrinB2 or NiV-F . Relative to their levels of CSE , only mutant C387A had a slight , and mutant K376 ( 19 ) a considerable , reduction in receptor ephrinB2 binding abilities ( Fig . S3 in Text S1 ) . In addition , all mutants analyzed bound NiV-F at roughly wt NiV-G levels , except for L181A , which bound NiV-F at greater than wt NiV-G levels ( Fig . S1B in Text S1 ) . Overall , our results indicate that: decreasing receptor binding [mutant K376 ( 19 ) ] decreased both region 9 and region 4N conformational changes; blocking region 9 blocked the region 4N conformational change; and blocking region 4N did not block the region 9 conformational change . Combined , these results indicate the following spatiotemporal order of three events: receptor binding , followed by 1 ) region 9 conformational change , 2 ) region 4N conformational change , and 3 ) G stalk domain exposure and F-triggering ( Fig . 7G ) . Based on all these findings , we propose the spatiotemporal F-triggering model depicted in Figure 7 . In the presence of the G head , the G stalk C-terminal F-triggering domain ( red ) is relatively “hidden” by other NiV-G sequences in the NiV-G head N-terminus and/or stalk C-terminus ( Fig . 7A ) . However , cell receptor binding to the NiV-G head causes conformational changes in the head ( Fig . 7B ) that allow exposure of a G stalk C-terminal domain ( Fig . 7C ) that can now interact with and trigger F to execute membrane fusion . In contrast , in virions expressing headless mutant 167 , the NiV-G stalk C-terminal F-triggering domain is always exposed ( Fig . 7D ) , triggering NiV-F prematurely , as virions exist in supernatants at 37°C for long periods of time prior to their collection , thus eliminating viral infectivity ( Fig . 7E ) . During cell-cell fusion , nearby target membranes are available when F is triggered , and thus 167 induces cell-cell fusion ( Fig . 7F ) .
Our results reveal a comprehensive mechanism of cell receptor-induced NiV membrane fusion triggering . Upon ephrinB2 binding , the NiV-G head undergoes at least two conformational steps in the head that result in exposure of a NiV-G stalk domain that interacts with and triggers F ( Fig . 7 ) . To our knowledge , this is the most detailed mechanistic molecular picture of paramyxovirus attachment protein linking receptor binding to F-triggering to date . Additionally , the headless NiV-G prematurely triggers F in virions , causing the loss of viral entry ( Fig . 3 ) , implying a crucial role for the NiV-G head in preventing premature F-triggering by a G stalk domain until receptor binding occurs . NiV-G/F interactions at viral or cell surfaces prior to receptor binding are well documented [3] . In addition , the first step of F-triggering , the transition time between F's pre-fusion and pre-hairpin intermediate conformations , occurs rather rapidly , with a half-life of ∼4 min [31] . Thus , pre-formed NiV-G/F complexes likely increase the efficiency of G-stalk/F interactions upon receptor binding and prior to G/F dissociation . Evidently , G/F interactions alone are insufficient for F-triggering and must somehow change for F-triggering to occur . It has previously been reported that: 1 ) a NiV-G mutant that lacks the C-terminal stalk domain 146–182 ( Δ3 ) , but retains the head and stalk N-terminal region , co-IP's with NiV-F [21] and 2 ) NiV-G mutants with added N-glycans to N-terminal stalk region 75–133 do not prevent G/F interactions [35] . Based on these findings and our results reported here , we speculate that bi-dentate G-F interactions involving both the NiV-G head and possibly the stalk N-terminus occur prior to receptor binding , and that either of these regions is sufficient to provide a detectable interaction with NiV-F . Full understanding of the residues involved in changes in these G/F interactions during membrane fusion will likely identify new potential targets for therapeutic intervention for the paramyxoviruses including NiV . It was recently reported that a headless PIV5 HN ( strain W3A ) can trigger PIV5-F , suggesting that a HN stalk hidden by the HN heads is exposed upon sialic acid receptor binding [22] . Notably , PIV5 W3A F alone can induce cell-cell fusion at 37°C and 42°C , but the presence of HN enhances fusion [22] , [23] . In contrast , for NiV and for most paramyxoviruses , wt F or even hyperfusogenic F mutants are unable to trigger membrane fusion in the absence of HN/H/G [29] , [32] . Another major functional difference between PIV5 and NiV is the type of receptors they bind , as sialic acid vs . protein receptor binding appears to modulate fusion via PIV5 HN-F association or NiV G-F dissociation , respectively [3] , [36] . Here , we report that a paramyxovirus attachment protein strictly required for membrane fusion can trigger F in the absence of its receptor binding head . Additionally , it was recently reported that a stabilized headless measles virus H stalk can trigger F [37] . Thus , our NiV results ( Figs . 1–3 ) combined with those for PIV5 [22] and MeV [37] suggest that despite the differences between PIV5 and NiV just described , fundamental aspects of the mechanism of membrane fusion triggering are conserved throughout the paramyxovirus family . However , as described in our introduction , within the paramyxoviridae family there are significant differences between the attachment glycoproteins of the paramyxovirinae and pneumovirinae subfamilies , thus it is possible that our three-step model of receptor-induced fusion triggering applies to the Paramyxovirinae but not to the Pneumovirinae subfamilies . Despite this apparent conservation , specifics on the receptor-induced mechanism of activation of HN/H/G may vary among paramyxoviruses [13] . Two recent reports indicate that rearrangement of the MeV H tetramer is crucial for F-triggering [38] , [39] . Oligomeric rearrangements of rigid monomers may possibly account for our results for NiV-G presented here; however , it is also possible that the mechanism of receptor-induced activation of NiV-G involves some level of secondary structural changes . Although no structural differences were observed between a soluble NiV-G head monomer bound or unbound to the ephrinB2 receptor , it is important to recognize that this study utilized only the NiV-G head , lacking the NiV-G stalk [25] . We previously reported , using circular dichroism ( CD ) , that receptor-induced secondary structural changes take place in a soluble NiV-G full ectodomain protein , but not in a soluble NiV-G head ( lacking the stalk domain ) [34] . Thus the stalk may be necessary to maintain NiV-G in the pre-receptor-activated conformation , and the lack of the stalk may result in the soluble NiV-G head converting prematurely to the receptor-activated conformation . Alternatively , the receptor-induced secondary structural changes in the NiV-G ectodomain detected by CD may occur solely in the stalk . In this case , the changes in Mab213 and Mab45 recognition we observed in the head may be purely a result of oligomeric rearrangements of rigid monomers . Mab213 and Mab45 bind two distinct NiV-G head epitopes located on different faces of the NiV-G monomer ( Fig . 7G ) making it less likely that oligomeric rearrangements of rigid head monomers solely account for our results . A combination of a future structure of the NiV-G tetramer and our knowledge of the binding epitopes of Mab213 and Mab45 provided here should help us distinguish between these possibilities . Although previous NiV studies uncovered G stalk mutations that modulate fusion , the domain ( s ) in NiV-G that trigger F have remained unknown . Our findings provide conclusive evidence that the NiV-G stalk is sufficient to trigger NiV-F . The precise residues in the NiV-G stalk exposed upon receptor binding and capable of F-triggering are yet to be determined . The finding that 167 , but neither 164 nor 173 , efficiently triggered F , though all three constructs were efficiently expressed ( Fig . 1B–D ) , suggests that the G stalk C-terminal residues 164–167 are important for F-triggering . Consistent with this , a recent NDV ectodomain crystal structure suggested that an equivalent stalk C-terminal region likely interacts with the HN head [20] . Thus , it was recently proposed that HN transitions from heads down to heads up conformations , exposing the stalk C-terminal F-triggering domain [22] . Our results are consistent with that model and further indicate that two conformational changes in the NiV-G head are necessary for the exposure of the stalk F-triggering domain . Furthermore , Ab167 , raised against mutant 167 , binds at least one epitope exposed after receptor binding , consistent with residues N-terminal of 167 being important for F-triggering . None of the antisera made against the full-length NiV-G ectodomain recognized the headless mutants tested , neither at the cell surface nor by Western blot analysis ( Fig . 1 ) . However , Ab167 , produced against mutant 167 , recognized the headless mutants , suggesting that the G stalk is somewhat immunogenic , and that the immunogenic stalk region ( s ) is relatively hidden in full-length NiV-G . The question arises: why are NiV-G mutants longer than 167 incapable of triggering NiV-F efficiently ? They contain the domain that ostensibly triggers F , present in mutant 167 , and they are expressed efficiently ( Fig . 1B–D ) . We speculate that residues C-terminal of 167 render the headless stalk more stable and thus unable to undergo a conformational change , possibly involving the unfolding of the 4HB stalk , to make it F-interactive . In other words , it is possible that the stalk needs to unravel in order to bind to and trigger F and that segments that extend beyond 167 are unable to undergo such unraveling until signaled by head residues upon receptor binding . Another possibility is that residues C-terminal from 167 fold over and directly “cover” residues N-terminal of 167 . However , the fact that the longer headless constructs are recognized well by Ab167 argues against this possibility . Overall , our data imply that wt NiV-G undergoes a receptor-induced conformational cascade that includes two sequential conformational steps in the head that result in exposure of a C-terminal stalk domain that triggers F to undergo its own conformational cascade . In contrast , the headless mutant 167 does not need to undergo such changes , triggering NiV-F either highly efficiently or prematurely , depending on whether F is at the cell or viral surface . These findings lead us to a mechanistic model of cell receptor-induced NiV and paramyxovirus membrane fusion and viral entry triggering ( Fig . 7 ) .
Expression plasmids for codon-optimized NiV-G and NiV-F genes , tagged or untagged at their C-terminus with the HA or AU1 tags , respectively , were previously described [40] , as was hyperfusogenic NiV-F mutant F3F5 [29] . The NiV-G headless mutants were constructed by inserting two stop codons ( TAA TAA ) at the corresponding sites . CHO ( CHOpgsA745 ) and Vero cells were cultured in Minimal Essential Medium alpha with 10% fetal bovine serum ( FBS ) . PK13 and 293T cells were cultured in Dulbecco's Modified Eagle's medium with 10% FBS . Rabbit antiserum ( Ab167 ) was produced by co-expressing NiV-M and headless NiV-G mutant 167 ( NiV-G sequences 1–167 ) using corresponding expression plasmids at a 1∶1 ratio in rabbits ( three DNA boosts ) . NiV-M was included to produce viral-like particles ( VLPs ) that incorporate mutant 167 protein in vivo [41] . Anti-NiV-G and anti-NiV-F polyclonal antisera or Mabs were produced previously in a similar fashion [15] , [29] , [32] , [40] . Binding of anti-NiV-G specific rabbit antibodies , mouse anti-HA Mab , or soluble ephrinB2 ( B2-hFc , R & D Systems ) , to cell surface NiV-G , were measured by flow cytometry . Primary antibodies were used at 1∶100 to 1∶1 , 000 dilutions , and B2-hFc at 10 nM , and PE-conjugated secondary antibodies ( Caltag ) at 1∶1000 dilution . Background mean fluorescence intensity ( MFI ) obtained by binding equal concentrations of primary and secondary reagents to PCDNA3 . 1 ( mock ) -transfected 293T cells was subtracted from the MFI of NiV-G expressing 293T or PK13 cells as indicated . NiV-F and wt or mutant NiV-G expression plasmids ( 1∶1 ratio , 2 µg total ) were transfected into 293T or PK13 cells ( ∼80% confluency ) , grown in 6-well plates . Cells were fixed in 0 . 5% paraformaldehyde 16–24 h post-transfection . To account for both syncytium size and numbers , cell-cell fusion was quantified by counting the number of nuclei within syncytia per 200X field ( 5 fields were counted per well per experiment ) . Syncytia were defined as 4 or more nuclei visualized within a common cell membrane [29] , [40] . Cells or pseudotyped NiV/VSV-rLuc virions expressing NiV-G and or -F were lysed in RIPA buffer ( Cell Signaling Technology ) and protease inhibitors ( cOmplete , Mini , Roche ) . Cell or viral lysates were then subjected to SDS-PAGE and NiV-G or NiV-F glycoproteins were subsequently detected by Western blotting using rabbit Ab167 or 806 or mouse anti-HA or anti-AU1 antibodies ( Caltag ) , at 1∶100–1∶3000 dilutions . Fluorescent secondary antibodies ( Li-Cor Biosciences ) were used at a dilution of 1∶10 , 000 , respectively , and detected with a Li-Cor Odyssey fluorimager . NiV/VSV pseudotyped virions expressing the renilla Luc reporter gene were produced as previously described [15] , [16] , [29] . Virions from viral supernatants collected 28 h post-infection were purified over a 20% sucrose cushion . Vero cells plated in 96-well were infected with the NiV/VSV-rLuc virions in infection buffer ( PBS+1% FBS ) for 2 h at 37°C over several logs of viral dilution . After 2 hours , Vero cell growth medium was added . 18–24 h post-infection , cells were lysed , and luciferase activity was measured in relative light units ( RLU ) using a renilla luciferase detection system ( Pierce ) and an Infinite M1000 microplate reader ( Tecan Ltd ) . Quantitation of viral genome copies was previously described [29] . RLU were plotted against genome copies per milliliter and regressed using GraphPad Prism . Approximately 20 hours post-transfection , 293T cells were washed with PBS and lysed in RIPA buffer ( Millipore ) supplemented with complete protease inhibitor ( cOmplete Mini , Roche ) . Cellular debris was pre-cleared by centrifugation and whole cell lysates ( supernatants ) were incubated with either 50 ul of µMACS anti-HA MicroBeads ( Miltenyi Biotec ) or 50 µl of polyclonal rabbit Ab167 and 100 ul of protein G MicroBeads ) for 30 min . at 4°C with rotation and then purified and eluted over μ columns ( Miltenyi Biotec ) following the manufacturers protocol . RIPA buffer was used for all wash steps . Cell lysate ( 10% of a well from a 6-well plate ) and purified elutions ( 45% ) were separated by either 10 or 12% PAGE to separate full-length G mutants or deletion mutants respectively . F and G proteins were detected by Western blot analysis as indicated above , using the indicated antibodies . Procedures were identical to those fully described in [33] . | The medically-important Paramyxovirus family includes the deadly Nipah virus ( NiV ) . After paramyxoviruses attach to a receptor at a cell surface , fusion between viral and cellular membranes must occur before the virus genetic material can enter the cell and replication of the virus inside the cell can begin . For most paramyxoviruses , viral/cell membrane fusion requires the concerted actions of two viral glycoproteins . After binding to a cell surface receptor , the viral attachment glycoprotein triggers the viral fusion glycoprotein to execute viral/cell membrane fusion so the genetic material of the virus can enter the cell . However , the mechanism of this receptor-induced triggering of membrane fusion is not well understood . We identified several sequential receptor-induced structural changes in the attachment glycoprotein of NiV that are part of the viral/cell membrane fusion-triggering cascade . Importantly , we propose a mechanism of cell receptor-induced paramyxovirus entry into cells , based on the findings described here , similarities between NiV and other paramyxoviruses , and other recent advances . |
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Infection with Citrobacter rodentium triggers robust tissue damage repair responses , manifested by secretion of IL-22 , in the absence of which mice succumbed to the infection . Of the main hallmarks of C . rodentium infection are colonic crypt hyperplasia ( CCH ) and dysbiosis . In order to colonize the host and compete with the gut microbiota , C . rodentium employs a type III secretion system ( T3SS ) that injects effectors into colonic intestinal epithelial cells ( IECs ) . Once injected , the effectors subvert processes involved in innate immune responses , cellular metabolism and oxygenation of the mucosa . Importantly , the identity of the effector/s triggering the tissue repair response is/are unknown . Here we report that the effector EspO , an orthologue of OspE found in Shigella spp , affects proliferation of IECs 8 and 14 days post C . rodentium infection as well as secretion of IL-22 from colonic explants . While we observed no differences in the recruitment of group 3 innate lymphoid cells ( ILC3s ) and T cells , which are the main sources of IL-22 at the early and late stages of C . rodentium infection respectively , infection with ΔespO was characterized by diminished recruitment of sub-mucosal neutrophils , which coincided with lower abundance of Mmp9 and chemokines ( e . g . S100a8/9 ) in IECs . Moreover , mice infected with ΔespO triggered significantly lesser nutritional immunity ( e . g . calprotectin , Lcn2 ) and expression of antimicrobial peptides ( Reg3β , Reg3γ ) compared to mice infected with WT C . rodentium . This overlapped with a decrease in STAT3 phosphorylation in IECs . Importantly , while the reduced CCH and abundance of antimicrobial proteins during ΔespO infection did not affect C . rodentium colonization or the composition of commensal Proteobacteria , they had a subtle consequence on Firmicutes subpopulations . EspO is the first bacterial virulence factor that affects neutrophil recruitment and secretion of IL-22 , as well as expression of antimicrobial and nutritional immunity proteins in IECs .
Citrobacter rodentium is an extracellular , mouse specific , intestinal pathogen used to model mechanisms of virulence employed by the human pathogens enteropathogenic and enterohemorrhagic Escherichia coli ( EPEC and EHEC ) and inflammatory bowel diseases [1] . In C57BL/6 mice , shedding of C . rodentium peaks around 8 days post infection ( DPI ) before being cleared , first via IgG opsonization of bacteria expressing virulence factors and phagocytosis by neutrophils and then through competition by the endogenous microbiota [2] . Infection with C . rodentium elicits robust tissue repair responses , which are characterized by production of IL-22 and cell proliferation leading to colonic crypt hyperplasia ( CCH ) [3 , 4] , as well as colitis . Although a number of host pathways involved in CCH have been identified [5 , 6] , the C . rodentium virulence factor/s implicated in eliciting the tissue repair response remain elusive . Both innate and adaptive immune responses are vital for C . rodentium elimination [1] . C . rodentium and its virulence factors are detected by pathogen recognition receptors ( PRRs ) such as toll-like receptors ( TLR ) -2 [7] and TLR-4 [8] and activate both the non-canonical ( caspase-11 ) [9] and canonical ( e . g . NLRP3 ) [10] inflammasome pathways in epithelial and myeloid cells . C . rodentium infection triggers expression of pro-inflammatory cytokines , e . g . TNF-α , Cxcl-1 ( KC ) , IL-6 and IL-23 , which activate innate lymphoid cells ( ILCs ) and induce differentiation of naïve T helper ( Th ) cells into Th1 , Th17 or Th22 effector cells secreting interferon-γ ( IFN-γ ) , IL-17A and IL-22 , respectively [1 , 11] . IL-22 triggers production of Reg family antimicrobial peptides including Reg3β and Reg3γ in intestinal epithelial cells ( IECs ) and plays a critical role in maintaining the epithelial barrier and controlling the bacterial burden [12 , 13] . At an early stage of the infection ( 4 DPI ) , ILC3 are the major source of IL-22 [14 , 15] whereas CD4+ T cells secrete IL-22 at a later stage ( after 9 DPI ) [13] . Importantly , Lee et al . have recently reported that CD11b+ Ly6C+ Ly6G+ neutrophils are also a main source of secreted colonic IL-22 in response to C . rodentium infection [16] . C . rodentium colonizes the apical surface of IECs while forming attaching and effacing ( A/E ) lesions , which are characterized by intimate bacterial interactions with the brush border microvilli [17] . Key to the C . rodentium infection strategy is the injection of multiple effectors into IECs via a type III secretion system ( T3SS ) . Following translocation , the effectors take control of cell signaling for the benefit of the adherent pathogen , including mitochondrial functions ( Map , EspF ) [18] , apoptosis ( NleB , NleH ) , necroptosis ( EspL ) , cellular trafficking ( EspG ) , phagocytosis ( EspJ , EspH , EspF ) , non-canonical and canonical inflammasome pathways ( EspI/NleA , NleF ) and innate immune responses ( NleC , NleD , NleE , Tir ) [19 , 20] . C . rodentium also encodes the T3SS effector EspO , an orthologue of OspE found in Shigella spp [21] . Importantly , EHEC O157:H7 encodes two EspO paralogs [22] . Recently , we reported that infection of mice with a C . rodentium mutant lacking the effector EspO ( ΔespO ) results in significantly higher bacterial load from 14 DPI compared to infection with wild type ( WT ) C . rodentium , which concur with reduced levels of colonic CD4+ T cells and C . rodentium-specific serum IgG antibodies [23] . In this paper , we report that EspO plays a role in triggering nutritional immunity , cell proliferation , mucosal innate immune responses , phosphorylation of STAT3 and expression of antimicrobial peptides .
Infection of C57BL/6 mice revealed that at 8 and 14 DPI , C . rodentium ΔespO triggered reduced CCH ( 48% and 40% reduction , respectively ) compare to WT; this phenotype was fully complemented with a plasmid encoding EspO ( pespO ) ( Fig 1A and 1B ) . By 21 DPI , no difference in the level of CCH was recorded between mice infected with WT , ΔespO-pespO or ΔespO ( Fig 1B ) . Ki-67 is a marker of cell proliferation expressed in all phases of active cell cycle but absent in resting cells [24] . Consistently , significant reduction ( 72% reduction ) in Ki-67 staining was seen in mice infected with ΔespO compared to mice infected with WT or the complemented strain at 8 DPI ( Fig 1C and 1D ) . Importantly , no difference in body weight was observed between mice infected with WT , ΔespO and ΔespO-pespO at 8 DPI and the uninfected control mice ( Fig 1E ) . As ΔespO cause a significantly reduced cell proliferation , a marker of tissue damage repair , we determined the outcome of infection of the highly susceptible Rag2-/- il2rg-/- mice ( n = 5 ) , which lack NK , ILCs , T and B cells . This revealed that while both WT and ΔespO triggered a similar decline in body weight ( Fig 1F ) , there was a small delay in mortality in mice infected with the ΔespO ( Fig 1G ) , which is consistent with the mutant causing less colonic damage . A previous study has recently shown that C . rodentium utilizes the T3SS to induce CCH as a means to oxygenate the mucosa and to drive bacterial expansion via CydAB-mediated aerobic respiration [25] . Enumeration of colony-forming units ( CFUs ) per gram of feces revealed that despite the significant difference in CCH there was no difference in bacterial shedding between WT , ΔespO and ΔespO-pespO up to 8 DPI ( Fig 1H ) . As C . rodentium T3SS effectors execute their function by altering cell signaling in IECs , we aimed to investigate the intracellular processes affected by EspO . To this end , we compared the proteomes of IECs purified from mice infected with WT , ΔespO and ΔespO-pespO at 8 DPI . Assessing purity by flow cytometry revealed that the IEC preparations were enriched by over 90% [26] . For the proteomic analysis , we only considered changes to protein abundance between WT and ΔespO if they were fully restored upon ΔespO complementation . Associated with the IEC proteomes were 773 C . rodentium proteins ( S1 Table ) , including structural ( e . g . EscN , EspA , B , D ) , chaperone ( CesF , CesT ) and T3SS effector ( Tir , EspF , EspH , EspM2 , EspI/NleA ) proteins , intimin , the global regulator of virulence RegA [27] and the essential periplasmic serine protease HtrA [28] . Consistent with the similar bacterial shedding ( Fig 1C ) , the proteomes of infected IECs revealed similar abundances of C . rodentium proteins across the challenged groups ( Fig 1I ) . As key to the C . rodentium infection strategy is formation of A/E lesions , which has a major impact on the shape and function of IECs , we first compared the abundance of brush border proteins in infected IECs . This revealed that Eps8 , Villin , Plastin , Ezrin and Espin , as well as actin binding proteins Profilin , Gelsolin , Cobl and Spectrin ( and their associated proteins ) were in similarly lower abundance in the infected groups compared to the uninfected control mice ( Figs 2A , 2B , S1A and S1B ) . These findings were confirmed by observations made in transmission electron microscopy ( TEM ) showing typical A/E lesions in colons infected with either the WT or ΔespO ( Fig 2C ) . We have recently reported that 1 , 447 proteins , mostly associated with metabolic processes ( e . g . ATP production in the mitochondria , lipid biogenesis ) , were in lower abundance in C . rodentium-infected IECs . In contrast , C . rodentium infection induced the creatine and cholesterol biogenesis pathways , as well as cholesterol efflux [18] . As cell proliferation is dependent on metabolic activity and ATP consumption , we compared the abundance of the metabolic enzymes between WT- and ΔespO-infected IECs . This revealed a similar profile in the infected IECs , with decreased abundance of proteins in the TCA cycle , oxidative phosphorylation ( Fig 3A and 3B ) and lipid metabolism ( Fig 3C ) , and increased abundance of proteins in the cholesterol , creatine and nucleic acid metabolism ( Fig 3C ) . Taken together , these results suggest that cellular processes involved in effacement of the brush border microvilli , disruption of the mitochondria and cell metabolisms are not related to CCH . Moreover , the data show that CCH is dispensable for proliferation of C . rodentium in vivo . In order to determine which biological processes are affected by EspO , we searched for proteins with differential abundance in WT- and ΔespO-infected IECs . This revealed 206 EspO specific proteins with altered abundance compared to WT and ΔespO-pespO ( Fig 4A; S2 Table ) . Among the EspO specific proteins , 41 were grouped under the GO term defense and reactive oxygen species ( ROS ) responses . Expression of the Nox2 system ( e . g . Ncf1 , Ncf2 , Ncf4 , Cyba ) was induced in IECs infected with WT , but to a significantly lesser extent in IECs infected with ΔespO ( Fig 4B ) . Nox2 activity is under the regulation of the calprotectin ( a heterodimeric complex made of S100a8 and S100a9 ) , which is translocated to the plasma membrane upon activation [29] . Whereas both S100a8 and S100a9 were in higher abundance in IECs infected with WT and ΔespO-pespO , they were in lower abundance in the ΔespO-infected IECs ( Fig 4B ) . As ROS and calprotectin have an antimicrobial activity , we extended the comparison to other antimicrobial proteins ( AMPs ) expressed in IECs infected with WT , ΔespO and ΔespO-pespO 8 DPI and 14 DPI . This revealed elevated abundance of 16 AMPs , including the antimicrobial peptides Reg3β and Reg3γ , lysozyme ( Lyz2 ) and proteins involved in nutritional immunity including lactotransferin ( Ltf , involved in binding and transport of iron ) , Lipocalin-2 ( Lcn2 , targeting the bacterial ferric-siderophore enterobactin ) , as well as calprotectin ( which sequesters Mn and Zn ) , in WT- and ΔespO-pespO-infected mice 8 DPI; compared to WT , these AMPs were found in significantly lower abundance following infection with ΔespO ( Fig 4A ) . The abundance of these proteins , with the exception of Camp , Ctsg and Reg3β , decreased at 14 DPI , with no significant difference between WT and ΔespO . Importantly , several of the identified AMPs were thought to be expressed only by immune cells ( Lyz2 , S100a8 , S100a9 , Mpo , Ctsg ) . However , recent studies have shown that these AMPs are expressed by other cells types upon cytokines stimulation: Lyz2 is expressed by crypt and Paneth cells [30] , while S100a8 and S1009 are produced by epithelial cells [31] . In addition , others AMPs , associated with neutrophil extracellular traps ( NET ) , could be found on the surface of IECs ( e . g . Mpo , Ctsg ) [32] . In order to validate the proteomics data , we quantified the levels of the Reg3β and Reg3ϒ mRNA in IECs by qPCR and fecal Lcn2 by ELISA . We used Dmbt1 ( a glycoprotein of the scavenger receptor cysteine-rich family targeting bacteria and reducing their adhesion to the cells ) and Indoleamine 2 , 3-dioxygenase 1 ( Ido1 , mediates tryptophan depletion and increases antimicrobial metabolites including kynurenine and 3-hydroxy-kynurenine [33] ) as controls . Whereas the mRNA levels of Reg3β and Reg3ϒ increased significantly following infection with both WT and ΔespO compared to control mice , the increase seen in the ΔespO was significantly lower compared to WT ( Fig 4C and 4D ) . A similar trend was detected for the abundance of fecal Lcn2 , quantified by ELISA ( Fig 4E ) . In contrast , the level of Dmbt1 and Ido1 mRNA increased significantly after infection , with no difference between WT and ΔespO ( Fig 4F and 4G ) . Taken together , these data show that EspO affects expression of antimicrobial peptides and nutritional immunity proteins in IECs and suggest the changes observed at the proteome may be partially regulated at the transcriptional level . The transcription factor STAT3 commonly regulates expression of genes encoding AMPs , proteins involved in ROS production [34] and cell proliferation [35] . To determine if STAT3 may be differentially activated in IECs infected with WT compared to ΔespO , STAT3 phosphorylation on Tyr 705 was accessed by western blotting . Total STAT3 and GAPDH were used as loading controls . Whereas , little STAT3 phosphorylation was observed in uninfected IECs , robust phosphorylation was detected in IECs infected with WT . Importantly , lower level of STAT3 phosphorylation was observed in IECs infected with ΔespO ( Fig 5A and 5B ) . No difference in the levels of total GAPDH was detected in the different mice whereas a small increased of total STAT3 is detected during infection . In a control experiment we determined if EspO itself can induce phosphorylation of STAT3 . For this , HeLa cells were infected with WT or ΔespO for 3 h and the level of STAT3 phosphorylation was measured by WB . IL-6 was used as a positive control . While IL-6 induced strong STAT3 phosphorylation , no phosphorylation was observed in cell infected with either the WT or ΔespO ( Fig 5C ) , suggesting that STAT3 phosphorylation is not a direct cell response to infection . Secreted by immune cells , IL-22 , which is a key cytokine needed to control C . rodentium infection [12] , triggers STAT3 phosphorylation and expression of AMPs [36] . IL-22 also plays a role in cell proliferation [37] . We therefore tested if IL-22 secretion into supernatants of colonic explants was reduced in explants that were previously infected with either C . rodentium wild-type or ΔespO . This revealed that levels of IL-22 were 57% lower in mice infected with ΔespO ( Fig 5D ) . In order to determine if the EspO influences expression of other cytokines , expression of Cxcl-1 produced by IECs and Ifn-γ produced by immune cells ( e . g . natural killers , macrophages and T cells ) was analyzed by qPCR in IECs and whole colonic tissue respectively . Whereas Cxcl-1 ( Fig 5E ) and Ifn-γ ( Fig 5F ) were induced during C . rodentium infection , no difference was observed between WT and ΔespO 8 DPI , suggesting that EspO selectively alters IL-22 related inflammation . ILC3 , at the early stage of the infection , and Th22 cells , at the later stage of the infection , are the major sources of IL-22 . Moreover , neutrophils have also been shown to be an important source of IL-22 during C . rodentium infection [16] . To identify the immune cell types responsible for IL-22 secretion , colonic immune cells populations were analyzed 8 DPI by FACS . Unexpectedly , no difference was observed in the total number of colonic ILC3 and T cells ( Figs 5G , 5I and S2 ) , or in the number of IL-22 producing ILC3 and T cells ( Fig 5H and 5J ) . Furthermore , no difference in other cell types producing IL-22 upon ex-vivo stimulation was seen between uninfected and infected colons ( Fig 5K ) . This suggests that EspO did not influence the abundance of IL-22-producing cells , but instead affected the expression or release of IL-22 or the positioning of IL-22-producing immune cell with respect to the colonic mucosal surface . To test this hypothesis , IL-22 mRNA level was analyzed by qPCR and whole colonic tissue . Whereas IL-22 expression was strongly induced during C . rodentium infection , no difference was observed between WT and ΔespO 8 DPI ( Fig 5L ) , suggesting that EspO alters either the release of IL-22 by immune cells or theirs localization in the tissue . The proteomics analysis predicted that compared with WT infection , ΔespO induces reduced chemotaxis ( activation score: -2 . 987; p-value 3 . 26 x 10−4 ) , cell movement of granulocytes ( activation score: -2 . 8; p-value 2 . 04 x 10−7 ) and immune response of neutrophils ( activation score: -2 . 543; p-value 3 . 30 x 10−5 ) ( Fig 6A ) . This is consistent with the predicted lower ROS production as well as the lower abundance of S100a8 and S100a9 , which promote chemotaxis [38] . In addition , Mmp9 , which digests extracellular matrix and opens tight junction allowing neutrophils transmigration [39] as well as Icam1 , a neutrophil ligand which promotes their adhesion to the epithelial cells [40] , were in higher abundance in IECs infected with WT and the ΔespO-pespO but to a lesser extent in ΔespO ( Fig 6A ) . Recently , neutrophils have been shown to be a main source of secreted IL-22 in the colon during C . rodentium infection [16] and colitis [31] . While we previously reported no global difference in total neutrophil numbers within inflamed tissue during infection with WT or ΔespO [23] , supporting our IL-22 expression data ( Fig 5K ) , neutrophil distribution within the inflamed colon was not assessed and could be altered after infection with ΔespO . To test this , colonic sections from mice infected with WT , ΔespO and ΔespO-pespO were stained with antibodies against Ly6G , C . rodentium and Hoechst stain ( nuclei ) . Uninfected sections were used as control . While the number of granulocytes infiltrated into the tissue increased during infection ( Fig 6B and 6C ) , the number was significantly lower in mice infected with ΔespO ( 68% reduction ) , suggesting that EspO affected neutrophils transmigration toward the site of bacterial attachment . These results suggest that by signaling inside IECs , EspO impacts on neutrophils chemotaxis to the site of C . rodentium colonization . As CCH , neutrophil recruitment and AMPs affect the composition of the gut microbiota , we hypothesized the deletion of espO , which affects these parameters , will impact on the nature of dysbiosis induced by C . rodentium . To test this , we compared the composition of tissue-associated microbiota between mice infected with WT and ΔespO 8DPI using 16S RNA sequencing . Whereas C . rodentium infection induced a dysbiosis with a decreased of Bacteroidetes , Firmicutes and Tenericutes and a proliferation of Proteobacteria , no difference was observed at the Phylum level between mice infected with WT or ΔespO ( Fig 7A ) . As expansion of C . rodentium in the colon has been linked to CCH [25] , we tested if infection with ΔespO affects at the abundance of other Enterobacteriaceae ( Fig 7B ) . Consistent with the similar level of shedding , the level of Citrobacter/Enterobacter were similar in mice infected with either WT or ΔespO ( Fig 7B ) . Moreover , the abundance of other genera was similar in the infected mice , suggesting that proliferation of Enterobacteriaceae is independent of CCH and is not affected by the abundance of AMPs . As Reg3γ specifically kills Gram-positive bacteria [41] , we analyzed the composition of tissue associated Firmicutes . Whereas , the abundance of most of the genera was similar in mice infected with either WT or ΔespO , the abundance of Aerococcus , Enterococcus and Anaerofuctis differ between the two infections , with the level in the ΔespO infected mice similar to that seen in the uninfected control mice ( Fig 7C ) . This is consistent with a previous report showing that Reg3-/- mice have similar numbers of mucosa-associated bacteria belonging to the Gram-negative Bacteroidetes phylum and an increase of some Firmicutes ( Eubacterium ) [42] . Taken together , these results suggest that by signaling inside IECs , EspO impacts on chemotaxis of neutrophils to the site of C . rodentium adhesion , secretion of IL-22 , phosphorylation of STAT3 , cell proliferation and expression of AMPs , which leads to a specific alteration in the abundance of particular Firmicutes genera .
One of the main hallmarks of infection with C . rodentium is induction of tissue damage repair responses , i . e . elaboration of IL-22 , secretion of AMPs and proliferation of transit amplifying cells [43] . In this study , we identified for the first time a T3SS effector , EspO , which once injected into infected IECs induces both processes . This raises the following question: what benefit C . rodentium gains from triggering tissue damage ? EspO is one of the smallest C . rodentium effectors ( 10 kDa ) , yet its deletion has profound effects on how the gut responds to infection . Induction of CCH is a complex event , involving a large number of biological pathways that are triggered directly by C . rodentium and by the host in response to the infection . Wnt signaling has been previously implicated in C . rodentium-induced CCH [44] . Roy et al . have shown that in Swiss–Webster mice , C . rodentium infection induces accumulation of β-catenin during the cell proliferation phase [5] . Our proteomics analysis did not reveal any hallmarks of the Wnt signaling pathway in infected C57BL/6 mice; the abundance of β-catenin was similar to the uninfected mice and none of the known Wnt target proteins were affected . Therefore , our data suggest that EspO delivered into IECs at the site of infection affects cell proliferation in a Wnt-independent fashion . A triple C . rodentium mutant ( ΔespH ΔcesF Δmap ) has been recently reported to colonize the colonic mucosa at a significantly lower level than WT , which was mirrored by reduced CCH and Ki-67 staining . It was proposed that C . rodentium induces CCH as a means for oxygenation of the mucosal surface , which sustains the preference of the pathogen for aerobic metabolism and promotes pathogenesis [25] . However , the cause and effect relationship between the reduced colonization and lower CCH remained unresolved . Here , we show that that deletion of espO also caused significant reduction in CCH , yet colonization of the mucosal surface was unaffected , suggesting an independence of these two processes . Indeed , we have recently shown that C . rodentium infection diverts ATP production from the mitochondria to glycolysis , which was associated with production of phosphocreatine . In this study , we found that while colonizing at the same levels , WT and ΔespO trigger similar changes to metabolism in IECs , including disruption of the mitochondria , suggesting that C . rodentium is able to extract oxygen independently of CCH . Induction of CCH seems to be influenced by inflammatory responses to infection . Indeed , deletion of aquaporin-3 , an IECs’ basolateral water channel which mediates uptake of H2O2 , has been shown to attenuate ROS responses , reduce CCH and impair C . rodentium clearance [45] , resembling the ΔespO phonotype . The abundance of proteins linked to the production of ROS was lower in IECs infected with ΔespO . This suggests the existence of a strong link between CCH , ROS and bacterial survival . Moreover , mice infected with C . rodentium have an increased level of serotonin [46] . Interestingly , rectal injection of serotonin ( 5-hydroxytryptamine ) in rat induced expression of Nox2 , production of ROS , neutrophils recruitment and an increase of colonic wall thickness similar with C . rodentium infection in mice [47] . The exact relation between ROS production , CCH and neutrophils recruitment is still unclear , but it is likely linked to the secretion of chemokines [48] . To date , little is known about the interaction between neutrophils and C . rodentium . One reason for this is that these cells are short lived and mostly transcriptionally inactive upon their arrival to the site of infection . While the purity of our IECs was greater than 90% , our proteomes contained proteins reported to be neutrophils specific . This is mainly due to incorrect protein annotation , as most studies have examined protein contents in resting cells and not in the context of an inflamed tissue; nonetheless , we cannot exclude the possibility that during the purification process we co-purified proteins from neutrophil NETs . While NETs have been shown to be induced by E . coli , it is yet unknown if they play a role in controlling C . rodentium infection . Neutrophils migration from the microcirculation to the tissues is a multistep process , which remains largely uncharacterized . It involves the Tlr4—Myd88 axis and requires chemokines ( e . g . Ccl-3 , Cxcl-1 ) , matrix metalloproteinase ( MMP ) activation and lipids ( lipid leukotriene B4 ) . While the overall number of colonic neutrophils was similar between mice infected with WT or ΔespO , their tissue penetration was altered . We found similar level of Cxcl-1 expression in IECs , suggesting that EspO does not affect this step . Mice deficient in IECs’ Myd88 are unable to control C . rodentium infection , as they cannot induce epithelial repair response that maintains the protective barrier , production of neutrophil chemokines and an efficient adaptive immune response . While these mice succumb to C . rodentium infection , they do not show signs of CCH . Importantly , bone marrow transplantation from WT mice into Myd88-/- mice restored CCH [6] . In addition , the T3SS effector Tir , in a process dependent on Y451 and Y471 , has been shown to modulate secretion of Cxcl-1 and recruitment of neutrophils 14 DPI [26] . Importantly , while exhibiting a similar level of shedding , mice infected with C . rodentium expressing Tir Y451A/Y471A presented reduced CCH 14 DPI . More recently , Cxcl-5 has been shown to mediate neutrophil infiltration during cancer or infection however , it requires further processing post secretion from epithelial cells . Mmp2 and Mmp9 have been shown to cleave Cxcl-5 and enhances it chemotactic activity [49] . Whereas we did not detect a significant increase of the abundance of Mmp2 , the abundance Mmp9 increase 4-fold in IECs infected with WT , but only 2 fold in IECs infected with ΔespO . It is possible that by modulating the secretion of Mmp9 , EspO indirectly modulates Cxcl-5 processing and neutrophil infiltration . Taken together , these results reinforce the link between neutrophils and CCH . However , the mechanism by which neutrophils contribute towards CCH remains unknown . Recent studies have shown that IL-22 can be secreted by neutrophils [16 , 31] , however infection of IL-22 deficient mice with C . rodentium results in greater CCH [12] . Expression of colonic IL-22 is induced under inflammatory conditions such as infection and IBD . Indeed , many of the IL-22-regulated proteins belong to the IBD susceptibility genes [50] . Mucosal IL-22 has a dual role in mediating mucosal healing ( through activation of activation of STAT3 and pro-proliferative genes ) and combating pathogens ( via expression of AMPs ) . IL-22 is an essential cytokine in the fight against C . rodentium infection [12] . IL-22 is produced by ILC3 prior to 8 DPI and by Th-17/22 T-cells after 8 DPI; importantly , the abundance of various IL-22 producing cells ( e . g . ILCs , Th22 ) was similar between uninfected , WT- and ΔespO-infected mice 8 DPI . The abundance of neutrophils recruited to the site of infection was the only difference we observed between WT and ΔespO at 8 DPI . The putative role of neutrophils as a source of IL-22 is supported by a recent report showing that depletion of neutrophils with anti-Gr-1 neutralizing antibody during C . rodentium infection resulted in significant reduction in IL-22 productions . The reduced number of neutrophils following infection with ΔespO was consistent with lower abundance of Mmp9 and was mirrored by a global decrease in expression of genes encoding AMPs , with the exception of Dmbt1 and Iod1 . However , while calprotectin , Reg3β and γ , Lcn2 and Lyz2 are regulated by IL-22 , Dmbt1 is mainly induced by IL-27 [51] and expression of Ido1is triggered by interferon gamma [52] , suggesting that EspO selectively modulates innate immune responses in IECs . It is important to note that even though the abundance of the IL-22 regulated antimicrobial proteins is reduced following infection with ΔespO , the residual levels are sufficient to mediate bacterial clearance ( albeit delayed ) , unlike il-22 KO mice which succumbed to C . rodentium infection [12] . As ΔespO is shed for a longer period of time and triggers a novel immune response compare to WT ( e . g . reduced abundance of T cells , neutrophils , IL-22 and C . rodentium-specific IgG ) [23] , the selective pressure for keeping EspO is not apparent . Our data show that by triggering damage repair responses EspO impacts on expression of antimicrobial peptides and the availability of trace minerals ( e . g . Fe , Mg , Zn ) , which are required for survival by all living organism . While C . rodentium can resist nutritional immune responses and toxicity of antimicrobial peptides , these host responses affect the composition of the gut microbiota . Indeed , infection with ΔespO , which attenuates ( yet not abolish ) nutritional immunity , is associated with specific dysbiosis , particularly affecting the abundance of Firmicutes . Similarly , infection of mice lacking Mmp9 , which is found in reduced abundance in ΔespO , with WT C . rodentium also resulted in increased abundance of Firmicutes , as well as Lactobacilli [53] . Although counterintuitive , the concept of bacterial T3SS effectors triggering inflammation is not new; indeed , multiple pathogens use this strategy as a means to disrupt both the epithelium and the microbiota in order to promote colonization [54] . T3SS effectors form a complex , yet robust , network that can resist sever perturbation ( e . g . deletions ) . Alongside essential effectors ( e . g . Tir , EspZ ) , C . rodentium encodes multiple accessory effectors , each making a refining contribution to the infection process . EspO is one such effector; its importance is emphasized by the fact that EHEC O157 contains two copies of the gene [22] . While we still do not know how EspO affects signaling ( reflected by changing the abundance of 206 host proteins ) in IECs , our data highlights a novel infection strategy involving activation of tissue healing responses as a means to trigger an advantageous nutritional immunity .
Wild type C . rodentium ICC169 ( 56 ) and ICC169 ΔespO ( ICC1333 ) were grown at 37ºC in Luria–Bertani ( LB ) with necessary antibiotics . espO was amplified from ICC169 genomic DNA using primers GCTGGATCCTAGAAGAAGGAGATATACCATGCCATTGTCAATAAGAAA and GCTGTCGACTCAGGATTTATTTGAGTTATTAATCTCGGTC and was cloned in pACYC184 plasmid to generate pICC1379 . The recombinant plasmid was confirmed by PCR and DNA sequencing ( GATC Biotech ) . All animal experiments complied with the Animals Scientific Procedures Act 1986 and UK Home Office guidelines and were approved by the Animal Welfare and Ethical Review Body ( AWERB ) at Imperial College London . The mouse experiments were performed under project licence PPL 70–8413 . Mouse experiments were designed in agreement with the ARRIVE guidelines [55] for the reporting and execution of animal experiments , including sample randomization and blinding . Pathogen-free female C57BL/6 or Rag2-/- il2rg-/- mice ( 18 to 20 g ) were inoculated by oral gavage with 200 μl of C . rodentium suspension ( ~5 x109 colony forming units ( cfu ) ) . Uninfected mice were mock treated with PBS ( 200 μl ) . The number of viable bacteria used was determined by retrospective plating . Number of viable bacteria per gram of stool was similarly determined by plating onto LB agar . Terminal colon ( 0 . 5 cm ) was fixed in 10% neutral buffered formalin and paraffin-embedded . Paraffin-embedded sections were then treated as previously described22 . Anti-intimin ( a gift from Professor Fairbrother , Montreal University ) , E-cadherin ( BD Biosciences ) and Ki67 ( Thermo Scientific ) were used as primary antibodies followed by secondary antibodies from Jackson ImmunoResearch . H&E stained tissues were evaluated blindly for CCH by measuring the length of well-oriented crypts from each section from all of the mice . Similarly , Ki-67 staining was assessed microscopically by measuring the distance from the bottom of the crypt to the last stained nuclei . Ki-67staining was expressed as a ratio over the total length of the crypt . Tissues were imaged with an Axio , images were acquired using an Axio camera , and computer-processed using AxioVision ( Carl Zeiss MicroImaging GmbH , Germany ) . For neutrophils staining , indirect immunofluorescence was performed on cryo-sections as previously described [56] . Chicken anti-intimin and rat anti-Ly-6G ( RB6-8C5; Santa Cruz ) were used as primary antibody follow by secondary antibodies from Jackson ImmunoResearch . Images were acquired using an AxioCam MRm camera and processed using AxioVision ( Carl Zeiss MicroImaging GmbH , Germany ) . Murine colonic tissues were fixed in 2 . 5% ( vol/vol ) glutaraldehyde/PBS and processed for electron microscopy . Samples for transmission electron microscopy were observed using a Phillips 201 transmission electron microscope at an accelerating voltage of 60 kV ( Philips , United Kingdom ) . IEC have been extracted as previously described [18] . Cell pellets were either kept frozen for proteomic analysis , Western blotting or RNA extraction . HeLa cells ( ATCC ) were maintained in low glucose Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with Heat-inactivated fetal calf serum ( 10% vol/vol ) ( FCS , Gibco ) , 2mM GlutaMAX ( Invitrogen ) , and 0 . 1 mM nonessential amino acids at 37°C under 5% CO2 atmosphere . C . rodentium was cultured in Luria Broth at 37°C , 200 rpm with appropriate antibiotics for 8 h and then subculture ( 1/500 ) in DMEM with low glucose and grown overnight at 37°C without agitation in 5% CO2 incubator . After 3 h of starvation in DMEM only , cells were infected for 3 h . HeLa cells were incubated with IL-6 ( Biovision , 50ng/ml ) for 30 min . prior to analyzing cell extracts by Western blotting , using Hax-1 as a loading control . IECs or HeLa cells were lysed in 50 mM Tris pH 7 . 4 , 150 mM NaCl , 2 mM EDTA , 1% NP-40 and 1% SDS . Following gel electrophoresis and transfer , membranes were washed with PBS 0 . 1% Tween , blocked in TBS ( 0 . 1% Tween , 3% BSA , 0 . 5% gelatin ) and probed with specific antibodies overnight . Blots were then incubated with secondary antibody ( Jackson ImmunoResearch ) , followed by EZ-ECL assay , according to the manufacturer's instructions ( Geneflow ) . Chemiluminescences were detecting using a Chemidoc ( Biorad ) . Polyclonal anti-GADPH ( Abcam ) , anti-Hax1 ( Genetex ) , anti-Stat3 and monoclonal anti- phopho-Stat3 ( Cell Signaling ) were used to detect the different proteins . Enterocytes mRNAs were isolated using a RNeasy minikit according to the manufacturer's instructions ( Qiagen ) . Samples were treated with RQ1 DNase I and reverse transcription was perform using RQ1 DNase I according to the manufacturer's instructions ( Promega ) . Targeted genes were amplified with specific primer pairs listed in S3 Table , using a 7300 Applied Biosystems instrument under standard cycle conditions for PowerUp SYBR Green Master Mix ( Thermo Fisher ) . Changes in gene expression levels were analyzed relative to the controls ( uninfected samples ) , with GAPDH as a standard , using the ΔΔCT method . Last centimeter of distal part of the colon has been excised , weighted and washed thoroughly with RPMI medium with 100ug/ml of streptomycin and 100U/ml penicillin . Tissues have been then culture in RPMI ( 10%FCS , P/S , L-Glu ) for 2h and placed in fresh media ( 0 . 1 ml / 10 mg of tissue ) . After 24h , the supernatant has been collected and centrifuged 15 min at 15000 rpm . IL-22 was then measured using Mouse IL-22 DuoSet ELISA ( R&D Systems ) according to manufacturer's instructions . Fresh stool pellets were resuspended in PBS-0 . 1% Tween20 at a w/v ratio of 100 mg of stool per 1 ml PBS . Samples were left shaking for 20 min , centrifuged at maximum speed for 10 min before freezing the supernatant . Fecal LCN-2 was then measured using Mouse Lipocalin-2/NGAL DuoSet ELISA ( R&D Systems ) according to manufacturer's instructions . IEC pellets isolated from WT , ΔespO and ΔespO-pespO C . rodentium infected and uninfected mice were analyzed as previously described [18] . Only unique peptides were used for quantification , considering protein groups for peptide uniqueness . Peptides with average reported S/N>3 were used for protein quantification . The IEC obtained from uninfected mice were used as controls for log2 ratio calculations . Differential expression p-values were computed based on a single-sample t-test . Specificity thresholds used to characterize the EspO-specific IECs proteins were define as p-value < 0 . 05 ( Student's t-distribution ) and log2 ratio > 0 . 59 or < -0 . 59 ( equivalent to 1 . 5 fold change ) compare to protein abundance following WT and ΔespO-pespO infections . Specifically regulated protein were uploaded in Ingenuity Pathway Analysis ( IPA ) ( Qiagen ) platform . Trends of activation/inhibition states of the enriched functions and regulators were inferred by the calculation of a z-score ( -2 < z-score > 2 ) . Colons were collected from mice and DNA was isolated using PowerSoil DNA Isolation Kit ( MO BIO Laboratories ) . For 16S amplicon pyrosequencing , PCR amplification was performed spanning the V3and V4 region using the primers 515F/806R of the 16S rRNA gene and subsequently sequenced using 500bp paired-end sequencing ( Illumina MiSeq ) . Reads were then processed using the QIIME ( quantitative insights into microbial ecology ) analysis pipeline with USEARCH against the Greengenes database . Cells were isolated from LI LP as previously described ( 53 ) using a digestion solution containing 25 μg/mL liberase TL ( Roche ) and 25 μg/mL Dnase1 ( Sigma Aldrich ) . For cytokine ex-vivo stimulation , 1–5 x 106 cells were incubated at 37°C with IL-1β ( R&D Systems; 100 ng/mL ) , IL-23 ( R&D Systems; 100 ng/mL ) , PMA ( Sigma-Aldrich; 50 ng/ml ) , Ionomycin ( Sigma-Aldrich; 2 . 5μg/ml ) and BD GolgiPlug ( BD Biosciences ) in 10% FCS DMEM ( Gibco ) for 3h . Single-cell suspensions were stained with Flexible Viability Dye eFluor 506 ( eBioscience ) and blocked with FcR Blocking Reagent ( Miltenyi ) for 15 minutes followed by 30 minutes of surface antigens staining with a combination of fluorescently conjugated monoclonal antibodies ( from BD Biosciences , eBioscience and Biolegend ) on ice . For experiments involving intranuclear transcription factor staining , cells were fixed , permeabilized and stained using Fix & Perm Buffer Kit according to the manufacturer’s instructions ( BD Biosciences ) . For intracellular cytokine staining , cells were fixed , permeabilized and stained using Fix/Perm kit according to the manufacturer’s instructions ( BD Biosciences ) . All the samples were acquired on a custom-configuration LSR Fortessa ( BD Biosciences ) and the data were analyzed on FlowJo10 software ( TreeStar ) . GraphPad Prism software was used for all statistical calculations . Statistical test used was Mann-Whitney compared to controls ( or as indicated in the figure ) . p-values < 0 . 05 were considered significant . For the microbiota , p-values were FDR corrected using Benjamini and Hochberg method . | Citrobacter rodentium is a gold standard model to study pathogen-host-microbiome interactions . Two of the hallmarks of C . rodentium infection are colonic damage repair responses and colitis; symptoms that are shared with inflammatory bowel diseases in humans . The processes leading to tissue damage repair responses and the implicated bacterial virulence factors are still elusive . In this paper , we show that the C . rodentium type III secretion system effector EspO plays a major role in triggering damage healing responses , recruitment of neutrophils to the colonic villi , secretion of IL-22 from colonic explants and expression of IL-22 regulated genes in intestinal epithelial cells . This paper is the first to report a bacterial virulence factor that impacts on both intestinal epithelial cell proliferation and immune responses . |
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Transcription factors have two functional constraints on their evolution: ( 1 ) their binding sites must have enough information to be distinguishable from all other sequences in the genome , and ( 2 ) they must bind these sites with an affinity that appropriately modulates the rate of transcription . Since both are determined by the biophysical properties of the DNA–binding domain , selection on one will ultimately affect the other . We were interested in understanding how plastic the informational and regulatory properties of a transcription factor are and how transcription factors evolve to balance these constraints . To study this , we developed an in vivo selection system in Escherichia coli to identify variants of the helix-turn-helix transcription factor MarA that bind different sets of binding sites with varying degrees of degeneracy . Unlike previous in vitro methods used to identify novel DNA binders and to probe the plasticity of the binding domain , our selections were done within the context of the initiation complex , selecting for both specific binding within the genome and for a physiologically significant strength of interaction to maintain function of the factor . Using MITOMI , quantitative PCR , and a binding site fitness assay , we characterized the binding , function , and fitness of some of these variants . We observed that a large range of binding preferences , information contents , and activities could be accessed with a few mutations , suggesting that transcriptional regulatory networks are highly adaptable and expandable .
The precise regulation of gene expression depends upon the specific binding of transcription factors to their cognate binding sites . For this process to be accurate , the sites for each factor need to be separable from all other sequences in the genome [1] , [2] . Many groups have studied specific protein-DNA interactions , and while nucleotide preferences are starting to be understood at the biophysical level for some DNA binding domains [3]–[5] , no universal DNA-recognition code has been discovered [6] . What has emerged is a consistent picture of binding site degeneracy . That is , for most factors there is a single consensus binding site that is bound with the highest affinity and an increasing number of lower affinity sites that vary from the consensus . At some point the degeneration is so great that all remaining sites show the same non-specific binding energy [7]–[9] . Using information theory , the amount of conservation within a set of binding sites ( information content ) , as well as the amount of information needed to specifically locate N sites in a genome of length L , can be quantified [1] , [10] . In bacteria , it has been shown that these values are identical for many factors , suggesting that the size of a factor's regulon constrains how specific it needs to be [1] , [11] , [12] . This relationship does not hold as well for individual transcription factors in eukaryotes though [13] , [14] , where gene regulation is often under the control of cooperatively acting factors [15] . Once bound to their target sequence , transcription factors can modulate the rate of expression over a range of activities . Differences in expression levels have been suggested and shown to vary with binding site strength [16]–[19] . Given this relationship , the range and continuity of binding affinities for a factor partially define the range and continuity of potential outputs for that factor [19] , [20] . These outputs in turn can significantly affect the phenotype and fitness of the cell and are selected to maximize cellular gain while minimizing cost [19] , [21] , [22] . Therefore , there is not only a selective advantage for transcription factors to specifically recognize and bind their target sites , but to bind them with an affinity that produces the maximally fit transcriptional output . Since both specific binding preferences and transcriptional activity are dependent on the distribution of binding energies for a factor , selection on one will ultimately affect the other . We are interested in understanding how plastic the informational and regulatory properties of a transcription factor are , and how transcription factors evolve to balance these functions . To address this , we developed an in vivo selection system in E . coli to select for functional variants of the transcription factor MarA with altered binding preferences , whose binding properties and activity could be further characterized . By functional , we mean that a variant could modulate the level of transcriptional output within a physiological range . This is in contrast to in vitro selection assays , like phage display , that generally select for high affinity binding to a single target sequence , and disregard the impact of these mutations on transcriptional activity . To do these selections , we wanted to use a monomeric , transcriptional activator whose binding sites have been characterized and structure had been solved . MarA fit these criteria . It is a monomeric , helix-turn-helix transcription factor in the AraC family [23] that can both activate and repress transcription in E . coli [24]–[26] . It regulates the expression of approximately 20 genes involved in exporting low levels of drugs and organic solvents from the cell [24] , [27] . The structure of the MarA-DNA complex suggests that specific recognition occurs through two alpha-helices that bind the major groove [28] , [29] . Additionally , MarA has two homologues in E . coli , Rob and SoxS , that have similar binding preferences [30] , suggesting that the MarA binding domain can be selected to recognize additional sites .
We generated a sequence logo from the 16 E . coli MarA binding sites summarized in Martin et al . [24] to visualize the natural binding preference of the protein and the relative contribution of each contacting residue to binding specificity ( Figure 1 ) . Sequence conservation follows a sine wave as seen for other transcription factors [31] , [32] . MarA specifically contacts the DNA through helices 3 and 6 . Bases contacted by helix 3 ( red helix on structure , DNA positions to ) have a greater information content than do those contacted by helix 6 ( blue helix on structure that intersects the sine wave , DNA positions to ) , suggesting that helix 3 is more important for specific DNA recognition . This is consistent with alanine-scanning mutagenesis data for MarA [33] . Three residues in helix 3 ( Trp42 , Gln45 , and Arg46 ) specifically contact DNA bases according to the MarA-DNA structure [28] ( Figure 1 ) . Interestingly , the structure does not predict a specific contact at position , but the sequence logo indicates a strong preference for ‘A’ at this position . The ‘C’ at position is completely conserved and only contacted by the tryptophan at residue 42 , suggesting this is a highly specific amino acid . To identify variants of MarA that have altered binding preferences , we randomized the three specifically contacting residues in helix 3 and selected for mutants that could bind a target DNA sequence and initiate transcription of the tetracycline resistance gene ( tet ) on the selection plasmid shown in Figure 2 . Both the promoter of the tet gene and helix 3 of the MarA protein were flanked by restriction sites that allowed promoter and binding domain variants to be cloned into the plasmid ( Figure 2 ) . Functional MarA protein-binding site pairs within this system activated tet and allowed for cell survival in tetracycline . As we increased the concentration of drug , we selected for higher affinity interactions [19] . Additional parameters can affect the rate of transcriptional initiation , most notably the position of the binding site relative to the polymerase [24] . Since we vary the binding site within a fixed promoter context , our selection should just be on the strength of the DNA-protein interaction . We performed our selection in the E . coli strain N8453 ( mar , sox-8::cat , rob::kan , see Materials and Methods ) to prevent activation by wild type MarA , or by the MarA E . coli homologues Rob and SoxS . Expression of MarA on the plasmid was controlled by an L-arabinose inducible promoter [34] . We needed to identify a promoter that was only functional when activated to have tet expression and cell survival dependent upon MarA binding . To identify one , we randomized the of the tet promoter construct ( Figure 2B ) and selected for a promoter sequence that allowed cell growth on tetracycline plates with L-arabinose ( induced expression of MarA ) but not on plates without it ( see Materials and Methods ) . The 6 . 5 bit binding site that we identified is marked in Figure 2B . The strength of this site was predicted using the model presented in [18] , and is an average site compared to all sites in the genome . In a single construct , we cloned 3 in-frame and 2 out-of-frame stop codons into helix 3 of the MarA binding domain and tested if the resulting truncated protein could express tet with this promoter . At 15 g/ml tetracycline and 0 . 1% L-arabinose , we observed significant growth with wild type MarA , and no growth with the truncated mutant ( data not shown ) , suggesting that in this condition activation of tet and cell survival is dependent upon binding by MarA . The MarA regulon in E . coli includes the arcAB operon , which when over-expressed shows increased tolerance to many antibiotics including tetracylcine [35] , [36] . To ensure that we are selecting for variants that directly activate tet , we performed a selection against the anti-consensus MarA binding site ( the worst possible binding site according to Figure 1: CGTTTGACCCGCCAGGGCG ) . We could not identify any protein variants that allowed for survival in 20 or 30 g/ml tetracycline , suggesting that differential regulation of the MarA regulon is not sufficient for cell viability . This does not exclude the possibility that the over-expression of the arcAB operon may reduce the selective pressure on tet production . Selection in this system is somewhat similar to selection in a natural system , where the fitness of a binder is dependent upon the relative contribution of multiply expressed genes . We have in essence added tet to the MarA regulon . Because of the high concentration of tetracycline used for selection , the fitness gain for expressing tet is probably much greater than for any other gene that it regulates . MarA binding domain mutants were selected against three variants of the 15 . 3 bit mar binding site ( Figure 1 ) that is found upstream of the mar operon in E . coli [24] . The three target sequences we selected against are named ‘GCA’ , ‘GAA’ and ‘GAC’ according to the bases present at positions , and ( Figure 1 and Figure 2B ) . We varied these bases because they are the most highly conserved ones contacted by helix 3 . Binding domain libraries were made as described in Materials and Methods . We transformed the N8453 cells with each library and selected for growth on plates at 20 and 30 g/ml of tetracycline +0 . 1% L-arabinose . Individual colonies were sequenced . Sequences of viable MarA binding domain variants are shown in Table 1 and sequence logos generated from these variants are shown in Figure 3 . Each binding domain is referenced by residues 42 , 45 and 46 . For example , wild type MarA is noted as WQR . Of the 18 sequenced binding domains selected against the MarA consensus ‘GCA’ binding site at 20 g/ml tetracycline , we identified 13 different variants , including that of the wild type protein , that could initiate tet transcription to a sufficiently high level for cell survival . Only 5 different variants were observed at 30 g/ml tetracycline , and no new variants were observed at this higher concentration as expected . Three of the 13 binding domains were represented by multiple codon sets further supporting that these variants are functional . Interestingly , only the ‘TCK’ variant selected against ‘GCA’ lacks an arginine at position 46 , but it retains a positively charged lysine residue at that position . Selection against the ‘GAA’ and ‘GAC’ binding sites showed much less variability in the number of identified functional MarA variants . We only identified two mutants that could activate the ‘GAA’ binding site and three that could activate ‘GAC’ . No colonies were observed when we selected against ‘GAC’ at the higher tetracycline concentration of 30 g/ml . We were interested in how the variability in the selected mutants compared to the natural variability at these residues . We blasted the E . coli MarA sequence against all bacterial genomes using BlastP with non-redundant protein sequences and default search parameters [37] . The top 250 hits were aligned by ClustalX [38] and sequence logos were generated using the Delila programs [39] ( Figure 3 , Natural ) . Both the natural and the experimentally selected binding domain variants show a strong preference for arginine at position 46 . Interestingly , tryptophan is highly conserved at position 42 in the natural binding domains , whereas it was only observed in two selected variants ( Table 1 ) . In a similar selection for specifically contacting residues in the engrailed homeodomain by phage display , experimentally and naturally selected variability correlated well [40] . Engrailed binds a more specific set of sequences than does MarA . Therefore , natural selection on binding by engrailed is probably directed to maintain high affinity to a single or small set of sites as was experimentally selected . Conversely , MarA has probably been selected to maintain affinity to a more degenerate set of sequences , which may explain the discordance between the naturally and experimentally selected binding domains . To identify the highest affinity MarA mutant for each of the three DNA binding sites , the protein binding domains in each library were competed against each other in liquid culture containing 30 g/ml tetracycline+L-arabinose for 24 hours . The competed cultures were mini-prepped , retransformed and individual variants were sequenced ( Materials and Methods ) . We expected the mutant that produced the highest tet output to be represented at the highest frequency in the competed population as seen in a similar experiment [19] . We sequenced 8 individuals from each library and observed only one protein variant for each target binding site: RQR for ‘GCA’ , SQR for ‘GAA’ and TRR for ‘GAC’ ( Table 1 , marked with ‘X’ in Best column ) . Interestingly , wild type MarA ( WQR ) was not identified as the most fit variant for its naturally evolved consensus binding site ‘GCA’ . We determined the relative affinity of wild type MarA and four selected MarA variants to 64 different binding sites using MITOMI ( Figure 4 ) . MITOMI ( Mechanically Induced Trapping of Molecular Interactions ) measures the relative thermodynamic association constant of a single transcription factor for a large number of DNA sequences using a microfluidics based approach . The relative amount of fluorescently-labeled protein associated with fluorescently-labeled DNA is quantified by microscopy for each binding site to determine interaction strengths [8] . The 64 sequences we measured binding to covered all combinations of bases at positions , and in the mar binding site ( Figure 1 ) . The 5 transcription factor variants chosen were wild type MarA ( WQR ) , the most fit binder for the wild type consensus binding site ( RQR ) , a double mutant that binds to the wild type consensus ( RTR ) , a double mutant that activates the ‘GAA’ site ( SAR ) , and the most fit mutant for the binding site ‘GAC’ ( TRR ) . We did not obtain reliable binding data for SQR , the most fit mutant for ‘GAA’ , and therefore did not include it in this study . For each of these five transcription factor variants , we set the binding affinity of the strongest site to 1 and scaled the strength of all other sites relative to that ( Figure S1 ) . To identify sequences that are similarly bound for each mutant , we clustered the DNA binding sites according to their relative affinities using Cluster [41] ( Figure 4 ) . Additionally , we we generated energy-based position weight matrices and logos [42] ( Figure 5 ) , and calculated the degree of similarity between all matrices as Kullback-Leibler Divergences ( KLD ) using the program MatCompare [43] ( see Materials and Methods ) . A KLD generally indicates that two matrices are significantly similar , and a KLD of 0 indicates that they are identical . All measured binding affinities , position weight matrices , and pair-wise KLD values are reported in Table S1 . MITOMI data for wild type MarA are consistent with the MarA sequence logo ( Figure 1 ) . Three sequences are tightly bound , ‘GCA’‘ACA’‘CCA’ , as seen in natural sites . A single mutation from a Trp at position 42 to an Arg has a dramatic effect on the binding preferences of the factor ( Figure 5 , KLD = 1 . 53 ) . The RQR mutant still specifically recognizes ‘GCA’ , but with a 1 . 6 fold reduced affinity relative to its most tightly bound site ‘TCC’ . As with wild type MarA , RQR has a strong preference for ‘C’ at position , but overall RQR is a less specific binder; the information content ( ) [1] for positions to is 3 . 03 and 2 . 27 bits for WQR and RQR respectively ( Figure 5 , Table 2 ) . The 2 . 46 bit RTR logo is significantly similar to the RQR logo ( KLD = 0 . 15 ) , but shows a slight decrease in degeneracy at position , as well as a switch in preference for ‘G’ over ‘T’ at position . Interestingly , the RQR and RTR mutants maintained the same relative difference in affinity between the bound sequences ‘GCA’ , ‘ACA’ and ‘CCA’ as wild type ( for both , data not shown ) , suggesting that the core binding preferences of wild type are somehow preserved in these variants although they are no longer the highest affinity sites . SAR is the least specific of the variants ( bits ) . It shows a preference for ‘A’ or ‘G’ at position , and almost no preference at positions and 0 . It does not strongly bind ‘GAA’ , the site it was selected against . Conversely , TRR appears to only bind its selected target site ‘GAC’ ( Figure S1 ) . While TRR is specific for this sequence , the relative difference in binding strength between ‘GAC’ and the non-specific background ( ) is much less than observed for WQR , RQR and RTR ( Figure S1 ) . As the logos in Figure 5 are generated from the calculated differences in binding energy from the strongest bound site to all single base-pair mutants ( see Materials and Methods ) , a low would result in a logo with a weak equiprobable conservation of all non-specifically bound bases at each position as observed for TRR . Given the MITOMI data , we can test two assumptions that underlie most thermodynamic DNA binding models: ( 1 ) that the energetic contribution of each nucleotide at each position is independent of neighboring bases and ( 2 ) that this contribution is purely additive to the overall binding affinity [7] , [44] , [45] . Using Scan , an information theory based program that predicts binding affinities based on an independent and additive model , we calculated the predicted affinity for each protein mutant to all 64 sequences [44] , and plotted this against the corresponding measured of binding ( Figure 6 , see Materials and Methods ) . Theoretically sites with an bits are predicted to be bound non-specifically , as [9] , [44] . For all mutants , except for SAR , predicted binding strength is highly correlated with actual binding for sites bits ( blue sequences in Figure 6 ) , and is poorly correlated for sites bits ( red sequences in Figure 6 ) . The experimental measurement of binding affinity for weakly bound sites has previously been shown to be less accurate than for strongly bound ones [9] . Because of this , we are not surprised by the weak correlation for the sites with an bits . If these sequences are truly bound non-specifically though , we would also expect the slope of the regression line to be 0 . For WQR , RQR and RTR we observe a slightly negative slope ( , and respectively ) , which suggests that to a small degree , binding energy does change as a function of sequence ( bound specifically ) for a fraction of these sites . This is evident for RQR , where sites bits lie close to the regression line for the positively bound sequences ( Figure 6 ) . We expect the specific/non-specific boundary to be closer to bits for this binding domain . Likewise , for TRR the non-specific boundary is probably at bits , but this deviation from 0 bits can be explained by the low , and subsequently biased model for TRR as previously mentioned . To approximate the non-specific binding energy for each mutant , we determined the intercept of the positive and negative site regression lines ( Table 2 ) . SAR appears to be almost completely non-specific from the MITOMI data , and we are not confident in the identified boundary between specific and non-specific binding for this mutant . Surprisingly , there appears to be a di-nucleotide binding preference for the RTR mutant ( Figure 6 ) . RTR binds ‘GC-C’‘GC-T’‘GC-G’‘GC-A’ and ‘TA-C’‘TA-T’‘TA-G’‘TA-A’ with almost equivalent energies between sites that have the same nucleotide at the third position ( = 0 . 99 ) . A simple independent and additive model would predict that a single mutation of a ‘G’ to ‘T’ at position or a ‘C’ to ‘A’ at position would not affect the binding energy of the site . Indeed , ‘TC-C’‘TC-T’‘TC-G’‘TC-A’ and is highly correlated to the equivalent ‘GC-N’ and ‘TA-N’ sites ( = 0 . 84 and 0 . 91 respectively ) , but ‘GA-N’ sites are not correlated and all sites have a greater than the RTR non-specific binding threshold of 3 . 30 kJ/mol . This clearly violates a simple independence assumption . To identify the in vivo binding preferences of the 5 MarA protein variants , we generated a library of selection plasmids for each mutant where positions , , , 0 and in the mar binding site were randomized ( Figure 2 ) . We transformed N8453 cells with these libraries and competed them against each other in 5 ml LB+50 g/ml tetracycline+ L-arabinose for 24 hours . The competed populations were mini-prepped and sequenced in a single sequencing reaction ( Figure S2 ) . Sequence logos were generated for all mutants as described in Materials and Methods ( Figure 7 ) . Higher affinity binding sites should be more fit and represented at a higher frequency in the competed population [19] . While the relative peak height for a given base at a given position within the chromatogram is correlated with the base frequency in the population , it can be biased by the identify of the neighboring bases . Therefore , this is a semi-quantitative representation of positional nucleotide frequency . In vivo binding preferences identified by this selection method are consistent with our MITOMI results . The wild type MarA protein ( WQR ) requires a ‘C’ at position and shows a strong preference for a ‘G’ at position . Unlike the MITOMI data , there is more variability at position in the selected sites , resulting in a large Kullback-Leibler Divergence between the corresponding WQR logos of 1 . 67 , but a decrease in KLD between WQR and the RQR and RTR mutants ( Table S1 ) . The RQR in vivo selected sites have an increased variability at positions and relative to the MITOMI data , but overall the resulting logos are nearly identical ( KLD = 0 . 15 ) . Similar results are observed for RTR ( KLD = 0 . 15 ) , which only shows a slight decrease in degeneracy at position in the experimentally selected sites . Interestingly ‘A’ is not observed at position in the RTR in vivo sites , even though ‘TAA’ is tightly bound according to the MITOMI data . The SAR mutant shows substantially less variability in the in vivo binding site selection as compared to the MITOMI data; the for positions to = 4 . 66 and 0 . 80 bits respectively . The concentration of tetracycline used for selection , imposes an energetic minimum that the factor must bind its site above to be viable [19] . This lack of variability in the SAR in vivo binding site selection suggests that unlike WQR , RQR and RTR , few SAR sites are above this threshold ( i . e . weakly bound ) . SAR is the only mutant to show a strong preference for ‘G’ at position , while all other mutants preferred a cytosine there . Differences in the SAR binding preferences observed in vivo and in vitro may also be accounted for by the presence of a unfavorable ‘C’ at position in the MITOMI binding site library ( Figure 1 ) , which could significantly reduce the binding affinity of all sites . TRR binds to a single site , ‘GAC’ , as expected . Interestingly , we observed a wide range of degeneracy at position , which does not appear to be directly contacted by any of the varied residues . There is a preference for ‘A’ at this position for all mutants , and it is completely conserved for SAR and TRR . We expect that the amount of observed variation at is not dependent upon specific contacts at that base , but on the energetic contribution of the rest of the binding site . That is , weak binding at positions , and by residual differences requires a base with a higher affinity ( ‘A’ ) at position for the site to be sufficiently strong in this selection . This suggests that degeneracy at a single position in a site is not completely defined by the residue that contacts it , but by the energy of the other contacts in the site . To quantify the extent of overlap in sites specifically bound by all mutants in vivo , we calculated the predicted binding strength ( ) of each mutant to the 64 potential binding site variants at positions to , and directly compared these affinities ( Figure 8 ) . Since the RQR logo has the lowest information content , we compared all mutants to it . Sequences that fall in the upper right quadrants in Figure 8 are predicted to be specifically bound by the two mutants compared ( positive for both ) . Sites in the lower left are predicted to not be bound by either . The remaining quadrants contain sites that are only bound by one mutant . As the RQR and RTR logos are significantly similar ( KLD = 0 . 14 ) , it is not surprising that their predicted affinities are highly correlated ( ) . Only a few sequences specifically bound by RQR are not bound by RTR ( lower right quadrant ) and no unique sequences are bound by RTR ( upper left quadrant ) suggesting that RTR is merely binding a subset of the sites bound by RQR ( Figure 8A ) . A similar result is observed for WQR , except that it binds a further reduced subset of the specifically bound RQR sites . There is no overlap in specifically bound sites by SAR and TRR with RQR , suggesting that these bind a completely orthogonal set of sequences ( Figure 8B ) . To better understand how mutations in the binding domain affect the transcriptional activity of MarA , we measured the expression of tet under the control of wild type MarA ( WQR ) with 11 different binding sites , and under the control of RQR with 15 different binding sites using quantitative PCR ( Figure 9 ) . We chose binding sites for each variant that covered a range of binding strengths based on the MITOMI data . For convenience , we normalized the output so that the relative expression of the ‘GCA’ binding site by WQR is 1 . For the WQR binding sites , the expression data correlate well with binding site strength ( for all sites , for the 3 tightly bound sites ) . The non-specifically bound sites show minor variability in their measured output . The expression data for the RQR bound sites do correlate with binding affinity but not as well ( for all sites ) and we observed much more variability in the non-specifically bound sites . The transcriptional output from the highest affinity RQR site is almost twice that of the strongest WQR site , suggesting that functionally this mutant can access a much larger dynamic range of outputs .
It is becoming increasingly clear that differences in transcriptional regulation are an important driving force in species diversification and evolution [46] , [47] . Fine scale differences in the expression level of an individual gene can be easily achieved by mutations in transcription factor binding sites contained within the associated cis-regulatory region [19] . Larger scale effects on the transcriptional network , and subsequently cellular phenotype , can be accessed through mutations in transcription factor binding domains which will impact the expression levels of all genes within their regulons [48] . As the systematic effects of transcription factor mutations are more difficult to characterize , few experimental studies have been done to probe their evolvability [5] . Since both the informational and regulatory properties of a transcription factor are determined by its binding site energy distribution [1] , [20] , we developed an in vivo selection assay to select for variants with altered binding preferences that still maintain a physiologically relevant transcriptional activity . Further in vivo and in vitro characterization of a subset of these mutants revealed that a large range of binding preferences , information contents and activities could be accessed with a few mutations suggesting that transcriptional regulatory networks may be easily adaptable . One way in which regulatory networks are believed to evolve is through the duplication of an existing transcription factor gene that is subsequently selected to recognize a unique set of targets [49] , [50] . It is unclear how readily this can happen . Maerkl and Quake observed that a relatively limited range of binding preferences could be accessed by single mutations in the basic helix-loop-helix protein MAX [5] . For MarA , we observed that we could get an orthogonal regulator with two mutations . The double mutant TRR is the most dramatic example . It is absolutely specific for ‘GAC’ , which no other variant specifically bound ( Figure S1 , Figure 8B ) . Likewise SAR bound its own unique set of sites that do not overlap wild type ( Figure 8B ) . Interestingly , both SAR and TRR have a lower for their highest affinity sites compared to mutants that bind the wild type consensus sequence . This suggests that a novel regulator may emerge or be engineered relatively easily , but may be initially limited in its range of potential activities . Gene duplication may not be the only pathway by which orthogonal regulators can evolve . WQR , RQR and RTR appear to have largely overlapping binding sites , where RTR and RQR have an incrementally increasing number of specifically bound sites ( Figure 8A ) . This suggests that a transcription factor could evolve to have an increased or decreased information content ( become more or less specific ) , while still maintaining the majority of its binding targets . An orthogonal regulator could potentially evolve through an intermediate with broader specificity like RQR or RTR ( Figure 10 ) . A mutation of this type would impact the relative expression levels of the genes controlled by the transcription factor , as seen in Figure 9 , and initially compromise the fitness of the cell [21] , but would presumably have a significant advantage over a mutation that leads to the loss of potential targets . Further selection could re-specify the transcription factor after becoming promiscuous to regulate a new set of sequences . As this broadening of specificity can be done relatively easily ( WQR can be converted to RQR by a single nucleotide mutation ) , this pathway may be highly tractable by evolution and useful for engineering regulatory networks . As previously mentioned , the information content of a transcription factor's binding sites is highly correlated to the amount of information needed to specifically locate its binding sites in the genome for bacterial systems [1] . This suggests that as the size of a bacterial factor's regulon increases or decreases , so does the selective pressure on binding site information . The decrease in information from WQR to RTR to RQR , also suggests that a transcription factor can easily evolve to expand or contract the size of its regulon . The overlap in binding sites between WQR , RQR and RTR may not be surprising as all were selected to bind the wild type consensus sequence ‘GCA’ . The dominant feature for these three mutants is a highly conserved ‘C’ at position ( Figure 5 , Figure 7 ) . One possibility is that the for this base is increased from WQR to RTR to RQR , and a stronger individual contact here compensates for a greater number of energetically unfavorable mismatches at positions and , decreasing the information content ( Table 2 ) . Interestingly , expanding the number of specifically bound sites for RQR also expands the range of transcriptional outputs nearly two fold ( Figure 9 ) . If RQR has a much greater range of potential activities , and largely similar binding preferences to wild type MarA ( WQR ) , why is it not observed in nature ? WQR has a greater information content to ratio than both RQR and RTR ( Table 2 ) , suggesting that it encodes the fewest number of specifically bound sites for its range of binding energies ( Table 2 ) . It also appears to have a large energetic gap between its three highest affinity sites and the background , which the other variants lack ( Figure 4 ) . These properties of the wild type MarA binding site distribution , and not just overall affinity , may be evolutionarily advantageous and thus selected , as an increased for all sites would decrease the likelihood of the factor binding the wrong location [51] , [52] , and fewer recognized sites would decrease the probability of spurious sites emerging in the genome [53] . Directly assaying the global effects of these mutations by RNA profiling and chromatin immunoprecipitation would dramatically improve our understanding of their cellular implications .
We modified the plasmid-based selection system described in [19] to select for and characterize MarA variants that have altered binding preferences ( Figure 2 ) . Griffith et al . generated an L-arabinose inducible MarA expression pBAD18 variant ( pBAD18-hisMarA ) [34] . We cloned the marA gene , the AraC regulated promoter and the araC gene from this plasmid into our pBR322-based selection system , allowing for us to control the expression of MarA by the addition of L-arabinose ( Figure 2A ) . An XhoI site was introduced about 10 residues upstream of the start of helix 3 by modifying the ‘CTG’ codon encoding the leucine at residue 30 to the synonymous codon ‘CTC’ by QuickChange [54] ( Figure 2 ) . An AgeI site exists immediately downstream of helix 3 . To make this a unique restriction site , we removed a second AgeI site present in a non-regulatory region upstream of the marA gene by QuickChange . To generate variants of the MarA-activated tet promoter ( Figure 2B ) , the selection plasmid was simultaneously digested with EcoRI and ClaI restriction enzymes for 2 hours at 37C ( NEB ) . Inserted promoter variants and libraries were generated by DNA synthesis ( Integrated DNA Technologies ) . We synthesized both strands of the DNA , and designed oligos to contain the appropriate overhang to be cloned into the EcoRI and ClaI sites . Digested plasmid and synthesized inserts were ligated overnight at 14C using T4 DNA ligase ( NEB ) . To generate binding domain variants ( Figure 2C ) , we used a similar method . Plasmid was digested with XhoI and AgeI simultaneously for 2 hours at 37C ( NEB ) . The digested plasmid was gel purified and ligated to complementary synthesized inserts that had XhoI and AgeI overhangs . To randomize the residues 42 , 45 and 46 , we synthesized the oligos with an equal mixture of all four bases at the first two positions of the codon , and an equal mixture of ‘G’ and ‘T’ at the third position of the codon to generate a more equal distribution of amino acids at each position . The ligated promoter and binding domain libraries were transformed into DH10B cells , recovered for 1 hour in LB , and plated on 100 ml LB+30 g/ml ampicillin plates . Cells were suspended from the plates in 10 ml LB and mini-prepped using the QIAquick miniprep kit ( Qiagen ) . To prevent activation of the tet gene by the endogenous MarA , Rob or SoxS proteins , selections were performed in the E . coli strain N8453 ( mar , sox-8::cat , rob::kan variant of GC4468 ) prepared by J . L . Rosner and R . G . Martin and obtained from R . E . Wolf . To identify a binding site that was only functional when activated , we transformed a library with a variant of the promoter construct shown in Figure 2B that contain the mar MarA binding site ( Figure 1 ) and a randomized hexamer . These plasmids also contain the wild type MarA protein . The library was transformed in N8453 cells by electroporation , recovered for 1 hour in 500 l LB at 37C , shaken at 225 rpm and plated on 5 g/ml tetracycline LB plates + L-arabinose . Individual colonies were picked and streaked on on 10 , 15 and 20 g/ml tetracycline LB plates+/− L-arabinose . Colonies that only grew on L-arabinose containing plates were sequenced . To identify binding domain variants that specifically bound different DNA sequences , libraries were transformed into N8453 cells by electroporation , recovered for 1 hour in 500 l LB at 37C , shaken at 225 rpm and plated on 100 ml LB plates containing 30 g/ml ampicillin + L-arabinose . Colonies that grew on the plates overnight were suspended in 10 ml LB containing 30 g/ml ampicillin + L-arabinose and grown at 37C , shaken at 225 rpm for 8 hours . 70 l of these cells were then plated on 25 ml LB agar plates containing 20 or 30 g/ml of tetracycline + L-arabinose . Individual colonies were picked , grown overnight , miniprepped by the QIAquick miniprep kit and sequenced . To identify the binding domain for each site that could produce the most tet transcript , libraries were transformed by electroporation into N8453 cells and plated on 5 g/ml tetracycline LB plates and grown overnight . These colonies were suspended in 5 ml LB with 5 g/ml tetracycline + L-arabinose and allowed to grow in liquid culture overnight . The following morning fresh 5 ml 30 g/ml tetracycline + L-arabinose cultures were inoculated with 100 l of the overnight culture and competed for 24 h . The competed library was miniprepped by a QIAquick miniprep kit , transformed into DH10B cells and plated on 30 g/ml ampicillin plates . Individual colonies were picked , grown up overnight , miniprepped and sequenced as described above . Binding site competitions for the 5 MarA selected variants were performed as described previously [19] , except that the libraries were transformed into N84533 cells and all media contained L-arabinose . Libraries were competed in 50 g/ml tetracycline for 24 hours and sequenced on a 96 capillary 3730xl DNA Analyzer ( Applied Biosystems ) . Nucleotide variation in the population of competed promoters was visualized using Finch TV ( Geospiza Inc ) . To generate sequence logos from these data ( Figure 7 ) , we measured the peak height of each base at each position in a chromatogram ( Figure S2 ) , and divided this height by the summed heights of all peaks at the position to calculate a relative nucleotide frequency . A standard position weight matrix was generate from these frequencies , and represented as a sequence logo using the Delila programs [39] . MITOMI ( Mechanically Induced Trapping of Molecular Interactions ) was performed according to Maerkl et al . [8] . The 64 variants of the mar binding site ( Figure 1 ) were synthesized by Integrated DNA Technologies . In vitro transcription and translation was done using the RTS E . coli HY kit ( Roche ) . Fluorescently labeled lysines were incorporated into the protein during in vitro translation by addition of tRNA-lys-bodipy-fl ( Promega ) . Protein and DNA fluorescence was measured using Genepix ( Molecular Devices ) . The of binding for each variant to each binding site was calculated using , where is the ideal gas constant , is the temperature of the experiment ( 295K ) and is the association constant as measured by MITOMI . The of binding was calculated for each binding site by subtracting the of binding for that site from the of binding from the highest affinity site for a protein variant . To generate the energy logos , we calculated a matrix for each variant by determining the difference in binding energy between the strongest bound site for that factor ( the consensus site ) and all single base-pair mutants . For example , to calculate the relative weights of each base at position for wild type MarA , we subtracted the measured binding energies of ‘ACA’ , ‘CCA’ , ‘GCA’ and ‘TCA’ from ‘GCA’ . We used the enoLogos webserver to convert these energies into a log-likelihood matrix [42] and generated logos using the Delila programs [39] . The matrices for all logos are given in Table S1 . To quantify the similarity in binding preferences between MarA variants , we used the program MatCompare to calculate the Kullback-Leiber Divergence ( KLD ) between the inferred sequence logos [43] . All pair-wise KLD values are reported in Table S1 . The relative affinity ( ) of a given binding model to all DNA sequences was calculated using the information theory based program Scan [44] . A library of mar binding sites was cloned into plasmids containing either the wild type MarA protein , or the RQR mutant . The library was transformed into N8453 cells , plated on 30 g/ml ampicillin and grown overnight . Individual colonies were grown overnight in 5 ml LB+30 g/ml ampicillin . Glycerol was added to 200 l of cells to a final concentration of 20% and stored at C . The remaining culture was mini-prepped and sequenced to determine which binding site was present . 11 different binding sites covering a range of affinities as determined by MITOMI were chosen for wild type MarA and 15 were chosen for the RQR mutant . These were not the same sites for both factors . Cultures were inoculated with the frozen samples and grown overnight in 5 ml LB cultures with 30 g/ml ampicillin and L-arabinose . A fresh 5 ml LB+30 g/ml ampicillin+L-arabinose culture was started at and grown to an . cells were added to RNAprotect Bacteria reagent ( Qiagen ) , and RNA was purified using the RNeasy Mini kit with on-column DNase digestion ( Qiagen ) . cDNA was made from 2 g of RNA using the Superscript III RT kit ( Invitrogen ) . QPCR was performed with the SYBR green mix from NEB . QPCR primers specific to the tet and marA gene were both used . The relative expression of the tet gene was determined by the ratio of tet abundance over marA abundance for each sample . | The main role of transcription factors is to modulate the expression levels of functionally related genes in response to environmental and cellular cues . For this process to be precise , the transcription factor needs to locate and bind specific DNA sequences in the genome and needs to bind these sites with a strength that appropriately adjusts the amount of gene expressed . Both specific protein–DNA interactions and transcription factor activity are intimately coupled , because they are both dependent upon the biochemical properties of the DNA–binding domain . Here we experimentally probe how variable these properties are using a novel in vivo selection assay . We observed that the specific binding preferences for the transcription factor MarA and its transcriptional activity can be altered over a large range with a few mutations and that selection on one function will impact the other . This work helps us to better understand the mechanism of transcriptional regulation and its evolution , and may prove useful for the engineering of transcription factors and regulatory networks . |
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Drosophila NURF is an ISWI–containing chromatin remodeling complex that catalyzes ATP–dependent nucleosome sliding . By sliding nucleosomes , NURF can alter chromatin structure and regulate transcription . NURF301/BPTF is the only NURF–specific subunit of NURF and is instrumental in recruiting the complex to target genes . Here we demonstrate that three NURF301 isoforms are expressed and that these encode functionally distinct NURF chromatin remodeling complexes . Full-length NURF301 contains a C-terminal bromodomain and juxtaposed PHD finger that bind histone H3 trimethylated at Lys4 ( H3K4me3 ) and histone H4 acetylated at Lys16 ( H4K16Ac ) respectively . However , a NURF301 isoform that lacks these C-terminal domains is also detected . This truncated NURF301 isoform assembles a complex containing ISWI , NURF55 , and NURF38 , indicating that a second class of NURF remodeling complex , deficient in H3K4me3 and H4K16Ac recognition , exists . By comparing microarray expression profiles and phenotypes of null Nurf301 mutants with mutants that remove the C-terminal PHD fingers and bromodomain , we show that full-length NURF301 is not essential for correct expression of the majority of NURF gene targets in larvae . However , full-length NURF301 is required for spermatogenesis . Mutants that lack full-length NURF exhibit a spermatocyte arrest phenotype and fail to express a subset of spermatid differentiation genes . Our data reveal that variants of the NURF ATP–dependent chromatin remodeling complex that recognize post-translational histone modifications are important regulators of primary spermatocyte differentiation in Drosophila .
The organization of DNA in nucleosomes has a vital function in regulating whether genetic information can be expressed . By altering nucleosome dynamics , genes can be rendered inaccessible or made available to the transcription machinery . There are a number of mechanisms by which altered chromatin states can be induced . Post-translational modification of the histone tails can change associations between histones and DNA and between neighboring nucleosomes , altering chromatin flexibility and conformation ( reviewed in [1]–[3] ) . A second approach is through the deployment of ATP-dependent chromatin remodeling factors ( reviewed in [4] , [5] ) . These multi-subunit protein complexes utilize the energy of ATP hydrolysis to alter the dynamic properties of nucleosomes . Traditionally they are divided into broad families depending on the catalytic subunit utilized , and their mechanism of altering nucleosomes - either nucleosome eviction , sliding or variant histone replacement . The principal activity of the ISWI family of ATP-dependent chromatin remodeling factors is energy-dependent nucleosome sliding [6]–[8] . The nucleosome remodeling factor ( NURF ) is one of the founding members of this family . By sliding nucleosomes , NURF can alternatively expose or occlude transcription factor binding sites , activating or repressing transcription . Consistent with this , studies of mutants that lack either the NURF-specific large subunit NURF301 , or the catalytic ISWI ATPase subunit have shown that NURF is required for both transcription activation and repression in vivo [9]–[12] . Gene targets that require NURF for activation include the homeotic selector genes engrailed , Ultrabithorax , ecdysone responsive genes and the roX non-coding RNA [9] , [12] , [13] , [14] . In contrast , NURF has been shown to regulate Drosophila innate immunity by repressing targets of the JAK/STAT pathway [15] . Although NURF is composed of four subunits , only the largest subunit ( in Drosophila NURF301; in humans bromodomain and PHD finger transcription factor ( BPTF ) ) is specific to NURF [8] , [16] . All other subunits are present in a number of other chromatin remodeling complexes . This suggests that targeting of NURF to promoters is likely to be a function of NURF301/BPTF . Consistent with this , NURF301/BPTF contains PHD ( plant homeo domain ) fingers and a bromodomain , motifs that have the potential to recognize specific histone modifications . The C-terminal PHD finger of BPTF/NURF binds the amino terminal tail of histone H3 trimethylated at lysine position 4 ( H3K4me3 ) and has been shown to recruit NURF to sites of H3K4me3 enrichment in humans [17] . In addition , structural studies of the bromodomain of BPTF predict that it can recognize histone H4 acetylated at lysine position 16 ( H4K16Ac ) [18] . This suggests that NURF may bind to sites of H4K16Ac , although in vitro studies indicate that H4K16Ac may also inhibit catalytic activity of the ISWI subunit of NURF and hence NURF-mediated chromatin remodeling [19] , [20] . Alternative splicing presents another mechanism by which the activity of chromatin modifying and remodeling factors may be regulated . Sequence analysis of 160 chromatin-associated proteins has revealed that 28% of these encode alternative splice forms in which domains critical for function are substituted [21] . Consistent with this , Shiekhattar and colleagues [22] have previously reported that function of human NURF can be regulated by the incorporation of a catalytically inactive isoform of the SNF2L subunit . In addition , splice variants of SNF2L that lack nuclear localization signals have also been identified [23] . This raises the possibility that multiple distinct NURF complexes exist with altered functions conferred by the incorporation of splice variants of the major subunits . In this report we examine whether Drosophila NURF is subject to alternative splicing . We show that three NURF301 isoforms occur , one of which lacks the PHD fingers and bromodomains that recognize the H3K4me3 and H4K16Ac marks . Using whole genome expression profiling we identify NURF target genes that require these domains and hence recognition of histone tail modifications . Our results indicate that NURF complexes that recognize H3K4me3 and H4K16Ac are not required for correct expression of the majority of NURF gene targets in larvae , but are obligatory for NURF function in spermatogenesis . Our data reveals that alternative splicing of the large specificity subunit NURF301 allows the elaboration of functionally distinct variants of the NURF ATP-dependent chromatin remodeling complex .
Inspection of the Drosophila genome sequence ( Release 5 . 2 annotation , http://flybase . bio . indiana . edu/ ) revealed that the locus encoding the NURF specificity subunit ( Nurf301 ) potentially generates three isoforms by alternative splicing ( Figure 1A ) . Nurf301-A corresponds to the previously described full-length isoform [8] . Nurf301-B contains an additional intron that deletes 60 bp from exon 6 . Most interestingly , Nurf301-C is a truncated isoform that terminates after the 7th exon . This truncation removes the C-terminal PHD fingers and bromodomain that are present in Nurf301-A and Nurf301-B . These domains are predicted to bind histone modifications , raising the possibility that distinct classes of NURF complexes exist , one built around NURF301-A or NURF301-B that can recognize the H3K4me3 and H4K16Ac histone modifications , and a second founded on NURF301-C that cannot . As a first step to verify this we confirmed these isoform predictions . RT-PCR using isoform-specific primer sets revealed that all three isoforms were expressed ( Figure 1B ) . No apparent differences in the distribution of expression were observed . Further support was provided by Northern analysis , which detected transcripts corresponding to the Nurf301-A , Nurf301-B and Nurf301-C isoforms ( Figure S1A ) . Additional evidence for the existence of the Nurf301-C isoform was provided by the presence of an EST ( LD30146 ) within the Berkeley Drosophila Genome Project collection that corresponds to the 3′ region of Nurf301-C ( Figure 1A ) . Moreover , sequence comparison of the Nurf301 genomic interval flanking exon 7 from Drosophila melanogaster , D . pseudobscura and D . mojavensis reveals nucleotide sequence conservation typical of coding sequence up to the predicted stop codon of Nurf301-C in all species ( Figure S1C ) . Finally , western analysis , using an anti-NURF301 antibody that recognizes an epitope shared by all predicted isoforms [15] , demonstrated that the transcripts encode bona fide protein isoforms . In extracts of third instar larval imaginal discs , protein species migrating at 301 kDa and 240 kDa were detected ( Figure 1C , lane 1 ) . These bands correspond with the predicted sizes of the NURF301-A and NURF301-B isoforms , which co-migrate , and the NURF301-C isoform . Neither band was observed in extracts of null Nurf301 mutants that disrupt all isoforms ( Figure 1C , lane 2 ) . We next verified which histone modifications were bound by the C-terminal PHD finger and bromodomain of full-length NURF301 . Single domains were expressed and purified as GST-fusion proteins and tested for the ability to bind either unmodified histone H3 and H4 tail peptides , H3K4me3 tail peptide or H4K16Ac tail peptide . Pull-downs were performed using either the peptides ( Figure 1D ) or GST-fusion proteins ( Figure 1E ) as baits . As observed previously [17] , the C-terminal PHD finger of NURF301 was able to bind H3K4me3 tail peptide but did not bind unmodified H3 tail peptide ( GST-PHD in Figure 1D and 1E ) . Likewise the bromodomain was able to bind H4K16Ac tail peptide but did not bind unmodified H4 tail peptide ( GST-Bromo in Figure 1D and 1E ) . Our data indicate that NURF complexes built around full-length NURF301-A or around NURF301-B should have the ability to bind both H3K4me3 and H4K16Ac histone modifications . However , NURF complexes composed of the NURF301-C isoform will lack this binding ability . To confirm whether truncated NURF301 isoforms would be able to assemble a NURF complex , we determined association with the three other subunits of the NURF complex: ISWI , NURF55 and NURF38 . Insect cells were co-infected with baculoviruses that express ISWI , NURF55 , NURF38 and a FLAG-tagged C-terminally truncated version of NURF301 ( FLAG-NURF301ΔC ) . FLAG-NURF301ΔC was then purified using anti-FLAG beads and association of other NURF subunits assayed by Western blotting . As shown in Figure 1F , ISWI , NURF55 and NURF38 co-purified with the truncated NUR301ΔC isoform . This defines a minimal ISWI- , NURF55- and NURF38-interacting region that is present within all NURF301 isoforms . It has previously been shown that the ISWI subunit alone displays remodeling activity , and that this does not require the presence of the other NURF subunits [24] . Thus , by associating with ISWI , all NURF301 isoforms should nucleate the formation of functional chromatin remodeling complexes . To distinguish these alternative NURF complexes , we have designated them NURF-A , NURF-B and NURF-C , corresponding to the NURF301 isoform utilized . For example , NURF-C indicates the NURF complex constituted from the short PHD finger- and bromodomain-deficient isoform NURF301-C . To address the function of these complexes , in particular to distinguish functions of the full-length NURF-A and NURF-B complexes from the NURF-C complex , we have made use of a cluster of EMS-induced mutations that truncate Nurf301 at exon 5 . As shown in Figure 1A , these mutations , which we term Nurf301ΔC mutations , generate shortened NURF301 isoforms that contain all of the conserved motifs present in NURF301 except the C-terminal PHD fingers and bromodomain . These mutations will ablate function of NURF301-A and NURF301-B while still expressing a protein variant ( NURF301ΔC ) that is a reasonable model for NURF301-C . As described previously [13] , Nurf3014 corresponds to a splice donor mutation that truncates NURF301 after amino acid 1528 , while Nurf30110 and Nurf30112 are amino acid to stop mutations that truncate NURF301 at amino acids 1557 and 1532 respectively . Western analysis of extracts of Nurf301ΔC mutants confirmed that they encode a truncated , but stable , protein of the expected molecular mass of 180 kDa ( Figure S1B ) . Comparison of these Nurf301ΔC mutants ( that lack NURF301-A and NURF301-B ) with either wild-type animals or a null Nurf301 mutant ( Nurf3012 ) that disrupts all NURF301 isoforms , allows the reasonable discrimination of the relative requirements of the full-length ( NURF-A and NURF-B ) and NURF-C complexes . As a first step , we used whole genome expression profiling to identify gene targets that show altered expression in either the null Nurf301 or Nurf301ΔC mutants relative to wild-type . mRNA was isolated from third instar larvae of either Nurf301 mutant strain and from the parental isogenic strain in which these mutants were generated . Samples were labeled and hybridized to Affymetrix Drosophila Genome 2 . 0 arrays . We then used the limma package [25] to determine genes with a statistically significant ( P<0 . 01 ) change in expression relative to the wild-type control in either Nurf301 mutant . As shown in Figure 2A , Only 20% ( 187 ) of the genes that were up-regulated , and 21% ( 136 ) of the genes that were down-regulated in null Nurf301 mutants were similarly changed in Nurf301ΔC mutants . This indicates that NURF-C is sufficient for expression of the majority of NURF target genes and that recognition of H3K4me3 and H4K16Ac is only obligatory for a subset of these in larvae . RT-PCR for selected transcripts with the most statistically-significant changes in expression confirmed the microarray results . As shown in Figure 2B , NURF targets that required full-length NURF301 included Lsp1γ , CG6296 and CG11893 . Expression of these genes was reduced in both mutants ( Figure 2B ) . Interestingly , real-time PCR analysis of transcript abundance ( Figure S2 ) indicated that levels of CG6296 and CG11893 were reduced more in Nurf301ΔC mutants than null Nurf301 mutants , raising the possibility of antagonistic functions of full-length and NURF-C complexes . In contrast , expression of ImpE2 and CG9036 was unaffected in Nurf301ΔC mutants ( Figure 2B ) , indicating that these are targets of NURF-C alone . Similarly , up-regulation of CG1304 , CG8942 and CG18186 only occurred in null Nurf301 mutants and was not observed in Nurf301ΔC mutants , suggesting that NURF-C is sufficient for repression of these . Further evidence that NURF-C is sufficient , and that recognition of H3K4me3 and H4K16Ac is not obligatory for correct expression of the majority of NURF target genes , was provided hierarchical clustering of the microarray data . The top 50 genes that showed the most statistically-significant difference in expression relative to wild-type ( lowest adjusted P-value ) in either the Nurf301ΔC ( Figure 2C ) or null Nurf301 mutants ( Figure 2D ) were selected . Genes and samples were then clustered according to expression in all three genotypes . This revealed that only 16% of genes that show altered expression when all NURF301 isoforms are removed ( null mutants ) were similarly affected when NURF-C is present ( Nurf301ΔC mutants ) . Conversely , most genes with altered expression in Nurf301ΔC mutants were similarly changed in null Nurf301 mutants . Previously we reported that null Nurf301 mutants exhibit de-regulated immune responses and develop inflammatory ( melanotic ) tumours [10] , [15] . In these studies it was shown that NURF acts as a co-repressor of a subset of JAK/STAT target genes [15] . Therefore we investigated whether repression of the JAK/STAT pathway requires interpretation of histone modifications by full-length NURF301 . First we determined whether any of the genes we previously showed to be regulated by NURF and the JAK/STAT pathway [15] were also affected in Nurf301ΔC mutants . Analysis of Nurf301ΔC microarray data revealed that just 14 out of the 119 NURF and JAK/STAT target genes identified before show any significant alterations in expression ( change P value<0 . 05 and fold change +/−2 . 5 ) , indicating that NURF301-C is sufficient for repression of the majority of JAK/STAT targets . The failure to induce the JAK/STAT pathway in Nurf301ΔC mutants was confirmed by determining lamellocyte numbers . In null Nurf301 mutants over-activation of the JAK/STAT pathway leads to the differentiation of large numbers lamellocytes , usually a rare hemocyte cell type ( compare Figure 3A and 3C ) , with lamellocyte frequency increasing from 0 . 44% to 57% ( Figure 3D ) . However , in Nurf301ΔC mutants ( Figure 3B ) lamellocyte numbers did not increase , exhibiting an average lamellocyte frequency of 1 . 4% , not statistically significant different from wild-type lamellocyte frequencies . As additional proof that repression of the JAK/STAT pathway does not require the NURF-A and NURF-B complexes , we examined whether Nurf301ΔC mutations interact genetically with the JAK/STAT pathway . A gain-of-function mutation in the Drosophila JAK ( hopTum ) induces temperature dependant constitutive activation of the JAK/STAT pathway causing the formation of melanotic tumors [26] , [27] . As we have reported previously [10] , null Nurf301 mutants are genetic enhancers of hopTum , increasing tumor incidence from 14 . 7% to 65 . 3% . In contrast Nurf301ΔC mutants were not enhancers of the hopTum mutation ( Figure 3E ) . Finally , we compared melanotic tumour frequencies in all Nurf301 mutant larvae . The development of inflammatory melanotic tumours is the ultimate morphological outcome of constitutive activation of the JAK/STAT pathway . As shown in Figure 3F , almost 100% of null Nurf301 mutants developed melanotic tumours . Conversely , only 6 . 6% of null Nurf301/Nurf301ΔC transheterozygous animals or 7 . 5% of Nurf301ΔC homozygous mutant larvae developed tumours . This is consistent with the reduced activation of immune targets in Nurf301ΔC observed during microarray analysis and the failure to increase lamellocyte numbers or activate the JAK/STAT pathway significantly . Taken together , these results indicate that repression of the JAK/STAT pathway by NURF does not require interpretation of histone modifications by the NURF-A and NURF-B complexes . Whole genome expression profiling data from larval stages indicated that correct interpretation of histone modification marks is required for only a subset of NURF function at larval stages . Indeed , unlike null Nurf301 mutants , Nurf301ΔC mutants are viable until adult stages . Interestingly , however , Nurf301ΔC mutants are both male and female sterile suggesting that full length NURF301 is obligatory for gametogenesis . To identify the defect in spermatogenesis , testes were dissected from wild-type and Nurf301ΔC mutant males and examined by phase contrast microscopy . As reviewed in Fuller ( 1993 ) [28] , gonial cells arise from stem cells located at the tip of the testis surrounding a specialized structure called the hub . Gonial cells undergo four transit-amplifying mitotic divisions to yield 16 primary spermatocytes . These cells then enter an extended G2 phase during which cell volume increases and transcription of genes required for meiosis and post-meiotic spermatid differentiation occurs . After two meiotic divisions spermatid differentiation occurs with the eventual production of motile sperm . As shown in Figure 4A , bundles of differentiated sperm were easily resolved by phase microscopy in live wild-type testes . However , in Nurf301ΔC mutant testes , no sperm bundles were observed indicating that mature sperm were not formed ( Figure 4B ) . Instead , large numbers of primary spermatocytes accumulated , suggesting that there is a defect in further differentiation and meiotic division of primary spermatocytes . This was confirmed by staining of testes with the DNA dye Hoechst 33342 to resolve nuclear morphology . In wild-type primary spermatocytes , the chromosome bivalents formed three distinct domains located near the nuclear periphery ( Figure 4C ) . In Nurf301ΔC mutants the bivalents formed three distinct territories but the chromosomes appeared more compact ( Figure 4D ) and progressive separation and fragmentation of the bivalents occurred ( Figure 4E and 4F ) . In rare cases primary spermatocytes in Nurf301ΔC mutants attempted the two meiotic divisions that generate spermatids . However , as shown in Figure 4H nuclei were highly disrupted and fragmented compared with wild-type spermatid nuclei ( Figure 4G ) . The defects that occur in primary spermatocyte differentiation in Nurf301ΔC mutants were strikingly similar to those that occur in a class of male sterile mutants known as meiotic arrest mutants . Mutants in the genes spermatocyte arrest ( sa ) , cannonball ( can ) , always early ( aly ) and meiosis I arrest ( mia ) result in the accumulation of mature primary spermatocytes and the absence of post-meiotic phases [29] . These gene products act to coordinate spermatid differentiation and meiotic cell cycle , with all four controlling transcription of spermatid differentiation genes in primary spermatocytes and aly also required for transcription of cyclin B and thus meiotic entry [30] . The similarity of the testes phenotypes of the Nurf301ΔC mutants and the meiotic arrest mutants led us to determine whether full-length NURF301 was also required for expression of spermatid differentiation genes and meiotic cell cycle regulators . As shown in Figure 5A , we first validated that all three NURF isoforms were expressed in testes . RT-PCR confirmed that the full-length Nurf301-A and Nurf301-B transcripts were expressed in testes , as was the Nurf301-C isoform that lacks the bromodomain and PHD fingers . We next tested whether transcription of meiotic cell cycle regulators was disrupted in the Nurf301ΔC mutants . Unlike aly mutants , in which transcription of cyclin B ( cycB ) is lost , cycB transcription in Nurf301ΔC mutants was unchanged relative to wild-type testes , as was transcription of twine and boule ( bol ) ( Figure 5B ) . We then assessed whether transcription of any of the spermatid differentiation genes known to be affected in can , sa , and mia mutants required NURF for expression . As shown in Figure 5B , of the genes examined , only expression of fuzzy onions ( fzo ) was reduced in Nurf301ΔC mutant testes when compared with wild-type testes . Moreover unlike can , sa , and mia mutants in which protein levels of Bol are reduced , in Nurf301ΔC mutant testes Bol levels were unaffected ( Figure S3C ) . These results were also confirmed by real-time analysis of transcript abundance ( Figure S4 ) . Failure to express fzo suggests that NURF-A is required to regulate expression of a subset of spermatid differentiation genes . However loss of fzo expression does not account for the meiotic arrest phenotype seen in Nurf301ΔC mutants , as fzo mutants show no defects in meiosis , only exhibiting a defect at later stages of spermiogenesis [31] . We thus examined if protein expression of any of the meiotic cell cycle regulators was affected in Nurf301ΔC mutants . Interestingly staining with antibodies against Cyclin B , revealed that levels of Cyclin B protein were decreased in primary spermatocytes of Nurf301ΔC mutants compared with wild-type primary spermatocytes ( Figure S3 ) . The failure to express Cyclin B protein is predicted to arrest spermatocytes at the G2-M transition . As cycB transcript levels are unaffected in Nurf301ΔC mutants ( Figure 5B ) , regulation is likely to occur through control of protein stability or protein translation . Sugimura and Lilly ( 2006 ) [32] have reported that Cyclin B protein levels can be regulated in female meiosis by the translational inhibitor Bruno ( Bru ) , which is encoded by the gene arrest ( aret ) . In Drosophila three Bruno family members occur . aret and bruno-2 ( bru-2 ) are located adjacent to each other on chromosome 2 L , while bruno-3 ( bru-3 ) is located on chromosome 3 L ( Figure S5 ) . All are RRM ( RNA-recognition motif ) containing proteins , with Bru and Bruno-2 ( Bru-2 ) being most similar [33] , [34] . Interestingly , we observed previously by whole genome expression profiling that levels of Bru family members were up-regulated in Nurf301 mutant tissues ( S . Y . K and P . B . , unpublished data ) . We therefore examined whether levels of Bru or Bru-2 were up-regulated in Nurf301ΔC mutants testes . Transcript levels of bru-3 were unaffected in Nurf301ΔC mutant testes ( data not shown ) . However , as shown in Figure 5C , both aret ( Bru ) and bru-2 transcript levels were elevated in mutant testes . Elevation of transcript levels was specific to the Bruno family members and not a general feature of this chromosome interval as levels of the flanking gene ( CG17218 ) and the intervening gene ( CG31862 ) were unchanged ( Figure 5C ) . In adults , normally four aret transcripts can be detected: three female-specific transcripts and a single male specific transcript [35] . Using primers that detect only the male-specific Bru transcript ( aret-RB ) we observed that levels of the male-specific transcript were elevated in Nurf301ΔC mutant testes . We conclude that the block to meiotic entry in Nurf301ΔC mutant testes is mediated by translational repression of Cyclin B expression caused by up-regulation of the Bru translational inhibitors . Next we analyzed whether regulation of fzo by NURF is direct and whether NURF binding coincides with histone modifications . We assayed NURF binding by chromatin immunoprecipitation ( ChIP ) using antibodies against NURF301 and were able to detect binding of NURF to fzo in testes ( Figure 6 ) . NURF-binding overlapped the promoter and transcription start site of fzo ( Figure 6B , primer sets 2 and 3 ) but was not detected in the far upstream region ( primer set 1 ) , coding region ( primer set 4 ) or 3′UTR ( primer set 5 ) . As shown in Figure 6C , NURF binding overlapped histone modifications bound by the PHD finger ( H3K4me3 ) and bromodomain ( H4K16Ac ) of NURF301 . We observed that H3K4me3 is detected in the vicinity of the transcription start site ( Figure 6C , primer sets 2 and 3 ) and peaks downstream of the start site ( primer set 4 ) . Only low levels of H3K4me3 were detected in the far upstream region ( primer set 1 ) or 3′UTR ( primer set 5 ) . H4K16Ac distribution showed a similar profile , however the peak in this modification occurred at the transcription start site ( Figure 6C , primer sets 2 and 3 ) and then declined in the body of the gene ( primer sets , 4 and 5 ) . In Figure 6D ChIP signal intensities for NURF , H3K4me3 and H4K16Ac are plotted relative to the corresponding input signals . This data confirmed that NURF-binding overlaps regions accumulating the H3K4me3 and H4K16Ac modifications . Finally we examined distribution of NURF301 in testes by immunostaining using antibodies that recognize all NURF301 isoforms . As shown in Figure 7 , nuclear NURF301 could be detected in spermatocytes , but interestingly distribution within the nucleus was dynamic . In early stage primary spermatocytes , NURF301 staining could be detected throughout the nucleus ( Figure 7A ) . However , during maturation of the primary spermatocytes NURF301 began to accumulate preferentially on the bivalents ( Figure 7A , mid ) until finally NURF301 was exclusively detected on the bivalents ( Figure 7A , late ) . Interestingly , NURF301 was only enriched on two of the three bivalents ( closed arrowheads in Figure 7A ) . The three bivalents correspond to the second , third , and X and Y chromosomes respectively . The paired second and third chromosomes appear larger and denser than the sex chromatin that surrounds the nucleolus and can be distinguished from these [36] . As such , NURF301 staining can be localized to the second and third chromosome bivalents . Upon completion of meiosis , nuclear levels of NURF301 decline substantially and cannot be detected in nuclei of round spermatids ( Figure 7A ) . Nurf301ΔC mutants , which lack the C-terminal PHD fingers and bromodomain , do not show appreciable enrichment of NURF301 on the bivalents ( Figure 7A , ΔC ) . Instead , NURF301 staining is distributed throughout the nuclei of primary spermatocytes suggesting that the C-terminal PHD fingers and bromodomain is required for NURF recruitment to chromatin in spermatocytes . In support of this , antibody staining using anti-H3K4me3 antibodies reveals that H3K4me3 is also selectively enriched on two of the three bivalents , like NURF ( Figure 7C ) . To establish what other factors play a role in recruitment of NURF to bivalents we examined NURF301 staining in late primary spermatocytes from aly mutants , which disrupt the Myb-MuvB/dREAM repressor complex tMAC [37] , [38] , and from mutants that lack the tTAFs Sa and TAF12L . Loss of both tMAC and tTAFs disrupted NURF301 staining . Although NURF301 was detected in the nuclei of early primary spermatocytes ( data not shown ) , NURF301 failed to localize to the bivalents in mature primary spermatocytes of aly , sa or TAF12L mutants ( Figure 7B ) . As tMAC and tTAFs are required for transcription of genes required both for spermatocyte differentiation as well as meiotic control we examined whether NURF301 staining was disrupted in mutants that block meiotic entry . Twine ( Twe ) is a germline-specific CDC25 that is required for entry of spermatocytes into meiosis . In twe mutants that fail to enter meiosis , NURF301 staining was unaffected ( Figure 7B ) . This suggests that NURF localization requires function of tMAC and tTAFs and that disrupted NURF staining is not an indirect consequence of a meiotic block .
Alternatively splicing represents a convenient mechanism for generating diversity within proteins and protein complexes . In this report we provide evidence that alternative splicing of the large NURF301 subunit of NURF generates functionally distinct chromatin remodeling complexes . We identify two classes of NURF complexes . The first comprises NURF variants composed of full-length NURF301 that have the ability to target the H3K4me3 and H4K16Ac histone modifications . The second consists of NURF complexes composed of a truncated isoform of NURF301 ( NURF301-C ) that lack this ability . Functional characterization of these variant complexes has allowed the identification of distinct subsets of target genes . We show that NURF complexes that recognize the H3K4me3 and H4K16Ac histone tail modifications are not required for correct expression of the majority of NURF targets in larvae but are obligatory for NURF function in spermatogenesis . The regulation of Drosophila NURF function by alternative splicing of the NURF301 specificity subunit may reflect a general phenomenon that will influence subunits and function of most chromatin remodeling enzymes . This is consistent with analyses of sequence databases that show that 28% of chromatin modifying proteins potentially encode alternative splice forms in which domains critical for function are substituted [21] . A good example of this is SNF2L , the catalytic subunit of human NURF , which generates an inactive isoform by alternative splicing [22] , [23] . Moreover , similar truncated NURF301 isoforms are also predicted to occur in other species . For example Andersen and Horwitz ( 2006 ) [39] have shown that the homologous C . elegans nurf-1 locus encodes a variant NURF-1A that lacks the PHD fingers and bromodomain . In addition gene predictions for human BPTF ( Swiss Institute of Bioinformatics ) and mouse BPTF ( UCSC gene predictions ) indicate the existence of similar shortened isoforms . We propose that these isoforms do not generate dominant-negative , variant NURF complexes like those produced by alternative splicing of SNF2L [22] . Rather functional NURF remodeling complexes are formed but these possess altered targeting specificity for histone post-translational modifications . Our data indicate that short NURF301 isoforms are co-expressed with full-length NURF301 in all tissues assayed , and can form a complex with all other NURF subunits . To date we have not uncovered evidence of tissue-specific or exclusive expression of any of the NURF301 isoforms suggesting that these variant NURF complexes co-exist in cells . Thus , functional distinction between these complexes does not seem to be achieved by altering tissue distribution , but rather by regulating their ability to be recruited to target promoters by changing their modified histone-binding properties . Our microarray analysis of null and truncating Nurf301ΔC mutants indicates that , for the majority of target promoters in larvae , full-length NURF isoforms are not obligatory for expression . However we do identify a subset of genes that require full-length NURF301 and a number of cases in which the variant NURF complexes appear to have antagonistic functions . For example , we have shown that expression levels of CG11893 and CG6296 are reduced by a greater margin in Nurf301ΔC than null Nurf301 mutants , raising the possibility that NURF-C isoforms antagonize function of full-length NURF complexes . The fact that Nurf301ΔC mutants affect expression of only a subset of NURF target genes in larvae is consistent with our previous analyses in which we showed that Nurf301ΔC mutants show proper expression of ecdysone target genes [13] . In addition , although null Nurf301 mutants do not express homeotic genes [9] , [10] Nurf301ΔC mutants do not show major developmental abnormalities indicating that Hox gene expression is for the most part unaffected . This contrasts with experiments in Xenopus that have shown dramatic axial patterning defects as a consequence of loss of H3K4me3 recognition by NURF [17] . One explanation for these differences may be that redundant stabilizing interactions exist on Drosophila Hox promoters that reduce the dependency on H3K4me3 for NURF binding to these promoters . This would be consistent with previous data showing that NURF can be recruited to target promoters through interaction with a number of transcription factors , for example the GAGA factor , the ecdysone receptor ( EcR ) and the Drosophila Bcl6 homologue [8] , [13] , [15] . It seems feasible that , on the majority of NURF targets , interactions with transcription factors may be sufficient for NURF recruitment and activity . Of the subset of NURF targets that do show altered expression in Nurf301ΔC microarrays , approximately equal numbers are up-regulated ( 227 ) and down-regulated ( 183 ) . While some of these may be indirect targets of NURF these data suggest that the C-terminal PHD fingers and Bromodomain may also be required for repression by NURF at some genes . This is confirmed in testes where expression of aret and bru-2 are both up-regulated in Nurf301ΔC mutants . There is some evidence that H3K4me3 and H4K16Ac are required for gene repression as binding of H3K4me3 by the PHD finger of ING2 has been shown to be required for transcriptional repression of cyclin D1 [40] , and H4K16Ac is required for rDNA silencing by NoRC [41] . However , it is worth noting that the C-terminal region of NURF301 contains a second PHD finger in addition to the distal PHD finger that binds H3K4me3 . This PHD finger has all the conserved hydrophobic residues in particular W32 that constitute the aromatic cage shown to be critical for methyl lysine recognition [18] , suggesting that it may also bind methylated lysine residues . Experiments are underway to determine the binding specificity of this PHD finger , but it is tempting to speculate that it binds methylated histone lysine residues involved in gene repression . We are currently examining the genome-wide distribution of NURF complexes by ChIP-Sequencing methodologies . Results of these experiments will allow us to determine whether genes derepressed in Nurf301ΔC mutants are direct targets of NURF and allow the overlap of NURF-binding with histone marks associated with gene repression to be determined . Finally , our data indicates that NURF has a key role in Drosophila spermatogenesis . We have shown that testis development in Nurf301ΔC mutants arrests at the primary spermatocyte stage . We demonstrate that expression of the spermatid differentiation gene fzo is lost in Nurf301ΔC mutants and that NURF-binding at fzo overlaps accumulation of H3K4me3 and H4K16Ac marks . Taken together these data suggests that interpretation of these histone modifications is required for NURF function at the fzo promoter . It is important to stress though that these results are correlative . Direct causal requirement for NURF recruitment on H3K4me3 and H4K16Ac would need to be confirmed by loss of NURF under conditions in which these marks are ablated . However , this is hampered by availability of suitable genetic backgrounds in which these modifications are completely removed . Unfortunately , Mof the principal H4K16 acetylase is male lethal as it is required for X-chromosome dosage compensation in males [42] . Moreover , Trithorax mutants , which survive to adult stages and have been used to examine fzo transcription [43] , are not null alleles and do not completely remove H3K4me3 deposition . The defects that occur in primary spermatocyte differentiation in Nurf301ΔC mutants are strikingly similar to those that occur in meiotic arrest mutants [30] . These include the testis-specific TAFs ( tTAFs ) nht ( dTAF4 ) , can ( dTAF5 ) , mia ( dTAF6 ) , sa ( dTAF8 ) and rye ( dTAF12 ) [44] . tTAF binding has been shown to be correlated with H3K4me3 accumulation on fzo , one of the NURF targets identified in this study , and leads to the displacement of the Polycomb repression complex [43] . It is possible that NURF is the link that integrates the H3K4me3 signal to displace Polycomb . However , meiotic arrest phenotypes are also observed in mutants that lack components of the testis-expressed Myb-MuvB/dREAM repressor complex tMAC [37] , [38] . tMAC has been suggested to interact with a testis-specific TFIID composed of tTAFs , in turn regulating transcription in primary spermatocytes [37] . Importantly , NURF is a sub-stoichiometric component of the Myb-MuvB/dREAM complex [45] . Data from C . elegans indicates that NURF antagonizes Myb-MuvB/dREAM [39] . This suggests interaction with tMAC may be an alternative route by which NURF regulates transcription in primary spermatocytes . In the future , determining the interactions between NURF , tMAC and tTAFs will help reveal the mechanisms by which these factors regulate transcription in primary spermatocytes .
