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The universally conserved J-domain proteins ( JDPs ) are obligate cochaperone partners of the Hsp70 ( DnaK ) chaperone . They stimulate Hsp70's ATPase activity , facilitate substrate delivery , and confer specific cellular localization to Hsp70 . In this work , we have identified and characterized the first functional JDP protein encoded by a bacteriophage . Specifically , we show that the ORFan gene 057w of the T4-related enterobacteriophage RB43 encodes a bona fide JDP protein , named Rki , which specifically interacts with the Escherichia coli host multifunctional DnaK chaperone . However , in sharp contrast with the three known host JDP cochaperones of DnaK encoded by E . coli , Rki does not act as a generic cochaperone in vivo or in vitro . Expression of Rki alone is highly toxic for wild-type E . coli , but toxicity is abolished in the absence of endogenous DnaK or when the conserved J-domain of Rki is mutated . Further in vivo analyses revealed that Rki is expressed early after infection by RB43 and that deletion of the rki gene significantly impairs RB43 proliferation . Furthermore , we show that mutations in the host dnaK gene efficiently suppress the growth phenotype of the RB43 rki deletion mutant , thus indicating that Rki specifically interferes with DnaK cellular function . Finally , we show that the interaction of Rki with the host DnaK chaperone rapidly results in the stabilization of the heat-shock factor σ32 , which is normally targeted for degradation by DnaK . The mechanism by which the Rki-dependent stabilization of σ32 facilitates RB43 bacteriophage proliferation is discussed .
The universally conserved molecular chaperone machines maintain cellular protein homeostasis by acting at almost every stage in the life of proteins [1] . In the bacterium Escherichia coli , the multifunctional DnaK ( Hsp70 ) chaperone machine ( the DnaK/DnaJ/GrpE complex ) performs key cellular functions under both physiological and stress conditions [2]–[4] . For example , it assists de novo protein folding and targeting to biological membranes , protein quality control , assembly or disassembly of oligomeric complexes , and signal transduction . It responds to multiple stresses leading to protein misfolding and aggregation [5] . Moreover , the DnaK machine controls the entire E . coli heat-shock response by binding specifically to the major stress sigma factor σ32 and facilitating its degradation by the membrane-anchored FtsH protease [6] . The multiple phenotypes associated with the loss of DnaK in E . coli attest to its central role in protein biogenesis [7] . The DnaK protein is composed of an N-terminal nucleotide-binding domain and a C-terminal substrate-binding domain connected by a conserved linker involved in conformational changes and stability [8]–[10] . While the ATP-bound DnaK exhibits a low affinity and fast exchange rate for its substrate , the ADP-bound form is characterized by high affinity and low exchange rates . Specific cochaperones regulate its switch from one state to the other , thus coordinating DnaK's various intracellular functions . For example , the cochaperone DnaJ ( Hsp40 ) stimulates DnaK's ATPase activity and targets specific substrates to DnaK [11] , resulting in the formation of a stable ADP-bound DnaK-substrate complex [12] . The nucleotide exchange factor GrpE stimulates ADP-ATP exchange by triggering substrate release , thus resetting DnaK's cycle [13] . All the DnaJ/Hsp40 cochaperones are characterized by the presence of a compact domain of about 70 amino acids , called the J-domain , which is essential for a functional interaction with Hsp70 . Therefore , these cochaperones are generally called JDPs for J-domain proteins [14] . JDPs have been divided into three classes . Adjacent to their J-domain , type I JDPs share a G/F-rich region , a zinc-binding domain and a C-terminal domain involved in substrate-binding [15] . Type II JDPs generally have a similar domain arrangement except that they do not possess a zinc-binding domain [16] . The type I and type II JDPs generally bind a large variety of unfolded substrates in response to stress and are thus considered generic cochaperones [17] . In contrast , type III JDP members only have the J-domain in common with the other JDPs , suggesting that they deliver specific substrates or confer specific cellular localization to Hsp70 [18] . In E . coli , the chaperone DnaK interacts with the type I and II JDPs , DnaJ and CbpA , respectively , as well as with the type III protein DjlA , which may confer to DnaK its membrane localization [7] , [19] . In general , the successful proliferation of viruses relies on both the efficient reprogramming of the host cell cycle and the rapid synthesis and subsequent folding of a large number of viral proteins necessary for genome replication , protein synthesis and capsid assembly [20] . Since molecular chaperones are generally involved in both processes , it is not surprising that many viruses utilize the host cellular chaperones at different stages in their life cycle . Among these chaperones , Hsp70 ( DnaK ) is often recruited by eukaryotic viruses to assist viral entry , replication , gene expression , folding and assembly of viral proteins , and to control the host cell cycle progression [21] , [22] . Some viruses , such as polyoma and Molluscum contagiosum , encode their own JDPs to hijack the host Hsp70 chaperone [22] . To date , the best characterized virus-encoded JDPs are the type III viral T-antigens from simian virus 40 ( SV40 ) , which use their N-terminal J-domain for transcriptional activation of viral genes , viral DNA replication and capsid morphogenesis , as well as modulation of the host growth control signaling pathways to facilitate viral replication ( review by [23] ) . Putative gene products showing sequence similarities with the J-domain also can be found in some mycobacteriophage and enterobacteriophage genomes ( [24] , [25]; http://phage . ggc . edu ) . In this work , we have identified and characterized the first functional bacteriophage-encoded JDP protein . We show that the ORFan gene 057w from the T4-related enterobacteriophage RB43 , encodes a bona fide type III DnaJ-like protein , named Rki , which specifically interacts with the E . coli host multifunctional DnaK chaperone . However , in contrast with other JDPs , Rki expression is highly toxic for E . coli growth and this toxicity is fully dependent on the presence of endogenous DnaK . Analysis of the rki mutant bacteriophage further revealed that interaction with DnaK is critical for RB43 proliferation . Finally , we show that recruitment of the host DnaK chaperone by Rki rapidly results in the stabilization of the heat-shock factor σ32 , thus facilitating bacteriophage proliferation .
The enterobacteriophage RB43 was originally isolated from sewage treatment plants in Long Island [26] . It shares only 40% ( 115/260 ) of its genes with T4 and is the representative member of a well-defined group of T4-related bacteriophages , including RB16 and most likely RB42 [27] , [28] . The ORFan gene 057w ( Uniprot Q56BZ1 ) of RB43 is the first gene of a locus of five genes of unknown function absent in bacteriophage T4 ( Figure 1A ) . Interestingly , this gene encodes a putative protein of 237 amino acids , with a N-terminal domain of about 75 amino acids having 63% similarity with the J-domain of the three known E . coli DnaJ cochaperones . Specifically , the essential His-Pro-Asp ( HDP ) tripeptide of the loop connecting helices II and III , as well as key residues from helices II and III of the DnaJ J-domain are well conserved . Nevertheless , the region corresponding to helix IV of DnaJ displays significantly lower sequence conservation ( Figure 1B ) [29] . With the exception of the four closely related 057w homologues found in other T4-related bacteriophages , i . e . , RB16 and RB42 , Klebsiella bacteriophage KP15 and Aeromonas bacteriophage 65 [27] , no significant sequence similarity with the remaining C-terminal region of the protein was found in databases ( Swiss-prot , TrEMBL ) . Since the C-terminal part of JDP proteins determines localization and/or substrate specificity , the lack of sequence similarity suggested to us that this family of bacteriophage-encoded JDP could recruit the host Hsp70 for specific and potentially novel bacteriophage-related function ( s ) . Surprisingly , in bacteriophage RB16 , gene 057w is fused in-frame with the downstream gene 058w ( Figure 1A; Uniprot Q56BZ0 ) . In this case , the stop codon of 057w ( TAA ) is replaced by a glutamate ( GAA ) at position 238 , resulting in a 565 amino acid long fusion protein ( Uniprot D9ICB9 ) . A similar fusion is also observed in the less related Klebsiella bacteriophage KP15 ( gp055; Uniprot D5JF99 ) , but not in RB42 or Aeromonas bacteriophage 65 ( http://phage . ggc . edu/blast/blast . html ) . This suggests that the adjacent 057w and 058w genes could somehow cooperate during bacteriophage infection ( see below ) . We first asked whether the 057w gene product indeed encodes for a functional JDP , by using domain swapping experiments [30] . Specifically , the J-domain of the E . coli DnaJ cochaperone was replaced by the putative J-domain of the 057w gene product , leading to the formation of the wild-type Jd57-DnaJ chimera . In addition , as a control for an inactive J-domain , we engineered the same chimera with the known inactivating His 38 to Gln ( H38Q ) substitution in the HPD tripeptide , equivalent to the well-characterized dnaJ259 mutant allele . This mutation abolishes functional interaction between a J-domain and its cognate Hsp70 [29] , [31] . Plasmids expressing various wild-type and chimeric proteins were introduced into an E . coli W3110 mutant strain lacking all three endogenous dnaJ homologs , namely dnaJ , cbpA and djlA [32] . As a consequence , this strain displays several DnaJ-dependent phenotypes such as temperature-sensitivity , resistance to bacteriophage lambda , and lack of motility [29] , [33] . Control experiments with plasmid-encoded wild-type DnaJ confirm that bacterial growth at the nonpermissive temperature of 43°C is indeed DnaJ-dependent ( Figure 1C ) . As anticipated , the Jd57-DnaJ chimera containing the wild-type bacteriophage J-domain efficiently rescues bacterial growth at high temperature , whereas the Jd57 ( H38Q ) -DnaJ mutant chimera does not . In agreement with the high temperature bacterial growth complementation result , bacteriophage λ growth and bacterial motility are also restored by expression of the wild-type Jd57-DnaJ but not by the mutant chimera ( Figure 1D and 1E ) . A control experiment presented in Figure 1F shows that the expression level of the chimeric proteins is comparable under the conditions tested . Taken together , these results demonstrate that the putative 057w gene product possesses a functional J-domain . Since similarity between the putative bacteriophage-encoded JDP and the other DnaJ family members is restricted to the J-domain , by convention this protein belongs to the type III group , like known JDPs of eukaryotic viruses [22] . We next asked whether the full length protein encoded by 057w displays some DnaJ function in vivo . However , multiple attempts to clone 057w under the control of its native promoter were unsuccessful , even when a pSC101 low copy number plasmid was used as a cloning vector . Finally , 057w was successfully cloned under the control of the tightly regulated ParaBAD promoter in the presence of glucose to minimize basal transcription levels and thus avoid toxicity . This plasmid construct was then tested for complementation of the temperature-sensitive phenotype of the triple dnaJ cbpA djlA mutant . As expected , expression of the full length bacteriophage JDP is highly toxic in the presence of L-arabinose inducer and is thus not capable of replacing the E . coli DnaJ protein ( Figure 2A ) . To investigate whether the severe toxicity is DnaK-dependent , we then expressed the bacteriophage JDP in the single dnaK , hscA or hscC mutants , the three Hsp70-encoding genes of E . coli [7] , and monitored its effect on bacterial growth . As in the wild-type strain , expression of the bacteriophage JDP exhibits a strong toxic effect in both hscA and hscC mutants . In sharp contrast , the JDP displays no toxicity when expressed in the single dnaK mutant ( Figure 2A and 2B ) , thus indicating that its toxicity is DnaK-dependent . As expected , the toxicity is restored when DnaK is co-expressed from a plasmid ( Figure 2B ) . Next , we showed that overexpression of the bacteriophage JDP harboring the H38Q inactivating mutation in its J-domain does not result in toxicity when expressed in the wild-type E . coli strain , thus demonstrating that the DnaK-dependent toxicity also requires a functional J-domain ( Figure 2B ) . Note that both the wild-type and H38Q JDP mutants showed comparable steady state expression levels ( Figure S1 ) . Toxicity of the bacteriophage JDP was exacerbated in the sole absence of DnaJ , the main cochaperone of DnaK in vivo ( and to a lesser extent in the presence of CbpA and DjlA ) , suggesting that the bacteriophage protein may compete with DnaJ for binding to DnaK during bacteriophage infection ( Figure 2A ) . Since the bacteriophage JDP and DnaK genetically interact , the gene 057w was named rki for RB43 DnaK-interactor . The contribution of the uncharacterized C-terminal domain of Rki to the DnaK-dependent toxicity remains unknown . Based on the predicted secondary structure of Rki ( and on the partial chymotrypsin proteolysis of purified Rki protein described below and in Figure S2 ) , we engineered a C-terminal deletion of Rki , after amino acid Met159 , within a predicted random coil region located approximately halfway through the putative C-terminal domain . The resulting Rki ( 1–159 ) construct was tested for both , its toxicity and its ability to replace the E . coli DnaJ cochaperone during bacterial growth at non-permissive temperature . Strikingly , robust overexpression of Rki ( 1–159 ) exhibits no toxicity at all , thus indicating that the C-terminal and the J-domain of Rki act in concert to exert toxicity . In addition , Rki ( 1–159 ) is also able to partially replace DnaJ as a functional DnaK cochaperone in vivo , even at the stringent temperature of 43°C ( Figure S1 ) . These results reveal that the DnaK-dependent toxic effect of Rki relies on both a functional J-domain and the C-terminal domain of unknown function . We next asked whether Rki and DnaK could indeed physically interact in vivo . To do so , an N-terminal Flag-tagged version of Rki was expressed in E . coli and used as bait in pull-down experiments . The results shown in Figure 2C clearly demonstrate that indeed Rki and DnaK form a complex . The fact that the H38Q substitution in Rki affects interaction with DnaK in this assay confirms that the interaction necessitates a functional J-domain ( Figure 2C ) . In addition , DnaJ participates in the complex with Rki and DnaK ( Figure 2C ) . However , as with DnaK , the complex between DnaJ and Rki is disrupted by the presence of the H38Q substitution in the Rki J-domain , thus indicating that the presence of DnaJ is DnaK-dependent and is not simply due to the formation of mixed oligomers between Rki and DnaJ . Taken together , these data demonstrate that Rki possesses a functional J-domain , which enables it to functionally interact with the host multifunctional DnaK chaperones in vivo . To further explore Rki functions in vitro , we purified both the wild-type Rki and Rki ( H38Q ) mutant proteins . SEC-MALLS experiments performed with purified Rki shows that Rki elutes as a single peak with an average molecular mass of 31 . 5 kDa ( Figure S2; Text S1 ) . This is in good agreement with the theoretical molecular mass of 29 . 15 kDa demonstrating that in contrast to the three E . coli J-domain cochaperones DnaJ , CbpA and DjlA , Rki is almost exclusively monomeric in solution . In addition , partial α-Chymotrypsin proteolysis followed by N-terminal sequencing of purified Rki indicates a two domain structure composed of the N-terminal J-domain ( residues 2 to 70 ) , a short putative linker region ( residues 71 to 75 ) and a larger C-terminal domain ( residues 76 to 237; Figure S2 ) . Purified Rki and Rki ( H38Q ) proteins were then tested for their ability to stimulate DnaK's ATPase activity in vitro under steady state condition , as described [33] . The results presented in Figure 3A show that Rki wild-type indeed stimulates DnaK ATPase , although less efficiently than does DnaJ . In contrast , Rki ( H38Q ) harboring the inactivating mutation the J-domain does not show any stimulation , thus indicating that Rki is capable of stimulating DnaK ATPase activity in a J-domain dependent manner . This result is in agreement with the domain swapping experiments shown in Figure 1 and further demonstrates that Rki possesses a bona fide JDP . We next asked whether Rki could assist DnaK in the refolding of the chemically denatured luciferase substrate . This assay is dependent on both a functional J-domain and a capacity to bind to and deliver an unfolded substrate to DnaK . A representative kinetic analysis of luciferase refolding in the presence of Rki , Rki ( H38Q ) or DnaJ is shown in Figure 3B . As expected , DnaJ efficiently stimulates DnaK-mediated refolding of luciferase . In contrast , both Rki and Rki ( H38Q ) do not stimulate DnaK's reactivation activity , even when Rki concentration was increased 2-fold above that of DnaJ . These results strongly suggest that although Rki interacts with DnaK both in vivo and in vitro , it does not possess a DnaJ-like , generic cochaperone function . This behavior is in sharp contrast to that of DnaJ , CbpA and DjlA [7] . It is known that DnaJ possesses intrinsic chaperone function , as it can bind unfolded substrate and efficiently prevents its aggregation [16] . The inability of Rki to support DnaK-mediated reactivation suggests that it may not efficiently bind denatured luciferase . Indeed , the results presented in Figure 3C clearly show that Rki alone does not prevent the aggregation of chemically denatured luciferase , even at a much higher cochaperone/substrate ratio , thus indicating , once more , that Rki displays no apparent generic chaperone function . In summary , the above results demonstrate that Rki specifically interacts with DnaK in a J-domain dependent manner . However , in sharp contrast with DnaJ , CbpA or DjlA , Rki is not capable of assisting DnaK as a generic cochaperone in vitro ( Figure 3B ) or throughout its multiple cellular tasks , as judged by its inability to replace DnaJ functions in vivo ( Figure 2 ) . To investigate a possible Rki function in vivo , we analyzed the presence of a putative bacteriophage promoter as well as the occurrence of rki transcripts during the course of RB43 infection . The putative rki gene promoter was identified and analyzed by a comparison with the consensus promoter described by Nolan et al . [34] . The putative rki promoter turns out to be very similar to the consensus RB43 early promoters with TAAAGT and TTGACA boxes located at −10 and −35 positions , respectively , and a consensus up element ( Figure 4A ) . Subsequently , we performed northern blot analysis using as controls two other genes known to be transcribed in the early ( g43 ) or late ( g37 . 2 ) phase of infection . As shown in Figure 4B , the rki transcript appears early , at 5 to 8 min following infection . These data are in agreement with the predicted presence of the rki early promoter . Finally , we asked whether the rki gene product is actually expressed during infection , by using a polyclonal antibody raised against Rki . The western blot analysis results presented in Figure 4C clearly show that , indeed , the Rki protein is expressed during the early phase of RB43 infection . It is known that at least some of the bacteriophage T4 proteins synthesized immediately following infection confer selective advantages to bacteriophages under specific environmental conditions , thus facilitating the timely progression from host to bacteriophage metabolism [35] . To examine such a possible role for Rki in vivo , we first engineered a deletion/replacement of rki by the gfp gene ( encoding green fluorescence protein ) by homologous recombination into RB43 genome . Because RB43 wild-type grows better on a dnaK mutant than on the isogenic wild-type strain in certain E . coli host backgrounds ( see below ) and because Rki toxicity is strictly DnaK-dependent , the Δrki::gfp mutant was isolated on a dnaK mutant host ( see the Materials and Methods section for details ) . Following recombination into the bacteriophage genome and PCR verification of the correct deletion/replacement , the absence of the Rki protein during bacteriophage infection at 30°C was confirmed by western blot analysis using polyclonal anti-Rki antibody ( Figure 5A ) . The RB43Δrki mutant and its isogenic parent were then tested for their ability to form plaques on wild-type E . coli hosts at various temperatures . Note that in sharp contrast with bacteriophage T4 , RB43 wild-type grows fairly well below 16°C and very poorly above 37°C ( Figure S3 ) . We found no significant difference at 37°C between the wild-type and RB43Δrki mutant when grown on the E . coli W3110 strain background . However , growth of RB43Δrki mutant was severely compromized at 14°C compared to the RB43 wild-type parent ( Figure 5B ) . The effect of the rki mutation on RB43 growth was significantly more severe when E . coli MC4100 was used as the host strain , as judged by the reduced plaque-forming ability of RB43Δrki mutant already observed at 30°C ( Figure 5C ) . To ensure that the phenotype was Rki-specific , we performed complementation experiments using Rki expressed from a plasmid under the control of an inducible promoter . The results presented in Figure 5D and Figure S4 , for MC4100 and W3110 strains respectively , show that the mutant growth defects are indeed due to the lack of Rki function . Taken together , these in vivo results indicate that although rki is not an absolutely essential gene , its presence confers a significant advantage to RB43 during infection , especially at more stringent temperatures ( i . e . , cold ) and can vary significantly depending on the particular E . coli host being infected . Next , we asked whether the phenotype of the Δrki mutant is indeed due to the lack of functional interaction with the host DnaK chaperone . To do so , we expressed the Rki ( H38Q ) mutant from a plasmid and tested its ability to complement the lack of Rki during bacteriophage infection . The results obtained in both the MC4100 ( Figure 5D ) and W3110 ( Figure S4 ) strain backgrounds clearly show that the J-domain mutant is not capable of complementing for the lack of Rki function at the non-permissive temperature of growth . In this case , expression of plasmid-encoded Rki ( H38Q ) was comparable to that of Rki wild-type ( Figure S4 ) . This result clearly demonstrates that Rki acts through a functional interaction with the DnaK chaperone in vivo during infection . The above results suggest that early during infection , Rki may recruit the DnaK chaperone function to directly facilitate various bacteriophage processes , such as transcription , DNA replication or protein folding . Yet , in sharp contrast with bacteriophages λ , P1 and P2 , T4 does not require the DnaK/DnaJ/GrpE chaperone machine for its DNA replication on an E . coli host [7] , [36]–[38] . Alternatively , Rki could inhibit a DnaK cellular function ( s ) detrimental to its proliferation , as it has been proposed for the host Hsp40 chaperone , which inhibits hepatitis B virus replication and capsule assembly [39] . To investigate this possibility , we first asked whether a mutation in dnaK restores growth to RB43Δrki . We used the MC4100 strain , which does not efficiently propagate RB43Δrki even at 30°C , a permissive temperature for a dnaK mutation , as a suitable host for such experiments ( Figure 5C ) [40] . We compared the ability of RB43 wild-type and RB43Δrki to form plaques on MC4100 and on its ΔdnaK52::CmR sidB1 ( BB1553 ) isogenic mutant derivative [40] . Note that the dnaK mutant strain BB1553 carries the sidB1 suppressor mutation in rpoH , allowing the cells to grow stably at 30°C [40] . As suspected , we found that the absence of DnaK efficiently suppresses the growth defect of RB43Δrki mutant ( Figure 6A ) . These results are in strong agreement with the DnaK-dependent toxicity of Rki and suggest that Rki , via its functional J-domain , could counteract some putative antagonistic function ( s ) of DnaK on bacteriophage RB43 growth . One of the main cellular functions of the DnaK/DnaJ/GrpE chaperone machine in E . coli is to control the entire σ32-dependent heat-shock response . Under normal conditions , it is known that DnaK and DnaJ can bind and target the heat-shock factor σ32 for degradation by the membrane-anchored FtsH protease , thus autoregulating their own synthesis and limiting that of the other σ32-dependent heat-shock proteins ( HSPs ) [41] . Following a heat stress , the DnaK chaperone is rapidly titrated away from σ32 by being recruited to unfolded and aggregated proteins , thus resulting in the stabilization of σ32 . In turn , stabilized σ32 binds to the RNA polymerase core leading to the transcription and induced transcription of more than one hundred HSP genes [42] , [43] . In agreement with such DnaK function , the deletion of dnaK leads to a 3–4 fold increase in the HSPs steady state levels , including the major stress chaperones ( e . g . , GroEL , HtpG , ClpB , IbpA/B ) and proteases ( e . g . , FtsH , Lon , ClpXP , HslUV ) . It is known that following infection with various eukaryotic viruses the synthesis of HSPs , including the chaperones Hsp27 , Hsp70 , Hsp40 and Hsp90 is induced ( review in [20] ) . In some cases , the increased level of HSPs directly helps viral replication as it has been observed with the SV40 , HIV-1 or CELO viruses [44]–[46] . Recently , Rawat and Mitra have shown that in human cell lines , the heat-shock factor 1 ( HSF1 ) , the major eukaryotic transcription factor that regulates transcription of the HSP genes in response to stress , is specifically induced during HIV-1 infection to directly drive viral gene expression and promote its own replication [44] . Taken all of the above observations together , we reasoned that immediately after infection , Rki may bind DnaK , thus triggering σ32 release and/or may somehow prevent its degradation by the FtsH protease . We first tested whether Rki expressed from a plasmid affects the levels of σ32 in the presence of DnaK . The results shown in Figure 6B demonstrate that indeed , expression of Rki rapidly leads to an increase in the endogenous σ32 levels . In sharp contrast , expression of the inactive Rki ( H38Q ) J-domain mutant does not affect σ32 levels , indicating that this process is DnaK-dependent . As expected , the level of HSPs was also concomitantly increased ( Figure 6C ) . Remarkably , further in vivo experiment revealed that co-overexpression of plasmid-encoded σ32 exacerbates Rki toxicity ( Figure S5 ) . This result strengthens the genetic link between Rki and σ32 and is in agreement with previous works demonstrating that high endogenous levels of σ32 are deleterious for E . coli at 30°C in the absence of a functional DnaK , possibly due to inappropriately high levels of HSPs [40] , [47] . We next asked whether the increased levels of endogenous σ32 in a wild-type E . coli background could help growth of the RB43Δrki mutant . The results presented in Figure 6D clearly show that indeed , expression of σ32 from a low-copy number plasmid fully suppresses the growth defect of RB43Δrki , even at the stringent temperatures of 22° and 39°C . The DnaK-dependent stabilization of σ32 by Rki suggests that Rki either inhibits DnaK , thus indirectly preventing σ32 transfer to FtsH at the membrane , or directly binds σ32 in complex with DnaK and prevents its degradation . To begin to answer such questions , we co-expressed a Flag-tagged Rki and wild-type σ32 in an ftsH mutant strain ( to avoid degradation of σ32 ) and performed in vivo pull-down experiments as described in Figure 2C . As a control , the same experiment was performed simultaneously with either a Flag-tagged DnaJ or the pBAD33 empty vector . The result presented in Figure 6E shows that as observed for DnaJ , Rki binds σ32 in vivo in the presence of DnaK . To investigate whether Rki binding to σ32 is dependent on DnaK , we next performed the same in vivo pull-down experiments using a DnaK depletion strain , in which chromosomally-encoded DnaK is under the control of a Tet-inducible promoter . Under the growth conditions tested , DnaK is barely detectable by western blot in the absence of anhydrotetracycline when compared to the isogenic wild-type strain ( Figure S6 ) . The results presented in Figure 6E clearly show that efficient binding of Rki to σ32 indeed depends on the presence of DnaK ( Figure 6E ) , thus suggesting that Rki could stabilize σ32 by acting directly on the DnaK-σ32 complex . How does stabilization of σ32 by Rki help RB43 growth ? Clearly , the increased levels of σ32 rapidly results in much higher intracellular levels of all HSPs , including GroEL which is absolutely essential for the proper folding of the bacteriophage RB43 major capsid protein Gp23 [48] . In agreement with this , Wiberg et al . ( 1988 ) showed that indeed the progeny yield of bacteriophage T4 increases dramatically when HSP synthesis is induced prior to bacteriophage infection , as does overexpression of the GroES/GroEL chaperone from a plasmid . However , in the case of Rki , induction of HSPs would occur shortly after infection , well before the synthesis of the capsid Gp23 protein . In sharp contrast with the full suppression exhibited by plasmid-encoded Rki and σ32 , we found that overexpression of GroESL only weakly suppresses the growth defect of RB43Δrki , as judged by the turbid plaques of RB43Δrki observed only at the less stringent temperature of 30°C on the MC4100 background ( Figure S6 ) . Nevertheless , in the context of infection , even a modest increase in GroEL levels could translate into an increase in Gp23 folding , resulting in a slight increase in bacteriophage production . Outside the laboratory , these seemingly minor increases would result in a small but significant selective advantage for maintaining rki in the genome . An alternative hypothesis is that stabilization of the heat-shock factor σ32 immediately after infection could directly help transcription of RB43 middle and/or late genes . Despite the fact that T4 encodes its own sigma factor gp55 for late transcription , it has been shown that a temperature upshift ( from 30° to 42°C ) performed a few minutes after infection by T4 dramatically affects transcription of late genes in the absence of σ32 , by an as yet unknown mechanism [49] . Such a mechanism involving σ32 could thus facilitate RB43 late gene expression under nonheat-shock conditions . In T4 , it is known that activation of transcription from middle promoters requires the host RNA polymerase and σ70 , as well as the two bacteriophage proteins MotA and AsiA [35] . Intriguingly , an in-depth comparative analysis of RB43 and T4 promoter regions neither detected middle promoter consensus sequences nor identified a motA ortholog in RB43 , thus suggesting a very different mechanism [34] . This work shows for the first time that bacteriophages can encode functional J-domain proteins capable of hijacking the host Hsp70 chaperone to facilitate viral proliferation . Our results show that , at least in the case of Rki , interaction with the host DnaK prevents degradation of the heat-shock factor σ32 via an unknown mechanism , thus conferring a selective advantage for RB43 under certain circumstances . As stated above , in bacteriophage RB16 the rki gene is fused with the downstream ORF058w , due to a single substitution in the stop codon of rki . The presence of the Rki-58 fusion protein , named Rki16 , during infection by RB16 was confirmed by western blot analysis , thus excluding the possibility of a DNA sequencing artifact ( Figure S7 ) . In addition , we found that overexpression of plasmid-encoded Rki16 fusion protein stabilizes σ32 and complements the RB43Δrki growth-sensitive phenotype , albeit considerably less efficiently than Rki . In agreement with this , the Rki-58 fusion protein is considerably less toxic than Rki ( Figure S7 ) . To date , nothing is known about the function of RB43 ORF058w , whose product possesses a weak similarity with an uncharacterized conserved domain PTZ00121 at its C-terminus ( http://www . ncbi . nlm . nih . gov ) . Yet , in the case of Rki , co-overexpression of the ORF058w gene product does not affect either the stabilization of σ32 or Rki toxicity ( Figure S7 ) . This indicates that within the limit of our experimental conditions , ORF058w does not significantly influence Rki function in RB43 . Interestingly , in addition to its J-domain protein Rki , the bacteriophage RB43 possesses two other uncharacterized small ORFan genes , namely ORF179c ( 61 amino acid residue gene product; Uniprot Q56BL9 ) and ORF191c ( 106 amino acid residue gene product; Uniprot Q56BK7 ) , whose gene products displays significant sequence similarity with the conserved zinc-binding domain of DnaJ , known to be critical for both substrate binding and activation of the DnaK chaperone cycle [50] . It is intriguing that RB43 potentially expresses several proteins that display homology with distinct domains important for DnaJ cochaperone function . One attractive hypothesis is that multiple DnaJ-like bacteriophage proteins could act in concert to hijack ( or inhibit ) the host DnaK/DnaJ/GrpE chaperone machine in order to facilitate bacteriophage proliferation under different environmental conditions .
Genetic experiments were carried out in E . coli K-12 MC4100 or W3110 strains . Strains MC4100 and BB1553 [40] , and AR3291 ( W3110 sfhC21 zad220::Tn10 ΔftsH3::KanR; [6] ) have been described . The strain FA1195 PtetdnaKJ is an MG1655 derivative in which the endogenous dnaKdnaJ promoter is replaced by the tetracycline promoter Ptet , together with the upstream terR repressor . In this case , expression of DnaK is dependent on the presence of anhydrotetracycline ( Frederic Angles , laboratory collection ) . Mutations were moved in different genetic backgrounds using bacteriophage P1-mediated transduction at 30°C . To construct the single , double or triple JDP mutants in the W3110 strain background , the Δ3 strain ( MC4100 dnaJ::Tn10-42 , ΔcbpA::kanR , ΔdjlA::ΩspcR; [33] ) was used as donor . The ΔdnaK52::CmR [2] , ΔhscC::kanR ( JWK0645; Keio Collection ) and ΔhscA::kanR ( JWK2510; Keio Collection ) mutant alleles have been described . Bacteriophages RB43 [26] , λcI , λcIdnaJ+ and P1 ( laboratory collection ) were maintained on W3110 at 30°C . The RB43Δrki::gfp deletion/replacement mutant was constructed as follows . The 717 bp long gfp gene was first amplified using primers RBGFP1 ( 5′- GAACGGAAAATGAGTAAAGGAGAAGAAC ) and RNGFP3 ( 5′-CATTACCGCTAATTTATTTGTAGAGCTCATCC ) . Thr 1212 bp region upstream rki was amplified using primers RB1 ( 5′-GCAGGATCCCTGGTGCAGACCGAACGG ) and RBGFP4 ( 5′-CTTTACTCATTTTCCGTTCCTCAAAATAAAAG ) , and the 835 bp region downstream rki was amplified using primers RBGFP2 ( 5′-CTACAAATAAATTAGCGGTAATGATATCTATG ) and RB3 ( 5′-CCCAAGCTTGGGCATGAGCCTTATCAACTGCTG ) . The three PCR fragments were assembled by the two-step fusion PCR method , resulting in a 2764 bp long fragment containing the gfp gene flanked by both the upstream and downstream genomic regions of rki . This fragment was then digested with HindIII and ligated into plasmid pMPMA6Ω previously digested with EcoRV and HindIII . Next , the E . coli B178 strain transformed with the resulting plasmid was grown to mid-log phase at 30°C in LB supplemented with ampicillin and 200 µl of the culture was then infected with 106 RB43 bacteriophages for 20 min at 20°C . Eight ml of LB amp were added and the culture was incubated at 37°C for 3 h with shaking until lysis occurred . Next , 100 µl of mid-log phase culture of B178 dnaK103 mutant strain was mixed with the bacteriophage lysate from above to obtain about 400 pfu per plate following overnight incubation at 30°C . Plaques were then transferred to nitrocellulose filters by Benton Davies method and DNA was bound with Stratalinker . The prehybridation took place at 68°C for 3 h and hybridation was carried out overnight at 68°C . These two steps were carried on with gfp DNA fragment labeled with Dig and the reaction tubes were boiled for 10 min . Then , the filters were washed at 65°C first in 2× SSC , 0 . 1% SDS and then in 0 . 1× SSC , 0 . 1% SDS before they were incubated with anti-Dig alkaline phosphate and revealed with NTB + BIPC . The genomes of both RB43 wild-type ( accession HE858210 ) and RB43Δrki::gfp mutant ( accession HE981739 ) used in this study were sequenced using the NGS/Illumina method ( LGC Genomics ) . Analysis of the wild-type genome revealed that the sequence ( with approximately 99% coverage ) of the RB43 bacteriophage used in this study differs from the published RB43 genome sequence by at least 107 nucleotides ( http://phage . ggc . edu/ ) . Apart from the Δrki::gfp deletion/replacement , 106 of these nucleotide differences were common to both RB43 wild-type and RB43Δrki::gfp mutant , whereas one mutation was different in the two bacteriophages but affected the same codon . This mutation was located in the hypervariable region of a putative adhesin gene 38 and corresponds to single nucleotide changes , CAA ( Gln100 ) to AAA ( Lys ) for the wild-type and to CGA ( Arg ) for the RB43Δrki::gfp mutant . Changes for lysine or arginine residues at this position in gp38 from bacteriophages RB42 and RB43 are known to facilitate recognition of the E . coli K-12 hosts [28] . Moreover , one mutation was found only in the RB43Δrki::gfp mutant . This mutation corresponds to a single nucleotide change GCT ( Ala5 ) to CCT ( Pro ) in gene 62 encoding for one subunit of the clamp-loader ( Gp44/Gp62 ) involved in T4 DNA replication and transcription of late genes [51] . Whether these mutations are linked to the simultaneous deletion of rki is unknown . Arguing against this possibility , the mutations were not present in another , independent , cold-sensitive RB43Δrki::gfp bacteriophage isolate . In addition , overexpression of the wild-type Gp62 from a plasmid did not rescue the cold-sensitive phenotype of either RB43Δrki ( Elsa Perrody , unpublished data ) . Plasmids pBAD22 , pBAD33 and pBAD24 ( Guzman et al . , 1995 ) , p29SEN ( Genevaux et al . , 2004 ) , pWKG90 ( pBAD22-DnaJ ) and pWKG90KPN ( pBAD22-DnaJ-H71T ) [30] , pGPPK [33] have been described previously . To construct plasmid pBAD22-DnaJFlag , containing DnaJ with the N-terminal Flag tag “MASDYKDDDDKSG” , the dnaJ gene was PCR-amplified using primers dnaJ-flagfor ( 5′-GCGAATTCATGGCAAGCGACTACAAAGATGACGACGATAAAAGCGGCATGGCTAAGCAAGATTATTACG ) and dnaJrev ( 5′-GCAAGCTTGCATGCTTAGCGGGTCAGGTCGTCAA ) , and pWKG90 DNA as template . The resulting PCR fragment was digested with EcoRI and SphI and ligated into pBAD22 previously digested with the same enzymes . Plasmid pBAD33-DnaJFlag was then constructed by subcloning of the EcoRV/SphI DnaJFlag fragment from pBAD22-DnaJFlag into EcoRV/SphI pBAD33 . Plasmid pBAD22-Rki was constructed as follows . The 714 bp long rki gene was PCR amplified using primers RB43DnaJfor ( GCGAATTCATGATTAACGAAAAAATGACA ) and RB43DnaJrev ( GCAGATCTAAGCTTTATGCGTCTAAGTGCTTGCG ) , digested with EcoRI and BglII and ligated into pBAD22 previously digested with the same enzymes . The pBAD22-rkiH38Q plasmid was constructed by the two-step PCR method using mutant primers H38Qfor ( 5′-CTCTGCGTAATCAGCCCGATCGTGG-3′ ) and H38Qrev ( 5′-CCACGATCGGGCTGATTACGCAGAG-3′ ) . To construct pBAD33-Rki , the rki gene was subcloned from pBAD22-Rki as an EcoRV/HindIII digested fragment and cloned into pBAD33 previously digested with the same enzymes . To construct pBAD24-RkiFlag , containing Rki with the N-terminal Flag tag , the rki gene was first PCR-amplified from pBAD22-Rki using primers RkiFlag-For ( 5′-GCTCCATGGCAAGCGACTACAAAGATGACGACGATAAAAGCGGCATGATTAACGAAAAAATGACACAT ) and Rki-Rev ( 5′- GAAAGCTTGGATCCTT ATGCGTCTAAGTGCTTGCGGAAAG ) . The resulting 749 bp fragment was digested with NcoI and BamHI and ligated into pBAD24 previously digested with the same enzymes . The same procedure was applied to construct pBAD24-Rki ( H38Q ) Flag using pBAD22-Rki ( H38Q ) as template . To construct pBAD33-RkiFlag , the rkiFlag gene from pBAD24-RkiFlag was subcloned as an EcoRV/HindIII digested fragment into pBAD33 previously digested with the same enzymes . To obtain p29SEN-Rki , the rki gene was PCR-amplified using primers EPw57f ( 5′-CGCAATTGTCATGATTAACGAAAAAATGACA ) and rkiCter-rev ( 5′- GCAAGCTTGGATCCTTATGCGTCTAAGTGCTTGCG ) and pBAD22-Rki as template . The resulting 733 bp fragment was digested with MfeI and HindIII and ligated into p29SEN digested with the same enzymes . The same procedure was followed for p29SEN-Rki ( H38Q ) with pBAD22-Rki ( H38Q ) as template . Construction of pET15b-RkiHis6 expressing Rki with an N-terminal 6×His tag was performed as follows . Both primers HisRB43Jfor ( 5′-GCCCCATGGGCAGCAGCCATCATCATCATCATCATAGCAGCATGATTAACGAAAAAATGACACAT-3′ ) and RB43J-rev ( 5′-GCAGATCTAAGCTTTATGCGTCTAAGTGCTTGCG ) were used to PCR amplify rki from pBAD22-Rki . The resulting rki-his PCR fragment was cloned as an NcoI/HindIII fragment into pBAD24 previously digested with the same enzymes . The rki-his gene was then subcloned as an NcoI/BglII digested fragment into pET15b vector ( Novagen ) previously digested with the same enzymes . The same procedure was used to construct pET15b-Rki ( H38Q ) His6 , except that pBAD22-Rki ( H38 ) was used as DNA template . The Rki-DnaJ and Rki ( H38Q ) -DnaJ chimeras containing the 77 amino acid long N-terminal J-domain sequence of Rki grafted into E . coli's DnaJ were constructed as described ( Kelley and Georgopoulos , 1997 ) . Briefly , the 231 bp long fragment containing the Rki J-domain was PCR amplified using primers BR43DnaJfor and SRB43JKpnIrev ( 5′-GCGGTACCCGCTGCGTGACGCGCTCGCAT ) , and RB43 DNA as template . The PCR products were cloned as EcoRI/KpnI fragements into pWKG90KPN plasmid [30] . To construct p29SEN-RpoH , the 855 bp long rpoH gene was PCR-amplified using primers rpohfor ( 5′-GAGAATTCCATATGACTGACAAAATGCAAAG ) and rpohrev ( 5′-GAAAGCTTGGATCCTTACGCTTCAATGGCAGCAC ) , and E . coli MG1655 genomic DNA as template . The PCR fragment was ligated as an EcoRI/HindIII fragment into EcoRI/HindIII p29SEN . All the constructs obtained by PCR were sequenced verified using the appropriate primers . Bacterial motility and bacteriophage λ plating assays were performed at 30°C as described [29] . To monitor bacterial viability , mid-log phase cultures of fresh transformants grown in LB medium ( tryptone 10 g/L , NaCl 10 g/L , yeast extract 5 g/L , thymine 20 g/L , NaOH 3 mM ) were serially diluted and spotted on LB agar plates ( agar 15 g/L ) supplemented when necessary with the appropriate antibiotics ( 100 µg/ml ampicillin , 20 µg/ml chloramphenicol , 20 µg/ml kanamycin ) and various L-arabinose or IPTG-inducers . Note that 0 . 2% glucose was added to overnight cultures in order to prevent Rki toxicity . RB43 plaque-forming ability was monitored as follows . Overnight cultures of E . coli grown in LB medium were diluted 1∶100 and grown with shaking to an OD600 of 0 . 7 at the indicated temperature . Cultures were then concentrated 10-folds in the same medium and bacterial lawns were prepared by mixing 300 µl of cells with 3 ml of pre-warmed H-Top medium ( tryptone 10 g/L , NaCl 8 g/L , Na citrate 2 . 4 g/L , glucose 3 g/L , NaOH 0 . 5 mM , agar 7 g/L ) and subsequently pouring the mix onto LB agar plates . Both , H-Top and LB plates were supplemented with the appropriate antibiotics and/or inducers when necessary . Serial dilutions of bacteriophage lysates were then spotted on bacterial lawns and plates incubated as indicated in the figure legends . Cells were grown in 100 ml LB supplemented with the appropriate antibiotics to an OD600 of 1 . 2 and harvested by centrifugation at 7000 rpm for 30 min at 4°C in a Beckman JA14 rotor . Pellets were resuspended in 1 ml of IP buffer ( 50 mM Tris-HCl buffer , pH 7 . 5 , 0 . 15 M NaCl , 20% ( v/v ) glycerol , 10 mM PMSF , 1 µl/ml Benzonase , 1 mg/ml lysozyme ) , sonicated twice for 20 sec and centrifuged at 14000 g for 30 min at 4°C . Supernatants were incubated 30 min at 25°C and 10 mM ADP , Hexokinase and 0 . 2% glucose was added for 15 min at 20°C . The samples were then incubated at 4°C for 2 h with 25 µl of anti-Flag M2-agarose suspension ( Sigma ) , washed with 6 ml TBS ( 50 mM Tris-HCl buffer , pH 7 . 5 and 0 . 15 M NaCl ) , and the bound proteins were eluted with 30 µl of TBS containing 5 µg of FLAG peptide ( Sigma ) . Proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) ( 4–12% Biorad ) . Bacterial cultures ( 16 ml/infection ) were grown at 30°C in LB medium to a density of 4 . 108 cells/ml . Cells were concentrated 4-fold in the same medium and bacteriophage infections were initiated by mixing 4 ml of cells with the adequate volume of RB43 bacteriophage stock ( multiplicity of infection ( MOI ) of 10 ) . Cells were then incubated at 30°C with shaking and infections were stopped at the desired time by adding 400 µl of pre-heated RNA lysis buffer ( 0 . 5 M Tris-HCl , 20 mM EDTA , 10% SDS , pH 6 . 8 ) and incubating in boiling water for 2 min . One volume of phenol was added and after mixing one volume of chloroform . Samples were incubated for 10 min at 30°c under shacking and centrifuged in order to collect the aqueous phase . Three more phenol/chloroform extractions were carried out . Nucleic acids were precipitated by mixing 2 . 5 volumes of prechilled ethanol ( −20°C ) in the presence of 0 . 3 M sodium-acetate at pH 5 . 7 and incubating at −20°C for at least 1 h . The pellet was collected by centrifugation ( 8000 rpm , 30 min ) , dried and resuspended in 250 µl of DEPC treated water . Small samples ( usually 5 µl ) were examined for RNA degradation and DNA contamination by electrophoresis on agarose gel containing ethidium bromide . Each infection allowed us to collect 200 to 300 µg of viral RNA . 50 µg of RB43 RNA , prepared as described above were treated with 100 units of DNAse I FPLCpure ( Amersham ) and precipitated by mixing 2 . 5 volumes of prechilled ( −20°C ) ethanol and 0 . 3 M sodium-acetate at pH 5 . 7 and incubating at −20°C for at least 1 h . Pellet was collected by centrifugation ( 8000 rpm , 30 min ) , dried and resuspended in 17 . 5 µl of DEPC treated water . A mix containing 10 µl of 10× MOPS , 17 . 5 µl of formaldehyde and 50 µl of formamide was prepared and mixed to the resuspended RNA . Samples were then incubated at 55°C for 15 min . Ten µl of 10× RNA loading buffer ( 1 mM EDTA pH 8 , 50% ( v/v ) glycerol , 0 . 25% bromophenol blue , 0 . 25% xylene cyanol FF ) and 5 µl of ethidium bromide ( 10 mg/ml ) were added to the mix . 10 µl of each samples were used for adjusting the loading amount of RNA by running on an agarose gel . Samples ( usually 15 µl ) were then electrophoresed at room temperature for 3 h in 1% agarose gel ( 180 ml final volume ) prepared in 1× MOPS containing 9 . 7 ml of formaldehyde . RNA integrity was verified under UV lamps . The resolved RNA population was subsequently transferred on a positively charged nylon membrane ( Hybond-N+ membrane from Amersham pharmacia biotech ) by salt diffusion over-night . RNA was UV cross-linked to the membrane and the efficiency of transfer was examined by methylene blue staining . The membrane was then prehybridized for 30 min at 68°C in hybridization buffer ( 7% SDS , 250 mM NaPi ( 0 . 77 M Na2HPO4/0 . 22 M NaN2PO4 mix ) , 2 mM EDTA ) . Prehybridization buffer was discarded and replaced by fresh buffer . 32P-labeled probe ( see below ) was added and hybridization was carried out over night at 68°C . The membrane was then washed twice at 60°C in a 5% SDS , 250 mM NaPi and 2 mM EDTA solution for 20 min and once in a 1% SDS , 250 mM NaPi , 2 mM EDTA solution for 30 min . Detection of the signals on autoradiograms was performed by exposure of the membrane at −80°C for 5–12 h in presence of an intensifying screen . Probes consisted of PCR products obtained by amplifying the desired fragments from the RB43 genomic DNA using the pfu polymerase . The PCR products were purified from 1 . 5% agarose gels using the Qiaquick gel extraction kit from Qiagen ( cat . No . 28706 ) . 2 µl of the PCR products were used as a template for a new PCR reaction containing 32P-αdCTP ( 1 . 5 mM MgCl2 , 2 µM of each primers , 2 mM of dATP , dTTP , dGTP , 0 . 2 mM dCTP ( 0 . 1 mM ) , 5 µl of 32P-αdCTP ( 10 µCi/µl ) , 5 units of Hot-start Taq ( Qiagen , 1× buffer ) . The PCR products were separated from free radioactivity by using Qiaquick gel extraction kit according to the furnisher . 100 µl of sonicated salmon sperm DNA ( 10 mg/ml ) were added and the mixture was incubated at 95°C for 2 min and then diluted in 1 ml of hybridization buffer . Location of the different primers used is indicated in Figure 4 and their respective sequences were: for gene 43 probe ( 700 bp long ) : 43gp43 . 0 ( 5′-ATGAATGAATTTTATCTATCA-3′ ) and 43gp43 . 3 ( 5′-CACGCCATAAATTTCGTATCC-3′ ) . For gene 37 probe ( 342 bp long ) : 43gp372am6 ( 5′-TAATTTGCCTTTACTCCCTACTGGA-3′ ) and 43gp372am3 ( 5′- GGATCGGAAGTATTCTATTTTGTGTT-3′ ) . For ORF057 probe ( 480 bp long ) : RB43Jfor ( 5′-GCGAATTCATGATTAACGAAAAAATGACA-3′ ) and 43gpJDTM2 ( 5′- CCAAGCTTACATCAAACCTTTACCTTCTTC-3′ ) . To avoid the toxic effect of protein overexpression in wild type E . coli , Rki and RkiH38Q were purified from the BL21ΔdnaKdnaJ strain [33] . Fresh overnight cultures were diluted 1∶100 in 500 ml of LB broth supplemented with 100 µg/µl ampicillin and grown with vigorous shaking at 30°C . At an OD600 of 0 . 3 , 2 mM IPTG was added for 2 h . Cells were harvested at 7000 rpm for 30 min at 4°C in a Beckman JA14 rotor and pellets were stored at −80°C . Pellets were resuspended in 20 ml of lysis buffer ( 50 mM NaH2PO4 , pH 8 . 0 , 300 mM NaCl , 10 mM Imidazole ) , 1 mg/ml lysozyme was then added and the cell suspensions were kept on ice for 30 min . After addition of Protease Inhibitor ( Roche ) , cells were sonicated 6×20 s on ice and centrifuged at 12000 rpm for 30 min at 4°C in a Beckman JA25 . 50 rotor . Supernatants were collected , 45% ammonium sulfate was added and samples were incubated overnight at 4°C under mild shaking . Samples were then centrifuged at 10000 rpm for 10 min at 4°C in Beckman JA25 . 50 rotor . The supernatants were dialyzed twice for 2 h in 2 L of lysis buffer in a Spectra/Por® Membrane , MWCO 12–14000 cut off 3 . 5 kDa . The dialysates were applied to a 4 ml of nickel-nitrilotriacetic acid columns preequilibrated with 10 ml of lysis buffer . The following steps were performed as described in the procedure from Qiagen for the purification of His6-tagged proteins from E . coli using nickelnitrilotriacetic acid superflow under native conditions , using lysis buffer supplemented with 20 mM imidazole as washing buffer and using 250 mM imidazole as elution buffer . The proteins were stored at −80°C in buffer containing 25 mM HEPES buffer , pH 7 . 6 , 0 . 4 M KCl , 1 mM DTT , 10% ( v/v ) glycerol . DnaK purification was performed as described [33] , and DnaJ and GrpE were purchased from Stressgen . ATPase activity was essentially carried out as described [52] , with minor modifications . Reactions were performed in 10 µl reaction buffer ( 30 mM HEPES buffer , pH7 . 6 , 40 mM KCl , 10 mM NaCl , 4 mM MgAc , 2 mM DTT , 0 . 29 mg/ml BSA , 0 . 1 mM ATP ) in presence of 1 µM DnaK , 1 µM GrpE , 1 µCi [γ32P]ATP , and increasing concentrations ( 0 . 2 , 0 . 4 , 0 . 6 or 0 . 8 µM ) of DnaJ , Rki or RkiH38Q . Three µl of 0 or the 20 min reaction were spotted on thin layer chromatography and migrated in migration buffer containing 0 . 15 M LiCl and 0 . 15 M formic acid . The amount of liberated γ-phosphate was quantified using phosphrimaging . Firefly luciferase aggregation was performed as described [33] , except that luciferase was denatured for 90 min at 25°C and aggregation kinetics were followed at 25°C . The protein concentrations used are described in the figure legend . Reactivation of firefly luciferase was performed essentially as described [33] . Briefly , 25 µM luciferase ( Sigma ) was denatured for 2 h at 22°C in 30 mM Tris-HCl buffer , pH 7 . 6 , 6 M guanidinium chloride , 5 mM DTT . Denatured luciferase was diluted to a final concentration of 0 . 125 µM into a reaction mixture ( 50 µl final ) containing 100 mM MOPS , 500 mM KCl , 50 mM MgCl2 , 20 mM creatine phosphate , 0 . 1 mg . mL−1 creatine kinase , 5 mM ATP , 0 . 015% bovine serum albumin , 0 . 5 µM DnaK and 0 . 125 µM GrpE . All components were incubated on ice . Refolding was initiated by adding either DnaJ or DnaJ mutant protein ( 0 . 125 µM each ) . The luciferase activity was measured at different time points after incubation at 22°C by using 10 µL of the luciferase assay system from Promega ( E1500 ) and a Berthold Centro LB960 luminometer . | Bacteriophages are the most abundant biological entities on earth . As a consequence , they represent the largest reservoir of unexplored genetic information . They control bacterial growth , mediate horizontal gene transfer , and thus exert profound influence on microbial ecology and growth . One of the striking features of bacteriophages is that they code for many open reading frames of thus far unknown biological function ( called ORFans ) , which have been referred to as the dark matter of our biosphere . Here we have extensively characterized such a novel ORFan-encoded protein , Rki , encoded by the large , virulent enterobacteriaceae bacteriophage RB43 . We show that Rki functions to control the host stress-response during the early stages of bacteriophage infection , specifically by interacting with the host DnaK/Hsp70 chaperone to stabilize the major host heat-shock factor , σ32 . |
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Bone morphogenetic proteins ( BMPs ) belong to the transforming growth factor β ( TGFβ ) superfamily of secreted molecules . BMPs play essential roles in multiple developmental and homeostatic processes in metazoans . Malfunction of the BMP pathway can cause a variety of diseases in humans , including cancer , skeletal disorders and cardiovascular diseases . Identification of factors that ensure proper spatiotemporal control of BMP signaling is critical for understanding how this pathway is regulated . We have used a unique and sensitive genetic screen to identify the plasma membrane-localized tetraspanin TSP-21 as a key new factor in the C . elegans BMP-like “Sma/Mab” signaling pathway that controls body size and postembryonic M lineage development . We showed that TSP-21 acts in the signal-receiving cells and genetically functions at the ligand-receptor level . We further showed that TSP-21 can associate with itself and with two additional tetraspanins , TSP-12 and TSP-14 , which also promote Sma/Mab signaling . TSP-12 and TSP-14 can also associate with SMA-6 , the type I receptor of the Sma/Mab pathway . Finally , we found that glycosphingolipids , major components of the tetraspanin-enriched microdomains , are required for Sma/Mab signaling . Our findings suggest that the tetraspanin-enriched membrane microdomains are important for proper BMP signaling . As tetraspanins have emerged as diagnostic and prognostic markers for tumor progression , and TSP-21 , TSP-12 and TSP-14 are all conserved in humans , we speculate that abnormal BMP signaling due to altered expression or function of certain tetraspanins may be a contributing factor to cancer development .
Bone morphogenetic proteins ( BMPs ) belong to the transforming growth factor β ( TGFβ ) superfamily of secreted polypeptides that regulate a variety of developmental and homeostatic processes [1 , 2] . The TGFβ ligands are synthesized as precursor proteins that can be subsequently processed by proteases [3] . Active TGFβ ligands bind to a heterotetrameric receptor complex composed of type I and type II receptors , leading to the phosphorylation of the type I receptor by the type II receptor . The phosphorylated type I receptor then phosphorylates and activates the receptor-regulated Smads ( R-Smads ) . The activated R-Smads form a complex with common-mediator Smads ( Co-Smads ) and enter the nucleus to regulate downstream gene expression . Malfunction of the TGFβ pathway can result in numerous somatic and hereditary disorders in humans , including various cancers , bone skeletal disorders , and cardiovascular diseases [4–7] . Multiple levels of regulation ensure proper spatiotemporal activity of TGFβ signaling in the correct cellular context [8–11] . Identifying factors involved in modulating the TGFβ pathway and determining their modes of action in vivo will not only provide valuable insights into our understanding of TGFβ signaling , but may also provide therapeutic targets for the many diseases caused by alterations in TGFβ signaling . C . elegans , with its wealth of genetic and molecular tools and its well-defined cell lineage , provides an excellent model system to study the functions and modulation of TGFβ signaling during the development of a whole organism at single-cell resolution . There are at least three TGFβ-related pathways in C . elegans: one that controls dauer formation , one that regulates axon guidance and cell migration , and a third BMP-like “Sma/Mab” pathway that regulates body size and male tail formation , among its multiple functions [12] . The Sma/Mab pathway includes a BMP-like molecule DBL-1 [13 , 14] , the type I receptor SMA-6 [15] , the type II receptor DAF-4 [16] , the R-Smads SMA-2 and SMA-3 , and the Co-Smad SMA-4 [17] . Loss-of-function mutations in any component of this pathway will cause small body size and male tail sensory ray formation defects [12] . We have previously shown that the Sma/Mab pathway also plays a role in patterning the C . elegans postembryonic mesoderm . The hermaphrodite postembryonic mesodermal M lineage arises from a single pluripotent precursor cell , the M mesoblast . During larval development , the M mesoblast divides to produce a dorsal lineage that gives rise to striated bodywall muscles ( BWMs ) and macrophage-like coelomocytes ( CCs ) , as well as a ventral lineage that produces BWMs and the sex muscle precursor cells , the sex myoblasts ( SMs ) ( [18]; Fig 1A and 1C and 1E ) . This dorsoventral asymmetry is regulated by the schnurri homolog sma-9 [19] . Mutations in sma-9 lead to a dorsal-to-ventral fate transformation in the M lineage ( [20]; Fig 1B and 1D and 1F ) . We have shown that mutations in the core components of the Sma/Mab pathway ( Fig 2A ) do not cause any M lineage defect on their own , but they suppress the dorsoventral patterning defects of sma-9 mutants , suggesting that SMA-9 regulates M lineage dorsoventral patterning by antagonizing Sma/Mab signaling [20] . Using this sma-9 M lineage suppression phenotype ( Fig 1A and 1C and 1E ) , we have recently identified two new modulators of the Sma/Mab pathway , the RGM protein DRAG-1 and the DCC/neogenin homolog UNC-40 , which directly associate with each other to positively regulate Sma/Mab signaling [21 , 22 , Fig 2A] . We further showed that their functions in modulating BMP signaling are evolutionarily conserved [22 , 23] . In this study , we describe our identification and analysis of additional sma-9 M lineage phenotype suppressors that function in modulating Sma/Mab signaling . One novel modulator is TSP-21 , which belongs to a family of transmembrane molecules called tetraspanins [24] . Tetraspanins are a distinct family of integral membrane proteins that have four conserved transmembrane ( TM ) domains separated by a small extracellular loop ( EC1 ) , a small intracellular loop ( IL ) and a large extracellular loop ( EC2 ) . They are known to interact with each other to form homo- and hetero-oligomers , and organize membranes into the so-called tetraspanin-enriched microdomains that are also enriched in cholesterol and glycosphingolipids [24–27] . There are 33 tetraspanins in humans and 21 in the C . elegans genome . The in vivo functions of most of these tetraspanins are not well understood . Here we provide evidence that TSP-21 , the C . elegans ortholog of human TSPAN4 , TSPAN9 and CD53 , is localized to the cell membrane and functions positively to regulate Sma/Mab signaling in the signal-receiving cells at the ligand-receptor level . We further show that two additional tetraspanins that belong to the C8 subfamily of tetraspanins , TSP-12 and TSP-14 , also function to promote Sma/Mab signaling . TSP-12 and TSP-14 can physically interact with each other , with TSP-21 , and with the type I receptor of the Sma/Mab pathway , SMA-6 . In addition , we find that mutants defective in glycosphingolipid biosynthesis exhibit defects in Sma/Mab signaling . Collectively , our results provide in vivo evidence supporting the roles of tetraspanins and glycosphingolipid-enriched membrane microdomains in modulating BMP signaling . Finally , we provide evidence that like TSP-12 and TSP-14 , which have been previously shown to function in promoting LIN-12/Notch signaling [28] , TSP-21 also appears to function in LIN-12/Notch signaling in a cell type-specific manner .
We have previously shown that mutations in all core components of the Sma/Mab pathway , but not the TGFβ-like dauer pathway , can suppress the M lineage phenotype of sma-9 ( 0 ) mutants [20] . Here we show that null mutations in unc-129 , which encodes a TGFβ-like molecule important for axon guidance [29] , or null mutations in genes that regulate body size but do not function in the Sma/Mab pathway , such as the β-spectrin gene sma-1 [30] , or the cuticle collagen gene lon-3 [31 , 32] , do not suppress the M lineage phenotype of sma-9 ( 0 ) mutants ( Table 1 ) . Similarly , mutations in the Sma/Mab pathway do not suppress the M lineage defect of let-381 ( RNAi ) , which also leads to a dorsal-to-ventral fate transformation defect in the M lineage by inactivating the FoxF/FoxC transcription factor LET-381 ( [33]; Table 1 ) . In contrast , two deletion alleles of sma-10 , which encodes a conserved , leucine-rich repeats- and immunoglobulin ( Ig ) -like domain ( LRIG ) -containing transmembrane protein that promotes Sma/Mab signaling in regulating body size [34 , Fig 2A] , do suppress the M lineage phenotype of sma-9 ( 0 ) mutants ( Table 1 ) . Motivated by the specificity of the BMP-like Sma/Mab pathway mutants in suppressing the sma-9 M lineage defect and our previous success from the sma-9 suppressor screen in the identification of evolutionarily conserved modulators of BMP signaling , such as DRAG-1/RGM and UNC-40/DCC/neogenin [21 , 22] , we performed a large-scale screen for sma-9 suppressors , named susm ( suppressor of sma-9 ) mutations , with the aim of identifying additional modulators of BMP signaling ( see Materials and Methods ) . Using a combination of linkage analysis , complementation tests and whole genome sequencing ( see Materials and Methods ) , we identified the corresponding genes for 32 susm mutations . As shown in Table 2 , our suppressor screen successfully and specifically identified mutations in all core members and known modulators of the Sma/Mab pathway . Intriguingly , we isolated lon-1 ( jj67 ) as a sma-9 suppressor and showed that an existing , strong loss-of-function allele , lon-1 ( e185 ) , also suppresses the sma-9 M lineage phenotype ( Tables 1 and 2 ) . lon-1 encodes a member of the cysteine-rich secretory protein ( CRISP ) family of proteins and is known to function downstream of , and be negatively regulated by , the Sma/Mab pathway [13 , 35 , Fig 2A] . The suppression of the sma-9 M lineage phenotype by lon-1 mutations and the increased expression of the Sma/Mab responsive reporter RAD-SMAD [21] in lon-1 ( jj67 ) mutants ( Fig 3B ) suggest that LON-1 may exert feedback regulation on Sma/Mab signaling , rather than being strictly regulated by this pathway ( Fig 2A ) . In addition to these factors known to function in Sma/Mab signaling , we also identified a novel factor defined by two non-complementing alleles , jj60 and jj77 ( Table 2 ) . We performed whole genome sequencing ( WGS ) of the novel complementation group that includes jj60 and jj77 to identify the corresponding gene . Four lines of evidence indicate that the corresponding gene is tsp-21 . 1 ) RNAi of tsp-21 suppressed the sma-9 M lineage phenotype ( Table 3 ) . 2 ) Both jj60 and jj77 contain molecular lesions in tsp-21 ( Fig 4A ) : jj60 contains a G-to-A change in nucleotide 1827 , resulting in a Glycine ( G ) to Glutamic acid ( E ) change of amino acid 109 ( G109E ) . jj77 contains a T-to-A change in nucleotide 3327 , resulting in a Valine ( V ) to Glutamic acid ( E ) change of amino acid 236 ( V236E ) . jj77 also carries a 84bp deletion ( between nucleotides 3202 and 3287 ) and a 6bp insertion ( ATCTCT ) , resulting in a 13 amino acid deletion and 2 amino acid insertion between amino acids 209 and 223 . 3 ) A DNA fragment containing the genomic sequence of tsp-21 ( 5kb upstream , entire coding region including introns , and 1 . 7kb downstream sequences for C17G1 . 8 in pJKL1005 , Fig 4B ) rescued the Susm phenotype of jj77 ( Table 3 ) . 4 ) A deletion allele of tsp-21 that we recently obtained , tm6269 , exhibited defects similar to those of jj77 and jj60 animals ( Fig 4B and Table 3 ) . tsp-21 encodes a conserved but previously unstudied 301 amino acid transmembrane protein of the tetraspanin family , TSP-21 ( Fig 4C ) . Based on the number of cysteine ( C ) residues in the large EC2 loop , tetraspanins can be classified into three groups , C4 , C6 and C8 [36 , 37] . TSP-21 belongs to the C6a group , with the following configuration of cysteine residues in EC2: CCG——CC——C——C ( Fig 4C ) . The closest vertebrate homologs of TSP-21 are TSPAN4 , TSPAN9 and CD53 ( Figs 4C and S1 ) . These proteins , except for CD53 , share the conserved C6a configuration in the EC2 loop as well as conserved transmembrane ( TM ) domains ( Fig 4C ) . The G109E mutation in jj60 affects the last residue of TM3 ( Fig 4A and 4C ) , and likely results in the production of a partial loss-of-function TSP-21 protein . The deleted residues of TSP-21 in jj77 mutants include one of the six highly conserved cysteine residues in EC2 ( Fig 4C ) . jj77 also contains a missense mutation in a conserved residue in TM4 ( V236E , Fig 4 ) , and is therefore likely a strong loss-of-function , or likely null , allele of tsp-21 . We have therefore used jj77 for all our subsequent analyses . Because our sma-9 suppressor screen is highly specific in identifying components of the BMP-like Sma/Mab pathway , we examined tsp-21 ( jj77 ) mutants for any additional Sma/Mab signaling defects , such as body size , male tail patterning and expression of RAD-SMAD , a Sma/Mab signaling reporter that we previously generated [21] . tsp-21 ( jj77 ) animals are smaller than wild-type animals ( Fig 2B–2E ) and exhibited reduced RAD-SMAD reporter expression ( Fig 3 ) . Unlike mutants in core members of the Sma/Mab pathway , tsp-21 ( jj77 ) mutant males can mate and they do not exhibit any significant male tail patterning defects ( based on examining 100 sides of tsp-21 ( jj77 ) male tails ) . These tsp-21 phenotypes are very similar to those exhibited by null mutants in two previously identified Sma/Mab pathway modulators , drag-1 and unc-40 [21 , 22] . Furthermore , like mutants in other members of the Sma/Mab pathway [20–22] , tsp-21 ( jj77 ) mutants exhibited no M lineage defects ( Table 3 ) . Finally , tsp-21 ( jj77 ) mutants showed no dauer defects , and tsp-21 exhibited no genetic interaction with daf-1 and daf-7 , two genes functioning in the TGFβ-like dauer pathway ( [12]; S1 Table ) . Collectively , these phenotypic analyses suggest that TSP-21 positively modulates the BMP-like Sma/Mab pathway , but does not appear to play a role in the TGFβ-like dauer pathway . The smaller body size of tsp-21 ( jj77 ) mutants allowed us to use genetic epistasis to determine where in the Sma/Mab pathway TSP-21 functions . We generated double mutants between tsp-21 ( jj77 ) and null mutations in various Sma/Mab pathway components ( Fig 2A ) and measured their body sizes . As shown in Fig 2E , dbl-1 ( wk70 ) ; tsp-21 ( jj77 ) and sma-3 ( jj3 ) ; tsp-21 ( jj77 ) double mutants were as small as dbl-1 ( wk70 ) and sma-3 ( jj3 ) single mutants , respectively . These observations are consistent with TSP-21 functioning in the Sma/Mab pathway in regulating body size . lon-1 ( jj67 ) ; tsp-21 ( jj77 ) double mutants were as long as lon-1 ( jj67 ) single mutants , while lon-2 ( e678 ) tsp-21 ( jj77 ) double mutants showed intermediate body size between lon-2 ( e678 ) and tsp-21 ( jj77 ) single mutants ( Fig 2E ) , suggesting that tsp-21 is likely to function upstream of lon-1 , but in parallel to lon-2 , in the Sma/Mab pathway . drag-1 ( jj4 ) ; tsp-21 ( jj77 ) and unc-40 ( e1430 ) ; tsp-21 ( jj77 ) double mutants , or drag-1 ( jj4 ) unc-40 ( e1430 ) ; tsp-21 ( jj77 ) triple mutants were significantly smaller than each respective single mutant ( Fig 2E ) , which is consistent with tsp-21 functioning in parallel to drag-1 and unc-40 . Taken together , these results indicate that TSP-21 functions at the ligand-receptor level to positively modulate Sma/Mab signaling . To determine how TSP-21 functions in the Sma/Mab pathway , we examined the expression and localization pattern of TSP-21 . We first generated integrated transgenic lines carrying a translational TSP-21::GFP fusion ( pJKL1004 , see Materials and Methods ) that contains the entire tsp-21 genomic region including 5kb 5’ sequences and 1 . 7kb 3’ sequences ( Fig 4B ) . This translational fusion rescued the Susm phenotypes of tsp-21 ( jj77 ) mutants ( Table 3 ) . Subsequently we generated the same fusion in the endogenous tsp-21 locus via CRISPR-Cas9 mediated homologous recombination ( see Materials and Methods ) . Both reporters showed that TSP-21::GFP is plasma membrane-localized and is expressed in a wide variety of somatic cell types , including the pharynx , intestine and hypodermis starting in embryos after the 100 cell stage ( Fig 5A–5C at mid-embryogenesis ) and peaking in L1 and L2 larvae ( Fig 5D–5L ) . The TSP-21::GFP signal in these tissues decreases in late larval and adult stage animals . TSP-21::GFP is also present at the surface of M lineage cells from the 1-M stage to the 16-M stage ( Fig 5S–5Z ) . In addition to expression in these tissues , the two TSP-21::GFP lines generated via the CRISPR-Cas9 system also showed GFP expression in the somatic gonad and vulva in L2-L4 larvae and adults ( Fig 5M–5O ) , as well as in the rectal epithelium in L4 larvae ( Fig 5P–5R ) . These observations suggest that the enhancer elements for tsp-21 expression in the somatic gonad , the vulva and the rectal epithelium lie outside of the 10 . 5kb tsp-21 genomic region included in pJKL1004 . We noticed that TSP-21::GFP is enriched in the basolateral side of intestinal cells while being absent from their apical sides ( Fig 5G–5I ) . A similar localization pattern has been reported for the type I receptor SMA-6 and the type II receptor DAF-4 [38] . In addition , while TSP-21::GFP in the M lineage cells is primarily plasma membrane localized , there is also a significant intracellular distribution of TSP-21::GFP in the M mesoblast ( Fig 5S and 5W ) . At present , the functional significance of either the asymmetric localization of TSP-21::GFP in intestinal cells or its intracellular localization in the M cell is not clear . The Sma/Mab pathway is known to function in the hypodermal cells to regulate body size and in the M lineage to regulate M lineage development . We next tested whether TSP-21 functions in these cell types to exert its role in Sma/Mab signaling . Using cell-type-specific promoters to drive tsp-21 expression , we found that forced expression of tsp-21 cDNA in hypodermal cells , but not in pharyngeal or intestinal cells , rescued the small body size phenotype of tsp-21 ( jj77 ) mutants ( Table 4 ) . Similarly , forced expression of tsp-21 cDNA in the M lineage also rescued the Susm phenotype of tsp-21 ( jj77 ) mutants ( Table 3 ) . Thus TSP-21 functions autonomously in the signal-receiving cells to promote Sma/Mab signaling . There are 21 tetraspanins in C . elegans . Our finding that TSP-21 functions in Sma/Mab signaling prompted us to ask whether other tetraspanins might also function in the Sma/Mab pathway . We therefore screened through the remaining 20 tetraspanin tsp genes by RNAi injection , testing whether any of them are involved in Sma/Mab signaling using the sma-9 suppression assay . Only tsp-12 ( RNAi ) resulted in a low penetrance ( 9 . 4% , n = 767 ) Susm phenotype ( Table 5 ) . We then tested a deletion allele of tsp-12 , ok239 , and found that it also exhibited the Susm phenotype ( Table 5 ) , suggesting that the tetraspanin TSP-12 also plays a role in modulating Sma/Mab signaling . Dunn and colleagues [28] have previously reported that TSP-12 and TSP-14 function redundantly to promote Notch signaling . We asked whether tsp-14 and tsp-12 might also share a redundant role in the Sma/Mab pathway , and found that tsp-14 ( RNAi ) enhanced the penetrance of the Susm phenotype of the tsp-12 ( ok239 ) mutation ( Table 5 ) . This effect appears to be specific since tsp-10 ( RNAi ) failed to enhance the penetrance of the Susm phenotype of the tsp-12 ( ok239 ) mutation ( Table 5 ) . Thus , TSP-12 and TSP-14 also function redundantly to promote Sma/Mab signaling , in addition to their role in Notch signaling . The dual functions of TSP-12 and TSP-14 in both the Notch and the Sma/Mab signaling pathways prompted us to examine whether TSP-21 also functions in the Notch signaling pathway . The LIN-12/Notch signaling pathway is known to function in the M lineage to promote the ventral fate: loss of LIN-12/Notch function results in a ventral-to-dorsal fate transformation in the M lineage , namely the loss of M-derived SMs and the gain of M-derived CCs ( [39 , 40]; S2 Fig ) . tsp-21 ( jj77 ) single mutants exhibit no M lineage defects . We therefore examined whether tsp-21 ( jj77 ) could enhance the M lineage defect of the lin-12 temperature sensitive , partial loss-of-function allele lin-12 ( n676n930ts ) by scoring the number of M-derived CCs . As shown in Table 6 , tsp-21 ( jj77 ) significantly enhanced the M lineage defect of lin-12 ( n676n930ts ) at both 20°C and 22°C , suggesting that TSP-21 functions to promote LIN-12/Notch signaling in the M lineage . However , tsp-21 ( jj77 ) failed to enhance the sterility and embryonic lethality of bn18ts , a mutation in the second Notch receptor gene in C . elegans , glp-1 [41] . The lack of genetic interaction between tsp-21 ( jj77 ) and glp-1 ( bn18 ) is consistent with the absence of TSP-21::GFP expression in the germline and early embryo , as described above . Tetraspanins often associate with each other and with other membrane or membrane-associated proteins to organize membranes into tetraspanin-enriched microdomains [24–26] . Our finding that in addition to TSP-21 , TSP-12 and TSP-14 also function in promoting Sma/Mab signaling suggested that these tetraspanins might interact with each other . We tested this hypothesis by using the mating-based split-ubiquitin system ( mbSUS , [42] ) in budding yeast . The mbSUS is based on the observation that a full-length ubiquitin can be reconstituted when the N-terminal ubiquitin domain ( Nub ) and the C-terminal ubiquitin domain ( Cub ) are brought into close proximity [43 , 44] . This system can be used to identify potential interactions between full-length membrane proteins or between a membrane protein and a soluble protein: a mutant form of Nub , NubG , that has reduced affinity for Cub , can only reconstitute with Cub via two interacting proteins . The reconstituted ubiquitin will direct ubiquitin-specific proteases to liberate PLV ( protein A , LexA and VP16 ) from Cub , which then enters the nucleus and activates transcription of reporter genes . We generated TSP-Cub fusions and Nub-TSP or TSP-Nub fusions ( see Materials and Methods , and S2 Table for a list of the plasmids generated ) , and tested pairwise interactions among the three tetraspanins , as well as interactions between these tetraspanins and the type I and type II receptors SMA-6 and DAF-4 , respectively . Results from these experiments are summarized in Fig 6 . TSP-12-Cub appeared to auto-activate reporter expression , while the TSP-14-Cub was not detectable on western blots ( see Materials and Methods ) . For the remaining three Cub fusions ( TSP-21 , SMA-6 and DAF-4 ) , we found that TSP-21 can associate with itself , as well as with TSP-12 and TSP-14 ( Fig 6 ) . In addition , SMA-6 can associate with both TSP-12 and TSP-14 , but not TSP-21 . We also detected a very weak interaction between DAF-4 and TSP-14 ( Fig 6 ) . The use of multiple positive and negative controls in these experiments ( see Materials and Methods , and Fig 6 ) indicated that the observed interactions are highly specific . For example , TSP-21 did not show any interaction with the C . elegans LKB homolog PAR-4 ( [45]; Fig 6 ) , or the plant potassium channel KAT1 ( [42]; Fig 6 ) , or with the C . elegans ABC transporter HMT-1 [46] . Except for the weak DAF-4-TSP-14 interaction , the other observed interactions all appeared to be particularly strong , as yeast growth on SC-Trp , -Leu , -Ade , -His , -Ura , -Met plates supplemented with 0 . 3mM of methionine was detectable only 2 days after streaking the mated yeast . Thus , TSP-21 can form both homo-oligomers and heteromeric complexes with TSP-12 and TSP-14 . These findings are consistent with our genetic evidence that all three tetraspanins function to promote Sma/Mab signaling . The strong interactions between SMA-6 and TSP-12 and TSP-14 suggest that these tetraspanins might function by directly recruiting the receptor molecules to specific membrane microdomains . Tetraspanin-enriched microdomains are also enriched in cholesterol and glycosphingolipids [24–26] . We therefore tested whether cholesterol and/or glycosphingolipids are required for Sma/Mab signal transduction . Our results suggest that Sma/Mab activity is influenced by glycosphingolipids but not cholesterol . C . elegans worm survival requires exogenous cholesterol [47 , 48] . In the lab , worms are normally fed with E . coli bacteria on agar plates supplemented with 5μg/mL cholesterol [49] . Using a method that can lead to nearly complete cholesterol depletion ( [50 , 51] and see Materials and Methods ) , we grew L1 or L4 worms on cholesterol-depleted plates and scored their phenotypes or their progeny’s phenotype , respectively , at the adult stage . We found no suppression of the M lineage phenotype when sma-9 ( cc604 ) worms were grown on cholesterol-depleted media , even though the worms were sterile , a known phenotype resulting from cholesterol depletion [47 , 48] . Thus cholesterol does not seem to be essential for Sma/Mab signaling . To determine the requirement of glycosphingolipids in Sma/Mab signaling , we generated double mutants between sma-9 ( cc604 ) and mutations that reduce or eliminate the activity of enzymes involved in glycosphingolipid biosynthesis [52] , S3 Fig ) , and examined their Susm phenotype . As shown in Table 7 , mutations in cgt-3 and bre-5 partially suppressed the sma-9 M lineage phenotype . cgt-3 encodes the ceramide glucosyltransferase that converts ceramide to glucosylceramide , a precursor of complex glycosphingolipids [53 , 54] . Previous work has shown that CGT-3 is the major enzyme among the three worm CGT proteins [54] . We found that a deletion allele of cgt-3 , ok2877 , which deletes most of the coding exons of cgt-3 , resulted in a late L1 or early L2 larval arrest and a partial suppression of the sma-9 ( cc604 ) M lineage defects ( Table 7 ) . cgt-3 ( ok2877 ) mutants exhibited additional defects in Sma/Mab signaling: the relative fluorescence intensity of the RAD-SMAD reporter in cgt-3 ( ok2877 ) mutants is only 58% of that in stage-matched wild-type animals ( see Materials and Methods ) and cgt-3 ( ok2877 ) mutants exhibited a smaller body size compared to stage-matched wild-type control animals ( 72% of wild-type body length , n = 23 ) . We also observed a low penetrance of the Susm phenotype in ye17 , an allele of bre-5 that encodes a β-1 , 3-galactosyltransferease involved in glycosphingolipid biosynthesis ( Table 7 , [55 , 56] ) . Taken together , our data suggest that glycosphingolipids are required for Sma/Mab signaling . The lack of a Susm phenotype for the other mutations affecting glycosphingolipid biosynthesis ( Table 7 ) may be because many of them are partial loss-of-function alleles , since null mutations in many of these genes result in lethality [52] . Alternatively , proper Sma/Mab signaling may require specific type ( s ) of glycosphingolipids . cgt-3 is widely expressed in multiple cell types in C . elegans [53 , 54] . We tested whether cgt-3 , and therefore glycosphingolipids , are required in the signal-receiving cells for proper Sma/Mab signaling . Expression of cgt-3 in the M lineage using the hlh-8 promoter partially , but significantly , rescued the Susm phenotype of cgt-3 ( ok2877 ) mutants ( Table 7 ) , suggesting that proper Sma/Mab signaling requires glycosphingolipids in the signal-receiving cells . The lack of complete rescue suggests that glycosphingolipids are also required outside of the signal-receiving cells to promote Sma/Mab signaling . During the course of our study , we observed that both cgt-3 ( ok2877 ) and bre-5 ( ye17 ) single mutants exhibited a low penetrance M lineage phenotype like that of a lin-12 ( lf ) mutant: extra M-derived CCs due to the fate transformation of M-derived SMs to CCs ( [39 , 40]; Table 8 and S2 Fig ) . We further found that cgt-3 ( ok2877 ) enhanced the penetrance of the M lineage defects of a hypomorphic lin-12 temperature sensitive allele , n676n930 , at a semi-permissive temperature ( Table 8 ) . These observations are consistent with previous findings by Katic and colleagues [57] showing that enzymes required for glycosphingolipid biosynthesis , such as BRE-5 , are required for promoting LIN-12/Notch signaling . The requirement of glycosphingolipids in LIN-12/Notch signaling appears to be distinct from their requirement in Sma/Mab signaling . Mutations in the Sma/Mab pathway fully restore the sma-9 ( 0 ) M lineage phenotype back to that of wild-type animals ( [20–22 , 40]; S2 Fig ) . However , lin-12 ( 0 ) ; sma-9 ( 0 ) double mutants exhibit a reversal of the M lineage dorsoventral polarity , so that the double mutants have 2 SMs born on the dorsal side and 2 M-derived CCs located on the ventral side ( [39 , 40]; S2 Fig ) . Careful examination of the position of the M-derived CCs in cgt-3 ( ok2877 ) ;sma-9 ( cc604 ) and bre-5 ( ye17 ) ;sma-9 ( cc604 ) mutants showed that a majority of the double mutant animals have their M-derived CCs located on the dorsal side ( S3 Table ) , indicating a suppression rather than a reversal of polarity . Taken together , our results support the notion that glycosphingolipids are required for both LIN-12/Notch and Sma/Mab signaling .
In this study , we identified TSP-21 , a C6a class tetraspanin , as a key factor promoting the BMP-like Sma/Mab signaling in C . elegans . tsp-21 mutants exhibit small body size and Susm phenotypes similar to that shown by mutants in core Sma/Mab pathway components . The TSP-21 protein is localized to the plasma membrane , and tsp-21 is expressed and functions in the signal-receiving cells at the ligand-receptor level to promote Sma/Mab signaling . We found that among the remaining 20 C . elegans tetraspanins , TSP-12 and TSP-14 function redundantly to also promote Sma/Mab signaling . How do these three tetraspanins function to promote Sma/Mab signaling ? We envision two possible , non-mutually exclusive , scenarios . In the first scenario , the three tetraspanins might promote clustering of the receptor complexes or the ligand-receptor complexes to modulate Sma/Mab signaling . Tetraspanins are known to homo- and hetero-oligomerize to organize membranes into tetraspanin-enriched microdomains , which are also enriched in tetraspanin-associated proteins [24–26] . Previous work has shown that in mouse , TSPAN12 promotes Norrin/β-catenin signaling by enhancing clustering of the Norrin receptor FZD4 [58 , 59] . In particular , TSPAN12 and Norrin can each enhance FZD4 clustering but work together cooperatively to further increase the clustering of the ligand-receptor complex to promote Norrin/β-catenin signaling [58] . We have shown that C . elegans TSP-12 , -14 and -21 can interact with each other in yeast and that both TSP-12 and TSP-14 can interact with the type I receptor SMA-6 . In addition , we found that glycosphingolipids , which are enriched in tetraspanin-enriched microdomains , are also required for proper Sma/Mab signaling . These findings suggest that TSP-21 , TSP-12 and TSP-14 may function by recruiting the receptor complex , or the ligand-receptor complex , to glycosphingolipid-enriched membrane microdomains containing TSP-21-TSP-12-TSP-14 , thereby increasing the local concentration of the receptors , or the ligand-receptor complexes , to promote Sma/Mab signaling ( Fig 7 ) . Supporting this model , SMA-6 , DAF-4 and TSP-21 are all localized to the basolateral membranes of the polarized intestinal cells ( [38]; this work ) . We envision that several previously identified positive modulators of the Sma/Mab pathway , including DRAG-1/RGM , UNC-40/neogenin , and SMA-10/LRIG , might be localized in these microdomains as well , as all three proteins are plasma membrane-localized , are expressed and function in the signal-receiving cells , and interact with the ligand and the receptors ( for DRAG-1 ) , or the receptors ( for SMA-10 ) , or with each other ( for DRAG-1 and UNC-40 ) [21 , 22 , 34] . Further biochemical and cell biological experiments are needed to determine the presence and subcellular localization of TSP-21-TSP-12-TSP-14-containing membrane microdomains , whether the Sma/Mab pathway receptors and modulators are indeed localized to these microdomains , and what other factors are also present there . Alternatively , but not mutually exclusively , the three tetraspanins might be involved in the trafficking of essential Sma/Mab pathway components . Tetraspanins have been found to be present in the plasma membrane or various types of intracellular membranous organelles , and multiple tetraspanins are known to regulate the processing and trafficking of associated proteins [60] . In C . elegans , TSP-12 and TSP-14 have previously been shown to function redundantly in promoting Notch signaling [28] . Their Drosophila and mammalian homologs , the TspanC8 tetraspanins , interact with the ADAM ( a disintegrin and metalloprotease ) protease ADAM10 to promote its maturation and trafficking to the cell surface , which in turn promotes Notch signaling [61–63] . TSP-12 and TSP-14 may function in a similar manner in promoting Sma/Mab signaling . Since both TSP-12 and TSP-14 can bind to the type I receptor SMA-6 in yeast , they may promote Sma/Mab signaling by regulating the trafficking of SMA-6 ( Fig 7 ) , and/or other players in the Sma/Mab pathway . Further work is needed to test this hypothesis . Since the role of TspanC8 tetraspanins in promoting Notch signaling is evolutionarily conserved [61–63] , it will be interesting to determine whether the role of TspanC8 tetraspanins in modulating BMP signaling is also evolutionarily conserved , and whether these tetraspanins function in a similar manner in promoting both BMP and Notch signaling . Using C . elegans as a model , Gleason and colleagues recently showed that the type I receptor SMA-6 and the type II receptor DAF-4 utilize distinct mechanisms for their intracellular recycling , providing physiological evidence supporting the roles of endocytosis and intracellular trafficking in regulating BMP signaling [38] . In light of the roles of multiple tetraspanins in regulating the processing and trafficking of associated proteins [60] , our findings , together with that of Gleason and colleagues [38] , highlight the usefulness of C . elegans as a model system in identifying cell biological mechanisms that regulate BMP signaling . The family of tetraspanin proteins is large: there are 21 tetraspanins in C . elegans and 33 tetraspanins in humans . Recent studies have implicated tetraspanins in multiple diseases and physiological processes in humans [60] . In particular , several tetraspanins , such as CD151 [64] , TSPAN12 [65] , and TSPAN8 [66] , among others , have been implicated in cancer initiation , progression and metastasis in mammals . These and other tetraspanins have emerged as diagnostic and prognostic markers , and possible therapeutic targets , for tumor progression ( for reviews , see [27 , 67] ) . However , the mechanism by which the mis-regulation of these tetraspanins contributes to cancer is not fully understood [27 , 67] . It is well known that mis-regulation of TGFβ signaling contributes to cancer initiation and progression [6 , 68] . CD151 is the only tetraspanin whose role in cancer has been directly linked to altered TGFβ signaling [69] . Sadej and colleagues showed that CD151 is required for TGFβ1-induced proliferation and scattering of breast cancer cell line MDA-MB-231 through regulating TGFβ-induced p38 phosphorylation , rather than canonical TGFβ-induced Smad phosphorylation . Furthermore , this function of CD151 in TGFβ signaling requires its interaction with the integrins [69] . How CD151-integrin interaction regulates TGFβ-induced p38 phosphorylation is not clear . Recently a study on the tetraspanin-interacting protein EWI-2 indirectly implicates two other tetraspanins , CD9 and CD81 , in regulating TGFβ signaling in melanoma growth and metastasis [70] . But the detailed mechanism on how these two tetraspanins regulate TGFβ signaling is not known . We have provided a direct in vivo link between BMP signaling and three tetraspanins , TSP-21 , TSP-12 and TSP-14 , in living animals using C . elegans as a model . Our genetic epistasis results showed that TSP-21 acts through SMA-3 , one of the R-Smads in the canonical BMP-like Sma/Mab signaling pathway ( Fig 2E ) . Due to the embryonic arrest of null mutants in the C . elegans integrin genes , we could not determine whether the function of TSP-21 in Sma/Mab signaling is dependent on integrins . We have found that strong-loss-of function mutations in one of the two C . elegans genes encoding the α subunit of integrin , ina-1 ( gm39 ) and ina-1 ( gm144 ) [71] , did not exhibit any Susm phenotype ( n = 53 for gm39 , and n = 109 for gm144 ) . But we cannot rule out the possibility that in these mutants residual ina-1 function or function of pat-2 , another gene encoding the α subunit of integrin [72] is sufficient to mediate Sma/Mab signaling . TSP-21 is orthologous to human TSPAN4 , TSPAN9 and CD53 , but is much more distantly related to CD151 ( whose C . elegans ortholog is TSP-17; S1 Fig ) . It is therefore possible that the differences between CD151 and TSP-21 in regulating TGFβ signaling are due to intrinsic biochemical differences between the two types of proteins . Alternatively , since TSP-21 regulates a BMP-like Sma/Mab signaling pathway , it is likely that tetraspanins can regulate both TGFβ signaling and BMP signaling , but via distinct downstream effectors . Interestingly , each of the three human orthologs of TSP-21 ( TSPAN4 , TSPAN9 and CD53 ) , as well as two out of the six human orthologs of TSP-12 and TSP-14 ( TSPAN10 and TSPAN33 ) , are expressed at elevated levels in certain cancer cell lines or tumors [73–75] In addition , one human ortholog of TSP-12 and TSP-14 ( TSPAN14 ) is genetically altered in non-small-cell lung cancer [76] . However , the functional significance of their overexpression or mutation in human cancers is not fully understood . We propose that the involvement of these tetraspanins in cancer may be partially due to their role in modulating the activity of TGFβ and/or BMP signaling . Previous genetic studies in C . elegans have led to the identification of key players in BMP signaling ( for example , [16 , 17] ) . A screen based on the body size phenotype has also been fruitful in identifying factors involved in modulating Sma/Mab signaling , such as SMA-10/LRIG [32] and LON-2/glypican [34 , 77] . Potential modulators of the Sma/Mab pathway may also exist among a collection of mutants with a small body size phenotype [78] . However , it may be difficult to identify the genes for which mutations produce only a subtle effect on body size , such as tsp-21 ( jj77 ) . Furthermore , since genes not functioning in the Sma/Mab pathway also regulate body size ( for example , [30–32 , 79] ) , not all mutations affecting body size will identify factors specifically functioning in the Sma/Mab pathway . The sma-9 suppressor screen appears to be a highly specific and sensitive means to identify new components of the Sma/Mab pathway: ( 1 ) Mutations in all ( except for crm-1 , see Table 1 ) previously identified Sma/Mab pathway members suppress the sma-9 M lineage phenotype ( Table 1 and Table 2 ) . In general , partial loss-of-function alleles for a given gene exhibited lower penetrance of the Susm phenotype compared to putative null alleles ( Table 2 ) , demonstrating that the suppression of the sma-9 M lineage phenotype is highly sensitive to altered levels of Sma/Mab signaling . ( 2 ) Mutations in other signaling pathways , such as the dauer pathway or the Wnt pathway , or mutations that exclusively affect body size without affecting Sma/Mab signaling , do not suppress the M lineage phenotype of the sma-9 mutant ( [20]; Table 1 ) . ( 3 ) Using this screen , we have identified three evolutionarily conserved modulators of the Sma/Mab pathway , DRAG-1/RGM [21] , UNC-40/neogenin/DCC [22] , and TSP-21/TSPAN4 , 9 ( this study ) . Additional modulators of this pathway probably exist , as our screen has only recovered single alleles of several genes known to function in Sma/Mab signaling and is , therefore , unlikely to be genetically saturated ( Table 2 ) . In summary , we have developed a highly specific and sensitive way to identify new modulators of the BMP pathway in C . elegans . This genetic approach has confirmed known regulators and identified novel players . Because of the high degree of conservation of the BMP pathway , the factors that we identify in our screen and the mode of their action that we decipher in C . elegans will be broadly relevant in understanding modulation of BMP signaling in other metazoans , including humans .
Strains were grown using standard culture conditions , as described by Brenner [49] . Analyses were performed at 20°C , unless otherwise noted . Cholesterol depletion conditions were following those described in Merris et al . [80] by replacing agar with agarose , and by growing bacteria OP50 and C . elegans worms on defined media , which contains 3 . 5mM Tris . HCl , 2mM Tris , 34mM NaCl , and 3 . 1g/L of ether-extracted peptone . Eggs or L4 hermaphrodite animals were placed on cholesterol-depleted plates and the resulting adult animals were scored for M lineage phenotypes . The following mutations and integrated transgenes were used: Linkage group I ( LG I ) : drag-1 ( jj4 ) , arIs37 ( secreted CC::gfp ) , bre-4 ( ok3167 ) , bre-5 ( ye17 ) ; LG II: sma-6 ( e1482 ) , pod-2 ( ye60 ) , cgt-3 ( ok2877 ) /mIn1[mIs14 dpy-10 ( e128 ) ] , jjIs2437[CXTim50 . 19[pCXT51 ( 5*RLR::deleted pes-10p::gfp ) + LiuFD61 ( mec-7p::rfp ) ] , sptl-1 ( ok1693 ) ; LG III: daf-4 ( m63 ) , daf-7 ( m62 ) , sma-2 ( e502 ) , sma-3 ( e491 ) , sma-4 ( e729 ) , lon-1 ( e185 ) , cup-5 ( ar465 ) , ina-1 ( gm144 ) , ina-1 ( gm39 ) , bre-2 ( ye31 ) , bre-3 ( ye26 ) , bre-3 ( ye28 ) , lin-12 ( n676n930ts ) , hT2[qIs48] , ccIs4438[intrinsic CC::gfp]; LG IV: daf-1 ( m40 ) , daf-1 ( m213 ) , fat-2 ( wa17 ) , fat-3 ( wa22 ) , fat-6 ( tm331 ) , tsp-12 ( ok239 ) , nT1[qIs51]; LG V: dbl-1 ( wk70 ) , fat-7 ( wa36 ) , sma-10 ( wk89 ) , sma-10 ( ok2224 ) , sma-1 ( ru18 ) , lon-3 ( ct417 ) , crm-1 ( tm2218 ) , him-5 ( e1467 ) , bre-1 ( ye4 ) , bre-5 ( ye17 ) , cgt-1 ( ok1045 ) , acs-1 ( gk3066 ) V/nT1[qIs51]IV;V; LG X: lon-2 ( e678 ) , tsp-21 ( tm6269 ) , sma-9 ( cc604 ) , jjIs2433[RAD-SMAD: CXTim50 . 1[pCXT51 ( 5*RLR::deleted pes-10p::gfp ) + LiuFD61 ( mec-7p::rfp ) ]] . tsp-21 and sma-9 are located 0 . 79 map unit apart from each other on the X chromosome . We therefore separated the tsp-21 ( jj77 ) mutation from sma-9 ( cc604 ) via recombination . Specifically , progeny from tsp-21 ( jj77 ) sma-9 ( cc604 ) /+ + heterozygous parents were scored for the number of CCs . Animals with 6 CCs ( jj77 cc604/+ + or + +/+ + or jj77 cc604/jj77 + ) were genotyped for jj77 homozygosity by PCR . jj77 cc604/jj77 + animals were selected and their progeny were further genotyped by sequencing the sma-9 gene in order to obtain jj77 +/jj77 + animals . Four independent recombinants were obtained , #570 , #778 , #898 and #954 . Each recombinant was then outcrossed with N2 three more times before further phenotypic analysis . All four recombinants behaved similarly regarding body size , RAD-SMAD and male tail patterning phenotypes . The lon-2 ( e678 ) tsp-21 ( jj77 ) double mutant was generated from a lon-2 ( e678 ) egl-15 ( n484 ) /tsp-21 ( jj77 ) heterozygous worms by identifying Lon-non-Egl recombinants , and scoring for the presence of the tsp-21 ( jj77 ) and the lon-2 ( e678 ) mutations by PCR genotyping . let-381 ( RNAi ) was performed via feeding following the protocol described in [33] . Other RNAi experiments were performed by injection . In general , gene specific fragments were amplified using RNAi clones from the Ahringer library [81] or the Vidal library [82] , or using N2 genomic DNA as template . dsRNAs were generated using the T7 Ribomax RNA Production System ( Promega ) and injected into gravid adult hermaphrodite animals of specific genotypes carrying CC::gfp . The resulting progeny were scored at the adult stage for the number of CCs . arIs37 ( secreted CC::gfp ) I; cup-5 ( ar465 ) III; sma-9 ( cc604 ) X animals lacking M-derived coelomocytes ( having a total of 4 CCs ) were treated with 50 mM ethyl methanesulfonate ( EMS ) . Individual F1 animals were picked to 3F1s per plate and their combined F2 progeny were screened for the restoration of M lineage-derived coelomocytes ( having a total of 5–6 CCs ) by direct visual examination using a fluorescence stereomicroscope . Plates that segregated 5–25% of animals with 6 CCs were kept for further analysis , including determining whether the mutations bred true , the degree of suppression for each suppressor mutation when homozygous and whether the mutations are dominant or recessive . By screening through 5 , 300 haploid genomes using the above method , we isolated 37 true-breeding sma-9 suppressors , named susm ( suppressor of sma-9 ) mutations ( jj49-jj85 , Table 2 ) . Four of these , jj68 , jj80 , jj81 and jj84 showed a relatively low degree of suppression ( near 30% , Table 2 ) , and were not further characterized in this work . jj58 might be a dominant mutation and was not further analyzed . All of the remaining susm alleles appear to be recessive , single locus mutations , although some suppressors exhibited partial dominance in their Susm phenotype ( Tables 1 and 2 ) . The suppressor mutations were then mapped to chromosome X or chromosome III based on their linkage to sma-9 ( cc604 ) X or to cup-5 ( ar465 ) III . Further complementation tests were carried out between each suppressor mutation and mutations in each known members of the Sma/Mab pathway , and between different suppressor mutations that did not affect known genes in the Sma/Mab pathway . LW0214 , which has arIs37 ( secreted CC::gfp ) I and sma-9 ( cc604 ) X introgressed into the CB4856 Hawaiian strain by 6x backcrossing , was used for mapping the sma-9 suppressors via snip-SNP mapping [83] and whole genome sequencing ( WGS ) [84] . LW0214 was tested using a panel of SNP markers and subsequently by WGS , and found to contain CB4856 SNPs for all six chromosomes except for the following regions that still contain N2 SNP markers: chromosome I—from the left end to -12 and from +24 to the right end; chromosome II—from the left end to -18; and chromosome X—between +1 . 73 and +11 . Snip-SNP markers used were described in Wicks et al . [83] and Davis et al . [85] . For the sma-9 suppressor mutations that appeared to affect known genes in the Sma/Mab pathway , either by complementation tests , or by whole genome sequencing ( WGS , see below ) , their molecular lesions were identified by sequencing PCR products spanning the entire genomic regions of the corresponding genes , which include dbl-1 , daf-4 , sma-6 , sma-2 , sma-3 , sma-4 , lon-1 , sma-10 and unc-40 . For jj69 that contains a single base pair change in the upstream regulatory region of sma-6 , a plasmid pJKL1060 , which contains 3kb of upstream sequences , the genomic coding region and 2kb of downstream sequences of sma-6 , was used to rescue the Susm phenotype of jj69 . Direct WGS of the homozygous suppressor mutant DNA was performed for some sma-9 suppressors . For others , the suppressors were simultaneously mapped and identified using the SNP-WGS method of Doitsidou et al . [84] . For the SNP-WGS method , each sma-9; suppressor mutant was crossed with LW0214 , which has sma-9 introgressed into the polymorphic Hawaiian strain CB4856 ( described above ) . Between 36 and 59 F2 progeny that were homozygous for both sma-9 and the suppressor mutation were collected . F3 generation worms from these F2 progeny were pooled for DNA extraction and library construction . Worm genomic DNA was prepared using the Qiagen Gentra Puregene Kit . 5μg of genomic DNA was used to prepare the sequencing library using the NEBNext DNA Sample Prep Master Mix Set 1 . Single-end 50bp short-read ( 51 cycle ) sequencing was performed on the HiSeq 2000 instrument ( Illumina ) , yielding 38 ~ 78 million reads ( 20 ~ 41 fold coverage ) per sample . For direct WGS ( jj58 , jj60 , jj61 , jj71 , jj77 ) , data analysis was done using the MAQGene platform [86 , 87] with the default setting . SNP variants on the X chromosome compared to the reference C . elegans genome ce6 W221 were analyzed . Genes with missense SNP variants in jj60 and jj77 , but not in jj58 and jj71 , were among the candidate genes that were targeted by RNAi for their ability to suppress the sma-9 ( cc604 ) M lineage defects by injection . These included C41A3 . 1 , K09C4 . 8 and C17G1 . 8 . Further PCR and sequencing confirmed the jj60 and jj77 mutations in C17G1 . 8 ( tsp-21 ) . For mapping additional suppressors using either direct WGS ( for jj2 , jj5 , jj7 , jj50 , jj52 and jj70 ) or SNP-WGS ( for jj49 , jj57 , jj62 , jj69 , jj71 , jj73 , jj78 and jj83 ) , sequence data were aligned to C . elegans reference genome version WS220 using BFAST [88] with default parameters . SNP calling was performed by SAMTOOLS [89] . A valid SNP call required a minimum read depth of three . ANNOVAR [90] was used for annotation of SNP coding potential . For SNP-WGS , Hawaiian SNPs were annotated with a custom Perl script . Scatter plots of heterozygous ( 0 . 2–0 . 7 fraction of total reads ) Hawaiian SNPs were generated as chromosome position vs . fractional total graphs . Mapping intervals were defined by visual inspection for gaps ( i . e . , Hawaiian SNP fraction <0 . 2 ) . Candidate suppressor genes were identified as homozygous ( fraction >0 . 8 ) , non-Hawaiian , nonsynonymous SNPs in the mapped interval . The SNPs in the identified suppressor genes were verified by PCR and sequencing . jj61 was mapped via SNP-WGS to the region on the X chromosome where lon-2 is located . Direct inspection of the sequence reads around the lon-2 region showed that jj61 contains a large deletion ( 11 . 8kb ) spanning the lon-2 region , which was subsequently verified by PCR and sequencing . sma-6 reporter and rescuing constructs pJKL840: sma-6p::nls::rfp::lacZ::unc-54 3’UTR pJKL1048: sma-6 ( jj69 ) p::nls::rfp::lacZ::unc-54 3’UTR pJKL1060: sma-6p::sma-6 rescuing construct tsp-21 reporter constructs pJKL1005: 5kb tsp-21p::tsp-21 genomic ORF::1 . 7kb tsp-21 3’UTR pJKL1004: 5kb tsp-21p::tsp-21 genomic ORF::gfp::1 . 7kb tsp-21 3’UTR pJKL998: 5kb tsp-21p::nls::gfp::lacZ::unc-54 3’UTR pZL11: 5kb tsp-21p::tsp-21 genomic ORF ( sgRNA target site modified ) ::gfp::1 . 7kb tsp-21 3’UTR Constructs for tissue-specific expression of tsp-21 pJKL1015: tsp-21p::tsp-21 cDNA::tsp-21 3’UTR pJKL1017: rol-6p::tsp-21 cDNA::tsp-21 3’UTR pJKL1018: elt-3p::tsp-21 cDNA::tsp-21 3’UTR pJKL1019: elt-2p::tsp-21 cDNA::tsp-21 3’UTR pJKL1020: hlh-8p::tsp-21 cDNA::tsp-21 3’UTR pJKL1021: myo-2p::tsp-21 cDNA::tsp-21 3’UTR The full-length tsp-21 cDNA clone yk1449c02 , which contains a SL1 trans-splice leader sequence , and full length 5’ and 3’ UTRs , was kindly provided by Dr . Yuji Kohara ( National Institute of Genetics , Japan ) . A point mutation in the coding region of tsp-21 in yk1449c02 was corrected by site-directed mutagenesis to generate pJKL994 . Transgenic animals were generated using the plasmid pRF4 or pJKL449 ( myo-2p::gfp::unc-54 3’UTR ) as markers . Integrated transgenic lines carrying pJKL1004[TSP-21::GFP] ( jjIs3113 and jjIs3114 ) were generated using gamma-irradiation . pJKL840[sma-6p::nls::rfp::lacZ::unc-54 3’UTR] was used for co-localization of TSP-21::GFP and sma-6p::nls::rfp . pTAA1[hlh-8p::cgt-3 . 1a ORF::unc-54 3’UTR] was used to test for function of cgt-3 in the M lineage . The following mix of plasmid DNAs was injected into the N2 gravid adults: ( 1 ) a Cas9 expression plasmid pDD162 [91] , ( 2 ) a tsp-21-specific sgRNA plasmid pZL10 , which has GAAACTGACACGGTAGAAGATGG replacing the unc-119 sgRNA in plasmid 46169 [92] , ( 3 ) the homologous repair template pZL11: 5kb tsp-21p::tsp-21 genomic ORF ( sgRNA target site modified ) ::gfp::1 . 7kb tsp-21 3’UTR , ( 4 ) a co-injection marker pCFJ90[myo-2p::mCherry] [93] . GFP knock-in events were screened via PCR using a primer in GFP ( ZL21: CGCATATCTTGGACGCCTAATTTG ) and a primer in the tsp-21 3’ region outside of the sequences included in pZL11 ( ZL22: TCCACACAATCTGCCCTTTCG ) . Single worm PCR of 250 F1s failed to detect any germline integration event . However , we checked the F2 generation for high transmission efficiency lines ( myo-2::mCherry positive ) and screened via PCR 5–10 F3 progeny from each of the three high transmission efficiency lines ( >50% ) . One of the three transgenic lines gave us two homozygous GFP knock-in strains: LW3670: jj93 ( tsp-21::gfp ) and LW3671: jj94 ( tsp-21::gfp ) . Total RNA was isolated from mixed-stage N2 or sma-6 ( jj69 ) worms using TRIzol Reagent ( Invitrogen ) . Reverse transcription was performed with SuperScript III First-Strand Synthesis System ( Invitrogen ) following the manufacturer’s instructions . The primers used to detect the cDNAs of sma-6 and act-1 are: sma-6 , MLF-34 and MLF-44; act-1 , NMA-163 and NMA-164 . Body size measurement and RAD-SMAD reporter assay were carried out as described in Tian et al . [22] . Dauer formation assay was carried out as described in Tian et al . [21] . Statistical analyses were performed using Microsoft Excel and GraphPad Prism ( http://www . graphpad . com/scientific-software/prism/ ) . GFP and RFP epifluorescence in transgenic animals was visualized either on a Leica DMRA2 compound microscope , where the images were captured by a Hamamatsu Orca-ER camera using the OPENLAB software , or on a Zeiss LSM 710 confocal microscope . Subsequent image analysis was performed using ImageJ and Photoshop CC . We identified tetraspanin homologs by running hmmsearch from HMMER 3 . 1b1 [94] with the hidden Markov model ( HMM ) profile for tetraspanins ( PF00335 . 15 ) from PFAM 27 . 0 [95] against the reference proteome set of the Quest for Orthologs consortium ( [96]; source URL , ftp:/ftp . ebi . ac . uk/pub/databases/reference_proteomes/QfO/QfO_release_2014_04 . tar . gz ) . hmmsearch was run with the arguments '-E 1e-06—domE 1e-06—incE 1e-06—incdomE 1e-06-A [alignment]' , which generated aligned regions of similarity to these core domains . Since the regions of homology were extracted from full-length proteins with an HMM , the specific residues extracted were generally a subset of the full protein; moreover , it was possible for two or more such regions to be independently extracted from a single protein chain , although this proved rare for tetraspanins . These regions were then realigned with MAFFT v7 . 158b [97] in L-INS-i , its slowest and most reliable mode , using the arguments '—localpair—maxiterate 1000' . The resulting alignments were purged of poorly aligned members by first running trimal v1 . 4 . rev15 [98] using the argument '-gt 0 . 5' , and then running BMGE 1 . 1 [99] using the arguments '-t AA-h 1-g 0 . 5:1' . This purged the alignments of any columns in which over 50% of the columns' positions consisted of gaps rather than amino acid residues , and then any sequences in which over 50% of the residues were gapped , yielding global alignments that lacked excessive loops and gaps . From the filtered alignments , we computed protein maximum-likelihood phylogenies , with a WAG model of amino acid evolution [100] and with pseudocounts for gaps , via FastTree 2 . 1 . 7 [101] , using the arguments '-pseudo-wag' . Confidence values for the branches of trees ( ranging from 0 . 00 to 1 . 00 ) were automatically computed by FastTree with 1 , 000 internal replicates . We visualized the resulting trees with FigTree 1 . 4 . 2 ( http://tree . bio . ed . ac . uk/software/figtree ) . Branch lengths were measured in average substitutions ( when comparing full sequences or their profiles ) among non-gap positions in the aligned sequences , with distances derived from the BLOSUM45 matrix , a correction for multiple substitutions , and an allowed maximum of 3 . 0 substitutions per individual site [102] . The split-ubiquitin yeast two-hybrid experiments were carried out following the detailed method described in Grefen et al . [103] . The bait CubPLV and prey NubG constructs were generated via PCR and recombinational in vivo cloning in yeast [103] . The resulting fusion constructs were recovered from yeast and transformed into E . coli and confirmed by sequencing . The primers , cDNA templates , and the names of the resulting bait and prey constructs are summarized in S2 Table . The bait and prey constructs were transformed into the haploid yeast strains THY . AP4 ( MATa ) and THY . AP5 ( MATα ) , respectively , and the resulting yeast strains were mated to generate diploid yeast cells carrying specific combinations of bait and prey constructs [103] . Interactions among each pair of bait and prey constructs were visualized by streaking diploid cells on SC-Trp , -Leu , -Ade , -His , -Ura , -Met plates that were supplemented with four different concentrations of methionine: 0mM , 0 . 075mM , 0 . 150mM and 0 . 300mM , respectively . Methionine can repress the expression of the CubPLV fusion , which is under the control of the Met-repressible MET25 promoter [42] . Growth was monitored for 2–9 days at 30°C . The plasmids KAT-1-Cub-PLV , NubG-KAT-1 ( in pNX33 vector ) and KAT-1-NubG ( in pXN21 vector ) [42] , HMT-1-Cub-PLV and HMT-1-NubG ( in pXN21 vector ) [46] were kindly provided by Sungjin Kim ( Cornell University ) and used as specificity controls . NubG fusions for PAR-4 , a protein unexpected to interact with any of the proteins tested , was included as another control for specificity of the interactions . The empty NubG vector was used as a control to determine if any Cub-PLV fusions can auto-activate the reporters . The vector expressing soluble wild-type Nub ( NubWT ) was used as a control to indicate expression of the Cub-PLV fusion . Additional confirmation of expression of each fusion protein came from western blot analysis using rabbit polyclonal anti-VP16 antibodies ( ab4808 , Abcam , for CubPLV fusions ) and monoclonal anti-HA antibodies ( Clone 12CA5 , Sigma , for NubG fusions ) . | The bone morphogenetic protein ( BMP ) signaling pathway is required for multiple developmental processes during metazoan development . Various diseases , including cancer , can result from mis-regulation of the BMP pathway . Thus , it is critical to identify factors that ensure proper regulation of BMP signaling . Using the nematode C . elegans , we have devised a highly specific and sensitive genetic screen to identify new modulators in the BMP pathway . Through this screen , we identified three conserved tetraspanin molecules as novel factors that function to promote BMP signaling in a living organism . We further showed that these three tetraspanins likely form a complex and function together with glycosphingolipids to promote BMP signaling . Recent studies have implicated several tetraspanins in cancer initiation , progression and metastasis in mammals . Our findings suggest that the involvement of tetraspanins in cancer may partially be due to their function in modulating the activity of BMP signaling . |
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Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years . Still , the analysis of proteomic data stays difficult because of limited sample size . This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units . The independent assumption ignores the associations among biomolecules with similar functions or cellular localization , as well as the interactions among them manifested as changes in expression ratios . Consequently , these methods often underestimate the associations among biomolecules and cause false positives in practice . Some studies incorporate the sample covariance matrix into the calculation to address this issue . However , sample covariance may not be a precise estimation if the sample size is very limited , which is usually the case for the data produced by mass spectrometry . In this study , we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data . The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database . We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group . The performance of the proposed T2-statistic is demonstrated using five published experimental datasets: the T-cell activation , the cAMP/PKA signaling , the myoblast differentiation , and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size . Compared with other popular statistics , the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication . We implemented the T2-statistic into an R package T2GA , which is available at https://github . com/roqe/T2GA .
Great progress has been made toward the development of high-throughput technologies and their application to biological and clinical research . In a quantitative experiment , genes or proteins with significant changes in expression are potential to have important roles in a given phenotype or phenomenon . Therefore , the analysis of quantitative experimental data generally produces a list of differentially expressed genes or proteins in order . The list may share some insights if the aim of the experiments is restricted to few targets . As regards high-throughput data , the list hardly provides biological understanding of the mechanisms being studied , since the data involve complicated regulations among biomolecules and the number of biomolecules is too large to examine all candidates individually . Systematically investigating the underlying mechanisms from a high-throughput data therefore become a new challenge . To confront the challenge , one idea is to apply pathway analysis to identify the genes or proteins that are known to be involved in a biological process or interaction based on the existing knowledge . Many approaches of pathway analysis have been developed over years concerning different methodologies . Some general reviews of pathway analysis approaches can be found in [1–4] . These approaches can be broadly divided into two major factions: a competitive null with a gene sampling model and a self-contained null with a subject sampling model . The null hypothesis of a competitive test suggests that the target pathway ( or a predefined gene set ) is differentially expressed as well as the rest of all pathways . Practically a competitive null is closely tied to ( although not necessarily ) a gene sampling approach [5] . A gene sampling model principally implies the independence assumption; the assumption presupposes that genes are expressed independently of each other so that these genes can be manipulated as the sampling subject to produce the null distribution . The null hypothesis of a self-contained test , on the other hand , suggests that the target pathway is not differentially expressed between distinct phenotypes . Using the phenotypes of the experiments as the sampling subject is the setup of a subject sampling model . In other words , a competitive null with a gene sampling approach let pathways compete with each other in order to rank these pathways and use the number of genes as the sample size in the meanwhile; whereas a self-contained null with a subject sampling approach examine each single pathway to determine if the pathway is indeed differentially expressed between phenotypes and use the number of experiments as the sample size . Intuitively , a competitive null aims to find the pathway that is most significant among all pathways; a self-contained null aims to find the pathway that is the most significantly expressed between phenotypes . The classification of null hypotheses and sampling methods is firstly suggested by Geoman et al [5] . Even both categories have their own pitfalls and benefits , the authors suggested using a self-contained null with a subject sampling model in pathway analysis . Competitive null with gene sampling model usually implies the independence assumption which may produce inaccurate small p-values , cause serious false positives [6] , and result misleading interpretations [5] . Further methodology issues of using a self-contained null can be found in [7–9] , and of using a competitive null can be found in [10] . Another issue of pathway analysis comes from the construction of test statistics . These statistics can be divided into univariate tests and multivariate tests . Univariate statistics , such as a modification or a weighted summation of the t-scores [11–14] , only focus on expression of genes or proteins and assume these biomolecules as independent units for statistical ease . The independence assumption ignores the associations among biomolecules with similar functions or cellular localization , as well as the interactions among them manifested as changes in expression ratios . In contrast , multivariate statistics take into consideration the associations among genes or proteins [15–20] . Some methodology studies [9 , 21–23] have evaluated univariate tests and multivariate tests with synthetic and experimental datasets . Compared with multivariate tests , univariate tests generally result a decrease in statistical power [21] and an increase in false positive rate [22] along with the rise of average correlation . Multivariate approaches usually incorporate the sample covariance matrix into calculation to address biological interaction . However , the sample covariance may not be precise enough to estimate the associations if the sample size is very limited . Compared with other gene expression data , proteomic data produced by mass spectrometry are more difficult to analyze systematically due to the limited number of experiments . This limitation causes current multivariate tests incompetent because the sample covariance will not be a robust statistic . The composition of pathway diagrams also become a challenge to pathway analysis . A pathway is a group of biomolecules that participate in a particular cellular process . The members of a pathway are usually defined by the tradition ( i . e . , the history of pathway discovery ) of molecular biology scientists . The structure of pathway diagrams is not standardized and therefore arises some issues to pathway analysis . First , the same pathway from different databases or other sources may have the same core members but different side members . Under different experimental conditions , the size of accessible biomolecules also changes . For example , the phosphorylation proteomic data may not provide information to the proteins not belong to phosphoproteome . Since the number of members within a pathway is not a constant , using this number as a parameter to determine if the pathway is significant or not may lead to inconsistent results . This issue arises with the assumption of a competitive null . Second , some molecules appear over pathways may play important roles as communication centers . For example , p53 appears in 38 pathways in the KEGG database . The shared members may interact or cooperate with each other and form a functional module . If this module is regulated ( i . e . , the members within the module are differentially expressed as they cooperate together ) , the subsets of this module may present in abundant pathways and make these pathways seemingly significant . To identify the regulated pathway among the significant pathways of common modules , biologists usually utilize the distinctive molecules participating in that specific pathway; whereas pathways do not contain distinctive molecules may be irrelevant to the underlying mechanism . However , most of the current approaches do not take consideration of this issue . The suggestion of irrelevant pathways due to the redundancy over the significant pathways usually causes confusion to data description . A recent study [24] also focuses on the second issue . They demonstrate how the shared members affect p-values , and try to address this problem under a competitive null . In this study , we introduced a multivariate test , based on the Hotelling’s T2-statistic , to perform pathway analysis for quantitative proteomic data . The most serious problem of analyzing proteomic data produced by mass spectrometry is the limited number of experiments . We usually obtain only a few replicates ( biological or technical ) per experimental condition . To manage this issue , we had two special designs in our test . First , instead of using the sample covariance matrix ( which is not robust when the sample size is limited ) , we use the covariance matrix that is constructed of the probabilistic confidence scores provided by the STRING and HitPredict databases . The proposed T2-statistic is then built of the protein expression profile and the covariance matrix to consider the expression level of individual proteins along with the associations among them . Second , we designed a self-contained model to produce a null distribution of altering protein expression while retaining the structure of protein associations . We are not capable of applying a subject sampling model because the number of experiments is too limited . In addition to the settlement of sample size issue , we designed an integrating procedure to categorize significant pathways as well as to avoid redundancy . The performance of the proposed T2-statistic is demonstrated using five public experimental datasets with different levels of biological complexity: the T-cell activation , the cAMP/PKA signaling , the effect of dasatinib on the BCR-ABL pathway , the differentiation process of myoblast , and the protective effect of myocilin via the MAPK signaling pathway . The first four datasets are proteomic data produced by mass spectrometry; the last dataset is a gene expression data of low sample size . We compared T2 with other popular statistics: DPA [25] , GSEA [26] , DAVID [27 , 28] , and IPA [29] . For most of the situations , T2 yielded more accurate descriptions in agreement with the discussion of the original publication .
We took four proteomic datasets of different biological complexity and experimental properties to demonstrate our approach . To be comprehensive , the testing datasets include the case of pathway activation and inhibition; also the case of signaling phosphoproteome and cellular proteome . We only used the final ratios provided by the datasets since they may have different integration approaches ( e . g . to combine the results of biological and/or technical repeats , to handle missing data or outliers ) under different experimental designs . The summarized ratio is also the most available format for quantitative proteomic data . In this situation , the sample size of data becomes only one , calculating a covariance matrix is not even possible . Our approach provide a solution to undertake this difficulty . We also applied our approach on a gene expression dataset of three samples to demonstrate that the general idea is applicable to other quantitative data of low sample size . To provide more generalized results , we choose two pathway databases and two protein-protein interaction databases: KEGG and Reactome provide pathway categories served as predefined gene sets , STRING and HitPredict contributes the confidence scores to estimate the covariance between protein expressions . As we mentioned in Introduction , subsets of one common active module may cause a lot of pathways statistically significant . These pathways may only have slight relevance to the target mechanisms ( Fig 1 ) . To avoid misinterpretation due to irrelevant pathways being reported , we identify delegates to categorize pathways into pathway groups . A pathway group is a set of pathways , in which the pathway being the superset of other pathways is defined as the delegate . Since other pathways do not show any distinctive proteins to support themselves , the significance of other pathways may simply originate from the regulation of the delegate . In other words , we want to avoid the situation that a pathway is enriched only because of some common proteins that are shared with other pathways . Our pathway integration procedure operates as follows . The set of the pathways being evaluated is denoted by P and each pathway P ∈ P is associated with a p-value . We iteratively perform the following steps until all the pathways in P are assigned to a specific pathway group M . A demonstrative example can be found in S1 Fig . The pathway integration procedure aims to find the pathways with the most sufficient information to represent current data . Please notice that this idea is not similar to pathway hierarchy in which higher level pathways are defined as the supersets over lower level pathways , in that case only the pathways of highest level are able to be delegates . One can only apply the integration procedure to pathways without hierarchy relationship . In addition , the procedure is only applicable to the statistical test using a self-contained null since the measure of significance is independent for each pathway .
We discussed the flow of signal transduction in time order since the dataset is a time-series of 5 min , 15 min , and 60 min experiments . In the beginning , the treatment anti-CD3ϵ activated TCR signaling pathway . The TCR signaling pathway in KEGG and IPA depicts the signals from the TCR receptors all the way to the IL-2 expression . Usually , the response of signal transduction comes rapid , we expected that the TCR signaling pathway should be enriched in early time points; the downstream from the TCR signaling pathway to the IL-2 expression should be enriched in late time points . From Table 2 we found that T2×ST , T2×HP , DAVID , and IPA enriched the TCR signaling pathway in all experiments; GSEA enriched the TCR signaling pathway in 5 and 15 min experiments only . The ranks of the TCR signaling pathway are top in T2 , DAVID , and IPA . The downstream of the TCR signaling pathway was illustrated in Fig 3 , there are three possible routes directed from TCR signaling . The original publication focused on the TCR/Ras/MAPK route to the IL-2 expression and the cytoskeleton remodeling response . The TCR/PI3K-Akt/mTOR route , on the other hand , is also important and has been discussed as a cluster in literature [44–47] . From Fig 3 we found the enriched pathways by T2×ST were well-grounded for the following reasons: To sum up above , we found the results of T2×ST fit our expectation: T2×ST enriched the TCR signaling pathway in the early time data , the route TCR/Ras/MAPK and TCR/PI3K-Akt/mTOR in time order , and the response of actin regulation , in both pathway databases . T2×HP , DAVID , and IPA enriched most of the expected pathways in KEGG , although the ranks of these pathways are occasionally low in IPA; T2×ST , T2×HP and DAVID also enriched most of the expected pathways in Reactome ( S1 Table ) ; GSEA failed to enrich half of the expected pathways in both pathway databases . We also found that there is no distinguishable difference between the result using a self-contained null ( T2×ST , T2×HP , DPA ) or a competitive null ( GSEA , DAVID , IPA ) in the TCR dataset . The target of the original publication is PKA substrates . As the authors described in their results; PGE2 induced a rapid and maximal increase in phosphorylation level after 1 min , and the level remained high before the number of substrates gradually returned to near-basal conditions after 60 min ( S2b Fig ) . From Table 2 we found T2×ST , T2×HP , DPA , and IPA enriched the cAMP signaling pathway for both 1 min and 60 min experiments . The downstream of the cAMP signaling pathway was illustrated in Fig 4 , there are four possible routes directed from cAMP signaling . The proliferation route was suggested by T2×ST , T2×HP , and IPA; and the cytoskeleton remodeling route also , with an addition of DPA . The DNA repair route was enriched by T2×ST , T2×HP , and DPA , in both pathway databases . The glycogen synthesis route , on the other hand , was enriched only by IPA . The reason is that T2 takes expression ratios as an important feature , whereas the mapped proteins of the “Glycolysis / Gluconeogenesis” pathway are all of low ratios ( min = −0 . 23710 , max = 0 . 14950 , mean = 0 . 02742 ) . Briefly , we found the results of T2 reasonable: T2×ST and T2×HP enriched the cAMP signaling pathway for both datasets; the PKA/Rap1/PI3K-Akt route in time order , and also the cytoskeleton remodeling route and the DNA repair route . DPA and IPA also enriched most of the expected pathways . GSEA and DAVID failed to enrich most of the expected pathways in both pathway databases . We also found that the result using a self-contained null ( T2×ST , T2×HP , DPA ) enriched more expected pathways than a competitive null ( GSEA , DAVID , IPA ) . Since the pathways competes with each others under a competitive null , the success of some pathways will obstacle other pathways . For example , the top 1 enriched pathway provided by DAVID for the 1 min data is “Ribosome” . The pathway includes 87 proteins , and 51 of them were mapping by the data . The cAMP signaling pathway , on the other hand , includes 73 proteins , but only 8 of them were mapping by the data . The high mapping rate of “Ribosome” will makes it harder for DAVID to enrich the target cAMP signaling pathway . This study aims to characterize the changes in protein expression underlying the phenotype conversion from mononucleated muscle cells to multinucleated myotubes . According to the their analysis , five functional clusters were identified in this dataset: cell cycle withdrawal ( 72hr/0hr ) , cell adhesion and migration ( 24hr/0hr ) , RNA metabolism ( both 24hr/0hr and 72hr/0hr ) , myofibril formation ( 72hr/0hr ) , and proteolysis , fusion , and ECM remodeling ( both 24hr/0hr and 72hr/0hr ) . The corresponding KEGG pathways were illustrated in Fig 5 . We chose the pathway “ECM-receptor interaction” as the target pathway in Table 2 because it is clearly stated to be differentially expressed in both experiments . From Table 2 we found the ECM-receptor interaction pathway was enriched by T2×ST , T2×HP and DAVID for both experiments . The original publication focused on the change of cellular phenotype accompanying myogenic differentiation and the development of myofibril . From Fig 5 we found the myofibril formation route was enriched by T2×ST , T2×HP , DAVID , and IPA . T2×ST and T2×HP further enriched muscle contraction related pathways , which were associated with myotube maturation as discussed in the original publication . The cell adhesion and migration play an essential role in the fusion of mononucleated myoblasts . Pathways related to adhesion and migration were enriched by T2×ST , T2×HP , and DAVID; those related to fusion were enriched by T2×ST , T2×HP , DAVID , and IPA . The elevation of lysosomal proteins contributed in remodeling intracellular components during the course of myotube formation . All tools enriched the lysosome pathway with an exception of IPA . The RNA metabolism and the cell cycle withdrawal routes represented the termination of proliferation since the growth factors and nutrition were removed from the medium . Related pathways were enriched by T2×ST , T2×HP and IPA . Briefly , T2×ST and T2×HP enriched all the pathways discussed in the original publication; the interpretation made by T2 fit the description of the dataset pretty well . In the meantime , DAVID and IPA also enriched most of the expected pathways , asides from muscle contraction pathways . DPA enriched most of the expected pathways in Reactome , but failed in KEGG; GSEA failed in both pathway databases . We also found that there is no distinguishable difference between the result using a self-contained null ( T2×ST , T2×HP , DPA ) or a competitive null ( GSEA , DAVID , IPA ) in the myogenesis dataset . This dataset , unlike the previous , is not a time series; it is a dose-comparison experiment . According to the original publication , two datasets shared nearly all identified proteins although 50nM dataset did down-regulate more phosphopeptides ( S2d Fig ) . Consequently , the authors only paid attention to the proteins that are regulated by both 5nM and 50nM dasatinib . The main target of dasatinib is the BCR-ABL signaling pathway , which is described in the pathway “Chronic myeloid leukemia ( CML ) ” of KEGG . From Table 2 we found the CML pathway was enriched by T2×ST , T2×HP , DAVID and IPA for both experiments . The downstream of the CML pathway was illustrated in Fig 6 , there are three possible routes directed from the inhibition of BCR-ABL . Since the two datasets shared nearly all proteins , we used the ranks of 5 nM dataset to represent the common pathways over two datasets . The original publication focused on the BCR-ABL/Ras/MAPK route and the connection between BCR-ABL signaling and apoptosis . From Fig 6 we found the enriched pathways by T2 were plausible for the following reasons: In short , we found the performance of T2 pleasant: T2×ST and T2×HP enriched the BCR-ABL/Ras/MAPK route , the BCR-ABL/PI3K-Akt/Apoptosis route , the JAK-STAT signaling pathway , and the pathways related to actin response . Both DAVID and IPA enriched some of the expected pathways . GSEA and DPA failed to enrich most of the expected pathways in both pathway databases . Generally there is no distinguishable difference between the result using a self-contained null or a competitive null , but T2 enriched more expected pathways than other methods . The dataset includes only one experiment , comparing the gene expression of myocilin expressed cells to control cells , under U0126 treatment . The authors concluded that myocilin has a protective effect to against apoptosis and further promotes cell survival and proliferation via the MAPK signaling pathways . They also experimentally confirmed that the Raf-MEK-ERK-MAPK cascade was activated by myocilin . From Table 2 we found T2×ST , T2×HP , DPA , and DAVID enriched the MAPK signaling pathway; all three methods under a self-contained null successfully enriched the target pathway , whereas only DAVID is under a competitive null . The upstream and downstream of the MAPK pathway was illustrated in Fig 7 . There are two possible upstream receptors of the MAPK signaling pathway: the GPCR receptors and the Notch receptors . In KEGG , both upstreams were enriched by T2×ST , T2×HP and DAVID , DPA only enriched the GPCR/Ras/MAPK route and IPA only enriched the Notch/Ras/MAPK route . In Reactome , both upstreams were enriched by T2×ST and T2×HP , DAVID only enriched the Notch/Ras/MAPK route ( S1 Table ) . The are two downstream routes , both are supported by the original publication . The results from both T2×ST and T2×HP suggested that the differentially expressed pathways are more upstream . This conclusion actually fit the discussion of the original publication , which suggests that myocilin may also regulate the upstream kinase of the MAPK signaling pathway . Briefly , both T2×ST and T2×HP successfully enriched the MAPK signaling pathway and its upstream; other tools enriched only some of the expected pathways . Generally speaking , we found that the proposed T2-statistic was able to enrich the pathways in agreement with the original publication , whereas the performances of DPA , GSEA , and DAVID were not stable . IPA , as a commercial software with high cost , also enriched most of the relevant pathways . Nevertheless some of the pathways are low-ranked and the numbers of enriched pathways are enormous . The results suggested that our multivariate design of the proposed T2-statistic does provide important information toward pathway analysis by considering the strength of interactions among proteins . In the meantime , our self-contained null hypothesis is capable of enrich relevant pathways by the significance of protein expression ratios , whereas the focus of the competition among pathways may neglect the clear distance between phenotypes . Both DAVID and IPA are based on the competitive null hypothesis , although DAVID performs a more stringent post hoc correction , they shared the failure of some relevant pathways . The tremendous numbers of enriched pathways also suggested IPA may report more false positive results . GSEA also applies a competitive null when the sample size is limited and its KS statistic is sensitive to small sample size . The unsatisfied results suggested that GSEA is not suitable for data of limited experiments . The design of DPA is similar to the proposed T2-statistic; both DPA and T2 are specifically designed for quantitative proteomic data , and they both use self-contained null hypotheses . The performance difference between DPA and T2 primarily comes from the aspect of statistic construction . The proposed T2-statistic outperformed DPA because it considers the strength of interactions among proteins . Briefly , in five testing datasets , the results using a self-contained null is generally more well founded than the results using a competitive null . The importance of applying the covariance matrix is to estimate accurate confidence interval . We illustrated an example in Fig 8 to demonstrate the situation that may cause inaccurate estimation . Both M1 and M2 are accurate null distributions since the data are normalized using proper covariance matrix and the distribution hence follows χ2 . M4 indicates that situation that an independent data are misinterpreted as a correlated data . This happens when we have false positive protein-protein interactions in the databases . In order to minimize the risk , we only use the confidence scores derived from directly experimental evidence . M3 represents the case that a correlated data are misinterpreted as an independent data . This may actually happen due to our incomplete knowledge of the biology system . In this case , the null distribution will not follow χ2 and the estimation of p-value will be inaccurate . Even so , for pathways of vary high expression ratios , applying the covariance matrix or not does not change its p-value dramatically . Here we demonstrated the impact toward p-values using permuted and purged confidence scores . We performed 100 experiments with 30% and 60% permuted confidence scores ( i . e . we reassign the score using the same score distribution ) and another 100 experiments with 30% and 60% purged confidence scores ( i . e . we randomly remove the scores from the PPI databases ) , and we checked if the expected pathways are still significant under current significance level ( α = 0 . 05 ) . From Table 3 we observed that: The construction of the proposed T2-statistic showed T2 is heavily dependent on expression ratios . After all , the null hypothesis for T2 is to test if the mean vector equals to zero . The contribution of applying the covariance matrix is to estimate p-values in a more accurate manner: to rescue some pathways with moderate expression ratios but their regulation directions are consistent with current knowledge of protein interaction , and to discard some pathways with inconsistency .
In this study , we presented a knowledge-based T2 approach to perform pathway analysis for quantitative proteomic data of a limited number of experiments . The proposed T2 is constructed as a multivariate statistic and the test of significance is under a self-contained null . We use the probabilistic confidence score provided by the STRING or HitPredict databases to approximate the covariance matrix of the protein profiles . The proposed T2-statistic is therefore able to reveal the influence of protein-protein interactions while performing the analysis . In addition , our pathway integration procedure is able to categorize pathways into pathway groups as well as to avoid redundancy . We performed the T2-statistic on five published quantitative proteomic dataset . In all cases , T2 was able to eliminate irrelevant pathways , as well as correctly identify relevant pathways that had been discussed in the original publication . The idea of incorporating biological evidence into conventional statistic can be widely applied to the analysis of quantitative proteomic data . | Pathway analysis is a common approach to quickly access the pathways being regulated in the experiments . There are numerous statistics to perform pathway analysis; most of them assume that the genes or proteins are independent of each other for statistical ease . This assumption , however , is unrealistic to the real biological system and may cause false positives in practice . A standard way to address this issue is to measure the associations among genes or proteins . Unfortunately , the estimation of associations requires sufficient sample size , which is usually not available for proteomic data produced by mass spectrometry . In this study , we propose a T2-statistic , which estimates the associations among gene products , to perform pathway analysis for quantitative proteomic data . Instead of calculating the associations directly from data , we use the confidence scores retrieved from protein-protein interaction databases . We also design an integrating procedure to reserve pathways of sufficient evidence as a regulated pathway group . We compare the proposed T2-statistic to other popular statistics using five published experimental datasets , and the T2-statistic yields more accurate descriptions in agreement with the discussion of the original papers . |
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Calorie restriction ( CR ) , the only non-genetic intervention known to slow aging and extend life span in organisms ranging from yeast to mice , has been linked to the down-regulation of Tor , Akt , and Ras signaling . In this study , we demonstrate that the serine/threonine kinase Rim15 is required for yeast chronological life span extension caused by deficiencies in Ras2 , Tor1 , and Sch9 , and by calorie restriction . Deletion of stress resistance transcription factors Gis1 and Msn2/4 , which are positively regulated by Rim15 , also caused a major although not complete reversion of the effect of calorie restriction on life span . The deletion of both RAS2 and the Akt and S6 kinase homolog SCH9 in combination with calorie restriction caused a remarkable 10-fold life span extension , which , surprisingly , was only partially reversed by the lack of Rim15 . These results indicate that the Ras/cAMP/PKA/Rim15/Msn2/4 and the Tor/Sch9/Rim15/Gis1 pathways are major mediators of the calorie restriction-dependent stress resistance and life span extension , although additional mediators are involved . Notably , the anti-aging effect caused by the inactivation of both pathways is much more potent than that caused by CR .