Flies were raised at 25°C . Selection of Nurf3012 , Nurf3014 and Nurf30112 mutants is described in [10] . aly1 , sa1 , twe1 and Taf12LKG00946 strains were obtained from the Bloomington Drosophila Stock Center . w1118 , Nurf3012 , and Nurf3014 wandering third instar larvae were staged using the blue-gut method [46] . mRNA was isolated by magnetic selection using µMacs columns according to manufacturer's instructions ( Miltenyi Biotec , Auburn , CA ) . Whole genome expression profiling was performed using Affymetrix Drosophila Genome 2 . 0 arrays as described in [13] . Statistical analysis was carried out using R version 2 . 5 . 1 ( http://www . R-project . org ) and the gcrma and limma libraries of Bioconductor version 2 . 0 ( http://www . bioconductor . org ) . Expression values were computed using gcrma [25] . Differential expression of genes was determined using an empirical Bayes approach within limma [47] , with the factor “genotype” ( wild-type , Nurf301 Null , Nurf301 ΔC ) . Moderated t statistics based on shrinkage of the estimated sample variance toward a pooled estimate and the corresponding P values were calculated for the Nurf301 Null vs . wild-type , and Nurf301ΔC vs . wild-type comparisons . P values were adjusted according to Benjamini and Hochberg [48] to control the false discovery rate and a threshold of 0 . 01 was used to select probe sets . Probe-sets with statistically significant changes relative to wild-type were determined using the decideTests function in limma . GCRMA computed expression values for all genes are listed in Dataset S1 . The top 500 and 200 genes with altered expression in respectively the null Nurf301 and Nurf301ΔC mutants are listed in Dataset S2 and Dataset S3 . Array datasets are available through ArrayExpress ( accession number E-MEXP-2011 ) . For confirmation of microarray expression data , mRNA was isolated from whole larvae as described above and reverse transcribed by Superscript II ( Invitrogen ) at 42°C . PCR was performed for 25–32 cycles as described [13] . Primer sets used are listed in Table S2 . For analysis of testis gene expression testes were dissected from wild-type and Nurf3014 homozygous mutant 3–5 day old male flies in ice-cold HyQ-CCM-3 culture medium ( HyClone ) containing protease inhibitors ( Complete , Roche ) . Samples of 25 pairs of testes were then centrifuged and washed in ice-cold 1×PBS containing protease inhibitors . Testes pellets were stored at −80°C until use . mRNA was isolated using mMacs columns as described above and reverse transcribed as above . Primer sets used for PCR are listed in Table S3 . Imaginal discs were dissected from 40 third instar larvae of each genotype . Samples were homogenized in SDS-PAGE loading buffer and run on 6% SDS-PAGE gels . Western analysis was using Rabbit anti-NURF301 ( [15]; 1∶1000 ) and anti-β-Tubulin antibodies ( MAb E7; 1∶100 ) . Testes were dissected from 3–5 day old male flies in ice-cold HyQ-CCM-3 culture medium ( HyClone ) containing protease inhibitors . Testes were fixed in PBT ( PBS containing 0 . 1% Triton-×100 ( Sigma ) ) containing 3 . 7% paraformaldehyde ( Sigma ) for 25 min on ice . Samples were washed in PBT , blocked in blocking solution ( PBT containing 10% FCS ) for 1 hour at room temperature . Primary antibody incubation was performed in blocking solution for 3 hours at room temperature . Mouse MAb F2F4 ( anti-Cyclin B ) was used at the dilution 1∶5 . Cy3-conjugated secondary antibodies ( Jackson ImmunoResearch ) were used at 1∶1000 in PBT for 1 hour at room temperature . Samples were mounted in Vectashield containing DAPI ( Vector Laboratories ) . For phase microscopy of live testes , testes were squashed in testes buffer ( 183 mM KCl , 47 mM NaCl , 10 mM Tris ( pH 6 . 8 ) ) containing 4 µg/ml Hoechst 33342 ) . Samples were observed using a confocal microscope ( Zeiss Axiovert 100 M ) . For anti-NURF301 and anti-H3K4me3 staining testes were dissected in testis buffer , fixed in Hepes buffer ( 0 . 1 M Hepes , 2 mM MgSO4 , 1 mM EGTA ( pH 6 . 9 ) ) containing 3 . 7% paraformaldehyde for 12 minutes at room temperature and squashed under a siliconized coverslip . Coverslips were removed after freezing in liquid nitrogen and slides stored in PBTw ( PBS containing 0 . 1% Tween-20 ( Sigma ) ) until use . Blocking and primary antibody incubation was performed in PBTw containing 10% FCS at 4°C overnight . Washes and secondary antibody incubation was performed in PBTw at room temperature as described above . Anti-NURF301 [15] and anti-H3K4me3 antibodies ( Abcam , Ab8580 ) were used at 1∶1000 and 1∶2000 respectively . Slides were mounted and viewed as above . Testes were dissected in batches of 25 from 3–5 day old male flies in ice-cold HyQ-CCM-3 culture medium ( HyClone ) containing protease inhibitors . Testes were fixed in PBS containing 1% formaldehyde at 25°C , washed three times using ice cold PBS containing protease inhibitors and pelleted after each wash by centrifugation at 260×g for 5 minutes at 4° . Fixed testes pellets were stored at −80°C until use . Samples were homogenized in ChIP Lysis buffer using a pellet pestle . Soluble chromatin was prepared using the protocol of the Chromatin Immunoprecipitation Assay Kit ( Upstate Biotechnology ) . Sample corresponding to 100 pairs of testes was used for anti-NURF301 ChIP , while sample corresponding to 50 pairs of testes each was used for anti-H3K4me3 and anti-H4K16Ac ChIP respectively . Samples were pre-cleared using Protein A-conjugated magnetic beads ( Dynal ) for thirty minutes at room temperature , followed by incubation with antibody coated Protein A-conjugated magnetic beads ( Dynal ) for 2 hours at room temperature . Immune complexes were recovered by magnetic selection , washed and eluted using the protocol of the Chromatin Immunoprecipitation Assay Kit . Target DNA abundance in ChIP eluates was assayed by quantitative PCR with addition of 0 . 2 µCi [α-32P]-deoxyadenosine 5′-triphosphate ( Perkin Elmer , specific activity 6000 Ci/mMol ) as a tracer before the amplification step . The following antibodies were used rabbit anti-NURF301 [15] , anti-H3K4me3 ( Abcam ab8580 ) and anti-H4K16Ac ( Serotec AHP417 ) . Primer pairs used for PCR are listed in Table S4 . The following biotinylated histone tail peptides were used: unmodified H3 ( 1–21 ) tail peptide ( Upstate Biotech , 12–403 ) ; H3K4me3 ( 1–21 ) tail peptide ( Upstate Biotech , 12–564 ) ; unmodified H4 ( 1–21 ) tail peptide ( Upstate Biotech , 12–405 ) ; H4K16Ac ( 1–20 ) tail peptide ( USBiological , H5110-15Q1 ) . GST-PHD ( NURF301 aa 2490–2553 ) and GST-Bromo ( NURF301 aa 2525–2669 ) were purified as described previously [8] , [17] . Peptide pull-downs were performed by incubating 1 µg tail peptide with 5 µg of either GST , GST-PHD2 or GST-Bromo protein in binding buffer ( 50 mM Tris , 150 mM NaCl , 0 . 1% NP-40 ) at 4°C overnight on a rotator . Complexes were incubated with 20 µL Immobilized Streptavidin ( Pierce ) for 2 hours at 4°C and precipitated by centrifugation at 1500 g for 1 min . Beads were washed four times in binding buffer and resuspended in SDS PAGE loading buffer . GST-fusion proteins were resolved on 10% SDS-PAGE gels , blotted onto PVDF membranes and visualized by Western blotting using HRP-coupled anti-GST antibodies ( GE Healthcare ) . A 10% input lane for each fusion protein was run as a control . For the reciprocal pull-down , reactions were performed identically except Glutathione-Sepharose beads ( GE Healthcare ) were used for the pull-down . Peptides were resolved on Novex 10–20% Tricine Gels ( Invitrogen ) , blotted onto PVDF membranes and visualized by staining with Streptavidin-conjugated HRP ( Vectastain Elite ABC kit , Vector Laboratories ) . A 10% input lane for each peptide was run as a control . NURFΔC complex was reconstituted by baculovirus-mediated co-expression in SF9 cells and purified as described previously [49] . Virus encoding FLAG-NURF301ΔC ( aa 1–1557 ) , HA-ISWI , MYC-NURF55 and AU1-NURF55 were used . NURFΔC complex was resolved on 4–20% SDS-PAGE gradient gels ( Invitrogen ) . Association of NURF subunits with NURF301ΔC was assayed by Western blotting using anti-FLAG ( Sigma , M2; 1/4000 ) ; anti-ISWI ( 1∶1000 ) , anti-MYC ( 9E10 , 1∶100 ) and anti-NURF38 ( [50]; 1∶2000 ) antibodies . | Changes in nucleosome dynamics have a profound effect on DNA transactions such as transcription , replication , and repair . Altered chromatin states can be induced by post-translational modification of the histone tails or energy-dependent nucleosome sliding mediated by ATP–dependent chromatin remodeling factors . Here we demonstrate that the Drosophila chromatin remodeling factor NURF is regulated by alternative splicing of its large subunit NURF301 . We show that three NURF301 isoforms occur . One of these lacks C-terminal protein domains that recognize the post-translational histone modifications H3K4me3 and H4K16Ac and that potentially allow recruitment of NURF to modified histone marks . Using whole genome expression profiling , we identify NURF target genes that require these domains and , hence , recognition of modified histone marks . Our results indicate that NURF complexes that recognize H3K4me3 and H4K16Ac are not essential for correct expression of the majority of NURF gene targets in larvae but are obligatory for NURF function in spermatogenesis . We show that NURF is an important regulator of spermatocyte differentiation in Drosophila . We suggest that alternative splicing provides a convenient mechanism to generate functional diversity of ATP–dependent chromatin remodeling complexes and allows the production of remodeling complexes with altered chromatin targeting specificities . |
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Genome-wide association studies ( GWAS ) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences . We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol , LDL cholesterol , HDL cholesterol , and triglyceride levels . The Swedish ( SE ) EUROSPAN cohort ( NSE = 656 ) was screened for candidate genes and the non-Swedish ( NS ) EUROSPAN cohorts ( NNS = 3 , 282 ) were used for replication . In total , 3 SNPs were associated in the Swedish sample and were replicated in the non-Swedish cohorts . While SNP rs1532624 was a replication of the previously published association between CETP and HDL cholesterol , the other two were novel findings . For the latter SNPs , the p-value for association was substantially improved by inclusion of environmental covariates: SNP rs5400 ( pSE , unadjusted = 3 . 6×10−5 , pSE , adjusted = 2 . 2×10−6 , pNS , unadjusted = 0 . 047 ) in the SLC2A2 ( Glucose transporter type 2 ) and rs2000999 ( pSE , unadjusted = 1 . 1×10−3 , pSE , adjusted = 3 . 8×10−4 , pNS , unadjusted = 0 . 035 ) in the HP gene ( Haptoglobin-related protein precursor ) . Both showed evidence of association with total cholesterol . These results demonstrate that inclusion of important environmental factors in the analysis model can reveal new genetic susceptibility loci .
Genome-wide association studies ( GWAS ) have identified more than 38 larger genetic regions which influence blood levels of total cholesterol ( TC ) , low-density lipoprotein cholesterol ( LDL-C ) , high-density lipoprotein cholesterol ( HDL-C ) and triglycerides ( TG ) [1]–[3] . These studies modeled basic anthropometric confounders , such as sex and age , while leaving out important environmental influences , such as diet and activity . This strategy is statistically suboptimal since the unexplained variation in the phenotype can increase the measurement error and as a result require larger sample sizes to detect a significant effect . Manolio [4] argued strongly for modeling of environmental covariates in GWAS and recommended lipid levels as a paradigmatic phenotype for studying the genetic and environmental architecture of quantitative traits . In order to explore the usefulness of including both environmental and genetic factors in the analysis model , we used lipid measurements from the EUROSPAN study , comprising 3 , 938 individuals for whom genome-wide SNP data ( NSNP = 311 , 388 ) were available [5] . We measured daily intake of food and physical activity at work and at leisure and modeled the influence of those environmental covariates on serum lipid levels in a GWAS . First , data from the Northern Sweden Population Health Study ( NSPHS ) were used as a discovery cohort to screen for SNPs that displayed the lowest p-values when the model was adjusted for environmental covariates . We then used the other , non-Swedish EUROSPAN cohorts for replication of our strongest associations in a candidate gene association study ( CGAS ) . We chose a population living in northern Sweden for the selection of candidate loci because it shows strong natural heterogeneity in certain lifestyle factors ( e . g . diet , activity ) , but homogeneity in other environmental aspects such as climate [6] . Whereas one group is living a modern , sedentary lifestyle found also in the southern part of Sweden and other western European countries , a subgroup of Swedes follows a traditional , semi-nomadic way of life based on reindeer herding . Reindeer herders typically show higher intake of game meat ( reindeer , moose ) , which has a high protein and low fat content , and lower intake of non-game meat , fish , and dairy products among other , lesser differences . They also exert more physical activity at work to tend their reindeer herds , but less activity at leisure [7] .
We performed a GWAS with a lifestyle-adjusted model which included not only sex and age , but also daily intake of game meat , non-game meat , fish , milk products , physical activity at work and at leisure as covariates . We focused on the 0 . 05% of all SNPs with the lowest p-values in the diet- and activity-adjusted model ( corresponding to about 150 SNPs per lipid ) . For total cholesterol , 88 of these were located in a gene and 14 in genes that have been associated with energy metabolism ( http://www . ncbi . nlm . nih . gov/omim/ ) . For LDL-C , 65 SNPs were located in a gene , of which 8 were functionally relevant . Several of the SNPs for LDL-C were identical with those affecting total cholesterol , as expected from the high correlation ( r = 0 . 91 ) between both phenotypes . For HDL-C , SNP rs2292883 , located in the MLPH gene ( Melanophilin ) , showed a genome-wide significant p-value ( p = 1 . 06×10−07 ) . 69 SNPs for HDL-C were located in a gene and 14 of those genes were reported as having a metabolic effect . Finally , for triglycerides , 63 SNPs were located in a gene , but only 4 SNPs in genes with a functional annotation of interest ( Table 1 and Table S1A , S1B , S1C , S1D ) . In order to evaluate the effect of including diet and activity covariates in the association analysis , we overlaid the p-values in the Manhattan plots from the NSPHS for the unadjusted and adjusted GWAS models ( Figure 1 , Figure 2 , Figure 3 , Figure 4 ) . More refined GWAS results separating the effect of adjusting for either diet or physical activity are presented in Figure S1A , S1B , S1C , S1D; and Figure S2A , S2B , S2C , S2D . As expected , the p-values for a number of SNPs were sensitive to the inclusion of both diet and activity covariates in the model . We matched the 0 . 05% SNPs with the lowest p-values ( top SNP list ) between the unadjusted and the adjusted model . For TC , 83 ( 53% ) SNPs were found in both top SNP lists . Those lists contained 102 ( 64% ) identical SNPs for LDL-C and 103 ( 65% ) for HDL-C . The analyses resulted in the same 74 ( 47% ) top SNPs for TG levels ( Table S1A , S1B , S1C , S1D ) . Finally , we compared the p-value changes of the resulting 39 candidate SNPs that are located in genes with a metabolic effect between the diet and activity-adjusted ( full ) model and the unadjusted ( restricted ) model resulting in an up to 27-fold p-value decrease ( Table 1 ) . A food- and activity-adjusted candidate gene association study of the final 39 candidate SNPs in the Scottish ( SC ) sample ( N = 714 ) was applied using similar lifestyle covariates ( Table 2; Table S1E , S1F , S1G , S1H; Table S2 ) . We replicated the effect of rs2000999 ( pSC , unadj = 6 . 16×10−03 , pSC , adj = 4 . 33×10−03 ) in the HP gene ( Haptoglobin-related protein Precursor ) on TC level and the effect of rs1532624 ( pSC , unadj = 2 . 40×10−09 , pSC , adj = 1 . 96×10−09 ) in CETP ( Cholesteryl ester transfer protein ) on HDL-C . In the Swedish cohort ( SE ) , the unadjusted genetic effect of rs2000999 in the HP gene is equivalent to a moderately large difference in average TC level of 20 . 21 mg/dl between the homozyguous genotypes ( MeanSE , unadj ( TC|A/A ) −MeanSE , unadj ( TC|G/G ) = 243 . 16−222 . 95 , Effect SizeSE , unadj = 0 . 41 , Effect SizeSE , adj = 0 . 44 ) ( Effect Size ( ES ) = ( MA/A−MB/B ) /SDpooled ) . Equivalent effects were observed in the Scottish replication sample ( MSC , unadj ( TC|A/A ) −MSC , unadj ( TC|G/G ) = 235 . 36 mg/dl−222 . 54 mg/dl = 12 . 82 mg/dl , ESSC , unadj = 0 . 29 , ESSC , adj = 0 . 52 ) . SNP rs1532624 in the CETP gene is associated with a large , unadjusted difference in HDL-C level of 9 . 99 mg/dl ( MSE , unadj ( HDL-C|A/A ) −MSE , unadj ( HDL-C|C/C ) = 68 . 14 mg/dl−58 . 15 mg/dl , ESSE , unadj = 0 . 73 , ESSE , adj = 0 . 48 ) in the discovery cohort and similar effects regarding direction and size in the replication cohort ( MSC , unadj ( HDL-C|A/A ) −MSC , unadj ( HDL-C|C/C ) = 69 . 79 mg/dl−60 . 75 mg/dl = 9 . 04 mg/dl; ESSC , unadj = 0 . 59 , ESSC , adj = 0 . 57 ) . We also performed an unadjusted candidate gene analysis of the 39 candidate SNPs in all non-Swedish ( NS ) EUROSPAN cohorts ( Scotland , Croatia , The Netherlands , and Italy , NNS = 3 , 282 ) and aggregated the results in a meta-analysis ( Table 2; Table S1I , S1J , S1K , S1L ) . We confirmed the effects of rs5400 ( pNS = 4 . 68×10−02 ) in SLC2A2 on TC . We again found that rs2000999 ( pNS , unadj = 3 . 54×10−2 ) in HP influences TC levels and rs1532624 ( pNS , unadj = 2 . 87×10−20 ) in CETP ( Cholesteryl ester transfer protein ) affects HDL-C levels . The unadjusted genetic effect of rs5400 is equivalent to a moderately large difference in mean TC level of 27 . 11 mg/dl between homozyguous genotypes ( MSE , unadj ( TC|A/A ) −MSE , unadj ( TC|G/G ) = 249 . 30 mg/dl−222 . 19 mg/dl , ESSE , unadj = 0 . 57 , ESSE , adj = 0 . 66 ) in the Swedish Cohort and a small total effect in all non-Swedish samples ( MNS , unadj ( TC|A/A ) −MNS , unadj ( TC|G/G ) = 236 . 69 mg/dl−223 . 34 mg/dl = 13 . 35 mg/dl , ESNS , unadj = 0 . 30 ) . No other associations , including LDL cholesterol or triglycerides levels , were replicated ( all p>0 . 05 ) . The genome-wide significant SNP rs2292883 in the Melanophilin ( MLPH ) gene found in the Swedish cohort was not confirmed .
Environmental covariates may either act as moderators , mediators or even suppressors , thereby affecting the discovery of genetic susceptibility loci [8] , [9] . Therefore , we conducted a GWAS , modeling genetic and important environmental effects , such as food intake and physical activity , on serum levels of classical lipids . To our knowledge , this is the first GWAS on blood lipid levels modeling environmental factors , in particular major food categories and physical activity , in international cohorts . Our analysis replicated one known locus in the CETP gene [1] and identified two other gene loci in the SLC2A2 and HP gene , respectively , involved in energy metabolism but not previously reported to be associated with cholesterol levels . SLC2A2 encodes the facilitated glucose transporter member 2 ( GLUT-2 , Solute carrier family 2 ) and is predominantly expressed in the liver . Mice deficient in GLUT-2 are hyperglycemic and have elevated plasma levels of glucagon and free fatty acids [10] . Mutations in GLUT-2 cause the Fanconi-Bickel syndrome ( FBS ) characterized by hypercholesterolemia and hyperlipidemia [11] , [12] . Cerf [13] argued that a high-fat diet causes a decreased expression of the GLUT-2 glucose receptor on β-cell islets . As a result , glucose stimulation of insulin exocytosis is impaired causing hyperglycemia , a clinical hallmark of type 2 diabetes . In addition , Kilpelainen et al . [14] found that physical activity moderates the genetic effect of SLC2A2 on type 2 diabetes . These studies suggest that these lifestyle factors could have masked genetic effects in previous , unadjusted GWAS . This is emphasized by the strong increase in statistical significance of the SLC2A2 polymorphisms after adjusting for diet and physical activity , indicating that the examined lifestyle factors modified the effect of this gene . Our supplemental results show that physical activity markedly moderated the genetic effect on total cholesterol . The HP gene encodes the Haptoglobin-related Protein Precursor ( Hp ) , which binds hemoglobin ( Hb ) to form a stable Hp-Hb complex and , thereby , prevents Hb-induced oxidative tissue damage . Asleh et al . [15] identified severe impairment in the ability of Hp to prevent oxidation caused by glycosylated Hb . Diabetes is also associated with an increase in the non-enzymatic glycosylation of serum proteins , so these authors suggested that there is a specific interaction between diabetes , cardiovascular disease and the Hp genotype . It results from the increased need of rapidly clearing glycosylated Hb-Hp complexes from the subendothelial space before they oxidatively modify low-density lipoprotein to form the atherogenic oxidized low-density lipoprotein . The p-value for association between the HP SNP rs2000999 and total serum cholesterol concentration decreased in the model adjusted for diet and physical activity , suggesting that the genetic effect is moderated by diet and physical activity . Our supporting material points out the moderating role of physical activity in particular . We also observed a highly significant association between rs1532624 in CETP and HDL-C levels . The CETP protein catalyzes the transfer of insoluble cholesteryl esters among lipoprotein particles . Variation in CETP is known to affect the susceptibility to atherosclerosis and other cardiovascular diseases [16] . Adjustment for diet and physical activity in our model caused an increase of the p-value of this SNP . Our supporting results indicate that the genetic effect is mediated by diet or by physical activity in a similar way . This study also has some limitations . First , we are aware that our candidate gene association approach covers only a very small fraction of all genomic loci , which is one of the potential reasons why some classical lipid-influencing genes , such as APOE , are not represented in our candidate SNP list . Therefore , our approach is not comprehensive and may have failed to identify other relevant lifestyle-sensitive genetic variants . Nonetheless , we decided to apply this approach to make the best out of the available lifestyle data . Second , our study provides only limited information on the role of individual lifestyle factors for a genetic variant . However , in this study we aimed at amplifying genetic effects by adjusting for a maximum amount of environmental variance in a single model and , therefore , we neglected some of these aspects here . Third , we did not model genetic covariates in known lipid-relevant genes which may also moderate the effect of other genetic predictors . This is due to the focus of this paper on gene-environment relationships . In summary , we have demonstrated that modeling environmental factors , in particular major food categories and physical activity , can improve statistical power and lead to the discovery of novel susceptibility loci . Such models also provide an understanding of the complex interplay of genetic and environmental factors affecting human quantitative traits . Inclusion of environmental covariates represents a much needed next step in the quest to model the complete environmental and genetic architecture of complex traits .
All EUROSPAN studies were approved by the appropriate research ethics committees according to the Declaration of Helsinki [17] . The Northern Swedish Population Health Study ( NSPHS ) was approved by the local ethics committee at the University of Uppsala ( Regionala Etikprövningsnämnden , Uppsala ) . The Scottish ORCADES study was approved by the NHS Orkney Research Ethics Committee and the North of Scotland REC . The Croatian VIS study was approved by the ethics committee of the medical faculty in Zagreb and the Multi-Centre Research Ethics Committee for Scotland . The Dutch ERF study was approved by the Erasmus institutional medical ethics committee in Rotterdam , The Netherlands . The Italian MICROS study was approved by the ethical committee of the Autonomous Province of Bolzano , Italy . The examined subjects stem from five different population-representative , pedigree-based cohorts from the EUROSPAN consortium ( http://www . eurospan . org ) . All studies include a comprehensive collection of data on family structure , lifestyle , blood samples for clinical chemistry , RNA and DNA analyses , medical history , and current health status . All participants gave their written informed consent [18] . A brief description of each population is given below: The Northern Swedish Population Health Study ( NSPHS ) represents a cross-sectional study conducted in the community of Karesuando in the subartic region of the County of Norrbotten , Sweden , in 2006 [5] . This parish has about 1500 eligible inhabitants of whom 740 participated in the study . The final sample consisted of 309 men and 347 women who were aged between 14 and 91 years . The inclusion of diet and activity covariates in the analytical model and according missing values reduced the effective sample size by less than 5% . The Orkney Complex Disease Study ( ORCADES ) is a longitudinal study in the isolated Scottish archipelago of Orkney [19] . Participants from a subgroup of ten islands ( N = 719 ) were used for the presented analysis . The sample comprised 334 men and 385 women aged between 18 and 100 years . The inclusion of diet and activity covariates in the analytical model and according missing values reduced the effective sample size by less than 5% . The VIS study is a cross-sectional study in the villages of Vis and Komiza on the Dalmatian island of Vis , Croatia , and was conducted between 2003 and 2004 [20]–[22] . 795 participants who had both genotype and phenotypic data available were analysed . This cohort included 328 men and 467 women with an age between 18 and 93 years . The Microisolates in South Tyrol Study ( MICROS ) is a cross-sectional study carried out in the villages of Stelvio , Vallelunga , and Martello , Venosta valley , South Tyrol , Italy , from 2001 to 2003 [23] . The 1 , 097 participants ( 475 males , 622 females , age between 18 and 88 years ) presented in this study are those for whom both relevant genotype and phenotype data were available . The Erasmus Rucphen Family Study ( ERF ) is a longitudinal study on a population living in the Rucphen region , the Netherlands , in the 19th century [24] . Fasting total cholesterol , HDL cholesterol and triglyceride levels were available . LDL cholesterol was estimated using the Friedewald formula [25] . The 918 individuals included in this study consisted of the first series of participants with 354 men and 564 women aged between 18 and 92 years . DNA samples were genotyped according to the manufacturer's instructions on Illumina Infinium HumanHap300v2 or HumanCNV370v1 SNP bead microarrays . Both arrays have 311 , 388 SNP markers in common that are distributed across the human genome . Analysis of the raw data was done in the BeadStudio software with the recommended parameters for the Infinium assay and using the genotype cluster files provided by Illumina . Individuals with a call rate below 95% and SNPs with a call rate below 98% , deviating from Hard-Weinberg equilibrium ( pHWE<1×10−6 ) or with a minor allele frequency of less than 1% were excluded from the analysis . Total cholesterol ( TC ) , low-density lipoprotein cholesterol ( LDL-C ) , high-density lipoprotein cholesterol ( HDL-C ) , and triglycerides ( TG ) were quantified by enzymatic photometric assays using an ADVIA1650 clinical chemistry analyzer ( Siemens Healthcare Diagnostics GmbH , Eschborn , Germany ) at the Institute for Clinical Chemistry and Laboratory Medicine , Regensburg University Medical Center , Germany . In the NSPHS cohort , we collected data with a food frequency questionnaire based on the Northern Sweden 84-item Food Frequency Questionnaire ( NoS-84-FFQ ) [26] . We included in the questionnaire several items on foods specific for the lifestyle in this geographic region , in particular on game consumption ( reindeer , moose ) . The answer options consisted of an 11-point format: 0 = “Never” , 1 = “less than 1 time per month” , 2 = “1 to 3 times per month” , 3 = “1 time per week” , 4 = “2 to 4 times per week” , 5 = “5 to 6 times per week” , 6 = “1 time per day” , 7 = “2 to 3 times per day” , 8 = “4 to 5 times per day” , 9 = “6 to 8 times per day” , 10 = “9 to 10 times per day” . The questionnaire was applied in electronic format by a trained study nurse as an interviewer . For each food item we calculated daily intake in gram per day as a standardized unit of measurement and aggregated the items to food categories , such game meat , non-game meat , fish , and dairy products . We evaluated the construct validity ( known-groups validity ) of the added items on game consumption in the NoS-84-FFQ questionnaire . We compared reindeer herders ( N = 94 ) versus non-reindeer herders ( N = 505 ) . We observed highly significant , large effect sizes in men ( ES = 1 . 25 , p = 9 . 7×10−04 ) and women ( ES = 1 . 15 , p = 2 . 9×10−05 ) in the expected direction corresponding with an approximately three times higher consumption of absolute overall game intake in reindeer herders compared to others . A similar approach was used for the measurement and analysis of dietary data collected with a food frequency questionnaire in the Scottish cohort ( Table S2 ) . In the NSPHS cohort , we used two self-report scales to measure overall physical activity at work and at leisure . The Work Activity Scale ( WAS , 6 items ) addresses typical occupational physical activities: sitting , standing , walking , lifting , and general indicators of physical activity , i . e . sweating and tiredness after work . The Leisure Activity Scale ( LAS , 4 items ) asks for various typical freetime activities such walking , cycling , other sporting activities , and sweating as a general indicator of physical activity . Participants reported the frequency of each activity on a 5-point rating scale ( 1 = “never” , 2 = “seldom” , 3 = “sometimes” , 4 = “often” , and 5 = “always” ) . Both scales showed satisfying internal consistency with Cronbach's α ( WAS ) = 0 . 73 and Cronbach's α ( LAS ) = 0 . 70 . A similar approach was used for the measurement and analysis of data on physical activity collected with a self-report questionnaire in the Scottish cohort ( Table S2 ) . | In this article we report a genome-wide association study on cholesterol levels in the human blood . We used a Swedish cohort to select genetic polymorphisms that showed the strongest association with cholesterol levels adjusted for diet and physical activity . We replicated several genetic loci in other European cohorts . This approach extends present genome-wide association studies on lipid levels , which did not take these lifestyle factors into account , to improve statistical results and discover novel genes . In our analysis , we could identify two genetic loci in the SLC2A2 ( Glucose transporter type 2 ) and the HP ( Haptoglobin-related protein precursor ) gene whose effects on total cholesterol have not been reported yet . The results show that inclusion of important environmental factors in the analysis model can reveal new insights into genetic determinants of clinical parameters relevant for metabolic and cardiovascular disease . |
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Kidney function depends on the nephron , which comprises a blood filter , a tubule that is subdivided into functionally distinct segments , and a collecting duct . How these regions arise during development is poorly understood . The zebrafish pronephros consists of two linear nephrons that develop from the intermediate mesoderm along the length of the trunk . Here we show that , contrary to current dogma , these nephrons possess multiple proximal and distal tubule domains that resemble the organization of the mammalian nephron . We examined whether pronephric segmentation is mediated by retinoic acid ( RA ) and the caudal ( cdx ) transcription factors , which are known regulators of segmental identity during development . Inhibition of RA signaling resulted in a loss of the proximal segments and an expansion of the distal segments , while exogenous RA treatment induced proximal segment fates at the expense of distal fates . Loss of cdx function caused abrogation of distal segments , a posterior shift in the position of the pronephros , and alterations in the expression boundaries of raldh2 and cyp26a1 , which encode enzymes that synthesize and degrade RA , respectively . These results suggest that the cdx genes act to localize the activity of RA along the axis , thereby determining where the pronephros forms . Consistent with this , the pronephric-positioning defect and the loss of distal tubule fate were rescued in embryos doubly-deficient for cdx and RA . These findings reveal a novel link between the RA and cdx pathways and provide a model for how pronephric nephrons are segmented and positioned along the embryonic axis .
The kidney eliminates metabolic waste in the body using highly specialized structures called nephrons . Individual nephrons are composed of a blood filter ( renal corpuscle ) , a tubule that recovers or secretes solutes , and a collecting duct [1] . The renal corpuscle contains epithelial cells called podocytes that form the slit-diaphragm filtration barrier and allow collection of substances from the blood [2] . In a number of vertebrate species , including some mammals , the renal corpuscle is connected to the tubule by a short stretch of ciliated epithelium called the neck segment that guides filtrate entry into the tubule [3–5] . The mammalian nephron tubule is subdivided into a series of proximal and distal segments connected to a collecting duct [1 , 6] . The polarized epithelial cells in the tubule segments have a unique ultrastructure and express a select cohort of solute transporters [1] . Thus , each segment is functionally distinct and performs the transport of particular solutes that are required for proper renal function . In higher vertebrates , three kidneys of increasing complexity arise sequentially from the intermediate mesoderm ( IM ) : the pronephros , the mesonephros , and the metanephros [7] . The pronephros and mesonephros degenerate in succession , with the metanephros serving as the adult kidney . Lower vertebrates , such as fish and amphibians , develop a pronephros during embryonic stages , and then form a mesonephros that will be used throughout their adult life [8–10] . Each of these kidneys contains the nephron as its basic functional unit [8] . To date , much of our knowledge of kidney development has come from gene-targeting studies in the mouse [7 , 11 , 12] . These experiments have identified a number of genes that play essential roles in the early stages of metanephros development , but there is a limited understanding of the molecular pathways governing the later stages of kidney ontogeny , when individual nephrons form and become segmented [7] . The zebrafish is an ideal genetic and developmental model system for dissecting the molecular mechanisms of nephron formation because of the anatomical simplicity of the pronephros , which contains two nephrons as opposed to the thousands of nephrons in a mammalian metanephros [9] . During zebrafish development , bilateral stripes of IM lying on either side of the trunk undergo a mesenchymal-to-epithelial transition to form the pair of pronephric nephrons . The anteriormost renal progenitors differentiate into podocytes , which migrate medially and fuse at the midline to form a single renal corpuscle . The nephrons also fuse posteriorly at the cloaca to form a shared exitway . From a functional standpoint , these pronephric nephrons have been thought to consist of three parts: ( 1 ) the blood-filtering renal corpuscle , ( 2 ) a very short tubule region that transports solutes , and ( 3 ) long pronephric ducts that convey the resulting waste to the cloaca [9] . Contrary to this model , recent studies have suggested that the ‘duct' region possesses regional segmentation , based on the restricted expression boundaries of solute transporter orthologues known to be expressed in the tubule segments of metanephric nephrons . For example , a rostral stretch of the pronephric duct expresses the endocytic receptor megalin ( lrp2 ) [13] and the sodium bicarbonate transporter NBC1 ( slc4a4 ) [14] , which are expressed in the proximal tubule in mammals . These reports raise the possibility that portions of the pronephros considered to be duct might in fact be tubule , thus suggesting that the organization of the zebrafish pronephros is more complex than previously appreciated . However , a complete model of the molecular anatomy of the zebrafish pronephros and whether there is a functional correlation to the segments of the mammalian nephron remain unclear . Furthermore , the pathway ( s ) directing segmentation of the pronephros along the embryonic axis are unknown . Numerous factors are known to control segmental patterning along the anterior-posterior ( A-P ) axis during vertebrate development and thus provide candidate pathways that might act to establish pronephros segmentation . Retinoic acid ( RA ) signaling is vital for directing the A-P regionalization of tissues deriving from all three germ layers , such as the hindbrain , paraxial mesoderm , and gut [15–19] . Control of RA production via retinaldehyde dehydrogenase ( RALDH ) synthesizing enzymes [20] and the degradation of RA via the CYP26 catabolizing enzymes establishes both the location and timing of RA signaling [21 , 22] . In addition to RA , the caudal ( cdx ) transcription factors ( Cdx1 , Cdx2 , and Cdx4 in mammals and cdx1a and cdx4 in zebrafish ) are responsible for determining vertebral identity and directing posterior body formation [23–31] . cdx genes are known to act as master regulators of the homeobox ( hox ) transcription factors [25] , and in turn , overlapping domains of hox gene expression along the A-P axis are thought to confer segmental identities [32] . In mice , loss of Cdx function causes posterior shifts in Hox gene expression that are associated with abnormal vertebral patterning , and posterior truncations due to defects in the extension of the embryo axis [23 , 25–27 , 33] . Similarly , studies in zebrafish have shown that the loss of cdx4 function or deficiency of both cdx1a and cdx4 causes shifts in hox gene expression domains , a shortened body axis , and altered patterning of the blood , vascular , and neural tissues [24 , 28–31] . These lines of evidence indicate that the cdx genes play essential roles in controlling cell fates along the embryonic axis; however , the molecular mechanisms underlying these effects have not been elucidated [34] . In this study , we undertook a functional genomics approach to identify new markers of the zebrafish pronephros . From this analysis , we found that the pronephros is composed of at least eight regions , including two proximal and two distal tubule segments . We explored how segmental identity is controlled during nephrogenesis by testing the roles of RA signaling and the cdx genes . We found that RA is required to induce proximal segment fates and prevent the expansion of distal segment fates , whereas the cdx genes are necessary for positioning the pronephros along the embryonic axis . Embryos deficient in cdx1a and cdx4 displayed a posterior shift in the location of the pronephros and formed proximal but not distal nephron segments . The cdx genes were found to control the expression boundaries of raldh2 ( aldh1a2 ) and cyp26a1 , suggesting a model in which the cdx pathway influences where the pronephros forms along the body axis by localizing the source of RA , while subsequent RA signaling acts to direct the segmentation of the pronephros .
To gain insight into the molecular mechanisms that control vertebrate renal development , we undertook a functional genomics approach to identify genes expressed in the kidney . We mined two gene collections , one comprising developmentally expressed genes from embryonic zebrafish cDNA libraries [35] and another compiled from an adult zebrafish kidney library [36] . Gene expression patterns were analyzed by whole-mount in situ hybridization using wild-type zebrafish embryos between the 5 somite stage and 144 hours post fertilization ( hpf ) . We identified a number of genes , including 15 solute transporters , that were expressed within specific subregions of the pronephros . In total , eight distinct regions could be visualized , with some genes expressed in more than one region ( Figure 1A ) . Representative examples of region-specific genes include wt1b , slc20a1a , trpm7 , slc12a1 , stc1 , slc12a3 , and gata3 , as compared to expression of cdh17 , which is found in all tubule and duct progenitors [37] ( Figure 1A ) . We investigated where the mouse or human orthologues of some of these genes are expressed in the mammalian metanephric kidney , and found that many corresponded to segment-specific domains within the nephron . For example , Slc9a3 is expressed in podocytes , the proximal convoluted segment ( PCT ) and proximal straight segment ( PST ) ( Figure 1B ) [38] . Slc20a1 is expressed throughout the entire nephron epithelium , although stronger expression was observed in proximal tubule segments ( Figure 1B ) . Transcripts for Slc13a3 are found in the PST [39] , while Slc12a1 is restricted to the thick ascending limb ( TAL ) and macula densa ( MD ) ( Figure 1B ) [40 , 41] . Slc12a3 is expressed in the distal convoluted tubule ( DCT ) ( Figure 1B ) [41] . Lastly , GATA-3 expression specifically marks the collecting ducts ( CD ) ( Figure 1B ) [42 , 43] . Based on this cross-species gene expression comparison , the following identities were assigned to the zebrafish pronephros segments we observed ( going from proximal to distal ) : podocytes ( pod ) , neck ( N ) , PCT , PST , distal early ( DE ) , corpuscle of Stannius ( CS ) , distal late ( DL ) , and the pronephric duct ( PD ) ( Figure 1A and 1E ) . Our division of PCT and PST within the tubule is based on the observation that the slc20a1a-expressing PCT cells undergo morphogenesis from a linear tube into a coiled structure by 5 days post-fertilization ( dpf ) , while the trpm7- and slc13a1-expressing PST segment maintains a linear structure ( Figure 1C ) . Expression of trpm7 and slc13a1 is discontinuous within the PST , an observation that has been shown recently to reflect the presence of two cell types in this region: transporting epithelia and multiciliated cells [44 , 45] . The renal corpuscle connects to the PCT via a short segment of cells that express the transcription factor rfx2 , and fails to express almost all of our PCT solute transporters ( Figure 1D ) . As rfx2 marks ciliated cells and rfx genes are essential regulators of ciliogenesis [46 , 47] , we hypothesize that this region corresponds to the ciliated neck segment found in other fish species as well as mammals [3–5] . However , a more detailed analysis is needed to confirm this hypothesis . In addition to the neck segment , rfx2 expression was also detected in presumptive ciliated cells along the length of the PST and DE segments , as described previously [44 , 45] . For the distal tubule , we adopted the DE/DL nomenclature used in Xenopus [48] , although the zebrafish DE appears analogous to the TAL segment in mammals and the DL appears analogous to the mammalian DCT segment according to our gene expression comparison . We included the CS as a discrete segment , as it initially arises from the tubular progenitors within the pronephros , but by 48 hpf , it is located just dorsal to the DE/DL boundary [49 , 50] ( unpublished data ) . The DL segment expresses slc12a3 and connects to the cloaca via a short segment that expresses gata3 and likely represents the PD . Our data are consistent with the notion that the zebrafish pronephric kidney resembles a ‘stretched-out”' mammalian nephron , and suggests that rather than being composed of mostly nephric duct ( as currently believed ) , it is made up of extensive proximal and distal tubule epithelium ( Figure 1E ) . Between 24 and 48 hpf ( the start of blood filtration ) , the pronephros undergoes significant morphogenesis , including the midline migration of podocytes and the growth/extension of the tubules [9] . In order to better quantitate these morphological changes , as well as to precisely define the anatomical boundaries of each segment , we mapped the expression domains of segment-specific markers relative to the somites by performing double whole-mount in situ hybridization with myosin heavy chain ( mhc ) at 24 and 48 hpf ( Figures 2 and S1–S4 ) . At 24 hpf , podocyte and neck progenitors are arranged in a slight curve at the level of somite 3–4 with the anterior boundary of the PCT level with somite 5 ( Figures 2 and S1 ) . By 48 hpf , the podocyte progenitors have fused at the midline ( level with somite 3 ) with the presumptive neck region forming a lateral extension that connects with the PCT also situated at the level of somite 3 ( Figures 2 and S3 ) . During this time , the length of the PCT , PST , and DE segments increased , possibly due to cell division within each segment ( Figure 2 and S1–S4 ) . This growth may provide the driving force that is responsible for the shift in the anterior boundary of the PCT from somite 5 to somite 3 between 24 and 48 hpf , and for the coiling morphogenesis of the PCT observed between 72 and 144 hpf ( Figure 1C ) . However , the DL segment did not increase in length between 24 and 48 hpf , indicating that there is not a uniform expansion in all segments during development . During juvenile development ( 2–3 wk post-fertilization ) the DL segment is proportionately larger than the other segments , suggesting that its expansion predominates at later stages of development ( unpublished data ) . Interestingly , at 24 hpf , we observed an overlap of the DL and PD expression domains at the level of somite 17 ( Figure 2 and Figure S2 ) . This overlap may indicate the presence of an additional segment ( such as a discrete CNT equivalent ) , though to date we have not discovered any genes expressed solely in this domain . In addition to mapping the morphological changes that occur between 24 and 48 hpf , we also noted segment specific changes in gene expression patterns during this time . For example , transcripts for the solute transporters slc13a1 ( inorganic sulphate transporter ) , slc13a3 ( sodium-dicarboxylate carrier ) , and slc22a6 ( organic anion transporter ) were all absent from the PST segment at 24 hpf but were found expressed at 48 hpf ( Figures 2 and S3; and unpublished data ) . Similarly , transcripts for trpm7 ( divalent cation-selective ion channel ) and slc41a1 ( Mg 2+ transporter ) were not found in cells of the CS at 24 hpf but could be detected at 48 hpf ( Figure 2 and unpublished data ) . The up-regulation of these genes likely reflects the maturation/differentiation of the segment epithelia before the onset of blood filtration , which begins around 40 hpf [51 , 52] . However , the glomerulus does not fully mature until 4 dpf , based on the size exclusion of different-sized fluorescent dextrans [53] . Further profiling of segment expression patterns at later stages is needed to investigate whether the maturation of the transporting epithelial is also an ongoing process . We next sought to characterize the developmental pathways that establish the segmentation pattern of the pronephros . Retinoic acid ( RA ) is essential for the development of numerous tissues during embryogenesis [25] . In vertebrates , a gradient of RA in the upper trunk is responsible for directing the A-P patterning of the hindbrain into segmental compartments [15 , 54] , and gain or loss of RA also affects vertebral identity [25] . Interestingly , RA has been reported to regulate pronephros formation in Xenopus [55] . To explore whether RA was needed for pronephros segmentation , we injected wild-type zebrafish embryos with a morpholino to retinaldehyde dehydrogenase 2 ( raldh2 ) , which encodes an enzyme required to produce RA [20] . We then examined raldh2 morphants at 48 hpf with our panel of segment-specific markers , in combination with mhc expression to map segment length and location relative to the somites . Embryos deficient in raldh2 had fewer podocytes , as evidenced by punctate staining of the podocyte markers wt1b , wt1a , and mafb ( Figure 3A and unpublished data ) . Both proximal tubule segments were slightly shortened , based on the reduced expression domains of slc20a1a ( PCT ) and trpm7 ( PST ) ( Figure 3 ) . Conversely , the distal tubule was expanded in length , with the clck transporter ( marking both the DE and DL ) expressed in a greater proportion of the cdh17-positive pronephric tubule ( note that the overall length of raldh2 morphants is reduced compared to wild-type ) ( Figure 3 ) . Each segment within the distal tubule was moderately expanded , with a lengthened DE shown by expanded slc12a1 expression , enlarged clusters of stc1-expressing cells that comprise the CS , and lengthened DL shown by slc12a3 expression ( Figure 3 ) . The expression domain of gata3 ( PD ) was also expanded ( Figure 3 ) . In contrast , the cloaca marker aqp3 was unchanged ( Figure 3A ) . These data suggest that RA signaling is necessary for podocyte formation and/or survival , as well as for establishing the normal pattern of nephron segmentation . Multiple enzymes are capable of synthesizing RA and a recent analysis of the neckless ( nls ) ( now called aldh1a2 ) mutant , which is defective in raldh2 , demonstrated that there are additional sources of raldh-like enzyme activity in the zebrafish embryo [56–58] . Based on this , we hypothesized that the nephron phenotype in raldh2 morphants represented the effect of reducing RA production , rather than a complete inhibition . To more fully block RA signaling , we utilized a competitive , reversible inhibitor of raldh enzymes , 4-diethylaminobenzaldehyde ( DEAB ) [59] , which has been used to effectively prevent de novo RA synthesis in zebrafish embryos [58 , 60] . Wild-type embryos were treated with DEAB starting at 60% epiboly ( early gastrula ) until the 15 somite stage , and nephron segmentation was assayed at 48 hpf by double whole-mount in situ hybridization using mhc expression to mark the somites . Expression of the podocyte markers wt1b , wt1a , and mafb was absent in DEAB-treated embryos , suggesting that podocyte development was completely blocked ( Figure 3 and unpublished data ) . Expression of the PCT and PST markers slc20a1a and trpm7 was also absent , suggesting that these segments had failed to be specified ( Figure 3 ) . In contrast , clck expression ( marking the DE and DL segments ) was dramatically expanded , such that it was present throughout the entire tubule territory ( Figure 3 ) . These findings suggest that the pronephric tubule adopts a distal tubule identity when RA synthesis is inhibited . Within this ‘distal-only' pronephros , the DE marker slc12a1 was expressed from the anterior limit of the tubule to almost the middle of its length , the DL marker slc12a3 was expressed from the middle of the tubule to near its posterior limit , and the PD marker gata3 was expanded by an additional four somite lengths ( Figure 3 ) . A marked expansion of stc1-expressing cells was also detected , with multiple clusters of cells arranged in bilateral stripes , as opposed to the small groups of stc1-expressing cells seen in wild-type embryos ( Figure 3 ) . Expression of aqp3 was not altered , suggesting that progenitors of the cloaca are unaffected by the inhibition of RA production over this developmental interval ( Figure 3 ) . It is not known if the observed DEAB phenotype represents a full loss of RA signaling , as trace amounts of maternal RA have been detected in the yolk [61] and as zygotic raldh2 transcripts are expressed prior to 60% epiboly [56 , 57] . Nevertheless , our results demonstrate that RA , produced by raldh2 and possibly one or more unknown raldh enzymes , plays an essential role in the formation of proximal nephron fates ( podocytes , PCT , PST ) and in suppressing the expansion of distal fates ( DE , CS , DL , PD ) . To determine more precisely when RA signaling was needed for the development of proximal segment fates , we performed a DEAB timecourse experiment ( Figure 3 ) . Blocking RA signaling from 90% epiboly ( late gastrula ) to the 5 somite stage caused a milder phenotype than the longer 60%- 15-somite exposure that was characterized by a loss of podocytes , reduced lengths of the PCT and PST segments , and small increases in the lengths of the distal segments ( Figure 3 ) . A slightly longer DEAB treatment , from 90% epiboly to the 10 somite stage , expanded distal segments further than the raldh2 morphants or DEAB 90% epiboly-5 somite treated embryos ( Figure 3 ) . In addition , examination of the slc20a1a and trpm7 expression patterns showed that the PST segment was ablated , while the PCT was only slightly shortened ( Figure 3 ) . Loss of PST identity was also confirmed by the absence of slc13a1 and slc22a6 transcripts , which are additional PST markers ( Figure 2 and unpublished data ) . These findings suggest that , at least for this DEAB time window , the expansion of distal fates occurs at the expense of the PST segment . Finally , we tested whether DEAB treatment during somitogenesis would affect pronephros segmentation . DEAB treatment from 5–15 somites or 10–15 somites had no effect on the segmentation pattern of the pronephros ( unpublished data ) , thus suggesting that an inhibition of RA signaling must be initiated prior to the 5 somite stage in order to affect nephron patterning . Taken together , our DEAB timecourse data indicate that RA signaling is required to induce proximal nephron fates and to prevent an expansion of distal fates , thereby establishing the normal pronephric segmentation pattern . Interestingly , our data suggest that different segments have different temporal requirements for RA signaling . RA is essential between 90% epiboly and the 5 somite stage to induce podocytes , between 90% epiboly and 10 somites to induce PST formation , and between 60% epiboly and 15 somites to form the PCT . The PCT segment is the most refractory to RA inhibition and is only lost following the longest DEAB treatment window ( 60% epiboly to 15 somites ) . Given these findings , we tested whether increasing the concentration of RA by exogenous treatment would promote proximal nephron fates at the expense of distal fates . We exposed wild-type zebrafish embryos to RA during similar developmental intervals used for our DEAB experiments , and then assayed segment marker expression at 24 hpf by double in situ hybridization with mhc to mark the somites . Wild-type embryos treated with 1 × 10−7 M RA between 90% epiboly and 5 somites developed a normal number of podocytes , as evidenced by wt1b expression , but displayed expanded proximal tubule domains , shown by expression of slc9a3 ( marking both the PCT and PST ) , slc20a1a ( PCT ) , and trpm7 ( PST ) ( Figure 4 ) . Conversely , the clck-expressing distal tubule domain was reduced , due to reductions in the length of the DL ( marked by slc12a3 ) and PD ( marked by gata3 ) ( Figure 4 ) . However , the DE segment ( marked by slc12a1 ) was unaffected ( Figure 4 ) . The position of the stc1-expressing CS segment cells was shifted posteriorly , and the level of expression was slightly reduced ( Figure 4 ) . A longer treatment window , from 60% epiboly–15 somites resulted in a more severe ‘proximalized' phenotype with a longer expanse of proximal tubule and a greater reduction in each distal tubule domain ( Figure 4 ) . In these embryos , the position of the stc1-expressing CS population was shifted even more posteriorly , and was located at the distal edge of the yolk sac extension ( Figure 4A ) . We next treated wild-type embryos with higher dose of RA ( 1 × 10−6 M ) over this same 60% epiboly–15 somites time window , as well as two shorter time periods: 60% epiboly–5 somites , and 90% epiboly–5 somites . Embryos treated for any of these time windows displayed a completely ‘proximalized' phenotype with the tubule domain being comprised entirely of proximal segment identities ( Figure 4 and unpublished data ) . In these embryos , the proximal marker slc9a3 was expressed throughout the cdh17-expressing tubule population ( Figure 4 and unpublished data ) . Within this ‘proximal-only' pronephros , the PCT marker slc20a1a was expressed from the anterior limit of the tubule to somite 13 , and the PST marker trpm7 was expressed from somite 14 to the posterior limit of the tubule , where the trpm7-expressing tubules fused at the prospective site of the cloaca ( Figure 4 and unpublished data ) . Expression of all distal segment markers was absent , suggesting that the DE , CS , DL , and PD had failed to be specified ( Figure 4 and unpublished data ) . These results show that exogenous RA treatment from gastrulation stages until the 5 somite stage is sufficient to ‘proximalize' the pronephros , suggesting that this time period is the critical window when RA signaling is required for proximo-distal patterning of pronephric progenitors . To further explore the notion that RA alters the patterning of renal progenitors prior to the 5 somite stage , we examined the expression patterns of the Notch ligands deltaC ( dlc ) and jagged2a ( jag2a ) , and the renal transcription factors wt1a , pax2a , pax8 , and evi1 , as these genes are detected in the IM during early somitogenesis and have been implicated in early nephron patterning [44 , 62–64] . Wild-type embryos were treated with DMSO , DEAB , or 1 × 10−6 M RA from 60% epiboly until the 6 somite stage , and then IM gene expression was assayed by whole-mount double in situ hybridization together with myoD to mark the somites . Transcripts for pax2a and pax8 , which label all pronephric progenitors , were found throughout the IM domain in a similar pattern in wild-type , DEAB-treated , and RA-treated embryos ( Figure S5 ) . Consistent with our previous results , wt1a expression was absent in DEAB-treated embryos , and wt1a was strongly up-regulated in RA-treated embryos ( Figure S5 ) . The expression domains of deltaC and jag2a , normally restricted to a proximal region of the IM adjacent to somites 2–5 , were absent in DEAB-treated embryos , and expanded posteriorly in RA-treated embryos ( Figure S5 ) . Expression of evi1 , found in the distal portion of the IM starting around somite 6 , was shifted anteriorly to somite 3 following DEAB treatment and reduced to the most posterior group of IM cells following RA treatment ( Figure S5 ) . These findings demonstrate that changes in RA dosage during gastrulation to early somitogenesis are associated with gene expression changes in IM progenitors , far in advance of their mesenchymal-to-epithelial transition that creates the pronephric tubules . In addition to RA , multiple tissues along the embryonic axis are segmentally patterned by the cdx genes [25 , 34] . Loss of cdx gene function leads to an expansion of anterior trunk fates and axial elongation defects that result in a loss/truncation of the posterior trunk and tail [23–30 , 34] . In zebrafish , cdx4–/– mutant embryos display expanded wt1a expression at the 15-somite stage , suggesting that the podocyte lineage might be expanded and thus implicating cdx genes in pronephros patterning [24] . We therefore examined the formation of the pronephros in cdx4–/– and cdx1a/4-deficient ( herein referred to as cdx-deficient ) to assess development of the renal corpuscle , tubule , and pronephric duct . In addition , we used differential interference contrast ( DIC ) optics to visualize the somite boundaries and determine the size and position of each segment relative to the somites . It is important to note that the size of posterior somites in cdx4–/– embryos , and both the size and number of the somites in cdx-deficient embryos , is greatly reduced toward the posterior , due to the axial elongation defect [24 , 29–30] . A similar defect has also been observed in mouse Cdx mutants [34] . At 24 hpf , wild-type embryos expressed wt1a in presumptive podocytes ( located adjacent to somite 3 , marked by an arrow in Figure 5A ) as well as a population of presumptive mesenchymal cells located next to somites 1–3 in a broad lateral domain ( indicated by a bracket and ‘M' in Figure 5A ) . cdx4–/– embryos had expanded wt1a expression at 24 hpf that ranged from a position anterior to somite 1 to approximately somite 7 , and cdx-deficient embryos showed an even more dramatic posterior expansion that reached somite 12 ( Figure 5A ) . As the expansion of wt1a in cdx mutant embryos could indicate increased numbers of podocytes and/or the mesenchymal population , we examined wt1b expression , which specifically marks podocytes [65] . Equivalent numbers of wt1b-expressing podocytes were formed in cdx4–/– mutant and wild-type embryos , but podocytes in cdx4–/– embryos were located more posteriorly , at the level of somite 5 ( Figure 5A ) . cdx-deficient embryos developed a normal number of wt1b-expressing podocytes , though they were arranged in a pair of somewhat irregular linear groupings ( rather than forming bilateral spherical clusters ) , and they were also located more posteriorly , adjacent to somite 7 ( Figure 5A ) . We conclude from these observations that the loss of cdx4 and cdx1a/4 function progressively expands wt1a expression without increasing the number of podocytes and leads to podocyte formation at more posterior locations along the embryonic axis . To assess tubule formation in cdx mutants , we examined the expression of cdh17 at 24 hpf . While cdx4–/– embryos formed complete tubules that fused at the cloaca , cdx-deficient embryos displayed cdh17 expression that was reduced and discontinuous , with the tubules failing to fuse ( Figure 5A ) . Consistent with the posterior shifts in podocyte position , the A-P position of the tubule , marked by cdh17 transcripts , was shifted caudally in both cdx4–/– and cdx-deficient embryos . In wild-type embryos , the tubule spans the length of somites 4–18 , but was located from somites 6–20 in cdx4–/– embryos , and from somites 8–13 in cdx-deficient embryos ( Figure 5A ) . We next characterized the tubule segmentation pattern in cdx mutant embryos by examining the expression patterns of slc20a1a ( PCT ) , trpm7 ( PST ) , slc12a1 ( DE ) , stc1 ( CS ) , slc12a3 ( DL ) , gata3 ( PD ) , and aqp3 ( cloaca ) . cdx4–/– embryos showed a slight expansion of the PCT , which spanned an additional two somites compared to wild-types ( Figure 5A and unpublished data ) . The PST , DE , CS , and PD were all shorter than normal in cdx4–/– embryos , with the PD displaying the most severe reduction in length ( Figure 5A and unpublished data ) . Transcripts for aqp3 were not detected in cdx4–/– embryos , suggesting a defect in cloaca development ( Figure 5A ) . These results indicate that loss of cdx4 leads to a slight expansion in PCT fate with corresponding reductions in more distal fates . In contrast to cdx4–/– embryos , cdx-deficient embryos showed discontinuous slc20a1a expression and failed to express trpm7 , slc12a1 , stc1 , slc12a3 , gata3 , or aqp3 ( Figure 5A ) . These results suggest that the tubule territory in cdx-deficient embryos acquires a PCT identity , while the remaining nephron segments fail to develop . These defects were not the result of delayed development , as at later developmental stages , cdx-deficient embryos continued to possess tubules that only expressed PCT-markers , and the tubules never fused caudally ( unpublished data ) . In addition , expression analysis of the podocyte marker mafB in cdx-deficient embryos at 48 hpf revealed that podocytes fail to fuse into a single renal corpuscle . Instead , the podocytes formed bilateral corpuscles that were dilated compared to wild-type and cdx4–/– embryos , presumably due to fluid accumulation ( Figure 5B ) . Consistent with this , cdx-deficient embryos had developed glomerular cysts by 72 hpf , as well as severe pericardial edema , indicative of renal failure ( Figure 5C ) . In contrast , cdx4–/– embryos never exhibited glomerular cyst formation or edema , suggesting that although various segment lengths were shortened , these embryos were able to maintain adequate kidney function ( Figure 5C ) . In summary , our expression analyses show that cdx deficiency causes a posterior shift in the location of the pronephros along the embryonic axis ( Figure 5D ) . While the podocytes and PCT populations formed relatively normally , the PST and distal tubule segments were reduced or absent in cdx4–/– and cdx-deficient embryos , respectively ( Figure 5D ) . Thus the cdx genes , acting either directly or indirectly , are required for the formation of the distal nephron segments and establishing the normal segmentation pattern of the pronephros . Given the largely opposite effects of cdx-deficiency and loss of RA signaling on nephron patterning , as well as the recent report that cdx genes control how the hindbrain responds to RA during its patterning [30 , 31] , we wondered if an interplay between these pathways was operative during pronephros segmentation . The location and level of RA within tissues is dependent on the expression of raldh-synthesizing enzymes and cyp26-degrading enzymes [15 , 54] . We therefore investigated the expression of these genes in cdx-deficient embryos during early somitogenesis , as our previous experiments suggested that the IM is being influenced by RA signaling at this time . At the 5 somite stage , expression of raldh2 in the paraxial mesoderm was expanded posteriorly in cdx4–/– embryos compared to wild-types ( Figure 6A ) . An even greater posterior expansion was seen in cdx-deficient embryos , with raldh2 transcripts being detected throughout the entire unsegmented paraxial mesoderm and tailbud region ( Figure 6A ) . Expression of the RA-catabolizing enzyme cyp26a1 in the upper trunk region was also expanded posteriorly in cdx4–/– embryos at the 5 somite stage , and more extensively expanded in cdx-deficient embryos ( Figure 6A ) . To visualize how the combined changes in raldh2 and cyp26a1 expression altered the source of RA along the trunk , we generated digital overlays of these expression patterns . This analysis suggested that the anterior boundary of RA production ( i . e . , the junction of the cyp26a1 and raldh2 expression domains ) was located more posteriorly in cdx4–/– mutants compared to wild-types , and that this posterior shift was more pronounced in cdx-deficient embryos ( arrows in Figure 6A ) . To better quantitate these posterior shifts , we examined expression of raldh2 and cyp26a1 at the 10 somite stage when the somites could be visualized by staining for myoD transcripts . We found that the raldh2 expression boundary in cdx4–/– and cdx-deficient embryos was shifted posteriorly by 1 and 2 somites , respectively , compared to wild-type embryos ( Figure 6B ) . The cyp26a1 domain in wild-type embryos occupied the region of somites 2–3 , just rostral and slightly overlapping with raldh2 , as shown by double in situ hybridization ( Figure 6B ) . In cdx4–/– embryos , cyp26a1 transcripts were detected in the region of somites 3–5 , whereas in cdx-deficient embryos they extended from somites 3–7 ( Figure 6B ) . These analyses reveal a striking correlation between the presumptive source of RA at the 10 somite stage and the axial position of the pronephros at 24 hpf in each genotype ( i . e . , the source of RA and the position of the pronephros both start at somites 3 , 5 , and 7 in wild-type , cdx4–/– , and cdx-deficient embryos , respectively ) . The combination of these data , together with the results from our DEAB experiments , suggest a model in which the cdx genes act upstream of raldh2 and cyp26a1 to localize the source of RA along the A-P axis , and that RA , in turn , acts on the IM to induce the proximal segments and prevent an expansion of the distal segment fates . If our model is correct , then inhibiting RA synthesis in cdx-deficient embryos should rescue pronephric positioning and the formation of the distal tubule segments . To test this , we treated cdx4–/– and cdx-deficient embryos with DEAB from 90% epiboly to the 5-somite stage and examined pronephros segmentation . In support of our model , we found that cdx4–/– and cdx-deficient embryos exhibited a one-somite anterior shift in the position of the pronephros , as shown by expression of cdh17 ( Figure 7 ) . In addition , the development of podocytes was abrogated , and the length of the PCT was reduced in DEAB-treated cdx4–/– and cdx-deficient embryos ( Figure 7 ) , consistent with our findings in wild-type embryos ( Figure 3 ) . We observed that the DE segment was increased in DEAB-treated cdx4–/– embryos , as shown by an expansion of the slc12a1 expression domain ( Figure 7 ) . DEAB treatment also increased the number of CS cells in cdx4–/– mutants , shown by the expression of stc1 , and increased the DL segment length , evidenced by expansion of the slc12a3 , romk2 , and clck expression domains ( Figure 7 and unpublished data ) . In cdx-deficient embryos treated with DEAB , formation of the DE , CS , and DL segments was rescued , shown by expression of slc12a1 , stc1 , and slc12a3 , respectively ( Figure 7 ) . A similar rescue of the expression of the DE and DL markers romk2 and clck was also observed ( unpublished data ) . These findings demonstrate that cdx gene function is not necessary to specify distal segment identity directly , but instead suggests that the abrogation of distal segment formation in cdx-deficient mutants is related to the level of RA that the renal progenitors are exposed to . Taken together with the above results , these finding provide good evidence that the pronephric positioning defect and failure to form the distal tubule identities in cdx-deficient embryos is caused by mis-localization of the RA source along the A-P axis .
Retinoid signaling plays essential roles in the A-P patterning of a number of diverse tissues in the embryo . During early development , a source of RA in the upper trunk ( cervical ) region is produced by the action of the RA synthetic enzyme , Raldh2 , which is expressed in the anterior paraxial mesoderm [54] . The coordinate expression of RA-catabolizing Cyp26 enzymes in surrounding tissues creates a so-called ‘sink' for this RA source [54] . Collectively , these enzymes are thought to create a gradient of RA activity that diffuses into surrounding tissues [34 , 54] . The functions of this RA source have been extensively studied in the developing hindbrain , where the effects of graded RA signaling are thought to create nested expression domains of RA-responsive genes that drive A-P segmentation of the hindbrain into a series of rhombomeres [15 , 54] . In addition to regionalizing the overlying neurectoderm , RA produced in the upper trunk paraxial mesoderm has been implicated in the regionalization of the underlying endoderm . Studies in zebrafish have shown that RA acts directly on the endoderm to specify hepatopancreatic progenitors that give rise to the liver and pancreas [17 , 18] . RA also influences mesodermal cell fate decisions during zebrafish development , including the formation of the pectoral fin field—which arises from the lateral plate mesoderm adjacent to the upper trunk somites [56 , 57 , 66]—and the heart [67] . In the latter case , inhibition of RA synthesis leads to an expansion of precardiac mesoderm , resulting in an excessive number of myocardial progenitors [67] . These findings indicate that , in addition to acting as an inducer of cell fates such as in the hindbrain and endoderm , RA also plays an important role in restricting certain cell fates . Our study now adds the IM as another mesodermal derivative that is patterned by RA . Our results show that RA production is essential during gastrulation and early somitogenesis for the induction of proximal nephron fates as well as to restrict the expansion of distal nephron fates . Over this period of development , RA is produced by the anterior paraxial mesoderm ( PM ) . The IM , which gives rise to the pronephros , is located lateral to the PM ( Figure 8 ) . Given the role of RA as a diffusible morphogen in other tissues , we hypothesize that RA diffuses from the PM and establishes a gradient along the IM , with high levels of RA inducing proximal fates and low RA levels being permissive for distal fates ( Figure 8 ) . Our time-course experiments with DEAB support this view , with the most severe reduction in proximal fates ( and concomitant expansion in distal segments ) corresponding to the longest treatment window . However , further work is needed to determine the nature of the RA gradient , as well as how dynamic fluctuations in retinoid availability [54] affect the dose and length of time that the renal progenitors are exposed to RA . A gradient-free model has recently been proposed for RA-dependent hindbrain patterning , based on the finding that sequential expression domains of the cyp26a1 , cyp26b1 , and cyp26c1 genes are essential for rhombomere boundary establishment [68 , 69] . It is unclear if a similar mechanism might operate during pronephros segmentation , as cyp26b1 and cyp26c1 do not show a nested pattern of expression in the IM . Overall , the effects of RA on the patterning of the IM can be regarded as ‘anteriorizing . ' Our finding that exogenous RA treatment induces proximal tubule fates to form throughout the pronephros supports this conclusion . Classically , RA is known as a ‘posteriorizing' factor due to its effects on the central nervous system , where an inhibition of RA signaling causes an expansion of anterior neural fates in the hindbrain [15 , 54] . Thus we conclude that RA can actually have both anteriorizing and posteriorizing activities , depending on the tissue in question . A unified way to characterize these effects would be to consider the upper trunk RA source as an organizing center , akin to the dorsal organizer in the gastrula , that locally patterns cell types in all three germ layers . Previous studies have implicated RA as a regulator of renal development . Animal cap experiments in Xenopus showed that RA , together with Activin , is sufficient to induce the formation of pronephric tubules [70] . A more recent study in Xenopus reported that overexpressing various RA antagonists results in a complete loss of the pronephros ( glomus , tubules , and duct ) [55] . This phenotype is more severe than what we observed and may reflect differences in the efficacy of DEAB to completely block RA production compared with other RA antagonists . The pronephric tubules in Xenopus are segmented into proximal and distal segments [48] , similar to the zebrafish pronephros , however a role for RA in Xenopus nephron segmentation has not been reported . In mammals , it has long been known that vitamin A deficiency causes severe renal malformations [71] . Targeted mutagenesis of the RAR genes in mouse , followed by elegant rescue experiments , established an important role for RA as a dose-dependent inducer of the GDNF receptor Ret [72 , 73] . GDNF is an essential regulator of ureteric bud branching morphogenesis , and loss of GDNF signaling results most frequently in renal agenesis [74] . Because ureteric bud branching is an essential prerequisite for nephrogenesis , RA serves a key role in stimulating nephron formation . At present it is not known whether RA is also involved in the proximodistal patterning of metanephric nephrons . Interestingly , Raldh2 transcripts are found in podocyte progenitors , whereas Cyp26a1 is expressed by the tubule anlagen during metanephros development , suggestive of a role for RA in mammalian nephron patterning [75] . Transplantation studies in frogs suggest that RA may act directly on pronephric precursors [55] . However , the downstream targets of RA in the IM are not known . In the hindbrain , the presumptive RA gradient is thought to regulate rhombomere segmentation by activating the expression of the anterior , 3' Hox genes , i . e . , those comprising the 1st through 5th paralog groups [15 , 54 , 76] . In zebrafish , transcripts for hoxb1a , hoxb1b , and hoxb5a are found in proximal portions of the IM , thus making them potential candidates for mediating the effects of RA during pronephros segmentation [34] . Future studies using single , double , and triple morpholino injections can test the importance of these hox genes for renal development . The effects of RA on pronephric segmentation may also be coordinated by the action of non-Hox pathways . Our results suggest the intriguing possibility that RA signaling targets may include renal transcription factors as well as members of the Notch signaling pathway . The gene evi1 encodes a zinc-finger transcription factor that has been implicated in patterning distal regions of the pronephros in Xenopus , and overexpression of evi1 was found to inhibit proximal segment formation [63] . These data are consistent with our results showing that expression of evi1 in the IM was expanded following DEAB treatment , and reduced following exposure to exogenous RA . Another renal transcription factor candidate is the odd-skipped related transcription factor 1 ( osr1 ) encoding a zinc-finger repressor . Recent studies in Xenopus and zebrafish have shown that osr1 is expressed in the ventral mesoderm during gastrulation and later in an anterior domain of the IM [77] . Morpholino-mediated knock-down of osr1 leads to defects in the formation of podocytes and proximal tubule progenitors [77] , consistent with Osr1 participating in a common pathway with RA . The Notch pathway may also interact with RA during nephron segmentation . Conditional knockout of Notch2 in the mouse metanephros results in a loss of podocytes and proximal tubule fates , whereas distal markers are relatively unaffected [78] . In zebrafish , Notch signaling has been shown to regulate the differentiation of multiciliated cells and principle cells in the pronephric tubules [44 , 45] . However , a role for Notch signaling in the formation of proximal nephron fates is also suggested by the expression pattern of the Notch ligands deltaC , jag1b , and jag2a , which are restricted to proximal portions of the intermediate mesoderm [62 , 64] . Simultaneous knockdown of jag1b/2a results in an abnormally small renal corpuscle and dysmorphic proximal tubules , consistent with a conserved role for Notch signaling in proximal nephron development [79] . Our finding that DEAB treatment abrogates deltaC and jag2a expression in the proximal IM , while exogenous RA expands their expression , supports a role for RA acting upstream of the Notch pathway . Our study provides evidence that cdx genes control the expression domains of raldh2 and cyp26a1 along the embryonic axis . The boundaries of both raldh2 and cyp26a1 are progressively shifted toward the posterior in cdx4 and cdx1a/4-deficient embryos , suggesting that the upper trunk source of RA is posteriorly shifted . We hypothesize that this posterior shift in RA production results in a posterior shift in the position of the pronephros ( Figure 8 ) . We propose that this effect , combined with the axial elongation defects , leads to reduced or absent distal segment fates . The ability to rescue distal segments by treating cdx mutants with a pulse of DEAB is consistent with this model , and also demonstrates that cdx function is not requisite for the induction of distal fates from the intermediate mesoderm . Thus additional , as yet unidentified pathways , are responsible for directing distal fates . While our data supports the notion that Cdx factors exert their effects by the regulation of RA signaling , it does not rule out the possibility that Cdx factors may also function to repress proximal fates independent of RA signaling . Given the mounting evidence that the upper trunk RA source is an important organizing center , we would predict that both the patterning and positioning of numerous organs would be affected in cdx mutants . Consistent with this , defects in several mesodermal fates that arise in the anterior trunk region have been observed in cdx-deficient embryos . Vascular precursors are progressively expanded when cdx activity is abrogated , and blood precursors are both reduced and shifted posteriorly in cdx mutants [24 , 29] . In addition to mesodermal defects , cdx mutants also display patterning defects in the neurectoderm that gives rise to the anterior spinal cord [30 , 31] . We hypothesize that many , if not all , of these defects in cdx mutants are caused by the abnormal localization of RA along the A-P axis . The loss of cdx gene function in both zebrafish and murine models has been shown to cause global shifts in hox gene expression in the mesoderm and neurectoderm [24 , 25 , 29–30 , 34] . Given the rostral shifts and expansions of both raldh2 and cyp26a1 expression observed in cdx4 and cdx1a/4-deficient embryos , Hox transcription factors are attractive molecules for regulating raldh2 and cyp26a1 expression . Defects in blood formation in cdx4-null zebrafish can be rescued by the overexpression of several hox genes [24] , and the overexpression of hoxa9a also results in a partial rescue of the axis elongation defect in cdx4–/– embryos [29] . Future studies are needed to examine whether hox gene overexpression ( s ) can rescue pronephros positioning and formation of distal segments in cdx mutant embryos . In conclusion , our studies have revealed an important link between the cdx genes and localization of RA , and provide evidence that RA signaling is a central determinant of pronephros A-P segmentation . Our results establish the zebrafish embryo as a simplified model of vertebrate nephron segmentation that will further our understanding of mammalian nephron segmentation , and provide insights into the causes of kidney birth defects and renal disease in humans .
Zebrafish were maintained and staged as described [80 , 81] . Tübingen strain wild-type embryos were used for all experiments . DEAB and all-trans retinoic acid ( Sigma-Aldrich ) were dissolved in 100% dimethyl sulfoxide ( DMSO ) to make a 1 M stock and aliquots were stored at −80°C . For DEAB and RA treatments: embryos were incubated in 1 . 6 × 10−5 M DEAB/DMSO in E3 embryo media , 1 × 10−6 M or 1 × 10−7 M RA/DMSO in E3 embryo media , or 1 . 6 × 10−5 M DMSO ( control ) in E3 in the dark over particular developmental intervals , then washed five times with E3 and then fixed at 24 or 48 hpf . These experimental treatments were fully penetrant and produced consistent results at the doses and treatment windows that were examined . raldh2 morpholino ( CAACTTCACTGGAGGTCATCGCGTC ) was injected into 1-cell wild-type embryos . Incrosses of kggtv205 heterozygous adults ( maintained on the Tübingen strain ) were used to obtain cdx4–/– embryos and were injected at the 1-cell stage with cdx1a morpholino ( CAGCAGATAGCTCACGGACATTTTC ) as described [29] to obtain cdx-deficient embryos . Both raldh2 and cdx1a morpholinos produced fully penetrant effects . Embryos were raised to appropriate stages and fixed in 4% paraformaldehyde ( PFA ) /1×PBST for gene expression analysis . For all reported gene expressions , at least 20 embryos were examined . Whole-mount in situ hybridization of zebrafish embryos was performed as previously described [24] . The expression patterns of cdh17 , clck , cyp26a1 , evi1 , gata3 , mhc , myoD , nbc1 , pax2a , pdzk1 , raldh2 , ret1 , sall1 , sglt1 , slc4a2 , slc20a1a , wt1a , and wt1b were previously reported [14 , 24 , 29 , 37 , 55 , 56 , 61 , 82–86] . For antisense probe production , we used the following IMAGE clone template plasmids , restriction enzymes for DNA linearization , and RNA enzymes: mafb: 7995399 , pExpress-1 , EcoR1 , T7; rfx2 , pBK-CMV , template was PCR amplified using primers GTGAATTGTAATACGACTCACTATAGGG and TTAACCCTCACTAAAGGGAACAAA , T7; slc9a3: 6996791 , pExpress-1 , EcoRI , T7; slc26a2: 4760214 , pBK-CMV , EcoRI , T7; slc13a1: 6793065 , EcoRV , t7; slc13a3: 4744276 , pCMV-sport6 . 1ccdb , EcoRI , T7; slc22a6: 4744276 , pBK-CMV , SalI , T7; slc12a1: pBK-CMV , EcoRI , T7; slc12a3: 7037010 , pExpress-1 , EcoRI , T7; stc1 was amplified from 24 hpf embryo cDNA using primers ATGCTCCTGAAAAGCGGATTT and TTAAGGACTTCCCACGATGGA and cloned into pGemTEasy , NcoI , Sp6 . Gene-specific primers spanning 700–1 , 000 bp of the coding sequence were used to amplify DNA fragments from E15 . 5/P0 kidney cDNA pools , and the PCR products of the right size were cloned into the pCRII-Topo vector ( primer sequences available upon request ) . DNA templates for riboprobe production were generated by PCR with T7 and Sp6 Ready Made primers ( Integrated DNA Technologies ) from PCRII-TOPO clones or T7 and T3 Ready Made primers from Bmap library clones . Digoxigenin-labeled anti-sense riboprobes were synthesized from the PCR product and purified with Micro Bio-spin columns P-30 Tris RNase-free ( Bio-Rad ) . Probes were diluted with prehybridization buffer ( 50% formamide , 5×SSC , pH4 . 5 , 50 μg/ml yeast tRNA , 1% SDS , 50 μg/ml heparin ) to 10 μg/ml and stored at −80 °C . Neonatal kidneys were dissected free of surrounding tissues except the ureter and fixed with 4% PFA at 4 °C for 24 h . After PBS washes , they were incubated with 30% sucrose at 4 °C overnight . Kidneys were swirled in five dishes of OCT to remove sucrose and mounted in OCT in a dry ice/ethanol bath . The OCT blocks were stored at −80 °C . Sections were cut at 20 μm and air dried . Sections were post-fixed with 4% PFA for 10 min , treated with 10 μg/ml proteinase K for 10 min and post-fixed for 5 min . Slides were acetylated ( 1 . 33% Triethanolamine , 0 . 065% HCl , 0 . 375% acetic anhydride ) for 10 min and dehydrated with 70% ethanol and 95% ethanol for 5 min each . Slides were air dried then incubated with 500 ng/ml digoxigenin-labeled riboprobes at 68 °C overnight . Hybridized sections were washed with 50% formamide , 1×SSC , pH4 . 5 for 30 min at 65 °C , treated with 2 μg/ml RNase for 15 min at 37 °C , and washed with 2×SSC , pH4 . 5 for 30 min , and twice with 0 . 2×SSC , pH4 . 5 for 30 min at 65 °C . Slides were washed three times at room temperature with 1×MBST ( 0 . 1 M maleic acid , 0 . 15 M NaCl , 0 . 1% Tween-20 , pH7 . 5 ) for 5 min each , and incubated with blocking solution ( 2% Boehringer Mannheim ( BM ) blocking reagent ) in 1×MBST , 20% heat-inactivated sheep serum ) for 1 h . After incubation with anti-digoxigenin antibody-AP ( Roche , 1:4000 ) at 4 °C overnight , sections were washed with 1×MBST at room temperature , 5 min for three times , then with NTMT ( 0 . 1 M NaCl , 0 . 1 M Tris-HCl , pH9 . 5 , 50 mM Mg2Cl , 0 . 1% Tween-20 , 2 mM Levimasole ) for 10 min , and developed with BM purple ( Roche ) . Color reactions were stopped with fixatives ( 4% PFA , 0 . 2% glutaraldehyde ) and sections mounted with glycergel mounting media ( DAKO ) . Images were captured with a Nikon DXM1200 digital camera attached to a Leitz DMRB microscope . | In the kidney , structures known as nephrons are responsible for collecting metabolic waste . Nephrons are composed of a blood filter ( glomerulus ) followed by a series of specialized tubule regions , or segments , which recover solutes such as salts , and finally terminate with a collecting duct . The genetic mechanisms that establish nephron segmentation in mammals have been a challenge to study because of the kidney's complex organogenesis . The zebrafish embryonic kidney ( pronephros ) contains two nephrons , previously thought to consist of a glomerulus , short tubule , and long stretch of duct . In this study , we have redefined the anatomy of the zebrafish pronephros and shown that the duct is actually subdivided into distinct tubule segments that are analogous to the proximal and distal segments found in mammalian nephrons . Next , we used the zebrafish pronephros to investigate how nephron segmentation occurs . We found that retinoic acid ( RA ) induces proximal pronephros segments and represses distal segment fates . Further , we found that the caudal ( cdx ) transcription factors direct the anteroposterior location of pronephric progenitors by regulating the site of RA production . Taken together , these results reveal that a cdx-RA pathway plays a key role in both establishing where the pronephros forms along the embryonic axis as well as its segmentation pattern . |
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Increased incidence of hand , foot and mouth disease ( HFMD ) has been recognized as a critical challenge to communicable disease control and public health response . This study aimed to quantify the association between climate variation and notified cases of HFMD in selected cities of Shanxi Province , and to provide evidence for disease control and prevention . Meteorological variables and HFMD cases data in 4 major cities ( Datong , Taiyuan , Changzhi and Yuncheng ) of Shanxi province , China , were obtained from the China Meteorology Administration and China CDC respectively over the period 1 January 2009 to 31 December 2013 . Correlations analyses and Seasonal Autoregressive Integrated Moving Average ( SARIMA ) models were used to identify and quantify the relationship between the meteorological variables and HFMD . HFMD incidence varied seasonally with the majority of cases in the 4 cities occurring from May to July . Temperatures could play important roles in the incidence of HFMD in these regions . The SARIMA models indicate that a 1° C rise in average , maximum and minimum temperatures may lead to a similar relative increase in the number of cases in the 4 cities . The lag times for the effects of temperatures were identified in Taiyuan , Changzhi and Yuncheng . The numbers of cases were positively associated with average and minimum temperatures at a lag of 1 week in Taiyuan , Changzhi and Yuncheng , and with maximum temperature at a lag of 2 weeks in Yuncheng . Positive association between the temperature and HFMD has been identified from the 4 cities in Shanxi Province , although the role of weather variables on the transmission of HFMD varied in the 4 cities . Relevant prevention measures and public health action are required to reduce future risks of climate change with consideration of local climatic conditions .
Hand , foot and mouth disease ( HFMD ) is an emerging infectious disease mainly caused by highly contagious intestinal viruses human enterovirus 71 ( EV71 ) and coxsackievirus A16 ( Cox A16 ) [1–3] . It is a human syndrome characterized by a distinct clinical presentation of fever , accompanied by oral ulcers and maculopapular rash or vesicular sores on the hands and feet , and sometimes the buttocks . HFMD transmission is through close personal contact , exposure to feces , contaminated objects and surfaces of an infected person . In recent decades , HFMD has become a growing public health threat to children , particularly those under the age of 5 [4–6] . Epidemics of HFMD are frequent and widespread in Asian countries , especially in China , Singapore , Malaysia and Japan , which have documented many large outbreaks of HFMD with severe complications and deaths predominantly among children [2 , 7–12] . At present , there is no specific curative treatment , and vaccine development is still in progress [13] . Weather variables might play a certain role in the transmission of the disease , as time series analysis in Guangzhou and Shenzhen , in China , showed that weather variation could affect the disease occurrence with a short lag period [14 , 15] . Under the context of global environmental change , the frequency of HFMD epidemics may be projected to increase in the future due to continued viral mutation , climate change , and the lack of health resources and effective surveillance systems in some regions [14 , 16–18] . Therefore , risk detection , early warning of HFMD cases with the capacity to predict a possible epidemic , and efficient public health response will be important to minimize the risk of epidemics and adverse impacts of HFMD . Intestinal viruses have a worldwide distribution . In tropical and semitropical areas , they are present throughout the year , whereas in temperate climatic zones , they are more common during summer and fall [19] . A previous literature review has indicated that HFMD typically occurs in the summer and early autumn [20] . Such seasonal distribution suggests climatic variations may play a certain role in the transmission of the disease , particularly in temperate areas . The fifth assessment report of the Intergovernmental Panel on Climate Change ( IPCC AR5 ) pointed out that the globe has experienced surface warming and projections of annual average temperature changes for 2081–2100 under Representative Concentration Pathways ( RCPs ) 2 . 6 and 8 . 5 , relative to 1986–2005 [21] . In China , increases in temperature have been observed in most regions from south to north during the last decade [22] . Climate change has also been identified as an important risk factor for transmission of infectious diseases , especially vector and food borne diseases [23] . Since 2008 , the China Ministry of Health has listed HFMD as a notifiable Class-C communicable disease , which has been included in the national communicable disease surveillance system and reporting network . Over six million cases had been reported up to the end of 2012 in China [17] . Studies have examined the association between HFMD and climate variables in selected regions [14 , 24–26] . Meteorological parameters , such as temperature and relative humidity , may affect the transmission and the frequency of HFMD . However , the effects of climate variables are not consistent in published studies , which could be due to various local climatic conditions , socioeconomic status and demographic characteristics in different regions . In particular , an understanding of the impact of seasonality and meteorological variables on disease transmission remains limited . A comparison among different cities within a Province may minimize potential confounding effects of socioeconomic inequalities and demographic differences , which will be used in this study to examine the role of climate variation on the incidence of HFMD , using Seasonal Autoregressive Integrated Moving Average ( SARIMA ) models . The purpose of this study was to identify , with SARIMA models , the impact of meteorological variables on HFMD in 4 major cities of Shanxi in northern China , using existing surveillance data , and to quantify the relationship between climate variation and the incidence of HFMD . This study will provide scientific evidence to assist public health policy-making to carry out efficient prevention and control of HFMD .
The study was approved by the Ethics Committee of Shanxi Medical University ( No . 2013091 ) , China , and conducted in accordance with its guidelines . Shanxi HFMD data were provided by the Shanxi Center for Disease Control and Prevention and were obtained from the National Surveillance System . No informed consent was required because no individual-level analysis was performed . The information contained in the patients’ records was anonymized and de-identified prior to analysis . Only aggregated data were analyzed and reported . Shanxi Province , which is located in North China , has a temperate , continental , monsoonal climate with four distinct seasons . The average temperature in January is in the range -16°C to -2°C and in July between 19°C and 28°C , the average rainfall is between 350 to 700 mm , and the average daily sunshine is between 7 to 9 hours . This study selected 4 major cities from north to south ( Datong , Taiyuan , Changzhi and Yuncheng ) , which have similar socioeconomic and demographic conditions ( Fig . 1 ) . Datong ( latitude 40°2′30" N and longitude 113°35′50" E ) is the northernmost prefecture-level city of Shanxi Province , with a population of 3 . 36 million by the end of 2012 ( data from the Shanxi Bureau of Statistics ) . Taiyuan ( latitude 37°43' 36" N and longitude 112°28′14" E ) is the capital and largest city of Shanxi , which is located at the centre of the province with an East-West span of 144 km and a North-South span of 107 km , and a population of 4 . 26 million in 2012 . Changzhi ( latitude 36°11′0" N and longitude 113°6′0" E ) is the southeast city of Shanxi Province , with a population of 3 . 37 million in 2012 . Yuncheng ( latitude 35°1′33" N and longitude 111°0′19" E ) is a southwestern city in Shanxi , with a population of 5 . 19 million in 2012 . Daily meteorological data including precipitation , average temperature , maximum temperature , minimum temperature , average relative humidity , and hours of sunshine for the study period from 1 January 2009 to 31 December 2013 , were obtained from the Shanxi Meteorological Administration . Daily meteorological data were aggregated on a weekly basis which comprised a total period of 261 weeks . Being a notifiable disease [17] , all clinical and hospital doctors are required to report cases of HFMD to the local Center for Disease Control and Prevention . The diagnosis criteria for HFMD cases were provided in a guidebook published by the Chinese Ministry of Health [27 , 28] . Patients with HFMD have the following symptoms: fever , papules and herpetic lesions on the hands or feet , rashes on the buttocks or knees , inflammatory flushing around the rash and fluid in the blisters , or sparse herpetic lesions on the oral mucosa . A recent data quality survey report has demonstrated that the data are of high quality in China , with reporting completeness of 99 . 84% and accuracy of the information reported to be 92 . 76% [29] . In addition , in order to reduce apparent underreporting and a large number of missing information of patients from the early stage of the surveillance system in 2008 , only the data from 2009 to 2013 were used for analysis . The weekly data of HFMD cases for the period were obtained from the China Information System for Disease Control and Prevention in Shanxi . According to our data , 90 . 9% , 91 . 0% , 90 . 3% and 97 . 8% HFMD cases were children aged 0–5 years in Datong , Taiyuan , Changzhi and Yuncheng , respectively . Therefore , we focused analysis on the incidence of HFMD among children aged 0–5 years in this study . The analysis includes descriptive , correlation and time series regression analyses . The meteorological variables data were calculated for intervals of 7 consecutive days , and transformed into a time series format . Descriptive analysis was performed by describing the distribution of climate variables and HFMD cases . Spearman rank correlation and partial correlation analysis were used to examine the association between each meteorological variable and the incidence of HFMD . In addition , given the potential lagged effect of the meteorological variables on disease transmission , cross-correlation analysis was also performed with relevant time lag values . Time series analysis was used to assess the effect of climatic variables on HFMD incidence . The plot of the observed HFMD incidence showed most of the cases occurred from May to July in the 4 cities ( Fig . 2A-2D ) . Furthermore , the plots of autocorrelation function ( ACF ) and partial auto correlation function ( PACF ) of HFMD cases ( Fig . 2E-2H ) showed the time series was non-stationary . With the temporal dependence of HFMD incidence , the need to use a SARIMA model was evident . The Seasonal Autoregressive Integrated Moving Average ( SARIMA ) model ( Box and Jenkins method ) has been recently applied in epidemiological studies [24 , 30–33] , and was used to describe current ( and future ) incidence of HFMD in terms of their past values in this study . SARIMA models extend basic ARIMA models and allow for the incorporation of seasonal patterns . A SARIMA model , which includes seasonal and non-seasonal components , is typically represented by ( p , d , q ) ( P , D , Q ) s , where p represents the order of autoregression ( AR ) , d is the order of differencing , and q is the order of the moving average ( MA ) . P , D , and Q are their seasonal counterparts , and s is the seasonal lag [34] . The long-term trend and seasonal components of each time series can be removed using SARIMA models [24] . In addition , we considered the weather during holidays may also affect the occurrence of HFMD . The development of a SARIMA model is a four-step process . Therefore , for the HFMD time series analysis , it was firstly necessary to stabilize the variance of the series by square root transformation , and seasonal and regular differencing was also applied . Secondly , in order to identify the order of MA and AR parameters , the structure of temporal dependence of stationary time series was assessed respectively , by the analysis of autocorrelation ( ACF ) and partial autocorrelation ( PACF ) functions . From the correlograms of the series , the p value may equal 0 , 1 or 2 for autoregressive parameters and q value may equal 1 , 2 or 3 for moving average parameters ( Fig . 3A-3D ) . Thirdly , parameters of the model were estimated by using the maximum likelihood method . The goodness-of-fit of the models was determined for the most appropriate model ( the lowest normalized Bayesian Information Criteria ( BIC ) and the highest stationary R square ( R2 ) ) , using the Ljung-Box test that measures both ACF and PACF of the residuals , which must be equivalent to white noise . The significance of the parameters should be statistically different from zero . Finally , the predictions were performed by using the best fitting model . The predictive validity of the models was evaluated by calculating the root mean square error ( RMSE ) , which measures the amount by which the fitted values differ from the observed values . The smaller the RMSE , the better the model is for forecasting . Therefore , the SARIMA model was developed and verified by dividing the data file into two date sets: the data from the 1st calendar week of 2009 to the 52nd calendar week of 2012 were used to construct a model; and those from the 1st calendar week to the 52nd calendar week of 2013 were used to validate it . For statistical analysis SPSS version 19 . 0 and Stata version 12 . 0 were used . The sensitivity analysis was conducted based on daily unit to check whether the number of weekly HFMD cases could affect the result estimates . Meanwhile , we controlled for day of the week and public holidays using categorical indicator variables . In addition , we graphically examined the exposure-response curves derived using a smoothing function [28 , 35–37] , and natural cubic splines [35] to control long-term trend and seasonality with 6 df per year for time [37] , which was done using the distributed lag non-linear models ( dlnm ) package in the software R .
Meteorological variables and number of HFMD cases show differences from the northern city to the southern city ( Table 1 ) . The northern city ( Datong ) has a lower temperature and relative humidity than the central city ( Taiyuan ) and southern cities ( Changzhi and Yuncheng ) ; while the southern cities have less sunshine than the central city and northern city . During the study period , the number of HFMD cases in Taiyuan and Changzhi were more than that in Datong and Yuncheng . In the 4 study cities , precipitation , temperature , relative humidity and hours of sunshine were positively correlated with incidence of HFMD ( p < 0 . 05 ) . Different meteorological variables may also be correlated with each other . For example , average temperature was positively correlated with maximum temperature in the 4 cities from north to south ( rs = 0 . 994 , 0 . 990 , 0 . 990 , 0 . 981; p < 0 . 001 , respectively ) , and also correlated with minimum temperature ( rs = 0 . 993 , 0 . 988 , 0 . 987 , 0 . 989; p < 0 . 001 , respectively ) . Accounting for these correlations , the association between meteorological variables and the number of HFMD cases were then analyzed using partial correlations . Results showed the associations of increased number of HFMD cases with increasing atmospheric temperature in the 4 cities ( p < 0 . 05 ) . In addition , the results showed statistically significant but weaker correlation for the association between relative humidity and the incidence of HFMD in Taiyuan , as well as the weaker correlation between precipitation and HFMD in Changzhi ( Table 2 ) . In order to estimate the values of parameters in fitted models , these models were diagnosed by analyzing the data with several SARIMA models without the weather variables , and the models in which the residual was not likely to be white noise were excluded . Therefore , the univariate SARIMA ( 0 , 1 , 1 ) ( 2 , 0 , 1 ) 52 model for Datong; SARIMA ( 2 , 1 , 3 ) ( 1 , 1 , 1 ) 52 model for Taiyuan; SARIMA ( 0 , 1 , 1 ) ( 0 , 1 , 1 ) 52 model for Changzhi; and SARIMA ( 0 , 1 , 1 ) ( 1 , 1 , 2 ) 52 model for Yuncheng had both the lowest Bayesian information criterion ( BIC ) and the highest R2 values and were the best to fit the HFMD cases , respectively ( Table 3 ) . The Ljung-Box test confirmed that the residuals of the time series were not statistically dependent ( p > 0 . 05 ) and the residuals on ACF and PACF plots showed the absence of persistent temporal correlation ( Table 3 ) ( Fig . 4 ) . The selected SARIMA model fitted the observed data from 2009 to 2012 . Then , the model was used to project the number of HFMD cases between January to December 2013 , and was validated by the actual observations . The validation analysis suggested that the model had reasonable accuracy over the predictive period in the 4 cities ( root-mean-square error ( RMSE ) = 1 . 763 , 4 . 505 , 4 . 907 and 2 . 817 , respectively ) ( Table 3 ) . The cross-correlation analyses showed the lag effects of the meteorological variables on the number of HFMD cases were different in the 4 cities . In Datong , HFMD was significantly positively associated with average temperature at lag 0 ( coefficients = 0 . 540 , p < 0 . 05 ) , mean maximum temperature at lag 0 ( coefficients = 0 . 520 , p < 0 . 05 ) , mean minimum temperature at lag 0 ( coefficients = 0 . 542 , p < 0 . 05 ) . In Taiyuan , the cases were significantly positively associated with average temperature at lag 1 week ( coefficients = 0 . 615 , p < 0 . 05 ) , maximum temperature at lag 1 week ( coefficients = 0 . 612 , p < 0 . 05 ) , minimum temperature at lag 1 week ( coefficients = 0 . 606 , p < 0 . 05 ) , relative humidity at lag 3 weeks ( coefficients = 0 . 224 , p < 0 . 05 ) . In Changzhi , the disease was significantly positively associated with average temperature , maximum and minimum temperature at lag 1 week ( coefficients = 0 . 479 , 0 . 472 and 0 . 465 , p < 0 . 05 ) , respectively . In Yuncheng , HFMD was significantly positively associated with average temperature at lag 1 week ( coefficients = 0 . 351 , p < 0 . 05 ) , maximum temperature at lag 2 weeks ( coefficients = 0 . 347 , p < 0 . 05 ) and minimum temperature at lag 1 week ( coefficients = 0 . 352 , p < 0 . 05 ) . To reduce potential multicollinearity , weekly average temperature , weekly mean maximum temperature and weekly minimum temperature were put into separate regression models ( Models 1 , 2 and 3 ) . In Model 1 , 2 and 3 , temperature ( average , maximum , and minimum , respectively ) was included , along with other meteorological variables . The results indicated that average temperature , and maximum and minimum temperatures with different lag times were significant in the SARIMA Model 1 , Model 2 and Model 3 , respectively ( Table 4 ) . Overall , SARIMA models with temperature were a better fit and validity than the models without the variable ( Stationary R-squared ( Stationary R2 ) increased , while the BIC decreased ) ( Table 3 and Table 4 ) . Other meteorological variables were not significantly included in the models , indicating their contribution was not statistically significant in this study . The models suggest that in Datong , a 1°C rise in weekly average temperature , weekly mean maximum temperature and weekly mean minimum temperature may be related to an increase in the weekly number of cases of HFMD of 0 . 8% ( 95%CI: 0 . 3%-1 . 2% ) , 0 . 6% ( 95%CI: 0 . 1%-1 . 0% ) and 0 . 7% ( 95%CI: 0 . 2%-1 . 3% ) respectively . In Taiyuan , a 1°C rise in weekly average temperature , weekly mean maximum temperature and weekly mean minimum temperature may be related to an increase in the weekly number of cases of HFMD of 1 . 4% ( 95%CI: 0 . 5%-2 . 7% ) , 1 . 0% ( 95%CI: 0 . 3%-1 . 8% ) and 1 . 1% ( 95%CI: 0 . 2%-2 . 1% ) respectively . In Changzhi , a 1°C rise in weekly average temperature , weekly mean maximum temperature and weekly mean minimum temperature may be related to an increase in the weekly number of cases of HFMD of 1 . 1% ( 95%CI: 0 . 1%-2 . 1% ) , 1 . 6% ( 95%CI: 0 . 4%-2 . 7% ) and 1 . 5% ( 95%CI: 0 . 3%-2 . 7% ) respectively . Finally , in Yuncheng a 1°C rise in weekly average temperature , weekly mean maximum temperature and weekly mean minimum temperature may be associated with an increase in the weekly number of cases of HFMD of 2 . 1% ( 95%CI: 0 . 4%-4 . 7% ) , 1 . 4% ( 95%CI: 0 . 3%-8 . 4% ) and 1 . 9% ( 95%CI: 0 . 1%-5 . 8% ) respectively ( Table 4 ) . Table 4 indicates that the incidence of the disease may rise with the increase of temperature , but only within a certain range of temperatures . The selected SARIMA model was used to project the number of HFMD cases in each city for the 52 weeks between January and December 2013 . The validation for January to December 2013 data showed a good fit between observed and predicted data ( Fig . 5 ) . A comparison between the models for the 4 cities , reveals that although a 1°C rise in temperature may cause a similar relative increase in the number of cases , the lag times for the effects of temperatures were shorter in Datong ( at lag 0 ) than those in Taiyuan , Changzhi and Yuncheng ( at lag 1 week ) . The lag times for the effects of maximum temperature were longer in Yuncheng ( at lag 2 weeks ) than those in Changzhi , Taiyuan and Datong ( Table 4 ) . In the sensitivity analysis , the results using daily data indicated that a 1°C rise in daily average temperature may be related to an increase in the daily number of cases of HFMD of 1 . 2% ( 95%CI: 0 . 1%-2 . 3% ) at lag 1 day , 1 . 6% ( 95%CI: 1 . 0%-2 . 2% ) at lag 8 days , 1 . 5% ( 95%CI: 0 . 5%-2 . 5% ) at lag 6 days , and 2 . 4% ( 95%CI: 0 . 1%-4 . 7% ) at lag 8 days in Datong , Taiyuan , Changzhi and Yuncheng , respectively . Although the analysis of daily data is likely to yield a more precise estimate compared to weekly data , the results were similar . Fig . 6 shows non-linear dose-response relationships for temperature with HFMD occurrence in the 4 cities , and an increase in HFMD occurrence within a short interval .
Although there are some time series studies in China on HFMD , these studies scarcely considered the potential impact of socioeconomic status . The findings in this study indicate that the occurrences of HFMD were positively associated with temperature in 4 major cities which have similar socioeconomic status and demographic characteristics . In addition , different lag effects of temperature were observed in selected regions from north to south . The results will be useful to assist public health responses in the different regions and informing local community and health authorities to better predict disease outbreaks . | Understanding of the impact of weather variables on HFMD transmission remains limited due to various local climatic conditions , socioeconomic status and demographic characteristics in different regions . This study provides quantitative evidence that the incidence of HFMD cases was significantly associated with temperature in Shanxi Province , North China . The delayed effects of weather variables on HFMD dictate different public health responses in 4 major cities in Shanxi Province . The results may provide a direction for local community and health authorities to perform public health actions , and the SARIMA models are helpful in the prediction of epidemics , determination of high-risk areas and susceptible populations , allocation of health resources , and the formulation of relevant prevention strategies . In order to reduce future risks of climatic variations on HFMD epidemics , similar studies in other geographical areas are needed , together with a longer study period to enable trend analysis which takes into consideration local weather conditions and demographic characteristics . |
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Although single genes underlying several evolutionary adaptations have been identified , the genetic basis of complex , polygenic adaptations has been far more challenging to pinpoint . Here we report that the budding yeast Saccharomyces paradoxus has recently evolved resistance to citrinin , a naturally occurring mycotoxin . Applying a genome-wide test for selection on cis-regulation , we identified five genes involved in the citrinin response that are constitutively up-regulated in S . paradoxus . Four of these genes are necessary for resistance , and are also sufficient to increase the resistance of a sensitive strain when over-expressed . Moreover , cis-regulatory divergence in the promoters of these genes contributes to resistance , while exacting a cost in the absence of citrinin . Our results demonstrate how the subtle effects of individual regulatory elements can be combined , via natural selection , into a complex adaptation . Our approach can be applied to dissect the genetic basis of polygenic adaptations in a wide range of species .
Historically , most studies pinpointing the genetic basis of polymorphic traits have focused on protein sequence changes of large effect , because these have been the most amenable to identification . For example , thousands of coding region mutations have been implicated in human diseases with Mendelian inheritance [1] . In contrast , non-coding mutations and polygenic traits have traditionally received far less attention . However this situation has radically changed with the advent of genome-wide association studies ( GWAS ) . In these studies , millions of SNPs can be tested for statistical association to any trait of interest . Two clear patterns have emerged from hundreds of human GWAS: most traits are highly polygenic , and most associations are in non-coding regions that are not in linkage disequilibrium with protein-coding changes ( and thus cannot be acting via changes in protein sequence ) [2–4] . For example , out of 697 loci associated with height , 86% are non-coding [5] . This suggests that natural selection will overwhelmingly result in polygenic cis-regulatory adaptations , composed of variants with very small individual effects , since selection acts on whatever heritable variation is available . Indeed , thousands of loci are involved in selection on height in Europeans , a clear example of polygenic adaptation [6] . Moreover , recent genome-wide comparisons of the proportions of adaptations in coding vs . non-coding regions in humans and sticklebacks support the prevalence of non-coding adaptations [7–9] . Two methods have successfully identified the genes underlying regulatory adaptations . One is QTL/association mapping , which has led to several beautiful examples of single-locus adaptations [10] . However QTL mapping in most species is only practical for loci of large effect , and thus is not well-suited for studying the evolution of complex traits , which are by definition polygenic . The second method is the “sign test” framework that we and others have developed [11–12] . The goal of this approach is to identify cases where selection has led to up- or down-regulation of multiple genes via independent mutations . First the cis-regulatory divergence between two species is quantified genome-wide via allele-specific expression ( ASE ) analysis in an F1 hybrid [11] . This results in directionality information for every gene ( e . g . for gene X , the species A allele is up-regulated compared to the species B allele ) . Any group of genes whose expression is evolving under the same selection pressure in the A and B lineages should have a similar frequency of A alleles up-regulated as in the entire genome . For example if 50% of genes with ASE have A allele up-regulation , then any random subset of these should have roughly 50% as well . If a strong deviation from 50% is detected for some gene set—e . g . all 20 genes in a pathway have the A alleles up-regulated—then this indicates the action of lineage-specific selection . Importantly , this approach does not make many assumptions of other methods for detecting selection such as constant population size , lack of epistasis , or neutrality of synonymous sites [11] . And unlike approaches that can rank genes but cannot determine which ( if any ) of them are inconsistent with neutrality ( such as FST , iHS , and the ratio of expression divergence between species to expression diversity within species ) , the sign test’s null model allows for confident identification of gene sets under lineage-specific selection [11] . Finally , the sign test is most powerful when many genes are involved , making it uniquely well-suited for studying complex traits . In the current work , we applied the sign test to ASE data from a hybrid between two species of budding yeast , Saccharomyces cerevisiae and S . paradoxus ( specifically the reference strains S288c and CBS432 , hereafter abbreviated Sc and Sp; note that these abbreviations refer specifically to the two reference strains , and not the two species as a whole ) . The results led us to focus on a specific mycotoxin ( a toxin produced by fungi ) called citrinin . Citrinin is produced by a number of Ascomycota fungi , including several species in the Aspergillus , Penicillium , and Monascus genera . It increases mitochondrial membrane permeability and causes oxidative damage via an unknown mechanism , and is a potent nephrotoxin in mammals [13–14] . Because citrinin is toxic to yeast growing on fermentable carbon sources , which do not require mitochondria , toxicity is likely caused by oxidative damage to other cellular components [13] . The transcriptional response may be an important means to mitigate citrinin toxicity , and in Sc , hundreds of genes are induced or repressed in response to even a low level of citrinin [15] . Although citrinin is a common food contaminant , making it a major health concern for both humans and livestock [13–14] , and has been the subject of hundreds of publications , the evolution of citrinin resistance has not been previously investigated .
In an effort to identify groups of genes subject to lineage-specific selection , we applied a sign test ( described above ) to allele-specific gene expression levels from an Sc/Sp hybrid grown in YPD ( rich glucose ) media [16] . Our objective was to identify any gene sets ( such as pathways or other functionally related groups ) with a biased directionality of ASE , implying the action of lineage-specific natural selection [11] . Testing a collection of publicly available gene sets ( see Methods ) , we found only one set with a highly significant bias: genes induced by exposure to citrinin , a naturally occurring mycotoxin , were over-represented among genes with Sp-biased ASE . Of 11 genes that were reported to be induced at least 10-fold in response to citrinin ( in Sc ) [15] , four were among the top 1% of Sp-biased ASE genes ( Fig 1; hypergeometric p = 2 . 7 x 10−6 ) . In fact , these four genes included the first and third most Sp-biased genes in the entire genome . In other words , Sp alleles have markedly higher expression of several genes that are induced in Sc’s citrinin response , even though ASE was measured in the absence of citrinin [16] . In contrast , none of the 11 citrinin-induced genes had even slightly Sc-biased ASE in the Sc/Sp hybrid . All 11 had some degree of Sp-biased ASE , and 10/11 were in the top 25% of Sp-biased genes ( Fig 1; p = 7 . 8 x 10−6 ) . The complete absence of citrinin-induced genes among Sc-biased genes , taken together with their 40-fold enrichment among the most strongly Sp-biased genes , is not consistent with a neutral model of gene expression evolution leading to random directionality of ASE [11]; instead , it suggests that lineage-specific selection shaped the cis-regulation of these genes . The results of our sign test ( Fig 1 ) suggested that Sp’s constitutive up-regulation of Sc’s citrinin-induced genes may confer a greater resistance to citrinin . To test this , we measured the growth of Sc and Sp in three concentrations of citrinin: 0 ppm ( parts per million ) , 300 ppm , and 600 ppm . At 300 ppm , Sc showed a clear shift towards slower growth , while Sp was essentially unperturbed ( Fig 2A and 2B ) . At 600 ppm , Sp was affected , but not nearly as strongly as Sc; the effect on Sp at 600 ppm was roughly similar to that on Sc at 300 ppm . These results are consistent with our hypothesis that Sp would show increased resistance . To investigate the evolutionary history of the resistance phenotype , we measured growth rates and cell densities at saturation ( “max OD” ) for a panel of 25 diverse S . paradoxus strains . These strains belong to four major clades , defined on the basis of partial genome sequences [17] , which also reflect their geographic origins: European ( including Western Russia ) , East Asian ( Eastern Russia and Japan ) , Hawaiian , and American . We also included Sc as an outgroup . Across all strains , the effect of citrinin on growth rate and on max OD was highly correlated ( Pearson r = 0 . 91; Spearman r = 0 . 96 ) , and strains fell into distinct clusters based on their sensitivities ( Fig 2C ) . The most sensitive group included Sc , the Hawaiian strain , both American strains , and 3/18 European strains . In contrast , the resistant group consisted of all four East Asian strains , and 15/18 European strains ( including the two most resistant strains , Sp and Q62 . 5 ) . Representing resistance as a binary trait on the S . paradoxus phylogeny , the most parsimonious explanation is that resistance evolved after the split between the Hawaiian/American clade and the European/East Asian clade , and was then lost in a subset of the European strains ( Fig 2D ) ( this loss could have occurred via new mutations , or admixture from a sensitive S . paradoxus strain; see Discussion ) . Any other scenario would require multiple independent gains and/or losses . These results indicate the difference between Sc and Sp ( Fig 2A and 2B ) is likely due to resistance being gained in a recent ancestor of Sp , as opposed to being lost in the Sc lineage . To determine whether citrinin resistance represents a more general pleiotropic trait—such as resistance to many different toxins—we compared our growth data ( Fig 2C ) to growth rates of the same strains in 200 diverse conditions [18] . These include several oxidative stress agents ( aminotriazole , paraquat , dithiothreitol , CdCl2 , and CoCl2 ) and dozens of other toxins . None of these showed a similar pattern of resistance across strains as we observed for citrinin: the maximum correlation across all 200 conditions was r = 0 . 44 ( n = 23 strains; not significant after correction for 200 tests ) . In fact , Sp showed the least resistance to all five oxidative stress conditions ( among 22 S . paradoxus strains and Sc ) , the opposite of our observation for citrinin . Together , these results suggest that citrinin resistance does not represent a more general resistance to toxins or oxidative stress . To further characterize the effect of citrinin on gene expression , we performed RNA-seq on Sc/Sp hybrid yeast exposed to 600 ppm citrinin , as well as in rich media lacking citrinin for comparison . This allowed us to measure the effect of citrinin on each gene’s ASE , and thus to measure the cis-regulatory contributions of each species to the induction or repression of each gene . We found strong agreement between our biological replicates ( r = 0 . 96–0 . 99; S1 Fig ) , and moderate concordance with published microarray data from the Sc/Sp hybrid [16] and Sc’s response to citrinin [15] ( S2 Fig ) . To determine if the transcriptional response to citrinin was species-specific , we analyzed the responses of Sc and Sp alleles separately in our hybrid RNA-seq data . We observed highly concordant responses to citrinin ( Fig 3A ) . In fact , among 114 genes with at least 3-fold induction or repression for alleles from both species , all of them responded to citrinin in the same direction for both ( r = 0 . 96 for these genes ) . As a result , ASE is broadly similar in rich media and in citrinin , as has been reported for the Sc/Sp hybrid in other conditions [16] . To identify candidate genes that may be contributing to the polygenic selection that we detected ( Fig 1 ) , we selected those with the strongest combination of citrinin-induction and Sp-biased ASE . To visualize this , we plotted each gene’s citrinin response against Sp/Sc ASE in the absence of citrinin ( Fig 3B ) . Requiring at least 4-fold citrinin induction and 4-fold Sp-biased ASE , we identified four genes; a fifth gene that was slightly below the ASE cutoff was included as well ( Fig 3B , red points ) . These constituted our top candidates for genes that may be involved in the evolution of citrinin resistance in Sp . Notably , no genes had both a 4-fold ASE bias and citrinin-response in any of the other three possible pairs of directions ( the three additional quadrants in Fig 3B ) , indicating the rarity of this combination . If citrinin resistance evolved after the split of European/East Asian and American/Hawaiian strains ( Fig 2D ) , we may expect that genes involved in this adaptation would show an ASE bias towards Sp ( European ) alleles in hybrids between these two clades , as we observed for Sc/Sp . To test this , we performed RNA-seq in a hybrid between Sp and an American S . paradoxus strain ( DBVPG6304 ) . We could assess ASE for 4/5 candidate genes; three of these showed Sp-biased ASE ( RTA1 , binomial p = 2x10-4; FRM2 , p = 0 . 05; CIS1 , p = 8x10-8 ) , while the fourth had strong American-biased ASE ( RSB1 , p = 3x10-55 ) . Therefore our prediction of ASE directionality was validated for 3/4 candidate genes . As an initial test of these five candidate genes , we deleted each gene individually from Sp and tested the effect on citrinin resistance ( all five were nonessential in YPD ) . For four of the five gene deletions , Sp resistance was significantly decreased ( Fig 4A ) ; the only exception was RSB1 , the same gene that did not show Sp-biased ASE in the Sp/American hybrid . These four genes are involved in a variety of functions: a mitochondrial glutathione peroxidase involved in the oxidative stress response ( GPX2 ) , an oxidoreductase also involved in oxidative stress response ( FRM2 ) , a lipid-translocating exporter of the plasma membrane ( RTA1 ) , and a mitochondrial protein of unknown function ( YLR346C , which we rename as CIS1 , for “CItrinin Sensitive knockout” ) . The functions of all four genes are consistent with a role in citrinin resistance: three are involved in mitochondria/oxidative stress , processes directly related to citrinin [13–14]; and the fourth , RTA1 , has been implicated in toxin resistance ( Sc strains missing RTA1 are highly sensitive to a mycotoxin called myriocin [19] , and over-expression confers resistance to multiple toxins [20] ) . Considering the four lines of evidence converging on these genes—induction in response to citrinin , constitutive up-regulation of their Sp alleles ( in two different hybrids ) , the effects of their knockouts on Sp citrinin resistance , and functional annotations—we focused further efforts on these four candidates . A complement to testing whether gene deletion leads to trait loss is to test whether over-expression leads to trait gain . To explore this , we used the CRISPR/Cas9 system , in which the Cas9 protein can be directed to a specific genomic site by use of a guide RNA ( gRNA ) complementary to the target DNA site . When a transcriptional activation domain is fused to a nuclease-dead mutant of Cas9 ( called dCas9 ) , the resulting protein acts as a strong activator of its target genes [21] . This is an attractive system for simultaneous over-expression of multiple genes , since multiple guide RNAs can be delivered to cells on a single plasmid . To test the efficacy of this system , we compared the induction levels of our four candidate genes using three different dCas9-activator fusion genes . One was a previously published fusion to VP64 ( a domain derived from a strong viral transcription factor ) [21]; the second was a dCas9-VP64 fusion that we created using a dCas9 gene with an alternative codon optimization; and the third was a dual-fusion that we created with VP64 at the C terminus and the Gal4 activation domain at the N terminus . We found that all three dCas9 constructs could induce all four genes , though the level of induction varied substantially between genes , with FRM2 achieving far higher induction than the other three ( Fig 4B ) , perhaps due to its lower basal expression level . Comparing the three dCas9 fusion proteins , we observed similar induction levels for all three , though our dual-fusion construct achieved slightly ( 1 . 2–1 . 8-fold ) higher induction for all four genes . We then used this dCas9 dual-fusion gene to over-express all four genes in a single strain ( Fig 4C ) . To measure the effect of over-expression on fitness , we performed direct competition between the over-expression strain vs . a control ( a strain with a dCas9 plasmid lacking any gRNAs ) , for 40 generations ( Fig 4D ) . We incorporated 6-base barcodes into each plasmid , and sequenced these before and after each competition to estimate strain abundances; each strain was tagged by three different barcodes , to reduce any barcode-specific biases . In the absence of citrinin , the over-expression strain had a small but consistent ( across 12 replicates ) fitness disadvantage of 0 . 6% per generation ( resulting in 20% lower abundance than the control after 40 generations ) . In contrast , in 300 ppm citrinin , the over-expression strain had a 0 . 8% fitness advantage ( 37% higher after 40 generations; t-test p = 3x10-5 comparing conditions ) . These results suggest that over-expression of these genes leads to a condition-specific fitness tradeoff . Cis-regulatory divergence in mRNA levels can be caused by changes at either the transcriptional or post-transcriptional ( e . g . mRNA stability ) level . We hypothesized that the major causal mutations may have affected transcription via changes in promoter sequences between Sc and Sp . To test this , we replaced the promoter region ( ~1 kb of noncoding DNA upstream of each gene ) of each of our four candidate genes in Sc with the orthologous promoter from Sp ( Fig 5A ) , using an approach that leaves no foreign DNA behind [22] . Boundaries were chosen to overlap stretches of perfect Sc/Sp conservation to ensure correct placement of the Sp sequence in the Sc genome , as well as to facilitate homologous recombination at the target site . In each “promoter-replacement” strain we performed quantitative PCR on the downstream gene , to assess the effect on mRNA levels . We found between 1 . 4 and 6 . 9-fold up-regulation of the four Sp promoters , compared to the orthologous Sc promoters . Comparing these results to the total cis-regulatory divergence based on our RNA-seq data in the Sc/Sp hybrid ( Fig 2A ) , we found good concordance ( Fig 5B , r = 0 . 92 ) . This suggests that the promoters are likely responsible for most , if not all , of the cis-regulatory divergence for these genes between Sc and Sp . To test the fitness effects of this cis-regulatory divergence , we pooled all four promoter replacement strains together with their Sc parent , and grew them in direct competition . We observed a similar pattern for 3/4 candidate genes , with the Sp promoter leading to 0 . 2–0 . 3% higher fitness in the presence of citrinin , but ~0 . 6% lower fitness in its absence ( Fig 5C; 18 replicates per condition; t-test p < 0 . 01 for each ) . The fourth gene , RTA1 , showed no detectable fitness difference in the two growth conditions ( p = 0 . 51 ) . These results suggest an overall similar condition-specific fitness tradeoff of the natural cis-regulatory divergence as we observed for the 4-gene over-expression strain , with each of three promoters contributing a small amount to citrinin resistance .
In this work , we have discovered a polygenic cis-regulatory adaptation , and investigated its genetic basis and effect on fitness . Our results establish that changes in the promoters of at least three genes have contributed to Sp’s recently evolved resistance to citrinin , which has a fitness cost in the absence of citrinin . The role of these genes in Sp’s citrinin resistance is supported by six lines of evidence: induction in response to citrinin , constitutive up-regulation of their Sp alleles ( compared to Sc and an American strain of S . paradoxus ) , functional annotations , decreased resistance via gene deletion in Sp , increased resistance via over-expression in Sc , and the fitness effects of promoter-replacements . Isolating the effects of individual promoter regions has allowed us to gain a deeper understanding of how distinct loci can contribute to polygenic adaptation . For example , the fitness cost of increased expression in the absence of citrinin may explain why these genes are induced in response to citrinin , as opposed to being expressed at constitutively high levels . Despite this cost , Sp does express all four genes at somewhat higher levels than Sc even without citrinin present ( Fig 3B ) , which may confer a net benefit if citrinin is encountered more often by Sp than by Sc . Alternatively , the fitness costs we observed in Sc may be reduced in Sp by compensatory changes at other loci . Having identified this gene expression adaptation , a natural question is what selective pressure ( s ) caused it . Although citrinin represents a plausible candidate , since it is a widespread naturally occurring toxin , it is also possible that something else ( e . g . another toxin ) was the actual selective agent , with citrinin resistance being a pleiotropic side-effect . Unfortunately there is no experiment that could unambiguously identify the selective agent , since pleiotropic effects of unknown/unmeasured traits are always a possibility; however our finding that citrinin resistance does not correlate with fitness across 200 other growth conditions [18] suggests that it is not a highly pleiotropic trait . An interesting aspect of our data is the strong correlation between maximum growth rate and maximum cell density across wild isolates ( Fig 2C ) . Similar correlations ( r = 0 . 85 and 0 . 77 ) were also observed between these same two variables among other strains of yeast [18 , 23] . Ibstedt et al . [23] proposed that the correlations between fitness components of natural isolates are not due to pleiotropic effects , but rather that selection has fixed multiple variants affecting different fitness components in particular lineages . In other words , the presence of a correlation between fitness components may , by itself , be evidence of selection on condition-specific fitness [23] . In the case of citrinin resistance in Sp , this interpretation is quite consistent with our detection of polygenic selection via the sign test . Another intriguing property of the resistance across S . paradoxus isolates is that resistance is roughly bimodal ( Fig 2C ) . This makes sense if resistance evolved once , and has not changed drastically since then in most of the strains tested here . The exception to this is the three European strains that have lost their resistance , perhaps due to new mutations and/or variants introduced via admixture with sensitive strains . Whether resistance was lost once or multiple times among European strains would be difficult to determine—although these three strains do not cluster together within the European phylogeny [17] , this represents only a genome-wide average phylogeny that many loci , including those involved in the loss of citrinin resistance , may not follow . Our inference that resistance evolved within S . paradoxus ( Fig 2D ) is based on parsimony , i . e . explaining a phylogenetic pattern with the fewest possible transitions . However if loss of citrinin resistance is more likely than gain then a scenario with an ancestral state of resistance , followed by three loss events ( one in Sc and two in S . paradoxus ) , could be more plausible . If this were the case , the lineage-specific selection that we detected with the sign test would more likely represent selection for lower expression of these genes in Sc , to avoid the fitness cost of their expression in the absence of citrinin ( Figs 4D and 5C ) . A relaxation of selection ( i . e . loss of constraint ) is another possible explanation for down-regulation [11] , though in this case would not be consistent with either the fitness effects of promoter-replacements ( which are large enough to be strongly selected ) , or the correlation of fitness components ( Fig 2C ) that suggests selection is acting on citrinin resistance within S . paradoxus [23] . Therefore regardless of the ancestral state , the differences we have observed between Sc and Sp are likely to be adaptive . In this work , we have only explored one facet of this polygenic adaptation , namely cis-regulatory divergence of mRNA levels in a specific environment ( YPD media at 30°C ) . Many other types of evolutionary changes may also be involved , such as changes in protein sequences , trans-acting factors , translation , etc . Moreover , it is quite likely that the cis-regulatory divergence of additional genes also contributes to Sp’s citrinin resistance; for example in our initial analysis ( Fig 1 ) , there were five genes with strong citrinin-induced upregulation and moderate Sp-biased ASE ( in the top 25% of genes ) that we did not pursue , as well as many more genes with weaker supporting evidence . Consistent with the idea that additional loci likely contribute , neither the four promoter replacements nor the 4-gene overexpression strain we tested can account for the large difference in resistance between Sc and Sp ( Fig 2A and 2B ) if we assume additivity of the promoter-replacement fitness effects and equivalence of the fitness measurements in these two experiments . For regulatory changes of RTA1 , for which we could not detect an effect on citrinin resistance , many possibilities exist—e . g . condition-specific effects ( gene-by-environment interaction ) , or genetic background-specific effects ( gene-by-gene interaction , i . e . epistasis ) , or effects that were too small to measure , or it may have no effect at all . It is clear that even for this one adaptation ( as well as every other polygenic adaptation ) , we are still far from having a complete understanding . Looking ahead , an important question is what approaches will be most useful for understanding the genetic basis of evolutionary adaptations . QTL mapping has been very effective in localizing large-effect loci down to specific genomic regions , and has led to the identification of several genes underlying adaptations [10] . However it does have limitations . First , an adaptive phenotype must be identified before starting QTL mapping; though in many cases , such as citrinin resistance , we do not know ahead of time which traits may be adaptive . Second , QTL mapping is generally only practical for loci of large effect ( with the exception of pooling-based approaches in yeast [24] ) , which may be rare among adaptations , given the highly polygenic nature of most traits [2–4] . Third , specific genes are not implicated; QTL peaks typically contain dozens or even hundreds of genes , of which only one may be involved in the trait . And fourth , even for loci of large effect , QTL mapping requires generating , genotyping , and phenotyping hundreds of F2 individuals to result in a reasonably sized QTL peak . In contrast , the sign test approach we have employed allows us to circumvent these limitations . First , no adaptive phenotype must be known a priori . Second , loci of very small effect can be identified . Third , specific genes are implicated . And fourth , no F2 crosses are required; a single F1 is sufficient to measure all cis-regulatory divergence via RNA-seq . Of course , the sign test is limited to polygenic cis-regulatory adaptations in which one direction of change ( up- or down-regulation ) dominates; but for these , which may be quite frequent ( see Introduction ) , the sign test can rapidly identify high-confidence candidate genes . Finally , the sign test can be applied to a wide range of species , including fungi , plants , and metazoans [25–31] . A major limitation of the sign test has been that it has previously relied on functional annotations of genes , thus limiting it to species with reasonably well-annotated genomes . However in this work , we found that gene sets and specific candidate genes can be defined on the basis of gene expression data alone ( such as the citrinin-response data of ref . 15 ) . Combined with other recent advances in genetic tools , such as CRISPR/Cas9 , we believe that pinpointing specific genes and genetic variants underlying complex , polygenic adaptations is now readily achievable across a wide range of species .
Allele-specific expression data from the Sc/Sp hybrid in YPD were obtained from [16] . Two genes with strong Sp-biased ASE due to being used as auxotrophic markers in Sc ( URA3 and MET17 ) were removed , leaving 4394 measured genes . The top 1% , 5% , and 25% of genes with the strongest ASE in each direction were submitted to FunSpec , a tool for gene set enrichment analysis [32] . The top enrichment at all three cutoffs for Sp-biased ASE ( Hypergeometric p < 0 . 01 after Bonferroni correction for multiple tests ) was the Gene Ontology set “response to toxic substance . ” Examination of the enriched genes revealed that their annotations were all derived from a single publication , measuring the transcriptional response to citrinin [15] . Thus subsequent analysis ( Fig 1 ) was performed using these induced genes as the set of interest . Induced genes were defined as those with at least 10-fold average induction ( Table 1 of ref . 15 ) , excluding four paralogous genes found to have cross-hybridization [15] . Out of 17 induced genes , 11 had ASE measurements [16] ( In decreasing order of Sp/Sc ASE ratio from ref . 16: RTA1 , CIS1 , RSB1 , FRM2 , ECM4 , FLR1 , AAD4 , GRE2 , GTT2 , YML131W , and YLL056C ) . None of these 11 genes were next to one another in the genome , so their ASE was likely caused by independent cis-regulatory changes , as required by the sign test [11] . No significant ASE directionality bias was observed for citrinin-repressed genes . To perform quantitative growth rate measurements ( Figs 2A , 2B , 2C and 4A ) , we grew strains in 96-well plates and measured OD600 at 12-minute intervals using an automated plate reader ( Tecan ) until cultures reached saturation . Experiments were performed at 30°C in YPD media [33] ( with or without citrinin ) . Citrinin ( Santa Cruz Biotechnology ) was dissolved in DMSO at a concentration of 20 , 000 ppm , prior to addition to growth media . An equal amount of DMSO was added to the no-citrinin controls , as was in each matched experiment with citrinin+DMSO . All growth assays were performed with each strain distributed across the plate ( e . g . alternating rows/columns for the different strains/conditions ) to minimize any spatial variation in conditions across the plate . Maximum growth rate was estimated by performing a linear regression on log10 ( OD ) values vs . time , for every set of 20 consecutive time points ( 4 hours ) , and the highest slope was recorded . Maximum OD was calculated across all time points . For clarity of presentation , we show growth rates as log-ratios relative to a fixed reference ( Sc in Fig 2C , and Sp in Fig 4A ) . For example in Fig 4A , a strain with a 2-fold greater reduction in max growth rate than Sp after citrinin exposure would have a value of 1; a 4-fold lower reduction in max growth rate than Sp would have a value of -2 . An Sc/Sp diploid hybrid yeast strain was produced by mating Sc ( BY4716: MATα lys2 ura3∷KAN ) and Sp ( CBS432: MATa ura3∷HYG ) , followed by selection on plates containing hygromycin B and G418 ( Sigma ) . Four single colony picks of the hybrid strain were grown overnight in 3 ml cultures of YPD medium at 30°C . Upon confirming that the cultures were in log phase , two of the replicates were treated with 91 μl of DMSO , while the other two were treated with 91 μl of DMSO containing citrinin such that its final concentration in the culture was 600 ppm . Cultures were allowed to continue shaking at 30°C for two additional hours followed by harvesting of the pellets by centrifugation . RNA was immediately isolated from the pellets using the MasterPure Yeast RNA Purification Kit ( Epicenter ) . Two μg of RNA from each of the samples was subsequently used to create sequencing libraries using the low-throughput protocol in the TruSeq RNA Sample Preparation Kit v2 ( Illumina ) . Individually barcoded libraries were pooled and sequenced ( resulting in a total of ~156 . 6 million single-end 101 bp reads ) on a single lane of an Illumina HiSeq 2000 instrument . Reads were trimmed to 50 bp and mapped to a concatenated reference containing both parental genomes [29] using Bowtie version 0 . 12 , allowing no mismatches [34] . Counts of reads mapping to a high-confidence curated ortholog set [35] were determined using htseq-count with the union option [36] . ASE was determined as the ratio of reads mapping to the Sp allele divided by those mapping to the Sc allele in the concatenated reference genome . Only genes with at least 20 reads mapping to each allele were retained . All reads are available in the NCBI SRA ( accession PRJNA270666 ) , and allele-specific read counts are given in S1 Table . To create the DVBPG6304/Sp hybrid , we started with diploid strains of each parent , and replaced one allele of the URA3 gene with an antibiotic resistance gene ( hyg in DVBPG6304 and kan in Sp ) . Transformants were selected on the corresponding antibiotic . The diploids were then sporulated to create haploids , and mixed to allow random mating . Hybrids were selected on plates containing hygromycin B and G418 . RNA-seq was performed as described above , with ~27 . 3 million single-end 36 bp reads generated . The complete coding regions of five candidate genes were replaced with the hphMX6 antibiotic resistance gene via PCR-mediated gene disruption [33] in Sp . Transformants were grown on hygromycin B ( Sigma ) . Molecular cloning was done with Gibson Assembly [37] . E . coli minipreps were performed with QIAprep Spin Miniprep Kits ( Qiagen ) . Transformation and preparation of competent E . coli DH5α were prepared using Mix & Go E . coli Transformation Kit & Buffer Set and Zymo Broth ( Zymo Research ) . Competent S . cerevisiae ( strain BY4741 ) was prepared either by standard lithium acetate transformation protocols [33] or using Frozen-EZ Yeast Transformation II Kit ( Zymo Research ) . Two yeast constructs were obtained from [38] via Addgene: p414-TEF1p-Cas9-CYC1t containing human codon optimized Streptococcus pyogenes Cas9 under control of the Tef1 S . cerevisiae promoter; and p426-SNR52p-gRNA . CAN1 . Y-SUP4t , expressing a guide RNA under control of the SNR52 polIII promoter . Catalytically inactive Cas9 ( dCas9 ) was made by by introducing the D10A and H840A mutations [39] . We next PCR amplified the Gal4 activator and linker domains from the pDEST23 plasmid from the ProQuest Two-Hybrid System ( Life Technologies ) , and fused it to the N-terminus of dCas9 . We also obtained an alternative dCas9 activator utilizing four repeats of the minimal domain of the herpes simplex viral protein 16 ( VP64 ) fused to the C-terminus of dCas9 [21] . We hypothesized that combining a Gal4 activator domain on the N-terminus with a C-terminal VP64 domain would produce a more potent activator than either activator fusion individually . To control for potential differences due to different codon optimizations , we cloned the VP64 activator domain from ref . 21 onto the dCas9 gene from ref . 38 . We also cloned the dCas9-VP64 activator into the pRS416 vector under control of the Tef1 promoter so that all Cas9 fusions were expressed under the same plasmid and promoter backgrounds . To build the Gal4-dCas9-VP64 activator , we linearized the pRS414-Tef1-nNLS-Gal4-dCas9-Cyc1t plasmid and added VP64 to the C-terminal end via Gibson Assembly , followed by cloning into the pRS416 vector background . We also made a gRNA plasmid from the p426-SNR52p-gRNA . CAN1 . Y-SUP4t for cloning new guides by replacing the 20 base specificity sequence for the Can1 . Y gRNA with a SacI restriction site . To activate the four candidate genes , we first designed two gRNAs for each gene . Because AG PAM sequences were reported to work as well as GG PAM sequences with dCas9 activators in human cell lines [40] , we tested gRNAs with both type of PAM . Guides were selected to target between 50–250 bp upstream of the transcription start site ( TSS ) of each gene , because this window produced the best activation of other genes as well as in published data [21] . Guides were built from 60 base oligonucleotides , which contained 20 bases of overlap for Gibson cloning on either side of a 20 base unique specificity sequence . These single-stranded oligos were directly cloned into SacI digested pRS425-SacI-gRNA vector via Gibson assembly using an oligo to vector ratio of >100:1 . This ratio was required because we found that Gibson Assembly less efficiently integrated single-stranded oligos than double-stranded fragments . PCR and Sanger sequencing were used to screen colonies . Confirmed clones were grown on LB carb media overnight and mini-prepped . These plasmids were then transformed into BY4741 containing the pRS416-dCas9-VP64 and grown on synthetic complete media plates lacking leucine and uracil for two days . From these plates , single colonies were selected and grown in liquid synthetic complete media lacking leucine and uracil . Activity of each gRNA was determined by qPCR . Interestingly , we found that none of the gRNAs designed with an AG PAM increased expression levels , suggesting that guides with an AG PAM may not be functional in S . cerevisiae . Once each gRNA was tested by qPCR , we sought to create a construct expressing a combination of guides , one to each of the four target genes . From each individual gRNA plasmid , we amplified the promoter , gRNA , and terminator using primers that produce unique overlaps to assemble the gRNAs in an ordered fashion . These PCR products were then assembled via Gibson Assembly in the absence of vector , followed by a second round of Gibson Assembly into pRS425 . By doing a two-step Gibson Assembly , we drastically reduced the rate of misassembled products resulting from the high sequence similarity of the fragments . All constructs were verified by sequencing . The four-gene over-expression strain in Fig 4C was created by transforming the pRS416-Gal4-dCas9-VP64 plasmid and pRS425 with 4 gRNAs into BY4741 . The control strain was identical to this , except using pRS425 with a nonfunctional gRNA . Strains were barcoded by transforming a pRS416 vector ( either empty pRS416 or pRS416-nGal4-dCas9-cVP64 for the dCas9-activator strains in Fig 4D ) containing a 6-base barcode . These were constructed by integrating an oligo with a random 6mer in the center flanked by sequences matching our sequencing primers , and sequences matching the vector at the NsiI site for Gibson Assembly . Assembled vectors were sequence verified and then transformed into each strain of interest . All barcodes were at least two nucleotides separated from each other . For each strain , we transformed three unique barcodes . Each barcoded strain was then grown to saturation and mixed evenly with the other strains of that background and frozen as glycerol stocks . Aliquots of the pooled barcoded strains were recovered in YPD media for 4 hours , and then diluted to OD 0 . 025 for the experiments . Yeast culturing and sample collection was performed using a cell-screening platform that integrates temperature-controlled absorbance plate readers , plate coolers , and a liquid handling robot . Briefly , 700 μl yeast cultures were grown in 48 well plates at 30°C with orbital shaking in either YPD media or YPD + 300ppm citrinin in Infinite plate readers ( Tecan ) . To maintain cultures in log phase , 23 μl of the culture was removed when it reached an OD of 0 . 76 , transferred to a well containing 700 μl of media , and then allowed to grow further . After seven such dilutions , 600 μl of the culture was collected at OD 0 . 76 and saved in a 4°C cooling station ( Torrey Pines ) . This amounted to approximately 40 culture doublings from the beginning of the experiment . Fresh media transfers were triggered automatically by Pegasus Software and performed by a Freedom EVO workstation ( Tecan ) . After sample collection , yeast plasmids were purified using the Zymoprep Yeast Plasmid Miniprep II kit ( Zymo Research ) . Purified plasmids were used as a template for PCR with barcoded sequencing primers that produce a double index to uniquely identify each sample . PCR products were confirmed by gel electrophoresis . After PCR , samples were combined and bead cleaned with Sera-Mag Speed Beads Carboxylate-Modified particles ( Thermo Scientific ) . Sequencing was performed using Illumina MiSeq . Reads were counted only if they were a perfect match to the expected 6 bp barcode and flanking sequences . In vivo site-directed mutagenesis , known as delitto perfetto [22] , was performed as described [40] . Briefly , the Kluyveromyces lactis URA3 gene was amplified using primers containing ~70 bp of homology to each Sc promoter . This PCR product was transformed into Sc , and correct incorporation into each promoter was verified by PCR . Each URA3 gene was then removed by transforming a PCR-amplified orthologous Sp promoter , containing enough flanking DNA sequence ( identical between Sc and Sp ) to allow specific targeting of the PCR product . Because the efficiency of delitto perfetto is maximized when transforming longer DNA molecules , as well as double-stranded DNA [33] , transforming long PCR products ( as opposed to shorter , single-stranded synthetic oligonucleotides ) is a useful modification . Counter-selection of the resulting transformants on 5-FOA ( Sigma ) allowed isolation of successfully engineered strains that had replaced each URA3 gene with the correct Sp promoter , which were then sequence-verified . To measure the expression levels of our four candidate genes ( Figs 4C and 5B ) , we followed our previously published methods for qPCR [41] . We first grew each strain in YPD , and harvested them in log-phase ( OD600 ~1 ) by centrifugation . We extracted total RNA with the MasterPure Yeast RNA Purification Kit ( Epicentre ) , and quantified them with a NanoDrop2000 spectrophotometer . Total RNA samples were diluted to a concentration of 500 ng/μL and then reverse transcribed into cDNA with SuperScript III RT ( Invitrogen ) , following manufacturer protocols . cDNA was diluted 1:100 prior to qPCR . qPCR was performed on an Eco Real-Time PCR machine ( Illumina ) following manufacturer’s protocols . To quantify changes in mRNA abundance , six control genes previously noted for their stability across conditions [42] were measured in each experiment: ACT1 , TDH3 , ALG9 , TAF10 , TFC1 , and UBC6 . All measurements were performed in triplicate . Data were analyzed using qBase Plus software ( Biogazelle ) [43] . | Adaptation via natural selection has been a subject of great interest for well over a century , yet we still have little understanding of its molecular basis . What are the genetic changes that are actually being selected ? While single genes underlying several adaptations have been identified , the genetic basis of complex , polygenic adaptations has been far more challenging to pinpoint . The complex trait that we study here is the resistance of Saccharomyces yeast to a mycotoxin called citrinin , which is produced by many other species of fungi , and is a common food contaminant for both humans and livestock . We found that sequence changes in the promoters of at least three genes have contributed to citrinin resistance , by up-regulating their transcription even in the absence of citrinin . Higher expression of these genes confers a fitness advantage in the presence of citrinin , while exacting a cost in its absence—a fitness tradeoff . Our results provide a detailed view of a complex adaptation , and our approach can be applied to polygenic adaptations in a wide range of species . |
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Seasonal influenza surveillance is usually carried out by sentinel general practitioners ( GPs ) who compile weekly reports based on the number of influenza-like illness ( ILI ) clinical cases observed among visited patients . This traditional practice for surveillance generally presents several issues , such as a delay of one week or more in releasing reports , population biases in the health-seeking behaviour , and the lack of a common definition of ILI case . On the other hand , the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues . In Europe , a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys , thus allowing a real-time estimate of the level of influenza circulating in the population . In this work , we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms , called syndromes . The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition . The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons , from 2011-2012 to 2016-2017 , with an average of 34 , 000 participants per season . Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries ( Pearson correlations ranging from 0 . 69 for Italy to 0 . 88 for the Netherlands , with the sole exception of Ireland with a correlation of 0 . 38 ) . The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season ( 2016-2017 ) based only on the available information of the previous years ( 2011-2016 ) . Furthermore , to broaden the scope of our approach , we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season ( Pearson correlations ranging from 0 . 60 for Ireland and UK , and 0 . 85 for the Netherlands ) and also to detect gastrointestinal syndrome in France ( Pearson correlation of 0 . 66 ) . The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries .
Seasonal influenza is an acute contagious respiratory illness caused by viruses that can be easily transmitted from person to person . Influenza viruses circulate worldwide causing annual epidemics with the highest activity during winter seasons in temperate regions and produce an estimated annual attack rate of 3 to 5 million cases of severe illness and about 250 to 500 thousand deaths around the world [1] . National surveillance systems monitor the influenza activity through a network of general practitioners ( GPs ) who report the weekly number of influenza-like illness ( ILI ) cases among the overall patients [2] . These traditional surveillance systems usually represent the primary source of information for healthcare officials and policymakers for monitoring influenza epidemics . However , due to the lack of specificity of influenza symptoms , they adopt quantitative indicators ( influenza-like illness ( ILI ) or acute respiratory illness ( ARI ) being the two most common ) which are defined at country level , while no defined standard exists at the international level [3–5] . One main reason might be that classification of ILI cases in GPs’ reports is usually based on common clinical symptoms observed among patients and , as with any syndromic-based disease surveillance , case definitions of “influenza-like illness” can vary [6–10] . They typically include fever , cough , sore throat , headache , muscle aches , nasal congestion , and weakness . Some previous works from hospital-based studies [11 , 12] , age-specific antiviral trials [7 , 13 , 14] and national surveillance activities [15] aimed at exploring suitable ILI case symptomatic descriptions but , so far , no unique definition has been widely adopted by the various national surveillance systems worldwide . For this reason , seasonal influenza surveillance in European countries remains rather fragmented . Only in recent years , some state members have adopted the case definition provided by the European Center for Disease Control and Prevention ( ECDC ) which defines an ILI case as the sudden onset of symptoms with one or more systemic symptoms ( fever or feverishness , malaise , headache , myalgia ) plus one or more respiratory symptoms ( cough , sore throat , shortness of breath ) [16] . Nevertheless , a significant fraction of European countries still adopts their own clinical case definition to compile seasonal influenza surveillance weekly reports . S2 Table highlights the existing issue in the heterogeneity of the ILI case definition in Europe [16–18] . In general , differences in seasonal influenza epidemics across European countries are characterised by heterogeneity in sentinel systems , climatic conditions , human mobility systems , as well as social contacts [19 , 20] . The result is a consequent heterogeneity in the prevalence of the disease among the population in the various countries which can present differences in severity during the same influenza season . This diversity makes it hard to have a unified , one-fits-all approach to influenza surveillance , let alone a unified ILI definition . Moreover , the ILI definition might change over time even for national sentinel systems [21] . For example , in Italy , the National Institute of Health ( Istituto Superiore di Sanità ) adopted the ECDC definition only in 2014 [22] . France is a peculiar example as it had double surveillance ( ILI and ARI ) up till 2014 ( Casalegno et al . [3] assessed the performance of various influenza case definitions in France between 2009-2014 ) . Mandl et al . [2] explicitly addressed the variation in the definition of ILI over time . In recent years the availability of novel digital data streams has given rise to a variety of non-traditional approaches for monitoring seasonal influenza epidemics [23–25] . Such new digital data sources can be exploited to capture additional surveillance signals that can be used to complement GPs surveillance data [26–29] . In this context , some so-called participatory surveillance systems have emerged in several countries around the world with the aim of monitoring influenza circulation through Internet reporting of self-selected participants [30–32] . One of these systems , the Influenzanet project [30] , has been established in Europe since 2011 and it is now present in ten European countries . In this study , we excluded from the analysis the country of Sweden , due to the fact that the Swedish cohort is solicited upon invitation when required and not on an annual basis [33] . The system relies on the voluntary participation of the general population through a dedicated national website in each country involved in the project . Data are obtained on a weekly basis through an online survey [34] where participants are invited to report whether they experienced or not any of the following symptoms since their last survey: fever , chills , runny or blocked nose , sneezing , sore throat , cough , shortness of breath , headache , muscle/joint pain , chest pain , feeling tired or exhausted , loss of appetite , coloured sputum/phlegm , watery/bloodshot eyes , nausea , vomiting , diarrhoea , stomach ache , or other symptoms . Differently , from most traditional surveillance systems , this participatory form of online surveillance allows the collection of symptoms in real-time and directly from the general population , including those individuals who do not seek health care assistance . The list of proposed symptoms has been chosen to include the various ILI definitions adopted by national surveillance systems in Europe and , at the same time , to get a comprehensive list of symptoms that could be clearly articulated and understood by participants and would allow the detection of various circulating flu-related illnesses . Even though participatory systems generally suffer from self-selection biases , causing the sample to be non-representative of the general population [35] , previous works have shown that the web-based surveillance data collected by Influenzanet can provide relevant information to estimate age-specific influenza attack rates [36 , 37] , influenza vaccine effectiveness [34 , 38 , 39] , risk factors for ILI [39–41] , and to assess health care seeking behaviour [39 , 42] . Moreover , it has been largely demonstrated that weekly ILI incidence rates computed from the web-based surveillance data by applying the ECDC case definition to the set of self-reported symptoms correlate well with the weekly ILI incidence reported by GPs surveillance [37 , 39 , 43] . An additional advantage of collecting symptoms directly from individuals among the general population in the various Influenzanet countries is that it is straightforward to compare the prevalence and the temporal dynamics of specific symptoms or groups of symptoms from one country to the other . In a previous work focused on France [44] , the authors proposed population-level indicators based on self-reported symptoms and analysed crowdsourced incidence estimates comparing them to official estimates provided by sentinel systems . In this work , we propose an approach that aims at addressing the heterogeneity of seasonal influenza epidemiological signals in the various European countries , focusing on the individual symptoms collected directly from the general population . The goal is to develop a mathematical framework able to extract , in an unsupervised fashion , the groups of symptoms that are in good correlation with the ILI incidence , as detected by traditional surveillance systems for each country without imposing an a priori a specific ILI case definition . By using the daily occurrence of symptoms in form of matrix , we employ an approach based on Non-negative Matrix Factorization ( NMF ) [45] , to extract latent1 features of the matrix that correspond to linear combinations of groups of symptoms . We assume that a specific combination of reported symptoms is the symptomatic expression of one or more illnesses experienced by the participants , i . e . of the syndromes affecting the individual . We can then select those groups of symptoms that better correlate with the sentinel-based ILI incidence , which will become our best approximation for the actual influenza-like illness signal for a specific country . The overall encouraging results suggest that such methodology can be employed as a near real-time flexible surveillance and prediction tool not constrained by any disease case definition . Thus , it can be employed to monitor a wide range of symptomatic infectious diseases or to nowcast the influenza trend , to help to devise public health communication campaigns .
This study was conducted in agreement with country-specific regulations on privacy and data collection and treatment . Informed consent was obtained from all participants enabling the collection , storage , and treatment of data , and their publication in anonymized , processed , and aggregated forms for scientific purposes . In addition , approvals by Ethical Review Boards or Committees were obtained , where needed according to country-specific regulations . In The United Kingdom , the Flusurvey study was approved by the London School of Hygiene and Tropical Medicine Ethics Committee ( Application number 5530 ) . In France , the Grippenet . fr study was approved by the Comité consultatif sur le traitement de l’information en matiére de recherche ( CCTIRS , Advisory committee on information processing for research , authorization 11 . 565 ) and by the Commission Nationale de l’Informatique et des Libertés ( CNIL , French Data Protection Authority , authorization DR-2012-024 ) . In Portugal , the Gripenet project was approved by the National Data Protection Committee and also by the Ethics Committee of the Instituto Gulbenkian de Ciência . In general , the inclusion criteria of participants in the data analysis vary depending on the specific aim of the study [35 , 39 , 49 , 50] . In our case , we included only the individuals registered on the Influenzanet national platforms who filled in at least one Symptoms Questionnaire ( hereafter referred to as “survey” ) per season . This was done to focus the analysis on participants for which we have some information . We had to necessarily exclude individuals who have registered on the platforms but who have not submitted any symptoms survey during any influenza season . This corresponds to the exclusion of 0 . 3% of the registered participants . Moreover , to reduce the noise due to low participation rates at the beginning of the data collection of each influenza season , we consider as starting point the first week for which the number of surveys corresponded at least to 5% of the total number of the surveys filled during the week with the highest participation for that season . This refers to the fact that at the beginning of the season , which is a period when the epidemic is still well below the epidemic threshold , the participation ( i . e . the number of symptoms surveys ) is rather low and therefore the signal to noise ratio can be very low too . Furthermore , we included only one survey per each week—the latest one—if more than one survey was submitted during the same week by the same participant . This exclusion corresponds to a small fraction of discarded surveys , approximately 5% of the total number of surveys; moreover , the distribution of the discarded symptoms and the submission time of the dropped surveys , are homogeneous2 . This exclusion criterion is essential to express the number of self-reported symptoms as probabilities in the final ILI syndrome emerging from our framework and to interpret the aggregation of symptoms as an “incidence” . S1 Table in the supporting information presents descriptive statistics for each country , namely: ( i ) the number of seasons analysed , ( ii ) the average number of participants per season , ( iii ) the average number of weekly surveys per season , ( iv ) the average percentage of surveys with at least one symptom , ( v ) the average number of surveys per participant per season and ( vi ) the average number of weeks within a single season . In this section , we describe the methodology employed to extract the latent features from the self-reported symptoms collected by the various Influenzanet platforms of the participating countries . Our approach relies on the assumption that a specific group of self-reported symptoms corresponds to the symptomatic expression of one or more illnesses , hereafter called syndromes , circulating among the population sample of Influenzanet . In our study we consider the 18 symptoms presented in the weekly Symptoms Questionnaire plus an additional symptoms-related variable , called “Sudden onset” , referring to the sudden appearance of symptoms , typically over the course of the previous 24 hours ( see Table 1 ) . This totalizes 19 symptom variables that we hereafter designate interchangeably as “symptoms” . The symptoms were treated as binary boolean variables having value 1 if the symptom is present and 0 if the symptom is absent . We then aggregated the reported symptoms across all participants to build a matrix X = [xij] , whose elements contain the occurrences of each symptom j ∈ {1 , ‥ , J} during each day i ∈ {1 , ‥ , I} . In other words , each element of the matrix corresponds to the number of times each symptom has been reported on each day of the influenza seasons under study . The result is a high-dimensional sparse matrix that can be linearly decomposed through a Non-negative Matrix Factorization ( NMF ) technique [45] . We opted for NMF since its non-negativity constraint offers the advantage of a straightforward interpretation of the results as positive quantities that can then be associated with the initial symptoms . This approach can be considered as a “blind source separation” problem [51] in which neither the sources nor the mixing procedure is known , but only the resulting mixed signals are measured . In our case , the time series corresponding to the daily symptoms counts are measured by the Influenzanet platforms and can be considered as the result of a linear mixing process driven by unknown sources , i . e . the latent syndromes . In the following we will use interchangeably the terms syndrome , source or component . According to this consideration , each element xij of the matrix X can be expressed as follows: x i j = ∑ k ∈ { 1 , ‥ , K } w i k h k j + e i j , ( 1 ) where the coefficients hkj describe the set of the unknown K sources , the factor wik represents the time-dependent mixing coefficients , and the terms eij correspond to the approximation error . The mixing equations Eq ( 1 ) can be equivalently expressed in matrix notation as: X = W H + E ( 2 ) where: W = [ w i k ] , H = [ h k j ] , E = [ e i j ] ( 3 ) It is worth stressing that in this representation the matrix X is known , while the matrices W and H are unknown and determined by the NMF algorithm . In particular , we used a variation of the NMF algorithm that minimizes the Kullback-Leibler divergence loss function [52] defined as follows: argmin W , H ∑ i , j x i j log ( x i j x ^ i j ) - x i j + x ^ i j , ( 4 ) where: x ^ i j = ∑ k w i k h k j . ( 5 ) To minimise the Kullback-Leibler divergence loss function , we adopted the multiplicative update rules described in [53] . Note that different initialisation of the matrices W and H might lead to different local minima , making the interpretation of the results not straightforward . To overcome this issue , we used an initialization technique called Non-negative Double Singular Value Decomposition [54] , that is based on a probabilistic approach equivalent to the probabilistic latent semantic analysis ( pLSA ) [55] , employed in the context of semantic analysis of text corpora . Since the two approaches of NMF and pLSA are equivalent ( see [56] for more details ) , the results of our matrix decomposition can be probabilistically interpreted as a mixture of conditionally independent multinomials , that we call p ( i , j ) . We can then write: π ( i , j ) ≈ p ( i , j ) = ∑ k p ( k ) p ( i , j | k ) = ∑ k p ( k ) p ( i | k ) p ( j | k ) , ( 6 ) where: π ( i , j ) = x i j / N , N = ∑ i , j x i j ( 7 ) and N is the total number of symptoms counts . According to Eq ( 6 ) , the total number of symptoms counts will be proportionally split among K latent sources according to p ( k ) , which is the probability to observe a specific component k; p ( i|k ) is the probability to observe a component k in a day i and p ( j|k ) is the probability to observe a specific symptom j in a component k , and they can be expressed as follows: p ( i | k ) = w i k / ∑ i w i k , ∑ i p ( i | k ) = 1 , p ( j | k ) = h k j / ∑ j h k j , ∑ j p ( j | k ) = 1 , p ( k ) = ∑ i w i k ∑ j h j k / N , ∑ k p ( k ) = 1 . ( 8 ) At this point , Eq ( 8 ) allows to determine the probability p ( i , k ) that , rescaled on the total number of symptoms counts N , yields the desired decomposition procedure , yik , which represents the contribution of a specific component k in a day i , given by the following expression: y i k = N p ( i , k ) = N p ( k ) p ( i | k ) ( 9 ) Thus , the final step in our approach is to determine the optimal number of components kmin to be used for the decomposition . A natural upper bound for k would be the total number of symptoms , i . e . 19 . We need to determine the number of components with the best trade-off between a model that best approximates the original matrix X and at the same time does not overfit the data . Each time we minimize the loss function Eq ( 4 ) for a specific number of components k , we obtain a candidate decomposition . To determine the best decomposition , we use an approximated model selection criterion , known as the Akaike Information Criterion ( AIC ) [57] . In particular , we employ the corrected version of the Akaike Information Criterion ( AICc ) proposed in [58] , valid for finite sample sizes . For each of the candidate decompositions generated by the various values of k , we estimate the value of AICc ( k ) , expressed as: A I C c ( k ) = - 2 L ( k ) + 2 P + 2 P ( P + 1 ) N - P - 1 , ( 10 ) where L ( k ) is the log-likelihood of the model with k components , defined in [56] as: L ( k ) = ∑ i , j x i j logp ( i , j ) . ( 11 ) P is the number of parameters of the model defined as: P = K ( I + J - 2 ) - 1 , ( 12 ) where K is the upper bound for the number of components , I is the total number of days and J is the total number of symptoms . The best candidate decomposition is the one that minimizes Eq ( 10 ) and we denote it as AICc ( kmin ) . The final result is a model , that we call y i k m i n , consisting of kmin components that best approximate the original matrix X . We applied the aforementioned framework to the data collected by the Influenzanet platforms in nine European countries throughout six influenza seasons ( from 2011-2012 to 2016-2017 ) . For each country , we applied the decomposition algorithm to the symptoms’ matrix X as represented in Eq ( 2 ) and , based on the AIC , we obtained the “optimum” number of components , kmin , for the decomposition . The daily counts of the emerged components are eventually aggregated weekly to allow the comparison with the weekly incidence reported by the traditional GPs surveillance . Among the kmin latent components , i . e . syndromes , extracted for each country , we identified the one that correlates better with the time series reported by the traditional GPs surveillance . In the following , we denote this component as IN_NMF . This component corresponds to the combination of symptoms that more closely represent the ILI time series recorded by the traditional surveillance , and hence , it can be used to build a data-driven , unsupervised ILI case definition , which is the ultimate goal of this study . To further evaluate the IN_NMF signal selected for each country , we also computed the Pearson correlation between: ( i ) the IN_NMF and the time series obtained by applying the ECDC case definition to the Influenzanet data ( hereafter called IN_ECDC ) ; ( ii ) the IN_NMF and the ILI incidence reported by the national surveillance systems per country ( hereafter called GP ) ; and ( iii ) the IN_ECDC and the GP . The reported correlations refer to the time series over the entire period analysed ( 2011-2017 ) . Additionally , we explored the predictive power of the proposed methodology in the following way: first , we trained the NMF decomposition framework with Influenzanet data only from 2011 to 2016 and then , we employed the resulting symptom weights to infer the weekly IN_NMF estimates during the 2016-2017 season . To assess the quality of this signal , we evaluated the Pearson correlation of the forecasted IN_NMF time series for 2016-2017 with both the GP time series and the IN_ECDC time series . Moreover , to broaden the scope of our framework in identifying syndromes not related to ILI ( e . g . gastrointestinal versus respiratory ) , we employed it to identify the syndrome related to gastrointestinal episodes by performing the Pearson correlation with data provided by the traditional official surveillance in France . We focused on the case of France due to the immediate data availability from the official surveillance . The Réseau Sentinelles in fact comprises a unique program of data collection about gastrointestinal illness episodes [59] . The identified component is denoted as IN_Gastro . For the entire analysis and simulations we used the Python programming language ( Python Software Foundation , version 2 . 7 , https://www . python . org/ ) .
S1 Fig in the supporting information depicts an exploration on the relative AIC values of a series of candidate models ( AICc ( k ) − AICc ( kmin ) , with k ∈ [1 , 6] ) , estimated according to Eq ( 10 ) . For the majority of the countries , the optimal decomposition consisted of kmin = 2 components , with the exceptions of the Netherlands and Belgium with kmin = 3 , and France with kmin = 4 . S2 , S3 , S4 , and S5 Figs in the supporting information depict for each country the respective time series of all the emerging kmin components and their symptoms composition . The component selected by our framework is highlighted by a blue square . These results show how our approach is capable of taking into account differences in ILI definition between countries since we can select the components that best correlate with the national ILI signal . In the left panel of Fig 1 , the IN_NMF component for each country is shown in comparison to the ILI signal as recorded by the traditional surveillance , GP . To allow for visual comparison , the IN_NMF time series has been rescaled on the GP time series with a fixed scaling factor . Specifically , the IN_NMF has been rescaled on the highest peak among all the GP time series for each country , hence the lower peak of the IN_NMF for the other peaks of the GP time series . Consequently , the performance of the selected ILI component cannot be evaluated in terms of amplitude and error with respect to the peak estimate . In the right panel of Fig 1 , the break-down of symptoms for each country’s IN_NMF component is expressed in terms of probabilistic contributions , denoted as p ( j|k ) , as described in Eq ( 6 ) . In terms of symptoms’ composition , IN_NMF appears to be stable across the various countries and consistent with the expected set of symptoms clinically associated with ILI . The top contributing symptoms are fever , chills and feeling tired , often reported in combination with a sudden onset of symptoms . Notably , each of these top three symptoms contributes for about 10% or more of the overall component composition . This is consistent across all the nine countries and it is the most important result of this study since it represents the basis towards the development of a common ILI definition . Small heterogeneities in the component composition across countries are most likely due to differences in the ILI case definitions used by sentinel doctors in each country which are reflected in the data that we use as ground truth . In principle , this issue might be overcome by using seroprevalence data as ground truth . For the sake of comparison , we have examined how our framework performs with respect to other similar approaches . For example , Goldstein et al . [60] have used two inference methods to estimate incidence curves from symptoms surveillance data . The first method essentially assumes that the distribution of symptoms is known . In our case , we have no such assumption; instead , we extract the symptoms and their probabilistic distribution from the observed data without making any a priori assumption on the distribution of symptoms . The second inference method proposed by Goldstein et al . [60] is closer to our framework and falls under the umbrella of the term “blind source separation” . The Non-negative Matrix Factorization can be formulated as an expectation-maximization problem [61] . The difference with our approach is that they assume as an initial condition that the expected weekly incidence is equal to 1 for each infection in their survey sample . Their approach is sensitive to the ratio of flu/non-flu distribution while NMF manages to overcome this problem . Table 2 reports all the Pearson correlations between the different time series as mentioned in the Data Analysis section . For all countries , the correlation between the IN_NMF component and the IN_ECDC is very high , ranging from 0 . 82 to 0 . 92 ( row ( i ) ) , thus showing that the IN_NMF signal captures symptoms highly compatible with those present in the ECDC ILI definition applied to the Influenzanet data . However , by carefully examining rows ( ii ) and ( iii ) , we note slight variations per country . For the Netherlands , Belgium , and Ireland the ILI incidence reported by the traditional surveillance ( GP ) was more strongly correlated with the IN_NMF component , than with the ILI incidence obtained by applying the ECDC ILI definition to the Influenzanet data ( IN_ECDC ) . For the United Kingdom , Spain , Denmark , and Portugal , the IN_NMF components perform equally well as the IN_ECDC . For Italy and France , the IN_NMF component had a slightly lower correlation ( about 11% and 7% less respectively ) with the traditional surveillance data ( GP ) than the IN_ECDC . Ireland is the only country for which we obtain a low correlation between the traditional surveillance data ( GP ) and both the IN_NMF and IN_ECDC , probably due to the limited number of participants in Influenzanet ( see S1 Table in the supporting information ) . Despite this , the IN_NMF performs much better than the IN_ECDC in capturing the ILI incidence trend in Ireland ( 0 . 38 vs 0 . 23 ) . This variation in performance is not an issue for the goal of this work since our focus is on paving the way towards a common cross-country ILI definition rather than finding the perfect signal that correlates best with the traditional national surveillance . Also , the loss in performance of IN_NMF vs GP with respect to IN_ECDC vs GP for Italy and France is only a small percentage . One might argue that , since it has been observed that people tend to go to the doctor if their symptoms are more severe or if the duration of the disease is longer [62] , the high correlation between the IN_NMF time series and the GP time series might be attributable to the fact that participatory surveillance only captures individuals with perceived severe symptoms , who did visit a doctor for their illness . Unfortunately , we cannot assess the severity of self-reported symptoms , but we can assess the fraction of participants who claimed they have visited a healthcare provider for their symptoms and , in line with previous studies , we found that the vast majority of participants did not seek medical consultation . Specifically , the percentages of participants who did seek medical consultation per country are: NL 12% , BE 22% , IT 23% , FR 26% , UK 14% , ES 17% , PT 17% , DK 11% , IE 16% . Moreover , to investigate the performance of our framework with respect to healthcare seeking behaviour , we employed two different approaches . First , we trained our framework only with the subset of self-reported symptoms from participants who consulted a medical doctor for their symptoms , obtaining the following Pearson correlations with the GP time series: NL 0 . 83 , BE 0 . 82 , IT 0 . 87 , FR 0 . 92 , UK 0 . 88 , ES 0 . 82 , PT 0 . 82 , DK 0 . 69 , IE 0 . 51 . Secondly , we trained our framework only with the subset of self-reported symptoms from participants who did not consult a medical doctor for their symptoms , obtaining the following Pearson correlations: NL 0 . 77 , BE 0 . 59 , IT 0 . 69 , FR 0 . 78 , UK 0 . 72 , ES 0 . 54 , PT 0 . 48 , DK 0 . 64 , IE 0 . 29 . We notice that since by default our framework selects as ILI component the one that best correlates with the official surveillance , the IN_NMF signal emerged represents better the data reported by the official surveillance systems . Unsurprisingly , the correlations are higher when we compare the same population of individuals who did seek medical consultation for their illness . On the other hand , it is of extreme importance that our framework is capable of extracting a relevant signal in the latter case since the population of individuals who do not seek healthcare is complementary to the one depicted by the official surveillance data . Finally , in order to assess the impact of the exclusion criterion for which we do not take into account duplicate reports from the same individual in a single week , we have determined the mean percentage of the symptoms discarded per country: NL 0 . 04% , BE 0 . 03% , IT 0 . 09% , FR 0 . 06% , UK 0 . 16% , ES 0 . 05% , PT 0 . 12% , DK 0 . 03% , IE 0 . 10% . Indeed , the duplicate report exclusion corresponds to a small number of symptoms discarded each week and the distribution of all discarded symptoms is homogeneous . The results of the prediction analysis described in the Data Analysis section are shown in S6 Fig . The fourth row of Table 2 ( iv ) reports the correlations of the forecasted IN_NMF time series and the national surveillance for the season 2016-2017 ( GP ) . The correlation between the two time series is good for all the countries , ranging from 0 . 60 to 0 . 85 . In supplementary information we depict the results of the prediction analysis described in the Data Analysis section . As already stated above , for the sake of visual comparison , the IN_NMF time series has been rescaled to the highest peak of the GP time series for each country , hence the lower peak for the other peaks . Consequently , the two time series cannot be evaluated in terms of amplitude and error . In Table 2 row ( v ) , we also report the correlation between the forecasted IN_NMF time series and the IN_ECDC time series emerged from applying the ECDC definition to the Influenzanet data for the season ( 2016-2017 ) . Also , in this case , the predicted trend of the ILI component have high correlations , ranging from 0 . 59 to 0 . 93 . Even if the focus of the paper is on the possibility of extracting a symptoms-based data-driven definition of ILI that is country specific , the forecasting capabilities of the framework represent an additional strengthening factor ( the forecasting potential of using participatory surveillance data , in combination with additional epidemiological signals has also been explored in a previous paper [29] ) . To further assess the robustness of the forecasts produced by the NMF framework , we have compared their accuracy with respect to a null model in two different ways . In the left panel of Fig 2 , we show the time series for the incidence of acute diarrhoea episodes ( GP_Gastro ) as detected by the official national surveillance in France , and the time series of the syndrome identified by our framework ( IN_Gastro ) . The Pearson correlation between the extracted syndrome and the official surveillance data is 0 . 66 . In the right panel of Fig 2 we depict the probabilistic contribution of each symptom to the IN_Gastro syndrome . Emerging symptoms , in this case , include also stomach ache , diarrhoea , and vomiting , which are in line with our expectations . Even if respiratory symptoms like runny nose or sneezing are also present , the contribution of fever and chills ( which were the main contributors to the IN_NMF signal ) is almost negligible . This suggests a rather good capability of our framework in discriminating between different syndromes . Despite limitations of the data availability , these preliminary findings indicate that the latent components of the decomposition , not related to ILI , may express syndromes related to allergies , common-cold or gastroenteritis . Understandably , additional adequate surveillance data are required to make a firm statement and reach a robust interpretation of the syndromes . Previous works have also focused on detecting gastrointestinal symptoms circulating among the general population through digital unstructured data [33 , 63–65] from participatory surveillance , big data , such as Twitter , as well as national pharmacy sales data . These examples show how crowdsourced digital health-related data , as well as passive digital traces generated on the web by individuals from the general population , can complement traditional and syndromic surveillance systems to estimate the circulation of gastrointestinal syndromes . This is particularly important because only a fraction ( about a third ) of individuals who reported gastrointestinal symptoms in France also declared that they visited a doctor . The NMF framework applied to the subset of data from participants who did not visit a doctor for their symptoms selected a component whose correlation with official surveillance data is 0 . 67 ( with respect to a correlation of 0 . 66 when using all the data ) . This shows that people tend to visit a doctor rarely and probably only if their symptoms are severe . The NMF framework is capable of providing robust results even if we focus the analysis only on those individuals who did not visit a doctor , for which we can safely assume that their symptoms were not severe . This approach has several limitations . As far as data are concerned , crowdsourced digital data are intrinsically biased due to the fact that the participants are self-selected and not representative of the general population , as extensively explored in a previous work [35] . However , such sample biases do not affect the robustness and accuracy of the epidemiological signal detected through participatory surveillance [37 , 39 , 43] . Previous works have shown that selecting groups with specific reporting patterns or combining data sources can improve the representativeness [28 , 66 , 67] . Extending this study , we will incorporate in our framework the user attributes to account for selection biases . Other issues could rise from the variable reporting behaviours along the season , individuals’ interpretation of the terms used for surveillance , and the correctness of their self-assessments . Some of these issues have been addressed partially in previous works [10 , 40 , 44 , 50 , 68] . In our approach , we assume that self-reported symptoms are consistent since Influenzanet data have been already proven to be accurate and reliable for ILI surveillance , even without providing any clinical confirmation . However , we are aware that one of the criticisms of online participatory surveillance is the lack of virological confirmation of influenza cases that would instead help to better assess the actual circulation of influenza in the population . To this respect , a pilot study has been developed in the United Kingdom by the national Influenzanet platform , called Flusurvey , which demonstrates that self-swabbing can be applied to an online cohort to conduct virological laboratory testing [69] . Moreover , in this work , we have not compared the performance of other machine learning algorithms besides NMF since this would go beyond the scope of this paper . Future work could explore the performance of other methods and clustering algorithms . Among the many algorithmic choices , LDA could be employed in a similar framework , since PLSA is simply a special case of LDA and Faleiros et al . [70] showed that indeed NMF with Kullback-Leibler divergence approximates the latent Dirichlet allocation ( LDA ) model under a uniform Dirichlet prior distribution . Finally , there are inherent socio-economic biases in influenza surveillance systems [71] due to the fact that in some countries traditional surveillance is based on primary healthcare which may be biased towards population with higher socioeconomic status . Even additional digital unstructured data sources are more representative of these population groups , thus even combining traditional and non-traditional data sources might fail in mitigating biases towards more at-risk groups .
The practice of seasonal influenza surveillance is affected by a lack of a common case definition for influenza-like illness across countries . Moreover , the seasonal influenza epidemics in the various European countries present a high degree of heterogeneity . To improve seasonal influenza surveillance beyond these issues , we propose an unsupervised probabilistic framework based on self-reported symptoms collected daily through a network of participatory web-based influenza surveillance platforms in Europe called Influenzanet . Our approach , which relies on a Non-negative Matrix Factorization of the daily symptoms matrix , is capable of producing an epidemiological signal that does not rely on a specific a priori case definition and that follows the temporal trend of influenza-like illness closely as detected by the traditional sentinel doctors surveillance in each country . The emerging signal successfully captures the ILI incidence trend estimated by the national surveillance data for all the nine countries included in this study . We also demonstrate that the proposed approach can be employed to forecast the forthcoming ILI incidence . Additionally , the proposed approach has the potential to be used to identify other illnesses , as shown here for gastrointestinal syndromes , although additional traditional surveillance data is needed to validate the generalisability of our framework . We can thus conclude that there is great potential in using symptoms directly collected from the general population to inform unsupervised algorithmic approaches aimed at detecting circulating bouts of illnesses without imposing an a priori case definition . The standardized technological and epidemiological framework and the ability to monitor symptoms from the general population , including individuals who do not seek medical assistance , provided by the Influenzanet participatory surveillance platforms , are what enables the application of unsupervised algorithmic approaches such as the one presented in this work . In the next future , we will include data from virologically confirmed influenza cases as ground truth to enhance the specificity of our framework . Regarding the forecasting capabilities of the framework , approaches from existing research on participatory flu surveillance suggest that the integration of real-time official data sources with the crowdsourced digital ones [72] [73] provide better forecasting performance . In our case , the weekly integration of sufficient traditional surveillance data in the framework could lead to a near-real-time selection of the component that better represents the symptoms in the ILI syndrome circulating among the general population . Finally , the flexibility provided by the participatory surveillance platforms in terms of symptoms that can be collected from the general population enables the possibility to extend the framework to other diseases , provided that traditional surveillance data are available to train the framework . | This study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting the temporal trends of influenza-like illness without relying on a specific case definition . The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years . Moreover , to broaden the scope of our approach , we applied it to the detection of gastrointestinal syndromes . We evaluated the approach against the traditional surveillance data and despite the limited amount of data , the gastrointestinal trend was successfully detected . The result is a near-real-time flexible surveillance and prediction tool that is not constrained by any disease case definition . |
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Across many environments microbial glycoside hydrolases support the enzymatic processing of carbohydrates , a critical function in many ecosystems . Little is known about how the microbial composition of a community and the potential for carbohydrate processing relate to each other . Here , using 1 , 934 metagenomic datasets , we linked changes in community composition to variation of potential for carbohydrate processing across environments . We were able to show that each ecosystem-type displays a specific potential for carbohydrate utilization . Most of this potential was associated with just 77 bacterial genera . The GH content in bacterial genera is best described by their taxonomic affiliation . Across metagenomes , fluctuations of the microbial community structure and GH potential for carbohydrate utilization were correlated . Our analysis reveals that both deterministic and stochastic processes contribute to the assembly of complex microbial communities .
The complete enzymatic deconstruction of polysaccharides ( e . g . , cellulose , chitin ) involves many carbohydrate active enzymes ( CAZymes ) including glycoside hydrolases ( GH ) , polysaccharide lyases , carbohydrate esterases , accessory activities ( e . g . , LPMO ) , and many accessory domains ( e . g . , CBM ) [1–4] . The glycoside hydrolases ( GH ) cleave glycosidic bonds in polysaccharides ( e . g . , cellulose ) and oligosaccharides ( e . g . , cellooligosaccharides ) and release short metabolizable products ( e . g . , cellobiose ) . According to the CAZy database [5] , many GH families , identified based on their structure , display substrate specificity . For example , most biochemically characterized proteins with domains from GH families 5 , 6 , 7 , 8 , 9 , 12 , 44 , 45 , and 48 act on cellulose . On the other hand , some GH families display mixed substrate specificity ( e . g . , GH16 ) . The identification of specific GH domains in sequenced genomes [6] and metagenomes [7] allows for the prediction of the potential for starch , cellulose , xylan , fructan , chitin , and dextran deconstruction ( i . e . , the potential to target carbohydrates according to functional annotation of genes ) [2 , 6 , 8 , 9] . To date , most identified GH are from bacteria and their distribution , across sequenced genomes , is phylogeneticaly conserved within genera [2 , 9 , 10] . Most bacteria have the potential to target starch and oligosaccharides and few lineages are associated with increased potential for complex carbohydrate deconstruction ( i . e . , potential polysaccharide degraders ) [2 , 9] . Besides some well-characterized microbial lineages involved in polysaccharide deconstruction ( e . g . , Clostridium , Streptomyces ) , the systematic investigation of sequenced bacterial genomes has revealed the richness and diversity of GH in poorly-characterized degrader lineages ( e . g . , Actinospica ) [6] . Microbial communities exposed to varying parameters , including carbohydrate supply [11] , fluctuate across environments [12–16] . As a consequence , changes in community composition have been associated with variations of environmental processes ( e . g . , plant material deconstruction , phosphate uptake ) [17–19] . Thus , the major challenges are ( i ) to understand which bacteria are involved in carbohydrate deconstruction , and ( ii ) to understand if the overall microbial community composition and potential for carbohydrate deconstruction are linked , across microbial populations and across environments . Does the environment select for specific GH , specific lineages , or both [10 , 20] ? In the first case , microbial communities would adapt through selection of adequate potential for carbohydrate processing independently of the lineage ( e . g . , by lateral gene transfer or other ways of convergent evolution ) . In the second hypothesis , microbial communities would adapt through selection of phylogenetically defined lineages endowed with specific potential for carbohydrate processing [20] . The first hypothesis implies that changes in functional potential and community composition are not connected whereas the opposite is the case for the alternative hypothesis . In order to address these questions , we investigated how changes in the potential for carbohydrate processing correlates with the change of bacterial communities composition across 13 broadly defined environments and across 1 , 934 sequenced microbiomes . Despite the lack of consistent quantitative estimation of the carbohydrate composition across environments , ecosystem-types are associated with specific supplies of carbohydrates . In soil [21] , sludge and wastewater ( referred to as sludge below ) [22] , and in the phyllosphere [23] , microbes are exposed to an abundant—and varying—complex mixture of carbohydrates ( e . g . , cellulose , xylan , and fructan from plant material and chitin from fungi and arthropods ) . In aquatic systems ( i . e . , marine , mats , and larger fresh water environments ) , the carbohydrate supply is reduced , and chitin is the most common polymer [24–26] . Microbes in digestive tracts ( i . e . , human gut , oral , and most animal samples ) are exposed to diverse and abundant substrates including plant polysaccharides and animal glycosaminoglycans found in food and produced by the host [27–29] . In other parts of the host ( e . g . , skin ) , the supply of carbohydrates is reduced and mostly composed of animal carbohydrates [30] . In corals and sponges , the supply of carbohydrates is reduced and reflects the chemical composition of prey ( i . e . , detritus and planktonic cells ) [31] . Finally , starch and glycogen , produced to store energy by many organisms [32 , 33] , and dextran associated with bacterial biofilm ( e . g . , dental plaque ) [34] are expected to be present in most environments . Investigating how changes of microbial community composition and changes of potential for carbohydrate processing correlate across environments will ( i ) help identify environment-specific potential for carbohydrate processing , ( ii ) and highlight new environmental lineages associated with potential for carbohydrate utilization , and ( iii ) provide a comprehensive framework for the interpretation of the mechanisms by which microbial communities adapt to varying carbohydrate supply .
First , in order to test how the environment affected the potential for carbohydrate utilization across ecosystems , we identified 130 . 2×106 sequences encoding putative glycoside hydrolases ( GH , ~0 . 5% of analyzed sequences ) in 1 , 934 annotated metagenomes from 13 broadly defined ecosystems ( S1 Table ) [35] . Across environments , we found that the potential for carbohydrate utilization varied extensively but , in many cases , matched the expected supply of carbohydrates . The frequency of sequences for GH ranged from 1 . 7 ( sponges ) to 172 ( human gut ) per sequenced genome equivalent ( i . e . , 3Mbp , SGE ) [7 , 36] . Broadly , the overall frequency of identified GH was high in most human—associated ecosystems , intermediate in the phyllosphere and animal samples and low in soil , sludge , mats , marine , fresh-water , coral , and sponge samples ( Fig 1A , S2 Table ) . Besides enzymes for oligosaccharides and starch , sequences targeting mixed substrates [i . e . , the other plant polysaccharides ( OPP ) , the other animal polysaccharides ( OAP ) , and other undefined carbohydrate ( Mixed ) ] dominated in most samples ( Fig 1B , S3 Table ) . Next , sequences for cellulose and fructan utilization were abundant in most human samples , intermediate in the phyllosphere and soil and low in the other ecosystem types . Xylanases were abundant in the human gut and intermediate in animal and phyllosphere samples only . Chitinases were abundant in mats and human skin samples whereas sequences for dextran utilization were abundant in human mouth and gut . Environments with expected abundant and diverse supply of carbohydrates ( e . g . , human gut , animal , phyllosphere , soil ) were associated with sequences for GH targeting many different substrates . Furthermore , the potential for carbohydrate processing was skewed in environments with a specific carbohydrate supply . In aquatic environments and the human mouth , the relative frequencies of sequences for GH targeting chitin and dextran were found to be higher than in other ecosystems , respectively ( Fig 1B , S3 Table ) . In some environments however ( e . g . , human skin and vagina ) , the prevalence of sequences for GH targeting specific substrates ( e . g . , cellulose and fructan ) did not systematically matched with the expected presence of substrates . When accounting for both the presence/absence and frequency of sequences for GH , across ecosystem-types we observed three clusters ( Fig 1C ) . The first cluster contained metagenomes from aquatic environments , sponge , and coral samples . In these ecosystems , the frequency of GH was extremely reduced . The second cluster contained metagenomes from soil , sludge , mats , and—more distantly related- animal samples . These ecosystems displayed intermediate and diverse GH frequency . Finally , the third group , composed of human samples and the phyllosphere , displayed abundant and diverse GH . Globally each ecosystem-type displays a specific potential for polysaccharide deconstruction matching the assumed carbohydrate supply . Sequences for GH were more frequent in human , animal , and phyllosphere samples than in “open” environments . These fluctuations could reflect variations in the actual GH abundance and/or variations of the average genomes size across environments . Indeed , for example , many lineages derived from the soil have large genomes ( e . g . , Streptomyces , phylum Actinobacteria ) whereas many host associated microbes have smaller genomes ( e . g . , Mycobacterium , phylum Actinobacteria ) [37 , 38] . Within ecosystems , extensive variations were also observed . These variations , likely reflect environmental fluctuation in microbial community composition [e . g . , human microbiome [39] , animals [27] , soil [40] , and marine ecosystems [41]] in response to specific environmental conditions ( e . g . , moisture , carbohydrate supply ) in sub-ecosystem types . For example “soil” represents many types of ecosystems ( e . g . , desert and forest ) associated with distinct carbohydrate supply and host to different communities [11] . Alternatively , these variations could reflect the variable GH content among functionally equivalent , and potentially interchangeable , lineages . For example , not all the potential cellulose degraders display the same GH content [6] . Next , we defined microbial communities of degraders as the collection of identified bacterial genera associated with the potential to target cellulose , xylan , fructan , dextran , chitin , OAP , OPP , or Mixed substrate . In order to identify the degrader communities , we used the taxonomic annotation of the detected GH sequences . As expected [2] , GH sequences for starch and oligosaccharides processing were associated with many genera . Traits for cellulose , xylan , and chitin were associated with tens to hundreds of genera . Finally the diversity of genera with the potential for metabolizing dextran and fructan was further reduced ( Fig 2 ) . The degrader community in human and animal metagenomes was strongly skewed toward few taxa from the Bacteroidetes , Actinobacterium , and Proteobacteria phyla . In both human gut and in animal samples , the pool of sequences for GH was dominated by sequence associated with Bacteroides whereas Streptococcus dominated in the human mouth , Propionibacterium in human skin , and Lactobacillus in human vagina . In corals and sponges , the few identified GH sequences were also derived from a reduced number of bacterial genera . In metagenomes from sludges , the community of degraders was moderately skewed toward few genera depending of the considered substrate ( e . g . , Clostridium for chitin and xylan ) . In the other environments the contribution of identified degraders to the pool of GH was more evenly distributed . Some of the identified degrader genera were detected in most ecosystem-types ( e . g . , Bacteroidetes , Bacillus ) whereas some were restricted to specific environments ( e . g . , Xylella ) . Across samples , sequences for the degrader community accounted for ~2 to ~82% ( median value ) of taxonomically identified sequences , in coral and vagina samples , respectively ( S1 Fig ) . In addition , variation in the composition of the degrader community correlated with the composition of the non-degrader community ( rSpearman = 0 . 69 , p = 0 . 001 , S2 Fig ) . This suggested that the environmental parameters are affecting both the degraders and the non-degraders . However , the carbohydrate supply , being a major factor affecting microbial community composition in terrestrial ecosystems [11] , is likely to act directly on the degrader community and indirectly on the non degraders through intergeneric association and competition [42] . Although similar numbers of degrader lineages are found across ecosystems , except in coral and sponge samples , host associated metagenomes displayed strong bias toward reduced number of degrader genera . These ecosystem-types constitute stable environments with constant supply of nutrient and little spatio-temporal variation . These stable and nutrient rich ecosystems promote the selection of specific lineages whereas “open” ecosystem-types , experiencing spatial and temporal variation of the nutrient supply harbor more diverse communities of degrader lineages [38] . This increased diversity likely results from spatial and temporal heterogeneity of open-environments and is likely to buffer the impact of fluctuating microbial community [43–45] . In contrast , in human and animal associated metagenomes , microbial communities are skewed towards few genera with increased GH-content and reduced genome size [46 , 47] , thus increasing the overall frequency of GH sequences . In these communities , carbohydrate processing , and thus the entire environment functioning , is more vulnerable to perturbation affecting degraders [29 , 48 , 49] . Interestingly , in environments where the GH distribution and the assumed carbohydrate supply do not match , identifying the degrader lineages highlighted two trends . First , in the human vagina , the high frequency of GH32 and 68 , targeting fructan , is associated with abundant Lactobacillus ( phylum Firmicutes ) . These enzymes are potentially involved in the biosynthesis and metabolism of fructose-derived exopolysaccharides and biofilms [2 , 39 , 50] . Next , in human skin , the high frequency of cellulases matched with abundant GH5 found systematically in Propionibacterium ( phylum Actinobacteria ) [2 , 9] . Although secreted by P . acnes isolates [51] , the exact function of these potential cellulases remains to be elucidated as the skin is not expected to contain large amount of cellulose . Thus , the prevalence of GH in a specific environment reflects the adaptation to nutrient supply , the requirement of GH for biosynthetic pathways ( e . g . , biofilms ) , and the phylogenetic conservatism of functional traits . Next , we essayed the conservatism of GH sequences in environmental potential degraders in order to test if the observed variation of the GH content across ecosystems mirrored the phylogeny or the environment . In total 493 identified bacterial genera with GH genes were identified . Most had the potential to degrade starch and oligosaccharides and just 77 major potential carbohydrate degraders were associated with GH for cellulose , xylan , fructan , dextran , chitin , OPP , OAP , and mixed substrates ( when excluding rare genera , i . e . <0 . 2 SGE/metagenome ) ( S3 Fig ) . Most of these genera contained known degraders ( e . g . , Clostridium , Xanthomonas ) [2 , 3 , 9] . In addition , several poorly-characterized genera were also identified ( e . g . , Basfia , Novosphingobium , Leeuwenhoekiella ) . Some degraders were cosmopolites ( i . e . , detected in most ecosystems , e . g . , Bacillus , Bacteroides ) , some were intermediate cosmopolites , identified in few environments ( e . g . , Caulobacter ) , and few were restricted to specific environments ( e . g . , Basfia ) . Next , among the identified lineages , some were specialists with GH for a reduced number of carbohydrates ( e . g . , Atopobium , a vaginal commensal , and Exiguobacterium , an environmental cosmopolite ) whereas some were generalists with the potential to target many substrates ( e . g . , Bacteroides , Bacillus , and Streptomyces ) ( S3 Fig ) . Among the major potential degraders , most cosmopolites and intermediate cosmopolites , except some Bacteroidetes , displayed conserved GH/SGE across environments ( Fig 3A ) . This suggested that , in most genera , the phylogeny strongly affects the GH content and this supported the phylogenetic conservatisms of GH at the genus level in sequenced bacterial genomes [2 , 9] . Conversely , in variable Bacteroides , Parabacteroides , and Flavobacterium , the environment is likely strongly affecting the GH content . This suggested that , depending on the phylum , both the phylogeny and the environment could explain the lineage-specific GH content . Thus , we next investigated the relative contribution of ecosystem and taxonomy on the genus specific GH content , across bacterial phyla ( S4 Fig ) . In most phyla , the taxonomic origin , not the ecosystem , was a major source of variation of the potential for carbohydrate degradation ( e . g . , >40% of the observed variation in Fusobacteria and Planctomycetes ) . However in some phyla ( e . g . , Thermotogae and Tenericutes ) the taxonomic affiliation accounted for <5% of observed GH/SGE variation . The environment-type and interactive effect between environment and taxonomy , also significantly affected the distribution of the GH in bacterial genera , accounting respectively for 1 . 5–17% and 0 . 7–13% of the observed variation ( S4 Fig ) . Thus , overall , our data suggested that first the taxonomy , and the associate phylogeny , and next the environment affected the genus-specific GH content . This was further confirmed by the significant correlation between overall community composition and the variation in functional potential for carbohydrate processing across environments ( n = 13 environment types , rmantel = 0 . 42 , p = 0 . 001 ) ( Fig 1C , S5 Fig ) and across samples ( n = 1 , 934 metagenomes , rmantel = 0 . 55 , p = 0 . 001 ) . Thus , despite variation across environments , the genus specific GH content is best described by the taxonomic affiliation of the considered lineages , at the genus level . Functional traits for carbohydrate processing are not randomly distributed among environmental bacterial genera . Next , we investigated the connection between the overall microbial community composition and the potential for carbohydrate processing , across metagenomes ( Fig 4 ) . This analysis highlighted the taxonomic and functional proximity of microbiomes within most environments ( Fig 4A and 4B ) . In addition , microbial communities from distinct environments but exposed to supposedly similar carbohydrates ( e . g . , animal vs . human gut ) , also overlapped structurally and functionally . This suggested that the overall microbial community composition and the potential for carbohydrate processing were linked . In order to test this connection , we assayed the dissimilarities in the potential for carbohydrate processing ( FBC ) and the overall taxonomic composition ( CBC ) across pairs of metagenomes ( Fig 4C ) . First , even some completely different communities ( i . e . , CBC~1 ) shared potential for carbohydrate processing ( i . e . , FBC<1 ) . This highlighted the central function of GH enzymes , their broad distribution across bacteria and environments [2 , 7 , 9] and converging functions in environmental communities regarding carbohydrate processing [52 , 53] . On the contrary , even taxonomically identical communities ( i . e . , CBC~0 ) displayed variation in their GH content ( i . e . , FBC>0 ) . This suggested that , although conserved in most bacterial genera , closely related lineages ( e . g . , species ) could possibly display variation of their potential for carbohydrate utilization [13] . Next , communities were more similar , compositionally and functionally , within the same environment than across environments . This supports ecosystem-specific GH composition ( Figs 1 and 4A ) and suggests that microbial community composition is a major factor affecting the overall potential for carbohydrate processing . Finally , within environments , compositional and functional dissimilarity correlated , the higher FBC being associated with higher CBC . As described here , shotgun metagenomics provided a path to depict the taxonomy and functional potential for carbohydrate processing of complex environmental microbial communities . Nevertheless , many limitations have been associated with this technique [7 , 13] . Specifically , we recognize that , fungi and other microeukaryotes , although important members of microbial communities , were not included in this study . Second , accurate annotation of individual sequences in databases depends on the availability of biochemically-characterized homologs . GH are among the most characterized enzymes and their predicted substrate specificity was derived from biochemically characterized bacterial homologs [2 , 5] . However GH sometime display broader substrate specificity than described here and although GH are essential for carbohydrate processing , many other enzymes are involved in this process . Third , DNA extraction and sequencing procedures are known to affect the distribution of identified sequences . However , these bias were shown to have limited impact on discrimination of microbial communities from distinct environments [54] . These issues are invariably associated with metagenomics and can affect our conclusions in unknown direction . We also recognize that GH , although central for the processing of carbohydrates , are not the only CAZymes involves in this process . Indeed GH are known to act synergistically with other CAZymes ( e . g . , LPMO ) and accessory domains ( e . g . , CBM ) in order to fully deconstruct complex substrate ( e . g . , plant cell wall ) [4 , 6] . Nevertheless , quantifying the distribution , the substrate specificity , and the taxonomic origin of sequences for glycoside hydrolases across 1 , 934 metagenomes provides an unprecedented opportunity for understanding organizing principles of the connection between community composition and the potential for carbohydrate processing , a key reaction in many environments [11] . First , a limited number of bacterial genera contribute to the pool of GH in the environment and their distribution produces ecosystem-specific potential for carbohydrate utilization . This reflects the limited distribution of genes for breaking down carbohydrate in bacterial lineages [2 , 9] . Across microbiomes , fluctuation in the community of the major degraders correlates with the non-degrader community thus confirming how important the carbohydrate supply is on the community of degraders [11] . As depicted here , the environment selects for both specific GH and specific lineages . In consequence , the assembly of microbial communities mirrors both deterministic and stochastic processes [55] . Indeed , in most ecosystems several ecologically similar , yet not identical , potential carbohydrate degraders can coexist and compete . This functional redundancy among degraders produces functionally similar but structurally distinct communities . Next , as suggested by Ferrenberg et al . , stable microbial communities are more influenced by stochastic processes [55] . Finally , although conserved in most bacterial genera , some lineages may display variation of the GH content within genus [56] . Interspecific variation within these genera may result in variable overall functional potential with little variation in the community structure , when characterized at the genus level [57] . Together , these variations can influence the relation between potential for carbohydrate deconstruction and the overall microbial community . In consequence , the microbial community structure cannot be inferred from the identified potential for carbohydrate utilization . However , within ecosystems , the potential for carbohydrate utilization is highly conserved , relative to the overall microbial community structure . This suggests that environmental parameters , including carbohydrate supply [11] , filter microbial lineages based on their potential for carbohydrate utilization . However the potential for carbohydrate utilization is constrained to specific lineages , at the genus level . In consequence , microbial community structure and function correlate and thus , knowing the microbial community composition ( at the genus level ) , one could potentially infer the distribution of traits for carbohydrate utilization . The phylogenetically conserved potential for polysaccharide utilization in bacterial genera detected in metagenomes in this study , and in sequenced bacterial genera [2 , 9] suggests that identifying the composition is essential to understand , and potentially predict , the distribution of genes involved in polysaccharide utilization in environmental microbial communities . In the future , increasing the diversity of reference genomes will provide a better understanding of the phylogenetic distribution of genes for carbohydrate utilization , especially in poorly-characterized lineages ( e . g . , Curtobacterium , Actinospica ) [6] . These lineages , even if poorly abundant , can contribute to the pool of GH [7] , and thus might potentially affect the processing of carbohydrate , an essential reaction in many environments .
Publically accessible SEED-annotated metagenomic datasets ( n = 1 , 934 ) were downloaded from the MG-RAST server , using the MG-RAST API ( S1 Table ) [35 , 58 , 59] , and datasets were grouped by features and biomes according to the bioportal ontology ( http://bioportal . bioontology . org/ontologies/ ) . In order to identify all the sequences associated with GH in the samples , sequences for each GH/CBM family , as defined in the CAZy database [5] , were extracted from the Pfam server and mapped against all sequenced genomes using SEED annotations [9 , 60] . SEED functional annotation of these traits was then used as a reference to investigate the SEED-annotated sequences provided by MG-RAST output files ( i . e . , XXX_650 . Superblat . expand . protein ) for functional annotations . The resulting hits and their corresponding sequences were then subjected to a Pfam_scan ( analysis ( PfamA 27 . 0 db , e-value<1×10−5 ) [61] to confirm functional annotations ( S4 Table ) . This approach allowed us to identify short sequences from metagenomes matching GH from sequenced bacterial genomes . The taxonomy of the identified GH , and the overall community composition ( at the genus level ) for each dataset , was retrieved using taxonomic annotation of the corresponding sequences using M5nr database [59 , 62] Glycoside hydrolases are among the most characterized enzymes . Many families have specific structure/function and display narrowed substrate specificity . GH families were assigned to substrate target categories according to the substrate specificities of characterized enzymes from bacteria , as stated in the CAZy database . GH families targeting cellulose , xylan , chitin , starch ( and glycogen ) , fructan , dextran , and oligosaccharides were identified [2 , 5 , 8 , 9] . Some GH families were identified as targeting Other Plant Polysaccharides ( i . e . , polysaccharides other than cellulose , xylan , starch , fructan ) , Other Animal Polysaccharides ( i . e . , polysaccharides other than starch-glycogen , chitin ) , and Mixed when targeting several substrates ( S4 Table ) . Statistical analyses were performed using ‘Stat’ ( v3 . 3 . 0 ) and ‘Vegan’ ( v2 . 4–1 ) packages in the R software environment ( v3 . 3 . 0 ) [63 , 64] . For clustering of environments , we summarized the data ( i . e . , we computed the median frequency of GH sequences per sequenced genome equivalent ( SGE ) , the GH composition , and to community composition ) by environment type . Then Bray-Curtis dissimilarities between pairs of environments were computed and the clustering was achieved by hierarchical clustering ( S6 Fig ) . For clustering based on the GH composition , we first selected metagenomic datasets containing at least 500 identified GH sequences , then the GH distribution was rarefied and dissimilarity was computed using Bray-Curtis index . Noteworthy , none of the datasets from Sponge or Coral was included in the analysis . Finally , for the clustering according to the community composition , datasets with more than 10 , 000 taxonomically identified hits were considered ( no dataset from Coral could be included in this test ) . Correlation between environment comparisons was achieved by running Mantel correlation test ( 999 permutations ) [63] on the corresponding distance matrices . The contribution of genera to the pool of GH sequences was achieved by analyzing the taxonomic origin ( at the genus level ) of identified GH sequences [2] . Then sequences for enzymes targeting specific substrate ( S2 Table ) were tallied by environment and by genus . Then , the total number of bacterial genera endowed with the potential to target the substrate was obtained . Major degrader genera were arbitrarily determined , for clarity of purpose , as bacterial genera contributing at least 8% of the identified GH for a considered substrate , in at least one specific environment . The impact of environment and taxonomy , and the associated phylogeny , on genus specific GH content was identified in bacterial genera in datasets with at least one genus-specific sequenced genome equivalent ( i . e . , 3Mbp ) . Next , we computed the median value for each GH family , in each genus , in each environment , per sequenced genome equivalent . Finally , we ran a PERMANOVA ( GH~Environment*Genus , with 500 permutations ) [63] , for each phylum . The results are expressed as percent estimated variance explained by genus , environment , and the interaction of genus by environment ( S5 Fig ) . | The deconstruction of complex carbohydrates ( e . g . , cellulose , chitin ) , mostly by microbes , releases short metabolizable oligosaccharides to the environment . This contributes to the functioning of an ecosystem and is essential for global carbon cycling . Carbohydrate degradation requires the production of carbohydrate active enzymes ( CAZymes ) . Among these , GH are the most abundant enzymes to break down polysaccharides into smaller products . However , not all the microbes have genes for all the glycoside hydrolases ( GH ) . In addition , microbial communities are dynamic assemblages and display important spatio-temporal variations . Thus , two major questions are , which microbes are associated with GH genes and which are involved in carbohydrate processing across environments . The bioinformatic challenge is therefore to collect enough metagenomic datasets and to reanalyze microbiomes in the light of GH genes . Here , we created a custom bioinformatic pipeline aimed at identifying sequences for GH in 1 , 934 sequenced microbiomes derived from 13 broadly defined ecosystems , including terrestrial and marine ecosystems as well as human and animal associated microbiomes . We linked changes in microbial community composition and functional potential for carbohydrate processing across environments . Our results suggest that a relatively small number of bacterial genera ( i . e . , the potential degraders ) , with increased number of GH genes target the substrates expected in their environment . These degraders display mostly conserved GH content across microbiomes . In each ecosystem however , the functional redundancy among potential degraders allows for slightly distinct communities with similar functional potential . Globally , linking variations of microbial community structure and function , across ecosystems , provides insight into how microbial communities may adjust to the supply of carbohydrates . In the future , this will help predict how change in microbial community composition , in response to environmental perturbation ( e . g . , global change ) , can affect the functional potential of microbial communities . |
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Although leprosy is largely curable with multidrug therapy , incomplete treatment limits therapeutic effectiveness and is an important obstacle to disease control . To inform efforts to improve treatment completion rates , we aimed to identify the geographic and socioeconomic factors associated with leprosy treatment default in Brazil . Using individual participant data collected in the Brazilian national registries for social programs and notifiable diseases and linked as part of the 100 Million Brazilian Cohort , we evaluated the odds of treatment default among 20 , 063 leprosy cases diagnosed and followed up between 2007 and 2014 . We investigated geographic and socioeconomic risk factors using a multivariate hierarchical analysis and carried out additional stratified analyses by leprosy subtype and geographic region . Over the duration of follow-up , 1 , 011 ( 5 . 0% ) leprosy cases were observed to default from treatment . Treatment default was markedly increased among leprosy cases residing in the North ( OR = 1 . 57; 95%CI 1 . 25–1 . 97 ) and Northeast ( OR = 1 . 44; 95%CI 1 . 17–1 . 78 ) regions of Brazil . The odds of default were also higher among cases with black ethnicity ( OR = 1 . 29; 95%CI 1 . 01–1 . 69 ) , no income ( OR = 1 . 41; 95%CI 1 . 07–1 . 86 ) , familial income ≤ 0 . 25 times Brazilian minimum wage ( OR = 1 . 42; 95%CI 1 . 13–1 . 77 ) , informal home lighting/no electricity supply ( OR = 1 . 53; 95%CI 1 . 28–1 . 82 ) , and household density of > 1 individual per room ( OR = 1 . 35; 95%CI 1 . 10–1 . 66 ) . The findings of the study indicate that the frequency of leprosy treatment default varies regionally in Brazil and provide new evidence that adverse socioeconomic conditions may represent important barriers to leprosy treatment completion . These findings suggest that interventions to address socioeconomic deprivation , along with continued efforts to improve access to care , have the potential to improve leprosy treatment outcomes and disease control .
Leprosy , also known as Hansen’s disease , is a chronic and potentially disabling infectious disease caused by Mycobacterium leprae that primarily affects peripheral nerves and skin [1 , 2] . Since the introduction of multidrug therapy ( MDT ) in 1982 , the global burden of leprosy has been significantly decreasing [3 , 4 , 5] . In 2017 , the World Health Organization ( WHO ) reported 210 , 671 new cases of leprosy , including 26 , 875 from Brazil [3] . In endemic countries , treatment defaulting is still an important obstacle to effective leprosy control and elimination [6 , 7] . Specifically , interruptions and defaults from treatment may result in incomplete cures and persisting sources of infection in affected communities . Concerns have also been raised that patient non-adherence to MDT has the potential to contribute to drug resistance [8] . Further , delays in leprosy diagnosis and inadequate treatment may lead to irreversible physical disabilities that can cause stigma and social disadvantages in affected people [4] . Leprosy patients are grouped for treatment purposes according to their number of skin lesions: cases are classified as paucibacillary ( PB ) if they have up to five skin lesions and multibacillary ( MB ) in the presence of more than five skin lesions [1 , 2] . The classification of PB versus MB defines the nature and duration of the treatment regimen . Broadly , the term defaulting from treatment describes when an individual with leprosy does not complete the full MDT treatment despite repeated efforts from health services to ensure treatment completion [2] . As recently systematically reviewed by Girão and colleagues ( 2013 ) , there exists a limited evidence base regarding the determinants of leprosy treatment default [9] . Current evidence suggests leprosy treatment default may be influenced by both personal characteristics ( e . g . , quality of life , socioeconomic position ) and medical factors ( e . g . , treatment regimen and guidance , clinic distance , drug shortages ) [9] . Further , some poverty-related variables , including a low number of rooms per household and low familial income , have also been associated with leprosy treatment default in one population-based study in central Brazil [6] . Utilizing individual participant data from more than 20 , 000 leprosy cases followed up between 2007–2014 as part of the 100 Million Brazilian Cohort , this study used a hierarchical approach to investigate the association of geographic and socioeconomic factors with ( i ) overall leprosy treatment default , ( ii ) leprosy treatment default in PB and MB subtypes , and ( iii ) leprosy treatment default within Brazilian geographic regions .
The cohort used in this study was derived from the 100 Million Brazilian Cohort created by the Centre for Data and Knowledge Integration for Health at Oswaldo Cruz Foundation ( CIDACS/FIOCRUZ , Salvador , Bahia , Brazil ) . The aim of the 100 Million Brazilian Cohort is to investigate the role of social determinants and the effects of social policies and programs on health , through the linkage of data from social programs with databases of health information systems [10] . The 100 Million Brazilian Cohort was built using the baseline information of the national registry for social programs , Cadastro Único ( CadÚnico ) , from 2001 to 2015 . CadÚnico contains administrative records of all families applying for social programs in Brazil . To date , the 100 Million Brazilian Cohort includes socioeconomic data on over 114 million individuals . The individual records were linked with nationwide health datasets , including the 2007–2014 leprosy registries from the ‘Sistema de Informação de Agravos de Notificação’ ( SINAN-leprosy ) , through a deterministic algorithm , using the CIDACS-RL tool ( https://gitHub . com/gcgbarbosa/cidacs-rl ) . The specific variables used to match both datasets were patients’ name , date of birth , sex , mother’s name and municipality of residence . To assess the accuracy of data linkage , we carried out a manual analysis with a random sample of 10 , 000 pairs . For a cutoff of 0 . 93 , sensitivity was 0 . 91 ( 95% CI 0 . 90–0 . 92 ) and specificity was 0 . 89 ( 0 . 88–0 . 90 ) . The full linked dataset was de-identified to ensure anonymity/confidentiality of personal information and was made available for research from January 2018 ( https://hdl . handle . net/20 . 500 . 12196/FK2/FNMRCA ) . CIDACS implemented strong data security rules to control access , use , and data privacy and integrity . The final subset of the 100 Million Brazilian Cohort used in this study was restricted to individuals who were diagnosed with leprosy after enrolment in the cohort between 1 January 2007 and 31 December 2014 . Family units within the dataset included at least one member aged over 15 years old , with the oldest member of each family designated as the ‘head of the family . ’ Individuals were excluded if they: ( i ) were diagnosed with leprosy prior enrollment in the cohort , ( ii ) belonged to family units without one member aged over 15 ( i . e . , children who were registered separately from their original families were excluded from the study ) , ( iii ) had less than 1 day of follow-up on SINAN-leprosy , and ( iv ) were relapsed cases . Records with missing data on the study outcome and/or covariates were also excluded . Only for the covariates of schooling and employment ( with missing values ≥10% ) , missing information were considered as an additional category ( Fig 1 ) . We constructed a theoretical framework in which variables were grouped in three levels and blocks according to a predefined hierarchy represented by the conceptual framework shown in Fig 2 [11] . The distal level included geographic variables: region of residence in the country and location of family home ( i . e . , urban versus rural ) . The intermediate level was related to the socioeconomic position in the community and included: ethnicity/skin colour ( according to the self-identified classification used in the Brazilian census ) [12] , the highest level of education , employment and per capita family income ( i . e . , presented relative to Brazilian minimum wage ) . For individuals aged less than 18 years , schooling and occupation of the ‘head of the family’ were used as a proxy indicator . The proximal level comprised a set of variables related to household conditions experienced at the family level and included: housing material , household water supply , sewage disposal system , the source of home lighting , waste collection and household density ( i . e . , individuals per room ) . Because sex and age were considered as confounders a priori , they were included in all analyses . The study outcome was leprosy treatment default defined as a binary variable ( i . e . , default versus cure ) among newly detected leprosy cases [2] . For PB cases , treatment completion comprises 6 monthly doses of MDT until 9 months . For MB cases , treatment completion comprises 12 monthly doses of MDT until 18 months . The term ‘defaulter’ refers to leprosy patients who does not complete these full MDT treatment regimens ( PB patients who does not attend treatment for more than 3 months and MB patients for more than 6 months ) , even after repeated efforts of health professionals to tracking patients for treatment completion [2] . We conducted a descriptive analysis assessing the role of each geographic and socioeconomic variable on the study outcome in bivariate analyses . Then , in a multivariate analysis , blocks of variables from distal to proximal levels were added in a sequence following a hierarchical approach [11] as shown in the conceptual framework . The study outcome was analysed using logistic regression with cluster-robust standard errors to account for familial clustering of covariates . Because of the low prevalence of this study outcome ( i . e . , in less than 10% ) , the odds ratio ( OR ) estimates and their 95% confidence intervals ( CI ) provided a close approximation of the risk ratios [13] . An effect-decomposition strategy was applied to fit three logistic regression models ( A , B , and C ) by including step-by-step blocks of variables [11] . Variables in each block that were associated with leprosy treatment default at a significance threshold of P<0 . 10 were included in the next level model , with all models adjusting for sex and age . As a secondary analysis , we investigated the associations by leprosy subtype and across geographic regions . Because MB leprosy cases have been reported to have higher rates of treatment default and onward transmission than PB cases [1 , 7 , 14–17] , we compared the associations by leprosy subtype ( i . e . , PB versus MB ) . In addition , reflecting the important regional differences in social inequalities in Brazil , we performed analyses stratified by region ( North , Northeast , Midwest and South/Southeast ) [12] . All P-values were calculated for 2-sided statistical tests , and all analyses were performed using Stata , version 15 . 0 ( StataCorp LLC , College Station , Texas , USA ) . No personally identifiable information was included in the datasets used for analysis . Further , all data included in this study were stored on secured servers within CIDACS with strict access restrictions . This study was performed under the international ( Helsinki ) , Brazilian and United Kingdom research regulations and was approved by three ethics committee of research: ( i ) University of Brasília ( UnB ) ( protocol n° 1 . 822 . 125 ) , ( ii ) Instituto Gonçalo Moniz/FIOCRUZ ( protocol n° 1 . 612 . 302 ) and ( iii ) London School of Hygiene and Tropical Medicine’s Research Committee ( protocol n° 10580–1 ) .
Among 20 , 063 new cases of leprosy , 1 , 011 ( 5 . 0% ) defaulted from treatment . The percentage of default varied from 6 . 4% in 2007 to 5 . 4% in 2014 . Approximately half of the leprosy cases ( N = 10 , 101 , 50 . 4% ) were female . The median age was 34 . 9y ( IQ 24 . 6–52 . 5y ) , and 17 , 179 ( 85 . 6% ) were aged 15y or more . The proportion of children less than 15 years ( 14 . 6% ) was nearly 2-fold the average of new child cases in Brazil ( 7 . 3% of all new leprosy cases during 2007–2014 ) [18] . The median per capita income in US dollar ( USD ) was 34 . 0 ( IQ 16 . 6–89 . 3 ) . 13 , 063 ( 65 . 1% ) were residents in the Northeast and North regions , 16 , 050 ( 80 . 0% ) lived in an urban setting and 14 , 511 ( 72 . 3% ) self-identified as having a ‘pardo’ ( mixed ) ethnicity . 10 , 858 individuals ( 54 . 1% ) had up to 5 years of schooling , 11 , 080 ( 55 . 2% ) had a per capita familial income up to a quarter of the Brazilian minimum wage , and 9 , 030 ( 45 . 0% ) were unemployed or students . The majority of the leprosy cases lived in generally favourable household settings , with 13 , 956 ( 69 . 6% ) residing in houses made of brick or cement , 13 , 797 ( 68 . 8% ) accessing water supply networks , 16 , 166 ( 80 . 6% ) accessing electricity through a home meter , 15 , 271 ( 76 . 1% ) having public waste collection , and 15 , 267 ( 76 . 1% ) residing in households with up to 1 individual per room . Nevertheless , 13 , 480 ( 67 . 2% ) of the leprosy cases did not report access to improved sanitation ( Table 1 ) . In bivariate analyses , individuals from the North region were the most likely to default from leprosy treatment ( OR = 1 . 61; 95%CI 1 . 29–2 . 01 ) as compared to South and Southeast residents ( Table 1 ) . Intermediate factors associated with defaulting were black ethnicity ( OR = 1 . 39; 95%CI 1 . 07–1 . 79 ) , no income ( OR = 1 . 52; 95%CI 1 . 17–1 . 97 ) and per capita familial income up to a quarter of the minimum wage ( OR = 1 . 57; 95%CI 1 . 29–1 . 91 ) . Proximal factors associated with defaulting were: residency in accommodations constructed of wood and mud ( OR = 1 . 16; 95%CI 1 . 01–1 . 33 ) , informal home lighting or no electricity ( OR = 1 . 61; 95%CI 1 . 38–1 . 88 ) , no public waste collection ( OR = 1 . 18; 95%CI 1 . 02–1 . 36 ) , and household density between 0 . 75–1 ( OR = 1 . 29; 95%CI 1 . 08–1 . 54 ) and > 1 individual per room ( OR = 1 . 59; 95%CI 1 . 34–1 . 87 ) ( Table 1 ) . In multivariate analysis , region of residence was also associated with treatment default in the distal model . Relative to the South/Southeast regions , the North , Northeast , and Midwest regions had increased odds of treatment default . Similar to the bivariate analyses , participants from the North region had the highest odds of defaulting from leprosy treatment in the full cohort ( OR = 1 . 57; 95%CI 1 . 25–1 . 97 ) ( Table 2 ) . Intermediate factors associated with treatment default in the full cohort included ethnicity and income . Participants who self-identified as black ( OR = 1 . 29; 95%CI 1 . 01–1 . 69 ) and those with with 'no income' ( OR = 1 . 41; 95%CI 1 . 07–1 . 86 ) and a per capita income up to 0 . 25 minimum wage ( OR = 1 . 42; 95%CI 1 . 13–1 . 77 ) also had an increased probability of default from treatment . Of note , educational attainment and unemployment status were not associated with the odds of default ( Table 2 ) . Among the proximal factors , no conventional home lighting or no electricity ( OR = 1 . 53; 95%CI 1 . 28–1 . 82 ) and a household density greater than one person per room ( OR = 1 . 35; 95%CI 1 . 10–1 . 66 ) were associated with increased probability of treatment default ( Table 2 ) . Housing material , water supply , sewage disposal , and waste collection were not associated with leprosy treatment default in the multivariate model . In the subgroup analyses of leprosy subtype , the directions of effect were broadly consistent across the PB and MB cases . The higher odds of treatment default among individuals from the North of Brazil remained consistent in this subgroup analyses , as residence in this region was most strongly associated with treatment default of MB leprosy cases ( OR = 1 . 65 95%CI; 1 . 24–2 . 18 ) . Regarding the intermediate factors , black ethnicity and income level up to 0 . 25 minimum wage were associated with treatment default only in MB patients ( Fig 3 ) . In relation to proximal factors , subgroup analyses of leprosy subtype showed that the use of informal home lighting or lack of electricity and a high household density ( >1 individual peer room ) remained associated with treatment default across both leprosy subtypes ( PB and MB ) ( Fig 4 ) . In the subgroup analyses by Brazilian regions , lowest income level ( i . e . , no income or income up to 0 . 25 minimum wage ) was associated with odds of defaulting among residents in the Northeast region . An association between moderate educational attainment ( i . e . , 6–9 years ) and treatment default was only found in the Northeast inhabitants ( Fig 5 ) . Subgroup analyses by region also revealed higher odds of treatment default associated with use of informal electricity or lack of electricity supply among residents in the North ( OR = 1 . 75; 95%CI 1 . 28–2 . 40 ) . Finally , a high household density ( >1 individual per room ) was associated with higher odds of treatment default of individuals living in the Midwest of Brazil ( OR = 1 . 52; 95%CI 1 . 14–2 . 03 ) ( Fig 6 ) .
Using data from over 20 , 000 participants followed for up to 8 years , this cohort study is the largest to date investigating risk factors for leprosy treatment default ( corresponding to 57 . 1% of the average of 35 , 130 new leprosy cases registered in all country during the same period ) [18] . Our results revealed that individuals living in Brazilian regions carrying the highest leprosy burdens ( i . e . , North , Northeast , and Midwest regions of Brazil ) also had increased odds of treatment default relative to the lower burden South and Southeast regions . As inadequately treated cases have the potential to contribute to onward transmission , this finding suggests that enhanced efforts to improve treatment completion in these communities could have the potential to contribute to disease control in the most affected regions . Additionally , our findings indicate that self-identification as having black ethnicity as well as markers of deprivation , related to income , access to electricity , and household crowding , were associated with higher odds of MDT default . These important findings advance on prior research by indicating that individuals living in precarious socioeconomic conditions are not only at increased risk of leprosy infections [19 , 20] , but also they have an increased risk of treatment default following diagnosis . Few published studies have investigated factors associated with leprosy treatment default [7 , 16 , 21 , 22] . Factors suggested as barriers to adherence include poor household conditions , alcohol use , lack of knowledge about the disease and MB subtype [6 , 7 , 17] . In addition , a systematic review pointed to the need for more robust evaluations in this field , approaching regional particularities , since these associated factors may vary depending on the study location [9] . The largest previous study conducted in Brazil included 79 municipalities at high risk for leprosy transmission located in the Midwest region [6] . This study found that only low familial income ( i . e . , less than the current minimum wage ) and reduced number of rooms ( i . e . , less than 3 per household ) were associated with treatment default [7] . Our study provided important new evidence that geographic ( i . e . , region of residence ) , socioeconomic ( i . e . , black ethnicity ) and household conditions ( i . e . , access to electricity ) —factors well established as determinants of leprosy transmission [19 , 23]—may also be associated with defaulting from MDT . Evidence from the literature on socioeconomic factors associated with treatment default in other high leprosy burden countries is also scarce . In a study conducted in Nepal , most defaulters from MDT were illiterate , labourers and belonged to low-income families [21] . Another study , based in India , found an association of literacy status , per capita income and socioeconomic position with leprosy treatment outcomes . Higher default rates were evident among individuals that only completed primary education , had low per capita income , and belonged to the most deprived social classes [22] . In our study , the higher default rates among low income individuals might suggest the great financial impact of leprosy diagnosis and treatment on the affected households [24] . Our data also showed that living in households with informal lighting or no electricity was strongly associated with treatment default , mainly in the North region . Despite having adequate coverages of electricity , rural electrification of Brazil has not yet reached 100% [25] . Lack of access to electricity is an indicator of extreme poverty in the rural population . The use of irregular or informal sources of home lighting in peri-urban and urban areas also reflects socioeconomic deprivation [25 , 26] and may be a marker for poor access to the healthcare system . Consistent with previous research [7 , 14 , 15] , our findings showed higher probabilities of default associated with geographic ( residence in the North region ) and socioeconomic factors ( black ethnicity and low income ) in individuals classified as MB leprosy , when compared to PB forms . With regards to the higher rates of default in MB leprosy cases , the longer duration of treatment for these patients may present an additional barrier to treatment adherence [7 , 17] . Treatment default represents one of the most relevant obstacles to controlling chronic infectious diseases that require long-term treatment , such as leprosy [6] . A mathematical modelling investigation indicated that non-compliance to MDT and relapse of leprosy might have a negative impact on leprosy eradication , leading to an increase in disease prevalence and related deaths worldwide [27] . For the year 2017 , Brazil was the country reporting the highest number of relapses ( 1734 ) to WHO [3] . Individuals classified as defaulters are at high risk of relapses and might have a higher chance of developing resistance to leprosy drugs , representing obstacles to this disease control [5 , 9] . Among the main interventions to achieve leprosy control , the WHO recommends the strengthening of social and financial support with a focus on underserved populations , along with the use of a shorter and uniform regimen for all types of leprosy [5] . The use of a uniform multidrug therapy ( U-MDT ) regardless of any type of classification has been pointed out as the best option to halve treatment duration for MB patients ( from 12 to 6 months ) which could potentially decrease MDT default [28] . The strengths and limitations of this study should be stated . By linking nationally collected data on leprosy to socioeconomic information collected from more than 114 million individuals residing in all regions of Brazil , this study had an unprecedented sample size of leprosy cases with which to explore risk factors for leprosy default . Additionally , the inclusion of more than 20 , 000 cases enabled us to conduct stratified analyses and confirm that the associations were generally robust across leprosy subtypes and geographic regions . Importantly , this analysis also highlighted new factors associated with leprosy treatment default that have not previously been investigated ( i . e . geographic location , ethnicity and household living conditions ) in Brazil , the country with the second highest burden of leprosy worldwide [3] . On the other hand , this study also has limitations . First , as our data were collected routinely and not primarily for research purposes , 16 . 1% ( 3 , 848/23 , 911 ) of the linked individuals were excluded from the final analyses for having missing data . Second , we were unable to explore other determinants of default , such as characteristics of health services , individuals’ knowledge about the disease , and psychosocial and clinical factors , as these data were not available in our database . Qualitative assessment could provide a better understanding about the influence of these aspects in treatment completion of leprosy patients , as evidenced by a larger study conducted in Nepal aiming to understand people’s coping , help-seeking and adherence behaviour [29] . Third , although unlikely for most analysed socioeconomic characteristics , variables such as education and work might have changed in the time gap between the date of entry in the cohort and leprosy diagnosis . Finally , the generalizability of our results are restricted to individuals enrolled in CadÚnico , which represents approximately the poorest half of Brazilians who have registered for the national social protection programs . Although our findings may not be applicable to all leprosy cases in Brazil , it is likely that the point estimates of the associations between the indicators of deprivation and leprosy treatment default could be more pronounced if the full population of Brazil was included in the study . Based on the study findings , we can conclude that poor socioeconomic conditions may constitute obstacles to leprosy treatment compliance . We also highlighted a remarkable association between black ethnicity and leprosy treatment default . However , the overall evidence on the correlation between ethnic background and leprosy is limited [30] , which point to the need for further research . Our results also showed striking evidence on association of geographic and socioeconomic characteristics with treatment defaulting among MB leprosy individuals , who are the most important source of this disease transmission [2] . Decreasing default rates from MDT treatment has the potential to reduce the occurrence of relapses and physical disabilities and , by decreasing the infectious reservoir , may ultimately contribute to the goal of leprosy elimination . An integrated approach is needed , including actions on social determinants of leprosy and the adoption of full access to uniform treatment regimens for all PB and MB patients [5 , 28] , irrespective of material wealth . Other aspects that influence treatment default of leprosy cases , including distance from household to health service , adverse events/toxicity and mainly patient understanding the importance of correct treatment for cure should be better investigated . In addition to early diagnosis and prompt chemotherapy , social policies that reach the poor also at great risk of leprosy has been appointed about 100 years ago as a key strategy playing an important role and constituting a priority strategy to achieve leprosy control [31] . | While the leprosy new case detection has been decreasing worldwide since the introduction of multidrug therapy ( MDT ) in the 1980s , treatment default remains an important risk factor for leprosy-associated disability and an obstacle to disease control and elimination . Treatment default occurs when an individual with leprosy does not take the prescribed number of doses required for treatment with MDT . We hypothesized that the frequency of defaulting may be influenced by geographic factors , especially as related to access to care , and socioeconomic factors , such as income , education , and household living conditions . To test this hypothesis , we investigated geographic and socioeconomic factors associated with leprosy treatment default among 20 , 063 new leprosy cases followed as part of the 100 Million Brazilian Cohort between 2007 and 2014 . In total , 5 . 0% of the leprosy patients defaulted from MDT . Among the associated factors , we found that having residency in the North and Northeast of Brazil , black ethnicity , low familial income , lack of formal electricity , and a high household density were associated with higher odds of leprosy treatment default . Overall , these findings highlight the need for tailoring MDT strategies for vulnerable populations in high-burden communities and suggest that social policies aiming to alleviate poverty should be investigated as potential tools for improving leprosy treatment completion . |
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Non-coding RNAs have important roles in regulating physiology , including immunity . Here , we performed transcriptome profiling of immune-responsive genes in Drosophila melanogaster during a Gram-positive bacterial infection , concentrating on long non-coding RNA ( lncRNA ) genes . The gene most highly induced by a Micrococcus luteus infection was CR44404 , named Induced by Infection ( lincRNA-IBIN ) . lincRNA-IBIN is induced by both Gram-positive and Gram-negative bacteria in Drosophila adults and parasitoid wasp Leptopilina boulardi in Drosophila larvae , as well as by the activation of the Toll or the Imd pathway in unchallenged flies . We show that upon infection , lincRNA-IBIN is expressed in the fat body , in hemocytes and in the gut , and its expression is regulated by NF-κB signaling and the chromatin modeling brahma complex . In the fat body , overexpression of lincRNA-IBIN affected the expression of Toll pathway -mediated genes . Notably , overexpression of lincRNA-IBIN in unchallenged flies elevated sugar levels in the hemolymph by enhancing the expression of genes important for glucose retrieval . These data show that lncRNA genes play a role in Drosophila immunity and indicate that lincRNA-IBIN acts as a link between innate immune responses and metabolism .
The fruit fly Drosophila melanogaster ( D . melanogaster ) is a widely used model system in immunological studies [1] . Drosophila has an elegant innate immune response that includes both the cellular and the humoral arms [2 , 3] . Activation of the cellular immune response involves mechanisms such as recognition , phagocytosis , encapsulation and the killing of parasites [4 , 5] . The humoral immune response is based on microbial recognition primarily by peptidoglycan recognition proteins leading to the production of antimicrobial peptides ( AMPs ) [6–9] . The humoral immune response is mainly mediated by two evolutionarily conserved NF-κB signaling pathways , the Toll and the Immune deficiency ( Imd ) pathway [10–12] . Recently , it has become evident that beside the protein coding genes that positively or negatively regulate the humoral and cellular innate immune responses , there is a multitude of short and long non-coding RNA genes that affect innate immune responses [13–16] . In between and within protein coding genes in the genome , there are thousands of uncharacterized non-coding RNA genes . Small non-coding RNAs ( <200 nucleotides ) are considered to have more of a “housekeeping RNA” role . However , the functions of long non-coding RNA ( lncRNA , >200 nucleotides ) genes are more diverse [17] . Although the number of lncRNAs is still a matter of debate , recent meta-analyses posit the human genome to give rise to >60 , 000 lncRNAs , albeit the majority is probably expressed at low levels [18 , 19] . In fruit flies , there are fewer lncRNAs in the genome and the ratio of lncRNAs to protein coding genes is lower than in humans [20] . The current lncRNA numbers can be found in the NONCODE Version v5 . 0 database ( www . noncode . org ) . The expression patterns of lncRNAs are highly specific to tissue , developmental stage and environmental conditions ( reviewed in [14 , 15] ) and they are thought to have tightly controlled biological roles . Recent studies have indicated that lncRNAs play an important functional role in innate immune responses , and specifically in innate immune cells . In mammals , lncRNA genes are expressed in monocytes , macrophages , dendritic cells , neutrophils , T-cells and B-cells [13] . A growing list of lncRNA genes , for example LincRNA-Cox2 [21] , Lethe [22] , PACER [23] and TNFα regulating hnRNPL interacting lncRNA ( THRIL ) [24] , has been found to control gene expression in immune cells [13] . To study the role of lncRNA genes in the Drosophila immune response , we performed transcriptome analysis in D . melanogaster upon a bacterial infection with the Gram-positive Micrococcus luteus ( M . luteus ) , giving particular emphasis to long non-coding RNA ( lncRNA ) genes . The most responsive of all transcripts was the lncRNA gene CR44404 , which was upregulated 1300-fold upon a M . luteus infection . Here , we show that CR44404 is highly induced by both Gram-positive and Gram-negative bacteria in Drosophila adults and by a parasitoid wasp infection in Drosophila larvae . Because of the inducible nature of the CR44404 gene , we named it lincRNA-IBIN ( Induced By INfection ) . Finally , we show that lincRNA-IBIN acts as a link between innate immune responses and metabolism by modulating the expression of genes regulating carbohydrate and peptide metabolism and affecting glucose levels in the hemolymph .
To investigate the importance of long non-coding RNAs in Drosophila immunity , we carried out a transcriptome analysis ( RNAseq ) of flies 24h after infection with the Gram-positive M . luteus in comparison to age and sex-matched uninfected controls . The RNA sequencing method used in this study recognizes polyadenylated non-coding RNAs , which are thought to represent the majority of long non-coding RNAs , although also ones without poly-A tails exist [25 , 26] . Prior to the transcriptome analysis , one of the Toll pathway target genes IM1 ( Immune induced molecule 1 ) , was measured from females and males upon M . luteus infection . IM1 was robustly induced in both male and female Drosophila ( S1A Fig ) , and males were chosen for the transcriptome analysis . LYS-type peptidoglycan containing Gram-positive bacteria are known to induce the classical Toll pathway target genes including a number of antimicrobial peptides ( AMPs ) ( e . g . [27] ) . As expected , AMPs were strongly upregulated in the transcriptome analysis upon a M . luteus infection , including Dro , Mtk , Drs , and multiple IMs ( Fig 1A , S1 Table ) . Noteworthy , the highest upregulation in infected flies was seen in a previously unannotated long non-coding RNA gene , CR44404 ( Fig 1A ) . The baseline expression of CR44404 is very low , and upon a M . luteus infection , it is induced by about 1300-fold . The induction of CR44404 expression was also shown to be comparable between males and females upon M . luteus infection ( S1B Fig ) . Besides CR44404 , there were only 15 other lncRNAs that were more than 3-fold upregulated upon infection ( Fig 1B , S2 Table ) . While findings from vertebrates indicate that lncRNAs have wide and important functions in immune responses [13–16] , cancer and metabolism [28 , 29] , the role of lncRNAs in Drosophila immunity has only begun to emerge . CR44404 was chosen for further analysis based on its intriguing expression pattern . CR44404 is 228 nucleotides long ( genomic loci 2R:17 , 671 , 068 . . 17 , 671 , 295 [+] ) and it is located between two protein coding genes; P32 and CG30109 . Therefore , CR44404 is classified as a long non-coding intergenic RNA ( lincRNA ) molecule . Although CR44404 is very close to the protein-coding gene P32 , the genes do not overlap . To confirm that CR44404 is an independent transcript , the expression levels of the adjacent genes were examined in the transcriptome analysis . Neither P32 nor CG30109 were affected by infection in the same way as CR44404 , the expression of which was ~1300-fold upon a M . luteus infection . Instead , P32 ( 1 . 17-fold ) and CG30109 ( 1 . 27-fold ) were not significantly induced by M . luteus infection at 24h time point , indicating that CR44404 is expressed independently from them . CR44404 is polyadenylated; it has a highly conserved cleavage signal sequence AAUAA towards the end of the full-length transcript . CR44404 does not contain open reading frames and based on the NCBI domain search tool [30] , it does not contain any predicted protein domains . According to RNA secondary structure predictions , CR44404 is multibranched ( contains 3–4 GC-rich branches ) and contains a variable amount of smaller ( hairpin ) loops connected to a bigger loop ( S2 Fig ) . Based on the high expression of CR44404 upon infection and its genomic location , we named the gene lincRNA-Induced By INfection ( lincRNA-IBIN ) . As lincRNA-IBIN was shown to be strongly induced by Gram-positive bacteria 24h p . i , we next infected male flies with either the Gram-positive M . luteus or the Gram-negative Enterobacter cloacae ( E . cloacae ) to measure the gene expression kinetics of lincRNA-IBIN during multiple time points ranging from 0-24h after infection ( Fig 1C ) . This revealed that lincRNA-IBIN is also induced by Gram-negative bacteria , and in both infections , the induction occurred within the first hours of infection and gradually increased towards the 24h time point ( Fig 1C ) . To further study the role the Toll pathway and the Imd pathway [10 , 11] , in the expression of lincRNA-IBIN , we knocked down MyD88 ( an adaptor protein functioning downstream of the Toll receptor in the Toll pathway ) , cactus ( a negative regulator of the Toll pathway ) and Relish ( an Imd pathway NF-κB factor ) . Thereafter , we infected the flies with M . luteus or E . cloacae and measured the lincRNA-IBIN RNA levels . Upon a M . luteus infection , the expression of lincRNA-IBIN was shown to be dependent on the expression of MyD88 , i . e the functional Toll pathway ( Fig 1D ) . Knocking down MyD88 upon a E . cloacae infection had no effect on the expression of lincRNA-IBIN ( Fig 1E ) , whereas knocking down Relish or using the RelishE20 null mutant inhibited the expression of lincRNA-IBIN , showing that it requires a functional Imd pathway in this context ( Fig 1E ) . Knocking down Relish or using the RelishE20 null mutant upon a M . luteus infection did not inhibit the expression of lincRNA-IBIN ( Fig 1D ) . The role of the Toll pathway activation to the expression of lincRNA-IBIN was further confirmed in uninfected flies by knocking down the inhibitor of the κB factor cactus , which strongly induced the expression of lincRNA-IBIN ( Fig 1F ) . Also , during the larval stage , lincRNA-IBIN was induced by the ectopic expression of the constitutively active form of the Toll receptor , Toll10b ( Fig 1G ) and by overexpression of the Imd molecule with the ubiquitous da-GAL4 driver ( Fig 1H ) . Next , we tested if the Osa-containing Brahma ( BAP ) complex is needed for the expression of the lincRNA-IBIN . The BAP complex is a group of protein-coding genes working together in remodeling chromatin [31] , and the complex has previously been reported to affect the Toll pathway-induced Drs-luc reporter in vitro in Drosophila [32 , 33] . Interestingly , when osa expression was knocked down , lincRNA-IBIN expression was strongly inhibited upon both a M . luteus ( Fig 1I ) and a E . cloacae ( Fig 1J ) infection . The knockdown of another BAP complex component brahma ( brm ) also reduced lincRNA-IBIN expression upon both infections ( Fig 1I and 1J ) . Moreover , because lincRNA-IBIN is strongly induced by a bacterial infection in Drosophila adults , indicating a role in the humoral immune response , we next studied whether lincRNA-IBIN is also induced during the cellular immune response by infecting Drosophila larvae with Leptopilina boulardi ( L . boulardi ) parasitoid wasps . Also in this context , the expression of lincRNA-IBIN was strongly induced ( Fig 2 ) . In conclusion , lincRNA-IBIN seems to have a rather broad role in the immune response , being induced by a bacterial infection in flies and by parasitoid wasps in larvae . The M . luteus -mediated induction of lincRNA-IBIN expression was shown to be dependent on the Toll pathway , whereas the E . cloacae -mediated induction requires the Relish/Imd pathway . In each studied case , lincRNA-IBIN expression was dependent on a functional BAP complex . This type of unspecific induction via both NF-κB pathways is rather uncommon in Drosophila and argues for a general immunity related function for lincRNA-IBIN . To understand the role of lincRNA-IBIN in Drosophila immunity , we investigated where lincRNA-IBIN was expressed and whether its effects were tissue-specific . Since lincRNA-IBIN expression is strongly infection-inducible , we reasoned that it is most likely expressed in immune-responsive tissues ( the fat body and hemocytes , the Drosophila blood cells ) . Wasp infection of Drosophila larvae led to the induction of lincRNA-IBIN expression in the fat body and hemocytes ( Fig 2A and 2B ) . Like in flies , osa RNAi in larval hemocytes ( HH> osaIR ) and fat bodies ( C564> osaIR ) kept the expression of lincRNA-IBIN close to the basal level ( Fig 2A and 2B ) . Because lincRNA-IBIN is a short gene and very strongly induced upon infection like AMPs , we next investigated whether lincRNA-IBIN is secreted into the plasma in similar manner as AMPs ( Fig 2C ) . First , we confirmed that hemocytes and plasma were separated by centrifugation ( S3 Fig ) . We also checked the expression of a hemocyte specific gene Hemolectin ( Hml ) and a fat body-specific gene Larval serum protein 1 alpha ( Lsp1α ) in each tissue sample ( Fig 2C , i and ii ) . A Hml signal was detected in the hemocyte fraction , whereas Lsp1α levels were high in fat body samples but not in hemocytes or in the plasma ( Fig 2C , i and ii ) . We did not detect lincRNA-IBIN in the plasma fraction in large quantities ( Fig 2C , iii ) . Pin-pointing the cellular localization of a lncRNA reveals typically more about its function than does the structure of the RNA . A RNA FISH ( RNA Fluorescent In Situ Hybridization ) protocol was performed with 3rd instar larval hemocytes . Uninfected w1118 larval hemocytes were used as a control for imaging the basal expression level and localization of lincRNA-IBIN ( Fig 2D , ii ) . Hemocytes from larvae overexpressing lincRNA-IBIN1 ( HH>lincRNA-IBIN1 ) and infected with L . boulardi were used to induce the expression of lincRNA-IBIN ( Fig 2D , iii ) , and they showed that lincRNA-IBIN ( pink labelling ) was primarily expressed in the nuclear compartment ( blue labelling ) of the cell . Therefore , we conclude that lincRNA-IBIN is expressed in immune responsive tissues , is not secreted into the plasma in large amounts and its cellular localization is mainly nuclear . This suggests that the function of lincRNA-IBIN may be in the regulation of gene expression , which is typical for lncRNAs [34–36] . To study the function of lincRNA-IBIN in uninfected and infected flies , we generated UAS-lincRNA-IBIN overexpression fly lines . Two of the generated lines , lincRNA-IBIN1 and lincRNA-IBIN7 , were selected for the following experiments . lincRNA-IBIN overexpression in the lincRNA-IBIN1 and lincRNA-IBIN7 lines was induced using the C564-GAL4 driver , which is expressed strongly in the fat body [37 , 38] ( Fig 3A ) . lincRNA-IBIN expression in uninfected flies was significantly increased in both overexpression lines ( Fig 3A , white bars ) , with higher expression levels in the lincRNA-IBIN7 line . 24 h after a M . luteus infection , the effect of the overexpression on the expression of lincRNA-IBIN was masked by the overwhelming endogenous expression of lincRNA-IBIN ( Fig 3A , black bars ) . To study the effect of the long-term exposure of flies to elevated levels of lincRNA-IBIN , we monitored the lifespan of flies overexpressing lincRNA-IBIN with the C564-GAL4 driver and controls . To ensure maximal lincRNA-IBIN expression , flies were cultured at +29°C for the duration of the experiment . Neither one of the lincRNA-IBIN overexpression lines ( lincRNA-IBIN1 and lincRNA-IBIN7 ) showed a statistically significant difference in the lifespan between flies overexpressing lincRNA-IBIN and controls ( S4 Fig ) . As lincRNA-IBIN was the most strongly induced gene upon a M . luteus infection , we first investigated whether overexpressing lincRNA-IBIN affected the survival of the flies against a septic infection with Gram-positive bacteria . For the survival experiment , we chose lincRNA-IBIN7 flies as these produced the highest overexpression without an infection . We first infected lincRNA-IBIN7 flies with M . luteus to prime the Toll pathway . 24h later , the flies were infected with the more pathogenic bacteria , Enterococcus faecalis ( E . faecalis ) [39] . Overexpression of lincRNA-IBIN7 ( C564-GAL4>lincRNA-IBIN7 ) improved the survival of the flies from the infection compared to the flies not overexpressing lincRNA-IBIN ( Fig 3B ) . MyD88 ( a positive regulator of the Toll pathway ) and cactus ( a negative regulator of the Toll pathway ) knock-down flies were used as controls . This indicates that lincRNA-IBIN positively affects immunity against the pathogenic Gram-positive bacteria E . faecalis . Next , we investigated whether lincRNA-IBIN regulates the central feature of the fly immune defense against Gram-positive bacteria , namely the production of AMPs via the Toll pathway . The expression of the Toll pathway mediated genes was monitored in flies overexpressing lincRNA-IBIN and controls after exposure to M . luteus for 24 hours ( Fig 3C and 3D ) . lincRNA-IBIN overexpression using both the lincRNA-IBIN1 and lincRNA-IBIN7 lines with the C564-GAL4 driver resulted in significantly elevated levels of IM1 upon infection ( Fig 3C ) . The expression of Drosomycin was elevated in C564>lincRNA-IBIN7 flies , whereas in the lincRNA-IBIN1 line the trend was similar , yet not significant ( Fig 3D ) . As expected , MyD88 knockdown decreased the expression of IM1 and Drosomycin upon infection , whereas cactus knockdown caused a strong induction of IM1 and Drosomycin expression also in the uninfected flies ( Fig 3C and 3D ) . To address the importance of lincRNA-IBIN in a situation where the expression of endogenous lincRNA-IBIN is prevented , we utilized the following experimental approach . Upon an E . cloacae infection , lincRNA-IBIN expression is fully dependent on Relish ( Fig 1E ) . Relish RNAi flies do not produce endogenous lincRNA-IBIN upon an E . cloacae infection , but in the Relish RNAi flies combined with the lincRNA-IBIN7 construct , lincRNA-IBIN is overexpressed ( Fig 3E ) . Next , we monitored the survival of Relish RNAi flies and Relish RNAi flies with the lincRNA-IBIN7 construct from an E . cloacae infection ( Fig 3F ) . Fig 3F demonstrates that lincRNA-IBIN overexpression provides protection against an E . cloacae infection . lincRNA-IBIN overexpression does not itself induce antimicrobial peptides ( Fig 3G and 3H ) , indicating that the protection is independent of the AMPs . Taken together , lincRNA-IBIN overexpression enhances the expression of target genes of the Toll pathway . Upon infection , lincRNA-IBIN overexpression gave flies a survival advantage . However , this is not due to the induction of AMPs itself , but results from a mechanism that prompts further investigation . As shown in Fig 2 , lincRNA-IBIN is expressed in immunogenic tissues in the fly , such as the fat body and hemocytes . Next , we examined the role of lincRNA-IBIN overexpression in the cellular response , i . e . the hemocytes . Phagocytic plasmatocytes are the main hemocyte type in uninfected larvae . Lamellocytes , which are formed upon a parasitoid wasp infection , function in the encapsulation of the wasp eggs and larvae [5 , 40 , 41] . To further investigate if lincRNA-IBIN has a role in two major components of the cellular immune response , namely the increase in hemocyte numbers and differentiation of lamellocytes , we utilized the hemocyte reporters ( msnCherry , eaterGFP ) to detect hemocytes with flow cytometer . The combination of the reporters with the hemocyte ( MeHH> for short , see materials and methods ) and fat body ( MeC564> ) drivers enabled us to detect the hemocytes and overexpress lincRNA-IBIN in these tissues . Driving lincRNA-IBIN expression in hemocytes ( MeHH>lincRNA-IBIN ) resulted in an increase in total hemocyte numbers in uninfected larvae ( S5A Fig ) , but did not induce ectopic lamellocyte formation ( S5A’ Fig ) . lincRNA-IBIN overexpression in the fat body ( MeC564>lincRNA-IBIN ) did not have an effect on hemocytes ( S5B–S5B’ Fig ) . lincRNA-IBIN could enhance the proliferation of hemocytes or their release from a reservoir located in segmental bands under the larval cuticle , called the sessile compartment [42 , 43] . To that end , we imaged whole larvae and checked for the existence of sessile bands . We did not observe any noticeable loss of sessile bands that could explain the increased hemocyte numbers ( S5C Fig ) . In L . boulardi-infected larvae , there was a slight decrease in the numbers of hemocytes in the lincRNA-IBIN7 line ( S5A Fig ) , but lamellocytes were not affected ( S5A’ Fig ) . Taken together , the overexpression of lincRNA-IBIN in hemocytes increases the hemocyte numbers , but does not affect hemocyte differentiation , in unchallenged Drosophila larvae . To identify the downstream pathways and targets of lincRNA-IBIN , we performed transcriptome profiling of flies overexpressing ( OE ) the lincRNA-IBIN7 construct with the C564-GAL4 driver . lincRNA-IBIN OE and control flies were either infected with M . luteus or E . cloacae , or they were left uninfected . In uninfected lincRNA-IBIN OE flies , lincRNA-IBIN expression was induced 166-fold ( Fig 4A ( 472±33 normalized number of reads for C564>IBIN vs . 2 . 8±0 . 62 normalized number of reads for w , IBIN ) . Also in this transcriptome analysis , target genes of the Toll pathway were induced in M . luteus-infected flies , and lincRNA-IBIN OE further elevated their levels ( S3 Table ) . Candidate target genes under lincRNA-IBIN regulation were searched for in the uninfected lincRNA-IBIN7 transcriptome data using a cut-off value of 2 for fold change . Genes with induced expression level from medium to high ( >10 reads ) in the treatment of interest were included in the analysis . Based on this criteria , 45 genes ( including lincRNA-IBIN ) were upregulated ( S4 Table ) and 21 genes were downregulated ( S5 Table ) in unchallenged lincRNA-IBIN OE flies . The top upregulated genes included one of the copies of Major heat shock 70 kDa protein , Hsp70Bb ( 32 . 9-fold ) , Niemann-Pick type C-2 ( Npc2e , 29 . 6-fold ) and Amyrel ( 6 . 8-fold; S4 Table ) . Among the most downregulated genes were εTrypsin ( 5 . 3-fold ) and βTrypsin ( 3 . 2-fold ) both involved in proteolysis ( S5 Table ) . A cluster analysis of the up- and downregulated genes revealed two major gene clusters , both of which are involved in metabolism ( Fig 4B ) . Eight out of forty-five genes upregulated in lincRNA-IBIN overexpressing flies belong to a carbohydrate metabolism/glycoside hydrolase gene cluster , and six out of twenty-one downregulated genes belong to a proteolysis / peptidase S1 gene cluster ( Fig 4B ) . To investigate the identified metabolism related gene clusters in more detail , we plotted normalized read values of selected genes including all the treatments ( uninfected , lincRNA-IBIN7 OE , E . cloacae and M . luteus infections with and without lincRNA-IBIN7 OE; Fig 4C & 4D ) . As shown in Fig 4C , overexpression of lincRNA-IBIN7 in uninfected flies causes the upregulation of six maltase genes ( Mal-A1 , Mal-A6 , Mal-A7 , Mal-A8 , Mal-A2 , Mal-B1 ) , whose function is to catalyze the hydrolysis of maltose ( disaccharide ) to glucose units ( monosaccharide ) . In addition , lincRNA-IBIN7 overexpression increased the expression of amylases ( Amy-p , Amy-d , Amyrel ) , which hydrolyze dietary starch into disaccharides . tobi ( target of brain insulin ) and Gba1a ( Glucocerebrosidase 1a ) are also involved in sugar metabolism , and were elevated by lincRNA-IBIN7 overexpression ( Fig 4C ) . Of note , the expression of some of these genes involved in carbohydrate metabolism was also elevated upon M . luteus and E . cloacae infections ( Fig 4C , blue and orange bars ) . This demonstrates that enhanced sugar metabolism is needed to fight infections as shown before [44–46] , and indicates that lincRNA-IBIN is involved in providing this metabolic switch . In contrast , many genes involved in peptide and protein catabolism were downregulated upon lincRNA-IBIN7 overexpression and infection ( Fig 4D ) . The enhanced ( S6B and S6C Fig ) or downregulated ( S6D Fig ) expression of selected metabolic genes upon lincRNA-IBIN overexpression ( S6A Fig ) was confirmed by qPCR also with the other lincRNA-IBIN overexpressing fly line , lincRNA-IBIN1 . According to the Flybase high throughput expression data ( www . flybase . org ) , all the identified metabolism genes are almost exclusively expressed in the midgut . Therefore we next analyzed whether lincRNA-IBIN is expressed in the adult midgut in response to septic injury . To ensure maximal induction of lincRNA-IBIN , we infected adult flies by pricking them with E . cloacae , and 24h later dissected the midgut tissues of the infected flies and controls . An E . cloacae infection induced the expression of lincRNA-IBIN also in the adult midgut ( Fig 4E ) . Furthermore , strong expression of the C564-GAL4 driver in the adult midgut was demonstrated ( S7A Fig ) , indicating that besides the fat body , lincRNA-IBIN is also overexpressed in the midgut when the driver C564-GAL4 is used ( S7B Fig ) . This is in line with the observed transcriptional changes when the overexpression of lincRNA-IBIN was driven using the C564-GAL4 driver . To further study changes in glucose metabolism during an infection , we measured glucose and trehalose levels from the hemolymph 24h after a septic injury with E . cloacae . Hemolymph from uninfected and infected flies ( 50 flies in each sample ) was collected for the analysis ( Fig 4F ) . An infection induced an increase in the glucose level in circulating hemolymph ( Fig 4G ) , whereas the level of circulating trehalose was unaltered ( S7C Fig ) . This suggests that increasing the hemolymph glucose level is important for the immune response . Next , to investigate the role of lincRNA-IBIN on the hemolymph glucose level , hemolymph from flies overexpressing lincRNA-IBIN7 with the C564-GAL4 driver and controls ( w; lincRNA-IBIN7 ) was collected as described ( Fig 4F ) . In addition to an infection ( Fig 4G ) , also lincRNA-IBIN7 overexpression caused a statistically significant increase in the glucose level in circulating hemolymph ( Fig 4H ) . In conclusion , these data imply that a septic infection with Gram-negative bacteria enhances the transcription of genes involved in carbohydrate metabolism , which leads to elevated sugar levels in the hemolymph . lincRNA-IBIN regulates in part this metabolic shift to ensure sufficient energy resources for the needs of the immune cells and tissues .
Drosophila melanogaster has been one of the most fruitful models for studying the immune response [1] . For example , identification of the regulators of the signaling cascades of innate immunity in Drosophila has greatly advanced our understanding of the control of mammalian immune responses [3 , 47] . Today , key pathways and proteins controlling the immune reactions in Drosophila are well documented [10 , 11 , 48 , 49] . However , while findings in vertebrates indicate that lncRNAs have wide and important functions in immune responses [13–16] , cancer and metabolism [26 , 27] , the role of lncRNAs in Drosophila immunity has only begun to emerge . Based on our Drosophila transcriptome analysis , only few lncRNA genes were up- or downregulated in response to an infection with the Gram-positive LYS-type peptidoglycan containing bacteria M . luteus . However , expression of the gene CR44404 , named as lincRNA-IBIN ( Induced By INfection ) , was highly upregulated during a M . luteus infection in flies . lincRNA-IBIN expression was also induced by the Gram-negative DAP-type peptidoglycan containing bacteria E . cloacae , indicating that activation of either the Toll or the Imd pathway induces its expression . As an infection with the parasitoid wasp L . boulardi also induced the expression of lincRNA-IBIN , this indicates that lincRNA-IBIN might have a general role in immunity . The expression patterns of lncRNAs have been found to be highly specific for tissue , developmental stage and context ( reviewed in [14 , 15] ) . When studying the expression levels of lincRNA-IBIN in larvae , the highest expression levels were found in the fat body . lincRNA-IBIN was also expressed in hemocytes , but was not secreted in considerable amounts into the plasma . The basal level of lincRNA-IBIN expression in these tissues was very low . However , the lincRNA-IBIN response to an infection was fast , as already at two hours after a bacterial infection the expression of lincRNA-IBIN was highly induced . This shows similar expression kinetics to AMPs and further argues for an important function for lincRNA-IBIN in immunity . The functional importance of lincRNA-IBIN was studied by overexpressing it in the fat body and in hemocytes with the UAS-GAL4-system . Overexpressing lincRNA-IBIN locally in the fat body resulted in elevated levels of the antimicrobial peptide Drosomycin and another Toll pathway target , IM1 , upon a M . luteus infection . lincRNA-IBIN overexpression also led to enhanced resistance against an infection with Gram-positive bacteria . To study the function of lincRNA-IBIN further , we performed a full transcriptome analysis of uninfected and infected flies overexpressing lincRNA-IBIN . The most upregulated gene in lincRNA-IBIN overexpressing flies was Hsp70Bb . The main function of the Hsp70 proteins , like other heat shock proteins , is to maintain the proper trafficking and folding of proteins [50] . In addition , Hsp70 expression has been shown to be induced by the Gram-negative Erwinia carotovora carotovora [51] and by medium from γ-irradiated Escherichia coli bacteria [52] . As a stress-responsive gene , the upregulation of Hsp70Bb could indicate that lincRNA-IBIN overexpressing flies are experiencing stress . However , no other heat shock genes were induced in lincRNA-IBIN OE flies , indicating that a general stress response is unlikely . Another highly upregulated gene in lincRNA-IBIN overexpressing flies was Npc2e . Npc2e binds microbial components , and it has been indicated to play a role in the Imd pathway [53] . In our data , Npc2e was also upregulated after E . cloacae and M . luteus infections ( S4 Table ) , but these changes were not significant after an FDR correction . Importantly , the lincRNA-IBIN OE transcriptome analysis revealed two main gene clusters , which were affected by lincRNA-IBIN overexpression , namely glycoside hydrolases ( upregulation ) and peptidases and proteases ( downregulation ) . Inflammatory responses from infection to cancer involve changes in metabolic pathways [54 , 55] . The activated immune cells switch from oxidative phosphorylation toward aerobic glycolysis; this is a well-known phenomenon in cancer cells called the Warburg effect , and it is also recognized as important for immune cells upon an infection [44 , 56 , 57] . The metabolic switch toward aerobic glycolysis leads to increased glucose consumption [58] . In Drosophila , the polysaccharide starch can be hydrolyzed into the disaccharide maltose and further on into the monosaccharide glucose by maltases . lincRNA-IBIN overexpression elevated the expression of multiple maltase genes , indicating that lincRNA-IBIN has a role in enhancing the catabolism of starch . Here we showed that besides the fat body cells and hemocytes , lincRNA-IBIN was also expressed in the Drosophila gut upon infection , which is the main site of starch metabolism . lincRNA-IBIN overexpression with the C564-GAL4 driver , which is also strongly induced in the midgut , led to elevated levels of free glucose in adult hemolymph providing an energy source for immune cells . lincRNA-IBIN did not seem to affect the insulin signalling pathway , which in humans , and in flies , controls peripheral as well as central nervous system -related aspects of metabolism [59 , 60] . Glucose and trehalose are the major circulating sugars in Drosophila , and the majority of the total sugar in the hemolymph is trehalose [61] . lincRNA-IBIN overexpression enhanced the expression of glycoside hydrolases and led to slightly elevated levels of hemolymph glucose , but not trehalose . Therefore , lincRNA-IBIN may improve the retrieval of glucose from dietary sugars from the gut , for example by enhancing the expression of maltases . More elevated glucose levels were seen by a septic infection . It is likely that upon infection , also other factors influence the elevation of glucose levels compared to lincRNA-IBIN overexpression alone; for example , the enhanced release of glucose from glycogen storages upon infection has been shown [44 , 46] . The expression of genes related to proteolysis was downregulated upon lincRNA-IBIN overexpression , indicating that the uptake of amino acids from food is reduced . Immune responses are tightly regulated as prolonged inflammation is costly to the host [62 , 63] . Dionne and coworkers showed that flies infected with Mycobacterium marinum undergo a process resembling wasting , where the flies progressively lose metabolic stores in the form of fat and glycogen and become hyperglycemic [45] . Hence , there must be delicately controlled mechanisms for the direct control of energy allocation to the immune response upon infection or inflammation . Here , we have characterized lincRNA-IBIN as one of the potential regulators of this switch . It is important to notice , however , that we have used UAS/GAL4-based overexpression of lincRNA-IBIN to study its function . We trust that this artificial overexpression system provides valid information about the effect of lincRNA-IBIN on its target genes , but one cannot exclude that some of the observed effects may be caused by non-physiological lincRNA-IBIN expression . For example , overexpressing a short RNA molecule , such as lincRNA-IBIN , may trigger antiviral immune responses . However , the normal life span and the lack of elevated expression of known virus infection-responsive genes such as Vago , AGO2 or Sting [64–66] in lincRNA-IBIN OE flies argues against a non-specific viral response . However , the more definite conclusion about the role of lincRNA-IBIN in Drosophila immunity will require the generation of a loss-of-function mutant . lincRNA-IBIN mutant flies would also be essential to address whether lincRNA-IBIN is required for the observed metabolic changes during an infection and to address if lincRNA-IBIN is required for normal innate immunity in Drosophila . lincRNA-IBIN , like most lncRNAs , demonstrates low evolutionary sequence conservation , and the lack of homologous sequences prevented the use of similar sequences for the identification of a function for lincRNA-IBIN . lncRNAs are composed of domains that permit either protein binding and/or base-pairing with RNA or DNA sequences [34 , 67–70] . Based on a secondary structure prediction , it is difficult to estimate whether lincRNA-IBIN interacts with protein , DNA or RNA molecules . The primary functions of lncRNAs can be traced according to their cellular localization . Based on our RNA fluorescent in situ hybridization analysis , lincRNA-IBIN was mostly localized within the nucleus . The nuclear localization suggests that lincRNA-IBIN localizes to its genomic target site ( s ) through RNA-DNA or RNA-protein interactions in the chromatin , where it could modulate chromatin regions or the expression of target genes by functioning as a guide , decoy or a scaffold for interacting molecules . An overview of lincRNA-IBIN functions is presented in Fig 5 . Taken together , lincRNA-IBIN has a role in both humoral and cellular innate immune pathways in larvae and adults . The basal expression level of lincRNA-IBIN is very low and it responds rapidly to an infection , but further studies are required to fully understand its role in different infection contexts . lincRNA-IBIN is expressed in immune responsive tissues and its expression is regulated by NF-κB signaling and the chromatin modeling BAP complex . In the gut , lincRNA-IBIN has a role in the activation of glycoside hydrolases . Finally , expression of lincRNA-IBIN elevates the levels of free glucose in the hemolymph . Based on our findings we postulate that lincRNA-IBIN has an important role in the metabolic switch required to provide additional glucose for immune cells during a systemic infection in Drosophila .