The effect of restricting calorie intake on life span extension has been known for more than 70 years [1 , 2] . Although many hypotheses on how calorie restriction ( CR ) modulates aging have been proposed , the underlying mechanism for CR is still elusive [3] . Evidence from genetic studies utilizing model organisms ranging from yeast to mammals points to an important role of nutrient-sensing/insulin/insulin growth factor I ( IGF-I ) pathways in life span modulation , suggesting a common evolutionary origin of aging regulation [4] . Furthermore , these signaling pathways have been implicated in mediating CR-induced life span extension in yeast , flies , and mice [4–6] . In yeast , the conserved Ras , Tor , and Sch9 signaling pathways integrate the nutrient and other environmental cues to regulate cell growth/division [7 , 8] . Deletion of SCH9 , a homolog of mammalian AKT and S6K [9 , 10] , enhances cellular protection against thermal and oxidative challenges , and extends yeast chronological life span ( CLS , defined as the survival of non-dividing cells ) as well as replicative life span ( RLS , defined as the number of daughter cells produced by a mother cell ) [11 , 12] . Similarly , the RAS2-null strain shows increased stress resistance and survival [13–15] . Recently , evidence has been presented that deficiency in TORC1 signaling also promotes longevity in both the replicative and chronological model systems [6 , 16 , 17] . Rim15 is a glucose-repressible protein kinase and a key integrator of signals transduced by the Sch9 , Ras , and Tor pathways in response to nutrients [18–20] . Nutrient depletion activates Rim15 , which in turn upregulates the expression of a variety of genes involved in G0 entry and stress response through the transcription factors Msn2/4 and Gis1 [21] . We have previously reported that life span extension associated with deficiencies in Sch9 and Ras2/cAMP/PKA is partially mediated by enhanced cellular protection against oxidative stress through the activation of SOD2 [13] . Both the stress response element ( STRE ) and post-diauxic shift motif ( PDS ) are present in the promoter region of SOD2 , suggesting the involvement of stress response transcription factors Msn2/4 and Gis1 [22 , 23] . In fact , deletion of MSN2/4 in ras2Δ and of RIM15 in sch9Δ mutants reverses or reduces life span extension [11] . Lack of Rim15 also abolishes the life span extension associated with a reduced activity of adenylate cyclase [13] , which is found downstream of Ras2 in the Ras/PKA nutrient sensing pathway . Moreover , Msn2/4 and Rim15 are negatively regulated by the TORC1 signaling , which promotes the cytoplasmic retention of Msn2/4 and Rim15 through the interaction with the 14-3-3 protein BMH2 [24 , 25] . Genetic data also suggest that Tor inhibits protein phosphatase 2A-dependent nuclear accumulation of Msn2 in response to stresses [26] . CR delays aging and prolongs chronological and replicative life span in yeast [27–30] . For RLS studies , CR can be modeled by maintaining yeast cells on reduced glucose concentration but otherwise complete ( rich ) medium [28 , 29] . CR fails to further extend the RLS of either sch9Δ or tor1Δ mutants , indicating that down-regulation of the Tor and Sch9 pathways may mediate CR effect in dividing yeast [6] . In liquid culture , yeast cells growing in glucose containing medium release and accumulate ethanol , which promotes cell death in wild-type cells during chronological aging [30] . Switching non-dividing yeast cells from ethanol-containing medium to water , which models the extreme CR/starvation condition that yeast encounter in the wild , extends not only the mean life span of wild-type cells but also that of sch9Δ mutants , indicating the presence of additional mechanism ( s ) controlled by CR [27 , 30] . Here we present results showing that the serine/threonine kinase Rim15 and the downstream stress resistance transcription factors Msn2/4 and Gis1 are required for chronological life span extension in mutants with defects in Ras/cAMP/PKA or Tor/Sch9 signaling as well as in calorie restricted cells . In addition , we show that calorie restriction/starvation doubles the chronological life span of the extremely long-lived mutants lacking both RAS2 and SCH9 , and that this 10-fold life span extension is only partially dependent on Rim15 . Our findings are consistent with the existence of a longevity regulatory network centered on the Ras/cAMP/PKA/Rim15/Msn2/4 and Tor/Sch9/Rim15/Gis1 pathways which play important roles in the mediation of CR-dependent stress resistance and life span extension . However , our results also indicate that mutations in Tor , Sch9 , and Ras signaling in long-lived mutants do not recapitulate the full effect of CR , and both Rim15/Msn2/4/Gis1-dependent and -independent mechanisms are required to achieve maximum life span extension .
Previously , we have shown that deficiencies in Ras and Sch9 signaling pathways extend yeast chronological life span through , in part , the activation of the stress response transcription factors Msn2/4 and protein kinase Rim15 , respectively [11 , 13] . Since Rim15 has also been shown as the integrating point of the Tor and Ras/PKA nutrient-sensing pathways and an important regulator for G0 entry [21 , 25 , 31] , we examined its role in yeast chronological life span extension caused by mutations in tor1Δ and ras2Δ mutants . The mean life span of rim15Δ mutant was slightly reduced ( 12% ) compared to that of wild-type ( DBY746 ) ( Figure 1A; Table S1 ) . Deletion of RIM15 abolished life span extension associated with deficiencies in Tor1 , Ras2 , or Sch9 ( Figure 1C and 1D; Table S1 ) , suggesting that the longevity regulatory network controlled by Tor , Sch9 , and Ras converges on Rim15 . Activation of cellular protection mechanisms represents an important survival strategy in yeast [32] . We tested the role of Rim15 in cellular protection in tor1Δ , sch9Δ , and ras2Δ mutants . Cells lacking Rim15 were hypersensitive to thermal and oxidative challenges ( Figure 1E ) . Deletion of Rim15 not only abolished protection against hydrogen peroxide , and to a lesser extent to heat , in sch9Δ ( Figure 1F ) , it also abolished any beneficial effect associated with attenuated Tor signaling ( Figure 1E ) . However , Rim15-mediated stress resistance only accounted for part of the stress resistance phenotype observed in ras2Δ mutant ( Figure 1F ) . Rim15 activates Gis1 , a transcription factor that binds to the PDS element ( AWAGGGAT ) , and induces a variety of stress response genes when cells enter stationary phase [23] . To determine the contribution of Gis1 to chronological survival and cellular protection , we monitored CLS of the gis1Δ mutant as well as cells lacking GIS1 in the long-lived genetic backgrounds . gis1Δ mutant had a mean life span similar to that of wild-type yeast ( Figure 1A; Table S1 ) . In contrast , the survival of the msn2Δ msn4Δ gis1Δ triple mutant was shorter than that of wild-type and resembled that of rim15Δ ( Figure 1A; Table S1 ) , in agreement with the gene expression profile data suggesting that Msn2/4 and Gis1 cooperatively mediate the Rim15 response to glucose limitation [19 , 21] . Deficiency in Gis1 almost completely abolished the mean life span of sch9Δ mutant ( Figure 1B ) , in agreement with our earlier finding regarding the role of Rim15 in mediating the effect of sch9Δ mutation in stress resistance and life span [11] . In the RAS2-null background , the enhanced survival effect was not fully dependent on Gis1 ( Figure 1D; Table S1 ) . This observation may be explained by the fact that Msn2/4 play an important role in the life span extension associated with ras2Δ [13] . With respect to cellular protection , 1-d-old msn2Δ msn4Δ mutant was hypersensitive to both heat and oxidative stresses as expected ( Figure 2A and unpublished data ) . At day 3 , however , the mutant showed more than 10-fold increase in resistance to heat , but not to hydrogen peroxide ( Figure 2A and unpublished data ) . This phenotype was not due to an adaptive mutagenesis , as the frequency of canavanine-resistant ( canR ) mutation did not differ significantly between msn2Δ msn4Δ mutant and that of wild-type ( Figure S1 ) . Furthermore , the day 3 heat resistant msn2Δ msn4Δ cells were still sensitive to stress challenges 1 d after being re-inoculated in fresh medium ( unpublished data ) . We showed that this compensatory activation of additional cellular protection in msn2Δ msn4Δ mutant at day 3 was Rim15/Gis1-dependent since it was abolished by deletion of either RIM15 or GIS1 ( Figure 2A ) . The enhanced thermal resistance of msn2Δ msn4Δ seen at day 3 was also abolished by the overexpression of Sch9 or , to a lesser extent , the constitutively active Ras2 ( ras2val19 ) , both of which inhibit Rim15/Gis1 ( Figure 2B ) . These results depict a Ras- , Tor- , and Sch9-controlled longevity regulatory network with Rim15 in the center transducing the signals to activate stress response genes and positively regulating life span ( Figure 5B ) . It is notable that the degree of dependence on stress response transcription factors downstream of Rim15 is quite different in sch9Δ and ras2Δ mutants , with the former depending primarily on Gis1 and the latter on both Msn2/4 and Gis1 . Tor , Sch9 , and Ras/cAMP/PKA control a dynamic transcriptional network that regulates the balance between cell growth and division [7 , 8] . Whereas cells lacking SCH9 are small in size ( ∼60% of that wild-type in volume ) and display a slow growth phenotype , tor1Δ mutants are only slightly smaller than wild-type cells ( ∼86% ) and grow at a normal rate ( Figure 3A ) . This may be due to the fact that Tor2 can function , in redundancy to Tor1 , in the TORC1 complex [33] . RAS2-null cells show a small increase in cell size ( by 10% in volume ) compared to wild-type . The combination of the ras2Δ and sch9Δ instead causes a further but small decrease in cell size ( Figure 3A ) . Since all three mutants are long-lived despite differences in cell size and growth rate , it appears that chronological survival can be uncoupled from the signaling involved in regulating cell growth and size . This is particularly important considering that some of the longest-lived mutants in higher eukaryotes are dwarfs and it is not clear whether life span extension can be separated from dwarfism [4] . The down-regulation of the Sch9 , Tor , or Ras pathways has been implicated in the mediation of the CR effect on longevity [6 , 28 , 34] . We have previously shown that extreme CR/starvation , in which stationary phase cells were switched to water , doubles the mean life span of wild-type yeast [30 , 35] . Furthermore , the life span of already long-lived sch9Δ is further extended by the removal of nutrients , suggesting that either the Sch9 pathway only partially mediates the CR effect or the mechanisms underlying CR are distinct from those triggered by the deletion of SCH9 [30] . To understand the role of Tor , Ras , and Sch9 signaling in CR , we monitored the survival of tor1Δ , ras2Δ , and ras2Δ sch9Δ mutants in water . As observed with sch9Δ , starvation/extreme CR increased mean life span of both TOR1- and RAS2-null mutants ( Figure 3B; Table S2 ) . The mean ( 50% survival ) and maximum ( 10% survival ) life span was markedly increased in CR ras2Δ mutant compared to CR wild-type strain . This was not the case for tor1Δ mutant . Although CR further extended the life span of tor1Δ , there was only 18% increase in mean CLS , and no difference in maximum CLS compared to that of wild-type under extreme CR ( Table S2 ) . Considering that Rim15 is required for chronological survival extension for all three long-lived mutants , these results suggest that the Rim15-controlled Msn2/4 and Gis1 are differentially activated in tor1Δ , sch9Δ , and ras2Δ mutants . The fact that ras2Δ sch9Δ double mutant survive longer than either one of the single mutants ( Figure 3C ) supports this conclusion and suggests that the full beneficial effect of CR may be accounted by the combined effect of down-regulation of both Ras2 and Sch9 signaling . To our surprise , however , extreme CR extended the survival of ras2Δ sch9Δ double knockout mutant , which reached a mean life span of approximately 10-fold of that wild-type grown and incubated in standard glucose/ethanol medium ( Figure 3C; Table S2 ) . This suggests an additive effect between down-regulation of both the Ras/cAMP/PKA and Sch9 pathways and dietary interventions . Alternatively , Ras/cAMP/PKA signaling could be down-regulated further by the inactivation of Ras1 by CR . In fact , Ras1 and Ras2 play redundant roles in the regulation of the cAMP/PKA pathway although their expression profile is different . Unfortunately , the ras1Δ ras2Δ double mutant could not be tested because it is not viable . To elucidate the roles of Rim15 and its downstream transcription factors in CR , we monitored the stress resistance and chronological survival of cells lacking Rim15 , Gis1 , and/or Msn2/4 incubated in water . This extreme CR treatment caused a ∼10-fold increase in oxidative defense in wild-type as well as in mutants lacking Msn2/4 ( Figure 4A ) . On the other hand , the gis1Δ , msn2Δ msn4Δ gis1Δ , and rim15Δ mutations prevented the enhancement in resistance to stress ( Figure 4A ) . The commonly used CR protocol in S . cerevisiae involves a reduction in glucose concentration from 2% to either 0 . 5% or 0 . 05% , which has been shown to extend both the replicative and chronological life span [28 , 29 , 36–38] . In addition to the switch to water , we also tested the effect of the calorie restriction by reducing the glucose concentration in the growth medium from 2% to 0 . 5% . This CR intervention led to an even higher increase in the resistance to both heat shock and oxidative stress ( Figure 4A ) . These effects of calorie restriction were also completely reversed by the lack of Rim15 or all three stress resistance transcription factors MSN2 , MSN4 and GIS1 , but not by the lack of either Msn2/4 or Gis1 alone ( Figure 4A ) . Under the extreme CR condition , mean life span of the msn2Δ msn4Δ and gis1Δ did not differ significantly from that of wild-type , whereas a ∼25% reduction in maximum life span ( measured as the age when 10% of the cells were still alive ) was observed in GIS1-null mutant ( Figure 4B; Table S2 ) . Lack of all three stress response transcription factors led to a 50% reduction of maximum life span compared to wild-type ( Figure 4B ) . By contrast , extreme CR/starvation failed to extend the longevity of Rim15-null mutant ( Figure 4B ) . The results obtained under glucose reduction CR ( 0 . 5% glucose ) were very similar to those under extreme CR with the exception that wild-type cells achieved a mean life span of 31 d instead of 12 d , and the deletion of MSN2/4 had a more marked negative effect on this CR-dependent life span extension ( Figure 4C ) . Taken together , these data suggest that the serine/threonine kinase Rim15 plays a central role in mediating the effect of CR on stress resistance and life span extension by positively regulating the activities of stress resistance transcription factors Msn2/4 and Gis1 . Activation of Msn2/4 and Gis1 leads to the expression of variety of stress response genes with STRE and PDS elements in their promoters . We employed the STRE- and PDS-driven reporter gene assay to examine the gene expression changes under extreme CR condition . One-day-old wild type cells carrying either STRE- or PDS-driven lacZ reporter gene were switched to water . Significant increase in both STRE- and PDS-driven transactivation was observed 2 h after the initiation of CR compared to cells maintained in SDC medium ( Figure 4D and 4E ) . PDS-dependent transactivation increased by 90% , whereas STRE activation increased by 40% , under the extreme CR condition by 8 h . This observation is in agreement with our survival data that Gis1 plays a more important role in extreme CR-induced longevity extension ( Figure 4B ) . The statistical analysis of data derived from genome-wide motif prediction and global expression profiles provides a powerful tool to infer transcriptional regulation in the cell [39] . We have previously reported that there is significant enrichment of STRE and PDS elements in the promoter regions of the genes upregulated in sch9Δ mutant compared to wild-type under normal culture condition ( SDC ) [40] . Here , we analyzed the expression of genes containing STRE ( Msn2/4 ) or PDS ( Gis1 ) elements in their promoter under extreme CR ( switching to water ) . Our data did not indicate an enrichment of either STRE or PDS element in genes upregulated under CR ( either 24 or 48 h ) in wild-type cells ( Table 1 ) . This is probably due to the fact that CR induced a significant but small increase ( 40% to 90% ) in transactivation of Msn2/4 and Gis1 ( Figure 4D ) , which could not be detected in the analysis of array data which was performed at a cutoff of 1 . 7-fold ( CR versus SDC ) . However , CR ( water ) did cause a significant increase in the expression of STRE- and PDS-containing genes in the sch9Δ mutant ( Table 1 ) . These findings are consistent with the fact that CR further extends the life span of sch9Δ mutant , and support the notion that pathways responsible for cellular protection and life span extension in long-lived genetic mutant and in CR-treated cells are overlapping , although their levels of activation are not identical . To determine whether the life span regulatory effects caused by deficiencies in the Ras/cAMP/PKA and Sch9 pathways were additive , we studied the ras2Δ sch9Δ double mutants . Cells lacking both RAS2 and SCH9 showed a mean CLS of 35 d , which is more than 5-fold that of wild-type cells ( Figure 5A; Table S1 ) . Surprisingly , extreme CR/starvation caused an additional doubling of the life span of the ras2Δ sch9Δ ( 10-fold that of wild-type in glucose/ethanol medium ) ( Figure 5A; Table S1 ) . In view of the important role of Rim15 in life span extension in both the long-lived ras2Δ and sch9Δ mutants as well as in the CR-dependent effects , we examined the role of RIM15 in the longevity regulation by ras2Δ sch9Δ . Lack of Rim15 only partially reversed the life span extension associated with deficiencies in both Ras2 and Sch9 ( from more than 5-fold to 2 . 5-fold , Figure 5A; Table S2 ) . The reversion was even less prominent in mutants under extreme CR , where the 10-fold life span extension was reduced to 7 . 5-fold ( Figure 5A; Table S2 ) . These data indicate that Rim15-independent pro-longevity mechanisms are activated in mutants lacking Ras2 and Sch9 signaling and that their beneficial effects are further potentiated by the extreme CR intervention .
Model organisms including yeast , worms , flies , and mice have been studied extensively to understand the mechanisms of aging . Here we present genetic evidence that both CR and evolutionarily conserved signal transduction proteins implicated in life span regulation , including Ras , Tor , and Sch9 , require the serine/threonine protein kinase Rim15 and the downstream stress resistance transcription factors Msn2/4 and Gis1 to extend life span . However , additional factors appear to be involved in the remarkable 10-fold life span extension observed in calorie restricted ras2Δ sch9Δ mutants . We have previously reported that life span extension in SCH9-null and adenylate cyclase deficient mutants depends on Rim15 [11 , 13] . Here we show that deletion of RIM15 also completely abolished the life span extension as well as the stress resistance phenotype caused by the deficiencies in Ras or Tor signaling . The activity of Rim15 has been shown to involve stress response transcription factors Msn2 , Msn4 , and Gis1 [19 , 20 , 21] . Deficiency in Gis1 led to a reversion of life span extension of the sch9Δ and , to a lesser extent , ras2Δ mutants . These data are consistent with the existence of at least two major life span regulatory pathways controlled by Ras/cAMP/PKA and Tor/Sch9 , both of which converge on Rim15 . The present data also point to an important role of stress response transcription factors controlled by Rim15 , Msn2/4 , and Gis1 , in mediating the pro-longevity effect in all long-lived genetic mutants with deficiencies in nutrient sensing pathways ( Figure 5B ) . To study the CR effect on yeast chronological survival , we took two different approaches , starvation and glucose reduction . The first one models the extreme condition that yeast encounter in the wild during complete starvation periods . The extreme CR may be considered as a dietary restriction since all the nutrients in addition to calories are removed from the culture . The reduction of glucose from 2% to 0 . 5% instead is the calorie restriction regimen commonly used in RLS and CLS studies [28 , 29 , 36–38] . Both CR interventions increased cellular protection and extended chronological survival of wild-type cells , with glucose reduction showing a more powerful effect . The difference may be explained , at least in part , by the onset of CR . Unlike the starvation paradigm , in which CR was initiated after cells had entered stationary phase , cells growing in low glucose medium were exposed to CR from the very beginning . In agreement with the hormesis hypothesis of CR [34 , 41] , it is possible that the mild stress imposed by CR early in life leads to an adaptive redirection of energy and resource from growth to survival . Another possibility is that the early reduction of glucose concentration causes changes in gene expression that affect stress resistance and survival at later stages . Others have shown that CR failed to increase the replicative life span of Tor1- or Sch9-deficient mutants [6] . Our results show that the CR effect requires Tor/Sch9-controlled protein kinase Rim15 and its downstream stress response transcription factors . However , extreme CR/starvation further extended the chronological life span of the already long-lived tor1Δ , sch9Δ , and ras2Δ mutants . This difference may be the result of the very different paradigms to study aging: RLS measures the budding potential of a mother cell , whereas the CLS measures the survival of non-dividing cells . It may also be due to the CR paradigms utilized , i . e . , glucose reduction but constant exposure to 0 . 5% glucose and other nutrients ( RLS ) versus starvation in water ( CLS ) . The amino acids or other nutrients still present in the RLS paradigm may block the effect of starvation/CR on the Ras pathway and other stress resistance transcription factors . In fact , RLS extension was also achieved by decreasing the amino acid content of the medium [29] . In our CLS starvation paradigm instead , all nutrients that may contribute to the activation of pro-aging pathways are removed . The CR effect was completely reversed in cells lacking the protein kinase Rim15 but not in the msn2Δ msn4Δ gis1Δ triple mutants , suggesting the presence of additional Rim15-dependent transcriptional factor ( s ) or signaling component ( s ) yet to be identified . Forkhead family transcription factors are evolutionarily conserved from yeast to mammals and have been implicated as mediators of insulin/IGF-I/Akt signaling pathway in the regulation of anti-aging genes in worms , flies , and mammals [42] . PHA-4 , a forkhead transcription factor orthologous to the mammalian Foxa , has been shown to mediate the dietary restriction effect in C . elegans [43] . Results from our preliminary studies on the single deletion mutants of the four known forkhead TFs in S . cerevisiae ( i . e . , Fhl1 , Fkh1 , Fkh2 , and Hcm1 ) are not consistent with a major life span regulatory role of these proteins ( unpublished data ) . Instead , data presented in this study point to zinc finger transcription factors Msn2/4 and Gis1 as key components of the CR-dependent pro-longevity pathway . Based on the database search , the immediate early genes of the Egr-1 family of C2H2-type zinc-finger proteins show the highest score of homology to Msn2/4 [44] . The Egr-1 family TFs have been implicated in a variety of cellular processes including differentiation , mitogenesis , DNA repair , senescence , and apoptosis [45 , 46] . Mammalian Sp1- and Kruppel-like transcription factors are among the candidates homologous to Gis1 . They are involved in insulin- and TGFβ-signaling . Interestingly , Gis1 also contains a jumonji domain , which is first described as a bipartite protein domain present in many eukaryotic transcription factors [47] . Recent evidence from several organisms has shown that a number of jmjC domain-containing proteins are histone demethylases , suggesting a role of Jumonji-domain–containing protein in chromatin remodeling [48] . Interestingly , the DNA binding activities of Egr-1 , Sp1 , and other zinc-finger TFs are sensitive to cellular redox state , and their dysfunction during aging may lead to age-associated pathophysiology [49–52] . While the existence of conserved domains in these yeast proteins is encouraging , it is still premature to speculate about their mammalian counterparts . Although the protein kinase Rim15 is required for life span extension in Ras2 , Tor1 , and Sch9-deficient mutants as well as in yeast under CR , our results indicate that pathways responsible for enhancing stress protection and life span extension in nutrient sensing-impaired genetic mutants and in cells under CR are not identical . On the one hand , the “full” activation of Rim15 and its downstream transcription factors , Msn2/4 and Gis1 , are required for the maximum life span extension , as the pro-longevity effects of ras2Δ , sch9Δ , and CR are additive ( Figures 3B and 3C , and 5A ) . On the other hand , Rim15 only accounts for part of the beneficial effect for ras2Δ sch9Δ mutant under CR , implicating the involvement of additional pro-survival mechanism ( s ) independent of the Rim15-centered nutrient-sensing pathways ( Figure 5A ) . Similar observations were also made in other model systems: CR can further increase the life span of the already long-lived Ames dwarf mice [53]; and it further extends the life span of insulin/IGF-I signaling-impaired chico flies [5] . We and others had shown that the down-regulation of Ras/cAMP/PKA signaling extends the yeast chronological and replicative life span [11 , 13 , 14 , 28] . However , the mammalian cAMP/PKA was only very recently implicated in the regulation of longevity in mice . The type 5 adenylyl cyclase knockout ( AC5-KO ) mice live 30% longer than their wild-type littermates [54] . CA5-KO mice do not show dwarfism , although they weigh slightly less than age-matched controls at 28 months . Similarly to mutants lacking Ras2 or with a reduced adenylate cyclase activity , mouse cells with CA5 disruption show enhanced resistance to oxidative stress , which may be mediated by the upregulation of MnSOD [13 , 54] . Interestingly , the CA5-KO mice have lower growth hormone level [54] , suggesting an attenuated GH/insulin/Akt signaling in these mice . In view of our yeast data showing that CR in combination with the down-regulation of the Ras/cAMP/PKA and Sch9 pathways reached a 10-fold life span increase , it will be interesting to determine the interaction between the insulin/Akt and Ras/cAMP/PKA pathways as well as their combined effect with CR in regulating life span in mammals . Considering the fact that Ras and Sch9 signaling pathways are partially conserved from yeast to mammal ( Figure 5B ) , it will also be important to explore the possibility that potential orthologs of Rim15 and of Msn2/4 and Gis1 may modulate aging in high eukaryotes .
All strains used in this study are derivatives of DBY746 ( MATα leu2–3 , 112 , his3Δ , trp1–289 , ura3–52 , GAL+ ) . Knockout strains were generated by one-step gene replacement as described previously [55] . Strains overexpressing SCH9 or ras2val19 were generated by transforming cells with plasmids pHA3-SCH9 ( a gift from Dr . Morano ) , or pMW101 ( plasmid RS416 carrying ClaI-ras2val19-HindIII fragment form pMF100 , a gift from Dr . Broach ) , respectively . For strains used in STRE- and PDS-lacZ reporter gene assay , the plasmid pCDV454 containing LacZ reporter under the control of a 37 bp SSA3-PDS region ( −206 to −170 ) [23] or the plasmid pMM2 containing four tandem repeats of STRE motif from the HSP12 sequence ( −221 to −241 ) [56] , was integrated into the URA3 locus of wild-type cells . The transcriptional specificity of these reporter genes were confirmed in the msn2Δ msn4Δ and gis1Δ background , respectively ( unpublished data ) . Yeast cells were grown in SDC supplemented with a 4-fold excess of the tryptophan , leucine , uracil , and histidine to avoid possible artifacts due to auxotrophic deficiencies of the strains . Yeast chronological life span was measured as previously described [11 , 57] . Briefly , overnight SDC culture was diluted ( 1:200 ) in to fresh SDC medium to a final volume of 10 ml ( with flask to culture volume of 5:1 ) and were maintained at 30 °C with shaking ( 200 rpm ) to ensure proper aeration . This time point was considered day 0 . Every 2 d , aliquots from the culture were properly diluted and plated on to YPD plates . The YPD plates were incubated at 30 °C for 2 d to 3 d , and viability was accessed by Colony Forming Units ( CFUs ) . Viability at day 3 , when the yeast had reached the stationary phase , was considered to be the initial survival ( 100% ) . Mean and maximum life span ( 10% survival ) was calculated from curve fitting ( one phase exponential decay ) of the survival data ( form pair matched , pooled experiments ) with the statistical software Prism ( GraphPad Software ) . For extreme CR/starvation , cells from 3-d-old SDC culture were washed three times with sterile distilled water , and resuspended in water . Water cultures were maintained at 30 °C with shaking . Every 2 d to 4 d , cells from the water cultures were washed to remove nutrients released from dead cells . For CR modeled by glucose reduction , overnight SDC culture was diluted ( 1:200 ) into fresh SC medium supplemented with 0 . 5% glucose . It is notable that the glucose reduction model employed here is different from that in replicative life span ( RLS ) studies . For RLS analysis , cells are maintained on reduced glucose but otherwise complete ( rich ) medium . In liquid chronological cultures , extracellular glucose was exhausted by day 1 in SC + 0 . 5% glucose as well as standard SDC cultures ( unpublished data ) . Unlike the extreme low glucose cultures ( SC + 0 . 05% glucose ) which reached saturation density of only a quarter of that standard SDC ones , there was no difference in saturation density between cultures with 2% and 0 . 5% glucose , suggesting 0 . 5% glucose is not a limiting factor on cell growth/division ( unpublished data ) . Heat shock resistance was measured by spotting serial dilutions ( 10-fold dilution started at OD600 of 10 ) of cells removed from SDC cultures onto YPD plates and incubating at either 55 °C ( heat-shocked ) or 30 °C ( control ) for 45 min to 150 min . After the heat-shock , plates were transferred to 30 °C and incubated for 2 d to 3 d . For oxidative stress resistance assays , cells were diluted to an OD600 of 1 in K-phosphate buffer , pH6 . 0 , and treated with 100 mM to 200 mM of hydrogen peroxide for 60 min . Serially diluted ( 10-fold ) control or treated cells were spotted onto YPD plates and incubated at 30 °C for 2 d to 3 d . Day1 SDC cultures were mixed with equal volume of 2× calcofluor ( 75 ng/ml in PBS , Molecular Probe ) . After 10 min incubation at room temperature in the dark , cells were washed once with PBS . Images were captured with a Leica fluorescence microscope . Diameter of the cell was measured using ImageJ ( http://rsb . info . nih . gov/ij/ ) . Cells were measured at long and short ( perpendicular ) axes . Diameter was expressed as the average of the long and short axes of the cell . 50 to 80 cells per genotype were measured . Day 1 wild-type cells carrying the STRE- or PDS-lacZ reporter gene ( grown in SDC ) were split into two portions . One was washed three times with sterile water and resuspended in water; the other was maintained in the original SDC medium . Cells were collected at 2 h , 4 h , and 8 h after the initiation of CR . Cell pellet from 1 ml of culture was lysed with Y-PER ( Pierce ) according to manufacturer's protocol . The protein concentration of the lysate was assayed with a BCA kit ( Pierce ) . 55 μl of lysate was mixed with 85 μl of substrate solution ( 1 . 1mg/ml ONPG in 60 mM Na2HPO4 , 40 mM NaH2PO4 , 10 mM KCl , 1 mM MgSO4 , 50 mM 2-mercaptoethanol , pH7 . 0 ) . Absorbance at 420 nm was read every 5 min until 30 min after the initiation of reaction . LacZ activities were determined by fitting the A420/time data to that of serial diluted recombinant β-galactosidase ( Promega ) . LacZ activity was normalized to the total protein in the lysate . A slight modified CR protocol was adopted , where 1 . 5-d-old cells were washed three times and incubated in water . 24 h and 48 h later , cells were collected for RNA extraction . These time points ( to obtain RNA samples at day 2 . 5 and day 3 . 5 ) were selected to avoid the general decrease in metabolism and consequently in gene expression that normally occurs at older ages ( day 4 to day 5 ) [57] . The cRNA generated from these samples was hybridized to Affymetrix GeneChip Yeast 2 . 0 array to obtain the measurement of gene expression . The “Invariant Set” approach was used for normalization at the probe level , and the “Model based” method to summarize and obtain expression for each probe set [58] . A detailed method for motif prediction and motif enrichment test has been described previously [40] . Briefly , for a given gene , if one or more binding sites of a transcription factor ( TF ) binding motif were found within the 800 bp region upstream of the start codon , it was defined as the target gene of that TF . A total of 51 motifs that can be associated with known TFs were used for motif prediction in all known yeast ORFs [39] . The cut-off value of motif matching score was set to 0 . 6 . The hypergometric test was employed to determine whether there was an enrichment of any motif in CR-induced genes ( upregulated more than 1 . 7-fold ) . Finally , we calculated the q-values for each test to correct the multiple testing errors using the “qvalue” package [59] .
Genes examined in this study from the Saccharomyces Genome Database ( http://db . yeastgenome . org/ ) are as follows: SCH9 , ( YHR205W ) ; RAS2 ( YNL098C ) ; TOR1 ( YJR066W ) ; RIM15 ( YFL033C ) ; MSN2 ( YMR037C ) ; MSN4 ( YKL062W ) ; and GIS1 ( YDR096W ) . | Reduction in calorie intake is a well-established intervention that extends the life span of a variety of biological model organisms studied . Calorie restriction also delays and attenuates age-related changes in primates , although its longevity-promoting effect has not been demonstrated . Here , we utilized a single cell organism , baker's yeast , to examine the role of evolutionarily conserved genes in life span regulation and their involvement in calorie restriction . The yeast mutants lacking Ras2 , Tor1 , or Sch9 are long-lived . The anti-aging effect observed in these mutants depends on the protein Rim15 and several key regulators of gene expression that are essential in inducing cellular protection under stress . The beneficial effects of calorie restriction are much smaller in yeast that are missing these proteins , indicating their essential role in promoting longevity . Our study also showed that by combining the genetic manipulation and calorie restriction intervention , yeast can reach a life span ten times that of those grown under standard conditions . This extreme longevity requires Rim15 and also depends on other yet-to-be identified mechanisms . Our findings provided new leads that may help to elucidate the mechanisms underlying the anti-aging effect of calorie restriction in mammals . |
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Extracellular capsules constitute the outermost layer of many bacteria , are major virulence factors , and affect antimicrobial therapies . They have been used as epidemiological markers and recently became vaccination targets . Despite the efforts to biochemically serotype capsules in a few model pathogens , little is known of their taxonomic and environmental distribution . We developed , validated , and made available a computational tool , CapsuleFinder , to identify capsules in genomes . The analysis of over 2500 prokaryotic genomes , accessible in a database , revealed that ca . 50% of them—including Archaea—encode a capsule . The Wzx/Wzy-dependent capsular group was by far the most abundant . Surprisingly , a fifth of the genomes encode more than one capsule system—often from different groups—and their non-random co-occurrence suggests the existence of negative and positive epistatic interactions . To understand the role of multiple capsules , we queried more than 6700 metagenomes for the presence of species encoding capsules and showed that their distribution varied between environmental categories and , within the human microbiome , between body locations . Species encoding capsules , and especially those encoding multiple capsules , had larger environmental breadths than the other species . Accordingly , capsules were more frequent in environmental bacteria than in pathogens and , within the latter , they were more frequent among facultative pathogens . Nevertheless , capsules were frequent in clinical samples , and were usually associated with fast-growing bacteria with high infectious doses . Our results suggest that capsules increase the environmental range of bacteria and make them more resilient to environmental perturbations . Capsules might allow opportunistic pathogens to profit from empty ecological niches or environmental perturbations , such as those resulting from antibiotic therapy , to colonize the host . Capsule-associated virulence might thus be a by-product of environmental adaptation . Understanding the role of capsules in natural environments might enlighten their function in pathogenesis .
Extracellular capsules , hereafter named capsules , constitute the outermost layer of some prokaryotic cells where they establish the first contact between the microorganism and its environment . They fullfill a myriad of roles , often linked to colonization and persistence . Their physical properties prevent dessication by retaining moisture near the cell surface , enhance survival in harsh environments , and protect cells from phagocytosis by grazing protozoa [1–4] . Capsules also play an essential role during infection; they downregulate pro-inflammatory cytokines [5] , protect cells against reactive oxygen species generated by the host [6] , and help bacteria to evade phagocytosis by macrophages and complement activation [3] . Capsules also reduce the efficiency of antibiotics [7] and cationic antimicrobial peptides [8] . These medical implications have driven the research on capsules and their roles , leading to the widespread perception that they are mostly associated with virulence [9 , 10] . This triggered the numerous studies on the genetic diversity of capsules in several prominent bacterial pathogens such as Streptococcus pneumoniae [11 , 12] , Escherichia coli [13] , Klebsiella pneumoniae [14 , 15] , Campylobacter jejuni [16] , and Acinetobacter baumanii [17] . Capsules can be synthesized through different genetic pathways ( Fig 1 and reviewed in [18–20] ) . Most capsules are high molecular weight polysaccharides made up of repeat units of oligosaccharides . In capsules synthesized through the Wzx/Wzy-dependent pathway or Group I [20] , the oligosaccharidic repeat unit is linked to an undecaprenyl phosphate acceptor in the cytoplasm by membrane-bound glycosyltransferases . This precursor is then transported across the inner membrane by the Wzx flippase and polymerized nonprocessively in the periplasm by the Wzy polymerase . In contrast , the nascent polysaccharidic chains of Group II and Group III capsules are polymerized in the cytoplasm and linked to a phospholipid acceptor before being transported across the inner membrane by the ATP-binding cassette ( ABC ) transporter . Group II and III capsules will be jointly referred to as ABC-dependent capsules . In spite of these differences , both the Wzx/Wzy- and the ABC- dependent pathways use homologous outer membrane proteins from the polysaccharide export family to transport the capsule across the outer membrane of diderm bacteria [21] . Both pathways are characterized by large operons that have a conserved region encoding the secretion machinery and a variable region encoding numerous polymer-specific enzymes . The latter defines the capsule serotype and includes enzymes for the synthesis of NDP-sugars , glycosidic linkages ( mainly by glycosyl-transferases ) , and sugar modification ( O-acetylation ) . Within-species serotype-diversity prompted the biochemical characterization of the oligosaccharide composition of capsules , ultimately leading to the development of serotype-specific vaccines [16 , 22 , 23] , and serotyping schemes for epidemic strains [24 , 25] . The synthesis of the polysaccharidic Group IV capsules relies on the Wzy polymerase but not on Wzx flippase , and depends on very diverse export machineries , including in certain cases proteins homologous to those of Group I [26 , 27] . Polysaccharidic capsules can also be produced by the synthase-dependent pathway , where a unique processive enzyme is responsible for the all the steps of initiation , polymerization and translocation of the capsule [28] . Some capsules are proteic , instead of polysaccharidic , notably the poly-γ-d-glutamate or PGA capsules produced by Bacillus anthracis [29] . To date very few studies have characterized the frequency and diversity of capsules across bacterial phyla , presumably because they are difficult to identify . Capsular systems have many poorly characterized components and are subject to frequent variation by homologous recombination and horizontal transfer , resulting in rapid genetic turnover [30] . Furthermore , the genetic pathways leading to the synthesis of lipopolysaccharides ( LPS ) , extracellular polysaccharides ( EPS ) , and capsules have many key homologous components that are difficult to disentangle [31 , 32] . Finally , there are few studies on the role of capsules in ecological settings other than the host , limiting the identification of new capsule secretion pathways . The understanding of capsule distribution and evolution across Prokaryotes has been hampered by the lack of computational tools to identify capsule systems in genomes . In order to tackle this limitation , we have built protein profiles to identify the key components of the different capsule biosynthesis pathways and defined models describing their expected frequency and genetic organization . We used them within MacSyFinder , a computational tool that allows the detection of macromolecular systems [33] , to identify capsule systems in more than 2500 complete prokaryotic genomes . We then searched for the presence of species with capsules in more than 6700 metagenomes . We aimed at answering the following questions: How many capsules are there in prokaryotic genomes ? Do multiple capsule groups co-occur and , if so , are there any correlations between capsule groups ? Which Prokaryotes encode capsules ? Which are the genetic and life-history traits associated to capsule prevalence ? What is the environmental distribution of Prokaryotes encoding capsules ? Our results uncovered novel intriguing patterns in the distribution of capsules , which have important biological implications and provide new insights into the evolutionary and ecological role of capsules .
We defined independent and customizable models describing the genetic composition and organization of eight groups and subgroups of capsules ( Fig 1 ) , based on the literature of the best-described experimental capsule systems [18–21 , 26 , 27 , 29 , 34] . This information was complemented with exploratory analyses of the diversity of these systems in other genomes ( see Methods ) . We identified 58 key components ( protein families ) involved in capsule synthesis . The majority of them regard the secretory and polymerization components of each capsule system , as well as the most common polymer-specific enzymes . Each component was associated with a hidden Markov model ( HMM ) protein profile , retrieved from PFAM ( 31 ) or built for the purpose of this study ( 27 ) ( S1 Table ) . The resulting computational tool—CapsuleFinder—uses as input the protein sequences of a genome , searches for the components of capsule systems using the HMM profiles and then delimits the systems based on the information provided in the models . There is no curated database with information on the organisms encoding and/or lacking capsule systems . The literature rarely mentions the absence ( or presence ) of a capsule for non-pathogens . Nevertheless , we sought to validate CapsuleFinder by comparing its results with those mentioned in two lists of some of the best-studied encapsulated Prokaryotes [19 , 35] . We successfully identified capsules in all 11 species that were reported as encapsulated and for which a complete genome sequence was available . To validate a broader set of systems , we randomly picked 100 species from our complete genome database . We then checked the literature for information on the presence of a capsule in the 40 species where a capsule system was detected ( S2 Table ) . There were 28 species for which we could find published reference to the presence or absence of a capsule . Among these , we found published experimental evidence for a capsule in 15 species and some positive information ( from either bioinformatic analyses or evidence in closely-related species ) for 10 others . The literature explicitly mentioned that no capsule had been observed for the remaining three species ( details S2 Table ) . It is difficult to say if these are false positives , which would give a false positive rate of ~8% , or if capsules actually exist in the species and the respective strains or conditions of expression were not yet identified . We have not attempted to quantify the rate of false negatives—cases where we missed an existing capsule—since there have been very few experimental efforts to show that a species lacks a capsule in a variety of environmental conditions . Yet , the analysis of our data showed a small number of cases where we missed some capsule systems and obtained some false positives . These are indicated in S3 Table . Even in the worst case , CapsuleFinder is able to identify all the best-known capsules whilst fetching few putative false positives ( S1 Text ) . We detected 2182 capsule systems in 1304 out of the 2643 genomes ( Fig 2 ) . The complete list of genomes and capsule systems is available in S1 Dataset . Around half ( 49% ) of the genomes , representing 52% of the species , encoded at least one capsule system . Group I capsules were the most frequent , representing ca . 70% of the total . ABC-dependent and synthase-dependent capsules were less frequent ( nearly 10% each ) , and subgroup CPS3 capsules were the most frequent among the latter . Group IV capsules ( 8 . 8% ) , and PGA proteic capsules were rarer ( 3 . 1% ) ( Fig 2 and S1 Dataset ) . We investigated the presence of capsule systems in all major taxonomic divisions of Bacteria and Archaea ( Fig 2 ) . The highly abundant Group I capsule was detected in all bacterial phyla represented by more than 20 available complete genomes ( except Spirochaetes and Tenericutes ) . PGA capsules , even if rare overall , were also present in most phyla . They were particularly abundant in Synergistetes , Planctomycetes , Bacillales and Fusobacteria ( Fig 2 ) . On the other hand , Group IV capsules were almost exclusively identified in γ-Proteobacteria and some subgroups were only identified in the taxa in which they were first described , e . g . , all Group IV_f capsules were identified in Francisella spp . ( Fig 2 ) . We identified at least five out of the eight capsule groups in α- and γ-Proteobacteria and in Actinobacteria . Following previous observations of capsule-like structures in Archaea [38–40] , and even if no experimental evidence has yet been given for their existence , we detected 47 capsule systems in 40 archaeal genomes . They were all synthase-dependent ( both subgroups ) or PGA capsules . Taken together , our results show that capsules are prevalent in Prokaryotes , where their frequency depends on the capsule group and on the phyla . The genetic loci encoding the experimentally studied capsule systems have remarkably different sizes . Since the number of genes in the capsule system is expected to have some impact on the complexity and evolution of capsules , we computed the number of genes of each system identified in our work ( see Materials and methods ) . These values are only approximate , because capsule systems surrounded by genes encoding enzymes involved in sugar metabolism cannot be delimited without ambiguity in the absence of experimental work . The Group I and ABC-dependent capsules were encoded by significantly more genes than the other capsule groups ( S1 Fig ) . Whereas the median Group I and ABC-dependent systems had between 19 and 16 genes , the synthase-dependent HAS ( hyaluronic acid ) capsule was encoded in three genes and the Syn_CPS3 in four ( S4 Table ) . These differences may be affected by the abovementioned inaccuracies in capsule loci delimitation and by the definition of the models . Nevertheless , our results show that some groups of capsules have loci of almost invariable sizes ( all Group IV capsules ) , whereas others showed very significant variation in the number of components ( especially Group I and ABC , see lower slopes in S1 Fig ) . These results give statistical support to the idea that the number of capsule components differs markedly between groups . We then searched to test if genome size was correlated with the number of genes encoding a capsule system . Genomes encoding capsule systems were generally larger than those lacking them ( Wilcoxon rank sum test , P < 0 . 001 ) , but the number of genes in the capsule loci showed no correlation with genome size when controlled for phylogenetic dependence ( S4 Table ) . This suggests that constraints on genome size have no significant effect on the complexity ( number of genes ) of each capsule system . We found that almost half of the genomes encoding capsules have more than one system ( 40% , Fig 3A ) . Strikingly , two environmental cyanobacteria encoded up to eight capsules , and 23 other species encoded between five and seven systems ( S5 Table for details ) . Among these 25 species , all with large genomes ( >4 . 5 Mb ) , we identified very few human-associated bacteria: a commensal Bacteroidetes , and some opportunistic pathogens of the Burkholderia cepacia complex . Instead , most of the 25 genomes were from mutualistic or environmental bacteria , including several α- and β-Proteobacterial rhizobia . The size of the genome was correlated with the number of capsules it encodes ( Spearman’s rho = 0 . 16 , P < 0 . 0001 after phylogenetic correction ) ( Fig 3B ) , and with the sum of all capsule components ( Fig 3B , and S4 Table for phylogenetic corrections ) . Hence , while the number of genes in a capsule system is not associated with genome size , larger genomes tend to encode more capsule systems , and thus have more capsule-associated genes . Nearly half of the genomes with multiple capsule systems encode several occurrences of the same capsule group ( 246 out of 537 ) . We analyzed their sequence similarity to test if they could have arisen by recent large segmental duplications . The systems were typically very divergent: 97% of the intra-genomic comparisons showed less than 80% sequence similarity at the homologous proteins used to identify the group ( see Methods ) . Systems of the same group were not found in tandem , as expected if they had resulted from recent duplications [41] and only eight ( out of 1004 ) pairs of consecutive systems were less than 10 kb apart ( S2 Fig ) . Furthermore , some genomes encoded two ( 238 ) , three ( 50 ) , and up to four ( in E . coli strain REL606 ) different capsule groups ( Fig 3C ) . Hence , multiple capsule systems do not seem to originate from recent segmental duplications . Remarkably , more than half of the genomes encoding an ABC-dependent capsule also encode a Group I capsule ( S6 Table ) , and all genomes coding for Group IV_s and Group IV_e capsules also code for at least one other capsule group . A non-random assortment of capsule groups would suggest epistatic interactions between capsules . To test this possibility , we analyzed the co-occurrence of capsule groups in the light of the underlying phylogenies ( see Materials and methods ) . We used Pagel’s method [42 , 43] to fit models of dependent evolution between capsule groups and compared them with models assuming independent evolution ( see Methods ) . We observed significant co-occurrence of Group I capsules and most of the other capsule groups ( Fig 3D and S7 Table ) , including ABC-dependent capsules and Group IV_s . We also observed frequent co-occurrence between PGA and Group IV_f capsules ( Fig 3D and S7 Table ) . In contrast , several groups of capsules showed unexpectedly low co-occurrence patterns suggesting the existence of negative epistatic interactions . For example , we only identified two co-occurrences of Group I and Syn_HAS . The family of Enterobacteria showed the most frequent co-occurrence of capsules from different groups and subgroups ( Fig 4 , see S8 Table for the complete list of genomes ) . Since it also includes several of the model organisms used to study the capsule—E . coli , S . enterica , K . pneumoniae–we analyzed these genomes more in detail . We detected seven out of the 24 different combinatorial possibilities offered by the four different capsule groups identified in the clade . In the line of the results mentioned in the previous paragraph , we observed a clear pattern of correlation between Group IV_s and Group I capsules in enterobacterial genomes ( Fig 3 ) . We observed that closely related genomes often encode different capsule systems . For instance , within the phylogenetic group B1 of E . coli , the two enteroaggregative pathotypes ( E . coli 55989 and E . coli O104 ) encode the same capsule groups , which differ from all the others of the same phylogroup . Similarly , the two only commensal strains ( ED1a and SE15 ) of the phylogroup B2 share the same capsular combination , which is different from all other B2 genotypes ( S8 Table ) . Finally , E . coli from phylogroup A , comprising a majority of commensal bacteria , often have at least three different capsule groups , which is significantly more than other clades including many pathogens , such as Shigella sp . and E . coli B2 ( two capsule systems per genome , on average ) . These results revealed an association between capsule groups and bacteria-host interactions . To conclude , the rapid genetic turnover of capsule systems within closely-related genomes [44] suggests that they can rapidly change to face environmental or lifestyle changes . The observation that multiple capsules are more frequently observed in commensals , mutualists or environmental bacteria seems at odds with the hypothesis of a tight association between capsules and pathogenesis . We classified bacterial species according to the degree of host-association they commonly exhibit ( S1 Dataset , see Methods for criteria and [45 , 46] ) and found that the probability of encoding a capsule depends on the lifestyle of the bacteria ( Fig 5A ) , even when accounting for genome size ( S9 Table ) . We then first tested whether free living species were more likely to code capsules than pathogens . We found that , indeed , capsules were slightly rarer in pathogenic species as opposed to free living species ( Fig 5A and S4 Fig ) . The lower frequency of capsules in pathogens remains qualitatively similar when commensals and mutualists ( or both ) are grouped together with free living species . Additionally , we observed no difference in genome size between pathogenic bacteria encoding a capsule system and the others , suggesting that the association between the presence of capsule and pathogenesis is independent of genome size . Many of the pathogenic bacteria in our dataset are facultative or opportunistic . These bacteria typically have environmental reservoirs and larger genomes than obligatory symbionts ( pathogens or mutualists ) [47 , 48] . We observed that many facultative pathogens encode capsules , in contrast to most obligate pathogens , independently of the differences in genome size between the groups ( Fig 5B and S1 Dataset ) . The difference between obligatory and facultative pathogens remained statistically significant when controlling for phylogenetic structure ( see Methods , Fig 5 and S5 Fig ) . Whereas very few obligate pathogens encoded a capsule , amongst which Shigella flexnerii and Mycoplasma mycoides , a small majority of the facultative pathogens encoded a capsule ( Fig 5B ) . This result does not change qualitatively when only human pathogens are taken into account . Facultative pathogens tend to start infections only at high infectious dose ( ID50 ) , to be motile , and to grow fast under optimal growth conditions [49] . These characteristics also tend to be associated with a lack of ability to kill professional phagocytes of the immune system or to survive in the intracellular milieu of these cells [49] . Since capsules may provide some resistance to phagocytosis , we enquired on the possible association between the capsule , minimum doubling time , and ID50 ( measured in humans as available for only 39 species , [49] ) . We observed that bacterial species that encode a capsule system ( Csp+ ) , show significantly lower minimum doubling times ( Fig 5C and S4 Fig ) , higher ID50 , are more likely to be motile , and are less likely to be able survive phagocytosis than those that do not encode a capsule ( Csp- ) ( Fig 5 , S3 and S4 Figs ) . Whereas the first association was significant even when controlling for genome size and pathogenicity ( S9 Table ) , and phylogenetic dependence ( S5 Fig ) , the two latter associations were not statistically significant due to lack of statistical power ( there is little data available for these traits ) . Overall , our results indicate that capsules are more readily associated with facultative pathogens with high infection doses and short minimal generation times . We analyzed microbiome data to confirm that capsule systems are frequent in environmental bacteria and in facultative pathogens ( that often have environmental reservoirs ) . Unfortunately , loci encoding capsule systems are too long and complex to be identifiable in the sequences of metagenomes . To circumvent this difficulty , we identified the presence of the species for which we had at least one complete genome in a large number of publicly available metagenomics datasets ( 16S rRNA ) . We used this information to quantify the abundance of each species and , using the species' complete genomes as a proxy , to predict the presence of capsules in these environments . Specifically , we searched for the presence of Csp+ in 16S datasets from four classes and numerous sub-classes of environments ( Fig 6A ) . This allowed both the qualitative and quantitative identification of bacterial species in 6700 environmental 16S datasets ( S8 Table , see Methods ) . We computed the abundance of Csp+ relative to Csp- species in the 16S datasets in qualitative ( number of species ) and quantitative ( number of 16S sequences ) ways ( see Methods ) . The percentage of Csp+ was similar in the 16S ( 53% out of 1197 bacterial species ) and in the genome ( 52% ) datasets . Csp+ were more frequently present and quantitatively more abundant than Csp- in all four classes of environments , even if this trend was not always significant ( Fig 6A and S6A Fig ) . Capsules allow Prokaryotes to withstand a series of stresses , from environmental disruptions to protozoa grazing , and are expected to be associated with broader environmental ranges . Indeed , Csp+ species were present in significantly more environmental subclasses than Csp- ( Fig 6B ) . Importantly , the number of environmental subclasses for a given species increased with its average number of capsules per genome ( Partial Spearman test , P < 0 . 001 , after correction for genome size ) . These results show that bacteria encoding capsule systems are able to colonize a larger variety of environments . The vast majority of previous studies focused on capsules of bacterial pathogens . To disentangle the relation between capsule and pathogenesis , we analyzed the presence of Csp+ species in human-associated datasets . We first checked that we were able to identify well-known pathogens in the host-associated environments . Indeed , we detected pathogens with Group I capsules , such as K . pneumoniae and S . pneumoniae , as well as pathogens with ABC capsule systems , namely Neisseria meningitidis , in samples of the human microbiome , and sometimes also in other environments ( S10 Table ) . The total abundance of species encoding capsules within the human host varied between body locations ( Fig 6C ) , and was higher overall than within the complete genome database ( 57% , binomial test , P = 0 . 005 ) . Csp+ species were more abundant than Csp- in all locations , and especially in the gut microbiota , which encompasses the largest fraction of bacteria in the human body . Likewise , clinical samples over-represented Csp+ species . Interestingly , we observed that the relative abundance of Csp+ and Csp- was strongly dependent on the human body sites ( ANCOVA , P < 0 . 001 , S6B Fig ) . Taken together , our results show that even if capsules are relatively rare among obligatory pathogens , they are very frequent in human microbiota where they are frequently associated with clinical conditions .
Capsules play important roles in bacterial virulence , but their study has been hampered by the lack of computational tools to identify them in genomes . Our tool , CapsuleFinder , identifies the eight major groups and subgroups of capsule systems in bacterial and archaeal genomes and is thus complementary to software designed to analyze very specific capsule systems , e . g . , the recently released Blast-based tool to identify capsular serotypes in Klebsiella spp . ( Kaptive , [50] ) . The models in CapsuleFinder can be modified to either increase specificity ( obtain systems closer to the experimental models ) or sensitivity ( to detect more distantly related systems ) . This can be done by changing the number , type and genetic organization of the components that are required to identify a system . Users can also add novel models and protein profiles to improve the tool , e . g . , to account for novel experimental data . If enough experimental serotype data is available for a given species , then the models can be specified in order to infer a putative serotype for the strains . The construction of our models was based on previous experimental studies restricted to a relatively small number of model organisms from Proteobacteria and Firmicutes . Capsules , like many extracellular structures [51] , are subject to rapid evolution and reorganization via recombination , complicating their detect from a small number of taxonomically restricted reference systems . In spite of this , we were able to identify them in many phyla of Prokaryotes—even in Archaea—with few putative false positives . Hence , we expect to have identified the majority of capsules of known groups in the complete genome database . The entire collection of capsule systems can be consulted in our database ( http://macsydb . web . pasteur . fr/capsuledb/_design/capsuledb/index . html and S1 Dataset ) . Further , the identification of capsule systems by CapsuleFinder opens the way for their comparative analysis , including the study of how horizontal transfer leads to serotype switching across bacteria [52 , 53] . Our analysis showed that a majority of Prokaryotes encodes at least one capsule system ( Fig 2 ) . Group I , PGA and Syn_CPS3 are the most widespread across the Bacteria whereas other groups were restricted to a few taxa , namely Group IV and Syn_HAS . Importantly , we found capsule systems in all phyla for which more than ten genomes were available . Future work will be necessary to assess if poorly sampled phyla—Chrysiogenetes , Deferribacteres , and Elusimicrobia—are effectively devoid of known capsule groups or if they encode novel groups of capsules . It will also be interesting to analyse capsule prevalence in newly discovered uncultivable phyla characterized by single-cell genomics since they may reveal novel capsule groups ( or variants of existing ones ) [37 , 54] . Given our results in the phyla with higher representation in the database , capsules might occur across all prokaryote phyla . Capsule-like structures have been described in Archaea [38–40] , where a previous bioinformatic study revealed the presence of proteins similar to those involved in the synthesis of the PGA capsule in one species [29] . We identified PGA capsule systems and also Syn_CPS3 systems in many genomes of Archaea . These two groups of systems have few components and we couldn't find data suggesting that they allow extensive serotypic variation . However , the lack of more complex capsule groups , should be subject to caution owing to the lack of experimental data . Furthermore , our tools to identify capsules were based on bacterial systems . Alternatively , the peculiarities of the cellular envelope of Archaea may explain the absence of certain capsule groups in the phyla . Most Archaea have a S-layer composed of glycans that might affect secretion or cell surface association of certain capsules . Our method may underestimate the number of capsule systems of the same group co-occurring in a genome owing to strict localization rules in our models to avoid false positives . For example , not all Group I Bacteroides thetaiotaomicron were detected because some operons lacked the minimum mandatory genes required to identify the gene cluster as a capsule system ( S3 Table ) . This suggests that some structural elements involved in capsule secretion might be shared between different systems . Yet , to date , the existence of genomes encoding multiple capsules of the same group had previously been documented in only a few species , namely Bacteroides spp [55 , 56] . In B . fragilis , a key commensal of the gut microbiome , there are several Group I capsule systems , some of which are implicated in the formation of intra-abdominal abscesses [57] . This species encodes a DNA inversion mechanism that combinatorically switches the expression of the different systems [58] , producing extremely diverse capsule structures that are thought to increase bacterial fitness in the intestinal milieu by virtue of their immunomodulatory properties [59] . In this case , capsule variation seems to evolve as a response to the rapid change of the human immune system [58] . Bacteria may also encode multiple capsules from different groups , as described for the PGA and Syn_HAS capsules encoded in different plasmids of Bacillus cereus biovar anthracis[60] . Co-expression of different capsule groups is thus possible , implicating that capsules will physically interact in the cell envelope . Our data suggests that capsule combinations can be even more complex , since this same strain encodes a Group I capsule in the chromosome , and some enterobacteria encode up to four different groups of capsules . The non-random patterns of co-occurrence of different capsule groups observed in this study suggest that capsule repertoires are affected by epistatic interactions ( Fig 3D ) . The nature of these interactions depends on whether the different capsules are expressed at the same time , thereby producing combinatorial diversity , or at different moments , e . g . , in response to different environmental cues . Positive epistasis may result from the synergistic combination of the properties of the different capsules , e . g . , different capsules may provide a broader range of environmental protections and capsule switching ( or variation in the proportions of each capsule group ) may facilitate escaping grazing protozoa , professional phagocytes of the immune system , or bacteriophages . Negative epistasis associated with co-expressed capsules may result from problems in accommodating different capsule structures in the cell envelope . Negative epistasis between capsules that are not co-expressed could be caused indirectly by the effects of the genetic background , e . g . , because some groups of capsules are more compatible with certain membrane structures ( pili , flagella , secretion systems ) than others . The mechanisms leading to the acquisition of multiple capsules will have to be studied in detail in the future , but our results already provide some clues . We observed that many genomes encode capsules of different groups , that capsules of the same group are very divergent in sequence and are encoded in distant regions in the genome ( or in different replicons ) . This suggests that capsules were independently acquired by multiple events of horizontal gene transfer . This fits the abundant literature showing that capsules vary rapidly within species by recombination and horizontal transfer [61–63] . It also explains why most capsule systems are encoded in a single locus , since this facilitates transfer [64] . Finally , the outcome of capsule transfer is likely to depend on the environmental challenges faced by the bacteria and will be affected by the abovementioned epistatic interactions . A substantial part of the previous literature on capsule systems has focused on bacterial pathogens and on the role of capsules as virulence factors . For instance , it has been shown that acquisition of certain capsule types by horizontal gene transfer in Neisseria meningitidis allowed the bacteria to increase in pathogenicity and going from non-pathogenic carriage to infectious state [52 , 53] . It was thus surprising that non-pathogens are more likely to encode capsules , and that , among pathogens , the ones establishing obligatory antagonistic interactions with their hosts typically lacked a capsule . The abundance of capsules across most phyla and environmental classes , and their rarity among obligatory pathogens , suggest they play important roles beyond pathogenesis . Indeed , the capsule also constitutes an advantage for commensal bacteria of the gut . To colonize the gut , the bacteria have to first withstand the harsh conditions of the stomach and then grow and multiply in the duodenum and colon , in the presence of bile salts . In Bifidobacterium longum , capsule expression would enhance survival in the stomach and allow growth under high concentrations of detergent-like bile salts in the duodenum [65] . Similarly , a study performed in yeast has shown that although capsules from environmental and pathogenic strains display similar composition and features , they fulfil different roles [66] . Capsules are an example of the ability of bacteria to evolve structures serving multiple purposes in different environments . Like other virulence factors , such as some iron capture proteins , while evolving as an adaptation to an environment they also confer an advantage during pathogenesis ( exaption ) , either during colonization or transmission across hosts [47 , 67] . Our data also shows that the presence of capsule systems , and especially multiple systems , is associated with broader environmental ranges . The ability to express different capsules , or combinations of them , can result in heterogeneity in the surface charge of bacterial cells which can in term influence important phenotypes such as cellular adhesion to tissues or surfaces , susceptibility to certain cationic peptides , etc . In the aforementioned B . cereus strain , the co-expression of the two capsules did not increase virulence in two different animal models , but rather favoured bacterial colonization and dissemination [60] . Similarly , previous studies in soil-borne nitrogen-fixing bacteria indicated that bacterial exopolysaccharides and lipopolysaccharides that can be similar to capsules are involved in species-specific interactions between the bacteria and the host [68] . This is consistent with our observation that capsule multiplicity increases environmental breadth , and suggests that it may also increase host range . Taken together , our study revealed an unsuspected prevalence of capsules in Prokaryotes , especially in environmental bacteria and facultative pathogens . Our results are in line with the multitude of roles proposed for capsules and are not consistent with the idea that capsules evolved to facilitate pathogenesis . Instead , they highlight that capsules might have an important role in facilitating bacterial adaptation to novel or changing environments . Interestingly , we found many capsule systems in soil bacteria , from which probably originated capsulated opportunistic multi-resistant bacteria such as Klebsiella pneumoniae , Enterococcus faecium , and Acinetobacter baumanii [69–72] . Capsules may have thus evolved primarily as an adaptation to a range of different environments , and this facilitated subsequent ecological transitions towards host colonization and pathogenesis .
We built a model for each group of capsule with the information we could obtain from the literature . We specified models with mandatory ( biologically essential components for a putative functional system , a majority of which , if not all , are required to identify and classify the systems ) , accessory ( non-essential components used to improve the annotation of the system ) , and forbidden components ( e . g . , those found in other capsule groups and not in the focal one , thus helpful to discriminate between the capsule groups , see below the example of Group IV capsules ) . Of note , due to the low conservation of some mandatory elements , for example Wzy polymerases , in some instances a system could be validated even if a certain number of mandatory components were not detected . This is controlled by the option min_mandatory_genes_required . The parameters used for the minimum quorum of mandatory genes were set based on the analysis of experimental systems and on our previous experiences with the development of similar models for protein secretion systems and CRISPR-Cas systems [33 , 36] . While these systems are very different , they have in common that certain components that are thought to be biologically necessary may not be identifiable by sequence analysis either because they evolve too fast , or because they can be replaced by analogues lacking sequence homology . Additionally , we specified that components should be encoded in a single locus ( defined as a series of genes respecting a maximal pre-specified distance between consecutive elements ) . When the available experimental data suggested that it was relevant to allow components to be encoded elsewhere in the genome , we defined them as loners in the models . Models were written in plain text , using a specific XML grammar , and can be modified by the user ( see http://macsyfinder . readthedocs . io/en/latest/ for details ) . For simplicity , we named the components after the protein names in the species that served as a biological model for each group of capsule . The names of the homologs to these proteins in other species with experimentally validated systems are listed in S1 Table . Polymer-specific enzymes were regarded as accessory in the models because they can be homologous to enzymes of other cellular processes [18] . We used MacSyFinder to search for capsule systems [33] . This program takes as input a proteome , a set of hidden Markov models ( HMM ) protein profiles ( one for each component of the system , see below ) , and models describing the number of components and their genetic organization ( Fig 1 ) . MacSyFinder identifies the individual components of each capsule system using hmmsearch from the HMMER package v3 . 1b2 [86] . A component was retained for further analysis when its alignment covered more than 50% of the length of the profile and obtained an e-value smaller than 0 . 001 . We used 58 different HMM protein profiles in our searches ( S1 Table ) , 31 retrieved from the PFAM 28 . 0 database ( http://pfam . xfam . org , [87] , last accessed November 2015 ) and 27 built in this study . Each protein profile was constructed as follows ( except when explicitly stated otherwise ) . We started from a well-described and experimentally-validated component of a system and used BLASTP v 2 . 2 . 28 [88] ( default settings , -v 4000 , e-value < 10−4 ) to search for homologs among complete genomes . To reduce the redundancy of the dataset ( i . e . , to remove very closely related proteins ) , we performed an all-against-all BLASTP v 2 . 2 . 28 analysis and clustered the proteins with at least 80% sequence similarity using SiLiX v1 . 2 . 9 ( http://lbbe . univ-lyon1 . fr/SiLiX , default settings ) [89] . We selected the longest sequence from each family as a representative . The set of representative sequences was then used to produce a multiple alignment with MAFFT v7 . 215 using the L-INS-i option and 1000 cycles of iterative refinement [90] . The alignment was manually trimmed to remove poorly aligned regions at the extremities , using SEAVIEW [91] . The HMM profile was then built from the trimmed alignment using hmmbuild ( defaults parameters ) from the HMMER package v3 . 1b2 [86] . We validated the method to identify capsule systems using two published lists of capsulated bacterial pathogens [19 , 35] . Since these lists were very short , and not necessarily meant to be exhaustive , we made a complementary validation on a random set of species from our dataset . We used the R function sample to randomly draw 100 species from a curated list of 1241 species in our database ( this list did not include genomes for which a genus but not a species was defined , such as Glacieola sp . ) . We identified capsule systems in 40 of the 100 species . We then sought to confirm the presence of capsule in the latter ( they include 52 . 5% of free-living , 30% facultative pathogens , 12 . 5% commensals and 5% of mutualists ) by analyzing the primary scientific literature . For those species for which we did not detect a capsule system , we did not seek further validation as negative results are not systematically reported . We identified the core genome of 131 enterobacterial genomes belonging mostly to E . coli and Salmonella spp . , but also Shigella spp . , Citrobacter , Cronobacter , Klebsiella , and Enterobacter ( see S8 Table for the complete list of genomes ) . We followed a previously published methodology [92] . Briefly , orthologs were identified as bidirectional best hits , using end-gap free global alignment , between the proteome of E . coli K12 MG1655 and each of the 130 other proteomes . We discarded hits with less than 60% similarity in amino acid sequence or more than 20% difference in protein length . The list of orthologs for every pairwise comparison was then curated to take into account the high conservation of gene neighborhood at this phylogenetic scale [93] . We defined positional orthologs as bidirectional best hits adjacent to at least four other pairs of bidirectional best hits within a neighborhood of 10 genes ( five genes upstream and five downstream ) . The core genome was defined as the intersection of pairwise lists of positional orthologs and consisted of 759 gene families . To control for phylogenetic independence of data at the genome-level , we aligned the 16S rRNA using secondary structure models with the program SSU_Align v0 . 1 [94] of 2440 bacterial genomes . The alignment was trimmed with trimAl v1 . 4 [95] using the option -noallgaps to delete only the gap positions but not the regions that are poorly conserved . The 16S rRNA phylogenetic tree was infered using IQTREE v . 1 . 4 . 3 [96] under the GTR+I+G4 model with the options–wbtl ( to conserve all optimal trees and their branch lengths ) , and–bb 1000 to run the ultrafast bootstrap option with 1000 replicates . Two hundred and eleven genomes from our database were excluded from the final phylogenetic tree because identical 16S sequences were already present in the multiple alignment . When data was analyzed at the species level , a 16S rRNA gene per species was chosen by the Bash function RANDOM ( from all the available genomes of the species ) from the secondary structure alignment and a new phylogenetic tree constructed as above . To build the core-genome phylogenetic tree of the Enterobacteria , we aligned each core gene family at the amino acid level with MAFFT v7 . 215 ( default options ) [90] , trimmed non-informative positions with BMGE v1 . 12 ( default options and—t AA ) [97] , and concatenated the alignments . The tree of the concatenate was built using IQTREE v . 1 . 3 . 10 under the GTR+I+G4 model [96] . In both trees , the model used was the one minimizing the Bayesian Information Criterion ( BIC ) among all models available ( option -m TEST in IQTREE ) . All phylogenetic corrections were done using the 16S rRNA tree of Bacteria . We restricted our phylogenetic controls to Bacteria , because the inclusion of Archaea reduced very much the phylogenetic signal ( resulting from a shorter multiple alignment ) and clumped together many species’ 16S sequences . The presence of phylogenetic signal in the evolution of traits was estimated with Pagel’s lambda using the phylosig function of the phytools package v . 0 . 5–20 for R [42] and the aforementioned 16S rRNA phylogenetic tree . To estimate the phylogenetic signal across capsule groups , instead of using the 16S rRNA tree , we built new trees comprising only the 16S rRNA sequences of the genomes for which we detected the given capsule groups . To control for the effect of the uncertainty in phylogenetic inference on the key positive results , we produced 1000 bootstrap trees ( options -wbtl -bb 1000 in IQTREE ) and randomly selected 100 of those trees . We then ran each key analysis ( those in the figures , either GEE , fitPagel or phylosig functions ) using the different trees . The distribution of the 100 P values of each analysis is presented in S5 Fig . We tested the significance of the co-occurrence of capsule groups , with the default method ( fitMk ) of the fitPagel function from the phytools package ( v0 . 5–52 maps v3 . 1 . 0 ) . This function assumes an ARD—all rates different , which allows different rates at all transitions- substitution model for both characters and gives the probability that they are independent ( the rates of transitions of each character are independent of the other character ) . We controlled the associations between traits for phylogenetic dependence whenever one of their lambda’s P values was less than 0 . 05 . We used the pic function to make independent contrast analysis of continuous data and the compar . gee function to analyze associations between discrete and continuous variables using generalized estimation equations ( GEE ) . Both were computed with the functions included in the ape v . 3 . 5 package for R [98] . We also controlled associations for the effect of genome size by fitting linear regression models using aov from R . We selected from MG-RAST the metagenomes matching at least one species of our complete genome database and obtained from four environmental categories ( subclasses indicated in S8 Table ) : ( i ) water ( fresh , marine and spring water ) , ( ii ) soil ( agricultural , dessert , forest , tundra and grasslands ) , ( iii ) air ( indoor , mammal ) , and ( iv ) host-associated ( human , other mammals , arthropods , aquatic organisms and plant ) . These categories are broad and heterogeneous ( they put together many different environments ) . They are used to provide a very coarse-grained classification of the type of environment of each species . We used 16S rRNA assembled reads to identify and quantify the presence of species from the complete genome dataset in the environmental samples . All analyses were performed at the species level rather than at the strain level because 16S rRNA does not allow resolving phylogenetic structure below the species level . For consistency with previous analyses , Archaea were also excluded from the 16S environmental datasets . First , for each metagenome we identified the 16S matching each of the species in our database using BLASTN v 2 . 2 . 28 ( selected hits with more than 97% sequence identity and with alignments covering at least 90% of the query sequence ) . The relative abundance of each species was then calculated by dividing the number of 16S rRNA sequences in each metagenome by the total number of sequences . This information was used to draw the frequency of species with capsule systems in each environmental category and subcategory . To validate the analysis , we searched for well-known pathogens and quantified the frequency in which they appeared across metagenomes of each environmental subcategory ( S11 Table ) . Sequence identities and similarities were calculated with needle function ( default settings ) included in the EMBOSS 6 . 6 package . Phylogenetic trees were produced with iTol v3 . 0 [99] . Statistical analysis and graphs were done with R version 3 . 2 . 0 and the packages ggplot2 and RColorBrewer , unless stated otherwise . PMCMR [100] , stats and NCstats [101] packages for R were used for post hoc pairwise multiple comparisons of mean ranks and data manipulation . We have made publicly available the methods to detect capsules . CapsuleFinder can be used locally using the program MacSyFinder [33] , freely available for download at https://github . com/gem-pasteur/macsyfinder . We recommend the use of our models without the option "all' ( as recommended in the documentation of the program ) . It can also be queried on a dedicated webserver within the Galaxy platform ( https://galaxy . pasteur . fr/root ? tool_id=toolshed . pasteur . fr/repos/odoppelt/capsulefinder/CapsuleFinder/1 . 0 . 2 ) . The protein profiles and capsule models used in this study are accessible at https://research . pasteur . fr/fr/tool/capsulefinder/ . The models are written in a simple XML grammar in plain text files to allow user modifications ( see documentation in http://macsyfinder . readthedocs . io/en/latest/ ) . The results of MacSyFinder can be visualized with MacSyView , available online at http://macsyview . web . pasteur . fr . The capsules detected in this study , their genomic localization and organization are collected in an accessible database , CapsuleDB , http://macsydb . web . pasteur . fr/capsuledb/_design/capsuledb/index . html . | Extracellular capsules protect bacterial cells from external aggressions such as antibiotics or desiccation , but can also be targeted by vaccines . Since little was known about their frequency across Prokaryotes , we created and made freely available a computational tool , CapsuleFinder , to identify them from genomic data . Surprisingly , its use showed that many bacterial strains , especially those with the largest genomes , encode several capsules . The frequencies of the different combinations of capsule groups depended strongly on the phyla and the groups themselves , suggesting the existence of epistatic interactions between capsules . Bacteria encoding capsule systems were found in many natural environments , and were frequent in the human microbiome . In contrast to their frequent association with virulence , we found many more capsules in non-pathogens or facultative pathogens than among obligatory pathogens . We suggest that capsules increase the environmental breadth of bacteria thereby facilitating host colonization by opportunistic pathogens . |
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Proteins containing DUF59 domains have roles in iron-sulfur ( FeS ) cluster assembly and are widespread throughout Eukarya , Bacteria , and Archaea . However , the function ( s ) of this domain is unknown . Staphylococcus aureus SufT is composed solely of a DUF59 domain . We noted that sufT is often co-localized with sufBC , which encode for the Suf FeS cluster biosynthetic machinery . Phylogenetic analyses indicated that sufT was recruited to the suf operon , suggesting a role for SufT in FeS cluster assembly . A S . aureus ΔsufT mutant was defective in the assembly of FeS proteins . The DUF59 protein Rv1466 from Mycobacterium tuberculosis partially corrected the phenotypes of a ΔsufT mutant , consistent with a widespread role for DUF59 in FeS protein maturation . SufT was dispensable for FeS protein maturation during conditions that imposed a low cellular demand for FeS cluster assembly . In contrast , the role of SufT was maximal during conditions imposing a high demand for FeS cluster assembly . SufT was not involved in the repair of FeS clusters damaged by reactive oxygen species or in the physical protection of FeS clusters from oxidants . Nfu is a FeS cluster carrier and nfu displayed synergy with sufT . Furthermore , introduction of nfu upon a multicopy plasmid partially corrected the phenotypes of the ΔsufT mutant . Biofilm formation and exoprotein production are critical for S . aureus pathogenesis and vancomycin is a drug of last-resort to treat staphylococcal infections . Defective FeS protein maturation resulted in increased biofilm formation , decreased production of exoproteins , increased resistance to vancomycin , and the appearance of phenotypes consistent with vancomycin-intermediate resistant S . aureus . We propose that SufT , and by extension the DUF59 domain , is an accessory factor that functions in the maturation of FeS proteins . In S . aureus , the involvement of SufT is maximal during conditions of high demand for FeS proteins .
Iron ( Fe ) is an essential nutrient for nearly all organisms . Fe is acquired from the environment and is transported into cells using specific uptake systems . Studies have shown that ~80% of the intracellular Fe is located in inorganic cofactors , called iron-sulfur ( FeS ) clusters , and heme in a respiring microorganism [1] . The metabolisms of most organisms are highly reliant on FeS cluster chemistry and a failure to properly assemble FeS clusters in proteins can result in widespread metabolic disorders , metabolic paralysis , and cell death [2 , 3 , 4] . FeS proteins function in diverse metabolic processes including environmental sensing[5] , carbon transformations [6] , DNA repair and replication [7 , 8] , RNA metabolism [9] , protein synthesis [10] , nucleotide , vitamin , and cofactor synthesis [11 , 12 , 13] , and cellular respiration [14 , 15 , 16] . FeS clusters are typically found in proteins as [Fe2S2] or [Fe4S4] clusters , but the use of complex FeS clusters has evolved for processes such as dinitrogen [17] , carbon monoxide [18] , and hydrogen metabolism [19] . Iron and sulfur ( S ) ions are often toxic to cells resulting in the evolution of tightly controlled mechanisms to synthesize FeS clusters from their monoatomic precursors [20 , 21] . Three FeS cluster biosynthetic systems ( Nif , Suf , and Isc ) have been described in Bacteria and Archaea for the synthesis of [Fe2S2] and [Fe4S4] clusters [22 , 23 , 24] . Bioinformatic analyses suggest that the Suf system is the most prevalent machinery in Bacteria and Archaea and perhaps the most ancient [25] . The Suf , Nif , and Isc systems utilize a common strategy to synthesize FeS clusters . First , sulfur is mobilized from free cysteine ( typically ) , using a cysteine desulfurase enzyme and subsequently transferred to either a sulfur carrier molecule ( SufU or SufE ) or directly to the synthesis machinery [24 , 26 , 27] . Monoatomic iron and sulfur , along with electrons , are combined upon a molecular scaffolding protein ( SufBD in S . aureus ) to form an FeS cluster [28] . The FeS cluster can be transferred directly from the scaffold to a target apo-protein or it can be transferred to a carrier molecule that subsequently traffics the cluster to a target apo-protein and facilitates maturation of the holo-protein [29] . Nfu and SufA serve as FeS cluster carriers in Staphylococcus aureus [4 , 30] . Nfu is necessary for virulence in models of infection [4 , 31] Most studies on bacterial FeS cluster assembly have been conducted using Escherichia coli and Azotobacter vinelandii . E . coli encodes for both the Suf and Isc systems [22] whereas A . vinelandii encodes for the Isc and Nif systems [32] . In contrast , few studies have been conducted on FeS cluster assembly in gram-positive bacteria such as Bacillus subtilis or S . aureus , which encode for only the Suf system [4 , 27] . Recent findings suggest that SufCDSUB are essential for S . aureus viability , confirming that Suf is the sole FeS cluster biosynthetic machinery used under laboratory growth conditions [4 , 33 , 34] . Dioxygen can accept electrons from cellular factors resulting in the spontaneous generation of reactive oxygen species ( ROS ) such as hydrogen peroxide ( H2O2 ) and superoxide [35 , 36 , 37] . FeS clusters are among the primary cellular targets of H2O2 and superoxide [38 , 39] . ROS readily oxidize solvent exposed [Fe4S4]2+ cofactors of enzymes such as aconitase ( AcnA ) [38 , 39] . Oxidation results in conversion to an inactive [Fe3S4]1+ cluster that can be repaired back to the active [Fe4S4]2+ state using Fe2+ and an electron [40] . Studies have implicated roles for cysteine desulfurase ( IscS ) and the putative Fe donors CyaY , YtfE , and YggX in the repair of oxidized clusters [40 , 41 , 42] . Cells also employ mechanisms to physically protect FeS clusters . The Shethna protein shields the FeS cofactor of dinitrogen reductase from dioxygen exposure [43] . Alternatively , protein domains can be situated in a manner that prevents oxidants from interacting with the FeS cluster . The pyruvate:ferredoxin oxidoreductase ( PFOR ) from Desulfovibrio africanus was found to have greater stability in the presence of dioxygen , relative to alternate PFOR enzymes , due to the presence of a domain that prevents the interaction of oxidants with its [Fe4S4]2+ cluster [44] . We have identified an open reading frame ( ORF ) in S . aureus that is often associated with the suf operon in a number of bacterial and archaeal genomes . The ORF ( SAUSA300_0875 ) encodes for a protein composed solely of a DUF59 domain and is annotated as SufT since it is often found in operons with a cysteine desulfurase ( i . e . SufS ) [45] . In eukaryotic cells , the CIA2 ( also identified as Fam96a/b or AE7 ) FeS cluster assembly factor ( s ) contain a DUF59 domain [46 , 47] . CIA2a and CIA2b act downstream of the cytosolic iron-sulfur assembly ( CIA ) machinery and are required for the maturation of FeS cluster proteins . A DUF59 domain is also present in the Arabidopsis thaliana chloroplast FeS cluster carrier , HCF101 , which is required for photosystem I maturation [48] . S . aureus is a leading cause of human infectious disease related morbidity and mortality worldwide . S . aureus forms surface associated communities referred to as biofilms that are critical for S . aureus pathogenesis and biofilm associated cells serve as the etiologic agents of recurrent staphylococcal infections ( reviewed here [49] ) . S . aureus also secretes a variety of toxins and enzymes into its extracellular milleu that are critical for biofilm formation , host colonization , nutrient acquisition and survival in the human host ( reviewed here [50] ) . About 60% of the secretome consists of peptide toxins ( phenol soluble modulins ( PSM's ) , which have multiple key roles in pathogenesis [51 , 52] . Since the 1990s the proportion of infections caused by community-associated methicillin resistant S . aureus ( CA-MRSA ) has been steadily increasing and has now reached near epidemic levels [53] . Vancomycin is a glycopeptide antibiotic that has traditionally been regarded as a last-resort drug for the treatment of MRSA infections [54] . Strains have recently emerged that display intermediate ( vancomycin intermediate-resistant S . aureus; VISA ) or high ( vancomycin resistant S . aureus; VRSA ) levels of resistance towards vancomycin [54 , 55] . Among the characteristics of VISA strains are decreased activity of peptidoglycan hydrolases and alterations in their cell wall that results in increased resistance to the lytic enzyme lysostaphin [55] . S . aureus provides an excellent model to assess the role of the DUF59 domain ( SufT ) in cellular physiology . In this report we present phylogenetic analyses indicating a widespread distribution for SufT and conservation of SufT homologs in bacterial and archaeal taxa that utilize the Suf system . These analyses also suggest that sufT was recruited to the neighborhood of sufBC over evolutionary time and for the most part retained . The bioinformatic analyses led us to hypothesize that SufT has a role in the maturation of FeS proteins . Results demonstrate an involvement of SufT in the maturation of FeS proteins during conditions imposing a high demand for FeS proteins . Moreover , epistasis studies show that the nfu and sufT mutations display synergy and the introduction of nfu in multicopy partially corrects the phenotypes of a sufT mutant . Deficiencies in the maturation of FeS proteins also result in increased biofilm formation , decreased exoprotein production , and the appearance of phenotypes consistent with vancomycin-intermediate resistant S . aureus ( VISA ) . We propose that SufT functions as an auxiliary factor for the maturation of FeS proteins with maximum usage during conditions of high FeS cofactor demand .
Of the 1669 complete genome sequences available as of October 2011 and compiled as part of our previously published work on the evolution of Suf [25] , 1092 ( 65 . 4% of total ) encoded for SufBC . Among these genomes , 761 ( 69 . 7% of total ) encoded for SufT . Of the 1669 genomes , 68 genomes contained sufT , but not sufBC . Five genomes contained sufT , but not sufB , iscU , or nifU , which encode for FeS cluster scaffolding molecules . These genomes were all from lactobacilli and the sufT homologues are in apparent operons with the genes encoding for either anaerobic ribonucleoside-triphosphate activating enzyme or serine dehydratase , which are FeS cluster-requiring enzymes [11 , 56] . Among the 761 genomes that encoded for sufT and sufBC , 374 of the sufT homologs were localized with sufBC ( suf operon associated ) and 387 sufT homologs were not associated with sufBC ( non-suf operon associated ) . Maximum likelihood phylogenetic reconstructions of SufT ( unrooted ) and SufBC ( rooted ) , followed by overlays of suf-operon associated and non-suf operon associated sufT , indicate that sufT has been recruited to and lost from the suf operon multiple times during its evolutionary history ( Fig 1 ) . However , the overall trend appears to be retainment once sufT was recruited to the suf operon . Mapping of the association of sufT with the suf operon on the SufBC tree indicates that sufT was not associated with the suf operon early during the evolution of taxa that used the Suf FeS cluster biosynthetic system and that it was recruited to the operon recently in its evolutionary history . Each SufT homolog identified contained a conserved cysteine residue , which was previously shown to be hyper-reactive [57] , but described FeS cluster-binding motifs were not recognized . Of the total ( n = 761 ) identified SufT homologs , the predominant structure contained only the DUF59 domain ( S1 architecture; ex . S . aureus SufT ) , but 198 encoded for additional N- and C-terminal motifs represented by nine primary modular structures ( Fig 2A ) . The most prevalent modular structure was the S2 architecture ( n = 88 ) , with a N-terminal motif that did not display homology to previously described domains . SufT within the S5 architecture ( n = 5 ) contained a N-terminal domain with homology to U-type FeS cluster scaffolds while SufT within the S7 architecture ( n = 3 ) harbored a N-terminal domain with homology to Rieske iron-oxygenase ferredoxins . Finally , SufT within the S9 architecture ( n = 1 ) contained a N-terminal domain with homology to serine acetyltransferases ( CysE ) . Characterization of the C-terminal motifs also revealed variation that was represented in four unique modular structures . These were characterized as SufT with C-terminal domains that have homology to PaaJ or acetyl-CoA acetyltransferase domains ( S3 architecture , n = 75 ) , P-loop NTPase domains ( S4 architecture n = 20 ) , DUF1858 domains ( S6 architecture , n = 4 ) and co-enzyme pyrroloquinoline quinone synthesis protein D ( PqqD ) domains ( S8 architecture , n = 2 ) . The S2-S9 architectures were mapped on the phylogenetic reconstruction of core DUF59 ( N- and C-terminal motifs were pruned from alignment block ) in order to determine if the modules are randomly distributed over the tree or if they are phylogenetically clustered . The overall pattern of clustering of the modular structures on the tree ( Fig 2B ) indicates that once these modules were fused to an ancestor of a given DUF59 containing protein , they were largely retained . This suggests that the N- and C-terminal motifs , and presumably their functionalities , are under strong selective pressure . We created and characterized a S . aureus ΔsufT mutant to test whether SufT has a role in the maturation of FeS proteins . A S . aureus ΔacnA strain is defective in utilizing glutamate as a source of carbon ( S1A Fig ) [58 , 59] . Nfu has a role in the maturation of AcnA in S . aureus [4] . The Δnfu and ΔsufT strains displayed growth defects in chemically defined media supplemented with glutamate as a carbon source ( hereafter 20AA glutamate medium ) ( Fig 3A ) , but the defect of the ΔsufT strain was less severe than that of the Δnfu strain . The WT , Δnfu , and ΔsufT strains had similar growth profiles in defined medium containing glucose as a carbon source ( hereafter 20 AA glucose medium ) ( S1B Fig ) . AcnA activity was assessed in the WT , ΔsufT , and Δnfu strains across growth . AcnA activity was decreased in strains lacking Nfu or SufT ( Fig 3B ) . The decreased AcnA activity in the ΔsufT strain could arise due to one of four scenarios: 1 ) decreased transcription of acnA , 2 ) decreased abundance of AcnA , 3 ) decreased occupancy of the [Fe4S4] cluster upon AcnA due to the decreased transcription of genes encoding FeS cluster biogenesis factors , or 4 ) decreased cluster occupancy upon AcnA due to the absence of SufT . Transcriptional activity of acnA was increased in the ΔsufT strain ( S2 Fig ) . This suggested that decreased AcnA activity in the ΔsufT strain was not the result of altered acnA transcription ( S2 Fig ) . We constructed acnA::TN strains containing a plasmid with a acnA_FLAG allele under the transcriptional control of a xylose inducible promoter ( pacnA ) . Introduction of pacnA allows for the control of acnA transcription and the simultaneous determination of AcnA_FLAG abundance . The acnA::TN ΔsufT strain was genetically complemented by re-introduction of the sufT allele at a secondary chromosomal location ( sufT+ ) . AcnA activity and AcnA abundance was assessed in the acnA::TN , acnA::TN ΔsufT , and acnA::TN ΔsufT sufT+ strains containing pacnA . AcnA activity was ~2-fold lower in the acnA::TN ΔsufT strain compared to the acnA::TN when activity was normalized to AcnA abundance in the same cell-free lysates ( Fig 3C ) . This phenotype was genetically complemented . Suf is encoded by the sufCDSUB operon in S . aureus . The transcriptional activity of sufC was increased ( ~2-fold ) in the Δnfu strain and mildly , but consistently , increased in the ΔsufT strain ( Fig 3D ) . Similar results were obtained in exponential and stationary growth . From Fig 3 we concluded that the absence of SufT results in decreased occupancy of the [Fe4S4] cofactor upon AcnA . Synthesis of the branched chain amino acids ( BCAA ) leucine and isoleucine requires the FeS cluster containing dehydratase enzymes isopropylmalate isomerase ( LeuCD ) and dihydroxyacid dehydratase ( IlvD ) , respectively [60 , 61] . Strains lacking either SufT or Nfu displayed growth defects in defined medium lacking leucine ( Leu ) and isoleucine ( Ile ) ( hereafter 18AA glucose medium ) ( Fig 4A ) , but displayed a growth profile similar to WT in 20AA glucose medium ( S1B Fig ) . We constructed leuC::TN , leuC::TN ΔsufT , leuC::TN ΔsufT sufT+ , ilvD::TN , ilvD::TN ΔsufT , and the ilvD::TN ΔsufT sufT+ strains carrying plasmids with either leuCD or ilvD under the transcriptional control of a xylose inducible promoter ( pleuCD and pilvD ) . The activities of LeuCD and IlvD were decreased in strains lacking SufT and these defects were restored by genetic complementation ( Fig 4B and 4C ) . We concluded that SufT is utilized in the maturation of multiple FeS cluster requiring enzymes . Staphylococcus aureus is a facultative anaerobe and can respire upon dioxygen or nitrate as terminal electron acceptors or grow fermentatively [62] . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured aerobically , as well as anaerobically in the presence or absence of nitrate before determining AcnA activity . The ΔsufT mutant had lower AcnA activity during respiratory growth , but AcnA activity was restored during fermentative growth ( Fig 5A ) . Microaerobic conditions also mitigated the growth defect of both the Δnfu and ΔsufT strains in 18AA glucose medium ( S3 Fig ) . Fermentative growth imposes a decreased demand for FeS clusters [63] . By inference , fermentative growth should result in decreased transcription of genes encoding for FeS assembly factors . Consistent with this prediction , the transcriptional activities of sufT , nfu , and sufC decreased when aerobically cultured cells were shifted to an anaerobic ( fermentative ) environment ( Fig 5B ) . We examined whether SufT functions to protect the AcnA FeS cluster via physical exclusion of dioxygen . Cell-free lysates were generated from the acnA::TN and acnA::TN ΔsufT strains containing pacnA . AcnA activity was assessed at periodic intervals before and after exposure of lysates to dioxygen . Dioxygen exposure resulted in decreased AcnA activity in both the parent and ΔsufT mutant ( Fig 5C ) , but the rate of decrease was statistically indistinguishable between the strains . Fermentatively cultured cells exposed to dioxygen ( reaeration ) increased sufC transcription suggesting that the resumption of respiratory processes results in an increased demand for FeS clusters ( Fig 6A and [4] ) . The transcription of sufT was also increased ( ~2 . 5-fold ) upon reaeration ( Fig 6A ) . The role of SufT in the maturation of AcnA upon reaeration was assessed . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured fermentatively before one set of the cultures was exposed to dioxygen while the other set was incubated anaerobically ( as previously described [40] ) . AcnA activity increased by ~30% in the parental strain upon dioxygen introduction ( Fig 6B ) . In contrast , AcnA activity decreased by ~20% in the ΔsufT mutant . The use of protein synthesis inhibitors allowed for the conclusion that the increased AcnA activity in the parental strain upon reaeration was due to de novo protein synthesis . These findings led to the conclusion that SufT has a role in FeS cluster assembly in cells attempting to resume respiratory processes , and thereby facilitates the adaptation of cells to shifts in dioxygen tensions . Reactive univalent species can damage or destroy solvent exposed FeS clusters [4 , 38 , 39] . We found that the ΔsufT , and sodA::TN ( encoding for the dominant aerobic superoxide dismutase [64] ) strains displayed decreased growth in the presence of paraquat , a redox cycling molecule that leads to increased accumulation of intracellular ROS ( Fig 7A ) . However , the phenotype of the ΔsufT mutant was less severe than that of the sodA::TN strain . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured , challenged with paraquat , and AcnA activity was determined . Challenging cells with paraquat resulted in ~15% and ~45% decrease in AcnA activity in the parent and ΔsufT mutant , respectively ( Fig 7B ) . The alkylhydroperoxidase system ( Ahp ) functions as an intracellular H2O2 scavenger and a S . aureus strain lacking Ahp accumulates intracellular ROS [4 , 65] . AcnA activity was assessed in the WT , ΔsufT , ahp::TN , and ahp::TN ΔsufT strains . AcnA activity was decreased ~25–30% in both the ahp and sufT strains and by ~75% in the ahp sufT double mutant strain ( Fig 7C ) . Four explanations could underlie the decreased AcnA activity observed in a ΔsufT strain upon ROS toxification: 1 ) the ΔsufT strain has decreased activities of ROS scavenging enzymes , 2 ) SufT is necessary for the repair of FeS clusters inactivated by ROS oxidation , 3 ) SufT is involved in physically shielding and/or excluding ROS from the enzyme active site and preventing damage , or 4 ) there is an increased need for SufT in FeS cluster assembly . The activities of the ROS scavenging enzymes catalase ( Kat ) and superoxide dismutase ( Sod ) were similar in the WT and ΔsufT strains across growth ( Fig 7D , S4 Fig ) . The acnA::TN and acnA::TN ΔsufT strains containing pacnA also displayed similar levels of Sod activity , both before and after paraquat treatment ( S5 Fig ) . We examined whether SufT is capable of physically shielding FeS clusters from univalent oxidants [43 , 44] . Cell-free lysates from the acnA::TN and acnA::TN ΔsufT strains containing pacnA were exposed to varying concentrations of H2O2 and AcnA activity was determined one minute post treatment . AcnA activity decreased with increasing H2O2 concentrations , but the decrease in AcnA activity was similar in the parent and ΔsufT mutant ( Fig 7E ) . Brief exposure to H2O2 can convert the active [Fe4S4]2+ cluster in AcnA into the inactive [Fe3S4]1+ cluster . This can be repaired to the [Fe4S4]2+ state by Fe2+ and an electron [40] . Cell-free lysates from the acnA::TN and acnA::TN ΔsufT strains containing pacnA were exposed to H2O2 . One-minute post challenge , the stress was terminated and reactivation of AcnA activity by factors in the lysate was monitored over-time . The rate of AcnA reactivation was similar in the parent and ΔsufT mutant ( Fig 7F ) . From Fig 7 we concluded that SufT is involved in the de novo assembly of FeS clusters in cells experiencing ROS stress . The phenotypic abnormalities of the ΔsufT mutant were exacerbated during respiration , during resumption of respiration in fermenting cells , and upon ROS challenge ( i . e . conditions imposing a high demand for FeS assembly ) . The transcription of core genes required for FeS assembly increased upon challenge with ROS or resumption of respiration [4] . We tested the hypothesis that SufT is required for FeS cluster assembly during conditions imposing a high demand for FeS clusters . Growth was monitored in either 20AA glutamate medium , or defined medium containing glutamate as a carbon source and lacking leucine ( Leu ) and isoleucine ( Ile ) ( hereafter 18AA glutamate medium ) . Growth in 18AA glutamate medium would impose a simultaneous requirement for the AcnA , LeuCD , and IlvD enzymes , and by inference , exert an increased requirement for FeS clusters . The ΔsufT strain displayed a growth defect in 20AA glutamate medium ( similar to Fig 3A; however the magnitude appears lower here due to the scale ) and this defect was exacerbated upon culture in 18AA glutamate medium ( Fig 8A ) . The acnA::TN and acnA::TN ΔsufT strains containing pacnA were cultured in the presence or absence of varying concentrations of xylose followed by assessing AcnA activity . The difference in AcnA activity between the parent and ΔsufT mutant increased in synchrony with increasing inducer concentrations ( Fig 8B and 8C ) . We next monitored sufT transcriptional activity with respect to the demand for FeS clusters using the acnA::TN strain carrying pacnA , as well as the sufT transcriptional reporter . The transcriptional activity of sufT increased in synchrony with increasing inducer concentrations ( Fig 8D ) . Mycobacterium tuberculosis contains a DUF59 containing protein ( Rv1466 ) that is part of the suf operon and is essential for viability ( Fig 1C and [66] ) . We examined whether Rv1466 could compensate for the loss of SufT in S . aureus . Rv1466 has a ~20 amino acid N-terminal extension when compared to the S . aureus SufT . Codon-optimized rv1466 and a truncated version of rv1466 ( trunc_rv1466 ) were introduced upon a multi-copy plasmid into the S . aureus ΔsufT strain and phenotypes were examined . The presence of trunc_rv1466 , but not rv1466 , rescued the growth defect of the ΔsufT strain in 18AA glutamate medium ( Fig 9A ) . The presence of trunc_rv1466 , but not rv1466 , displayed a dominant effect and inhibited growth of the ΔsufT strain in 20AA glucose medium ( Fig 9B ) . Epistatic relationships between sufT , nfu , and sufA were investigated by phenotypically examining mutant strains lacking one , two , or all three maturation factors . The ΔsufA strain did not display a defect in AcnA activity , relative to the WT strain , and the ΔsufA ΔsufT double mutant phenocopied the ΔsufT strain ( Fig 10A ) . The phenotypic effects of the ΔsufA and Δnfu mutations displayed an additive effect . AcnA activity in the Δnfu mutant was ~65% of WT while the activity in the ΔsufA ΔsufT double mutant was ~50% . AcnA activity was near the limit of detection in the Δnfu ΔsufT double mutant ( ~2% ) . The Δnfu ΔsufT ΔsufA triple mutant had AcnA activity similar to the Δnfu ΔsufT strain . AcnA activity in the acnA::TN Δnfu ΔsufT strain containing pacnA was also nearly undetectable relative to its isogenic parental strains ( Fig 10B ) . This suggested that the low AcnA activity in the Δnfu ΔsufT strain was not solely the outcome of decreased acnA transcription . Growth was examined in media that impose varying demands for FeS proteins ( 20AA glucose , 20AA glutamate , or 18AA glutamate media ) . The ΔsufA strain did not display a growth deficiency in any of the media examined ( Fig 10C–10E ) . The ΔsufA ΔsufT double mutant phenocopied the ΔsufT strain in 20AA glucose and 20AA glutamate medium , but the effects of the mutations were additive in 18AA glutamate medium . The Δnfu ΔsufA double mutant phenocopied the Δnfu strain in 20AA glucose and 20AA glutamate media , but the effect of the mutations were additive in 18AA glutamate medium . The phenotypes of the Δnfu and ΔsufT mutations displayed synergism . The Δnfu ΔsufT double mutant displayed a severe growth defect in each media examined . The Δnfu ΔsufT ΔsufA triple mutant strain largely phenocopied the Δnfu ΔsufT strain in each media . The Δnfu ΔsufT double mutant also displayed severe growth defects in complex medium . Growth of S . aureus in tryptic soy broth ( TSB ) results in the consumption of glucose , the release of fermentative byproducts such as acetate , and acidification of the medium [67 , 68] followed by the uptake of the fermentative byproducts resulting in alkalization of the growth medium . Therefore , the pH and acetate profile of the spent medium correlates with the cells ability to uptake and utilize fermentation products [67 , 68 , 69] . We monitored optical densities , pH of the spent medium , and acetate concentrations in the spent medium over time in cultures of the WT , ΔacnA , Δnfu , ΔsufT , and Δnfu ΔsufT strains . The Δnfu ΔsufT double mutant and ΔacnA strains displayed pronounced differences during post-exponential growth reaching lower final optical densities ( S6A Fig ) . The pH of the medium from the Δnfu ΔsufT and ΔacnA mutants did not re-alkalinize ( S6B Fig ) nor was acetate utilized ( S6C Fig ) . The interactions amongst sufT , nfu , and sufA were further examined by introducing each gene upon a multi-copy plasmid ( psufT , pnfu and psufA , respectively ) and assessing whether they impart phenotypic suppression to the ΔsufT or Δnfu strains . The ΔsufA strain did not have decreased AcnA activity , and therefore , suppression was not examined in this strain . The presence of psufA appeared to increase AcnA activity mildly in both the WT and ΔsufT strains , but a statistically significant phenotypic rescue was not observed ( S7A Fig ) . AcnA activity decreased in the Δnfu strain carrying psufA . AcnA activity was increased in the ΔsufT strain carrying pnfu ( increase of ~250% ) , while the presence of pnfu had little effect on AcnA activity in the WT ( Fig 11A ) . The presence of psufT slightly decreased AcnA activity in the WT , while it did not alter AcnA activity in the Δnfu strain ( S7B Fig ) . Growth profiles of the WT and ΔsufT strains carrying empty vector or pnfu were examined in 20AA glutamate medium . The presence of pnfu partially mitigated the growth defect of the ΔsufT strain in 20AA glutamate medium ( S8 Fig ) . The phenotypes of the ΔsufT strain were mitigated during fermentative growth , which imposes a low demand for FeS clusters . We reasoned that Nfu is utilized to fulfill the demand for FeS cluster assembly in the ΔsufT strain during fermentative growth . After fermentative culture the acnA::TN Δnfu ΔsufT strain containing pacnA displayed levels of AcnA activity that were near the limit of detection ( ~2% ) , whereas the acnA::TN ΔsufT and acnA::TN Δnfu strains had AcnA activity similar to the parent ( Fig 11B ) . Microaerobic growth in 18 AA glucose medium was also examined . The Δnfu and ΔsufT strains displayed growth profiles that did not significantly deviate from that of the WT ( Fig 11C ) . However , the Δnfu ΔsufT double mutant displayed a large growth defect . From Figs 10 and 11 , S7 and S8 Figs , we concluded that 1 ) the phenotypic effects of the nfu and sufT mutations are synergistic , 2 ) overproduction of nfu partially alleviates the phenotypes of the ΔsufT strain , and 3 ) either Nfu or SufT is sufficient for AcnA maturation during fermentative growth . Biofilm formation and exoprotein production were assessed in strains lacking FeS cluster assembly factors . Agr is the dominant activator for transcription of exoproteins and toxins , as well as the phenol soluble modulins ( PSMs ) . Therefore , an Δagr strain was included as a positive control [51] . A strain lacking AcnA has been proposed to have increased Agr activity [70] . Since a Δnfu ΔsufT strain phenocopied the acnA::TN mutant , the acnA::TN strain was also examined . Exoproteins were extracted from the spent medium supernatant and analyzed using SDS-PAGE . S . aureus encodes for eight PSMs that are small peptides comprising ~60% of the total exoproteome and are visualized on SDS-PAGE as one band [51] . The Δnfu ΔsufT , Δnfu ΔsufT ΔsufA , and the Δagr strains were deficient in exoprotein production ( Fig 12A ) . For ease of comparative analyses , only the band corresponding to PSMs is displayed . Static growth of WT in TSB does not induce biofilm formation , and therefore , biofilm formation was examined in biofilm inducing medium ( Fig 12B and 12C , [71] ) . Biofilm formation was also assessed in strains lacking Agr and SigB , which negatively and positively influence biofilm formation , respectively [72 , 73] . Strains deficient in the maturation of FeS proteins displayed varying degrees of biofilm formation . The Δnfu ΔsufT double mutant displayed the largest increase in biofilm formation ( ~4 . 5 fold ) . The acnA::TN strain formed biofilms at a similar extent as the WT ( Fig 12B and 12C ) . We examined vancomycin sensitivities of strains lacking FeS cluster assembly factors . The Δnfu ΔsufT double mutant displayed a large increase in resistance towards vancomycin during growth ( Fig 13A ) . In growth inhibition curves we found that the Δnfu ΔsufT strain was not completely resistant towards vancomycin , but rather , it displayed an inhibition response more characteristic of vancomycin-intermediate resistant Staphylococcus aureus ( VISA ) ( S9 Fig and [74] ) . Vancomycin resistant strains display alterations in their cell walls resulting in increased resistance towards lysis by lysostaphin [55 , 74] . The Δnfu ΔsufT double mutant displayed the greatest resistance towards lysis by lysostaphin ( Fig 13B ) . Decreased activity of peptidoglycan hydrolases is a hallmark of VISA strains [55 , 74] . Peptidoglycan hydrolase activity was monitored using zymographic analysis upon heat-killed WT cells as a substrate . The Δnfu ΔsufT double mutant displayed the largest alterations in the activities of peptidoglycan hydrolases ( Fig 13C ) .
Staphylococcus aureus SufT is composed solely of a DUF59 domain . Alternate proteins containing DUF59 domains participate in FeS cluster assembly , but the function ( s ) of the DUF59 domain itself has not been described [46 , 47 , 48] . The goals of this study were to determine if SufT has a role in FeS cluster assembly , and if so , begin to dissect its in vivo functional role . Phylogenetic analyses found that sufT was recruited to the same chromosomal location as sufBC , and once recruited , it was largely retained . These findings suggested that sufT was recruited to the operon to refine the functionality of Suf-mediated FeS cluster assembly . Amongst the genomes analyzed , only five organisms encoded for SufT , but not the FeS cluster scaffolding proteins SufB , IscU , or NifU . The five organisms identified were lactobacilli and within these genomes the SufT homolog was located within apparent operons that encode known FeS cluster requiring proteins . The informatics and phylogenetic findings strongly suggested a role for SufT in FeS cluster assembly . The S . aureus ΔsufT strain displayed physiological abnormalities consistent with SufT having a role in the maturation of FeS proteins . Further , the phenotypes of the S . aureus ΔsufT strain closely resembled those of a strain lacking the FeS cluster carrier Nfu [4] . Aside from a role in de novo FeS cluster assembly , alternate possibilities for the observed deficiencies manifest in the ΔsufT strain were considered . The ΔsufT strain did not have altered H2O2 or superoxide scavenging activities . SufT was not required for the physical exclusion of H2O2 from the AcnA active site or the repair of the H2O2 damaged FeS cluster upon AcnA . These findings suggested that SufT likely functions in the de novo assembly of FeS clusters upon apo-proteins . Genes encoding for proteins with functional overlap often display synergistic ( superadditive ) phenotypic effects when the gene products are absent or non-functional [75] . The phenotypes associated with nfu and sufT were synergistic . This was most evident during fermentative growth where there is a lower demand for FeS clusters . The phenotypes of the Δnfu and ΔsufT strains were nearly indistinguishable from the WT strain , but the Δnfu ΔsufT double mutant displayed a large growth defect and exhibited AcnA activity near the limit of detection . Introduction of nfu in multicopy to the ΔsufT strain led to partial mitigation of the phenotypes of this strain . Taken together , these findings led to the conclusion that both SufT and Nfu function as non-essential , accessory factors in the maturation of FeS proteins . Lending further support to this conclusion , subsequent to our informatics analyses , the genome of Oligotropha carboxidovorans was sequenced and found to encode for a protein consisting of a fusion of the N-terminus of Nfu and SufT ( Locus tag: OCA5_c02770 ) . SufT , Nfu , and SufA are auxiliary FeS cluster maturation factors leading to the question of why S . aureus encodes for three such factors . The simplest explanations are that there is a degree of specificity for each auxiliary factor with respect to their target apo-proteins or that they have different functions . Vinella et al . have recently proposed an expanded model , which visualizes a dynamic cellular network of proteins that varies with growth stage or growth condition allowing for rapid calibration to alterations in the cellular demand for FeS protein maturation [76] . During such a scenario , certain auxiliary proteins and pathways would be preferred during normal growth and alternate auxiliary proteins and pathways during stress conditions . The findings presented herein are consistent with the model proposed by Vinella et al . [76] . During routine aerobic growth , Nfu was the dominant auxiliary factor required for the maturation of AcnA . However , upon the overproduction of AcnA , the need for SufT for AcnA maturation was increased . The cellular need for SufT was also increased when cells were resuming respiration , toxified with ROS , or grown in 18AA glutamate medium; three conditions that impose a high demand for de novo FeS cluster assembly . The transcriptional activity of sufT also increased as the cellular demand for FeS clusters increased . These findings lend strong support to a model wherein SufT is a dominant factor involved in the maturation of FeS proteins in cells experiencing a high demand for FeS clusters . The epistasis experiments further strengthen the idea that certain accessory proteins are preferentially utilized when confronted with a high demand for FeS clusters . SufA was dispensable for growth under all conditions tested . However , SufA dependent phenotypes were manifest in strains lacking either Nfu or SufT and simultaneously cultured upon a medium imposing a high demand for FeS proteins . Therefore , we propose that SufA facilitates FeS protein maturation in S . aureus under conditions imposing a very high demand for FeS clusters . It is tempting to speculate that cells encode for multiple accessory maturation factors to respond to a gradation of demand for FeS cluster assembly , however , this awaits further experimentation . It is currently unclear what genetic or biochemical elements dictate the increased usage of SufT or SufA upon increased FeS cofactor demand . Possible explanations include different functionalities , increased stability of a particular factor under stress conditions , or an increased rate of FeS cluster synthesis or FeS protein maturation under select cellular conditions . A similar scenario has been described to exist between the Suf and Isc FeS cluster biosynthetic machineries . In Escherichia coli , Suf is preferred under ROS stress and Fe limiting conditions , whereas Isc is the preferred FeS assembly system during conditions imposed by routine laboratory cultivation [77 , 78] . What is the role of SufT in FeS cluster assembly ? The genetic findings presented make it tempting to speculate that SufT functions in the carriage of FeS clusters , but further biochemical analyses will be necessary to make this conclusion . It also worth noting that the SufT homologues analyzed in Fig 1 contain only one strictly conserved cysteine residue . With the exception of monothiol glutaredoxins , described FeS cluster carriers contain two or more cysteines utilized in FeS cluster ligation [79] . Biofilm formation and exoprotein production are critical in the infectious lifecycle of S . aureus [49 , 50] . We previously found that a strain lacking Nfu is attenuated for virulence in models of infection [4] . In this report we found that a strain that was crippled in its ability to maturate FeS proteins displayed significantly increased biofilm formation and decreased exoprotein production . Vancomycin is a last resort drug in the treatment of CA-MRSA infections and the genetic and molecular mechanisms underlying resistance to vancomycin are an active area of research [54] . Strains defective in FeS protein maturation also displayed an intermediate resistance to vancomycin and multiple phenotypes associated with VISA strains . The Δnfu ΔsufT strain phenocopied a ΔacnA strain in growth experiments , but it did not phenocopy this strain in phenotypes involved in virulence . S . aureus encodes for the FeS cluster utilizing two-component regulatory system ( TCRS ) AirSR [5] . AirSR alters the transcription of genes encoding for peptidoglycan hydrolases , as well as those required for biofilm formation [5 , 80] . AirR directly binds to the promoter region of Agr [80] . AirSR is also implicated in vancomycin resistance and a strain lacking AirSR displays VISA like phenotypes [80] . Therefore , the accumulation of apo-AirSR in the Δnfu ΔsufT strain may underlie the virulence phenotypes witnessed . An alternate explanation is that the altered Agr activity in these strains results in altered virulence phenotypes . Apart from its roles in toxin production and biofilm formation , Agr has also been implicated in modulating vancomycin resistance in S . aureus [51 , 81 , 82] . Regardless of the mechanism ( s ) underlying the phenotypes presented , these findings highlight the importance of efficient FeS cluster assembly for multiple phenotypes critical for pathogenesis and antibiotic resistance . In summary , we have identified a role for SufT , and by extension DUF59 , in the maturation of FeS proteins . We propose a model wherein SufT is an auxiliary FeS protein maturation factor whose usage is selectively increased during growth conditions necessitating increased FeS cluster assembly in S . aureus . An increased demand for FeS clusters may have been an evolutionary driving force to recruit sufT to the suf operon thereby increasing the efficiency and control of de novo FeS cluster assembly .
Restriction enzymes , quick DNA ligase kit , deoxynucleoside triphosphates , and Phusion DNA polymerase were purchased from New England Biolabs ( Ipswich , MA ) . The plasmid mini-prep kit , gel extraction kit and RNA protect were purchased from Qiagen ( Hilden , Germany ) . Lysostaphin was purchased from Ambi products ( Lawrence , NY ) . Oligonucleotides were purchased from Integrated DNA Technologies ( Coralville , IA ) and sequences are listed in S1 Table ( oligonucleotides used in this study ) . Trizol ( Life Technologies ) , High-Capacity cDNA Reverse Transcription Kits ( Life Technologies ) , and DNase I ( Ambion ) was purchased from Thermo Fisher Scientific ( Waltham , MA ) . Tryptic Soy Broth ( TSB ) was purchased from MP Biomedicals ( Santa Ana , CA ) . An acetic acid quantification kit was purchased from R-BioPharma ( Darmstadt , Germany ) . Unless specified all chemicals were purchased from Sigma-Aldrich ( St . Louis , MO ) and were of the highest purity available . Unless otherwise stated , the S . aureus strains used in this study ( listed in Table 1 ) were constructed in the S . aureus community-associated USA300 strain LAC that was cured of the native plasmid pUSA03 , which confers erythromycin resistance [83] . The USA300 LAC genome differs from USA300_FPR3757 only by a few single nucleotide polymorphisms [84 , 85] . Unless specifically mentioned , S . aureus cells were cultured as follows: 1 ) aerobic growth at a flask/tube headspace to culture medium volume ratio ( hereafter HV ratio ) of 10; 2 ) anaerobic growth at a flask/tube headspace to culture medium volume ratio of 0 , as described earlier [4]; 3 ) in 96-well microtiter plates containing 200 μL total volume ( detailed procedure below ) . Liquid cultures were grown at 37°C with shaking at 200 rpm unless otherwise indicated . Difco BioTek agar was added ( 15 g L-1 ) for solid medium . When selecting for plasmids , antibiotics were added at the final following concentrations: 150 μg mL-1 ampicillin ( Amp ) ; 30 μg mL-1 chloramphenicol ( Cm ) ; 10 μg mL-1 erythromycin ( Erm ) ; 3 μg mL-1 tetracycline ( Tet ) ; 125 μg mL-1 kanamycin ( Kan ) ; 150 ng mL-1 anhydrotetracycline ( Atet ) . For routine plasmid maintenance , liquid media were supplemented with 10 μg mL-1 or 3 . 3 μg mL-1 of chloramphenicol or erythromycin , respectively . Escherichia coli DH5α was used as a cloning host for plasmid constructions . All clones were passaged through RN4220 and transductions were conducted using phage 80α [86] . All S . aureus mutant strains and plasmids were verified using PCR or by sequencing PCR products or plasmids . All DNA sequencing was performed by Genewiz ( South Plainfield , NJ ) . Unless otherwise stated , JMB1100 chromosomal DNA was used as a template for PCR reactions . To create the ΔsufT deletion strain ( JMB1146 ) , approximately 500 base pairs upstream and downstream of sufT gene ( SAUSA300_0875 ) were amplified using PCR with primer pairs 0875up5EcoRI and 0875up3NheI; 0875dwn5MluI and 0875 dwn3BamHI ( S1 Table ) . Amplicons were gel purified and fused using PCR and the 0875up5EcoRI and 0875 dwn3BamHI primers . The resulting amplicon was gel purified , and digested with BamHI and SalI , followed by a ligation into similarly digested pJB38 resulting in pJB38_ΔsufT . The plasmid pJB38_ΔsufT was isolated and subsequently transformed into RN4220 before transducing into JMB1100 . A single colony was inoculated into 5 mL of TSB-Cm and cultured overnight at 42°C followed by plating 25 μL on TSA-Cm to select for colonies containing a single recombination event . Single colonies were inoculated into 5 mL of TSB medium and were grown overnight , followed by a dilution of 1:25 , 000 before plating 100 μL onto TSA containing Atet to select against plasmid containing cells . Colonies were screened for Cm sensitivity and for the ΔsufT mutation using PCR . The sufT::tetM strain was created by digesting the pJB38_ sufTΔ with MluI and NheI and inserting the tetM gene between the upstream and downstream regions of sufT . The DNA encoding for Tet resistance ( tetM ) was amplified using PCR with Strain JMB1432 as a template and the G+tetnheI and G+tetmluI primers before digesting and ligating into similarly digested pJB38_ΔsufT . The resulting plasmid ( pJB38_ΔsufT::tetM ) was passaged though E . coli , before it was transformed into RN4220 . The ΔsufT::tetM mutant was constructed as described above . Plasmids for genetic complementation , transcriptional analyses , and insertion of epitope tags to allow protein detection by western blots were constructed by subcloning digested PCR products into similarly digested vectors or by using yeast homologous recombination cloning ( YRC ) as previously described [87 , 88] . The pLL39_sufT and pCM28_sufT plasmids were created using the 0875_5BamHI and the 0875_3SalI primer pair . The pCM11_sufT was created using the 875gfpKpnI and 875gfpHindIII primer pair . The pCM11_acnA was made using the AcnApHindIII and AcnApKpnI primer pair . The Mycobacterium tuberculosis rv1466 was codon optimized and synthesized by Integrated DNA technologies ( IDT; Coralville , IA ) and cloned into pCM28 using the native S . aureus sufT promoter using YRC . The full-length construct was constructed using amplicons generated using the following primer pairs: pCM28YCC and Ycc875p3; ycc875p5 and 875pMT3; 875pMT5 and 875pCM28 3 . The truncated version was created using the same primers except MT875trunk5 and MT875trunk3 replaced ycc875p5 and Ycc875p3 , respectively . Growth was assessed in 200 μL cultures grown at 37°C in 96-well plates using a BioTek 808E Visible absorption spectrophotometer . Culture optical density was monitored at 630 nm . The staphylococcal-defined medium has been described previously [4] . Strains cultured overnight in TSB were inoculated into minimal medium or TSB to a final optical density ( OD ) of 0 . 025 ( A600 ) units . For assessing nutritional requirements , cultures were harvested and treated as above , except that the cell pellet was washed twice to prevent carryover of rich medium components . For aerobic growth the shake speed was set to medium . For microaerobic growth the plate was incubated statically . The four growth medium formulations utilized for nutritional analyses were: 1 ) 20AA glucose medium , containing the 20 canonical amino acids and 14 mM glucose as a source of carbon; 2 ) 18AA glucose medium , containing 18 canonical amino acids and lacking leucine and isoleucine and 14 mM glucose as a source of carbon; 3 ) 20AA glutamate medium , containing the 20 canonical amino acids and 44 mM glutamate as a source of carbon , and 4 ) 18AA glutamate medium , containing 18 canonical amino acids and lacking leucine and isoleucine and 44 mM glutamate as a source of carbon . To examine vancomycin sensitivity , cultures were inoculated into TSB in the presence or absence of varying concentrations of vancomyin ( 0 . 025–1 . 5 μg/mL ) . Growth inhibition was assessed after 4 hours of growth . Paraquat sensitivity assays were conducted upon solid tryptic soy broth agar ( TSA ) plates containing 0 or 30 mM of paraquat . Overnight cultures ( ~18 hours of growth ) were serial diluted in 1X phosphate buffered saline and 10 μL of each dilution was placed on plates of the solid medium . The plates were incubated at 37°C for 15 hours before the growth was assessed . Strains cultured overnight in TSB-Erm medium were diluted into fresh TSB-Erm medium to a final OD of 0 . 1 ( A600 ) and cultured , with shaking , at a HV ratio of 10 . At periodic intervals culture density and fluorescence were assessed as described previously [4] . Fluorescence data were normalized with respect to a strain not carrying a GFP-based transcriptional reporter to normalize for background fluorescence values . The resulting data were normalized to the culture OD . Finally for ease of comparative analyses the data were normalized relative to the wild-type ( WT ) strain , or as specified in the figure legend . Anaerobic culture conditions were achieved as described earlier [4 , 89] . Cells were cultured to exponential growth , aerobically , as described above . The cultures were then split and one set of cells was cultured at a HV ratio of zero in capped microcentrifuge tubes and anaerobiosis was verified by the addition of 0 . 001% resazurin to control tubes [4 , 89] . mRNA abundances of genes were examined from a previously described cDNA library [4] . Strains were cultured overnight in TSB and cells were harvested by centrifugation . Cell pellets were washed twice with 1X phosphate buffered saline and resuspended in lysis buffer ( recipe above ) in the presence of 5 μg/mL of lysostaphin . The lysostaphin mediated decrease in optical densities ( A600 ) was recorded periodically . Protein concentration was determined using a copper/bicinchonic acid based colorimetric assay modified for a 96-well plate ( 47 ) . Bovine serum albumin ( 2 mg/mL ) was used as a standard . Western blot analyses were conducted as described previously [4 , 88] . Strains cultured overnight in TSB ( ~18 hours ) were diluted into fresh TSB to a final OD of 0 . 1 ( A600 ) . Periodically , aliquots of the cultures were removed , optical density was determined , and the cells and culture media were partitioned by centrifugation at 14 , 000 rpm for 1 minute . Two mL of either the culture supernatant or sterile TSB , which served to provide a pH reading for the point of inoculation , were combined with 8 mL of distilled and deionized water and the pH was determined using a Fisher Scientific Accumet AB15 pH mV Meter . The concentration of acetic acid in spent media was determined using the R-Biopharm Enzymatic BioAnalysis kit following the manufacturer's suggested protocol . Biofilm formation was examined as described elsewhere , with minor changes [71 , 97] . Briefly , overnight cultures were diluted into biofilm media ( TSB supplemented with 3% NaCl and 0 . 5% glucose ) , added to the wells of a 96-well microtiter plate and incubated statically at 37°C for 22 hours . Prior to harvesting the biofilms , the optical density ( A590 ) of the cultures was determined . The plate was subsequently washed with water , biofilms were heat fixed at 60°C , and the plates and contents were allowed to cool to room temperature . The biofilms were stained with 0 . 1% crystal violet , washed with water , destained with 33% acetic acid and the absorbance of the resulting solution was recorded at 570 nm and standardized to an acetic acid blank and subsequently to the optical density of the cells upon harvest . Finally the data were normalized with respect to the WT strain to obtain relative biofilm formation . Spent medium supernatants were obtained from overnight cultures , filter sterilized with a 0 . 22 μm ( pore-size ) syringe filters , and standardized to culture optical densities ( A600 ) . Zymographic analyses of bacteriolytic proteins were conducted using standard methods described elsewhere [98] and samples were separated upon a 12% SDS gel incorporated with 0 . 3% ( vol/vol ) heat killed USA300_LAC cells [98] . To determine exoprotein profiles , the spent media supernatant was concentrated using standard trichloroacetic acid precipitation . The resultant protein pellets were resuspended in laemelli buffer and equal volumes were separated upon a 12% SDS gel . The taxonomic distribution of Suf was determined via BLASTp analyses of publically available genome sequences in October of 2011 as part of a previous study [25] . This distribution of Suf was characterized using the KEGG gene viewer [99] , with manual verification using BLASTp or using sequence alignments . 1094 genomes out of a total of 1667 genome sequences ( 65 . 6% of total ) encoded for SufBC . Genomes that encoded for SufBC were then screened for the presence of SufT using BLASTp . sufT was considered to be associated with the suf operon if they were within four open reading frames from sufBC and appeared to be transcribed from a common promoter . SufT sequences were compiled and aligned with ClustalW specifying default parameters [100] . The aligned sequences were manually truncated to the minimal SufT sequence or positions 1 to 99 of SufT from Thermoplasma acidophilum ( Kegg ID: Ta0200 ) . Phyml was used to reconstruct the evolutionary history of the SufT alignment block specifying the Blosum62 substitution model and gamma distributed rate variation [101] . The topology of the tree was evaluated using Chi2-based likelihood ratio tests . The phylogenetic reconstruction was projected with the Interactive Tree Of Life ( Itol ) web program [102] . The N- and C-terminal sequences that were pruned from the alignment block were subjected to BLASTp against the Conserved Domain Database ( CDD ) using an evalue of 0 . 01 [103] . Identified motifs in both N- and C- terminal motifs were compartmentalized into modular structures based on the presence of unique sequence motifs . These N- and C-terminal motifs were mapped onto the SufT phylogenetic tree using the Itol program . Furthermore , SufT was mapped onto a concatenated SufBC phylogenetic tree using the Itol program . The concatenated SufBC tree was constructed as previously described [25] . | Iron-sulfur ( FeS ) clusters are inorganic cofactors that are used for diverse cellular processes including cellular respiration , DNA replication and repair , antibiotic resistance , and dinitrogen fixation . A failure to properly assemble FeS clusters in proteins results in widespread metabolic disorders , metabolic paralysis , and oftentimes cell death . Therefore , the biosynthesis of FeS clusters is essential for nearly all organisms . Proteins containing DUF59 domains are widespread in Eukarya , Bacteria , and Archaea . Proteins containing DUF59 domains have roles in FeS cluster assembly , but the function ( s ) of the DUF59 domain is unknown . Moreover , the function ( s ) of proteins containing DUF59 domains are largely unknown . Staphylococcus aureus SufT is composed solely of a DUF59 domain , which provides a unique opportunity to examine the role ( s ) of this domain in cellular physiology . In this report we show SufT to be an accessory factor utilized in FeS cluster assembly during conditions imposing a high-demand for FeS proteins . We also show that deficiencies in the maturation of FeS proteins result in alterations in the ability of S . aureus , an epidemic human pathogen , to form biofilms , produce exoproteins , and resist antibiotic stress . |
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A global increase in invasive infections due to group A Streptococcus ( S . pyogenes or GAS ) has been observed since the 1980s , associated with emergence of a clonal group of strains of the M1T1 serotype . Among other virulence attributes , the M1T1 clone secretes NAD+-glycohydrolase ( NADase ) . When GAS binds to epithelial cells in vitro , NADase is translocated into the cytosol in a process mediated by streptolysin O ( SLO ) , and expression of these two toxins is associated with enhanced GAS intracellular survival . Because SLO is required for NADase translocation , it has been difficult to distinguish pathogenic effects of NADase from those of SLO . To resolve the effects of the two proteins , we made use of anthrax toxin as an alternative means to deliver NADase to host cells , independently of SLO . We developed a novel method for purification of enzymatically active NADase fused to an amino-terminal fragment of anthrax toxin lethal factor ( LFn-NADase ) that exploits the avid , reversible binding of NADase to its endogenous inhibitor . LFn-NADase was translocated across a synthetic lipid bilayer in vitro in the presence of anthrax toxin protective antigen in a pH-dependent manner . Exposure of human oropharyngeal keratinocytes to LFn-NADase in the presence of protective antigen resulted in cytosolic delivery of NADase activity , inhibition of protein synthesis , and cell death , whereas a similar construct of an enzymatically inactive point mutant had no effect . Anthrax toxin-mediated delivery of NADase in an amount comparable to that observed during in vitro infection with live GAS rescued the defective intracellular survival of NADase-deficient GAS and increased the survival of SLO-deficient GAS . Confocal microscopy demonstrated that delivery of LFn-NADase prevented intracellular trafficking of NADase-deficient GAS to lysosomes . We conclude that NADase mediates cytotoxicity and promotes intracellular survival of GAS in host cells .
Since the 1980’s , there has been a sustained , worldwide increase in the incidence of severe , invasive infections due to group A Streptococcus ( Streptococcus pyogenes or GAS ) , particularly necrotizing fasciitis and streptococcal toxic shock syndrome [1–3] . The reasons for the emergence of invasive GAS disease are incompletely understood; however , a partial explanation may be the global dissemination of a clonal group of strains of the M1T1 serotype . The invasive M1T1 strains harbor bacteriophage-associated genes encoding such virulence factors as the pyrogenic exotoxin SpeA and the secreted DNase Sda1 ( also called SdaD2 ) , both of which have been associated with GAS pathogenicity in model systems . In addition , these strains secrete NAD+-glycohydrolase ( NADase ) , a property that generally was not present among M1 strains isolated prior to 1988 [4–6] . NADase is encoded by nga , which is located in an operon together with ifs , encoding an intracellular inhibitor that dissociates from NADase upon NADase secretion , and slo encoding the cholesterol-dependent cytolysin/hemolysin , streptolysin O ( SLO ) [4 , 7–9] . Genomic analyses of multiple M1 isolates from the past century indicate that the invasive M1T1 strain acquired a 36-kb chromosomal region that includes the nga and slo genes prior to emergence of this strain in the 1980s [10–12] . The association of NADase activity with contemporary invasive M1T1 isolates has suggested that production of the enzyme might contribute to virulence . Physical association of NADase with hemolytic activity in GAS culture supernatants led to early misidentification of NADase and SLO as a single protein , although subsequent studies clearly separated the two [13–15] . A new paradigm for the interaction of NADase and SLO was proposed by Madden et al . , who found that NADase could be translocated into the cytosol of epithelial cells in vitro after its secretion from GAS bound to the cell surface [16] . Translocation required the concomitant expression of SLO , which suggested a model in which NADase associates with SLO on the epithelial cell surface and is transferred across the cell membrane in a process dependent on SLO . These and subsequent studies provided evidence that SLO-mediated delivery of NADase augmented the cytotoxic effect of SLO and induced epithelial cell apoptosis [16 , 17] . NADase-deficient mutants were found to have reduced virulence in mice compared to wild type GAS , supporting a role of the enzyme in pathogenesis of invasive infection [18 , 19] . The exposure of human oropharyngeal keratinocytes to GAS that produce both SLO and NADase , but not to those producing SLO alone , results in depletion of intracellular NAD+ and ATP . This finding is consistent with the enzymatic function of NADase to hydrolyze cellular NAD+ to nicotinamide and adenosine diphosphoribose and , secondarily , to deplete cellular ATP [20] . In previously published work , we used isogenic mutants deficient in SLO or NADase to study the role of each toxin in enhancing intracellular survival of GAS . These studies revealed that NADase-deficient GAS are more efficiently killed after internalization by keratinocytes compared to SLO+NADase+ GAS [21] . The increased survival of NADase-producing strains is associated with failure of GAS-containing vacuoles to fuse with lysosomes to form an acidic , bactericidal compartment [21] . While these observations have suggested a role for NADase in GAS pathogenesis , prior studies have been limited in their capacity to distinguish effects of NADase from those of SLO , since SLO is itself a cytotoxin and is required to deliver NADase to host cells . The goal of the present investigation was to distinguish effects of NADase from those of SLO during interaction of GAS with human epithelial cells . To this end , we developed a system that delivers NADase to the cytosol of host cells independently of SLO . Utilizing the anthrax toxin platform to deliver enzymatically active or inactive forms of recombinant NADase to cells infected with various GAS strains , we have obtained direct evidence that the catalytic activity of NADase is a critical effector of GAS intracellular trafficking and intracellular survival . Anthrax toxin , the major virulence factor of Bacillus anthracis , is an A-B type toxin composed of the catalytic moieties , lethal factor ( LF ) and edema factor ( EF ) , and the receptor binding/pore forming protective antigen ( PA; MW 83 kDa ) . Upon release by the bacteria , PA83 binds to its cellular receptors and is cleaved by cell surface furin to a 63 kDa form ( PA63 ) , which then self-assembles to form a heptameric or octameric prepore [22–24] . The prepore binds the enzymatic LF and/or EF moieties to form complexes that are subsequently endocytosed [25 , 26] . The low pH of the endosome causes the PA prepore to undergo a conformational change into the pore form , which inserts into the endosomal membrane and translocates the catalytic LF and EF moieties into the cytoplasm [27–29] . The intrinsic activity of the anthrax toxin system for intracellular delivery of its catalytic components can be harnessed to translocate heterologous proteins into the cytosol of its target cells . Fusing the non-catalytic N-terminal PA-binding domain of LF ( LFn , residues 1 to 263 ) ) [30] to any of a variety of unrelated “cargo” proteins enables them to undergo PA-dependent translocation to the cytosol . Examples include a cytotoxic T lymphocyte epitope from Listeria monocytogenes , the gp120 portion of the HIV-1 envelope protein , and the activity domains of Pseudomonas exotoxin , diphtheria toxin , or shiga toxin [31–35] . In the current study , we fused LFn to NADase or its variants and utilized the anthrax toxin platform to deliver enzymatically active or inactive forms of the enzyme to human oropharyngeal keratinocytes independently of SLO . Results of in vitro infection experiments utilizing this system provide direct evidence that the enzymatic activity of NADase is a critical effector of GAS intracellular trafficking and survival .
Functional analysis of GAS NADase has been complicated by the necessity to co-express its endogenous inhibitor IFS ( Immunity Factor for Streptococcal NADase ) to prevent toxicity to the cell that produces the active enzyme [8 , 9] . IFS must be removed for NADase to be enzymatically active . Previously , expression and purification of NADase in E . coli was achieved by directing secretion of recombinant NADase to the periplasmic space , allowing IFS to remain in the cytosol [9 , 36] . In our hands , the yield was low with this approach , and a portion of IFS remained in the NADase-containing fraction , presumably due to incomplete exclusion of cytosolic proteins in the periplasmic preparation . In order to produce sufficient quantities of NADase free of IFS , we developed a novel scheme for expression and purification . Because initial experiments indicated low expression levels of NADase and its fusion constructs , the nga and ifs gene sequences were codon-optimized for expression in E . coli . We then exploited the high-affinity binding of IFS to NADase to purify native and variant forms of the enzyme and various fusion constructs using His6-tagged IFS ( S1 Fig ) . In the first step , we purified the NADase-IFS-His6 complex , which bound to a Ni-charged resin . His6-tagged IFS was then released from untagged NADase by denaturing the two proteins with guanidinium chloride . A second round of affinity chromatography was used to separate His6-tagged IFS , which was retained by the Ni column , from untagged NADase in the flow through fraction . NADase was then refolded slowly by removal of guanidinium chloride by dialysis . High protein purity was achieved by Q column purification of proteins after the first Ni column affinity purification , and then again after renaturation of IFS-free NADase constructs . Each of the purified recombinant proteins migrated predominantly as a single band of the expected molecular size on SDS-PAGE ( Fig 1A ) . In addition to native NADase , two variant forms were expressed and purified , both as individual proteins and as fusions to LFn . Variant 190NADase lacks the N-terminal 190 amino acids required for SLO-dependent translocation of NADase [37]; NADaseG330D harbors a point mutation that almost completely abrogates NAD-glycohydrolase activity [6 , 38 , 39] . Since the protocol involved protein denaturation and renaturation , we confirmed that the purified LFn-NADase , LFn-190NADase , and NADase proteins retained similar levels of NAD-glycohydrolase activity ( Fig 1B ) . The Kcat value for LFn-NADase was estimated at 4200 reactions/sec , which compares favorably with published estimates of 3700 and 8000 reactions/sec , determined for purified NADase using a highly sensitive HPLC-based assay [36 , 38] . LFn-NADaseG330D and NADaseG330D lacked detectable catalytic activity . However , both LFn-NADaseG330D and NADaseG330D were able to compete with NADase for binding of IFS after renaturation ( S2 Fig ) . We also analyzed the secondary structure of purified recombinant NADaseG330D by circular dichroism spectroscopy and found nearly identical results as those for purified recombinant ( and enzymatically active ) NADase ( S2 Fig ) . Together , these analyses provide evidence that renaturation of the enzymatically inactive variants LFn-NADaseG330D and NADaseG330D restored the native conformations of the purified proteins . We tested the ability of LFn-NADase and its variants to interact with and translocate across PA pores in planar bilayers in vitro as measured by ion conductance . Occlusion of pores in DPhPC bilayers was monitored for 60 sec following addition of each recombinant protein ( final concentration 1 μg/ml ) to the cis compartment . All of the constructs tested ( LFn , LFn-NADase , LFn-190NADase , LFn-NADaseG330D , and LFn-190NADaseG330D ) blocked conductance rapidly ( within 20 sec ) and almost completely ( Fig 2A ) . Subsequently , translocation was initiated by addition of KOH to the trans compartment to increase the pH to ~7 . 5 . Translocation of free LFn and LFn-190NADase , as measured by return of ion conductance , was rapid ( within ~80 sec ) and essentially complete ( ~80–90% ) ( Fig 2B ) . LFn-NADase took longer ( 240 sec ) to achieve comparable translocation . LFn-NADaseG330D and LFn-190NADaseG330D constructs were less efficiently translocated , with about ~60% translocation achieved in 240 sec . Interestingly , addition of IFS to the cis compartment ( final concentration 6 μg/ml ) before addition of KOH to the trans compartment prevented translocation of LFn-NADase ( Fig 2B ) . To test whether the binding of NADase to IFS prevents NADase unfolding , which is necessary for translocation , we used differential scanning fluorimetry to measure the melting temperature of NADase , IFS , and NADase-IFS complex . The melting temperatures were determined to be 43°C , 60°C and 76°C , respectively ( S3 Fig ) . Thus , the tight binding of IFS increases the Tm of NADase by more than 30°C , presumably preventing its unfolding , a required step for the translocation of LFn-NADase across PA pores . Having determined that LFn-NADase could be translocated through PA pores in an artificial membrane in vitro , we investigated whether PA pores could mediate delivery of LFn-NADase into human oropharyngeal keratinocytes . We reasoned that NAD+-glycohydrolase activity of LFn-NADase would deplete cellular energy stores resulting in inhibition of protein synthesis . Accordingly , NADase and its variants were tested for PA-mediated translocation into OKP7 cells by measuring inhibition of cellular protein synthesis . In the presence of PA , LFn-NADase and LFn-190NADase were efficiently translocated , with half-maximal inhibition of protein synthesis observed at a LFn-NADase concentration of ~1 nM in the cell culture medium ( Fig 2C ) . LFn-190NADase gave an almost identical result , a finding that implies the N-terminal domain of NADase involved in SLO-mediated translocation is dispensable for delivery of the enzyme by the anthrax toxin system . In the absence of PA , no LFn-NADase translocation was observed ( S4 Fig ) , and , as expected , NADase and 190NADase also did not translocate . Translocation of the enzymatically inactive forms of NADase , LFn-NADaseG330D and LFn-190NADaseG330D , did not inhibit cellular protein synthesis , even at concentrations up to 1 , 000 times that required for inhibition by LFn-NADase ( Fig 2C ) . A key determinant of translocation by PA is the phenylalanine clamp , a structure formed by the F427 side chains within the lumen of the PA pore [40] . We tested LFn-NADase and LFn-190NADase for cytotoxicity in the presence of PA F427H , a mutant form of PA , which forms pores that lack the ability to mediate translocation . The F427H mutation completely blocked LFn-NADase translocation ( Fig 2C ) , implying PA-mediated translocation is dependent on interaction with the Phe clamp and occurs through the central pore . It has been suggested that introduction of NADase into host cells exerts cytotoxic effects that are independent of NAD+-glycohydrolase activity of the protein [38] . The anthrax toxin delivery system enabled us to test this hypothesis in the absence of other GAS virulence factors . We found that exposure of OKP7 keratinocytes to LFn-NADase in the presence of PA resulted in rounding , pyknosis , and uptake of propidium iodide indicating loss of cell viability ( Fig 3 ) . Treatment with LFn-NADase resulted in 52% cell death as assessed by propidium iodide staining . In addition , treatment with LFn-NADase caused significant cell loss when compared to untreated cells , presumably due to cells becoming non-adherent upon loss of viability . In contrast , identical exposure to enzymatically inactive LFn-NADaseG330D in the presence of PA caused no cytotoxicity compared to untreated cells ( 1% cell death for each condition ) . These results provide direct evidence that the cytotoxic effects of NADase are due solely to its enzymatic activity . Previous studies on the effects of NADase on epithelial cells have utilized model systems in which cells are exposed to live GAS in vitro [17 , 21] . In order to compare effects of NADase delivered by the anthrax toxin system with those associated with exposure to live GAS , we measured intracellular NAD-glycohydrolase activity under both conditions . Our goal was to determine the concentration of LFn-NADase to be added to OKP7 cells so that the subsequent PA-mediated delivery would result in an intracellular NADase activity comparable to that achieved by exposure to live GAS in prior studies . We found that addition of LFn-NADase to a concentration of 10 nM achieved a level of NADase activity in the cytosol of the keratinocytes that corresponded to approximately 50% of that associated with infection by NADase-producing GAS strain 188 at an MOI of 10 ( Fig 4 , S5 Fig ) . Infection of OKP7 cells with GAS is associated with survival of 10 to 15% of intracellular bacteria , whereas fewer than 1% of GAS deficient in NADase activity survive intracellularly for 24 hours [21] . We reasoned that anthrax toxin-mediated delivery of exogenous NADase might rescue intracellular GAS that did not produce enzymatically active NADase . We found that addition of 10 nM LFn-NADase to OKP7 cells in the presence of 20 nM PA increased the intracellular survival of GAS strain 188 G330D , which expresses enzymatically inactive NADase , by 14-fold , from 0 . 35% at 24 hours to 5% ( Fig 5A ) . Thus , addition of exogenous NADase restored intracellular GAS survival to an extent roughly commensurate with the amount of NADase activity delivered to the cytosol of the host cell , i . e . , approximately 50% of the activity associated with infection of the cell by the parent strain , 188 . Addition of 1 nM LFn-NADase had a lesser and not statistically significant effect , increasing survival at 24 h of 188 G330D 2 . 5-fold to 0 . 9% . The ability of exogenously delivered NADase to restore intracellular GAS survival was dependent on the catalytic activity of the protein: addition of LFn-NADaseG330D had no effect on the 24-hour survival of GAS within OKP7 cells , even at 100 nM ( Fig 5B ) . The process of SLO-dependent translocation of NADase across the eukaryotic cell membrane requires a 190-aa domain in the amino terminus of the NADase protein , a part of the molecule that is dispensable for enzymatic activity [37] . As suggested by the protein synthesis inhibition assays ( Fig 2C ) , we found that LFn-190NADase could function in lieu of LFn-NADase to increase the intracellular survival of 188 G330D , albeit slightly less efficiently than LFn-NADase ( 7-fold increase in survival versus 14-fold for LFn-NADase , Fig 5C ) . These results imply that the catalytic domain of NADase plays a dominant role in the intracellular survival of GAS . However , the small but reproducible improvement in survival imparted by LFn-NADase compared to LFn-190NADase suggests that the N-terminal translocation domain has an as-yet-unidentified function in enhancing intracellular survival . The anthrax toxin delivery system allowed us to evaluate the contribution of NADase to GAS intracellular survival in the absence of SLO . Because SLO is ordinarily required to translocate NADase during GAS infection , it has not been possible previously to assess the role of NADase on GAS intracellular survival independently of SLO . To address the discrete contribution of each toxin , we added LFn-NADase and PA during infection of OKP7 cells with 188 SLO- and assessed the effect on intracellular survival ( Fig 5D ) . Delivery of LFn-NADase increased intracellular survival of 188 SLO- by ~8 fold , from 0 . 25% to 2 . 0% . Thus , delivery of NADase prolongs the survival of both 188 G330D and 188 SLO- strains , results that imply both SLO and NADase are required for maximum resistance to intracellular killing . The fact that SLO-independent delivery of NADase partially corrects the survival defect of an SLO-deficient strain indicates that the reduced survival of SLO- GAS is due in part to the absence of NADase delivery , but also that SLO possesses NADase-independent activities that contribute to the intracellular survival of GAS . Thus , the synergistic action of SLO and NADase mediates optimal intracellular survival . Previous studies have implicated SLO and NADase in GAS resistance to killing by epithelial cells . After internalization , SLO-deficient mutants are contained within endosomes or autophagosomes that fuse with lysosomes , an event associated with acidification of the GAS-containing vacuole and efficient bacterial killing [21 , 41] . The anthrax toxin system allowed us to assess directly the ability of NADase to interfere with fusion of the GAS-containing compartment with lysosomes . We found that delivery of exogenous LFn-NADase to the cytosol of GAS-infected OKP7 cells reduced the co-localization of 188 G330D with the lysosomal marker LAMP-1 ( Lysosomal–Associated Membrane Protein 1 ) by 4-fold , from 41% to 10% at 6 h of infection ( P<0 . 001 , Fig 6 ) . Delivery of LFn-NADase also inhibited trafficking of 188 SLO- to a LAMP-1-positive compartment , reducing co-localization with LAMP-1 from 86% to 51% ( P<0 . 05 ) . These findings correlate with the effects of LFn-NADase on the intracellular survival of 188 G330D and 188 SLO- and provide direct evidence that NADase contributes to GAS intracellular survival by interfering with lysosomal fusion to the GAS-containing vacuole . Because the effect of LFn-NADase on endosomal trafficking was evident within the first few hours of infection , it seemed likely that the survival of intracellular GAS was largely determined during this time period . We found that a delay of only 2 hours in the addition of LFn-NADase to cells infected with 188 G330D largely abrogated the 24-hour survival benefit of LFn-NADase compared with that conferred by addition of the toxin at the time of initial infection ( Fig 7 ) . This result is consistent with the finding that , in the absence of NADase expression , GAS are trafficked to a degradative compartment by lysosomal fusion as early as 1 hour after infection ( Fig 6 ) .