The UAS-GAL4 -based system in Drosophila was utilized in most experiments to achieve the silencing or overexpression of genes in the F1 progeny [71] . The brm ( transformant ID #37720 ) , osa ( #7810 ) , MyD88 ( #25399 ) and Relish ( #108469 ) UAS-RNAi lines , and the isogenized w1118 flies ( w1118iso ) that were used as controls , were obtained from the Vienna Drosophila Resource Center ( www . vdrc . at ) . cactus UAS-RNAi flies ( 5848R-3 ) were obtained from the National Institute of Genetics Fly Stock Center in Japan . RelishE20 mutant flies were a kind gift from prof . Dan Hultmark . The Imd overexpression line that overexpresses the Imd protein under the control of UAS , was originally a kind gift from prof . Jules Hoffmann . The constitutively active Toll10b mutant [72] was originally obtained from the Bloomington Drosophila Stock Center at Indiana University . C564-GAL4 flies , driving the expression of a UAS-construct in the fat body [37 , 38 , 73] and some other tissues , were a kind gift from Prof . Bruno Lemaitre ( Global Health Institute , EPFL , Switzerland ) . The Daughterless-GAL4 ( Da-GAL4 ) flies drive the expression of a UAS-construct ubiquitously [74] . The combination of the HmlΔ-GAL4 ( w1118; P{Hml-GAL4 . Δ}2P{wUAS-2xEGFP}AH2 ) driver [75] and the He-GAL4 ( P{He-GAL4 . Z} ) driver [76] ( HH-GAL4 ) was used to drive the expression of the UAS-constructs in hemocytes [77] . The GAL4 drivers were backcrossed into the w1118 background that was used as a control in crosses without a GAL4-driver . The hemocyte reporter lines eaterGFP ( for plasmatocytes ) [78] and MSNF9mo-mCherry ( for lamellocytes , hereafter called msnCherry ) [79] were obtained from Robert Schulz’s laboratory . The lines were recombined to create the msnCherry , eaterGFP reporter line . The msnCherry , eaterGFP reporter was further crossed with C564-GAL4 to obtain msnCherry , eaterGFP;C564-GAL4 ( MeC564> for short ) . msnCherry , eaterGFP; HmlΔ-GAL4; the He-GAL4 ( MeHH> ) line was a kind gift from I . Anderl . To create lincRNA-IBIN overexpressing fly lines , the full-length gene for lincRNA-IBIN was cloned into the EcoRI and BcuI ( SpeI ) restriction sites in the pUAST vector using the following primers ( with restriction sites underlined ) : CR44404_F: TAAGCAGAATTCCACAATCTAAAGTTAACTTGCC and CR44404_R: CACACAACTAGTGTTTATTTTCTTTCTATGGTTG . The produced plasmids were injected into the w1118 background in Best Gene Inc . , USA ( thebestgene . com ) . Ten lines producing red-eyed transformants were generated , and two lines ( lincRNA-IBIN1 and lincRNA-IBIN7 ) with good overexpression of lincRNA-IBIN were selected for experiments . For the experiments , 10–15 virgin females were crossed with 5–7 males per vial containing mashed-potato , syrup and yeast-based fly food medium . Crosses were kept at +25°C and flies transferred daily into fresh vials . The vials with eggs were transferred to +29°C after one day of egg laying and kept there until the experiments at the larval or adult stage unless otherwise stated . Test groups and controls were kept at the same conditions at all times . After testing that target genes of the Toll pathway were induced in a similar manner in the progeny male and female flies , male flies were used for the transcriptome analysis with and without a M . luteus infection . The expression of CR44404 was tested in both female and male flies and found to be equivalent , after which male flies were used in all of the subsequent experiments . For the lifespan experiment , lincRNA-IBIN overexpressing flies ( lincRNA-IBIN1 and lincRNA-IBIN7 lines ) were crossed with C564> driver flies at +25°C . After one day , the eggs were transferred to develop at +29°C for a maximum lincRNA-IBIN overexpression for the entire lifespan of the flies . Twice a week , the number of the flies was recorded and the flies transferred to fresh food . Micrococcus luteus ( M . luteus ) was cultured on Luria-Bertani ( LB ) agar plates under Streptomycin selection ( final concentration 100 μg/ml ) and left to grow at 29°C for 2–3 days . Enterobacter cloacae ( E . cloacae ) was cultured on LB agar plates under Nalidixic acid selection ( final concentration 15 μg/ml ) and the plates were incubated overnight at 37°C . The concentrated bacterial culture used for pricking the flies was prepared by collecting the colonies from the plate into 100μl of 50% glycerol in phosphate buffered saline ( PBS; 137 mmol/l NaCl , 2 . 7 mmol/l KCl , 10 mmol/l Na2HPO4 , 1 . 8 mmol/l KH2PO4 ) . Enterococcus faecalis ( E . faecalis ) was cultured in Brain-Heart-Infusion ( BHI ) medium and incubated at 37°C with shaking ( 225 rpm ) overnight . The absorbance of the E . faecalis bacterial culture grown overnight was measured with a spectrophotometer at 600nm after which it was diluted 1:25 in 5 ml of BHI medium and left to grow for 2–3 hours at 37°C with shaking ( 225 rpm ) until the absorbance at 600nm was 0 . 75 . Then 2 ml of the bacterial culture was centrifuged at 700 x g for 5 min and the supernatant was discarded . The pellet was resuspended into 100 μl of 50% glycerol in PBS and the bacterial concentrate was used for pricking the flies . For bacterial infections , 0–2 day old male flies were collected and placed at +29°C for 48h , after which a septic injury to the flies was caused by pricking them in the thorax with a thin sharp tungsten wire dipped into a concentrated bacterial culture . For the activation of the Toll pathway , flies were infected with M . luteus ( Gram-positive bacteria ) and incubated for 24h at 25°C . For measuring AMP expression levels , infected flies and non-infected controls were incubated at 25°C for the duration of the infection , harvested , and their RNAs were extracted as described below . For survival experiments , M . luteus infected flies ( 24h , 25°C ) were subsequently infected with E . faecalis and incubated at RT . The survival of the flies was monitored for 48h , as described earlier [39] . To activate the Imd pathway , the flies were infected with the Gram-negative bacterium E . cloacae , and the flies were incubated at 25°C for the duration of the infection . 2nd instar larvae were infected with strain G486 of L . boulardi parasitoid wasps by placing 20 female wasps in vials with larvae . After two hours at room temperature , the wasps were removed and the larvae were transferred back to +29°C . 48 hours later the larvae were dissected to collect the hemocytes , plasma and fat bodies . The infection status of the larvae was checked by visually confirming the presence of L . boulardi eggs or larvae . For the first transcriptome analysis ( Fig 1A and 1B ) , total RNAs from uninfected or M . luteus -infected ( 24h p . i . ) w , osaIR male flies were extracted with the TRI reagent . For the second transcriptome analysis ( Fig 4 ) , total RNAs were extracted from uninfected male flies with lincRNA-IBIN overexpression ( C564>lincRNA-IBIN7 ) , uninfected controls ( w1118 , lincRNA-IBIN7 ) , M . luteus -infected lincRNA-IBIN OE and control flies ( 24 h p . i . ) and E . cloacae -infected lincRNA-IBIN OE and control flies ( 6 h p . i . ) . All the sample groups were crossed at the same time and kept in the same conditions until collection . The resulting RNA samples were DNase treated with the RapidOut DNA removal kit ( Thermo Scientific ) . The quality of the total RNA samples was ensured with the Advanced Analytical Fragment Analyzer and found to be good . Total RNA samples were pure , intact and all samples were of similar quality . The preparation of the RNA libraries and Illumina HiSeq 2500 sequencing were carried out in the Finnish Microarray and Sequencing Centre ( Turku , Finland ) . The RNA libraries were prepared according to the Illumina TruSeq Stranded mRNA Sample Preparation Guide ( part # 15031047 ) : Firstly , the poly-A containing RNA molecules were purified using a poly-T oligo attached to magnetic beads . Following purification , the RNA was fragmented into small pieces using divalent cations under an elevated temperature . The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers . Strand specificity was achieved by replacing dTTP with dUTP in the Second Strand , followed by second strand cDNA synthesis using DNA Polymerase I and RNase H . The incorporation of dUTP in second strand synthesis quenches the second strand during amplification , because the polymerase used in the assay is not incorporated past this nucleotide . The addition of Actinomycin D to First Stand Synthesis Act D mix ( FSA ) prevents spurious DNA-dependent synthesis , while allowing RNA-dependent synthesis , improving strand specificity . These cDNA fragments then have the addition of a single 'A' base and subsequent ligation of the adapter: the Unique Illumina TruSeq indexing adapter was ligated to each sample during the adapter ligation step for later pooling of several samples in one flow cell lane . The products were then purified and enriched with PCR to create the final cDNA library . Typically , the RNAseq library fragments are in the range of 200–700 bp and the average size of the fragments is 250–350 bp . The samples were normalized , pooled for the automated cluster preparation and sequenced with an Illumina HiSeq 2500 instrument using TruSeq v3 sequencing chemistry . Paired-end sequencing with a 1 x 50 bp read length was used , followed by a 6 bp index run . The technical quality of the HiSeq 2500 run was good and the cluster amount was as expected . In both transcriptome analyses , the reads obtained were aligned against the Drosophila melanogaster reference genome ( BDGP6 assembly , downloaded from the Illumina iGenomes website and originally derived from Ensembl ) . The reads were associated with known genes based on RefSeq annotations derived from UCSC database and the number of reads associated with each gene was counted using the featureCount method . The counts were normalized using the TMM normalisation method of the edgeR R/Bioconductor package . The number of reads is represented as RPKM values ( Reads Per Kilobase of exon per Million reads mapped ) . RPKM = total gene reads / [mapped reads ( millions ) x total length of gene exons ( kb ) ] . Genes with expression values ( read number ) of less than 0 . 125 across the treatments were considered to be expressed at low levels and excluded from the analysis . For extracting RNA from whole flies or larvae , 3 x 5 individuals per phenotype were collected and snap-frozen on dry ice or in liquid nitrogen . For RNA extraction from the fat body , fat bodies from 3rd instar larvae were dissected with forceps under a stereomicroscope and washed by dipping them three times into a 20 μl drop of 1 x PBS . In total , three biological replicates were prepared and pools of whole fat bodies from ten larvae per each biological replicate were used . Samples were snap-frozen in liquid nitrogen and stored at -80°C until RNA extraction . For RNA extraction from hemocytes and plasma , 55–60 larvae per replicate were washed , placed in a drop of 1 x PBS on a multiwell glass slide and dissected with forceps to release hemolymph . To separate hemocytes and plasma from hemolymph , suspensions were centrifuged at 2500 x g for 10 min , after which the plasma was carefully pipetted into a clean tube . Hemocytes and plasma samples were snap-frozen in liquid nitrogen and stored at -80°C until RNA extraction . For RNA extraction from adult guts , flies were dipped in 70% ethanol and dissected on a glass slide in 15 μl of 1 x PBS . The midgut region of the gut was separated and washed in a second drop of 1 x PBS . Guts from 10 flies per sample were pooled and centrifuged at 2000 x g for 2 min , after which PBS was removed and guts snap-frozen in liquid nitrogen and stored at -80°C until extraction . To start the RNA extraction , a sufficient amount of the TRI reagent ( MRC , Fisher Scientific ) was added to the frozen whole flies , larvae or tissues . Whole flies , larvae , fat body and gut tissues were quickly thawed and homogenized in the TRI reagent using a micropestle ( Fisher Scientific ) . Hemocytes were homogenized in the TRI reagent by pipetting up and down for a minimum of ten times . Plasma samples were quickly thawed and suspended in the TRIzol LS reagent ( Thermo Fisher Scientific ) by pipetting up and down ten times . Thereafter , total RNAs were extracted according to the manufacturer’s ( TRI reagent or TRIzol LS ) instructions . RNA pellets were dissolved in nuclease-free water , and the RNA concentrations and the purity were determined by a Nano-Drop 2000 ( Thermo Scientific ) measurement . Quantitative real-time PCR ( qRT-PCR ) was carried out with the iTaq Universal SYBR Green One-step kit ( Bio-Rad , Hercules , CA , USA ) using total RNAs ( approximately 40 ng/sample ) as templates . RpL32 or ND-39 was used as a housekeeping gene to normalize differences in RNA amounts between samples . In the experiments presented in Fig 3C , the amounts were standardized to 40 ng of total RNA/sample . This is because no products/mRNA from genes that are considered to have a housekeeping role are normally found in the plasma . Expression levels of genes in the test groups and controls were measured within the same qRT-PCR experiment . If the samples within an experiment did not fit in one 96-well plate , a reference sample was measured in all plates to make internal normalization between plates possible . In the qRT-PCR experimental figures , one uninfected control sample ( indicated in the figure legend ) was set to 1 , to calculate fold-induction values . The primers used are listed in Table 1 . Individual 3rd instar wasp-infected and uninfected msnCherry , eaterGFP;C564>lincRNA-IBIN ( MeC564>lincRNA-IBIN ) , msnCherry , eaterGFP; HmlΔ>; He > lincRNA-IBIN ( MeHH>lincRNA-IBIN ) and control larvae were placed in a 20 μl drop of cold 8% BSA in 1 x PBS and dissected carefully with forceps . Carcasses were removed and the bled hemolymph was pipetted into a vial with 80 μl of 8% BSA in 1 x PBS . Ten larvae were dissected per cross and each cross was replicated three times . The samples were run with a BD Accuri C6 flow cytometer ( BD , Franklin Lakes , NJ , USA ) , using a gating strategy established in [80] . In short , GFP-positive cells were detected in the FL1 ( 510/15 BP filter ) and mCherry-positive cells in the FL3 ( 610/20 BP filter ) . GFP-only , mCherry-only and non-labelled hemocytes were used to establish the gates . Some of the GFP fluorescent signal was detected in the non-primary FL3 detector , and this was corrected for by subtracting 9% from the signal . To check how well centrifuging separated the hemocyte and plasma fractions , five late 3rd instar HH>GFP larvae were bled in 100 μl of 1 x PBS . The vials were centrifuged for 10 minutes at 2500 g at +4°C . The supernatant containing the plasma was pipetted into another vial ( ~90 μl ) and the hemocyte pellet was re-suspended in 90 μl of 1 x PBS . Plasma and hemocyte samples were run with a flow cytometer and the numbers of GFP-positive hemocytes in both fractions were determined . Late 3rd instar larvae were gently washed in a drop of water with a brush , dried on a piece of tissue paper and placed on a glass slide dorsal side facing up in a drop of 70% ice-cold glycerol . A coverslip was placed on the larvae and they were stored at +4°C overnight . The next day , the immobilized larvae were imaged with a Zeiss AxioImager M2 with Apotome 2 , with an EC Plan Neofluar 5x/0 . 16 objective . A Colibri LED light source was used to excite GFP ( LED 470 nm ) and mCherry ( LED 555 nm ) and images were captured with an AxioCam HRm CCD camera . Images were processed with ImageJ ( Version: 2 . 0 . 0-rc-59/1 . 51j ) and Adobe Photoshop CS4 . Ten larvae per cross were imaged . For detecting the cellular localization of lincRNA-IBIN , the RNA fluorescence in situ hybridization ( RNA FISH ) method with Cy3-tagged probes labeling the lincRNA-IBIN molecules was used . Late 3rd instar male larvae were washed in a drop of water with a brush and the hemolymph of two larvae per sample type ( four biological replicates ) was carefully bled out from the larvae in 20 μl of ice-cold 1 x PBS on a multiwell glass slide well , avoiding contamination from other tissues . Hemocytes were left to adhere for one hour in a humidified chamber at RT . Samples were fixed with cold 3 . 7% paraformaldehyde in 1 x PBS for 5–10 min and washed with 1 x PBS for 3 x 5 min . Samples were permeabilized with 1 x PBS + 0 . 1% Triton X-100 for 5 minutes and washed with 1 x PBS until there was no foam , and the mask around the wells was dried carefully with a tissue paper . The samples were blocked with 3% BSA in 1 x PBS at +4°C o/n . The RNA FISH protocol was performed by using the QuantiGene ViewRNA Assay ( Affymetrix ) and the probes for lincRNA-IBIN and RpL32 for Drosophila are now available in their catalog . For the hybridization of lincRNA-IBIN and RpL32 probes ( control ) and a negative “no probe” control , pre-warmed diluents and humidified chambers were used , and the incubator temperature ( +40°C ) was monitored . The Working Probe Set Solution was prepared by diluting each probe set 1:100 in Probe Set Diluent QF: 20 μl drops were prepared for each sample by combining 0 . 2 μl of Probe Set and 19 . 8 μl of Probe Set diluent QF . The previous solution was aspirated from the wells and replaced with 20 μl of the Working Probe Set Solution and the samples were incubated in humidified chambers for three hours at +40°C . Working Probe Set Solution was aspirated and the wells were washed three times with Wash Buffer ( this was used in all the washes ) . 20 μl of PreAmplifier Mix solution per sample was prepared by diluting PreAmplifier Mix 1:25 in Amplifier Diluent QF and added to samples and incubated at +40°C for 30 min . After washing three times , Amplifier Mix solution was prepared by diluting Amplifier mix 1:25 in pre-warmed Amplifier Diluent QF , added to the samples and incubated at +40°C for 30 min . After three washes , the Label Probe Mix Solution was prepared by diluting Label Probe Mix 1:25 in Label Probe diluent QF , added to the samples and incubated at +40°C for 30 min . Samples were washed three times and were left for 10 min in the wash buffer for the final wash . The samples were mounted with 20 μl of ProLong Gold Antifade Mountant with DAPI ( Thermo Fisher Scientific ) . Cover glasses were pressed on and the slides were left to harden overnight in the dark , transferred to +4°C for a day and imaged . The samples were imaged with a Zeiss LSM 780 confocal microscope with a Plan Apochromat 63 x/1 . 4 oil immersion objective . A pulsed diode laser was used to excite DAPI ( 405 nm ) and a diode laser ( 561 nm ) was used to excite Cy3 for imaging lincRNA-IBIN and RpL32 . Images were captured using a Quasar spectral GaAsP PMT array detector and camera allowing fast spectral imaging . Images were processed with ImageJ ( Version: 2 . 0 . 0-rc-59/1 . 51j ) and Adobe Photoshop CS4 . lincRNA-IBIN overexpressing ( C564>lincRNA-IBIN7 ) and control flies ( w1118 , lincRNA-IBIN7 ) were allowed to eclose for 2 days , collected in fresh vials and kept at 29°C for two days prior to collecting the hemolymph . For experiments with infected and uninfected flies , w1118 flies were collected as above . w1118 flies were kept in fresh vials at 25°C for one day , after which half of them were infected by septic injury with a E . cloacae -contaminated needle . Flies were kept at 25°C for another 24 h prior to collecting the hemolymph . The hemolymph was collected by pricking the flies in the thorax with a thin sharp tungsten wire sterilized in 70% ethanol . Pools of 50 pricked flies were collected on ice in 0 . 5 ul microtubes with small holes punctured in them and placed in 1 . 5 μl microtubes . The flies were centrifuged at 5000 x g for 5 min , after which 0 . 8 μl of hemolymph was collected from the bottom of the 1 . 5 ml tube and diluted 1:100 in Trehalase Buffer ( TB; 5 mM Tris pH 5 . 5 , 137 mM NaCl , 2 . 7 mM KCl ) . The samples were snap-frozen in liquid nitrogen and stored at -80°C . Glucose and trehalose were analyzed using a colorimetric assay ( Sigma Glucose ( GO ) assay kit , GACO20 ) based on the glucose oxidase ( GO ) enzyme following the protocol described in [81] . First , a trehalase stock was prepared by diluting 3 μl of porcine trehalase ( Sigma-Aldrich; T8778-1UN ) with 1 ml of TB . Samples were heat-inactivated for 5 min at 70°C , and divided into two 40 μl aliquots; one treated with an equal amount of trehalase stock to break down trehalose into free glucose , and the other left untreated by adding an equal amount of TB only . The samples were then incubated at 37°C overnight . Glucose standards were prepared by diluting 16 μl of a 1 mg/ml glucose stock solution with 84 μl of TB . 2-fold standard dilution curves were generated . Next morning , a 30 μl aliquot of each sample , the dilution series and a blank were loaded onto a 96-well plate and 100 μl of the GO reagent ( GAGO20 Glucose assay kit , Sigma-Aldrich ) was added . The plate was sealed with parafilm and incubated at 37°C for one hour . To stop the reaction , 100 μl of 12 N sulfuric acid ( H2SO4 ) was added on the samples , after which the absorbance at 540 nm was measured using the Wallac Envision 2104 Multilabel Reader ( PerkinElmer ) . The amount of glucose and trehalose ( glucose + trehalose—glucose ) in the samples were determined according to the glucose standard curve . To verify that the C564-GAL4 driver is expressed in the guts of adult flies , C564>GFP males and females were dissected in a drop of 1 x PBS and their guts were removed . The guts were checked for GFP expression using a stereomicroscope fluorescence adapter ( NIGHTSEA , MA , USA ) with a Royal blue LED ( 440–460 nm ) for excitation and a 500 nm long-pass filter . Images were captured with Nikon DS-Fi2 camera . The first transcriptome analysis ( Fig 1A and 1B ) data was analyzed using the R package Limma . The package uses a modified t-test to generate an FDR ( false discovery rate ) corrected p-value ( adjusted p-value ) for each comparison . In the second transcriptome data analysis , the comparison between lincRNA-IBIN overexpression and controls ( Fig 4A , S4 Table and S5 Table ) was done using a two-tailed t-test ( unequal variances assumed ) with a 5% false discovery rate ( FDR ) correction using the Benjamini-Hochberg method [82] . In Fig 4B , genes that had a normalized read number >10 in the treatment of interest and an expression fold change >2 were included in the cluster analysis performed with the DAVID Bioinformatics resources 6 . 8 ( https://david . ncifcrf . gov ) [83 , 84] online tool . For Fig 4C and 4D , pairwise comparisons between uninfected control sample and different treatments were carried out using a two-tailed t-test assuming unequal variances . Statistical analyses of gene expression by qRT-PCR results were carried out using a two-tailed t-test for two samples assuming equal variances . Statistical analyses of fly survival experiments were carried out using the log-rank ( Mantel-Cox ) test with Prism 6 ( GraphPad ) . Data on hemocyte quantifications and types were plotted and analyzed with R version 3 . 3 . 2 ( 2016-10-31 ) , Copyright 2015 The R Foundation for Statistical Computing . Data were analyzed using analysis of variance ( ANOVA ) followed by Tukey’s HSD post hoc test when requirements for normality and homoscedasticity were met , and in other cases a non-parametric Kruskal-Wallis rank sum test followed by Dunn’s post hoc test were applied . The level of statistical significance was established as p < 0 . 05 . | Drosophila melanogaster is a powerful genetic model for studying the innate immune mechanisms conserved from flies to humans . With recent methodology , such as whole transcriptome analyses , novel non-protein coding genes in addition to protein coding genes are being increasingly identified . These long and short non-coding RNA genes are located between and within protein coding genes in the genome , and their functions are largely uncharacterized . In humans , such RNA genes have been shown to affect numerous physiological processes including immune responses . In Drosophila , very few non-coding RNA genes have so far been characterized in detail . In this study , we have identified and characterized an immune-inducible long non-coding RNA gene , lincRNA-IBIN . lincRNA-IBIN is induced by exposure to bacteria as well as the parasitoid wasp , Leptopilina boulardi , suggesting a general role in humoral and cellular innate immunity . Accordingly , forced expression of lincRNA-IBIN enhances the expression of genes involved in carbohydrate catabolism and elevates hemolymph glucose levels in Drosophila . These results indicate that lincRNA-IBIN acts as a link between immunity and metabolism in Drosophila . As research in Drosophila has often resulted in the identification of evolutionarily conserved mechanisms also in mammals , it remains to be studied whether long non-coding RNA genes regulate metabolism upon an infection also in humans . |
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The fungal pathogen Candida glabrata has risen from an innocuous commensal to a major human pathogen that causes life-threatening infections with an associated mortality rate of up to 50% . The dramatic rise in the number of immunocompromised individuals from HIV infection , tuberculosis , and as a result of immunosuppressive regimens in cancer treatment and transplant interventions have created a new and hitherto unchartered niche for the proliferation of C . glabrata . Iron acquisition is a known microbial virulence determinant and human diseases of iron overload have been found to correlate with increased bacterial burden . Given that more than 2 billion people worldwide suffer from iron deficiency and that iron overload is one of the most common single-gene inherited diseases , it is important to understand whether host iron status may influence C . glabrata infectious disease progression . Here we identify Sit1 as the sole siderophore-iron transporter in C . glabrata and demonstrate that siderophore-mediated iron acquisition is critical for enhancing C . glabrata survival to the microbicidal activities of macrophages . Within the Sit1 transporter , we identify a conserved extracellular SIderophore Transporter Domain ( SITD ) that is critical for siderophore-mediated ability of C . glabrata to resist macrophage killing . Using macrophage models of human iron overload disease , we demonstrate that C . glabrata senses altered iron levels within the phagosomal compartment . Moreover , Sit1 functions as a determinant for C . glabrata to survive macrophage killing in a manner that is dependent on macrophage iron status . These studies suggest that host iron status is a modifier of infectious disease that modulates the dependence on distinct mechanisms of microbial Fe acquisition .
Candida glabrata has emerged as an opportunistic fungal pathogen that causes life-threatening infectious disease in humans [1] . The dramatic rise in the number of immunocompromised individuals due to HIV infection and tuberculosis , and as a result of immunosuppressive regimens in cancer treatment and transplant interventions , have provided fertile ground for unchecked C . glabrata proliferation . While Candida species now account for over 10% of all bloodstream infections [2] , the poor susceptibility of C . glabrata to antifungal therapeutics is in great part responsible for the high mortality rate of up to 50% associated with C . glabrata candidemia [3] , [4] . The limited knowledge of virulence factors that contribute to the pathogenesis of C . glabrata demands insights into the biology of this opportunistic pathogen that contribute towards the successful colonization of the mammalian host . Iron ( Fe ) is an essential metal for virtually all organisms . The ability of Fe to cycle between the reduced ferrous ( Fe2+ ) and oxidized ferric ( Fe3+ ) forms endows it with redox versatility that is utilized in both catalysis and structural biology . However , Fe2+ also has the potential to generate damaging reactive oxygen species ( ROS ) via Fenton/Haber Weiss chemistry [5] and , as a consequence , organisms have evolved sophisticated homeostatic mechanisms to tightly regulate the acquisition , utilization , storage and mobilization of Fe [6] , [7] . Eukaryotic Fe homeostasis has best been described in the budding yeast , Saccharomyces cerevisiae that possesses two high-affinity mechanisms of Fe acquisition [7] . In an aerobic environment , Fe is oxidized and largely insoluble [8] and one mechanism relies on the reduction of environmental Fe sources by cell surface reductases and transport through a ferroxidase/permease complex composed of the Fet3 and Ftr1 proteins , respectively . The activity of the Fet3 Cu-dependent ferroxidase is essential for Fe transport through Ftr1 and , thus , reductive high-affinity Fe assimilation is dependent on adequate cellular Cu bioavailability . Alternatively , S . cerevisiae expresses cell surface siderophore transporters . Synthesized and secreted by most bacteria and fungi as well as some higher plants , siderophores are low molecular weight organic chelators with high affinity for Fe3+ ( binding constants between 1023 M and 1049 M ) . Given the low Fe bioavailability in an aqueous environment ( free Fe3+ concentration below 10−9 M at pH 7 ) and the further strict active withholding of Fe within mammalian hosts ( free serum Fe concentration of approximately 10−24 M ) [9] , siderophores represent a powerful and widespread mechanism for microbial Fe scavenging . This is underscored by the fact that bacteria and fungi have evolved transporters that allow them to utilize siderophores they themselves do not produce ( xenosiderophores ) and several fungi , such as Candida species , and bacteria do not possess the ability to synthesize siderophores yet express transporters for xenosiderophores [7] . Although the mechanisms of Fe homeostasis in Candida albicans , similar to most fungi , differ significantly from that of S . cerevisiae and C . glabrata [10] , iron-regulated CaSit1 shares high homology with S . cerevisiae siderophore transporters and its deletion compromises utilization of fungal ferrichrome-type hydroxamate siderophores . CaSit1 has been shown to be required for the invasion and penetration of human epithelial cells in an in vitro model of oral candidiasis but was not required for murine systemic candida dissemination [11] , possibly reflecting the sterile environment of the serum in which siderophore concentrations are predicted to be negligible . The absence of an identifiable heme receptor in C . glabrata present in Candida albicans [12] , suggests that C . glabrata may rely predominantly on the solubilization of the circulating exchangeable Fe pool to meet its requirements for Fe . Given that mammals are host to a formidable number of microbial species , the aggressive exploitation of local siderophore reservoirs that allows for high-affinity Fe uptake from virtually any host ligand is likely to play a critical role in microbial virulence . Mammalian cellular immunity relies heavily on the action of macrophages , cells that also play a critical role in systemic Fe homeostasis [13] . A key mechanism through which macrophages mediate microbial containment is through the localized reduction of serum transferrin-bound Fe , decreased Fe export and the depletion of Fe from the macrophage phagosome , an intracellular compartment that surrounds ingested microorganims . These macrophage activities promote the killing of microorganisms by depriving them of Fe essential not only for cellular biochemistry but also for incorporation into microbial ROS-detoxifying enzymes , such as catalase or superoxide dismutase , that are critical to resistance to high levels of oxidative stress generated in the phagosome [14] . Emerging evidence for increased infections in patients with Fe overload disease highlights the strict requirement for tight host Fe restriction in microbial containment [15] . Hereditary hemochromatosis ( HH ) is the most common single-gene disorder affecting Caucasian individuals of European descent [16] . Mutations associated with HH are found in the gene encoding the Fe exporter , ferroportin ( Fpn ) , in HAMP , encoding the peptide hormone hepcidin and negative regulator of Fpn , as well as in genes encoding regulators of HAMP expression . Hepcidin expression and release from the liver leads to targeted Fpn degradation at a systemic level resulting in a block in dietary Fe uptake through enterocytes and a block in Fe release from macrophage and hepatocyte stores [17] . Although several of the mutations that underlie HH lead to similar outcomes of pathophysiological Fe overload in parenchymal organs , the clinical presentations differ with regard to macrophage Fe levels which are low when hepcidin levels are low or fails to be recognized by Fpn , and high when hepcidin levels increase or Fpn Fe-export activity is compromised . Given the critical role of macrophages in immunity , the dominant effects of Fpn activity on macrophage Fe status may be predicted to impinge on their immuno-protective functions . Here we describe the C . glabrata Sit1 siderophore transporter , its regulation under Fe deficiency and substrate siderophore specificity . In infection assays using both mouse and human macrophage cell lines we observe a strong Sit1-mediated , siderophore-dependent increase in C . glabrata survival that is blunted with increased macrophage Fe levels . Genetic manipulation of macrophage Fe status to parallel that which is observed in human diseases of Fe overload revealed an inverse correlation between macrophage Fe and C . glabrata dependence on siderophore-Fe as indicated by yeast survival rates as well as by SIT1 expression in the macrophage phagosome . Consistent with this , infection of primary macrophages derived from a mouse model of Ferroportin disease in which macrophage Fe levels are chronically elevated demonstrated enhanced C . glabrata survival that is independent of Sit1-mediated siderophore utilization . These observations are consistent with reports of greater microbial burden in patients with Fe overload disease [15] , [18] , [19] , [20] , [21] and predict that mutations affecting macrophage Fe levels may not only compromise microbial containment , but may also dictate the dependence of microbes on distinct mechanisms of Fe uptake .
Fe acquisition is a critical microbial virulence determinant due to the Fe-restricted nature of the mammalian host . The great majority of body Fe is compartmentalized within red blood cells bound as heme to hemoglobin . Several microorganisms , including C . albicans , can access this rich Fe source by secreting hemolytic factors that results in red blood cell lysis [22] . Heme can then be taken up , often by means of a dedicated receptor ( such as Rbt5 and Rbt51 in C . albicans [12] ) and the porphyrin ring hydrolysed by intracellular heme oxidases [23] . To test whether red blood cells represent an efficient source of Fe for C . glabrata , we grew the wild type strain as well as that of C . albicans and S . cerevisiae , on sheep blood agar plates containing 5% whole blood . Formation of a hemolytic halo surrounding yeast colonies was evaluated after 48–72 hours growth at 30 °C or 37 °C with 5% CO2 . Unlike the robust growth and associated hemolytic activity observed for C . albicans , C . glabrata and S . cerevisiae displayed very weak hemolysis when grown on blood sheep agar ( Figure 1A ) . Furthermore , whereas C . albicans was able to efficiently utilize heme as a source of Fe at sub-micromolar hemin concentrations , C . glabrata required 100-fold higher hemin concentrations to achieve the same level of growth . In the same way , higher hemin concentrations were required for a slower growth of C . glabrata due to hemin toxicity when compared to C . albicans ( Figure 1B ) . The observed differences in growth when heme is the sole Fe source may due to the absence of an identifiable heme receptor in the C . glabrata genome that is present in C . albicans . To address this possibility , C . albicans and C . glabrata were cultured in Fe-deficient medium supplemented or not with 20 µM zinc protoporphyrin ( ZPP ) for 3 hours . In contrast to heme , which is non-fluorescent due to the quenching of the intrinsic porphyrin fluorescence by Fe , ZPP fluoresces and its cellular uptake can be evaluated by in vivo microscopy . Figure 1C shows that in contrast to C . albicans that showed intense intracellular fluorescence , this was not observed for C . glabrata , the intracellular fluorescence of which did not rise above background as determined by comparison to the signal obtained by growth under Fe deficiency alone . This suggests that C . glabrata is not capable of taking up heme . Thus , given its weak hemolytic activity and inability to efficiently import heme , we predict that C . glabrata relies primarily on circulating host Fe sources to satisfy its requirements for Fe . Analysis of the Candida glabrata genome [24] predicts the existence of orthologues to the Saccharomyces cerevisiae high affinity reductive Fe assimilation pathway as well as a single open reading frame , CAGL0E04092g , encoding a predicted membrane protein with 14 transmembrane domains and a high degree of identity ( 72% ) to the siderophore-Fe transporter , Arn1 , that we have designated SIT1 ( Siderophore Iron Transporter 1 ) ( Figure 2A and Figure S1A ) . Computational topological analyses of Sit1 identified sequence signatures characteristic of members of the Major Facilitator Superfamily of transporters ( data not shown ) . In addition , Sit1 orthologues predicted to exist across the ascomycetes , and to lesser extent basidiomycetes , share high sequence identity within the carboxyl terminus of the protein . Protein BLAST analysis [25] of a 51 amino acid sequence comprising the last extra-cellular loop ( from amino acid residue 531 to 581 ) exclusively returned known siderophore transporters and uncharacterized proteins predicted to encode fungal siderophore transporters ( data not shown ) . This SIderophore Transporter Domain ( SITD ) is conserved in many pathogenic fungi ( Figure 2A ) . To investigate the effects of Fe availability on SIT1 expression , C . glabrata was grown under conditions of Fe-repletion or Fe-deficiency . Figure 2B shows that steady state SIT1 mRNA levels are elevated under Fe deficiency and that this elevation is sustained throughout the time-course of the experiment over 6 hours . To analyze the levels of Sit1 protein as a function of Fe availability , a Flag epitope was introduced at the carboxyl terminus of SIT1 and the fusion gene , encoding a functional protein , was integrated at the endogenous SIT1 genomic locus ( Figure S1B ) . Similar to what was observed for SIT1 mRNA , Sit1Flag fusion protein expression is low under Fe repletion but robustly accumulates under conditions of Fe deficiency ( Figure 2C ) . Siderophore production is common among most microorganisms and is a major mechanism of Fe solubilization and acquisition . The very high Fe-binding constants observed for siderophores of fungal origin ( approximately 1030 M at pH 7 ) are several orders of magnitude higher than that of the Fe2+ chelator BPS , which inhibits the Fet3/Ftr1 pathway of Fe2+ uptake . We reasoned that if a siderophore biosynthetic pathway exists in this microorganism , C . glabrata would exhibit growth in the presence of BPS . As shown in Figure 2D , growth of the wild type strain was severely compromised in Fe-deficient medium chelated with 100 µM BPS . This is consistent with a predicted lack of siderophore biosynthetic machinery in C . glabrata based on the absence of identifiable orthologues to the gene encoding ornithine-N5-oxygenase that catalyses the first committed step in fungal siderophore biosynthesis ( the DNA and protein sequences of the Aspergillus nidulans ornithine-N5-oxygenase SidA were used to perform the BLAST analyses ) [26] . To address the role of the Sit1 transporter in xenosiderophore utilization , a mutant lacking the SIT1 gene , as well as the reconstituted strain in which the wild type SIT1 gene was integrated in the sit1Δ strain at the endogenous genomic locus , were generated and evaluated for growth in Fe-deficient medium alone or in the presence of several structurally distinct fungal and bacterial siderophore substrates . Supplementation of Fe-deficient media with the fungal hydroxamate-type siderophores ferrichrome , ferrirubin or coprogen promoted robust growth of the wild type and SIT1-reconstituted strains ( Figure 2D ) . In contrast , the sit1Δ mutant strain was unable to use siderophore-Fe , strongly supporting the notion that Sit1 is a siderophore transporter capable of utilizing these xenosiderophores . Neither the ester-linked siderophore triacylfusarine C , nor the bacterially-derived siderophores enterobactin , salmochelin , yersiniabactin or desferrioxamine were able to complement the growth of C . glabrata under iron deficiency ( Figure S1C ) suggesting that these siderophores are either not transported by Sit1 or C . glabrata lacks a mechanism of releasing the Fe from these siderophores intracellularly . The subcellular localization of Sit1 was evaluated using the Sit1Flag and Sit1mCherry fusion proteins . Previous studies in S . cerevisiae have shown that during Fe deficiency the Arn1 siderophore transporter is not localized to the plasma membrane but rather to intracellular endosomal vesicles [27] . Trafficking of Arn1 to the plasma membrane is a substrate-dependent process that occurs in a dose-dependent manner [28] , [29] . In contrast to this , indirect immunofluorescence as well as live microscopy of C . glabata Sit1 revealed strong accumulation of the Sit1 protein at the plasma membrane under Fe deficiency in the absence of siderophore substrate and this accumulation was largely unaltered by the addition of ferrichrome ( Figure 2E and Figure S1D ) . High-affinity elemental Fe uptake requires the activity of the Fet3/Ftr1 ferroxidase/permease complex , with the protein complex assembled in the ER and 4 Cu ions loaded onto Fet3 within the Golgi . Under Cu deficiency , apo-Fet3-Ftr1 is assembled and targeted to the plasma membrane , but the absence of ferroxidase activity abolishes high-affinity Fe2+ transport through Ftr1 [30] . As such , elemental Fe uptake is a copper-dependent mechanism . In contrast , siderophore transporters are not known to require Cu for activity and would be predicted to be functional under low Cu conditions . Figure 2F shows that siderophore utilization is functional under Cu deficiency as imposed by growth in the presence of the Cu chelator BCS suggesting that Fe-siderophore transport is likely to be critical as the sole mechanism of high affinity Fe acquisition under conditions of Cu deficiency . The ability of C . glabrata to exploit local xenosiderophore reservoirs in the absence of an endogenous siderophore biosynthetic pathway suggests a selective advantage imparted by this mechanism of Fe uptake that may impact on the survival of this fungal pathogen . Fe acquisition is challenging within the mammalian host , in which Fe is tightly bound to proteins and ligands in extracellular and intracellular environments . The further active depletion of Fe from the phagosomal compartment amplifies the microbicidal potency of macrophages as microorganisms defend themselves from ROS under conditions that are expected to compromise ROS-detoxifying enzyme activity . We tested whether Fe deficiency would increase the susceptibility with which C . glabrata resists macrophage killing and if this might be circumvented by exposure of C . glabrata to substrate siderophore . Using the mouse macrophage-like cell line , J774A . 1 , isogenic wild type , sit1Δ and sit1Δ::SIT1 C . glabrata strains were grown under Fe limiting conditions and briefly incubated in the absence or presence of ferrichrome prior to co-culture with activated macrophages ( Figure 3A ) . C . glabrata cells were recovered by cell lysis and survival was evaluated by quantitating colony forming units ( CFU ) and comparing percentage yeast survival relative to the wild type strain grown under Fe deficiency ( represented by a dashed line ) . Figure S2A shows that when C . glabrata cells were grown under Fe deficiency prior to macrophage infection , differences were not observed in the survival of the three strains to macrophage killing . However , growth of both the wild type and reconstituted sit1Δ::SIT1 strains in the presence of ferrichrome significantly increased survival when compared to the sit1Δ strain , indicative of enhanced C . glabrata ability to survive macrophage killing as a result of siderophore-Fe utilization ( Figure 3B , left columns ) . This suggests that Sit1-mediated ferrichrome utilization provides a survival advantage to phagocytosed C . glabrata cells that is not observed in the strain that cannot internalize this Fe source . To address whether the decreased survival of the sit1Δ strain could be attributed to altered levels of phagocytosis of the mutant yeast cells rather than decreased Fe satiety , CFU were monitored 2 hours post-infection and no significant differences between the wild type , sit1Δ and sit1Δ::SIT1 strains were observed in the presence or absence of ferrichrome , strongly suggesting equally efficient phagocytosis of the three yeast strains by macrophages ( Figure S2A ) . To test whether the Sit1-dependent increase in C . glabrata survival in the presence of siderophore was a consequence of macrophage Fe status , macrophages were Fe-loaded prior to infection with the C . glabrata strains and the levels of ferritin , the primary intracellular Fe storage protein , were evaluated by immunoblotting . As shown in Figure S2B , activated macrophages cultured under Fe supplementation ( lane 4 ) showed higher ferritin protein levels than those cultured in normal medium ( lane 2 ) . Under these conditions of exogenous Fe loading , differences in survival were not observed between the wild type , sit1Δ and sit1Δ::SIT1 yeast strains grown in the presence or absence of ferrichrome ( Figure 3B , right columns ) . Furthermore , significantly higher CFU were recovered from Fe-loaded macrophages under all conditions tested when compared to infection of macrophages growing in the absence of Fe supplementation , suggesting that elevated Fe compromises the ability of macrophages to contain C . glabrata proliferation within the phagosome . To ascertain whether Fe-loading compromises macrophage activation and the ability to mount an inflammatory response , secreted levels of TNF-alpha , an acute phase inflammatory cytokine secreted by activated macrophages , were assayed and no significant changes were observed between that secreted from activated Fe-loaded macrophages and activated macrophages cultured in standard growth media ( Figure S2C ) . Despite the fact that the overall mechanisms of innate and adaptive immunity are relatively conserved between mice and humans , there are striking immunological differences between the two species [31] . In particular , there is some controversy over whether human macrophages express functional inducible nitric oxide synthase ( iNOS ) . This enzyme catalyzes the production of NO from L-arginine that , in the oxidative environment of the phagosome , reacts with superoxide leading to the generation of peroxynitrite , a powerful oxidant used by macrophages and neutrophils in microbial killing during the oxidative burst . Given this and other differences in mouse and human macrophage immunobiology , we asked whether Sit1-mediated siderophore utilization enhanced C . glabrata survival to killing by human macrophages . To test this , we used the U937 cell line derived from a hystiocystic lymphoma whose differentiation and activation have been previously described [32] , [33] . Upon differentiation into mature macrophages , U937 cells were infected with the wild type , sit1Δ and sit1Δ::SIT1 C . glabrata strains grown under Fe deficiency , with or without a brief exposure to ferrichrome , and yeast CFU quantitated after 18 , 24 and 36 hours of co-culture . Figure 3C shows that when exposed to ferrichrome , the wild type and SIT1 reconstituted strains displayed enhanced survival at all time points in contrast to the sit1Δ mutant strain that is unable to transport the siderophore . Pre-loading U937 cells with Fe by culturing in the presence of 20 µM FAC promoted the growth of C . glabrata and abrogated the requirement for Sit1-mediated siderophore utlization in enhanced C . glabrata survival ( Figure 3D ) . These results recapitulate the observations made in the J744A . 1 mouse macrophage cell line and suggest that Sit1-mediated siderophore utilization protects C . glabrata against the intracellular microbicidal activities of macrophages . Given the generation of ROS in the phagosome of activated macrophages ( Figure S2D ) , indicative of higher levels of oxidative stress , we hypothesize that siderophore-Fe plays a role in providing the Fe required for DNA replication and cellular proliferation as well as for defence against oxidative stress . Moreover , these results indicate that macrophage Fe-withholding from phagocytosed microorganisms is fundamental for the containment of microbial growth and also impacts on the mechanisms available for microbial iron uptake , imposing a strict dependence of C . glabrata on the availability and utilization of xenosiderophores via Sit1 . Based on the effect of macrophage Fe loading on C . glabrata Sit1-mediated siderophore-dependent survival , we asked whether mutations that alter macrophage Fe homeostasis might similarly modulate the dependence of C . glabrata on siderophore-Fe acquisition . The sole mammalian Fe exporter Fpn is expressed on the surface of enterocytes , macrophages , hepatocytes and placental cells where it plays a fundamental role in systemic Fe homeostasis . Type IV hemochromatosis , or Ferroportin disease , is an autosomal-dominant condition that arises from missense mutations in Fpn that compromise regulated Fe export activity . Two often contrasting clinical presentations observed in patients result from distinct mutations in Fpn that either abolish the hepcidin binding site or compromise Fe export , leading to differential effects on macrophage Fe levels [16] , [34] , [35] , [36] . We modulated macrophage Fe status by stably expressing a cDNA encoding the FPNC326Y hepcidin-insensitive disease mutation , associated with low macrophage Fe levels , or the FPNH32R allele identified in the flatiron mouse that fails to localize to the plasma membrane , leading to Fe-overload . Using ferritin protein levels as a biomarker of macrophage Fe loading , macrophages expressing the FPNH32R transgene were chronically iron-loaded in the absence of hepcidin when compared to macrophages expressing the wild type gene due to the loss in ability to export cellular Fe ( Figure 4A , lanes 1 and 3 ) . Incubation with hepcidin increased ferritin levels in cells expressing wild type FPN , as would be predicted from Fpn internalization and degradation , resulting in increased cellular Fe accumulation ( Figure 4A , lane 2 ) . In contrast , hepcidin had only a mild effect on FPNH32R-expressing macrophages ( Figure 4A , lane 4 ) . Given that Fpn functions as a dimer [37] , this modest increase in ferritin levels may reflect the contribution of hepcidin-sensitive endogenous wild type homodimers that correctly localize to the plasma membrane . In contrast , macrophages expressing the FPNC326Y transgene show lower than wild type ferritin levels when grown under Fe-replete conditions in the presence of hepcidin , consistent with a chronic Fe efflux under conditions that induce Fpn turnover of the wild type protein ( Figure 4B , lanes 2 and 4 ) . Indirect immunofluorescence in macrophages expressing the wild type and mutant FPN transgenes was performed to monitor protein localization . Both the wild type and FpnC326Y-expressing cells , but not cells expressing FpnH32R , show plasma membrane ferroportin accumulation in the absence of hepcidin ( Figure S3A ) . Exposure to hepcidin leads to a predominantly intracellular localization of the wild type protein , whereas the hepcidin-insensitive FpnC326Y protein remains at the cell surface . Using these macrophages with altered Fe homeostasis , we ascertained whether genetically programmed differences in macrophage Fe levels impinge on the dependence of C . glabrata on siderophore-Fe for resistance to macrophage killing activities . Macrophages stably expressing the wild type and mutant forms of FPN were infected with wild type , sit1Δ and the SIT1-reconstituted C . glabrata strains that had been grown under Fe deficiency and exposed or not to ferrichrome . As shown in Figure 4C , wild type and SIT1-reconstituted C . glabrata yeast cells , but not sit1Δ cells , showed enhanced survival to macrophages expressing wild type Fpn only when grown in the presence of ferrichrome prior to infection , as interpreted from the increased number of CFU when compared to the wild type C . glabrata strain grown in the absence of FC ( dashed line set at 100 ) . No differences were observed in the survival of the three C . glabrata strains grown in the absence of ferrichrome supplementation ( data not shown ) . This recapitulates the results obtained in non-transfected J774A . 1 mouse macrophages and human differentiated U937 cells in the absence of Fe supplementation ( Figure 3B , left columns ) . In contrast , siderophore-dependent differences in C . glabrata survival were not observed between the yeast strains recovered from the chronically Fe-loaded macrophages expressing the FPNH32R transgene , supporting our previous results using exogenously Fe-loaded macrophages ( Figure 3B , right columns ) . This is consistent with the high levels of labile intracellular Fe in FPNH32R macrophages that elevate the abundance of ferritin and might be inefficiently withheld from C . glabrata . In contrast , C . glabrata recovered from FPNC326Y macrophages cultured in the presence of hepcidin exhibit increased dependence on siderophore-Fe utilization for survival when compared to those recovered from macrophages expressing wild type Fpn ( Figure 4D ) . This is consistent with the refractility of FpnC326Y to hepcidin-stimulated degradation and Fe levels in these macrophages that are lower than wild type . To ascertain whether Fe overload or deficiency might compromise the ability of macrophages to mount an inflammatory response , the levels of TNF-alpha were quantified from the culture medium of activated macrophages expressing the FPN , FPNC326Y and FPNH32R transgenes and no difference was found between cell lines ( Figure S3B ) . Taken together , these data suggest that elevated macrophage Fe accumulation increases Fe accessibility to internalized C . glabrata . This ameliorates the strict requirement for Sit1-mediated siderophore utilization observed in macrophages with limited intracellular Fe . The results presented here suggest that Sit1-dependent siderophore utilization enhances C . glabrata survival to activated macrophages in a manner that is dependent on macrophage labile Fe status . SIT1 characterization demonstrated that expression is responsive to environmental Fe bioavailability , showing low levels of expression under Fe repletion and robust mRNA and protein accumulation under Fe deficiency ( Figure 2B and 2C ) . Using a reporter strain expressing a SIT1mCherry fusion gene from the endogenous SIT1 locus , we ascertained whether internalized C . glabrata cells sense differences in genetically-programmed macrophage Fe status . As shown in Figure 5A by live microscopy , C . glabrata cells phagocytosed by macrophages expressing the FPNC326Y transgene showed enhanced Sit1mCherry accumulation when compared to yeast internalized by macrophages that were Fe-loaded by virtue of FpnH32R expression , suggesting that C . glabrata cells experienced different degrees of Fe deficiency in these macrophages . Fluorescein-conjugated dextran beads were included in the infection medium to allow for a clearer visualization of intra- versus extra-cellular yeast . To quantitatively evaluate the influence of macrophage Fe status on the Fe satiety of C . glabrata , SIT1 transcript levels were monitored in yeast cells recovered from macrophages by semi-quantitative RT-PCR . Figure 5B shows that cells recovered from FPNH32R-expressing macrophages exhibited lower levels of the SIT1 transcript when compared to cells recovered from FPNC326Y-expressing macrophages two and four hours post-infection , consistent with an increased Fe satiety within the phagosome . These results support the hypothesis that chronic perturbations in macrophage Fe levels translate into altered microbial Fe availability that may impact on the mechanisms of microbial Fe uptake . Given the effects of macrophage Fe status on the dependence of C . glabrata siderophore utilization using well-established macrophage cell lines , we ascertained whether this could be recapitulated in an ex-vivo infection of primary mouse macrophages . Bone marrow cells were recovered from the femurs of control mice and of the flatiron ( ffe ) mouse model of human Ferroportin Disease , which harbors the FPNH32R mutation [36] . Cells were differentiated into mature macrophages in culture and endogenous Fpn expression stimulated by incubating cells in 10 µM ferric ammonium citrate for 2 days prior to activation and infection with C . glabrata ( Figure 6A ) . Consistent with the results obtained using the J774A . 1 and U937 cell lines , when grown in the presence of ferrichrome , C . glabrata wild type and SIT1-reconstituted cells showed enhanced survival to the killing activities of wild type primary macrophages when compared to the sit1Δ strain ( Figure 6B ) . However , siderophore-dependent survival was significantly diminished in infected ffe macrophages , supporting our previous observations that increased macrophage labile Fe pools promote the growth of C . glabrata independently of siderophore utilization . Computational analysis of the Sit1 SITD ( Figure 2A ) revealed a high degree of conservation among orthologous siderophore transporters in a large number of fungal species , including several animal and plant pathogens , a representative number of which are shown in Figure S4 . To begin to evaluate a potential role for the SITD in Sit1-mediated siderophore utilization in C . glabrata , the predicted extracellular topological orientation of the carboxyl-terminal loop between transmembrane domains 13 and 14 was first evaluated experimentally . Sit1 was tagged with a 2XFLAG epitope within the carboxyl-terminal loop between residues G542 and D543 ( Figure S5A ) . Cells expressing this tagged Sit1 protein , or the empty vector , were cultured under Fe deficient conditions and the accessibility of the anti-Flag antibody for its cognate epitope evaluated in non-permeabilized cells by indirect immunofluorescence . As shown in Figure S5B , plasma membrane accumulation of the Flag antibody was observed in non-permeabilized cells expressing carboxyl-loop-Flag Sit1 . This immunofluorescence signal was absent in cells expressing vector alone and is consistent with an orientation of this loop region , and therefore the SITD , toward the extracellular face of the plasma membrane . To test whether the Sit1 SITD may be of functional importance , we mutated the conserved residue , Y575 to alanine ( Figure S4 ) , and tested whether this might compromise Sit1-mediated ferrichrome utilization . A strain carrying the SIT1Y575A allele integrated at the endogenous genomic locus ( Figure S5C ) was strongly impaired in its ability to use ferrichrome as an Fe source ( Figure 7A ) despite exhibiting levels of protein expression ( Figure S5D ) and plasma membrane localization ( Figure 7B and Figure S5E ) comparable to the wild type strain . The observed decrease in the ability of the Sit1Y575A mutant to utilize ferrichrome prompted us to evaluate the ability of this strain to survive macrophage killing when grown in the presence of this siderophore . In contrast to wild type C . glabrata cells , neither the sit1Δ nor the strain bearing the Sit1Y575A substitution in Sit1 showed the Sit1-mediated siderophore-dependent increase in the ability to survive macrophage killing , suggesting that although this mutation does not lead to a complete loss of Sit1 function ( see Figure 7A ) , the remaining activity is insufficient to meet the cellular demand for Fe within the phagosome ( Figure 7C ) .
Candida glabrata has risen from relative obscurity as a relevant opportunistic human fungal pathogen to an increasing cause of serious infectious disease particularly in immunocompromised individuals . Yet , the significant phylogenetic relatedness of C . glabrata to Saccharomyces cerevisiae argues for some fundamental differences in the biology of this microorganism , either through the functions of the as yet poorly studied 400 genes unique to Candida glabrata and/or through the enhancement or acquisition of novel functions of shared genes . Our results underscore a role for siderophore utilization in the successful existence of C . glabrata as a human commensal as well as during pathogenesis , primarily with respect to survival to phagocytosis by mammalian macrophages . Furthermore , our studies support a pivotal contribution for unregulated host Fe acquisition in the outcome of fungal infection . The severe Fe restriction imposed by mammalian hosts as an antimicrobial strategy dictates that Fe acquisition is a bona fide virulence determinant . The existence of clinical correlations between elevated Fe bioavailability and increased microbial burden extend beyond diseases of Fe overload for which the higher susceptibility to bacterial infection , notably to Vibrio vulnificus , has been described [15] , [18] . In the same way , transfusion-dependent thalassemia patients present with increased susceptibility to widespread microbial infection that correlates with the degree of Fe overload [19] . Treatment of HH and thalassemia patients is further complicated by the fact that the sole FDA-approved Fe chelator desferrioxamine , a naturally occurring bacterial siderophore , effectively further solubilises Fe in a form that is readily utilized by a diversity of bacterial and fungal species , thus elevating the potential for infectious disease in these patients . Individuals with malignant hematological disorders , such as acute myeloid leukemia , who typically present with very high serum Fe , serum ferritin and transferrin saturation , are known to sustain increased mycoses , especially by Candida and Aspergilli [38] , [39] . Furthermore , clinical evidence also indicates that Fe overload is an independent risk factor for post-transplant infection [20] , [21] and correlates with poor prognosis and in patients infected with HIV , a virus known to depend on and manipulate target cellular Fe accumulation [40] . What then are the implications of Fe overload diseases to the susceptibility to fungal infections ? Previous studies support the notion that optimal immunity operates within a range of physiological Fe concentrations [41] . This is ensured in part through Fpn activity , mobilizing dietary and systemic Fe , and the regulatory action of hepcidin . Hereditary Hemochromatosis presents with different genetic etiologies that impart distinct effects on macrophage Fe levels . Using mutations associated with Ferroportin Disease , an autosomal dominant disease in humans , we provide evidence that C . glabrata dependence on the siderophore-Fe uptake machinery is inversely correlated to labile macrophage Fe levels and that Fe overloaded macrophages are markedly less efficient at killing and containing C . glabrata . We infer that this results from the inability of Fe-laden macrophages to withhold this essential metal from internalized C . glabrata cells given the decreased dependence of C . glabrata on siderophore-Fe , decreased SIT1 expression indicative of increased Fe satiety , increased C . glabrata fitness and the increased levels of host cell ferritin expression . Previous studies have shown that activated macrophages accumulate Cu within the phagosome [42] suggesting perhaps that inefficiently withheld macrophage Fe can be mobilized through reductive Fe uptake and supporting a host-dependent modulation of high-affinity mechanisms of Fe assimilation . This notion of a host modulatory action over the mechanisms utilized for microbial Fe acquisition is further evidenced in a study by Lesuisse [43] that compared the effects of exposure of C . albicans to either fetal bovine serum or synthetic Fe-deficient medium supplemented with distinct Fe sources . The results obtained showed that reductive Fe uptake from ferric citrate was downregulated in the presence of serum , as evidenced by decreased Fe uptake and reduced ferrireductase activity , whereas that from ferrichrome-type siderophores was elevated . In contrast to Fe-loaded macrophages , the low macrophage Fe levels associated with FpnC326Y expression impose Fe deficiency on phagocytosed C . glabrata cells , as demonstrated by elevated SIT1 expression and further supported by the increased dependence on ferrichrome and Sit1 for proliferation . As a result of chronic Fe-deficiency , hepcidin-insensitive macrophages are expected to represent a formidable challenge for microbial proliferation . The FPNC326Y disease-associated mutation results in a similar clinical outcome to that observed in patients carrying HFE mutations present in approximately 80% of HH patients and exhibiting an allelic frequency of over 10% in Caucasians of European descent [13] . Given the high prevalence of HFE mutations , it has been postulated that this mutation was positively selected for conferring protection during outbreaks of infections caused by intracellular pathogens such as Chlamydia and Yersinia [44] , [45] highlighting the intimate association between host Fe status and infectious disease . Whilst siderophore utilization is also undoubtedly critical to microbial colonization of healthy immunocompetent individuals in its state of commensalism , our study prompts an as yet unresolved question in the field regarding the origin of xenosiderophore substrates that are utilized by non-siderophore producers such as Candida and Cryptococcus species . A recent study by Ghannoum et al . characterizing the mycobiome of the oral cavity of healthy individuals identified in this host milieu a complex and dynamic profile of fungal species , the number of which parallels that observed for bacteria [46] . Among the fungi found to colonize the oral cavity of humans are several siderophore-producers that include Aspergillus and Fusarium species known to copiously produce siderophores that include coprogens and ferrichromes , both substrates for Sit1-facilitated utilization in C . glabrata . In particular , Aspergillus fumigatus , one of the most prevalent air-borne human pathogens , synthesizes chemically distinct siderophores for the solubilization of extracellular Fe as well as for its safe intracellular storage [47] . In fact , siderophore utilization by this saprophytic fungus has been shown to be required for virulence , in contrast to reductive Fe assimilation [48] , and the conidia of a siderophore-deficient mutant exhibit decreased levels of stored Fe and higher sensitivity to oxidative stress [47] . Given the ubiquitous nature of siderophore production and the mixed nature of the mammalian microbiome localized siderophore reservoirs are likely available to C . glabrata and other non-siderophore producers . In particular , it is tempting to speculate that the higher microbial burden associated with immunocompromised patients generates higher concentrations of microbial siderophores that might tip the balance towards the transition from commensalism to pathological C . glabrata proliferation and subsequent infectious disease . Given the inability of C . glabrata to efficiently use heme , Sit1-dependent siderophore utilization represents a likely mechanism for high-affinity Fe uptake under conditions of Cu insufficiency , conditions under which elemental high affinity Fe uptake is compromised . This suggests that siderophore utilization might contribute toward the virulence of C . glabrata in both the Fe- and Cu-restricted mammalian host [49] . Sit1 is syntenic to its orthologous gene in S . cerevisiae , Arn1 , with which it shares high sequence identity that translates into similar siderophore substrate specificity , which includes ferrichromes and coprogen . Yet , Sit1 and Arn1 differ in their subcellular localization . The routing of trans-Golgi Arn1 away from the plasma membrane and to the vacuolar protein-sorting pathway requires the monomeric clathrin adaptor , Gga2 [50] . Given the existence of a putative orthologue for Gga2 in C . glabrata , it is unclear whether differences in transporter localization reflect distinct protein-protein interactions or another mechanism of siderophore transport altogether - pore versus receptor , for instance . Interestingly , C . albicans Sit1 as well as S . cerevisiae Enb1 [7] , also localize predominantly to the plasma membrane in either the absence or presence of substrate siderophore [51] . It is tempting to speculate that under the pressure of harsh competition for Fe and local xenosiderophore reservoirs , C . glabrata and C . albicans have evolved to become more competitive for siderophore binding by expressing the transporter constitutively at the cell surface . Further studies are required to elucidate the mechanism of siderophore recognition and uptake by Sit1 as well as its intracellular fate , an aspect of siderophore utilization that is currently poorly understood in fungi .
C . glabrata strains , BG2 and BG14 ( ura3::KAN ) , were a gift from Dr . Brendan Cormack at the Johns Hopkins School of Medicine , Maryland . Growth under Fe-replete conditions was achieved by supplementing media with 5 µM ferrous ammonium sulfate . Fe-deficient media contained 100 µM bathophenantroline disulphonate ( BPS ) . For spot assays , cells were grown in synthetic complete medium ( SC ) to mid-logarithmic phase and serial dilutions were spotted on SC or SC supplemented with 100 µM BPS or 150 µM bathocuproine sulfonate ( BCS ) with or without the indicated siderophores . Ferrichrome , desferrioxamine , hemin and zinc protophorphyrin were purchased from Sigma Aldrich , St . Louis , MO; ferrirubin , coprogen , enterobactin , salmochelin , triacylfusarine C and yersiniabactin were purchased from EMC Microcollections , Tuebingen , Germany . Alexa Fluor-conjugated dextran was purchased from Invitrogen , Carlsbad , CA . For macrophage infection experiments , yeast cells cultured in low Fe medium for 5 hours were exposed or not to ferrichrome [10 µM] 30–35 minutes and the yeast cultures were washed three times with PBS prior to infection . The BG14 strain was used to generate the sit1Δ strain by the PRODIGE PCR-based approach [52] . The SIT1FLAG knock-in cassette was created by overlap PCR of the SIT1 ORF and 964 bp upstream promoter was used to generate the in-frame SIT1FLAG-fusion DNA subcloned upstream of the nourseothricin resistance ( NATR ) cassette with a further 400 bp of the SIT1 3'UTR cloned downstream of NATR . The sit1Δ::SIT1mCherry strain was generated by targeted integration of a SIT1mCherryNATR cassette cloned into pBM46 . Generation of SIT1 tagged with two FLAG epitopes between residues G542 and D543 within the carboxyl-terminal loop was generated by overlap PCR . The Sit1Y575A mutation was generated by site-directed mutagenesis ( QuikChange Site-Directed Mutagenesis Kit , Stratagene , La Jolla , CA ) and either integrated at the endogenous genomic locus or expressed episomally . For C . glabrata transformation , overnight cultures were transformed with 5 µg DNA using the Frozen-EZ Yeast Transformation II Kit ( Zymo Research , Orange , CA ) according to the manufacturers instructions . Transformants were selected by growth on YPD media containing 200 µg/ml nourseothricin ( Werner BioAgents , Jena , Germany ) at 30 °C for 2–4 days . Cells were cultured in SC containing ferrous ammonium sulfate [5 µM] in the absence or presence of BPS [80 µM] and samples harvested at the indicated time points and processed for RNA and protein extraction . Total RNA was extracted using the hot acid phenol method [53] . PCR-amplified probes were gel-purified and radiolabeled with alpha-P32-dCTP ( GE Healthcare , Piscataway , NJ ) using Random Primed DNA Labeling Kit ( Roche Applied Science , Indianapolis , IN ) . Semi-quantitative PCR was performed on cDNA generated by reverse transcription of isolated total RNA ( Superscript III first strand; Invitrogen , Carlsbad , CA ) using primers specific for SIT1 and ACT1 . SIT1 expression levels were normalized against those of ACT1 . Total protein was extracted in ice-cold lysis buffer ( Tris 25 mM , pH7 . 5 , NaCl 150 mM , 1 mM EDTA ) supplemented with protease inhibitors ( Roche complete tablet; Roche Applied Science , Indianapolis , IN ) using the Triton X-100/glass bead method and total protein quantified using BCA reagent ( Pierce , Rockford , IL ) . Sit1Flag was detected using an HRP-conjugated α-Flag antibody ( Sigma Aldrich , St . Louis , MO ) . An alpha-Pgk1 antibody was used as loading control . The sit1Δ::SIT1mCherry cells were visualized with a Zeiss Axio Image widefield fluorescence microscope . In vivo visualization of macrophages infected with C . glabrata strain sit1Δ::SIT1mCherry was performed using a Zeiss Axio Observer Z1 fluorescence microscope . Indirect immunofluorescence of Fpn was observed and captured using a motorized Zeiss Axio Observer Z1 . Cells were fixed in methanol at −20 °C for 5 minutes , incubated with Fpn antibody ( a gift from Drs . Jerry Kaplan and Ivana de Domenico , University of Utah ) and incubated with a secondary fluorochrome-conjugated antibody ( Alexa-fluor 488; Molecular Probes , Invitrogen , Carlsbad , CA ) . J774A . 1 macrophages seeded at 2×105 cells/ml were activated with 1 µg/ml LPS and 5 ng/ml LPS for 3 hours and 2 µM 2′ , 7 dichlorofluorescein diacetate ( DCFH-DA ) added during the last 15 minutes . Fluorescence was monitored 450 nm excitation and 530 nm emission using a fluorescence plate reader ( Perkin-Elmer 1420 Victor 3 ) . The mouse macrophage cell line J774A . 1 was maintained in Dulbecco's Modified Eagles Medium 4 . 5 g/L D-glucose ( Gibco , Invitrogen , Carlsbad , CA ) , 10% FBS , 100U penicillin and 100 mg/ml streptomycin , at 37 °C and 5% CO2 . For iron loading experiments , J774A . 1 macrophages were cultured in 20 µM ferric ammonium citrate for 2 days and washed 3 times with warm DMEM prior to infection with C . glabrata . Macrophages seeded at 2×105 cells/ml overnight were activated with IFN-gamma [5 ng/ml] ( Sigma Aldrich , St . Louis , MO ) and LPS [1 µg/ml] ( Sigma Aldrich , St . Louis , MO ) 3 hours prior to infection with C . glabrata strains at a multiplicity of infection of 1:4 ( macrophage:yeast ) . At the indicated time points infected macrophages were lysed with water , and dilutions of the lysates plated on YPD . Colony forming units ( CFU ) were counted after growth at 30 °C for 2 days . U937 cells were maintained in RPMI-1640 medium ( Gibco , Invitrogen , Carlsbad , CA ) , supplemented with 2 mM glutamine , 10% FBS , 100U penicillin and 100 mg/ml streptomycin , at 37 °C and 5% CO2 . Differentiation of these pro-monocytic cells into mature macrophages was induced by the addition of 10 nM phorbol myristate acetate ( PMA ) to 1×106 cells/ml for 48 hours . Cells were washed 3 times with warm culture medium and incubated for a further 48 hours at 37 °C and 5% CO2 . LPS ( 1 µg/ml ) was added to the macrophages 3 hours prior to infection with C . glabrata ( MOI of 1:1 ) and yeast cell recovery and CFU counting performed as described above . For primary macrophage infection assays , mouse femurs were kindly supplied to us by Drs . Jerry Kaplan and Ivana De Domenico , at the University of Utah . Primary bone marrow cells were harvested by flushing the femurs of 3–5 month-old female control ( C3H ) or flatiron ( ffe ) mice and macrophage differentiation induced by culturing in RPMI 1640 containing 20% FBS , 20 µM ß-mercaptoethanol , 2 mM L-glutamine , 100U penicillin and 100 mg/ml streptomycin and GM-CSF [3 ng/ml] . Macrophages were seeded for infection assays at 2×105 cells/ml . For the generation of stable cell lines expressing wild type and mutant forms of FPN , full-length mouse FPN cDNA ( TrueORF , OriGene , Rockville , MD ) was used as a template to introduce the H32R and C326Y mutations through site-directed mutagenesis . FPN , FPNH32R and FPNC326Y plasmid DNA were stably transfected into J774A . 1 macrophages using Lipofectamine reagent ( Invitrogen , Carlsbad , CA ) . Total protein was extracted in lysis buffer as described above supplemented with 0 . 1% SDS and a phosphatase inhibitor cocktail ( HALT , Pierce , Rockford , IL ) . Antibodies against ferritin and α-tubulin were purchased from Abcam ( Cambridge , MA ) . IL-6 and TNF-alpha secretion was measured by enzyme-linked immunosorbent assay ( ELISA ) ( R&D Systems , Minneapolis , MN ) . Fluorescence was measured using a SpectraMax Plus luminometer ( Molecular Devices , Sunnyvale , CA ) . The SIT1 DNA , protein sequence and functional description were deposited in GenBank with the Accession Number HQ734814 . | Candida glabrata is a major human pathogen due to its low susceptibility to conventional antifungal drugs and the dramatic increase in the number of immunocompromised individuals suffering from HIV AIDS , cancer , and diabetes . Iron overload is one of the most common genetically inherited diseases and reports suggest increased susceptibility of these patients to bacterial infection . The ability of microorganisms to obtain iron from their environment is a major determinant in their fitness and hence in their ability to cause infectious disease . Here we demonstrate that the siderophore iron carrier is critical for C . glabrata survival after ingestion by mouse and human macrophage immune effector cells . Through the generation of macrophage models of human iron overload disease we demonstrate that ingested C . glabrata cells sense altered macrophage iron levels , and that the Sit1 siderophore-iron transporter functions as a critical determinant in the ability of C . glabrata to survive macrophage killing in a manner that is dependent on macrophage iron status . Our results reveal a role for siderophore-iron as a source of iron during C . glabrata infection , suggest additional therapeutic intervention strategies , and support a pivotal contribution for a common human iron overload disease in the mechanisms used for Fe acquisition in C . glabrata . |
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Trimeric autotransporter adhesins ( TAAs ) are a major class of proteins by which pathogenic proteobacteria adhere to their hosts . Prominent examples include Yersinia YadA , Haemophilus Hia and Hsf , Moraxella UspA1 and A2 , and Neisseria NadA . TAAs also occur in symbiotic and environmental species and presumably represent a general solution to the problem of adhesion in proteobacteria . The general structure of TAAs follows a head-stalk-anchor architecture , where the heads are the primary mediators of attachment and autoagglutination . In the major adhesin of Bartonella henselae , BadA , the head consists of three domains , the N-terminal of which shows strong sequence similarity to the head of Yersinia YadA . The two other domains were not recognizably similar to any protein of known structure . We therefore determined their crystal structure to a resolution of 1 . 1 Å . Both domains are β-prisms , the N-terminal one formed by interleaved , five-stranded β-meanders parallel to the trimer axis and the C-terminal one by five-stranded β-meanders orthogonal to the axis . Despite the absence of statistically significant sequence similarity , the two domains are structurally similar to domains from Haemophilus Hia , albeit in permuted order . Thus , the BadA head appears to be a chimera of domains seen in two other TAAs , YadA and Hia , highlighting the combinatorial evolutionary strategy taken by pathogens .
Adherence to the host is a key event in bacterial pathogenesis . The mediators of this process , called adhesins , form a heterogenous group that vary in architecture , domain content and mechanism of binding . Trimeric autotransporter adhesins , also referred to as OCAs for oligomeric coiled-coil adhesins , form a new class of adhesins recently defined from pathogenic proteobacteria [1]–[4] . The best studied TAAs are important virulence factors: YadA of Yersinia enterocolitica , a species causing enteritis , mesenteric lymphadenitis , and reactive arthritis in humans [5] , [6]; NadA of Neisseria meningitidis [7] , an agent of meningitis and sepsis; UspA1 and A2 of Moraxella catarrhalis [4] , [8] , a prominent species in respiratory tract infections; Hia and Hsf of Haemophilus influenzae [9] , [10] , an organism causing meningitis and respiratory tract infections , and BadA of Bartonella henselae [11] , which is the agent of cat scratch disease . In the context of the AIDS pandemic , Bartonella henselae has also emerged as the agent of bacillary angiomatosis , an uncontrolled proliferation of blood vessels resulting in tumor-like masses of cells in patients with impaired immune systems . All TAAs follow a head-stalk-anchor architecture in the direction from amino- to carboxy-terminus of the protein [4] . Whereas head and stalk are assembled from an array of analogous domains , the anchor is homologous in all TAAs and represents the defining element of this family [3] . It trimerizes in the outer membrane to form a 12-stranded pore [12] , through which the head and the stalk exit the cell . After export is completed , the C-terminal end of the folded stalk occludes the pore . The head , which is projected above the cell surface by the stalk , mediates a range of molecular interactions such as autoagglutination and attachment to host tissue , typically via proteins of the extracellular matrix , e . g . collagen , fibronectin , or laminin . Two head structures , a complete one from YadA [13] and a partial one from Hia [14] , have been solved by X-ray crystallography , revealing fundamentally different trimeric complexes with novel folds . Of the experimentally studied TAAs , Bartonella henselae BadA is the longest representative , at over 3000 residues , and extends approx . 240 nm from the bacterial cell surface ( Figure 1 ) . BadA has been shown to bind collagen and fibronectin [11]; although the location of the binding sites has not been determined , comparison to other TAAs suggests that they reside in the head . The BadA head is composed of two parts , the N-terminal of which is clearly homologous to the head of YadA , while the C-terminal has no detectable similarity to proteins of known structure . We have recently produced a comprehensive , web-based annotation platform for TAAs [15]; as part of this work , we found that the C-terminal part of the BadA head in fact consists of two domains . Here we report the crystal structure of these two domains , which closely resemble parts of the Hia head structure despite an extremely low sequence similarity . Based on our data and a homology model to the YadA head , we present the structure of the full BadA head .
With a size of 3082 residues per monomer , BadA ( gi|119890727| ) is considerably larger than other well-studied TAAs , such as YadA ( 455 res . ) , Hia ( 1098 res . ) , UspA1 ( 863 res . ) or NadA ( 364 res . ) . Although very long ( 240 nm+/−10 nm , Figure 1 ) , it preserves the head-stalk-anchor architecture typical of TAAs . The sequence is highly repetitive and the presence of 24 conserved connectors , called neck sequences [4] , allowed us to define domain boundaries and to parse out the head , stalk and anchor regions [11] . We found that the head region falls into two parts , separated by a neck sequence ( Figure 2A ) . The first part was evidently similar to the head of YadA ( Protein Data Bank ( PDB ) code 1p9h ) , as well as to the heads of many other TAAs of unknown structure , due to the periodic occurrence of degenerate SVAIG motifs , which form the inner β-strands of the trimeric β-helix [13] . The second part however showed no discernible similarity to any protein of known structure , even when using advanced sequence comparison and fold prediction tools ( see Methods ) . By sequence comparisons to other TAAs , we determined that this part in fact consists of two separate domains , one containing a highly conserved Gly-Trp ( GW ) motif near its N-terminus and the other a conspicuous Gly-Ile-Asn ( GIN ) motif near its C-terminus [15]; we therefore named the former “Trp-ring domain“ and the latter “GIN domain” . Based on this analysis , we decided to determine the structure of these two domains . The fragment chosen for crystallization extended from the end of the YadA-like head domain to the end of the first stalk segment ( residues 375 to 536 , Figure 2A ) . The recombinantly expressed BadA construct runs as a trimer of three 17 kDa subunits on calibrated size exclusion columns ( data not shown ) . To assay the stability of the trimers , we subjected the protein to proteolytic treatment with trypsin and chymotrypsin . In both cases we obtained fragments of approx . 14 kDa ( Figure 2B , Figure S1 ) , which could still form trimers . Mass spectrometric analysis showed that the carboxy-terminal part of the construct was particularly prone to digestion , suggesting a flexible conformation . The termini of the protease-resistant fragment are marked by red arrows in Figure 2A . The CD spectra of the digested and undigested forms indicated well-folded proteins consisting primarily of β-sheets ( Figure 2C ) . Thermal denaturation curves with CD detection showed that unfolding was also very similar in these proteins and proceeded cooperatively as a two-step process ( Figure 2D ) , with a first plateau at 75°C and complete unfolding at 92°C . This elevated stability seems common to TAA domains , as shown for the anchor of YadA [16] and for the complete YadA protein [17] . The two-step denaturation may reflect the presence of two domains in our construct . The single tryptophan residue close to the amino-terminus ( Trp387 ) allowed us to perform fluorescence measurements . The λmax of 320 nm , which is typical for buried Trp residues in folded proteins , does not change significantly in the trypsinized and chymotrypsinized fragments , but the intensity of the emission signal increases substantially ( Figure 2E ) . Proteolysis of the amino-terminal sequence may have removed quenching residues from the vicinity of the tryptophans . The undigested protein was crystallized under a variety of conditions . The crystals typically grew to 300×200 µm in size , were well ordered , and diffracted up to a resolution of 1 . 1 Å . All crystals tested , although of different shape and from different crystallization conditions , belonged to space group P1 with cell constants of a = 29 . 87 , b = 51 . 14 , c = 58 . 62 , α = 65 . 87° , β = 76 . 6° , γ = 82 . 08° . In order to solve the structure of this fragment , a variety of heavy atom derivatives were prepared and data were collected , but none of the crystals showed binding . This was not unexpected , as the protein does not contain cysteine or methionine residues , which commonly bind heavy metal compounds . For the same reason , we could not use selenomethionine-based MAD-phasing . The continued failure to determine the structure experimentally led us to re-explore the protein with bioinformatic tools . We had failed to identify potential homologs through either sequence comparisons or fold recognition , but a new method for detecting distant sequence similarity by comparing profile Hidden Markov Models with each other had just been developed in our department ( see Methods ) . The two best matches obtained with this method , albeit with low statistical confidence , were to the structure of Haemophilus Hia ( PDB code 1s7m ) . The two BadA domains each gave a separate match – the first domain to the C-terminal part of Hia and the second to the N-terminal part . These matches were intriguing , as Hia is also a TAA and the conserved GW and GIN motifs , which we had identified as key signatures of these domains [15] , were also present in the domains from Hia . The inverted order of the two domains in the Hia structure relative to BadA provides a rationale for the inability of less sensitive methods to detect the relationship between the two proteins . We therefore attempted to solve the BadA structure by molecular replacement with homology models based on the Hia structure . To this end , we built full-atom models for each domain , as described in the Methods section . Molecular replacement searches returned two solutions , which were further refined , and a trimeric model of residues 385 to 498 of BadA could be built into the electron density map ( underlined in Figure 2A ) . The statistics given in Table 1 demonstrate the high overall quality of this structure . The termini of the construct were not resolved , as expected from the results of proteolytic digestion , which suggested that they are unstructured . The overall structure of the construct is rod-like , with a length of 10 nm and an approximate diameter of 2 . 5 nm . Superposition of the three protein chains shows root mean square deviations ( r . m . s . d . ) of ∼1 . 2 Å , with the main differences in the termini and in the loops . Although the structural variability near the termini is probably an artifact of expressing a truncated construct , the overall r . m . s . d suggest an intrinsic flexibility of the three protein chains while the B-factors are low and equally distributed all over the protein chain except for the coiled-coil part . The three chains are tightly intertwined and each can only assume its structure in the context of the other two . 72 of 114 residues from each chain ( 63% ) are involved in intersubunit contacts , including 16 residues in the hydrophobic core of the trimer ( Figure 3 ) . More than 50 hydrogen bonds ( as defined by a cutoff distance of 3 . 5 Å ) are formed between any two chains . There are , however , no inter-subunit salt bridges and only two intra-subunit ones ( Asp394 - Lys418 , and Asp 481 - Lys469 ) . Overall , 5070 Å2 from each chain , corresponding to half of its total surface area of 10040 Å2 , are buried in the trimer . The structure consists of four distinct elements , as anticipated from the sequence analysis ( Figures 2A , 3 and 4 ) : the Trp-ring domain , named for the peculiar arrangement of the highly conserved Trp residues ( Figure 5 ) , the GIN domain , a neck sequence , and a short segment of the coiled-coil stalk of BadA . The Trp-ring domain forms a β-prism of interleaved , five-stranded β-meanders parallel to the trimer axis . Each of the three β-sheets forming the sides of the prism consists of the β1- β2 hairpin of one chain , β3I of the next chain and the β4II- β5II hairpin of the last chain , as viewed clockwise from the N-terminus; the strand order is β2- β1- β3I- β5II- β4II ( Figure 4B ) . The GIN domain ( residues 435–466 ) also forms a β-prism of 5-stranded β-meanders , albeit not interleaved and perpendicular to the trimer axis . Its five β-strands ( β7- β11 ) are extended N-terminally by the region connecting GIN with the Trp-ring domain in the next chain ( β6 ) I and C-terminally by the first residues of the neck sequence in the last chain ( β12II ) , again as viewed clockwise from the N-terminus ( Figure 4C ) . The neck sequence serves as a connector , which makes the transition from the wide diameter of the β-prisms to the narrower diameter of the coiled-coil stalk . Although being largely devoid of regular secondary structure , the neck forms an extended network of hydrogen bonds ( Figure 6 ) . The coiled coil following the neck is a part of the extended stalk domain of BadA [11] . In our construct , we had included 50 residues from the N-terminal part of the BadA stalk , but only two heptads are visible in the structure , the rest being disordered . Comparison of the BadA structure with the two previously determined TAA head structures from YadA [13] and Hia [14] shows that shared domains are structurally nearly identical ( Figure 7 ) , even when , as in the case of the Trp-ring and GIN domains , their sequence similarity is barely detectable . In comparing these two domains between BadA and Hia , we find that the structural conservation extends to the conformation of hydrophobic residues in their core . The main differences are two insertions in Hia , one between strands β1 and β2 of the Trp-ring domain and the other between strands β9 and β10 of the GIN domain . Two apparent differences , concerning the relative order of the two domains and the seemingly missing N-terminal strand in Hia GIN , are in fact artifacts of the way the Hia construct was cloned out of the full length gene . Hia contains multiple Trp-ring-GIN-tandems , and the Hia construct was cloned such that the C-terminal GIN domain of one tandem appeared N-terminally to the Trp-ring domain of the next tandem . In the process , the N-terminal strand of the GIN domain , which is detectable in the sequence , was omitted . All three proteins contain necks , which are structurally nearly identical ( Figure 7 ) . The similarity of the necks in BadA and YadA was easily detected at the sequence level . The Hia neck , however , was not recognized before due to sequence divergence , which includes the insertion of a domain of 44 residues ( Figure 7 ) . The nearly identical backbone structure in the three necks is the result of a conserved network of mainchain hydrogen bonds and does not seem to involve sidechain interactions , beyond the formation of a small hydrophobic core ( Figure 6 ) . The charge network reported in the YadA neck [13] is not present in BadA or Hia and seems to be a specific feature of YadA . In light of these observations , it is hard to understand the exceptional sequence conservation of necks , which is typically in the range of 50% identity between any two necks [4] . The part of the BadA head which is not included in our construct shows extensive sequence similarity to the YadA head , allowing us to model it by homology ( see Methods ) . The β-roll domain was modeled using a template-based approach , since YadA contains 8 repeats ( whose inner strands carry the conspicuous SVAIG sequence motif ) and BadA 11 . Two features of BadA could not be modeled for lack of a structural template: ( I ) the N-terminal segment from the signal sequence cleavage site to the first turn of the β-roll , which is presumably unstructured and also not resolved in the YadA structure , and ( II ) an insertion in the last turn of the β-roll , which is present in many TAA head domains , but not in YadA [15] . The similarity between YadA and BadA not only encompasses the left-handed β-roll domain , but also the neck connector and a short coiled-coil segment . Thus , even though the coiled-coil segment N-terminal to the Trp-ring domain was not resolved in our BadA construct , we could merge the model to the structure without gaps by aligning the registers of the coiled coils and modeling the missing part with parametric equations [18] . The resulting model for the complete BadA head is shown in Figure 8 . We have determined the structure of two domains from the head of the Bartonella henselae adhesin BadA . Surprisingly , these domains are structurally nearly identical to two domains from the Haemophilus adhesin Hia , despite their similarity being essentially undetectable by sequence comparisons . This is due to the short length of the individual domains , to two large inserts in Hia , and to the seemingly reversed order of the domains in the two proteins . The reversed order is due to the way in which the Hia fragment was constructed; in fact , Trp-ring and GIN domains also occur in tandem in Hia , albeit in multiple copy , and the Hia fragment combines the C-terminal GIN domain of one tandem with the N-terminal Trp-ring domain of the next . The near-identity of the structures is underscored by our ability to solve the BadA structure by molecular replacement , using the Hia structure as a modeling template . From this we conclude that TAA domains retain their structure closely , irrespective of their molecular context , and that their strongly interleaved nature prevents structural divergence , even after considerable sequence divergence has occurred . These findings support our previous proposal that the structure of TAAs can be elucidated by a “dictionary approach”: domains are identified by bioinformatics , compared to a database containing the structures of representative exemplars for each domain , modeled and assembled into complete fibers using the nearly invariant structure of connectors such as the necks and coiled-coil segments [19] . We have laid the bioinformatics groundwork for this approach with a sensitive online system for the annotation of TAA domains [15] and are now in the process of selecting and solving representative exemplars for each domain type . An important question relates to the role of this part of the BadA head in the adhesive properties of the entire molecule . BadA has been reported to bind to collagen and fibronectin [11] , while the homologous Vomps A , B and C of Bartonella quintana , which only have the YadA-like part of the head and lack the domains described here , only bind to collagen [20] , [21] . For this reason , we suspected that fibronectin binding by BadA would reside in the Trp-ring-GIN tandem . However , attempts to show this remained ambiguous . The fragment binds in isolation to endothelial and epithelial cells and shows a certain affinity for fibronectin in sandwich dotblots , but cannot be co-immunoprecipitated with fibronectin and is insufficient to preserve fibronectin binding in a stalk deletion mutant ( Riess , Wagner , Kempf , Ursinus , Linke and Martin , unpublished; Kaiser et al . , submitted ) . We note in this context that the binding affinity of individual heads could be quite low , given their high density on the cell surface . A conspicuous structural feature of the Trp-ring domain are the many open hydrogen bond donor and acceptor groups at the edges of the three β-sheets forming the prism . In the only complex between a bacterial adhesin and fibronectin known to atomic resolution ( PDB accession 1o9a; [22] ) , the interaction is mediated by β-sheet extension along such open edges ( “β-zippers” ) . It is attractive to consider that a similar binding mechanism applies to the Trp-ring domain .
Protein expression and purification of the fragment shown in Figure 2 were performed as described [11] . Note that the fragment was originally considered to be part of the stalk because it showed no homology to the Yersinia YadA head [11] , [23] . The oligomeric size of the purified protein was verified by gel-sizing chromatography on a calibrated analytical S200 column ( GE Healthcare ) which was coupled to a MiniDAWN Tristar detector ( Wyatt ) , allowing in addition molecular mass determination by static light scattering . For protease resistance assays , the BadA fragment ( 0 . 5 mg/ml ) was incubated at room temperature in 20 mM MOPS/KOH pH 7 . 2 , 150 mM NaCl with 10 µg/ml of either trypsin or chymotrypsin for 10 min . Reactions were stopped by addition of 1 mM PMSF , and samples were subsequently analyzed by SDS-PAGE and mass spectrometry . In preparation for MS analysis , the proteolytically treated protein was re-purified by ion-exchange and gel-size exclusion chromatography . LC HR MS measurements were performed with an Agilent 1100 series HPLC with a Waters Symmetry C4 3 . 5 µm column ( 2 . 1×100 mm ) , coupled to a micrOTOFLC mass spectrometer ( ESI- TOF , Bruker Daltonics , Bremen , Germany ) . Protein was eluted from the HPLC column using buffer A ( H2O/0 . 05% TFA ) and buffer B ( CH3CN/0 . 05% TFA ) with a gradient from 20–80% buffer B at a flow of 250 µl/min . Circular dichroism ( CD ) spectra of proteins ( 12 µM ) were recorded in PBS at 200–240 nm with a J-810 Spectropolarimeter ( Jasco ) , using 1 mm cuvettes . The signal output was converted into molar ellipticity . Thermal stability was monitored by CD spectroscopy using a Peltier-controlled sample holder unit . Temperature profiles at 210 nm were recorded in 1°C increments with 0 . 2° pitch from 25°C to 100°C . In all cases a temperature probe connected to the cuvette was used to provide an accurate temperature record . The fraction of protein in the unfolded conformation , fU , was calculated as fU = ( yF−y ) / ( ( yF−yU ) , where yF and yU represent the values corresponding to folded and unfolded states , respectively , and y being the observed value . Tryptophan fluorescence was measured at room temperature in PBS buffer at protein concentrations of 30 µM in a FP-6500 spectrofluorometer ( Jasco ) with λex = 293 nm and λem = 300–400 nm . Bacterial colonies of BadA+ and BadA− strains grown on blood agar [11] were fixed with 2 . 5% glutaraldehyde in PBS directly on the agar plates for 20 min at ambient temperature and kept for 20 hours at 4°C . For scanning electron microscopy , colonies were postfixed with 1% osmium tetroxide in 100 mM Phosphate buffer pH 7 . 2 for 1 h on ice , dehydrated in ethanol and critical-point-dried from CO2 . The samples were sputter-coated with 8 nm gold-palladium and examined at 20 kV accelerating voltage in a Hitachi S-800 field emission scanning electron microscope . For transmission electron microscopy , glutaraldehyde-fixed cells were covered with 2% agarose and blocks containing single colonies were cut out . After postfixation with 1% osmium tetroxide in 100 mM Phosphate buffer pH 7 . 2 for 1 h on ice , these blocks were rinsed with aqua bidest , treated with 1% aqueous uranyl acetate for 1 hr at 4°C , dehydrated through a graded series of ethanol and embedded in Epon . Ultrathin sections were stained with uranyl acetate and lead citrate and viewed in a Philips CM10 electron microscope . For on-section immunolabeling , cells were fixed with 2 . 5% glutaraldehyde in PBS , dehydrated in a graded series of ethanol at progressive lower temperature from 0°C down to −40°C , infiltrated with Lowicryl HM20 and UV-polymerized at −40°C . Unspecific binding sites on ultrathin sections were blocked with 0 . 5% bovine serum albumin and 0 . 2% gelatine in PBS . Ultrathin sections were then incubated with a BadA specific rabbit IgG antibody ( 10 µg/ml; raised against the C-terminal part of the BadA head [11] ) followed by protein A-10 nm gold conjugates ( gift from Dr . Y . Stierhof , Tübingen ) . Sections were stained with 1% aqueous uranyl acetate and lead citrate and analysed in a Philips CM10 electron microscope at 60 kV using a 30 µm objective aperture . Crystals of the BadA head fragment were obtained at 291 K by the vapor diffusion hanging drop method against one ml of a reservoir solution . Crystal drops were prepared by mixing 1 µl of protein at 11 mg/ml concentration with 1 µl of reservoir solution . Crystals were obtained with 0 . 05 M ammonium sulfate , 0 . 05 Bis-Tris pH 6 . 5 , 30% v/v pentaerythrol ethoxylate with a size of 150×100×100 µm . Single crystals were flash-frozen in their mother liquid and data collection was performed at 100 K . The crystal system is triclinic P1 with cell constants of a = 29 . 87 Å , b = 51 . 140 Å , c = 58 . 62 Å – α = 65 . 87 , β = 76 . 60 , γ = 82 . 08 . The crystals contained one trimer in the asymmetric unit , diffracted to a resolution limit of 1 . 13 Å and showed a solvent content of 41% . A high and low resolution data set was collected at beamline ID29 , ESRF ( European synchrotron radiation facility ) . Data were indexed , integrated and scaled with the XDS program package [24] . High and low resolution data were merged using the XSCALE subroutine of the XDS package . The homology of the N-terminal part of the BadA head with YadA was found using PSI-BLAST [25] ( E-value of 5e-07 in the first iteration ) . Searches for distant homologs of known structure to BadA were performed with three standard programs that use sequence-profile comparisons ( PSI-BLAST ) , sequence-HMM comparisons ( SAM-T02 [26] ) , and profile-profile comparisons ( COMPASS [27] ) . In addition , we used a structure prediction metaserver ( 3D-Jury [28] ) . None of these tools yielded significant matches . More recently we developed a tool based on HMM-HMM comparisons , which was shown to be at least twice as sensitive in detecting distant homologs as the methods listed above; this tool , HHsearch [29] , was implemented in a web server , HHpred [30] . The homology between Hia and BadA was detected using the HHPred server , running HHsearch 1 . 1 . 4 . in its default settings , albeit with low statistical significance ( E-values of 0 . 93 and 3 . 9 and probability of 66% and 30% for the Trp-ring and GIN domains , respectively ) . Note that with the current HHsearch version 1 . 5 . 0 , HHpred returns good statistical significances ( probabilities of 80–90% ) for the matches between BadA and Hia , but only if the compositional bias correction is turned off in the ‘more options’ field . Sequences of corresponding domains were manually aligned with respect to secondary structure arrangement and conserved residues . Homology models based on the structures of YadA and Hia were built with the nest program from the Jackal package ( http://wiki . c2b2 . columbia . edu/honiglab_public/index . php/Software:Jackal ) . Each chain was modeled separately , then all chains were combined together and sterical clashes were removed with the profix program , again from the Jackal package . The YadA-like domain of the BadA head was modeled on a template structure containing three partially overlapping core sections from the YadA structure and a following neck sequence . Preparing this template was necessary , as this domain in BadA is significantly longer than in YadA; it has 11 head repeats instead of 8 . Moreover , we had to introduce a break in the last repeat before the neck , since BadA has a conserved insertion in that place ( data not shown ) for which we do not have a structural template . The coiled-coil segment preceding the Trp-ring domain has a periodicity of 11 and was constructed with BeammotifCC [18] . Its transition from the neck into the Trp-ring domain was modeled based on the structures of YadA and Hia . The model of the full head of BadA was constructed using the solved structure described here and the two models mentioned above . The necessary structural superimpositions were done with VMD [31] . The structure of the BadA head fragment was solved by molecular replacement using models based on the PDB coordinates of the partial head of Haemophilus Hia ( 1s7m ) . Two subdomains of this model were independently placed using the program MOLREP [32] and initially refined in REFMAC [33] . To improve this model , the program packages ARP/wARP [34] , Coot [35] , and REFMAC were used to rebuild sidechains and to add missing residues . A random set of 5% of the data were neglected during the refinement process and marked as test set for cross-validation . Atoms were refined anisotropically and TLS parameters for the three independent protein chains were defined using REFMAC [36] . ARP/wARP was used to build the solvent structure . Together , this procedure returned a final model consisting of 2870 non-hydrogen atoms and 438 water molecules ( corresponding to residues 385–498 in chains A and C and to residues 385–495 in chain B ) . Together with the hydrogen atoms generated for all amino acid residues , a crystallographic R/Rfree-factor of 0 . 156/0 . 184 was achieved . Model superposition was performed by the programs top3d or LSQ included in the CCP4 program package [37] . Secondary structure elements were defined according to DSSP criteria ( http://molbio . info . nih . gov/structbio/basic . html ) . Figures were prepared using the programs DINO ( http://www . dino3d . org/ ) and Rasmol ( http://www . openrasmol . org/ ) . The x-ray structure was deposited in the Protein Data Bank ( PDB , access code 3D9X ) . The model of the full BadA head can be downloaded from http://protevo . eb . tuebingen . mpg . de/coordinates/ . | The ability to adhere is an important aspect of the interaction between bacteria and their environment . Adhesion allows them to aggregate into colonies , form biofilms with other species , and colonize surfaces . Where the surfaces are provided by other organisms , adhesion can lead to a wide range of outcomes , from symbiosis to pathogenicity . In Proteobacteria , colonization of the host depends on a wide range of adhesive surface molecules , among which Trimeric Autotransporter Adhesins ( TAAs ) represent a major class . In electron micrographs , TAAs resemble lollipops projecting from the bacterial surface , and in all investigated cases , the adhesive properties reside in their heads . We have determined the head structure of BadA , the major adhesin of Bartonella henselae . This pathogen causes cat scratch disease in humans , but can lead to much more severe disease in immunosuppressed patients , e . g . , during chemotherapy or after HIV infection . Surprisingly , domains previously seen in other TAA heads are combined in a novel assembly , illustrating how pathogens rearrange available building blocks to create new adhesive surface molecules . |
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Drosophila C virus ( DCV ) is a natural pathogen of Drosophila and a useful model for studying antiviral defences . The Drosophila host is also commonly infected with the widespread endosymbiotic bacteria Wolbachia pipientis . When DCV coinfects Wolbachia-infected D . melanogaster , virus particles accumulate more slowly and virus induced mortality is substantially delayed . Considering that Wolbachia is estimated to infect up to two-thirds of all insect species , the observed protective effects of Wolbachia may extend to a range of both beneficial and pest insects , including insects that vector important viral diseases of humans , animals and plants . Currently , Wolbachia-mediated antiviral protection has only been described from a limited number of very closely related strains that infect D . melanogaster . We used D . simulans and its naturally occurring Wolbachia infections to test the generality of the Wolbachia-mediated antiviral protection . We generated paired D . simulans lines either uninfected or infected with five different Wolbachia strains . Each paired fly line was challenged with DCV and Flock House virus . Significant antiviral protection was seen for some but not all of the Wolbachia strain-fly line combinations tested . In some cases , protection from virus-induced mortality was associated with a delay in virus accumulation , but some Wolbachia-infected flies were tolerant to high titres of DCV . The Wolbachia strains that did protect occurred at comparatively high density within the flies and were most closely related to the D . melanogaster Wolbachia strain wMel . These results indicate that Wolbachia-mediated antiviral protection is not ubiquitous , a finding that is important for understanding the distribution of Wolbachia and virus in natural insect populations .
As obligate intracellular parasites , viruses have intricate associations with their hosts . Many viruses have deleterious effects on their host including virus induced pathology , morbidity and mortality . For this reason a suite of antiviral defence responses have evolved . Some of these responses are conserved across different kingdoms , while others are unique to closely related groups of organisms . For example , viruses that infect insects encounter some host defences that are distinctive to invertebrates , such as the peritrophic matrix . There are a number of motivations for studying antiviral responses in insects . Insects are a useful model for research on innate immune responses , and because of the evolutionary conservation in many of these pathways , this research may lead to an increased understanding of antiviral immunity in mammals ( reviewed in [1] ) . It is also important to understand insect antiviral responses for other reasons . Viruses cause diseases in both pest insect species and beneficial insects . Also insects are involved in the transmission of many viruses that cause serious disease in humans , other animals and plants . Thus there are diverse reasons for wanting to control virus infection in insects and understanding antiviral responses in insects may facilitate strategies to achieve this . The vinegar fly , Drosophila melanogaster , is an appropriate model for the study of antiviral responses . The Drosophila cellular antiviral responses include both the intrinsic RNAi pathway and inducible immune pathways [2]–[6] . In addition to host antiviral defences , D . melanogaster are also protected from RNA viruses when infected by the intracellular bacterium , Wolbachia pipientis [7] , [8] . In D . melanogaster the interaction between Wolbachia and virus has important implications for the outcome of viral infection . Recent studies on antiviral responses in Drosophila have utilised the most pathogenic of the Drosophila viruses , Drosophila C virus ( DCV ) . A member of the Dicistroviridae family , DCV is a natural pathogen of D . melanogaster found in both wild and laboratory fly populations [9] , [10] . Following injection of DCV into the hemocoel of adult D . melanogaster , flies typically die within 4–6 days [11] . In contrast , following injection of DCV into Wolbachia infected flies , the accumulation of infectious DCV particles is delayed and flies live for 12–14 days [7] , [8] . Wolbachia-mediated antiviral protection is not limited to DCV . Wolbachia infection also protects flies from mortality induced by a second member of the Dicistroviridae family Cricket paralysis virus ( CrPV ) and a member of the Nodaviridae family Flock House virus ( FHV ) [7] , [8] . In addition , antiviral protection has been demonstrated in a number of D . melanogaster genetic backgrounds and using closely related Wolbachia strains that naturally occur in D . melanogaster , namely wMelCS and wMelPop [7] , [8] . Wolbachia are predicted to infect from 20–70% of insect species [12]–[14] , which raises the possibility that Wolbachia may potentially influence virus infection across a large number of insect species . Bacteria of the genus Wolbachia are maternally inherited intracellular symbionts , which are best known for their propensity to manipulate host reproductive systems [15] . Wolbachia infect a wide range of arthropods and filarial nematodes and are classified into 7–8 phylogenetic supergroups based on analysis of the sequence of a number of Wolbachia genes ( see [16] and references therein ) . The majority of known Wolbachia strains that infect insect species belong to either supergroup A or B [17] , [18] . The Wolbachia that occur in D . melanogaster are very closely related strains from the Mel clade of supergroup A [19] . It is currently not known whether antiviral protection is mediated by diverse strains of Wolbachia . The fly species , D . simulans is infected by up to six strains of Wolbachia that span across both supergroup A and B [18] , [20] , including three supergroup A strains wAu , wRi and wHa and one supergroup B strain wNo [18] , [20] . Here we tested whether Wolbachia-mediated protection extends to insects other than D . melanogaster and whether each of the Wolbachia strains could protect D . simulans from virus infection . Our results show that some , but not all , of the Wolbachia strains protected naturally infected D . simulans lines from virus-induced mortality .
Wolbachia strains closely related to wMel have previously been shown to protect their natural host D . melanogaster from accumulation of DCV particles and DCV-induced mortality [7] , [8] . To establish whether wMel can protect D . simulans from DCV , we assayed Me29 , a D . simulans line that was transinfected with wMel [21] ( Table 1 ) . Me29 flies infected with wMel and the genetically paired population that had been cured of Wolbachia infection were challenged with DCV and mortality was recorded for 15 days ( Figure 1A ) . For flies both with and without Wolbachia the mortality in PBS injected controls was negligible . All DCV injected wMel-free flies died by 8 days post infection ( dpi ) , with a median survival time of 6 days . In contrast , at 15 dpi about 50% of wMel infected flies remained alive . These results indicate that the presence of wMel mediates a significant decrease in DCV induced mortality in Me29 flies . The accumulation of infectious DCV particles was assayed in Me29 flies with and without wMel . The titre of infectious virus in homogenates from flies collected 2 dpi was significantly different in flies with and without wMel ( p<0 . 002; Figure 1B ) . The titre of virus in flies without Wolbachia was estimated to be about 2600-fold greater than in Me29 flies infected with wMel . By 10 dpi there were no surviving Wolbachia-free flies and the virus titre in the surviving wMel infected flies had increased to a level similar to that of Wolbachia-free flies at 2 dpi . This indicates that the presence of wMel in Me29 flies delays rather than prevents DCV accumulation . D . simulans populations are naturally infected with a range of Wolbachia strains . To analyse whether diverse strains could protect from DCV induced mortality we assayed four D . simulans lines CO , DSR , DSH and N7NO , which are naturally infected with wAu , wRi , wHa and wNo , respectively ( Table 1 ) . Each of the four fly lines was treated with tetracycline to produce a genetically paired line without Wolbachia infection . Flies with and without Wolbachia were challenged by injection with DCV or mock infected with PBS ( Figure 2 ) . In all cases less than 10% mortality occurred in the mock-infected flies , indicating that in the absence of virus fly survival was stable over the course of the experiments . The CO flies without Wolbachia had a median survival time of 8 days following DCV injection ( Figure 2A ) . Strikingly , the wAu-infected CO flies survived DCV infection; more than 90% were alive when the experiment was terminated at 30 dpi . The wRi-infected DSR flies had significantly better survival ( p<0 . 0001 ) than Wolbachia-free DSR flies ( Figure 2B ) . The median survival times following DCV infection were 14 dpi as compared to 6 dpi for flies with and without wRi , respectively . Thus presence of either wAu or wRi in D . simulans can mitigate DCV-induced mortality . Not all Wolbachia strains protected flies from DCV induced mortality . The median survival time of DSH and N7NO flies challenged with DCV was 4 days regardless of Wolbachia infection status for fly lines infected by wHa or wNo , respectively ( Figure 2C and 2D ) . While there was a small but statistically significant ( p = 0 . 001 ) difference between the survival curves for the DSH flies with and without wHa infection for the representative experiment shown in Figure 2C , a significant difference was evident in only 2 out of 4 experiments replicated on independent cohorts of flies ( data not shown ) . Taken together , the minor difference in survival and non-reproducible nature of the result suggests that it is unlikely that this difference is biologically relevant , and as such we interpret the results as indicating that there is no protection against DCV induced mortality in the DSH flies infected with wHa . There was no difference between the survival curves of N7NO flies with and without wNo infection ( p = 0 . 7 ) . To investigate whether protection would be evident for these lines challenged with reduced amounts of virus we decreased the concentration of DCV injected by 10- or 100-fold . Even at these lower doses of virus no Wolbachia-mediated antiviral protection was observed in DSH and N7NO flies ( data not shown ) . DCV accumulation was assayed in each D . simulans line in the presence or absence of Wolbachia ( Figure 3 ) . DCV infected flies were assayed at 2 dpi and the DCV titre was compared for each fly line with and without Wolbachia infection . The average DCV titre was approximately 800-fold lower in CO flies infected with wAu compared to paired Wolbachia-free flies , and an unpaired t test showed this to be a significant difference ( p<0 . 05; Figure 3A ) . Interestingly , although wAu infected flies survived DCV infection ( Figure 2A ) , virus continued to accumulate beyond 2 dpi and high titres of DCV were observed in wAu-infected flies harvested at both 10 and 30 dpi ( Figure 3A ) . This shows that these flies did not clear the virus infection . The titre of DCV was similar when comparing flies with and without Wolbachia at 2 dpi for each of the three other fly lines assayed ( Figure 3B–D ) . Having identified that some but not all Wolbachia strains mediate protection against DCV in the D . simulans lines tested , we next investigated whether antiviral protection was consistent across different viruses . Flies with and without Wolbachia were challenged by injection with FHV or mock infected with PBS ( Figure 4 ) . In all cases mortality in the mock-infected control flies was negligible . The CO flies without Wolbachia infection reached 100% mortality within 7 days of injection with FHV ( Figure 4A ) . Similar to challenge with DCV the wAu-infected flies survived FHV infection; more than 90% were alive when the experiment was terminated at 24 dpi . The wRi-infected DSR flies had significantly better survival ( p<0 . 0001 ) than Wolbachia-free DSR flies ( Figure 4B ) . The median survival times or DSR flies challenged with FHV were 10 days as compared to 7 days with and without wRi , respectively . Thus median time to death was reduced in both DCV and FHV infections for wRi-infected DSR flies . No virus-induced mortality was observed in wAu-infected CO flies for either virus . Not all of the fly lines were protected from FHV-induced mortality by Wolbachia infection . The median survival time of DSH flies challenged with FHV was 6 days regardless of the presence or absence of wHa ( Figure 4C ) and there was no significant difference in the survival curves ( p = 0 . 4 ) . For the N7NO line there was no difference between the survival curves with and without wNo infection ( p = 0 . 5; Figure 4D ) . To investigate whether virus protection correlated with the density of the Wolbachia in the fly lines , we utilized quantitative PCR to determine Wolbachia density from pools of 5 male flies from each fly line . Estimates of abundance for a single copy Wolbachia gene were determined and then normalized against abundance of a single copy host gene to determine relative abundance of Wolbachia ( Figure 5 ) . The three Wolbachia strains ( wMel , wRi and wAu ) that gave strong antiviral protection in the D . simulans lines , were significantly more abundant in these flies than the strains that gave no protection ( wHa and wNo ) .
Many insect species are infected with Wolbachia , raising the possibility that Wolbachia-mediated antiviral protection could be a widespread phenomenon . Wolbachia strains vary both between host species and within a host species ( for example [20] ) . Naturally occurring Wolbachia strains in D . melanogaster ubiquitously protect against DCV [7] , [8] , however these strains are very closely related [19] . Wolbachia is maternally inherited and therefore has a close association with its host . Using D . simulans fly lines that are naturally infected by different Wolbachia strains we showed that some strains did not mitigate virus-induced mortality . Strains wAu and wRi protected the CO and DSH host flies respectively . In contrast , neither wHa nor wNo protected their host lines from DCV induced mortality . Phylogenetic analysis indicates that the D . simulans Wolbachia strains wAu and wRi are most similar to wMel . Whereas of the phylogenetic supergroup A strains , wHa is the most divergent to wMel , and wNo belongs to supergroup B [18] , [20] . This may suggest that there is a Wolbachia feature involved in antiviral protection , which is conserved among strains more closely related to wMel . With the exception of the Me29 flies infected by wMel , natural host-Wolbachia combinations were used . The D . simulans Wolbachia strains are known to be associated with different mitochondrial haplotypes [22] and we did not control for host nuclear genetic background which can have an impact on virus infection [7] . As a consequence it is not possible to rule out that intrinsic variability in susceptibility to virus that is linked to the host background has an influence on the outcome of Wolbachia-mediated protection in our experiments . Indeed there is variation in the time to death of Wolbachia-free D . simulans lines used in this study when challenged with DCV ( Figure 2 ) , although interestingly these same Wolbachia-free lines showed similar time to death when challenged with FHV ( Figure 4 ) . Antiviral protection was observed in both D . melanogaster and D . simulans when infected with wMel . This indicates that antiviral protection mediated by Wolbachia can be transferred between different host species . Since protection against DCV was not seen in all the fly lines infected with the Wolbachia strains , we tested whether there is specificity in protection against different viruses . Infection of D . melanogaster by Wolbachia protected the flies from all RNA viruses tested [7] , [8] . Although each of these viruses was a non-enveloped , positive sense RNA virus , the viruses come from a broad spectrum of virus families . Compared to DCV the most divergent of these viruses is FHV . DCV is a member of the Dicistroviridae family and has a single genomic RNA that is not capped but is polyadenylated [9] . The genome is a bicistronic mRNA from which the structural and non-structural polyproteins are translated via internal ribosome entry sites [23]–[25] . DCV RNA replication occurs on membranes derived from the golgi [26] . In contrast , the nodavirus FHV genome comprises two mRNA sense RNAs which are capped but not polyadenylated and a third subgenomic RNA is synthesised during replication [27] . FHV genome replication occurs on mitochondrial membranes [28] , [29] . Interestingly , although DCV and FHV have distinct infection cycles the same Wolbachia strains protected D . simulans lines from both DCV and FHV induced mortality . This suggests that the mechanism of protection from virus-induced mortality may be common across diverse viruses , although it is not currently known what the mechanism of viral pathogenesis is in flies infected with either DCV or FHV . It remains to be seen whether the same host-Wolbachia combinations that do or do not protect against DCV and FHV have similar outcomes for other viruses , or indeed other types of pathogens . Concurrent with protection from virus induced mortality in D . melanogaster was a delay in accumulation of DCV [8] . Here a similar result was seen with wMel protection in D . simulans , the amount of infectious virus accumulated 2 dpi was significantly lower in Wolbachia infected flies . By 10 dpi the DCV titre in Wolbachia infected flies was similar to the day 2 titre for Wolbachia-free flies . It would be tempting to speculate that the resistance to DCV accumulation protects the flies from DCV induced mortality , however , the results observed with the D . simulans Wolbachia strains complicate this interpretation . The CO flies infected with wAu survived DCV infection beyond 30 dpi , whereas the Wolbachia-free flies were clearly susceptible to DCV-induced mortality . wAu infected flies had by 10 dpi accumulated high titres of DCV and the virus titre remained high at 30 dpi . This shows that wAu infected flies were tolerant of DCV infection , that is the virus accumulated but did not cause mortality [30] . Interestingly , although wRi-infected DSR flies were protected from DCV induced mortality , at 2 dpi there was no difference in virus accumulation in flies with and without wRi . We cannot rule out that accumulation was delayed in wRi-infected flies earlier than 2 dpi . Taken together our results indicate that Wolbachia-mediated antiviral protection could arise in flies in two ways . Wolbachia can interfere with the virus infection cycle to delay virus accumulation , that is , it can induce resistance to virus infection in the host . In addition Wolbachia infection can protect flies from the pathogenesis associated with virus infection , that is , it can increase host tolerance to virus infection . The processes or mechanisms involved in resistance and tolerance may be the same , independent or overlap . Our results show that Wolbachia strains can induce both resistance and tolerance to DCV infection , but importantly prolonged resistance is not a requirement for protection against DCV-induced mortality . These results are consistent with those reported for FHV in Wolbachia infected D . melanogaster , where there was no difference in FHV accumulation 6 dpi but Wolbachia infection protected flies from FHV induced mortality [7] . The strains of Wolbachia that mediate antiviral protection were anticipated to be present at higher density in infected flies [31] , [32] . We confirmed the density of Wolbachia in the particular fly lines used in this study correlated with protection . The density of Wolbachia was assayed in whole flies as previous assays have shown that in addition to reproductive tissues somatic tissues are commonly infected with Wolbachia [33] , [34] . Further experiments controlling the density of a single strain are required to determine if high Wolbachia density is a pre-requisite for antiviral protection . The mechanisms or processes by which Wolbachia protects the host from virus are not yet understood . The correlation of high bacterial density of the strains that protect the host suggests that Wolbachia density may be important for antiviral protection . Potentially protection may require a threshold of Wolbachia density to be exceeded , which would be consistent with protection being a consequence of competition between the two intracellular microbes for limited host resources . Antiviral protection may also be dependent on the distribution of Wolbachia between tissue or cell types . Wolbachia have been identified in a range of somatic and reproductive tissues in insects and are known to display variable tissue tropism depending on infecting strain and host combination [33]–[35] . Late in infection DCV is widely distributed in Drosophila tissues including both reproductive and somatic tissues [36]–[38] , giving abundant opportunity for overlap with Wolbachia distribution . However , little is known about the spread of virus from the initial infection site or if replication of the virus is equivalent in all of the susceptible tissues . It is possible that there are tissues or cell types that are critical to virus replication or pathogenesis and that Wolbachia-mediated protection occurs by exclusion or regulation of virus in these tissues . In addition , if particular tissues are critical for pathogenesis , tolerance may be a result of protection of those tissues . The relatively close phylogenetic relationships of the strains that do confer antiviral protection compared to non-protective strains , suggests that other features of the Wolbachia strains could determine the outcome of virus infection . Protection via both resistance and tolerance could be induced by modulation of host antiviral responses by Wolbachia . For example , proteins from the ankyrin family , which can play a role in innate immune pathways , vary considerably both in number and sequence between Wolbachia strains [39]–[42] . Interestingly defence against bacterial infection in flies via the melanisation response has been shown to involve both resistance and tolerance effects [43] . Wolbachia are able to rapidly invade host populations and are often maintained at high prevalence in these populations [44] . In many cases this is achieved at least in part by Wolbachia manipulation of host reproductive systems to increase the prevalence of infected individuals in the host population . For example the Wolbachia strains wRi , wHa and wNo used in this study induce cytoplasmic incompatibility in D . simulans , however wAu does not manipulate host reproductive systems [45]–[48] . In the absence of strong reproductive parasitism , theory predicts that to be maintained in a host population Wolbachia must provide a fitness advantage to the female host ( reviewed in [49] , [50] ) . Wolbachia-mediated protection from viruses and other pathogens [51] may confer this fitness advantage . It is therefore likely that the interactions between Wolbachia and viruses such as DCV impact on the distribution of both microbes in insect populations .
Plaque purified DCV isolate EB [11] and FHV [52] were propagated and purified from DL2 cells [53] . DL2 cells were maintained in Schneider's media supplemented with 10% FBS , 1 x glutamine and 1 x penstrep ( Invitrogen ) at 27 . 5°C . Cells grown in 75 cm2 flasks were infected with either DCV or FHV at a low multiplicity of infection ( <1 ) and harvested at 4–5 dpi . Cells were lysed by two rounds of freeze-thawing and cell debris removed by centrifugation at 5 , 000 rpm for 5 min . The virus was purified from the supernatant by pelleting through a 6 ml 10% sucrose cushion at 27 , 000 rpm at 12°C for 3 hours in a SW28 swing bucket rotor ( Beckman ) . The resuspended virus was layered onto a continuous 10–40% w/v sucrose gradient and centrifuged at 27 , 000 rpm at 12°C for 3 hours in a SW41 swing bucket rotor ( Beckman ) . The virus-containing fractions were harvested , diluted in 50 mM Tris pH 7 . 4 and virus was pelleted by centrifugation at 27 , 000 rpm , 12°C for 3 hours . The virus was resuspended in 50 mM Tris pH 7 . 4 at 4°C overnight , aliquoted and stored at −20°C . The concentration of tissue culture infectious units ( IU ) of each virus preparation was determined by replicate TCID50 analysis on two separate frozen aliquots , as previously described [8] . All Wolbachia infected fly lines were obtained from the culture collection in the O'Neill lab and were maintained on standard cornmeal diet at a constant temperature of 25°C with a 12-hour light/dark cycle . The D . simulans fly line Me29 is infected with wMel . The wMel infection was established by injection of Wolbachia containing cytoplasm from D . melanogaster Wien 5 embryos into D . simulans NHaTC embryos [21] . The other D . simulans lines are naturally infected with Wolbachia strains as previously described and are listed in Table 1 [45]–[47] , [54] . Virus-free populations of each of the Wolbachia containing fly line were prepared essentially as previously described [55] . Briefly , flies were aged for at least 20 days , transferred to fresh media ( supplemented with dry yeast ) and allowed to lay eggs for up to 16 hours . The eggs were collected from the surface of the media and treated for 4 minutes in 1 . 7% ( w/v ) sodium hypochlorite solution to remove the chorion . After treatment the eggs were thoroughly rinsed with water , transferred to moist filter paper and placed on fresh virus-free media . Virus-free flies were maintained separately from untreated stocks . To generate fly lines free of Wolbachia each virus-free Wolbachia infected fly line was treated with 0 . 03% tetracycline [45] . Following the tetracycline treatment flies were held for more than four generations to recover before being used for experiments . Drosophila were infected with DCV , FHV or mock infected by microinjection of virus or PBS into the upper lateral part of the abdomen . Samples were injected using needles pulled from borosilicate glass capillaries and a pulse pressure micro-injector into 4–7 day old male flies that were anaesthetised with carbon dioxide . For each fly line assayed , three groups of 15 flies were injected with virus and one group of 15 flies were injected with PBS . After injection flies were maintained in vials at a constant temperature of 25°C with a 12 h light/dark cycle and mortality was recorded daily . Mortality that occurred within one day of injection was deemed to be due to injury . Each experiment was replicated using independent cohorts of flies . Survival curves were compared using Kaplan-Meier analysis and log-rank statistics reported ( GraphPad Prism ) . For each assay described in this paper a fresh aliquot of either DCV or FHV was defrosted and diluted to 1×108 IU/ml before use . The accumulation of infectious DCV particles in both Wolbachia infected and uninfected flies was measured . For each of the five fly lines , groups of flies with and without Wolbachia were injected with DCV as for survival bioassays . At designated times post injection , two pools of four live DCV injected flies were collected and frozen at −20°C . Flies from all Wolbachia infected and uninfected fly lines were collected at 2 dpi . For Me29 , DSR and CO flies infected with Wolbachia samples were also collected at 10 days post injection; for N7NO and DSH containing Wolbachia and all tet-treated lines there were not enough live flies remaining at 10 days for collection . For CO-Wolbachia flies an additional collection was included at 30 dpi . Each pool of four flies was homogenised in 100 µl of PBS with two 3 mm beads ( Sigma-Aldrich ) using a Mini BeadBeater-96 ( Biospec Products ) for 60 seconds . The homogenates were clarified by centrifuging at 14 K for 8 minutes . The virus–containing supernatant was aliquoted and stored at −20°C . Virus titre was determined using the TCID50 assay as previously described [8] . The two replicates for each fly population were assayed on different days to control for between-day variation in TCID50 assays . Statistical analysis of the data was done using unpaired t tests to compare the geometric means of the duplicate samples between flies of each line with and without Wolbachia at 2 dpi ( GraphPad Prism ) . For each fly line 200 eggs were collected and incubated on fresh food with a constant temperature of 25°C for 10 days . Freshly emerged flies were collected for 8 hours , aged to 4 days old and then five male flies from a single collection were pooled . For each fly line a total of 10 pools of flies were collected from independent bottles and the DNA extracted using a DNeasy Blood and Tissue Kit as per manufacturers instructions ( Qiagen ) . The relative ratio of Wolbachia to fly genomic DNA was determined by quantitative PCR . Each 10 µl qPCR reaction included 5 µL of Sybr Green qPCR Supermix-UDG ( Invitrogen ) , 1 µL of DNA template and 1 µM each of the forward and reverse primers . Primers for Wolbachia were designed from an alignment of the sequence of the WSP genes from all five Wolbachia strains ( wspFQALL 5′ GCATTTGGTTAYAAAATGGACGA 3′ and wspRQALL 5′ GGAGTGATAGGCATATCTTCAAT 3′ ) and for the host gene RPS17 ( Dmel . rps17F 5′CACTCCCAGGTGCGTGGTAT 3′ and Dmel . rps17R 5′GGAGACGGCCGGGACGTAGT 3′ ) . Reactions were done in duplicate in a Rotor-gene thermal cycler ( Corbett Life Sciences ) with the following conditions: one cycle of 50°C 2 min , 95°C 2 min , followed by 40 cycles of 95°C 5 sec , 60°C 5 sec , 72°C 10 sec . A third technical replicate was done where necessary and DNA extracted from flies without Wolbachia was used as a negative control . Ratios were calculated in Qgene and statistical analysis included Mann-Whitney t test to compare differences of the means . EF423761 wsp wRi; DQ235409 wsp wAu; AF020074 wsp wNo; AF020073 wsp wHa; NM_079278 RPS17 . | Many human , animal and plant viruses are transmitted between hosts by insect vectors . Understanding the processes that control virus infection in insects may facilitate strategies that aim to control the spread of important viral pathogens . Infection of the model insect Drosophila with the endosymbiotic bacteria Wolbachia can significantly affect the outcome of infection with pathogenic viruses . Wolbachia is widespread in insects , so here we tested the generality of antiviral protection across diverse strains of the bacteria . We show that some , but not all , strains of Wolbachia protect flies from pathogenic viruses . These results have implications for proposed strategies utilising Wolbachia to control the spread of insect-transmitted viral diseases , such as dengue . |
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Var genes encode the major surface antigen ( PfEMP1 ) of the blood stages of the human malaria parasite Plasmodium falciparum . Differential expression of up to 60 diverse var genes in each parasite genome underlies immune evasion . We compared the diversity of the DBLα domain of var genes sampled from 30 parasite isolates from a malaria endemic area of Papua New Guinea ( PNG ) and 59 from widespread geographic origins ( global ) . Overall , we obtained over 8 , 000 quality-controlled DBLα sequences . Within our sampling frame , the global population had a total of 895 distinct DBLα “types” and negligible overlap among repertoires . This indicated that var gene diversity on a global scale is so immense that many genomes would need to be sequenced to capture its true extent . In contrast , we found a much lower diversity in PNG of 185 DBLα types , with an average of approximately 7% overlap among repertoires . While we identify marked geographic structuring , nearly 40% of types identified in PNG were also found in samples from different countries showing a cosmopolitan distribution for much of the diversity . We also present evidence to suggest that recombination plays a key role in maintaining the unprecedented levels of polymorphism found in these immune evasion genes . This population genomic framework provides a cost effective molecular epidemiological tool to rapidly explore the geographic diversity of var genes .
Quantifying the diversity of major surface antigens underlying immune evasion of HIV 1 and 2 and Influenza A has been central to characterizing the transmission dynamics of these important human pathogens . In addition , documentation of variation data has provided a basis for the development of candidate vaccine targets [1 , 2] . Surprisingly , in-depth molecular epidemiological sampling and population genomic analyses of the var genes encoding the major blood stage surface antigen of the malaria parasite , Plasmodium falciparum erythrocyte membrane protein 1 ( PfEMP1 ) , has not been done . This is largely due to the inherent difficulties in the population genomic analysis of highly diverse multigene families . We set out to develop and evaluate a rapid , molecular epidemiological population genomic framework to investigate var gene diversity in natural parasite populations , due to the importance of these genes to the biology of P . falciparum . To achieve chronic infection , malaria parasites evade the host immune response by switching PfEMP1 isoforms through differential expression of members of the var multigene family [3–5] . PfEMP1 is expressed on the surface of blood stage parasites known as trophozoites [6] and the transmission ( early gametocyte ) stages [7] . Parasite adhesion occurs in the deep vasculature of host tissues by binding of PfEMP1 to host endothelial cell receptors . Some PfEMP1-adhesion interactions are proposed to lead to severe disease manifestations such as cerebral and placental malaria ( reviewed in [8] ) . Variant specific anti-PfEMP1 antibodies are believed to contribute to the regulation of parasite density in a manner that decreases the incidence of clinical disease [9–13] . This immunity may reduce the duration of infection in a variant-specific manner to drive the dynamics of multiple infections in semi-immune children [14] and induced infections in humans [15] . Immunity to PfEMP1 can thereby influence transmission by reducing persistence . It may also reduce transmission by regulating the density of asexual blood stages with potential to become transmission stages and by directly targeting early gametocytes to prevent the maturation of transmission stages [16] . Consequently , diversity of PfEMP1 or var genes is able to promote transmission success by immune evasion . Describing the diversity of var genes presents a more complex problem than assessing the diversity of the single copy major surface antigen genes of HIV and Influenza A . Individual P . falciparum genomes have repertoires of var genes that can recombine with other repertoires during the obligatory sexual phase of the life cycle in the mosquito [17–19] . There is also circumstantial evidence for ectopic recombination among var genes within the same genome , possibly during both meiosis and mitosis [20–22] . Therefore , there is enormous potential to generate diversity , even among closely related genomes . Var genes are large ( 5–12 kb ) and complex [23–26] , encoding variable numbers and classes of the adhesion domains , Duffy binding like ( DBL: α , β , δ , ɛ , and γ classes ) and cysteine interdomain rich ( CIDR: α , β , and γ classes ) [26] . Both size and complexity preclude population genomic analysis of the full var gene sequences . The DBLα encoding domain was used previously as a marker of var genes in investigations of diversity [21 , 22 , 27–30] and expression [31–33] . The small size ( ∼1 kb ) and ubiquitous presence of DBLα among var genes [24–26 , 34] make this domain a suitable population genomic marker . Previous analyses of var gene diversity have examined DBLα domain sequences from the var gene repertoires of allopatric ( distantly related ) [22 , 27 , 30] or just a few sympatric ( closely related ) [28 , 29] isolates . These studies have established that any pair of DBLα sequences from the same genome were on average as diverse as any pair from two different genomes , with a range of 45%–100% amino acid identity [3 , 22 , 27 , 28] . This has made it impossible to identify var gene orthologs among genomes . Limited overlap ( shared DBLα sequences ) among var repertoires from sympatric isolates has also been reported [27–29] , suggesting that many genomes must be sampled to see the extent of diversity of these genes in natural populations . Given the importance of var genes to transmission , a systematic sequencing effort and population genomic analysis is needed to examine var gene diversity in sufficient depth to estimate levels of antigenic diversity within natural populations . A high-throughput population genomic framework was developed to address sampling issues specific to the molecular epidemiological analysis of diverse multigene families . This allowed the random sampling of the var gene repertoires of culture-adapted and field isolates of P . falciparum by sequencing DBLα domains as population genomic markers ( Figure 1 ) . Var genes were sampled from a “global” collection of isolates , including clones 3D7 and HB3 used in genome sequencing projects ( [34]; D . Wirth , personal communication ) . DBLα sequences from these isolates were used to validate the framework . The “global” DBLα sequences were combined with available data from previous studies and compared to that obtained from a local population of Papua New Guinea ( PNG ) . The results show immense levels of diversity among the var genes with strong evidence of geographic structuring of variation . We demonstrate patterns of similarity among sequences that suggest the widespread action of recombination in creating and maintaining diversity .
We tested a population genomic framework ( Figures S1–S4; Text S1 ) to sample and analyze the var gene sequences of 25 cloned P . falciparum lines/isolates from widespread geographic origins ( global; see Materials and Methods ) . This gave 4 , 754 reads ( median = 161 . 5; range = 43–311 per isolate ) of high quality DBLα sequences , which after removing redundancy , resulted in 608 consensus sequences ( median = 20; range = 10–53 per isolate ) , with an average of seven times coverage . The GenBank accession numbers for these sequences can be found in Table S1 . Artifacts were limited due to the multiple reads contributing to each DBLα sequence and by the use of a 96% DNA sequence identity cut-off ( grouping reads from a single isolate with less than 4% sequence differences into a single consensus ) . To calculate the number of distinct DBLα sequences among isolates , individual repertoires were compared with each other , again using an arbitrary 96% DNA sequence identity cut-off . This identified shared DBLα sequences ( i . e . , overlap ) among var repertoires . It also defined all of the distinct DBLα sequences in the global population sample . We named these distinct sequences “DBLα types” rather than alleles because the possibility of ectopic recombination means that the ancestry of a particular var gene sequence will be unlikely to be linked to physical location . It should be noted that by defining types in this way , synonymous and nonsynonymous mutations were treated equally . We identified 534 distinct DBLα types from the 608 sequences obtained from the global isolates . This large number of types confirmed the wide-ranging amplification capacity of the degenerate primers ( see Materials and Methods , Text S2 ) . Some var gene types were shared among isolates as shown by the fact that the number of DBLα sequences was greater than the number of DBLα types in the population . When 480 DBLα sequences ( 404 types ) obtained from the GenBank database ( see Materials and Methods ) were combined with these data , 1 , 088 DBLα sequences and 895 DBLα types were defined . We did not have information for the number of reads contributing to this additional data . Local sampling of 30 P . falciparum isolates from the population of Amele , PNG ( see Materials and Methods ) gave 3 , 080 reads ( median = 85 . 5; range = 8–238 per isolate ) . Analysis of the sequence data resulted in 460 DBLα sequences ( median = 15; range = 2–30 per isolate ) with each sequence having an average of 6 . 7 times coverage . The GenBank accession numbers for these sequences can be found in Table S1 . We defined 185 DBLα types from the sequences obtained . The ratio of sequences to types for the global sampling ( ratio = 1 . 21 ) was much less than that of Amele local sampling ( ratio = 2 . 5 ) , demonstrating greater overlap amongst var gene repertoires from the Amele isolates than those from the global collection . This overlap is defined more precisely below . An important question in malaria population genomics is “how many isolates need to be sampled to find the majority of var gene diversity in a population ? ” To answer this question , we applied an ecological method to the data . We measured how the rate of DBLα type discovery changed with sample size in a manner similar to a species richness curve [35] . This method was previously used to measure heterozygosity in the human blood group antigens [36] . The number of DBLα types was plotted against the number of DBLα sequences as the var gene repertoire from each isolate was sampled . For sampling of global isolates , the curve did not plateau even though we sampled more than 1 , 000 DBLα sequences from 59 isolates ( the 25 global isolates we sampled plus 34 var repertoires available from GenBank including the entire 3D7 var repertoire ) , showing that the majority of the var gene diversity in the global P . falciparum population was not defined ( Figure 1 ) . When we plotted the cumulative diversity for the Amele local population , the curve began to deviate from that of the global sample after sampling just 50 DBLα sequences ( approximately three isolates ) and tended toward a plateau after sampling 460 DBLα sequences ( 30 isolates ) . Thus , while var gene diversity was immense on a global scale it was restricted in this local population of PNG . The shape of the curve suggested that a large proportion ( around 50%–60% ) of common types were sampled in the local population of Amele , although this may not represent all of the diversity in the Madang region or in PNG . The higher ratio of DBLα sequences to DBLα types and the flattening of the cumulative curve for Amele in comparison to that for global indicated a greater degree of var repertoire overlap among Amele isolates than among global isolates . To quantify this overlap in the two populations , we considered the proportion of those types that were found in both of a pair of isolates; a statistic designated the pairwise type sharing ( PTS ) ( see Materials and Methods ) . We found that average PTS among Amele isolates ( or within the population ) was significantly greater than for global isolates ( Figures 2 and S5 ) . The PTS statistic estimated that 7% of var genes were shared among Amele isolates , but that a negligible number of var genes were shared among global isolates . When the two populations were compared , again the PTS was negligible ( Figure 2 ) . Note that we removed other PNG isolates from the global population ( see Materials and Methods ) for this analysis as these showed significantly higher PTS values with Amele isolates ( unpublished data ) . The higher degree of overlap in Amele in relation to that among global isolates demonstrated the presence of geographic population structure . In particular , there was higher overlap among the contemporary Amele isolates and among those isolates and PNG isolates , also from Amele , but collected at different time periods ( see Materials and Methods ) . Some of this will be due to the presence of a few high-frequency types in Amele ( Table S2 ) . To test whether there was any geographic population structure , we examined the distribution of Amele types in the global population sample . The majority of the defined Amele types ( 61 . 6% ) was unique to PNG . The remaining 71 types were found in Africa ( 24 . 8% ) , Asia ( 3 . 2% ) , and Latin America ( 3 . 8% ) ; two were found in all malaria endemic regions and ten types were found in either Africa and Asia ( 2 . 2% ) ; Africa and Latin America ( 2 . 7% ) ; or Asia and Latin America ( 0 . 5% ) ( Table S2 ) . The geographic restriction of Amele types to PNG demonstrates the existence of geographic population structure . Nevertheless , a substantial fraction of types has a cosmopolitan distribution indicating that different geographic regions do not have completely different repertoires of var genes . Having demonstrated extremely high levels of diversity among the var gene sequences , we wanted to explore potential structuring within the data . For example , do sequences broadly fall into a few groups ? Or is every sequence equally different from all others ? A natural approach to analyzing such structure within the data is to construct a phylogenetic tree for the sequences from a multiple-sequence alignment of the translated DNA sequences ( Figure S6 ) . The phylogeny estimated from the aligned DBLα types from Amele showed mostly distant relationships with long external branches and short , unresolved internal branches ( Figure 3A ) . This “star-like” phylogeny suggests that the sequences are so diverse that little structuring to the variation is apparent . One evolutionary force known to result in “star-like” trees is recombination [37] , suggesting that frequent exchange between different var gene copies may be occurring . This tree shape may also result from a rapidly expanding population or other forms of natural selection [37] . However , it is also possible that the “star-like” quality may simply reflect a low quality multiple sequence alignment ( Figure S6 ) . This showed it was difficult to assess the relatedness of var gene sequences using multiple sequence alignments . Alternatively , pairwise analyses will maximize the quality of an alignment and highlight the most highly related fragments of a pair . Quality scores from these alignments are better estimates of the relationship among potentially recombined sequences because they are based on the percent similarity as well as the length of the alignment ( i . e . , the partial alignment of a pair of recombined sequences will have a higher score than a pair of sequences with an equal number of polymorphisms across their entire length; see Materials and Methods ) . The distribution of the quality scores can then be used to identify potential clustering of even weakly related sequences . Therefore , we also considered the distribution of the quality scores from pairwise sequence alignments . Pairwise alignments of the translated DBLα sequences ( i . e . , prior to type definition ) showed an average amino acid similarity of 66% ( range = 22%–100% ) . The distribution of quality scores among Amele DBLα sequences showed a bimodal distribution with a small cluster of high scoring alignments to the right ( Figure 3B ) . These clusters corresponded to the sequences that were shared among isolates ( i . e . , types ) and those that had a high degree of similarity . The majority of sequences , however , show more distant relationships , resulting in a distribution of quality scores that appears approximately Normal ( Figure 3B ) . One consequence of recombination is that different regions of pairs of sequences will be related to each other to different degrees , and the more recombination , the stronger the homogenizing force . High levels of recombination , therefore , might “average” out the quality score across the sequence and result in the approximately Normal distribution , while low levels of recombination are expected to result in more “jagged” distributions . Overall , these analyses suggested that recombination is a major force in the generation and maintenance of variation in the var genes . Given the antigenic nature of the var genes , we suggest that recombination is an important source of novelty for the P . falciparum var gene repertoire . However , the evidence presented here is only circumstantial , and the extreme diversity of the sequences makes application of existing population genetic approaches to detecting recombination problematic . We are currently developing novel population genetic approaches to learn about genetic exchange among the var gene sequences . To determine whether previously observed var gene structural groups found in the 3D7 genome [24 , 25] were represented within the Amele sample , we used a phylogenetic approach to identify var gene groupings among translated DBLα types obtained from the Amele population . This was done by observing the Amele types that clustered with 3D7 DBLα sequences upon which the groupings were partly based [24 , 25] . We observed that Amele DBLα types clustered significantly with all six groups identified for the DBLαCIDRα region of var genes [25] ( Figure S7 ) . Therefore , sequences with high levels of similarity to the DBLα domains of var gene structural groups previously defined in the 3D7 genome [25] were present in the Amele population . It should be noted that the structural groupings were based upon the DBLα plus CIDRα head-structure sequences [25] , whereas we compared only DBLα sequences; therefore , the only group that formed a significant cluster was group A ( Figures 3A and S7 ) . Recently , an alternative grouping of var genes has been published based on the number of cysteine residues and short amino acid motifs within the DBLα domain [31] . We found all six of these alternative groups within the Amele population ( Table S2 ) . Therefore , the degenerate PCR primers amplified a broad range of DBLα sequences from this natural population . These results demonstrate that the functional specialization and restricted evolution of var gene groups suggested from analysis of a single genome [24] and African clinical isolates [31] were also observed in a random sample of a natural population of P . falciparum from PNG . Serological analyses have predicted that the var genes expressed during severe disease are conserved among clinical parasite isolates [38] . Recent investigations have shown group A var genes to be associated with the rosetting phenotype and severe disease [31 , 39–41] . Although the most conserved ( i . e . , high prevalence ) var genes in the Amele population were not restricted to a particular var gene group , at least four group A var genes were found in more than 20% of isolates ( Table S2 ) . These are clearly of interest as candidate vaccine targets . The var1csa gene , previously found to have a global distribution [42–44] , was detected in our dataset . Our analyses confirmed that var1csa had a high prevalence globally , but this gene was rare among Amele isolates ( Table S2 ) . This further demonstrates geographic differences among the two populations .
While var gene sequences have been extensively explored in terms of conserved features associated with adhesion [24–26 , 31 , 43 , 45–47] , we have examined the primary sequence diversity because of its role in evasion of host immunity and consequent P . falciparum transmission . We present a population genomic framework to randomly sample the diversity of var genes by sequencing the ubiquitous marker , DBLα , in natural P . falciparum populations . The framework can be considered a molecular epidemiological tool for malaria with the specific application to diverse multigene families . It defines the diversity and distribution of var genes in natural populations . The framework could also be applicable to other diverse systems such as additional var gene domains ( e . g . , DBLα , CIDR ) ; other multigene families encoding putative malaria surface antigens ( e . g . , vir genes in Plasmodium vivax ) ; and the immune evasion genes of other pathogens ( e . g . , variant surface glycoprotein [vsg] genes of Trypanosoma brucei ) . Importantly , the cumulative diversity curve addressed the issue of incomplete sampling of individual repertoires by defining the number of isolates that were needed to see the majority of diversity in a local population ( i . e . , 30 isolates for Amele ) . If multiple infections are prevalent , which is true for many malaria populations [48–50] , the extent of var gene diversity for the population could still be defined by plotting the cumulative curve . Analysis of var gene diversity on a global and local scale using the above-described framework showed very different patterns of diversity . We were unable to sample all of the diversity in the global population even though we compared over 1 , 000 DBLα sequences . The global range of var gene diversity is likely to be so large that it would take an enormous number of isolates to observe its full extent . When var genes were sampled from the local population of Amele , a region less than 10 km in diameter in Madang Province , PNG , the majority of var gene diversity was defined . Whether this is a representation of the var gene diversity of Madang , or PNG in general , remains to be determined by further spatial sampling within PNG . The variation between populations indicates important differences in their evolutionary history . For example , the presence of high frequency types in Amele might indicate the effect of selective sweeps in parts of the genome that led to the fixation of specific var types . In addition , reduced population size , population bottlenecks , and lower levels of multiple infection may all potentially have played a role in reducing var gene repertoire diversity within PNG . Unfortunately , it is only by obtaining full genomic sequences for multiple isolates for each locality that the different hypotheses can be compared . Nevertheless , our findings have important implications for the understanding of local immunity in different geographic localities . Analysis of relationships among the DBLα types found in Amele showed that P . falciparum has unprecedented levels of genetic diversity of a major surface antigen on a small geographic scale . This finding is particularly striking when compared to data on other prevalent human pathogens sampled locally and globally . The linear phylogenies of ha and env genes of Influenza A and HIV , respectively , are highly structured such that ancestral sequences can be traced even among isolates from distant locations [1 , 2] , whereas the DBLα types from Amele were so diverse that the phylogeny showed only distant relationships . We have sequenced only a 500-bp fragment ( DBLα ) as a marker of var genes ( >5 kb ) in a limited number of global isolates and in one local population . Thus , we have described only the “tip of the iceberg” of total var gene diversity . This gives an indication of the vast archive of antigenic diversity available to P . falciparum and may explain why immunity to malaria is nonsterlizing and develops slowly . To determine whether all var genes sampled are under similar selective pressures , it is important to know whether or not they are expressed during natural infection . In our dataset , the presence of pseudogenes could only be defined by frame shifts or stop codons in the DBLα domain . T . brucei has many vsg pseudogenes that have been proposed to be an archive of further antigenic diversity [51] . It is conceivable that P . falciparum also recombines fragments of var pseudogenes into functional var genes to increase its antigenic repertoire . For this reason , we included putative pseudogenes in the population genomic analysis of var genes where possible . In addition , only one or a few members of the var multigene family is expressed at a time [6]; therefore , sampling all possible transcripts from the population will require many times more sampling than for the present study . We therefore compared the Amele types to those found to be expressed in clinical isolates from the nearby population of Maiwara [32] . Interestingly , we found that 4 . 3% of the Amele DBLα types were expressed in the Maiwara isolates ( Table S2 ) . When a neighbor-joining tree was constructed , 22 . 4% of Amele DBLα types clustered with significant bootstrap support with the expressed sequences from Maiwara ( unpublished data ) . This indicates that many of the var genes sampled in the Amele population were likely to be functional genes . Our investigation of the sequence diversity among var genes in the Amele local population suggested that obtaining further types from Amele will only identify additional diverse var genes with presumably novel immunological properties . Previous studies have suggested that recombination occurs among var genes by analyzing distantly related genomes [21 , 22] and the progeny of a genetic cross [20] . Our results , namely the phylogenetic and pairwise alignment quality score analyses , suggested that recombination is an important factor in generating polymorphism among the var genes within a natural population . Ultimately , mutation must be the source of amino acid changes . However , the generation of novel combinations to which the host immune system has not previously been exposed is likely to be equally important to immune evasion . The complex nature of the sequence data requires new statistical tools to further define recombination among var genes in natural populations . As mentioned earlier , we are currently addressing this issue . The restriction of var gene diversity in PNG , var genes in PNG which were predominantly unique to that location and the extremely limited overlap among global repertoires points to the existence of geographic population structure . Such structure has been observed for microsatellite [52] and single nucleotide polymorphism [53] markers . The majority of the broadly distributed var genes were found in Africa , but this may be an artifact because almost half of the global population ( 29 isolates ) was from Africa giving a higher probability of finding matching var genes in that region . As P . falciparum originated in Africa [54] , it is likely that the PNG population is a subpopulation of the ancestral population . The possibility of such geographic population structure warrants further investigation because genomes with different var repertoires may be introduced to other transmission systems with consequences for immune evasion and malarial disease . An alternative explanation is temporal variation among the Amele isolates collected in 1999 and the global isolates collected over a period of 20 y . However , our data also showed that certain var genes were maintained in the PNG population over long periods of time . Given the relative importance of var genes to P . falciparum survival [10–11] , host immunity [12–19] , and the potential utility of certain PfEMP1 subgroups [39 , 45 , 55] and domains [56–58] for malaria vaccination , it will now be a priority to observe spatial and temporal variation on small and large geographic scales using our validated molecular epidemiological framework .
For global sampling , 25 P . falciparum isolates or cloned lines were chosen for their single distinct genotypes and diverse origins . From Africa there were three field isolates from Zimbabwe ( collected by S . Mharakurwa from Sahumani Province , Zimbabwe in 1999 . Ethical approval was obtained from the Medical Research Council of Zimbabwe ) ; four field isolates from Dienga in Southeast Gabon , 50 km from Bakoumba ( collected by FMN , ethical approval was obtained by the Ethics Committee for Research in Human Medicine , International Center for Medical Research , Gabon ) ; D6 cloned from isolate Sierra Leone I/CDC from Sierra Leone , West Africa [59]; isolates K39 , M24 , and 3118 from Kenya ( donated by W . Watkins , Wellcome Trust Research Laboratories , Kenyatta National Hospital , Nairobi , Kenya ) . PNG isolates were KF1776 ( B . Tiwari , unpublished data ) , KF1916 [13] , and Muz12 , Muz37 , Muz51 , and Muz106 [60] collected from Amele , Madang Province , PNG , in 1986 and 1990 , respectively . From Asia there were isolates SK44 , PO18 , PO19 , and AMB47 from Thailand ( donated by Wellcome Trust Research Laboratories , Faculty of Tropical Medicine , Mahidol University , Bangkok , Thailand ) ; W2 cloned from Indochina III/CDC , isolated from a Lao refugee in 1980 [59]; isolate MRC20 from India ( donated by H . Joshi , S . K . Subbarao , and C . R . Pillai , Malaria Research Centre , Delhi ) . From Latin America there was HB3 cloned from the isolate Honduras-I/CDC and isolated in Honduras , Central America , in 1980 [61]; and 7G8 cloned from IMTM22 isolated in Manaus , Brazil , in 1980 [62] . Parasites were cultured as described previously [13] . W2 , 7G8 , and D6 clones were propagated in the laboratory of D . F . Wirth , Harvard School of Public Health . DBLα sequences downloaded from the GenBank database consisted of a total of 480 sequences from 34 isolates . These included 3D7 ( number of DBLα sequences ( n ) = 57 ) from the Malaria Genome Project [34]; three isolates from India: R35 ( n = 9 ) , R15 ( n = 9 ) , and R1 ( n = 7 ) [63]; four isolates from Sudan: SD101 ( n = 2 ) , SD105 ( n = 14 ) , SD106 ( n = 2 ) , and SD126 ( n = 4 ) [22]; ten isolates from Kenya: KEN1 ( n = 2 ) , KEN2 ( n = 8 ) , KEN3 ( n = 9 ) , KEN4 ( n = 6 ) , KEN5 ( n = 1 ) , KEN13 ( n = 5 ) , KEN15 ( n = 5 ) , KEN16 ( n = 6 ) , KEN19 ( n = 11 ) , and KEN20 ( n = 2 ) [33]; one isolate from Cape Verde: CV ( n = 8 ) [33]; four isolates from the Solomon Islands: S2 ( n = 28 ) , S55 ( n = 22 ) , S99 ( n = 25 ) , and N72 ( n = 15 ) [27]; two isolates from the Phillipines: PH1 ( n = 26 ) and PH4 ( n = 29 ) [27]; two isolates from Vanuatu: VAN232 ( n = 4 ) and VAN230 ( n = 3 ) [34]; one isolate from PNG: AN143 ( n = 22 ) ; one isolate from Thailand: Dd2 ( n = 22 ) [27]; one isolate from Africa: NF36 ( n = 23 ) [27]; three isolates from Venezuela: VZ1 ( n = 32 ) , VZ2 ( n = 15 ) , and VZ3 ( n = 14 ) [28]; and one isolate from Brazil: A4 ( n = 33 ) [33] . Epidemiological sampling of a local population involved the selection of isolates from 1 , 082 field isolates collected for other studies in our laboratory [64] . This local population was called “Amele . ” Field isolates were randomly collected in November/December of 1999 from residents of a cluster of six Amele villages in Madang on the north coast of PNG , where intense transmission of P . falciparum malaria occurs [65] . Daily inoculation rates range from 0 . 19–1 . 44 infective bites/person/night ( 68–526 per y ) [66] . Amele isolates were collected as blood samples from children aged between 6 mo and 11 y , with asymptomatic infection . Ethical approval for the study was obtained from the Medical Research Advisory Committee of PNG . Whole genomic DNA was obtained from laboratory isolates as previously described [67] . Field isolate genomic DNA was obtained by one of two methods . Isolates from Gabon and Zimbabwe ( from the global collection ) were stored as whole blood spots on 3MM filter paper ( Whatman , http://www . whatman . com ) , and DNA was extracted using the Chelex method . Field isolates from Amele were stored in Guanidine and extracted using Glassmilk ( Qbiogene , http://www . qbiogene . com ) . Isolates or cloned lines were genotyped to confirm that they contained single , distinct genomes . This was done by analysis with 12 genome-wide microsatellite markers [68] and/or MSP2 fingerprinting [69] . The DBLα sequencing protocol is outlined in Figure S1A . DBLα repertoires were amplified with two sets of degenerate primers to homology blocks D ( forward primer ) and H ( reverse primer ) [26] . One set of primers known as “AF” and “BR” were described previously [33] . PCR conditions were 10 ng ( cultured isolate ) or 1 μl ( field sample ) genomic DNA , 2 mM dNTPs , 2 . 4 mM MgCl2 , 50 pmol each primer , 2 . 5 Units Taq Polymerase ( Promega , http://www . promega . com ) . Reactions were performed in a Perkin Elmer 9600 thermal cycler at 95 °C for 4 min , 30 cycles of 95 °C for 1 min , 42 °C for 1 min , 60 °C for 1 min , and a final cycle of 60 °C for 7 min . A second set of primers , known as “AB” ( DBLαAB-F: AGRAGYTTYGCNGAYATHGG and DBLαAB-R: AACCAYCTYAARTAYTGNGG ) were used to correct the amplification of nonspecific human sequences from field samples , which we found to be a problem with the AF/BR primers . PCR conditions were 10 ng ( cultured isolate ) or 1 μl ( field sample ) genomic DNA , 2 mM dNTPs , 3 . 5 mM MgCl2 , 20 pmol each primer , 2 units AmpliTaq Gold DNA Polymerase ( Applied Biosystems , http://www . appliedbiosystems . com ) . Reactions were performed in a Perkin Elmer 9600 thermal cycler at 95 °C for 10 min; 35 cycles of 95 °C for 30 s; 48 °C for 30 s; 60 °C for 45 s; and a final cycle at 60 °C for 7 min . PCR products of approximately 350–450 bp were cloned using the TopoTA PCR cloning kit ( Invitrogen , http://www . invitrogen . com ) according to the manufacturer's instructions . At least 96 colonies were picked from each cloning experiment and cultured in 1 ml of LB broth supplemented with 25 μg/ml of Kanamycin in deep-well culture plates . Plasmid DNA was extracted using the Millipore 96-well plasmid extraction kit ( Millipore , http://www . millipore . com ) according to the manufacturer's instructions , except for the vacuum steps that were replaced with a single centrifugation step encompassing both lysate removal and DNA binding . The bacterial lysate was added to the lysate clearing plate , which was then stacked on a plasmid plate with a waste collection plate on the bottom . Centrifuge adapters for stable stacking of plates are available from Millipore . Centrifugation for 40 min resulted in the plasmid DNA binding to the plasmid plate . This was followed by two wash cycles as for the vacuum protocol . Up to eight plates ( 768 clones ) could be extracted in a few hours using this adaptation . Cycle sequencing was performed using the Big Dye Terminator Sequence Reaction mix ( Applied Biosystems ) according to the manufacturer's instructions , for both strands using M13 Universal forward and reverse primers . Sequencing reactions were run on an ABI3700 ( Applied Biosystems ) at the core sequencing center at the Department of Zoology , University of Oxford . The primary sequence analysis protocol is outlined in Figure S1B and describes how raw sequence data was processed . Raw sequence data for each isolate was screened for vector contamination and end-trimmed where necessary using the automated functions in the DNA sequence analysis program Sequencher ( Gene Codes , http://www . genecodes . com ) . Alignment was then performed at 96% DNA sequence identity to allow DBLα sequences from the same gene to align yet separate sequences from different genes . This limit was based on an analysis of data from two isolates: 3D7 [33] and 1776 ( B . Tiwari , unpublished data ) available from GenBank at the time of project design . Available sequences from these two isolates were aligned at incremented percentage similarity until they no longer aligned to each other , giving a value of 96% . If duplicate genes are present and diverged only within the 96%–100% identity range , these will be sampled as one var gene . Hence , the number of DBLα sequences within an isolate may also be a measure of within-isolate diversity if each isolate is sampled equally . Note that these numbers may also be normalized for sequence input by dividing by the number of reads . To determine whether this was a possibility , the 59 3D7 gene sequences were aligned at 96% identity . A total of 54 types remained after alignment . This showed that there was redundancy within this genome , and thus there may be redundancy within other genomes . After alignment as described , the resulting consensus DBLα sequences were considered a sample of the var gene repertoire . Multiple sequence reads for each consensus sequence reduced the possibility of PCR , cloning , and sequencing artifacts . For definition of types , comparison among different var gene repertoires was performed . Again a 96% identity cut-off was used to align matching consensus DBLα sequences among repertoires . The number of unique DBLα sequences ( types ) was then defined for the population ( i . e . , a group of isolates ) . Var genotypes were then defined based on the range of DBLα types ( Figure S1B ) . To summarize the relatedness between the var gene repertoires from two isolates , we used a simple statistic PTS . If isolate A has a repertoire of nA distinct types and isolate B has a repertoire of nB distinct types , and a total of nAB types are shared by the two isolates , we define: Because the isolate repertoires are not fully sampled , estimates of PTS from empirical data are likely to be downward biased . However , as long as there are not systematic differences between the number of reads obtained for isolates from different geographic locations the sample estimates of PTS are still useful statistics on which to base a comparative analysis of diversity . Note that if each isolate had a repertoire of exactly one var gene , sample PTS is equivalent to expected homozygosity , and the apportionment of PTS within and between populations would be equivalent to measuring Wright's fixation indices . PTS can therefore be thought of as a simple extension of FST to the case where multiple gene family members of unknown allelic status have been collected . DBLα types were translated to amino acid sequences using the program Transeq from the EMBOSS toolkit at the European Bioinformatics Institute ( http://www . ebi . ac . uk ) . Translations that contained two or more stop codons were not analyzed further as this may indicate a frame shift and may artificially result in a lack of homology to other DBLα protein sequences . Phylogenetic analysis of DBLα types was done by constructing multiple alignments of translated DBLα types using stand-alone Clustal W downloaded from the European Bioinformatics Institute Website ( http://www . ebi . ac . uk ) [70] . Multiple alignments were then imported into MEGA version 3 . 0 [71] where neighbor-joining trees were constructed using the p-distance model to define pairwise distances between sequences . Primer sequences were removed for the phylogenetic analysis . Pairwise alignments of “good” protein sequences ( described above ) were constructed using the GCG ( Genetics Computer Group , http://www . accelrys . com/products/gcg ) program “BestFit . ” BestFit makes an optimal alignment of the best segment of similarity between two sequences using a Smith and Waterman local homology algorithm . Percent identity and percent similarity are indicators of the quality of the alignment . For scoring similarities among protein sequences , BestFit uses the blosum62 comparison matrix [72] . We also examined quality scores because percent identity or similarity scores ignore the length of the alignment . Quality scores for pairwise alignments are related to the degree of similarity but also account for length of the alignment and gaps . These will more accurately quantify the relationship among potentially recombined sequences . For example , a mosaic sequence may have 50% similarity to each of its donors across the entire length of the alignment . This would be considered equivalent to the similarity among a pair of sequences with 50% differences spread across the length of the alignment . However , the pairing of a mosaic sequence with its donor will have a higher quality score because it will score 100% similarity for half of the alignment . The calculation of pairwise quality scores was done using the following formula: Where: “CmpVal” is the comparison value for an amino acid pair based on an amino acid comparison table ( PAM matrix ) and “Total” is the total number of matches for that pair in the alignment . Therefore , each match in the alignment adds points to the quality score and mismatches and gaps subtract from it . | Malaria parasites live in red blood cells of the human host for part of the life cycle , during which a family of diverse antigens known as PfEMP1 are placed on the surface . PfEMP1 variants switch by sequential expression of up to 60 var genes . This allows the parasite to evade immune detection within an individual host , enhancing its chances to be transmitted to the mosquito vector in situations where mosquitoes are seasonally available . Methods to rapidly assess var gene diversity in parasite populations are needed to measure antigenic diversity and define relationships with malaria transmission . Using a specialized framework , we completed the first systematic sampling of var genes from parasite genomes obtained from the same ( Papua New Guinea [PNG] ) and different ( global ) populations . Globally , there was no limit to the number of var genes because parasites rarely shared var genes . In PNG , var gene numbers were restricted due to high levels of sharing , and most were only found in that population . Recombination was important to the evolution of var genes in PNG . The data suggest there are distinct var genes in different populations , which may have consequences for the spread of malarial disease from one geographic area to another . |
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Sporadic Creutzfeldt-Jakob disease ( sCJD ) cases are currently subclassified according to the methionine/valine polymorphism at codon 129 of the PRNP gene and the proteinase K ( PK ) digested abnormal prion protein ( PrPres ) identified on Western blotting ( type 1 or type 2 ) . These biochemically distinct PrPres types have been considered to represent potential distinct prion strains . However , since cases of CJD show co-occurrence of type 1 and type 2 PrPres in the brain , the basis of this classification system and its relationship to agent strain are under discussion . Different brain areas from 41 sCJD and 12 iatrogenic CJD ( iCJD ) cases were investigated , using Western blotting for PrPres and two other biochemical assays reflecting the behaviour of the disease-associated form of the prion protein ( PrPSc ) under variable PK digestion conditions . In 30% of cases , both type 1 and type 2 PrPres were identified . Despite this , the other two biochemical assays found that PrPSc from an individual patient demonstrated uniform biochemical properties . Moreover , in sCJD , four distinct biochemical PrPSc subgroups were identified that correlated with the current sCJD clinico-pathological classification . In iCJD , four similar biochemical clusters were observed , but these did not correlate to any particular PRNP 129 polymorphism or western blot PrPres pattern . The identification of four different PrPSc biochemical subgroups in sCJD and iCJD , irrespective of the PRNP polymorphism at codon 129 and the PrPres isoform provides an alternative biochemical definition of PrPSc diversity and new insight in the perception of Human TSE agents variability .
Transmissible spongiform encephalopathies ( TSE ) are neurodegenerative disorders affecting a large spectrum of mammalian species that share similar characteristics , including a long incubation period ( which in man may be measured in decades ) and a progressive clinical course resulting in death [1] . The most common form of human TSE is an idiopathic disorder named sporadic Creutzfeldt-Jakob disease ( sCJD ) . sCJD is not a uniform disorder in terms of its clinical and neuropathological phenotype . It remains unclear whether this variability is related to variations in the causative TSE agent strains , or to the influence of the methionine/valine polymorphism at codon 129 of the PRNP [2] , [3] . A key event in the pathogenesis of TSE is the conversion of the normal cellular prion protein ( PrPC , which is encoded by the PRNP gene ) into an abnormal disease-associated isoform ( PrPSc ) in tissues of infected individuals . Conversion of PrPC into PrPSc is a post-translational process involving structural modifications of the protein and resulting in a higher β-sheet content [4] . PrPC is completely degraded after controlled digestion with proteinase K ( PK ) in the presence of detergents . PrPSc is N-terminally truncated under such conditions , resulting in a PK resistant core , termed PrPres [5] . PrPres , also named PrP 27–30 , is a disease marker for TSE and the presence of PrPSc seems to correlate with infectivity [5] , [6] . According to the prion hypothesis , PrPSc is the infectious agent in TSE [7] and , in the last decades , several lines of evidence have indicated that particular biochemical properties of PrPSc , such as solubility in N-lauroylsarcosine , PK resistance and electromobility in western blotting ( WB ) can be used to distinguish between different prion agents or strains [8] , [9] . In sCJD , two major PrPres types have been described by WB: in type 1 PrPres , the unglycosylated fragment is 21 kDa , while in type 2 , the apparent molecular weight of this unglycosylated fragment is 19 kDa [3] . Protein N-terminal sequencing revealed that type 2 isoform derives from preferential cleavage of the protein during PK digestion at amino acid 97 , while in type 1 preferential cleavage occurs at amino acid 82 [10] . sCJD cases can be subclassified according the PrPres isoform and the PRNP codon 129 methionine ( M ) /valine ( V ) polymorphism , resulting in 6 major subypes: MM1 , MM2 , MV1 , MV2 , VV1 and VV2 . Interestingly , these subtypes appear to carry distinct pathological and clinical features , [2] , [3] , and it has been proposed that type 1 and type 2 isoforms in sCJD might correspond to different TSE agent strains . However , the description of PrPres isoforms which appear to be distinct from type 1 and type 2 , and the increasing number of reports describing the coexistence of type 1 and type 2 PrPres in different areas or the same area in the brain from a single sCJD patient , calls into questions the subclassification system described above in sCJD [11]–[14] . Here , in a large group of cases including 41 sCJD and 12 iCJD patients , we confirmed that type 1 and type 2 PrPres can be observed as a mixture in a substantial number of patients . However , using two novel assays described here , PrPSc from these patients with mixed PrPres types are homogeneous irrespective of the brain area considered . Moreover , based on these novel PrPSc biochemical properties , four distinct subgroups were observed in our cohort of sCJD patients . Similar findings were observed in iCJD cases from two countries and differing sources of infection .
A total of 41 French cases of sCJD , each of which had frozen tissue ( 2–4 g ) available from preferentially 5 brain regions: ( occipital , temporal and frontal cortex , cerebellum and the caudate nucleus ) , were included in this study . All six currently defined classes of s-CJD patients ( MM1-MM2-MV1-MV2-VV1-VV2 ) were represented in our panel ( Table 1 ) . Moreover , 12 cases of iatrogenic CJD ( iCJD ) , linked to contamination by growth hormone ( GH ) or dura mater grafts , from patients originating either from United Kingdom ( UK ) or France , were also investigated ( Table 1 ) . None of the patients had a familial history of prion disease and , in each case , the entire PRNP coding sequence was analyzed , either by denaturating gradient gel electrophoresis and/or direct sequencing . All patients died from CJD during the period 1997–2004 . Additionally , five cases of Alzheimer's disease were included as non-CJD controls . In all cases , informed consent for research was obtained and the material used had appropriate ethical approval for use in this project . For each sample , a 20% brain homogenate ( weight/volume ) in 5% glucose was prepared using a high-speed homogenizer ( TeSeE Precess 48 system ) . The homogenates were then filtered through a 20 Gauge needle before storage at −80°C . Various factors have been reported to influence the results of PrPres analysis by WB , including tissue pH and the effect of Cu2+ ions [15]–[17] . In order to limit these factors , each homogenate was diluted a 100-fold in a single non-CJD control brain homogenate prior to further investigation . A WB kit ( TeSeE WB kit Bio-Rad ) was used following the manufacturer's recommendations . Three different monoclonal PrP-specific antibodies were used for PrP detection: Sha31 ( 1 µg/ml ) [18] , 8G8 ( 4 µg/ml ) [19] and 12B2 ( 4 µg/ml ) [20] , which recognized the amino acid sequences YEDRYYRE ( 145–152 ) , SQWNKPSK ( 97–104 ) and WGQGG ( 89–93 ) respectively . After incubation with goat anti-mouse IgG antibody conjugated to horseradish peroxidase , signal was visualized using the ECL western blotting detection system by enhanced chemiluminescent reaction ( ECL , Amersham ) . Molecular weights were determined with a standard protein preparation ( MagicMark , Invitrogen ) . PrPSc detection was carried out using sandwich ELISA test ( TeSeE CJD , Bio-Rad ) used following the manufacturer's recommendations . The assay protocol includes a preliminary purification of the PrPSc ( TeSeE purification kit ) consisting in ( i ) digestion of PrPC with PK , ( ii ) precipitation of PrPSc by buffer B and centrifugation , ( iii ) denaturation of PrPSc in buffer C at 100°C , before immuno-enzymatic detection . In this ELISA , the capture antibody 3B5 recognizes the octarepeat region of PrP [19] , while the detection antibody 12F10 binds to the core part of the protein [18] . PK resistance of the PrPSc portion recognized in the ELISA test was determined by measurement of the ELISA specific signal recovered from a series of homogenate aliquots digested with different concentrations of PK in buffer A′ reagent ( TeSeE Sheep/Goat purification kit ) . Each sample was first diluted in normal brain homogenate ( between 100- and 10 , 000-fold ) until obtaining a signal between 1 . 5 and 2 absorbance units after digestion with 50 µg/ml of PK . Triplicate of equilibrated samples were then submitted to a PK digestion with concentrations ranging from 50 to 500 µg/ml , before PrPSc precipitation and ELISA detection . Results were expressed as the percentage of residual signal when compared to the 50 µg/ml PK digestion ( lowest PK concentration ) . In each assay , two standardized controls ( scrapie and BSE from sheep ) were used as an internal standard . About 20% of samples were randomly selected and submitted to two independent tests separated in time as to assess inter-assay variation . The ELISA test used in this study was adapted from the Bio-Rad TeSeE test , validated at CEA for EU strain typing studies in ruminants and designed to distinguish BSE in sheep from scrapie . The principle was to measure conformational variations in PrPSc by applying two differential PK digestions under the modification of detergent conditions ( SDS sensitivity ) . For each sample , PK digestion was performed under two conditions: ( i ) two aliquots of 250 µl of 20% homogenate were mixed either with 250 µl of A reagent ( TeSeE purification kit ) containing 20 µg of PK , ( ii ) with 250 µl of A′ reagent ( N-lauroylsarcosine sodium salt 5% ( W/V ) , sodium dodecyl sulfate 5% ( W/V ) containing 55 µg of PK , All the tubes were then mixed by inversion 10 times and incubated at 37°C ( in a water bath ) for exactly 15 min . Subsequently , 250 µl of reagent B ( Bio-Rad purification kit ) /PMSF ( final concentration 4 mM ) were added , mixed and the tubes were centrifuged for 5 minutes at 20 , 000 g at 20°C . Supernatants were discarded and tubes dried by inversion onto an absorbent paper for 5 min . Each pellet was denatured for 5 min at 100°C with 25 µl of C reagent ( Bio-Rad purification kit ) . The samples were diluted in 250 µL of R6 buffer containing 4 mM of serine protease inhibitor AEBSF ( 4-2-aminoethyl benzenesulfonyl fluoride hydrochloride ) , and , if desired , further serially diluted in R6 buffer . ELISA plates were then incubated for two hours at room temperature and , after three washes , antibody detection ( TeSeE CJD , Bio-Rad ) was added for two hours at 4°C . The ratio of the absorbance obtained in the two conditions ( A/A′ ) was calculated using appropriate dilutions providing absorbance measurements ranging from 0 . 5 to 2 . 5 absorbance units in A conditions . For each plate , the same three control samples ( one MM1 , one VV2 and one MM GH ) were included . To avoid inter-assay variations , final results were expressed as a normalized ratio established by dividing the ratio obtained for the analyzed sample by the one obtained for a VV2 sample selected as standard .
For each sCJD case , PrPres profile was determined from five brain areas using both Sha31 and 8G8 antibodies . A single PrPres type ( type 1 or type 2 ) was observed in investigated brain areas of most of the MM1 ( n = 11 ) , and all the areas from MV1 ( n = 8 ) , VV1 ( n = 1 ) and MM2 ( n = 3 ) sCJD cases of our panel . However , in several cases initially classified as MM1 ( n = 2 ) , and in a majority of VV2 ( n = 5 ) or MV2 ( n = 6 ) cases , some brain areas harboured mixed electrophoretic pattern characterized by two distinct bands at 19 and 21 kDa , indicating the coexistence of PrPres type 1 and type 2 ( Table 1 and Figure 1A ) . Moreover , in individual patients some brain areas were found to be type 1 , while another area could be found to be type 2 ( Table 1 and Figure 1A ) . Since both Sha31 and 8G8 gave similar results this phenomenon cannot be attributed to some antibody peculiarity in PrP recognition ( Figure 1B and 1C ) . Antibody 12B2 is specific for the amino acid sequence 89–93 that is located N-terminally of the type 2 cleavage site ( amino acid 97 ) . In principle , this antibody is unable to recognize type 2 PrPres . Systematic western blotting with 12B2 consistently demonstrated the presence of the 21 kDa band , characteristic for type 1 PrPres , in nearly all type 2 classified samples , regardless of the PRNP codon 129 polymorphism ( Figure 1D ) . In a limited number of type 2 samples , 12B2 failed to detect a type 1 band ( Figure 1E and 1F , lane 2 , 3 ) . Using Sha31 or 8G8 , mixed type 1/type 2 PrPres profiles were observed in several iCJD cases ( Figure 2A ) , regardless of their national origin or mode of infection . In most ( but not all ) samples initially classified as type 2 , the 12B2 antibody revealed the presence of a 21 kDa band , characteristic of type 1 PrPres ( Figure 2B ) . Together these findings point to the existence of variable amounts of type 1 PrPres molecules in all or nearly all type 2 classified patients ( Table 1 ) . In sCJD and iCJD patients who harboured a single WB PrPSc type in the different brain areas , as assessed by Sha31 , a single ELISA PK resistance profile ( Table 1 and Figure 3A ) and a comparable ratio in strain typing assay ( Table 1 and Figure 3B ) were observed in all brain areas . Surprisingly , in each patient harbouring both type 1 and 2 PrPres , either in the same or in different brain areas , a single ELISA PK digestion profile ( Table 1 and Figure 3C and 3D ) and a comparable signal ratio in strain typing assay ( Table 1 and Figure 3B ) was also observed , irrespective of region assayed . MM1 and VV2 samples but also MM2 and VV1 samples , which harboured similar apparent PrPSc content ( as assessed by ELISA ) were artificially mixed in different proportions . Using WB , a mixed type 1+2 profile could , or could not , be observed depending on the mixture proportions ( Figure 3E and 3F ) . Both PrPSc resistance ELISA assay ( Figure 3G and 3H ) and strain typing ELISA ( not shown ) were able to discriminate the different mixtures from the original isolates and from each other . These results clearly demonstrate that the uniformity of PrPSc biochemical properties , as demonstrated by both PrPSc resistance ELISA and strain typing ELISA , in patients harbouring different PrPres isoforms cannot be attributed to a lack of discriminative power of these techniques . Together , these data strongly indicate that , despite possible variations in PrPres type on WB analysis , patients with either sCJD or iCJD appear to harbour a single PrPSc isoform in their brain . According to the results from PrPSc PK resistance assay and strain typing ELISAs , sCJD patients could be split into four groups ( Table 1 , and Figure 3A and 3B ) . The first group was characterized by a strong PK resistance ( Figure 3A ) and a low ratio in strain typing assay ( Figure 3B ) . Group 1 could be readily differentiated from Group 2 which showed a higher sensitivity regarding PK digestion , as well as an increased signal ratio in strain typing assay , when compared to Group 1 . Two other PrPSc groups were also observed . Group 3 harboured an intermediate PK lability in the PrPSc resistance ELISA and ratio in the strain typing ELISA , when compared to Group 1 and 2 . Group 4 had a very high PK-sensitivity and ratio in the strain typing ELISA . No overlapping in PK resistance profile or ratio value in strain typing assay were observed between the four determined groups ( Table 1 ) . Group1 was composed of sCJD MM and MV patients , harbouring predominantly type 1 PrPres while Group 2 consisted in VV and MV patients harbouring predominantly type 2 or type 1+2 PrPres . Groups 3 and 4 were respectively composed with VV1 and MM2 patients from our sCJD panel . Striking differences were observed in the PrPSc properties between the different iCJD cases and all four groups relying on PrPSc signatures observed in sCJD cases were identified ( Table 1 , and Figures 3B and 4 ) . As it might have been expected from sCJD cases observations , Group 1 PrPSc properties was identified in MM1 UK dura mater graft patients ( n = 2 ) ( Figure 4A ) while Group 2 PrPSc features were observed in UK VV2 ( n = 2 ) ( Figure 4D ) and MV2 ( n = 1 ) ( Figure 4B ) GH patients . Surprisingly , a typical Group 2 PrPSc signature was also observed in one out of the three MV1 French GH patients ( type 1 in all brain areas ) . Meanwhile , all investigated MM1 and two out of the three MV1 French GH cases ( Figure 4A ) harboured identical PrPSc properties than Group 3 sCJD ( Figure 4E ) . Finally , a Group 4 sCJD PrPSc signature ( Figure 4F ) was observed , using both PrPSc resistance ELISA ( Figure 4E ) and strain typing ELISA ( Figure 3B ) , in a French dura mater VV1 case ( n = 1 ) , which harboured a type 1 PrPres WB profile in every investigated area . Taken together , these observations support the concept that , in iCJD patients , variability in the PrPSc biochemical properties is not related to the route of infection or the PRNP codon 129 genotype . It also indirectly suggests that the range of different PrPSc properties observed in iCJD might be related to those in the source of infection ( likely to have been a sCJD case ) .
In this study , detection , by WB , of the coexistence of two PrPres types in about 30% ( 13/41 ) of cases is consistent with already published data [12] , [14] . This observation could suggest the existence in brain from a single patient of different abnormal PrP species . Although two main PK cleavage sites are associated with PrPres type 1 and type 2 ( respectively amino acid 82 and 97 ) , N-terminal sequencing revealed in all investigated cases the presence of a whole spectrum of overlapping cleavage sites . Moreover in a part of investigated cases this technique demonstrated the presence ( i ) of variable but consistent level of type 1 PrPres in patients classified type 2 using WB and ( ii ) in some patient classified type 1 , of low amount of type 2 PrPres [10] . These observations could suggest that , rather than a pure type 1 or type 2 PrPres , PK digestion of a PrPSc specific conformer generate variable mixture of PrPres fragments ( with presence of dominant or sub dominant type 1 or type 2 PrPres ) , which WB usually failed to reveal accurately because its intrinsic technical limits [14] . Antibodies either harbouring higher affinity to PrP ( like Sha31 ) [18] or probing specifically type 1 PrPres ( like 12B2 ) [20] , now allow a better perception of such mixture . However , investigations carried out using artificial mixture of type 1 and type 2 brain homogenate , even using high affinity anti-PrP antibodies , clearly indicate the current limits of WB discriminative power [14] . Together , these data suggest that WB analysis of PrPres on its own could be misleading for adequate discrimination between PrPSc variants in CJD . Both PrPSc PK resistance ELISA and strain typing ELISA are based on the characterization the N terminal part of the PrPSc PK digestion either by increasing PK amount or modifying detergent conditions . While WB profile could be compared to a snapshot picture of PrPres fragments generated by PK digestion process , these assays reflect the dynamics of the PK cleavage rather than its final result ( different forms of PrPres ) . Consequently they could provide different but also more accurate perception of the PrPSc conformers . Our findings from the PrPSc capture immunoassays clearly indicate that in a single patient , irrespective of brain area , sCJD associated PrPSc displays uniform biochemical properties , regardless of the regional variation of type 1 and type 2 isoforms determined by WB . Such findings support the idea of the presence of a specific TSE agent in each brain and the accumulation of a single associated PrPSc conformer . Because the limited size of our cohort of cases , an in depth comparison between the PrPSc signature ( as established in this study ) and the Parchi classification system is not possible . However , despite this limitation , two major groups were identified in our panel according to the PrPSc properties . The first major group was constituted with patients harbouring a highly PK resistant PrPSc ( MM1 and MV1 patients ) . The second group included patients harboring a PK labile PrPSc ( VV2 and MV2 patients ) . Using both lesion profile and clinical parameters [2] , two major forms of sCJD are commonly recognized . The first sCJD form , named “classical” , is characterized by a “rapid evolution” ( usually around 4 months ) , and affects most of the MM1 and MV1 patients . The second sCJD form , named “atypical” , affects VV2 and MV2 with a longer symptomatic evolution ( usually longer than 6 months ) and a late dementia . Despite inter-individual variations , sCJD Groups 1 and 2 , as we defined them on biochemical criteria were consistent with this classification . Both VV1 and MM2 sCJD cases are extremely rare; they respectively represent 1% and 4% of the identified sCJD cases . According to the literature , these patients have clinical features and lesion profiles that are very different from other sCJD patients [2] . However , in our study as in previously published studies , WB did not identify any distinct biochemical difference from other type 1 and type 2 cases . In contrast , both the strain typing ELISA and PrPSc resistance assays clearly differentiated these cases from Group 1 and Group 2 cases . This finding , which is consistent with clinico/pathological observations carried out in patients , could indicate that there are indeed differences in PrPSc that distinguish these VV1 and MM2 cases from other sCJD groups . Although prion strains can only be identified definitively by bioassay , molecular in vitro tools to characterize PrPSc are more and more widely used for the rapid identification of particular agents , such as BSE in cattle , sheep , rodent and humans ( vCJD ) [20] , [21] . This has come to be termed “molecular strain typing” and although widely employed , the exact relationship between PrPSc biochemistry and the biological properties of the agents responsible remain to be determined . In sCJD , the presence of four distinct PrPSc biochemical forms apparently correlated to clinico-pathological phenotypes as defined by Parchi et al . [2] could be an indication of the involvement of different TSE agents . iCJD cases are a consequence of accidental human to human TSE transmission , most likely representing transmission of sCJD . The identification in iCJD cases of the four PrPSc signatures identified in sCJD is consistent with the existence of distinct prions associated with these biochemical forms . Three examples of human-to-human transmission of variant CJD through blood transfusion have now been identified . While all blood donors were MM at codon 129 PRNP , the recipients had either a MM ( n = 2 ) or a MV genotype ( n = 1 ) . Despite this genotype difference there appears to have been conservation of the disease phenotype and PrPres type in all “secondary” vCJD cases [22]–[25] . These observations could suggest that in case of inter-human transmission , difference in donor/recipient genotype could result in un-altered abnormal PrP signature . Our identification of MM GH iCJD cases harbouring similar PrPSc signature as a VV1 sCJD case or of a VV dura mater iCJD case similar to MM2 sCJD might indicate preservation of a specific PrPSc biochemical signature after human to human transmission between individuals of different codon 129 genotypes . Treatment with extracts of GH contaminated by CJD has lead to a high number of iCJD cases in France and the UK . The codon 129 genotypes of the affected individuals in the two countries differ , with the French cohort predominantly MM and MV and the British cohort MV and VV [26] . In the absence of any clear explanation for this finding , it was suggested that it might be due to contamination of different batches of GH with different prion strains from individuals of differing PRNP codon 129 genotypes . Our identification of different biochemical forms of PrPSc in GH French patients and in UK patients is consistent with this hypothesis . The variability observed within the French GH cases could signify involvement of different prion strains , consistent with multiple contaminated GH batches in the French epidemic . The identification in this study of different PrPSc species in CJD patients with the same PRNP polymorphism at codon 129 and WB PrPres profile offers a new perspective on our understanding of the relationship between PrP biochemistry , prion disease phenotype and agent strain . We highlight two novel approaches to analysing PrPSc in sCJD and iCJD and offer evidence that these analyses provide potentially-strain associated information , which appears to be lacking from the conventional WB assay . | Prion diseases are transmissible neurodegenerative disorders characterized by accumulation of an abnormal isoform ( PrPSc ) of a host-encoded protein ( PrPC ) in affected tissues . According to the prion hypothesis , PrPSc alone constitutes the infectious agent . Sporadic Creutzfeldt-Jakob disease ( sCJD ) is the commonest human prion disease . Although considered as a spontaneous disorder , the clinicopathological phenotype of sCJD is variable and substantially influenced by the methionine/valine polymorphism at codon 129 of the prion protein gene ( PRNP ) . Based on these clinicopathological and genetic criteria , a subclassification of sCJD has been proposed . Here , we used two new biochemical assays that identified four distinct biochemical PrPSc subgroups in a cohort of 41 sCJD cases . These subgroups correlate with the current sCJD subclassification and could therefore represent distinct prion strains . Iatrogenic CJD ( iCJD ) occurs following presumed accidental human-to-human sCJD transmission . Our biochemical investigations on 12 iCJD cases from different countries found the same four subgroups as in sCJD . However , in contrast to the sCJD cases , no particular correlation between the PRNP codon 129 polymorphism and biochemical PrPSc phenotype could be established in iCJD cases . This study provides an alternative biochemical definition of PrPSc diversity in human prion diseases and new insights into the perception of agent variability . |
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Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations . To understand how this occurs , there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion . In this work , we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules , functional organization , and the optimal metabolic phenotype of Rhizobium etli , a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris . To experimentally characterize the metabolic phenotype of this microorganism , we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions , and during nitrogen fixation with P . vulgaris . By integrating these data into a constraint-based model , we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R . etli . Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities . Consistent with modular activity in metabolism , we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation . In this work , we explore the relationships among topology , biological function , and optimal activity in the metabolism of R . etli through an integrative analysis based on modeling and metabolome data . Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids , peptides , and lipids . More fundamentally , we supply evidence that such modular organization during functional nitrogen fixation is a robust property under different environmental conditions .
With the advent of bioinformatics and high-throughput technologies , plentiful sources of information stored in databases are now available to help unveil how a variety of biological entities interact among each other and to elucidate how these interacting networks support phenotypic behaviors in microorganisms . Notably , a variety of studies accomplished with these networks have contributed to elucidation of some fundamental organizing principles by which the cell presumably regulates and coordinates its vital biological functions . Among these organizing properties , structural modularity is a systemic property that has been observed in a variety of biological networks , which range from genetic transcriptional regulation to metabolic activity [1] , [2] , [3] , [4] , [5] . On an even more fundamental level , there is evidence that these modules inferred from network topology can be associated with functional modules; these latter are defined as a set of biological components ( metabolites , proteins , or genes ) that coordinately participate in accomplishing a specific biological function in the cell [6] . On the other hand , the idea that optimization principles guide metabolic activity in microorganisms has been an interesting hypothesis that , in combination with genome-scale metabolic reconstructions , has resulted in a successful framework for a systematic , quantitative , and predictive scheme in systems biology [7] . Briefly , the optimization problem in this context is reduced to identification of the metabolic flux along the network that ensures maximal production of a specific array of metabolites representing a specific phenotypic state in the microorganism . An optimal metabolic phenotype constitutes the basis of constraint-based modeling , and its application domain has been extended to a variety of organisms in the past few decades [8] , [9] . In this context , an immediate and fundamental question emerges: how do these structural modules organize and coordinate among themselves to support an optimal metabolic phenotype in microorganisms ? . Even though this enterprise is far from being solved , some remarkable advances have been reported in the field . For instance , a recent experimental and in silico study on a metabolic reconstruction for Escherichia coli supported the idea that feedback inhibition in metabolic units , called modules , constitutes a mechanism capable of inducing an optimal growth rate [10] . Equally relevant , there are studies that have pointed out that modular organization on a genomic scale may be a natural strategy for coordinating the transcriptional and metabolic activities required for ensuring that cells evolve and efficiently respond to environmental perturbations [1] , [5] . Despite these and other advances , the study of the principles governing the metabolic organization in cells is in its infancy , and additional discoveries are required for surveying how structural modules in a metabolic network link together to efficiently achieve their biological functions [10] . In this work , by using a systems biology description , we supply computational and experimental evidence that suggests that structural and functional modularities are robust properties when the cell operates under its optimal metabolic phenotype at different physiological conditions . To support our conclusions , we carried out an integrative study involving computational modeling and high-throughput sequencing technology for characterizing the metabolic activity of Rhizobium etli CFN42 during symbiotic nitrogen fixation in symbiotic association with Phaseolus vulgaris ( bean plant ) . Here , this organism is our benchmark model , a decision that was favored based on the availability of 1 ) a computational description of its genome-scale metabolic reconstruction , 2 ) an integrative description among high-throughput technologies at nitrogen fixation stages , and 3 ) the valuable physiological knowledge already available that describes the metabolic activity during nitrogen fixation by this organism in symbiotic association with P . vulgaris [11] . Hence , to elucidate the metabolic activity of R . etli during nitrogen fixation , constraint-based modeling was applied on an updated version of the metabolic reconstruction for this organism ( iOR450 ) [11] . At present , the metabolic reconstruction of R . etli is an integrated network of 402 reactions involving the participation of 450 genes and 377 metabolites . Unlike our previous studies , here we used metabolome technology to experimentally support our in silico interpretations of R . etli metabolism under two physiological conditions: when it fixes nitrogen in symbiosis with P . vulgaris , and under free-living conditions with succinate and ammonium as carbon and nitrogen sources , respectively ( see the Materials and Methods section ) . Thus , by applying capillary electrophoresis and mass spectrometry ( CE-MS ) [12] , [13] to R . etli and its products , we report the relative abundances of 220 metabolites under these physiological conditions . To improve the interpretations obtained from the constraint-based modeling , we used metabolome data to identify metabolites with meaningful biological roles in bacterial nitrogen fixation . Consequently , this information was used to guide the reconstruction of a more proper objective function ( OF ) to computationally simulate this biological process . Next , with the results of our integrative analysis carried out between the constraint-based modeling and the metabolome data for R . etli , we concluded that the metabolic activity at optimal nitrogen fixation is supported by structural modules with well-defined functional activities . Furthermore , our in silico study let us show that those modular structures tend to be robust during changes in environmental conditions . Overall , our study supplies evidence that modular organization in metabolic networks is required for promoting an optimal metabolic phenotype in microorganisms .
In addition to transcriptome and proteome approaches , metabolome technology represents a third complementary approach to characterize the phenotypic state of a microorganism , through quantitative and qualitative descriptions of its metabolite concentrations . In order to elucidate the metabolic capabilities and organizing properties of R . etli metabolism , we investigated the metabolome profiles of this bacterium in two physiological situations: during nitrogen fixation with P . vulgaris and under free-living conditions ( see the Materials and Methods section and reference [11] ) . Biological samples from each physiological condition were analyzed in triplicate using CE-MS technologies [12] , [13] , and the output was used to sense the relative abundance levels of metabolites under each physiological condition . Metabolome measurements were carried out through a facility service at Human Metabolome Technology Inc . , Tsuruoka , Japan . Briefly , the samples were prepared following an experimental protocol supplied by Human Metabolome Technology Inc . , and the ionic metabolites were separated through electric fields . Having separated the ionic metabolites by capillary electrophoresis the samples were subjected to spectrometric analysis , and their identities were selectively detected by monitoring ions over a large range of m/z values . Thus , spectrum profiles were compared in consultation with the Human Metabolome Technology database and normalized with respect to internal controls for uncovering and estimating their identities and metabolic abundances in both physiological conditions . The relative peak area observed by spectrometry and the average , standard deviation , and log-ratio obtained for each metabolite are reported in Dataset S1 in the supporting information and visually depicted in Figure 1 . Overall , high-throughput analysis led us to identify and characterize the abundances of 220 ionic metabolites ( 93 cations and 127 anions ) under both physiological conditions . A biological analysis of these results led us to arrive at the following conclusions: Undoubtedly , the above descriptions are valuable for elucidation of the metabolic landscape during bacterial nitrogen fixation in R . etli; however , in order to explore the relationships among network topology , functionality , and optimal metabolic phenotype , it is necessary to move toward an integrative , quantitative , and predictive description . To this end , metabolome data were used for improving and assessing the flux metabolic activity inferred from constraint-based modeling when applied on the genome-scale metabolic reconstruction of R . etli . Thus , from the set of 220 metabolites experimentally detected , we identified 119 of the 377 metabolites that participate in the metabolic reconstruction reported for R . etli OR450 [11] . This metabolic set covered 32 . 75% of the total number of metabolites participating in the metabolic reconstruction and constituted our experimental dataset for improving , supporting , and assessing the results that emerged from the constraint-based modeling . Constraint-based modeling is a paradigm in systems biology for exploring the metabolic phenotype in cells under specific environmental conditions and/or subject to genetic perturbations [16] , [17] , [18] . In particular , Flux Balance Analysis ( FBA ) has proven to be a useful computational tool for surveying the metabolic phenotype capacity in a microorganism and to simultaneously evaluate its coherent description with high-throughput technology [9] , [11] . Briefly , once the genome-scale metabolic reconstruction for an organism has been completed , FBA can be divided into two main steps: 1 ) the reconstruction of an OF that simulates a particular phenotypic state for the microorganism ( for instance , the growth rate or maximal nitrogen fixation , to name two ) , and 2 ) the search for a flux distribution along the entire network that maximizes the selected OF when the system is at steady-state [7] . Among the variety of applications that can be tackled with this systems-level framework , bacterial nitrogen fixation carried out by R . etli is an example of how an integrative study using computational modeling and high-throughput technology can contribute to understanding , predicting , and elucidating the metabolic activity during this biological process [11] . Based on these previous achievements , we selected this organism as our model system for surveying the relationships among structural organization , functionality , and the maximal phenotype during bacterial nitrogen fixation . Before proceeding with our in silico analysis , we took advantage of the metabolome data to refine and improve a previous FBA carried out for this organism [11] . Thus , given the metabolic reconstruction iOR450 , we improved the FBA by suggesting a more proper OF , one capable of modeling bacterial nitrogen fixation in a more accurate and realistic fashion . Reconstruction of an OF is a crucial issue in constraint-based modeling for computationally mimicking the metabolic activity of a microorganism , in this case , bacterial nitrogen fixation . For this reason , an OF is mainly defined in terms of those metabolites whose permanent production is essential for sustaining bacterial nitrogen fixation [11] , [19] . Currently , the OF for modeling bacterial nitrogen fixation has been entirely constructed in terms of a query in the scientific literature [11] , [19]; however , the metabolome data depicted in Figure 1 open the possibility for identifying new potential metabolites that were not included in previous analyses due to a lack of physiological evidence . The issue described above is extremely important because it directly influences the quality of the in silico interpretations and the coherent description based on the combination with high-throughput data . In order to proceed with this improvement , our strategy was based on the argument that metabolites detected at higher concentrations at the symbiotic nitrogen fixation stage , compared with those observed under free-living conditions , may mirror their fundamental participation in sustaining a functional phenotype during the biological process . Hence , under this assumption , we identified those metabolites whose concentrations increased during nitrogen-fixing activity by applying a statistical test on the log-ratio data , as shown in Figure 1 and Dataset S1 in the supporting information . To reduce the probability of including false-positive results , we limited the selection of metabolites to only those that obeyed the following conditions: 1 ) the log-ratio increased by at least by 2-fold during nitrogen fixation , with a p-value<0 . 01 ( see the Materials and Methods section ) ; and 2 ) once the candidate metabolite was included in the OF , there existed at least a solution for the metabolic flux distribution that maximized the OF at steady state . A graphical representation of those metabolites obeying the first criterion is shown in Figure 2 ( A ) ( blue points ) . Consequently , by applying the second criterion over this metabolite subset , we identified nine metabolites that potentially have an important role in driving and supporting bacterial nitrogen fixation . Having identified candidate metabolites , we integrated them into the OF and applied linear optimization to identify the metabolic flux distribution that maximized the metabolism of nitrogen-fixing bacteria ( see the Materials and Methods section ) . The time line for the metabolic components , integrating the previous OF in bacterial nitrogen fixation for R . etli ( ZFix ) , including the one obtained with the criteria described above , is summarized in Figure 2 ( B ) and the Materials and Methods section . As a result , the OF reported in this study shows a significant number of components that were not included in previous versions , and among them are the following: 3-phospho-d-glycerate ( 3pg ) , 2-oxoglutarate ( akg ) , l-arginine ( arg-L ) , l-aspartate ( asp-L ) , citrate ( cit ) , CMP ( cmp ) , fumarate ( fum ) , malate ( mal-L ) , and l-tryptophan ( trp-L ) [see Figure 2 ( B ) ] . To assess the consequences of this improvement and compare the results reported here with those obtained in our previous study , we performed FBA on the metabolic reconstruction for R . etli ( iOR450 ) , taking into account the new OF depicted in Figure 2 ( B ) . To proceed with this comparative analysis , our simulation was carried out under equivalent conditions as those described in reference [11] , which can be summarized as follows: In this context , FBA carried out for iOR450 with the new OF led us to conclude that the consistency coefficients for genes ( ηGenes ) and proteins ( ηEnzymes ) were 0 . 61 and 0 . 71 , respectively [see Figure 2 ( D ) ] . Given that the new OF was based on the experimental metabolome profile , we argue that our in silico analysis moves towards a more accurate and improved description of metabolic activity during bacterial nitrogen fixation . In order to evaluate the metabolic implications of these results and compare our results with previous reports carried out for bacterial nitrogen fixation by R . etli , we report in Table 1 the consistency coefficients and the degrees of coverage obtained in each case . Here , the degree of coverage is defined as the fraction of genes ( proteins ) , from the total number of genes ( proteins ) obtained from constraint-based modeling , that were detected by transcriptome or proteome technology ( see the data reported in reference [11] ) . As Table 1 shows , the values of the consistency coefficients for genes and proteins had slight variations in each case . However , as Table 1 chronologically shows , the OF has systematically improved two variables: the total number of genes ( proteins ) obtained from constraint-based modeling , and the subset of genes ( proteins ) belonging to this group detected by transcriptome or proteome technologies . Thus , the OF reconstructed in this work led us to increase the degree of coverage compared with previous cases: 72 of 101 enzymes predicted in silico were experimentally identified with high-throughput technologies . In agreement with this finding , 187 genes were experimentally identified by high-throughput technologies from the 306 genes predicted in the in silico analysis . In general terms , we conclude that while the consistency coefficients slightly varied among them , the OF constructed here induced the activity of additional reactions for the biosynthesis of new metabolites in bacterial nitrogen fixation . We consider that the latter issue is a crucial step in moving toward a more accurate description for characterizing and understanding the metabolic activity supporting bacterial nitrogen fixation and eventually uncovering their fundamental organizational principles at this biological level . The predominance of a hierarchical organization in a metabolic network seems to have advantages that can be observed on short-term and longer-term time scales , because structural modularity has been suggested as a fundamental network property by which cells evolve and orchestrate their physiological responses [3] , [4] , [20] . In order to elucidate the relation between structural modules and biological functionalities in the metabolic activity of R . etli , we focused first on identification of the structural modules in the genome-scale metabolic reconstruction for iOR450 and , consecutively , analyzed the biological roles of their metabolic components . In terms of the first issue , modular structures were identified by considering a pure topological criterion , which has been suggested as a useful method for surveying the organization in a biological network [4] , [5] . Hence , as we explained in the Materials and Methods section , we defined a metric of closeness for each pair of metabolites in the network by calculating the inverse square of the minimal path length between them . Consequently , by taking into account that the metabolites integrating a module are those whose numerical values of the metric tend to be similar , we classified those metabolites with a similar pattern of closeness through a hierarchical clustering analysis ( see the Materials and Methods section ) . As a result of this analysis , nine topological modules were identified over the entire metabolic reconstruction [see Figure 3 ( A ) and ( B ) ] . Even though the previous finding supplied information about the metabolic organization underlying the reconstruction , it was necessary to carry out a functional analysis to determine how these modules can be linked and support a specific metabolic phenotype . To this end , we proceeded to characterize the biological role of the set of metabolites that comprised each topological module defined in Figure 3 ( B ) . The functional composition of each module was determined by identifying the biological roles of their components , which in turn were defined according to the classification reported in the KEGG database [6] . By considering only those metabolites appearing in the KEGG database classification , we noted that the identified metabolites fell into three main biological groups: nucleic acids , peptides , and lipids [see Figure 3 ( C ) ] . This functional analysis suggested that most of the modules were formed by a heterogeneous composition among the three biological functionalities described above; however , we noted that two modules were characterized by a well-defined functional activity , i . e . , production of nucleic acids and peptide groups [see modules one and seven in Figure 3 ( C ) ] . Intuitively , one can expect that the metabolites integrating a functional module can be characterized by their coordinate response to physiological changes in such a way that the relative concentrations of the metabolites comprising the modules be up or down-regulated in a coherent fashion under both physiological conditions . To qualitatively assess this assumption , we first identified those metabolites that were experimentally detected by high-throughput technology in each modular structure defined in Figure 3 ( B ) . Consequently , in order to estimate the coordinate behavior of metabolites inside modules , we evaluated their relative changes under the two physiological conditions: nitrogen fixation versus free-living conditions . Notably , most of the metabolites experimentally detected suggested that components inside modules up-regulate their concentrations during bacterial nitrogen fixation compared to free-living conditions [see Figure 3 ( D ) ] . This finding supports the intuitive idea that coherent activity occurs inside the metabolic modules reported in Figure 3 ( B ) . However , converse to this global trend , we observed a few metabolites with an opposite behavior [see the black lines in Figure 3 ( D ) ] . These components are potentially central metabolites with a specific regulatory control that is perhaps required for transforming the functional background during bacterial nitrogen fixation , a hypothesis to be evaluated in future studies . A list of metabolites integrating each of the structural modules , those metabolites that were experimentally detected by high-throughput technology , the ratio obtained under the two physiological conditions , and their functional classifications according to the KEGG database are depicted in Dataset S2 in the supporting information . The metabolic profile in Figure 1 represents valuable data for surveying how metabolites are organized during bacterial nitrogen fixation on a network scale . In particular , modular organization in biological systems has been suggested as a common property in biological networks at different biological levels [4] , [5] . Here , as discussed above , we have supplied evidence that structural modules for the metabolic reconstruction of R . etli can be considered a potential organizing principle by which the bacterium orchestrates its physiological response during nitrogen fixation ( see Figure 3 ) . However , these modules were identified through a static description , in which physiological information associated with a specific environment was completely absent . In order to study how the topological modules described in Figure 3 can work together to support an optimal phenotype for R . etli , we applied FBA to simulate the metabolic flux activity during bacterial nitrogen fixation . The aims in this section are 2-fold: 1 ) identification of the metabolic flux profile that acts during bacterial nitrogen fixation and to evaluate to what degree the components of the structural modules participate in supporting maximal nitrogen fixation , and 2 ) evaluation of the extent to which the metabolic array required for reaching a maximal phenotype for bacterial nitrogen fixation is robust under perturbations of the physiological conditions . As we described above , data-driven reconstruction of the OF represents a significant contribution to simultaneously improving the predictive scope of constraint-based modeling and unveiling the structural organization for cell metabolism . Hence , we proceeded to apply FBA to the metabolic reconstruction for R . etli to obtain the flux metabolic distribution that maximizes the metabolome-driven OF described above . The solution was found by solving the linear optimization problem , which was subject to thermodynamic and enzymatic constraints , over the entire set of biochemical reactions in the metabolic reconstruction ( see the Materials and Methods section and Dataset S3 in the supporting information ) . Having applied FBA , we identified those reactions required for optimizing nitrogen fixation and , with them , reconstructed a subnetwork involving only their corresponding substrates and products ( see Materials and Methods ) . To dissect the functional participation of the metabolites integrating this subnetwork , we took into account the modular classification previously defined in Figure 3 ( B ) and Dataset S2 in the supporting information . As expected , we identified the metabolites that potentially participate in support of bacterial nitrogen fixation , their biological roles , and their distributions along the modules ( see Figure 4 ( A ) and the Materials and Methods section ) . The metabolic properties inferred from this subnetwork were such that 47 . 96% ( 47 of 98 ) of the metabolites predicted by constraint-based modeling were experimentally detected inside the bacteroids for R . etli . Even though this percentage of alignment with computational modeling is relatively low , this finding represents a significant advance towards a more realistic method for the study of the metabolic activity of bacterial nitrogen fixation . As far as our knowledge extends , this metabolome study is the first performed for Rhizobiaceas in such a way that it defines a benchmark point for future improvements . According to the functional classification described in Figure 3 ( A ) and in Dataset S2 at supporting information , our in silico analysis suggested that nitrogen fixation requires a variety of metabolites , mostly peptides and lipids , belonging to diverse structural modules . Converse to the idea that all the metabolites in a specific module participate during this biological process , we found that a metabolic heterogeneity—in terms of both the number of components and module classification—is required for optimizing bacterial nitrogen fixation in R . etli . Notably , the subnetwork depicted in Figure 4 ( A ) shows a few metabolites that participate in the synthesis of nucleic acids ( dark green dots ) . These findings are in qualitative agreement with the fact that R . etli bacteroids do not grow during symbiotic nitrogen fixation with P . vulgaris ( bean plant ) . In order to distinguish the modules obtained from pure topological criteria from modules specific for a physiological condition , here after we denote these latter as functional modules . At this stage , a question that immediately emerges is to what extent the functional modules depicted in Figure 4 ( A ) are robust under external environmental perturbations , and also whether this network array can be used as a fingerprint to characterize a functional phenotype in bacterial nitrogen fixation . For the purpose of evaluating whether the metabolic organization shown in Figure 4 ( A ) can be used as a fingerprint to define the optimal phenotype for bacterial nitrogen fixation in R . etli , we explored to what extent this topological structure is robust during changes under external environmental conditions . To this end , we evaluated the robustness of this metabolic array through the systematic reduction of uptake rates of two carbon sources: succinate and inositol . While the plant supplies succinate to the bacteroid as the main carbon source , inositol is an internal metabolite that has been detected at high concentrations inside bacteroids in nodules . As has been experimentally verified , both metabolites are important components in the support of nitrogen fixation in Rhizobiaceas [11] . To explore how the availability of inositol and succinate alter the metabolic phenotype shown in Figure 3 ( A ) , we constructed a phenotype phase plane over these carbon sources [see Figure 4 ( B ) ] . In agreement with the physiological knowledge for Rhizobiaceas , we observed that according to the uptake rate , carbon sources in bacterial metabolism were reduced , and a reduction effect on nitrogen fixation was obtained [see the black region of low nitrogen fixation in Figure 4 ( B ) ] . To evaluate the topological changes at different points in the phenotype phase plane , i . e . , with different succinate and inositol uptake rates , we selected a subset of 20 points along the metabolic phase plane . As depicted by the diagonal line in Figure 4 ( B ) , uptake rates for both carbon sources were selected such that their fluxes simultaneously decreased from 20 to 0 nmol of gDW/hr . Then , for each one of the selected uptake rate conditions , we graphically represented the metabolic subnetwork that resulted from FBA . As described in the Materials and Methods section , these subnetworks were constructed by considering only those metabolites participating in the biochemical reactions required for optimization of the phenotype under defined conditions of succinate and inositol uptake rates [see Figure 4 ( C–F ) ] . To quantify the topological variations that resulted under different external conditions , we defined an overlapping coefficient as the fraction of metabolites that overlap these networks ( see the Materials and Methods section ) . With the purpose of quantifying the differences among all pairs of subnetworks , we constructed an overlapping matrix whose calculated numerical entries indicated the overlapping coefficient for each pair of subnetworks obtained at different succinate and inositol uptake rates . The comparative analysis over the 20 points depicted in Figure 4 ( B ) is shown in Figure 5 . This study led us to conclude that the metabolic profile required for optimizing bacterial nitrogen fixation does not change in a significant way for a wide range of uptake rates for either carbon source [see Figure 5 ( A–B ) ] . Hence , our study supplies evidence that , while limited carbon sources reduce the bacterial phenotype , the metabolic organization supporting bacterial nitrogen fixation tends to be robust at different succinate and inositol uptake rates . In other words , even though there was a significant reduction for a phenotype , the topology of the metabolic network did not change in a significant fashion . In light of these results , we concluded that functional nitrogen fixation , at low or high rates , seems to be achieved through a conservative metabolic profile involving those metabolites required for support of optimal nitrogen fixation under specific environmental conditions ( see Figure 5 ) . Notably , this property opens the possibility for the use of network topology for characterizing cellular phenotypes through a specific pattern of metabolites and potentially distinguishing functional from dysfunctional states associated with a specific biological system . This latter approach is an avenue to be addressed in the future . Converse to the naive idea that a reduced phenotype in bacterial nitrogen fixation is the consequence of a broken modularity in metabolism , our study supplies evidence that a reduced efficiency of a phenotype can be mainly a consequence of a decrement in flux activity along the network , but without a significant rupture of the functional modules defined in Figure 4 ( A ) . In this contextual scheme , modular organization in metabolism seems to be a necessary condition for supporting not only the maximal but a suboptimal functional phenotype in bacterial nitrogen fixation ( see Figure 4 ) .
A description of the integration between high-throughput data and computational modeling is a central issue in systems biology that is required for moving toward a quantitative and predictive analysis of the metabolic activity in microorganisms . This enterprise has relevant implications , not only in solving practical issues in biotechnology but also in supplying schemes that contribute to unraveling the principles and mechanisms that regulate and organize living systems . In this work , we have explored the relationships between three biological concepts in metabolic networks: structural modularity , biological functionality , and optimal ( maximal ) phenotype . Here , the relationships among these concepts were studied at a systems level for metabolism in bacterial nitrogen fixation , an important biological process participating in the balance of nitrogen in the biosphere [19] . Hence , by using constraint-based modeling and measuring metabolome data under two physiological conditions , we explored the metabolic organization in R . etli bacteroids while they fix nitrogen in symbiotic association with P . vulgaris ( bean plants ) . Unlike our previous study [11] , here we present a refined version of the OF used for simulating bacterial nitrogen fixation based on our taking into account metabolome data . When constraint-based modeling was applied in this new context , we concluded that 71% of the metabolic reactions predicted in silico were justified by the proteome and microarray data previously stored in the GEO database [see Figure 2 ( D ) and the Materials and Methods section] . On the other hand , based on the metabolic reconstruction for R . etli , we identified nine structural modules in which some of the metabolites components fell in one of the following biological roles: nucleic acids , peptides or lipids ( see Dataset S2 in supporting information ) . Furthermore , by considering the metabolome profile of 220 metabolites under two physiological conditions , we supplied evidence that most of the metabolites integrating each one of these modules works in a coordinated fashion by increasing their metabolite concentrations at nitrogen fixation stages [see Figure 3 ( D ) ] . This latter finding supports the idea that metabolites inside a module tend to respond in a coordinated fashion . This systems-level description supplies evidence that the diverse metabolites forming the modules participate to support an optimal phenotype in bacterial nitrogen fixation . Even more fundamentally , we note that the network representation for those arrays of metabolites associated with an optimal metabolic performance is robust under different external conditions [see Figures 4 and 5] . In light of this study's findings , the main contributions can be summed up as follows:1 ) we have supplied evidence that a robust modular organization at the metabolic level underlies optimal bacterial nitrogen fixation for R . etli; 2 ) we have proposed how these functional modules interact together for supporting bacterial nitrogen fixation; and 3 ) given the robustness observed for these functional modules when there are physiological changes , we suggest that these can be used as fingerprints to associate the active topological structure in a network with optimal phenotypic behavior . Why the metabolic activity is so robust under environmental changes ? , maybe a common explanation can be found in other complex systems , for instance the social organization in factories , where even though the production of cars can change as a consequence of external factors—as low or high demand—the hierarchical and modular organization among employees is still maintained for covering a specific demand in an efficient way . Finally , even though a variety of avenues should be addressed in future research to enrich this study , we envision that this unified description will contribute to the design of experiments for evaluating the interrelation and the role that these modules have in supporting functional states in bacterial nitrogen fixation . This latter issue is fundamental for uncovering the principles by which the cell organizes its metabolism to support functional states in bacterial nitrogen fixation .
The bacterial strain used was R . etli CFN42 wild type . Culture media and growth conditions for R . etli and plant experiments were performed as previously described by our group in reference [11] . Metabolic flux distribution supporting nitrogen fixation in R . etli was predicted in silico by using constraint-based modeling [19] . Briefly , simulations were carried out by defining a mathematical function , called the OF , for computationally mimicking the metabolism of bacterial nitrogen fixation and identifying the flux distribution that maximizes it at a steady-state behavior . The OF , ZFix , is composed of some key compounds , which are classified in two groups: 1 ) metabolites essential for sustaining nitrogen fixation , and 2 ) metabolites that are imported or exported to the bacteroid and establish the symbiotic relationship between R . etli and the plant ( these are denoted by the index [e] ) . Thus , we write the OF as follows:where glycogen , histidine , lysine , polyhydroxybutyrate , valine , alanine , aspartate , and ammonium are denoted as glycogen , hist[c] , lys , phb[c] , val[c] , ala[e] , asp[e] , and nh4[e] , respectively . Similarly , mal , trp , arg , cit , cmp , fum , 3pg , and akg denote malate , tryptophan , arginine , citrate , CMP , fumarate , 3-phospho-d-glycerate , and 2-oxoglutarate , respectively . All these metabolites are required to support an effective symbiotic nitrogen fixation , and their spatial location in the cytoplasm is indicated by the index [c] . For the purpose of obtaining a computational profile of metabolic fluxes , we assumed that the metabolic state of the bacteroid during nitrogen fixation is one that optimizes the OF , ZFix . This problem was mathematically solved as a linear optimization algorithm subject to enzymatic and thermodynamic constraints , i . e . , where Si , j represents the stoichiometric coefficient of metabolite i participating in the jth reaction . Thermodynamic and enzymatic constraints in each metabolic reaction were characterized through the parameters αj and βj ( for a detailed numerical description of these parameters , see the lower and upper bounds in Dataset S3 in supporting information ) . For the sake of simplicity , all the coefficients ( ci ) were chosen as a unit along all the analyses . Linear optimization programming was carried out using the Tomlab optimization package from Matlab . Metabolome data supply valuable information to survey the metabolic phenotype associated with a biological process , and in turn , elucidate the components integrating the OF used in the constraint-based modeling . With the purpose of carrying out FBA in the metabolic reconstruction for R . etli , a more realistic OF for simulating bacterial nitrogen fixation was obtained by identifying those metabolites with a statistically significant change during bacteroid activity compared to levels under free-living conditions . To this end , we calculated the corresponding intensity log-ratio for each metabolite between nitrogen fixation and free-living conditions . Taking into account the three experimental replicates obtained for each physiological condition , a one-side statistical t-test was applied to determine those metabolites that significantly increased their relative quantity between the two physiological conditions ( see Figure 1 ) . Those metabolites with a p-value lower than 0 . 01 and with a ratio higher than 2 during nitrogen fixation were selected as potential candidates to be included in the OF , see region I in Figure 2 ( A ) . Finally , these sets of metabolites were separately included in the OF and were accepted if their effect on nitrogen fixation activity was not reduced to one-third of the corresponding previous OF . To assess the predictive scope of the computational model , we downloaded transcriptome and proteome data so that we could integrate a set of genes whose presence and upregulation suggested an important role for sustaining nitrogen fixation in R . etli . Earlier characterizations of nodules formed after 18 days of nodulation with root plants under both experimental conditions were carried out by our group and previously stored in public databases . The complete dataset of the transcriptome analysis is freely available at GEO ( http://www . ncbi . nlm . nih . gov/geo ) , with accession numbers GPL10081 for the R . etli platform and GSE21638 for data on free-living and symbiotic forms . In addition , to enrich the set of genes required for model assessment , proteome data obtained for R . etli bacteroids were downloaded from the ProteomeCommons . org Tranche by using the following hash:BY/eCcVjwTWN1+m+2ArvJ0QVnesGx5Ekgd4wUOASACfm/ueNl7YI3iLf4xz0lnGsepV5LkpMWOQOrZtjYExlNpQkIBcAAAAAAAABjA = = . Bacteria were grown in minimal medium and were harvested at the exponential phase . Free-living cells were collected by centrifugation and washed once with double-distilled water , and immediately 2 ml of methanol and the internal standards were added and the mixture was treated in an ultrasonic bath for 30 s . A 1 . 6-ml cell suspension was transferred to centrifuge tubes , 1 . 6 ml of CHCl3 plus 640 µl of milliQ water was added , and the mixture was vortexed and then centrifuged at 2 , 300×gat 4°C for 5 min . A 1 . 5-ml aliquot of the aqueous layer was filtered through a Millipore 5-kDa cutoff filter . Then , the sample was fully dried by using a centrifugal evaporator . The bacteroid metabolome extraction followed the same method , except by day 18 after inoculation with root plant , bacteroids were extracted in a self-Percoll gradient in accordance with methods previously described [21] . Measurements of extracted metabolites were performed by using CE coupled with electrospray ionization–time-of-flight analysis and MS with electrophoresis buffer ( solution ID H3302-1021; Human Metabolome Technologies Inc . , Tsuruoka , Japan ) . To assess agreement between in silico predictions and interpretations of high-throughput data , we defined a consistency coefficient that quantified the fraction of genes predicted to be upregulated in silico and simultaneously detected or induced by proteome or transcriptome technologies ( ηGene ) . Simultaneously , we defined a consistency coefficient that quantified the fraction of active enzymes that were predicted by constraint-based modeling and identified by high-throughput technology ( ηEnzyme ) . To proceed with this evaluation , E jkegg ( G jkegg ) was denoted as the set of enzymes ( genes ) that conformed to the j-esime metabolic pathways in the KEGG database , with j ranging from 1 to 22 . Similarly , the set of enzymes ( genes ) that integrated the i-esime metabolic pathway in the reconstruction and the set of enzymes that were detected by high-throughput data were denoted E jRec ( G jRec ) and E jHT ( G jHT ) , respectively . Finally , the set of enzymes obtained from constraint-based modeling were denoted E jiModel andG jiModel . In order to evaluate and create a proper frame of comparison between in silico predictions and high-throughput data , we defined the consistency coefficient as the fraction of enzymes ( genes ) that were actively predicted in silico and were identified by high-throughput technology as follows:This ratio ranged from 0 to 1 and constituted our central parameter to assess and quantify the agreement between the constraint-based modeling and high-throughput data . To assess the output from FBA , we selected 22 metabolic pathways and evaluated the degree of coherence between the flux activity predicted in silico with data for the proteome and transcriptome previously reported for R . etli during nitrogen fixation ( see reference [11] ) . Topological analysis was accomplished by representing the set of biochemical reactions in the metabolic reconstruction as an undirected network . In this network , nodes represent the metabolites and edges indicate their participation in a specific metabolic reaction [4] . Thus , we linked the metabolites in the reactants to all the products in the biochemical reaction , and this procedure was repeated for all the reactions included in the metabolic reconstruction , to finally obtain the results shown in Figure 3 ( B ) . In the case of the FBA , the subnetworks depicted in Figure 4 ( A ) , ( C–F ) were obtained when we applied these rules only to those metabolic reactions for which the flux obtained from FBA was different from 0 . In order to analyze those metabolites with biological roles , the following metabolites were excluded from the entire network representation: pi[e] , ala-L[e] , accoa[e] , glu-L[e] , pi[c] , fdp[c] , nh4[e] , nh3[c] , nh3[e] , h[e] , ppi[c] , o2[c] , o2[e] , nadph[c] , nadp[c] , nadh[c] , nad[c] , n2[c] , n2[e] , h2o2[c] , h2o[c] , h2o[e] , h[c] , fdred[c] , fdox[c] , fadh2[c] , co2[c] , co2[e] , nh4[c] , and fad[c] . A detailed list of the metabolic reconstruction iOR450 used in this work , including the metabolic reactions and metabolite abbreviations , is provided in Dataset S3 in supporting information . Modular composition along the entire metabolic reconstruction was obtained by a clustering analysis applied on a matrix whose entries represented the inverse squares of minimal path lengths for pairs . Specifically , the clustering analysis was performed by calculating the shortest path length between every pair of genes ( i . e . , dij is the shortest path length between gene i and gene j ) . Next , we calculated the association function , defined as 1/dij2 , for all pairs of metabolites along the metabolic reconstruction . This parameter gives a measure of the closeness among genes , amplifying the parameter for pairs of metabolites with low path lengths and minimizing pairs of metabolites located at remote distances . With the purpose of identifying topological modules along the network , these sets of parameters were used as input to perform a hierarchical clustering [3] , [4] , [5] , [22] , see Figure 2 ( A ) . A detailed description of the modules identified by this algorithm and the corresponding biological roles for some of the components are shown in Dataset S2 . The clustering analysis was performed using a hierarchical agglomerative average-linkage clustering algorithm , considering Kendall'sτ value as the similarity metric . In order to compare the similarities and differences that emerged from the functional modules obtained along the phase plane region depicted in Figure 4 , we define the following coefficient:where ηi and ηj represent the number of metabolites that appeared in only module i or j , respectively , and indicates the number of metabolites in common between modules i and j . Clearly , this coefficient ranges from 1 to 0 , depending on the complete or null analogy between the i and j functional subnetworks . | Biological networks are an inherent concept in systems biology that is useful in elucidating how biological entities—as metabolites or proteins—work together in supporting specific phenotypes in microorganisms . Notably , topological analyses carried out over these networks have shown that modular organization is a ubiquitous property at different levels of biological organization , in such a way that modular organization may serve as an organizing principle governing the metabolic activity in microorganisms . With the aim of elucidating the relationship among functional modules , network topology , and optimal metabolic activity , here we present an integrative study that combines computational modeling and metabolome data for evaluation of the metabolic activity of the soil bacterium Rhizobium etli during symbiotic nitrogen fixation with Phaseolus vulgaris . As a result , we supply experimental and computational evidence supporting the concept that the optimal metabolic activity during this biological process is guided by modular structures in the metabolic network of R . etli . Even more fundamentally , we suggest that these biochemical modules interact among each other to ensure an optimal phenotype during nitrogen fixation . Finally , through the in silico analysis on the genome scale metabolic reconstruction for R . etli , we give some examples that suggest that these modular structures supporting nitrogen fixation are robust to external physiological conditions . |
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Infection with Epstein-Barr virus ( EBV ) is highly prevalent worldwide , and it has been associated with infectious mononucleosis and severe diseases including Burkitt lymphoma , Hodgkin lymphoma , nasopharyngeal lymphoma , and lymphoproliferative disorders . Although EBV has been the focus of extensive research , much still remains unknown concerning what makes some individuals more sensitive to infection and to adverse outcomes as a result of infection . Here we use an integrative genomics approach in order to localize genetic factors influencing levels of Epstein Barr virus ( EBV ) nuclear antigen-1 ( EBNA-1 ) IgG antibodies , as a measure of history of infection with this pathogen , in large Mexican American families . Genome-wide evidence of both significant linkage and association was obtained on chromosome 6 in the human leukocyte antigen ( HLA ) region and replicated in an independent Mexican American sample of large families ( minimum p-value in combined analysis of both datasets is 1 . 4×10−15 for SNPs rs477515 and rs2516049 ) . Conditional association analyses indicate the presence of at least two separate loci within MHC class II , and along with lymphocyte expression data suggest genes HLA-DRB1 and HLA-DQB1 as the best candidates . The association signals are specific to EBV and are not found with IgG antibodies to 12 other pathogens examined , and therefore do not simply reveal a general HLA effect . We investigated whether SNPs significantly associated with diseases in which EBV is known or suspected to play a role ( namely nasopharyngeal lymphoma , Hodgkin lymphoma , systemic lupus erythematosus , and multiple sclerosis ) also show evidence of associated with EBNA-1 antibody levels , finding an overlap only for the HLA locus , but none elsewhere in the genome . The significance of this work is that a major locus related to EBV infection has been identified , which may ultimately reveal the underlying mechanisms by which the immune system regulates infection with this pathogen .
Epstein-Barr virus ( EBV ) belongs to the herpes virus family , which consists of double-stranded DNA viruses , composed of a DNA core surrounded by a nucleocapsid and a tegument , with relatively large genomes ( 100–200 genes ) . There are currently eight known human herpesviruses , which include herpes simplex virus type I , herpes simplex virus type II , varicella-zoster virus , EBV , cytomegalovirus , human herpesvirus 6 , human herpesvirus 7 , and Kaposi sarcoma herpesvirus . Infection with EBV is common , with over 90% of the world's adult population estimated to be infected [1] . EBV is thought to be typically transmitted through contact with saliva , infecting B lymphocytes and epithelial cells of the oropharynx [2] . The virus is shed consistently into saliva during primary infection , but intermittent shedding can continue for years afterwards . Following initial infection , EBV establishes a dormant , lifelong infection ( mainly in memory B cells ) and retains the potential to reactivate [3] , [4] . In developing countries primary EBV infection usually occurs during infancy or early childhood , and is typically without clinical symptoms or presents as a mild febrile illness . In more affluent countries infection in childhood is still common , but for approximately one third of individuals primary infection occurs during adolescence or early adulthood and is associated with a higher risk of infectious mononucleosis [5] . EBV infection has also been associated with some malignant conditions including Burkitt lymphoma , nasopharyngeal carcinoma , some gastric cancers , and Hodgkin lymphoma [6]–[11] . EBV has been studied extensively . It was the first human tumor virus identified , and its viral genome was the first to be fully described [12]–[13] . While genetic risk factors have been reported for particular EBV-infected subpopulations ( e . g . , individuals with Hodgkin lymphoma , infectious mononucleosis , and multiple sclerosis [14]–[19] ) , there is still much that remains unknown about inter-individual differences in antibody response to EBV exposure and potential adverse outcomes related to infection with this pathogen . Here we have quantified antibody titer to the Epstein-Barr virus nuclear antigen 1 ( anti-EBNA-1 ) , which reflects history of infection with this pathogen , in a randomly ascertained sample ( i . e . , one that is not enriched for a particular phenotype ) of Mexican Americans and used genome-wide linkage and association analyses and expression profiling on lymphocytes to identify underlying genetic loci .
IgG antibodies to EBNA-1 were quantified in plasma samples from 1 , 367 randomly ascertained and naturally infected Mexican American participants of the San Antonio Family Heart Study ( SAFHS ) , which represent 63 families , many of which are large genealogies ( Table S1 ) . While seropositivity to infection with EBV is sometimes measured using IgG antibodies against the viral capsid antigen ( VCA ) , here we refer to EBV seropositivity as based on measurement of anti-EBNA-1 antibodies , which are produced during latent EBV infection . In this study , 48% of individuals are categorized as seropositive for anti-EBNA-1 antibodies , 24% have intermediate ( seroindeterminate ) levels , and 28% are seronegative . EBNA-1 seroprevalence is similar in men and women and does not change substantially with age within the examined age range of 16–94 years ( Figure 1 ) , indicating that most subjects underwent primary infection before adulthood . Given the prevalence of EBV infection and mode of transmission , we assume that essentially all individuals have been exposed to this pathogen multiple times during their lifetime , and therefore seronegative status should be informative in that those individuals failed to mount an antibody-mediated immune response to EBNA-1 ( or mounted only a response so weak as to lead to undetectable antibody levels ) . While the measured antibody assay generates a quantitative read-out of antibody levels , conceptually the antibody-mediated immune response may be viewed as a yes/no trait , in which the immune system did or did not mount an antibody response after exposure , and thus it may also be justifiable to discretize the assay results . We have performed all analyses presented below on both the quantitative antibody phenotype and the dichotomized serostatus phenotype . For clarity , in the text we pay more attention to the results with the quantitative phenotype . In addition , we view the quantitative trait as more informative and the statistical results to be more reliable , partly because the cut-off levels for discretization are somewhat arbitrary . However , the results for both traits are presented in the Figures and Tables , and the findings are highly consistent with one another . Heritability rates of EBNA-1 serological phenotypes were estimated using a variance components approach , and shared environment was accounted for by using a “household” random effects model [20] . EBNA-1 serological measures are significantly heritable , at 43% ( p≪10−9 ) and 68% ( p≪10−9 ) for the quantitative antibody titer and discrete serostatus traits , respectively ( Table 1 ) , indicating that host-genetic factors are important determinants of immune response ( as we have previously found to be the case for other pathogens [21] ) . However , household effects , which are one measure of shared environmental exposures and which were based on reported co-habitation at the time of blood draw for serological assaying , are not significant in this sample . Given the high prevalence of this pathogen , this observation may indicate that an individual is just as likely to be infected by someone who does not share the same residence as they are by someone who does . To localize any underlying genetic factors , we performed a variety of genome-wide analyses using nearly 1 million available SNPs . The SAFHS consists of extended families that provide information on linkage as well as association . We therefore performed joint analysis of linkage and association , thereby using both information sources available in families in order to localize the responsible loci . After Bonferroni correction for the number of SNPs analyzed , multiple genome-wide significant SNPs ( most significant p-values of 3 . 3×10−9 and 8 . 3×10−11 for the quantitative and dichotomous serological trait , respectively ) were found in the human leukocyte antigen ( HLA ) region on chromosome 6 ( Figure 2 , Table S2 ) . The HLA region is also implicated by linkage analysis itself , with maximum LOD scores of 1 . 27 and 3 . 05 for the quantitative and discrete trait , respectively ( Figure S1 ) . No genome-wide significant evidence of linkage and/or association was found elsewhere in the genome . Having identified the HLA region as a major locus , we performed association analysis conditional on linkage , in order to separate out the association signal from individual SNPs ( see Methods section for more detail ) . The most significant association p-value for the quantitative trait occurs with SNPs rs477515 and rs2516049 ( p = 1 . 4×10−8 ) ( Table 2 ) , two SNPs that are in complete linkage disequilibrium ( LD ) with each other and are located within the HLA-DRB1 gene in the HLA class II region . The most significant result for the discrete trait is for SNP rs9268832 in HLA-DRB9 ( p = 2 . 2×10−9 ) . In all , 5 and 19 SNPs reach genome-wide significance for the quantitative and discrete EBNA-1 traits , respectively , and all are located in the HLA region of chromosome 6 . As shown by the quantile-quantile plot , the HLA region accounts for the entirety of the deviation from the diagonal line of expected p-values under the null hypothesis , and there was no evidence of any inflation in p-values once the HLA region was excluded from the plot ( Figure S2 ) . To confirm our findings , we generated anti-EBNA-1 antibody measurements ( using the same assay ) in plasma samples from 589 participants ( representing 39 , mainly extended families [Table S1] ) in a separate Mexican American family cohort from San Antonio , the San Antonio Family Diabetes/Gall Bladder Study ( SAFDGS ) [22] . EBNA-1 seroprevalence in this cohort was estimated to be somewhat higher at 85% . Heritability estimates are 37% ( p = 1 . 0×10−6 ) and 42% ( p = 1 . 9×10−3 ) for the quantitative and discrete traits , respectively , nearly identical to the prior estimates from the SAFHS . Linkage analysis yielded LOD scores of 3 . 37 and 2 . 22 in the HLA region on chromosome 6 for the quantitative and dichotomous traits , respectively ( Figure S3 ) . Also , genome-wide joint linkage and association analysis points to significant results only for chromosome 6 , and not elsewhere in the genome ( Figure S4 ) . We then performed association analysis conditional on linkage of all SNPs from the extended HLA region , and after Bonferroni correction for the number of SNPs analyzed we obtained significant evidence of association with the same two SNPs ( rs477515/rs2516049 ) ( most significant p-values of 4 . 1×10−6 and 8 . 8×10−7 for the quantitative and discrete traits , respectively ) ( Table 2 ) . Given the unambiguous replication of the HLA locus , including evidence of linkage and association in the extended HLA region in both datasets , we also performed joint analysis of both pedigree cohorts ( SAFHS+SAFDGS ) using all available SNPs in the extended HLA region , in order to use all available information for identification and prioritization of the most candidate SNPs within this region . The minimum p-values for association analysis conditional on linkage for the combined dataset were even more significant and occurred at the very SNPs that gave the most significant results for each dataset alone ( p = 3 . 1×10−13 and p = 3 . 9×10−12 for SNPs rs477515/rs2516049 for the quantitative and discrete traits , respectively ) ( Figure 3 , Table 2 ) . The HLA region is highly complex and exhibits considerable and long-range LD . In order to determine whether a single or multiple haplotype block harbors genetic variants influencing the serological EBNA-1 phenotypes , we performed several rounds of conditional association analysis within the extended HLA region using the combined sample of both studies . We first conditioned on the most significant SNP for the quantitative trait ( rs477515/rs2516049 ) and identified one additional significant SNP ( rs2854275 , located within the HLA-DQB1 gene in the MHC class II region ) that was independently associated with EBNA-1 at a genome-wide level of significance ( Table 3 , Figure S5 ) . After conditioning on both independent SNPs ( rs477515/rs2516049 and rs2854275 ) , no additional SNPs were significant for the quantitative antibody trait . This suggests that at least two haplotype blocks harbor variants influencing EBNA-1 seroreactivity . The pattern of LD among SNPs giving genome-wide significant association evidence with either EBNA-1 quantitative or dichotomous EBNA-1 trait is shown in Figure S6 . In order to pinpoint the most likely gene ( s ) influencing anti-EBNA-1 antibodies , we used an integrative genomics approach based on available expression profiles from 1 , 243 peripheral blood mononuclear cell ( PBMC ) samples ( collected at the same point in time as the plasma samples used for antibody assays ) from SAFHS study participants . Specifically , we examined whether the SNPs that are significantly associated with anti-EBNA-1 antibody status are also significantly associated with expression levels of any nearby gene transcripts ( which would suggest that such SNPs are putative cis-regulatory variants of these transcripts ) , and whether those transcript levels in turn are significantly associated with antibody status . Using the 41 SNPs that were significantly associated with EBNA-1 seroreactivity in the combined SAFHS+SAFDGS sample ( with either the quantitative and/or discrete trait , and in the initial and/or subsequent association analysis , i . e . the SNPs included in Table 2 and Table 3 ) , we conducted association analyses on the 150 expressed transcripts from the extended HLA region , yielding 6 , 750 SNP-transcript pairs . After Bonferroni correction , we observed significant association results ( conditional on linkage ) for four SNP-transcript pairs ( Table S3 ) . SNPs rs204999 and rs10947261 are significantly associated with expression of the housekeeping gene RPS18 ( p = 4 . 8×10−5 and p = 3 . 0×10−4 , respectively ) , and SNPs rs9273327 and rs2854275 are significantly associated with PBX2 expression . PBX2 is a gene involved in B-cell and certain T-cell leukemias . Our results indicate that that these SNPs ( or variants in LD with them ) may be putative cis-acting regulators of these genes . However , the evidence of association is only of moderate strength for any SNP-transcript pair ( the p-values are significant after Bonferroni correction for the number of transcripts analyzed within the extended HLA region , but not if one were to impose a genome-wide multiple testing correction ) . In addition , the distance between SNPs and probes is fairly large compared to commonly observed , strong cis-acting expression nucleotides . Other SNP-transcript pairs yielded suggestive , but not statistically significant , results after correction for multiple testing . We subsequently looked at whether any HLA transcripts were significantly correlated with EBNA-1 seroreactivity . However , the expression levels of RPS18 and PBX2 , which we had found to be potentially cis-regulated by SNPs associated with EBNA-1 antibody phenotypes , were not significantly correlated with the EBNA-1 traits ( p = 0 . 17 and p = 0 . 82 , for RPS18 and PBX2 respectively , for the quantitative trait ) . Among the other HLA transcripts , the expression level of HLA-DRB1 is most significantly associated with both anti-EBNA-1 traits ( quantitative: p = 2 . 8×10−5; discrete: p = 3 . 0×10−4 ) ( Table S4 ) . Thus , in conclusion , while our integrative genomic analyses point to potential candidate genes , the evidence obtained does not yield overwhelming support for a particular candidate gene . A potential explanation may be the fact that the relevant differences in HLA function are attributable to alteration in protein sequence or binding affinity rather than gene expression level . As the HLA region is well known to play a role in many aspects of the immune system , it may not be surprising that genetic variants therein appear to influence anti-EBNA-1 antibody levels . To examine whether the identified locus is specific to EBNA-1 , or whether it plays a role in determining antibody titers more generally , we assessed whether the EBNA-1-associated SNPs are also significantly associated with antibodies directed at other herpesviruses or other pathogens . IgG antibody titers had previously been measured on the same plasma samples [23] for 12 other pathogens , including 5 herpesviruses . We focused on the top two independent SNPs associated with the quantitative EBV antibody titer in the SAFHS , and found no evidence of association of SNPs rs477515/rs2516049 or rs2854275 with any of these other pathogens , suggesting that the identified loci are specific to EBV ( Table 4 ) and not some general IgG HLA control mechanism . Several types of cancer have been linked to infection with EBV , we therefore looked for evidence of genetic overlap between anti-EBNA-1 antibody traits and published susceptibility loci for two common EBV-related cancers , nasopharyngeal carcinoma ( NPC ) and Hodgkin lymphoma ( HL ) . A comparison with Burkitt lymphoma was not made , however , as genetic association loci were not available in the published literature . The results for association analysis ( conditional on linkage ) for EBNA-1 quantitative and discrete traits for NPC-related SNPs are presented in Table 5 , and Table 6 presents the results for HL-related SNPs . Two NPC SNPs , rs2860580 and rs28421666 , were significantly associated with the EBNA-1 traits , after applying a Bonferroni correction to account for testing 23 SNPs . Both SNPs are located on chromosome 6 , in HLA class I and II regions , respectively . Similarly , four HL SNPs that were found to be significantly associated with EBNA-1 trait ( rs204000 , rs9268542 , rs2395185 , and rs2858870 ) were also located in the HLA region . EBNA-1 traits were not significantly associated with cancer-related SNPs that are located outside the HLA region . Because EBV infection has been associated with certain autoimmune diseases , in particular systemic lupus erythematosus ( SLE ) and multiple sclerosis ( MS ) , we examined whether there is evidence for overlap of genetic factors influencing these traits and anti-EBNA-1 antibody traits by investigating whether any of the previously reported SNPs significantly associated with these disorders also show association with EBNA-1 antibody traits . Table 7 provides the results of the association analysis ( conditional on linkage ) for EBNA-1 quantitative and discrete traits with SLE-associated SNPs taken from the literature , and Table 8 focuses on SNPs associated with MS . After applying a Bonferroni correction for examining 47 SLE-relevant SNPs , rs9268832 and rs9271366 were significantly associated with both EBNA-1 serological traits , and rs3135391 with the discrete EBNA-1 trait . Notably , all three of these SNPs are located in the HLA region on chromosome 6 . None of the 41 SLE-associated SNPs outside of the HLA region show any evidence of association to EBNA-1 . A comparison with 30 genome-wide significant MS SNPs from published reports yielded similar results , with statistically significant association results , for both EBNA-1 traits with SNP rs9271366 , and also for the discrete EBNA-1 serostatus trait with SNPs rs3129860 and rs3135388 , all of which are located within the HLA region ( Table 8 ) . As with SLE , there was no evidence for association of EBNA-1 with MS-associated SNPs from anywhere else in the genome .
In this study , we estimated the seroprevalence rate of EBV infection as 48% seropositive in the study population of 1 , 367 Mexican American participants from the SAFHS . Our estimate is lower than estimates of EBV prevalence for other adult populations [24] , but this study characterized anti-EBNA-1 antibody titers , while many other estimates are based on measurements of IgG antibodies against EBV VCA . Typically , anti-VCA antibody titers will give a slightly higher estimate , as some anti-VCA positive individuals will subsequently fail to also make anti-EBNA-1 antibodies [25] . In addition , there may be other variations between assays and their cutoff values . When we include the indeterminate samples as seropositive in our analysis , the estimate increases to 70% EBV seropositivity , which is close to estimates for the U . S . general adult population of 73% to 90% [26] . In the replication study ( SAFDGS ) , the same assay yielded a higher seroprevalence estimate ( 85% ) . The reason for this difference is unclear , but may be related to simple threshold effects that magnify differences between assays being run at two separate points in time when dichotomizing quantitative assay read-outs . A comparison of our heritability estimates for anti-EBNA-1 antibody titers ( h2 = 0 . 43 for the SAFHS , and h2 = 0 . 37 for the SAFDGS ) with estimates for anti-VCA antibody titers ( h2 = 0 . 32–0 . 48 [27] ) shows that they fall within the same range . Our study identified multiple , significant associations of anti- EBNA-1 antibody measures with genetic factors located in the HLA region , which contains genes related to immune function in humans . These associations were not found for seroreactivity to 12 other pathogens examined in this study , and therefore appear to be specific to EBNA-1 . HLA class I genes are involved in the presentation of peptides ( including viral antigens ) from within the cell , which attract CD8+ T lymphocytes ( cytotoxic T cells ) to destroy cells presenting foreign antigens . Previous research identified genetic loci within this region that were associated with the development of infectious mononucleosis upon primary infection with EBV , suggesting that HLA class I polymorphisms influence T cell response during primary EBV infection and viral persistence [17] . The HLA class I region has also been implicated in the development of classical Hodgkin lymphoma among EBV-positive individuals [14]–[16] . HLA class II genes are involved in presenting peptides from outside the cell to CD4+ T lymphocytes ( helper T cells ) , which in turn stimulate B cells to produce antibodies . In our study , genes significantly associated with anti-EBNA-1 antibody levels belong to HLA class II . This supports earlier reports of an association between HLA class II and EBV susceptibility among individuals with multiple sclerosis [17]–[19] . EBV primarily targets resting B cell lymphocytes , which are induced to proliferate , and also infects epithelial cells of the nasopharynx and oropharynx . EBV infects B lymphocytes via attachment to the target cell by binding of the viral major envelope glycoprotein gp350 to complement receptor type two , CD21 , on the cell surface [28] . Subsequent penetration of the cell membrane requires a complex of three glycoproteins: gH and gL , which have functional homologs in other herpesviruses; and gp42 , which is EBV-specific . Glycoprotein gp42 binds to HLA-DR on the host cell , and in this way HLA class II molecules serve as cofactors for EBV infection of B cells [29] . In our study , we demonstrate a significant association between EBV serostatus and SNP rs477515/rs2516049 , which is located in gene HLA-DRB1 within the HLA-DR gene cluster , and the expression level of HLA-DRB1 is also significantly correlated with both EBNA-1 serological traits , though we did not observe evidence indicating that the particular EBNA-1-associated SNPs are likely cis-acting regulators of this gene . Nearby genes HLA-DRA and HLA-DRB9 do appear to be associated with significant EBNA-1 SNPs , but their expression levels are not significantly correlated with the examined antibody traits . Nonetheless , based on our results , these genes appear to be the best candidates for playing a role in EBV susceptibility in the study population . This may be related to viral penetration of B cells , but it is more likely due to specific haplotype and T cell recognition , as HLA class II genes are also involved in the presentation of viral antigens to T cells , which is important in suppressing proliferation of EBV-infected B cells . HLA-DR is a class II cell surface receptor that serves as a ligand for the T cell receptor . The primary function of HLA-DR is the presentation of peptide antigens to the immune system , and it is closely linked to HLA-DQ , another molecule that presents antigens to T cells . In our study , after conditioning on the top SNP ( rs477515/rs2516049 ) there was significant association of EBNA-1 serostatus and a second independent SNP ( rs2854275 ) that is located within the HLA-DQB1 gene . After binding to the foreign antigen , T cells stimulate B-cells to produce anti-EBV antibodies . Our finding of significant SNPs located in the HLA-DR and HLA-DQ genes may relate to the efficiency of cell surface antigen presentation to T cell receptors in EBNA-1 seropositive individuals . Under normal circumstances , the host immune system is capable of limiting the proliferation of EBV-infected B cells through natural killer ( NK ) cell and T cell responses . However , some copies of the virus will become latent in memory B cells , at concentrations of approximately 1 to 50 per 106 in cells within peripheral blood in healthy individuals [30] . Although most individuals will not experience clinical symptoms after EBV enters latency , a small percentage may develop cancer following a re-activation of infection . During active infection , the virus produces approximately 100 different viral proteins that are involved in viral replication and modulating the immune response in the host . However , during latent infection only about 10 proteins are produced by the virus , including EBNA-1 . This protein , which was used in our study to quantify EBV antibody titer , correlates closely with past infection [31] and is expressed on all EBV-associated tumors . EBNA-1 is suggested to elicit poor CD8+ T cell response [32] and it is considered to be a universal viral oncogene [2] . EBV-associated malignancies are higher in particular geographic locations as well as among certain ethnic groups , indicating that both environmental exposures and genetic factors are likely involved in disease risk . Cancers linked to EBV infection include: Burkitt lymphoma , which is prevalent in Africa and for which malaria may be a cofactor [6]; nasopharyngeal carcinoma , which is more common among individuals from South China [33] , [34]; Hodgkin lymphoma , which is reported to have a higher incidence in Hispanic and Asian/Pacific Islander populations [35]; parotid tumors in patients from Alaska [8]; and some gastric cancers [9] . EBV infection is also associated with post-transplant lymphoproliferative disorders [36] . Serological evidence points to high EBNA-1 antibody titers prior to the onset of clinical symptoms for several of these malignancies [7] , [37] . In our study , we found an overlap between EBNA-1 traits and NPC susceptibility loci in HLA-A and HLA-DR/DQ genes , and with HL loci in BTNL2 , and HLA-DR genes . It has been suggested that the presentation of EBV-derived peptides is in some way involved in the pathogenesis of EBV-related cancer [16] . We also found suggestive evidence for association of top SNPs with the expression of EGFL8 , which has previously been implicated in cancer progression , and NCR3 , a gene that encodes a natural cytotoxicity receptor that may aid NK cells in lysing tumor cells . Studies indicate that EBV may be implicated in the development of autoimmune disease , including systemic lupus erythematosus ( SLE ) and multiple sclerosis ( MS ) [38] , [39] . In SLE , patients may be unable to keep latent EBV infection in check , possibly due to defective T cell response to the virus . Molecular mimicry appears to play a role in SLE autoimmunity , as humoral immune response initially targets the proline-rich repeat motif PPPGMRPP , antibodies against which also cross-react with the EBNA-1 peptide PPPGRRP [40] , [41] . In our study we provide evidence for shared genetic factors that influence both anti-EBNA-1 antibody status and autoimmunity , as shown by a significant association between EBNA-1 serological measures and SLE SNPs rs3135391 , rs9268832 and rs9271366 , all located in the HLA region . SNP rs9268832 was both the top SNP associated with the EBNA-1 discrete serostatus trait in our study of Mexican Americans and the top SNP identified in a Spanish SLE cohort [42] . Given that this SNP lies within the HLA class II pseudogene DRB9 , it has been suggested that this association is due to the composite effect of SNPs rs3130490 ( located in the MSH5 gene ) and rs3129768 ( located between HLA-DRB1 and HLA-DQA1 genes ) . Dysregulation of the MSH5 gene , which plays a role in immunoglobulin class switching , allowing B cells to generate different classes of antibody but with the same specificity , is proposed to contribute to risk of developing SLE . While SNPs located within this gene were not statistically significant in our sample after adjusting for multiple testing , other genetic factors that appear to influence both EBNA-1 serostatus and SLE include HLA-DR and HLA-DQ loci , possibly due to mechanisms shared across various autoimmune and inflammatory diseases . Susceptibility to MS was previously shown to be associated with HLA genes , and with HLA-DRB1 in particular [43] , which in our study was significantly related to EBNA-1 serostatus . Indeed , our results , which are based on genome-wide investigation , support an earlier report of association between anti-EBNA-1 antibody titer level and HLA-DRB1 in an MS cohort [18] . That study observed that HLA-DRB1*15 positive individuals had a higher level of anti-EBNA-1 antibody titer and greater risk of developing MS , indicating that HLA genetic influence on MS risk may also involve control of EBV infection . In addition to HLA-DRB1 , MS has been associated with changes in the expression of a number of other genes , including other HLA-DR genes and HLA-DQ genes [44] . We found evidence for a significant overlap between anti-EBNA-1 antibodies and three MS HLA SNPs ( rs3129860 , rs3135388 , and rs9271366 ) , which are associated with HLA-DR and HLA-DQ genes , and may be related to a more general autoimmune/inflammatory response . In summary , the results of our study indicate that genetic determinants in the HLA region are important in the immune response to EBV and subsequent regulation of infection with this pathogen . Variation in EBNA-1 antibody titer among individuals may be due in part to variation in B cell permeability to EBV infection and/or differences in cell surface antigen presentation , as indicated by a statistically significant relationship between genetic factors within the HLA II region and EBV serostatus ( defined here by the level of anti-EBNA-1 antibodies ) that was not found for the other pathogens examined . Further investigation may reveal the underlying mechanisms by which these HLA genes potentially limit EBV viral load , possibly influencing risk for developing autoimmune disease or cancer in infected individuals .
The study and protocols were approved by the Institutional Review Board at the University of Texas Health Science Center at San Antonio , and informed consent was obtained from all participants . Individuals in this study included 1 , 367 members of extended , multi-generational families from the Mexican American community in San Antonio , Texas , and surrounding region . They were recruited during the years 1991–1995 for participation in the San Antonio Family Heart Study ( SAFHS ) , which seeks to identify genetic risk factors for cardiovascular disease [45] , and were ascertained without regard to any specific disease phenotype . Participants included 551 men and 816 women , who ranged in age from 16–94 years and represented 63 families ( Table S1 ) . These families had up to 6 generations and the largest consisted of 101 phenotyped individuals . Included in the study were 267 sibships , with an average size of 3 . 3 and size range of 2–12 . Significant findings were replicated for a separate sample of 589 Mexican Americans participating in the San Antonio Diabetes/Gallbladder Study ( SAFDGS ) . The SAFDGS seeks to investigate the genetic influences underlying type II diabetes mellitus and gallbladder disease , and it is similar in design to the SAFHS except that recruitment was based on a single diabetic proband in each pedigree , and the sample is therefore enriched for diabetics [21] , [46] . Please note that this is a very weak form of ascertainment in Mexican Americans from San Antonio , where lifetime prevalence of diabetes approaches 30% . In fact , 20 years after the initiation of both studies , the prevalence rates of major diseases , such as heart disease , diabetes , and obesity , are not significantly different between these two component studies . The SAFDGS participants consisted of 39 families , representing up to 6 generations , and included 115 sibships , which ranged in size from 2–9 ( average size of 3 . 2 ) . Analyses were also run on the combined data set ( SAFHS+SAFDGS ) , which included 1 , 956 phenotyped individuals . Familial relationships were confirmed based on genotype composition using PREST [47] . Blood samples were collected from participants after an overnight fast , at the time of recruitment ( 1991–1995 ) using EDTA vacutainers . Frozen plasma aliquots were obtained as previously described [48] , along with the buffy coat for DNA extraction , and stored at −80°C . Plasma samples were thawed just prior to antibody determinations , and IgG antibodies to Epstein-Barr virus nuclear antigen 1 ( EBNA-1 ) were measured using a commercially available enzyme-linked immunosorbent assay ( ELISA ) kit ( IBL-America , Minneapolis , MN ) . Seropositive/seronegative status was determined according to the manufacturer's instructions using the following absorbance values: seronegative if ≤0 . 9; indeterminate if >0 . 9 and <1 . 1; and seropositive if ≥1 . 1 . Antibody titers to 12 comparative pathogens were also obtained and included: Chlamydophila pneumoniae , Helicobacter pylori , Toxoplasma gondii , cytomegalovirus ( CMV ) , herpes simplex type I virus ( HSV-1 ) , herpes simplex type II virus ( HSV-2 ) , human herpesvirus 6 ( HHV-6 ) , varicella zoster virus ( VZV ) , adenovirus 36 ( Ad36 ) , hepatitis A virus ( HAV ) , influenza A virus , and influenza B virus [23] . For Ad36 , a previously described serum neutralization test was utilized for measuring antibodies [49] , and analyses were run in duplicate , with specimens assigned as seropositive if both replicates had neutralization titers ≥1∶8 , otherwise they were considered to be seronegative . Serostatus for all other pathogens was determined using the same criteria as for EBNA-1 ( i . e . , seronegative if ≤0 . 9; indeterminate if >0 . 9 and <1 . 1; and seropositive if ≥1 . 1 [23] ) . DNA was extracted from the lymphocyte samples from study participants . SNPs were typed using several versions of Illumina's SNP genotyping BeadChip microarrays ( HumanHap550v3 , HumanExon510Sv1 , Human1Mv1 and Human1M-Duov3 ) , according to the Illumina Infinium protocol ( Illumina , San Diego , CA ) . SNP genotype data underwent extensive processing prior to analysis: SNPs with a low call rate , that were monomorphic or those comprising <10 individuals with the minor allele were excluded from analysis . Additional SNPs were excluded if Hardy-Weinberg Equilibrium test statistics were equivalent to p≤10−4 ( calculated using SOLAR [50] while taking relationships properly into account ) , leaving a total of 944 , 565 SNPs for further analysis . Allele frequencies were computed using maximum likelihood estimates in SOLAR [50] . SNP genotypes were checked for Mendelian consistency using Simwalk [51] . MERLIN [52] was used to impute missing genotypes conditional on relatives' genotypes , with a weighted average of possible genotypes being used when an individual's genotype could not be inferred with certainty . Multipoint identity-by-descent ( MIBD ) matrices , based on a subset of 28 , 219 informative SNPs that were not in LD with one another , were calculated with LOKI [53] . The chromosomal maps used in the analyses were based on those generated by deCODE genetics [54] . Both anti-EBNA-1 quantitative antibody titer and discrete serostatus traits were analyzed . Statistical analyses of the sample of related individuals were performed using a variance components ( VC ) approach with the computer software package SOLAR [50] . Due to the sensitivity of VC analyses to non-normality , the quantitative antibody titer trait was transformed prior to analysis using an inverse , rank-based normalization to ensure a standard normal distribution of this phenotype . For the genetic analysis of the discrete EBNA-1 serostatus trait within a VC framework , a liability threshold model was used , in which serostatus was assumed to reflect an unobservable underlying quantitative liability , with individuals above a threshold being seropositive , and those below being seronegative [55] , [56] , individuals with indeterminate serostatus were excluded from analysis . All analyses included sex , age ( at the time of sample collection ) , and their interactions as covariates . Although the SAFDGS was enriched for diabetics , diabetes status was found to not be a significant predictor of EBNA-1 antibody status and therefore was not included in further analyses . Narrow-sense heritability , or the proportion of phenotypic variance attributable to the aggregate effects of additive genetic variation , was estimated along with the influence of shared environmental factors , which were modeled using a “household” random effects component [20] . Individuals living together at the time of the blood draw were considered members of the same household . Details concerning the length of cohabitation , however , were not available . Because the SAFHS includes extended families , which provide information on both linkage and association , we performed several analyses in order to maximize the amount of information obtained from this sample . We performed genome-wide linkage analysis , using MIBDs based on 28 , 219 SNPs , to identify regions of the genome that may harbor genetic variants influencing EBNA-1 serological traits . We also performed joint genome-wide linkage and association analysis , based on 944 , 565 SNPs that were available for 1 , 367 individuals , in order to have more power for localizing the responsible loci . The joint analysis was conducted under a VC model in which the linkage component was implemented as a regular VC-based random effects linkage model , and an additive measured genotype model was used for the association component . Two-times the natural logarithm of the likelihood ratio of the joint linkage and association test was assumed to be distributed as a 50∶50 mixture of chi-squared random variables with 1 and 2 degrees of freedom , respectively . In order to remove the long-distance linkage effect and hone in on the shorter-range association signal , and thus achieve better differentiation among SNPs , we then performed association conditional on linkage for the extended HLA region ( nucleotide positions 29 , 677 , 984 to 33 , 485 , 635 ) , based on all 5689 available SNPs . As population substructure may result in spurious associations in GWAS studies , we corrected for this by using principal components analysis to model differences in ancestral contributions among study participants [57] . R princomp [58] was used to run the principal components analysis on a subset of 11 , 512 autosomal SNPs ( determined to be in low mutual linkage distribution [LD] ) in 345 genotyped founders , and offspring were assigned PC values averaged over their parents , in order to not accidentally remove true pedigree differences . The first five principal components were included as additional covariates in all statistical analyses ( these account for ∼3% of the variance in the genotype scores , indicating that there is in fact little evidence for stratification in the Mexican American cohort ) . Given that there is a large amount LD in the HLA region on chromosome 6 , we ran multiple conditional analyses on the top EBNA-1 SNPs , in order to determine the number of independent significantly-associated SNPs . In addition , LD specific to the Mexican American study population , and appropriately taking the relatedness into account , was calculated using SOLAR [50] and regional plots , based on this information , were generated using LocusZoom [59] . Transcriptional profile data were available for PBMCs from 1 , 243 study participants , collected at the same time as the plasma samples used for EBNA-1 screening , as previously described [60] . Raw and normalized expression values are available under the accession number E-TABM-305 at: http://www . ebi . ac . uk/arrayexpress . Briefly , sample quality was examined by comparing the number of expressed probes ( p≤0 . 05 ) , mean expression across expressed probes , and mean correlation ( across expressed probes ) with other samples , and 1 , 243 samples were deemed to give high quality expression profiles . Transcripts with significant expression at a false discovery rate ( FDR ) ≤0 . 05 were identified using a one-sided binomial test ( based on counts of samples with successful and unsuccessful detection at p≤0 . 05 ) , yielding 20 , 634 significantly detected probes . Subsequently , we performed “background noise correction” , log2 transformation , and quantile normalization . We then tested whether SNPs that were significantly associated with EBNA-1 antibody measurements were also significantly associated with the quantitative expression levels of neighboring transcripts ( i . e . , whether the candidate SNPs are putative cis-regulatory variants ) , and whether those transcripts were in turn associated with EBNA-1 antibody measures . Prior to these analyses , in order to detect and remove the impact of suspected as well as unknown confounding variables on expression levels , we performed principal components ( PC ) analysis ( after inverse , rank-based normalization of transcripts ) on the expression profile data ( details on methodology are being prepared for publication elsewhere ) . For detection of putative cis-acting expression quantitative trait nucleotides , the top 50 expression PCs were regressed out , followed by additive measured-genotype-based association analysis ( conditional on linkage ) on transcripts in the HLA region . Before correlating expression levels with anti-EBNA-1 traits , we examined ( by regression analysis ) the relationship between each of the top 50 expression PCs to the antibody traits , and regressed out all of the top 50 PCs except those that were significantly related to the antibody traits ( so that we would not accidentally remove any true connection between expression and antibody traits ) . | Many factors influence individual differences in susceptibility to infectious disease , including genetic factors of the host . Here we use several genome-wide investigative tools ( linkage , association , joint linkage and association , and the analysis of gene expression data ) to search for host genetic factors influencing Epstein-Barr virus ( EBV ) infection . EBV is a human herpes virus that infects up to 90% of adults worldwide , infection with which has been associated with severe complications including malignancies and autoimmune disorders . In a sample of >1 , 300 Mexican American family members , we found significant evidence of association of anti–EBV antibody levels with loci on chromosome 6 in the human leukocyte antigen region , which contains genes related to immune function . The top two independent loci in this region were HLA-DRB1 and HLA-DQB1 , both of which are involved in the presentation of foreign antigens to T cells . This finding was specific to EBV and not to 12 other pathogens we examined . We also report an overlap of genetic factors influencing both EBV antibody level and EBV–related cancers and autoimmune disorders . This work demonstrates the presence of EBV susceptibility loci and provides impetus for further investigation to better understand the underlying mechanisms related to differences in disease progression among individuals infected with this pathogen . |
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Regions of the genome that have been the target of positive selection specifically along the human lineage are of special importance in human biology . We used high throughput sequencing combined with methods to enrich human genomic samples for particular targets to obtain the sequence of 22 chromosomal samples at high depth in 40 kb neighborhoods of 49 previously identified 100–400 bp elements that show evidence for human accelerated evolution . In addition to selection , the pattern of nucleotide substitutions in several of these elements suggested an historical bias favoring the conversion of weak ( A or T ) alleles into strong ( G or C ) alleles . Here we found strong evidence in the derived allele frequency spectra of many of these 40 kb regions for ongoing weak-to-strong fixation bias . Comparison of the nucleotide composition at polymorphic loci to the composition at sites of fixed substitutions additionally reveals the signature of historical weak-to-strong fixation bias in a subset of these regions . Most of the regions with evidence for historical bias do not also have signatures of ongoing bias , suggesting that the evolutionary forces generating weak-to-strong bias are not constant over time . To investigate the role of selection in shaping these regions , we analyzed the spatial pattern of polymorphism in our samples . We found no significant evidence for selective sweeps , possibly because the signal of such sweeps has decayed beyond the power of our tests to detect them . Together , these results do not rule out functional roles for the observed changes in these regions—indeed there is good evidence that the first two are functional elements in humans—but they suggest that a fixation process ( such as biased gene conversion ) that is biased at the nucleotide level , but is otherwise selectively neutral , could be an important evolutionary force at play in them , both historically and at present .