A role for NADase in the virulence of GAS was suggested by the association of NADase production with M1T1 GAS isolates from invasive infections , beginning in the 1980s . Subsequent studies by Caparon and coworkers established a compelling model for SLO-dependent translocation of NADase into host cells , and intoxication of the cells was shown by our group to result in depletion of cellular NAD+ and ATP [16 , 20] . Experiments with NADase-deficient mutants supported a role for NADase in synergistic cytotoxicity with SLO , in induction of apoptosis , and in enhancing intracellular survival of GAS internalized by epithelial cells [17 , 38] . However , these functions of NADase during GAS infection have been inferred almost entirely from comparisons with mutants that lacked NADase or produced an enzymatically defective protein . The requirement of SLO for translocation of NADase has made it difficult to analyze the biological effects of NADase separately from those of SLO , which is required for NADase delivery , but which also has intrinsic cytotoxicity due to its pore-forming activity . An additional level of experimental complexity arises from the tightly bound endogenous inhibitor of NADase , IFS , whose co-expression is required for NADase production , but which must be removed to restore enzymatic activity . In the current study , the anthrax toxin system provided a tractable platform to deliver enzymatically active , highly purified , IFS-free NADase or variant forms to the cytosol of human oropharyngeal keratinocytes . This system permitted direct investigation of the function of NADase in the cell biology of GAS infection , independent of the effects of SLO . We found that SLO-independent cytosolic delivery of LFn-NADase inhibited protein synthesis in oropharyngeal keratinocytes in a dose-dependent manner ( Fig 2C ) . Nearly identical inhibition was observed upon delivery of LFn-190NADase , which lacks the N-terminal domain of NADase required for SLO-mediated translocation , but preserves the catalytic domain . By contrast , LFn-NADase G330D , an enzymatically inactive variant , had no inhibitory effect , even at high doses . Consistent with these results , sufficient doses of NADase delivered by the anthrax toxin system resulted in cytotoxicity and cell death that was dependent on the catalytic activity of the protein ( Fig 3 ) . These results support the view that the intrinsic cytotoxic activity of NADase on eukaryotic cells depends on the enzymatic activity of the toxin . Depletion of cellular NAD+ and ATP is expected to have a broad range of inhibitory effects on cellular functions . It remains possible that the synergistic toxicity of NADase with SLO also involves a second , non-enzymatic mechanism , as suggested by Chandrasekaran et al , although the molecular basis for such an effect has not been determined [38] . Previous studies found that SLO was required for prolonged GAS intracellular survival in keratinocytes [21 , 41] . Shortly after bacterial internalization , GAS production of SLO results in damage to the endosomal membrane , which exposes the bacteria to the cytosol where they become ubiquitinated . Ubiquitin is a signal for targeting intra-cytosolic bacteria to autophagosome-like compartments [21 , 42] . Fusion of lysosomes with these compartments leads to their maturation into degradative autolysosomes and efficient bacterial killing . Autophagosomes containing NADase-deficient GAS appear to follow this pathway; however , the step of lysosomal fusion is impaired for autophagosomes containing NADase-producing GAS , and this impairment is associated with enhanced intracellular survival [21] . The anthrax toxin system allowed us to study directly whether NADase prevents lysosomal fusion with GAS-containing vacuoles in infected cells . We found that cytosolic delivery of NADase inhibited the co-localization of GAS 188 G330D ( expressing an enzymatically inactive NADase ) with the lysosomal marker LAMP-1 ( Fig 6 ) . Inhibition of lysosomal fusion was associated with a 14-fold increase in intracellular survival to a level approaching that of the NADase-producing parent strain . Delivery of enzymatically inactive NADase G330D had no effect on GAS intracellular survival , supporting the essential role of enzymatic activity in enhancing intracellular survival . In similar experiments , we tested the effect of NADase delivery on the intracellular survival of the SLO-deficient GAS strain 188 SLO- . Supplying exogenous NADase partially rescued survival of 188 SLO- , a result that implies that SLO contributes to GAS intracellular survival in part through delivery of NADase , but also through function ( s ) , such as pore-formation , independent of NADase translocation . These data are consistent with the observation that a GAS strain producing a non-pore-forming SLO that is competent for NADase translocation ( SLO Y2552A ) was defective for intracellular survival [21] . Results of these experiments provide the most direct evidence to date on the contribution of NADase to the cell biology of GAS infection . Use of the anthrax toxin delivery system isolated the effects of NADase from those of SLO and defined an unambiguous role for NADase in cytotoxicity for host epithelial cells and in enhancing GAS intracellular survival . Both functions were dependent on cytosolic delivery of NADase and on the enzymatic activity of the toxin to degrade NAD+ . Together , these findings provide a plausible molecular basis for the association of NADase expression with GAS virulence .
The OKP7/bmi1/TERT ( OKP7 ) keratinocytes used in this study are immortalized normal human soft palate keratinocytes [43 , 44] . These cells were a gift of James Rheinwald and were provided through the Harvard Skin Disease Research Center . OKP7 cells were cultured in keratinocyte serum-free medium ( KSFM , Gibco/Invitrogen ) as described previously [41] . GAS strain 188 and its mutant derivatives were used in this study . GAS strain 188 is an isogenic unencapsulated mutant of the M type 3 necrotizing fasciitis isolate 950771 [45] . Use of an unencapsulated mutant allowed efficient internalization of GAS by human cells in vitro because the hyaluronic acid capsule inhibits GAS internalization . Escherichia coli XL1-Blue was used as a host for molecular cloning ( NEB ) and was grown in Luria-Bertani ( LB ) medium ( Novagen ) . GAS was grown in L3 medium as described with two modifications: the final CaCl2 concentration was 0 . 015% and type 1-S bovine hyaluronidase was omitted [46] . Generation of LFn-NADase-IFS constructs . The LFn-NADase-IFS-encoding construct was created by first PCR-amplifying separately the LFn-encoding sequence [47] and the nga-ifs genes from GAS genomic DNA . These amplicons were then used as templates for overlap PCR to generate the LFn-NADase-IFS-encoding DNA fragment , incorporating a BamHI restriction site inserted between the LFn-encoding sequence and nga-ifs . This product was subsequently cloned into pET43 . 1a vector ( Invitrogen , Grand Island , NY ) between the NdeI/XhoI restriction sites such that the in-frame fusion construct generated a His6-tag at the C-terminus of IFS . Protein expression from the LFn-NADase-IFS-encoding construct , named MRW001 , was insufficient for downstream studies . To improve expression , a DNA fragment encoding NADase G330D-IFS ( enzymatically inactive NADase and IFS ) was codon-optimized for expression in E . coli and synthesized by GENEWIZ , Inc ( South Plainfield , NJ 07080 ) . Codon-optimized nga-ifs was generated by OuikChange site-directed mutagenesis ( Agilent Technologies ) of the codon-optimized NADase G330D-IFS-encoding construct . These two constructs served as templates for PCR to generate DNA constructs encoding NADase , NADase G330D , 190NADase ( aa 190–451 ) , and 190NADase G330D using appropriate primers . Each of these PCR products was cloned between the BamHI/XhoI restriction sites in MRW001 , in place of nga-ifs ( S1 Fig ) . Generation of NADase constructs . Codon-optimized DNA fragments encoding NADase , NADase G330D , and 190NADase were amplified by PCR using appropriate primers and cloned into the NdeI/XhoI restriction sites of pET43 . 1a to incorporate a C-terminal His6-tag on the IFS protein . Generation of IFS and LFn constructs . In the first step , DNA fragments encoding an N-terminally His6-tagged Sumo protein , codon-optimized IFS , and LFn were amplified by PCR in separate reactions [47 , 48] . These PCR products served as template for overlap PCR to generate the His6-Sumo-IFS and His6-Sumo-LFn-encoding constructs , which were subsequently cloned into the NdeI/XhoI restriction sites in the pET43 . 1a vector . Recombinant proteins used in this study are described in Table 1 . LFn-NADase , LFn-190NADase , NADase and their variants were expressed in BL21 ( DE3 ) cells ( Invitrogen ) using IPTG induction . Proteins were initially purified using Ni-charged metal affinity chromatography . Each partially purified protein preparation was loaded onto a High Performance Q column ( GE ) in buffer A ( 20 mM Tris , pH 7 . 5 ) , washed with buffer A , and eluted with a gradient of 0 to 1 M NaCl in the same buffer . The proteins were then denatured in 6 M guanidinium chloride , pH 8 . 0 , and the His-tagged IFS was removed from untagged LFn-NADase proteins by Ni-charged metal affinity chromatography . The IFS-free proteins were renatured by dialysis into buffer A containing 350 mM NaCl and 5 mM DTT . The renatured proteins were subsequently dialyzed in buffer A containing 5 mM DTT . Finally , the proteins were subjected to another round of Q column purification . Protein solutions were filter sterilized and stored at -80°C . LFn and IFS fused with N-terminally His6-tagged Sumo protein were overexpressed using IPTG in BL21 ( DE3 ) cells ( Invitrogen ) . The proteins were initially purified using Ni-charged metal affinity chromatography . Sumo was removed by cleavage with Sumo protease , and the reaction was monitored by SDS-PAGE . N-terminally His6-tagged Sumo and Sumo protease were removed from the now untagged protein of interest using Ni-charged metal affinity chromatography . DTT ( 5 mM ) was added to the final protein eluate for NADase constructs . Recombinant wild type PA and PA F427H were overexpressed in the periplasm of E . coli BL21 ( DE3 ) , purified by anion-exchange chromatography , and converted to the prepore form of PA using a protocol published elsewhere [50] . NADase and NADaseG330D were dialyzed against 10 mM sodium phosphate , 0 . 5 mM DTT , pH 8 . 0 , and introduced at a concentration of 3 . 55 μM ( determined by A280 measurements ) into a stoppered 0 . 1 cm quartz cuvette . Equal concentration of the two proteins was confirmed by SDS-PAGE and Coomassie staining . CD spectra were measured in a JASCO J-815 Spectropolarimeter at 20°C from 185–260 nm in 0 . 5 nm steps with a 1 nm bandwidth . Five scans were averaged and smoothed , a background buffer-only spectrum was subtracted , and the data for the two protein species were plotted and overlayed to assess similarity . Differential scanning fluorimetry was used to calculate the melting temperature of NADase , IFS , or NADase-IFS complex . A 10 μM solution of each protein was prepared in PBS containing 5X SYPRO Orange ( Sigma ) , and the solution was dispensed in wells of a 96-well PCR plate . The plate was subjected to a temperature scan from 10 to 93°C at a rate of 1°C min−1 in an ABI Prism 7300 real time PCR instrument ( Applied Biosystems/Invitrogen ) using an excitation wavelength of 492 nm; fluorescence emission was recorded at 610 nm . Fluorescence emission of SYPRO Orange in aqueous solution increases upon binding to hydrophobic regions of proteins exposed by temperature-induced protein unfolding . The peak of the curve of the first derivative of the measured fluorescence intensity , plotted as a function of temperature , represents the melting temperature of the protein . NADase activity of the recombinant proteins was determined as described ( Bricker et al . , 2002 ) . Briefly , two-fold serial dilutions of NADase , LFn-NADase , or LFn-190NADase were incubated with 0 . 67 mM NAD+ for a period of 1 h at 37°C . The reaction was then terminated by the addition of 2 M NaOH and the fluorescence of uncleaved NAD+ was allowed to develop for 1 h , at which point the plates were read in a fluorimeter with excitation/emission wavelengths of 355nm/560nm . Samples without NADase served as controls . The results were expressed as fraction of total NAD+ that was cleaved at a given NADase concentration . Thirty-five nM NADase was added to 17 . 5 nM , 35 nM and 70 nM of LFn-NADase G330D , LFn-190NADase G330D and NADase G330D in a 96-well plate . Seventy nM IFS , sufficient to completely inhibit enzymatic activity of 35 nM NADase , was then added to the wells . To this mixture , 0 . 67 mM NAD+ was added and the reaction incubated for a period of 1 h at 37°C . The reaction was then terminated by the addition of 2 M NaOH and the fluorescence of uncleaved NAD+ was allowed to develop for 1 h at which point the plates were read in a fluorimeter with excitation/emission wavelengths of 355nm/560nm . Samples without NADase served as controls . The results were expressed as percentage inhibition of NADase activity . Complete cleavage of NAD was labeled as 0% inhibition of NADase activity and no cleavage was labeled as 100% inhibition of NADase activity . OKP7 cells were grown in 6-well dishes at 37°C in 5% CO2 to approximately 70% confluence ( ~2x105 cells/well ) . Cells were washed and incubated in KSFM containing GAS at a multiplicity of infection ( MOI ) of 10 unless otherwise indicated or supplemented with 20 nM PA and LFn-NADase at 10−8 , 10−9 , or 10−10 M for 2 h . A control lacking PA protein was also included . Fifteen min prior to harvesting cells , clindamycin ( 10 μg/ml ) was added to prevent NADase production by GAS during sample processing . For intracellular NADase measurements , cells were washed , trypsinized , and permeabilized by incubation in PBS containing saponin ( 0 . 005% w/v ) and protease inhibitors for 20 min at 37°C . Cells were removed by centrifugation for 2 min at maximum speed on a bench-top centrifuge and the supernatant containing cytosolic material was passed through a 0 . 2 μm filter . This filtrate , the cytosolic fraction , was kept on ice until NADase measurement . NADase activity was determined as previously described [17] . Experiments were performed three times . Intracellular activity was represented as the percentage NAD+ substrate depletion . Planar phospholipid bilayer experiments were performed in a Warner Instruments Planar Lipid Bilayer Workstation ( BC 525D , Hamden , CT ) . Planar bilayers were formed by painting a 35 mM solution of 1 , 2-diphytanoyl-sn-glycerol-3-phosphocholine ( DPhPC ) in n-decane ( Avanti Polar Lipids , Alabaster , AL ) on a 200 μm aperture of a Delrin cup in a Lucite chamber . The aperture separated two compartments , each containing one ml of 100 mM KCl , 1 mM ethylenediaminetetraacetic acid ( EDTA ) , and 10 mM each of sodium oxalate , potassium phosphate , and 2- ( N-morpholino ) ethanesulfonic acid ( MES ) , pH 5 . 5 . Both compartments were stirred continuously . Upon formation of a bilayer membrane , up to 5 μg PA prepore ( 25 pM ) was added to the cis compartment in the presence of a constant voltage of +20 mV with respect to the trans compartment . After incorporation of PA pores as monitored by conductance across the membrane , the cis compartment was perfused to remove any free PA . Once the current had stabilized , 1 μg of LFn-NADase or a variant was added to the cis compartment , and interaction with PA channels was monitored by the decrease in conductance . After occlusion of PA pores had reached a steady state , excess LFn-NADase was removed by perfusion of the cis chamber . KOH was then added to the trans compartment to raise the pH of the buffer to 7 . 5 . An increase in conductance indicated that the pH gradient between the cis and trans compartment had triggered the translocation of LFn-NADase across the PA pore into the trans compartment . OKP7 cells were plated in a 96-well plate at a density of 104 cells/well approximately 40 h prior to the protein synthesis inhibition assay . PA ( 20 nM ) and LFn-NADase diluted in KSFM were added to the plates . The plates were then incubated at 37°C for 24 h , after which toxin-containing medium was removed and was replaced with L-Leucine-deficient F-12 medium supplemented with L-[4 , 5-3H] Leucine ( Perkin Elmer ) . The plates were incubated for 1 h at 37°C . Next , the plates were washed with ice-cold PBS , liquid scintillation cocktail was added , and incorporation of radioactivity in the cells was measured in a scintillation counter . Results were normalized and expressed as a fraction of the radioactivity incorporated in OKP7 cells that were not treated with toxin . OKP7 cells were infected at an MOI of 10 with GAS that had been grown to exponential phase ( A600nm~0 . 25 ) and washed twice in KSFM . When appropriate , PA ( 20 nM ) and LFn-NADase ( 1 nM or 0 . 1 nM ) were added to the cells at the time of infection . Infected cell monolayers were treated with 20 μg/ml penicillin G and 200 μg/ml gentamicin for 45 minutes beginning 1 h 15 min post-infection . At 2 h post-infection , viable intracellular bacteria were quantified as described previously [51] . To determine intracellular survival at later time points , infected monolayers were washed at 2 h post-infection and fresh medium containing penicillin G ( 1 μg/ml ) , but not PA or LFn-NADase , was added . Infected monolayers were incubated for 4 h or 24 h post-infection , at which times the total intracellular CFU were determined as above . OKP7 cells were cultured on coverslips in 24-well plates . Cells were infected with GAS as described above except that antibiotics were omitted to prevent cellular uptake of non-viable bacteria . Instead , extracellular bacteria were removed by extensively washing the cells with PBS at 2 h post infection , after which the cells were incubated in fresh KSFM . Infected cells were processed 1 h , 3 h , or 6 h post-infection . At each of these time points , monolayers were washed three times with PBS and extracellular GAS were stained with Alexa Fluor 660-conjugated anti-GAS IgG at 4°C for 15 min in the dark . Excess unbound antibody was removed by washing with PBS . Subsequently , cells were fixed and permeabilized by incubation in ice-cold methanol at −20°C for 5 min . Cells were then washed three times with PBS and incubated at room temperature for 1 h with mouse anti-LAMP-1 IgG . After three washes in PBS , cells were incubated for 1 h with goat anti-mouse Alexa Fluor 568-conjugated IgG and with Alexa Fluor 488-conjugated anti-GAS IgG at room temperature in the dark for 1 h . Slides were mounted using Prolong Gold ( Molecular Probes ) and stored at room temperature in the dark for 16–24 h prior to imaging . Confocal microscopy was performed at the Harvard Digestive Diseases Center core facility as previously described [51] . Images were acquired and analyzed using Slidebook 5 and Slidebook 6 ( Intelligent Imaging Innovations , Denver , CO ) . For quantification , co-localization of intracellular bacteria with LAMP-1 marker was determined from three independent experiments . Images were evaluated by an observer who was blind to the experimental conditions . At least 100 intracellular bacteria were scored for each experiment . OKP7 cells were cultured on coverslips in 24-well plates and grown to 40–50% confluence . Cells were then incubated with medium containing 20 nM PA and either 100 nM LFn-NADase or 100 nM LFn-NADase G330D for a period of 48 h . Cells that were not treated with any toxin served as a negative control . Cells were then washed with PBS and incubated with 500 μl of PBS containing 1 μg/ml propidium iodide for a period of 30 min at room temperature in the dark . Cells were then visualized under a Nikon Eclipse TS100 fluorescence microscope with a standard TRITC filter set ( Ex 535/50 , Em 610/75 , DM 565 ) and images were acquired . For easy visualization , images showing dead cells stained with propidium iodide were colored red and merged with bright-field images showing the total number of cells ( ImageJ software ) . Significance of differences between experimental groups was assessed by Student’s t-test . P values of less than 0 . 05 were considered statistically significant . | Invasive infections due to group A Streptococcus ( S . pyogenes or GAS ) have become more frequent since the 1980s due , in part , to the emergence and global spread of closely related strains of the M1T1 serotype . A feature of this clonal group is the production of a secreted enzyme , NAD+-glycohydrolase ( NADase ) , which has been suggested to contribute to GAS virulence by intoxication of host cells . For NADase to exert its toxic effects , it must be translocated into the host cell by a second GAS protein , streptolysin O ( SLO ) . SLO is a pore-forming toxin that damages cell membranes in addition to its role in translocating NADase . In order to distinguish effects of NADase on host cell biology from those of SLO , we used components of anthrax toxin to deliver NADase to human throat epithelial cells , independently of SLO . Introduction of NADase into GAS-infected cells increased the intracellular survival of GAS lacking NADase or SLO , and the increase in bacterial survival correlated with inhibition of intracellular trafficking of GAS to lysosomes that mediate bacterial killing . The results support an important role for NADase in enhancing GAS survival in human epithelial cells , a phenomenon that may contribute to GAS colonization and disease . |
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Circadian rhythm is fundamental in regulating a wide range of cellular , metabolic , physiological , and behavioral activities in mammals . Although a small number of key circadian genes have been identified through extensive molecular and genetic studies in the past , the existence of other key circadian genes and how they drive the genomewide circadian oscillation of gene expression in different tissues still remains unknown . Here we try to address these questions by integrating all available circadian microarray data in mammals . We identified 41 common circadian genes that showed circadian oscillation in a wide range of mouse tissues with a remarkable consistency of circadian phases across tissues . Comparisons across mouse , rat , rhesus macaque , and human showed that the circadian phases of known key circadian genes were delayed for 4–5 hours in rat compared to mouse and 8–12 hours in macaque and human compared to mouse . A systematic gene regulatory network for the mouse circadian rhythm was constructed after incorporating promoter analysis and transcription factor knockout or mutant microarray data . We observed the significant association of cis-regulatory elements: EBOX , DBOX , RRE , and HSE with the different phases of circadian oscillating genes . The analysis of the network structure revealed the paths through which light , food , and heat can entrain the circadian clock and identified that NR3C1 and FKBP/HSP90 complexes are central to the control of circadian genes through diverse environmental signals . Our study improves our understanding of the structure , design principle , and evolution of gene regulatory networks involved in the mammalian circadian rhythm .
Circadian rhythm is a daily time-keeping mechanism fundamental to a wide range of species . The basic molecular mechanism of circadian rhythm has been studied extensively . It has been shown that the negative transcriptional–translational feedback loops formed by a set of key circadian genes are responsible for giving rise to the circadian physiology . In mammals , the master clock resides in the suprachiasmatic nucleus ( SCN ) and the SCN orchestrates the circadian clocks in peripheral tissues by directing the secretion of hormones such as glucocorticoids . Through many years of molecular and genetic studies , at least 19 key circadian genes—Per family ( Per1/Per2/Per3 ) , Cry family ( Cry1/Cry2 ) , Bmal1 ( Arntl ) , Clock , Npas2 , Dec1/Dec2 ( Bhlhb2/Bhlhb3 ) , Rev-erbα/β ( Nr1d1/Nr1d2 ) , Rora/Rorb/Rorc , Dbp/Tef/Hlf , and E4bp4 ( Nfil3 ) —have been identified in mammals [1] . As is now commonly accepted , Arntl and Clock proteins form a complex that positively regulates the transcription of Per and Cry family genes through activating the cis-regulatory element E-box in their promoters . Per and Cry family proteins form a complex that inhibits Arntl/Clock transcriptional activity , thus completing the negative feedback loop . Other key circadian genes such as Dbp and Nfil3 controlling the D-box element and Rora/Rorb/Rorc and Nr1d1/Nr1d2 controlling the RRE ( Rev-erb/Ror element ) have also been shown to be important to the mammalian circadian rhythm . Since 2002 , there have been a series of microarray experiments aimed at identifying circadian oscillating genes at the genome-wide level in various tissues of mammalian species , including mouse , rat , rhesus macaque , and human ( Table S1 ) . These experiments usually identified hundreds of circadian oscillating genes , suggesting that the circadian rhythm drives a genomewide circadian oscillation of gene expression . However , microarray data are intrinsically noisy , and further , these microarray experiments differed in the animals that they used , experimental conditions , and sampling times , etc . Indeed , these microarray experiments have so far not been compared or integrated . In a few cases where two tissues were studied in a single experiment , the overlap of circadian oscillating genes between tissues was very limited [2] , [3] . Assuming that a set of common circadian genes exists in most tissues and cell types , integration of different circadian microarray datasets in multiple tissues could potentially identify such a common set of circadian genes [4] . Comparison of circadian oscillating genes and their oscillating patterns across different tissues can help us understand the tissue-specific functions of circadian rhythm . Comparison across different mammalian species can also shed light on the molecular mechanisms that lead to their different physiologies and behaviors . Because many known key circadian genes such as Arntl/Clock , Nr1d1/Nr1d2 , and Dbp/Nfil3 are transcription factors , transcriptional regulation must have played an important role in the genome-wide circadian oscillation of gene expression . Ueda et al . constructed a small-scale gene regulatory network consisting of 16 genes and 3 cis-regulatory elements based on in vitro luciferase reporter assays [5] . However , the construction of a circadian gene regulatory network at the system level based on promoter analysis alone has been almost impossible due to the difficulties in transcription factor binding site prediction [6] . The existence of other cis-regulatory elements associated with circadian oscillation has remained elusive . On the other hand , there are a large body of microarray experiments from transcription factor knockout or mutant animals currently available at public databases . Incorporating the knockout or mutant microarray experiment results with the promoter sequence analysis can greatly facilitate the identification of functional transcription factor binding sites . In general , construction and analysis of gene regulatory networks involved in the mammalian circadian rhythm will improve our understanding on how key circadian genes are driving circadian-controlled genes , and will pave the way for more detailed quantitative modeling of the mammalian circadian rhythm .
We searched for circadian oscillating genes in 21 circadian time series microarray data covering 14 tissues in mouse ( Table S1 ) by fitting them to cosine functions with different phases , and extracted circadian phase information for circadian oscillating genes . We identified 9 , 995 known genes showing circadian oscillations in at least one tissue ( Table S2 ) . The number of genes showing circadian oscillation in multiple tissues decreases rapidly as the number of tissues increases , whereas the consistency of their circadian phases across tissues as measured in p-values of circular range tests improves rapidly ( Figure 1 ) . We identified 41 common circadian genes , defined as the genes showing circadian oscillation in at least 8 out of 14 tissues in mouse ( Table 1 ) . 13 out of 19 previously known key circadian genes were among the common circadian genes that we identified in this study . Other known key circadian genes: Rorb , Cry2 , Rora , Npas2 , and Hlf were found to be circadian oscillating in one , three , three , four , and five tissues , respectively . Bhlhb3 was not found to be circadian oscillating in any tissue . 39 of these common circadian genes showed significant consistency ( p<1/3 in circular range test ) of their circadian phases across all tissues . We surveyed tissue-specific gene expression profiles in a mouse tissue gene expression atlas [7] for the circadian oscillating genes in different tissues . To cross-validate the circadian phase data with the tissue gene expression data , we created a binary matrix of 1 or 0 to denote the presence or absence of circadian oscillations in 14 tissues in circadian phase data and compared it to the gene expression matrix in 61 tissues from the tissue gene expression atlas . For each pair of tissues from the two matrices , we calculated a correlation coefficient . The circadian data in liver , kidney , skeletal muscle , adrenal gland , and white adipose tissue correctly correlated best with their corresponding tissues in the tissue gene expression atlas , whereas SCN correlated equally well with preoptic and hypothalamus , and brown adipose tissue correlated equally well with adipose tissue and brown fat . These results reflected the fact that sufficiently high gene expression levels are the prerequisite to be detected as circadian oscillating in our collection of microarray datasets . To investigate if the differences in the circadian phases of circadian oscillating genes across tissues are caused by the differences in their gene expression levels , we calculated the variances of circadian phases and the variances of gene expression for circadian oscillating genes across the seven tissues common to our circadian datasets and the tissue gene expression atlas . There is no significant correlation ( r = 0 . 01 , p = 0 . 71 ) between these two variances . For example , the gene expression level of Per2 is 27 times higher in adrenal gland than in skeletal muscle , but this has no effect on the consistency of circadian phases of Per2 between the two tissues . In fact , the common circadian genes have significantly higher variances of gene expression across the 61 tissues than those from the same number of randomly selected genes . We observed that the correlation coefficients rij between the tissue gene expression data of the common circadian gene pairs ( i , j ) negatively correlated with their circadian phase differences ( r = −0 . 22 , p<10−8 ) . The gene pairs positively correlated in their tissue gene expression patterns had a significantly lower circadian phase difference than expected by random , whereas the gene pairs negatively correlated in their tissue gene expression patterns had a significantly larger circadian phase difference than expected by random ( Figure S1 ) . Therefore , the common circadian genes with similar gene expression patterns across tissues also tend to have similar circadian phases . The circadian gene regulation may share a similar mechanism that gives rise to tissue-specific gene expression . We clustered the 21 circadian phase datasets using hierarchical clustering . The datasets from the same tissue or biologically closely related tissues were clustered together , suggesting that the differences in circadian phases between tissues resulted from their biological differences ( Figure 2 ) . To ensure that these differences between tissues were also reproducible between experiments , we used circular ANOVA to identify the circadian oscillating genes shared between two tissues but associated with significantly different circadian phases between these tissues . There were 12 circadian oscillating genes shared between two SCN datasets and at least two liver datasets . Among them , Per1 , Per2 , Nr1d2 , and Avpr1a showed a significant ( p<0 . 01 ) advance of about 6 hours in their circadian phases in SCN datasets compared to liver datasets , whereas Dnajb1 , Hmgb3 , Hsp110 , and Pdcd4 showed no significant differences in their circadian phases between SCN and liver ( Figure 3 ) . To test if such differences also exist between SCN and whole brain tissues , we also compared SCN with 3 whole brain datasets . There were 12 circadian oscillating genes shared between two SCN datasets and at least two whole brain datasets . Per2 , Nr1d2 , and Tuba8 again showed a significant advance of about 6 hours in their circadian phases in SCN datasets compared to whole brain datasets , whereas Hmgb3 , Hsp110 , Sgk , and Fabp7 showed no significant differences in their circadian phases between SCN and whole brain . Further examination validated that the known key circadian genes including Per1 , Per2 , Cry1 , Arntl , Nr1d1 , and Nr1d2 all showed around 6 hour advances in circadian phases between SCN and non-SCN tissues in general , whereas heat shock proteins showed consistent circadian phases across all tissues . There were 15 circadian oscillating genes shared between 3 heart datasets including whole heart , atria , and ventricle and at least 3 liver datasets . Comparing the heart datasets with the liver datasets , Bhlhb2 ( p<0 . 001 ) and Tspan4 ( p = 0 . 006 ) had circadian phase 5–6 hours earlier in heart than liver whereas Dscr1 ( p = 0 . 002 ) had circadian phase 8 hours later in heart than liver . Other known key circadian genes such as Per1/Per2 , Arntl , and Nr1d1/Nr1d2 showed consistent circadian phases between heart and liver . Comparing the whole brain datasets with the liver datasets , Tfrc , St3gal5 , and Tspan4 had circadian phases more than 4 hours earlier in whole brain than liver , whereas Hist1h1c , Tsc22d1 , Myo1b , Litaf , and BC004004 had circadian phases more than 4 hours later in whole brain than liver . Among the 1 , 269 rat genes identified as circadian oscillating genes in rat liver , 1 , 137 of them had homologues in mouse . 232 of them overlapped with 944 mouse liver circadian oscillating genes in at least 2 mouse liver datasets . We used the circular ANOVA test to identify the circadian oscillating genes shared in both mouse and rat livers but with significantly different circadian phases . 10 genes had significantly ( p<0 . 01 ) different circadian phases between mouse and rat livers . The circadian phases of BC006779 , Cdkn1a , Svil , Uox , Ak2 , Nr1d1 , Mtss1 , Nudt16l1 , and Gss were 4–6 hours later in rat liver than mouse liver , whereas Hsd17b2 was in anti-phase between mouse and rat livers ( Figure S2 ) . Among 803 rat skeletal muscle ( SKM ) circadian oscillating genes , 703 of them had homologues in mouse and 64 of them overlapped with 440 mouse SKM circadian oscillating genes . Among the overlapping genes , 34 of them did not show circadian phase differences larger than 4 hours between mouse and rat SKM . 22 of them had circadian phases more than 4 hours later in rat SKM than mouse SKM . Cpt1a , Pdk4 , and Ucp3 , involved in lipid metabolism , showed a 5–8 hour delay in their circadian phases in rat SKM compared to mouse SKM . 8 genes had circadian phases more than 4 hours earlier in rat SKM than in mouse SKM . Among them , Fkbp5 and Sgk , which are controlled by the glucocorticoid receptor element ( GRE ) , had about 6 hour advance in their circadian phases in rat SKM compared to mouse SKM . There were 11 circadian oscillating genes common to mouse liver and SKM , and rat liver and SKM . The 4–5 hour delay in circadian phases in rat compared to mouse was observed in both liver and SKM for all 11 circadian genes except Dynll1 . Among 603 rhesus macaque adrenal gland circadian oscillating genes , 560 had homologues in mouse and 170 overlapped with 4 , 162 mouse adrenal gland circadian oscillating genes . We found significant differences in circadian phases also between these two species . Among the overlapping genes , 47 did not show circadian phase differences larger than 4 hours between mouse and macaque , whereas 66 had circadian phases more than 4 hours later in the macaque adrenal than in the mouse adrenal . Known key circadian genes , Arntl , Dbp , Nr1d1 , and Bhlhb2 , showed about 8 hour delay in their circadian phases in the macaque adrenal compared to the mouse adrenal . Although Per2 did not satisfy our criteria ( p<0 . 01 ) to be a circadian oscillating gene in macaque adrenal , this gene has a circadian phase at CT21 ( p = 0 . 03 ) , which is also about 8 hours later than that in mouse . Similarly , heat shock proteins , Hsp110 , Hspa8 , Dnaja1 , and Dnajb6 , had circadian phases around CT16 in the mouse adrenal but around CT0 in the macaque adrenal . Cold inducible protein ( Cirbp ) had a circadian phase around CT7 in the mouse adrenal but around CT16 in the macaque adrenal , in anti-phase with heat shock proteins in both mouse and macaque . On the other hand , there were also 57 genes showing circadian phases more than 4 hours early in the macaque adrenal than in the mouse adrenal . In the human circadian SKM microarray study , there were only two circadian time point measurements: CT1 and CT13 . Hence we can only roughly estimate the circadian phases to be either CT1 or CT13 in human SKM . Among the common circadian genes , Per1 , Per2 , Nr1d2 , and Dbp had circadian phases around CT1 , whereas Arntl and Cry1 had circadian phases around CT13 in human SKM . Our estimates of circadian phases for Per1 and Per2 in human SKM were in good agreement with the study in human peripheral blood mononuclear cells where a 2 hour sampling time was used throughout 72 hours [8] . The heat shock proteins , Dnaja1 , Dnajb4 , and Hspa4 , had circadian phases around CT13 , consistent with the peak of common body temperature at CT10 in human [8] . Next , we made a three-species comparison of circadian phases in the SKMs of mouse , rat , and human . We found 12 circadian oscillating genes common to SKM in all three species ( Table 2 ) . After we rounded the circadian phases in mouse and rat to their closest time points , CT1 or CT13 , we observed that Per2 , Arntl , Dbp , Ppp1r3c , and Ablim1 had conserved circadian phases between mouse and rat , but were 12 hours away from those of human . Epm2aip1 , G0S2 , and Maf had conserved circadian phases between mouse and human but 12 hours away from those of rat . Finally , D19Wsu162e , Myod1 , Pfn2 , and Ucp3 had conserved circadian phases among all three species . We searched for the Gene Ontology ( GO ) categories significantly over-represented in circadian oscillating genes in each mouse tissue using GOminer program [9] . We further tested the associations of GO categories with any specific circadian phase intervals using Fisher's test with a rotating window method . The list of significant biological processes associated with circadian phases in different tissues is shown in Table S3 . The most common of these biological processes were steroid biosynthesis , heat shock response , and protein folding . Steroid biosynthesis was associated with CT22 in liver , kidney , adrenal , brown adipose tissue ( BAT ) , and white adipose tissue ( WAT ) . Heat shock response or protein folding were associated with CT16 in SCN , liver , kidney , adrenal , aorta , BAT , WAT , calvarial bone , and whole brain , due to a large number of heat shock proteins consistently showing circadian phases near CT16 in most tissues . In liver , carbohydrate and amino acid metabolism were associated with CT17 and CT15 respectively , consistent with the rise of activities after light off in mouse . In BAT , WAT , and adrenal , lipid metabolism was associated with CT22 . Negative regulation of protein kinase activities was associated with CT17 in prefrontal cortex and CT21 in whole brain . There were also notable differences in the circadian phases of some biological processes between tissues . For example , protein translation was associated with CT20 in SCN but CT9 in WAT . Organ development was associated with CT22 in heart and BAT but CT10 in adrenal . To test the association of transcription factor ( TF ) regulation with the circadian oscillation of gene expression , we predicted the TF binding sites on the mouse promoters of circadian oscillating genes in each tissue using positional weight matrix ( PWM ) based methods . We first tested whether there was a significant over-representation of TF PWM binding sites on the promoters of circadian oscillating genes using the Fisher's exact test . Among the significant TF PWMs , we again tested their associations with any specific phase intervals using the Fisher's test with a rotating window method . To remove the redundancy in TF PWMs , we grouped the TF PWMs into TF families and averaged the associated circadian phases of significant TF PWMs within the same TF families . The results are shown in Table S4 . EBOX , AP-2 , CRE , SP1 , and EGR were the top 5 TF families associated the circadian phase in most tissues . However , unlike the consistent circadian phases of the common circadian genes across tissues , the associated circadian phases of the significant TF families varied considerably among different tissues . EBOX was associated with CT12 in the majority of tissues including SCN , liver , aorta , adrenal , WAT , brain , atria , ventricle , and prefrontal cortex , but it was associated with CT0 in skeletal muscle , BAT , and calvarial bone . CRE was consistently associated with CT11 in SCN , liver , aorta , heart , adrenal , calvarial bone , prefrontal cortex , and ventricle , but with CT20 in atria . Two other known TF families related to circadian rhythm , RRE and DBOX , were detected to be associated with circadian phase only in two tissues . RRE was associated with CT0 in liver and WAT . DBOX was associated with CT16 in aorta and adrenal . We obtained microarray data from TF knockout or mutants for Clock , Arntl , Npas2 , Nr1d1 , Rora/Rorc , Egr1/Egr3 , Dbp/Hlf/Tef , and Ppara in various mouse tissues , together with Cebpa/Cebpb/Cebpd/Cebpe transfection microarray data in NIH3T3 cells . To study the systematic effects of glucocorticoids , cAMP , and temperature on the circadian rhythm , we included microarray data from Nr3c1 ( glucocorticoid receptor ) , Pka , and Hsf1 knockouts or mutants in response to DEX ( glucocorticoid agonist ) , cAMP , and heat stimulation , respectively , compared with wild type mouse . We also included microarray data from a light response mouse model in order to identify light sensitive genes in mouse SCN [10] . The complete list of knockout or mutant microarray experiments used in this study is shown in Table S5 . We assumed that the target genes of TFs will be significantly down-regulated in the knockout or mutant compared with the wild type mouse in the case of activators , and up-regulated in the case of repressors , such as Nr1d1 . To identify the direct targets of TFs in knockout or mutant experiments , we required that the significantly affected genes in the knockout or mutant must have at least one putative binding site of their corresponding TFs in the promoter regions . Under these criteria , we identified 320 EBOX , 295 RRE , 43 DBOX , 492 EGRE , 455 CRE , 326 GRE , 122 HSE , 607 CEBP , and 516 PPRE controlled genes respectively ( Table S6 ) . For these genes , we extracted their mean circadian phases if they have consistent circadian phases across multiple tissues ( p<1/3 , circular range test ) . We observed that EBOX was significantly associated with CT12 ( p<10−6 , Fisher's exact test ) , RRE with CT1 ( p<10−6 ) , DBOX with CT15 ( p<10−5 ) , HSE with CT17 ( p<10−6 ) ( Figure S3 ) . Based on these regulatory interactions , we constructed the gene regulatory network for the circadian oscillating genes in mouse . In Figure 4 , we show a network consisting of the circadian oscillating genes identified in at least 7 mouse tissues . Among the 81 circadian oscillating genes identified in at least 7 tissues , 53 of them can be included through 88 regulatory interactions with 9 cis-regulatory elements in our network . Their circadian phases were represented by different colors in the color wheel . We were able to identify almost all known transcription regulatory interactions for common circadian genes in the literature , except EBOX → Per1 , EBOX → Nr1d1 , EBOX → Ppara , RRE → Nr1d1 , and RRE → Cry1 . To further complete our network , we supplemented these missing gene regulatory interactions with known protein interaction information ( Per/Cry Arntl/Clock and Fkbp:Hsp90 Nr3c1 ) and protein phosphorylation information ( Csnk1d → Per/Cry and Gsk3b → Nr1d1 ) from the literature . These relationships are shown in red color in Figure 4 . Two well-known negative feedback loops can be reconstructed from this analysis: Arntl/Clock → EBOX → Per1/Per2 Arntl/Clock and Nr1d1/Nr1d2 RRE → Arntl/Clock → EBOX → Nr1d1/Nr1d2 . Two feedforward loops are attached to the negative feedback loops through Arntl/Clock → EBOX → Dbp → DBOX → Per1/Per2 acting as an alternative route of Arntl/Clock → EBOX → Per1/Per2 and Nr1d1/Nr1d2 RRE → Nfil3 DBOX → Per1/Per2 Arntl/Clock acting as an alternative route of Nr1d1/Nr1d2 RRE → Arntl/Clock . Bhlhb2 inhibiting EBOX is also regulated by EBOX and Nr1d1 inhibiting RRE is also regulated by RRE , therefore forming two auto-regulatory loops . The effects of food and light act on common circadian genes directly through GRE and CRE respectively . GRE controls Per1 and Per2 , while CRE controls Per1 , Rora , Nr1d2 , and Nfil3 . As shown in Figure 4B , the effect of temperature acts on common circadian genes rather indirectly through the route HSE → Hsp90aa1 → Fkbp/Hsp90 Nr3c1 → GRE → Per1/Per2 . Nr3c1 and the Fkbp/Hsp90 complex are also components of another negative feedback loop , Nr3c1 → GRE → Fkbp5 → Fkbp/Hsp90 Nr3c1 , which may play an important role in glucocorticoid stimulation . Nr3c1 is also under the control of CRE and therefore may be responsive to light stimulation . Nr3c1 and the Fkbp/Hsp90 complex feed into EBOX by regulating Per1/Per2 through GRE . In turn , EBOX controls both components of the Fkbp/Hsp90 complex , i . e . , Fkbp5 directly and Hsp90aa1 indirectly through EBOX → Ppara → PPRE → Hsp90aa1 . Therefore , Nr3c1 and Fkbp/Hsp90 play central role of integrating the regulatory inputs from diverse environmental signals into circadian genes in our network ( Figure 4B ) .
By combining all available circadian microarray data in mouse , we identified a set of common circadian genes showing circadian oscillations with consistent circadian phases in a wide range of tissues . However , the majority of circadian oscillating genes were restricted to a small number of tissues , with large variations in their circadian oscillation phases , suggesting that they are likely circadian-controlled genes that are driven by common circadian genes under their different tissue environments . The 6 hour phase delay of known key circadian genes such as Per1 , Per2 , and Nr1d1 in non-SCN tissues compared to SCN has been noted by others previously and has been explained by the time-lapse needed to transmit the regulatory signals from SCN to peripheral tissues . However , we also observe genes such as heat shock proteins showing consistent phases in all tissues including SCN , which coincide with the phase of circadian oscillation of body temperature in mouse . The circadian oscillation of body temperature may hence be the driving force that synchronizes the circadian oscillation of heat shock proteins throughout the body , which may be independent of the regulation of circadian rhythm in peripheral tissues by SCN . After integrating tissue gene expression data with circadian rhythm data , we were surprised to find that the common circadian genes show a high degree of variation in gene expression across tissues in spite of the universal presence of circadian rhythms in different tissues . This indicates that the circadian rhythm gene regulatory network is robust against the variations in gene expression levels of its key components in different tissues . Interestingly , we observed that the common circadian genes with similar gene expression patterns across tissues also tended to have similar circadian phases . Thus , the gene regulatory network responsible for generating “spatial” expression variation across tissues may be also responsible for generating the “temporal” expression variation . We applied promoter analysis on the circadian oscillating genes in different mouse tissues and identified a suite of transcription factors that potentially play important roles in circadian rhythm . Bozek et al . used a similar promoter analysis approach on several mouse circadian microarray datasets and identified TFs including Sp1 , AP2 , STAT1 , HIF-1 , and E2F to be associated with circadian oscillating genes [6] . However , they considered neither tissue differences nor the association of TFs with specific circadian phases . Furthermore , using sequence based promoter analysis alone to identify significant TFs that regulate circadian oscillating genes is problematic . First , it is almost impossible to distinguish the multiple TFs binding to identical or similar DNA motifs . For example , in addition to Arntl/Clock , a number of other TFs such as Usf and c-myc also bind the EBOX motif . Second , it is difficult to separate the direct and indirect regulatory interactions . For example , although we identified the association of TFs such as SP1 , E2F , and A2P with circadian oscillating genes , it is more likely that these TFs are associated with other key circadian TFs such as Arntl/Clock , and act as parts of the transcription machinery . To overcome these problems , we utilized a number of mouse TF knockout or mutant microarray experiments to construct a systematic gene regulatory network for circadian rhythm in mouse . We compared our network with a small-scale gene regulatory network constructed by Ueda et al . using a reporter assay for 16 common circadian genes in mouse [5] . Among the nine E/E'BOX controlled genes identified by Ueda et al . , Per1 , Per2 , Bhlhb2 , Bhlhb3 , Cry1 , Dbp , Nr1d1 , Nr1d2 , and Rorc , we identified five , Per2 , Per3 , Bhlhb2 , Dbp , Nr1d2 , and also Rora instead of Rorc . Among the seven DBOX controlled genes identified by Ueda et al . , Nr1d1 , Nr1d2 , Rora , Rorb , Per1 , Per2 , and Per3 , we only identified Per3 . Among the six RRE controlled genes identified by Ueda et al . , Clock , Npas2 , Arntl , Nfil3 , Rorc , and Cry1 , we identified four , with Rorc and Cry1 being the exceptions . In fact , Cry1 was significantly up-regulated in the Nr1d1 knockout experiment , but we did not identify any canonical RRE binding site in its promoter , suggesting our criterion for putative RRE may be too stringent . Ueda et al . showed that the transcriptional activities of EBOX , RRE , and DBOX reach their maximums at CT7 . 5–CT11 . 5 , CT21 . 0–CT23 . 0 , and CT11 . 0 , respectively . The circadian phases associated with EBOX and RRE in our network were consistent with Ueda et al . 's results whereas the circadian phase associated with DBOX was around CT15–CT16 in our network . An important question in circadian physiology is how environmental factors such as food , light , and temperature affect the circadian clock . Upon food intake , adrenal gland secretes glucocorticoids that activate the glucocorticoid receptor ( Nr3c1 ) . It was known that the activated Nr3c1 positively regulates Per1 through a glucocorticoid responsive element ( GRE ) in the Per1 promoter . Here we show that the direct targets of Nr3c1 also include other common circadian genes such as Per2 and Fkbp5 . Upon cAMP stimulation , PKA phosphorylates CREB1 , which in turn up-regulates downstream genes through the cAMP responsive element ( CRE ) . One component of PKA , Prkar1a , was among the common circadian genes that we identified with a phase at CT2 . 5 . Other components of PKA were also found to be oscillating with phases around CT0 . The rhythmic oscillation of the mRNA levels of PKA components may suggest that the cAMP signaling pathway is circadian oscillating even in the absence of light stimulation , as many microarray experiments were conducted in 12 h dark:12 h dark ( DD ) condition . It is known that the Per1 promoter contains a functional CRE responsive to cAMP stimulation . Our analysis of PKA mutant microarray data identified additional CRE controlled common circadian genes such as Nr1d2 , Nfil3 , and Rora . In addition , CRE also controls two kinases , Csnk1d and Gsk3b , playing important roles in post-transcriptional regulation of common circadian genes . Csnk1d is a key kinase that phosphorylates PER1 proteins in the cytoplasm , which leads to their degradation . Thus , cAMP stimulation not only elevates the mRNA levels of Per1 , but also the phosphorylation state of PER1 proteins in the cytoplasm . Gsk3b has been shown to phosphorylate and stabilize Nr1d1 protein . The inhibition of Gsk3b activities by lithium has also been implicated in the treatment of bipolar and circadian disorders [11] . In mouse , the response to light has long been suggested to be acting through the cAMP signaling pathway . We identified 28 light sensitive genes in mouse SCN from the light response microarray experiment . Seven of them are PKA controlled genes that we identified from PKA knockout experiments . There are only two genes , Egr1 and Pim3 , among the common circadian genes . They were not among the CRE controlled genes identified from PKA knockout experiments . But a closer examination showed that both genes have conserved CREs between human and mouse in their promoters , therefore strongly suggesting that they too were controlled by CRE . As a key TF in heat response , Hsf1 mainly controls heat shock proteins , whose circadian phases are significantly enriched around CT16 , coinciding with the phase of daily body temperature oscillation in mouse . Hsp90aa1 is a direct target of Hsf1 . Fkbp5 and Hsp90 form a complex inactive glucocorticoid receptor and transmit the impact of heat stimulation indirectly on Per1/Per2 . Kornmann et al . suggested that temperature might entrain the circadian rhythm through the direct regulation of Hsf1/Hsf2 on Per2 [12] . However , we found no evidence of such direct regulation either from the Hsf1 knockout experiment or from the Per2 promoter analysis . Instead , our result suggests an indirect regulation of Hsf1 on Per2 through the glucocorticoid receptor . Similar crosstalk between glucocorticoid stimulation and cAMP stimulation may also exist , as our results showed that the promoter of glucocorticoid receptor Nr3c1 also contained CRE and was responsive to cAMP signaling . Cebp family proteins have a significant number of inputs to common circadian rhythm genes such as Per2 , Dbp , and Nfil3 . Cebpa showed circadian phase at CT7 in four tissues , Cebpb at CT11 in six tissues , and Cebpd at CT14 in two tissues . Their circadian phases suggest that they may be driven by Arntl/Clock through EBOX , thereby forming additional feedback loops . Npas2 has been considered to be a substitute for Clock in forming a hetero-dimer with Arntl . We only obtained 47 Npas2 regulated genes from Npas2 knockout experiment and only one gene , Cirbp , was among the common circadian gene . Therefore , Arntl/Npas2 may have only played a minor role in circadian rhythm comparing to Arntl/Clock . Metabolism and cell cycle are among the many important biological processes controlled by the circadian rhythm . Pfkp , a key enzyme which controls glycolysis and shows circadian phase around CT23 in 7 tissues , is regulated by RRE . Ces3 , a key enzyme in fatty acid metabolism showing circadian phase around CT17 in 6 tissues , is controlled by DBOX . Ppara , a key TF regulating fatty acid metabolism showing circadian phase around CT7 in three tissues , is controlled by EBOX and may drive the circadian oscillation of other downstream metabolic genes . The circadian oscillations in the cAMP signaling pathway as discussed earlier will also undoubtedly affect the metabolism . In liver , the main metabolic organ , carbohydrate and amino acid metabolism , were associated with CT17 and CT15 respectively . In adipose tissues such as BAT and WAT , lipid metabolism was associated with CT22 . We also observed the association of CT0 with steroid biosynthesis in a wide range of tissues . These results are consistent with the observation that the metabolic activities rise after light off ( dusk ) in mouse . Cdkn1a or p21 , a cyclin dependent kinase inhibitor controlling the progression of cell cycle at G1 phase has the circadian phase at CT22 in 10 tissues and is controlled by RRE . Another kinase , Wee1 , controlling the progression of cell cycle into M phase , has circadian phase at CT14 in 5 tissues and is controlled by DBOX . Cdkn1a and Wee1 are two valves controlling the G2/M and G1/S checkpoints in cell cycle progression , respectively . They have almost opposite circadian phases and receive inputs from the negative limb Nr1d1 and the positive limb Dbp in the circadian rhythm , respectively , which leads to the orchestrated progression of the cell cycle by circadian clock . The mouse has been the most extensively studied mammalian model organism for circadian rhythm . The scarcity of microarray experiments with circadian and TF knockouts or mutants in non-mouse mammals makes it difficult to construct systematic gene regulatory networks for non-mouse mammals . But the comparison between the microarray experiments in mouse and a few microarray experiments in other mammals including rat , macaque , and human , have revealed significant differences between species both in terms of circadian oscillating genes and their circadian phases . The known key circadian genes showed a 4–5 hour phase delay in rat compared to mouse and 8–12 hours phase delay in macaque and human compared to mouse , which probably reflects the fact that mouse and rat are nocturnal animals whereas macaque and human are diurnal . Interestingly , the circadian phases of heat shock proteins are well aligned with the peaks of body temperature in mouse , rat , and human . The anti-phase relationship between EBOX controlled genes and RRE controlled genes is preserved among mouse , rat , macaque , and human . Therefore , the negative feedback loops in the center of the mammalian circadian rhythm , consisting of Per1/Per2 , Cry1 , Arntl , Clock , and Nr1d1/Nr1d2 , must have been well conserved among mammalian species . Meanwhile , the diversity in the circadian oscillating genes and their phases among these four species suggests that a significant amount of gene regulatory interactions in the circadian gene regulatory network have been rewired during evolution . Future comprehensive studies on the structure and dynamics of circadian gene regulatory networks in different mammalian species will advance our understanding of the molecular basis of their physiological and behavioral differences .
We collected all available circadian microarray data from different laboratories for mouse , rat , rhesus macaque ( Macaca mulatta ) , and human . The total mouse data consisted of 21 datasets covering 14 tissues including two datasets in SCN , five datasets in liver , three datasets in whole brain , one dataset in kidney , aorta , heart , skeletal muscle ( SKM ) , adrenal gland , brown adipose tissue ( BAT ) , white adipose tissue ( WAT ) , calvarial bone , prefrontal cortex , atria , and ventricle . The three datasets in whole brain were from three different mouse strains: C57BL/6J , AKR/J , and DBA/2J . The rat data consisted of one dataset in liver and one dataset in skeletal muscle . The macaque data consisted of one dataset in adrenal gland . The human data consisted of one dataset in skeletal muscle . The complete list of all circadian microarray datasets used in this study is shown in Table S1 . Most circadian microarray experiments were conducted in a time series of every 4 hours . The human microarray experiment was only conducted at CT1 and CT13 . For simplicity , we did not distinguish the light conditions , i . e . , 12 h light:12 h dark ( LD ) or 12 h dark:12 h dark ( DD ) , under which the animals were kept during the experiments . In order to have a more complete and consistent analysis of the data from different experiments , we decided to re-analyze all the datasets by our own method rather than simply taking the gene lists from the original publications . For the datasets where the CEL files were available , we normalized the data by RMA method in “affy” package . For the datasets where only normalized data were available , the normalization step was skipped . We used the method similar to that described in [2] to analyze all microarray data . Namely , cosine functions Aij ( t ) = cos ( 2πt/Ti−φj ) where Ti = 20+i , φj = 2πj/60 , 0 ≤ i ≤8 , and 0 ≤ j ≤59 were used as the reference time series of circadian oscillation . The gene expression time series of each probe set on the microarray were fitted to each cosine function time series Aij ( t ) and the cosine function with highest correlation coefficient was chosen . A p-value <0 . 01 in the regression for the best cosine function was used as the criterion for circadian oscillation , and we estimated a false positive rate of about 10% for this cutoff using a random permutation test . When the experimental replicas at each time point were available , we further carried out a one-way ANOVA test on the time series using time points as factor and p-value <0 . 05 as an additional criterion . For the probe sets satisfying the criteria for circadian oscillation , the gene expression time series were again fitted to the cosine functions with fixed 24 hrs period but changing phases , Bj ( t ) = cos ( 2πt/T−φj ) , where T = 24 , φj = 2πj/144 , and 0≤ j ≤ 143 . The circadian phase was calculated from the best fitted Bj ( t ) as φj*24/2π . We were unable to obtain the microarray data in [2] so we only extracted circadian gene lists with their circadian phase information . In the human SKM study , vastus lateralis muscles were taken from exercised and non-exercised legs of 4 patients at CT1 ( 8AM ) and CT13 ( 8PM ) . We used circadian time and exercise state as two factors in two-way ANOVA . A p-value <0 . 05 in the circadian time comparison was used as the criterion for circadian oscillation . We estimated the circadian phase to be either CT1 or CT13 , depending on when the average expression value was the highest in human SKM . The R package “Circular” was used to analyze the circadian phases obtained from circadian microarray datasets . For each circadian microarray dataset , the probe sets were annotated by R package “annaffy” and only the probe sets corresponding to known genes were used in the analysis . The probe sets that passed circadian oscillation criteria and that corresponded to the same genes were merged by the following procedure . First , a circular range test was used to assess the consistency of phases estimated from the different probe sets for the same genes , where p<1/3 was used as the criterion to take into account the 4 hour intrinsic errors in phase estimation as the animals were sampled every 4 hours in most experiments . Then , a circular mean function was used to calculate the mean circadian phases from the consistent probe sets . The same procedure was used to combine the different datasets for the same tissue , i . e . , five datasets for liver , two datasets for SCN , three datasets for whole brain . In liver and whole brain , we only selected the genes identified as circadian oscillating in at least two out of five liver datasets or two out of three whole brain datasets , respectively . In SCN , we selected the genes identified as circadian oscillating in one out of two SCN datasets considering the small number of circadian oscillating genes in Ueda et al . 's SCN dataset [2] . We identified 9 , 955 circadian oscillating genes in at least one out of 14 tissues ( Table S2 ) . The number of circadian oscillating genes in different number of tissues was plotted using the “barplot” function in R and is shown in Figure 1A . The circular range test was also used to describe the consistency of phases of circadian oscillating genes across tissues . The distribution of p-values of circular range tests in different number of tissues was plotted using the boxplot function in R and is shown in Figure 1B . We defined the 41 circadian oscillating genes identified in at least 8 out of 14 tissues as common circadian genes , and these are shown in Table 1 . The microarray data of 61 mouse tissues after gcrma normalization were downloaded from the mouse tissue gene expression atlas website: http://symatlas . gnf . org [7] . We selected the probe set with the highest average expression value across tissues to represent the genes with multiple probe sets . To remove the non-detected probe sets , we filtered out the probe sets with gene expression values lower than 100 in all 61 tissues . We obtained the expression profiles for 19 , 168 genes across 61 tissues . For 9 , 955 circadian oscillating genes identified in at least one tissue , we created a matrix of 1 or 0 to denote the presence or absence of circadian oscillation in 14 tissues . For 8 , 029 genes having both circadian data and tissue expression data , we calculated the correlations between the circadian 1 or 0 matrix with the matrix of log2 ( gene expression ) in 61 tissues in tissue gene expression atlas . We searched for the tissues in tissue data having the highest correlation coefficient with the tissues in circadian data . Liver ( r = 0 . 29 , p<10−15 ) , kidney ( r = 0 . 23 , p<10−15 ) , skeletal muscle ( r = 0 . 10 , p<10−15 ) , adrenal gland ( r = 0 . 06 , p = 10−7 ) , and white adipose tissue ( r = 0 . 18 , p<10−15 ) in circadian data have the highest correlations with their corresponding tissues in the tissue data , whereas SCN in circadian data correlates equally well with preoptic and hypothalamus ( r = 0 . 22 , p<10−15 ) in tissue data and BAT correlates equally well with adipose tissue and brown fat ( r = 0 . 19 , p<10−15 ) . For the seven tissues having both circadian data and tissue data: liver , heart , BAT , WAT , kidney , adrenal gland , and SKM , we calculated the variances of circadian phases in circadian data using the “circular var” function for the circadian oscillating genes identified in at least two tissues , and the variances of log2 ( gene expression ) in tissue data across the tissues where the circadian oscillations have been identified in circadian data . The correlation coefficient of these two variances is 0 . 01 ( p = 0 . 71 ) . For the 37 common circadian genes identified in at least 8 tissues having tissue data , the median of variances of log2 ( gene expression ) across 61 tissues was 2 . 28 . In comparison , the expected median of variances of log2 ( gene expression ) for the same number of randomly selected genes was 0 . 54 based on 106 random simulations . The correlation coefficients rij between the tissue gene expression profiles of the common circadian gene pairs ( i , j ) were negatively correlated with their circadian phase differences dij ( r = −0 . 22 , p<10−8 ) . To further demonstrate the relationship between rij and dij , we defined two functions y+ ( x ) = median ( dij ( rij>x ) ) and y− ( x ) = median ( dij ( rij<x ) ) for −1 ≤ x ≤ 1 . We plotted y+ ( x ) and y− ( x ) in Figure S1 . y+ ( x ) for x>0 is significantly lower than the median of dij for all gene pairs ( 5 . 84 ) and reaches the minimum 3 . 068 at x = 0 . 64 , whereas y− ( x ) for x<0 is significantly higher and reaches the maximum 9 . 939 at x = −0 . 26 . These results indicated that the common circadian genes with positive correlations in their tissue gene expression profiles tended to have closer circadian phases , whereas those with negative correlations in tissue gene expression profiles tended to have larger differences in their circadian phases . We used the median of phase differences of circadian oscillating genes shared by two tissues as the distance measure of global phase dissimilarity between two tissues . We use these distances to cluster the phases of circadian oscillating genes in all 21 datasets using hierarchical clustering with complete linkage ( Figure 2 ) . For the mouse tissues where multiple datasets were available , i . e . , liver , SCN , whole brain , and heart ( whole heart , atria , and ventricle ) , we conducted pair-wise comparisons of the phases across tissues , using the “circular ANOVA” function for the genes identified as circadian oscillating in at least two datasets in each tissue under comparison . The same method was used to compare mouse liver data with rat liver data . To compare the circadian oscillating genes across species , rat , macaque , and human gene symbols were converted to mouse orthologs using the HomoloGene database of NCBI ( build 56 , http://www . ncbi . nlm . nih . gov/HomoloGene ) . Gene symbols of circadian oscillating genes identified in each tissue in mouse , rat , macaque , and human were uploaded to Gominer [9] for Gene Ontology ( GO ) annotation and enrichment analysis . We selected the biological processes significantly over-represented in circadian oscillating genes in each tissue using False Discovery Rate ( FDR ) less than 0 . 05 as the criterion . For the circadian oscillating genes in each enriched biological process , we further tested their associations with any specific phase intervals using the Fisher's exact test with a rotating window method . In each 1 , 000 equally spaced phase intervals of size 4 hours between CT0 and CT24 , the Fisher's test was applied to test the association between the biological process and the phase interval . The smallest p-value among Fisher's tests in all intervals was obtained to represent the significance of the association . The significant biological processes ( p<0 . 005 ) in each tissue were colored using a color circle to represent their associated circadian phases . We visualized the significant biological processes as GO maps created by Cytoscape program ( version 2 . 5 ) . The significant biological processes were represented by the nodes and their hierarchical GO relationship was represented by the directed edges between them so that close-related biological processes were clustered together . All GO maps in different tissues can be found in our website ( http://www . picb . ac . cn/circadian/ ) . We manually selected the most representative biological processes for each GO cluster and summarized the result in Table S3 . Transcriptional start sites ( TSSs ) information of mouse and human were integrated from three databases: DataBase of Transcriptional Start Site ( DBTSS ) [13] , [14] , the CAGE ( Cap-Analysis Gene Expression ) database of Fantom3 ( Functional annotation of mouse ) project [15] , and the NCBI RefSeq database [16] . The criteria to select the TSSs were as follow: for DBTSS TSSs , the proportion of confident cDNA clones ( non-exonic start clones , i . e . , the clones mapped to the non-exonic regions of the genome ) was not less than 0 . 75; for CAGE TSSs , the total number of corresponding CAGE tags was not less than 2 and can be mapped around the 5′ end of a known mRNA . If no TSS can be found for the gene from either DBTSS or CAGE under the above criteria , the 5′ end of the mRNA in RefSeq ( human build 36 . 1 , mouse build 36 ) was used as the TSS of the gene . Human ( hg18 or NCBI build36 . 1 ) and mouse ( mm8 or NCBI build 36 ) genome sequences were downloaded from UCSC . The 3000bp flanking sequences of each TSS were extracted from the genome as the promoter regions . As the CAGE database was based upon the older versions of human and mouse genome , i . e . , hg17 and mm5 , we mapped the CAGE TSSs to the new version of genomes using liftOver program in UCSC . In addition , orthologous promoter regions of mouse ( mm8 ) vs . human ( hg18 ) genome alignment results were also downloaded from UCSC . Three positional weight matrix ( PWM ) based motif searching programs , match [17] , motifscan [18] , and profilestas [19] , were used to identify the putative transcriptional factor binding sites ( TFBS ) on the extracted promoter regions . All vertebrate PWMs in TRANSFAC 11 . 2 were used as inputs in these programs . For the match program , we used the cut-off profile that minimizes the false positive rate , i . e . , minFP profile in TRANSFC 11 . 2 . For the motifscan program , we used a third-order background model by the CreateBackgroundModel program [20] to distinguish between the motifs that occurred frequently throughout the genome and the ones that were specific to the promoter regions . For the profilestas program , we first used the profilestas package to generate the scoring matrix and scoring threshold that minimized the false positive rate for each PWM . Then we used the patser program [21] to scan the promoter sequences and select the TFBSs above the scoring thresholds . The putative TFBSs predicted from all three programs have been compared and yielded very similar results . For simplicity , all the promoter analysis results presented in this paper were based on the match program . We first tested the significant over-representation of putative TFBSs among a total of 568 PWMs on the promoters of circadian oscillating genes using the Fisher's exact test ( p<10−4 ) , using the promoters of all known genes as the background . Among the significant PWMs , we again tested their associations with any specific circadian phase interval in each tissue using the Fisher's exact test with a rotating window method as described above in the GO analysis ( p<0 . 005 ) . To remove the redundancy in PWMs , we grouped the PWMs into TF families according to their classifications in the TRANSFAC database and we averaged the associated circadian phases of significant TF PWMs in the same TF families using the “mean” function in the R “circular” package . The results are summarized in Table S4 . The detailed information about TF enrichment and their associations with any specific circadian phase intervals can be found in our website ( http://www . picb . ac . cn/circadian/ ) . We collected microarray data in different tissues or cell types from knockout or mutant mice , including liver and skeletal muscle in a Clock mutant , atrium and ventricle in a cardiomyocyte-specific Clock mutant , liver in a liver-specific conditional Nr1d1 mutant , aorta in Arntl and Npas2 knockout or mutant , liver in a Rora/Rorc knockout , liver and kidney in a Dbp/Hlf/Tef knockout , liver in a Ppara-null mice on Sv129 background treated by the Ppara agonist Wy14643 , NIH 3T3 cells under Cebpa/b/d/e transfection , S49 cells in a Pka knockout under cAMP stimulation , cortex and thymus in a Egr1/Egr3 knockout , liver and primary chrodrocytes in a Nr3c1 ( glucocorticoid receptor ) knockout treated by the glucocorticoid agonist deamethasone ( DEX ) , and embryonic fibroblast in a Hsf1 knockout under heat shock ( Table S5 ) . We also included the microarray experiment in the SCN of mouse exposed to 30 minute light pulse at 1 hour after the light off period compared to a dark pulse [10] . For the knockout or mutant mice microarray data where time series were available , we applied a two-way ANOVA using genotypes and time series as factors . The p-values and fold changes in the genotype comparison were used . For the knockout or mutant mice microarray data where external treatments such as Wy14643 , cAMP , DEX , and heat were available , we applied a two-way ANOVA using genotypes and treatments as factors . Here the p-values and fold changes in cross-interactions between two factors were used . For Dbp/Hlf/Tef and Egr1/Egr3 knockout or mutant and Cebpa/b/d/e transfection experiments , we applied one-way ANOVA using genotypes as factor . For Rora/Rorc , Arntl , and Npas2 knockout or mutant experiments , we applied the LIMMA program using genotypes as the factor . In the Rora/Rorc knockout or mutant experiment , Rora knockout , Rorc knockout , Rora/Rorc double knockout were treated as the same genotype . In Pka knockout or mutant experiment , only the data at 0 hr and 2 hr of cAMP stimulation were used to include the directly affected genes in the cAMP signaling cascade . In Dbp/Hlf/Tef knockout or mutant experiments , the averages of log2 ( p-value ) and log2 ( fold change ) in three experiments: triple knockout vs . wild type in liver , triple knockout vs . triple heterozygotes in liver , and triple knockout vs . wild type in kidney were used as the overall log2 ( p-value ) and log2 ( fold change ) . Ppara knockout data were obtained from the third and fourth study in [22] . To combine the results in third and fourth studies , we extracted the probe sets with consistent log fold changes of Ppara knockout effect of both studies . The maximum of p-values of both studies and mean of log fold changes were used . For Egr1/Egr3 knockout in cortex and thymus and Nr3c1 knockout in liver and primary chrodrocytes , the significantly affected gene lists were simply merged in two tissues or cell types . In all knockout or mutant data , a p-value less than 0 . 01 and a |log2 ( fold change ) |>0 . 5 were used to identify the significantly up- or down-regulated genes in the knockout or mutant . To reliably identify Arntl/Clock and Nr1d1/Rora/Rorc controlled genes , we combined the evidences from multiple datasets . Arntl/Clock controlled genes were identified as those satisfying two out of the five conditions: down-regulated in the Clock knockout in liver , down-regulated in the Clock knockout in skeletal muscle , down-regulated in the cardiomyocyte-specific Clock knockout in atria , down-regulated in the cardiomyocyte-specific Clock knockout in ventricle , and down-regulated in the Arntl knockout in aorta . As Nr1d1 , a repressor , was significantly down-regulated in the Arntl or Clock knockout or mutant , the significant up-regulation in the Arntl or Clock knockout or mutant was also considered to be the evidence for Nr1d1 controlled genes . Thus , Nr1d1/Rora/Rorc controlled genes were identified as those satisfying one out of the seven conditions: up-regulated in the Clock knockout in liver , up-regulated in the Clock knockout in skeletal muscle , up-regulated in the cardiomyocyte-specific Clock knockout in atria , up-regulated in the cardiomyocyte-specific Clock knockout ventricle , up-regulated in the Arntl knockout in aorta , up-regulated in the Nr1d1 conditional knockout , and down-regulated in the Rora/Rorc knockout . Dbp/Hlf/Tef , Ppara , Egr1/Egr3 , Pka , Nr3c1 , and Hsf1 controlled genes were identified as those that were significantly down-regulated in a knockout or mutant mouse compared to the wild type mouse . CEBP controlled genes were identified as those that were significantly up-regulated in Cebpa/b/d/e transfected cells compared to the control cells . We identified 380 Arntl/Clock , 1 , 166 Nr1d1/Rora/Rorc , 53 Npas2 , 53 Dbp/Hlf/Tef , 627 Cebp , 536 Ppara , 710 Egr1/Egr3 , 464 Pka , 341 Nr3c1 , and 425 Hsf1 controlled genes from the knockout or mutant experiments . To identify the direct target genes of transcription factors in knockout or mutant experiments , we required that the significantly affected genes in a knockout or mutant must have at least one putative binding site of their respective transcription factors in the promoter regions . We identified 320 EBOX , 295 RRE ( Rev-erb/Ror element ) , 47 Npas2-regulated element , 43 DBOX , 607 CEBP , 516 PPRE ( peroxisome proliferator responsive element ) , 492 EGRE ( Egr element ) , 455 CRE ( cAMP response element ) , 326 GRE ( Glucocorticoid response element ) , and 122 HSE ( Heat shock element ) directly controlled genes after combining with the promoter analysis ( Table S6 ) . | Circadian rhythm is universally present from unicellular organisms to complex organisms and plays an important role in physiological processes such as the sleep–wake cycle in mammals . The mammalian circadian rhythm presents an excellent system for studying gene regulatory networks as a large number of genes are undergoing circadian oscillation in their expression levels . By integrating all available microarray experiments on circadian rhythm in different tissues and species in mammals , we identified a set of common circadian genes lying in the center of the circadian clock . Significant differences in the circadian oscillation of gene expression among mouse , rat , macaque , and human have been observed that underlie their physiological and behavioral differences . We constructed a gene regulatory network for the mouse circadian rhythm using knockout or mutant microarray data that have previously received little attention . Further analysis revealed not only additional feedback loops in the network contributing to the robustness of the circadian clock but also how environmental factors such as light , food , and heat can entrain the circadian rhythm . Our study provides the first gene regulatory network of the mammalian circadian rhythm at the system level . It is also the first attempt to compare gene regulatory networks of circadian rhythm in different mammalian species . |
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The human blood fluke Schistosoma mansoni causes intestinal schistosomiasis , a widespread neglected tropical disease . Infection of freshwater snails Biomphalaria spp . is an essential step in the transmission of S . mansoni to humans , although the physiological interactions between the parasite and its obligate snail host that determine success or failure are still poorly understood . In the present study , the B . glabrata embryonic ( Bge ) cell line , a widely used in vitro model for hemocyte-like activity , was used to investigate membrane properties , and assess the impact of larval transformation proteins ( LTP ) on identified ion channels . Whole-cell patch clamp recordings from Bge cells demonstrated that a Zn2+-sensitive H+ channel serves as the dominant plasma membrane conductance . Moreover , treatment of Bge cells with Zn2+ significantly inhibited an otherwise robust production of reactive oxygen species ( ROS ) , thus implicating H+ channels in the regulation of this immune function . A heat-sensitive component of LTP appears to target H+ channels , enhancing Bge cell H+ current over 2-fold . Both Bge cells and B . glabrata hemocytes express mRNA encoding a hydrogen voltage-gated channel 1 ( HVCN1 ) -like protein , although its function in hemocytes remains to be determined . This study is the first to identify and characterize an H+ channel in non-neuronal cells of freshwater molluscs . Importantly , the involvement of these channels in ROS production and their modulation by LTP suggest that these channels may function in immune defense responses against larval S . mansoni .
Schistosomiasis , a neglected tropical disease afflicting over 250 million people worldwide [1] , is caused by parasitic flatworms of the genus Schistosoma . Schistosoma spp . have a two-host life cycle involving sexual reproduction within a mammalian host and asexual reproduction within a snail intermediate host . The pathology associated with the intestinal form of human schistosomiasis arises in chronic infections when eggs released by female worms occupying mesenteric veins become trapped in the liver ( and other organs ) and elicit an intense inflammatory response leading to the formation of granulomas that damage tissues and block circulation [2 , 3] . Eggs from ruptured intestinal capillaries exit the host by fecal excretion , and upon exposure to freshwater , hatch to release the free-swimming snail-infective miracidia . Upon infection of snails , miracidia transform through two sporocyst stages , ultimately completing their life cycle by the production and release of free-swimming cercariae , the human-infective stage [4] . Because of the absolute dependency of human schistosome transmission on the snail host , one of the keys to sustained control of schistosomiasis is to block or eliminate the snail’s participation in the life cycle . The freshwater snail Biomphalaria glabrata serves as the most common invertebrate host of S . mansoni , the most widely distributed species of Schistosoma [5] . Hemocytes ( phagocytic immune cells ) of B . glabrata , genetically-selected for susceptibility or resistance to infection by larval S . mansoni , have been shown to react differentially to invading miracidia . Circulating hemocytes of susceptible strains do not recognize and kill invading larvae , whereas in resistant snails developing larvae are rapidly encapsulated by hemocytes and killed within 24–48 hours of infection [6–8] . Hemocyte larvicidal activity has been linked to the production and release of reactive oxygen species ( ROS ) , mainly hydrogen peroxide ( H2O2 ) , and the reactive nitrogen species , nitric oxide [9 , 10] . Although hemocytes of both resistant and susceptible B . glabrata strains produce H2O2 , resistant hemocytes generate and release higher levels than susceptible cells [11] , and this production appears to depend on the extracellular signal regulated protein kinase ( Erk ) [12] . However , a critical question arising from these observations is what are the signaling mechanisms that regulate ROS responses ? A critical period of larval development in the snail host is 24–48 hours post-infection , when the newly invading miracidium completes its transformation to the primary sporocyst stage . Larval killing depends on the ability of circulating hemocytes to recognize and encapsulate the newly formed sporocyst [4 , 13–15] . Among various sporocyst factors that may be contributing to hemocyte reactivity are glycoproteins that are released during the miracidium-to-sporocyst transition . In vitro studies have shown that these larval transformation proteins ( LTPs ) [16] modulate phagocytic activity , motility , and ROS production in B . glabrata hemocytes [17–21] , and disrupt hemocyte immune signaling [22–24] . However , questions regarding specific mechanisms by which LTPs modulate hemocyte immune responses remain unanswered . For over four decades a cell line derived from embryos of a schistosome-susceptible strain of B . glabrata , the B . glabrata embryonic ( Bge ) cell line [25] , has served as an in vitro model for the study of larval schistosome-snail host interactions in schistosomiasis . Bge cells share many characteristics with B . glabrata hemocytes including their morphology , adhesive properties , phagocytic activity , and larval encapsulation response [26] . In fact , co-culture of Bge cells with S . mansoni larvae results in the development of the parasite from the miracidium to the final cercarial stage , similar to the development that occurs with susceptible B . glabrata strains [27–30] . We have therefore adopted Bge cells as an in vitro model system to study the molecular interactions between snail cells and S . mansoni LTP . Because ion channels in the plasma membrane of human immune cells , including eosinophils , macrophages , neutrophils and lymphocytes , play important roles in immune responses , often by regulating the production and release of ROS [31] , we explored the role ion channels may play in signaling and ROS production in Bge cells . Using the whole cell patch clamp technique , we discovered an LTP-sensitive H+ channel that serves as the dominant ion conductance of Bge cell membranes . In addition , using a fluorescent probe to measure intracellular ROS , we also found that this channel mediates the production of ROS , thus suggesting a possible function for H+ channels in snail immune responses .
The Bge cell line was originally obtained from American Type Culture Collection ( ATCC CRL 1494 ) and is currently available through the BEI Resources ( https://www . beiresources . org ) . Cells were maintained at 26°C under normoxic conditions in complete Bge ( c-Bge ) medium consisting of 22% Schneider’s Drosophila Medium , 0 . 45% lactalbumin enzymatic hydrolysate , and 7 . 2 mM galactose supplemented with 10% heat-inactivated fetal bovine serum and 1% penicillin/streptomycin [25 , 28] . Bge cells were passaged at 80% confluency . S . mansoni eggs were isolated , hatched , and miracidia cultured in vitro as previously described [28] . Approximately ~ 5000 miracidia/mL in Chernin’s balanced salt solution ( CBSS; 47 . 9 mM NaCl , 2 . 0 mM KCl , 0 . 5 mM Na2HPO4 , 0 . 6 mM NaHCO3 1 . 8 mM MgSO4 , 3 . 6 mM CaCl2 and pH 7 . 2 ) [32] supplemented with glucose ( 1 mg/mL ) , trehalose ( 1 mg/mL ) , penicillin G ( 100 units/mL ) and streptomycin sulfate ( 0 . 05 mg/mL ) adjusted to pH 7 . 2 ( CBSS+ ) were then plated in a 24-well tissue culture plate and incubated at 26°C under normal atmospheric conditions to allow in vitro transformation of miracidia to primary sporocysts . The LTP-containing culture medium was collected after 48 hr , and the newly transformed primary sporocysts were washed once with CBSS+ . The LTP and CBSS+ wash were combined , filtered with a 0 . 45 μm Nalgene syringe filter ( Thermo Scientific , Waltham , MA ) , and concentrated using 3 kDa molecular weight cut-off ultrafiltration tubes ( Amicon Ultra Centricon , Billerica , MA ) . A NanoDrop ND-1000 spectrophotometer ( NanoDrop Technologies , Wilmington , DE ) was used to determine the protein concentration , after which a protease inhibitor cocktail ( Calbiochem , Billerica , MA ) was added . Multiple collections of LTP were pooled and stored in aliquots at -20°C . To denature LTP , pools were boiled at 100°C for 5 min . Bge cells ( ~4 x 106 ) were plated in 60x15 mm petri dishes in c-Bge medium , and allowed to attach overnight . In order to make recordings under defined ionic conditions , cells were washed 3X with CBSS before recording and kept in this buffer during subsequent manipulations . In experiments involving the treatment of Bge cells with ZnCl2 , 10 mM HEPES replaced NaH2PO4 in CBSS due to the insolubility of Zn3 ( PO4 ) 2 . Adherent cells were viewed with an Axioskop microscope equipped with a 63X water-immersion objective ( Carl Zeiss , Thornwood , NY ) . Bge cells were imaged with a CCD camera and viewed on a monitor . Patch electrodes fabricated from borosilicate glass capillaries had resistances of 3–7 MΩ when filled with a solution containing ( in mM ) 60 K-gluconate , 1 CaCl2 , 1 MgCl2 , 1 Mg-ATP , 10 HEPES , and 5 EGTA . The bathing solution for recordings was a slightly modified version of CBSS consisting of ( in mM ) : 47 NaCl , 2 KCl , 0 . 5 NaH2PO4 , 0 . 6 NaHCO3 , 1 . 8 MgSO4 , 3 . 6 CaCl2 . The pH of the pipette solution and external CBSS was adjusted to 5 or 7 with KOH or HCl . Modified versions of the internal and external solutions are stated in the Results section where they are used . Pressure-ejection pipettes were modified patch electrodes with tip diameters of ~2 μm . A Picospritzer II ( General Valve Corp . ) was used to apply 5–10 PSI of pressure to ejection pipettes . Patch clamp recordings were made with an Axopatch 200B amplifier ( Molecular Devices , Sunnyvale , CA ) , with data read into a PC through a Digidata 1440 A interface . The computer program pClamp 10 ( Molecular Devices ) controlled data acquisition , voltage steps , and pressure application by the Picospritzer . Data were filtered with a low-pass Bessel filter at 2 kHz before digitization at 10 kHz . The fluorescent probe 2’7’-dichlorofluorescein-diacetate ( DCFH-DA; Sigma-Aldrich , St . Louis , MO ) was used to measure ROS production in Bge cells following a method described previously with hemocytes [33] . Bge cells ( ~1 . 5 x 105 ) in suspension were washed 3X with CBSS before incubation in CBSS ( control ) , CBSS containing either 30 μg/mL LTP , 1 mM ZnCl2 or 30 μg/mL LTP + 1 mM ZnCl2 for 1 hr at 26°C . After treatment , cells were washed 3X with CBSS and centrifuged at 1000 rpm for 10 min . The final cell pellets were then re-suspended in 150 μL of CBSS containing 10 μM DCFH-DA , and distributed in three wells of a 96-well black-walled plate ( BD Falcon ) . The oxidation of DCFH-DA to fluorescent 2’7’-dichlorofluorescein ( DCF ) was measured in triplicate at 10 min intervals for up to 60 min using a Bio-Tek Synergy fluorescence plate reader ( Winooski , VT ) with excitation and emission wavelengths of 485 ± 20 and 528 ± 20 , respectively . Data analysis was conducted with Origin software ( Microcal , Northhampton , MA , USA ) . Five independent replicates of each experiment were conducted , with the raw data presented as mean ± SEM , and ratios of means of treated groups to controls presented separately . For molecular analysis of H+ channels , the hydrogen voltage gated channel 1 ( HVCN1 ) gene was identified in the nonredundant NCBI database , and sequence comparisons were conducted with PCR products from Bge cells and B . glabrata hemocytes . Schistosome-susceptible ( NMRI ) and resistant ( BS-90 ) B . glabrata strains were maintained in laboratory colonies in 10-gallon aquaria at 26°C under 12:12 hr light/dark cycling . Hemolymph , containing hemocytes , was collected by headfoot retraction [34] and immediately transferred to Eppendorf tubes containing an equal volume of CBSS on ice . Hemocytes were then pelleted by centrifugation at 1000 RPM for 10 min and washed 3 times in CBSS . Bge cells , grown in a flask to ~80% confluency , were detached mechanically using a cell scrapper , transferred to a 15 mL conical tube and pelleted by centrifugation as described for hemocytes . Total RNA was extracted from Bge cells and hemocytes of both B . glabrata strains using TRIzol reagent . Normalized concentrations of isolated total RNA samples were subjected to cDNA synthesis reactions using the GoScriptTM Reverse Transcription System ( Promega Corp . , Madison , WI ) . The cDNA was then used as the template for PCR using primers for the B . glabrata voltage-gated H+ channel 1-like gene ( BgHVCN1-like; Forward 5’-TGCTATGGGCTTAGCTTACTTC-3’; Reverse 5’-ATGTAGGGTCTTCAAACCATTCT-3’ ) that were designed using the predicted mRNA sequence for the gene with the National Center for Biotechnology Information ( NCBI ) database ( Accession number XM_013231505 ) . The expected amplicon size is ~362 bp , ~65% of the coding DNA sequence . As a positive control , primers for B . glabrata α–tubulin ( Forward 5’ -GTGAGACTGGCTGTGGTAAA-3’; Reverse 5’ -GGGAAGTGAATCCTGGGATATG-3’ ) with Accession number XP_013094834 . 1 were used to amplify an expected product of ~643 bp . Gel electrophoresis of the PCR products was performed followed by Big Dye sequencing at the University of Wisconsin Biotechnology Center DNA Sequencing Facility ( Madison , WI ) . The resulting nucleotide sequences were used in a search using BLASTn search against the non-redundant nucleotide NCBI database to confirm that the PCR amplified product encoded an HVCN1-like protein . Patch clamp data were analyzed with Clampfit ( Molecular Devices , Sunnyvale , CA ) and Origin Pro ( Microcal , Northhampton , MA ) . One-way RM-ANOVA and post-hoc statistical analyses were conducted in Origin Pro to assess significance . Results are presented as means ± SEM . The asterisks ( * ) represent p < 0 . 05 in all figures .
Whole cell patch clamp recordings were made from Bge cells to explore their membrane properties . Voltage steps from -75 to 25 mV for 500 msec induced an outward current that activated rapidly and then weakly inactivated in ~10–20 msec before stabilizing ( Fig 1A , control trace , top ) . To identify the ions responsible for this current , we manipulated the composition of the recording solutions . When Cl- was replaced by gluconate in the internal and bathing solutions , voltage steps induced currents similar to those seen with control solutions ( Fig 1A , second trace from top ) . Further substitution of Cs+ for K+ in the internal solution reduced the current to roughly 68% of control currents ( Fig 1A , third trace from top ) . The mean peak and plateau current amplitudes for these solutions are shown in Fig 1B . For gluconate and Cs+ substitution , current amplitudes were not significantly different from the control . Thus , Cl- and K+ replacement experiments indicated that these are not major permeating ions . In addition , comparisons of the Nernst potentials ( equilibrium potential for each ion based on internal and external concentrations ) with reversal potentials in current-voltage relationships did not support channels selective for Na+ or Ca2+ ( Supplemental S1 Fig ) . These results suggested that the major ions in our recording solutions do not permeate the membranes of Bge cells . H+ channels play important roles in many types of immune cells [35] , so we explored the possibility that H+ channels reside in the membranes of Bge cells . Subjecting Bge cells to pH gradients ( by adjusting the pH of the pipette and bathing solutions–see Methods ) [36] altered the current elicited by voltage steps and shifted the relationship between current and voltage ( Fig 2 ) . A gradient of two pH units ( pH 5in/pH 7out ) reduced the current amplitude at all voltages and shifted the reversal potential in the plot of peak current versus voltage in the negative direction by 17 . 5 mV ( Fig 2B , dashed line ) . Reversing the pH gradient ( pH 5out/pH 7in ) shifted the peak current-voltage plot in the opposite direction with a positive shift in the reversal potential of 27 . 5 mV ( Fig 2B , dotted line ) . Plots of plateau current versus voltage showed similar shifts ( Supplemental S2 Fig ) . Table 1 presents the reversal potentials along with the Nernst potentials for H+ . The shifts are in the direction of the H+ Nernst potential but smaller in magnitude because the H+ concentration is much lower relative to the concentrations of other ions in the solutions . Channels permeable to other ions generally result in H+ current reversal potential shifts that are less than the change in the H+ Nernst potential [37] . The effects of pH gradients on membrane currents are consistent with the presence of an H+ channel in Bge cell membranes . As an additional test for the presence of H+ channels we applied the H+ channel blocker Zn2+ [36 , 38] . Pressure application of 1 mM ZnCl2 from a glass pipette onto a Bge cell significantly reduced both peak and plateau currents elicited by voltage steps from -50 to 20 mV ( Fig 3A ) . This blockade was reversible , as demonstrated by current recovery after ZnCl2 removal ( Fig 3A , wash trace ) . Time course plots in which ZnCl2 was perfused onto cells through the bathing medium showed a 3 . 5-fold reduction in current amplitude ( Fig 3B and 3C ) , from 621 ± 4 pA to 177 ± 1 pA ( N = 4 ) , supporting the presence of H+ channels in Bge cell membranes . Although other actions of Zn2+ cannot be ruled out , the block of membrane current is consistent with the presence of H+ channels in Bge cells . As larval schistosome proteins have been shown to modulate a variety of snail hemocyte immune functions [14 , 15] , we tested the effects of S . mansoni LTP on Bge cell membrane current . Pressure application of LTP onto Bge cells dramatically increased the peak and plateau currents evoked by steps from -50 mV to 20 mV ( Fig 4A ) . LTP increased the current significantly by over 2-fold ( 478 ± 6 pA ) compared to control ( 212 ± 4 pA ) , and this increase only partially reversed with a 17% decrease ( 397 ± 7 pA ) following removal of LTP . Recovery was slow , and 5 min after LTP removal the current had decreased only slightly ( Fig 4B and 4C ) . Plotting current versus time also illustrated the opposite effects of LTP and ZnCl2 on Bge cells ( Fig 4C ) . This plot showed a >2-fold increase in current amplitude in the presence of LTP ( Fig 4C blue circles ) and a >2-fold reduction in the presence of ZnCl2 ( Fig 4C , red triangles ) compared to control ( Fig 4C black squares ) . The reversal of block by ZnCl2 was rapid and essentially complete , while the reversal of enhancement by LTP was slow . Moreover , when heat-denatured LTP was pressure-applied onto Bge cells , we observed no significant change compared to control current amplitudes ( Fig 5C and 5D ) , indicating that the action of LTP on H+ channels depends on heat-labile factors . To determine whether LTP increased Bge cell membrane current by opening H+ channels , we applied LTP and ZnCl2 simultaneously , and observed no statistically significant change ( Fig 5 ) , indicating that ZnCl2 counters the effect of LTP . Finally , we noted that current-voltage curves shifted in the presence of LTP and ZnCl2; LTP caused a 9 mV right-shift from control , toward the H+ Nernst potential , while ZnCl2 caused a 23 mV left-shift , away from the H+ Nernst potential ( Fig 6 ) . These results are consistent with the blockade of H+ channels by ZnCl2 and enhancement of H+ channels by LTP . Because H+ channels contribute to ROS production in mammalian immune cells [35 , 39] , we measured the generation of ROS in Bge cells with the fluorescent probe 2’7’-dichlorofluorescein-diacetate ( DCFH-DA ) . We observed a rapid and robust fluorescence increase that reflects constitutive ROS production . ZnCl2 and LTP + ZnCl2 inhibited this activity by ~50% compared to the untreated control ( F3 , 16 = 24 . 26 , p < 0 . 05 ) . These results demonstrate a linkage between H+ channels and the production of ROS in Bge cells . LTP alone produced a small apparent increase in ROS production , but this increase was not statistically significant . This suggests that at control level of H+ current in Bge cells , other factors limit ROS production ( Fig 7A and 7B ) . To identify putative H+ channel proteins expressed by Bge cells and B . glabrata hemocytes we searched the B . glabrata genome ( https://www . vectorbase . org/organisms/biomphalaria-glabrata ) using Blastp for homologues of human HVCN1 protein . The closest match was an HVCN1-like protein ( BgHVCN1-like , Accession number . XM_013231505 ) with 31% identity to human HVCN1 . This sequence contained the motif RLWRVTR , which is consistent with the H+ channel consensus sequence RxWRxxR [36] . A segment of the predicted sequence was then used to design primers for polymerase chain reactions ( PCR ) . Using cDNA from Bge cells and B . glabrata hemocytes ( NMRI and BS-90 strains ) as templates , PCR using the primers stated in the Methods section yielded amplicons of similar size with 99% sequence identity ( E = 0 . 0 ) to the predicted B . glabrata HVCN1-like sequence ( Supplemental S3 Fig ) . The amplified products encode 120 amino acid stretch of the 186 residues predicted for molluscan HVCN1-like protein . These results indicate that mRNA with the predicted sequence for a BgHVCN1-like gene is present in both Bge cells and hemocytes .
This investigation revealed the presence of functional ion channels in Bge cell membranes . pH manipulations altered the voltage dependence of membrane currents in a manner consistent with a dominant H+ permeability . Since the H+ concentration was several orders of magnitude lower than the other ions in our solutions , even low permeabilities to other ions can make large contributions to the observed reversal potentials and move them away from the H+ Nernst potential . Thus , although currents did not reverse at the H+ Nernst potential , the shifts were in the appropriate direction and supported the hypothesis that H+ channels are the predominant ion permeability in the plasma membrane of Bge cells . We also found that the H+ channel blocker Zn2+ significantly reduced the current through Bge cell membranes , providing additional support for the presence of H+ channels . Finally , we identified and sequenced an HVCN1-like transcript expressed in both this snail cell line and B . glabrata hemocytes , suggesting a functional linkage between these cell types . Thus , three independent lines of evidence support the conclusion that Bge cells express functional H+ channels . With few exceptions [40] , previous studies focusing on ion channels in molluscs almost exclusively have involved neuronal cell systems and/or emphasized Na+ , K+ , Ca2+ or Cl- channel activities [41–43] . To our knowledge , this is the first report of a functional H+ channel in non-neuronal cells of freshwater gastropods . Similar to the well documented association between H+ channels and ROS production in mammalian immunocytes [39] , we also found that blockade of the H+ channel with Zn2+ significantly abrogated Bge cell ROS production , indicating a functional association between channel-mediated H+ flux across the membrane and the oxidative response . This finding is significant since the formation and release of several ROS , especially H2O2 , and RNS are known to be involved in the killing of larval S . mansoni by B . glabrata hemocytes [9 , 10] . It is possible that , as in mammalian immune cells [39 , 44] , changes in membrane potential associated with ROS production also require a compensatory activation of H+ channels to maintain pH balance in immunocyte-like molluscan cells . It is important to note that hemocytes from both resistant and susceptible strains of B . glabrata snails are capable of generating ROS [11 , 32] , but differ both qualitatively and quantitatively in their responses [11] . Since Bge cells were originally derived from a S . mansoni-susceptible Puerto Rican strain of B . glabrata [25] , it is likely that hemocytes from a related susceptible strain ( NMRI ) also share both molecular and functional similarities to Bge cells . These shared characteristic have been well-documented in previous studies [26 , 45 , 46] , supporting the use of this cell line as a hemocyte-like model , as well as a general model for Biomphalaria-schistosome interactions [29 , 47] . Based on the presence and expression of the HVCN1-like gene in B . glabrata hemocytes , it is quite possible that voltage-gated H+ channels are also involved in regulating cellular ROS production as demonstrated in Bge cells . Proteins released during the S . mansoni miracidium-to-sporocyst transformation ( LTP ) have been shown to modulate a variety of functions in both hemocytes and Bge cells [14 , 24 , 45] . Such a role is supported by our finding of an LTP-induced potentiation of H+ channel activity . Exposure to LTP elicited a rapid and sustained enhancement of Bge cell membrane current . Because the reversal potential moved toward the H+ Nernst potential , it is likely that LTP increased the current through H+ channels . This activity was heat-labile , suggesting that the channel-active LTP component ( s ) may be a protein ( s ) with irreversible or slowly reversing action . However , it remains unclear whether the regulation of Bge cell H+ channels by schistosome LTPs results from factors thought to play a role in host-parasite compatibility [48–50] or other , yet unidentified , larval factors . The H+ channel may play a role in co-evolutionary mechanisms , known to affect oxidant-antioxidant levels during parasite-host interaction [51] . Identifying the active components of LTP and determining whether this response reflects the action of a single or multiple species will require further investigation . Despite the channel stimulating action of LTP , LTP treatment of Bge cells resulted in no statistically significant increase in ROS production . These results are consistent with previous findings that exposure of B . glabrata hemocytes to excretory-secretory products of larval S . mansoni exerted little effect on the production of ROS [52] . However , the question remains as to why LTP-stimulated H+ channel activation failed to enhance ROS production . Based on the H+ current data , it might be speculated that LTP binding to Bge cells is linked to the opening of H+ channels through receptor-mediated activation of a channel-associated signaling pathway , possibly through interactions with pathogen recognition receptors such as fibrinogen-related proteins , Toll-like receptors , or bacterial binding proteins that have been implicated in B . glabrata immunity [50 , 53–55] . Mitogen-activated and extracellular-signal regulated protein kinases shown to function in molluscan immunity [12 , 22] could also play a role in signaling to the H+ channel . A final possibility is that LTP may be acting directly on the channel protein itself to induce opening . The consequence of H+ channel modulation would be an alteration or disruption of H+ ion balance and intracellular pH , but without stimulating ROS production . This may , in turn , serve as a potent anti-immune mechanism used by sporocysts for countering host ROS-mediated effector responses . Thus , H+ channels , while serving an important role in maintaining pH balance within Bge cells and hemocytes , may also be manipulated by schistosome larvae to reduce their immune efficacy . Since Bge cells were originally derived from a S . mansoni-susceptible PR albino strain of B . glabrata [25] , it is likely that hemocytes from a related susceptible strain ( NMRI ) also share sensitivity to H+ channel–reactive anti-immune proteins , thereby supporting a compatible snail-schistosome interaction . In conclusion , Bge cells possess a functional H+ channel that is responsible for a dominant conductance of their plasma membrane . ROS production is dependent on H+ channels . Exposure of cells to heat-labile LTP stimulates channel opening and H+ flux , but has little if any effect on the generation of ROS . Although H+ channels have not been tested directly in B . glabrata hemocytes , PCR amplification and amplicon sequencing demonstrated the presence of HVCN1-like transcripts in both susceptible and resistant B . glabrata strains . Thus , the association of the Bge cell H+ channel activity with cellular ROS production and the channel’s response to schistosome LTP suggest a role in regulating larval schistosome-snail interactions . Future identification of the specific mechanism ( s ) tying together these activities should provide important insights into host-parasite compatibility in this system . | Schistosoma mansoni is one of four major species of human blood flukes that , together , infect over 250 million people worldwide . Transmission of S . mansoni to humans requires infection of freshwater intermediate host snails , Biomphalaria spp . , in order to complete its life cycle . The B . glabrata embryonic ( Bge ) cell line , derived from a Puerto Rican strain of snail host shares characteristics with circulating hemocytes , the molluscan immune cells , and serves as an in vitro model for snail immune function . Electrical recordings from Bge cells demonstrated the presence of H+ channels that allow hydrogen ions ( H+ ) to cross the membrane . Furthermore , blocking these channels inhibited the production of reactive oxygen species ( ROS ) , an immune defense mechanism shared by Bge cells and hemocytes . Interestingly , Bge cell exposure to proteins produced by S . mansoni larvae exerted the opposite effect , enhancing H+ movement across the cell membrane . An H+ channel-encoding gene was expressed in both Bge cells and hemocytes suggesting that hemocytes may share similar functions with Bge cells . |
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Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies . Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences . Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously . In particular , new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies , which cannot be revealed by pairwise analyses alone . In this paper , we develop a method for estimating time-varying spike interactions by means of a state-space analysis . Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework . We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters . This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons , thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis . Furthermore , the method can estimate dynamic higher-order spike interactions . To validate the inclusion of the higher-order terms in the model , we construct an approximation method to assess the goodness-of-fit to spike data . In addition , we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data , e . g . , data from awake behaving animals . The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics . Finally , we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand .
Precise spike coordination within the spiking activities of multiple single neurons is discussed as an indication of coordinated network activity in the form of cell assemblies [1] comprising neuronal information processing . Possible theoretical mechanisms and conditions for generating and maintaining such precise spike coordination have been proposed on the basis of neuronal network models [2]–[4] . The effect of synchronous spiking activities on downstream neurons has been theoretically investigated and it was demonstrated that these are more effective in generating output spikes [5] . Assembly activity was hypothesized to organize dynamically as a result of sensory input and/or in relation to behavioral context [6]–[10] . Supportive experimental evidence was provided by findings of the presence of excess spike synchrony occurring dynamically in relation to stimuli [11]–[14] , behavior [14]–[19] , or internal states such as memory retention , expectation , and attention [8] , [20]–[23] . Over the years , various statistical tools have been developed to analyze the dependency between neurons , with continuous improvement in their applicability to neuronal experimental data ( see [24]–[26] for recent reviews ) . The cross-correlogram [27] was the first analysis method for detecting the correlation between pairs of neurons and focused on the detection of stationary correlation . The joint-peri stimulus time histogram ( JPSTH ) introduced by [11] , [28] is an extension of the cross-correlogram that allows a time resolved analysis of the correlation dynamics between a pair of neurons . This method relates the joint spiking activity of two neurons to a trigger event , as was done in the peri-stimulus time histogram ( PSTH ) [29]–[31] for estimating the time dependent firing rate of a single neuron . The Unitary Event analysis method [25] , [32] , [33] further extended the correlation analysis to enable it to test the statistical dependencies between multiple , nonstationary spike sequences against a null hypothesis of full independence among neurons . Staude et al . developed a test method ( CuBIC ) that enables the detection of higher-order spike correlation by computing the cumulants of the bin-wise population spike counts [34] , [35] . In the last decade , other model-based methods have been developed that make it possible to capture the dependency among spike sequences by direct statistical modeling of the parallel spike sequences . Two related approaches based on a generalized linear framework are being extensively investigated . One models the spiking activities of single neurons as a continuous-time point process or as a discrete-time Bernoulli process . The point process intensities ( instantaneous spike rates ) or Bernoulli success probabilities of individual neurons are modeled in a generalized linear manner using a log link function or a logit link function , respectively [36]–[38] . The dependency among neurons is modeled by introducing coupling terms that incorporate the spike history of other observed neurons into the instantaneous spike rate [38]–[40] . Recent development in causality analysis for point process data [41] makes it possible to perform formal statistical significance tests of the causal interactions in these models . Typically , the models additionally include the covariate stimulus signals in order to investigate receptive field properties of neurons , i . e . , the relations between neural spiking activities and the known covariate signals . However , they are not suitable for capturing instantaneous , synchronous spiking activities , which are likely to be induced by an unobserved external stimulus or a common input from an unobserved set of neurons . Recently , a model was proposed to dissociate instantaneous synchrony from the spike-history dependencies; it additionally includes a common , non-spike driven latent signal [42] , [43] . These models provide a concise description of the multiple neural spike train data by assuming independent spiking activities across neurons conditional on these explanatory variables . As a result , however , they do not aim to directly model the joint distribution of instantaneous spiking activities of multiple neurons . In contrast , an alternative approach , which we will follow and extend in this paper , directly models the instantaneous , joint spiking activities by treating the neuronal system as an ensemble binary pattern generator . In this approach , parallel spike sequences are represented as binary events occurring in discretized time bins , and are modeled as a multivariate Bernoulli distribution using a multinomial logit link function . The dependencies among the binary units are modeled in the generalized linear framework by introducing undirected pairwise and higher-order interaction terms for instantaneous , synchronous spike events . This statistical model is referred to as the ‘log-linear model’ [44] , [45] , or Ising/spin-glass model if the model contains only lower-order interactions . The latter is also referred to as the maximum entropy model when these parameters are estimated under the maximum likelihood principle . In contrast to the former , biologically-inspired network-style modeling , the latter approach using a log-linear model was motivated by the computational theory of artificial neural networks originating from the stationary distribution of a Boltzmann machine [46] , [47] , which is in turn the stochastic analogue of the Hopfield network model [48] , [49] for associative memory . The merit of the log-linear model is its ability to provide a well-defined measure of spike correlation . While the cross-correlogram and JPSTH provide a measure of the marginal correlation of two neurons , these methods cannot distinguish direct pairwise correlations from correlations that are indirectly induced through other neurons . In contrast , a simultaneous pairwise analysis based on the log-linear model ( an analogue of the Ising/spin-glass analysis in statistical mechanics ) can sort out all of the pair-dependencies of the observed neurons . A further merit of the log-linear model is that it can provide a measure of the ‘pure’ higher-order spike correlation , i . e . , a state that can not be explained by lower-order interactions . Using the viewpoint of an information geometry framework [50] , Amari et al . [44] , [45] , [51] demonstrated that the higher-order spike correlations can be extracted from the higher-order parameters of the log-linear model ( a . k . a . the natural or canonical parameters ) . The strengths of these parameters are interpreted in relation to the lower-order parameters of the dual orthogonal coordinates ( a . k . a . the expectation parameters ) . The information contained in the higher-order spike interactions of a particular log-linear model can be extracted by measuring the distance ( e . g . , the Kullback-Leibler divergence ) between the higher-order model and its projection to a lower-order model space , i . e . , a manifold spanned by the natural parameters whose higher-order interaction terms are fixed at zero [44] , [52]–[54] . Recently , a log-linear model that considered only up to pairwise interactions ( i . e . , an Ising/spin-glass model ) was proposed as a model for parallel spike sequences . Its adequateness was shown by the fact that the firing rates and pairwise interactions explained more than % of the data [55]–[57] . However , Roudi et al . [54] demonstrated that the small contribution of higher-order correlations found from their measure based on the Kullback-Leibler divergence could be an artifact caused by the small number of neurons analyzed . Other studies have reported that higher-order correlations are required to account for the dependencies between parallel spike sequences [58] , [59] , or for stimulus encoding [53] , [60] . In [60] , they reported the existence of triple-wise spike correlations in the spiking activity of the neurons in the visual cortex and showed their stimulus dependent changes . It should be noted , though , that these analyses assumed stationarity , both of the firing rates of individual neurons and of their spike correlations . This was possible because the authors restricted themselves to data recorded either from in vitro slices or from anesthetized animals . However , in order to assess the behavioral relevance of pairwise and higher-order spike correlations in awake behaving animals , it is necessary to appropriately correct for time-varying firing rates within an experimental trial and provide an algorithm that reliably estimates the time-varying spike correlations within multiple neurons . We consider the presence of excess spike synchrony , in particular the excess synchrony explained by higher-order correlation , as an indicator of an active cell assembly . If some of the observed neurons are a subset of the neurons that comprise an assembly , they are likely to exhibit nearly completely synchronous spikes every time the assembly is activated . It may be that such spike patterns are not explained by mere pairwise correlations , but require higher-order correlations for explanation of their occurrence . One of the potential physiological mechanisms for higher-order correlated activity is a common input from a set of unobserved neurons to the assembly that includes the neurons under observation [25] , [61]–[63] . Such higher-order activity is transient in nature and expresses a momentary snapshot of the neuronal dynamics . Thus , methods that are capable of evaluating time-varying , higher-order spike correlations are crucial to test the hypothesis that biological neuronal networks organize in dynamic cell assemblies for information processing . However , many of the current approaches based on the log-linear model [44] , [45] , [53] , [55] , [56] , [61] , [62] , [64] , [65] are not designed to capture their dynamics . Very recently two approaches were proposed for testing the presence of non-zero pairwise [66] and higher-order [67] correlations using a time-dependent formulation of a log-linear model . In contrast to these methods , the present paper aims to directly provide optimized estimates of the individual time-varying interactions with confidence intervals . This enables to identify short lasting , time-varying higher-order correlation and thus to relate them to behaviorally relevant time periods . In this paper , we propose an approach to estimate the dynamic assembly activities from multiple neural spike train data using a ‘state-space log-linear’ framework . A state-space model offers a general framework for modeling time-dependent systems by representing its parameters ( states ) as a Markov process . Brown et al . [37] developed a recursive filtering algorithm for a point process observation model that is applicable to neural spike train data . Further , Smith and Brown [68] developed a paradigm for joint state-space and parameter estimation for point process observations using an expectation-maximization ( EM ) algorithm . Since then , the algorithm has been continuously improved and was successfully applied to experimental neuronal spike data from various systems [38] , [69]–[71] ( see [72] for a review ) . Here , we extend this framework , and construct a multivariate state-space model of multiple neural spike sequences by using the log-linear model to follow the dynamics of the higher-order spike interactions . Note that we assume for this analysis typical electrophysiological experiments in which multiple neural spike train data are repeatedly collected under identical experimental conditions ( ‘trials’ ) . Thus , with the proposed method , we deal with the within-trial nonstationarity of the spike data that is expected in the recordings from awake behaving animals . We assume , however , that dynamics of the spiking statistics within trials , such as time-varying spike rates and higher-order interactions , are identical across the multiple experimental trials ( across-trial stationarity ) . To validate the necessity of including higher-order interactions in the model , we provide a method for evaluating the goodness-of-fit of the state-space model to the observed parallel spike sequences using the Akaike information criterion [73] . We then formulate a hypothesis test for the presence of the latent , time-varying spike interaction parameters by combining the Bayesian model comparison method [74]–[76] with a surrogate method . The latter test method provides us with a tool to detect assemblies that are momentarily activated , e . g . , in association with behavioral events . We test the utility of these methods by applying them to simulated parallel spike sequences with known dependencies . Finally , we apply the methods to spike data of three neurons simultaneously recorded from motor cortex of an awake monkey and demonstrate that a triple-wise spike correlation dynamically organizes in relation to a behavioral demand . The preliminary results were presented in the proceedings of the IEEE ICASSP meeting in 2009 [77] , as well as in conference abstracts ( Shimazaki et al . , Neuro08 , SAND4 , NIPS08WS , Cosyne09 , and CNS09 ) .
For a given number of neurons , , we can construct state-space log-linear models that contain up to the th-order interactions ( ) . While the inclusion of increasingly higher-order interaction terms in the model improves its accuracy when describing the probabilities of spike patterns , the estimation of the higher-order log-linear parameters of the model may suffer from large statistical fluctuations caused by the paucity of synchronous spikes in the data , leading to an erroneous estimation of such parameters . This problem is known as ‘over-fitting’ the model to the data . An over-fitted model explains the observed data , but loses its predictive ability for unseen data ( e . g . , spike sequences in a new trial under the same experimental conditions ) . In this case , the exclusion of higher-order parameters from the model may better explain the unseen data even if an underlying spike generation process contains higher-order interactions . The model that has this predictive ability by optimally resolving the balance between goodness-of-fit to the observed data and the model simplicity is obtained by maximizing the cross-validated likelihood or minimizing the so-called information criterion . In this section , we select a state-space model that minimizes the Akaike information criterion ( AIC ) [73] , which is given as ( 11 ) The first term is the log marginal likelihood , as in Eq . 10 . The second term that includes is a penalization term . The AIC uses the number of free parameters in the marginal model ( i . e . , the number of free parameters in ) for . Please see in the Methods subsection ‘Selection of state-space model by information criteria’ for an approximation method to compute the marginal likelihood . Selecting a model that minimizes the AIC is expected to be equivalent to selecting a model that minimizes the expected ( or average ) distance between the estimated model and unknown underlying distribution that generated the data , where the ‘distance’ measure used is the Kullback-Leibler ( KL ) divergence . The expectation of the KL divergence is called the KL risk function . One of the goals of a time resolved analysis of spike correlation is to discover dynamical changes in the correlated activities of neurons that reflect the behavior of an animal . This implies the necessity of dealing with the within-trial nonstationarity that is typically present in the data from awake behaving animals . However , we know from other correlation analysis approaches that , if not well corrected for , nonstationary spike data bear the potential danger of generating false outcomes [25] , [83] . Here we deal with the within-trial nonstationarity of the data by using the state-space log-linear model while assuming identical dynamic spiking statistics across trials ( across-trial stationarity ) . In order to correctly detect the time-varying correlation structure within trials , we apply to the state-space log-linear model a Bayesian model comparison method based on the Bayes factor ( BF ) [74]–[76] , and combine it with a surrogate approach . The BF is a likelihood ratio for two different hypothetical models of latent signals , e . g . , in our application , different underlying spike correlation structures . Using the BF , we determine which of the two spike correlation models the spike data supports . By computing the BF for a particular task period in a behavioral experiment , we can test whether the assumed correlation structure appears in association with the timing of the animal's behavior . In the following , we denote a specific task period of interest by the time period . In this study , the BF , , is defined as the ratio of the marginal likelihoods of the observed spike patterns , , in the time period under different models , or , assumed for the hidden state parameters , ( 12 ) By successively conditioning the past , the BF is computed by the multiplication of the bin-by-bin one-step BF given at time as . Here , the bin-by-bin BF at time , , can be calculated as ( see the Methods subsection , ‘Bayesian model comparison method for detecting spike correlation’ ) , ( 13 ) where is the space of the interaction parameters , , for the model , ( ) . In Eq . 13 , is the filter density and is called the one-step prediction density , both of which are obtained in the Bayesian recursive filtering algorithm developed in the Methods section ( cf . Eqs . 25 , 26 and Eqs . 31 , 32 ) . Therefore , the bin-by-bin BF at time , , is the ratio of the odds ( of opposing models ) found by observing the spike train data up to time ( filter odds , the numerator in Eq . 13 ) to the odds predicted from without observing the data at time ( prediction odds , the denominator in Eq . 13 ) . Thus , an unexpected synchronous spike pattern that significantly updates the filter odds for the interaction parameters from their predicted odds gives rise to a large absolute value for the BF . Because the posterior densities are approximated as a multivariate normal distribution in our filtering algorithm , the BF at time can be easily computed by using normal distribution functions . Please see the subsection , ‘Bayesian model comparison method for detecting spike correlation’ , in the Methods section for the derivation of Eq . 13 and detailed analysis of the BF . The BF becomes larger than 1 if the data , , support model as opposed to as an underlying spike correlation structure and becomes smaller than 1 if the data support model as opposed to . Alternatively , it is possible to use the logarithm of the BF , known as the ‘weight of evidence’ [75] which becomes positive if the data support model as opposed to model and negative in the opposite situation . Below , we display the results for the BF in bit units ( logarithm of the BF to base 2 ) , i . e . , the weight of evidence . By sequentially computing the bin-by-bin BF , we can obtain the weight of evidence in a period as the summation of the local weight of evidence: . An intuitive interpretation of the BF values is provided in the literature [74] , [76] . For example , in [76] , a BF ( weight of evidence ) from 1 . 6 to 4 . 3 bit was interpreted as ‘positive’ evidence in favor of against . Similarly , a BF from 4 . 3 to 7 . 2 bit was interpreted as ‘strong’ evidence , and a BF larger than 7 . 2 bit was found to be ‘very strong’ evidence in favor of against . While the classical guidelines are useful in practical situations , they are defined subjectively . Thus , in this study , in order to objectively analyze the observed value of the BF , we combine the Bayesian model comparison method with a surrogate approach . In this surrogate method , we test the significance of the observed BF for the tested spike interactions by comparing it with the surrogate BFs computed from the null-data generated by destroying only the target spike interactions while the other structures such as the time-varying spike-rates and lower-order spike interactions are kept intact . The BF in a behaviorally relevant sub-interval can be computed from the optimized state-space log-linear model fitted to the entire spike train data in . Here , for the purpose of testing spike correlation in the sub-interval , we recommend to use in the state model because the autoregressive parameters are optimized for entire spike train data , which are not necessarily optimal for the sub-interval . Similarly , a typical trial-based experiment is characterized by discrete behavioral or behaviorally relevant events , e . g . movement onset after a go signal or a cue signal for trial start , etc . Thus , on top of the expected smooth time-varying change in the spike-rate and spike-correlation , sudden transitions may be expected in their temporal trajectories . Because we use time-independent smoothing parameters ( i . e . , hyper parameter in Eq . 8 ) that were optimized to entire data ( see the EM algorithm in the Methods section ) , such abrupt changes may not be captured very well . This may cause a false detection or failure in the detection of the spike correlation at the edge of a task period . For such data , we suggest applying the Bayesian model comparison method to state-space models which are independently fitted to each of the task periods ( or relatively smooth sub-intervals within each task period ) .
In this study , we introduced a novel method for estimating dynamic spike interactions in multiple parallel spike sequences by means of a state-space analysis ( see Methods for details ) . By applying this method to nonstationary spike train data using the pairwise log-linear model , we can extend the stationary analysis of the spike train data by the Ising/spin-glass model to within-trial nonstationary analysis ( Figure 3 ) . In addition , our approach is not limited to a pairwise analysis , but can perform analyses of time-varying higher-order spike interactions ( Figure 4 ) . It has been discussed whether higher-order spike correlations are important to characterize neuronal population spiking activities , assuming stationarity in the spike data [54]–[59] . Based on the state-space model optimized by our algorithm , we developed two methods to validate and test its latent spike interaction parameters , in particular the higher-order interaction parameters , which may dynamically change within an experimental trial . In the first method , we selected the proper order for the spike interactions incorporated in the model under the model selection framework using the approximate formula of the AIC for this state-space model ( In Methods , ‘Selection of state-space model by information criteria’ ) . This method selects the model that best fits the data overall across the entire observation period . The selected model can then be used to visualize the dynamic spike interactions or for a performance comparison with other statistical models of neuronal spike data . However , more importantly , the detailed structure of the transient higher-order spike interactions needs to be tested locally in time , particularly in conjunction with the behavioral paradigm . To meet this goal , we combined the Bayesian model comparison method ( the Bayes factor ) with a surrogate method ( In Methods , ‘Bayesian model comparison method for detecting spike correlation’ ) . The method allows us to test for the presence of higher-order spike correlations and examine its relations to experimentally relevant events . We demonstrated the utility of the method using neural spike train data simultaneously recorded from primary motor cortex of an awake monkey . The result is consistent with , and further extended the findings in the previous report [8]: We detected an increase in triple-wise spike interaction among three neurons in the motor cortex during the preparatory period in a delayed motor task , which was also tightly locked to the expected signals . Although the analysis was done for a limited number of neurons , smaller than the expected size of an assembly , it demonstrates that the nonstationary analysis of the higher-order activities is useful to reveal cooperative activities of the neurons that are organized in relation to behavioral demand . Of course , further analysis is required to strengthen the findings made above including a meta-analysis of many different sets of multiple neurons recorded under the same conditions . In this study , we adopted the log-linear model to describe the higher-order correlations among the spiking activities of neurons . There are , however , other definitions for ‘higher-order spike correlation’ . An important alternative concept is the definition based on cumulants . Using the cumulants of an observed count distribution from a spike train pooled across neurons , Staude et al . developed an iterative test method that can detect the existence of a high amplitude in the jump size distribution of the assumed compound Poisson point process ( CPP ) model for the pooled spike train [34] , [35] . This method can detect an assembly from a few occurrences of synchronous spike events to which many neurons belong to , typically by using the lower-order cumulants of the observed spike counts . In contrast , the information geometry measure for the higher-order spike correlation used in this study aims to represent the correlated state that cannot be explained by lower-order interactions . Consequently , the information geometry measure extracts the relative strength of the higher-order dependency to the lower-order correlated state . Therefore , the presence of positive higher-order spike correlations does not necessarily indicate that many neurons spike synchronously whenever they spike because such activities can be induced by positive pairwise spike correlations alone [65] , [85] , [86] ( see also Figure 8A and B in the Methods section ) . In contrast , the cumulant-based correlation method by Staude et al . [34] infers the presence of ‘higher-order correlation’ for such data by determining the presence of high amplitudes in the jump size distribution of the assumed CPP model . Yet another important tool for analyzing higher-order dependency among multiple neurons is the copula function , a standardized cumulative distribution function used to model the dependence structure of multiple random variables ( see [87]–[89] for an analysis of neurophysiological data using the copula , including an analysis for higher-order dependency [89] ) . In summary , it should be remembered that the analysis method used for the higher-order dependency among neuronal spikes inherits its goal from the assumed model for spike generation as well as a parametric measure defined for the ‘higher-order’ spike correlation [34] , [62] . Although we face a high-dimensional optimization problem in our settings , we are able to successfully obtain MAP estimates of the underlying parameters because of the simplicity of the formulation of the state-space model: The use of the log-concave exponential family distributions [50] , [90] in both the state and observation models guarantees that the MAP estimates can be obtained using a convex optimization program . At each bin , the method numerically solves a nonlinear filter equation to obtain the mode of the posterior state density ( the MAP estimates , see Eqs . 28 , 29 , and 30 in Methods ) . With only a few ( 3–8 ) Newton-Raphson iterations , the solution reaches a plateau ( the increments of all the elements of the updated state space vector are smaller than ) . The entire optimization procedure can be performed in a reasonable amount of time: On a contemporary standard laptop computer it takes no longer than 30 seconds to obtain smooth estimates of a full log-linear model for neurons ( bins , Figure 4 ) , which includes 100 EM iterations . The method is even faster when approximating the posterior mode using the update formula Eq . 30 without any iterations , using the one-step prediction mean as an initial value . This fast approximation method suggested in [69] could even be utilized in a real-time , on-line application of our filter ( the filtering method applied to a single trial , , using predetermined hyper-parameters ) at the cost of estimation accuracy . The pairwise analysis can be applied up to about neurons simultaneously to derive time-dependent pair interactions . However , the current version of the algorithm does not scale to a larger number of neurons because the number of spike patterns that need to be considered suffers from a combinatorial explosion . The major difficulty arises from the coordinate transformation from the -coordinates to the -coordinates that appear in the non-linear filter equation ( Eq . 31 in Methods ) . The coordinate transformation is required in this equation to calculate the innovation signal , i . e . , the difference between the observed synchrony rates , , and the expected synchrony rates ( -coordinates ) based on the model . We numerically derived the exact -coordinates by marginalizing the dimensional joint probability mass function computed from the -coordinates . Thereby , a full knowledge of the probability mass function is required even if the model considers only the lower-order interactions . Because this is a common problem in the learning of artificial neural networks [46] , [47] , [91] , sampling algorithms such as the Markov chain Monte Carlo method have been developed to approximate the expectation parameters , , without having to compute the partition function [92] . The inclusion of such methods allows us to analyze the time-varying low order spike interactions from a larger number of parallel spike sequences . Recent progress [93] , e . g . , in the mean field approach and/or the minimum probability flow learning algorithm for an Ising model , may allow us to further increase the number of neurons that can be treated in this nonstationary pairwise analysis . Nonetheless , the method presented here , which aims at a detailed analysis of the dynamics in higher-order spike interactions , may not easily scale to massively parallel spike sequences that can be analyzed by other methods such as those based on the statistics pooled across neurons . Thus , we consider it to be important to combine the detailed analysis method proposed in this study with other state-of-the-art analysis techniques in practical applications . For example , test methods based on population measures such as the Unitary Event method and cumulant-based inference method [34] , [35] allow us to detect the existence of statistically dependent neurons in massively parallel spike sequences . If the null-hypothesis of independence among those neurons is not rejected in these methods , we can exclude those neurons from any further detailed analysis of their dynamics using the methods proposed in this study . Several critical assumptions made in the current framework need to be addressed . First , it was assumed in constructing the likelihood ( Eq . 7 ) that no spike history effect exists in the generation of a population spike pattern . Second , we assumed the use of identically and independently distributed samples across trials when constructing the likelihood ( Eq . 7 ) . The first assumption may appear to be strong constraint given the fact that individual neurons exhibit non-Poisson spiking activities [94] . However , as in the case of the estimation of the firing rate of a single neuron , the pooled spike train across the ( independent ) trials is assumed to obey a Poisson point process because of the general limit theorem for point processes [30] , [31] , [95] , [96] . This is because most of the spikes in the pooled data come from independent different trials . They are thus nearly statistically independent from each other , even if the individual processes are non-Poisson . Similarly , in our analysis , we used statistics from a pooled binary spike train , assuming independence across trials: The occurrences of joint spikes in the binary data pooled across trials are mostly independent of each other across bins . Because these joint spike occurrences are sparse ( i . e . , they rarely happen closely to each other in the same trial ) , it is even more feasible to assume their statistical independence across bins . Third , however , while pooling independent and identical trials ( the second assumption ) may validate the first assumption of the independence of the samples across bins , that assumption of independently and identically distributed samples across trials has itself been challenged [97] , [98] and is known to be violated in some cases , e . g . , by drifting attention , ongoing brain activity , adaptation , etc . It is possible that the trial-by-trial jitter/variation in the spike data causes spurious higher-order spike correlation . Thus , as discussed in the section on the application of our methods to real neuronal data , it is important to examine the stationarity of the spike train data across trials . Note that , not only the firing rates , but also the spike synchrony can be shaped on a longer time scale by repeatedly practicing a task [19] . In fact , the current analysis method can be used to examine the long-term evolution of pairwise and higher-order spike interactions across trial sessions by replacing the role of a bin with a trial , assuming within-trial stationarity . It will be a challenge to construct a state-space log-linear model that additionally applies a smoothing method across trials ( see [98] for such a method for a point process model ) . The present method is left with one free parameter , namely the bin-width . The bin-width determines the permissible temporal precision of synchronous spike events . Very large bin-widths result in binary data that are highly synchronized across sequences , while very small bin-widths result in asynchronous multiple spike sequences . In the latter case , we might overlook the existing dependency between multiple neural spike sequences due to disjunct binning [99] ( but see [57] , [100] that aim to overcome such a problem by modeling the spike interactions across different consecutive time bins ) . Within our proposed modeling framework , which focuses on instantaneous higher-order spike correlations , it is important to catch the innate temporal precision of the neuronal population under investigation using the appropriate bin-width . Thus , the choice may be guided by the biophysical properties of the neurons . However , it may be of advantage to derive the bin-width in a data-driven manner . For example , in the context of an encoding problem , the proper bin-width can be chosen based on the goodness-of-fit test for single neuron spiking activities [101] , conditional on the spiking activities of the other neurons [40] . For questions about the relation of coincident spiking to stimulus/behavior , the bin-width may be selected based , for example , on the predictive ability of an external signal . For this goal , it is important to search the optimal bin-width using elaborate methods such as those developed in the context of the Unitary Event analysis method [99] ( see [25] for a review of related methods ) . A substantial number of studies have demonstrated that stimulus and behavioral signals can be decoded simply based on the firing rates of individual neurons . At the same time , it has been discussed whether spike correlations , particularly higher-order spike correlations , are necessary to characterize neuronal population spiking activities [54]–[58] or to encode or decode information related to stimuli [53] , [60] , [102] . At this point in time , a smaller number of dedicated experiments have supported the conceptual framework of information processing using neuronal assemblies formed by neurons momentarily engaged in coordinated activities , as expressed by temporally precise spike correlations ( see [6] , [7] , [9] , [10] for reviews of these experiments ) . Nevertheless , it is possible that the current perspective on this subject has been partly formed by a lack of proper analysis approaches for simultaneously tracing time-varying individual pairwise spike interactions , and/or their higher-order interactions . Indeed , we demonstrated by the time-resolved higher-order analysis that three cortical neurons coordinated their spiking activities in accordance with behaviorally relevant points in time . Thus our suggested analysis methods are expected to be useful to reveal the dynamics of assembly activities and their neuronal composition , as well as for testing their behavioral relevance . We hope that these methods help shed more light on the cooperative mechanisms of neurons underlying information processing .
In this subsection , we review the known mathematical properties of a log-linear model for binary random variables . These properties are used in constructing recursive filtering/smoothing formulas in the next section . Using the multi-index , ( see the Results subsection ‘Log-linear model of multiple neural spike sequences’ ) , the probability mass function ( Eq . 1 ) , , where and ( ) , and the expectation parameters ( Eq . 2 ) are compactly written as ( 14 ) and ( 15 ) where is a feature function , here representing an interaction among the neurons indicated by the multi-index , ( Eq . 3 ) . The - and -coordinates are dually flat coordinates in the exponential family probability space [44] , [50] , and the coordinate transformation from one to the other is given by the Legendre transformation [40] , [50] . From Eq . 14 , the log normalization function , , is written as ( 16 ) The first derivative of the log normalization function , , with respect to ( ) , provides the expectation parameter , : ( 17 ) Let be the negative entropy of the distribution: ( 18 ) Eqs . 17 and 18 complete the Legendre transformation from -coordinates to -coordinates [44] , [50] . The Legendre transformation transfers the functional relationship of and to the equivalent relation in the dual coordinates , and . The inverse transformation is given by Eq . 18 and . Using the log normalization function , we can obtain the multivariate cumulants of with respect to the random variables , . The cumulant generating function of the exponential family distribution is given as . Let us compactly write the partial derivative with respect to ( i . e . , ) as . Then , the first order cumulant is given as , as shown in Eq . 17 . In general , the cumulants of the exponential family distribution are given by the derivatives of the log normalization function . Thus , the second derivative of yields the second-order cumulant , ( by the cup , , we mean the multi-index representation of an union of the elements of the two multi-indices , e . g . , if and , then ) : ( 19 ) for . is known as the Fisher metric with respect to the natural parameters . Eqs . 17 and 19 are important relations used in this study to construct a non-linear filtering equation for a dynamic estimate of the natural parameters because we approximate the log-linear model ( Eq . 14 ) with a precision of up to a ( log ) quadratic function ( cf . Eqs . 28 and 29 ) . Similarly , the higher-order derivatives yield higher-order multivariate cumulants . For example , the third-order derivative yields the third order cumulant , , where . The pseudo distance between two different distributions , and is defined using the Kullback-Leibler ( KL ) divergence ( 20 ) We represent distribution by using -coordinates as , and by using -coordinates as . Here , we used for the -parameters of ( and for -parameters of ) in order to differentiate it from the representation of distribution in the -coordinates ( and the representation of in the -coordinates ) . Then , the KL-divergence between the two distributions , and , is computed as [44] , [50] ( 21 ) We develop a non-linear recursive Bayesian filtering/smoothing algorithm and its optimization method in order to trace dynamically changing spike interactions from parallel spike sequences . To reach this goal , we use the expectation-maximization ( EM ) algorithm [68] , [73] , [103] , [104] , which is known to efficiently combine the construction of the posterior density of a state ( the natural parameters ) and the optimization of the hyper-parameters . This method maximizes the lower bound of the log marginal likelihood , Eq . 10 . Using Jensen's inequality and nominal hyper-parameters , , the lower bound of the log marginal likelihood with hyper-parameters is given by ( 22 ) Here , represents a negative entropy . The maximization of the lower bound with respect to is equivalent to maximizing the expected complete data log-likelihood in Eq . 22 , known as the -function: ( 23 ) The expectation in the above equation is read as . We maximize the -function by alternating the expectation ( E ) and maximization ( M ) steps . In the E-step , we obtain the expected values with respect to in Eq . 23 using a fixed . In the M-step , we obtain the hyper-parameter , , that maximizes Eq . 23 . The resulting is then used in the next E-step . The details of each step are now given as follows . The method developed in the previous subsection is applicable to a full log-linear model , as well as a model that considers an arbitrary order of interactions . In order to select the most predictive model among the hierarchical models in accordance with the observed spike data , we select the state-space model that minimizes the Akaike information criterion ( AIC ) for a model with latent variables [73] , [105]: ( 40 ) Here , is the log marginal likelihood ( Eq . 10 ) and is the number of free hyper-parameters in the prior distribution . For the th order model , the number of natural parameters is given by . The number of free parameters in the prior distribution is computed as , where each term corresponds to the number of free parameters in , , and . Note that the AIC applied to the state-space model is sometimes referred to as the Akaike Bayesian information criterion ( ABIC ) [73] . In the following , we derive the approximation method to evaluate the AIC for the state-space log-linear model . The log marginal likelihood , Eq . 10 , can be written as ( 41 ) We make a log quadratic approximation to evaluate the integral . To accomplish this , we denote ( 42 ) with ( 43 ) The Laplace approximation of the integral in Eq . 42 is given as [80] ( 44 ) By applying Eqs . 42 , 43 and 44 to Eq . 41 , the log marginal likelihood is approximated as ( 45 ) We confirmed that the log-quadratic approximation provided a better estimate of the marginal likelihood than the first order approximation used in [77] by comparing them with a Monte Carlo approximation of the integral in Eq . 42 . We select the state-space log-linear model that minimizes the AIC ( Eq . 40 ) , where the log marginal likelihood is approximated using Eq . 45 . For comparison with the AIC , we compute two other information criteria that employ different forms of the penalization term . The Bayesian information criterion [79] , [80] ( also known as Schwartz's criterion ) are obtained by replacing the penalization term of Eq . 40 , , with : ( 46 ) Shimodaira's predictive divergence for indirect observation models ( PDIO ) [81] is given as ( 47 ) Here , we redefine as a one-dimensional vector of free hyper-parameters , while denotes the one-step operator of EM iteration . To obtain the Jacobian matrix for the EM operator , we follow the algorithm described in Meng and Rubin [111] . In this method , the Jacobian matrix was approximated using a numerical differentiation of the EM operator . By perturbing one hyper-parameter and then computing a one-step EM iteration , numerical differentiations of the hyper-parameters with respect to the perturbed hyper-parameter were obtained . An entire Jacobian matrix was approximated by repeating the process while changing the hyper-parameter to be perturbed . In this subsection , we formulate a method for detecting the hidden structure of spike interaction by means of a Bayesian model comparison based on the Bayes factor ( BF ) [74]–[76] . The BF is a ratio of likelihoods for the observed data , based on two different assumptions about the hidden states ( model and ) . Here we reiterate the definition of the BF for model as opposed to model used in this paper ( cf . Eq . 12 ) : ( 48 ) The BF becomes larger than 1 if the data , in a time period , supports model as opposed to model , and becomes smaller than 1 if the data supports model as opposed to model . The BF can be computed from the one-step prediction and filter density obtained in the method developed in the preceding subsection . From Eq . 48 , the BF can be rewritten as ( 49 ) Let us define the bin-by-bin BF for the spike data at time as ( 50 ) Using Bayes' theorem , we obtain [74]–[76] ( 51 ) for . Using Eq . 51 , we can rewrite the bin-by-bin BF as ( 52 ) where and denote spaces that the natural parameters occupy , supported by models and , respectively . Here , and are the filter density and one-step prediction density , respectively . By sequentially computing Eq . 52 , we can obtain the BF with respect to the sub-interval as . A test with the following models in the sub-interval allows us to detect a momentarily active cell assembly of more than two neurons by the presence of simultaneously positive th-order spike interactions . In the th-order log-linear model of neurons , the natural parameters , ( ) , represent the th-order spike interactions among neurons denoted in index . We examine whether a subset of neurons among the total neurons simultaneously exhibit positive th-order interactions . Let be the subset of neurons from neurons , e . g . , from if and . Let be an -subset from , e . g . if . Then , the model where the subset neurons simultaneously exhibit positive th-order interactions ( ) and its complementary hypothesis ( ) are represented as ( 53 ) ( 54 ) for . The remaining parameters are real: ( and . excluding ) . These parameters are integrated out in Eq . 52 . The above definition of the assembly is a clique [12] , a subset in which each neuron is connected to every other neuron through the positive th-order interactions . Depending on the assembly structure one wishes to uncover , other models can be tested such as one in which neurons bounded in non-exclusive manner . | Nearly half a century ago , the Canadian psychologist D . O . Hebb postulated the formation of assemblies of tightly connected cells in cortical recurrent networks because of changes in synaptic weight ( Hebb's learning rule ) by repetitive sensory stimulation of the network . Consequently , the activation of such an assembly for processing sensory or behavioral information is likely to be expressed by precisely coordinated spiking activities of the participating neurons . However , the available analysis techniques for multiple parallel neural spike data do not allow us to reveal the detailed structure of transiently active assemblies as indicated by their dynamical pairwise and higher-order spike correlations . Here , we construct a state-space model of dynamic spike interactions , and present a recursive Bayesian method that makes it possible to trace multiple neurons exhibiting such precisely coordinated spiking activities in a time-varying manner . We also formulate a hypothesis test of the underlying dynamic spike correlation , which enables us to detect the assemblies activated in association with behavioral events . Therefore , the proposed method can serve as a useful tool to test Hebb's cell assembly hypothesis . |
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Trypanosoma cruzi has three distinct life cycle stages; epimastigote , trypomastigote , and amastigote . Amastigote is the replication stage in host mammalian cells , hence this stage of parasite has clinical significance in drug development research . Presence of extracellular amastigotes ( EA ) and their infection capability have been known for some decades . Here , we demonstrate that EA can be utilized as an axenic culture to aid in stage-specific study of T . cruzi . Amastigote-like property of axenic amastigote can be sustained in LIT medium at 37°C at least for 1 week , judging from their morphology , amastigote-specific UTR-regulated GFP expression , and stage-specific expression of selected endogenous genes . Inhibitory effect of benznidazole and nifurtimox on axenic amastigotes was comparable to that on intracellular amastigotes . Exogenous nucleic acids can be transfected into EA via conventional electroporation , and selective marker could be utilized for enrichment of transfectants . We also demonstrate that CRISPR/Cas9-mediated gene knockout can be performed in EA . Essentiality of the target gene can be evaluated by the growth capability of the knockout EA , either by continuation of axenic culturing or by host infection and following replication as intracellular amastigotes . By taking advantage of the accessibility and sturdiness of EA , we can potentially expand our experimental freedom in studying amastigote stage of T . cruzi .