Understanding the forces that have shaped the evolution of the human genome is one of the most exciting problems in modern genomics . Two approaches to this problem are focused on identification and characterization of those genomic regions that have evolved the slowest and fastest along the human lineage [1]–[5] . The slowest evolving regions may contain elements that cannot be disturbed without disrupting essential function . The fastest evolving regions may harbor elements whose function is unique to our species lineage . To eliminate non-functional regions , both of these complementary approaches begin with a search for regions that are conserved throughout mammalian history or longer . The ultra conserved elements [1] maintain this conservation along the human lineage , and have been shown to be under purifying ( negative ) selection [6] , strongly suggesting that they are functionally important to our species , although in ways that are still largely unknown . By contrast , several groups have searched for positive selection along the human lineage by focusing on those previously slowly evolving regions of the genome that have evolved most quickly along the human lineage [4] , [5] , [7] . These regions , such as those in the set of Human Accelerated Regions ( HARs ) [7] , may include some of the genetic changes that make our species biologically unique . Indeed , biological characterization of the topmost elements on this list of candidates has proven fruitful: HAR1 is part of a novel RNA gene ( HAR1F ) that is expressed during neocortical development [3]; HAR2 ( or HACNS1 ) is a conserved non-coding sequence that has been shown to function as an enhancer in the developing limb bud with the human-specific sequence enhancing expression in the presumptive anterior wrist and proximal thumb [8] . Since the HARs were identified based on an excess of fixed differences between the human reference genome and sequences that are highly conserved among chimp , mouse and rat , such differences could have arisen at any time within the 5 million years that have elapsed since our common ancestor with the chimpanzee . As such it is important to recognize that even if such differences resulted from positive selection for advantageous mutations , they may have occurred so long ago that we have little power to find evidence for such selection using only the present day sequences available to us . Furthermore , as previously noted [7] , positive selection might not be the sole explanation for the rapid evolution that is evident in the HARs . Biased gene conversion ( BGC ) may also hasten the fixation of mutations in a local manner independent of any fitness benefits [9] , [10] . BGC arises as a byproduct of recombination between homologous chromosomal regions . In this process DNA double stranded breaks are repaired and the alleles from one chromosome are copied to the other , with a bias for conversion of A or T ( weak hydrogen bonding ) alleles to G or C ( strong hydrogen bonding ) alleles [11]–[14] . A neutral locus can thus mimic the rapid evolution of loci under positive selection [9] , [10] , and furthermore , BGC may in fact drive fixation of deleterious alleles [15] , the precise opposite of a positive , adaptive evolutionary effect . One of the most powerful tools for identifying those regions that have been subjected to directional selection comes from examining the distribution of allele frequencies segregating within a species . For example , analysis of this distribution , known as the site frequency spectrum ( SFS ) , allows for the identification of loci that have been involved in selective sweeps in the last few hundred thousand years . Analysis of the SFS has been used to identify targets of natural selection that may be responsible for genetic traits that are uniquely human , such as language [16] or cognition [17] . In the current work we investigate the top 49 HARs that were identified as having a 5% false discovery rate [3] . But rather than restricting our attention to the core elements , which are 100–400bp in length , we consider the polymorphism in a set of 22 chromosomal human samples in a 40kb neighborhood of each of these HAR elements , with an eye to capturing perturbations in the SFS at linked sites , and/or regionally biased patterns of allele fixation . Our samples are drawn from a single population , the Yoruba from Ibidan , Nigeria in order to avoid confounding issues of population admixture as well as to take advantage of a greater degree of variation in this population . We use an adaptation of several techniques previously developed [18]–[21] to enrich genomic DNA from our sample individuals for the target genomic neighborhoods . The enriched DNA is then subject to high throughput sequencing followed by genome-wide mapping of many overlapping sequences to determine genotypes at sites in the target regions , and hence derive the site frequency spectra . With these spectra in hand , it is possible to test for the hallmarks of BGC . We employed an approach that compares the separate site frequency spectra for the weak-to-strong ( i . e . A or T to G or C ) ( W2S ) and strong-to-weak ( S2W ) mutations to determine if any shift towards high frequency , normally characteristic of a selective sweep , is biased towards one of the two sets of mutations . This signal would indicate an ongoing process in the current human population . Similarly , one can compare the proportion of W2S changes among already fixed substitutions on the human or chimp lineage to that among the still segregating sites . A W2S bias in fixed differences relative to polymorphisms would indicate that the regions have historically been subject to a BGC-like biased process . On the other hand , the spectra may contain evidence of positive selection . Various techniques have emerged in recent years to search for signatures of positive selection using population genetic data [22]–[24] , but many are based on the phenomenon of genetic hitchhiking [25] , [26] in which fixation of beneficial mutations results in a skew in the site frequency spectrum . One such approach [27] is based on the composite likelihood of allele frequencies wherein the probability of the observed allele frequency at each polymorphic position is calculated based on its distance from a site under putative positive selection . This probability explicitly takes into account the strength of recombination and selection . A variant of this approach has been implemented [28] in the SweepFinder program that we use herein . It has been previously used [29] in a genome-wide search for sweeps at the scale of 500kb , since that study's data was restricted to loci with common polymorphisms . The power of that approach is probably limited to finding sweeps not much older than 200 , 000 years but has the attractive property that it is robust to demographic history [29] . Unlike other approaches that have been used [22] , [23] , [30] , [31] it also does not require that the sweep be ongoing or differentially concluded in separate populations . Since we have discovered many novel polymorphisms by resequencing our samples , we use this method to take a more focused look at our 40kb HAR neighborhoods in search of adaptive evolutionary forces .
When the HARs were first described , a strong W2S substitution bias was noted in the human-specific substitutions in these elements [3] , [7] . This bias was extremely pronounced in HARs 1 , 2 , 3 , and 5 , but also noticeable as a general trend in the entire set 1–49 . This evidence suggested that BGC could have had a historical role in the evolution of the HARs . Here we analyze our list of segregating sites in the 40kb HAR neighborhoods to determine if such bias is still ongoing in the human population . In each region , we separately computed the derived allele frequency spectra for the W2S mutations and the S2W mutations . We then tested for an offset in the spectra between the two categories with a two-sided Mann Whitney U ( MWU ) test ( see Materials and Methods ) . This test has been shown to have good power to detect fixation bias [34] . We found a significant ( p 0 . 05 ) difference in 11 out of 49 harseq regions ( Table 1 ) . In all 11 significant harseq regions , the offset was for the W2S mutations to be segregating at higher derived allele frequencies than S2W . This implies that regardless of the rate of introduction of W2S or S2W mutations , it is the W2S mutations that are more likely to reach high frequency and eventually fix in the human population . This is certainly consistent with a mechanism of gene conversion that favors selection of G or C alleles from a heterozygote or some other selective force generally favoring higher GC content . The novel features of this result are that it indicates that this process is ongoing and not confined to the core 100–400bp HAR elements . This ongoing W2S fixation bias distinguishes the harseq regions from ctlreg regions and Seattle SNPs regions . Significant MWU tests were observed at none of ctrlreg50-62 ( Supplementary Table 2 in Text S1 ) and five out of 62 Seattle SNPs regions ( Supplementary Table 4 in Text S1 ) , one of which has higher derived allele frequencies in S2W compared to W2S mutations . The distribution of the MWU test statistic in the test regions is also biased towards W2S mutations compared to the 62 Seattle SNPs regions ( Supplementary Figure 6 in Text S1 ) . Applying the MWU test to simulations of a neutral coalescent model ( see Materials and Methods ) showed that the p-values from this test accurately reflect the fraction expected by chance from a neutrally evolving locus ( Supplementary Figure 2 in Text S1 ) . The two harseq regions with the greatest offset were harseq21 and harseq34 ( Figure 1 ) . We note that across these two 40kb regions , the ratio of W2S to S2W segregating sites is not extreme; it is the W2S shift towards higher frequency in the population that is significant . These ratios are consistent with the smoothed ratios reported in the top several thousand conserved candidate HAR elements [7] even though our 40kb regions do not comprise largely conserved regions . It is natural to theorize that BGC will be a stronger driving force in areas of high recombination and studies have shown there to be a good correlation with the male recombination rate in particular [35] . We examined the recombination rates in the enclosing 1Mb windows as determined by the deCODE project [36] . Harseq21 is an outlier in that it is contained in a genomic region of extremely high recombination rate ( male 4 . 29 cM/Mb , sex-averaged 3 . 43 cm/Mb , in contrast to genome-wide averages of 0 . 93 cM/Mb and 1 . 29 cm/Mb respectively ) . But this is not true of harseq34 ( 0 male and 1 . 42 cm/Mb sex-averaged ) . For the remainder of the regions with a significant p-value on the MWU test , the rates vary . An additional ( not unrelated ) factor that has been strongly correlated with biased substitutions is chromosomal position near telomeres [35] . With the exception of harseq1 this is not the case for the regions with significantly shifted W2S spectra ( Table 1 ) . We also performed the MWU test for the shift in W2S sites toward higher frequencies after pooling all the segregating sites in our 49 harseq regions , and found a p-value . By contrast , the test was not significant ( p = 0 . 26 ) for the pooled data in our 13 control regions , thereby controlling for possible systematic bias in our sequencing and genotyping techniques . We thus conclude that in many of the neighborhoods of the top 49 HARs there is an ongoing force driving W2S mutations to higher frequency in the human population . Since the HARs were essentially defined based on fixed differences between the human and chimp reference genomes in otherwise strongly conserved elements [7] , we compared such human/chimp fixed differences to the segregating sites in our human samples . Our set of fixed differences was based on high quality base calls from the reciprocal best alignments [37] of human and chimp genomes . We further restricted this list to the locations within our regions for which we had the above-mentioned 35-fold coverage for an individual in our sample . Finally , we removed from this initial fixed difference list those positions that we found to be segregating in our samples , or that appeared in the dbSNP129 database [38] ( see Materials and Methods ) . The latter two filters removed 6 . 7% of the fixed differences at high coverage positions . Since we do not have information on sites that are segregating in the chimp population , we could not remove those , but would expect the number to be similarly small . We separated the mutations in our segregating site set into the categories: W2S , S2W , and neither . We similarly divided the set of fixed differences , regardless of whether the substitution occurred on the chimp or human lineage . As in reference [35] , we performed a variant of the McDonald-Kreitman ( MK ) test to compare the W2S∶S2W ratios in the sets of mutations and the sets of substitutions ( see Materials and Methods ) . We found a significant ( p 0 . 05 ) difference between the substitution patterns of segregating versus fixed sites in 11 of the 49 harseq regions ( Table 2 ) . In all but one ( harseq39 ) of these 11 , the fixed substitutions had relatively more W2S mutations ( compared to S2W ) from the ancestral form than did the segregating sites . Four of the 11 fell in the 40kb neighborhoods of the top 11 HARs , and indeed the strongest result ( p = 0 . 00015 ) was for harseq1 . This is not surprising , since the HAR1 element has 18 fixed differences , all W2S [3] . By contrast , none of the ctlreg regions and nine out of 62 Seattle SNPs regions had a significant p-value on this test ( Supplementary Tables 2 and 4 in Text S1 ) . All of the nine significant Seattle SNPs regions had a higher W2S∶S2W ratio in fixed differences than in segregating sites . Thus , the harseq regions have a much stronger signal for historical fixation bias than our control regions and a somewhat stronger signal than the genic Seattle SNPs regions . Applying the MK test to simulations of a neutrally evolving primate phylogeny ( see Materials and Methods ) showed that the p-values from this test accurately reflect the fraction expected by chance from a neutrally evolving locus ( Supplementary Figure 3 in Text S1 ) . We also recapitulate and expand the finding [7] that this bias towards W2S fixation is associated with telomeres , since 7 of the 11 significant regions under the MK test were found either in the karyotype band containing a telomere or the one immediately adjacent . ( This count includes the two cases of harseq19 and harseq36 from chromosome 2 that fall adjacent to the ancestral telomeric fusion event at 2q14 . 1 . ) Although many of the 11 regions ( including ones near telomeres ) have elevated recombination rates , it is also worth noting that 5 of the 11 are in regions with much lower than average male recombination rates ( including the two near the chromosome 2 fusion site , and harseq11 on chrX ) . One noteworthy negative result from the MK test , ( and the MWU test as well ) is harseq2 . It had been noted [7] that the core HAR2 element showed a strong bias towards W2S fixations , and that this extended to a region of 1kb . Here we find no significant signal for either the MK or MWU test in our 40kb neighborhood of that element ( Supplementary Table 2 in Text S1 ) . Another noteworthy negative result on these tests is the harseq6 region , which has an extremely elevated rate of mutation as estimated either by nucleotide diversity [39] or by the number of segregating sites [40] ( Supplementary Table 1 in Text S1 ) , but apparently no strong bias towards weak-to-strong fixation ( Supplementary Table 2 in Text S1 ) . Although the MK test , like the MWU test , is consistent with the mechanism of BGC favoring fixation of G or C alleles , in fact only one of our regions ( harseq1 ) had significant results in both tests . The complementary evidence from the MWU test ( a total of 20 of our 49 test regions are W2S-significant on one test or the other ) indicates that the ongoing bias in favor of W2S mutations has also probably led to human specific substitutions in otherwise conserved elements . Because BGC is posited to operate on a scale much smaller than the 40kb of our target sequencing regions , perhaps operating at localized recombination hotspots , and because the ( 100–400bp ) HAR elements at the core of our target regions were suspected of arising in part due to BGC [7] , we wanted to test whether the MWU and MK signals depended on these core elements . We therefore performed the same tests after masking out the central 500bp , 1kb , 5kb or 10kb of each region . We found that signals of both ongoing and historical fixation bias are fairly robust to removing sequences including and flanking the core HAR element . For the MWU test , all but two ( harseq1 , harseq18 ) of the 11 regions that were significant at the 5% level for the MWU test were still significant at that level with the central 5kb omitted . Seven of the 11 remained significant even with 10kb omitted ( Table 1 ) . For the MK test , the results were slightly more sensitive to masking . Considering the 11 regions with a significant result on that test ( including the one favoring S2W fixation ) , 9 ( 7 ) of these were still significant with the central 1kb ( 5kb ) masked out ( Table 2 ) . We conclude that the evolutionary forces behind these results is not confined to the small HAR elements themselves , but rather that any bias in the substitutions found in the HARs is likely a byproduct of the forces acting at a larger scale . To test whether there might be other localized elements within the 40kb regions driving these results we performed the tests under a set of fifteen overlapping 5kb masks ( centered at a 2 . 5kb spacing along each region ) . Among the 11 MWU significant regions , 5 were still significant at 10% under this regime , while 3 completely lost significance ( p 20% ) for at least one such mask ( Table 1 ) . Of the 11 MK significant regions , 5 were still significant at 10% ( including harseq1 ) under this regime , while 1 completely lost significance ( p 20% ) under at least one mask ( Table 2 ) . It is worth noting that the harseq1 result reflects the fact that of the 105 segregating sites we found in that 40kb neighborhood , 71 were S2W and only 19 W2S ( Supplementary Table 2 in Text S1 ) . We conclude from this set of tests that the evolutionary forces behind W2S fixation bias are not necessarily highly local . If fixation bias relies on recombination hotspots and BGC , we have to posit that such hotspots extend over a long range of bases , or are somehow temporally and spatially variable ( cf . [41] ) . To determine if the results for the MWU and MK tests on the HAR neighborhoods are consistent with a model of GC-biased evolution , we performed simulations under a model of BGC ( see Materials and Methods ) . Of 499 simulations , for the MK test 130 were significant ( p ) with W2S bias ( none significant with S2W bias ) , and for the MWU test 51 were significant with W2S bias ( one significant with S2W bias ) . The union of the significant W2S simulations on the two tests comprised 169 cases while the intersection comprised 12 cases . Compared to the simulations , the 49 HAR regions had significantly more than the expected number of MWU W2S cases ( p = 0 . 003 for binomial probability of at least 11/49 cases using the simulation rate of 51/499 ) , but the MK test does not ( p = 0 . 77 binomially comparing 10/49 to 130/499 ) . On the other hand , the small ( one case ) intersection of MWU and MK tests in the HAR regions is not unexpected based on the simulations . That is , using the fraction 12/499 ( = 0 . 024 ) of the MWU and MK intersection in the simulations as the expected rate , the small fraction 1/49 ( = 0 . 020 ) in the HAR regions is not statistically significant using either a binomial ( p = 0 . 67 ) or Poisson ( p = 0 . 67 ) test . Finally , for neither the simulations ( Fisher's Exact test p = 0 . 74 ) nor the HAR regions ( p = 0 . 42 ) is there a significantly greater correlation between the MWU and MK results than expected by chance . Together , these analyses indicate that the MWU and MK results for the 49 HAR regions are consistent with a model of GC-biased evolution in terms of the overlap between the tests , although the number of MWU cases is enriched compared to the simulation model . For each of the studied regions , we used the SFS to calculate two population genetic statistics that can sometimes indicate positive selection: Tajima's D [42] , which is based on the folded SFS , and Fay and Wu's H [43] . Neither of these statistics exceeded the value of in any region ( Supplementary Table 1 in Text S1 ) . We next compared the distributions of these two statistics in the 49 harseq regions and the 13 ctlreg regions , to those for the same population ( YRI ) in 104 genic regions resequenced by Seattle SNPs ( see Materials and Methods ) . We found the ctlreg regions to be indistinguishable from the Seattle SNPs for these statistics , while the harseq regions were only mildly more negative for Tajima's D ( Wilcoxon rank sum p = 0 . 08 ) and not significantly different for H ( Supplementary Figure 1 in Text S1 ) . These results strongly suggest that the site frequency spectrum in harseq regions is indistinguishable from that found in either our control regions ( ctlreg ) , or in the Seattle SNPs data set . Thus we have no reason to believe that harseqs represent some kind of genomic outlier with respect to recent selective events . Examination of these statistics calculated separately for the W2S and S2W segregating sites ( Supplementary Figure 7 in Text S1 ) shows that the W2S subset in the harseq regions has a significantly more negative value of H than in the Seattle SNPs , which is consistent with the shift to higher derived allele frequencies for this subset noted above using the MWU test . To test for evidence of a selective sweep , we analyzed the spatial variation of derived allele frequencies at the segregating sites from our 22 chromosomal samples in each of the target 40kb regions using the SweepFinder program . This software determines a composite likelihood ratio ( CLR ) statistic comparing the hypothesis of a complete selective sweep at the location to the null hypothesis of no sweep using Test 2 from [28] . We tested along a grid of 1000 points in each target region ( see Materials and Methods ) . This test has been shown to be robust to demographic deviations from the standard neutral model in its ability to use an arbitrary background site frequency spectrum [29] . We tested with two such backgrounds: the first from the pooled set of all the data in the 49 harseq regions plus 13 ctlreg regions , the second from the same population ( YRI ) as our samples but with frequencies taken from the Seattle SNPs resequencing data [33] for a large set ( 104 ) of genic regions . It should be noted that using the SFS from our data as the background to define the neutral model should be particularly conservative in that we are testing any given region for deviations from that neutral model . To determine the significance of the maximum CLR values , we performed coalescent simulations of each target region and ran the SweepFinder program on each simulated set of segregating sites ( see Materials and Methods ) . We report as a p-value the fraction of simulations of each target that had a CLR greater than or equal to the actual maximum CLR for that target ( Supplementary Table 2 in Text S1 ) The harseq regions with the most significant five SweepFinder p-values are listed in Table 3 . These are nearly all at the 95% confidence level for either of the two background distributions used , but we note that none are individually significant after a conservative Bonferroni correction , given the 49 harseq regions that were tested . Since these may nevertheless harbor mutations that were selected for in the human lineage , here we briefly note some of their characteristics that can be seen in tracks from the UC Santa Cruz Genome Browser ( Supplementary Figure 4 in Text S1 ) . Unlike the other four SweepFinder hits , which all contain introns or exons of coding genes , harseq25 is in a gene “desert” . The nearest known gene , approximately 1Mb away on chromosome 4 , is ODZ3 , which is a transmembrane signaling protein most highly expressed in brain . Note that harseq25 also has a significant result on the MWU test discussed above . Evidence for a sweep in the harseq9 region is intriguing because it encompasses the 42-codon long , second exon of the PTPRT gene , a phosphatase with possible roles in the central nervous system . However , the human amino acid sequence of this exon matches the other primates chimp , gorilla , and orangutan , except where chimp has an obviously non-ancestral ThrAla substitution . The human sequence does have a single GA substitution near the 3′ splice site just upstream of this exon , but it falls in a position between the polypyrimidine ( Py ) tract and the AG acceptor site , for which the consensus sequence across many splice sites is evenly divided among the 4 nucleotides . The location of harseq11 on chromosome X places its evidence for a sweep in the first intron of the 2 . 4Mb long dystrophin gene DMD . The evidence for a sweep in harseq16 is offset to one end of its region , about 20kb from an apparent pseudogene comprising a single coding exon with a 270-codon open reading frame ( ORF ) that is probably derived from the Poly-A binding protein PABPC1 . The harseq24 region encompasses the second through fourth exons of the SKAP2 gene with the strongest evidence for a sweep about 10kb from the closest exon , but closer to a LINE transposable element that is present also in chimp , orangutan , and rhesus macaque ( but lost in gorilla ) . Although the above evidence for selective sweeps is not statistically significant , and none of it seems to point directly to a mutation in a core HAR element based on the position of the SweepFinder peak CLR values , it is important to note that while having the advantage of robustness to demography and recombination rate , our tests would not likely have power to detect sweeps that occurred beyond the last 200 , 000 years [29] . Under an assumption that substitutions in the HAR elements occurred uniformly over the last 5 million years and that most of these substitutions were adaptive , we estimate ( see Materials and Methods ) that we would be able to detect fewer than 8 with our tests . Therefore this negative result should not be interpreted as ruling out a role for adaptive evolution in the HARs .
In the era of comparative genomics , strong signals of conservation across multiple species serve as signposts that can indicate regions where evolutionary forces may be preserving functional elements that are subject to purifying selection ( e . g . [6] ) . By contrast , signals of positive selection pointing to adaptive changes in one lineage are harder to find , often employing sets of polymorphic sequences from multiple individuals of the same species . We exploited the two recently developed techniques of genomic enrichment and high throughput sequencing to characterize the polymorphism in a single human population across 40kb neighborhoods of the 49 HARs ( harseq regions ) . We investigated the harseq regions because the HARs were defined based on a presumption that the human lineage specific fixed differences therein might have arisen due to adaptive evolutionary forces . On the other hand , it has been emphasized by some that the presumably evolutionarily neutral mechanism of BGC can influence the frequency spectra at polymorphic positions , or cause fixation of alleles in a way that partially mimics the action of adaptive evolution . Indeed , fixation bias was noted in connection with the limited set of human specific alleles for some of the HARs when they were first described [7] . With the extensive novel polymorphism in our samples , we were able to carefully characterize fixation bias — both historical and ongoing — in the harseq regions and to conduct tests for recent selective sweeps across these regions . Our deep resequencing data is noteworthy because it eliminates issues of SNP ascertainment bias that could have skewed previous investigations of polymorphism near HARs . We applied several established population genetic tests , as well as an application of the MWU test , to identify differences in the fixation patterns of W2S and S2W mutations . Consistent with published reports [7] , [9] , [35] , we find evidence of historical W2S fixation bias in harseq regions . Using a MK test , we compared the proportion of W2S mutations among already fixed substitutions on the human or chimp lineage to that among the still segregating sites in our samples . We found that 11 of our 49 regions show statistically significant evidence of historical bias in allele fixation , with all but one favoring W2S fixation . These results strengthen and expand previous findings by identifying signals for W2S bias in much larger regions flanking the core HAR regions in an ascertainment-free population sample . This study goes beyond previous approaches by also looking at ongoing W2S fixation bias in the segregating site frequency spectrum . We performed a MWU test using only sites that are still segregating in the human population , separating out W2S from S2W mutations . This second test is designed to detect a phenomenon of bias that is currently driving W2S mutations to higher frequency in the population than S2W mutations . We found statistically significant evidence for this bias ( and none in the opposite direction ) in the regions flanking 11 of 49 HARs . For both of our tests , we showed that the core HAR element is generally not the main source of the signal that we detected , since the signal usually remains strong even when we mask out the central 1kb or even 5kb of the region . This is not consistent with BGC due to a recombination hot spot that has remained in the same location for millions of years , because the length scale of the effect of BGC is set by the length of the heteroduplex tract formed during recombination that needs to be repaired , which is thought to be 500bp ( e . g . [44]–[46] ) . However , it is consistent with a model in which the location of recombination hotspots drift fairly rapidly over evolutionary time scales , but may be denser in some regions [41] , [47]–[50] . It is noteworthy that there was little overlap in the regions identified by these two tests , one for older W2S fixations and the other for present day forces toward fixation , with a total of 20 found in one or the other . Although this is consistent with the hypothesis that the regional focus of BGC , which may be recombination hot spots , drifts significantly on a time scale of many hundreds of thousands or millions of years , we also found from simulations of GC-biased evolution over these time scales that the relatively minimal overlap between the tests is not unexpected . Another explanation for W2S fixation bias near HARs is selection for increased GC-content or individual fitness-improving GC alleles . To investigate these hypotheses and to attempt to disentangle the possible roles of BGC and positive selection in shaping the HARs , we applied a recently developed powerful method for detecting selective sweeps . Selection was previously investigated in much larger ( 500kb ) regions using more sparse polymorphic loci [29] . That study found 101 regions with strong evidence for a selective sweep within 100kb of a known gene . Here , we found only 5 possible candidates for such sweeps among our 49 target regions ( and none that were significant after correction for multiple hypothesis testing ) . Three of these candidates overlap regions with significant evidence of historical ( 2 ) or ongoing ( 1 ) W2S bias . As we are dealing with a lineage-specific evolutionary period of about 5 million years , and these tests can only see back a few hundred thousand years , it is quite possible that the original signal for selective sweeps in these regions has already decayed beyond our ability to recognize it in human population genetic data . That is , the lack of evidence for recent sweeps does not rule out the possibility that some of the excess substitutions in HARs were fixed by older selection . Similarly , the evidence for GC-biased evolution based on current population genetic data may not fully reflect patterns of polymorphism in the past . Consistent with the idea that HAR regions may have experienced positive selection too long ago to be detected with population genetic methods , very few positively selected regions in the human lineage have been identified to date , despite the existence of numerous public databases . Selective sweeps that have been identified have typically been the product of very recent events in human history , such as dairy farming affecting the lactase gene [51] or climate differences influencing a salt sensitivity variant [52] . Such environmental or cultural changes result in differences in the genetic makeup of disparate human populations , and such differences can be exploited to find evidence of recent , possibly still ongoing , selective sweeps . An alternative hypothesis that deserves consideration is that HARs may have an unusually high level of recent substitution due to a recent relaxation in purifying selection along the human lineage ( e . g . [53] ) . Using previously described methods [7] , we compared estimates of the rates of substitution in the 49 HAR elements to the neutral rate . We find that the human substitution rate exceeds the expected neutral rate in all 49 HARs , while this is true for the chimp substitution rate in only 10 HARs . Furthermore , in 33 HARs the human substitution rate significantly exceeds the neutral rate ( Poisson p-value ) while none of the chimp substitution rates significantly exceed the neutral rate . This evidence argues against the hypothesis that these HAR elements are the product of relaxed selection . We have focused in our study on 40kb neighborhoods of 49 HAR elements ( and 13 similar control regions ) because of their intrinsic interest but also because the scope of our study was appropriate to the state of the art of recently emerged enrichment and sequencing technologies . As larger data sets become available we will be able to apply our analysis on a genome-wide scale . Such analysis should give us insights into the properties associated with genomic regions that display this ongoing W2S fixation bias and their potential biological consequences . Despite the evidence that the unusually high level of recent substitution in the more extreme HAR elements , such as HAR1 and HAR2 , could be due to the process of BGC , there is ample evidence that these genomic elements remain functional , and thus the effect of BGC was to mutationally stress but not destroy these elements . HAR1 shows a very strong pattern of compensatory substitutions within its RNA helix structures , indicating a selective force to maintain these helix structures . The W2S substitutions all strengthen the RNA helices of HAR1 , and in one case , a substitution appears to extend one of them . Human HAR1 and HAR2 both show evidence of specific function , the former by its highly specific expression pattern during neurodevelopment and the latter by its ability to enhance gene expression during limb development . Whether the human-specific evolutionary changes to these elements reflect a process that was essentially like swimming upstream against an onslaught of non-selective BGC just to keep in place on the fitness landscape , or whether the mutational stress pushed these elements into a configuration that enabled some positive selection for higher fitness in humans , remains to be seen .
Genomic DNA for our samples was obtained from the NHGRI Sample Repository for Human Genetic Research distributed by the Coriell Institute for Medical Research [Camden NJ] [54] . All of the 11 samples were chosen from the Yoruba from Ibidan Nigeria ( YRI ) HapMap population . In particular , the samples were chosen as a subset of the Seattle SNPs P2 panel [33] . The Seattle SNPs PGA-VDR ( and Coriell repository numbers were ) : DY01 ( NA18502 ) , DY03 ( NA19223 ) , DY04 ( NA19201 ) , DY17 ( NA19143 ) , DY18 ( NA18517 ) , DY19 ( NA18856 ) , DY20 ( NA19239 ) , DY21 ( NA18871 ) , DY22 ( NA19209 ) , DY23 ( NA19152 ) , DY24 ( NA19210 ) . Sample preparation as described below was similar for all samples , except that prior to processing , the DY01 , DY03 , DY04 samples were subject to whole genome amplification ( WGA ) using the Repli-G Kit ( Qiagen N . V , The Netherlands ) according to manufacturer's specifications . It was found that WGA caused a loss of coverage in some isolated target regions , most notably in the 28kb section of the harseq1 region with coordinates hg18:chr20:61 , 183 , 966–61 , 212 , 244 , except for the core HAR1 element at chr20:61 , 203 , 919–61 , 204 , 081 . The primary target regions consisted of 20kb extensions in both directions from the 49 most statistically significant Human Accelerated Regions ( HARs ) identified as having a 5% false discovery rate [3] . Additional 40kb control regions were chosen in neighborhoods of 13 of the set of 34 , 498 vertebrate conserved elements that had extremely low LRT scores in the test used to define the HARs . The enrichment arrays were obtained from Nimblegen Systems ( Madison WI ) who designed the probes on their 385K array based on our specifications of the coordinates of the 62 target genomic regions ( Supplementary Table 1 in Text S1 ) . Probes were chosen to tile the target regions from both DNA strands . The design process avoided probes in highly repetitive sequences as described previously [19] , [21] . The fraction of bases in the target regions covered by the probes ranged from 98% ( harseq12 ) to 65% ( harseq4 ) with a median of 88% . Details of probed bases are available upon request . The library preparation of the samples for SOLiD ( Applied Biosystems , Foster City , CA ) sequencing generally followed the manufacturer's protocols for barcoded SOLiD System 2 . 0 Fragment Library Preparation ( samples DY01 , DY03 , DY04 ) and SOLiD System 3 . 0 Barcoded Fragment Library Preparation ( remaining samples ) , with changes as necessary for enrichment on the Nimblegen arrays as noted in the following: Samples were sheared to approximately 100bp using the Covaris S2 System Program B ( Covaris Inc , Woburn , MA ) . End repair was performed with End-It DNA End-Repair Kit ( Epicentre Biotechnologies , Madison , WI ) per manufacturer's instructions . Single stranded oligos for the P1 and P2-barcoded SOLiD adaptors were ordered from Invitrogen Corporation and annealed per the SOLiD protocol to form double stranded adaptors , which were ligated to the end-repaired DNA fragments using the Quick Ligation Kit ( New England BioLabs , Ipswich , MA ) per manufacturer's directions , leaving a nick at the 3′ end of each genomic DNA strand where the 5′ end of the adaptor was not phosphorylated . Samples DY01 , DY03 , DY04 were size selected to 150250bp from a 6% polyacrylamide gel and purified via ethanol precipitation . This was followed by nick translation and ligation mediated PCR ( LMPCR ) amplification ( 6 cycles ) in a combined reaction per the SOLiD protocols using Takara ExTaq polymerase ( Takara Bio , Madison , WI ) . After dividing into 10 aliquots , an additional 10 cycles of ExTaq LMPCR amplification were performed on these samples in preparation for array hybridization . Samples DY17–DY20 were first nick translated without amplification using Pfu polymerase ( Stratagene , La Jolla , CA ) . Samples DY21–DY24 were nick translated and ExTaq LMPCR amplified ( 6 cycles ) . Quantitation using a DNA 2100 BioAnalyzer ( Agilent Technologies , Waldbronn , Germany ) showed that the PCR associated with nick translation prior to size selection mainly served to amplify the nick translated adaptors . After the nick translation and any initial LMPCR , all of samples DY17–DY24 were size selected on an E-Gel SizeSelect 2% agarose gel ( Invitrogen ) per manufacturer's instructions . These size selected samples were then ExTaq LMPCR amplified ( 6 , 9 or 10 cycles ) in preparation for array hybridization . Array hybridization to the Nimblegen arrays was performed on the Nimblegen Hybridization System 4 station under Mix Mode “B” for 64 to 70 hours using the Nimblegen Sequence Capture Kit per manufacturer's instructions . Prior to hybridization , samples DY21–24 were pooled to a total of 5 . 7g . All the other samples were hybridized individually , in amounts ranging from 1 . 9g to 8 . 0g per sample . For competitive hybridization to probes on the array that might nonselectively bind repetitive DNA , Human Cot-1 DNA ( Invitrogen ) that had been Covaris sheared to approximately 100bp was added to the hybridization mix in a 5∶1 ratio by weight . Additionally , to block the adaptor ends of the denatured , single stranded DNA fragments from binding to each other , a 10∶1 molar excess of adaptor oligos ( P2 with an unused barcode sequence and P1 ) was also added . At the completion of hybridization , the slide was washed with the Nimblegen Sequence Capture Wash and Elution kit per manufacturer's directions , and the enriched DNA was eluted with 350L of C purified water using an affixed SA200 SecureSeal Hybridization Chamber ( Grace Bio-Labs , Bend OR ) . A secondary elution with an additional 350L was also taken . Quantitative real-time PCR ( qPCR ) was performed on the eluted material to determine the rough fraction of target DNA and to compare the primary and secondary elutions . For this purpose qPCR amplicons within the target were compared to amplicons not in the target regions . It was generally found that the primary elution captured more than 95% of the target DNA ( data not shown ) . By normalizing to pre-enrichment material , and taking into account the fact that the 2 . 1Mbp target region comprised approximately 0 . 1% of the entire human genome , it was estimated that more than 35% of the eluted material fell in the target regions ( data not shown ) . The eluted material was ExTaq LMPCR amplified ( samples DY01 for 19 cycles , samples DY03–04 for 15 cycles , pooled samples DY21–24 for 10 cycles , samples DY17–20 for 12 cycles , ) in preparation for the emulsion PCR step of SOLiD sequencing that was performed in the UC Santa Cruz Genome Sequencing Center . Samples DY01 , DY03 , DY04 were processed with the SOLiD Version 2 system , producing 35 bases of sequence information for each read . The remaining samples were processed with the SOLiD Version 3 system , producing 50 bases of sequencing information for each read . The 35mer ( DY01 , DY03 , DY04 ) or 50mer ( remaining samples ) sequencing reads were mapped to the whole human genome using the bwa program [55] which generates mappings and associated quality scores in the sam format [56] that can be processed with the samtools suite . The bwa program is aware of the colorspace nature of the SOLiD sequencing reads , and uses a dynamic programming algorithm to infer the best nucleotide sequence for the read [55] . All reads were also mapped to the DNA of the Epstein-Barr Virus , which was used to transform the Coriell cell lines from which the supplied genomic DNA was extracted . For the female samples , the Y chromosome was excluded from the mapping . For the male samples , the pseudo-autosomal region of the Y chromosome was excluded from the mapping . The set of mappings for each sample was then filtered to the regions covered by the probes on the Nimblegen enrichment array described above . To eliminate spurious pileups caused by overamplification of particular molecules in the library preparation process , the mapped reads were further filtered to select at most 4 reads from each strand at a given genomic starting position . Where there were more than 4 , the 4 with the highest total read quality ( not the best mapping quality , which would bias against reads containing non-reference alleles ) from the SOLiD instrument were selected . Between 40% and 60% of the reads for a given sample were successfully mapped , and of those reads , between 33% and 48% mapped to bases covered by the probes on the enrichment array ( Supplementary Table 3 in Text S1 ) . For samples DY01 , DY03 , DY04 about 50% of the latter reads were lost in the “maximum 4 per strand” pileup elimination step , while only 11% to 23% were lost in this step in the remaining samples ( Supplementary Table 3 in Text S1 ) . This was likely due to the difference in LMPCR cycles used for the different samples as noted above . To determine the coverage at each position in the target region and the consensus genotypes for each sample , the command “samtools pileup -v” was used with default parameters for its consensus calling model . Possible confounding of the genotypes due to contamination by paralogous sequences was avoided in two ways . First , as noted , only genotypes at positions delimited by the Nimblegen probes were used in the analysis and these probes were designed to avoid repetitive sequences . Second , the bwa mapping algorithm assigns low mapping quality to reads that are not genome-wide unique , and the samtools consensus caller requires high mapping quality . To filter SNPs from among the not homozygous reference genotypes , “samtools . pl varFilter” was run with default parameters , except that the maximum read depth was set to 425 , because with up to 4 reads of length 50 on each strand , it was possible to get coverage of 400 . This command filters out potential SNPs when more than 2 fall within a 10bp window , on the grounds that there might be an insertion/deletion event rather than separate SNPs , and also filters out reads with RMS mapping quality value less than 25 . A similar quality filter was applied to the genotype calls that were homozygous reference . Because of stochastic variation in the composition of reads from the two chromosomes of each diploid individual , low coverage might cause an erroneous homozygous call in a true heterozygote . Therefore a further filter restricted the subsequent analysis to the SNP or homozygous reference calls made for a sample only at positions for which the coverage was 35 or greater . For each target region , the count of the union of such positions across all samples is listed in Supplementary Table 1 in Text S1 . As shown in Supplementary Figure 5 in Text S1 , the vast majority of the segregating sites that remain after the application of our 35× coverage filter are in Hardy-Weinberg Equilibrium , with only 0 . 3% having a p-value less than 0 . 05 . For purposes of all subsequent analysis , an ancestral allele at each position in the target regions was determined from the Enredo-Pecan-Ortheus ( EPO ) pipeline [57] , [58] as published on the 1000 Genomes website [59] . This pipeline determines the common ancestor of human and chimp at a locus by considering alignments of the human , chimp , orangutan , and rhesus macaque genomes . From the sets of filtered genotype calls in the 11 diploid samples as described above all the segregating sites were selected . A set of filters was applied to this list to produce the final set of segregating site derived ( i . e . non-ancestral ) allele frequencies ( DAFs ) for all downstream analyses . To avoid skewing the DAFs towards higher frequencies , segregating sites with less than 8 chromosomal samples were eliminated . Also eliminated were any positions with more than 2 alleles among the reference , ancestral , or sample alleles , or where the ancestral allele was not determined by the EPO pipeline . Lowercase values of the EPO ancestral allele , which result from various cases without complete evidence in all species were not eliminated . The SweepFinder program [28] was applied to the allele frequencies for the final list of segregating sites to determine the composite likelihood ratio ( CLR ) of a selective sweep at each one of a grid of 1000 positions across each target region . The model used in this program requires a background derived allele frequency spectrum . Two such backgrounds were used . First , all of the DAFs from all filtered segregating sites in our sample were aggregated and used as input to the command “SweepFinder -f” , which accounts for missing data using a Broyden-Fletcher-Goldfarb-Shanno ( BFGS ) algorithm . We refer to this as the “harseq” background . A second , presumably more neutral background was obtained from the African-Derived ( AD ) YRI subset of 24 individuals in the Seattle SNPs P2 panel . The DAFs for all segregating sites in all 104 genes resequenced for this panel by Seattle SNPs [33] were included . As for the harseq background , the “seasnp” background was obtained with the command “SweepFinder -f” applied to these DAFs . The resulting “seasnp” frequency spectrum ran from 1 to 47 and was reduced to a spectrum running from 1 to 21 , as needed by SweepFinder with our data , by hypergeometric weighting the relevant components of the input allele frequencies at each target allele frequency . ( 1 ) Dividing by the sum of the in Eqn 1 produces a valid frequency spectrum that sums to 1 . A similar hypergeometric weighting was also required to reduce the spectrum to a range from 1 to 19 for the harseq1 , 2 and ctlreg60 regions . In the latter regions missing data reduced the maximum number of samples at the segregating sites to 20 . P-values for our SweepFinder results were obtained via coalescent simulation conditional on the observed number of segregating sites in focal region , the observed coverage of this region , and the estimated recombination rate for a given region according to the pedigree data of [36] assuming a human effective population size of . The second point is important here in that our resequencing of both the harseq regions and the control regions was not perfectly complete , but instead was partial owing to an inability to design proper probes for our Nimblegen enrichment procedure ( see above ) in certain genomic segments . For each region examined we performed coalescent simulations under the standard neutral model which has been shown to be conservative for the SweepFinder procedure [29] . From the set of fixed differences between human and chimp in the 49 core HAR elements we use the EPO determined ancestral allele ( see above ) to count 206 as the total number of human lineage specific substitutions . Since a small number of substitutions are expected to occur by chance even in constrained elements , we used the number of substitutions on the chimp lineage as an estimate of the minimum number of non-adaptive substitutions in each HAR . These total 16 for all 49 HARs . So , we approximate that at most 190 ( = 206-16 ) substitutions were adaptive in humans . In reality some of the excess nucleotide changes for a given HAR were probably segregating at the same time on the same haplotype . So , 190 is most likely an overestimate of the number of adaptive events in HARs . But suppose there were indeed 190 separate adaptive substitutions and that these occurred uniformly over the last 5 million years . Further assume that any sweep from the last 200 , 000 years could be detected by SweepFinder . Then , 4% of the 190 adaptive substitutions ( i . e . , 7 . 6 sweeps ) should be in the detectable time frame . Since the number 190 and the percentage 4% are both upper bounds , we conclude that at most 8 and probably much fewer than 8 sweeps would be detectable by our SweepFinder analysis even if all 49 HARs were shaped by adaptive evolution . Our finding of no significant sweeps after Bonferroni correction and 5 significant before correction is therefore consistent with expectations . To determine if mutations from an ancestral weak ( A or T ) basepair to a strong ( G or C ) basepair ( W2S mutations ) are more likely to spread in the population represented by our samples , we compared W2S segregating sites to S2W segregating sites for each target region and for the aggregate set of segregating sites . We performed a Mann-Whitney U ( MWU ) test for a difference between the W2S and S2W derived allele frequency spectra . The test was performed in the R language with the command “wilcox . test ( paired = FALSE , alternative = two . sided ) ” . The resulting “location” parameter was normalized to 22 samples and is positive if W2S mutations are segregating at higher frequencies than S2W . The resulting p-values are in Supplementary Table 2 in Text S1 . To determine if relatively more W2S mutations fixed along the human or chimp lineages than are segregating in the human population represented by our samples , we first determined the high mapping quality chimp reference bases that differ from the human reference using reciprocal best alignments of the chimp and human genomes [37] . This set was then restricted to the positions in our target regions for which we had a genotype call for at least one sample with read depth of coverage of 35 or greater as discussed above . From this set of fixed differences we removed any for which the EPO ancestral allele was not determined as discussed above , or for which we had a segregating site , or which appeared in dbSNP release 129 [38] . The remaining fixed differences as well as the segregating sites were divided into W2S or S2W ( or other ) . A McDonald Kreitman-like ( MK ) test on the resulting 2×2 contingency table was performed in the R language with the command “fisher . test ( alternative = two . sided ) ” . The resulting p-values are in Supplementary Table 2 in Text S1 . For the significant cases it was easy to determine from the data in the contingency table if the fixed differences favored S2W mutations relatively more than the segregating sites ( column “S2W” in Table 2 ) . For the target regions with significant p-values on either the MWU or MK tests , we tested whether the significance was due to a restricted locus within the region , by removing all segregating sites and fixed differences under a mask of a given size at a given position within the region and rerunning the test with the remaining data ( Table 1 and Table 2 ) . We downloaded the data for the 104 genes resequenced with the Seattle SNPs P2 panel . The genotypes for our 11 YRI samples ( which are a subset of the P2 panel ) and the coordinates for the genotyped segregating sites were obtained from the global “prettybase” file mapped to UCSC hg18 coordinates . The total set of positions genotyped was obtained from the individual gene “genbank” files by excluding the features defined as “Region not scanned for variation” and aligning the remaining regions to the hg18 coordinates of the full extent of the genic region sequenced specified in the associated “ucscDataFile” . Given these coordinates and the genotypes at the segregating sites , the same techniques as described above for our resequencing data was applied to derive ancestral alleles and human/chimp fixed differences , and to perform the MWU and MK tests . Our data was derived from ( probed ) regions of a relatively tight size distribution ( Supplementary Table 1 in Text S1 ) . By contrast , the sizes of Seattle SNPs variation-mapped genic regions varied widely . Some were rather small and contained few segregating sites . Therefore , we included only genic regions with a minimum of 10kb variation-mapped and a minimum of 40 segregating sites . Additionally , a small number of the genic regions were excluded because of data missing from the “prettybase” file or because there were no associated high quality reciprocal best human chimp differences as described above , possibly because of paralogous genes in one or the other lineage . The remaining set of results for the MWU and MK tests on 62 genic regions ( Supplementary Table 4 in Text S1 ) were used for comparison to our 49 harseq regions . To determine if the p-values for the MWU and MK tests were accurate , we also conducted the tests on sets of simulated data under a neutral model . For the MWU test , we performed coalescent simulations using Hudson's ms program [60] . For each simulation we generated 22 samples at 85 segregating sites ( the average number of W2S plus S2W segregating sites in the 49 harseq target regions ) and then randomly assigned the sites as either W2S or S2W in Bernoulli trials using the W2S∶S2W ratio from the 49 harseq regions of 2057∶2114 . After calculating the MWU test p-value for each simulation , the fraction of simulations with p-value less than a given value was computed , as well as the subset of that fraction in which the W2S spectrum was offset towards higher derived allele frequencies ( Supplementary Figure 2 in Text S1 ) . For the MK test , for each simulation we separately derived a set of human-chimp fixed differences and a set of segregating sites . The fixed differences were derived using the phyloBoot program from the PHAST package [61] . We used a phylogenetic model and substitution rate matrix derived from 4-fold degenerate amino-acid coding synonymous sites across the genome as an unbiased neutral model . The equilibrium GC-content of this model was adjusted to reflect the genome-wide average GC-content . From the primate sequences so generated , we extracted positions containing a human-chimp difference that could also be unambiguously assigned an ancestral allele based on the macaque allele at that position . Each simulation used 335 such sites , ( the average number of fixed differences in the 49 harseq target regions ) which were divided based on whether they were W2S or S2W ( or other ) . The segregating sites for each simulation were derived from 101 ( the average total number of segregating sites in the 49 harseq target regions ) Bernoulli trials , randomly dividing them as W2S or S2W ( or other ) according the corresponding ratios in the fixed differences from all of the phyloBoot simulations . After calculating the MK test p-value for each simulation , the fraction of simulations with p-value less than a given value was computed , as well as the subset of that fraction for which the ratio of W2S∶S2W was higher for the simulated fixed differences than for the simulated segregating sites ( Supplementary Figure 3 in Text S1 ) . Simulations of GC-biased evolution due to BGC were generated using a forward time Wright-Fisher model of a population . Simulations were run at a population size of 10 , 000 , which is approximately comparable to the long term human effective population size . Details of the simulation method can be found in [62] and references therein . Briefly , we model BGC as a selection process in which each W2S ( S2W ) mutation adds ( subtracts ) some normal deviate fitness value to the haplotype on which it is found . This model is approximately equal to the normal shift model [63] if we were only to consider the W2S subset of mutations . Simulations were run for 40 * N generations as a burnin period to reach stationarity , at which point we modeled a vicariance event representing the human chimp divergence . After the population split we ran the two populations for 6 . 5 units of 4N generations , to approximate the divergence time between humans and chimps . We assume the strength of BGC acting was 4NB = 1 . 3 as recently estimated from human data [10] , [64] . We also assumed a ratio of 4NB∶4Nu of 1 . The MWU and MK tests were performed as above using a single sample from the chimp lineage and 50 samples from the human lineage . Association between MWU and MK tests on simulated ( and HAR region ) data was assessed using Fisher's Exact Test on the 2×2 contingency table defined by the counts of significant or not significant tests: {MWU , notMWU}×{MK , notMK} . The numbers of significant tests by either or both MWU and MK were compared between the simulated and HAR region data using binomial and Poisson tests . | The search for functional regions in the human genome , beyond the protein-coding portion , often relies on signals of conservation across species . The Human Accelerated Regions ( HARs ) are strongly conserved elements , ranging in size from 100–400 bp , that show an unexpected number of human-specific changes . This pattern suggests that HARs may be functional elements that have significantly changed during human evolution . To analyze the evolutionary forces that led these changes , we studied 40 kb neighborhoods of the top 49 HARs . We took advantage of recently developed DNA sequencing technology , coupled with methods to isolate genomic DNA for our target regions only , to determine the genotypes in 22 chromosomal samples . This polymorphism data showed no significant evidence for adaptive selective sweeps in HAR regions . By contrast , we found strong evidence for a nucleotide bias in the fixation of mutations from A or T to G or C basepairs . Our work reveals that this bias in the HAR neighborhoods is not just an historic phenomenon , but is ongoing in the present day human population . This finding adds credence to the possibility that non-selective forces , such as biased gene conversion , could have contributed to the evolution of several of these regions . |
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Stress granules ( SGs ) are non-membranous cytoplasmic aggregates of mRNAs and related proteins , assembled in response to environmental stresses such as heat shock , hypoxia , endoplasmic reticulum ( ER ) stress , chemicals ( e . g . arsenite ) , and viral infections . SGs are hypothesized as a loci of mRNA triage and/or maintenance of proper translation capacity ratio to the pool of mRNAs . In brain ischemia , hippocampal CA3 neurons , which are resilient to ischemia , assemble SGs . In contrast , CA1 neurons , which are vulnerable to ischemia , do not assemble SGs . These results suggest a critical role SG plays in regards to cell fate decisions . Thus SG assembly along with its dynamics should determine the cell fate . However , the process that exactly determines the SG assembly dynamics is largely unknown . In this paper , analyses of experimental data and computer simulations were used to approach this problem . SGs were assembled as a result of applying arsenite to HeLa cells . The number of SGs increased after a short latent period , reached a maximum , then decreased during the application of arsenite . At the same time , the size of SGs grew larger and became localized at the perinuclear region . A minimal mathematical model was constructed , and stochastic simulations were run to test the modeling . Since SGs are discrete entities as there are only several tens of them in a cell , commonly used deterministic simulations could not be employed . The stochastic simulations replicated observed dynamics of SG assembly . In addition , these stochastic simulations predicted a gamma distribution relative to the size of SGs . This same distribution was also found in our experimental data suggesting the existence of multiple fusion steps in the SG assembly . Furthermore , we found that the initial steps in the SG assembly process and microtubules were critical to the dynamics . Thus our experiments and stochastic simulations presented a possible mechanism regulating SG assembly .
Cells suffer from various environmental stresses including heat shock , chemicals , hypoxia , starvation , osmotic shock , ultraviolet irradiation , and viral infections . Cells respond to these stresses resulting in either survival or apoptosis . Assembly of stress granules ( SGs ) , which are non-membranous cytoplasmic aggregates of mRNAs and related proteins with a size in the order of 0 . 1–2 μm , is one form of cellular response to a stress [1–5] . SGs are reported to contain RNA-binding proteins ( e . g . HuR ) , translation initiation factors ( e . g . eIF4E , eIF4G , eIF3 , and PABP-1 ) , 40S ribosomal subunit , self-oligomerizing proteins ( e . g . TIA-1 and G3BP ) , nuclear transport proteins ( e . g . importin α1 and importin 8 ) , and signaling proteins ( e . g . TRAF2 , RACK1 , and Raptor ) in addition to mRNAs [2 , 5–7] . The 60S ribosomal subunit , HSP90 and ARE-binding proteins hnRNPA1 and hnRNPD are excluded from SGs . Inclusion of the translation initiation factor eIF2α and heat shock protein HSP70 are reported to be cell-type and stress-type specific [8] . S1 Fig shows the translation initiation steps ( thin lines and narrow characters ) together with pathways related to SG assembly ( thick lines and bold characters ) . It has been reported that the SG assembly is usually initiated by the phosphorylation of eIF2α on Ser51 [1 , 8–10] . This phosphorylation inhibits translation initiation by reducing the level of eIF2 ∙ GTP ∙ tRNAMet ternary complex [1 , 11] . The observations led to the hypothesis that SGs act as sites for storing and/or sorting of untranslated mRNAs [1 , 4 , 12 , 13] . It has also been hypothesized that SGs maintain the proper ratio of translation capacity to the pool of mRNAs in response to environmental stress [14 , 15] . In fact , global translation repression is not required for the assembly of SGs [16 , 17] . In addition , exclusion of mRNAs encoding HSP70 and HSP90 from SGs [1 , 2 , 18 , 19] is consistent with these hypotheses , because this enables HSP70 and HSP90 proteins translated under a stress condition to act as chaperones regulating misfolded proteins outside SGs . Thus , the assembly of SGs offers a chance for a cell to decide its own fate . The roles of SGs are typically found in brain ischemia . The ischemic treatment of neurons located in the hippocampal CA3 region led to the phosphorylation of eIF2α resulting in the inhibition of protein synthesis [20 , 21] . Reperfusion with a normal oxygen content solution recovered the protein synthesis in these neurons . However , the recovery did not occur in pyramidal neurons within the hippocampal CA1 region [21–23] . In parallel , the SG assembly was observed in hippocampal CA3 neurons but not in CA1 neurons [20 , 21] . It is known that the CA1 region is more vulnerable to ischemia than the CA3 region [24 , 25] . The fact that no SG is assembled in hippocampal CA1 pyramidal neurons by ischemic treatment is a possible explanation for their vulnerability highlighting the role of SGs described above . In addition , SGs are emerging to play an important role in neurodegenerative disorders including ALS ( amyotrophic lateral sclerosis ) and FTLD ( frontotemporal lobar degeneration ) [20] . These observations support a view that SGs play an important role in the survival of neurons . TIA-1 plays a critical role in the induction of SG assembly ( S1 Fig ) . TIA-1 contains one prion-related domain ( PRD ) and three RNA recognition motifs ( RRM1 , RRM2 , and RRM3 ) [6 , 26] . PRD enables TIA-1 to self-aggregate , while RRM2 and RRM3 bind mRNAs with a different nucleic acid sequence specificity [1 , 6 , 12 , 18 , 27–30] . TIA-1 is required for SG assembly because TIA-1 dominant-negative mutants block SG assembly in response to stress . On the other hand , overexpression of TIA-1 was sufficient to induce SG assembly in the absence of a stress [8 , 29] . TIAR and G3BP are also known to promote aggregate formation in SG assembly [9 , 17 , 31] . Phosphorylation of eIF2α is catalyzed by kinases PKR , PERK , GCN2 , and HRI by the following stress indicators: PKR by heat shock , UV irradiation , and viral infection; PERK by ER stress; GCN2 by starvation; HRI by hypoxia [32–38] . In addition to the initiation of SG assembly by the phosphorylation of eIF2α , it also promotes polysomal disassembly resulting in the accumulation of untranslated mRNPs [33] . From these observations , it is postulated that self-aggregating proteins such as TIA-1 play a major role in aggregate formation , while eIF2α phosphorylation provides constituents of SGs , thus both acting synergistically in the SG assembly . In addition , it is also postulated that TIA-1 actively escorts untranslated mRNA to SGs [6] . SGs were assembled after a short latent period ( <10 min ) by the application of arsenite . The number of SGs reached a peak after approximately 20–30 min ( ~30 SGs ) , and then subsequently decreased [39] . The physical size of SGs was small early in the assembly , and they maturated into larger size later [37 , 40 , 41] . SGs distributed within the cytoplasm without preference to location at the beginning of the assembly , and were gradually confined to the perinuclear region later as a response to arsenite [39] . Microtubules were reported to be critical for SG assembly , because inhibition of their function abrogated SG assembly [41 , 42] . In addition , SGs were reported to move on microtubules by the dynein motor [37 , 42] . It is reasonable to assume that the dynamics of SG assembly should be closely related to its biological roles . The time course over which SG assembly occurs is important in determining how quickly the cell can respond to a stressful environment . The number and the size of SGs can be considered as measures of responsiveness of a cell to environmental stress . The distribution of SGs in a cell can indicate the location in a cell where the translational silencing occurs , and where the most effective loci of translational silencing and recovery take place at specific mRNAs . Although there are reports on the dynamics of SGs as shown above , mechanisms that regulate these dynamics are still largely unknown . We approached these problems by devising experiments and conducting computer simulations . SG simulation is not an extension of conventional deterministic simulation ( DS ) , which is based on differential equations including continuous variables , and concentrations . Since SGs are discrete countable structures , they should be expressed in terms of numbers instead of a concentration . As a result , a stochastic simulation ( SS ) was employed [43] . In our method of SS , molecules undergo a random walk ( RW ) changing their location , making chance collisions leading to a reaction described by a probability function Pr calculated from the classical binding reaction rate constant k . Thus our SS program controls coordinates and states of every single molecule . Simulation results not only showed good agreements with our observations of SG assembly , but also predicted a gamma distribution relative to SG size . The experimental data also yielded a gamma distribution . In addition , we clarified critical parameters determining the dynamics of SG assembly .
First we investigated the assembly of SGs in HeLa cells . To monitor the process of SG assembly in living cells , HeLa cells were transiently transfected with an expression plasmid for green fluorescent protein ( GFP ) -tagged TIA-1 , an SG-nucleating protein . 40 h after transfection , cells were stimulated with arsenite , and the time-lapse fluorescence images were acquired every 5 min for 55 min . SGs were clearly observed at 10 min ( green dots in Fig 1A ) . The size of SGs was small at this stage , but grew larger at a later time ( 50 min ) . In contrast , the number of SGs reached a maximum at 30 min , then decreased as a function of time . These dynamics to exemplify SG assembly are qualitatively shown in Fig 1B and 1C . A small number of SG assembly was observed at 5 min reaching a maximum ( 31 . 7 ± 2 . 7 , N = 9 ) at 30 min , and then decreased to 22 . 0 ± 2 . 8 at 55 min ( Fig 1B ) . During these observable changes in the number of SGs , the average size increased monotonically with time ( Fig 1C ) . In this quantification , the size of SGs were measured by the number of pixels ( Materials and Methods ) . S1 Movie demonstrates the overall dynamics clearly and was similar to previous reports [37 , 39–41] . To validate our SG size measurement , we quantified SGs differently by integrated fluorescent intensity aimed at SG size measurement by volume instead of diameter ( Materials and Methods ) . The time course of SG assembly and its evolution of average size were not changed significantly ( left and middle panels in S2A Fig ) . Next , the TIA-1 requirement for the assembly of SG was tested . TIA-1 possesses three RRMs at the N-terminus , along with a glutamine-rich PRD at the C-terminal region , both of which are essential for TIA-1-mediated SG assembly [9 , 29] . Previous studies reported that the expression of the C-terminal fragment of TIA-1 , which contains the PRD alone , dominantly suppresses SG assembly [9 , 29] . Therefore , we constructed an expression vector encoding GFP-tagged PRD ( GFP-PRD in S2B Fig ) , and examined if the expression of the PRD would affect the SG assembly . COS-7 cells were transiently transfected with either GFP alone ( as control ) or GFP-PRD , and incubated for 48 h . The cells were then treated with 0 . 5 mM arsenite for 50 min and the assembly of SGs was assessed by immunofluorescence microscopy ( Fig 2A and 2B ) . In control cells expressing GFP alone , approximately 90 ± 76% of the cells formed SGs in response to arsenite . In contrast , cells expressing GFP-RPD scarcely showed SG assembly ( 10 . 4 ± 2 . 4% ) ( Fig 2B ) . These results clearly indicate that TIA-1 was required for the assembly of SG upon application of arsenite in COS-7 cells as was reported previously [8 , 29] . We focused on the spatio-temporal dynamics of TIA-1-dependent SG assembly ( i . e . time courses of the number and the size , and the spatial distribution ) in a whole cell . We intended to construct a minimal model instead of a consolidative one that included all pathways shown in S1 Fig . Thus , in the model , SG assembly was initiated by and depended on the self-aggregation of TIA-1 ( Fig 3A ) because our experiments clearly showed the requirement of TIA-1 ( Fig 2 ) . Neither translocation of TIA-1 from the nucleus to the cytoplasm nor de novo synthesis of TIA-1 upon stress application was included in our model , because our experimental data excluded these possibilities ( S3 Fig ) . In addition , TIA-1 translocation was not seen in data from other laboratories [44 , 45] . The O-GlcNAc modification of proteins in the translational machinery [46] was also excluded for simplicity . The latency before the assembly of SGs was observed both in our ( Fig 1B ) and other laboratory’s experiments [39] . Latencies are often observed in biological phenomena . In signal transductions , latency emerges after multiple steps of activation cascade , which acts as rate limiting steps . Among them , the assembly of filamentous actin ( F-actin ) is one typical example [47–52] . The oligomerizations of globular actin ( G-actin ) , which are nucleation steps , are the rate-limiting steps in F-actin assembly . In light of this , we employed a model in which a single TIA-1 ( TIA1 ) formed a dimer ( TIA2 ) , and a trimer ( TIA3 ) ( 3-step model shown in Fig 3A ) . These were the rate-limiting steps in SG assembly . Then TIA3 bound with TIA1 assembling TIA* containing four TIA1 molecules . TIA* further bound with TIA1 , TIA2 , TIA3 , and TIA* resulting in the formation of a larger TIA*aggregate . These steps of SG enlargement are analogous to the elongation step of F-actin . SGs thus formed were assumed to be transported on microtubules . In the present model , TIA1 was a complex of TIA-1 , mRNA and its binding proteins for simplicity . In our SS , molecules undergo random walk ( RW ) by changing their location upon stochastic jumps in 3D space . During a jump , two TIA1 undergo a chance collision ( top panel of Fig 3B ) . This can lead to a binding reaction between two TIA1s . The probability of the binding reaction Pr is calculated by Eq 1 ( Materials and Methods ) , which is a function of the classical rate constant k , time between a jump τ , which is calculated from diffusion coefficient D , collision radius Rc , and calculation time step Δt . The important aspect of Pr is that its usage guarantees the convergence of SS to DS at an infinite number of molecules for any selection of τ , Rc , or Δt as long as Pr<1 [43] . Our SS guarantees this , and we used the same theory and algorithm in the present SSs . The mathematical bases of our SS method are found elsewhere [43 , 53] . In the SS of SG assembly , TIA1 , TIA2 , and TIA3 underwent RW in a 3D model cell with a diameter and thickness of 12 and 1 . 5 μm , respectively ( bottom panel of Fig 3B ) . A nucleus at the center with a diameter and thickness of 4 and 1 . 5 μm was located . SG movement along microtubules was reported [37 , 54] . We assumed that TIA* with 12 or larger number of TIA1s , which was tentatively defined as SG , underwent 1D radial RW simulating transportation on microtubules . Since hindered diffusion of large particles such as SGs was reported [2 , 55] , SG transportation on microtubules was expected to lead to an effective collision between large SGs resulting in a replication of experimentally observed SG dynamics , which are discussed in the following sections . Coordinates and state of every molecules including SG were stored in tables within the SS program , and were updated upon the occurrence of an event ( jump , collision , and reaction ) . The number of TIA1 molecules in a single TIA* ( and also SG ) was stored in the table of the SS program . The size of SG was calculated using this table ( Materials and Methods ) , and the total number of TIA1 in a 3D model cell , which is the summation of the number of TIA1s in all complexes including monomers , was monitored and kept constant . To test the validity of our SS in the 3D model cell shown above , we ran simulations of simple reactions . SS results for the binding reaction ( blue and red open circles in the left panel of Fig 3C ) agreed almost perfectly with those of DS ( blue and red lines ) . The initial number of molecules were 2724 for A and B , and 0 for C . SS results for dimerization reaction , which appeared in the present SG simulation , also agreed almost perfectly with those of DS ( right panel in Fig 3C ) . Insets are snapshots at the beginning and at the end of SSs . These tests of SSs showed clearly that our SS could be applied to the simulation of SG assembly . All SS parameter values in the SG simulation are summarized in S1 Table . Prs for each reaction were calculated by the SS program using Eq 1 with given parameter values of k , D , Rc , τ , and Δt before the start of SS ( Cf . Material and Methods ) . We ran SSs with an initial number of TIA1 equivalent to 9081 ( blue dots at 0 min in Fig 4 ) , which corresponded to a concentration of 100 nM in our 3D model cell . Each dot in Fig 4 indicates a single molecule with different colors for different molecular species . At 6 . 7 min , a small number of TIA2 ( yellow dots , one of which is indicated by a yellow arrowhead ) and TIA3 ( green dots , one of which is indicated by a green arrowhead ) were formed , and a small number of small SGs were also found ( red circles , one of which is indicated by a red arrow surrounded by white line ) . TIA* containing TIA1 smaller than 12 are indicated by small red dots ( one of which is indicated by a red arrowhead ) . At 16 . 7 min , both the number and the size of SGs increased . SGs gradually moved to perinuclear regions at later points in time ( 26 . 7 , 40 , and 60 min ) . These dynamics are clearly seen in the S2 Movie . Note that the number of TIA1 decreased with time as seen by the clearing up of blue dots from the background in the cytoplasmic space . If we plotted the number of SG as a function of time , SS results ( black line in Fig 5A ) agreed well with our observations ( white circles , which were replotted from Fig 1B ) . Gray areas indicate standard deviation ( SD ) at each time point for multiple SSs ( N = 10 ) . SS with smaller runs ( N = 5 ) gave almost the identical result ( S4 Fig ) . This suggests that experimental data with N = 9 is sufficient for analyses . The size of SGs increased monotonically as discovered in the experiment ( black line in Fig 5B ) . White circles were replotted from Fig 1C . Note that the time courses were sublinear both in the experiment and the SS . Next we compared the simulated special distribution of SG with that of experiments . SGs were localized around the nucleus at 50 min during the experiments ( left panel in Fig 5C ) . We found the same distribution in SSs at 50 min ( right panel in Fig 5C ) . There were negligible SGs seen at a distance from the nucleus . If this was compared to an earlier time , distributions from both experiments ( 35 min ) and simulations ( 35 min ) agreed qualitatively ( S5A and S5B Fig ) . Thus we replicated the dynamics of SG assembly qualitatively in our SSs . We tested a simpler model than that shown in Fig 3A , where there were two molecular species , TIA1 and TIA* ( 1-step model shown in S6A Fig ) . TIA* that contained 12 or larger number of TIA1 was defined as SG as in the model shown in Fig 3A . SS results showed that neither latency nor the peak in the time course of the number of SG was found . If we plot the time to 99% of the saturated number of assembled SG by varying kf1 and kf2 in the model shown in S6A Fig , it was far shorter than the experimental observation on the time to peak ( S6B Fig ) . Thus , this simple model could not replicate our experimental observations . We also tested a complex model by employing TIA4 in addition to TIA1-3 ( 4-step model shown in S6C Fig ) . This model also replicated the observed time course of number of SGs ( S6D Fig ) . Next we compared latencies from three SS models and our experiment . The latency was defined as the time to 10% of the maximum number of SGs , which was 5 . 6 min in our experiment ( S7A Fig ) . Latencies for 1- , 3- , and 4-step models were 8 . 3x10-3 , 3 . 8 , and 4 . 2 min , respectively ( S7B Fig ) . These results clearly showed the importance of multiple steps before the assembly of SG . We also tested a DS model , which was based on ‘winners-share-all’ dynamics ( S8A Fig ) . This mechanism was consistent with the observation that almost all SG resources ( TIA-1 ) were collected and shared into a small number of SGs during the course of their assembly [11] . Snapshots of DS results seemed to be consistent with observations ( upper panels of S8B Fig ) . However , there was no latency in the time course of the number of SG ( lower panel of S8B Fig ) . Furthermore , this type of simulation could not involve SG transportation on microtubules , and therefore , neither movement on microtubules of assembled SGs nor their fusion could occur . In fact , large SGs , shown by reddish dots , did not move at all ( S3 Movie ) , which was inconsistent with our experimental observation ( Cf . S1 Movie ) . Thus a DS model shown here was not representative of experimental observations of SG assembly . Next we investigated the dynamics of each molecular species in the model . First we analyzed the time course of complexes ( Fig 6A ) . As the increase in the number of SG , TIA1 decreased monotonically . TIA2 and TIA3 increased rapidly just after the start of the simulation , and then decreased monotonically . The peak number of TIA2 and TIA3 was 66 and only 9 , respectively in this SS . The small number of TIA3 was expected from Fig 4 . This indicates that TIA3 lifetime was relatively short , and quickly made a transition back to TIA2 or forward to TIA* . The number of SG after a peak decreased ( Fig 5A ) . In this decreasing phase , stepwise decrements in the number of SG in a single SS were observed in SSs ( top panel of Fig 6B ) . These decrements should be correlated to some event . We hypothesized that the fusion of SG occurred at time points of these decrements , and found that each decrement coincided exactly with the occurrence of single fusion events . In fact , two SGs at 3200 sec ( white arrowheads in the bottom left panel in Fig 6B ) fused into one larger SG at 3210 sec ( white arrowhead in the bottom right panel ) . Thus , in our simulation , the fusion of SGs was a major reason for the decrease in the number of SGs after attaining its peak . Next we tested the spatial distribution of SGs . In our simulations , the probability of antero- and retrograde transport were 0 . 4 ( pm ) and 0 . 6 ( pn ) , respectively , resulting in perinuclear localization of SGs at 60 min ( left panels of Fig 6C ) . It was expected that if these probabilities were changed , the distribution would also be changed . In fact , SG distributed dispersedly within the cytoplasm ( middle panels ) or near plasma membranes ( right panels ) for pm/pn of 0 . 5/0 . 5 and 0 . 6/0 . 4 , respectively . Thus the probability of antero- and retrograde transport of SGs on microtubules mainly determined the SG distribution in our simulation . We were interested in the SG size distribution , because it might provide us with a better perspective on the dynamics of SG assembly . Bars in the top panel of Fig 7A indicate SG size distribution at 55 min as a result of the simulation , which was fitted by a gamma distribution with a shape and scale parameters of 4 . 50 and 0 . 073 , respectively . The distribution in experiments at 55 min was also fitted by a gamma distribution with a shape and scale parameters of 2 . 54 and 0 . 093 , respectively ( lower panel of Fig 7A ) . There were significant differences between the simulation and the experiment . To explore the reason , we hypothesized that small SGs were not detected in our fluorescence measurement , and estimated that the number of GFP-TIA1 molecules in the smallest SG was 139 ( Materials and Methods ) . If we eliminated SGs smaller than this in our simulated data , the distribution was much similar to that by the experiment with a shape and scale parameters of 1 . 70 and 0 . 14 , respectively ( middle panel of Fig 7A ) . In addition , the shape and scale parameters were almost unchanged by the change in N from 5 to 100 ( S9 Fig ) , suggesting that the SG size distribution was fitted reasonably well with a gamma distribution in our simulation . These results suggest that small SGs were not detected in our fluorescence measurement . To further validate our SG size measurement , we draw SG size distribution using data by integrated fluorescent intensity ( Materials and Methods ) . The SG size was again approximated by a gamma distribution with a shape and scale parameters of 3 . 58 and 0 . 059 , respectively ( right panel in S2A Fig ) . The fact that the SG size was fitted by a gamma distribution both in our experiment and SS suggests that the SG assembly is an accumulation of successive random events , rather than ensembles of single random events . Next we analyzed the change in the dynamics of SG assembly by investigating the change in kinetic parameters using data shown in S10 and S11 Figs . First we investigated the sensitivity of number of SGs at the time of the peak and at 60 min . The peak number of SGs was sensitive to k1-1 , k1-2 , kb2 , and kb3 , but insensitive or only weakly sensitive to other parameters ( black bars in the top panel of Fig 7B ) . In contrast , the number at 60 min was only weakly sensitive to parameters tested ( gray bars in the top panel of Fig 7B ) . The time to peak was sensitive to k1-1 , k1-2 , k2-2 , kb2 , kb3 , and kb4 . But the latency was only weakly sensitive to parameters tested ( bottom panel of Fig 7B ) . Sensitive parameters for the number and the time are shown in solid red and blue boxes in the middle panel of Fig 7B , respectively . Overall , the dynamics of SG assembly was mainly sensitive to rate-limiting steps , and thus they determined the dynamics of SG assembly . Next we investigated the sensitive parameters for determining sublinearity seen in the evolution of the size of SG ( Fig 5B ) . We found that the sublinearity was sensitive to k1-4 and pm/pn ( S12 Fig ) . Larger k1-4 or pm/pn ratio increased the sublinearity . We investigated how microtubules contributed to the multiple aspects of SG assembly . To this purpose we deleted microtubules from our SS by removing 1D radial RW during the SG assembly . Thus SGs underwent 3D RW as other species ( TIA1-3 , and TIA* ) . The D was the same as that for 1D radial RW of SGs . The simulated deletion of microtubules dramatically changed the dynamics . SGs were distributed throughout the cytoplasmic space ( bottom panel of S13A Fig . The peak of the spatial distribution shifted from perinuclear to distant position ( S13B Fig ) , which was similar to that in the presence of SG transportation on microtubules with pm/pn of 0 . 5/0 . 5 ( middle panel of Fig 6C ) . There was no decrease in the number of SGs in the time course of their assembly ( S13C Fig ) . SG was small and distributed at bins of smaller sizes ( S13A and S13D Fig ) . In addition , the normalized SG size distribution was largely different from that in the presence of microtubules ( S13E Fig ) . While the distribution skewed positively in the presence of microtubules , it skewed negatively in their absence . These results strongly suggest that three experimental observations , the decrease in the number of SGs after the peak , the spatial distribution , and the size distribution were regulated by a single common mechanism of 1D radial RW of SGs on microtubules .
We intended to elucidate possible mechanisms for the dynamics of SG assembly in a whole cell both by experiments and mathematical models . To the best of our knowledge , this is the first occurrence to demonstrate SSs of SG assembly in a whole cell model . The SSs result replicated our experimental observations . In addition , the SSs result predicted a gamma distribution describing the SG size , which was also found in our experiments . One of important roles of computer simulations is to show a common mechanism for multiple phenomena . Our SSs replicated multiple aspects of our experimental observations including the decrease in the number of SGs after the peak , perinuclear spatial distribution , and the gamma distribution of SG size with a common mechanism of 1D RW of SG on microtubules . We employed 1D RW of SG in the radial direction . The assumption of radial structure of microtubules does not hold at perinuclear region . However , overall orientation can be assumed to be radial [56 , 57] . Thus we employed this simple assumption in our minimal model . Although the involvement of dynein in the retrograde transportation of SGs was suggested [41 , 42] , there is no firm evidence for the involvement of kinesin in the anterograde transportation of SGs . However , we observed anterograde movement of SGs in our experiments ( Cf . S1 Movie ) . In addition , mRNP was reported to move anterogradely on microtubules driven by kinesin motor protein [37] . Therefore , we hypothesized that SGs was transported anterogradely on microtubules in addition to retrograde movement . We have shown that the SG distribution was modified by probabilities of antero- and retrograde transportation ( pm and pn ) of SG ( Fig 6C ) . If motor proteins associated with a SG are modified posttranslationally by constituents of a SG in cell type- and stress-specific fashion , the probability of the movement direction would be modified too , and the SG distribution will be different by cell type- and stress-specific fashion . This might have a direct relationship to the biological roles of SG . The distribution of SG size was fitted with a gamma distribution both in SSs and experiments ( Fig 7A and S9 Fig ) . It is important to suggest that if the shape and/or scale parameter is different for different type of cells or stresses , existence of different kinetics and/or modifications in SG assembly mechanisms might exist . Thus analysis of SG size distribution will give us additional information for clarifying the mechanisms of SG assembly . A limitation to detect small SGs by fluorescence microscopy could result in a gamma distribution of SG size with different shape and scale parameters ( top and middle panels of Fig 7A ) . However , different quantification method of SG size by integrated fluorescent intensity also gave a gamma distribution ( right panel of S2A Fig ) , suggesting that SG size was intrinsically gamma distributed . Sublinearity in the SG size evolution in experiments was more pronounced than in SSs . ( Fig 5B ) . We have shown that the sublinearity was regulated by pm/pn ( S12 Fig ) . This suggests that the distribution of SG and the sublinearity in the SG size evolution might have a relation . This result was beyond our expectation . Further experiments and simulations are required to further reveal its mechanism and biological role . The difference in the latency between 3- and 4-step models was small , and the latency in a 4-step model was still shorter than the experiment . This may suggest more number of rate-limiting steps are required for replicating experimental observations . However , there is an another possibility of the existence of preprocess before TIA1 . In any case , the present study strongly suggested the existence of multiple rate-limiting steps before the assembly of SGs . In some of the simulation cases , the latency was so long that there was no observable decrease in the number of SG during 60 min of simulations , nor could the peak time be identified ( Cf . S14 Fig ) . If we carefully looked at the evolution of SG size , we found that there was a catastrophic decrease in the SG size before SG assembly in many long latency cases ( blue arrow in the bottom right panel of S15 Fig ) . This should be caused by a stochastic disassembly of SGs , and in extreme cases , no SG would be assembled . For example , such cases were observed for large values of kb2 and kb3 , where latency and the time to peak were not measured because of no SG assembly ( Cf . S11 Fig ) . In the present study , we could not compare the size of SGs between experiments and simulations in an absolute fashion , because we could not know experimentally how many TIA-1 existed in a SG . If this could be measured , models and kinetics would be greatly improved enabling a better replication of the experiments and proposing additional predictions to describe the dynamics of SG assembly . It is also important to know the minimum size of observable SGs and the number of TIA-1 molecules within it .
Full-length TIA-1 or a C-terminal fragment of TIA-1 ( TIA-1-PRD ) was subcloned into pEGFP vector by PCR . HeLa and COS-7 cells were maintained in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal bovine serum ( FCS ) , L-glutamate , penicillin and streptomycin . For transient transfection assays , cells grown on a 35-mm-diameter glass bottom dish were transfected with the appropriate expression plasmids using Effectene transfection reagent ( QIAGEN ) according to the manufacturer’s protocol . 36 h after transfection , culture medium was exchanged with fresh DMEM/10%FCS without phenol red . The cells were incubated for another 4 h and then stimulated with arsenite ( 0 . 5 mM ) . Fluorescence images of the living cells were captured using a TE-2000E inverted microscope ( Nikon , Japan ) , equipped with a Planfluor 40× objective ( numerical aperture , 0 . 6 ) , a CoolSNAP HQ charge coupled device ( CCD ) camera ( Photometrics ) , and a xenon lamp . For GFP imaging , a B-2A filter set [a dichroic mirror ( 500 nm , a long-pass ) , an excitation filter ( 450–490 nm ) , an emission filter ( 515 nm cut-on ) ; Nikon , Japan] was used . Image binning was set to 2×2 . Fluorescence images were acquired every 5min from the same field . Fluorescence microscopic images of fixed cells were captured using an inverted Olympus IX81 microscope equipped with a QImaging Retiga EXi digital camera ( IEEE1394 ) and the Universal Imaging Metamorph software ( Molecular Devices ) . The assembly of SGs was determined from the fluorescence images using the Metamorph software package . First , the region of the entire cell area and the cytoplasmic area were manually located and the position of the region of the interest ( ROI ) was set in each fluorescence image in a time-series stack . Next , a median filter ( 25×25 ) was applied to the raw images and the resulting images were used as the background images . Images of SGs were generated by background image subtraction , by which the background images were appropriately subtracted pixel-by-pixel from the raw images . The number and the size of the SGs were quantified using transfluor assay module of the Metamorph software package and the pit-detection algorithm with the parameter describing approximate minimum width of 4 pixels ( ~1 . 3 μm ) was applied to the SG images . We then measured the area size of individual SG , the number of SGs in the cytoplasmic region of the cell , and the x and y coordinates of the each SG dot relative to the nuclear centroid of the cell . In addition , we applied different quantification method , in which a fluorescence spot was surrounded by a circle ( ROI ) using Find Spots command of Metamorph , and ROI higher than the threshold level of fluorescent intensity was defined as SG . Then , the spatial integration of fluorescent intensity within a ROI was calculated aimed at measuring the volume of a SG instead of its diameter . The false-positive dots were carefully checked by looking and manually eliminated from the measurement . Cells grown on glass coverslips were transfected with appropriate plasmids as indicated . Cellular SG assembly was then stimulated with 0 . 5 mM arsenite for 50 min . The cells were then fixed with 1% paraformaldehyde in PBS for 10 min . After washing with PBS , the cells were permeabilized with 0 . 1% Triton X-100 for 5 min , and incubated in the blocking solution BlockAce ( Snow Brand Milk Products ) for 1 h . Cells were then incubated with anti-eIF4E ( Santa Cruz ) antibody for 50 min in PBS containing 2% BSA , washed three times with PBS , and incubated with an Alexa Fluor 546-conjugated rabbit anti-mouse antibody for 30 min . The stained cells on coverslips were washed three times with PBS and were mounted in FluorSave Reagent ( Calbiochem ) . SG assembly was assessed by determination of the number of cells expressing at least two SGs per cell . We assumed that a SG began to assemble by aggregating TIA1 upon stress application . TIA1 was assumed to contain TIA-1 , mRNA , and related proteins for simplification . TIA1 forms dimer ( TIA2 ) , trimer ( TIA3 ) , and larger complex ( TIA* ) . TIA* grew further by binding TIA1 , TIA2 , TIA3 , and TIA* . Reaction probability Pr in SSs was calculated by the following equation [43]: Pr=k/{NA ( 4/3 ) πRc3 ( 2−e−Δt/τ1−e−Δt/τ2 ) /Δt} . ( 1 ) k , NA , Rc , and Δt are binding reaction rate constant between molecule 1 and 2 , Avogadro’s number , collision radius , and calculation time step , respectively . τ1 and τ2 are waiting times before the jump for molecule 1 and 2 , which were calculated by using τ = λ2/6/D . λ and D were jump lengths and diffusion coefficient . The convergence of our SS to DS is guaranteed as long as Pr<1 as shown in the main text , and a theory and analyses for this convergence are found in our previous paper [43] . Rc was used for testing an occurrence of a collision , and Pr was used for decision of actual occurrence of a reaction by the collision . Thus , Rc was not the direct parameter for reactions . In this sense , Rc was not an important parameter in our SS . However , it should be noted that the spatial accuracy of the occurrence of a reaction worsens by the increase in Rc . In contrast , small Rc gives a better spatial accuracy , but Pr would be larger than 1 . This leads to a strategy for the selection of Rc , in which small Rc is selected as long as Pr<1 . Rcs between TIA1 , TIA2 , and TIA3 were 20 nm according to this consideration . The same Rc was employed for the reaction between TIA* and other species ( TIA1 , TIA2 , and TIA3 ) . Rc between two TIA*s was calculated by the summed radii of two TIA*s . Each radius of a TIA* was calculated by the following equation: RTIA*=R0⋅nTIA11/3 , ( 2 ) where R0 is 10 nm and nTIA1 is the number of TIA1 in a single TIA* . The Pr between two TIA*s was assumed to be 1 . TIA* contained 12 or a higher number of TIA1 moved on microtubule by 1D RW . Other species underwent diffusion in a cytoplasmic 3D space . Simulations were run on computers consisting of an Intel Core i7-4770 processor ( 3 . 4GHz ) with Windows 8 OS ( 64 bit ) . SS program was written in C language . Parallelization by Open MP or MPI was not applied in the present SSs . The computational time was about 24 h for a 60 min simulation of SG assembly with an initial number of about 15 , 000 molecules . SS program running on Windows PC can be found in “doi . org/10 . 6084/m9 . figshare . 1295250” on the web . DS model was constructed by A-Cell software [58 , 59] . The model description files can be downloaded from http://www . ims . u-tokyo . ac . jp/mathcancer/A-Cell/A-CellModels/index . html . SS yields a different result for every run because of the use of random variables in the simulations . So we averaged SS results from 5 or 10 runs and calculated SD using Origin Pro 8 . 5 . 0 J SR1 from Origin Lab Corporation . Fitting of SG size distribution with gamma distribution was performed by using R with fitdsitr ( ) function of MASS package . Sensitivity Se was calculated using the following equation: Se=∂PCPC∂PSPS , ( 3 ) where PC and PS are characterizing and simulation parameters , respectively . Sensitivities of a single parameter were calculated at each value in the given range of a parameter shown in S10 and S11 Figs , and they were averaged giving an average sensitivity in the range , which was used in Fig 7B . We defined a characterizing parameter as sensitive , when Se was larger than 25% of the absolute maximum value of sensitivity for the number or the time in SG assembly . We estimated the minimum size of a SG to be detected in our fluorescence measurements . It was reported that ~200 nM EGFP fluorescence signal could be detected above typical cellular autofluorescence [60] . We employed a 1 . 3 μm square window for detecting SGs as shown in the previous section . If spherical shape of a SG was assumed within the window , the number of GFP-TIA1 molecules in the smallest SG was estimated using the following equation: nTIA1=Cf⋅43πr3⋅NA , ( 4 ) where Cf , r , and NA are 200 nM , 0 . 65 μm , and Avogadro’s number , respectively . These yielded nTIA1 of 139 . | Cells suffer from various environmental stresses such as heat shock and viral infection . In response to a stress , small non-membranous cytoplasmic aggregates , stress granules ( SGs ) , are assembled . SGs contain mRNAs and related proteins . Hippocampal CA1 neurons located in the brain , which are vulnerable to ischemia , do not assemble SGs , while CA3 neurons , which are resilient to ischemia , assemble SGs . The dysfunction of SGs has been reported in human diseases including pathogenic viral infection . These observations led to a hypothesis that SGs play an important role in cell fate decisions , and the dynamics of SG assembly would regulate cell fate . However , the conditions that determine the number and distribution of SGs in a cell in response to a stress are largely unknown . We approached this problem by experiments and simulations . Our stochastic simulations replicated the observations . Furthermore , we found that initial steps in the SG assembly process were important to the dynamics of SG assembly , and that SG size resembled the gamma distribution both in simulations and experiments , suggesting the existence of multiple steps in the SG assembly process . To the best of our knowledge , this work was the first to show SG assembly in a whole cell by stochastic simulations . |
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Despite the presence of significant levels of systemic Interferon gamma ( IFNγ ) , the host protective cytokine , Kala-azar patients display high parasite load with downregulated IFNγ signaling in Leishmania donovani ( LD ) infected macrophages ( LD-MØs ) ; the cause of such aberrant phenomenon is unknown . Here we reveal for the first time the mechanistic basis of impaired IFNγ signaling in parasitized murine macrophages . Our study clearly shows that in LD-MØs IFNγ receptor ( IFNγR ) expression and their ligand-affinity remained unaltered . The intracellular parasites did not pose any generalized defect in LD-MØs as IL-10 mediated signal transducer and activator of transcription 3 ( STAT3 ) phosphorylation remained unaltered with respect to normal . Previously , we showed that LD-MØs are more fluid than normal MØs due to quenching of membrane cholesterol . The decreased rigidity in LD-MØs was not due to parasite derived lipophosphoglycan ( LPG ) because purified LPG failed to alter fluidity in normal MØs . IFNγR subunit 1 ( IFNγR1 ) and subunit 2 ( IFNγR2 ) colocalize in raft upon IFNγ stimulation of normal MØs , but this was absent in LD-MØs . Oddly enough , such association of IFNγR1 and IFNγR2 could be restored upon liposomal delivery of cholesterol as evident from the fluorescence resonance energy transfer ( FRET ) experiment and co-immunoprecipitation studies . Furthermore , liposomal cholesterol treatment together with IFNγ allowed reassociation of signaling assembly ( phospho-JAK1 , JAK2 and STAT1 ) in LD-MØs , appropriate signaling , and subsequent parasite killing . This effect was cholesterol specific because cholesterol analogue 4-cholestene-3-one failed to restore the response . The presence of cholesterol binding motifs [ ( L/V ) -X1–5-Y-X1–5- ( R/K ) ] in the transmembrane domain of IFNγR1 was also noted . The interaction of peptides representing this motif of IFNγR1 was studied with cholesterol-liposome and analogue-liposome with difference of two orders of magnitude in respective affinity ( KD: 4 . 27×10−9 M versus 2 . 69×10−7 M ) . These observations reinforce the importance of cholesterol in the regulation of function of IFNγR1 proteins . This study clearly demonstrates that during its intracellular life-cycle LD perturbs IFNγR1 and IFNγR2 assembly and subsequent ligand driven signaling by quenching MØ membrane cholesterol .
Visceral Leishmaniasis ( VL ) , a potentially fatal visceralizing disease , afflicts millions of people worldwide and is caused by infection with Leishmania donovani ( LD ) , an obligate-intracellular trypanosomatid protozoan . During the past decades , a large body of evidences supported the notion that the cytokine interferon gamma ( IFNγ ) plays a decisive role in anti-leishmanial defense [1] , [2] . A primary defect that may lead to pathogenesis in VL is the failure to activate parasitized macrophages ( MØs ) to eliminate LD in response to IFNγ [3] . Intriguingly , the presence of elevated levels of serum IFNγ in human VL [4]–[8] and high expression of IFNγ mRNA in lymphoid organs [9] do not reconcile with large parasite burden observed at the active stage of the disease . The remarkable predominance of the Th1 cytokine IFNγ along with impaired MØ effector function indicates a MØ specific desensitization to the available IFNγ stimulus . This was evident from several in vitro studies [3] , [10] , [11] showing gross inhibition of the IFNγ signaling pathways in the LD infected MØs ( LD-MØs ) , but the exact mechanism that triggers the inhibition remained unknown till date . IFNγ binds to specific cell surface receptor IFNγR which consists of two heterodimeric subunits , IFNγR1 ( α , ligand binding subunit ) and IFNγR2 ( β , signal-transducing subunit ) . Signal transduction of IFNγ is initiated by its binding to IFNγR1 and subsequent receptor subunit multimerization [12] . IFNγR1 colocalizes partly with the ganglioside GM1 , a classical marker of specialized cholesterol-rich membrane microdomains termed lipid-rafts [13] . Subsequent evidences disclosed that membrane lipid-rafts are intimately involved in the process of IFNγ mediated signal transduction . Remarkably , irrespective of various cell types used in different reports , disruption of the plasma membrane rafts by cholesterol depletion using methyl-β-cyclodextrin ( mBCD ) or cholesterol sequestration with filipin reversibly affected not only the generation of the IFNγ inducible tyrosine phosphorylation of signal transducer and activator of transcription 1 ( STAT1 ) but also productive transcriptional signaling [14]–[16] . This is congruent with the prevalent hypothesis of lipid raft involvement in IFNγ mediated signaling [17] . Membrane proteins such as G protein coupled receptors , transporters , or ion channels have been shown to be localized or enriched in lipid rafts . The structure- activity analysis shows that many of the cholesterol effects are due to specific sterol-protein interactions as shown in case of a number of membrane bound receptors such as those for cholecystokinin ( type B ) , oxytocin and nicotinic acetylcholine ( nACh ) [18] . Refined structure of nACh receptor ( nAChr ) has been shown to have internal sites capable of containing cholesterol , whose occupation bolsters the protein structure [19] . Both oxytocin and serotonin1A receptors contain the strict cholesterol consensus motif ( CCC ) , and in both the cases cholesterol incorporation lead to dramatic increase in agonist affinity [20] , [21] . Previous studies from our group have shown that during their intracellular life-cycle LD parasites disrupt the membrane rafts of MØs by quenching cholesterol from the membrane . This was associated with defective antigen presenting function , which could be corrected by liposomal delivery cholesterol [22] , [23] . We also showed that systemic administration of cholesterol through liposomal delivery in LD infected hamsters has strong therapeutic efficacy [23] , in agreement with the clinical practice of using cholesterol either directly [24] or as an adjunct to treatment [25] . It may be recalled that Kala-azar patients show progressive decrease in serum cholesterol as a function of splenic parasite load [26] . Therefore we investigated the potential involvement of LD mediated manipulation of MØ membrane cholesterol in IFNγ signaling modulation . Herein we report for the first time that the earliest activation induced signaling event of IFNγ receptor oligomerization is inhibited due to quenching of the MØ membrane cholesterol by the LD parasites . Restoration of the MØ membrane cholesterol by exogenous delivery not only reinstates the receptor subunit interaction but also restores subsequent downstream signaling as well as the IFNγ mediated leishmanicidal property .
Previous reports revealed that Leishmania spp . infected MØs are refractory to exogenous IFNγ mediated clearance of intracellular parasites due to defective IFNγ signaling [3 , 11 , 27 , and 28] . In this study , the molecular mechanism/s triggering the impairment of IFNγ signaling in LD-MØs have been investigated . To investigate the mechanistic basis of defective IFNγ signaling in LD-MØs , BALB/c derived peritoneal MØs ( PEC ) and homologous cell-line of RAW 264 . 7 were used . While performing the preliminary experiments ( Figure S1-S3 ) , RAW 264 . 7 cells yielded results comparable to the native system of PEC; therefore all the subsequent experiments were performed using these cells unless otherwise mentioned . Of note , JAK2 phosphorylation , which is the proximal most activation event down-stream to IFNγR1 engagement [12] , [29]–[32] was uncoupled ( Figure S3A ) , from the intact IFNγ-IFNγR1 interaction in LD-MØs ( Figure S2B ) . It has been reported that ligand induced receptor multimerization is the crucial and sufficient trigger for the activation of most of the cytokine receptors [33]–[36] , including IFNγ receptors [37]–[41] . Thus , we hypothesized that abrogation of earliest signal of JAK2 phosphorylation by IFNγR1 engagement in LD-MØs most likely involve the impairment of immediate upstream event of IFNγR subunit multimerization . To address this possibility , we utilized the fluorescence resonance energy transfer ( FRET ) method , a powerful spectrofluorometric technique that has been used to determine molecular interactions with higher resolution [42] . We assessed FRET between cell surface IFNγR1 and IFNγR2 molecules that were fluorescently labeled with receptor specific antibodies tagged with DyLight488 or DyLight594 respectively . As shown in Figure 1A ( panel a ) , the emission spectrum excited with 485 nm light showed reciprocal changes in donor emission peak ( 517 nm ) versus acceptor emission ( 620 nm ) , indicating that IFNγ treatment promoted energy transfer between IFNγ-R1 and -R2 in normal cells . In marked contrast , stimulation of LD infected cells with IFNγ could induce minimal or no FRET signal between IFNγR1 and IFNγR2 ( Figure 1A , panel b and c ) . The FRET efficiency was calculated in each of these conditions . As this was found to be low in infected cells ( E = 0 . 166 in 4 hr and E = 0 . 042 in 12 hr LD infection ) compared to that ( E = 0 . 465 ) of normal IFNγ stimulated condition , LD infection must have significantly inhibited ligand induced receptor association . Thus the results indicate prominent reduction in the ligand induced IFNγR1-IFNγR2 FRET efficiency , which is reduced by 64 . 6 % at 4 hr and 90 . 9 % at 12 hr post LD infection than the normal condition . To confirm the observation , we coimmunoprecipitated IFNγR1 and IFNγR2 from ligand stimulated or unstimulated normal and infected MØs . The results ( Figure 1B ) indicate that IFNγ induced copurification of the IFNγR2 subunits with IFNγR1 in normal condition of the MØs . Conversely , infection for 4 hr markedly reduced the extent of IFNγR2 coprecipitation with IFNγR1 even in IFNγ stimulated condition ( Figure 1B ) , while the level of coprecipitated IFNγR2 went below detection level with increasing time of infection . Taken together , the results in this section imply that LD infection inhibits IFNγ induced receptor oligomerization in MØs . Association with lipid-raft membrane domains is known to regulate the oligomerization of various cell surface receptors [43]–[45] . It is also accepted that localization of the IFNγR complex within intact lipid raft microdomains governs their efficient signal transduction capability [14]–[16] . The aforementioned results suggested that LD infection inhibits IFNγ induced receptor oligomerization in MØs , despite having unaltered initial ligand-receptor association . We therefore speculated that differential membrane partitioning behavior of the subunits might affect the IFNγ-R1 and -R2 interaction . It was therefore necessary to examine whether LD infection of the host cells delocalizes the IFNγR chains from the lipid raft microdomains that in consequence might hinder efficient oligomerization of the receptor subunits upon IFNγ mediated stimulation . To study the abrogation of IFNγ induced IFNγR subunits partitioning , if any , under parasitized conditions , MØ membrane extracts with 1% Triton X-100 were subjected to optiprep density gradient separation and examined by immunoblotting to analyze the distribution of IFNγR subunit proteins ( Figure 2 ) . Cholesterol-rich lipid raft domains were characterized by the enrichment of known raft marker protein flotillin1 , while the fractions containing the non-raft portions were identified with the surrogate marker of transferrin receptor [46] , [22] . The success of the biochemical isolation process was confirmed by determining the lipid raft constituent glycosphingolipid GM1 in each fraction by dot blot . Careful analysis of the fractionation study revealed that the constitutive association of IFNγR1 with the detergent resistant lipid raft fractions was discernibly disoriented at steady-state of the early infected MØs where appreciable amount of IFNγR1 was present in the detergent soluble fractions . Whereas , with increasing severity of the LD infection , the IFNγR1 protein was nearly completely delocalized from the lipid raft fractions and were recovered from the detergent soluble higher density fractions ( Figure 2 ) . On the contrary , the localization pattern of the IFNγR2 subunit showed a more or less diffused distribution throughout the density gradients of the steady state of the normal MØs . Upon IFNγ mediated activation , the IFNγR2 subunit is recruited to the lipid raft fractions almost to entirety which is accompanied by robust receptor subunit interaction , as indicated in the above mentioned energy transfer and coIP experiments ( Figure 1 ) . Intriguingly , the IFNγR2 subunit distribution pattern was markedly altered from the very early hours of infection through the late hours , when bulk of the proteins were recovered from the non-raft fractions , denoting complete detergent solubilization . Nevertheless , at early hours of LD infection stimulation of the cells with IFNγ could elicit IFNγR2 redistribution albeit to a lesser extent and this partial IFNγR2 recruitment to the raft domains was reflected in concomitant decline in the efficiency of receptor subunit interaction and signaling potency . Notably , at late hours of LD infection , despite having simultaneous presence of both IFNγR1 and IFNγR2 within the non-raft fractions , the IFNγ mediated stimulation of the MØs failed to induce any detectable subunit interaction as evidenced by the absence of FRET or coIP positivity at 12 hr postinfection . The results were further validated in situ by confocal colocalization study , which recapitulated the observations of the floatation studies for the receptor distribution patterns . Analysis of the colocalization pattern of the individual subunits of the IFNγR assembly with the GM1 rich microdomains was quantified and expressed in terms of the Pearson's correlation coefficient ( PCC ) values . The PCC values obtained by colocalization analysis of constitutively raft associated IFNγR1 proteins with GM1 at the steady state of the naïve MØs were considered as basal , and each experimental condition assessed thereafter was represented as the function of this optimal ( 100% ) colocalization efficiency . The results in Figure 3 , demonstrate that the percentage colocalization of IFNγR1 with GM1 molecules upon IFNγ mediated activation of the normal MØs remain unchanged ( 102% ) , signifying preassociation of the IFNγR1 protein with the GM1 enriched domain . The results also reveal that , there is a considerable decrease in the percentage of colocalized IFNγR1 with GM1 in the macrophage membrane at 4 hour post LD infection ( 71% ) than the basal condition . However , IFNγ stimulation could elicit a minor increase in the colocalization pattern ( 86 . 9% ) sufficient to elicit a partial activation of signaling cascade . With increasing time of infection at 12 hr post infection , we observed a sharp decline in the percentage of the colocalized IFNγR1 ( 32 . 7% ) along with absolute failure of IFNγ to induce any detectable increase in the colocalization pattern at this condition ( 34 . 2% ) . Comparison of the percentage colocalization of the IFNγR2 with GM1 at the steady-state of the MØs showed a steep decline both in the early as well as the late hours of LD infected MØs than the uninfected control . In agreement with the floatation studies , IFNγ activation could elicit a partial increase in the colocalization efficiency at the early hours of LD infection ( 62 . 5% ) , though absence of the same at late hours of infection denotes a conspicuous block in the IFNγ triggered translocational behavior of IFNγR2 . To investigate further , we stimulated the uninfected and LD infected MØs with biotinylated IFNγ and isolated the lipid rafts . Total receptors and engaged receptors were immunoprecipitated using anti-IFNγR1 and streptavidin-agarose respectively . The activated forms of the signaling complex were determined by resolubilization of the immunoprecipitates of streptavidin-agarose , reprecipitation by p-Tyr antibody and immunoblotting with antibodies against distinct components of the IFNγ signaling complex namely IFNγR1 , JAK1 , JAK2 and STAT1 . As depicted in Figure 4 , endogenous IFNγR1 distribution was less affected during early ( 4th ) hours of infection , but with increasing severity of infection it was completely delocalized from the raft fractions . Most striking of these results is that , though we could recover some ligand engaged IFNγR1 from non-raft portions at 4 hr post infection , IFNγR2 was only detectibly coimmunoprecipitated from the raft fractions . Another significant finding of this experiment was that though endogenous IFNγR1 and engaged IFNγR1 could be immunoprecipitated from both raft and non-raft fractions after 3 minutes of IFNγ stimulation from 4 hr LD infected macrophages , the phosphorylated ( activated ) IFNγR1 was only detected in the immunecomplexes from the raft fractions along with other activated signaling intermediates . It is worthy of note that at late hours of infection , though engaged IFNγR1 ( Figure 4 ) and IFNγR2 ( Figure 2 ) were simultaneously present in the non-raft fractions , they could not generate any productive interaction as evidenced by the complete absence of IFNγ triggered CoIP and/or phosphorylation signal . These results denote critical importance of cholesterol rich microenvironment for proper IFNγR functioning . The involvement of rafts in early IFNγ signaling events such as induction of STAT1 tyrosine phosphorylation has previously been studied using methyl-ß-cyclodextrin ( mBCD ) as well as filipin and nystatin [14]–[16] , all of which perturb the raft integrity via cholesterol depletion or sequestration [47] . Extending and verifying our original observation of decreased MØ membrane cholesterol in LD infected in vivo model [23] , our in vitro system of infected RAW 264 . 7 cells also showed reduction in membrane cholesterol as function of time ( Figure 5A ) , the kinetics of which exactly coincided with the time points of waning IFNγ signaling . To clearly establish the role of cholesterol in IFNγ mediated receptor assembly , we replenished the LD-MØs with exogenous cholesterol in liposomal form ( CH-Liposome ) . Figure 5B shows that cholesterol supplementation induced relocalization of IFNγR1 within the raft domains regardless of the time points of investigation after LD infection . Interestingly , in cholesterol replete MØs , the engaged receptors were entirely recovered from the raft fractions ( Figure 6A ) . This cholesterol induced IFNγR1 relocalization was prominently associated with a marked increase in IFNγR2 interaction , which was readily detected from the immunecomplexes isolated by total IFNγR1 and/or engaged IFNγR1 in the IFNγ stimulated condition ( Figure 6A ) . The restored pattern of IFNγR subunit multimerization was more conclusively verified in FRET experiments , where cholesterol supplementation reinstated the IFNγ inducible receptor subunit interactions ( Figure 6B ) . To establish unambiguously the specific role of cholesterol in influencing the earliest step of IFNγR signaling in reconstituted membrane rafts of LD-MØs , we treated the infected MØs with liposomes prepared with oxidized cholesterol analogue of 4-cholestene-3-one ( AN-Liposome ) . The results of coimmunoprecipitation ( Figure 6A ) and FRET ( Figure 6B ) analyses between IFNγR subunits in AN-Liposome treated infected MØs showed that treatment with AN-liposome failed to restore IFNγR translocations as also the subunit interactions by IFNγ stimulation . In order to investigate whether the enhanced IFNγR oligomerization in cholesterol-replete cells directly involves action of cholesterol rather than its indirect effect via increased bulk membrane rigidity , we treated LD-MØs with DPPC , a phospholipid previously shown to induce increased membrane rigidity in model membranes [48] . The data in Figure 5B , 6A reveal that in contrast to cholesterol supplemented cells , the DPPC treated cells , despite having comparable bulk membrane rigidity as indicated by the FA values ( Figure 6B , insets ) , failed to induce IFNγ receptor interaction indicating specific requirement of cholesterol in IFNγR subunit assembly . To corroborate the finding of cholesterol specific IFNγR subunit interaction , RAW 264 . 7 cells were treated with mBCD , which disrupts lipid rafts by specific depletion of cholesterol [49] . As shown in Figure 7A , stimulation of mBCD treated cells with IFNγ failed to induce association of IFNγR2 subunit with engaged IFNγR1 , which was found to be localized in the non-raft membrane fractions . Comparable to the LD infected macrophages , mBCD treatment completely abolished the activation of IFNγ signaling components while cholesterol replenishment of these cells restored the subunit assembly ( Figure 7 ) and activation pattern of IFNγ induction ( Figure 7A ) . In additional control experiments , conditions of mBCD treatments in RAW 264 . 7 cells were first determined to ensure unaffected cell viability and unaltered expression of surface IFNγR and components of STAT1 activation axis ( data not shown ) . LPG is a GPI anchored molecule secreted/shed by the parasites after internalization in MØs by phagocytosis [50] , [51] . It is known to increase the total membrane rigidity . Consistent with previous reports [52]–[54] , LPG induced significantly higher bulk membrane rigidity upon incubation with MØs ( Figure 8A ) and was incorporated substantially within GM1 enriched membrane domains . However , it could not induce IFNγR1 delocalization from the more buoyant lipid raft fractions as indicated by dot blot analyses of individual membrane fractions of optiprep gradient ( Figure 8B ) . This intactness of IFNγR1 localization in the LPG incubated MØs was also reflected in undiminished IFNγ functionality in these cells as assessed by the gamma activated sequence driven luciferase ( GAS-Luc ) reporter system ( Figure 8C ) , a finding supported previously by Ray et al . [11] . Notably , pharmacological depletion of membrane cholesterol from the LPG-incubated MØs completely disoriented the IFNγR1 localization pattern and disrupted the membrane rafts as indicated by the even distribution of GM1 molecules along the isolated density gradients of the cell membrane ( Figure 8B ) . Importantly , repletion of cholesterol in the system of LPG-incubated mBCD-treated MØs restored both the localization and functionality pattern of IFNγR1 ( Figure 8B , 8C ) . As the infection in vivo propagates through amastigotes which lack LPG [53] , we asked the question if their role in IFNγ signaling inhibition was different from that of promastigotes . The result depicted in Figure 9 demonstrates that for all the tested time points of amastigote infection , deactivation of the IFNγ signaling takes place as early as 5 min post-IFNγ stimulation . The result was consistent with previous report [55] of immediate deactivation of early IFNγ signaling at the level of JAK2 phosphorylation in LD amastigote infected primary human MØ , post 10 min of rIFNγ stimulation . Collectively the above results strongly imply that membrane cholesterol is critically involved in the initiation of IFNγ signaling , probably by direct modulation of IFNγ receptor functionality . There are number of reports on the existence of a common cholesterol recognition/interaction amino acid consensus ( CRAC ) motif in various proteins [56] including several membrane proteins that directly interact with cholesterol [57] . Very well characterized CRAC motif conforms to the pattern ( L/V ) -X1-5-Y-X1-5- ( R/K ) , in which X1-5 represents between one to five residues of any amino acid [56] , [57] . As an initial effort to explore the possibility that the functionality of constitutively raft associated IFNγR1 protein is directly modulated by the interaction of cholesterol , we tried to detect the cholesterol recognition motif in it . Motif analysis of IFNγR1 indeed identified two CRAC sites within the IFNγR1 subunit protein ( Figure S4 ) . Multiple sequence alignment showed it to be highly conserved because the domain is present in many of the homologous proteins in different species ( Table S1 ) nullifying its chance occurrence in murine IFNγR1 . As denoted by Jamin et al . the CRAC motif is typically located adjacent to a transmembrane α-helix that positions this sequence within the nonpolar core of the bilayer , where it can form contacts along the length of the cholesterol molecule [58] . Sequence analysis of IFNγR1 predicted the localization of the CRAC motif ( −269 to −280 ) within a transmembrane α-helical portion ( Figure S5 ) . Accordingly , we designed a peptide reproducing the CRAC motif of IFNγR1 protein ( −269 to −280 ) to analyze the binding of the motif with cholesterol liposome . It should be noted that the tyrosine residue in this motif has been shown by mutational analysis to be critical for cholesterol binding in other proteins [58] . Therefore , another peptide was designed to introduce substitution at positions 275 and 277 to replace tyrosine , intended to disrupt/attenuate the binding of the peptide with cholesterol . We used surface plasmon resonance ( SPR ) to examine the binding of IFNγR1 peptides to liposomal formulation of cholesterol or its oxidized analogue 4-cholestene-3-one . Binding of cholesterol to the Wt IFNγR1 peptide is indicated by the high association rate ( ka = 2 . 69×103 M−1 s−1 ) and affinity ( KD = 4 . 27×10−9 M ) compared to those of the mutant ( ka = 27×100 M−1 s−1 , KD = 3 . 77×10−7 M ) . The specificity of the cholesterol-IFNγR1 peptide interaction was also evident from the 100 fold lower affinity of the Wt peptide for the cholesterol analogue ( Table 1 and Figure 10 ) . We assessed whether cholesterol repletion could restore IFNγ induced leishmanicidal effects of the infected MØs . The results depicted in Figure 11 reveal that IFNγ stimulation of the cholesterol repleted MØs mediated effective restoration of the parasiticidal effects independently of the status of infection progression . The data ( Figure 11 A–C ) showed 2 . 41 fold ( p = 0 . 0003 vs infected control ) and 2 . 16 fold ( p<0 . 0001 vs infected control ) reduction respectively in the intracellular amastigote number and percent of infected cells , when stimulated by IFNγ at 4 hr post infection , along with 2 . 51 fold ( p = 0 . 0009 ) enhanced generation of NO . The observations of parasiticidal effects and NO production were remarkably similar in MØs stimulated with IFNγ at later time points of infection ( Figure 11 A' , B' , C' ) , suggesting an effective reversal of the IFNγ unresponsiveness of the infected MØs . As anticipated , AN-Liposome or DPPC-Liposome treatment of the LD infected MØs failed to reinstate the MØ responsiveness to IFNγ stimulation . To ascertain the direct correlation of cholesterol mediated IFNγ signaling with NO production and consequent leishmanicidal effect without involvement of any secondary mediator , L-NMMA was added to cholesterol treated cells . This indeed completely abrogated the enhanced NO production and amastigote killing potential of cholesterol replenished MØs in response to IFNγ ( Figure 11 D , D' ) . We finally assessed the generalized biological response of cholesterol promoted IFNγ signal enhancement in MØs . Analysis of the IFNγ induced transcriptional activity by GAS driven luciferase reporter activity in cholesterol-supplemented systems of CH-Liposome treated LD infected cells and mBCD treated cells demonstrated enhanced bioactivity of IFNγ , as manifested by higher GAS-Luc expression . This suggests that efficient MØ activation could be achieved by cholesterol treatment of deactivated MØ systems .
Our study reinforces the previous findings that LD-MØs are refractory to IFNγ mediated signaling , as evident from the inability to clear intracellular parasites ( Figure S1 ) . This was due to defective IFNγ signaling as apparent from a number of functional assays such as defective downstream signaling such as JAK2 phosphorylation ( Figure S3A ) and IFNγ driven GAS-Luc activity ( Figure S3B ) under parasitized condition . Interestingly enough , the defect is not due to non-availability of IFNγR on the cell surface ( Figure S2A ) or inability of the ligand to bind to the cognate receptor as there was a comparable binding affinity ( KD ) in terms of binding of 125I-IFNγ between normal and infected conditions ( Figure S2B ) . It was also evident that LD-MØ did not show any generalized defect as IL-10 mediated STAT3 phosphorylation ( Figure S3C ) and internalization of 125I-IFNγ remained unaffected ( Figure S2C ) . The ligand receptor interaction of IFNγ-IFNγR1 is a sequential multistep process which involves the binding of bivalent IFNγ to the extracellular domain of the cognate receptor subunit of IFNγR1 in a ratio of 1∶2 [59] , [60] . This provides the binding scaffold for IFNγR2 , the second subunit of the receptor complex [61] , [62] . The receptor subunit assembly is unlikely to occur randomly at the plasma membrane of the stimulated cells but is confined to specific cholesterol rich membrane microdomains termed rafts . This can be inferred from indirect evidences derived by the use of cholesterol quenching drugs , which show perturbation of IFNγ signaling by depletion of membrane cholesterol [14]-[16] . The defective IFNγ signaling in LD-MØs was not due to LPG because treatment of normal MØs with exogenous LPG showed neither decrease in fluorescence anisotropy ( FA ) nor altered IFNγ mediated promoter activity ( Figure 8 ) . The defective signaling induced by promastigote mediated infection could also be reproduced with LPG deficient amastigotes ( Figure 9 ) ; implying that the effect is independent of LPG despite the intercalation of LPG into membrane raft ( Figure 8B and ref [54] ) and increased the total membrane rigidity ( Figure 8A and ref [54] ) . From the fractionation study ( Figure 2 ) and confocal microscopy ( Figure 3 ) , it was evident that IFNγR1 is raft associated whereas IFNγR2 is largely non-raft associated . Upon ligand driven stimulation , IFNγR2 moves to the raft fractions in normal condition ( Figure 2 ) . During infection , restraint in IFNγR2 recruitment in raft fraction even in the presence of IFNγ , suggests that the association of IFNγ-R1 and -R2 in LD-MØ is defective . This observation was further corroborated from co-IP studies ( Figure 1B ) and confirmed by FRET analysis ( Figure 1A ) . An elegant study showed that the diffusion coefficient of both GPI linked and native I-Ek are dependent on cholesterol concentration [63] . Our study also showed that membrane cholesterol decreases in LD-MØ as a function of time ( Figure 5A ) . Our results thus support the notion that the decreasing membrane cholesterol due to the presence of intracellular parasites affects the stimulation dependent mobility of IFNγR2 into rafts as shown in the case of T cell receptors at the plasma membrane [64] . It was also observed that stimulation of normal MØs with IFNγ causes clustering of the entire IFNγ signaling complex in the membrane raft ( Figure 4 ) which was absent under parasitized condition . The effect caused by the intracellular parasites was mimicked by quenching of cholesterol from the membrane by mBCD and restored by liposomal delivery of cholesterol , as evident from FRET analysis ( Figure 7B ) and fractionation studies ( Figure 7A ) . This observation indicates that intracellular parasites may cause the above defects by quenching cholesterol from the membrane . The specificity of cholesterol in inducing effective IFNγ inducible receptor interaction was assessed by supplementation of the LD-MØs with the oxidized cholesterol analogue 4-cholestene-3-one ( Figure 5B , 6A , 6B ) , which is known not to induce lipid ordered domain or raft formation due to its head group oxidation and differential partitioning behavior compared to cholesterol [65] , [66] , [23] . The results imply that the oxidized cholesterol analogue could not induce any productive interaction between the receptor subunits of IFNγR1 and IFNγR2 , substantiating an obligatory role of cholesterol in maintaining the functional integrity of IFNγR . The membrane packing of cholesterol is supported by a hydrogen bond between the cholesterol OH group and the amide bond of sphingolipids [49]; this interaction is presumably absent in the case of the oxidised analogue because of the lack of OH group . To assess whether increasing the membrane rigidity could restore the propensity of IFNγR1 and IFNγR2 subunit interaction , we supplemented the cell with DPPC-liposomes ( Figure 5B , 6A , 6B ) to deliver saturated phospholipids , which increased the membrane rigidity very efficiently . Intriguingly and paradoxically , the increased membrane viscosity resulting from the delivery of DPPC ( Figure 5 , 6 ) or LPG ( Figure 8 ) , as evident from fluorescence anisotropy values , was insufficient to potentiate IFNγ induced receptor subunit interaction . Collectively these results suggest a direct role of cholesterol in stabilizing the IFNγR interactions , rather than an indirect effect of bulk membrane rigidity to increase the chance interaction of the receptor subunits by random proximity . Membrane proteins such as G protein coupled receptors , transporters , or ion channels have been shown to be localized or enriched in lipid rafts . The role of cholesterol in receptor conformational stabilization has been reported by several groups , where stability of the active conformation of the receptors depends on the presence of cholesterol with specific molecular interaction between protein and adjacent cholesterol , such as cholecystokinin ( type B ) , oxytocin and nACh receptors [18] . nAChr is reportedly shown to be stabilized in its activated form by a direct action of cholesterol [19] . There are spectroscopic analyses showing that variation in cholesterol concentration can perturb protein secondary structure; this has been shown in photosystem II [67] . Because of nonavailability of pure IFNγR1 receptors ( available in the form of chimera with Fc receptor , which itself contains cholesterol binding motif ) we could not study the conformation and determine kinetic values of IFNγR1 in association with cholesterol-liposome/analogue-liposome . As there are reports of cholesterol binding CRAC motifs [ ( L/V ) -X1-5-Y-X1-5- ( R/K ) ] [57] in a number of membrane proteins , the presence of such a motif in IFNγR was searched for . Sequence analysis of IFNγR1 showed their presence in the transmembrane domain . Kinetic studies revealed conspicuous interaction of such motifs with cholesterol liposomes , but not with analogue liposomes , which differed by two orders of magnitude ( KD: 4 . 27×10−9 M versus 2 . 69×10−7 M; Figure 10 ) . Consistent with previous reports , the interaction is tyrosine dependent as the tyrosine substituted peptide failed to interact with cholesterol since the association rate was very slow and/or the affinity for cholesterol was 100 fold reduced . These observations , thus clearly denote that Leishmania parasites during their intracellular life cycle by modulating membrane cholesterol , exert significant effect on the IFNγR function with subsequent modification of IFNγ mediated intracellular parasite killing . These results thus represent the first evidence of interaction of cholesterol and cholesterol binding motif in IFNγR1 . It corroborates all the previous reports [14]–[16] of cholesterol dependent IFNγR1 functional regulation . One would expect cholesterol-liposome to show leishmanicidal effect in association with IFNγ . Indeed this study clearly showed such effect coupled with NO generation in LD-MØs upon treatment with the above combination . The effect was cholesterol specific because the leishmanicidal effect was not observed with analogue liposome or with DPCC-liposome . This observation lends credence to our previous reports showing strong therapeutic role of liposomal-cholesterol in infected hamster model [23] . Survival strategy of the parasite LD might as well include specific degradation and/or negative regulation of expression of protein molecules of the infected cells such as STAT1 [68] and IFNγR1 [69] - two important components of the IFNγ signaling pathway . The lack of reproducibility of these results in our system is perhaps attributable to the difference in cell system or parasite strains used for infection . The mechanism by which LD modulates membrane cholesterol is yet unknown . Given the fact that dynamic equilibrium between the pools of free membrane cholesterol and cytoplasmic droplets of cholesterol esters is very tightly controlled [70] , diminished membrane cholesterol in LD-MØs , may result from overall decrease in total cellular cholesterol . It may be recalled that in VL patients a progressive decline in serum cholesterol as a function of splenic parasite load [26] is indicative of reduced cellular cholesterol since serum cholesterol and total cellular cholesterol are in dynamic equilibrium [71] . Interestingly , in L . major infected macrophages decrease in cellular cholesterol is causally related to reduced mRNA level of HMG-CoA reductase , the rate limiting enzyme of cholesterol biosynthetic pathway [72] . Alternatively , different reports [73] , [74] also suggest that LD may exploit host cholesterol for its entry and survival , interfere with cholesterol trafficking or may exchange host cholesterol for ergosterol , with biophysical characteristics grossly different than cholesterol . Thus , these contentions warrant further studies and are currently under our investigation . We would like to emphasize here that we have specifically dealt with the earliest and critical most step of IFNγ signaling occurring within seconds ( 180 secs ) of receptor ligation and demonstrate that LD infection can efficiently impose a block at this step by specific disruption of the receptor oligomerization by lowering the macrophage membrane cholesterol level . It should be mentioned here that IFNγ signaling amplitude depends on the sustained durability of the activation phenomenon and is very tightly regulated by multiple negative regulatory mechanisms which include dephosphorylation of the activated signaling intermediates by inducible activity of phosphoprotein phosphatases [75] , several minutes after ligand–receptor interaction . Seminal study by Blanchette et al . show that this basic physiological phenomenon of signal duration restrain is efficiently utilized by the opportunistic parasite Leishmania spp . , to attenuate the IFNγ signaling amplitude [76] which in turn affect the overall IFNγ bioactivity in LD infected macrophages which likely contribute in part to the inhibition of complete restoration of the IFNγ signaling pattern in cholesterol treated LD infected macrophages ( Figure 11A , A' , B , B' , C , C' , E ) . Of note , constitutive SHP-1 preassociation with JAK2 in the steady-state of normal cells as reported by various groups [77] , [78] denotes an active repression of JAK2 autophosphorylation . Experimental verification ( Figure S6 ) denotes that in cholesterol depleted systems with segregated IFNγ receptor subunits as in established LD infected macrophages , the SHP-1 species remained associated with the JAK2 proteins , preventing its autophosphorylation upon ligand stimulation , which was in sharp contrast to uninfected macrophages where the SHP-1 is released from JAK2 to induce its autophosphorylation within minutes ( 3 minutes ) after ligand stimulation . Interestingly , cholesterol supplementation in LD infected cells reinstated the JAK2 activation profile and release of SHP-1 from JAK2 immediately upon IFNγ stimulation ( our unpublished observation , data not shown ) . There is a report that Leishmania parasites deliver effector proteins into host cells by exosome-based secretion and can modulate host-cell function [79] . Thus it is not unlikely that uninfected MØs in the infected splenic environment may trap parasite derived antigen ( s ) and display similar defects in IFNγ signaling like infected MØs . It is worthy of mention that the late established phase of VL is marked with increased production of IL-10 [80] . IL-10 , which has a prominent IFNγ desensitizing role acting directly at the level of STAT1 deactivation [81] , might account for the chronic infection in the presence of high circulating IFNγ in VL , providing an additional negative regulatory step on IFNγ signaling . The central point that evolved from this study is that intracellular LD quench membrane cholesterol; this in turn affects the lateral mobility of IFNγR2 into the raft , IFNγR1-R2 multimerization and subsequent downstream signaling in response to IFNγ . This defect could be corrected by exogenous delivery of liposomal cholesterol leading to intracellular parasite clearance . Based on this study , it can be reasonably speculated that cholesterol may also influence the conformation of IFNγR1 . Thus in VL , cholesterol-liposomal formulation of IFNγ treatment may emerge as a promising therapeutic strategy both in drug-sensitive and -resistant cases as it is directed specifically towards host than the parasites .