Trypanosoma cruzi is the causative agent of Chagas’ disease , which affects 6–7 million people mainly in Latin America[1] . The parasite is transmitted by a reduviid bug through its contaminated feces , and enters into the mammalian host when the bite site is rubbed or scratched . Chagas’ disease can also be acquired by vertical and perinatal transmissions , blood transfusion or organ transplant , and oral transmission through contaminated food[2 , 3] . Approximately 30 to 40% of infected people develop chronic disease 10 to 30 years after acute infection[1] . Common chronic phase manifestations include cardiomyopathy , arrhythmias , megaviscera , and polyneuropathy . There are currently two drugs available to treat Chagas’ disease; benznidazole and nifurtimox . Both drugs are effective in acute phase of the infection , but efficacy becomes limited once the disease proceeds to chronic phase[4] . Because most parasite carriers do not get a timely diagnosis or have access to the medication , many of them proceed to chronic phase unnoticed or without proper treatment . In addition , aforementioned drugs are known to cause adverse side effects in roughly 40% of the patients[1] . Thus , development of safer new drugs that are effective in chronic phase is a pressing matter . T . cruzi has distinct developmental stages in its life cycle ( Reviewed in [5] ) . In a reduviid bug , the parasite replicates in a form called epimastigote . It differentiates into metacyclic trypomastigote , a non-replicative infectious form , in the insect rectum before being defecated . Trypomastigotes enter a mammalian host and invade the host cell , where they transform into flagella-less amastigotes and replicate intracellularly . Amastigotes differentiate into highly motile blood stream trypomastigotes as they emerge out of the host cells . The parasites then travel through blood stream to infect another host cell , or to be picked up by an insect vector to complete the life cycle . In order to develop a chemotherapeutic agent effective in chronic phase of the disease , it is important to identify a drug target that is essential for the parasite in amastigote stage . Yet , it is difficult to perform a knockout study in this stage of T . cruzi due to inaccessibility of the parasite in the host cells . Accordingly , conventional method to obtain a transgenic amastigote starts with epimastigote transfection . However , this procedure potentially introduces unwanted bias in the resulting amastigote population by selecting for transfectants that are better-fitted in epimastigote or trypomastigote stages . Even though transient transfection of trypomastigote allows to bypass metacyclogenesis step[6] , it still requires active invasion of the host cells and trypomastigote-amastigote differentiation , which still may introduce some bias . To overcome this issue , we focused our attention on extracellular amastigote ( EA ) to utilize it as a tool for direct experimental manipulations . EA can be obtained either by spontaneous appearance in T . cruzi-host co-culture , or by inducing differentiation of trypomastigote in vitro at low pH[7] . It has been reported that EA is morphologically very similar to intracellular amastigote[7–10] and expresses surface glycoprotein SSP-4 , which is a hallmark of amastigote stage parasite[7 , 9 , 11] . EA is capable of infecting culture host cells by inducing the host actin polymerization[12 , 13] , even if the host cell is not a professional phagocyte[8 , 10 , 12–15] . Notably , EA is able to establish infection and kill mice when inoculated intraperitoneally[8] , and free amastigotes can be found in blood stream of infected animals[9] . Traditionally , amastigotes were considered to be non-infectious , but above findings shed light on the important role of EA in T . cruzi dissemination process . Molecular mechanisms of EA take-up by the host cells are beginning to be elucidated as well[16–20] . In the present study , we demonstrate that EA can replicate free of host cells and can be utilized in variety of assays , including exogenous gene expression and CRISPR/Cas9-mediated gene knockout . We also show that susceptibility of proliferating axenic amastigotes to benznidazole and nifurtimox is close to that of intracellular amastigotes in host 3T3 cells . Our strategy to utilize EA expands methodological freedom in amastigote study , and contributes to advance our understanding in this pathogenic stage of T . cruzi .
Epimastigote of T . cruzi Tulahuen strain ( provided by Dr . Takeshi Nara , Juntendo University ) was maintained in liver infusion tryptose ( LIT ) medium supplemented with 10% heat-inactivated FBS at 28°C . Metacyclogenesis was performed in RPMI medium [21] , and differentiated metacyclic trypomastigotes were isolated by DEAE ion-exchange chromatography[22] . Metacyclic trypomastigotes were added to the 3T3-Swiss Albino fibroblast cell culture for infection , and amastigote-containing culture was maintained in DMEM supplemented with 10% FBS and penicillin/streptomycin at 37°C under 5% CO2 in a humidified incubator , until tissue-derived trypomastigotes emerged out to the culture supernatant . Axenic amastigotes were obtained by in vitro amastigogenesis according to the method described by Tomlinson et al . [7] The tissue-derived trypomastigotes were collected from culture supernatant by centrifugation at 2000 ×g for 15 min , and were transformed into amastigotes by incubation in DMEM buffered with 20 mM MES ( pH 5 . 0 ) and supplemented with 0 . 4% bovine serum albumin ( BSA ) for 24 h at 37°C . To obtain intracellularly-derived amastigotes , infected 3T3 cells were detached from a culture flask by trypsin treatment , and centrifuged at 100 ×g for 5 min . The cells were washed by PBS to remove trypomastigotes and EA , and were suspended in 1 mL of Phosphate Saline Glucose buffer ( 1:9 mixture with 1% glucose ) [23] . To release intracellular amastigotes , the cell suspension was passed through a syringe with 27 G needle 40 times to lyse the host 3T3 cells . Unbroken host cells and debris were removed by centrifugation at 100 ×g for 3 min . The intracellular amastigotes in the supernatant were purified by anion-exchange chromatography[23] . All-in-One Fluorescence Microscope BZ-X710 ( Keyence Co . Ltd . , Japan ) was used to capture images of parasites , using appropriate filter sets . For objective lens , S PL FL ELWD ADM 20xC ( NA0 . 45 ) and CFI Plan Apo λ60xH ( NA1 . 40 ) Nikon lenses were used . TRIzol Reagent ( Thermo Fisher Scientific Inc . , USA ) was used to extract total RNA from epimastigote , tissue-derived trypomastigote , intracellular amastigote , EA derived by in vitro amastigogenesis , and axenic amastigotes cultured in LIT medium for 1 , 3 , 5 , and 7 days . Extracted RNAs were treated with DNase I ( Thermo Fisher Scientific ) to eliminate potential genomic DNA contamination , and the samples were purified by phenol/chloroform/isoamyl alcohol extraction and ethanol precipitation . Reverse-transcription was performed by using 1 μg of above RNAs , 0 . 5 μg of Oligo ( dT ) 12-18 Primer ( Thermo Fisher Scientific ) , and Superscript III ( Thermo Fisher Scientific ) . cDNA was synthesized at 50°C for 1 h , and the enzyme was heat-inactivated at 70°C for 15 min . The reaction product was diluted 10 fold before being used for the following qPCR assay . Target gene IDs and sequences of primers used for qPCR are listed in S1 Table . For amplification reaction , TB Green Premix Ex Taq II ( Tli RNaseH Plus ) ( Takara Bio Inc . , Japan ) was mixed with 2 μL of diluted cDNA , 0 . 2 μM each gene-specific forward and reverse primers , and ROX reference dye in the total reaction volume of 20 μL . qPCR was performed according to the manufacturer’s instruction by using StepOnePlus Real-Time PCR System ( Thermo Fisher Scientific ) . Specificity of the reaction was verified by melting curve analysis , and the gene expression was quantitated by relative standard curve method using StepOne Software v2 . 3 . Expression levels were normalized by GAPDH as an internal control . Benznidazole ( Sigma-Aldrich Co . LLC , USA ) and nifurtimox ( Sigma-Aldrich ) were dissolved in DMSO and dispensed into 96 well microplate , and T . cruzi axenic amastigotes in LIT medium ( 1 × 106 cells in 100 μL ) was added to each well . The final concentration of DMSO was 0 . 5% . After 48 h of incubation at 37°C under 5% CO2 in a humidified incubator , 10 μL of resazurin solution ( Sigma-Aldrich ) was added at a final concentration of 3 mM[24] . The plates were incubated for additional 5 h , and the reaction was stopped by addition of 50 μL 3% SDS . Amount of resorufin was quantitated by scanning the microplate by SpectraMax Gemini fluorescent plate reader ( Molecular Devices , LLC . , USA ) at ex . 560 nm/em . 590 nm . EC50 was calculated by fitting the dose response curves with non-linear regression analysis , using " ( inhibitor ) vs . normalized response" model of GraphPad Prism7 software ( GraphPad Software Inc . , USA ) . Host 3T3 cells were seeded onto 96-well black , clear bottom microplate ( Corning Inc . , USA ) at 5 × 103 cells/well in 100 μL of DMEM supplemented with 10% FBS for 4 h to allow cell attachment . Tissue-derived trypomastigotes were added at multiplicity of infection ( MOI ) of 20 , and incubated at 37°C for 24 h . Uninfected trypomastigotes were removed by washing with PBS , followed by addition of 100 μL of DMEM ( 10% FBS ) containing benznidazole or nifurtimox dissolved in DMSO . The final concentration of DMSO was 0 . 5% . After 48 h incubation at 37°C , the media was removed from wells and cells were fixed by addition of 100 μL of 4% formaldehyde for 15 min at room temperature . After fixation , the nuclei were stained by adding 100 μL of 1 . 0 μg/mL Hoechst 33342 ( Thermo Fisher Scientific ) and 0 . 05% Triton X-100 ( Wako Pure Chemical Industries , Ltd . , Japan ) for 15 min , followed by washing wells with PBS 4 times . The plates were imaged by a fluorescence microscopy . Host cells containing more than three amastigotes were considered as infected . EC50 was calculated by fitting the dose response curves with non-linear regression analysis , using “ ( inhibitor ) vs . normalized response" model of GraphPad Prism7 software . The plasmid constructs and the corresponding cell lines are summarized in Supplementary S1 Fig . Expression vector pTREX-attR derived from pTREX-n[25] was provided by Dr . Takeshi Nara . For constitutive expression of EGFP , EGFP gene was amplified by PCR using forward ( 5’-CTCTAGAATGGTGAGCAAGGGCGAGGAGCT-3’ ) and reverse ( 5’- GCTCGAGTTACTTGTACAGCTCGTCCATGCC-3’ ) primers , and ligated into XbaI and XhoI sites of pTREX-attR to generate pTREX-EGFP . For construction of amastigote-specific EGFP expression vector pTREX-EGFP-amastin 3’UTR , the fragment containing amastin 3’UTR upstream of tuzin site[26] ( GenBank: U25030 . 1 ) was amplified using forward ( 5’-GTACAAGTAACTCGAGCGGGTGCATCCACCGTCT-3’ ) and reverse ( 5’-TCGTAAATGGCTCGAGCGCAGGGCGGGCAGCGGC-3’ ) primers . The resulting 800 bp fragment was ligated into 3’ end of the EGFP gene at XhoI site using In-Fusion HD cloning kit ( Takara Bio ) . Streptococcus pyogenes Cas9 sequence ( RefSeq . NC_002737 ) , twice-repeated sequence of the SD40 nuclear localization signals and EGFP sequence were synthesized and ligated into XbaI and XhoI site of pTREX-attR to generate pTREX-Cas9-EGFP . Amastin 3’UTR sequence was inserted into pTREX-Cas9-EGFP as described above to generate pTREX-Cas9-EGFP-amastin-3’UTR . To generate pTREX-mDsRed-Bsd plasmid for expression of mDsRed and blasticidin resistant gene , full-length mDsRed was amplified by PCR using forward ( 5’-TGCTCTAGAATGGCCTCCTCCGAGAACGT ) and reverse ( 5’-CCGCTCGAGCTACAGGAACAGGTGGTGGC ) primers , and resulting fragment was subcloned between XbaI and XhoI sites . Neomycin resistance gene was then replaced by blasticidin selection marker[27] at PspXI and NheI sites . Transfection was carried out using Basic Parasite Nucleofector Kit 2 ( Lonza Inc . Switzerland ) . Briefly , 2 × 107 epimastigote cells in their log phase were suspended in 100 μL of Nucleofector buffer with provided supplement solution , and 20 μg of plasmid was added to the mixture . Electroporation was carried out using program X-14 of Amaxa Nucleofector device ( Lonza ) unless otherwise stated . To generate a stable cell line harboring pTREX-EGFP-amastin-3’UTR ( EGFP-ama ) , pTREX-Cas9-EGFP ( Cas9 ) and pTREX-Cas9-EGFP-amastin-3’UTR ( Cas9-ama ) , the transfectants were selected in LIT medium containing 500 μg/mL G418 for over 4 weeks at 28°C . For transient expression of mDsRed , axenic amastigotes were electroporated with pTREX-mDsRed-Bsd plasmid as described above . The transfected amastigotes were cultured in LIT medium at 37°C under 5% CO2 in a humidified incubator , or applied onto 3T3 cell culture for an infection experiment . To enrich positive transfectants by blasticidin S resistant marker , axenic amastigotes were transferred to LIT medium immediately after electroporation , and 50 μg/mL of blasticidin S ( Wako Pure Chemical Industries ) was added 24 h later . Cells were monitored for the next 6 days for mDsRed expression , and fraction of mDsRed positive amastigotes were quantitated under fluorescence microscopy . Blasticidin-selected transgenic amastigotes were subsequently applied onto 1 × 105 cells of host 3T3 in 24-well plate at MOI of 40 . After 2 days of incubation , the cells were washed 3 times with DMEM to remove amastigotes remained outside of the host cells . Replication of internalized mDsRed-positive amastigotes was monitored for the next 2 days under fluorescence microscopy . gRNA was purchased from IDT ( Integrated DNA Technologies , Inc . , USA ) as two synthetic RNA oligonucleotides , Alt-R CRISPR crRNA and tracrRNA . The target sequences of EGFP , TcCgm1 and mDsRed ( negative control ) were GGTGGTGCAGATGAACTTCA , TAGCCGCGATGGAGAGTTTA and GGACGGCACCTTCATCTACA , respectively . Gene-specific crRNA and universal tracrRNA were annealed to make a complete gRNA , according to company’s protocol . Transfection of gRNA into Cas9-expressing epimastigote was carried out essentially in the same manner as plasmid transfection described above , except 5 μg gRNA was used . For amastigote transfection , 1 × 107 Cas9-ama-expressing EAs were collected immediately after amastigogenesis in pH 5 . 0 , and were resuspended in Nucleofector buffer . After the electroporation , EAs were transferred to 5 mL of LIT medium and incubated at 37°C under 5% CO2 . Cell growth was monitored by counting surviving cell number using Burker-Turk hemocytometer . Propidium iodide was mixed to the EA culture prior to the counting to aid in distinguishing viable amastigotes from dead parasites . For quantitation of EGFP knockout efficiency , percentage of EGFP-positive population was calculated by analyzing the total cell count in bright field images and the EGFP-positive cell count in fluorescent images , using Hybrid Cell Count software of Keyence microscope . Fluorescence intensity of EGFP-positive/negative cut-off was determined by analyzing the images of WT parasites as the background fluorescence . Total of 1000 parasites were analyzed for transfected epimastigotes , and 500 were analyzed for transfected amastigotes . gRNA-transfected Cas9 cells or Cas9-ama cells were harvested at 2 days after electroporation . Cell pellets were stored at -80°C until use . The cells were resuspended in a buffer containing 50 mM Tris-HCl ( pH 7 . 5 ) , 20 mM NaCl , 10% sucrose , 0 . 1% Triton X-100 and 1×cOmplete EDTA-free Protease Inhibitor Cocktail ( F . Hoffmann-La Roche , Ltd . , Switzherland ) for lysis , and cell debris were cleared by a centrifugation for 1 min at 8000 rpm . Guanylyltransferase assay was carried out by incubating 4 μg of cleared lysate in a reaction mixture containing 50 mM Tris-HCl ( pH 7 . 5 ) , 10 mM MgCl2 , 2 mM DTT and 40 μM [α-32P]-GTP for 20 min at 30°C[28] . The reaction was terminated by addition of SDS loading buffer , and the products were resolved on a 10% SDS-PAGE . Radiolabeled enzyme-GMP covalent adducts were visualized by BAS-2500 phosphorimager and quantitated by Image Gauge 4 . 0 software ( Fujifilm Corp . , Japan ) .
We first tested the multiplication capability of amastigote outside of the host cell . EA was obtained by differentiation of tissue-derived trypomastigotes by incubating the parasite in acidic DMEM , buffered with MES ( pH 5 . 0 ) and supplemented with 0 . 4% BSA , for 24 h at 37°C . EA was subsequently cultured in LIT medium ( 10% FBS ) or DMEM ( 10% FBS ) at 28°C or 37°C , and their growth was monitored for the next 10 days . Axenic amastigotes replicated most efficiently in LIT medium at 37°C ( Fig 1A , closed circle ) . In this condition , amastigotes continued to proliferate for a week before replication slowed down and eventually ceased past day 8 . Intracellular amastigotes obtained by host cell rupture also replicated in LIT medium at 37°C , and showed similar growth pattern as in vitro-derived EA ( Fig 1A , closed triangle ) . DMEM at 37°C did not support the growth of axenic amastigote ( Fig 1A closed square ) . At this temperature , amastigotes in all groups retained typical round morphology throughout the observation period ( Fig 1B ) . On the other hand , EAs replicated only for a few days in LIT medium when incubated at 28°C ( Fig 1C , closed circle ) . In addition , some amastigotes started to transform into intermediate morphologies on day 5 , which resembles epimastigote , trypomastigote and spheromastigote , based on their shapes and nuclear staining patterns ( Fig 1D ) . By day 8 , the number of those intermediate forms were up to 30% of the total number of the parasites in LIT 28°C ( Fig 1C , closed and open circles ) . Axenic amastigotes in DMEM at 28°C did not replicate , although their morphology remained as round form throughout the observation period ( Fig 1C , closed square ) . These results are consistent with previously reported observations that EA can replicate free of host cells , given appropriate media condition and temperature setting[10 , 29 , 30] . We also found that amastigotes derived from both in vitro amastigogenesis and from host cell rupture have comparable proliferation capability in axenic environment ( Fig 1A , closed circle and triangle ) . For the rest of experiments in this paper , we used LIT medium at 37°C to maintain axenic amastigote culture . We use the term “EA” for extracellular amastigote in general or amastigote soon after in vitro amastigogenesis , and “axenic amastigote” for amastigote cultured free of host cells for more than two days . Since trypanosomatids transcribe their mRNAs polycistronically , the amount of each mRNA is controlled mainly post-transcriptionally , and 3’UTR plays a major role in differential gene expression in trypanosomatids ( Reviewed in [31] ) . Amastin is a family of surface glycoproteins , which is most abundantly expressed in amastigote stage of T . cruzi[32] . To investigate whether axenic amastigotes maintain amastigote-specific 3’UTR-mediated gene regulation during prolonged cultivation , we generated a cell line that expresses EGFP under the control of amastin 3’UTR[26] . Control cells harboring EGFP construct without stage-specific 3’UTR expressed EGFP in all developmental stages ( S2 Fig , EGFP ) , whereas EGFP-amastin 3’UTR cell line ( EGFP-ama ) expressed EGFP in intracellular amastigote stage but not in epimastigote or trypomastigote stages ( S2 Fig , EGFP-ama ) . When non-fluorescent , tissue-derived trypomastigotes of EGFP-ama cell line were transformed into EA by in vitro amastigogenesis , EGFP signal became apparent as trypomastigotes differentiated into round flagella-less form ( Fig 2A , Extracellular amastigote ) . Proliferating axenic amastigotes in LIT medium continued to express EGFP , and retained the fluorescence at the same level even after 1-week of host-free replication ( Fig 2A , Axenic amastigote ) . These results indicate that in vitro-transformed EA and axenic amastigotes use similar differential gene expression system to that of intracellular amastigote , and that amastigote-specific 3’UTR regulation persists even after 1 week of axenic cultivation . To verify whether the endogenous genes in axenic amastigote are also under stage-specific regulation , the amount of selected mRNAs; amastin , paraflagellar rod protein and TcAc2 , were analyzed by RT-qPCR . Differential expression of the target genes were microarray-identified and qPCR-verified previously[33] and analyzed by RNA-seq more recently[34] by other groups . δ-Amastin is known to be expressed abundantly in amastigote stage[35] . In axenic amastigote , mRNA of δ-amastin remained in similar level to that in intracellular amastigote isolated from host cells throughout 1 week of axenic cultivation , which is around 4–6 fold higher than the level of trypomastigote or epimastigote ( Fig 2B ) . Remarkable upregulation of δ-amastin mRNA during in vitro amastigogenesis is consistent with RNA-seq data , in which peak expression of δ-amastin coincides with the timing of trypomastigote-amastigote transition[34] , and the fact that the surface of EA is already rich in amastigote-specific glycoproteins by the time it finishes differentiation from trypomastigote[7 , 9] . Paraflagellar rod protein , a key component of flagellum , was significantly downregulated as trypomastigote transformed into EA , and remained roughly 10 fold less than that of trypomastigote during axenic cultivation ( Fig 2B ) . This low expression in axenic amastigote was roughly the same level in intracellular amastigote , reflecting their flagellar-less morphology . TcAc2 is a thiol-dependent reductase and is a virulence factor , also known as Tc52[36] . It was significantly upregulated in epimastigote , comparing to trypomastigote , axenic amastigote and intracellular amastigote , as expected from the previous report[33 , 34] . We observed temporal upregulation of TcAc2 during in vitro amastigogenesis , which is presumably due to a stress response of the parasite to acidic environment[36] . In all three target genes examined , expression levels in axenic amastigote was comparable to that in intracellular amastigote , and clearly distinct from trypomastigote and epimastigote , even after 1 week of host-free replication . To further characterize the nature of proliferating axenic amastigotes and to explore its potential usage in drug screening assay , we compared the efficacy of benznidazole and nifurtimox , clinical drugs for Chagas’ disease , against axenic amastigotes and intracellular amastigotes . We employed resazurin redox assay to quantitate the cell viability of axenic culture . For intracellular amastigote assay , host-amastigote co-cultures were fixed and stained by Hoechst to identify infected 3T3 cells . Percent infection was calculated and were normalized to untreated controls to derive the relative inhibition . Intracellular amastigote assay ( Fig 3 , open circle ) provided EC50s of 3 . 20 ( ± 0 . 36 ) μM and 0 . 53 ( ± 0 . 031 ) μM for benznidazole and nifurtimox , respectively , which are comparable to previously reported EC50s[37] . Estimated EC50s of benznidazole and nifurtimox for axenic amastigote ( Fig 3 , closed circle ) were 1 . 32 ( ± 0 . 074 ) μM and 0 . 39 ( ± 0 . 028 ) μM , respectively . Dose response curves of intracellular amastigotes tend to show steeper Hill Slope than axenic amastigotes , both for benznidazole and nifurtimox . Nonetheless , these results indicate that axenic amastigotes and intracellular amastigotes have similar susceptibility to the tested trypanocidal compounds . Taking advantage of the fact that axenic amastigote is fairly robust , we explored the possibility of using EAs for exogenous DNA transfection by standard electroporation method . Nucleofector system was used as electroporation device and reagent , and pTREX-mDsRed-Bsd plasmid was used to visualize transient expression of a fluorescent marker , mDsRed . Expression of mDsRed became visible 1 day after electroporation ( Fig 4A ) , and continued to be detectable at least for the next 6 days . Eight pre-programed pulse settings ( U-06 , U-33 , V-06 , V-33 , X-01 , X-06 , X-14 , and Y-01 ) were tested to determine suitable pulse condition for EA electroporation . Highest transfection efficiency , 7 . 4 ±0 . 8% , was achieved by X-14 program , whereas maximum survival rate was observed with X-01 and X-06 programs ( S2 Table ) . We selected X-14 program as our standard protocol for EA transfection , as it is also suited for epimastigote transfection[38] . Since axenic amastigote culture is sustainable at 37°C for approximately 1 week without major deterioration ( Fig 1 ) , we next subjected the mDsRed-Bsd transfected EAs to blasticidin selection to enrich mDsRed-positive population . After pTREX-mDsRed-Bsd plasmid was electroporated into EAs , the parasites were transferred to LIT medium and cultured at 37°C . Blasticidin S was added 24 h later , and the percentage of mDsRed-expressing amastigotes were monitored for the next 6 days . Without addition of blasticidin , fraction of mDsRed-positive amastigotes gradually decreased after transfection ( Fig 4B , Bsd - ) . On the other hand , percentage of mDsRed-positive amastigotes increased in presence of blasticidin , since many of mDsRed-negative amastigotes died during the selection period ( Fig 4B , Bsd + ) . Effect of drug selection peaked on 5 days after blasticidin addition , or 6 days post electroporation . Further selection beyond 5 days did not seem to benefit the population enrichment . This is primarily due to gradual loss of proliferation capability of axenic amastigote after 1 week of cultivation , as seen in Fig 1A . Next , blasticidin-selected transfectants from above were used to infect 3T3 cells . Axenic amastigotes were allowed to infect host cells for two days , and amastigotes remained outside of the host cells were washed away . The host-parasite co-culture was incubated for additional two days before visualizing and quantitating the prevalence of mDsRed-expressing amastigotes in 3T3 cells . Transfectants were successfully internalized by the host cells and established productive infection , despite the 6-day-long axenic cultivation ( Fig 4C ) . Infection efficiencies of blasticidin-selected ( Bsd + ) and non-selected ( Bsd - ) transfectants were 19 . 6% and 29 . 8% , respectively ( Fig 4D ) . When blasticidin-selected EAs were used for infection , percentage of mDsRed-expressing amastigotes in total intracellular amastigotes was 36 . 0% , whereas that of non-selected amastigotes was only 2 . 0% ( Fig 4D , black bars ) . These proportions are well correlated with the fractions of mDsRed-positive population in initial transfectants applied onto 3T3 cells for infection ( Fig 4B ) . Blasticidin-selected amastigotes differentiated into trypomastigotes and emerged out to culture supernatant 4 days post infection ( S1 and S2 Movies ) . These results suggest that axenic amastigotes can be utilized for electroporation-mediated exogenous gene transfer , and selectable marker is useful to enrich positive transfectants without significantly impairing the ability of amastigotes to infect host cells and to proceed to the next stage of life cycle . Drug target research against T . cruzi entails validation of gene essentiality especially in amastigote stage . Even though CRISPR/Cas9 system offers effective knockout strategy in T . cruzi[27 , 39–44] , it is troublesome to perform this in amastigotes , because intracellular amastigotes are shielded by the host cell and direct access for experimental manipulation is hindered . Utilization of EA as an experimental tool potentially offers an alternative mean to bypass such issue , and allows us to perform knockout studies solely in amastigote stage . To this end , we investigated whether CRISPR/Cas9 system can function in EA to knockout a target gene and yield measurable growth phenotype to allow evaluation of the target essentiality , either as an axenic culture or as intracellular amastigotes followed by host infection . For a proof of concept , we first transfected gRNA against EGFP into the stable Cas9 cell line , which harbors EGFP as a Cas9 fusion protein , to confirm the functionality of the system and to estimate the knockout efficiency ( Fig 5 ) . The fraction of EGFP-positive population dropped to 2% in epimastigote and to 4% in amastigote at 1 day post transfection . We routinely achieved knockout efficiency higher than 95% in both epimastigote and amastigote using conventional electroporation method , based on the fluorescence intensity . Analysis of genomic DNA confirmed that CRISPR/Cas9-mediated knockout introduced mutations in the target DNA . ( S3 Fig ) . We then targeted an endogenous gene , TcCGM1 as a model target . TcCgm1 is T . cruzi homologue of T . brucei mRNA capping enzyme TbCgm1 , which is responsible for cap 0 formation on SL RNA and is essential for the proliferation of T . brucei[28] . We transfected gRNA against TcCGM1 into epimastigote and amastigote stages of T . cruzi . Guanylyltransferase activity of TcCgm1 , along with the other capping enzyme TcCe1 , can be detected in the cell lysate by incubating the total protein with [α-32P]-GTP in presence of metal cofactor . The resulting enzyme-[32P]-GMP covalent intermediate can be visualized as a radiolabeled band in SDS-polyacrylamide gel . After transfection with gRNA against TcCGM1 , signal of TcCgm1-[32P]-GMP became weak comparing to the control cells that received gRNA with unrelated sequence ( Fig 6A and 6B ) . Radiolabeled signal of the other guanylyltransferase , TcCe1 , was relatively unaffected . The amount of TcCgm1-[32P]-GMP was reduced to 32% in epimastigote and to 49% in axenic amastigote 2 days after gRNA transfection . We then monitored the phenotype of TcCGM1 knockout cells after gRNA transfection . In epimastigote , Cas9 cells transfected with TcCGM1-gRNA halted the growth on the day after electroporation ( Fig 6C ) . Deformation of the knockout cells started to appear on day 2 , and became clearly noticeable on day 3 post transfection ( Fig 6D ) . TcCGM1-knockout epimastigotes tended to be large , and often possessed multiple flagella . Nuclear staining revealed that many cells contained abnormal number or size of nuclei or kinetoplasts . Much smaller spots of unknown nature were observed in some cases ( Fig 6D , arrow head ) . In axenic amastigote , growth of Cas9-ama cells transfected with TcCGM1-gRNA was also suppressed ( Fig 6E ) . For the first 3 days , there was no apparent morphological changes in knockout cells , comparing to the amastigotes received control gRNA . However from day 4 , TcCGM1-knockout amastigotes started to display irregular shapes . Deformation became more noticeable on day 5 ( Fig 6F ) . Unlike knockout epimastigotes , not many cells possessed multiple nuclei or kinetoplasts , except occasional large cells that show abnormal Hoechst staining pattern ( Fig 6F , arrow head ) . For intracellular amastigote assay , gRNA-transfected EAs were applied onto 3T3 cells 1 day after electroporation at MOI of 20 , and allowed to infect host cells for 2 days . Amastigotes remained outside of the host cells were washed away , and host-parasite co-culture was incubated for additional 2 days . Infected 3T3 host cells were then fixed and stained by Hoechst to identify intracellular amastigotes ( S4 Fig ) to calculate the percent infection . Fraction of host cells infected by amastigotes transfected with TcCGM1-gRNA and control gRNA were 4 . 6% and 13 . 6% , respectively ( Fig 6G ) . This outcome is in agreement with the transfectants’ cell growth monitored as axenic cultures ( Fig 6E ) . Taken together , these results show that essentiality of a target gene in EA can be analyzed after CRISPR/Cas9-mediated knockout by monitoring growth phenotype of the amastigotes , either as axenic culture or as intracellular amastigotes followed by host invasion .
T . cruzi goes through distinct life cycle stages as they travel between insect vectors and mammalian hosts . Being able to isolate and study individual stage is crucial in understanding the parasite biology . Epimastigote stage of T . cruzi has been routinely used for basic cell biology research and drug development study , because of the easiness of culture maintenance . On the other hand , amastigote had received little attention as a subject of direct experimental manipulation due to complication and inaccessibility in the host co-culture , even though this life cycle stage is most relevant in host-parasite interaction and drug development studies . Here , we demonstrated that EA can be proliferated as axenic culture at least for one week without major morphological change or loss of stage-specific gene expression . Susceptibility of EA and intracellular amastigote to benznidazole and nifurtimox was comparable in terms of EC50 values . We also demonstrated that a plasmid vector can be delivered directly into EA for transient gene expression , and transfectants can infect host 3T3 cells and replicate just like bona fide amastigotes even after 6 days of axenic culturing in presence of selective agent . CRISPR/Cas9 system can function in Cas9-expressing axenic amastigotes when gRNA is transfected by conventional electroporation . These new methodologies open up the possibility to carry out stage-specific experiments in a truly amastigote-specific manner . It is widely accepted that amastigote of T . cruzi is an obligate intracellular parasite . Host metabolic factors such as Coenzyme Q10 and Akt-related pathways including glucose and lipid metabolisms have been implicated as key growth regulators of intracellular amastigote[45 , 46] . Our result indicates that components in LIT medium can compensate for such nutrient needs , at least for a short while , to sustain the growth of axenic amastigote . Although it was previously demonstrated that EA can uptake exogenous glucose[47] , it is unlikely that glucose by itself allows axenic proliferation , because DMEM contains higher concentration of glucose than LIT medium , yet EA kept in DMEM did not replicate at all ( Fig 1A ) . Optimum nutrients required for axenic amastigote cultivation beyond 1 week remain to be investigated . Also , whether the technique of genetic manipulation during temporal axenic cultivation is applicable to other strains of T . cruzi or not must be investigated in future studies . There are few instances in previous literatures that EA increased in number during host-free incubation in Y strain[30] and Brazil strain[10 , 29] , but those observations were not followed up . Recently , several techniques for high-throughput inhibitor screening against T . cruzi host co-culture have been developed ( Reviewed in [37] ) . However , those systems are specialized for phenotypic assays in compound screenings . In order to identify a drug target , to probe into a mode of action of drug candidates , or to investigate the biological role of specific gene , there is still a great need to directly investigate the amastigote itself . Preceding examples of utilization of axenic amastigotes can be found in Leishmania drug screening studies[48–53] . It must be noted , however , that inhibitory compounds identified by axenic assay and host co-culture assay do not perfectly overlap[49 , 52] . This discrepancy originates in part from physiological differences between intracellular and extracellular forms of Leishmania amastigotes , namely proteome[54] and transcriptome[55] . In T . cruzi , some differences between the two forms of amastigotes have also been reported . For example , EA is more resistant to complement-mediated lysis than intracellular amastigote . Hundred percent of intracellular amastigote is lysed in fresh serum but EA is completely resistant in case of Tulahuen strain[56] . Also , EA is more infectious to the cultured host cell than intracellularly-derived amastigotes[56] . Since literature on drug treatment of T . cruzi axenic amastigotes is extremely limited[29] and this present study is the first instance of directly comparing the dose response curves of EA to that of intracellular amastigotes in host cells , it definitely requires further investigations to see whether T . cruzi axenic amastigotes can be utilized for inhibitor screening assays in general . Our data indicate that Hill Slopes tend to be shallower in axenic amastigote comparing to intracellular amastigote ( Fig 3 ) . This might partly be resulted from different counting schemes in our experiments , i . e . , resazurin assay of axenic amastigote reflects redox activity of viable parasites , whereas percent host infection of intracellular amastigote does not account for the population dynamics of the parasite within individual host cell . Alternatively , steepness of the dose response curves may be associated with complexity of the target molecule or pathway , and presence or absence of the host cells could affect “tipping point” of the lethality . Since benznidazole and nifurtimox both affect wide range of cellular machinery by generation of reactive oxidant species , it would be interesting to see whether target-specific trypanocidal compounds also produce similar slope trends in dose response profiles . If significant phenotypic discrepancies are found between EA and intracellular amastigotes in drug sensitivity or selectivity , those inhibitors potentially provide valuable insights into host-parasite interaction and cellular biology of T . cruzi amastigote . Considering the amount of information we can extract , there is no doubt that parasite-host co-culturing is the most relevant system in terms of phenotypic assay[37] . It surely requires further investigation to determine the relevance and practicality of the use of EA in drug screening . Nonetheless , the use of EA can give us an easy and fast evaluation of compound susceptibility of amasitigote itself . In the co-culture system , passing the host cell barrier is one of the top criteria that the compounds are selected for . However , it is not uncommon that subsequent chemical modification significantly improves the permeability of the compound to cell membrane through lead optimization process[57–59] or by drug delivery system[60] . Therefore , the use of naked amastigote in early-stage compound screening may give chance to some candidate compounds that are otherwise dropped out , and expand the options of starting materials to move forward with the next step of drug development . There are some studies utilizing EA in T . cruzi in the past , however their objectives were mostly limited to investigation of signaling factors involved in trypomastigote-to-amastigote differentiation[30 , 61] or host invasion[16–20] . To our knowledge , the present study is the first instance of utilizing T . cruzi EA for direct transfection for exogenous gene expression and endogenous gene knockout . Previously , Padmanabhan et al . demonstrated that trypomastigote can be transfected for later infection and differentiation to produce transgene-expressing intracellular amastigotes[6] . In their report and also in our hands , plasmid transfection efficiency of trypomastigote is about 5% . One advantage of using EA instead of trypomastigote is that electroporation can be followed by proliferation and selection of transfectants to enrich positive population to compensate for low transfection efficiency . In our present study , fraction of mDsRed-positive EA was initially about 4% , but reached to 37% after 5 days of blasticidin selection ( Fig 4B ) . It may be possible to improve the enrichment efficiency by using a selection marker that requires shorter selection period , or by improving the culture medium to allow longer cultivation of axenic amastigote . It is , of course , feasible to take advantage of a fluorescence activated cell sorter prior to the host infection[6] to obtain homogeneous population of transgenic amastigotes instead . Another advantage of EA transfection is that we can bypass active host invasion ( as supposed to “passive” mode of infection by amastigotes ) and trypomastigote-to-amastigote differentiation that may introduce unwanted bias to transfectants , which is crucial when studying stage-specific cellular functions . For example , in a drug target research , one would like to produce knockout parasites in search for a target gene that is essential in clinically-relevant amastigote stage . However , if a candidate gene was essential in trypomastigote stage , we cannot obtain a knockout amastigote by infection and differentiation of the lethal trypomastigote . Our strategy of using axenic amastigotes enables evaluation of essential genes truly in amastigote-specific manner . Knockout study of some target genes may yield different outcomes between axenic amastigotes and host intracellular amastigotes . If so , those cases provide us with opportunities to examine the involvement of host factors in amastigote-specific gene functions . In summary , having direct access to amastigote as experimental tools may greatly expand methodological freedom to investigate basic cellular biology of T . cruzi and potentially provide valuable insights into the drug development study in the future . | We developed an experimental system to study amastigote stage of Trypanosoma cruzi as a proliferable axenic culture . Use of axenic amastigotes allows us to directly introduce exogenous gene into T . cruzi amastigote and select for drug resistant parasite to enrich the transfectants . Our strategy bypasses differentiation steps involved in conventional epimastigote transfection procedure to obtain transgenic amastigotes . Gene knockout can also be performed in amastigote-specific manner , using Cas9-expressing extracellular amastigotes . Drug sensitivity could also be assessed during 1-week axenic growth period . Our method potentially leads to variety of new experimental strategies to make amastigote-stage-specific manipulations and analyses possible . |
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Many biological systems perform computations on inputs that have very large dimensionality . Determining the relevant input combinations for a particular computation is often key to understanding its function . A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs . In systems neuroscience , the corresponding method is known as spike-triggered covariance ( STC ) . This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems . So far , most studies used the STC method with weakly correlated Gaussian inputs . However , it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment . In such cases , the stimulus covariance matrix has one ( or more ) outstanding eigenvalues that cannot be easily equalized because of sampling variability . Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs . In many cases , these modes obscure the significant dimensions . We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more . This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode ( s ) . Analyzing the responses of retinal ganglion cells probed with Gaussian noise , we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons .
How do neurons encode sensory stimuli ? One of the primary difficulties in answering this long-standing problem is the fact that sensory stimuli have high dimensionality . For example , responses of many visual neurons are affected by image patterns that require at least a pixel grid for their description as well as a temporal history spanning multiple time bins or basis functions . Determining what input combinations affect the neural responses is a key step in characterizing the neural computation . Broadly speaking , to detect the presence of certain features in the environment over a range of distances and light conditions , one needs to disambiguate the presence of this feature at a weak contrast from the presence of a similar , but different feature presented at a higher contrast . This can only be achieved with nonlinear functions that depend on multiple input components , such as the presence of an edge of correct orientation and the absence of the edge orthogonal to it [1] . In support of these arguments , the responses of neurons in different sensory modalities are found to be sensitive to multiple input combinations . Examples include vision [2]–[7] , audition [8]–[10] , olfaction [11] , somatosensation [12] and mechanosensation [13] . Neurons respond with all-or-none responses termed spikes . The goal of different methods for characterizing neural feature selectivity is to determine how the probability of eliciting a spike from a neuron depends on its inputs . The underlying assumption is that this dependence of spike probability on input parameters will have a low-dimensional structure . Finding either the linear input dimensions that modulate the spike probability ( we will refer to these dimensions as relevant ) or quadratic forms of inputs [14]–[16] is the focus of much of the current research in the field . Much of the analysis of neural selectivity for multiple input combinations has been carried out using uncorrelated ( “white noise” ) or weakly correlated inputs . With such inputs , the relevant input dimensions can be found using a computationally inexpensive method known as spike-triggered covariance ( STC ) [6] , [7] , [17]–[22] . The STC method works by comparing the change in variance along different dimensions in the input space across all stimuli and across stimuli that elicited a spike . The dimensions along which the variance is found to be significantly different represent the relevant input dimensions for the response of a particular neuron . The method is not limited to strictly Gaussian inputs provided that the inputs are still circularly symmetric [23] , which is another example of an input distribution without correlations . In principle the STC method can also be used with correlated Gaussian stimuli [7] , [20] . The case of correlated stimuli - especially with strong correlations , where the second moment of the covariance spectrum may be infinite - is important for neural coding . This is because signals in the sensory environment possess such correlations in both the second and higher orders [24]–[30] . Because the properties of a cell's relevant subspace may change depending on the stimulus statistics as a result of adaptation [31] , [32] , it may not be sufficient to study neural coding using uncorrelated stimuli . Here we show that with strongly correlated inputs , the significance analysis for determining which of the dimensions obtained by the STC method are relevant for neural spiking needs to be modified to take into account a rather complicated covariance structure of randomly selected inputs drawn from such input ensembles . The nonuniform covariance structure , which has properties akin to the graph laplacian in small-world networks [33] , breaks the symmetry in the input space , and therefore may obscure many significant dimensions . The most prominent aspect of the natural scenes covariance structure is the presence of the so-called “coherent” mode [34] . This stimulus dimension approximately corresponds to the zero frequency input component and has a corresponding eigenvalue that is at least times larger than the mean eigenvalue of the input covariance matrix . Even in datasets of fairly large size , the extremely large variance along the coherent mode obscures many of the truly relevant dimensions for neural spiking ( Fig . 1 ) . These effects are also reproduced in our analysis of the responses of ganglion cells from the salamander retina probed with -type naturalistic Gaussian stimuli . We identify a close relationship between the covariance structure derived from natural scenes to that defined by the Spiked-Wishart matrix model [35] , [36] . This allows us to explain the effects in the context of the STC method using results from random matrix theory , and suggest ways to bypass sampling variability along the outstanding modes .
Mathematically , the first step in the STC method is to compute the covariance matrix of stimuli that lead to a spike and the covariance matrix of all stimuli : ( 1 ) ( 2 ) Here , is the number of recorded spikes , is the number of stimulus frames , is the value of the stimulus along the th dimension at time , the hat denotes that this stimulus triggered a spike , the bar denotes the average across the input distribution and is the average across the distribution of inputs that triggered a spike ( the so called “spike-triggered-average” ) . As the second step , one computes the difference between these covariance matrices: ( 3 ) and finds the eigenvalues that are significantly different from zero . The corresponding eigenvectors span the neuron's relevant subspace . To determine statistical significance of the eigenvalues , they need to be compared to the null distribution , which is the distribution of eigenvalues of the matrices . The matrices are formed assuming no association between the stimulus and the neural response , i . e . by using random spike times chosen at the same rate found for real neurons . If the spike train has particular temporal structure ( e . g . bursting , a refractory period ) , the is obtained by random shifts of the spike train with periodic boundary conditions [20] . Significant eigenvalues of can be positive or negative . The procedures for determining statistical significance are detailed in Materials and Methods . The final step of the STC method is to remove stimulus correlations from the estimate of dimensions found to be significant . This can be done by multiplying them with the ( pseudo ) inverse of ( see Materials and Methods ) . The method which we use to find the optimal rank of the pseudoinverse matrix is detailed in [22] , [37] and for completeness described in Materials and Methods . We note that this approach , Eqs . ( 1 ) – ( 3 ) , of finding the relevant stimulus dimensions by diagonalizing is equivalent to seeking eigenvectors of the following matrix [20]: ( 4 ) This matrix describes a change in the second moment between the distributions stimuli that elicit a spike and that of all stimuli , after subtracting the mean stimulus . Despite the fact that , their eigenvectors coincide . In another formulation , instead of subtracting the matrix in Eq . ( 3 ) , the stimulus is decorrelated ( “whitened” ) prior to its spike triggered characterization [7] . For completeness , the details of this method are brought in Materials and Methods . Throughout the manuscript , we will refer to this method as the “one centered” method , because the null distribution is centered around the identity matrix , rather than a matrix of zeros , as in Eq . ( 3 ) . Correspondingly , we will refer to the version of the STC method obtained by diagonalizing Eq . ( 3 ) as the “zero-centered” method . In essence , both the one-centered and the zero-centered versions are similarly affected by inhomogeneous sampling variability . The authors of [7] proposed a slightly different definition of the null distribution and a nested hypothesis technique for significance testing . For the model cell simulations we used both significance analysis methods , in both the “zero-centered” and “one-centered” STC formulations , and obtained similar results . For the rest of this paper we will refer to our significance testing method as the “global” one , and focus mainly on the “zero-centered” formulation of the STC method . Using this combination the important effects of the strong stimulus correlations on the analysis are more easily understood . We begin with an illustration of the problems that arise when the STC method is used to analyze neural responses to strongly correlated Gaussian noise ( Fig . 1 ) . We simulated a model neuron where the neuronal responses were modulated by stimulus projections onto a single dimension ( termed here the relevant feature ) . The stimuli were constructed to match the second-order statistics from the set of images in the van Hateren dataset [38] ( see Materials and Methods ) . In this example obtained for dataset of a moderate size , no eigenvalues fell outside of the % confidence intervals ( % significant bounds for the largest and smallest rank-ordered eigenvalues ) . Yet , the spike train contains enough signal about the cell's input-output function to identify the relevant feature for this level of significance . Specifically , the variance along the relevant dimension in the spike-triggered stimulus ( ) is much smaller than can be explained by random spike times ( Fig . 1E ) . To understand the origin of such masking of the relevant feature ( s ) , we consider the eigenstructure of covariance matrices for stimulus ensembles with strong pairwise correlations . For example , in the case of natural scenes that exhibit long range correlations over a very wide range of spatial scales [27] , [39] , principal component analysis ( PCA ) yields one outstanding eigenvalue ( for example , see eigenvalue marked in Fig . 2A ) . The corresponding eigenvector has all positive components [28] , [30] and is often referred to as the “coherent mode” [34] . To understand why such a coherent mode appears , one can consider the case where the correlations decrease only slightly over the range of image patches used to compute the covariance matrix . In this case , the correlation values in different image patches will be approximately the same . Such a matrix will have one outstanding eigenvalue with a corresponding eigenvector that has equal weights for all stimulus dimensions [40] . Small differences in the amount of covariation for pixel pairs with different spatial separation will lead to deviations in components of the coherent mode from each other , but the basic structure will remain the same as long as the mean of the correlation values exceeds the standard deviation of their fluctuations [40] . In fact , shuffling entries in the sample covariance matrices of natural stimuli yields matrices whose spectra follow the analytical predictions exactly [40] , [41] . These analytical predictions generalize the Wigner semicircle law [42] for matrices whose elements have a non-zero mean: ( 5 ) where and are the mean and variance of matrix elements . The distribution follows the semicircle law with the addition of one outstanding mode that appears once the mean of matrix elements exceeds their standard deviation . The eigenvector corresponding to the outstanding eigenvalue is . The semicircle law appears because matrices are no longer positive-definite after shuffling . However , the outstanding eigenvalue is located at exactly the same value as the outstanding eigenvalue of the natural scenes covariance matrix ( see Fig . 2C ) . In our analysis of the van Hateren database , the largest eigenvalue tends to be at least times larger than the second largest eigenvalue . This shows how strong the coherent mode is compared to other modes . The principal components ranked below the coherent mode form a collection of orthogonal “edge detectors” , some of which correspond to an eigenvalue still much larger than the mean eigenvalue of , a signature of the stimulus' heavy-tailed covariance spectrum . Such large disparities in variance along the different dimensions in the stimulus space make it problematic to directly compare changes in variance induced by the observation of spikes along these different dimensions . The detailed structure of sampling variability in the estimation of eigenvectors and eigenvalues can be understood in terms of the Spiked Wishart ensemble [35] , [36] . In the Spiked Wishart matrix model , the true ( population ) covariance eigenvalues are all equal to one , except for a small number of outstanding modes with eigenvalues larger than one , where is the stimulus dimensionality . The distribution of sample covariance eigenvalues for a finite number of inputs has a positive bias , with the following analytical expressions [36]: ( 6 ) ( 7 ) ( 8 ) where and is the number of samples . The distribution representing the “bulk” of eigenvalues is the so called Marčenko-Pastur distribution given by: ( 9 ) ( 10 ) This distribution corresponds to the sample covariance eigenvalues obtained when the true covariance is the identity matrix . Using numerical simulations we verified that , although the Spiked Wishart ensemble is only an approximation to the covariance matrices derived from natural stimuli , Eqs . ( 7 ) and ( 8 ) accurately describe the scaling of the variance and the mean of sample eigenvalues as increases . In addition to biases in eigenvalue estimates , there are also biases in the estimation of eigenvectors . The dot product between the true ( population ) th eigenvector and the th eigenvector of the sample covariance approaches ( 11 ) In other words , the “mixing” of the outstanding sample eigenvectors seen in Eq . ( 11 ) ( note the dependence of this mixing on through ) as well as the variance and bias in the sample eigenvalues seen in Eq . ( 7 ) means that whitening cannot be exact . In the context of the spike triggered covariance the consequences of such properties of the distribution of sample eigenvalues are twofold . First , Eq . ( 8 ) indicates that the variance of the outstanding eigenvalues around their mean increases with the square of their value and is inversely proportional to the number of samples . Thus , for sample sizes that are not much larger than the stimulus dimensionality ( in the simulation results presented in Fig . 3A ) , the increased variance of the outstanding sample eigenvalue means that and will not cancel each other exactly along that vector . Second , the mean estimate contains a positive bias relative to the population values , cf . Eq . ( 7 ) . The combination of these two effects widens the null-distribution used to test the significance of the resulting eigenvectors , effectively masking features that should otherwise be identified as being relevant . One way to compensate for the symmetry breaking effects caused by strong correlations in the input space is to equalize variances before applying the STC method . This is the essence of the “one-centered” formulation of the STC method [7] . In principle , this “whitening” should work with Gaussian stimuli with any covariance structure . However , as discussed above , in the case of strongly correlated stimuli , the estimation of eigenvalues ( i . e variances along different dimensions in the input space ) possesses strong variability , cf . Eq . ( 7 ) . As a consequence , normalization by a variance estimated from one part of the dataset does not fully remove correlations in a different subset of the data . With increasing dataset size , the estimate of the variance along the coherent mode improves . However , because the absolute value of variance is not relevant in the pre-whitening method , dimensions with smaller variance can cause just as much contamination as the coherent mode . In addition , the estimation of variance along dimensions corresponding to just larger than remains poor for large . If the sample eigenvalue estimation error diverges as , as follows from Eq . ( 8 ) . In other words , as the number of samples and increase , the bulk of the distribution narrows , and new eigenvalues separate from the bulk . It is these eigenvalues with intermediate values that are poorly determined and make it problematic to equalize variance along different dimensions . Another signature of this phenomenon is that for these dimensions , as follows from Eq . ( 11 ) . Thus , these dimensions are poorly estimated from the sample covariance and , as a consequence , the variance along one stimulus dimension in the training set will be inappropriately used to normalize variance along a different stimulus dimension in the test set . Altogether , we observed that pre-whitening stimuli did not improve the estimation of relevant stimulus features compared to the zero-centered method , compare panels A and B in Fig . 3 . Intuitively , in the zero-centered method the dimensions with the largest variance provide the largest uncertainty in variance estimation , whereas in the one-centered version the problematic dimensions change depending on the dataset size , and are not easily identified a priori . We have also explored the possibility of using a pseudoinverse of the covariance matrix instead of the full inverse to normalize variance along different dimensions ( see Materials and Methods for details ) . When using the pseudoinverse ( instead of the inverse ) , stimulus dimensions with small variance in the stimulus ensemble are removed to avoid noise amplification along these dimensions ( see Materials and Methods for details ) . However , an immediate consequence of choosing a small pseudoinverse order is that the stimulus dimensionality is reduced to . This implies that the effective of the problem is now , i . e . times larger than . This could work well in some cases as illustrated in Fig . 3I . Here , in simulations based on a small number of spikes , the use of pseudoinverse can help recover one or two significant features while the standard zero-centered method fails to find any . However , the use of pseudoinverse only helps within a very narrow band of small pseudoinverse orders . This band may be difficult to determine when analyzing real neural data . In addition , this procedure limits the reconstruction to a linear combination of only a few leading stimulus dimensions . In many cases , the relevant features do include components along stimulus dimensions with smaller variance , and in those cases , the effective increase in will not improve the performance of the STC method . Indeed , one observes that in cases where two significant dimensions are obtained by using substantial reduction in dimensionality of the pseudoinverse , the resulting dimensions have the subspace projection onto the model features of whereas this value is when using the full inverse and a larger number of spikes to obtain for a comparable effective ( Fig . 3I ) . Finally , in the regime where ( i . e . “almost full” inverse ) , the prewhitening approach works just as well as the “zero-centered” formulation , and a relatively high value of the signal-to-noise ratio parameter is required for recovery of the full relevant subspace . As another way to compensate for the symmetry breaking effects caused by strong correlations in the input space , we propose to modify the “zero-centered” formulation of the STC method in the following way . Because the largest drop in variance is between the coherent mode and other dimensions , we propose here to test the significance of changes in variance separately along the coherent mode and in the subspace orthogonal to it . Explicitly , to do the analysis in the dimensional subspace , the coherent mode is projected out of all stimuli . If is a stimulus vector and normalized to length , one can perform the STC analysis using instead of where: ( 12 ) In this approach the correct number of relevant dimensions is determined by evaluating significance in the subspace orthogonal to the coherent mode and then adding back their projections on the coherent mode from the corresponding eigenvectors evaluated in the full input space ( see below ) . We find that considering the coherent mode separately from the rest of stimulus dimensions reduces the value of for which the full relevant subspace is found to be significant by a factor of ( Fig . 3C ) . This improvement can be approximated from Eqs . ( 7 ) and ( 8 ) . Assuming the cell's relevant subspace is exactly orthogonal to the coherent mode , the extremal values of the null distribution are distributed as . The variance of is: ( 13 ) This implies that the number of stimuli sufficient for identifying the relevant features as significant increases with as: ( 14 ) Upon removal of the coherent mode , the minimum value of for which the signal to noise ratio will be high enough to identify the relevant dimensions scales as corresponding to the stimulus' second principal component . Therefore the improvement is proportional to . In our simulations ( Fig . 3A , C ) this corresponds to a predicted fold improvement . Given that our model features were not exactly orthogonal to the coherent mode , and that the spectrum obtained from the van Hateren dataset has a heavy tail and does not conform exactly to the Spiked Wishart ensemble , an approximate fold improvement represents a good agreement with the prediction . It is noteworthy that the minimum requirement on the dataset size for obtaining the correct number of relevant dimensions is actually smaller with correlated stimuli than it is for white noise stimuli for the same neuron ( compare Fig . 3 panels A–D ) when the model parameters were matched such that the firing rate remains constant across different stimuli statistics . Another important point is that considering the coherent mode separately is different from simply discarding a “DC-like” component that could be found to be significant by the STC . This is because when is small , no dimensions are found to be significant with the coherent mode as part of the stimulus ensemble ( Fig . 1 ) . An important consideration is that the final analysis can include the components of the relevant dimensions onto the coherent mode . This is possible for two reasons . First , the coherent mode does not represent an arbitrary dimension in the input space but is one of the eigenvectors of the sample covariance matrix . Second , the significant eigenvectors of have a form , where is the th eigenvalue corresponding to the th eigenvector of the sample covariance matrix , and describes one of the relevant features [20] . Because of these two properties , eigenvectors evaluated in the full input space and in the subspace orthogonal to the coherent mode differ only in their components along the coherent mode ( see Materials and Methods for the details of the derivation ) . This makes it possible to analyze cells with features that have nonzero components along the coherent mode . We have verified that this approach also works in a large number of cases where the relevant stimulus dimensions have a large projection on the coherent mode ( Fig . 4 ) . One concern is that when such neurons are probed with a relatively small number of stimuli , then projecting the coherent mode out may “push” the relevant feature into the null eigenvalue distribution . This does not appear to be a problem in our simulations for ( Fig . 4B ) . If this does happen , the relevant subspace should be the one spanned by both the eigenvectors found to be significant in the full stimulus space and those found to be significant in the subspace orthogonal to the coherent mode . We now demonstrate the importance of this correction scheme by analyzing recordings of 22 salamander retinal ganglion cells ( RGCs ) . These neurons were probed with a correlated noise stimulus whose covariance matrix was matched to that of natural visual stimuli . Without correcting for the presence of the coherent mode , the STC analysis yielded no significant dimensions for a third of the cells , and very few for the rest ( Fig . 5 ) . This happens because the eigenvalue corresponding to the coherent mode injects large eigenvalues into the null eigenvalue distribution ( as seen in Eq . ( 8 ) ) , thus masking the cell's true relevant features . Following the correction , the number of significant dimensions per cell increased from to ( see Fig . 5A for the full population values ) . The dimensionality of the relevant subspace increased for 21 out of 22 cells . For one cell , we were unable to find a significant dimension either before or after the correction of the method . The distributions of null eigenvalues used to determine which of the eigenvectors of are significant ( Fig . 5B , C ) became much more narrow when evaluated in the subspace orthogonal to the coherent mode .
The goal of this work was to extend the range of applicability of a computationally simple method of spike-triggered covariance to strongly correlated stimuli . While the STC method in principle can be used with strongly correlated Gaussian stimuli , our results show that the inhomogeneous sampling variability can in practice make it difficult to recover the correct relevant subspace . We have characterized the effects generated by strong Gaussian correlations using simulations of two model neurons in a wide range of dataset sizes ( which could also be viewed as an inverse measure of the neuron's level of internal noise ) . Results from random matrix theory , and specifically the Wigner and Spiked Wishart ensembles , suggest that the origin of these issues can be traced to the estimation bias and variance of covariance matrices with vastly different eigenvalues . We demonstrate that by considering the coherent mode , which corresponds to the largest eigenvalue , separately from the rest of stimulus dimensions , one can improve the method's sensitivity by . One qualitative lesson offered by these analyses is that while the bulk of the eigenvalues of is a good proxy for the width of the null distribution in the case of white noise inputs , but not in the case of strongly correlated inputs . Furthermore , our analysis suggests that sampling variability along the secondary outstanding modes corresponding to the next few principal components may have similar masking effects to the ones reported here for the coherent mode . Possible solutions to the full problem may include performing a sequence of analyses in subspaces of decreasing dimensionality , orthogonal to several leading principal components . However , the payoff from this procedure is ( at most ) of order which in our case is . At the same time , one runs the risk of losing the ability to resolve the remaining dimensions because of the reduced signal to noise ratio . Another potential solution is to correct for the estimation bias and variance in eigenvalues and eigenvectors , described by Eqs . ( 7 ) and ( 8 ) . However , this procedure is difficult computationally and in most cases can only be done for simple eigenvalue distributions [43] . The treatment of the artifacts caused by a large coherent mode present in the data has been previously discussed in analyses of stock-markets [44] , [45] , evolution of proteins [46] , and Human Immunodeficiency Virus ( HIV ) mutations [34] . In these cases , the extra dimension was removed and the resulting covariance structure was compared against the Marčenko-Pastur eigenvalue distribution that assumes no correlation between the variables and uniform variable variances . The case of reverse correlation experiments discussed here is different from these analyses because the spike triggered ensemble is compared to the full stimulus distribution . In addition , our analyses provide two important novel contributions . First , we show there is a crucial difference between discarding the coherent mode and projecting it out . This is because of the way the coherent mode injects noise into the null distribution . Second , the approach described here also permits the inclusion of the components of the relevant dimensions along the coherent mode in the final results . We hope that the ideas for treating the coherent mode presented here will also be relevant in other areas of computational biology .
Experimental data were collected using procedures approved by the Institutional Animal Care and Use Committee of Princeton University , and in accordance with National Institutes of Health guidelines . Experimental and surgical procedures have been described previously [47] . Each stimulus frame was randomly drawn from a multivariate Gaussian distribution with zero mean and covariance matrix , In the correlated stimulus case , the population covariance was computed from the covariance of pixels patches from the van Hateren image database [38] ( with no downsampling ) . In the uncorrelated ( “white” ) case , was the identity matrix . We describe two approaches for determining significance of candidate features that were previously described in the literature: global and nested . When applied to our datasets , both of the approaches yielded similar results . Within the STC method , stimulus correlations need to be removed from the estimates of eigenvectors obtained by diagonalizing matrix . This correction is needed , because the eigenvectors of have a form , where describe components of one of the relevant features [20] . As described above , one may wish to use a pseudoinverse , instead of the full inverse of the matrix to minimize noise amplification at higher spatial frequencies . Assuming that the eigenvalues are ordered to be monotonically decreasing , the pseudoinverse of order is given by ( 18 ) In the analysis of data from retinal ganglion cells , the optimal order of the pseudoinverse was determined in the following way . The dataset was divided into the training and test sets . The features were computed by diagonalizing the matrix , cf . Eq . ( 3 ) , in either the full input space or in the space orthogonal to the coherent mode using the training set . Following that , the optimal pseudoinverse order was selected as the one that yielded decorrelated features that convey the most information about , or give the largest predictive power for , the neural response . Explicitly , ( 19 ) ( 20 ) where is the probability distribution of the projections of stimuli onto the significant eigenvectors ( ) , decorrelated by . are the decorrelated significant features , and: ( 21 ) ( 22 ) As an alternative to removing stimulus correlations from the eigenvectors of , one can remove stimulus correlations from each of the stimulus vectors , prior to the diagonalization of , a procedure that is known as pre-whitening [7] . The sample stimulus covariance matrix from Eq . ( 2 ) can be written in terms of eigenvalues and eigenvectors as ( 23 ) We can now define a matrix . Then , the analogue of in the “one-centered” formulation is given by: ( 24 ) This procedure is equivalent to whitening each of the stimulus frames independently ( by multiplying it with ) and then computing the spike-triggered covariance . In the limit of infinite data , the null hypothesis corresponds to . In this case . For a dataset of finite size , the null distribution is computed from many realizations of the matrix ( 25 ) where is defined by Eq . ( 16 ) . The eigenvalues of ( most of which are close to ) can then be compared to the null eigenvalue distribution , using either the nested or global comparison tests described above . In Fig . 3 we analyzed the simulated spike trains using every pseudoinverse order of . The prewhitening is then done using this matrix Eq . ( 18 ) instead of the full rank matrix . Performing the pre-whitened STC analysis using all pseudoinverse orders is equivalent to testing models . Therefore , the confidence interval of the null distribution should be adjusted from the percentile range to , where is the Dunn-Šidák correction: ( 26 ) We recall that according to Ref . [20] , the significant eigenvectors of can be written as ( 27 ) Thus , the eigenvectors of represent a sum of projection operators onto the principal components of the stimulus ensemble . When we perform the STC method in the subspace orthogonal to the first principal component of the stimulus , the eigenvectors of can be written as ( 28 ) ( the coherent mode is exactly the vector ) . Comparing expressions for the eigenvectors of and , one observes that there is a one-to-one correspondence between them . This correspondence can be identified based on proportionality in components along second , third , and other principal components: ( 29 ) for any . In sum , once the eigenvector is found to be significant in the subspace orthogonal to , the eigenvector that should be identified as significant in the full stimulus space is that satisfies the condition of Eq . ( 29 ) . The nonlinearity was chosen to be a logistic function because such functions maximize the cell's noise entropy and thus minimize the assumptions imposed on the cell's response [48] . Using the models , we generated simulated spike trains in response to either a white or a correlated noise stimulus . The model used in Fig . 3 ( model “” ) had a two dimensional relevant subspace with features orthogonal to the coherent mode . The probability of spiking was modeled to increase when the projection of the stimulus on either of the preferred features was large in absolute value ( representing a logical OR function ) . If are the preferred model features and is the stimulus presented at time ( here , the 's and are dimensional vectors ) then the probability of a spike at time is: ( 30 ) where and are parameters that determine the width and ( soft ) thresholds of the sigmoid nonlinearities for the model . We have also considered the case where the projection of the stimulus on the features was not taken in absolute value , corresponding to a monotonic nonlinearity . In that case ( model “” , used in Fig . 1 ) the model was one dimensional , so the probability of a spike is ( 31 ) The effects described above were observed for both symmetric ( Fig . 3 ) and monotonic ( Fig . 1 ) nonlinearities . The second model had one relevant input feature with a large component along the coherent mode . In this case , the probability of a spike was modeled as: ( 32 ) where and are the width and the threshold of the sigmoid nonlinearity of this model . In units of the standard deviation of the projection of the stimulus on the model features ( , ) the model parameters were chosen to be: ( 33 ) ( 34 ) ( 35 ) The overlap measure we use when the dimensionality of the relevant subspace is greater than one is given by [49]: ( 36 ) where and are matrices that hold the model and computed features , respectively , is the input dimensionality , and is the number of relevant features in the model . | In many areas of computational biology , including the analyses of genetic mutations , protein stability and neural coding , as well as in economics , one of the most basic and important steps of data analysis is to find the relevant input dimensions for a particular task . In neural coding problems , the spike-triggered covariance ( STC ) method identifies relevant input dimensions by comparing the variance of the input distribution along different dimensions to the variance of inputs that elicited a neural response . While in theory the method can be applied to Gaussian stimuli with or without correlations , it has so far been used in studies with only weakly correlated stimuli . Here we show that to use STC with strongly correlated , -type inputs , one has to take into account that the covariance matrix of random samples from this distribution has a complex structure , with one or more outstanding modes . We use simulations on model neurons as well as an analysis of the responses of retinal neurons to demonstrate that taking the presence of these outstanding modes into account improves the sensitivity of the STC method by more than an order of magnitude . |
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Toxoplasma gondii establishes a chronic infection by forming cysts preferentially in the brain . This chronic infection is one of the most common parasitic infections in humans and can be reactivated to develop life-threatening toxoplasmic encephalitis in immunocompromised patients . Host-pathogen interactions during the chronic infection include growth of the cysts and their removal by both natural rupture and elimination by the immune system . Analyzing these interactions is important for understanding the pathogenesis of this common infection . We developed a differential equation framework of cyst growth and employed Akaike Information Criteria ( AIC ) to determine the growth and removal functions that best describe the distribution of cyst sizes measured from the brains of chronically infected mice . The AIC strongly support models in which T . gondii cysts grow at a constant rate such that the per capita growth rate of the parasite is inversely proportional to the number of parasites within a cyst , suggesting finely-regulated asynchronous replication of the parasites . Our analyses were also able to reject the models where cyst removal rate increases linearly or quadratically in association with increase in cyst size . The modeling and analysis framework may provide a useful tool for understanding the pathogenesis of infections with other cyst producing parasites .