Use of mice was approved by the Institutional Animal Ethics Committee of Indian Institute of Chemical Biology , India . All animal experimentations were performed according to the National Regulatory Guidelines issued by CPSEA ( Committee for the Purpose of Supervision of Experiments on Animals ) , Ministry of Environment and Forest , Govt . of India . Anti-mouse IFNγR1 ( MAB1026 , Clone- 2E2 . 4 ) was obtained from RnD systems , anti-mouse IFNγR2 ( #559917 , Clone- MOB-47 ) , biotinylated anti-rat IgG , biotinylated anti-hamster IgG , Flottilin1 , transferrin receptor ( biotinylated CD71 ) , antiSTAT1α , Streptavidin-PE and recombinant mouse IFNγ were purchased from BD Pharmingen , San Jose , California . Anti-LPG antibody ( CLP003A , Clone- CA7AE ) was from Cedarlane , Ontario , Canada . FBS , RPMI 1640 medium , M199 medium , Penicillin-streptomycin and sodium bicarbonate were obtained from Invitrogen Life Technologies . Cholesterol , HEPES , 2-β ME ( beta mercaptoethanol ) , paraformaldehyde , ponceau S , FITC conjugated cholera toxin B subunit ( CTxB-FITC ) , L-NMMA , 1 , 6-diphenyl-1 , 3 , 5-hexatriene ( DPH ) and Western Blocker Solution for HRP detection systems were purchased from Sigma-Aldrich ( St . Louis , MO ) . Prestained protein molecular weight marker , Bradford protein assay reagent and Laemmli buffer were from Bio-Rad , Hercules , CA . Hybond ECL nitrocellulose membrane and Hypercassette were from Amersham Biosciences . pJAK1 , pJAK2 , p-Tyr were purchased from Santa Cruz Biotechnology . JAK1 , JAK2 ( Santa Cruz Biotechnology ) were kind gift from Dr . Santu Bandyopadhyay , IICB ( Kolkata , India ) . Streptavidin-HRP was purchased from Zymed Laboratories . Phosphatidylcholine ( PC ) ( egg lecithin ) and 1 , 2-dipalmitoyl-sn-glycero-3-phosphocholine ( DPPC ) was from Avanti Polar Lipids , Inc . , Alabaster , Alabama and 4-cholestene-3-one was purchased from ICN ( Irvine , CA ) . All the amino acids were purchased from Novabiochem , Merck . TFA , EDT , thioanisole , TIS were purchased from Merck , Germany . LD strain AG83 ( MHOM/IN/1983/AG83 ) , originally obtained from an Indian kala-azar patient [82] , was maintained in Golden hamsters as described previously [83] . Promastigotes obtained after transforming amastigotes from the spleen of infected animals were maintained in culture in Medium 199 supplemented with 10% FCS at 22°C . The culture was replenished with fresh medium every 72 h . Murine MØ-like tumor cell , RAW 264 . 7 was obtained from American Type Culture Collection . Cells were maintained in complete medium at 37°C with 5% CO2 in a humidified atmosphere . BALB/c mice were obtained from the animal facility of the institute and were housed under conventional conditions , with food and water ad libitum . BALB/c mice were injected with 2 ml of 4% starch i . p . The peritoneal cells ( PEC ) were harvested 2 days after the starch injection and were rested for 24 h before any treatments on these cells . In the meanwhile , the nonadherent cells were washed out to generate pure adherent populations . PEC and RAW 264 . 7 cells were allowed to adhere for 4 hr at 37°C in presence of a 5% CO2 atmosphere , after which the nonadherent cells were removed by gentle washing with serum-free medium . The adherent MØs , after overnight incubation in complete medium , were challenged with LD promastigotes at a MØ to parasite ratio of 1∶10 and incubated further for 6 hr at 37°C . After initial attachment of 6 hr , excess parasites were washed with serum-free medium , and this was considered as the initial most ( 0 hr ) time point of infection and the infection was allowed to progress for the indicated time periods thereafter , followed by rIFNγ treatment with the suboptimal 10 U/ml , optimal 100 U/ml and supraoptimal dose of 1000 U/ml . The cultures were terminated after indicated timepoints and treatments , by washing and immediate fixing with methanol , followed by staining with Giemsa stain and counted under a light microscope ( Leica ) . The parasite load was expressed as the number of amastigotes per MØ and % infected MØs as described earlier [22] . In some experiments freshly prepared lesion derived LD amastigotes were used for infection at a multiplicity of infection ( m . o . i ) of 10 . The dose of 100 U/ml of rIFNγ was chosen for optimum cell-activation as no additional benefit was obtained for the higher dose tested . Nitric oxide ( NO ) generation was monitored by using the Griess reaction as described previously [84] , and the results were expressed in µM of nitrite accumulation . In some experiments , MØs were cultured in the presence of NG-monomethyl-L-arginine ( L-NMMA ) a competitive inhibitor of inducible NO synthase , added to a final concentration of 5 µM [85] . Membrane expression of IFNγR1 and IFNγR2 were investigated on resting RAW 264 . 7 cells with incubation of 1×106 cells with anti-mouse IFNγR1 mAb or anti-mouse IFNγR2 mAb for 30 min at 4°C followed by two washes with cold PBS supplemented with 1% FCS and 0 . 05% sodium azide ( PBS-azide ) . For secondary labeling , cells were stained with biotin-conjugated anti-hamster antibody for 30 min at 4°C and were washed twice with cold PBS-azide and were then labeled with PE-conjugated Streptavidin . After the final wash with cold PBS-azide , cells were fixed with cold 2% paraformaldehyde in PBS . Membrane protein expression was analyzed with a FACSAria II flow cytometer ( Becton Dickinson , Mountain View , CA ) . Radioiodination of rIFNγ was performed as previously described [86] . rIFNγ ( 5 µg at 0 . 5 µg/µl ) was radioiodinated with 5 µl ( 500 µCi ) of Na125I ( 17 mCi/µg , BRIT , India ) in the presence of 25 µl of 0 . 15 M potassium buffer , pH 7 . 4 , and 10 µl chloramine-T ( 5 mg/ml ) for 2 minutes . After neutralization of the reaction with 10 µl each of sodium metabisulfite ( 10 mg/ml ) , potassium iodide ( 70 mg/ml ) , and BSA ( 20 mg/ml ) , the reaction mixture was chromatographed over a 10-ml Sephadex G-10 column equilibrated with a Tris/NaCl/BSA buffer ( 10 mM Tris-HCl , pH 7 . 4 , 0 . 15 M NaCl , and 0 . 33 mg/ml BSA ) . Fractions of 500 to 600 µl were collected , and the fraction containing the greatest activity was used for receptor binding studies . The specific activity of 125I-IFNγ was 110 µCi/µg of protein . Anti-mouse IFNγR1 ( 10 µg ) was radioiodinated by following the method essentially described in [87] , with minor modifications . Briefly , 50 µl anti-mouse IFNγR1 was incubated with 25 µl ( 500 µCi ) Na125I ( 17 mCi/µg , BRIT , India ) in the presence of 125 µl of 0 . 15 M potassium buffer , pH 7 . 4 , and 50 µl chloramine-T ( 5 mg/ml ) for 4 min . The neutralization of the reaction was done as above and readily sieved on a 10-ml Sephadex G-10 column . Fractions of 400 µl were collected , and the fraction containing the greatest activity was used for receptor binding studies . The specific activity of 125I -labeled anti-IFNγR1 was in the range of 100–105 µCi/µg of protein . Saturation binding assays were performed as previously described with brief modifications [10] . To enumerate receptor numbers , 125I-IFNγ was added to final concentrations of 5–800 pM to 2×107 cells per ml of RPMI medium containing 2% fetal bovine serum in duplicates . After 180 min of incubation at 4°C , cells were washed thoroughly with ice cold PBS . The radioactivities associated with separated cell pellet and the supernatant were quantified in a scintillation counter ( Packard ) to determine free and bound 125I-IFNγ respectively . To determine nonspecific binding , the amount of 125I-IFNγ bound to the cells in the presence of a 200-fold excess of unlabeled ligand was determined . Specific binding was calculated by subtracting nonspecific binding from the total radioactivity bound . After correcting for nonspecific binding , data of 125I-IFNγ saturation binding experiments were fitted to a one-site binding hyperbola and analyzed with non-linear regression analysis to determine KD and Bmax values using GraphPad Prism 5 . 0 ( GraphPad , San Diego , USA ) to obtain parameters of equilibrium binding , where Bmax denotes the maximal density of receptor sites and KD denotes the radioligand equilibrium dissociation constant . In addition , 125I-IFNγ saturation binding data were displayed using Scatchard plots where the X-axis is specific binding ( Bound ) and the Y-axis is specific binding divided by free radioligand concentration ( Bound/Free ) [88] . 125I-labeled murine IFNγ and 125I-labeled anti-mouse IFNγR1 were used respectively to determine the ligand induced and constitutive bulk flow endocytosis mediated IFNγR internalization and were performed as previously described [11] , [89] with minor modifications . For internalization studies , 2×106 cells were replenished with ice-cold RPMI containing 2% fetal calf serum . Parallel wells received either 125I-labeled IFNγ or 125I-labeled anti-mouse IFNγR1 and were incubated for 180 minutes in 4°C , after which unbound radioactivity was removed with brief wash with ice cold PBS and incubations were continued in 37°C medium without ligand to allow internalization to occur . At intervals thereafter , cells were immediately added with ice cold PBS and transferred to ice , to stop internalization . Membrane-associated ligand or antibody was removed from the cells with low pH acid wash buffer ( 0 . 2 M acetic acid , 0 . 5 M NaC1 , pH 2 . 5 , [90] ) . Internalized ligand or antibody was determined by solubilization of the acid-treated cells in 1 M NaOH . Radioactivity was measured with a scintillation counter ( Packard ) . The amount of radioactivity remaining with cells that had been kept at 4°C all along before acid treatment was taken to represent ‘nonspecific’ acid-resistant radioactivity and was subtracted from the values obtained from the 37°C incubations to provide an estimate of specific ligand internalization . The data were fitted to linear regression analysis program in GraphPad Prism 5 . 0 ( GraphPad , San Diego , USA ) to obtain the rate of internalization by following the methods of Liu et al . [91] . Cell lysis and western blotting were carried as described previously [92] with fewer modifications . In general , uninfected or LD infected RAW 264 . 7 cells were treated with or without 100 U/ml of rIFNγ for 3 min , 37°C . After washing twice with ice-cold PBS , cells were lysed in 1X RIPA Buffer from Cell signaling technology , Danvers , MA ( 20 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1 mM Na2EDTA , 1 mM EGTA , 1% NP-40 , 1% sodium deoxycholate , 2 . 5 mM sodium pyrophosphate , 1 mM beta-glycerophosphate , 1 mM Na3VO4 , 1 µg/ml leupeptin , with recommended addition of 1 mM PMSF immediately before use ) . In some experiments , cells were treated with 10 ng/ml of rIL-10 for 15 min , 37°C [93] . Protein concentration was determined using the Micro BCA protein assay kit ( Pierce Chemical Co . Rockland , IL ) . Proteins ( 20 µg/lane ) were separated by SDS-PAGE on a 10% gel under reducing conditions and electrotransferred to nitrocellulose membranes ( HYBOND ECL , Amersham , Piscataway , NJ ) in a transfer buffer consisting of 20 mM Tris-HCl , 150 mM glycine , and 20% methanol . Membranes were incubated at 4°C overnight in blocking buffer ( Western Blocker Solution , Sigma ) . Primary and secondary Abs were diluted as recommended by the manufacturer in blocking buffer and incubated with the membranes for 1 hr , at room temperature with five washes in between , with wash buffer ( 20 mM Tris , 500 mM NaCl , pH 7 . 4 ) . Detection of HRP-conjugated Abs was performed using SuperSignal West Pico Chemiluminescent Substrate ( Pierce , Rockland , IL ) . Interferon gamma driven luciferase reporter activity was essentially done as in ref . [94] . RAW 264 . 7 cells were transfected with the pGAS cis-reporter plasmids or the pCIS-CK negative control plasmid by electroporation [95] by Genepulser X cell ( Bio-Rad , Hercules , CA ) . Cells from three electroporations were pooled to eliminate differences between individual transfections and the mixture was then equally divided among the wells of a 24-well cluster dish ( Corning CoStar Corp . , Cambridge , MA ) . They were next allowed to adhere to the substratum for 4 hr before the medium was changed . After 24 hr ( which was needed to reduce the background levels of luminescence ) , the medium was again changed and were either infected with LD or left uninfected . After the initial attachment of the parasites with the cells for 6 hr , the cells were washed and the infection was allowed to progress for the indicated time periods . Thereafter , cultures were incubated for 8 hr with medium alone or medium that contained 100 U/ml IFNγ . The cells were harvested in 300 µl of passive lysis buffer ( Promega Biotech , Madison , WI ) , and 50 µl aliquots of the clarified extracts were used to assay luciferase activity using Luciferase assay kits/reagents from Promega Biotech according to the manufacturer's protocols . Luciferase activity was normalized to the levels of the protein content . The average is reported along with the standard deviation of the mean . The pGAS cis-reporter plasmid and the pCIS-CK negative control plasmid were purchased from Stratagene , La Jolla , CA . Unilamellar liposomes were prepared from PC with either cholesterol or an analogue of cholesterol ( 4-cholesten-3-one ) at a molar ratio of 1∶1 . 5 as described in ref . 22 , 23 . For liposome preparation with DPPC , we followed the methods of Giraud et al . [96] . DPPC ( 13 . 8 mg ) was dissolved in a chloroform-methanol ( 9∶1 v/v ) mixture . The solvent was removed by evaporation under vacuum , followed by overnight drying of the resulting film under vacuum to remove the residual solvents . The dry film of DPPC was rehydrated in PBS , pH 7 . 4 , then alternately heated in a water bath at 60°C and mixed via a vortex mixer at room temperature for 5 min to allow full hydration of the phospholipid and finally sonicated using an ultrasound 2 mm tip in microprobe sonicator . According to the conventional procedure , repetitive 30-sec cycles of sonication/ice cooling were performed . Samples were briefly centrifuged to remove titanium particles . For fluorescence resonance experiments , anti-mouse IFNγR1 and anti-mouse IFNγR2 were conjugated to DyLight488 or DyLight594 ( Pierce; N-hydroxy succinimide ( NHS ) ester-activated fluorescent dyes ) respectively , according to the manufacturer's protocol . Naïve or infected MØs were stimulated with/without IFNγ for 3 min , 37°C to induce FRET signals . The stimulation was terminated by immediate transfer of cells to ice and washing with ice-cold PBS . The cells were then fixed with 2% paraformaldehyde/PBS at 4°C . After washing the paraformaldehyde and quenching the excess formaldehyde for 15 min with 0 . 1% NaBH4 in PBS , staining was followed with excess antibodies to respective IFNγR subunits at 4°C , for 1 hr . Following 3 washes with cold buffer , cells were resuspended in PBS . Spectroscopic analysis of cells in a quartz cuvette was done with to 485 nm excitation and recording the emission spectra from 500 nm to 700 nm with emission and excitation slit of 2 . 5 nm . FRET efficiency ( E ) was obtained from the measured fluorescence intensity of the donor ( Dylight488- IFNγR1 ) at its maximal emission wavelength ( 519 nm ) in the presence and absence of the acceptor ( Dylight594- IFNγR2 ) using the equation with E ( % ) = ( 1 – Ida/Id ) , where Ida and Id are the fluorescence intensities in the presence and absence of acceptor , respectively [97] , [98] . For co-immunoprecipitation , experimental cells , treated as indicated in the figure legends , were aspirated off the medium and washed with ice cold PBS , lysed in Cell Lysis Buffer ( 1X ) , Cell signaling technology ( 20 mM Tris pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1% Triton X-100 , 2 . 5 mM sodium pyrophosphate , 1 mM β-glycerophosphate , 1 mM Na3VO4 , 1 µg/ml Leupeptin with 1 mM PMSF added immediately prior to use ) . After 10 min on ice , lysates were collected by scraping , sonicated for 15 s ( 0 . 3 s bursts ) , centrifuged at 10 , 000 r . p . m . for 10 min at 4°C and the supernatant was recovered . Total protein concentrations were determined using the Micro BCA protein assay kit ( Pierce Chemical Co . ) with BSA as standard . Lysates were pre-cleared using Protein A/G plus Agarose beads ( Pierce Chemical Co . ) . 200 µl of precleared supernatants were incubated overnight at 4°C with specific primary Ab to IFNγR1 or control unrelated whole rabbit IgG , and the immune complexes were collected on 20 µl of 50% bead slurry Protein A/G Plus-Agarose ( prewashed and preblocked with 2% BSA for 2 hour , at 4°C in 25 mM Tris , 150 mM NaCl; pH 7 . 2 ) for 2 h , at 4°C , with gentle shaking . Precipitates were washed 5 times with 500 µl of 1X Cell Lysis Buffer , kept on ice during washes and microcentrifuged for 60 seconds at 4°C at 2500 x g , each time . The pellet was resuspended with 20 µl of 2X Laemmli sample buffer [ ( Bio-Rad , Hercules , CA ) , 62 . 5 mM Tris-HCl , pH 6 . 8 , 25% glycerol , 2% SDS , 0 . 01% Bromophenol Blue , 5% β-mercaptoethanol] , vortexed for 30 seconds and boiled for 5 min before resolving in SDS-PAGE gels and probed with antibodies as detailed in the figure legends and detected as previously stated . Total crude cell membranes were isolated as described in ref . [99] . Cells were homogenized in 1 ml of buffer [10 mM Tris-HCl ( pH 7 . 4 ) , 1 mM EDTA , 200 mM sucrose] and protease inhibitor mix ( Roche Diagnostics , Mannheim , Germany ) ] . The nuclei and cellular debris were removed by centrifugation at 900 x g for 10 min at 4°C . The resulting supernatant was centrifuged at 110 , 000 x g for 75 min at 4°C to obtain the crude membrane pellet . Purification method of detergent-soluble and -insoluble fractions was adapted from ref . [100] , with very few modifications . The crude membrane pellet was solubilized in 1 ml of Lysis buffer ( Caveolae/ Rafts Isolation Kit , Sigma ) containing 1% Triton X-100 and was agitated end to end for 30 min at 4°C . The sample was mixed with OptiPrep and to a final OptiPrep concentration of 35% . Then the density gradient was made of 5 layers of OptiPrep with 35 , 30 , 25 , 20 , and 0% . The volume of each layer was 2 ml except for 1 ml of 0% OptiPrep . Samples were centrifuged at 200 , 000 x g for 4 h at 4°C . After centrifugation , nine fractions of 1-ml from the top to bottom of the ultracentrifuge tube were collected . As described in manufacturer's instruction , caveolin-1 positive fractions were found in fractions 2–4 ( 25–35% OptiPrep ) counting from the top ( data not shown ) , which were also enriched in GM1 content . Ganglioside GM1 was visualized with an HRP-conjugated cholera toxin B subunit ( 1∶2000 , Sigma ) in dot blots with 2 µl sample from each gradient fraction . In some experiments , fractions 2–4 or fractions 7–9 were combined , and are referred to as Triton-insoluble glycolipid enriched membranes ( rafts ) or Triton-soluble ( non-raft ) fractions , respectively . Equal volumes ( 50 µl ) of each fraction were analyzed directly by western blotting . Experimental cells were grown on glass-coverslips and treated as indicated in figure legends and after final treatment , washed with cold wash buffer . Cells , preblocked in ice cold blocking buffer ( 1X PBS with 2 % FBS ) for 30 minutes in 4°C were stained with anti-mouse IFNγR1 or anti-mouse IFNγR2 in blocking buffer diluted to a predetermined optimal concentration and incubated at 4°C in the dark . The reaction was stopped by adding ice cold wash buffer and washed three times . For staining with secondary Ab , biotin-conjugated Fc specific anti-IgG was added and stained as described above and followed by streptavidin-PE . The cells were counterstained with FITC-CTxB following the protocol described above . The cells were then fixed with 1% paraformaldehyde , mounted with 90% glycerol on a glass slide , and observed under a laser scanning microscope [Leica ( Mannheim , Germany ) TCS SP2 AOBS system] by using a 63 oil-immersion objective . Double-stained images were obtained by sequential scanning for each channel to eliminate the “cross-talk” of chromophores and to ensure reliable quantification of colocalization [101] . Images were acquired and processed for colocalization analysis in TIFF format . Colocalization was estimated using Pearson's correlation coefficient [102] analyzed by ImageJ software ( rsbweb . nih . gov/ij/ ) . Total IFNγR1 or engaged IFNγR1 were immunoprecipitated from pooled ‘rafts’ fraction ( 2 , 3 and 4 ) and the ‘non-raft’ fraction ( 7 , 8 and 9 ) as described previously [103] , [104] with minor modifications of both . Cells were stimulated for 3 minutes at 37°C with biotinylated rIFN-γ [Recombinant murine IFN-γ was conjugated to EZ linked N-hydroxysulfosuccinimide-biotin ( Pierce , Rockford , IL ) according to the manufacturer's instruction . Cells were incubated with 0 . 5 ng/ml biotin-conjugated IFNγ , which is the calculated approximate equivalence to 100 U/ml of bioactive unconjugated IFNγ in our system and gave unaltered stimulation index in terms of STAT1 phosphorylation and pGAS-Luc activity in supportive experiments as with the unconjugated rIFNγ ( data not shown ) ] . Stimulation was terminated by immediate washing with ice cold PBS and harvesting cells in lysis buffers . To immunoprecipitate total amount of IFNγR1 from isolated raft and non-raft fractions , 0 . 5 ml of the pooled raft and non-raft fractions were added to 0 . 5 ml of cell lysis buffer , were pre-cleared , and the signaling complex was immunoprecipitated overnight at 4°C with anti-IFNγR1 ( 10 µg/ml ) and the recovery of the pulled immunecomplex was done exactly as described before . Alternatively , engaged IFNγR1 were immunoprecipitated from pooled raft and non-raft fractions with streptavidin-agarose beads . Pooled ‘rafts’ fraction ( 2 , 3 and 4 ) and the ‘non-raft’ fraction ( 7 , 8 and 9 ) pre-cleared of non-specific binding proteins by incubation with 50 µl of agarose beads ( prewashed and suspended to 50% in 1X cell lysis buffer ) . Pre-cleared lysates were incubated with streptavidin-agarose beads ( 50 µl of even suspension of 50% bead slurry ) for 2 hr at 4°C with gentle end over end shaking , washed using our immunoprecipitation protocol and procured from the beads and analyzed as before . Control experiments confirmed that no proteins bound to streptavidin-agarose beads if cells were left untreated with biotinylated IFNγ ( data not shown ) . Where indicated , the IFNγ signaling complex pulled down by immobilized streptavidin-agarose was dissociated in 50 µl of PBS containing 1% SDS by boiling for 10 min and diluted 20-fold with lysis buffer before it was subjected to a second immunoprecipitation using 10 µg/ml of an anti-pTyr Ab . The washing , procurement and immunoblot analysis were done exactly as mentioned before . For some experiments , cells were treated with mBCD ( Sigma-Aldrich , St . Louis , MO ) to disrupt lipid rafts . In the initial set of experiments , cells were treated with different concentration of mBCD ranging from 2 to 20 mM in the absence of serum for 30 min at 37°C , after which cell viability was assessed by trypan blue exclusion and the cell membrane cholesterol content and the cell-membrane fluidity were measured ( data not shown ) . Only conditions that allowed for cell viability comparable to mock treatment were used for these studies . It was observed that RAW 264 . 7 cells maintained the ability to exclude vital dyes such as trypan blue after treatment with 5 mM mBCD though treatment with the same essentially caused an acute depletion of cholesterol content from the cell membrane almost equivalent to the conditions of prevailing in MØ with 12 hr LD infection . The membrane fluorescence and lipid fluidity of cells were measured following the method described by Shinitzky and Inbar [105] . Briefly , the fluorescent probe DPH was dissolved in tetrahydrofuran at 2 mM concentration . To 10 ml of rapidly stirring PBS ( pH 7 . 2 ) , 2 mM DPH solution was added . For labeling , 106 cells were mixed with an equal volume of DPH in PBS ( Cf 1 µM ) and incubated for 2 hr at 37°C . Thereafter the cells were washed thrice and resuspended in PBS . The DPH probe bound to the membrane of the cell was excited at 365 nm and the intensity of emission was recorded at 430 nm in a spectrofluorometer . The FA value was calculated using the equation: FA = [ ( I∥ -I ⊥ ) / ( I∥ + 2I ⊥ ) ] , where I∥ and I ⊥ are the fluorescent intensities oriented , respectively , parallel and perpendicular to the direction of polarization of the exciting light [106] , [22] . Sample for cholesterol estimation was essentially prepared following the methods described in ref [107] . The crude plasma membrane fraction was isolated as described above from the experimental cells and suspended in a minimum volume of PBS . An aliquot was used for protein measurement . The rest of the pellet was extracted with 2∶1 methanol/chloroform , followed by 0 . 5 ml of chloroform and 0 . 5 ml of water . The methanol/chloroform ( lipid phase ) layer was dried under vacuum . The dry lipid was suspended in 200 µl of 1X Reaction buffer provided in Amplex Red Cholesterol Assay Kit ( Molecular Probes' Invitrogen ) and membrane cholesterol quantitation was performed exactly according to manufacturer's instructions . A method modified from ref . [108] was employed . Lyophilized lipophosphoglycan ( LPG ) , purified from LD ( a kind gift from Prof SJ Turco , University of Kentucky , Lexington , USA ) , was solubilized in double–distilled water to 1 mM stock and frozen in −20°C as aliquots . Stock solution was diluted in PBS and added to the MØ cultures to a final concentration of 10 µg/ml and was incubated at 37°C in 5% CO2 and high humidity for indicated time duration , in each experiment . The cells were washed three times in cell–culture medium with 5% FCS at room temperature to remove unbound LPG followed by washing three times in PBS at 4°C to terminate the experiment . Sequence analysis of IFNγR1 protein was done with ScanProsite software ( EXPASY TOOLS ) to find out consensus motif for cholesterol binding and the peptides were designed accordingly . Peptide designed and synthesized from wild type IFNγR1 protein , reproducing cholesterol binding motif ( CRAC motif , residues −269 to −280 ) was designated as Wt IFNγR1 peptide ( 269- VILVFAYWYTKK- ) . Point mutant IFNγR1 peptide ( 269- VILVFAAWATKK- ) reproduces the tyrosine to alanine substituted form of Wt IFNγR1 peptide . The peptides were synthesized on Rink Amide MBHA resin using standard solid phase Fmoc chemistry [109] with a capping step with 5% acetic anhydride and 5% lutidine in DMF after each coupling using PS3 peptide synthesizer ( Protein Technologies Inc , USA ) . Fmoc-amino acids were activated with HBTU in presence of HOBt and DIEA . Peptides were cleaved from the resin and side-chain protecting groups were removed by incubating with 94% TFA , 2 . 5% EDT , 1 . 5% thioanisole , 1 . 5% water , 0 . 5% TIS for 3 hrs at room temperature after which peptides were precipitated with ice-cold diethyl ether . Peptides were then purified by HPLC ( Waters , USA ) on a reverse phase µbondapak C-18 column using 0–80% acetonitrile in 0 . 01% TFA and molecular weight determined by MALDI-TOF/TOF analyzer ( Applied Biosystem , USA ) . Binding experiments were done by surface plasmon resonance ( SPR ) as described [110] , on a BIAcore 3000 system ( Biacore AB , Piscataway , NJ ) . L1 sensor chip ( Biacore ) with hydrophobically modified dextran layer was used to for SPR experiment to study binding of peptide to immobilized liposomes . Freshly prepared liposome were immobilized on L1 sensor chip up to response unit of 2000–2500 at flow rate of 5 µl/min in PBS ( pH = 7 . 2 ) . To remove any multilamellar structures from the lipid surface , we injected NaOH ( 50 µl , 10 mM ) at a flow rate of 50 µl /min , which resulted in a stable baseline corresponding to the lipid monolayer linked to the chip surface . The negative control BSA was injected ( 25 µl , 0 . 1 mg/µl in PBS ) to confirm complete coverage of the nonspecific binding sites . After complete association required dissociation time was given to wash loosely bound liposomes and to achieve stable base line . Kinetic experiments were performed with serial dilution of the peptides . Concentration of the peptides used were 0 . 02 nM , 0 . 2 nM , 2 nM , 20 nM , 200 nM in PBS ( pH = 7 . 2 ) . Peptides binding measured by observing the change in SPR angle as 90 µl of peptide analyte flowed over the immobilized liposome for 3 min at flow rate 30 µl/min . L1 sensor chip was regenerated using 200 µl of 20 mM CHAPS at flow rate of 50 µl/min . Different dilutions of protein samples in buffer were injected at a flow rate of 30 µl/min . The response was monitored as a function of time ( sensorgram ) at 25°C . SPR experiments were conducted in duplicate at each concentration to confirm the bindings were repeatable . Multiconcentration data were globally fit , and residuals were calculated and used to assess the goodness of fit with BIAevaluation 4 . 1 software ( Biacore , Piscataway , NJ ) . Each experiment was performed thrice , and representative data from one set of these experiments are presented; the interassay variation was within 10% . Two-tailed Student's t test was performed to ascertain the significance of the differences between the means of the control and the experimental groups . We considered values of P<0 . 05 to be statistically significant . P value <0 . 001 were considered extremely significant ( *** ) , P value ranging between 0 . 001 to 0 . 01 were very significant ( ** ) , P value 0 . 01 to 0 . 05 as significant ( * ) and P value >0 . 05 were not significant ( ns ) ( Student's t test ) . Error bars indicate mean ± SD . Data was analyzed by using Prism 5 . 0 ( GraphPad , San Diego , CA ) . | The disease Visceral Leishmaniasis or Kala-azar is extending its base in the Indian subcontinent and elsewhere . The emergence of drug resistant cases is aggravating the problem further . The kala-azar patients do not respond to the host-protective cytokine IFNγ at the active stage of the disease , the cause of which is unknown . This research is designed to understand how cell surface receptors for IFNγ respond under parasitized condition . Our results clearly showed that the Leishmania parasites during their intracellular life-cycle make the host cell membrane fluid by quenching cholesterol from the membrane , which renders the IFNγR nonfunctional despite their physical presence on the cell surface . Upon supplementation of cholesterol in infected MØs , the infected cells regain responsiveness to IFNγ coupled with intracellular parasite killing . Thus supplementation of cholesterol together with IFNγ may be a new approach to treat drug unresponsive Kala-azar cases . |
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Although of fundamental importance in developmental biology , the genetic basis for the symmetry breaking events that polarize the vertebrate oocyte and egg are largely unknown . In vertebrates , the first morphological asymmetry in the oocyte is the Balbiani body , a highly conserved , transient structure found in vertebrates and invertebrates including Drosophila , Xenopus , human , and mouse . We report the identification of the zebrafish magellan ( mgn ) mutant , which exhibits a novel enlarged Balbiani body phenotype and a disruption of oocyte polarity . To determine the molecular identity of the mgn gene , we positionally cloned the gene , employing a novel DNA capture method to target region-specific genomic DNA of 600 kb for massively parallel sequencing . Using this technique , we were able to enrich for the genomic region linked to our mutation within one week and then identify the mutation in mgn using massively parallel sequencing . This is one of the first successful uses of genomic DNA enrichment combined with massively parallel sequencing to determine the molecular identity of a gene associated with a mutant phenotype . We anticipate that the combination of these technologies will have wide applicability for the efficient identification of mutant genes in all organisms . We identified the mutation in mgn as a deletion in the coding sequence of the zebrafish microtubule actin crosslinking factor 1 ( macf1 ) gene . macf1 is a member of the highly conserved spectraplakin family of cytoskeletal linker proteins , which play diverse roles in polarized cells such as neurons , muscle cells , and epithelial cells . In mgn mutants , the oocyte nucleus is mislocalized; and the Balbiani body , localized mRNAs , and organelles are absent from the periphery of the oocyte , consistent with a function for macf1 in nuclear anchoring and cortical localization . These data provide the first evidence for a role for spectraplakins in polarization of the vertebrate oocyte and egg .
The animal-vegetal axis is the first axis to form in the vertebrate embryo; however , the cellular and genetic pathways by which it is specified are the least well understood . In lower vertebrates , such as frogs and fish , this axis forms during early oogenesis , while in mouse , it becomes apparent during oocyte maturation . The earliest morphological marker of asymmetry in the vertebrate oocyte is the Balbiani body , a transient structure composed of organelles , including mitochondria , endoplasmic reticulum ( ER ) and Golgi ( reviewed in [1] and [2] ) . The Balbiani body is found in the oocytes of invertebrates such as Drosophila [3] and vertebrates , including Xenopus , mouse and human [4]–[9] . In most mammals , the molecular composition and function of the Balbiani body is unknown . In mouse , however , the Balbiani body contains Trailer hitch ( Tral ) protein , which has been implicated in mRNA localization and translation in Drosophila and in P body assembly in human cells [10] , [11] . The presence of Tral in the Balbiani body of the mouse suggests a function for this structure in RNA metabolism [6] . Studies of the Xenopus Balbiani body have shown that in addition to ER , mitochondria and Golgi , it contains germ plasm RNAs and germinal granules , and it is thought to localize these factors to the vegetal cortex of the oocyte during early oogenesis [5] , [12] , [13] ( reviewed in [2] ) . Recent studies have described a Balbiani body in zebrafish oocytes that behaves similarly to the Xenopus Balbiani body [14]–[17] . In both zebrafish and Xenopus , the Balbiani body is the first morphological marker of polarity and predicts the future animal-vegetal axis . In primary oocytes , it forms adjacent to the oocyte nucleus on the future vegetal side of the oocyte and then becomes localized to the vegetal cortex as oogenesis proceeds [5] , [14]–[18] . Upon localization to the vegetal cortex , germ plasm RNAs and germinal granules are deposited and the Balbiani body disassembles . Although a highly conserved structure , the mechanisms of Balbiani body formation , function , and disassembly have remained elusive , in large part due to a lack of genetic and molecular data . The zebrafish bucky ball ( buc ) gene is the only vertebrate gene known to be required for Balbiani body formation [15] , [17] . buc encodes a novel protein , in the absence of which a Balbiani body fails to form . In buc mutants , vegetal RNAs are not localized , reflecting a defect in animal-vegetal polarity of the oocyte [15] , [17] . We report a second zebrafish gene required for animal-vegetal polarity of the oocyte and egg . The magellan ( mgn ) mutant was identified based on a defect in which cytoplasm localizes around the yolk rather than at the animal pole of the egg [19] . We found that during oogenesis , mgn mutant oocytes display an asymmetric localization of the oocyte nucleus , a novel enlarged Balbiani body phenotype and an absence of vegetally-localized RNAs , stable microtubules , and organelles at the oocyte cortex . To identify the gene disrupted in mgn mutants , we positionally cloned the mgn gene , utilizing a novel DNA capture method to enrich for genomic DNA spanning the interval containing our candidate gene . This technique involves hybridization of region-specific oligonucleotides to long genomic DNA fragments , extension of the oligonucleotides using labeled nucleotides , capture of the labeled fragments along with the genomic DNA template and flanking regions , and finally , isolation of the captured genomic DNA , which is then processed for massively parallel sequencing . Using this technique , we identified a 31 base pair deletion in the zebrafish ortholog of microtubule actin crosslinking factor 1 ( macf1 ) , a highly conserved cytoskeletal linker protein belonging to the spectraplakin family of proteins . This is one of the first examples in which genomic DNA enrichment combined with massively parallel sequencing has been used to determine the molecular identity of a gene associated with a phenotype . macf1 function has been characterized in several types of polarized cells including epithelial cells , neurons , and muscle cells . Our analysis of the mgn mutant reveals a new role for macf1 in the oocyte , providing insight into the role of spectraplakins during vertebrate oogenesis .
In zebrafish , animal-vegetal polarity of the egg becomes morphologically apparent upon egg activation . Prior to activation , the cytoplasm of the egg is intermingled with the yolk . Activation of the egg through contact with water causes two striking changes to the egg: the cortical granules fuse with the plasma membrane and exocytose their contents into the perivitelline space causing the chorion to expand; and the egg contracts , resulting in segregation of cytoplasm from the yolk to the animal pole to form the blastodisc ( Figure 1A ) . The zebrafish mgn mutant was identified based on a defect in activated eggs . We found that mgn mutant eggs exhibit variable expansion of the chorion . In addition , we observed that following cytoplasmic segregation , cytoplasm was variably distributed around the yolk rather than being restricted to one pole ( Figure 1B ) , suggesting a defect in animal-vegetal polarity of the egg . To determine if the mgn mutation causes defects during oogenesis , we examined mutant and wild-type oocytes by histology , using hematoxylin and eosin dyes to visualize structures such as the Balbiani body , cortical granules and yolk . In wild-type mid to late stage I oocytes , the nucleus was centrally located and the Balbiani body , which forms on the future vegetal side of the nucleus and translocates to the vegetal cortex , was detected either near the nucleus ( n = 2/15 ) , between the nucleus and future vegetal cortex ( n = 6/15 ) , or at the vegetal cortex ( Figure 2A; n = 7/15 ) . In contrast , the nucleus of mutant oocytes was asymmetrically localized ( Figure 2B ) and the Balbiani body was only infrequently found at the cortex ( n = 2/20 ) . In the majority of mutant oocytes , the Balbiani body was observed near the nucleus ( Figure 2B; n = 12/20 ) or between the nucleus and oocyte cortex ( n = 6/20 ) . In addition , the Balbiani body of mutant oocytes was surrounded by pale pink staining that appeared speckled . This staining was not observed in wild-type oocytes . The asymmetric positioning of the oocyte nucleus was also seen in stage II mutant oocytes , whereas in wild-type oocytes , the nucleus remained centrally located ( compare Figure 2C and 2D ) . In addition , we found that in wild-type stage II oocytes , cortical granules were located radially around the central nucleus ( Figure 2C; n = 25/25 ) , whereas in mutant oocytes , cortical granules were asymmetrically located and were found in the middle of the oocyte , including within and around a region distinct to mutant oocytes that stains lightly with eosin ( Figure 2D; n = 31/31 ) . Stage III of oogenesis is characterized by an accumulation of yolk in the middle of the oocyte , which is thought to drive the cortical granules to the oocyte cortex ( Figure 2E; n = 17/17 ) [20] . In stage III mgn mutant oocytes , as in wild-type , yolk accumulated in the oocyte and the cortical granules typically moved to the oocyte periphery . In mutant oocytes , however , there was a variable and uneven distribution of cortical granules around the cortex and a small number of cortical granules remained within the yolk ( Figure 2F; n = 22/23 ) , likely due to their abnormal localization during stage II . These defects in cortical granule localization may result in the variable chorion expansion defect in mgn mutants ( Figure 1 ) . In zebrafish , as in Xenopus , the Balbiani body contains germ plasm components that include germ plasm RNAs and germinal granules [14] , [15] , [17] . Studies in Xenopus indicate that the Balbiani body is required for localization of mRNAs to the vegetal cortex [12] , [13] , ( reviewed in [2] ) and recent work in zebrafish shows that distinct localization elements within the 3′UTR of dazl mRNA localize it to the Balbiani body and vegetal cortex of the oocyte [14] . To investigate the function of the Balbiani body in mgn mutant oocytes , we examined the localization of dazl and a second conserved germ plasm mRNA , vasa . In wild-type oocytes , vasa mRNA is localized to the Balbiani body during early stage I of oogenesis , transiently localizes to the vegetal cortex during late stage I , and then becomes localized around the cortex of the oocyte during stage II ( Figure 3A and 3B ) . In mgn mutant oocytes , vasa mRNA localized to the Balbiani body during early stage I , but it was predominantly localized to the middle of the oocyte during late stage I and stage II ( Figure 3C and 3D; n = 15/16 ) . In wild-type oocytes , dazl mRNA is localized to the Balbiani body during early stage I and then becomes localized to the vegetal cortex during late stage I where it remains during stage II ( Figure 3E and 3F ) . In mutant oocytes , dazl mRNA initially localized to the Balbiani body during stage I , and like vasa mRNA , was predominantly found in the middle of the oocyte in late stage I and stage II , rather than localized to the vegetal cortex ( Figure 3G and 3H; n = 24/25 ) . These results show that germ plasm mRNAs localize to the Balbiani body of mutant oocytes during early stage I and that Mgn is required for the subsequent localization of the Balbiani body and associated RNAs to the vegetal cortex during late stage I . In addition , the persistence of both vasa and dazl mRNA in a central domain of stage II mutant oocytes may reflect a failure of the Balbiani body to disassemble at the end of stage I of oogenesis . Using SSLP ( simple sequence length polymorphism ) markers in bulk segregant analysis , followed by fine mapping of homozygous mutant versus heterozygous sibling females using SSLP and SNP ( single nucleotide polymorphism ) markers , we localized the mgn mutation to a 2 . 02 Mb interval ( Figure 4F ) based on the Sanger Institute Ensembl zebrafish genome sequence assembly ( Zv7/danRer5 ) . Based on data from 976 meioses and 27 recombination events , we then calculated that the molecular lesion would be in the zebrafish macf1 gene . To date , a full-length zebrafish macf1 transcript has not been sequenced , however , the longest predicted zebrafish macf1 transcript is 23 , 975 base pairs ( bp ) ( NCBI RefSeq accession: XM_001920059 . 1 ) . Additionally , while macf1 is highly conserved , the frequency of multiple , alternatively spliced isoforms in other organisms [21] suggested that prediction of ovarian transcript sequences would be difficult . We therefore chose to utilize increasingly affordable next-generation sequencing technologies to sequence the entire 298 , 606 bp predicted macf1 genomic interval ( Zv7/danRer5 , Ensembl 52 genebuild ) as well as approximately 17 kb of sequence 5′ to the predicted gene . To do this economically , we had to enrich for genomic sequence specific to our target macf1 region . This was accomplished using a genomic sequence capture and release method . This method involves designing 22–27mer long region-specific oligonucleotides ( oligos ) that are hybridized to genomic DNA , extended using biotinylated nucleotides , and then affinity purified using streptavidin coated magnetic beads to capture the genomic template DNA ( modification of method in [22] ) . The captured DNA is then dissociated from the beads , sheared , and cloned into a library for massively parallel sequencing ( Figure 4A ) . We designed oligos approximately every 8 kb spanning a 319 . 8 kb region , performed the capture and sequencing , and then analyzed a 315 . 6 kb region that included all of macf1 and ∼17 kb of sequence 5′ to the gene . We obtained a 243-fold average enrichment for this region , as determined from the sequence analysis . The sequencing results also showed a 31-fold average read depth for unique sequence , covering 99 . 9% of our targeted region ( Figure 4B ) . Data were obtained for the entire predicted coding sequence with the exception of three small gaps . The genomic intervals surrounding these gaps were then amplified by PCR and sequenced by Sanger sequencing . A four bp and 12 bp gap were not confirmed by Sanger sequencing and no base changes were found in these intervals . Sanger sequencing did , however , confirm a gap of 31 bp . The 31 bp deletion causes a frame-shift at amino acid codon 5315 of the longest predicted zebrafish Macf1 protein isoform ( NCBI RefSeq accession: XP_001920094 . 1 ) , resulting in a premature stop codon and subsequent truncation of the predicted protein ( Figure 4C ) . One additional base change in predicted coding sequence was found by massively parallel sequencing but was not confirmed by Sanger sequencing . Based on the mgn mutant phenotype , we expected macf1 to be expressed during stage I of oogenesis . In situ hybridization experiments showed that , consistent with a function during early oogenesis , macf1 mRNA is expressed in stage I oocytes and additionally , has a restricted localization pattern during later oogenesis ( Figure 4D ) . In contrast , in situ hybridization analysis showed that in mgn mutant oocytes , macf1 mRNA levels appeared to be significantly reduced ( Figure 4E ) . This suggests that the transcript is subject to nonsense-mediated decay and is consistent with the presence of a premature stop codon in macf1 caused by the deletion in mgn mutants . To confirm that the deletion in macf1 is the only change in coding sequence in the interval to which the mgn mutation maps , we further narrowed the interval and then sequenced the remaining candidate genes . We initially attempted to identify SNPs to narrow the interval by PCR amplification and sequencing of non-coding regions , but we were unsuccessful in identifying polymorphisms . Therefore , we performed a second sequence capture experiment with wild-type and mutant fish from a new map cross to attempt to more efficiently identify any rare SNPs in the region and examine additional genes in the interval . For the second sequence capture , we designed oligos to a 613 . 9 kb region that consisted of the macf1 gene plus approximately 86 kb of sequence 5′ to macf1 and 230 kb of sequence 3′ to macf1 ( Figure 4F ) . We massively parallel sequenced the captured genomic DNA from a wild-type ( AB strain ) fish and a mutant ( TU strain ) fish and analyzed 616 kb of targeted sequence and surrounding sequence that was captured . For the wild-type sample , we obtained a 72-fold average enrichment and a 29-fold average read depth for unique sequence , covering 94% of our targeted region ( Figure 4G ) . For the mutant sample , we obtained a 43-fold average enrichment and a 17-fold average read depth for unique sequence , covering 97% of our targeted region ( Figure 4G ) . Using these data , we identified a SNP ( called SNP36 ) 14 . 35 kb from the 5′ end of macf1 . No genes are known or predicted to exist in this 14 . 35 kb region . We were unable to identify useful SNPs 3′ to the macf1 gene using the sequence capture data; therefore , we used a previously identified SSLP marker ( z53477 ) to narrow the interval 3′ to macf1 . We identified one recombinant fish from 311 meioses at SNP36 and 10 recombinants from 578 meioses at z53477 . The interval between z53477 and SNP36 contains part of si:dkey-190l1 . 2 ( similar to vertebrate gamma-aminobutyric acid B receptor , 2 ) , as well as the full sequence of 13 genes . Ten genes including macf1 are present in the second sequence capture interval ( sla1 ( Src-like-adaptor 1 ) , wisp1a ( novel protein similar to vertebrate WNT1 inducible signaling pathway protein 1 ) , ndrg1 ( N-myc downstream regulated gene 1 ) , si:rp71–45k5 . 2 ( novel protein ) , zgc:113424 ( novel forkhead domain-containing protein ) , si:rp71–45k5 . 3 ( novel protein ) , si:rp71–45k5 . 4 ( novel protein similar to vertebrate proteasome subunit , alpha type 2 ) , zgc:91910 ( novel protein ) , bmp8 ( bone morphogenetic protein 8 ) and macf1 ) . Examination of the coding and predicted coding sequences revealed that there were no sequence changes in any of the genes in the interval . Three genes in the mgn interval that were not included in the captured region were cloned from ovarian cDNA ( si:dkey-190l1 . 1 ( elongator complex protein 2 ) , si:ch211–254e15 . 2 ( UPF0436 protein C9orf6 homolog ) , si:ch211–254e15 . 1 ( novel protein similar to vertebrate catenin , alpha-like 1 ) ) and were sequenced by Sanger sequencing . No sequence changes were found in the predicted coding regions of these genes . Based on these data , we conclude that the mgn mutant phenotype is caused by the deletion in macf1 . macf1 belongs to the highly conserved spectraplakin family of proteins . Spectraplakins are multifunctional cytoskeletal linker proteins characterized by an N-terminal actin binding domain consisting of Calponin homology domains , a globular plakin domain that mediates interactions with various types of cell junctions , a plakin repeat domain that can interact with intermediate filaments , a spectrin repeat domain that allows dimerization , and finally a C-terminal microtubule binding domain that consists of an EF-hand and GAS2 domain [23] , [24] . The mgn deletion creates a stop codon in the spectrin repeat domain that is predicted to result in a loss of part of the spectrin domain and all of the microtubule binding domain ( Figure 4H ) . Thus , the truncated protein is not expected to bind to the microtubule cytoskeleton . It is well established that spectraplakins such as macf1 are able to interact with both the actin and microtubule cytoskeletons ( reviewed in [21] , [24] ) . To determine if the cytoskeleton is affected in mgn mutant oocytes , we first used an antibody to acetylated tubulin to visualize stable microtubules . We found that in wild-type stage I oocytes , stable microtubules were present throughout the oocyte but did not appear to be attached to a microtubule organizing center ( MTOC ) ( Figure 5A and 5A′; n = 13/13 ) . In zebrafish as in other organisms , the centrosome , which serves as an MTOC , is inactivated early in oogenesis . Stable microtubules were also present in mgn mutant oocytes , but their presence was significantly reduced at the periphery of the oocyte ( Figure 5B and 5B′; n = 23/24 ) . We next examined the actin cytoskeleton in wild-type and mgn mutant oocytes . In both wild-type and mutant oocytes , we found actin filaments localized to the nucleus and at the oocyte cortex ( Figure 5C and 5D , n = 15 wild-type and n = 13 mutant oocytes ) . Thus , the mutation in macf1 causes a loss of stable microtubule localization to the periphery of the oocyte , but it does not appear to affect the actin cytoskeleton . These results suggest that , consistent with loss of the predicted Macf1 microtubule binding domain , the defects seen in mgn mutants may be caused by a loss of tethering of stable microtubules to the oocyte cortex . Given the defect in cortical localization of RNAs and stable microtubules , we investigated if mgn mutant oocytes also exhibit defects in the localization of organelles by labeling ER and mitochondria with the membrane dye , DiOC6 . In addition to cytoplasmic ER and mitochondria , DiOC6 labels the aggregate of ER and mitochondria that , along with germ plasm mRNAs and other organelles , composes the Balbiani body ( reviewed in [1] , [2] ) . DiOC6 staining revealed that in wild-type oocytes , ER and mitochondria are found throughout the oocyte and in addition , are concentrated within the Balbiani body , which translocates from the nucleus to the oocyte cortex during mid to late stage I ( Figure 6A , n = 80/80 ) . In mutant oocytes , ER and mitochondria , as well as the Balbiani body itself , are typically absent from the periphery of the oocyte ( Figure 6B and 6C , n = 46/64 ) . In many oocytes , ER and mitochondria were tightly concentrated around the nucleus of the oocyte , as well as absent from the periphery ( Figure 6C; n = 24/64 ) . The defect in the peripheral localization of ER and mitochondria persists into stage II of oogenesis when in wild-type oocytes , the Balbiani body has disassembled and ER and mitochondria are localized throughout the oocyte ( Figure 6D ) . In mgn mutants , DiOC6 staining was concentrated in the middle of stage II mutant oocytes ( Figure 6E ) , consistent with a function for macf1 in the localization of ER and mitochondria to peripheral regions of the oocyte . The lack of ER and mitochondria localization to the periphery of mgn mutant oocytes is strikingly similar to the absence of acetylated tubulin in these regions ( Figure 5A and 5B ) . To confirm the absence of ER and mitochondria at the oocyte periphery observed with DiOC6 staining , we examined stage II oocytes by transmission electron microscopy ( TEM ) . TEM revealed a uniform distribution of ER and mitochondria throughout wild-type oocytes ( compare cytoplasmic and peripheral domains in Figure 7A′ and 7A″; n = 4 ) . In mgn mutant oocytes , the central cytoplasmic domain was similar to that of wild-type oocytes ( Figure 7B′; n = 7 ) ; however , in peripheral regions , very little ER and no mitochondria were present ( Figure 7B″ ) . These TEM results are consistent with our observations from the DiOC6 staining and further support a role for macf1 in the peripheral localization of organelles . In addition to the frequent loss of cortical Balbiani body localization , DiOC6 staining revealed a difference in Balbiani body size in mutant compared to wild-type oocytes . We found that in early stage I mutant oocytes , the Balbiani body was comparable in size to that of wild-type oocytes ( Figure 6F; 50–70 micron diameter ) ; however , in mid to late stage I oocytes ( 70–110 micron diameter ) the Balbiani body of mutant oocytes was significantly larger than in wild-type oocytes ( Figure 6F , average Balbiani body size = 12 microns versus 18 microns in 70–90 micron wild-type and mutant oocytes , respectively , and 18 microns versus 29 microns in 90–110 micron wild-type and mutant oocytes , respectively ) . Thus , the Bb is similarly sized in early stage I mgn mutant and wild-type oocytes but becomes abnormally large beginning in mid stage I of oogenesis in mgn mutants . The asymmetric localization of the nucleus in mutant stage I and II oocytes that we observed by histology was also apparent in DiOC6 stained oocytes ( Figure 6B ) . In 70–90 micron wild-type oocytes , the average distance between the centroid of the nucleus and the centroid of the oocyte was 6 . 3 microns ( n = 15 ) , whereas in 70–90 micron mutant oocytes , it was 12 microns ( n = 13 ) . Thus , there is a significant ( p<0 . 05 ) difference in the position of the nucleus in mutant mid stage I oocytes . It is not yet clear if the mislocalized nucleus causes the defect in Balbiani body size , is a consequence of it , or is an independent defect of mgn mutant oocytes .
The mechanisms guiding the establishment and maintenance of polarity in the vertebrate oocyte are not well understood . Here we have identified a novel function for macf1 in formation of the animal-vegetal axis during zebrafish oogenesis . macf1 belongs to the spectraplakin family of cytoskeletal linker proteins , which are highly conserved amongst species . Mutations in the Drosophila homolog of macf1 , short stop ( shot ) , were first identified in screens for neuromuscular specificity and axon guidance [25] , [26] and for integrin-mediated adhesion [27]–[29] . Subsequent work has defined several additional roles for shot in processes that include oocyte determination and tracheal cell fusion [30] , [31] . The diverse functions of shot have been attributed to the presence of multiple isoforms , and recent work has focused on defining tissue-specific domain requirements [32] . The function of macf1 in vertebrates has largely been characterized in cell culture experiments , since the mouse macf1 knockout results in embryonic lethality [33] , [34] . As in Drosophila , vertebrate macf1 has diverse functions . In Hela cells , for example , macf1 is required for the cortical localization of CLASP2 , a microtubule tip binding protein [35] . During mouse embryogenesis , macf1 is required for Wnt signaling , where it functions in translocating Axin to the cell membrane [34] . Our isolation of the zebrafish mgn mutant provides a unique opportunity to now study the role of macf1 during vertebrate oogenesis . We found that mgn is required during zebrafish oogenesis for formation of the animal-vegetal axis of the oocyte and egg . The only other vertebrate gene known to be required for this process is the zebrafish bucky ball ( buc ) gene , which encodes a novel protein [15] , [17] . In buc mutants , a Balbiani body does not form and RNAs are mislocalized , leading to a defect in animal-vegetal polarity of the oocyte and egg [15] , [17] . Similar to buc mutants , mgn mutant females produce eggs that display abnormal animal-vegetal polarity . In mgn mutant oocytes , however , a Balbiani body forms during early stage I as in wild-type oocytes , but it becomes abnormally large and typically does not localize to the oocyte cortex during mid stage I as in wild-type . Interestingly , a null mutation in Kinesin heavy chain or P-element insertions in the kinesin-associated mitochondrial adaptor , milton , which cause its overexpression , result in overgrowth of the Balbiani body in the Drosophila ovary [36] . Although similar to mgn mutants , the Balbiani body defects in these milton mutants affect the localization of mitochondria to the Balbiani body without disrupting RNA localization and are largely resolved during late oogenesis when the microtubule cytoskeleton reorganizes [36] . Thus , mgn is the only gene known to be required for regulating the size of the Balbiani body , as well as for localization of the Balbiani body and its associated mRNAs to the oocyte cortex . Macf1 may be directly required for these processes through binding to cytoskeletal elements within the Balbiani body . Alternatively , misregulation of Balbiani body size and localization in mgn mutants may be caused by a disruption of the stable microtubule cytoskeleton that prevents localization of the Balbiani body to the vegetal cortex of the oocyte . Analysis of mid stage I mgn mutant oocytes revealed an asymmetric localization of the nucleus that is not seen in wild-type oocytes at this stage and which persists into stage II . It is possible that this mislocalization of the oocyte nucleus is a consequence of the Balbiani body enlargement; however , it is also plausible that the nuclear mislocalization represents an independent defect in nuclear anchoring . Establishment and maintenance of a centralized nucleus is an active process , and it is well established that actin filaments , microtubules , and intermediate filaments play roles in nuclear migration and anchoring in various cell types ( reviewed in [37]–[39] ) . Nuclear anchoring is mediated by a family of proteins , all of which contain KASH ( Klarsicht , ANC-1 , Syne homology ) domains at their C-termini . The KASH domains target the proteins to the outer nuclear envelope , while the N-termini of these proteins tether them to the cytoskeleton ( reviewed in [40] , [41] ) . Interestingly , the KASH domain of Nesprin-3a interacts with the actin-binding domain of MACF1 in coimmunoprecipitation experiments in vitro [42] . It has recently been reported that vertebrate Macf1 isoform-3 localizes to the outer nuclear envelope and that the N-terminus associates with Nesprin-3 in COS-7 cells [43] . These data suggest that Macf1 may play a role in tethering the nucleus to the microtubule cytoskeleton . Such a function for Macf1 would be consistent with the defect in nuclear positioning that we observe in mgn mutant oocytes . In addition to the abnormally large Balbiani body and the nuclear positioning defect , DiOC6 staining and TEM revealed that the cytoplasmic ER and mitochondria that surround the Balbiani body were absent from the periphery of the oocyte . It has long been recognized that microtubules are essential for establishing and maintaining the structure of the ER ( reviewed in [44] ) and for localizing mitochondria to various cellular domains ( reviewed in [45] , [46] ) . Knockdown of the Kinesin-binding protein Kinectin in cultured cells results in a collapse of ER and mitochondria to the center of the cell [47] , demonstrating the involvement of motor proteins in the maintenance of ER architecture and mitochondrial localization . In addition , in cultured mouse cells mutant for the conventional kinesin heavy chain , kif5b , mitochondria display a perinuclear distribution and an abnormal absence from the cell periphery [48] . Several non-motor proteins have also been implicated in mediating ER-microtubule interactions ( reviewed in [44] , [49] ) . Climp-63 , for example , is an integral ER membrane protein that binds to microtubules , anchoring the ER to the cytoskeleton . Overexpression of a mutant Climp-63 lacking the microtubule binding domain results in displacement of endogenous Climp-63 and clustering of ER around the nucleus [50] . These phenotypes are similar to those of mgn mutant oocytes , in which ER and mitochondria are frequently concentrated around the nucleus and are absent from the periphery . This suggests that Macf1 may be required to maintain ER architecture and to localize mitochondria in the oocyte by positioning stable microtubules at the oocyte periphery . Alternatively , Macf1 may bind directly to ER , mitochondria and microtubules . We found that mgn is required to localize stable microtubules to peripheral regions in zebrafish oocytes . Microtubule-cortex interactions are mediated by a growing class of microtubule-associated proteins called microtubule plus-end-tracking proteins ( +TIPS ) . +TIPS mediate cortical interactions through association of microtubule ends with the cortical actin cytoskeleton and with the plasma membrane ( reviewed in [51] , [52] ) . Macf1 ( Acf7 ) has been identified as a +TIP in mouse endodermal cells where it functions to coordinate the actin and microtubule cytoskeletons in the cytoplasm and at the cell cortex and to maintain polarity in response to wound healing [33] . In Drosophila tendon cells , Macf1 ( Shot ) organizes the microtubule network at the muscle-tendon junction by forming a complex with the +TIPs EB1 and APC [53] , which regulate cortical targeting of microtubules ( reviewed in [51] , [54] ) . Recent studies have shown that Macf proteins bind to EB1 directly and exhibit plus end tracking in vivo [55] . These data are consistent with a function for Macf1 in cortical localization of microtubules . Together , the loss of stable microtubules at the oocyte periphery and the absence of peripheral ER and mitochondria in mgn mutant oocytes suggest that in the zebrafish oocyte , ER and mitochondria associate with stable microtubules and that Macf1 may function to tether the stable microtubule cytoskeleton to the cortical actin cytoskeleton . In the absence of functional Macf1 , the microtubule cytoskeleton is disrupted and the ER and mitochondria collapse into the middle of the cell . To identify the molecular lesion associated with the mgn phenotype , we identified a candidate gene using positional cloning methods and then used a novel targeted sequence capture technique combined with massively parallel sequencing to sequence the candidate gene and exclude nine neighboring genes in the interval . Identification of mutations by traditional positional cloning techniques is time consuming and can be hindered by a variety of factors including limited genome annotation and complex genetic loci , which make prediction of transcripts difficult . As a result , there is increasing interest in utilizing next-generation sequencing technologies to identify mutations by sequencing large regions of genomic DNA . It was recently demonstrated that a point mutation in the encore gene in Drosophila could be identified by whole genome resequencing [56] . Although extremely efficient , this method would be prohibitively costly in an organism with a large genome , such as zebrafish or mouse . A more cost-effective approach was recently used to identify a mutation in the mouse Megf8 gene [57] . This approach utilized positional cloning techniques to identify a 2 . 2 Mb interval containing the mutation . The authors then constructed a BAC contig spanning this region and sequenced 15 BACs with massively parallel sequencing to identify a point mutation in Megf8 . While this method is effective and more cost-efficient than whole genome resequencing , the construction of BAC contigs remains labor intensive and time consuming . We sought to utilize an efficient and cost-effective method for region-specific enrichment of genomic DNA to allow for massively parallel resequencing of targeted genomic intervals . In recent years , several methods have been developed for genomic enrichment [58]–[66] . A method for in-solution hybrid selection was recently reported based on hybridizing long biotinylated RNA sequences to DNA that has been randomly sheared and amplified [66] . Oligonucleotide tiling arrays were recently used to target and identify mutations in the mouse kit gene , human neurofibromin gene , and in seven human autosomal recessive ataxia-associated genes [61]–[63] . In addition , array-based genomic sequence capture of a 40 Mb linkage interval combined with massively parallel sequencing was used to identify TSPAN12 as the mutated gene in patients with familial exudative vitreoretinopathy [64] . Array based capture of a 2 . 9 Mb interval was also used to identify mutations in taperin ( C9orf75 ) as the cause of nonsyndromic deafness DFNB79 [65] . We used an in-solution method to capture a targeted region based on enzymatic extension of a hybridized oligo to a targeted genomic region using biotin-labeled nucleotides based on a previously established method [22] . This region-specific extraction ( RSE ) method reliably and efficiently extracts long genomic DNA fragments with streptavidin-coated microparticles [22] , [73] . Because RSE does not require shearing the genomic DNA prior to capture , as the previously described methods do , it retrieves large regions of genomic DNA with a very small number of oligos ( each spaced about 8 kb apart ) , making experimental design simple and cost-efficient . Using this method , we were able to achieve 99 . 9% coverage of our 300 kb targeted region and 97% of our 600 kb targeted region , and we demonstrated that this method is effective in the identification of significantly larger deletions than had previously been shown [61] . Based on the excellent read depth and coverage that we obtained , our method is equally effective at identifying point mutations and SNPs , as we did here . We used a combination of targeted genomic DNA capture and massively parallel sequencing to identify a deletion in zebrafish macf1 that affects Balbiani body function and causes a defect in oocyte polarity . Although conditional targeting of mouse macf1 ( acf7 ) in skin epidermis [67] and brain [68] has recently been reported , the function of vertebrate macf1 has primarily been studied in cell culture due to the lethality of the macf1 ( acf7 ) knockout in mice . Until our identification of the mgn mutant , a requirement for vertebrate macf1 had not been examined in the oocyte . We have shown that macf1 is required for cortical localization of the Balbiani body , RNAs , microtubules , and organelles in the zebrafish oocyte and that disruption of macf1 function affects polarity of the oocyte . Further studies will be required to define the molecular function of macf1 during oogenesis and will be particularly valuable for understanding the mechanism by which cell polarity is regulated in the ovary .
The animal work in this study was approved by the Institutional Review Board of the University of Pennsylvania School of Medicine . Phenotype characterization was carried out using the p6cv allele of mgn [19] . Oocytes were staged according to Selman et al . [20] . Ovaries were dissected from euthanized females and fixed overnight at 4°C in 4% paraformaldehyde . Following washes in PBS , ovaries were processed for histology or dehydrated in MeOH prior to in situ hybridization . For histological analysis , ovaries were embedded in JB-4 Plus plastic resin ( Polysciences ) and 5 micron sections were cut using a microtome . Sectioned ovaries were stained for 20 minutes with hematoxylin ( Sigma-Aldrich ) , washed in distilled water , stained for 20 minutes with eosin Y ( Polysciences ) , washed in distilled water , and cleared with 50% EtOH . Stained sections were coated with Permount ( Fisher ) and then coverslipped . Whole mount in situ hybridization was performed as previously described [69] . Following staining , oocytes were embedded in JB-4 Plus Plastic resin and sectioned as described above . The vasa DIG labeled probe was generated using pTY27 [70] and the dazl probe was made using pCRII zdazl ( gift from Kunio Inoue ) . Genomic DNA was pooled from 18 mutant and 24 sibling ( wild-type and heterozygous ) females . Using these pools , mgn was mapped to chromosome 19 using SSLP markers that cover the genome as described [71] . The SSLP markers z31313 and z24515 , which flank the mgn mutation , were used to genotype individual fish . SNP and SSLP markers were generated for further fine mapping . Fine mapping was performed using the SNP marker SNP3 ( 5′-CGT AGG CGT TGC ATA ACT GA-3′ and 5′-GCA AGC AAT CAT ACG CAC AT-3′ ) and either z53070 or CAmarker1 ( 5′-TTG AAG GGT CAC GTT TGA CA-3′ and 5′-CAA GGG TGA AGG GTG AAG AG-3′ ) , which are approximately 7 . 7 kb apart . 976 meioses were analyzed , and 16 recombinants at SNP3 and 11 recombinants at z53070/CAmarker1 were used to narrow the interval to a 2 . 7 cM ( 2 . 02 Mb based on Ensembl Zv7/danRer5 ) interval . For preparation of genomic DNA , fish were euthanized and then flash frozen in liquid nitrogen . 100 mg of tissue from each fish was homogenized and genomic DNA was isolated using the Qiagen Genomic-tip 100/G protocol ( Qiagen ) . Genomic DNA was enriched from two mgn mutant females by region specific extraction ( RSE ) [22] , [72] for two overlapping regions: 1 ) A 319 . 8 kb interval on chromosome 19 ( 32 , 557 , 347–32 , 877 , 103; Zv7/danRer5 ) where the molecular lesion associated with the mgn phenotype was predicted to be based on meiotic mapping; and 2 ) A 613 . 9 kb interval on chromosome 19 ( 32 , 326 , 938–32 , 942 , 969; Zv7/danRer5 ) that extended the 319 . 8 kb region on both sides of macf1 to examine flanking genes and identify SNPs . Genomic DNA from a wild-type ( +/+ ) AB fish was also enriched for the 613 . 9 kb interval to identify SNPs to narrow the interval . RSE was carried out by Generation Biotech at CHOP/UPenn on a Qiagen BioRobot EZ1 . For this capture technology , oligonucleotide primers are hybridized to targeted areas of the genome by exploiting sequence elements that are unique to the region of interest . The bound oligos are extended with biotinylated nucleotides to label the targeted DNA segments . Streptavidin coated magnetic microparticles are then added to the reaction mix to isolate the targeted DNA along with flanking regions [22] , [72] , [73] . The 30 microliter RSE reaction mix consisted of a pre-mixed set of targeting oligos ( 41 primers for the 319 . 8 kb region , 77 primers for the 613 . 9 kb region; oligo design and sequences are below ) combined with 600 ng of genomic DNA . The genomic DNA was denatured and an automated capture performed , followed by washing and elution in pre-loaded reagent cartridges . After RSE , the enriched DNA from each sample was removed from the microparticles by heating the solution at 80°C for 15 minutes to disrupt the biotin-streptavidin complex [22] . The microparticles were magnetically collected and the eluate , which contained the enriched material , was retained . The samples were then tested by quantitative PCR ( QuantiTect ) at three and six loci in the 319 . 8 kb and 613 . 9 kb target regions , respectively , and at the beta-actin 2 locus on chromosome 3 as off-target . These quantitative assays showed enrichment of the target region with low amounts of off-target material in each sample . Specifically , for each 25 microliter reaction , 8 microliters of sample was combined with 1X Qiagen Quantitect Probe PCR master mix ( Cat # 204345 ) , 0 . 4 micromoles each of forward and reverse primers ( Integrated DNA Technologies , IDT , Iowa ) and 0 . 2 micromoles probe ( IDT ) . Six 1:3 serially diluted zebrafish genomic DNA standards were run in duplicate for each locus as well as a single negative control . Forty cycles of 95°C for 15 seconds , 60°C for 1 min were run after the initial denaturation at 95°C for 15 minutes . Fluorescence was collected at 60°C . The primer sequences are: A custom software program was used to identify the oligos for the enrichment . It utilizes and integrates several free , open source or publicly available software solutions to automatically generate appropriate oligo sets at user-defined intervals , spanning genomic regions of interest . In this experiment , 41 capture oligos were used to target the 319 . 8 kb region ( Table 1 ) and 77 oligos for the 613 . 9 kb region ( Table 2 ) . The oligos were designed to target unique zebrafish sequence spanning the region of interest at approximately 8 kb intervals . Through the functionalities embedded in the software program , the UCSC web browser ( http://genome . ucsc . edu ) was first accessed to retrieve repeat- and SNP-masked DNA sequences for the target region in order to exclude these positions as capture points . The program is designed in such a way that the user enters a large genomic region ( currently up to 1 Mb ) , which is then parsed into smaller regions in which the oligos are designed . After generating the oligos , the user is able to redesign small regions in the event that the oligo selection was poor or missing for that smaller region . The preliminary oligo set was then checked via Blat against a local Blat server to ensure that the selected sequences only match to one region . Currently the local Blat server supports human , mouse and zebrafish genomes; more genomes can be added . In a final step , the software program was used to test for cross-reactivity to ensure minimal homo- and hetero-dimerization and hairpin formation . This step ensures that all selected oligos will work well together in a multiplexed format . The GC content for the final oligo set was between 48–52% and the Tm range was 57–61°C . Enrichment values were calculated using the following formula: ( Reads that map to the region examined/total number of reads ) / ( size of interval examined/genome size ) . Enrichment was calculated based on an estimated genome size of 1 . 6 Gb . For the first sequence capture , enrichment of mgn mutant DNA = ( 280884/5861492 ) / ( 315606/1 . 6×109 ) = 243 . For the second sequence capture , enrichment of wild-type DNA = ( 430216/15480724 ) / ( 616031/1 . 6×109 ) = 72 . For the second capture , enrichment of mgn mutant DNA = ( 259817/15639239 ) / ( 616031/1 . 6×109 ) = 43 . Ovaries were dissected from euthanized females and treated with 3 mg/ml collagenase for 10 minutes in Medium 199 ( Invitrogen ) to improve post-fixation morphology ( the majority of follicle cells were still present ) . For acetylated tubulin staining , ovaries were then fixed in 4% paraformaldehyde for 2 hours at room temperature , washed , dehydrated in MeOH and then stained essentially as described [74] . Anti-acetylated tubulin antibody ( Sigma ) was used at a dilution of 1:350 . For rhodamine phalloidin staining , dissected ovaries were fixed overnight at 4°C in Actin Stabilizing Buffer and stained as described [75] . Dissected ovaries were fixed for DiOC6 staining in 4% paraformaldehyde overnight at 4°C . Following washes in PBS , ovaries were stained in 5 micrograms/ml DiOC6 ( Calbiochem ) for 2 hours at room temperature or overnight at 4°C . Confocal microscopy was performed using a Zeiss LSM 510 Laser Scanning Inverted Microscope . Images of acetylated microtubule staining were median filtered using ImageJ to decrease background . Oocyte and Balbiani body sizes were calculated using measurements made with ImageJ software . Because fixed oocytes are not uniformly round , Balbiani body and oocyte diameters were determined by measuring the areas of each from single confocal sections and then calculating the diameters of circles based on the areas . For each oocyte , the optical section with the largest oocyte area was used to measure oocyte size , and the section with the largest Balbiani body area was used to measure Balbiani body size . For calculations of nucleus position , the centroids of the oocyte and the nucleus were determined from single confocal sections using ImageJ . For these studies , Excel was used to calculate statistical significance , which was determined by performing a two-sample unequal variance t-test where alpha = 0 . 05 . Ovaries from mgn mutant or heterozygous siblings were dissected and fixed in 2 . 5% glutaraldehyde+ 2% paraformaldehyde in 0 . 1 M Sodium Cacodylate overnight at 4°C . Fixed samples were rinsed in 0 . 1 M Sodium Cacodylate buffer , post-fixed with 2% osmium tetroxide , dehydrated in graded ethanol , and embedded in Epon . 70 nm thin sections were stained with uranyl acetate and bismuth sub-nitrite [76] , [77] . Stained sections were examined with a JEOL JEM 1010 electron microscope and imaged with a Hamamatsu CCD camera and AMT 12-HR software . Reagents and supplies were purchased from Electron Microscopy Sciences , Fort Washington , PA . | How the axes of the embryo are established is an important question in developmental biology . In many organisms , the axes of the embryo are established during oogenesis through the generation of a polarized egg . Very little is known regarding the mechanisms of polarity establishment and maintenance in vertebrate oocytes and eggs . We have identified a zebrafish mutant called magellan , which displays a defect in egg polarity . The gene disrupted in the magellan mutant encodes the cytoskeletal linker protein microtubule actin crosslinking factor 1 ( macf1 ) . In vertebrates , it can take years to identify the molecular nature of a mutation . We used a new technique to identify the magellan mutation , which allowed us to rapidly isolate genomic DNA linked to the mutation and sequence it . Our results describe an important new function for macf1 in polarizing the oocyte and egg and demonstrate the feasibility of this new technique for the efficient identification of mutations . |
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Nephron progenitor number determines nephron endowment; a reduced nephron count is linked to the onset of kidney disease . Several transcriptional regulators including Six2 , Wt1 , Osr1 , Sall1 , Eya1 , Pax2 , and Hox11 paralogues are required for specification and/or maintenance of nephron progenitors . However , little is known about the regulatory intersection of these players . Here , we have mapped nephron progenitor-specific transcriptional networks of Six2 , Hoxd11 , Osr1 , and Wt1 . We identified 373 multi-factor associated ‘regulatory hotspots’ around genes closely associated with progenitor programs . To examine their functional significance , we deleted ‘hotspot’ enhancer elements for Six2 and Wnt4 . Removal of the distal enhancer for Six2 leads to a ~40% reduction in Six2 expression . When combined with a Six2 null allele , progeny display a premature depletion of nephron progenitors . Loss of the Wnt4 enhancer led to a significant reduction of Wnt4 expression in renal vesicles and a mildly hypoplastic kidney , a phenotype also enhanced in combination with a Wnt4 null mutation . To explore the regulatory landscape that supports proper target gene expression , we performed CTCF ChIP-seq to identify insulator-boundary regions . One such putative boundary lies between the Six2 and Six3 loci . Evidence for the functional significance of this boundary was obtained by deep sequencing of the radiation-induced Brachyrrhine ( Br ) mutant allele . We identified an inversion of the Six2/Six3 locus around the CTCF-bound boundary , removing Six2 from its distal enhancer regulation , but placed next to Six3 enhancer elements which support ectopic Six2 expression in the lens where Six3 is normally expressed . Six3 is now predicted to fall under control of the Six2 distal enhancer . Consistent with this view , we observed ectopic Six3 in nephron progenitors . 4C-seq supports the model for Six2 distal enhancer interactions in wild-type and Br/+ mouse kidneys . Together , these data expand our view of the regulatory genome and regulatory landscape underpinning mammalian nephrogenesis .
The mammalian metanephric kidney maintains fluid homeostasis . The number of individuals afflicted with kidney disease is on the rise , and reduced nephron number has been associated with disease outcome [1] . In the mouse , genetic studies have demonstrated that nephrons are generated from a Six2+ progenitor pool in a regulatory process requiring the transcriptional action of Six2 for progenitor maintenance [2] . Human SIX2 shows an expression and activity similar to its murine counterpart suggesting that mouse Six2 and human SIX2 likely have similar functions [3] . Consistent with this view , human mutations in SIX2 are associated with renal hypodysplasia and the malignant transformation of progenitor cells in Wilms’ tumor , a pediatric nephroblastoma [4–6] . There is an increasing interest in the relationship between nephron progenitors , their output , and congenital and acquired kidney disease [1 , 7] . Further , new approaches to modulate nephron progenitor outputs to generate kidney structures in vitro call for a better understanding of regulatory processes at play in vivo [8–10] . Nephron progenitor specification and nephron progenitor maintenance are dependent on a number of additional transcriptional regulatory factors including Hoxa/c/d11 , Osr1 , Wt1 , Sall1 , Eya1 , Pax2 , and Six1 . Previous studies of mouse mutants in these genes suggest complex hierarchical interactions amongst these factors [11–27] . Identification of their genomic targets and target regulatory mechanisms are essential to determine the nephrogenic regulatory network . Direct nephron progenitor ChIP-seq studies have identified a broad range of potential transcriptional targets of Six2/SIX2 action in the mouse and human kidney , respectively , and verified predicted enhancer modules for several of these targets [3 , 28 , 29] . Six2 interacts at cis-regulatory modules of genes expressed both in the nephron progenitors and their committed nephron-forming descendants through enhancers co-engaged by differentiation-inducing transcriptional complexes formed in response to canonical Wnt signaling [28 , 29] . Interestingly , a potential role for Hox11 paralogs within Six2-predicted cis-regulatory modules is suggested by the strong enrichment of AT-rich homeobox motifs in Six2 ChIP-seq peaks [28] . The genomic targets of Wt1 have also been analyzed by ChIP experiments of embryonic mouse kidneys [30–32] . Though the approach was not specific to nephron progenitors , these studies revealed the interplay with many genes expressed in , and critical for , nephron progenitors , including Fgf and Bmp family members [30–32] . Sall1 ChIP-seq has also shed light on its active roles in nephron progenitors and repressive actions on development of nascent nephrons , respectively [29] . Interestingly , a subset of Six2- and Sall1-bound regions overlap suggesting these factors co-associate and target analysis predicts genes regulating the nephron progenitor population [29] . With a working model that multi-factor binding will highlight key regulatory nodes of the nephron progenitor pathway [33–37] , we utilized ChIP-seq analysis to identify a subset of putative regulatory elements associated with multiple transcription factors . gRNA/Cas9-mediated ablation of ‘regulatory hotspots’ adjacent to Six2 and Wnt4 highlight the significance of these enhancer elements in regulating target gene expression . Additional analyses of the regulatory landscape surrounding Six2 identified insulator-bound elements which constrain enhancer function . In support of this finding , deep sequencing of the Br mutant mouse identified an inversion of Six2 and Six3 loci altering enhancer specificity . These studies highlight the critical role of multi-factor input and proper enhancer context for directing appropriate target gene expression .
To extend our understanding of the transcriptional regulatory networks operating within mouse nephron progenitors , we developed a transgenic approach to overcome the limited availability and inconsistency of working antibodies for key transcriptional components , and complications that arise from the diverse expression of regulatory factors elsewhere in the kidney . In this transgenic strategy , an epitope-tagged transcription factor-of-interest is expressed exclusively within the nephron progenitor compartment using a Six2 distal enhancer ( DE ) previously shown to recapitulate Six2-like , nephron progenitor restricted expression ( Fig 1A [28] ) . This approach obviates the need to enrich for the progenitor population in whole kidney samples simplifying ChIP procedures and avoiding potential artifacts introduced by tissue dissociation and fluorescence-activated cell sorting ( FACS ) . We also took advantage of established tagging methods which have been utilized to successfully isolate protein:DNA complexes[38–41] . Each transcription factor-of-interest is appended with a BioTag-FLAG ( BF ) epitope at the C-terminus of the target protein . Co-production of an EGFP-BirA enzyme on the transgene through an IRES element also allows both ready visualization of transgenic kidneys and biotinylation of the biotin-recognition motif ( BioTag ) enabling an additional mode of isolation of factor-associated DNA or protein complexes through streptavidin affinity purification ( Fig 1A ) . Though the biotin tagging strategy proved successful ( S1C Fig ) and provided a secondary purification option , we did not utilize it for any ChIP experiments as anti-FLAG antibodies were sufficient for all of the studies presented here . To rigorously assess the efficacy of this strategy and to develop a protocol for whole kidney ChIP , we first generated Six2-BFtg mice to determine whether Six2 ChIP-seq generated with the transgenic line ( Six2-BF ) replicates Six2 ChIP-seq using a Six2-specific antibody ( Six2-ab ) [3] . Six2-BF was restricted to the Six2+ nephron progenitors as indicated by specific detection of the anti-FLAG epitope ( Fig 1C ) . FLAG ChIP-seq from Six2-BF+ kidneys identified 6808 Six2-associated regions in the Six2-BF data , with 90% of these peaks overlapping with Six2-ab peaks ( Fig 1B ) . The two datasets were relatively correlated ( R2 = 0 . 69 ) and , as expected , overlapping peaks were ranked higher than Six2-BF unique peaks indicating the variability in the data reflects marginal peak calls ( S1A Fig ) . The most enriched motif discovered from the top 1000 peaks in the Six2-BF ChIP-seq dataset ( ‘TCANGTTTCA’ , 47% , p-value = 10−1190 , Fig 1D ) matched and agreed with the identified Six2 motif from our previous ChIP studies ( S1B Fig; [3 , 28 , 29] ) . The motif was relatively centered within the peaks suggesting direct binding of Six2 to the motif ( Fig 1D ) . Additionally , electrophoretic mobility shift assays ( EMSA ) utilizing recombinant Six2 and a Six2 motif identified within the Six2-DE showed a strong interaction of Six2 protein with its DNA target ( S2A Fig ) . Mutational analysis on this Six2-binding site demonstrated that the most conserved bases in the consensus ( 1T , 6T and 9C ) were critical individually for effective protein-DNA interaction ( S2A Fig ) . Wt1 and bHLH recognition motifs were also significantly enriched in Six2 binding regions ( S1F Fig ) consistent with an expected role for Wt1 within the progenitor compartment [31 , 42] , and an unidentified role for a bHLH factor . To interrogate the regulatory functions of Six2-BF , we performed GREAT Gene Ontology ( GO ) analysis [43] on Six2-BF peaks . Six2-BF peaks were highly enriched near genes associated with kidney development as reflected by the top GO term ‘ureteric bud development’ ( Fig 1E ) . In summary , the FLAG transgenic strategy robustly reproduced Six2-ab ChIP-seq data generated from wild-type kidneys identifying expected Six2 target and gene associations . These whole kidney-derived datasets significantly extend the depth of Six2 ChIP-seq peaks identified from earlier reports ( [28]: 3907 peaks , [29]: 4306 peaks ) . While our transgenic strategy is useful for targets for which there are no working antibodies or when a progenitor-specific ChIP is desired , expression levels of the tagged protein or affinities of the FLAG antibody versus protein-specific antibodies ( if one exists ) may affect the number of relevant peaks discovered . Interestingly , although peaks identified uniquely with the Six2-ab showed lower levels of enrichment , these peaks still enriched for the Six2 motif at a similar level ( 46% ) and were linked to kidney development GO terms suggesting a biological relevance to the interactions ( S1A Fig ) . As nearly all Six2-BF peaks are contained within the larger Six2-ab dataset ( ~90% , Fig 1B ) , for a more complete analysis of Six2 bound target regions we used the latter dataset for subsequent analyses . Having validated the transgenic strategy for generation of nephron progenitor specific ChIP-seq data , we established additional transgenic mouse lines to identify regulatory interactions mediated by other transcriptional regulators in nephron progenitors . Viable and phenotypically normal founders were generated for Hoxd11 ( Hoxd11-BFtg ) and Osr1 ( Osr1-BFtg ) . Immunostaining with anti-FLAG antibodies confirmed the restriction of Hoxd11-BF and Osr1-BF to Six2+ nephron progenitors and validated the use of both transgenic lines for ChIP-seq analyses ( Fig 1C ) . We also attempted to generate transgenic lines for Wt1 , Hoxa11 , Pax2 , Sall1 , and Eya1 but were unsuccessful in producing any founder animals . Further , we were not able to obtain transgenic progeny which survived past birth from the original Hoxd11-BF founder . These observations suggest transgene and/or transgenic line dependent lethality ( see Discussion ) . To map Hoxd11- and Osr1-associated genomic regions within nephron progenitors , we performed FLAG ChIP-seq on E16 . 5 Hoxd11-BFtg/+ and Osr1-BFtg/+ kidneys identifying 7776 Hoxd11-BF and 5032 Osr1-BF associated regions ( Fig 1D ) . Osr1-BF protein levels were markedly lower and this may account for the lower number of target sites identified ( Fig 1C ) . Both Hoxd11-BF and Osr1-BF peaks showed typical enhancer features: similar to the Six2-BF dataset the majority of the peaks were located >5kb from the transcription start site ( TSS ) within intronic ( Six2-BF:46 . 6% , Hoxd11-BF: 46 . 2% , Osr1-BF: 44 . 7% ) or intergenic regions ( Six2-BF: 46 . 5% , Hoxd11-BF: 48 . 7% , Osr1-BF: 43 . 6% ) ( S1D and S1E Fig ) . GREAT analysis identified an enrichment for both factors near genes associated with processes related to metanephric kidney development ( Fig 1E ) . Using the same workflow adopted above for analysis of Six2 interactions , we identified the top DNA motif enriched in Hoxd11-BF ( ‘TTTATGG’ , 38% , p-value = 10−1033 , Fig 1D ) and Osr1-BF datasets ( ‘GCTNCTG’ , 45% , p-value = 10−1438 , Fig 1D ) . Both motifs were well-centered within each peak dataset ( Fig 1D ) . Multiple Hox factors are expressed in nephron progenitors and each may exhibit distinct binding preferences . While the predicted Hoxd11 motif has a prominent AT-rich Hox factor consensus feature , the motif differs from that identified through protein-DNA binding microarray ( PBM ) studies in vitro ( ‘TTTACGA’ , [44] , S2B Fig ) . EMSA analysis confirmed Hoxd11 binding and the relative importance of the bases 2T and 4A which are conserved in both the PBM and ChIP-seq based predictions , while the 1T and the 5T/C positions , which differed between the two predicted motifs , were not important for binding in vitro ( S2B Fig ) . The Osr1 motif identified from our ChIP-seq data closely resembled that predicted from PBM studies ( ‘GCTACTG’ , [44] ) though no strong preference for the 4th nucleotide position was seen in the in vivo motif . EMSA demonstrated Osr1 bound to the predicted Osr1 binding site within the Six2-DE ( GCTGCTG ) . Interestingly , substituting an A in the 4G position to more closely reflect the PBM motif enhanced the Osr1 interaction ( S2C Fig ) . These findings suggest that in vivo regulatory processes may prefer weaker binding , potentially adding greater flexibility to transcriptional interactions . Wt1 and bHLH motifs were also enriched in each peak dataset , as was observed for Six2-BF peaks ( S1F Fig ) . A Wt1-like binding motif was predicted within all three datasets suggesting Wt1 co-regulation within Six2 , Hoxd11 and Osr1 transcriptional networks . Other groups have published Wt1 ChIP from the whole embryonic kidney or glomerulus [30–32] but no nephron progenitor-specific Wt1 data has been generated . We attempted to generate a viable Wt1-BF transgenic line but failed , so we adopted a recently developed protocol for enriching nephron progenitors by magnetic-activated cell sorting ( MACS ) [45] , and performed ChIP-seq with a Wt1-specific antibody on E16 . 5 nephron progenitors ( Wt1-NP , S3A Fig ) . Compared to a Wt1 ChIP from the whole kidney ( Wt1-kidney ) which we generated from the same stage ( S3A Fig ) , the recovered motif from the Wt1-NP dataset , ‘CCTCCCCCNC’ , closely matches the motif identified in our own whole kidney dataset , and published non-nephron progenitor-restricted Wt1 kidney ChIP data ( S3B Fig , [30–32] ) . The motif also matched the predicted Wt1 motif that was highly enriched in the earlier Six2 , Osr1 , and Hoxd11 datasets ( S1F Fig ) . The motif was centered in the ChIP peak dataset supporting direct DNA binding ( S3B Fig ) . The nephron progenitor-specific Wt1 ChIP shared >50% of peaks with our whole kidney dataset . Shared target genes with roles in kidney development were focused on genes involved nephron progenitor maintenance and differentiation , while those unique to the whole kidney also targeted genes associated with podocytes ( S3F Fig ) . This suggests that our Wt1-NP ChIP is representative of regulatory functions for Wt1 within nephron progenitors . The majority of peaks showed an intergenic ( 35% ) and intronic ( 33% ) distribution ( S3D Fig ) . However , Wt1 showed significant enrichment near promoters within 5kb of the TSS ( 25% , S3C and S3D Fig ) , significantly more promoter enrichment than observed with the other factors ( between 3 . 1 and 5 . 3% , S1E Fig ) , potentially reflecting Wt1’s binding preference to a cytosine-rich motif and GC enrichment at promoters . This result is in contrast to the Wt1 ChIP-seq performed by Motamedi et al . , who found peaks to be enriched more distally [31] . However , if we performed GREAT analysis with the ‘basal plus extension’ parameter which includes larger regulatory domains compared to the more restricted ‘single nearest gene’ parameter which was utilized in all of our analyses , we observe a greater enrichment for Wt1-NP peaks 50-500kb from the TSS ( S3C Fig ) . Importantly , the GREAT parameters recovered ‘ureteric bud development’ and ‘metanephric nephron morphogenesis’ terms which are consistent with Wt1 kidney functions ( S3E Fig ) . To investigate potential co-operative actions of Six2 , Hoxd11 , Osr1 , and Wt1 in nephron progenitors , we analyzed all pairwise overlaps of transcription factor binding sites , and evaluated the statistical significance of such two-factor overlap . Not all genome fractions are accessible to transcription factor binding , and binding of many transcription factors correlates with open chromatin [46] . For simplicity , our statistical analysis is built on the assumption that only open chromatin , identified by utilizing the Assay for Transposase Accessible Chromatin with high-throughput sequencing ( ATAC-seq ) within nephron progenitors ( see Methods for details of the approach and access to data ) , is accessible to any of the DNA binding factors analyzed in the current study . We found that the greatest significance of co-binding is observed between Six2 and Hoxd11 ( -log10p = 320 at all Six2 sites where Hoxd11 is bound and -log10p = 361 at all Hoxd11 sites where Six2 is bound ) , and Six2 and Wt1 ( -log10p = 106 at all Six2 sites where Wt1 is bound and -log10p = 123 at all Wt1 sites where Six2 is bound ) interacting regions . The weakest co-association is between Wt1 and Hoxd11 ( -log10p = 6 for each pairwise association ) , although still significant ( Fig 2A ) . Potential target genes for each factor ( based on GREAT analysis , [43] ) were also subjected to pairwise comparisons . Hoxd11 , Osr1 , and Wt1 share the majority of their target genes with Six2 ( ratio greater than 0 . 60 or 60% , Fig 2A and 2B ) . Hoxd11 shows the greatest overlap with Six2 ( 0 . 80 or 80% ) , although all pairwise overlaps showed that nearly half of the comparators target genes are shared with any one factor . These results suggest that these factors likely cooperate in regulatory actions within nephron progenitors . Next , we overlapped all four datasets to identify sites where all factors converge in the potential regulation of target genes . We recovered 373 putative cis-regulatory modules where Six2 , Hoxd11 , Osr1 , and Wt1 associated within 1kb of each other ( Fig 2B , S1 Table ) . Regions co-bound by all four factors displayed the strongest Six2 binding . In addition , Six2 peaks bound by any three-factor combination were on average stronger than two-factor combinations , while Six2 peaks bound by any factor in combination with Six2 were stronger than Six2-only peaks ( S1H Fig ) . We refer to regions co-bound by all four factors as ‘regulatory hotspots’ hypothesizing that these may play a key role in nephron progenitor programming . Consistent with this view , regulatory hotspots were enriched around genes annotated to developmental processes such as ‘ureteric bud development’ ( Fig 2C ) . Further , two regulatory hotspots are known from published studies to drive transgenic reporters with expression profiles reflecting the putative target genes: a region ~60kb upstream of Six2 which corresponds to the Six2-DE used in our transgenic strategy and the Wnt4-DE 50kb upstream of Wnt4 ( Fig 2D , [28] ) . Sall1 is also bound at the Six2 distal enhancer but not at the Wnt4 enhancer site[29] . Six2 is largely restricted to the nephron progenitors while Wnt4 expression is absent from nephron progenitors but activated on progenitor induction in the formation of differentiating renal vesicles [2 , 47] . Thus , engagement of the four factors can occur on target genes for nephron progenitors or genes activated shortly after the onset of nephrogenesis . Other putative targets of regulatory hotspots include Fgf9 which is expressed by nephron progenitors and is involved in regulating their maintenance [48] , and Pax8 which regulates nephron progenitor differentiation [49] ( S1 Table ) . Tsc22d1 and Mgat5 also represent putative targets and knockouts of these genes are reported to generate kidney phenotypes [50 , 51] ( S1 Table ) . Regulatory information may also converge on a common target through alternative enhancer usage . To examine this possibility , we intersected the predicted target gene sets for each factor and identified 1744 genes sharing Six2 , Hoxd11 , Osr1 , and Wt1 associated peaks ( Fig 2B , S2 Table ) . The set of genes identified as having all four factors co-associated at one putative cis-regulatory module or dispersed through multiple interactions sites are predicted to define a set of genes with a significant role in nephron progenitors or their derivatives; we termed this group ‘core targets’ ( Fig 2C , S2 Table ) . This set includes genes expressed in nephron progenitors and implicated in progenitor maintenance and self-renewal including Six2 , Pax2 , Sall1 , Sox4 , and Gas1 [2 , 19 , 52–54] . However , the ‘core targets’ also included genes normally activated downstream in the induced/developing nephron such as Wnt4 , Lhx1 , Pax8 , Hes1 , and Irx1/2 [47 , 49 , 55–57] . To determine whether interactions amongst these transcription factors exist in vivo , we performed immunoprecipitations with Six2 antibodies from E16 . 5 kidney nuclear lysates . Six2 was able to co-immunoprecipitate Hoxd11 and Wt1 ( Fig 2E ) ; however , the absence of a working Osr1 antibody precluded analysis of this factor although recent studies show Six2 and Osr1 complex in vitro [17] . Six2 is also purported to complex with Sall1 [29] though we could not replicate this interaction with available antibodies in our assay . Taken together , these data provide evidence for endogenous , multi-protein complexes among three of the four factors . We sought to identify whether Six2 , Hoxd11 , Osr1 , and Wt1 are each involved in activating or repressing gene expression in nephron progenitors . First , we generated RNA-seq expression profiles of E16 . 5 Six2TGCtg/+ kidney cortex preparations FAC-sorted for GFP+ ( Six2+ ) or GFP- ( Six2- ) cells . Six2+ cells would represent the nephron progenitor population ( both self-renewing and recently induced ) and Six2- cells would largely represent stromal cells as well as ureteric bud tip cells and endothelial cells . Genes with a TPM ( Transcripts Per Kilobase Million ) value >5 and a fold difference >3 between the two cell types were identified: 246 genes were enriched in the Six2+ fraction and 545 genes were enriched in the Six2- cortex fraction ( Fig 3A , S3 Table ) . We asked whether ChIP-seq peaks of any of the transcription factors or the regulatory hotspots are preferentially located adjacent to differentially expressed genes . The results show that peaks from all ChIP-seq datasets occur significantly more often around genes enriched in the Six2+ cells ( Fig 3C ) consistent with a specific role in regulating the nephron progenitor cell versus other cell types of the kidney cortex . However , regulatory hotspots near Foxd1 , a marker of self-renewing stromal progenitors [58] , and Wnt11 , a ureteric tip marker required for normal kidney development [59] ( S1 Table ) , raises the possibility that the four factors may also work together to repress these genes within nephron progenitors . Nephron progenitor cells can be divided into Cited1+/Six2+ self-renewing progenitors and Cited1-/Six2+ differentiating progenitor cells [60] . To address the relationship between regulatory hotspots and programs of progenitor maintenance or commitment , we performed RNA-seq analysis to identify progenitor-specific and early induction gene sets . For the former , a transcriptional profile was generated for E16 . 5 Cited1+; RFP+ cells from Cited1-nuc-TagRFP-Ttg/+ kidneys while Six2+; GFP+ cells from Six2TGCtg/+ P2 kidneys were used to generate the latter dataset ( S4 Table , [61] ) . As expected , Cited1 levels were appreciably lower in the P2 Six2+ cells ( 200 . 9 TPM in E16 . 5 Cited1+ cells vs . 3 . 8 TPM in P2 Six2+ cells ) while Wnt4 transcripts were markedly increased ( 9 . 0 TPM in E16 . 5 Cited1+ cells vs . 219 . 1 TPM in P2 Six2+ cells ) supporting our classification of these datasets ( S4 Table ) . As expected , a comparison of the genes with a TPM >5 and a fold difference >3 between the two cell types showed self-renewing nephron progenitor-specific genes such as Cited1 and Osr1 enriched in the E16 . 5 Cited1+ cell dataset whereas genes involved in progenitor differentiation such as Pax8 and Wnt4 were enriched in the P2 Six2+ cell dataset ( Fig 3B , S4 Table ) . Six2 and Hoxd11 displayed similar enrichment near genes up-regulated in either self-renewing nephron progenitors or in differentiating progenitors ( 1 . 4–1 . 5 fold; Fig 3C ) consistent with roles in promoting the progenitor state , and either preventing or priming nephron forming programs . Osr1 and Wt1 interactions were slightly enriched near genes associated with self-renewing nephron progenitors ( 1 . 4-fold vs 1 . 0-fold for Osr1 , 1 . 2-fold vs 1 . 0-fold for Wt1; Fig 3C ) . Interestingly , the regulatory hotspot associated gene lists showed a higher enrichment around genes upregulated in differentiating cells versus self-renewing progenitors ( 1 . 6-fold vs . 1 . 3-fold; Fig 3C ) . Next , for each single factor or combination of factors we compared the percent of target genes in distinct transcriptional categories: nephron progenitor enriched ( E16 . 5 Six2+ cells ) , self-renewing nephron progenitor enriched ( E16 . 5 Cited1+ cells ) , or differentiating nephron progenitor enriched ( P2 Six2+ cells ) relative to the whole transcriptome . Target genes unique to any single factor were not enriched in any of these categories ( ≤1 . 6% for each ) compared to the whole transcriptome ( ≤1 . 6% for each ) suggesting that single factor input has no particular relevance to nephron progenitor function . Similar observations hold when Hoxd11 co-targeting is examined with Osr1 and Wt1 , ( ≤1 . 8% ) . but not with a Six2 binary combination ( ≥2 . 5% ) suggesting that Hoxd11 has a strong preference for co-regulation of target genes with Six2 ( Fig 3D ) . Generally , the greatest enrichments are observed when all four factors are bound near the target gene in any category ( 2 . 4–7 . 2% ) consistent with co-regulatory input by multiple factors impacting target gene regulation to the greatest extent . In agreement with our earlier analyses , the four-factor overlap has a preference for genes expressed upon differentiation rather than in self-renewing progenitors ( 5 . 9% compared to 2 . 4%; Fig 3D ) . While we have described target genes with known functions in kidney development , we wanted to identify potentially novel candidate genes which are targets of co-regulation either by one cis-regulatory module or dispersed through multiple interactions sites . Target genes of interest include Shisa2 and Shisa3 which are enriched in self-renewing nephron progenitors ( S2 Table ) . Shisa2 is a modulator of Wnt and Fgf signaling , specifically attenuating such signals . The majority of mutant mice exhibit dwarfism and half die postnatally . Shisa3 is a related family member although no overt phenotype was observed for the null allele [62] . Pdgfc and Pdgfa are enriched in the self-renewing and differentiating progenitors , respectively ( S2 Table ) . Their conserved expression in these cell populations of developing mouse and human kidneys have been reported [63–65] . Pdgfa and Pdgfc double mutants have a reported deficiency in cortical renal mesenchyme , however , the mutant kidney phenotype was not analyzed in detail [66] . Ccnd1 ( cyclin D1 ) is a putative target that shows a nearly 7-fold increase in expression in P2 Six2+ cells versus E16 . 5 Cited1+ cells ( S2 Table ) . In situ hybridization confirms strong Ccnd1 in E15 . 5 pretubular aggregates and early differentiating nephrons ( www . gudmap . org , [67 , 68] ) . This suggests that the regulatory networks may directly modulate cell cycle dynamics and balance progenitor proliferation or alternatively may prime putative enhancers of Ccnd1 for rapid activation upon nephron progenitor induction . Sema5a and Epha4 are predicted targets with a similar ~6-7-fold increase in expression in differentiating progenitors confirmed by in situ studies ( S2 Table; www . gudmap . org , [67 , 68] ) suggesting factor regulation of targets genes controlling local cell interactions . To examine the functional significance of ‘regulatory hotspots” , we focused on Six2-DE ( chr17: 85747271–85749534; Fig 4A ) and Wnt-4 DE ( chr4:137216986–137217756; Fig 5A ) elements previously verified in transgenic reporter assays [28] . To examine the requirement for each enhancer , we used CRISPR/Cas9 gene editing technology to delete each enhancer in B6SJLF1/J mice . The Six2-DE deletion and Wnt4-DE deletion were confirmed in founder lines by PCR and Sanger sequencing of products ( Six2∆DE: chr17:85747284–85749542; Wnt4∆DE: chr4:137216991–137217771 ) . For the Six2-DE knockout , we examined kidneys at E16 . 5 and observed no obvious difference in the size of wildtype , Six2∆DE/+ and Six2∆DE/∆DE kidneys ( Fig 4B ) . Six2+ and Wt1+ nephron progenitors were present in Six2∆DE/∆DE kidneys though Six2 levels appear reduced relative to wild-type embryos ( Fig 4C and 4D ) . Nephron structures were formed as reflected by the presence of podocytes and proximal tubules , labeled by Wt1 and LTL ( Lotus tetragonolobus lectin ) , respectively ( Fig 4C ) . The Six2∆DE/∆DE mice were viable; no phenotype was observed . To more accurately assess the effect of the distal enhancer deletion on Six2 expression , we used qPCR to measure relative Six2 levels in nephron progenitors of E16 . 5 kidneys . A 40% reduction of Six2 mRNA was measured in Six2∆DE/∆DE nephron progenitors compared to wildtype ( Fig 4E , p-value = 0 . 006 ) ; higher levels than in mice heterozygous for a Six2 null allele ( Six2CE/+ , [69] ) where Six2 transcripts were reduced approximately 50% relative to wild-type as expected ( Fig 4E ) . The levels of Pax2 mRNA , which is not dependent on Six2 [2] , were relatively similar across all genotypes showing a Six2-specificity for the Six2-DE deletion . Strikingly , when Six2 levels were further reduced by combining a Six2∆DE allele with a Six2 null allele ( either Six2CE/+ or Six2GCE/+ [69] ) , the resultant Six2∆DE/CE embryos exhibited severely hypoplastic kidneys at E16 . 5 , with a complete absence of Six2+ nephron progenitors , mirroring the phenotype of complete removal of Six2 activity ( Fig 4B and 4D , [2] ) where only a few glomeruli ( Wt1+ ) and tubules ( LTL+ ) have formed by E18 . 5 ( S4 Fig ) . As early as E11 . 5 , at the outset of active kidney morphogenesis , Six2∆DE/GCE kidneys were devoid of Six2+ nephron progenitor cells but filled with Pax8+ differentiating nephron progenitors as in Six2 protein null mutant kidneys ( Fig 4G , [2] ) . Taken together these results demonstrate that Six2-DE accounted for approximately 40% of Six2 expression and by combining one Six2-DE allele with a Six2 null allele , the remaining Six2 mRNA levels ( predicted to be 30% of wild-type levels ) were insufficient for Six2-mediated maintenance of the nephron progenitor state . Next , we investigated a ‘regulatory hotspot’ predicted to function in progenitor differentiation . Deletion of the Wnt4 distal enhancer resulted in mutant kidneys that are ~25% smaller than those from wildtype animals ( p = 0 . 2e-4; Fig 5B and 5C ) . Nephrons developed in Wnt4∆DE/∆DE kidneys as reflected by presence of both LTL+ proximal tubules and Wt1+ podocytes ( Fig 5D ) and Wnt4∆DE/∆DE mice are viable . Interestingly , in situ hybridization revealed that expression of Wnt4 is significantly reduced in renal vesicles but remained largely unchanged in the renal medulla of Wnt4∆DE/∆DE kidneys consistent with an overall reduction of Wnt4 mRNA levels in Wnt4∆DE/∆DE kidneys measured by qPCR ( Fig 5F ) . Thus , the Wnt4-DE plays a functional role in regulating Wnt4 mRNA levels in forming nephrons ( Fig 5E ) . The Wnt4∆DE/∆DE phenotype was less severe than Wnt4 protein null mutants where the severely hypoplastic kidney lacks nephron tubules and glomeruli [47]; indeed , low levels of Wnt4 RNA were detected in Wnt4∆DE/∆DE kidneys ( Fig 5D; arrows in Fig 5E ) . When the Wnt4∆DE allele was combined with a Wnt4GCE protein null allele [69] , Wnt4∆DE/GCE kidney size and nephron structures were further reduced , though kidneys were still larger than Wnt4 null kidneys ( Fig 5B–5D ) and Wnt4 mRNA levels were markedly reduced in whole kidney PCR ( Fig 5E and 5F ) . Taken together these results indicate a dose-dependent reduction in kidney size through reduced nephrogenesis upon decreasing Wnt4 activity . Further , residual levels of Wnt4 activity in Wnt4∆DE/GCE kidneys were sufficient to drive low levels of nephrogenesis . Clearly , the Wnt4-DE plays a role in maintaining appropriate levels of Wnt4 transcripts in the nephrogenic program to ensure a normal program of kidney development . Six2 lies ~60 kb from a related family member Six3 , although significant Six3 expression is not observed in the self-renewing nephron progenitors ( S4 Table ) indicating a specificity in Six2-DE interactions . Topologically associating domain ( TAD ) boundaries have been described as CTCF-enriched sites which serve as insulators and prevent promiscuous enhancer activity [70] . We performed CTCF ChIP-seq using E16 . 5 purified nephron progenitors to identify CTCF-bound regions of the genome ( Fig 6B and S3G and S3H Fig ) . We identified strongly bound CTCF sites between the Six2 and Six3 locus , most of which are consistent with ENCODE data analyzing whole P0 kidney samples ( S5B Fig , ENCODE experiment ENCSR143WOK , submitted by Richard Myers , HAIB , [71] ) and predictions from Hi-C on mouse ES cells ( TAD: chr17:85640660–85680660 , S5A Fig , [70] ) . We hypothesize that this region serves as a TAD boundary to prevent the Six2-DE from engaging the Six3 promoter . Consistent with this view , there is a marked bias in the engagement of regulatory factors in nephron progenitors to the Six2 side of this putative boundary , 5’ to the Six2 transcriptional start site ( Fig 6B ) . With these insights into regulation of Six2 , our attention was drawn to the Brachyrrhine ( Br ) mouse , an X-irradiation induced mutant that displays kidney hypoplasia and frontonasal dysplasia , and maps to the Six2 region of chromosome 17 [72] . Though Br mutants have significantly reduced Six2 expression in the kidney and craniofacial tissues , no mutation has been found in the Six2 transcription unit or within 1 . 8 kb upstream of the start codon which includes the Six2-PE elements [72] . Interestingly , Six2 is ectopically expressed in the developing lens of Br heterozygous and homozygous animals , a normal site of Six3 expression [72] . Given that irradiation induces large-scale genomic rearrangements , we speculated that the Br mutation led to a chromosomal rearrangement that removed Six2 from enhancer ( s ) directing normal regulatory input to the nephron progenitor population , placing Six2 under the control of Six3 regulatory elements normally inaccessible the other side of a CTCF-dependent boundary element . Next generation sequencing and sequence alignment identified the underlying sequence change in the Br mutant ( S1 Supplemental Material and Methods ) . The main feature was a large inversion of 324 , 596bp including both the Six2 and Six3 loci . The inversion moves Six3 ~206kb from the Six2-DE , actually further than in the wild-type organization , but importantly the inversion removes the intervening TAD boundary ( Fig 6B ) . In contrast , Six2 is repositioned on the other side of this boundary element within Six3’s unchartered regulatory territory ( Fig 6B ) . In addition to the inversion , two small deletions were detected: a 4 , 630bp deletion ( chr17:85414584–85419213 ) 5’ to the Six3 TSS in the intron of Camkmt , and a 5bp deletion between the Six2-PE and Six2-DE ( chr17:85743809–85743813 , Fig 6A and 6B ) . The results from the sequencing and computational analysis were confirmed by allele-specific diagnostic PCR assays ( S5C and S5D Fig ) . The inversion also separates the last 4 exons of Camkmt from the rest of the transcription unit . However , Camkmt has a TPM of only 2 . 26 in the E16 . 5 Cited1+ nephron progenitor cells and homozygous mutant mice are viable ( International Mouse Phenotyping Consortium , http://www . mousephenotype . org/ , Release 5 . 0 [73] ) , so Camkmt is unlikely to contribute to the kidney phenotype . The rearrangement predicts: i ) ectopic Six2 expression in Six3’s normal expression domain , the lens , as Six3 enhancers can now target Six2 , and ii ) an abnormal interaction between the Six2-DE and Six3 promoter resulting in ectopic Six3 expression in nephron progenitors . To directly examine interaction of Six2-DE with Six2 and Six3 promoters , we performed 4C-seq [74] using Six2-DE as the view point . As expected , in wildtype kidneys Six2-DE interacts with a broad region that includes Six2 transcription start site ( TSS ) ( Fig 6B ) , with the local maxima 7 . 2 kb upstream of Six2 TSS . Noticeably , Six2-DE interaction was restricted by the TAD boundary between Six2 and Six3 ( Fig 6B and S5B Fig ) and no interaction was observed around the Six3 TSS . In kidneys from Br/+ embryos , strong Six2-DE contacts were now observed in the segment of the inverted region that was repositioned between the Six2-DE and CTCF-bound TAD boundary element ( Fig 6B and S5B Fig ) . As expected , with the loss of one wildtype allele in Br/+ embryos , Six2-DE interactions with Six2 TSS , and in general with the region on Six2 side of the TAD boundary , were significantly reduced . A relatively strong , de novo interaction of the Six2-DE was observed ~15 kb upstream of Six3 TSS ( Fig 6B and S5B Fig ) consistent with the model of Six3 expression driven , at least in part , by the Six2-DE in the Br allele . Importantly , the predicted Six2-DE/Six3 upstream contact in the Br allele occurs over a distance of 200 kb from the Six2-DE to the Six3 TSS , a longer interval than the ~130 kb that separates these non-interacting elements in the wild-type allele ( S5B Fig ) . Therefore , the differential interaction of Six2-DE with Six2 TSS and Six3 TSS between wildtype and Br/+ cannot be attributed to shortened distance from Six2-DE to Six3 TSS . Rather , this data supports specific regulation by Six2-DE to Six2 and Six3 that is defined by the TAD boundary . As a result of the altered chromatin architecture introduce by the genomic inversion , ectopic Six2 expression has been reported in the lens of Br/Br mutants [72] , and Six3 expression was reduced in this structure ( S5E Fig ) . Quantitative PCR detected Six3 expression in the kidneys of Br/+ mice at E13 . 5 ( Fig 6C ) and Six3 protein was detected in Six2+ nephron progenitors ( Fig 6D ) . Br/Br mutants resemble Six2 null mutants and have no nephron progenitors at this stage [2] ( Fig 6C and 6D ) . When Br/Br mutants were examined at E11 . 5 , they showed a similar loss and premature differentiation of nephron progenitors as in Six2 null mutants but interestingly low-levels of Six3 were detected in differentiating progenitors ( Fig 6E ) . Ectopic Six3 was also observed in the cranial base of Br mutants at E14 . 5 concomitant with decreased Six2 levels ( S5F Fig ) . Taken together these data lend additional weight to the importance of the Six2-DE in directing Six2 expression and reveal higher order principles of topological organization acting in conjunction with this enhancer to provide target gene specificity to the regulatory landscape .
Our ChIP studies reveal a complex regulatory architecture of the nephron progenitors . Examining co-binding of the four factors suggests each of these genes is itself a target of their combined actions through auto and cross-regulatory inputs , as are a number of other transcriptional regulatory components important for kidney development and nephron progenitor maintenance such as Sall1 and Pax2 ( S2 Table ) . By combining our data with insight from previous studies , a hierarchical network starts to emerge . For example , mutational analyses have demonstrated a requirement for the Hox11 paralogues to activate Six2 expression in metanephric mesenchyme [12] . Hox11 members complex with Pax2 and Eya1 binding to an enhancer that lies within ~1kb of the Six2 TSS , in the Six2-PE [75] . Hox11 acts as an activator of Six2 activity and mutations in Hox motifs results in loss of reporter activity in transgenic assays [76] . We have also shown that the Hox motif within the Six2-DE is required for reporter activity [28] . Consistent with this data , Hoxd11 is bound at the Six2-DE ( Fig 2D ) . However , we did not observe a significant Hoxd11 association to the Six2-PE as reported [76] . This discrepancy may result from preferential enhancer usage at different developmental stages . Two previous studies assayed reporter activity of the ~1kb Six2-PE at E11 . 5 [75 , 76] while our studies assayed Six2-DE activity at E15 . 5 [28] . Hox11 may be required at the Six2-PE to help initiate Six2 expression , but maintenance of expression may then rely , at least partially , with the Six2-DE where Hoxd11 is engaged at E15 . 5 . Additionally , Osr1 and Wt1 are enriched at the Six2-DE compared to the PE ( Fig 2D ) , as is Sall1 [29] , supporting multifactor input at the DE as an important mechanism of Six2 regulation . However , Six2 is bound at both the PE and DE , though PE association is weaker ( Fig 2D ) , suggesting both may contribute at some level to the maintenance and autoregulation by Six2 itself . Unfortunately , technical difficulties preclude detailed temporal analysis of engagement in the small numbers of cells that are the foundation of the nephron progenitor pool . When assessing the targets unique to any transcription factor combination , the greatest enrichment for genes with expression within the nephron progenitors , either in self-renewing or differentiating cells , generally occurred when they were complexed with Six2 ( Fig 3D ) . Hoxd11 showed the lowest levels of enrichment for these targets when engaged with Osr1 or Wt1 in the absence of Six2 , suggesting that its primary regulatory functions rely on engaging with Six2 . Taken together , these data suggest Six2 acts as a master regulator: co-engagement with Six2 predicts a higher probability of regulatory functions within nephron progenitors . Osr1 has been described as a transcriptional repressor in vertebrate kidney development [77] . Xu et al . showed that Osr1 works with the Groucho family members and represses activation of a Wnt4 enhancer specifically in Six2+ nephron progenitors [17] . Consistent with this result , Osr1 associates with the Wnt4 enhancer in our ChIP assay ( Fig 2D ) . Additionally , other genes that are not present in the nephron progenitors but rather in differentiating structures such as Pax8 and Lhx1 are also bound by Osr1 suggestive of a repressive role ( Fig 3C , S2 Table , [49 , 56] ) . However , Osr1 is also bound near genes actively expressed in nephron progenitors such as Six2 and Osr1 itself ( Fig 3C , S2 Table , [2 , 16 , 69] ) . Therefore , our data suggest a more complex relationship than Osr1 simply repressing transcription at all engaged targets . Further , our previous ChIP studies supported dual roles for Six2 in activating transcription within nephron progenitors but also engaging at targets silent in progenitors but activated as progenitors differentiate towards nephrons [3 , 28] . Similarly , Hox11 has been characterized as an activator , specifically of Six2 expression [76] . Consistent with this view , Hoxd11 , is bound near nephron progenitor-specific genes but like Six2 binding is also prominent around differentiation targets ( Fig 3C , S3 Table ) . Similarly , these observations extend to Wt1 nephron progenitor targets . Engagement most likely reflects dual activator and repressor actions of these complexes and which activity could be dependent on currently unidentified co-bound factors . Conversely , factor engagement at differentiation-specific gene targets may facilitate or enable subsequent activation of enhancers for differentiation-associated genes following the induction of nephron progenitors . In this scenario , multi-factor engagement may be necessary but not sufficient for target activation for differentiation associated genes . Additional factors or modification of existing transcriptional components following progenitor commitment may modify the action of these regulatory complexes . We observed a highly significant overlap of transcription factor binding in nephron progenitors . Motif analysis of each ChIP dataset showed de novo , factor-specific motifs were the most enriched ( Fig 1D ) , supporting direct protein-DNA binding . Additionally , our EMSA assays confirmed factor binding to each motif ( S2 Fig ) . On the other hand , previous studies have shown that Six2 can complex with a number of transcription factors , including Hoxa11 [28] and Osr1 [17] in vitro , and Eya1 and Sall1 both in vitro and in vivo [29 , 78 , 79] . Osr1 has also been shown to interact with Wt1 in vitro [80] . These studies support protein-protein interactions amongst these factors and may account , in part , for the multi-factor co-localization on specific genomic targets . Additionally , our kidney immunoprecipitation data suggests that Six2 can interact with Wt1 and Hoxd11 ( Fig 2E ) , confirming such complexes exist in vivo . However , without confirming the co-association of these factors on any genomic loci at the same time and in the same cell , we can only suggest their combined function . The association of each factor with its own DNA target and co-association with each other adds to the difficulty of predicting the actions of the regulatory circuit . Further , it is likely that there are significant components yet to be discovered . For example , all of the ChIP datasets recovered a bHLH motif amongst the most-significantly enriched motifs ( S1F Fig ) . Whereas Myc is a bHLH transcription factor that has been shown to complex with Eya1 and Six2 in the kidney [79] , and loss of function Myc mutants argue for a role in kidney development [81] , the recovered motif is distinct from the conventional Myc-Max target site [44] , suggesting a role for another , unidentified family member . In addition to identifying target genes with known function during kidney development , we also uncovered novel putative targets of the four factors ( see S2 Table for list of all target genes ) . Bmper is a secreted protein that interacts with Bmp proteins and inhibits their function [82] . Inactivation of Bmper in the kidney leads to mild hypoplasia [83]; Bmp signaling plays important roles in the progenitor self-renewal and differentiation [84] . Six2 and the other factors may help fine-tune the level of Bmp signaling through activation of Bmper . Rspo1 is a secreted protein that binds to G protein-coupled receptors that activate Wnt signaling and its function has been implicated in multiple developmental systems [85] . Rspo1 could have a role in modulating Wnt signaling in the nephron progenitor niche although Rspo1 mutants have no obvious kidney phenotype , these mutants have not been analyzed in depth [85] . We also identified other modulators of Wnt signaling within our data . Shisa2 is reported to attenuate Wnt and Fgf signaling during development [62] . Shisa2 is expressed in the nephron progenitors along with its related family member Shisa3 ( S2 Table ) . Other targets like Tsc22d1 and Mgat5 are reported to display kidney phenotypes . Mgat5 is expressed in differentiating structures including podocytes ( S2 Table , Eurexpress , www . eurexpress . org , [86] ) and shows a glomerular phenotype [51] . Tsc22d1 is expressed in the nephron progenitors ( S2 Table ) and mutants have small kidneys [50] . Given the current associations of known targets with kidney development and disease , it is likely that functional analysis of new targets predicted here will identify additional regulators of mammalian kidney development . From our analyses , the majority of significant targets fall under the control of all four factors . These genes fall into multiple functional categories from transcriptional regulators like Six2 , Sall1 , and Pax2 to signaling factors like Fgf9 and Wnt4 to cell cycle regulators such as Ccnd1 and matrix proteins such as Lamb1 ( S2 Table ) . This suggests that these transcription factors control many different aspects of progenitor cell biology . Fewer targets with known kidney functions emerge from the interaction maps where one of more the factors was not bound at the putative regulatory region ( S5 Table ) . However , Eya1 , Wt1 , and Bcam lacked an Osr1 association in combined factor interaction analysis ( S6 Table ) but are well known for their early roles in the kidney program [18 , 21] . Bcam , encodes a surface receptor which binds laminin and is expressed at increasing levels in differentiating progenitors ( S6 Table ) . Knockouts display glomerular abnormalities suggesting important functions in the kidney [87] . Phgdh , a Six2-independent target with highest expression in nephron progenitors ( S6 Table ) participates in L-serine synthesis and knockouts are embryonic lethal [88] . Enhancers directing Six2-like and Wnt4-like reporter gene expression [28] , identified as ‘regulatory hotspots’ co-bound by Six2 , Hoxd11 , Osr1 , and Wt1 in the data here , were shown to play roles in regulating activity of both gene targets . Kidney phenotypes were observed in embryos homozygous for the enhancer deletion ( Wnt4-DE ) or when combined with protein null mutations ( Six2-DE and Wnt4-DE ) . While the study identified functional enhancer regions , neither works alone in regulating normal transcript levels in the target cell type . An alternative proximal enhancer has been documented for Six2 [76 , 89] . This proximal enhancer lies a few hundred base pairs upstream of Six2’s transcriptional start site and strongly binds Six2 , but not the other regulatory factors analyzed here . Alternative enhancers have not been functionally demonstrated for Wnt4 . In summary , our studies provide evidence to support a focus on multifactor input to prioritize functional analysis of large datasets emerging from ChIP-seq studies . CRISPR/Cas9 deletion of an enhancer region >100kb from the TSS for Sox2 that is co-bound by multiple transcription factors regulating pluripotency ( Oct4 , Sox2 , Nanog , and Klf4 [90 , 91] ) provides another example of this strategy to identify strong , bone-fide components of the regulatory genome . Individual enhancer action depends on the larger context of the chromosomal landscape . Our demonstration that the inversion in Br mutant strain , repositions Six2 and Six3 in a new regulatory landscape modifying enhancer interactions that likely contribute to altered features of each gene’s regulation . Each gene exists in a distinct TAD that is likely enforced by the action of a CTCF-dependent boundary element between the two genes . In Br heterozygous and mutant alleles , Six3 is ectopically expressed in nephron progenitors: the boundary element no longer separates the Six3 promoter from the Six2-DE . We hypothesize that this enhancer , and potentially undefined regulatory information 5’ to this enhancer , dominate over other regulatory information that might be present within the Six3 flanking region . As a result , the Six2-DE drives Six3 expression in nephron progenitor cells while Six3 expression is lost from its normal lens expression domain . Even though Six3 was detected in nephron progenitors in Br/+ mutants , Six3 is a member of a functionally divergent sub-group of Six factors [92] . Consequently , Six3 activity failed to compensate for loss of Six2 and Br/Br mutants resemble Six2 null mutants [72] . Interestingly , even though there is no alteration in Six2-PE position relative the Six2 gene , the Six2-PE is not sufficient to drive levels of Six2 which maintain nephron progenitor development in the context of the inversion . Thus , if the Six2-PE were capable of sustaining normal Six2 levels , the inversion may prevent Six2-PE engagement with regulatory factors necessary for its activation . Alternatively , there may be distinct enhancers other than the Six2-PE that are required for Six2’s expression . Six2-bound putative regulatory regions lie upstream of Six2-DE ( S8 Table , Fig 6B ) and these would be predicted to disengage from Six2 regulation in the Br inversion . Topological domains are highly conserved between cell types and across mammalian species [70] . Recent studies have shown that alterations in TADs and CTCF site orientation can affect chromosome architecture and result in altered gene expression [93 , 94] . Specifically , several limb malformations in the human were attributed to the rearrangement of TADs and disrupted boundaries . When genetically modeled in the mouse , altered gene expression suggests a mechanism for driving the limb malformations [93] . The type of topological rearrangements described here could play a role in a subset of the congenital anomalies of the kidney and urinary tract ( CAKUT ) syndrome . Importantly , these micro-rearrangements would not be detected in traditional exome screens . Even whole genome sequencing approaches required tailored alignment algorithms to uncover the junction fragments for the rearrangements as performed here . Together , these studies highlight the importance of non-coding DNA and chromatin architecture to the appropriate regulation of gene expression and the resulting phenotypic consequences incurred by rearranging the regulatory landscape . In addition to Six2 , Hoxd11 , and Osr1 , we attempted to generate transgenic lines for other important regulatory factors including Wt1 , Hoxa11 , Pax2 , Sall1 , and Eya1 . Our goal was to build an extensive regulatory network for the nephron progenitor population and more precisely identify the targets and combinatorial actions of these major players in vivo . However , despite considerable efforts , we were unable to establish correctly expressing founder lines for these factors . The Six2-DE is not only active in the kidney but Six2 is expressed in the developing brain , ear , tendons , and smooth muscle [95] and we observed transgene expression in the brain and ear . Some transgenic lines showed a circling behavior , consistent with inner ear defects , along with insufficient kidney expression and thus were not utilized . The ectopic action of a sub-set of factors in these other sites of Six2-DE activity may have resulted in severe defects and subsequent lethality . Alternatively , there could be dominant effects within the kidney itself from elevating levels of that factor in the normal nephron progenitor context , though this seems less likely given the absence of a kidney phenotype in Six2 , Hoxd11 , or Osr1 transgenic strains , a Six2-BF binding profile that was comparable to the native Six2 protein , and the levels of Six2-DE activity . Despite these technical limitations , we were able to generate a core transcriptional network of four factors important for kidney development . Overlap with additional factors may not add much greater insight; comparison with Sall1 and β-catenin targets reveals many of the same nephron progenitor-specific target genes and a lack of nephron progenitor relevant independent regulation by these factors ( S1 , S5 and S6 Tables ) . Therefore , the data presented here is likely to highlight some of the most critical regulatory elements and target genes which modulate nephron progenitor programs .
All surgical procedures , mouse handling , and husbandry were performed according to guidelines issued by the Institutional Animal Care and Use Committees ( IACUC ) at the University of Southern California and after approval from the institutional IACUC committee . The transgenic construct utilized by Park et al . [28] to test Six2-DE enhancer activity was modified to insert PacI and SwaI sites for cloning downstream of the Hsp68 minimal promoter followed by an IRES-NLS-GFP-BirA cassette . However , the IRES-NLS-GFP-BirA cassette was not included in the generation of the Six2-BF line . Each transcription factor of interest ( Six2 , Hoxd11 , and Osr1 ) was amplified from E15 . 5 kidney cDNA with a BioTag-3XFLAG sequence on the 3’ C-terminus and inserted into the transgenic vector using PacI and SwaI sites . Transgenes were purified and injected as previously described [28] . F0 animals were genotyped and transgenic animals bred to confirm germline transmission . Embryonic offspring were also analyzed for correct expression patterns of the transgene in the developing kidney . Cas9-mediated removal of the Six2-DE ( chr17:85747271–85749534 ) was performed by identifying optimal gRNAs flanking the enhancer utilizing the CRISPR Design Tool ( crispr . mit . edu; 5’ gRNA: gttaccatctacggtgatgc , chr17: 85747271–85747290; 3’ gRNA: gatatgattctcccgagctt , chr17: 85749515–85749537 ) . The gRNAs were cloned into the pX330-U6-Chimeric_BB-CBh-hSpCas9 plasmid ( Addgene ) as described ( ‘One-page Protocol for Cloning Using CRISPR Cas9 Backbone Plasmids’ found at http://www . genome-engineering . org/crispr/; [96] ) . Pronuclear injection of 2 . 5ng/μl of each construct into B6SJLF1/J x B6SJLF1/J zygotes ( The Jackson Laboratory ) with transfer to Swiss Webster ( The Jackson Laboratory ) pseudopregnant females was performed in house . A similar strategy was used to create the Wnt4-∆DE mouse ( chr4:137216986–137217756 ) . The CRISPR Design Tool ( crispr . mit . edu ) was utilized to identify optimal gRNAs flanking the enhancer ( 5’ gRNA: aggctgacaagcgaagttac , chr4:137216986–137217008; 3’ gRNA: atgtcggttgattaataatc , chr4: 137217756–137217778 ) . The following primers were used to generate complexes for in vitro transcription of the gRNAs using the MEGAshortscript T7 Transcription Kit ( Ambion ) : Six2-DE 5’ gRNA F: AATAATACGACTCACTATAAGGCTGACAAGCGAAGTTACGTTTTAGAGCTAGAAATAGC , Six2-DE 3’ gRNA F: AATAATACGACTCACTATAATGTCGGTTGATTAATAATCGTTTTAGAGCTAGAAATAGC , Wnt4-DE 5’ gRNA F: AATAATACGACTCACTATAGAGGCTGACAAGCGAAGTTACGTTTTAGAGCTAGAAATAGC , Wnt4-DE 3’ gRNA F: AATAATACGACTCACTATAGATGTCGGTTGATTAATAATCGTTTTAGAGCTAGAAATAGC , Common R: AAAAGCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAAC . Cytoplasmic injection of 100ng of each gRNA and 50ng of Cas9 mRNA ( TriLink Biotech ) into B6SJLF1/J x B6SJLF1/J zygotes with transfer to Swiss Webster pseudopregnant females was performed in house . To establish lines , F0 animals were born and genotyped to confirm presence of the deletion . One animal carrying a deletion from chr17:85747284–85749542 for the Six2-DE and a deletion of chr4:137216991–137217771 for the Wnt4-DE ( each confirmed by Sanger sequencing of PCR product ) was mated to C57BL/6J ( The Jackson Laboratories ) to establish the line . Genotyping primers: Six2-DE flank F: GCAGAATGAGATTCTGACAGCCCAG Six2-DE flank R: CAAGGATGTCTTGTTTGGTCCTTGAGTGAG Six2-DE internal F: GAGGCCCATAAATAAAGCTGGGACG Six2-DE internal R: CTCCAGTGACAGATACCACTCTTACTG Wnt4-DE flank F: AAGCCATGAGGAAAAGAGGGTT Wnt4-DE flank R: TTCTCAACCCCAAACCCCACC Wnt4-DE internal F: AGTGTGAGGCACTGTGTAGC Wnt4-DE internal R: GTGGATGCTGCCTTATGGGT Six2TGCtg or Cited1-nuc-TagRFP-Ttg lines utilized for FACS were previously described [69] ( http://www . gudmap . org/index . html ) . Six2GCE , Six2CE , and Wnt4GCE mice were previously described [69] . The Br mutant mouse and mapping are previously described [97] and experimental protocols were approved by the University of Hawaii Institutional Animal Care and Use Committee . Wildtype or transgenic kidneys were utilized for ChIP . Hoxd11-BFtg/+ and Osr1-BFtg/+ kidneys were sorted out prior to ChIP by visualizing GFP+ kidneys . ChIP from E16 . 5 whole kidneys was carried out as previously described [3] . Nephron progenitor purification by MACS prior to ChIP was carried out is described in Brown et al . , 2015 [45] . Briefly , E16 . 5 mice kidney cortex cells were dissociated with collagenase/pancreatin as described in Brown et al . , 2015 [45] , fixed with 1% formaldehyde for 10 min in room temperature in AutoMACS running buffer . The fixed cells were then processed following published protocols [74] to generate 4C libraries . Dpn II and NlaIII were used in the first and second restriction enzyme digestions , respectively . In order to create a view point from Six2-DE , the primers used in 4C-PCR: Six2DE DpnII reading F: tccctacacgacgctcttccgatctGTTCTGAAAGAGCCGTGTAGGGATC Six2DE NlaIII noReading R: gtgactggagttcagacgtgtgctcttccgatcGGGGCCCATAAATCGTGATTCAAC The capitalized letters indicate the complimentary sequences to the genomic view point and the remainder Illumina adaptor sequences . The 4C-libraries were then indexed by PCR and sequenced by NextSeq500 . 4C-seq data were analyzed following the workflow provided by 4C-ker [98] . Briefly , the data was mapped to a reduced genome containing 25 bp regions from DpnII sites genome-wide and were subsequently quantified in 3 kb windows to show enrichment . The 4C-seq data is deposited on GEO ( GSE90017 ) . All ChIP-seq sequences were mapped to the mouse reference genome ( mm10 ) using Novoalign software ( Novocraft; parameters: single-end reads trimming 10 bp , polyclonal read filter: 7 , 10 0 . 4 , 2 , maximum alignment score acceptable: 120 ) . Mapped ChIP-seq and input data were analyzed using QuEST 2 . 4 software [99] using a “transcription factor” setting . The false discovery rate ( FDR ) for detecting the bound regions was evaluated by allocating the same number of mapped reads from a separate mouse input library and performing QuEST analysis using the same parameters . We generated multiple replicates for each ChIP-seq experiment ( except Hoxd11 due to technical issues ) , and used the replicate containing the most peaks using the same peak calling parameters for downstream analyses ( see S1G and S3A Figs , G for peak overlap between replicates . The smaller replicates all had >50% overlap with the larger replicate ) . To account for the innate differences between transcription factors in binding to the genome , we used different parameters in calling peaks of different transcription factors . We used high ratio of peaks with motif and low variability of motif-peak distances as our standard in determining the validity of a data set . See S7 Table for ChIP-seq data information and parameters in peak calling . In this paper , overlapping sites are defined as those with ChIP-seq peak center distance <150 bp from each other unless otherwise specified . To evaluate the statistical significance of two sets of peaks overlapping each other ( Fig 2A ) , we performed binomial test with the null hypothesis that peaks fall randomly into open chromatin regions in nephron progenitors . To determine the open chromatin regions in nephron progenitor , we performed ATAC-seq [100] in MACS-purified nephron progenitors [45] . We called ATAC-seq peaks with QuEST using the ‘transcription factor’ setting following threshold of fold enrichment > 10 . Then we extended the ATAC-seq peak coordinates by 150 bp to both sides , resulting in a total size of accessible chromatin as 21856500 bp . This process is modeled as following: NA , B∼Binom ( NA , pB ) pB=300∙NBSizeaccessiblegenome where NA , B is the number of peaks in set A that overlap with set B , and pB is the probability of a randomly located peak overlapping with set B . Information on all ChIP-seq samples presented in the paper can be found in S7 Table . The ChIP-seq and ATAC-seq data is accessible from GEO ( GSE90017 ) . All de novo motif discovery work was carried out using MEME ( Multiple Em for Motif Elicitation , [101] ) . To find the most enriched motifs for each transcription factor , MEME was run on a pool of 100 bp sequences around the predicted peak center for the top 1000 ChIP-seq peaks called from each data set ( or all peaks if number of the total peaks is less than 1000 ) . The locations of motif within 300 bp of peaks are found by FIMO ( Find Individual Motif Occurrences , [102] ) with data set-specific p-value threshold setting ( Six2 motif: 2e-4; Hoxd11 motif: 2e-4; Osr1 motif: 2e-4; Wt1 motif: 2e-5; bHLH motif: 2e-5 ) . We model the appearance of a motif near a set of peaks as following: NA , m∼Binom ( NA , pm ) pm=Nm300∙NA where NA , m is the number of motif m found in +/-150 bp of the peak set A; pm is the probability of finding such motif near a matched random set with the same number of peaks in A . Since promoter regions of genes are GC-rich , resulting higher rate of discovering GC-rich motif ( Wt1 , Osr1 and bHLH ) than TA-rich motif ( Six2 , Hoxd11 ) in promoter regions . To address this bias , we created the matched random set of peaks by picking genomic coordinates with the same distances to the nearest TSS as the observed peaks set , but with permutated nearest genes . We then screen the matched random peaks for the same motif to obtain Nm . GREAT GO analysis was performed utilizing the online GREAT program , version 2 . 0 [43] . Gene regulatory domains utilized for region annotation were defined as minimum 5 . 0 kb upstream and 1 . 0 kb downstream of the TSS , and extended up to 500 . 0 kb to the nearest gene’s minimal regulatory domain ( ‘single nearest gene’ option ) . GO Biological Processes annotations were assessed for each peak category . To infer the statistical significance of a set of ChIP-seq peaks found near a set of genes ( Fig 3 ) , we performed the following analysis . We assigned +/-500 kb from TSS of a gene as its ‘regulatory domain’ . We then calculated the probability of a selected set of ChIP-seq peaks falling into the merged regulatory domains of a list of selected genes . This is modeled by a binomial process with the null hypothesis that each peak falls uniformly throughout the genome . NA , x is the number of peaks in set A that fall into the regulatory domains defined by the gene list Gx . px is the probability of a peak falling into regulatory domains defined by the gene list Gx , assuming the peak randomly falling on any position in the genome . Size ( RRi ) is size of a regulatory region after resolving the overlap with any nearby regulatory regions . We found that each observed set of peaks fall into any random sets of regulatory domains more often than expected , which is not observed when doing the same experiment using random sets of genomic coordinates . To control this background over-representation , we obtained a background enrichment ratio over random sets of regulatory domains by rA , x=1n∑i=1nNA , xri/ ( NApxri ) where rA , x is the normalizing factor for a specific set of peaks A . NA , ri is the number of peaks from set A that fall into a the regulatory domain defined by a random list of genes Gxri which contains the same number of genes as Gx . Therefore , the final binomial model we used in the analysis is NA , x∼Binom ( NA , pxrA , x ) Cortical tissues of E16 . 5 or P2 kidneys from Six2TGCtg/+ or E16 . 5 Cited1-nuc-TagRFP-Ttg/+ embryos were dissociated as described in Brown et al . , 2015 [45] . The dissociated cells were resuspended in autoMACS buffer ( Miltenyi Biotec ) and passed through a 40 μm nylon filter to obtain single cells . The respective GFP+ , GFP- , or RFP+ cells were then isolated with the BD FACSAria II . RNA was isolated from FACS isolated cells using the QIAGEN RNeasy Micro Kit . RNA was submitted to the USC Epigenome Center for library preparation and sequencing on the Illumina HiSeq 2000 . All RNA-seq reads were aligned to the mouse reference genome ( mm10 ) using the TopHat2 [103] . Sequences have been deposited in GEO , accession number GSE90017 . Quantification of RNA-seq reads to generate RPKM was performed by Partek Genomics Suite software , version 6 . 6 ( St . Louis , MO , USA ) . TPM was calculated by dividing the RPKM by the mapping ratio of the library to exon regions of the genome . To identify genes differentially expressed in a cell type , we select those with a fold difference > 3 , TPM > 5 and p-value < 0 . 05 . Sample information can be found in S7 Table . A complete list of all annotated genes and their coordinating RNA-seq data can be found in S3 and S4 Tables , and coordinating ChIP-seq data can be found in S8 Table . Gene ontology analysis of gene lists was carried out by PANTHER [104] . For the gene set analysis in Fig 3D , we selected the enriched gene lists using the following metrics: nephron progenitor-enriched ( TPM > 10 in E16 . 5 Six2GFP+ cells and fold change > 2 in E16 . 5 Six2GFP+ vs . Six2GFP- cells ) , self-renewing nephron progenitor-enriched ( TPM > 10 in E16 . 5 Cited1RFP+ cells and fold change > 2 in E16 . 5 Cited1RFP+ vs . P2 Six2GFP+ cells ) and differentiating nephron progenitor-enriched ( TPM > 10 in P2 Six2GFP+ cells and fold change > 2 in P2 Six2GFP+ vs . E16 . 5 Cited1RFP+ cells ) . S5 and S6 Tables are pre-filtered to show the nephron progenitor-enriched genes only , but entire lists can be viewed by releasing the filter . qPCR reaction was performed with Luna Universal qPCR Master Mix Protocol ( New England Biolabs ) on a Roche LightCycler 96 System . The primers used in this paper includes: GAPDH F: AGGTCGGTGTGAACGGATTTG GAPDH R: TGTAGACCATGTAGTTGAGGTCA Six2 F: CACCTCCACAAGAATGAAAGCG Six2 R: CTCCGCCTCGATGTAGTGC Pax2 F: AAGCCCGGAGTGATTGGTG Pax2 R: CAGGCGAACATAGTCGGGTT Wnt4 F: AGACGTGCGAGAAACTCAAAG Wnt4 R: GGAACTGGTATTGGCACTCCT In situ hybridizations were performed on frozen sections as previously described ( https://www . gudmap . org/Research/Protocols/McMahon . html ) . The primer sequences used to generate Wnt4 probe template are: F: GAGAAACTCAAAGGCCTGATCCA R: TAATACGACTCACTATAGGGGGCTTTAGATGTCTTGTTGCACG EMSA was carried out using Glutathione S-transferase ( GST ) -tagged recombinant proteins purified from bacterial lysates . To produce the protein , bacterial expression constructs ( pDEST15 backbone ) were prepared using the Gateway system . BL21-AI One Shot ( Life Technology ) chemically competent cells were transformed and grown to OD600 = 0 . 6 before induction with 0 . 2% L- ( + ) -arabinose ( Sigma ) for 3 hrs at 37 °C . The bacteria pellets were resuspended in lysis buffer ( 20 mM Tris-HCl , 150 mM NaCl , 1% TritonX-100 , 1x protease inhibitor , 5 mM DTT ) and incubated with 1 mg/mL lysozyme ( Sigma ) . The lysates were sonicated with Branson digital sonifier at 50% amplitude for 90 s . After sonication , supernatant of the lysates were incubated with 5mL glutathione-agarose beads ( Sigma ) per 1 L bacteria culture for 1 hr at 4°C . The beads were washed with 1% TritonX-100/PBS and eluted with elution buffer ( 20 mM Tris-HCl , 150 mM NaCl , 15 mg/mL ( 50 mM ) reduced glutathione , 1x protease inhibitor ) . The eluted protein was concentrated to at least 10 mg/mL using Amicon Centrifugal Filter Unit with the right filter size . The concentrated protein was diluted with PBS and concentrated again to exchange buffer . To perform the EMSA experiments , the recombinant protein was incubated with biotinylated DNA probe for 30 min at room temperature , then the mixture was run through a native TBE gel . The gel was transferred to a nitrocellulose membrane , which was then illuminated using the LightShift Chemiluminescent EMSA Kit ( Pierce ) . The sequences of DNA probes can be found in S2 Fig . Nuclear lysates were prepared from E16 . 5 whole kidneys using the Nuclear Complex Co-IP Kit ( Active Motif ) . Normal rabbit IgG or Six2 ( Proteintech , 11562-1-AP ) antibodies were crosslinked with dimethyl pimelimidate to Dynabeads Protein G ( Thermo Fisher Scientific ) using the Protein A/G SpinTrap Buffer Kit ( GE Healthcare ) . Nuclear extracts were incubated overnight with beads at 4°C . Samples were washed 5x with TBS+0 . 1% Triton X-100 , and proteins subsequently eluted with 0 . 1M Glycine-HCl , pH 2 . 9 . Samples were run on a 10% SDS-PAGE gel , transferred to nitrocellulose , and subjected to standard Western blotting protocols using Six2 ( Proteintech ) , Hoxd11 ( Abcam , ab60715 ) , or Wt1 ( Santa Cruz , sc-192 ) antibodies . Kidneys were isolated at the appropriate stage and fixed in 4% PFA for 1 hour . Cryosections were immunostained as previously described [28] . Antibodies used include Six2 ( Proteintech , 11562-1-AP ) , FLAG ( Sigma , F1804 ) , Wt1 ( Abcam , ab89901 ) , pan-cytokeratin ( Sigma , C2931 ) , Pax8 ( Abcam , ab13611 ) , Ecad ( Sigma , U3254 ) , Six3 ( Rockland , 200-201-A26S ) , and LTL-FITC ( Vector Labs , FL-1321 ) . Images were acquired on a Nikon Eclipse 90i epi-fluorescent microscope or Zeiss LSM 780 inverted confocal microscope . Purified genomic DNA from one wildtype and two Br/Br animals was sequenced on the Illumina Hi-seq 2000 and mapped to the mm10 genome using Bowtie2 . Specific details of the mapping and allele characterization are described further in S1 Supporting Information . Sequences have been deposited in GEO , accession number GSE90017 . | Nephrons , the filtering units of the kidney , derive from nephron progenitors . Deficiencies in nephron number increases the risk of kidney disease . An understanding of the regulatory programs governing progenitor actions has important translational potential . Several transcription factors regulate the nephron progenitor population . However , their target interactions are largely unknown . Here , we mapped and intersected the genome-wide binding sites for four such factors in mouse nephron progenitor cells in the developing kidney: Six2 , Hoxd11 , Osr1 , and Wt1 . The intersectional data highlight a high-value set of putative enhancer elements linked to genes regulating nephron progenitor properties . We validate the function of two such enhancer elements regulating the levels of Six2 , a key transcriptional regulatory factor in nephron progenitor maintenance , and Wnt4 , a critical signaling factor controlling the mesenchyme to epithelial transition of induced nephron progenitors . Further characterization of the Six2 regulatory landscape identified higher order regulatory interactions that ensure appropriate enhancer-promoter specificity . CTCF-bound sites between Six2 and the adjacent Six3 locus likely act as boundary elements to define topological interactions domains separating enhancer elements thereby providing distinct tissue specificity to each gene’s expression . An inversion of this region in the Brachyrrhine ( Br ) mutant mouse reverses Six2 and Six3 expression domains , placing Six3 under control of the Six2 enhancer element above resulting in kidney-specific expression , while Six2 expression shifts to the lens , a normal expression domain for Six3 . Together , these data expand our view of the regulatory genome and regulatory landscape underpinning mammalian nephrogenesis . |
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ClinicalTrials . gov NCT03474198 , NCT01659437
Buruli ulcer ( BU ) , or Mycobacterium ulcerans disease , is a necrotizing skin disease driven by production of the immunosuppressive and cytotoxic macrolide-like toxin , mycolactone . Treatment for BU shifted from surgery and skin grafting to antibiotic therapy following pre-clinical and clinical evidence that combination , as opposed to single drug , therapy could be highly efficacious in killing M . ulcerans , stopping disease progression , reversing tissue damage and preventing relapse after treatment [1–3] . The original regimen of rifampin ( RIF , 10 mg/kg ) and streptomycin ( STR , 15 mg/kg ) given daily has both the benefit and the drawback of the inclusion of an injectable drug: patient and provider adherence is better assured but daily injections disrupt work , study , and recreation and burden the healthcare system . Although ototoxicity and other complications of STR treatment were not initially observed in West African patients based on self-report , more objective audiometric testing showed that hearing loss does indeed occur [4] . In Australia , which also has a large number of cases , all-oral antibiotic therapy combined with surgery has been in practice for many years [5] . In March of 2017 , the BU Technical Advisory Group for the World Health Organization Global BU Initiative , due to difficulties in obtaining STR and preliminary findings of non-inferiority in a clinical trial ( NCT01659437 ) in West Africa , recommended replacing STR with oral clarithromycin ( CLR , 15–30 mg/kg ) [6] . Compared to other chronic mycobacterial diseases such as leprosy and tuberculosis ( TB ) requiring antibiotic treatment for 6 to 24 months , depending on the form of the disease in each case , the treatment of BU with either regimen is relatively short at only 2 months . With the achievement of an all-oral regimen in hand , the next goal is to find ways to shorten treatment to one month or less . Previous studies in our laboratory established that clofazimine ( CFZ ) at a dose of 25 mg/kg , when used in combination with RIF , is as effective as either standard regimen ( i . e . , RIF+STR or RIF+CLR ) in reducing footpad swelling , production of mycolactone , and the M . ulcerans burden in a mouse footpad model of BU [7] . The regimen was comparable to RIF+STR and was significantly superior to RIF+CLR in preventing relapse after treatment for 6 weeks . Subsequently , research in our group with a mouse model of TB has shown that the dose of CFZ can be halved from 25 to 12 . 5 mg/kg with no loss of efficacy and a reduction in the transient skin discoloration associated with the drug [8 , 9] . The MIC for CFZ against both M . tuberculosis and M . ulcerans is approximately 0 . 25–0 . 5 μg/ml [7] . Recent studies in our group have shown that increasing the dose of RIF and the long half-life rifamycin , rifapentine ( RPT ) shortens the treatment duration needed to prevent relapse in mouse models of TB [10 , 11] . More recently , we found that replacing RIF with high-dose RIF or RPT resulted in greater bactericidal activity compared to RIF+CLR in a mouse footpad model of BU ( T . Omansen et al . , 2017 , P1621 , ECCMID , p . 191 , WHO Meeting on BU , p . 128 ) . Likewise , Chauffour et al . [12] showed that the regimen of RPT 10 mg/kg together with CLR achieved superior bactericidal effects and sterilizing efficacy at least comparable to that of RIF+STR in a mouse footpad model of BU . One complication in the treatment of BU in humans is a paradoxical worsening of the clinical appearance of lesions [13] . Given that CFZ has both antimicrobial and anti-inflammatory properties and treats erythema nodosum reactions in leprosy , it has the additional potential advantage over STR and , possibly , CLR to reduce the risk of such paradoxical worsening . Here , we evaluated the bactericidal and sterilizing efficacy of a rifamycin plus CFZ at the lower CFZ dose of 12 . 5 mg/kg and at escalating doses of RIF from 10 to 20 or 40 mg/kg and of RPT from 10 to 20 mg/kg . The results indicate that increasing the rifamycin dose can significantly shorten the treatment duration necessary to achieve culture negativity and prevent relapse when combined with CFZ , which may be a superior alternative to CLR or STR as a companion drug of the rifamycin .
M . ulcerans 1059 ( Mu1059 ) , originally obtained from a patient in Ghana , was generously provided by Dr . Pamela Small , University of Tennessee . Autoluminescent Mu1059 ( Mu1059AL ) was generated in our laboratory [14 , 15] . These strains both produce mycolactone A/B , and this toxin kills macrophages and fibroblasts in vitro [16 , 17] . The Mu1059AL strain was passaged in mouse footpads before use in these studies . The bacilli were harvested from footpads with grade 2 level swelling , i . e . , swelling with inflammation[18] . All animal procedures were conducted according to relevant national and international guidelines . The study was conducted adhering to the Johns Hopkins University guidelines for animal husbandry and was approved by the Johns Hopkins Animal Care and Use Committee , #MO17M13 . Johns Hopkins University is in compliance with the Animal Welfare Act regulations and Public Health Service Policy and also maintains accreditation of its program by the private Association for the Assessment and Accreditation of Laboratory Animal Care International . RIF , CFZ , and STR were purchased from Sigma ( St . Louis , MO ) . RPT and CLR were prepared from Priftin ( Sanofi ) and generic CLR ( Aurobindo Pharma , Hyderabad , India , Dayton , NJ , USA ) tablets , respectively , purchased at a pharmacy . Stock RIF and RPT ( with brief sonication ) suspensions were prepared every two weeks in distilled water; STR and CLR solutions were prepared weekly in water; and CFZ was suspended weekly in an 0 . 05% ( w/v ) agarose solution in distilled water . All drugs were given 5 days per week in 0 . 2 ml . RIF ( 10 , 20 , and 40 mg/kg ) , RPT ( 10 and 20 mg/kg ) , CFZ ( 25 mg/kg and 12 . 5 mg/kg ) , and CLR ( 100 mg/kg ) were administered by gavage . STR ( 150 mg/kg ) was administered by subcutaneous injection ( S1 Table ) . Doses for CLR and STR were chosen based on mean plasma exposures ( i . e . , area under the concentration-time curve over 24 hours post-dose ) compared to human doses . BALB/c mice ( N = 292 ) , age 4–6 weeks ( Charles River , Wilmington , MA ) , were inoculated in both hind footpads with approximately 4 . 42 log10 ( 2 . 65 x104 ) CFU of Mu1059AL in 0 . 03 ml PBS , resulting in a mean ( ±S . D . ) CFU count of 3 . 53±0 . 37 log10 M . ulcerans per footpad three days after infection . Treatment began 38 days after infection when footpad swelling increased to approximately grade 2 [18] , and there were 5 . 31±0 . 28 log10 CFU/footpad . Treatment with RIF+STR , RIF+CLR , RIF+CFZ , RPT+CFZ and RIF or RPT alone was administered for up to 6 weeks for the combination regimens and up to 4 weeks for the monotherapy regimens . Footpads were harvested before treatment initiation ( Day 0 ) and after 1 , 2 , and 4 weeks of treatment from mice ( 6 footpads from 3 mice ) for CFU and relative light unit RLU counts to assess luminescence and mycolactone detection . For relapse determinations , 10 mice ( 20 footpads ) were held without treatment for approximately 12 weeks after completing a 4- or 6-week combination regimen treatment ( See the overall experiment scheme , including each regimen evaluated , in S1 Table ) . Mice were euthanized if they reached grade 3 swelling on a scale of 0–4 , as described [18] . Footpad tissue was harvested , minced with fine scissors , suspended in 1 . 5 ml PBS , serially diluted , and plated on Middlebrook selective 7H11 plates ( Becton-Dickinson , Sparks , MD ) . Plates were incubated at 32°C and colonies were counted after 8–12 weeks of incubation . Autoluminescence was assessed using a Turner Designs ( TD 20/20 ) luminometer in both intact footpads and in suspensions of minced footpads in PBS . Values in the latter tended to be approximately 5 times higher than those obtained in intact footpads and only the suspension values are reported here . The values are reported as relative light units ( RLU ) . Samples of footpad tissue were stored in PBS at -20°C prior to mycolactone quantification . Mycolactone was extracted from 50 μl of tissue homogenate with 0 . 2 ml of acetonitrile containing 100 ng/ml of the internal standard , itraconazole . The standard curve and quality controls were prepared in blank mouse EDTA plasma . After centrifugation , the supernatant was transferred into an autosampler vial for LC-MS/MS analysis . Separation was achieved with a Thermo Betasil Phenyl ( 50 × 2 . 1 mm , 3 μm ) column at 40°C with a gradient . Mobile phase A was water containing 0 . 1% formic acid and mobile phase B was acetonitrile containing 0 . 1% formic acid . The gradient started with mobile phase B was held at 20% for 0 . 5 minutes and increased to 100% over 0 . 5 minutes; 100% mobile phase B was held for 2 minutes and then returned back to 20% mobile phase B and allowed to equilibrate for 2 minutes . Total run time was 5 minutes with a flow rate of 0 . 3 ml/min . The column effluent was monitored using a Sciex triple quadrupole 4500 mass spectrometry detector ( Sciex , Foster City , CA , USA ) using electrospray ionization operating in positive mode . The spectrometer was programmed to monitor the following Multiple Reaction Monitoring transitions: 765 . 4 → 429 . 3 for mycolactone and 705 . 3 → 392 . 0 for itraconazole . Calibration curves for mycolactone were computed using the area ratio peak of the analysis to the internal standard using a quadratic equation with a 1/x2 weighting function over the range of 0 . 5 to 100 ng/ml . DNA from each individual colony was extracted by boiling in 1X TE ( Tris-EDTA , pH 8 . 0 ) buffer for 5 minutes; 5 μl of the supernatant was then used for PCR . Specific primers , forward primer MU_rpoF 5’ CGACGACATCGACCACTTC 3’ and reverse primer MU_rpoR 5’ CGACAGTGAACCGATCAGAC 3’ , were used to amplify a 400 bp region encompassing the rifampin resistance-determining region . The PCR product was then sequenced to identify the presence of any mutation . Colonies from untreated control groups were used as a negative control . GraphPad Prism 6 was used to compare group means by student’s T test and analysis of variance and group proportions by Fisher’s exact test , and to determine Spearman’s correlation coefficients to evaluate RLU and CFU correlations .
Treatment was initiated five weeks after inoculation of M . ulcerans strain Mu1059AL , when the mean footpad swelling index was approximately 1 . 75 on a scale of 0–4 [21] . The mean CFU count on treatment initiation ( Day 0 ) was 5 . 31 ± 0 . 28 log10 , and the mean RLU count was 239 . 3 ± 84 ( Fig 1 ) .
Treatment for BU was revolutionized in the last 20 years after work in a mouse model like that employed here demonstrated that a combination of RIF10 plus either STR or amikacin could prevent the development of swelling and treat established lesions [18 , 20 , 21] . In humans , RIF+STR is efficacious but the inclusion of STR has the drawbacks of ototoxicity and a requirement for daily injections for 8 weeks [4] . In March , 2017 , a WHO technical advisory group recommended the adoption of an all-oral regimen of RIF+CLR on the basis of a series of clinical trials [6 , 22–24] . While better tolerated , the recommended regimen still requires 8 weeks of treatment . Therefore , shortening the duration of treatment required to safely eradicate M . ulcerans and its mycolactone toxin is now a major goal of research to improve BU treatment . Increasing the rifamycin exposure by using higher doses of RIF or RPT increase the sterilizing activity of combination therapy in mouse models of TB [10 , 11] . The treatment-shortening potential of anti-TB regimens containing daily RIF doses as high as 35 mg/kg [25] and RPT doses as high as 20 mg/kg [26] are now being evaluated in phase 2/3 trials ( NCT02581527 , NCT03474198 , NCT02410772 ) . We hypothesized that similar dose increases would shorten the duration of all-oral treatments for BU . Indeed , we found that , while monotherapy with the standard dose of RIF10 had a modest impact on swelling , mycolactone production , and bacterial burden , increasing the rifamycin dose had a significant impact on all three parameters , particularly on mycolactone levels and bacterial burden . Whereas RIF20 alone may have lagged behind the other high-dose rifamycins as monotherapy , they all had efficacy similar to that of the control regimens after 2–4 weeks of treatment . Combining high-dose RIF or RPT together with CFZ resulted in superior reduction of CFU burden and footpad swelling compared to rifamycin monotherapy , particularly after week 2 . More importantly , these combinations significantly reduced the proportion of mice relapsing after 4 weeks of treatment compared to both RIF10+STR and RIF10+CLR . Combining CFZ with RIF20 , RIF40 or RPT20 completely prevented relapse after 4 weeks of treatment , whereas RIF10+STR and RIF10+CLR treatment for 2 additional weeks was still associated with relapse in 15% and 65% of footpads , respectively . These results suggest that all-oral high-dose rifamycin and CFZ regimens have the potential to reduce the treatment duration from 8 weeks to 4 weeks without reducing efficacy . This would represent a substantial advance over the current standard of care . CFZ was recently repurposed for treatment of multidrug-resistant TB as a component of a short-course regimen now endorsed by WHO [27] . There may be additional significant advantages to replacing CLR with CFZ in all-oral regimens for BU . RIF significantly induces human metabolism of CLR , lowering average CLR concentrations by 73–87% and compromising its activity as a companion agent [28 , 29] . While CLR concentrations remain above the MIC when CLR is administered with standard doses of RIF to BU patients [30] , the inductive effect of high-dose rifamycins is expected to be even greater and more consistent from person-to-person . The inductive effect of high-dose rifamycins is expected to be even greater and more consistent from person-to-person . CFZ , on the other hand , has no known unfavorable interactions with RIF . At the 100 mg human dose providing plasma exposures similar to the 12 . 5 mg/kg dose in mice , CFZ has better gastrointestinal tolerability than CLR . Given the anti-inflammatory activity of CFZ , treatment with this drug may reduce the possibility of paradoxical reactions [5 , 13] . While the precise mechanisms underlying the induction of paradoxical reactions remain uncertain , accumulations of macrophages/giant cells have been observed . CFZ has been shown to accumulate in macrophages and to inhibit TNF production and boost anti-inflammatory IL-1RA production , including in dermal macrophages [31] . Giant cells form in response to both CD4-mediated and innate immune mechanisms [32] . Accordingly , we speculate that CFZ may modulate at least some of the inflammatory signals that may be involved in the induction of paradoxical reactions . Finally , CFZ is expected to become more readily available now due to its recommended use in the treatment of multi-drug resistant TB . Clinical trials are also underway ( NCT03474198 ) or in the planning stages for combining rifamycins with CFZ in treatment of drug-susceptible TB based on treatment-shortening effects in a mouse model [8 , 33 , 34] . The main side effect of CFZ is skin discoloration . However , it should be emphasized that the discoloration is dose- and duration-dependent , is less noticeable in pigmented skin , and resolves completely after treatment completion [35–38] . We believe it is unlikely that 4 weeks of treatment producing plasma exposures observed with the 12 . 5 mg/kg dose used here in mice would produce noticeable or disconcerting skin discoloration . Any skin discoloration is expected to be a minor , short-lasting effect , particularly in comparison to the gastrointestinal intolerance associated with CLR [5 , 39] and the toxicity and discomfort with STR [4] . CFZ may also be safer in BU patients with other co-morbidities . Caution in treating with CLR has been urged in patients with coronary heart disease in whom an increase in death has been observed after a two-week course of CLR . Deaths were apparent after patients were followed for one year or longer ( NCT00121550 ) [40] . O’Brien et al . [5] noted that severe antibiotic complications developed at a median time of 4 weeks in Australian patients treated with the currently used oral regimens for BU , either RIF+CLR or RIF+fluoroquinolone , and were associated with reduced renal function . Relapse of BU after antimicrobial treatment is thought to be rare , unlike in the preceding era when surgery without antibiotic treatment inevitably missed covert areas of infection . These observations suggest that there are new opportunities to shorten treatment durations with more potent drug combinations . To test the new regimens studied here , we used the stringent outcome measure of relapse prevention and found that high-dose rifamycins together with CFZ prevented relapse more effectively than RIF+STR and RIF+CLR despite shorter durations of treatment . Based on these promising results with drugs that are already in clinical use , these regimens warrant evaluation in clinical trials seeking to shorten the treatment of BU . | Buruli ulcer , a neglected tropical skin disease caused by Mycobacterium ulcerans , is treatable since 2004 with antibiotics instead of surgery . Treatment with either rifampin plus streptomycin or , more recently , rifampin plus clarithromycin requires taking the drugs daily for 8 weeks . Streptomycin is administered by injection and may result in hearing loss . Clarithromycin often causes gastrointestinal discomfort . Our goal is to identify a regimen that is both shorter and associated with fewer side effects . Rifampin , previously an expensive drug , is well tolerated not only at the standard dose of 10 mg/kg but at doses of 20 and 40 mg/kg . The related rifamycin , rifapentine , has a longer half-life and is also well tolerated . We tested in a mouse model of Buruli ulcer whether higher doses of these rifamycins together with clofazimine , a drug that has transient skin pigmentation side effects but no toxicities , could effectively reduce lesion size , the number of bacteria , and production of the mycolactone toxin , in a shorter time than that for the existing drug regimens . We found that treatment for 4 weeks with a high dose rifamycin plus clofazimine is as effective as 8 weeks of the current standard regimens of rifampin plus streptomycin or rifampin plus clarithromycin . |
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The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability . The low success rates of computational design protocols and the extensive in vitro optimization often required , highlight the challenge of designing proteins that perform essential biochemical functions , such as binding or catalysis . One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins . The structural complexity of the functional motif largely determines how readily one can find host protein structures that are “designable” , meaning that are likely to present the functional motif in the desired conformation . One promising route to enhance the “designability” of protein structures is to allow backbone flexibility . Here , we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins—Rosetta Functional Folding and Design ( FunFolDes ) . We performed extensive computational benchmarks , where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein . An observation consistent with several experimental studies that have revealed function-stability tradeoffs . To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo “functionless” fold , which represent two typical challenges where the designability problem arises . The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies , making them valuable candidates for vaccine design endeavors . Overall , we present an accessible strategy to repurpose old protein folds for new functions . This may lead to important improvements on the computational design of proteins , with structurally complex functional sites , that can perform elaborate biochemical functions related to binding and catalysis .
Proteins are one of the main functional building blocks of the cell . The ability to create novel proteins outside of the natural realm has opened the path towards innovative achievements , such as new pathways [1] , cellular functions [2] , and therapeutic leads [3–5] . Computational protein design is the rational and structure-based approach to solve the inverse folding problem , i . e . the search for the best putative sequence capable of fitting and stabilizing a protein’s three-dimensional conformation [6] . As such , a great deal of effort has been placed into understanding the rules of protein folding and stability [7 , 8] and its relation to the appropriate sequence space [9] . Computational protein design approaches focus on exploring two interconnected landscapes related to sampling of the conformational and sequence spaces . Fixed backbone approaches use static protein backbone conformations , which greatly constrain the sequence space explored by the computational algorithm [9] . Following the same principles of naturally occurring homologs , which often exhibit confined structural diversity , flexible backbone approaches enhance the sequence diversity , adding the challenge of identifying energetically favorable sequence variants that are correctly coupled to structural perturbations [10] . Another variation for computational design approaches is de novo design , in which protein backbones are assembled in silico , followed by sequence optimization to fold into a pre-defined three-dimensional conformation without being constrained by previous sequence information [11–13] . This approach tests our understanding of the rules governing the structure of different protein folds . The failures and successes of this approach confirm and correct the principles used for the protein design process [7 , 8] . One of the main aims of computational protein design is the rational design of functional proteins capable of carrying existing or novel functions into new structural contexts [14] . Broadly , there are three main approaches for the design of functional proteins: redesigning of pre-existing functions , grafting of functional sites onto heterologous proteins , and designing of novel functions not found in the protein repertoire . The redesign of a pre-existing function to alter its catalytic activity [15] or improve its binding target recognition [16] can be considered the most conservative approach . It is typically accomplished by point mutations around the functional area of interest and tends to have little impact on global structure of the designed protein . On the other extreme , the design of fully novel functions has most noticeably been achieved by applying chemical principles that tested our fundamental knowledge of enzyme catalysis [17 , 18] . Between these two approaches resides protein grafting . This method aims to repurpose natural folds as carriers for exogenous known functions . It relies on the strong structure-function relationship present in proteins , to endow an heterologous protein with an exogenous function by means of transferring a structural motif that performs such function [3–5 , 19–22] . At the biochemical level , grafting approaches have been used to design high binding affinity protein-protein interactions , by stabilizing binding motifs removing the entropic cost of binding ( e . g . flexible peptides ) [21] , and also by extending the binding interfaces to allow for additional energetically favorable interactions . The extended interfaces also provide opportunities to tune the specificity of the designed proteins [21] . On the practical side , some of the most notable applications of protein grafting thus far , have been the design of novel viral inhibitors [21 , 23] and epitope-focused immunogens for vaccine design [3–5] . Following this strategy one can easily imagine applications to functionalize protein-based biomaterials [24] or to design novel biosensors [25] . The importance of robust grafting approaches to functionalize heterologous proteins is related to the fact that the proteins that naturally perform these functions , may lack the best biochemical properties in terms of size , affinity , solubility , immunogenicity and other application specific factors . Thus far , the most successful grafting approaches are highly dependent on structural similarity between the functional motif and the insertion region in the protein scaffold . When the functional motif and the insertion region are identical in backbone conformation , the motif transfer can be performed by side-chain grafting , i . e . mutating the target residues into those of the functional motif [3 , 5] . In much more challenging scenarios , full backbone grafting may be used in conjunction with directed evolution [19] . Nevertheless , motif transfer is limited between very similar structural regions , which greatly constrains the subset of putative scaffolds that can be used for this purpose . The lack of compatibility between the putative scaffolds and the functional sites has been referred to as a “designability” problem [26 , 27] , which refers to the likelihood of a protein backbone to host and stabilize a structural motif . The designability problem becomes more obvious as the structural complexity of the functional motif grows , drastically limiting the types of functional motifs that can be transferred . Previously , we have demonstrated the possibility of expanding protein grafting to scaffolds with segments that have low structural similarity . To accomplish that task , we developed the prototype protocol Rosetta Fold From Loops ( FFL ) [4 , 21] . The distinctive feature of our protocol is the coupling of the folding and design stages to bias the sampling towards structural conformations and sequences that stabilize the grafted functional motif . In the past , FFL was used to obtain designs that were functional ( synthetic immunogens [4] and protein-based inhibitors [21] ) and where the experimentally determined crystal structures closely resembled the computational models . However , the structures of the functional sites were structurally very close to the insertion segments of the hosting scaffolds . The architecture of FFL was intrinsically limited in the types of constraints available and the grafting of linear , single segment functional motifs . Here , we present a complete re-implementation of FFL with enhanced functionalities , simplified user interface and complete integration with other Rosetta protocols . We have called this new , more generalist protocol Rosetta Functional Folding and Design ( FunFolDes ) . We benchmarked FunFolDes extensively , unveiling important technical details to better exploit and expand the capabilities of the protocol . Furthermore , we challenged FunFolDes with two design tasks of transplanting viral epitopes to heterologous scaffolds , and by doing so probe the applicability of the protocol . The design tasks were centered on using distant structural templates as hosting scaffolds , and functionalizing a de novo designed protein , —FunFolDes succeeded in both challenges . These results are encouraging and provide a solid basis for the broad applicability of FunFolDes as a strategy for the robust computational design of functionalized proteins .
The original prototype of the Rosetta Fold From Loops ( FFL ) protocol was successfully used to transplant the structural motif of the Respiratory Syncytial Virus protein F ( RSVF ) site II neutralizing epitope into a protein scaffold in the context of a vaccine design application [4] . FFL enabled the insertion and conformational stabilization of the structural motif into a defined protein topology by using Rosetta’s fragment insertion machinery to fold an extended polypeptide chain to adopt the desired topology [28] which was then sequence designed . Information from the scaffold structure was used to guide the folding , ensuring an overall similar topology while allowing for the conformational changes needed to stabilize the inserted structural motif . The final implementation of FunFolDes is schematically represented in Fig 1 , and fully described in Materials and Methods . Our upgrades to FFL focused on three main aims: I ) improve the applicability of the system to handle more complex structural motifs ( i . e . multiple discontinuous backbone segments ) ; II ) enhance the design of functional proteins by including binding partners in the simulations; III ) increase the control over each stage of the simulation improving the usability for non-experts . These three aims were achieved through the implementation of five core technical improvements described below . Insertion of multi-segment functional sites . Most functional sites in proteins typically entail , at the structural level , multiple discontinuous segments , as is the case for protein-protein interfaces , enzyme active-sites , and others [29 , 30] . FunFolDes handles functional sites with any number of discontinuous segments , ensuring the native orientations of each of the segments . These new features enhance the types of structural motifs that can be handled by FunFolDes , widening the applicability of the computational protocol . Structural folding and sequence design in the presence of a binding target . Many of the functional roles of proteins in cells require physical interaction with other proteins , nucleic acids , or metabolites [31] . The inclusion of the binder has two main advantages: I ) explicit representation of functional constraints to bias the designed protein towards a functional sequence space , resolving putative clashes derived from the template scaffold; II ) facilitate the design of new additional contact residues ( outside of the motif ) that may afford enhanced affinity and/or specificity . Region-specific structural constraints . FunFolDes can collect from full-template to region-specific constraints , allowing greater levels of flexibility in areas of the scaffold that can be critical for function ( e . g . segments close to the interface of a target protein ) . The type of distance constrains used in the protocol are soft constrains with score penalties if the defined standard deviation is exceeded in the upper and lower bounds . Furthermore , FunFolDes is no longer limited to atom-pair distance constraints [32] and can incorporate other types of kinematic constraints , such as angle and dihedral constraints [33] , which have been used extensively to design beta-rich topologies [8] . On-the-fly fragment picking . Classically , fragment libraries are generated through sequence-based predictions of secondary structure and dihedral angles that rely on external computational methods [34] . We leveraged internal functionalities in Rosetta so that FunFolDes can assemble fragment sets on-the-fly . Using this feature , we can assemble fragment sets based on the structure of the input scaffold . Sequence-based fragments remain an option , however this feature removes the need for secondary applications , boosting the usability of FunFolDes . Lastly , the on-the-fly fragment picking enables the development of protocols with mutable fragment sets along the procedure . Compatibility with other Rosetta modules . Finally , FunFolDes is compatible with Rosetta’s modular xml-interface—Rosetta Scripts ( RS ) [35] . Enabling customization of the FunFolDes protocol and , more importantly , cross-talk with other protocols and filters available through the RS interface . We devised two benchmark scenarios to test the performance of FunFolDes . One of these aimed to capture conformational changes in small protein domains caused by sequence insertions or deletions , and the second scenario assessed protocol performance to fold and design a binder in the presence of the binding target . Typical protein design benchmarks are assembled by stripping native side chains from known protein structures and evaluating the sequence recovery of the design algorithm [9] . The main design aim of FunFolDes is to insert structural motifs into protein folds while allowing flexibility across the overall structure . This conformational freedom allows the full protein scaffold to adapt and stabilize the functional motif’s conformation . This is a main distinctive point from other approaches to design functional proteins that rely on a mostly rigid scaffold [2 , 3 , 11 , 19 , 30 , 36] . For many modeling problems , such as protein structure prediction , protein-protein and protein-ligand docking , and protein design , standardized benchmark datasets are available [37] or easily accessible . Devising a benchmark for designed proteins with propagating conformational changes across the structure is challenging , as we are assessing both structural accuracy as well as sequence recovery of the protocol . To address this problem , we analyzed structural domains found repeatedly in natural proteins and clustered them according to their definition in the CATH database [38] . As a result , we selected a set of 14 benchmark targets labeled T01 through T14 ( Fig 2A ) . A detailed description on the construction of the benchmark can be found in the Materials and Methods section . Briefly , for the benchmark we selected proteins with less than 100 residues , where each test case was composed of two proteins of the same CATH domain cluster . One of the proteins is the template , and serves as a structural representative of the CATH domain . The second protein , dubbed target , contains structural insertions or deletions ( motif region ) , to which a structural change in a different segment of the protein could be attributed ( query region ) . The motif and query regions for all the targets are shown in Fig 2A and quantified by the percentage of overall secondary structure in S1A Fig . To a great extent , these structural changes due to natural sequence insertions and deletions are analogous to those occurring in the design scenarios for which FunFolDes was conceived . Using FunFolDes , we folded and designed the target proteins while maintaining the motif segment structurally fixed , mimicking a structural motif insertion . Distance constraints between residues were extracted from the template in the regions of shared structural elements of the template and the target , and were used to guide the folding simulations . To check whether FunFolDes enhances sequence and structural sampling , we compared the simulations to constrained ab initio ( cst-ab initio ) simulations [33] . As Rosetta conformational sampling is highly dependent upon the fragment set [39] , in this benchmark we also tested the influence of structure- and sequence-based fragments . The performance of the two protocols was analyzed regarding global and local recovery of both structure and sequence . Structural recovery was assessed through two main metrics: ( a ) global RMSD of the full decoys against the target and ( b ) local RMSD of the query region . When evaluating the distributions for global RMSD in the designed ensembles , FunFolDes outperformed cst-ab initio by consistently producing populations of decoys with lower mean RMSD ( mostly found below 5 Å ) , a result observed in all 14 targets ( Fig 2B , S1B Fig ) . This result is especially reassuring considering that FunFolDes simulations contain more structural information of the target topology than the cst-ab initio simulations . The local RMSDs of the query unconstrained regions presented less clear results across the benchmark ( S1B Fig ) . In 13 targets , FunFolDes outperformed cst-ab initio , showing lower mean RMSDs but in some targets with minor differences . When comparing fragment sets ( structure- vs sequence-based ) , both achieved similar mean RMSDs in the decoy populations; nonetheless , the structure-based fragments more often reached the lowest RMSDs for overall and query RMSDs ( Fig 2B , S1 Fig ) . This is consistent with what would be expected from the structural information content within each fragment set . When paired with the technical simplicity of use , time-saving and enhanced sampling of the desired topology , the structure-based fragments are an added value for FunFolDes . We also quantified sequence recovery , both in terms of sequence identity and similarity according to the BLOSUM62 matrix [40] ( Fig 3A ) . In all targets , FunFolDes showed superior recoveries than cst-ab initio , and at the levels of other design protocols using Rosetta [10] ( Fig 3A ) . This type of metrics has been shown to be highly dependent on the exact backbone conformation used as input [9 , 10] . Given that FunFolDes is exploring larger conformational spaces , as a proxy for the quality of the sequences generated , we used the target’s Hidden Markov Models ( HMM ) [41] and quantified the designed sequences that were identified as part of the target’s CATH superfamily according to its HMM definition ( Fig 3B ) . FunFolDes decoy populations systematically outperformed those from cst-ab initio ( Fig 3B ) . The performance of the two fragment sets shows no significant differences . In summary , this benchmark highlights the ability of FunFolDes to generate close-to-native scaffold proteins to stabilize inserted structural motifs . FunFolDes aims to refit protein scaffolds towards the structural requirements of a functional motif . It is thus critical , to explore within certain topological boundaries , structural variations around the original templates . This benchmark points to several variables in the protocol that resulted in enhanced structural and sequence sampling . The computational design of proteins that can bind with high affinity and specificity to targets of interest remains a largely unsolved problem [42] . Within the FunFolDes conceptual approach of coupling folding with sequence design , we sought to add the structure of the binding target ( Fig 1 ) to attempt to bias sampling towards functional structural and sequence spaces . Previously , we used FFL to design a new binder ( BINDI ) to BHRF1 ( Fig 4A ) , an Epstein-Barr virus protein with anti-apoptotic properties directly linked to the tumorigenic activity of EBV [21] . FFL designs bound to BHRF1 with a dissociation constant ( KD ) of 58–60 nM , and after affinity maturation reached a KD of 220±50 pM . BINDI was designed in the absence of the target and then docked to BHRF1 through the known interaction motif . A striking observation from the overall approach was that the FFL stage was highly inefficient , generating a large fraction of backbone conformations incompatible with the binding mode of the complex . To test whether the presence of the target could improve structural and sequence sampling , we leveraged the structural and sequence information available for the BINDI-BHRF1 system and benchmarked FunFolDes for this design problem . As described by Procko and colleagues , when comparing the topological template provided to FFL and the BINDI crystal structure , the last helix of the bundle ( helix 3 ) was shifted relative to the template ensuring structural compatibility between BINDI and BHRF1 ( Fig 4B ) . We used this case study to assess the capabilities of FunFolDes to sample closer conformations to those observed in the BINDI-BHRF1 crystal structure . In addition , we used the saturation mutagenesis data generated for BINDI [21] to evaluate the sequence space sampled by FunFolDes . A detailed description of this benchmark can be found in the Materials and Methods section . Briefly , we performed four different FunFolDes simulations: I ) binding target absent ( no_target ) ; II ) binding target present with no conformational freedom ( static ) ; III ) binding target present with side-chain repacking ( pack ) ; IV ) binding target present with side-chain repacking plus minimization and backbone minimization ( packmin ) . no_target simulations generated a low number of conformations compatible with the target ( <10% of the total generated designs ) ( S2A Fig ) . Upon global minimization more than 60% ( S2A Fig ) of the decoys were compatible with the binding target , at the cost of considerable structural drifts for both binder ( mean RMSD 3 . 3 Å ) and target ( mean RMSD 7 . 7 Å ) ( Fig 4C ) . These structural drifts reflect the energy optimization requirements by the relaxation algorithms but are deemed biologically irrelevant due to the profound structural reconfigurations . In contrast , simulations performed in the presence of the target clearly biased the sampling to more productive conformational spaces . RMSD drifts upon minimization were less than 1 Å for both designs and binding target ( Fig 4C ) . Global structural alignments of the designs fail to emphasize the differences of the helical arrangements ( S2B Fig ) . Thus , we aligned all the designs on the conserved binding motif ( Fig 4A ) and measured the RMSD over the three helices that compose the fold . FunFolDes simulations in the presence of the target sampled a mean RMSD of 3 Å ( lowest ≈ 2 Å ) compared to the BINDI structure ( Fig 4D ) , with the closest designs at approximately 2 Å , while the no_target simulation showed a mean RMSD of 4 . 5 Å ( lowest ≈ 2 . 5 Å ) . While we acknowledge that these structural differences are modest , the data suggests that they can be important to sample conformations and sequences competent for binding . We also analyzed Rosetta energy distributions of designs in the unbound state for the different simulations . We observed noticeable differences for the designs generated in the absence ( no_target ) and the presence of the binding target , -320 and -280 Rosetta Energy Units ( REUs ) , respectively ( Fig 4E ) . This difference is significant , particularly for a small protein ( 116 residues ) . We also observed considerable differences for the binding energies ( ΔΔG ) of the no_target and the bound simulations with mean ΔΔGs of -10 and -50 REUs , respectively ( Fig 4E ) . The energy metrics provide interesting insights regarding the design of functional proteins . Although the sequence and structure optimization for the designs in the absence of the target reached lower energies , these designs are structurally incompatible with the binding target and , even after refinement , their functional potential ( as assessed by the ΔΔG ) is not nearly as favorable as those performed in the presence of the binding target ( Fig 4F ) . These data suggest that , in many cases , to optimize function it may be necessary to sacrifice the overall computed energy of the protein , a common proxy to the experimental thermodynamic stability of the protein [43] . The existence of stability-function tradeoffs has been the subject of many experimental studies [44 , 45] , however , it remains a much less explored strategy in computational design , where it may also be necessary to design proteins with lower stability to ensure that the functional requirements can be accommodated . This observation provides a compelling argument to perform biased simulations in the presence of the binding target , which can be broadly defined as a “functional constraint” . To evaluate sequence sampling quality , we compared the computationally designed sequences to a saturation mutagenesis dataset available for BINDI [21] . The details of the dataset and scoring scheme can be found in the methods and S2 Fig . Briefly , point mutations beneficial to the binding affinity to BHRF1 have a positive score , deleterious mutants a negative score , and neutral score 0 . Such a scoring scheme , will yield a score of 0 for the BINDI sequence . Designs performed in the presence of the binding target obtained higher mean scores as compared to the no_target designs ( Fig 4G ) . The pack simulation , showed the highest distribution mean , having one design scoring better than the BINDI sequence . In some key positions at the protein-protein interface , the pack designs clearly outperformed those generated by the no_target simulation , when quantified by a per-position score ( Fig 4H ) ; meaning that amino-acids productive for binding interactions were sampled more often . This benchmark provides an example of the benefits of using a “functional constraint” ( binding target ) to improve the quality of the sequences obtained by computational design . Overall , the BINDI benchmark provided important insights regarding the best FunFolDes protocol to improve the design of functional proteins . To further test FunFolDes’s design capabilities , we sought to transplant a contiguous viral epitope that is recognized by a monoclonal antibody with high affinity ( Fig 5A ) . For this design , we used the RSVF site II epitope ( PDB ID: 3IXT [46] ) as the functional motif . This epitope adopts a helix-loop-helix conformation recognized by the antibody motavizumab ( mota ) [46] . In previous work we have designed proteins with this epitope , but started from a structurally similar template , where the RMSD between the epitope and the scaffold segment was approximately 1 Å over the helical residues . Here , we sought to challenge FunFolDes by using a distant structural template where the local RMSDs of the epitope and the segment onto which it was transplanted were higher than 2 Å . We used MASTER [47] to perform the structural search ( detailed description in Materials and Methods ) and selected as template scaffold the structure of the A6 protein of the Antennal Chemosensory system from the moth Mamestra brassicae ( PDB ID: 1KX8 [48] ) ( Fig 5A ) . The backbone RMSD between the conformation of the epitope and the insertion region in 1kx8 is 2 . 37 Å ( Fig 5B ) . The A6 protein is involved in chemical communication and has been shown to bind to fatty-acid molecules with hydrophobic alkyl chains composed of 12–18 carbons . Two prominent features are noticeable in the structure: two disulfide bonds ( Fig 5A ) and a considerable void volume in the protein core ( S3 Fig ) , thought to be the binding site for fatty acids . These features emphasize that the initial design template is likely not a very stable protein . In the design process we performed two stages of FunFolDes simulations to obtain a proper insertion of the motif in the topology ( Fig 5C ) . A detailed description of the workflow and metrics used for selection ( S3 Fig ) can be found in the Materials and Methods . A striking feature of our designs , when compared to the starting template , is that they had a much lower void volume , showing that FunFolDes generated structures and sequences that yielded well packed structures ( S3 Fig ) . We started by testing experimentally seven designs . Those that expressed in bacteria were further characterized using size exclusion chromatography coupled to a multi-angle light scatter ( SEC-MALS ) to determine the solution oligomerization state . To assess their folding and thermal stability ( Tm ) we used Circular Dichroism ( CD ) spectroscopy , and finally to assess their functional properties we used surface plasmon resonance ( SPR ) to determine binding dissociation constants ( KDs ) to the mota antibody . Out of the seven designs , six were purified and characterized further . The majority of the designs were monomers in solution and showed CD spectra typical of helical proteins . Regarding , thermal stabilities we obtained designs that were not very stable and did not unfold cooperatively ( 1kx8_02 ) , however we also obtained very stable designs that did not fully unfold under high temperatures ( 1kx8_07 ) ( S4 Fig ) . The determined binding affinities to mota ranged from 34 to 208 nM , which was an encouraging result . Nevertheless , compared to the peptide epitope ( KD = 20 nM ) and other designs previously published ( KD = 20 pM ) [4] , there was room for improvement . Therefore , we generated a second round of designs to attempt to improve stability and binding affinities . Driven by the observation that the native fold has two disulfide bonds , in the second round , we tested eight designed variants with different disulfide bonds and , if necessary , additional mutations to accommodate them . The disulfide bonded positions were selected according to the spatial orientation of residues in the designed models , with most of the disulfide bonds being placed at distal locations from the epitope ( >20 Å ) . All eight designs were soluble after purification and two were monomeric: 1kx8_d2 and 1kx8_3_d1 , showing CD spectra typical of helical proteins ( Fig 5D ) with melting temperatures ( Tms ) of 43 and 48°C ( Fig 5E ) , respectively . Remarkably , 1kx8_d2 showed a KD of 1 . 14 nM ( Fig 5F ) , an improvement of approximately 30-fold compared to the best variants of the first round . 1kx8_d2 binds to mota with approximately 20-fold higher affinity than the peptide-epitope ( KD ≈ 20 nM ) , and 50-fold lower compared to previously designed scaffolds ( KD = 20 pM ) [4] . This difference in binding is likely reflective of how challenging it can be to accomplish the repurposing of protein structures with distant structural similarity . Post-design analyses were performed to compare the sequence and structure of the best design model with the initial template . The global RMSD between the two structures is 2 . 25 Å . Much of the structural variability arises from the inserted motif , while the surrounding segments adopt a configuration similar to the original template scaffold . The sequence identity of 1kx8_d2 as compared to the native protein is approximately 13% . The sequence conservation per-position ( Fig 5G ) was evaluated through the BLOSUM62 matrix , where positive scores are attributed to the original residue or favorable substitutions and negative if unfavorable . Overall , 38 . 5% of the residues in 1kx8_d2 scored positively , and 61 . 4% of the residues had a score equal or lower than 0 . This is particularly interesting , from the perspective that several residues , unfavorable according to BLOSUM62 , yielded well folded and functional proteins . To further substantiate our experimental results , we performed structure prediction simulations of the designed sequences , where we observed that 1kx8_d2 presents a higher folding propensity than the WT protein ( S5A Fig ) . To evaluate if the predicted models presented the correct epitope conformation , we performed docking simulations and observed that they obtained lower binding energies than the native peptide-antibody complex , within similar RMSD fluctuations ( S5A Fig ) . The successful design of this protein is a relevant demonstration of the broad usability of FunFolDes and the overall strategy of designing functional proteins by coupled folding and design to incorporate functional motifs in unrelated protein scaffolds . Advances in computational design methodologies have achieved remarkable results in the design of de novo protein sequences and structures [7 , 8 , 11] . However , the majority of the designed proteins are “functionless” and were designed to test the performance of computational algorithms for structural accuracy . Here , we sought to use one of the hallmark proteins from de novo design efforts–TOP7 [13] ( Fig 6A ) –and functionalize it using FunFolDes . The functional site selected to insert into TOP7 was a different viral epitope from RSVF , site IV , which is recognized by the 101F antibody [49] . When bound to the 101F antibody , site IV adopts a β-strand-like conformation ( Fig 6B ) , which in terms of secondary structure content is compatible with one of the edge strands of the TOP7 topology ( Fig 6C ) . Despite the secondary structure similarity , the RMSD of the site IV backbone in comparison with TOP7 is 2 . 1 Å over 7 residues , and the antibody orientation in this particular alignment reveals steric clashes with TOP7 . Therefore , this design challenge is yet another prototypical application for FunFolDes , and we followed two distinct design routes: I ) a conservative approach where we fixed the amino-acid identities of roughly half of the core of TOP7 and allowed mutations mostly on the contacting shell of the epitope insertion site; and II ) a sequence unconstrained design where all the positions of the scaffold were allowed to mutate . We attempted five designs for recombinant expression in E . coli and two ( TOP7_full and TOP7_partial ) were selected for further biochemical and biophysical characterization , one from each of the two design strategies mentioned above . According to SEC-MALS , both behaved as monomers in solution , with TOP7_partial showing higher aggregation propensity . Both TOP7_full and TOP7_partial ( S6 Fig ) were folded according to CD measurements . TOP7_full showed a CD spectrum ( Fig 6D ) very similar to that of native TOP7 [13] . We observed that TOP7_full was much less stable than the original TOP7 ( Fig 6E ) ( Tm = 54 . 5°C ) . To quantify the functional component of TOP7_full , we determined a KD of 24 . 2 nM with 101F ( Fig 6F ) , within the range measured for the native viral protein RSVF ( 3 . 6 nM ) [49] . Importantly , the KD for TOP7_full is 2400 fold lower than that of the peptide-epitope ( 58 . 4 μM ) [49] , suggesting that productive conformational stabilization and/or extra contacts to the scaffold were successfully designed . Per-residue structural similarity and sequence recovery were evaluated for TOP7_full against TOP7 ( Fig 6G ) . Most conformational changes occur on the site IV insertion region and displacement of the neighboring alpha-helix , with the overall backbone RMSD being 1 . 5 Å . Remarkably , the sequence identity of the most aggressive design ( TOP7_full ) is only 28% , and using the BLOSUM62 based scoring system , we observe that most of the TOP7_full residues were actually favorable , obtaining positive scores . This low conservation is especially relevant considering that intensive studies on TOP7 have revealed the importance of beta-sheet conservation in order to keep its foldability [22 , 50 , 51] . Sequence folding prediction experiments showed that TOP7_full has a similar folding propensity to TOP7 and docking simulations also show lower binding energies as compared to the native peptide-antibody complex , reinforcing the experimental results obtained ( S5B Fig ) , In summary , our results show that FunFolDes repurposed a functionless protein by folding and designing its structure to harbor a functional site , which in this case was a viral epitope . Previously , these computationally designed proteins with embedded viral epitopes were dubbed epitope-scaffolds and showed their medical applicability as immunogens that elicited viral neutralizing antibodies [4] .
The robust computational design of proteins that bear a biochemical function remains an important challenge for current methodologies . The ability to consistently repurpose old folds for new functions or the de novo design of functional proteins could bring new insights into the determinants necessary to encode function into proteins ( e . g . dynamics , stability , etc . ) , as well as , important advances in translational applications ( e . g . biotechnology , biomedical , biomaterials , etc . ) . Here , we present Rosetta FunFolDes , that was conceived to embed functional motifs into protein topologies . This protocol allows for a global retrofitting of the overall protein topology to favorably host the functional motif and enhance the designability of the starting structural templates . FunFolDes has evolved to incorporate two types of constraints to guide the design process: topological and functional . The former entails the fragments to assemble the protein structure and sets of spatial constraints that bias the folding trajectories towards a desired topology; and the latter are the structure of the functional motif and the binding target . Our methodological approach fills the gap between conservative grafting approaches where the structure of the host scaffold is mostly fixed ( Rosetta Epigraft and Motifgraft [19 , 52] ) and the full de novo assembly of non-predefined protein topologies bearing functional motifs [53 , 54] . FunFolDes lies in between , by affording considerable structural flexibility to the host scaffold within the boundaries of its topology . In our view , FunFolDes is the most appropriate tool in situations where the structural mimicry of the functional motif is distant from the receiving scaffold’s site and overall conformational adaptations are necessary to design viable protein structures and sequences . We have extensively benchmarked FunFolDes , leveraging natural structural and sequence variation of proteins within the same fold , as well as deep mutational scanning data for the computationally designed protein BINDI [21] . In our first benchmark , we observed that FunFolDes biases the sampling towards improved structural and sequence spaces . Improved sampling may contribute to solve some of the major limitations in protein design , related to “junk” sampling , where many designs are not physically realistic , exhibiting flaws according to general principles of protein structure . Importantly , higher quality sampling will likely contribute to improve the success rate of designs that are tested experimentally . The BINDI benchmark allowed us to test FunFolDes in a system with extensive experimental data , which included both sequences and structures . Perhaps the most interesting observation was that designs that were theoretically within a sequence/structure space productive for binding , were far from the energetic minimum accessible to the protein fold in the absence of the binding target . This observation resembles the stability-function tradeoffs that have been reported from in vitro evolution experimental studies [44 , 45] . The large majority of the design algorithms are energy “greedy” and the sequence/structure searches are performed with the central objective of finding the global minimum of the energetic landscape . By introducing functional constrains into the simulations , FunFolDes presents an alternative way of designing functional molecules and skew the searches towards off-minima regions of the global landscape . We anticipate that such finding will be more relevant for protein scaffolds that need to undergo a considerable structural adaptation to perform the desired function . If confirmed that this finding is generalized across multiple design problems , it could be an important contribution for the field of computational protein design . Furthermore , we used FunFolDes to tackle two design challenges and functionalized two proteins with two distinct viral epitopes generating synthetic proteins that could have important translational applications in the field of vaccine development . In previous applications , FFL always used three-helix bundles as design templates , here we diversified the template folds and used an all-helical protein that is not a bundle ( 1kx8 ) and a mixed alpha-beta protein ( TOP7 ) , clearly demonstrating the applicability to other folds . For the 1kx8 design series , we evaluated the capability of using distant structural templates as starting topologies as a demonstration of how to functionally repurpose many naturally occurring protein structures available . We obtained stable proteins that were recognized by an anti-RSV antibody with high affinity , showing that in this case , we successfully repurposed a distant structural template for a different function , a task for which other computational approaches [55] would have limited applicability . We see this result as an exciting step forward towards using the wealth of the natural structural repertoire for the design of novel functional proteins . In a last effort , we functionalized a “functionless” fold , based on one of the first de novo designed proteins–TOP7 . For us , this challenge has important implications to understand the design determinants and biochemical consequences of inserting a functional motif into a protein that was mainly optimized for thermodynamic stability . We were successful in functionalizing TOP7 differently than previous published efforts . Previously , TOP7 was mostly used as a carrier protein with functional motifs fused onto loop regions or side chains grafted in the helical regions [22 , 50 , 51] , while our functional motif was embedded in the beta-sheet region . Exciting advances in the area of de novo protein design are also yielding many new proteins [11–13] , which could then be functionalized with FunFolDes , highlighting the usefulness of this approach . Interestingly , we observed that the functionalized version of TOP7 showed a dramatic decrease in thermodynamic stability as compared to the parent protein . While this observation can be the result of many different factors , it is compelling to interpret it as the “price of function” , meaning that to harbor function , TOP7 was penalized in terms of stability , which would be consistent with our findings in the BINDI benchmark and the experimental studies on stability-function tradeoffs . Recently , there have also been several de novo proteins designed for functional purposes [56]; however , these efforts were limited to linear motifs that carried the functions , and the functionalization was mainly accomplished by side-chain grafting [3 , 5] , relying on screening a much larger number of designed proteins . In the light of all the technical improvements , FunFolDes has matured to become a valuable resource for the robust functionalization of proteins using computational design . Here , we presented a number of important findings provided by the detailed benchmarks performed and used the protocol to functionalize proteins in design tasks that are representative of common challenges faced by the broad scientific community when using computational design approaches .
Rosetta Functional Folding and Design ( FunFolDes ) is a general approach for grafting functional motifs into protein scaffolds . It’s main purpose is to provide an accessible tool to tackle specifically those cases in which structural similarity between the functional motif and the insertion region is low , thus expanding the pool of structural templates that can be considered useful scaffolds . This objective is achieved by folding the scaffold after motif insertion while keeping the structural motif static . This process allows the scaffold’s conformation to change and properly adapt to the three-dimensional restrictions enforced by the functional motif . The pipeline of the protocol ( summarized in Fig 1 ) proceeds as follows: To test the ability of FunFolDes to recover the required conformational changes to stabilize a given structural motif , we created a benchmark of 14 target cases of proteins with less than 100 residues , named T01 to T14 . Each target case was composed of two structures of the same CATH superfamily [38] . One of the structures was representative of the shared structural features of the CATH family; we called this structure the reference . The second protein within each target case can present two types of structural variations with respect to the reference: I ) an insertion or deletion ( indel ) region and II ) a conformational change . Direct structural contacts between these two regions make it so that the indel region is likely the cause for the conformational change . We called this second structure the target ( Fig 2 , Table 1 ) . For each template protein we generated approximately 10000 decoys with FunFolDes by folding the target with the following conditions: 1 ) the indel region was considered as the motif , meaning that its structural conformation was kept fixed and no mutations allowed; 2 ) residue-pair distance constraints were derived from the secondary structure elements conserved between reference and the target ( constrained region ) ; 3 ) the region of the protein which showed the largest structural variations ( query region ) was constraint-free throughout the simulation . FunFolDes simulations were compared with constrained ab initio ( cst-ab initio ) simulations , the key difference being that the cst-ab initio simulations allowed for backbone flexibility in the motif region . The comparison between both approaches provides insights on the effects of a static segment in the folding trajectory of the polypeptide chain . In both scenarios a threshold was set after the folding stage where only decoys that had less than 5 Å RMSD from the template were carried to the design stage . The importance of the input fragments was assessed in our benchmark . Both protocols were tested with sequence-based fragments from FragmentPicker and structure-based fragments generated on-the-fly by FunFolDes . Comparison between the two types of fragments provides insight into how to utilize FunFolDes in the most productive manner . Structural recovery was evaluated by RMSD with the target structure . Global RMSD , understood as the minimum possible RMSD given the most optimal structural alignment , was used to assess the overall structural recovery of each decoy population . Local RMSD , was evaluated for the unconstrained ( query ) region and the motif by aligning each decoy to the template through the constrained segments ( excluding the motif ) . This metric aimed to capture the specific conformational changes required to accommodate the motif into the structure ( Fig 2B , S1B Fig ) . Sequence recovery was evaluated through two different criteria , sequence associated statistics and Hidden Markov Model ( HMM ) [41] . For the sequence associated statistics , we quantified sequence identity and similarity according to BLOSUM62 for the core residues of each protein , as defined by Rosetta’s LayerSelector [7] . Motif residues , that were not allowed to mutate , were excluded from the statistics . In the second criteria , position specific scoring matrices with inter-position dependency known as Hidden Markov Model ( HMM ) were used to evaluate fold specific sequence signatures . In this case , the closest HMM to the template structure provided by CATH was used to query the decoys and identify those that matched the HMM under two conditions: I ) an e-value under 10 and II ) a sequence coverage over 50% . Although these conditions are wide , they were within the ranges found between members of CATH superfamilies with high structural and sequence variability like the ones used in the benchmark . To assess the performance of FunFolDes in the presence of a binding target we recreated the design of BINDI as a binder for BHRF1 [21] , the BHRF1 binding motif from the BIM-BH3 protein ( PDB ID:2WH6 [66] ) was inserted into a previously described 3-helix bundle scaffold ( PDB ID:3LHP [3] ) . Four different design simulations were performed , one without the binder ( no_target ) and three in the presence of the binder ( static , pack and packmin ) . The difference between the last three relates to how the binding target was handled . In the static simulations the binding target was kept fixed and no conformational movement in the side chains was allowed throughout the protocol . In the pack simulations the side chains of the binding target were repacked during the binder design stage . Finally , in the packmin simulations the binding target side-chains were allowed to repack and both side-chains and backbone were subjected to minimization . In all cases , the two terminal residues on each termini of the binding motif were allowed backbone movement to optimize the insertion in the 3-helix bundle scaffold . For each of these simulations , approximately 20000 decoys were generated . For the no_target simulations the FunFolDes designs were docked to BHRF1 using the inserted motif as guide to assess their complementarity and interface metrics . In all the simulations , a final round of global minimization was performed , where both proteins of the complex were allowed backbone flexibility . During this minimization , the rigid-body orientation between the design and target was kept fixed . The final ΔΔG of the complexes was measured after the minimization step to enable comparisons between the no_target decoys and the remaining simulation modes . Structural changes related to this minimization step were evaluated as the global RMSD between each structure before and after the process , this measure is referred to as RMSD drift . Structural evaluation includes global RMSD against the BINDI crystal structure ( PDB ID: 4OYD [21] ) as well as local RMSDs against regions of interest in BINDI . In the Local-RMSD calculations the structures were aligned through the inserted motif , as its conformation and orientation relative to BINDI were kept fixed throughout all simulations . The local RMSD analysis was performed over all the helical segments contained in the structures ( all H ) , which provided a measurement of the structural shifts on the secondary structure regions of the designs . To evaluate the sequence recovery we leveraged BINDI’s saturation mutagenesis data analyzed by deep sequencing performed by Procko et al [21] . The experimental fitness of each mutation was summarized in a score matrix where a score was assigned to each amino-acid substitution for the 116 positions of the protein ( S2C Fig ) . In summary , point mutations that improved BINDI’s binding to BHRF1 are assigned positive scores while deleterious mutations present negative values . These scores were computed based on experimental data where the relative populations of each mutant were compared between a positive population of cells displaying the designs ( binders ) and negative populations ( mutants that display but don’t bind ) , these experiments have been described in detail elsewhere [21] . Upon normalization by the BINDI sequence score , a position sequence specific matrix ( PSSM ) was created . Like the original data , this matrix also assigns a positive score to each point mutation if it resulted in an improved binding for the design . This normalization provides a score of 0 for the BINDI sequence , which is useful as a reference score . To experimentally validate the capabilities of FunFolDes and insert functional sites in structurally distant templates , we grafted the 11 residues from the site II epitope from the Respiratory Syncytial Virus ( RSV ) protein F ( PDB ID:3IXT [46] ) , residues 256 to 276 in chain P ( NSELLSLINDMPITNDQKKLMSN ) , into heterologous scaffolds . This is a continuous , single segment , helix-loop-helix conformation epitope . The main objective was to challenge the capabilities of FunFolDes to reshape the structure of the scaffold to the requirements of the functional motif . We searched for insertion segments with RMSDs towards the site II structure higher than 2 Å . The structural searches were performed using MASTER [47] where we used the full-length site II segment as a query against a subset of 17539 protein structures from the PDB , composed of 30% non-redundant sequences included in the MASTER distribution . The RMSD between the query and segments on the scaffolds were assessed using backbone Cαs . All matches with RMSDCα < 5 . 5 Å relative to site II were further filtered by protein size , where only proteins between 50 and 100 residues were kept . These scaffolds were then ranked regarding antibody-binding compatibility , where each match was realigned to the antibody–epitope complex and steric clashes between all glycine versions of the scaffold and antibody were quantified using Rosetta . All matching scaffolds with ΔΔG values above 100 REU were discarded under the assumption that their compatibility with the antibody binding mode was too low . The remaining scaffolds were visually inspected and PDB ID: 1kx8 [48] ( RMSDCα = 2 . 37 Å ) was selected for design with FunFolDes . The twenty-one residues from the site II epitope ( motif ) as present in 3IXT were grafted into a same sized segment ( residues 79–100 ) of 1kx8 using the NubInitioMover . Up to three residues in each insertion region of the motif were allowed backbone flexibility in order to model proper conformational transitions in the insertion points . Atom pair constraints with a standard deviation of 3 Å were defined for all template residues , leaving the motif segment free of constraints . The generous standard deviation was defined to allow for necessary conformational changes to retrofit the motif within the topology . The total allowed deviation from the template was limited to 5 Å to ensure the retrieval of the same topology . In this design series we used sequence-based fragments generated with the 1kx8 native sequence . Three cycles of design/relax were performed on the template residues with the FastDesignMover . A first generation of 12500 designs was ranked according to Rosetta energy . From the top 50 decoys , only one presented the motif without distortions on the edges derived from the allowed terminal flexibility . This decoy was used as template on the second generation of FunFolDes to enhance the sampling of properly folded conformations , with the same input conditions as before . In the second generation , the top 50 decoys according to Rosetta energy were further optimized through additional cycles of design/relax . The final designs were again selected using a composite filter based on Rosetta energy ( top 50 ) , buried unsatisfied polar atoms ( <15 ) , cavity volume ( < 75 Å3 ) and we obtained a final set of 15 candidates from which we prioritized 6 upon the inspection of the computational models . In addition , we also quantified the secondary structure prediction using PSIPRED [64] , all the tested designs had more 65% ( ranging from 65% to 92% ) of the residues with correct secondary structure prediction . The final designs were manually optimized , this process entailed the removal of designed hydrophobic residues in solvent exposed positions , in this designs series we performed between 2 and 4 mutations obtaining 7 designs from the previous 6 . After the initial characterization , designs with added disulfide bridges were generated to improve protein stability and affinity ( S3 Fig , S4 Fig ) . To do so , we use the Rosetta DisulfidizeMover , which screened the designed models for pairs of residues with favourable three-dimensional orientations to host disulfide bonds . Upon the placement of the disulfide bond , the neighbouring residues within 10 Å of the disulfide , were designed to optimize the residue interactions and improve the packing of the designed region . In a second effort to test the design capabilities of FunFolDes we sought to insert a functional motif in one of the first de novo designed proteins–TOP7 ( PDB ID: 1QYS [13] ) Six residues from the complex between the antibody 101F and the peptide-epitope , corresponding to residues 429–434 in chain P ( RGIIKT ) on the full-length RSV F protein [49] , were grafted into the edge strand of the TOP7 backbone using FunFolDes . The choice between epitope and hosting scaffold was made based on the secondary structure adopted by the epitope and the structural compatibility of TOP7 , the RMSDCα between the epitope an the insertion segment was 2 . 07 Å . To ensure that the majority of the β-strand secondary structure was maintained throughout the grafting protocol , the epitope motif was extended by one residue and a designed 4-residue β-strand ( KVTV ) pairing with the backbone of the C-terminal epitope residues was co-grafted as a discontinuous segment into the adjacent strand of the TOP7 backbone . With this strategy we circumvented a Rosetta sampling limitation , where often times extensive sets of constraints to achieve backbone hydrogen-bonds on beta-strands are necessary [8] . After defining the motif consisting of the epitope plus the pairing strand and the sites of insertion on the TOP7 scaffold , FunFolDes was used to graft the motif . Backbone flexibility was allowed for the terminal residues of the functional motif and a β-turn connection between the two strands was modelled during the folding process ( NubInitioMover ) . During the folding process , the 101F antibody was added to the simulation in order to limit the explored conformational space productive for binding . Finally , the NubInitioLoopClosureMover was applied to ensure that a proper polypeptide chain was modelled and no chain-breaks remained , a total of 800 centroid models were generated after this stage . Next , we applied an RMSD filter to select scaffolds with similar topology to TOP7 ( < 1 . 5 Å ) and a hydrogen bond long-range backbone score ( HB_LR term ) to favour the selection of proteins with proper beta-sheet pairing . The top 100 models according the HB_LR score and <1 . 5 Å to TOP7 , were then subjected to an iterative sequence-design relax protocol , alternating fixed backbone side-chain design and backbone relaxation using the FastDesignMover . Two different design strategies were pursued: I ) partial design—amino acid identities of the C-terminal half of the protein ( residues 45 through 92 ) were retained from TOP7 while allowing repacking of the side chains and backbone relaxation; II ) full-design—the full sequence space in all residues of the structure ( with the exception of the 101F epitope ) was explored . No backbone or side chain movements were allowed in the 6-residue epitope segment whereas the adjacently paired β-strand was allowed to both mutate and relax . Tight Cα atom-pair distance constraints ( standard deviation of 0 . 5 Å ) were used to restrain movements of the entire sheet throughout the structural relaxation iterations . From the 100 designs generated , only those that passed a structural filter requiring 80% beta-sheet secondary structure composition after backbone relaxation were selected for further analysis . The 93 designs passing this filter were evaluated with a composite filter based on REU score ( Top 50 ) , hydrogen-bond long-range backbone interactions ( < -113 ) and core packing ( > 0 . 7 ) . The selected designs were finally submitted to human-guided optimisation to correct for hydrophobic residues that were designed in solvent exposed positions ( 1–3 ) and shortening of the connecting loop between the two inserted strands using the Rosetta Remodel application [67] . Interestingly , in an attempt to reproduce the same grafting exercise with MotifGraftMover [55] , this resulted in non-resolvable chain breaks when trying to graft either the two segment-motif or the epitope alone into the TOP7 scaffold . DNA sequences of the designs were purchased from Twist Bioscience . For bacterial expression the DNA fragments were cloned via Gibson cloning into a pET21b vector containing a C-terminal His-tag and transformed into E . coli BL21 ( DE3 ) . Expression was conducted in Terrific Broth supplemented with ampicillin ( 100 μg/ml ) . Cultures were inoculated at an OD600 of 0 . 1 from an overnight culture and incubated at 37°C with a shaking speed of 220 rpm . After reaching OD600 of 0 . 7 , expression was induced by the addition of 1 mM IPTG and cells were further incubated for 4-5h at 37°C . Cells were harvested by centrifugation and pellets were resuspended in lysis buffer ( 50 mM TRIS , pH 7 . 5 , 500 mM NaCl , 5% Glycerol , 1 mg/ml lysozyme , 1 mM PMSF , 1 μg/ml DNase ) . Resuspended cells were sonicated and clarified by centrifugation . Ni-NTA purification of sterile-filtered ( 0 . 22 μm ) supernatant was performed using a 1 ml His-Trap FF column on an ÄKTA pure system ( GE healthcare ) . Bound proteins were eluted using an imidazole concentration of 300 mM . Concentrated proteins were further purified by size exclusion chromatography on a Superdex 75 300/10 GL or a Hiload 16/600 Superdex 75 pg column ( GE Healthcare ) using PBS buffer ( pH 7 . 4 ) as mobile phase . For IgG expression , heavy and light chain DNA sequences were cloned separately into pFUSE-CHIg-hG1 ( InvivoGen ) mammalian expression vectors . Expression plasmids were co-transfected into HEK293-F cells in FreeStyle medium ( Gibco ) using polyethylenimine ( Polysciences ) transfection . Supernatants were harvested after 1 week by centrifugation and purified using a 5 ml HiTrap Protein A HP column ( GE Healthcare ) . Elution of bound proteins was accomplished using a 0 . 1 M glycine buffer ( pH 2 . 7 ) and eluents were immediately neutralized by the addition of 1 M TRIS ethylamine ( pH 9 ) . The eluted IgGs were further purified by size exclusion chromatography on a Superdex 200 10/300 GL column ( GE Healthcare ) in PBS buffer ( pH 7 . 4 ) . Protein concentrations were determined by measuring the absorbance at 280 nm using the sequence calculated extinction coefficient on a Nanodrop ( Thermo Scientific ) . Far-UV circular dichroism spectra of designed scaffolds were collected between a wavelength of 190 nm to 250 nm on a Jasco J-815 CD spectrometer in a 1 mm path-length quartz cuvette . Proteins were dissolved in PBS buffer ( pH 7 . 4 ) at concentrations between 20 μM and 40 μM . Wavelength spectra were averaged from two scans with a scanning speed of 20 nm min-1 and a response time of 0 . 125 sec . The thermal denaturation curves were collected by measuring the change in ellipticity at 220 nm from 20 to 95°C with 2 or 5°C increments . Multi-angle light scattering was used to assess the monodispersity and molecular weight of the proteins . Samples containing between 50–100 μg of protein in PBS buffer ( pH 7 . 4 ) were injected into a Superdex 75 300/10 GL column ( GE Healthcare ) using an HPLC system ( Ultimate 3000 , Thermo Scientific ) at a flow rate of 0 . 5 ml min-1 coupled in-line to a multi-angle light scattering device ( miniDAWN TREOS , Wyatt ) . Static light-scattering signal was recorded from three different scattering angles . The scatter data were analyzed by ASTRA software ( version 6 . 1 , Wyatt ) To determine the dissociation constants of the designs to the mota or 101F antibodies , surface plasmon resonance was used . Experiments were performed on a Biacore 8K at room temperature with HBS-EP+ running buffer ( 10 mM HEPES pH 7 . 4 , 150 mM NaCl , 3mM EDTA , 0 . 005% v/v Surfactant P20 ) ( GE Healthcare ) . Approximately 1200 response units of mota or 101F antibody were immobilized via amine coupling on the methyl-carboxyl dextran surface of a CM5 chip ( GE Healthcare ) . Varying protein concentrations were injected over the surface at a flow rate of 30 μl/min with a contact time of 120 sec and a following dissociation period of 400 sec . Following each injection cycle , ligand regeneration was performed using 3 M MgCl2 ( GE Healthcare ) . Data analysis was performed using 1:1 Langmuir binding kinetic fits within the Biacore evaluation software ( GE Healthcare ) .
FunFolDes is available as part of the Rosetta software suite and is fully documented in the Rosetta Commons documentation website as one of the Composite Protocols . All data and scripts necessary to recreate the analysis and design simulations described in this work are available at https://github . com/lpdi-epfl/FunFolDesData . | The ability to use computational tools to manipulate the structure and function of proteins has the potential to impact many facets of fundamental and translational science . Due to our limited understanding of the principles that govern protein function and structure , the computational design of functional proteins remains challenging . We developed a computational protocol ( Rosetta FunFolDes ) to facilitate the insertion of functional motifs into heterologous proteins . We performed extensive in silico benchmarks , and found that when the design of function is required the global energy minima may not be the optimal solution , in line with previously reported experimental studies . Further , we used FunFolDes to design two novel functional proteins , displaying two viral epitopes that can be of interest for vaccine development . The designed proteins were experimentally characterized , showing that functionalization was successfully achieved . These results highlight the capability of FunFolDes to address common challenges on the design of functional proteins . In particular , the reduced structural compatibility between functional sites and host scaffolds , effectively enabling the repurposing of old protein folds for new functions . Overall , FunFolDes provides new means to accomplish the challenging task of functionalizing computationally designed proteins . |
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Regulatory interactions buffer development against genetic and environmental perturbations , but adaptation requires phenotypes to change . We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus . We find extensive variation in gene expression in this network throughout development in a natural population , some of which has a heritable genetic basis . Switch-like regulatory interactions predominate during early development , buffer expression variation , and may promote the accumulation of cryptic genetic variation affecting early stages . Regulatory interactions during later development are typically more sensitive ( linear ) , allowing variation in expression to affect downstream target genes . Variation in skeletal morphology is associated primarily with expression variation of a few , primarily structural , genes at terminal positions within the network . These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role .
The process of development is a balancing act in an unpredictable world: it is often remarkably resilient , producing consistent phenotypes in the face of new mutations and environmental perturbations , but it is also adaptable , allowing for the evolution of novel phenotypic traits in response to environmental change . The first property is essential for the survival of individuals and the second for the persistence of populations . Yet these two critical aspects of development seem diametrically opposed , since buffering stabilizes phenotypes while adaptation permanently changes them [1]–[3] . An outstanding challenge for both systems biology and evolutionary biology is understanding the molecular mechanisms that allow development to buffer phenotypes while retaining flexibility [4] , [5] . Key to understanding both buffering and adaptation is measuring how , and to what extent , variation in developmental gene function impacts downstream phenotypes [6] , [7] . At its core , buffering must result from reduced variation in underlying developmental processes such as cell fate specification and morphogenesis . Although the detailed molecular mechanisms underlying developmental buffering are not clear , they likely involve redundancy , modularity , feedback loops , and threshold ( nonlinear ) interactions among regulatory molecules that reduce the downstream consequences of variation within regulatory networks [8] , [9] . The extent to which variation in molecular processes such as transcription , splicing , translation , and phosphorylation during early development affects later processes and , eventually , organismal phenotypes remains poorly understood . Measuring such variation during development is also critical for understanding adaptation , as it provides the raw material for natural selection . Several well studied cases demonstrate that alleles segregating in wild populations influence morphology in ecologically significant ways by changing specific regulatory interactions during development that in turn alter gene expression [10]–[12] . It remains unclear , however , whether such alleles are common within natural populations and how they are able to influence morphology despite buffering during development . In this study , we examine three pertinent questions for understanding the relationship between developmental buffering and evolvability: How much variation in the expression of developmental regulatory genes exists within a natural population ? What impact does this variation in gene expression have on downstream genes within a regulatory network ? And finally , how does expression variation during development influence the morphological phenotypes that lie at the interface between organism and environment and are therefore potential targets of natural selection ? To address these questions , we measured naturally occurring variation in gene expression within a well-defined developmental gene regulatory network , as well as the impact of expression variation on downstream gene expression and on morphology . The network of >100 genes that we examined ( Figure 1 ) contains all of the genes known to be involved in axial and cell type specification during embryogenesis in the purple sea urchin , S . purpuratus [13]–[24] as well as many of the important genes involved in mesoderm specification and the formation of the larval skeleton . Most of the >200 connections in this network are direct intermolecular interactions identified from extensive experimental studies including knock-downs , over-expression assays , cell transplantations , protein:DNA binding assays , and transient transfection assays . The network thus provides a detailed account of gene regulatory interactions necessary , though not sufficient , for early development . Three additional features make this gene regulatory network particularly useful for studying the consequences of variation in gene expression during development . First , the network spans the major phases of development: from the unfertilized egg , through embryonic patterning and cell fate specification , to morphogenesis and terminal cellular differentiation . It thus offers an opportunity to examine how changes in the expression of regulatory genes influence phenotypes throughout development . Second , the network includes all of the regulatory genes and many of the structural genes known to be involved in the formation of the larval skeleton , a discrete and readily imaged three-dimensional structure [25] . This allows one to quantify covariation between molecular processes and the morphological structures they produce within the context of a known topology of largely direct regulatory interactions ( Figure 1 ) . Because variation in the size and shape of the larval skeleton influences fitness [26]–[30] , it provides a relevant phenotypic readout of the consequences of variation in gene expression throughout the network . Third , wild populations of S . purpuratus harbor high levels of genetic diversity [31] , [32] , including substantial variation within cis-regulatory elements that mediate regulatory interactions within the network [33]–[36] . This genetic variation provides a natural source of perturbation that allows one to measure the impact of functional variation on developmental processes and morphology . Because these perturbations derive from natural rather than induced mutations , they are directly relevant to understanding the operation and evolution of development in the wild . We measured variation in gene expression throughout the gene regulatory network at multiple developmental time points in crosses of outbred individuals of S . purpuratus collected from a natural population . We also measured the phenotypic impact of this variation , both on downstream gene expression and on the resulting morphology of the larval skeleton . While several previous studies have examined correlations between natural variation in gene expression and ecologically significant traits from single genes [10] , [12] , [37] , [38] or within short pathways [6] , [39] , [40] , this is the first study we are aware of that has sought to quantify expression variation throughout an extensive gene regulatory network spanning development from embryogenesis to the production of organismal traits , and to relate variation in gene expression throughout a developmental network and across developmental stages to specific morphological trait consequences .
Variation in developmental gene expression within a population can arise from many sources , including genetic differences and non-genetic parental influences such as egg quality , which contribute to resemblance among relatives , as well as from environmental influences and stochastic processes , which do not . Because the cultures we analyzed were derived from a controlled cross with known parents ( the NCII breeding design ) , we were able to estimate the magnitude of genetic and non-genetic parental contributions ( collectively , parental effects ) , based on correlations in expression levels among related individuals relative to the population as a whole . In the NCII design , male and female contributions each provide a direct estimate of the additive ( heritable ) genetic contribution to gene expression variation [41] . In the case of the male effects , the estimate is direct , as sperm contribute to offspring phenotypes almost exclusively through genetic effects . In the case of maternal effects , however , estimates can be distorted by differences in additional maternal contributions such as mRNA or nutrients loaded into the egg ( which may themselves have a genetic component ) . For a variety of reasons ( discussed below and in Text S1 ) , we believe non-genetic maternal effects to be relatively minor subsequent to the first time point we examined . We observed pervasive parent-of-origin effects on gene expression throughout development . The expression levels of most genes in the network ( 72/74 ) showed significant paternal and/or maternal effects at one or more of the time points sampled . For most of these genes , we could ascribe a direct contribution from genetic variation at multiple time points ( Table S1 ) : some 70% of the genes ( 52/74 ) showed significant paternal effects , evidence of widespread genetic influences on quantitative variation in gene expression , since paternal effects are likely to be largely genetic [41] . In most cases , the magnitude of variation explained by parent-of-origin was modest , consisting of quantitative differences in the timing or level of gene expression on the order of 10%–15% relative to mean expression level among families ( Table S1 ) . For instance , up-regulation of Nkx2 . 2 , which encodes a transcription factor , was delayed in offspring of one female relative to other families despite reaching typical levels later ( Figure 2A ) , while expression levels of SM30-E , which encodes a dominant component of the biomineral matrix of the skeleton , were on average higher and lower in offspring of two different males relative to other families ( Figure 2B ) . In a few cases , differences in gene expression among families were more substantial . The key regulatory gene Pmar , whose product forms part of a double-negative gate that restricts expression of skeletogenic genes to a subset of cells in the early embryo [42] , was expressed at sustained high levels in the offspring of one female long after it was barely detectable in other families ( Figure 2C ) . Transcripts of VEGFR , which encodes a receptor that mediates critical cell fate decisions [43] , were abundant in the very early embryo in offspring of just one female ( Figure 2D ) , perhaps reflecting maternal loading of transcripts . Because we assayed gene expression in whole embryos , we cannot distinguish whether these differences involved increased expression within their normal spatial domains or ectopic expression involving additional cells . What is clear , however , is that there was no detectable impact of these two anomalous expression profiles on the subsequent expression of known downstream targets , even though Pmar and VEGFR both encode key regulators of the skeletogenic portion of this regulatory network . Zygotic transcription occurs at very low levels during the first few cell cycles after fertilization in S . purpuratus and increases dramatically by 4th–5th cleavage [44] , [45] . This corresponds approximately to our time point 1 , which should thus show strong maternal contributions to variation in transcription levels . Beginning with time point 2 , maternal and paternal genetic contributions should be equal in magnitude , and any significantly larger maternal effects would constitute evidence for maternal-specific genetic and non-genetic ( e . g . , environmental ) contributions to gene expression variation [41] . Consistent with an important role for genetic influences on gene expression , we found that maternal effects were significantly greater than paternal effects only at the first time point ( p = 0 . 004 , Wilcoxon ) ( Figure 3 ) . In contrast , the distribution of paternal effects remained fairly constant across developmental time points , with an important contribution to the average parental effect at each time point coming from a few transcripts with relatively high paternal contributions ( Figure S1B ) . Furthermore , we observed a shift from a modest , though significant , correlation between the magnitude of maternal and paternal effects at time point 1 ( Spearman Rho = 0 . 54 , p = 4×10−5 ) to higher correlations at later time points ( Spearman Rho = 0 . 87 , p = 2 . 2×10−5 ) , consistent with predominately zygotic transcription after the first time point . The magnitude of parental influences on variation in gene expression clearly changed during development ( Kruskal-Wallis , p = 2 . 9×10−5 , χ2 = 61 . 21 , df = 6 ) ( Figures 3 and S1B ) , with the first two time points showing significantly greater variation in parental effects among families than later time points ( mean σ2est = 0 . 286 versus 0 . 064; Wilcoxon , p = 3 . 48×10−8 , W = 32 , 208 ) ( Figure S1A ) . Differences in the magnitude of parental variances among time points remained significant even after excluding time point 1 ( Kruskal-Wallis , p = 2 . 4×10−3 , χ2 = 18 . 44 , df = 5 ) , suggesting that the zygotic component of genetic influences on variation in gene expression changes throughout development and that genetic contributions to gene expression are at least as great during early development as they are during morphogenesis . Importantly , we obtain qualitatively similar results when we examine only statistically significant paternal effects ( Kruskal-Wallis , p = 1 . 5×10−3 , σ2est = 0 . 038 versus 0 . 026 p = 0 . 09 Wilcoxon ) or when we look at mean parental contributions for genes only at the times in which they are known from prior research ( see above ) to be involved in direct regulatory interactions ( protein:DNA and protein:protein ) ( Kruskal-Wallis , p = 0 . 039 , σ2est = 0 . 18 versus 0 . 07 p = 0 . 051 Wilcoxon ) . Taken together , these results suggest that this wild population harbors substantial amounts of genetic variation that influence gene expression during even the earliest stages of embryonic and larval development . This finding is consistent with the high levels of genetic variation in known cis-regulatory sequences that previous studies have documented in wild populations of S . purpuratus , including SNPs within experimentally validated transcription factor binding sites regulating the expression of FoxB , Endo16 , and SM50 [33]–[36] . In order to understand the impact that variation in the expression of regulatory genes has on downstream targets , we first examined correlation coefficients ( r2 ) between pairs of genes for which there is experimental evidence of a direct regulatory interaction . We restricted these analyses to time points when each interaction is known to occur ( http://sugp . caltech . edu/endomes/; no data are available for time point 7 ) . The information in this database has been painstakingly compiled , and represents arguably the most complete picture of a developmental gene regulatory network to date [13]–[24] . It is not , however , perfect: most noted regulatory interactions are likely to be real , but many others remain to be identified . This has a minor impact on the comparison of correlation coefficients between known interactors as compared with random pairs of genes thought not to interact . However , inadvertently including some actual interactions in the background model makes the test more conservative , so this is not a major concern . As expected , correlations over all stages were , on average , significantly stronger between genes that are known to interact than between genes with no known regulatory interactions ( p<0 . 01 , permutation ) . However , the strength of these correlations changed dramatically during development . During the first two time points , r2 values were no greater , on average , for interacting genes ( whether via protein:DNA or protein:protein interactions ) than for non-interacting pairs ( Figure 4A ) . In contrast , correlations rose significantly during the subsequent time points , suggesting that functional dependencies between genes become quantitatively stronger and more tightly correlated later in development ( p<0 . 01 for each of time points 3–6 , permutation ) . Importantly , correlations among pairs of genes expressed in the same tissue were often negative and were not , on average , greater than those between random pairs of genes ( Figures S2 and S3 ) . Changes in correlations over development are thus unlikely to stem from differences in tissue composition or developmental rates among broods . Regulatory interactions also differed qualitatively during development . Some gene pairs showed strong dependencies throughout development ( e . g . , GataE→Fmo1 , 2 , 3 , Figure 5A ) , suggesting that the expression level of the downstream target was sensitive to variation in the expression level of its immediate upstream regulator . In other cases , the expression of downstream genes appeared largely insensitive to quantitative variation in a direct upstream regulator beyond some threshold level of expression ( e . g . , Dri→CyP , Figure 5B ) . This second example suggests a situation in which the downstream gene is effectively buffered from variation in the expression level of the upstream regulator , at least within the bounds of normal variation encountered in the wild population . On the basis of these patterns of correlation , we next classified each experimentally validated pair-wise regulatory interaction as being either sensitive ( expression levels of the two genes show a statistically significant correlation; e . g . , Figure 5A ) or insensitive ( switch-like or Boolean; no quantitative relationship between expression levels above a threshold; e . g . , Figure 5B ) . Because the quantitative and qualitative nature of a regulatory interaction can change during development ( Figure 5C ) , we analyzed each interaction independently at each time point at which it occurs . To ensure an equal power to detect sensitive edges at each stage of development , we quantified overall levels of variation at each time point ( Table S2 ) and observed no relationship between these numbers and the number of edges classified as sensitive/insensitive . Furthermore , because the average expression levels of the genes used in this test were approximately equal at each time point , we can rule out influences from changes in the accuracy of expression measurements between time points . Interestingly , the relative proportion of sensitive and insensitive edges changed over time ( Figure 4B; χ2 = 10 . 91 , p = 0 . 032 ) . The proportion of sensitive edges increased substantially from early to later stages of development when considering zygotic transcription only . The first time point marks an exception . However , at this time a large proportion of transcripts present are still maternally derived [44] , [45] , and covariances between genes are both significantly greater and more structured than at subsequent stages of development as revealed by comparisons of genetic covariation matrices across time points ( see Methods and Text S1 ) . Correlations among genes at this earliest time point we sampled are thus likely confounded by differences in maternal provisioning among females affecting sets of genes in unison rather than causal relationships among interacting genes in the network , a result we discuss further below . Insensitive regulatory interactions may allow more genetic variation to accumulate within the population by buffering downstream phenotypes from the consequences of mutations impacting the expression of upstream genes . To test this possibility , we compared the size of paternal effects , the most conservative estimator of genetic influences [41] , for genes upstream of insensitive versus sensitive regulatory interactions . We observed significantly greater paternal effects upstream of insensitive interactions than sensitive ones ( mean σ2paternal = 0 . 029 for insensitive versus 0 . 016 for sensitive , p = 0 . 037 , Kolmogorov-Smirnov ) . In order to account for changes over developmental time in average paternal effects and in the proportion of insensitive interactions , we repeated the analysis incorporating the effects of developmental time point in the model and the result was still significant ( p = 0 . 048 ) . Thus , insensitive regulatory interactions may contribute to buffering and may influence the distribution of genetic variation across the network . While gene expression is an important intermediate phenotype , the ultimate products of developmental gene regulatory networks are the morphological and physiological traits upon which natural selection directly acts . To better understand how natural variation in gene expression within the network influences ecologically relevant traits , we next examined associations between variation in gene expression throughout the network and variation in the morphology of the larval skeleton . Because the size and shape of the larval skeleton is closely associated with feeding rates and survivorship [27]–[29] , morphological variation in this structure ( Figure 6A ) is likely to be subject to natural selection . After measuring the skeletons of larvae from each culture at time point 7 using established morphological landmarks [46] , [47] , we carried out a principle components analysis ( PCA ) , and found that the first three principle components collectively explained ∼89% of overall variation in size and shape ( Table S3 ) . These factors showed strong evidence of parental effects , particularly maternal effects ( Table S4 ) , as one might expect given the evidence for a relationship between variation in maternal egg quality and skeletal shape in echinoderms [47] , [48] . For each gene at each time point , we then calculated Pearson's correlation coefficient between transcript abundance and each of these first three principal components . After correcting for multiple comparisons , eight genes showed statistically significant associations between gene expression at one or more time points and one or more of the principle components of skeletal variation ( Figure 6B; Table S5; Text S1 ) . Although our gene expression measurements were based on whole embryos , five of the eight genes are transcribed only in skeletogenic cells at the time points we examined , suggesting that their effects on skeletal variation are exerted through this cell type alone . Six of the genes reside within the skeletogenic subnetwork: three ( SM30-E , Msp130 , SM50 ) encode abundant protein components of the biomineral matrix of the skeleton [25] , [49] , another ( C-lectin ) encodes one of the most abundant proteins in the cells that secrete the skeletal matrix [50] , and two ( FoxB and Hex ) encode transcription factors that are direct activators of the four structural genes just mentioned [14] . The remaining two genes , Dkk and Su ( H ) , are regulators of Wnt and Notch signaling , respectively [20] , [51] , whose functions in sea urchin development have primarily been studied before the onset of skeletogenesis . Interestingly , Dkk is an abundant component of the phosphoproteome of adult skeletal matrix [49] , raising the possibility that it has a more direct role in skeletogenesis . Because the majority of associated genes ( five of eight ) are expressed exclusively within skeletogenic cells , correlations between their expression and skeletal morphology could be explained by differences in the number of skeletogenic cells among families . For two reasons , this is unlikely to be the case . First , as mentioned above , correlations in the expression of skeletogenic cell genes among families are no greater than background , and are often negative . Second transplant experiments that artificially increase skeletogenic cell number by more than 2-fold , which is far outside the normal range of variation , have no measurable impact on the size or shape of the larval skeleton [52] , [53] , arguing against a direct link between skeletogenic cell number and skeletal morphology . These results indicate that: ( 1 ) natural variation in the expression level of several genes within the network has an impact on an ecologically important structure in the larva; ( 2 ) the genes with the largest impact are located at the termini of this gene regulatory network; and ( 3 ) these genes primarily encode cell type-specific structural proteins and their immediate regulators . Analyses focused on single gene associations ( previous section ) may overlook cases in which an upstream regulator affects a morphological trait through its influence on other genes . To investigate this possibility , we carried out a two block partial-least squares ( 2B-PLS ) analysis [54] . This technique , which bears some similarity to PCA , is applied to two sets of data , in this case gene expression measures and morphological measures , and seeks to find weighted groupings within each dataset that together maximize the correlation between the two sets of traits . This multivariate correlation is quantified by the RV statistic [55] , which is analogous to Pearson's r2 . Using 2B-PLS analysis , we found a modest but statistically significant overall relationship between variation in the expression of active genes and in skeletal morphology ( RV = 0 . 163 , p = 0 . 002; permutation ) . A large weight in this association ( 49% ) was attributable to the first pair of factors . 84% of the weighting in the skeletal factor of this first pair was associated with the body rod length and thus with the overall length of larvae ( Tables S6 and S7 ) . Interestingly , the strongest associations showed a strikingly nonrandom distribution among developmental time points . The 10% most highly weighted genes in the first expression factor showed a strong enrichment ( 11 of 22 expression measures ) for expression at time point 1 ( p = 5 . 42×10−7 , χ2 = 25 . 108 , df = 1 ) . A further seven of these top 10% expression measures corresponded to later stages of development ( Table S8 ) , including two associations ( time points 5 and 6 ) with SM30-E , which encodes a major component of the skeletal matrix . Only a few expression measures ( three of 22 ) among the most heavily weighted components of the first gene expression axis involved regulatory genes at intermediate time points: Pmar , which encodes a transcription factor critical in skeletogenic cell fate specification , and SM50 and SM27 , both of which encode components of the skeletal matrix with primary functions in later in development ( Figure 6C ) . Next , we examined the influence of gene expression on multidimensional variation in skeletal morphology by calculating the total contribution of each expression measure as the weighted sum of its loadings on each of the six 2B-PLS factors . The 11 gene-time point combinations that comprised the upper tail of the distribution ( top 5% of scores ) were again heavily weighted towards the top and bottom of the network ( Figure 6D; Table S9 ) . Six of the expression measures correspond to transcription factors expressed during the first time point , while two others involve Pmar , which was also associated with skeletal size ( previous paragraph ) , at time points 2 and 3 . Included in this list were only two genes expressed later in development: SM30-E ( time points 5 and 6 ) , a second gene that was also associated with skeletal size in our PCA-based analysis ( previous paragraph ) , and FoxO ( time point 6 ) , which encodes a transcription factor that regulates the epithelial-to-mesenchymal transition that skeletogenic cells undergo prior to commencing biomineralization ( L . Saunders and D . McClay , submitted ) . Thus , the 2B-PLS analyses indicate that the strongest gene expression-morphology associations are concentrated bimodally , in very early development and in terminal differentiation . Since skeletal variation and many of the genes uncovered by our correlation analyses are both influenced by maternal effects , the correlations we observed between them could be due to covariation with a common maternal influence . To test this possibility , we sought evidence of a statistically significant correlation between gene expression and a principal component of skeletal variation by including a maternal parental term as an additional factor in our linear models . For any gene-time point expression measures that remain significant predictors of skeletal variation even after accounting for maternal effects , we can reject the hypothesis that maternal effects are the sole factor influencing correlations between gene expression and skeletal variation . Using this approach , we could not reject a model that included only maternal influences for the majority of genes within the network that showed some prior association with skeletal variation . However , for four genes ( FoxO , SM30-E , Msp130 , and C-lectin ) we found significant support ( p<0 . 01 ) for non-maternal co-variances at time points 4 and/or 5 ( Figure 6E ) . The relationship between the expression of these four genes and skeletal morphology appears to be independent of maternal effects , and thus is most likely due to genetic contributions . Because our ability to detect maternal genetic contributions is confounded by non-genetic components of egg quality and is limited by the modest size of the cross , it is certainly possible that additional associations between gene expression and morphology have a genetic component .
We observed extensive variation in gene expression within the gene regulatory network in a natural population . Most involves modest changes in the timing or level of gene expression , although two genes in particular ( Pmar and VEGF1R ) showed striking differences ( Figure 2 ) . Based on our NCII breeding design , we found evidence for a genetic component to expression variation for most genes ( Table S1 ) . The majority of these showed statistically significant paternal influences even during early development and despite the modest size of the cross , suggesting that genetic contributions to variation are often substantial . This result is consistent with the extensive genetic variation within regulatory elements ( including transcription factor binding sites ) that previous studies have reported for S . purpuratus [33]–[36] . Average levels of genetically based variation in gene expression remained appreciable throughout development , with a modest drop after the second time point ( Figures 3 and S1 ) . These results are consistent with a previous study of genetic contributions to gene expression during development [57] , and extend them to natural populations and to a broad range of developmental stages . Understanding the phenotypic consequences of variation in gene expression within wild populations is important for three reasons , which we discuss below: ( 1 ) much of it must be buffered so as to avoid adversely affecting later developmental processes , ( 2 ) it may influence quantitative variation in ecologically significant organismal traits such as morphology , and ( 3 ) it may form the basis for future adaptations . An important question is whether the variation in gene expression encountered in nature is buffered or propagated across a gene network during development [2] , [5] , [58] . Consistent with what one would expect of transcriptional regulators and their targets , we found that covariation in gene expression was generally higher for direct regulatory interactions than for random pairs of genes within the network ( Figure 4A ) . However , the levels of covariation we observed were generally modest or nonexistent , indicating that the effects of variation in gene expression are buffered throughout the network . One possible mechanism for buffering transcriptional regulation is through threshold , or switch-like , interactions . Switch-like regulation of target gene expression can arise in several ways , including cooperative binding of transcription factors [59]–[61] and transcription factor saturation at binding sites [62] , [63] . In either case , changes in transcription factor concentrations above a certain threshold would have relatively little impact on the expression of their target genes . We found that many regulatory interactions in S . purpuratus show either switch-like behavior ( e . g . , Figure 5B ) or a low degree of correlation , suggesting that transcriptional regulatory interactions can contribute to developmental buffering . Other regulatory interactions within the network , however , showed stronger correlations between regulator and target ( e . g . , Figure 5A ) . An important topic for future research will be understanding what makes these interactions different . Do they represent cases in which cooperative regulatory interactions play a less important role in regulating gene expression levels or where transcription factor binding sites are rarely saturated ? The fact that less buffered interactions often lie upstream of terminal differentiation genes may point to an important difference in how genes are regulated during cell fate specification versus differentiation . Our results indicate that variation in gene expression is buffered particularly well during early development . One manifestation of this is an overall increase in covariation between the expression levels of neighboring genes within the network as development proceeds; in early embryos , the expression levels of genes involved in direct regulatory interactions are no more correlated than those of random pairs of genes , while at later stages , expression levels of direct interactors are significantly more correlated than random pairs ( Figure 4A ) . Another indication of a change in the degree of buffering during development is a substantial increase in the proportion of sensitive regulatory interactions over time , from about one-third during early embryogenesis to about two-thirds at later stages ( Figure 4B ) . These qualitative shifts in the nature of regulatory interactions may be related to changes in the overall function of the network during development . Early development involves a series of binary decisions between distinct cell fates . Buffering may be especially important during this time , to ensure the fidelity of all-or-nothing cell fate decisions and to allow for the proper integration of distinct sets of regulatory inputs . Buffering may also be important during early development to screen out environmental variation that might otherwise influence these critical processes . Consistent with this expectation , we previously reported minimal influences of ecologically relevant thermal stress on gene expression throughout this network [64] . Later development , in contrast , involves the inherently quantitative processes of growth and morphogenesis . Quantitative regulatory interactions among genes in a network may be especially important during post-embryonic stages in order to fine-tune these quantitative processes in response to nutrition , pathogens , physical conditions , and other environmental factors [65] . Note that buffering and cell fate specification are not necessarily related processes . Binary cell fate decisions may depend on a threshold response to a molecular cue , but that cue may be a property other than transcript abundance of a single upstream gene . Conversely , quantitative processes like growth could be based on the additive effects of many switch-like regulatory interactions of small effect . Nor is buffering a necessary component of cell fate specification , since there is no reason why a network with reduced variation in gene expression could not execute binary cell fate decisions . Our results also suggest that developmental buffering can influence the accumulation of genetic variation that influences development in wild populations . We observed more genetically based variation in the expression of genes upstream of switch-like ( insensitive ) regulatory interactions than in those upstream of quantitative ( sensitive ) interactions . Our results further suggest that genetic variation in the natural population is not distributed randomly across the gene network , but is instead a by-product of the change in developmental mechanisms from cell fate specification in the early embryo to morphogenesis and growth during later stages . We hypothesize that by masking variation during early development , switch-like regulatory interactions may allow cryptic genetic variation to accumulate over evolutionary time at nodes that operate early within the network . Cryptic genetic variation is evolutionarily significant , because mutation or stress can unmask it , sometimes with dramatic phenotypic consequences [9] , [66] , [67] . Our results may also help to explain the observation that expression profiles are often less conserved between species during very early development than at subsequent stages [68] , a pattern that has long been noted for morphology across developmental stages [69]–[72] . The larval skeleton of sea urchins plays critical roles in feeding , defense , and orientation [26]–[28] , [30] , and natural variation in this structure has an impact on mortality [27] , [29] . Thus , genetic variation that influences the size and shape of the larval skeleton will likely be a target of natural selection in the wild . We found that variation in the expression of a few genes within the network is associated with quantitative variation in the morphology of the larval skeleton ( Figure 6 ) . These genes fall into two sets: one expressed during early embryogenesis and the other during skeletogenesis . Correlations between the first set of genes and skeletal morphology are unlikely to represent a causal relationship that is mediated through transcription of the network genes we examined . Support for this inference comes from the lack of correlation between the expression of genes in the middle portion of the network ( Figure 6C–6F ) and skeletal variation: if early gene expression influenced the skeleton through the network , the expression of intermediate genes should also be significant in the 2B-PLS analysis . Another reason is that the association between early genes and skeletal variation is fully explained by the maternal parent . Thus , it seems likely that a single maternal input , such as egg size or provisioning , independently influences variation in the expression of early genes and variation in larval morphology . The existence of such an input would explain why expression levels , as well as breeding values , are relatively highly correlated at time point 1 ( Figures S2 and S3; Text S1 ) and is supported by experiments demonstrating that variation in egg size and quality influence the morphology of the larval skeleton [27] , [47] , [48] . Nonetheless , we cannot rule out the possibility that these genes act through the network via processes other than transcription or through interactions outside of this network such as changes in cell size . In contrast , the second set of genes are likely candidates for directly contributing to variation in the larval skeleton . All are expressed during formation of the skeleton , most are expressed exclusively within skeletogenic cells , and most encode protein components of the biomineral matrix itself . That many of these genes change expression in response to targeted chemical manipulations that alter of the size and structure of the larval skeleton [73]–[75] and that they covary with skeletal morphology independent of maternal effects ( Figure 6 ) provides further support for a direct connection between their expression and skeletal formation . These results are consistent with the idea that the position of a gene within the network has consequences for quantitative genetics and that mutations affecting the expression of terminal structural genes are among the most likely to influence variation in the size and shape of the larval skeleton . Importantly , this hypothesis can be tested directly by manipulating gene expression levels experimentally and measuring trait consequences . Terminal genes may influence morphological variation simply because fewer molecular events separate them from the trait of interest than is the case for genes that operate farther upstream , providing less opportunity for buffering . Other studies have also found that the expression of terminal genes can influence an organismal trait , as with abdominal pigmentation in Drosophila [10] , [76] , [77] . It is important to bear in mind , however , that terminal genes are not always major contributors to phenotypic variation . In metabolic networks , variation in genes at the top of the network often have the largest impact [78]–[80] , while in developmental networks , genes at intermediate positions sometimes contribute the most to trait variation [37] , [38] , [81] . A clue as to why these cases differ may lie in the nature of gene interactions . Most of the interactions that regulate the structural genes of the sea urchin larval skeleton are quantitative rather than switch-like , which allows variation in immediate upstream regulators to influence the expression levels of structural genes , whereas changes in more upstream genes are buffered . In light of the number of regulatory interactions that appear to be buffered in this network , associations between genes encoding transcription factors such as FoxO and skeletal morphology become all the more surprising . Unlike structural genes such as SM30-E , the impacts of changes in transcription factor expression are necessarily indirect , and it is worth considering why some transcription factors , but not others , quantitatively influence morphology . Differences in molecular mechanisms of action may provide important clues . For instance , forkhead proteins , including FoxO , often act as pioneer transcription factors , binding directly to condensed chromatin and setting the stage for lineage-specific transcription by recruiting chromatin remodelers and additional transcription factors [82]–[84] . Forkhead transcription factors have also been proposed to play a special role in fine-tuning gene expression [85] . Although the regulatory targets of FoxO remain unknown in sea urchins , it is tempting to speculate that the quantitative association between FoxO expression and skeletal morphology stems from a potentially rate-limiting role in priming the chromatin landscape for transcription . More generally , this result highlights the need to better understand the molecular mechanisms that link genes with the organismal phenotypes that they influence . Several elegant studies of adaptation in wild populations have traced ecologically important phenotypes to changes in the transcriptional regulation of a single gene during development [10] , [12] , [38] , [86] . Moving forward , a central challenge in evolutionary genetics is understanding why certain genes , and not others , contribute to adaptation . Systematically measuring variation and correlations across a gene network and across developmental time , as we have done here , provides one promising approach . For a gene to contribute to adaptation , it must harbor variants that influence its function , and this variation must be associated with an ecologically significant organismal trait . Based on these criteria , C-lectin , FoxO , and SM30-E are the strongest candidates among the genes we examined for contributing to adaptive changes in the size and shape of the larval skeleton in S . purpuratus . Additional candidates are FoxB , Msp130 , and SM50 , though with somewhat weaker support . Most of the remaining genes we assayed show no clear correlation with skeletal morphology and seem less likely to contribute . Five of the six candidates are terminal differentiation genes and the other is a transcription factor expressed during differentiation , suggesting that network position is a significant factor in the genetics of adaptation for the larval skeleton . Several independent lines of evidence suggest that the larval skeleton of S . purpuratus should be able to evolve adaptively . First , this species has an enormous effective population size , non-assortative mating , and extensive gene flow [32] , [87] , [88] , all features that favor the efficient operation of natural selection . Second , populations of S . purpuratus harbor substantial genetic variation ( 0 . 5%–3% ) in noncoding regions of the genome [31] , [36] , [89] , providing abundant genetic variation upon which selection can act . This includes extensive genetic variation within empirically validated cis-regulatory elements and transcription factor binding sites [33]–[36] . Third , evidence presented in this study indicates that some of this genetic variation has an impact on phenotypic variation in the size and shape of the larval skeleton . Adaptation requires a fourth component that is more difficult to assess , namely the ecological circumstances that favor phenotypic change . As mentioned earlier , the skeleton plays several important roles in larval feeding , defense , buoyancy , and swimming , making it a likely target of selection [26] , [28]–[30] . The remarkable diversity of skeletal size , shape , and configuration among living species of sea urchins points to extensive adaptive changes over the past 250 million years of evolution [72] , [90] , while signatures of positive selection within the cis-regulatory elements of genes in the skeletogenic network indicate recent adaptive changes within S . purpuratus [34]–[36] . Thus , adaptation in skeletal size and shape has likely been an important part of the evolutionary history of sea urchins , and the variation we document here has the potential to contribute to future adaptation . Our results reinforce the idea that the position of a gene within a regulatory network position has important evolutionary consequences [6] . Some regulatory interactions within the developmental network of sea urchins can buffer more variation than others , and the distribution of these interactions is enriched during embryogenesis relative to later development . As a consequence , the accumulation of genetic variation affecting developmental mechanisms within the population is likely to be nonrandom across the network and across developmental time . We also found that genetically based variation in the expression of a subset of genes is correlated with variation in an ecologically relevant trait , the larval skeleton . This variation is also not randomly distributed across the network , but enriched in genes that encode structural proteins and that have cell type-specific expression . This suggests that the genetic basis for adaptation in the larval skeleton is likely to come primarily from genes with terminal positions in the network . Empirical investigation of well characterized gene regulatory networks may eventually allow one to formulate predictions about the genetics of adaptation .
The adult sea urchins used for the cross were collected during a single SCUBA dive from a population in the Santa Barbara Channel , Santa Barbara , California ( US ) . Individuals were shipped overnight to Durham , North Carolina and held in aquaria containing artificial sea water ( Coralife , Oceanic Systems Inc . ) at 12°C for <48 h prior to spawning . Gametes were obtained from similarly sized individuals ( a proxy for similar age ) and fertilization was carried out following standard procedures [91] . With the resulting gametes , we set up a North Carolina II breeding design [41] , [92] consisting of all pair-wise crosses between six male and six female urchins . Each culture was raised in a randomized design in artificial sea water at 12°C in controlled climate chambers at the Duke Phytotron ( Durham , North Carolina ) . Temperature was monitored continuously using sensors distributed throughout the growth chambers . Because we made use of a randomized design , neither micro-environmental variation nor sampling order should influence our estimates of parent-of-origin effects ( or additive genetic variation ) . Uncontrolled variation did , however , introduce phenotypic variation across the experiment that improved our ability to detect patterns of correlation between pairs of genes at each time point . The 36 pools of zygotes generated by our cross were reared in replicate , and all 72 cultures were sampled at seven developmental time points: 10 , 18 , 24 , 28 , 38 , 45 , and 90 h post-fertilization . These time points span very early embryogenesis through a free-swimming larva capable of feeding ( Figure 1A ) . All 72 cultures came from crosses that yielded at least 95% fertilization rates , but 22 cultures had somewhat higher rates of malformed embryos , as assessed by no or irregular early cell divisions . We excluded these cultures from analyses at time points 1 and 2 because we could not reliably separate healthy from malformed embryos . However , we were able to include data from these cultures in our analyses for time points 3 to 7 by taking advantage of the fact that healthy embryos begin swimming between time points 2 and 3 , allowing a clean physical separation of healthy from malformed embryos . A minimum of several hundred embryos were sampled from each culture at each time point for RNA extractions , with numbers varying somewhat owing to changes in total RNA per embryo during development . RNA extractions were carried out using the Qiagen RNeasy 96 kit ( Qiagen ) , quantified on a NanoDrop ( Thermo Scientific ) , and adjusted to between 10 and 100 ng/µl with water . RNA integrity was checked in ten samples using an Agilent 2100 Bioanalyzer . None of the samples showed evidence of RNA degradation . Genomic DNA extractions were carried out using the Qiagen DNeasy mini kit ( Qiagen ) , DNA quantity measured on a NanoDrop , and adjusted to between 20 and 100 ng/µl with buffer AE . We used Illumina's DASL platform ( cDNA-mediated annealing , selection , extension , and ligation ) [93] to measure gene expression from these samples . This assay is based on the technology in the widely used GoldenGate genotyping platform and is well suited to measuring the expression of a moderate number of genes in a large number of samples . Briefly , fluorescent probes complementary to the targets of interest are bound to beads etched with a barcode . These probes are then annealed to cDNA in solution and captured in a plate . The expression levels of individual beads is read using a laser that simultaneously captures information about the quantity of cDNA bound to any bead as well as the barcode of that bead , thus identifying the identity of the fluorescent transcripts being interrogated . On average , ∼30 individual beads are measured per probe per sample . We worked with Illumina to design a custom DASL assay containing 384 probes that targeted exons of 77 genes , based on annotations from SpBase [15] mapped to sea urchin build 2 . 1 ( www . spbase . org ) . Where possible , we validated sequences from the sea urchin genome sequence against targeted sequencing efforts available in GenBank . We chose three to six probes with Illumina final scores >0 . 8 ( App version 6 . 4 . 1 . 0 . 0 . 0:2 . 0 . 0 ) for each gene . Illumina recommends using three probes per gene to improve measurement precision . We included more probes when possible so that we could identify poorly performing probes on the basis of the correlations of all probes targeting the same transcript . The full set of probe sequences and the genes they target are available upon request . DASL assays were carried out on the Illumina BeadStation by the Duke Genotyping Core Facility at the Duke Institute for Genome Sciences & Policy . RNA and gDNA samples , used for quality control , were processed and run separately . Raw bead-level data was recorded directly instead of passing through the Illumina BeadStudio software . At each stage of development , gene expression measures were normalized to the expression of RBM8A following correction for background fluorescence ( Text S1 ) . Note that , as with all methods for measuring transcript abundance , measurement error on the DASL platform is generally higher when expression levels are very low . However , relatively few genes in our set are expressed at very low levels during the developmental stages when they are known to participate in regulatory interactions . To facilitate comparisons among genes , we normalized trait variances by the square of the mean expression of each gene over all cultures . The square root of this quantity is thus the coefficient of variation , an easily interpretable quantity in terms of percent change relative to the mean , and is recommended for comparing variances among traits [94] . An advantage of our network is that gene activity is defined both by expression and functional experiments . The coefficient of variation thus represents variation around a mean level of expression sufficient for gene function and is , thus , likely a more accurate predictor of the impact of variation in expression than an un-scaled measure of a gene's variance ( i . e . , some genes are naturally higher expressed than others , and a variance of one unit is likely more important for a gene expressed at only a few copies per embryo , than for a gene expressed at a very high level ) . In our analyses of this gene regulatory network , we benefitted from extensive prior work by other labs , much of which has been compiled into a Biotapestry [95] database available at ( http://sugp . caltech . edu/endomes/; Figure 1B ) . From this database we extracted an XML representation of the network ( version: 12 May 2009 ) and parsed the resulting information using custom scripts . In the XML data , time series information is recorded as gene activity reports every 3 h from 6–30 h post-fertilization . Since we reared our culture at a different temperature than that of the Biotapestry representation ( 12°C rather than 15°C ) , we converted times using published developmental schedules [91] . Edges between genes are also recorded along with the tissues in which the interaction occurs and the times over which the interaction occurs . At each developmental stage , genes were determined to be “active , ” ( rather than “expressed” ) if they are expressed at detectable levels and take place in one of the regulatory interactions noted in this database ( Table S10 ) . Several nodes in the graphic representation of the network involve interactions between multiple proteins . In deciding which gene expression correlations would represent this node , we deferred to the edge representations in the XML data ( e . g . , the proteins TCF and β-catenin often work together to active transcription , but only β-catenin is listed in the XML representation ) . Genes that act as their own regulators were not included in subsequent analyses as we were not considering between time-point correlations . In order to design probes for the DASL assay , we needed to assign each gene represented in the network to an annotated gene in the sea urchin genome project ( SpBase . org ) [15] . In the vast majority of cases , the assignment was either intuitive or could be quickly determined by matching probes , primers , or DNA sequence from prior publications to the sea urchin gene models . One exception was the node annotated as “FvMo1 , 2 , 3 , ” which represents three members of the flavin-containing monooxygenase gene family that are co-expressed in developing pigment cells and are strongly suspected to be co-regulated [96] . Unfortunately , it was not possible to unambiguously assign these genes to the current sea urchin genome assembly . We thus chose to use the well annotated SpFmo2 gene as a proxy for the activity of this cluster both because it had been used as a proxy in recent analyses of the network [97] and because its putative cis-regulatory region contains likely binding sites for both annotated upstream regulators of the cluster ( Gcm and GataE ) [98] . We measured the size and shape of the larval skeleton at time point 7 ( 90 h post-fertilization ) by marking eight established morphological landmarks in three dimensions [99] on images from a dense z-stack taken at 3 µm intervals and used these landmarks to define distances corresponding to lengths of the skeletal rods that make up the larval skeleton . Images were taken of 18–30 larvae per culture , measured using ImageJ , and rod lengths calculated using a custom Python script . We took two approaches to examining the relationship between gene expression variation and variation in the larval skeleton . First , we used PCA to collapse our measures to three factors , which capture 88% of the between-culture variation in skeletal rod lengths and show strong parent-of-origin ( particularly maternal ) effects ( Text S1 ) . We then measured correlations between each factor and the expression of each gene at each time point ( Table S5 ) . For the second approach , we carried out a two block partial least-squares analysis [54] , [55] to test for components of the gene expression variation and skeletal variation data that together maximized the correlation between the two datasets . See Text S1 for further details of both approaches . Correlations between the expression levels of interacting genes vary both quantitatively and qualitatively ( e . g . , Figure 5 ) . To better understand how these patterns of correlation change over development , we employed two methods . First , we asked if r2 values between directly interacting genes were , on average , stronger than those between active genes with no known regulatory interactions . To address this question , we compared the average r2 values of all interacting genes at each time point to the average of all non-interacting genes . To test for statistical significance , we compared the observed results to 10 , 000 permutations of the data by randomizing the edges but keeping the overall network topology intact . With the second method , we asked a slightly different question: How does the qualitative nature of regulatory interactions change over development ? We classified each edge ( regulatory interaction ) in our curated network representation into one of two types: ( 1 ) Switch-like or “insensitive” interactions , in which a downstream gene is insensitive to quantitative variation in the upstream gene , and ( 2 ) “sensitive” interactions , in which there is a statistically significant ( p<0 . 01 ) relationship between variation in an upstream gene and its downstream targets as assessed by standard linear regression . Both sets of analyses were conducted with and without repressive regulatory interactions with no impact on the statistical significance of the results . Differences in tissue composition among broods could affect gene expression variation and increase the apparent correlations in expression values for genes expressed in the same tissue . To test for this possibility , we made use of the fact that most of the genes analyzed in our study are classified into one ( or more ) of nine non-overlapping domains/tissues of expression at each time point in the Biotapestry database ( covering time points 1–6 ) from which we extracted information about network topology . Figure S2 shows all pairwise correlations for each gene marked as expressed within a tissue ( plus all tissues in the upper right ) . The large number of negative correlations observed within tissues argues that changes in tissue composition are not a driving factor underlying gene expression correlations . This observation is supported by formal statistical analyses: at no time point are correlations within a tissue greater than between random genes ( all p>0 . 2 , 1 , 000 permutations ) , a result that holds when the analysis is restricted to genes expressed in one , and only one , tissue per time point . For thoroughness , we also conducted this analysis using correlations among breeding values ( Figure S3 ) with similar results ( both qualitatively and formally ) . A requirement for the operation of natural selection is the presence of additive genetic variance , that is genetic variation that has a significant average effect on a phenotype across a range of environments and genetic backgrounds . One metric of additive genetic variance is four times the paternal or maternal covariance among half-sibs , namely individuals that share one parent , but not the other . In this experiment , estimates of these parental contributions were estimated using a North Carolina II breeding design using six outbred male and female S . purpuratus individuals of approximately the same age as described more fully in Text S1 . Parental effects in the NCII design are typically estimated using ANOVA methods . However , ANOVA methods are not well suited for estimating error terms or significance in the face of missing data . When analyzing gene expression data , we therefore converted the standard mixed-effect linear model underlying the NCII design into a Bayesian hierarchical mixed-effect model by adding priors on the genetic and residual variances and fitting the model using a Gibbs sampler , implemented in the MCMCglmm package in R [100] . We took this approach for two reasons . First , due to filtering for quality control of the gene expression measures , certain samples with low quality expression measurements were removed , resulting in an unbalanced design . Likelihood-based methods , such as REML and Gibbs samplers inherently tolerate unbalanced designs , while ANOVA methods require complicated adjustments [41] . Second , since our sample sizes were small for estimating variances , asymptotic closed form confidence-interval estimates on variance components such as those produced from REML or ANOVA methods , are not reasonable , and return confidence intervals that span zero for low variance estimates . Bayesian credible intervals can be much more interpretable in these situations as they do not depend on asymptotic assumptions and can be enforced to be positive and asymmetric . In our analyses , we used a diffuse inverse gamma prior centered at 10% of the expression of each gene for each variance term ( see Text S1 for fuller discussion ) . If fewer than 20 samples had expression levels above background for a given gene at a given time point , no variance measures were calculated . The significance of specific linear models was calculated using DIC-based model comparisons , as discussed more fully in the Supporting Information . To confirm the validity of our approach , we also conducted more traditional REML-based analyses ( Text S1 ) . The two sets of results were highly concordant . With an NCII design , it is theoretically possible to estimate the genetic covariances between genes . Owing to the relatively small size of our breeding design , however , we cannot accurately estimate genetic covariances between individual pairs of genes . We can , however , make attempts to compare the full set of genetic covariances ( the G-matrix ) between time points . One measure of genetic constraints in this case is the variance of the eigenvalues for the G-matrix at each time point: high variances indicate that the G-matrix is constrained primarily in one or a few directions , while low variance ( all eigenvalues having similar magnitudes ) is indicative of a relative lack of constraint . Via permutations tests , we can see that the G-matrices at each time point are more structured than random ( Text S1 ) , as one would expect from a network of interacting genes . However , only at time point 1 is the variance among eigenvalues statistically distinguishable from ( and larger than ) other time points , highlighting the greater extent to which genes at time point 1 ( including those with no known interactions ) share a common genetic influence ( Text S1 ) . Data are available from the Dryad Digital Repository [101] . | Animal development is highly stereotypic in the face of changing environmental conditions and individual genetic differences . At the same time , developmental processes can evolve rapidly , suggesting that selectable genetic variation is hidden beneath this apparent stability . To better understand the relationship between these seemingly opposed properties of robustness and evolvability , we measured how natural variation in gene expression propagates across a network of interacting genes underlying early development in sea urchins . We found that gene interactions are not equal across development: expression variation is well buffered during early development by the use of on/off switch-like ( rather than continuously tunable ) regulatory mechanisms , while during later development it has a greater impact on the expression of downstream target genes and on morphology . Using a breeding design , we were able to detect a substantial genetic component to the observed variation in gene expression . Interestingly , the degree of genetic contribution was greatest during early development and specifically at points of switch-like regulation , suggesting that the properties of developmental gene regulatory networks that underlie robustness also promote the accumulation of genetic variation that could seed adaptation . |
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