Toxoplasma gondii , an obligate intracellular protozoan parasite , is an important foodborne pathogen that can cause various diseases including lymphadenitis and congenital infection of the fetuses in humans . Infection occurs through ingestion of food or water contaminated with cysts or oocysts . The acute stage of infection is characterized by proliferation of tachyzoites in various nucleated cells . IFN--mediated immune responses limit tachyzoite proliferation [1]–[3] and the parasite establishes a chronic infection by forming cysts containing bradyzoites , primarily in the brain ( Figure 1 ) . Chronic infection with T . gondii is one of the most common parasitic infections in humans . It is estimated that 500 million to 2 billion people worldwide are infected with the parasite [4] , [5] . During the chronic stage of infection , bradyzoites slowly replicate within the cysts and cyst sizes increase in response . In immunocompromised individuals such as those with AIDS and organ transplants , cysts can rupture resulting in release of bradyzoites , conversion of bradyzoites into tachyzoites , and proliferation of tachyzoites , which can cause life-threatening toxoplasmic encephalitis [6] , [7] ( Figure 1 ) . Even in immunocompetent host , T . gondii cysts occasionally rupture during the chronic stage of infection [8] . In these cases , tachyzoite growth is controlled by the host's immune response , but the parasite is most likely able to form small numbers of new cysts ( Figure 1 ) . Such natural rupture of cysts and the formation of new cysts are thought to result in a wide range of T . gondii cyst sizes observed in the brains of chronically infected mice . There is currently only limited information on the immune responses to the cyst stage of T . gondii [9] , [10] . It was generally considered that T . gondii cysts cannot be recognized by the immune system . However , our recent study revealed that the T cells have the capability to remove tissue cysts from the brains of infected mice [9] . Marked decreases in cyst numbers occur during the T cell-mediated anti-cyst immune responses , suggesting that the immunity-mediated removal of the cysts can prevent formation of new cysts ( Figure 1 ) . Therefore , host-pathogen interactions during the chronic stage of T . gondii infection appear to have two distinct processes . One is a natural rupture of tissue cysts that can result in formation of new cysts . The other is the T cell-mediated cyst removal not associated with formation of new cysts . In order to better understand the dynamics of host-pathogen interactions during chronic T . gondii infection , in the present study we developed a set of biologically based models of cyst growth and removal including both natural rupture and immunity-mediated removal of tissue cysts and compared these models with actual data on distribution of sizes of T . gondii cysts obtained from the brains of chronically infected mice .
Previous studies by Hooshyar et al . [11] provided limited snapshots of the cyst size distributions in the brains of infected mice during the period of 2–4 months after infection . Typically , sizes of T . gondii cysts are viewed in terms of diameter . However , volume is biologically a more appropriate measure to indicate the size of cysts since it is expected to be proportional to the number of bradyzoites in a cyst . Hooshyar et al . assumed the shape of a cyst was ellipsoidal and measured the two diameters of the ellipsoid [11] . Based on their data , the mean volumes of cysts at 2 , 3 , and 4 months after infection were ( ) , ( ) , and ( ) , respectively . The number of cysts examined was 17 for each time point . There is a significant difference in the cyst volume between months 2 and 3 ( , ) , 2 and 4 ( , ) , but not 3 and 4 ( , not significant ) . These studies support the assumption that cyst volume reaches a steady state distribution within 4 months after infection . In order to have a larger data set of cysts in the steady state during the chronic stage of infection , we measured sizes of over 200 cysts of T . gondii in the brains of mice at 6 months after infection . Female Swiss-Webster mice ( Taconic , Germantown , NY ) were infected intraperitoneally with 10 cysts of the ME49 strain ( a type II strain ) as previously described in [12] . T . gondii has three predominant clonal genotypes ( types I , II , and III ) [13]–[15] . Type II constitutes a majority of clinical cases of toxoplasmosis and asymptomatic infections in humans in North America and Europe [13] , [15] , [16] . Six months later , the brain of each of four mice was triturated in 1 ml of PBS [9] . Mouse care and experimental procedures were performed in accordance with established institutional guidance and approved protocols from the Institutional Animal Care and Use Committee . Four to six aliquots ( 20 microliters each ) of each brain suspension were applied to microscopic examination using a Nikon Eclipse 90i microscope and a photograph was taken on each T . gondii cyst detected at ×400 magnification with a Nikon DS-Ri1 digital camera . Photographs of 50–56 cysts from each brain , a total of 213 cysts from four mice , were recorded ( see Figure 2 for a photograph of a typical cyst ) . We measured the diameter of each cyst from two different angles using NIS Elements BR analysis 3 . 2 software ( Figure 3; see also supplemental data ) . We calculated the volumes of each cyst using the two measured diameters by assuming an ellipsoidal shape: , where is the larger diameter and is the smaller diameter . There have been several attempts to understand the biology of Toxoplasma gondii infection through mathematical modeling [17] , [18] , however , none of these previous efforts have tried to model the growth and distribution of cysts as a function of their volume . Because in this study we are solely interested in the distribution of cyst volumes , we do not explicitly model population of free bradyzoites , tachyzoites , and uninfected target cells and , instead , simply assume new cysts are being formed at some rate . See Figure 4 for a schematic of the within-host system and Table 1 for definitions of the functions used in our model . Biologically represents the rate at which uninfected target cells become infected by free parasites and begin forming intracellular cysts . Following [19] , we model the growth of these cysts using a partial differential equation ( PDE ) structured by both time and cyst volume . Specifically , ( 1 ) where is the density of bradyzoite cysts of volume at time , is the cyst growth rate , i . e . the rate at which the bradyzoite population grows within a cyst , and is the cyst removal rate , i . e . the sum of the rate at which encysted cells are either cleared by the immune response or through natural cyst bursting . Conceptually , the PDE defined in Equation ( 1 ) describes how the density of cysts of size at time develops over time . For example , the first term on the left hand side of Equation ( 1 ) describes the ‘movement’ of cysts of size along the time variable . Since movement along the time axis is constant , we can think of the cysts as being carried along a conveyer belt along the variable . The second term describes how growth ‘stretches’ or ‘compresses’ the distribution of with cyst growth . For example , if we are considering the density of cysts in a region where is increasing with , then the density of cysts will be stretched out along the variable as larger cysts move more quickly along the axis . In contrast , if is decreasing with then will be compressed along as smaller cysts ‘catch up’ with the larger cysts . Finally , if is constant with respect to , similar to with the time variable , the density of cysts can be envisioned as moving along the axis on conveyer belt . The removal term on the right hand side of Equation ( 1 ) describes the rate at which the cyst density is being ‘siphoned off’ via the removal process . If the removal rate decreases/increases with , then larger cysts are removed at a lower/higher rate . If is constant with respect to , then the total density of cysts of a particular age ( i . e . ) will decline exponentially with time . Although these two cyst removal processes differ in that bursting can ultimately leads to the production of new cysts while immune response clearance does not , their effects on the relative distribution of cysts as a function of volume are indistinguishable and , hence , combined in Equation ( 1 ) . Biologically , both and likely vary with the immune response state of the host . However , since we are focusing on the steady state of the system where the immune response state of the host is constant , we do not explicitly model this dependency . For simplicity , we assume that all new cysts have an initial volume . Based on our definition of as the rate at which new cysts are formed , according to [20] the boundary condition for Equation ( 1 ) satisfies the equality , ( 2 ) The general solution of Equation ( 1 ) can be obtained using the method of characteristics [21] . First , an inverse function must be determined to find the correspondence between size and time . Depending on a cysts initial volume , , the current volume , , can be determined by some function that depends on the elapsed time since infection . This function , is the solution to , where is growth rate . From equation ( 2 ) , the equation for is the boundary condition . Then , the general solution is: ( 3 ) where is the boundary condition ( inflow of all new cysts into the system ) , is the characteristic curve through the time-size domain that is defined by solving the inverse equation above , is the initial time we wish to model . See Calsina and Saldana for a complete derivation [21] . Although Equation ( 1 ) can be explicitly solved as a function of time ( e . g . see [21] ) , here we focus solely on the steady state solution . Letting represent the steady state solution of Equation ( 1 ) , that is , . Under this condition , Equation ( 1 ) simplifies to the following ordinary differential equation ( 4 ) where is a combined function of the cyst growth and removal functions: ( 5 ) Note that is the derivative of with respect to . Equation ( 4 ) has a general solution of ( 6 ) where represents the steady state density of newly formed cysts and satisfies the boundary condition defined in equation ( 2 ) with . Because the combined function is a function of both and the first parameters of growth and removal functions , and respectively , cannot be uniquely identified . Instead , they can be estimated only as ratios of one another , i . e . , in this setting . Our data on cyst volume represents a random sample from the larger cyst population , in order to fit our models to this data we generate a probability density function from our steady state solution . We investigate the steady state solution in Equation ( 6 ) under several different forms of growth and removal functions; see the function definitions in Table 2 . We divide cyst density by the total cyst population size , to get a probability density function for cyst size . Specifically , ( 7 ) where represents the parameters of a given combined function ( e . g . or and ) . Using this probability density function , it follows that the negative log-likelihood of a particular model and parameter set given a random sample of observed cyst volumes is simply , ( 8 ) For each model in Table 2 we estimated the corresponding model parameters by minimizing based on the observed data using the NMinimize routine in Mathematica 8 . 1 . The minimal value and the total number of independent parameters were used to calculate the AIC value for each model . AIC and parameter estimates are also presented in Table 2 .
We measured two diameters on each of 213 cysts detected in the brains of 4 mice at 6 months after infection in order to have a larger size of data on volume of cysts in the steady stage during the chronic stage of infection . Distributions of diameters measured and volume of cysts calculated from the diameters by assuming that cysts are in an ellipsoidal shape are shown in Figure 5 . While the probability distribution on the diameter scale ( Figures 5 ( c ) and ( d ) ) is unimodal , the probability distribution on the volume scale ( Figures 5 ( a ) and ( b ) ) does not show modality . This difference is due to nonlinear transform between volume and diameter [22] . See the Methods section for calculation of the volume . We developed a differential equation model to investigate the cumulative effects of unknown growth and removal functions on the cyst- size distribution . As a means for model selection , the Akaike information criterion ( AIC ) [23] was used to evaluate and compare different models; see Table 2 . Based on information entropy , AIC is an estimate of the relative information lost for a given model . The AIC value of a model is calculated using its negative log-likelihood at the maximum-likelihood estimation ( MLE ) parameters and the number of parameters . Therefore , AIC provides a trade-off between a model's complexity and its goodness of fit . The AIC of a given model is the difference between the lowest observed AIC value and the AIC value of the model [24] . We explored three different growth functions and eight different removal functions . A schematic illustrations of these functions are shown in Figure 6 . More detailed descriptions of the function formalities can be seen in Holling [25] . To determine the cyst growth model that can fit best to the experimental data , we explore three different hypotheses as follows . The first hypothesis is that cysts grow at a constant rate i . e . , such that the cyst volume increases linearly with time ( indices 1–8 in Table 2 ) . Because bradyzoite number within a cyst increases with its size , this hypothesis corresponds to a per capita growth rate of bradyzoites that is inversely proportional to the number of bradyzoite , implying that bradyzoite replication is finely regulated and asynchronous within the cyst . The second hypothesis is that the cyst volume increases exponentially with time ( indices 9–16 in Table 2 ) . This corresponds to a constant per capita growth rate of within-cyst bradyzoites , implying that bradyzoites replicate independently of each other within the cyst . The third hypothesis is that the cyst volume grows logistically with time i . e . ( indices 17–24 in Table 2 ) . This hypothesis implies that bradyzoite replication is regulated within the cyst in a simple density dependent manner in which the per capita growth rate declines linearly with cyst volume . The AIC scores indicate that hypotheses two and three are not supported by the data . Therefore , we focused on various removal functions under hypothesis one . In regard to the cyst removal rate , models with constant ( index 1 ) , one-parameter type II ( index 4 ) , two-parameter linear ( index 6 ) , two-parameter type II ( index 7 ) , and two-parameter type III ( index 8 ) functions all fell within 2 . 5 AIC units of the best model ( index 5 ) , which is a model with a one-parameter type III function . We can , however , clearly reject models where cyst removal rate increases linearly ( index 2 ) or quadratically ( index 3 ) in association with increases in cyst volume . Comparison between probability distributions of the experimental data and the models using constant growth function ( indices 1–8 ) in Figure 5 .
We have developed a mathematical framework to select the most appropriate mathematical descriptions for the growth and removal processes of T . gondii cysts through parameter fitting of experimental data obtained from the brains of chronically infected mice . Population growth often satisfies a linear or logistic growth function [26] . However , experimental data here supports a constant growth rate model , i . e . , . We calculated the volumes of cysts by assuming that cysts are in an ellipsoidal shape . We also performed the same analysis by assuming that cysts are in a spherical shape using the effective diameter ( data not shown ) . In both cases , we reached the same conclusion . We assumed the cyst volume is proportional to the number of bradyzoites within the cyst . Therefore , a constant volume growth rate indicates that the number of parasites within the cyst increases linearly over time and the per capita growth of bradyzoites is inversely proportional to the number of parasites within a cyst . This probably suggests that bradyzoites do not replicate synchronously but each bradyzoite divide independently to produce a single new bradyzoite within a certain time interval . For example , a cyst may start with a given number of bradyzoites and a single new bradyzoite may be formed through replication every few hours . This is a contrast to tachyzoites of T . gondii or merozoites of malaria parasite . The tachyzoites and merozoites are the acute stage form of these parasites and they proliferate quickly after invading into host cells . On the other hand , tissue cysts of T . gondii are formed in the chronic stage of infection and the major purpose of cysts is most likely to persist within host cells , rather than proliferate . Therefore , it appears that tachyzoites and bradyzoites within cysts are under distinct regulatory mechanisms to control their proliferation . While it may be surprising that the bradyzoites replicate in a way analogous to a factory producing a product , there may be factors such as nutrient availability , immune response , and other stress factors that may limit their replication . Based on the analyses on cyst growth described above , we performed parameter fitting of various removal functions with the constant growth rate . The best model was a one-parameter type III function; however several other removal functions performed similarly well and are indistinguishable from one another . Based on the AIC criteria , performances of the following functions ( constant , type II , type III , and type III with two parameters ) are indistinguishable for the constant growth rate model . Thus , the current data cannot distinguish between several removal functions . However , our analyses were able to reject models where cyst removal rate increases linearly or quadratically with increases in cyst volume . This result would suggest that removal of cysts is the outcome of a complex of multiple biological mechanisms . In this study , we considered two removal processes: natural rupture and immune-mediated removal . Natural rupture of cysts may not occur simply based on the volume of cysts . It may also depend on cell-types of cyst-containing host cells and location of cysts in the brain . It has been shown that T . gondii can form cysts in both glial cells and neurons [27]–[29] . Removal of cysts by immune T cells and phagocytes could be independent of the sizes of the cysts contained in the infected host cells . To determine the specific removal function that fits best in experimental data , we would need to collect data on the transient dynamics and conduct corresponding studies . Moreover , the current study on steady state can only determine the ratio between the parameters and . Transient data are also needed to estimate these parameters separately . Recent studies suggested possible contributions of chronic infection with T . gondii with important diseases such as cryptogenic epilepsy and Alzheimer's disease [30] , [31] . Thus , it is crucial to understand the mechanisms of host-pathogens interactions in the brain during the chronic stage of infection with this parasite for defining the pathogenesis of this common infection . The present study provided valuable information that may improve our understanding in this aspect . This study also demonstrated a power of mathematical modeling to provide the information that will be difficult to obtain directly from biological studies . In the present study , we obtained the data at only one time point of the chronic stage of infection . Having the data from multiple time points including the acute stage of infection and larger samples numbers at each time points will assist in understanding of dynamics of cyst growth and removal during the course of infection with T . gondii . These data will also assist in better understanding of the roles of natural rupture of cysts and immune response-mediated removal of cysts in the pathogenesis of cerebral infection with the parasite . | A large portion of people worldwide are chronically infected with T . gondii . Chronic infection with this parasite is characterized by formation of tissue cysts . Bradyzoites slowly replicate within cysts during the chronic stage of infection leading to a corresponding increase in cyst size . Cysts occasionally rupture and release bradyzoites that invade nearby host cells and convert into tachyzoites which can quickly proliferate . Tissue cysts can also be targeted by immune T cells and phagocytes for removal . We developed a differential equation model to investigate the cumulative effects of unknown growth and removal functions on the cyst-size distribution . We then used the AIC to select models that best fit experimental cyst size distribution data obtained from the brains of chronically infected mice . The results suggest that the within-cyst growth of bradyzoites is finely-regulated asynchronous such that the per capita growth rate is inversely proportional to the number of bradyzoites . While it may be surprising that the bradyzoites replicate in a way analogous to a factory producing a product , there may be factors such as nutrient availability , resource allocation , immune response , and other stress factors that may limit replication in cysts . |
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Multiple Acyl-CoA Dehydrogenase Deficiency ( MADD ) is a severe mitochondrial disorder featuring multi-organ dysfunction . Mutations in either the ETFA , ETFB , and ETFDH genes can cause MADD but very little is known about disease specific mechanisms due to a paucity of animal models . We report a novel zebrafish mutant dark xavier ( dxavu463 ) that has an inactivating mutation in the etfa gene . dxavu463 recapitulates numerous pathological and biochemical features seen in patients with MADD including brain , liver , and kidney disease . Similar to children with MADD , homozygote mutant dxavu463 zebrafish have a spectrum of phenotypes ranging from moderate to severe . Interestingly , excessive maternal feeding significantly exacerbated the phenotype . Homozygous mutant dxavu463 zebrafish have swollen and hyperplastic neural progenitor cells , hepatocytes and kidney tubule cells as well as elevations in triacylglycerol , cerebroside sulfate and cholesterol levels . Their mitochondria were also greatly enlarged , lacked normal cristae , and were dysfunctional . We also found increased signaling of the mechanistic target of rapamycin complex 1 ( mTORC1 ) with enlarged cell size and proliferation . Treatment with rapamycin partially reversed these abnormalities . Our results indicate that etfa gene function is remarkably conserved in zebrafish as compared to humans with highly similar pathological , biochemical abnormalities to those reported in children with MADD . Altered mTORC1 signaling and maternal nutritional status may play critical roles in MADD disease progression and suggest novel treatment approaches that may ameliorate disease severity .
Multiple acyl-CoA dehydrogenase deficiency ( MADD ) , also known as glutaric aciduria type II ( GA-II , OMIM #231680 ) , is a rare autosomal recessive inherited metabolic disorder first described in 1976 [1] . The precise incidence and prevalence are unknown but are likely underreported given the variability in clinical presentation . MADD is caused by mutations in electron transfer flavoprotein genes A ( ETFA ) , B ( ETFB ) or the ETF dehydrogenase ( ETFDH ) [2] . The ETFA and ETFB gene products , ETFα and ETFβ respectively , form an ETF heterodimer located in the mitochondria matrix [3] . This complex receives electrons from at least nine distinct dehydrogenases that are involved in fatty acid β-oxidation , amino acid and choline metabolism [4] , [5] , [6] , [7] . Patients with MADD are classified by disease severity with type 1 having severe neonatal-onset with congenital anomalies , rapid deterioration and death [8] . Type 2 patients with MADD do not have congenital anomalies but still have a severe course with death usually during the few years of life [9] . Finally , type 3 patients have later onset and an overall milder course . However they still have hypoglycemia , metabolic acidosis , cardiomyopathy , hepatomegaly , kidney defects and neurological manifestations such as encephalopathy and leukodystrophy [10] , [11] . Current treatments are mainly aimed at relieving symptoms though anecdotal reports of improvement after administration of riboflavin or Coenzyme Q have been reported [11] . While all types of MADD can be caused by ETFA , ETFB or ETFDH mutations , it is not understood why there is such variability in disease severity . Several reports indicate a marked buildup of fatty acids , amino acid or toxic compounds in multiple organs in patients with MADD . However , comprehensive cellular and molecular analyses have not been possible as there are no animal models available that recapitulates the spectrum of abnormalities seen in patients with MADD . The first animal model of MADD was created by inactivating the zebrafish etfdh gene [12] . This mutant zebrafish was named xavier ( xav ) with conserved metabolic abnormalities also observed in MADD patients including increased levels of acylcarnitines and glutaric acid . However xav mutant zebrafish did not recapitulate morphological defects observed in MADD patients . This may be due to early lethality seen in this model prior to later stages of organogenesis . Using forward genetic screening for mutants with abnormal livers , we identified a mutant zebrafish called dark xavier ( dxavu463 , termed hereafter as dxa ) due to its phenotype of a dark fatty liver and hepatomegaly . Dxa mutant zebrafish have a nonsense mutation in the etfa gene resulting in widespread abnormalities broadly similar to those observed in MADD patients . We found large increases of acylcarnitines and glutaric acid in dxa mutants associated with multiple abnormalities of various organs including brain , liver , kidneys and heart . Marked accumulation of neutral lipid drops including cerebroside sulfate and free cholesterol in multiple organs was also observed . Analyses by mass spectrometry [13] found a large increase in triacylglycerides in dxa mutants but also a significant decrease of phosphatidylserine species which was also observed in human tissue derived from a patient with MADD [14] . The multiple defects seen in dxa mutant zebrafish closely recapitulate many core abnormalities observed in human patients with MADD . Interestingly , dxavu463 mutant developed hyperplasia with increased cell size in multiple organs including brain , liver and kidney suggesting activation of mTORC1 signaling . Excessive maternal feeding also exacerbated the phenotype in dxa mutants . We confirmed that mTORC1 signaling is highly elevated in dxa with increased phosphorylation of S6 and 4E-BP1 . Treatment of dxa zebrafish with rapamycin alleviated a subset of signaling and cellular proliferation abnormalities suggesting that targeting mTORC1 signaling could be a rational therapeutic approach for patients with MADD .
We identified dxa mutants during a forward genetic screen using ENU mutagenized zebrafish . Homozygous dxa mutants had a large and dark colored liver at 7 days post fertilization ( dpf ) ( Figure 1A ) . However , when more closely examined , dxa mutants had a broad spectrum of defects during development and post developmental stages ( Figure 1A ) . About 20% of mutants had severe congenital defects ( type I ) that included a small head and cardiac edema , these larvae died by 5–6 dpf . Approximately 18% of mutants were type II with moderate defects including an abnormal head and dark liver , intestine and brain . These died by 7–8 dpf . The remainder of the mutants ( approximately 62% ) classified as type III had mild defects that were morphologically close to wild type zebrafish except for a darker appearing liver , intestine and brain ( n = 218 ) . Type III mutants lived for 10 dpf in the unfed state whereas control siblings live for 10–12 dpf . Overall , type I , II and III mutants accounted for approximately 25% of the total zebrafish in each cross suggesting the dxa phenotype was due to a defect in an autosomal recessive gene . This was later confirmed ( see below ) as a mutation in the etfa gene known to be involved in mitochondrial function . Given the potential for metabolic influences on mitochondrial disease , we studied whether maternal overfeeding prior to egg laying could influence the phenotype . One week of extra feeding caused a dramatic shift in severity with 57% of dxa mutant zebrafish now classified as type I , 32% type II and 11% type III ( n = 151 ) ( Figure 1B ) . This result suggests that the maternal nutritional state dramatically affects the severity of dxa zebrafish and may also explain similar phenotypic variability reported in patients with MADD . To identify the mutant gene in dxa zebrafish , we performed conventional linkage mapping and were able to map the likely gene to approximately 0 . 18 cM from the galk2 gene , located on zebrafish chromosome 25 ( 1/547 recombination , data not shown ) . Whole genome sequencing of dxavu463 and control zebrafish ultimately identified a mutation in the etfa gene approximately 360 kb from galk2 . This G to T mutation introduces a premature stop codon ( G290X ) in etfa ( Figure 1C ) . Zebrafish etfa has 80% homology to the human ETFA gene suggesting a highly conserved function ( sequence alignment not shown ) . Whole mount in situ hybridization of etfa mRNA shows maternal and ubiquitous expression during early development with subsequent high expression levels maintained in the midbrain and blood vessels at 30 hours post fertilization ( hpf ) as well as liver and pectoral fins at 2 dpf ( Figure S1A ) . Immunofluorescent staining with an anti-Etfa antibody also revealed high expression of Etfa protein in neural progenitor cells located adjacent to the ventricles of the brain of wild type larvae ( Figure 1D , head , inset ) . We also saw strong expression in neuromast hair cells as well as kidney , liver and skeletal muscle of the pectoral fin of wild type at 9 dpf ( Figure 1D , trunk , inset , n = 9/9 ) . However , negligible Etfa protein was detected in dxa zebrafish ( Figure 1D , bottom panel , n = 9/9 . There is some residual signal in the dxa zebrafish that we interpret as non-specific binding of the secondary antibody to the outer pial membranes of the brain and outer eye ( Figure 1D ) . Immunoblot analyses also detected a very minimal amount of Etfa protein in the dxa mutant ( Figure S1C ) . This also supports a loss of function mutation due to non-sense mediated decay of etfa mRNA given the location of the premature stop codon in exon 10 and the in situ expression data ( Figure 1D lower panel , Figure S1B . 10/10 ) . This expression pattern of etfa further supports an important role in high energy demanding cell types such as neural progenitors within the brain , hepatocytes and kidney tubule cells . While genetic testing of affected patients is ideal , MADD can be strongly suspected in symptomatic children who exhibit increased serum acylcarnitines and glutaric aciduria [7] . Using tandem mass spectroscopy to determine acylcarnitine levels , we found significantly higher level of multiple long- , medium- and short-chain acyl-CoA species and isovalerylcarnitine in the dxa mutant larvae compared to control siblings ( Figure S2A ) . This suggests that dysregulation of mitochondrial β-oxidation is highly similar in dxa to that observed in patients with MADD . Further analysis of organic acids using gas chromatography-mass spectrometry found approximately 6 . 5 µg of glutaric acid per dxa larvae ( Figure S2C ) , but no detectable amount seen in control siblings ( Figure S2B ) . This pattern is highly reminiscent of that seen in patients with MADD , also known as glutaric aciduria Type II ( GA-II ) . Hepatic steatosis is a central sign in MADD , likely resulting from defective fatty acid β-oxidation that may be exacerbated during episodes of hypoglycemia . Dxa mutants exhibit progressive accumulation of dark colored granules in multiple organs including brain , liver and intestine after 6 dpf ( see Figure 1A ) . Oil Red O ( ORO ) staining in whole mounts of type II dxa mutant larvae and coronal sections showed massive accumulations of neutral lipid in the brain , liver and intestine as well as blood vessels at 8 dpf ( Figure 2A , B ) . Interestingly , toluidine blue staining of thick sections used for Transmission Electron Microscopy ( TEM ) revealed many heterogeneous sized vacuoles in the liver with brown colored drops in the cytosol of hepatocytes ( Figure 2C ) . As glycosphingolipid can include glucose or galactose and sulfate groups ( cerebroside sulfate ) that can be stained with toluidine blue , the brown drops within hepatocytes may contain cerebroside sulfate . Intensive Periodic Acid Schiff ( PAS ) staining was also seen in dxa liver ( Figure 2C , middle ) suggesting that these lipid drops are comprised of cerebroside sulfate as liver glycogen is normally undetectable in unfed 8 dpf zebrafish . Additional support that the drops do not contain glycogen is supported by TEM analyses where we did not observe any glycogen containing granules at 6 or 8 dpf ( data not shown ) . We also found high levels of free cholesterol in the cytosol of dxa hepatocytes using filipin staining ( Figure 2C , right ) . However , we did not see lipid and free cholesterol accumulation in dxa liver at 6 dpf although the mutants already exhibit hepatomegaly and enlarged hepatocytes ( Figure S3A–D ) Enlarged cell size was a consistent phenotype at later stages ( Figure S3E , F ) . Dxa hepatocytes were approximately three times larger those seen in control siblings ( Figure S3G ) . These results suggest that intrinsic abnormalities of hepatocytes led to both lipid and cholesterol accumulation in dxa mutant zebrafish . We then analyzed cellular ultrastructure using TEM to investigate possible organelle defects . The internal mitochondrial cristae density was markedly decreased in type II dxa hepatocytes at 6 dpf although total mitochondrial size was not changed ( Figure 2D ) . Strikingly , we found extremely large mitochondria with minimal cristae in type II mutants just 2 days later at 8 dpf ( Figure 2E ) . From these TEM results , we conclude that the “vacuoles” we saw in the mutant liver are actually grossly swollen mitochondria ( Figure 2C , E ) . This suggests that Etfa is required for mitochondrial maintenance as well as energy metabolism . We assessed mitochondrial function in dxa mutants by measuring oxygen consumption over time . We found significantly decreased oxygen flux in dxa mutant zebrafish compared to sibling controls ( Figure S4 ) . This strongly supports an impairment of mitochondria function in etfa mutant cells . It has been reported that many patients with severe neonatal onset MADD have polycystic kidney disease though Bohn et al . and Harkin et al . reported that these kidneys were pathologically distinct from typical polycystic kidney disease [15] , [16] . We did note high Etfa expression within pronephric tubules of wild type kidneys ( Figure 1D , trunk ) . Histological analysis of toluidine blue stained sections of dxa zebrafish kidney showed clear abnormalities possibly resulting from hypertrophy of the pronephric tubular epithelium . We also found a large number of prominent vacuoles in both type II and III dxa kidney epithelium compared to wild type ( Figure 3A ) . TEM analysis of type II dxa mutants showed similar to hepatocytes , they are very large mitochondria with minimal cristae ( Figure 3C , 2E ) . This finding implies that the “cystic kidney” pathology ascribed to patients with MADD may be due to massively swollen mitochondria in kidney tubule cells . We further found lipid and free cholesterol accumulation in the cytosol of dxa mutant kidney cells ( Figure 3B , D ) . However , we did not see significant increases of lipid and free cholesterol in dxa mutants that have only mild defects at earlier stages , though hypertrophic kidney tubule cells with swollen mitochondria are already present . ORO staining also revealed extensive lipid drops in the brain ( Figure 2A ) . We then analyzed brain sections to more precisely determine the location and cell types that contain lipid . We found large lipid accumulation in the ventricular zone ( VZ ) of the brain where the neural progenitor cells are found ( Figure 4A ) . Within the same region of dxa mutant brain , we also found cerebroside sulfate containing lipid drops at 9 dpf ( Figure 4B ) . Interestingly , type II mutants with more severe defects have VZ cells with very large nuclei compared to other neurons , but these large cells had pale intracellular staining ( Figure 4B ) . TEM revealed that these cells do not have a discernible subcellular structure other than swollen nucleus and mitochondria ( Figure 4C , D ) . These findings are suggestive of ongoing necrosis although we could not identify ruptured cell membranes . Of note we did not see enlarged nuclei at 6 dpf but swollen mitochondria were still observed in the type II mutant brain ( data not shown ) . These results suggest progressive and rapid brain damage after 6 dpf in dxa zebrafish . To analyze whether those abnormal cells are neural progenitors , we performed immunostaining for Sox2 in both type II and III dxa larvae at 8 dpf . We found increased numbers of Sox2 positive cells in both type II and III mutants ( n = 9/9 ) . Statistical analysis of type II mutants showed a 75% increase of Sox2 positive cells in the dorsal part of the VZ at 8 dpf ( Figure S5 ) . Finally , we found that brain lipid-binding protein ( BLBP ) positive neural progenitor cells were increased and distorted in their morphology though had decreased processes within the white matter of dxa mutants ( Figure 4H , asterisk and yellow magnified inset , n = 6/6 ) . Altogether , these results suggest that neural progenitor cells in the dxa mutant are hyperplastic and hyperproliferative . They may also be unable to properly generate neurons given the abnormal appearing grey matter observed in dxa mutant brain ( Figure 4 ) . Given the overt increases in lipids seen by Oil Red O staining , we performed lipid profiling with mass spectrophotometry ( MS ) to identify differences of lipid molecular species between control and dxa mutant larvae at 8 dpf . We found moderately decreased monoacylglycerol ( MAG ) and diacylglycerol ( DAG ) in the dxa mutant though only the MAG decrease was statistically significant . However a large increase of triacylglycerol ( TAG ) was observed in the dxavu463 mutants ( Figure S6 ) . Using MS analyses of glycerophospholipids , we also found significant decreases in phosphatidylserine ( PS ) species ( Figure S6 ) . By contrast , the two most abundant phospholipid species phosphatidylcholine ( PC ) and phosphatidylethanolamine ( PE ) did not show any statistically significant differences between control and dxa mutant zebrafish . These results provide a rationale for future lipid modifying therapies in patients with MADD and will help focus future experiments on lipid abnormalities seen in etfa mutant zebrafish . High Etfa expression was found within neuromast cells that are zebrafish sensory organs ( Figure 1D ) . Interestingly , type I and II dxa mutants had a decreased response to touch stimulation ( data not shown ) that was correlated with the severity of defects and increased age . Neuromast cells had short or absent kinocilia in type II dxa mutant and using ORO staining we found the dark granules seen in dxa neuromasts are comprised of lipids ( Figure 5A , seen in 10/10 mutant larvae examined ) . Given these widespread lipid abnormalities in sensory structures , we also looked for alterations in sensory nerves tracts . Using acetylated tubulin , we noted decreased staining with a disorganized appearing , “kinked” axonal track in type II dxa mutants ( Figure 5B , n = 8/9 ) . We then examined expression of myelin basic protein ( MBP ) , the most abundant myelin associated protein in the brain and spinal cord . We found decreased MBP staining in dxa type II mutants ( Figure 5C , n = 6/6 ) but did not see significant changes in type III mutants ( data not shown , n = 5/6 ) . Using TEM to examine myelination in the spinal cord , we again found abnormally increased size and morphology of mitochondria in the Mauthner axon track ( sensory pathway mediating escape responses ) as well as decreased myelination ( Figure 5D ) . These hypomyelination findings are reminiscent of the leukodystrophy reported in patients with MADD [17] . The markedly enlarged cells in dxa mutant brain , liver and kidney suggests that mTORC1 could be involved as signaling through this kinase is a key controller of cell size [18] . We previously showed activation of this pathway in zebrafish causes increased cell size that can be reversed with rapamycin , a potent mTORC1 inhibitor [19] . We then evaluated the phosphorylation status of the mTORC1 downstream effectors S6 ribosomal protein and 4E-BP1 by immunofluorescence . We found markedly elevated phospho-S6 and phospho-4E-BP1 in dxa mutants especially in neural progenitor cells ( n = 4/6 ( phospho-S6 ) , 6/6 ( phospho-4E-BP1 ) ) and pial cells ( n = 6/6 ( phospho-S6 ) , 6/6 ( phospho-4E-BP1 ) ) on the midbrain and central canal of the hindbrain ( Figure 6A , B ) . Increased level of phospho-4E-BP1 was seen more broadly than phospho-S6 in dxa mutants notably within midline cells and the central canal of the hindbrain as well as the intestine ( Figure 6B ) . Immunoblots of dxa mutants also revealed increased mTORC1 signaling compared to control larvae at 6 dpf ( Figure S7A–C ) . We also found increased phospho-S6 and phospho-4E-BP1 in the kidney and liver at 8 dpf ( Figure 6B ) . Even at earlier time points before the pathology was overt , we still found increased mTORC1 signaling in the liver ( Figure S7D ) . Given these findings we hypothesized that mTORC1 inhibition could potentially rescue dxa mutants . However , rapamycin treatment from 3–9 dpf was not able to suppress the dxa mutant phenotype ( data not shown ) . To address whether activated mTORC1 observed in dxa mutants is rapamycin sensitive , we treated with rapamycin daily from 5 dpf to 8 dpf at a concentration of 300 nM . Rapamycin freely crosses the blood brain barrier in zebrafish and typically inhibits mTORC1 downstream completely [19] . In contrast to results we have obtained with other zebrafish models of human disease , we found that levels of phospho-S6 and phospho-4E-BP1 were not fully suppressed in the brains of treated larvae ( Figure 6C ) . In the liver rapamycin did in fact suppress phospho-S6 levels but phospho-4E-BP1 actually appeared to be increased ( Figure 6D ) [20] . This intriguing finding suggests a novel regulation of mTORC1 downstream effectors in dxa mutant zebrafish . We previously found that tsc2 mutant zebrafish with prominent mTORC1 activation had both increased cell size and increased proliferation [19] . To assess proliferation in dxa mutants , we analyzed the proportion of kidney and liver cells expressing proliferating cell nuclear antigen ( PCNA ) at 8 dpf . Very few PCNA positive cells were detected in control siblings but a highly increased proportion of cells express PCNA in type III dxa mutant at 8 dpf ( Figure 7A ) . We quantified these differences in the liver and found 1/246 PCNA positive cells in control larvae versus 20/245 in type III dxa mutant zebrafish , this difference was statistically significant , p<0 . 006 ( Figure 7B ) . We then treated with rapamycin from 5 to 8 dpf to verify if suppression of mTORC1 signaling could rescue aspects of the dxa phenotype . While mutants treated with rapamycin still developed a fatty liver , there was a clear decrease in cellular proliferation ( Figure 7B ) . This result suggests that mTORC1 may be considered as a potential therapeutic target for some of the pathological features seen in patients with MADD .
MADD is a complex genetic disease with multi-organ involvement and widespread biochemical abnormalities . These features likely reflect the impairment of multiple acyl-CoA dehydrogenases with each enzyme normally handling different substrates . Additional MADD complexity may be due to distinct mutations in ETFA , ETFB or ETFDH . While patients with ETFDH mutations predominate in the literature , this is possibly due to a bias of genetic testing for relatively milder forms of MADD that are compatible with longer survival . Patients with ETFA mutations in contrast may have a more severe course and rapidly succumb to this disease prior to an accurate clinical , biochemical and genetic assessment . MADD is now screened in newborns in many countries and the true prevalence of all genotypes should eventually emerge from prospective analysis of confirmed positive cases . Comprehensive analysis of MADD features and pathological mechanisms including genotype/phenotype relationships has been severely hampered by the lack of genetic animal models that recapitulates key features of MADD . In this study we analyzed a novel zebrafish model with a loss of function mutation of the etfa gene . Remarkably , dxa zebrafish recapitulates many key MADD features including biochemical abnormalities , a phenotypic spectrum from severe ( type I and II ) to moderate ( type III ) and multi-organ defects of the brain , liver and kidney . The shared phenotype of hepatic steatosis and dysmorphic kidneys seen in patients with MADD and dxa mutant zebrafish are likely due to defects of fatty acid β-oxidation as well as disruptions of amino acid and choline metabolism . C4 ( butyryl ) and C5 ( isovaleryl ) acylcarnitines and glutaric acid were highly elevated , this confirms the remarkable conservation of zebrafish and human mitochondrial function . However we did note species-specific differences . For example , patients with MADD have elevations of C14:1 but we also observed increases of fully saturated C16 and C18 in zebrafish . This suggests that the substrate availability for very long chain acyl-CoA dehydrogenase ( VLCAD ) in the zebrafish diet differs from the human fatty acid pool and that zebrafish primarily oxidize saturated fatty acids . In contrast to MADD/GAII , GA Type I is caused by mutation in glutaryl-CoA dehydrogenase ( GCDH ) , however this is one of dehydrogenases coupled to the ETF complex . Gcdh knockout mice did not have any obvious brain defects , but on a high lysine diet , these mice had neuronal loss , defective myelination and swollen mitochondria [21] , [22] . Though abnormalities of neural progenitor cells were not reported , their results suggest that accumulation of glutaric acid may be sufficient to cause defective myelination and mitochondrial abnormalities although the clinical differences between GA-I and GA-II support distinct pathological mechanisms for each disease . Strikingly , we found the severity may be caused by the nutritional state of the parents as extra feedings prior to egg fertilization produced a much higher proportion of type I and II mutants in each cross . Ongoing studies in our laboratory will investigate whether this mechanism is due to additional metabolic “stress” or from alterations of key maternal proteins , lipids or mRNA in the yolk . However , our findings indicate that a better understanding of nutrition and overfeeding may positively impact fetuses with MADD and could reduce severe congenital anomalies . The low frequency of this disease and the lack of prenatal diagnosis in families without a previously diagnosed proband makes this scenario unlikely but given the trend towards precise genetic diagnoses for all aspects of medicine , maternal diet potentially exacerbating the MADD phenotype may be a crucial finding . mTORC1 signaling is a key mediator of cell size control and differentiation . Using other zebrafish models of human disease , rapamycin treatment reversed abnormalities of cell size and mTORC1 signaling in the brain [19] . In marked contrast , brain abnormalities and other aspects of mTORC1 signaling in dxa zebrafish appeared to be rapamycin resistant . Phospho-S6 was entirely inhibited in the liver of dxa zebrafish but levels of phospho-4E-BP1 were actually elevated by rapamycin . It was previously shown that rapamycin inhibits phosphorylation of S6 and 4E-BP1 differentially [20] . This group reported that S6K and S6 phosphorylation were readily abolished throughout the duration of rapamycin treatment but phosphorylation of 4E-BP1 can recover despite initial inhibition and repeated application of rapamycin . We do not understand the mechanism leading to rapamycin resistant mTORC1 signaling in the brain and liver but speculate it may be related to increased amino acids in dxa zebrafish that could activate Rag proteins [23] . Increased leucine for example is sufficient to cause Rag GTPase dependent translocation of mTORC1 to lysosomes [24] . Isovaleric Co-A dehydrogenase requires the ETF complex and loss of function mutations in the ISOVALERYL-CoA DEHYDROGENASE ( IVD ) gene are known to cause accumulation of isovaleric acid , a metabolite of leucine [25] , [26] . Leucine accumulation in dxa may then be activating mTORC1 . We found markedly increased leucine levels in dxa larvae supporting this potential mechanism ( Figure S8 ) . We also found markedly increased p62/sequestosome 1 in dxa mutant zebrafish ( data not shown ) that was recently shown to be essential to activate mTORC1 [27] . These findings suggest that restricting intake of leucine and other branched amino acids may be important in MADD to suppress symptoms due to mTORC1 activation . However , increased aerobic glycolysis was observed in etfdh mutant zebrafish , this may compensate for a failure of mitochondrial beta oxidation [12] . Increased glycolysis may provide key intermediates for cell proliferation [28] and elevated mTORC1 signaling could further increase glycolysis by modulating transcription of genes required for this metabolic process [29] . This may represent a compensatory mechanisms and inhibition of mTORC1 with rapamycin could exacerbate the MADD phenotype or precipitate a metabolic crisis . We have seen no evidence for this in our animal model but caution against the use of mTORC1 inhibitors in patients with MADD outside of well regulated clinical trials . The myelination defects in etfa mutant zebrafish are notable given the severe neurologic deficits including encephalopathy that is usually seen in patients with MADD . Lysosomal disorders such as metachromatic leukodystrophy ( MLD ) [30] , Krabbe disease [31] and Gaucher disease [32] all have accumulation of cerebroside sulfate that appears to cause myelination defects in nerve system as well as hepatomegaly . We also see accumulation of cerebroside sulfate in radial glia and hepatocytes in dxa mutant larvae . It is possible that inhibition of autophagy by mTORC1 activation might contribute to symptoms in MADD . The markedly increased p62 levels in dxa mutants supports such a mechanism . In conclusion , we report the first animal model of MADD due to mutations of the etfa gene . Dxa mutant zebrafish larvae have an array of biochemical and pathological features that strongly indicates this is a relevant model for MADD . Dxa zebrafish can be effectively employed to generate and test further hypotheses about disease specific mechanisms . In addition , dxa mutant zebrafish will be invaluable for future in vivo chemical screens to identify therapeutic compounds that may ameliorate disease aspects of MADD and potentially other mitochondrial disorders .
Zebrafish strains used in this study included AB* and dxavu463 . Embryos were obtained from natural matings and raised at 28 . 5°C in egg water ( 0 . 3 g of sea salts/L ) . For overfeeding experiments , we gave an extra meal of TetraMin Tropical Flakes daily for one week prior to fertilization of eggs . The normal diet is twice a day meal of brine shrimp and Tropical Flakes Monday through Friday and once day on Saturday and Sunday of each week . Short term extra feeding does not cause any obvious phenotypes . 5 pairs of heterozygous siblings were used for this experiment . We fed normally one week and each pair of each was mated . Then we gave the same zebrafish extra food for the subsequent week and mated again . This cycle was repeated three times . Antisense digoxigenin-labeled RNA probe for etfa was produced using a DIG-RNA labeling kit ( Ambion ) . Embryos were fixed in 4% paraformaldehyde overnight , and dehydrated in 100% methanol at −20°C . Whole mount hybridization was performed using standard protocols [33] . BCIP/NBT ( Vector laboratories ) mixture was used as a chromogenic substrate . In situ images were acquired using a Zeiss Axioscope and Nikon Coolpix 4500 digital camera . To avoid staining variation , 3 control and 3 dxa mutant larvae were processed together in the same slide glass . Slides were processed in a Sequenza Slide Rack . Embryos were fixed in 4% paraformaldehyde from overnight to two days at 4°C . Fixed embryos were embedded in 1 . 2% agarose/5% sucrose and saturated in 30% sucrose at 4°C for 1 to 2 days . Tissue blocks were frozen in 2-methyl butane . 10 µm sections were collected on microscope slides using a Leica cryostat . Sections were kept in −80°C before use . Sections were rehydrated in 1× PBS and blocked in 5% sheep serum in PBS for 2 hours , they were then incubated with primary antibodies to Etfa ( Genetex , #GTX124324 , dilution 1∶300 ) , Sox2 ( abcam , #97959 , dilution 1∶500 ) , PCNA ( Sigma , #P8825 , dilution 1∶3000 ) , BLBP ( abcam , #ab32423 , dilution 1∶500 ) , phospho-S6 ribosomal protein ( Cell Signaling #2215 Ser235/236 , dilution 1∶300 ) , and phospho-4E-BP1 ( Cell Signaling #2855 Thr37/46 , dilution 1∶300 ) overnight at 4°C , washed 10 minutes×3 times with 1× PBS and then incubated for 2 hours with Alexa Fluor conjugated goat anti-rabbit secondary antibodies . Sections were then washed with 1× PBS for 30 minutes and mounted in Vectashield with DAPI ( Vector laboratories ) . Antigen retrieval for PCNA staining was performed for 30 minutes of boiling in 10 mM sodium citrate before blocking . Images were acquired using Zeiss Axiovert 200M microscope with Zeiss AxioCam MRm and Hamamathu digital camera . Digital images were processed using Adobe Photoshop CS5 and Adobe illustrator CS5 . All images received only minor modifications with control and mutant sections always processed in parallel . Fixed samples were rinsed with PBS-DT ( 1× PBS , 0 . 5% Triton X-100 , 2% DMSO ) and both control and mutant were incubated with blocking solution ( PBS-DT , 5% goat serum ) for 2 hours at room temperature in a single tube . The antibody against acetylated-tubulin ( Sigma , #T7451 , dilution 1∶500 ) was used overnight at 4°C . Larvae were rinsed with PBS-DT 3 times ( 10 minutes each ) . Secondary was a goat Cy3-anti-mouse for overnight at 4°C . Specimens were rinsed with 700 µL of PBS-Tween for 10 minutes and repeated 5 times . Zebrafish were fixed in 4% PFA and mounted in glycerol before being imaged . For whole mount staining at larvae stage , larvae were fixed in 4% PFA overnight . Control and dxa mutant larvae were rinsed three times ( 5 minutes each ) with 1× PBS/0 . 5% Tween-20 ( PBS-Tween ) . After removing PBS-Tween , larvae were stained with mixture of 300 µL of 0 . 5% ORO in 100% isopropyl alcohol and 200 µL of distilled water for 15 minutes . Larvae were then rinsed with 1× PBS-Tween for three times . Larvae were rinsed twice in 60% isopropyl alcohol for 5 minutes each . They were briefly rinsed in PBS-Tween and fixed in 4% PFA for 10 minutes . Larvae were mounted in glycerol prior to imaging . For high resolution ORO staining on transversely sectioned larvae , 10 µm sections were dried at room temperature for 5 minutes . 150 µL of working ORO solution was added to slides and stained for 30 seconds . They were then washed with tap water and mounted using Vectashield with DAPI . For free cholesterol staining on transversely sectioned larvae , slides were soaked with 1× PBS for 5 minutes , then Filipin complex diluted 1∶500 ( Sigma , F-976 ) was added directly to slides and stained for 1 minute in the dark . Slides were washed with PBS and mounted with 75% glycerol . Images were taken using the DAPI channel of a fluorescent microscope . Frozen sections were used for PAS staining . The PAS stain was conducted in the Translational Pathology Core laboratory at Vanderbilt University using a DAKO Artisan Link Staining System . Glycerophospholipids from zebrafish larvae were extracted using a modified Bligh and Dyer procedure [34] . Forty of 8 dpf larvae of each genotype , either mutant or sibling control were homogenized in 800 µl of ice-cold 0 . 1 N HCl∶CH3OH ( 1∶1 ) using a tight-fit glass homogenizer ( Kimble/Kontes Glass Co , Vineland , NJ ) for about 1 minute on ice . The suspension was then transferred to cold 1 . 5 mL Eppendorf tubes and vortexed with 400 µl of cold CHCl3 for 1 min . Centrifugation ( 5 minutes at 4°C , 18 , 000× g ) to separate the two phases . Lower organic layer was collected , an odd carbon internal standard was added and solvent evaporated . The resulting lipid film was dissolved in 100 µl of isopropanol∶hexane∶100 mM NH4COOH ( aqueous ) 58∶40∶2 ( mobile phase A ) . Quantification of glycerophospholipids was achieved by the use of an LC-MS technique employing synthetic odd-carbon diacyl and lysophospholipid standards . Typically , 200 ng of each odd-carbon standard was added per sample . Glycerophospholipids were analyzed on an Applied Biosystems/MDS SCIEX 4000 Q TRAP hybrid triple quadrupole/linear ion trap mass spectrometer ( Applied Biosystems ) and a Shimadzu high pressure liquid chromatography system with a Phenomenex Luna Silica column ( 5-µm particle size ) using a gradient elution as previously described [35] , [36] . Individual species were identified based on their chromatographic and mass spectral characteristics . This analysis allows identification of the two fatty acid moieties but does not determine their position on the glycerol backbone ( sn-1 versus sn-2 ) . Neutral lipids from zebrafish ( forty of 8 dpf larvae/sample ) were extracted by homogenization in the presence of internal standards ( 500 ng 14∶0 monoacylglycerol and 24∶0 diacylglycerol and 1 µg 42∶0 triacylglycerol ) in 2 ml 1× PBS and extracting with 2 mL ethyl acetate∶trimethylpentane ( 25∶75 ) . A dried lipid film was dissolved in 1 mL hexan∶sopropanol ( 4∶1 ) and passed through a bed of Silica gel 60 Å to remove remaining polar phospholipids . Solvent from the collected fractions was evaporated and lipid film was redissolved in 90 µl 9∶1 CH3OH∶CHCl3 , containing 10 µl of 100 mM CH3COONa for MS analysis essentially as previously described [36] , [37] . Samples were analyzed in triplicates and p-values determined using Student's t-test . Forty 9 dpf control and dxa mutant larvae were lysed using pellet pestles ( Sigma , #Z359947 ) and passed through a 25 gauge syringe in 150 µL of PBS . For acylcarnitine analysis , the total lysate was placed into a 96 well plate containing stable isotope labeled internal standards ( Cambridge Isotope Laboratories , Andover , MA ) and acylcarnitine analysis performed according to the published methods [38] . Briefly , the lysate was dried under nitrogen , reconstituted with fifty µL of acetonitrile and one µL was injected into a Xevo-TQS tandem mass spectrometer ( Waters Corp . Waltham , MA ) . Acylcarnitines were quantified against an isotope–labeled internal standard of the nearest chain-length using the parent ions of the carnitine-specific fragment of m/z 85 . For organic acid analysis , the total lysate was made up to a final volume of 2 . 5 mL using deionized water , acidified to pH 2 . 0 and the acid fraction extracted three times into equal volumes of ethyl acetate . The pooled organic phases were dried down under a stream of nitrogen at room temperature and trimethylsilyl derivatives analyzed by gas chromatography-mass spectrometry using an Agilent 7890A gas chromatograph fitted with a 5975C Mass Selective Detector ( Agilent Technologies , Santa Clara , CA ) using a method initially developed for urine and vitreous humor analysis [39] . The acylcarnitine and organic acid assays are validated and in routine clinical use and were also previously used for analyses of etfdh mutant zebrafish [12] . In brief , samples were fixed in 2 . 5% gluteraldehyde for 1 hour then transferred to 4°C overnight . Samples were washed 3 times in 0 . 1 M cacodylate buffer then incubated for 1 hour in 1% osmium tetraoxide and washed with cacodylate buffer . Samples were dehydrated through a graded series of ethanol , then incubated in ethanol and propylene oxide ( PO ) . Samples were infiltrated with 25% Epon 812 resin and 75% PO for 35 minutes , then 50% Epon 812 resin and 50% PO for 1 hour then exchanged with new 50% Epon 812 resin and 50% PO and incubated overnight . Samples were exchanged with 75%: 25% ( resin: PO ) , then pure epoxy resin for 3–4 hours , then overnight . Finally , the resin was exchanged with epoxy resin for 3 hours , embedded in epoxy resin and polymerized at 60°C for 48 hours . Sectioning and Imaging: 500 nm to 1 µm thick sections were collected using a Leica Ultracut microtome . Thick sections were stained with 1% toluidine blue and . 70–80 nm ultra-thin sections were cut from this block and collected on 300-mesh copper grids and stained with 2% uranyl acetate ( aqueous ) for 16 minutes and then with lead citrate for 12 minutes . Samples were imaged on the Philips/FEI Tecnai T12 electron microscope at various magnifications . Average basal oxygen flux was quantified by high resolution respirometry using the Oroboros O2k Oxygraph ( Oroboros Instruments , Innsbruck , Austria ) . Ten larvae ( 8 dpf ) per chamber were maintained in Instant Ocean at 28°C and initially equilibrated to room air . Measurements of oxygen concentration were recorded every 2 seconds with no stirring . When the measured O2 concentration stabilized , chambers were stirred at 100 rpm for as short a time as possible to permit recording of a new stable O2 concentration , which was reflective of the true O2 concentration in solution . Stirrers were then turned off . This process was repeated until no further decrement in O2 concentration was measured and the fish were no longer motile . Average oxygen flux was calculated from the change in O2 concentration over time from the beginning of the experiment to the end . Data are from 4 measurements made with 10 larvae of each genotype . Forty control siblings and homozygote dxa mutant larvae from 3 to 5 clutches were homogenized in 100–750 µL of 0 . 1 M TCA , containing 10 mM sodium acetate , 100 µM EDTA , 5 ng/ml isoproterenol as an internal standard and 10 . 5% methanol at pH 3 . 8 . Samples were centrifuged at 10 , 000× g for 20 minutes . Supernatant was removed and stored at −80 degrees . Samples of the supernatant were then analyzed for biogenic monoamines and/or amino acids . Leucine was quantified with a Waters AccQ-Tag system with a Waters 474 Scanning Fluorescence Detector . Ten µL samples of the supernatant are diluted with 70 µL of borate buffer to which 20 µL aliquots of 6-Aminoquinol-N-Hydroxysuccinimidyl Carbamate and 10 µL 250 pmol/µL α-aminobutyric acid ( as internal standard ) are added to form fluorescent derivatives . 10 µL of sample was then injected into the HPLC system , and separation of the amino acids accomplished by means of a Waters amino acid column and supplied buffers using a specific gradient profile . Error bars in Figure S3 and S6 represent standard error of the mean ( SEM ) , error bars in Figure 7 and S4 represent standard deviations ( SD ) . Student's t-test was used to determine statistical significance . All animal experiments were done with the approval of the Vanderbilt University IACUC . | Mitochondrial disorders have multiple genetic causes and are usually associated with severe , multi-organ disease . We report a novel zebrafish model of mitochondrial disease by inactivating the etfa gene . Loss of this gene in humans causes multiple acyl-Co dehydrogenase deficiency ( MADD ) that manifests with brain , liver , heart , and kidney disease . While presentations are variable , many children with MADD have a severe form of the disease that rapidly leads to death . We report that etfa gene function is highly conserved in zebrafish as compared to humans . In addition we uncovered potential disease mechanisms that were previously unknown . These include the impact of maternal nutrition on disease severity in their offspring as well as the role mTOR kinase signaling . Inhibition of this kinase with the drug rapamycin partially reversed some of the symptoms suggesting this may be a new approach to treat mitochondrial disorders . |
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The yellow fever mosquito Aedes aegypti inhabits much of the tropical and subtropical world and is a primary vector of dengue , Zika , and chikungunya viruses . Breeding populations of A . aegypti were first reported in California ( CA ) in 2013 . Initial genetic analyses using 12 microsatellites on collections from Northern CA in 2013 indicated the South Central US region as the likely source of the introduction . We expanded genetic analyses of CA A . aegypti by: ( a ) examining additional Northern CA samples and including samples from Southern CA , ( b ) including more southern US populations for comparison , and ( c ) genotyping a subset of samples at 15 , 698 SNPs . Major results are: ( 1 ) Northern and Southern CA populations are distinct . ( 2 ) Northern populations are more genetically diverse than Southern CA populations . ( 3 ) Northern and Southern CA groups were likely founded by two independent introductions which came from the South Central US and Southwest US/northern Mexico regions respectively . ( 4 ) Our genetic data suggest that the founding events giving rise to the Northern CA and Southern CA populations likely occurred before the populations were first recognized in 2013 and 2014 , respectively . ( 5 ) A Northern CA population analyzed at multiple time-points ( two years apart ) is genetically stable , consistent with permanent in situ breeding . These results expand previous work on the origin of California A . aegypti with the novel finding that this species entered California on multiple occasions , likely some years before its initial detection . This work has implications for mosquito surveillance and vector control activities not only in California but also in other regions where the distribution of this invasive mosquito is expanding .
Dengue , Zika , and chikungunya are severe mosquito-borne infectious diseases that are of growing concern in tropical and sub-tropical regions , and yellow fever is re-emerging in many regions [1] . The viruses causing these diseases are primarily transmitted by the mosquito vector Aedes aegypti . The incidence of dengue has increased 30-fold in the past 50 years [2] with an estimated 96 million new cases annually [3] . More than 2 billion people are at risk of infection by one of the four dengue serotypes [4] . Following in the footsteps of a widespread chikungunya epidemic in Asia and the New World , Zika virus has rapidly emerged around the globe . It has spread to more than 40 countries , in some cases causing serious birth defects including microcephaly [5 , 6] . With no effective vaccines and limited antiviral therapeutics available for these diseases , vector control remains critically important . The ancestor of the domestic form of A . aegypti is a zoophilic sub-species called formosus [7] . It is likely from sub-Saharan Africa , where it can still be found [7] . Outside of Africa , A . aegypti is a domestic mosquito that primarily bites humans , lays eggs in manmade water-containers , and can disperse over long distances using human transportation systems [8] . As such , it is a highly successful invasive species that has colonized most tropical and subtropical regions [7 , 8] . A . aegypti likely migrated to the Americas in European slave ships in the fifteenth through seventeenth centuries [7 , 9] , and these ships probably provided an environment that helped select for traits that increased the success of the domestic form of A . aegypti [7] . The first documented epidemic of yellow fever in the New World was in the Yucatan in 1648 , and yellow fever was common in Atlantic seaports from the seventeenth through nineteenth centuries , presumably fueled by the arrival of African slaves and infected sailors [8] . Today , populations of A . aegypti are distributed throughout most of the southern United States , especially below the 33-degree north latitude line [10] . Populations are sporadically found in the Mid-Atlantic States and New England , including an overwintering population in Washington D . C . [10 , 11] . Locally transmitted disease from A . aegypti is not common in the United States , but there were locally transmitted cases of Zika in Miami-Dade County , Florida and Cameron County , Texas in 2016 [12] . There has been local transmission of dengue and chikungunya in Florida as recently as 2013 and 2014 , respectively [13 , 14] . Population genetics plays important roles in both understanding the natural history of A . aegypti and in implementing vector control . Validated genetic markers can be used to determine the source of new invasions [15 , 16] , detect bottlenecks potentially caused by vector control or founder effect , determine a population’s susceptibility to different classes of insecticide , infer connectivity or isolation between populations , and determine if seasonal appearance of A . aegypti is due to new introductions each year or overwintering . Mosquitoes are more likely to be present and abundant in an area where they can survive the winter , and overwintering is especially concerning in cases where the vectored viruses can also persist through the winter , either in inseminated females or through vertical transmission from mother to eggs . ( There is evidence for transovarial transmission of dengue and vertical transmission of Zika in A . aegypti [17 , 18] . ) A stable , overwintering population would be expected to be genetically stable from year to year , although this can be difficult to distinguish from a population that is re-founded each year from the same source population . California ( CA ) has an extensive mosquito-monitoring program , and historically A . aegypti were only occasionally detected near airports and other ports of entry [19] . Breeding populations were first reported in 2013 from Fresno , Madera , and San Mateo . Gloria-Soria et al . concluded that the likely origin of these populations was the South Central US , particularly Houston or New Orleans [20] . Through the end of 2016 , A . aegypti had been found in 96 cities and census designated places from 12 different counties in CA [21 , 22] . In this analysis , we have built upon our previous work by adding 13 new samples from CA , including 8 from southern CA ( Fig 1A ) . The primary goals of the study are 1 ) to determine whether A . aegypti populations in CA originated from a single or from multiple introductions , 2 ) to characterize the genetic structure of CA A . aegypti populations , and 3 ) to determine if the genetic data are consistent with overwintering by A . aegypti , especially in the northern parts of CA . We found clear genetic differentiation between the Northern and Southern CA populations and found support for the hypothesis that at least two introductions of A . aegypti into CA are responsible for the current populations within the state .
A total of 34 samples of A . aegypti mosquitoes from 12 sites in CA and 16 sites from across the southern United States and northern Mexico were considered in analyses ( Table 1 ) . The mean sample size per collection was 39 individuals ( range: 6–150 ) . Ten of the California samples were collected between May and September of 2015 , and an additional eight were collected in 2013–2014 . In several cases , multiple collections were made from the same site in different years , or in different areas of the same site in a single year ( as noted in Table 1 ) . All mosquitoes from 2015 were collected as adults or eggs from traps and were shipped as adults to our laboratory for analysis . The collections made prior to 2015 are described elsewhere [15 , 16 , 20] . To avoid biased sampling of siblings , when ovitraps were the source of our sampling we used eggs from four or more traps from any locality with no more than six genotyped individuals per trap . Given that Aedes aegypti are “skip ovipositors” ( normally laying one or a few eggs in multiple containers ) [23] , the use of multiple traps should be sufficient to minimize the sampling of siblings . For convenience , we have grouped the samples into five broad geographic regions referred to throughout this paper as Southern California , Northern California , Southwest US , South Central US , and Southeast US . The regions are described in Table 1 and shown in Fig 1B . Whole genomic DNA was extracted from 286 whole adult mosquitoes from ten CA sites collected in 2015 using the Qiagen DNeasy Blood and Tissue kit according to manufacturer instructions , including the optional RNAse A step . All individuals were genotyped at 12 highly variable microsatellites , as in Brown et al . [15] . The microsatellite loci are A1 , B2 , B3 , A9 ( tri-nucleotide repeats ) , and AC2 , CT2 , AG2 , AC4 , AC1 , AC5 , AG1 , and AG4 ( di-nucleotide repeats ) [15 , 24] . These loci have been validated previously for their ability to distinguish A . aegypti populations around the world [15] . A total of 107 individuals from ten CA samples ( as noted in Table 1 ) were genotyped at 50 , 000 single-nucleotide polymorphisms using the high-throughput genotyping chip , Axiom_aegypti1 [25] . Cost prohibited the genotyping of all individuals , so we chose 5 Northern and 5 Southern CA populations instead . After excluding individuals that did not genotype at all microsatellites ( likely due to poor DNA quality ) , we chose 6–12 arbitrary individuals from each population . These data were pruned as described below . Genotyping was subsequently conducted by the Functional Genomics Core at University of North Carolina , Chapel Hill . All SNP data is available in S1 File as a VCF file , and all microsatellite data is available in S2 File . Additionally these data will be publicly available at Vectorbase . org , Population Biology Project ID: VBP0000177 . All microsatellite loci were tested for within-population deviations from Hardy-Weinberg equilibrium and for linkage disequilibrium among loci pairs using the online version of GENEPOP [26 , 27] with 10 , 000 dememorizations , 1 , 000 batches , and 10 , 000 iterations per batch for both tests . To correct for multiple testing , a Bonferroni correction was applied at the 0 . 05 α level of significance . Observed heterozygosity ( HO ) and expected heterozygosity ( HE ) were calculated using the software GenAlEx 6 . 5 [28 , 29] , and allelic richness was estimated by rarefaction ( N = 30 ) using the software HPRARE [30] . To identify likely genetic clusters and possible origins for each cluster , we used a Bayesian clustering method implemented by the software STRUCTURE v . 2 . 3 . 4 [31] . STRUCTURE identifies K genetic clusters and estimates what proportion of each individual’s ancestry is attributable to each cluster , with no a priori location information about the individuals . Twenty independent runs were conducted at K = 1–15 for the full set of CA and North American reference populations and at K = 1–12 for the subset of just CA populations . We ran each for 600 , 000 generations with 100 , 000 discarded as burn-in , assuming an admixture model and correlated allele frequencies . The optimal number of K clusters was chosen using the guidelines from Prichard et al . [31] and the Delta K method [32 , 33] . The results were visualized using the program DISTRUCT v . 1 . 1 [34] . To further explore population structure , discriminant analyses of principle components ( DAPC ) , Principle Component Analyses ( PCA ) , and plots illustrating FST values were created using the Adegenet package v . 2 . 0 . 2 . [35] , available on R software v . 3 . 2 . 4 and RStudio v . 0 . 99 . 893 [36] . DAPC optimizes variation between clusters while minimizing variation within them . Data are transformed using a PCA and then clusters are identified using discriminant analysis . We assessed genetic differentiation among population pairs by calculating FST values with GenoDive v . 2 . 0b27 [37] . We also ran isolation by distance ( IBD ) analyses for all populations and for all CA populations using Genodive v . 2 . 0b27 . While the SNP chip has 50 , 000 probes , only 27 , 674 passed the initial stringent testing requiring unambiguous genotyping , biallelic and polymorphic markers , and Mendelian inheritance [25] . Further filtering was done using PLINK v . 1 . 9 to exclude alleles showing up in <1% of samples as these could be genotyping errors , as well as loci not conforming to Hardy-Weinberg expectations ( threshold of 0 . 00001 ) , and those that genotyped in <98% of the samples [38 , 39] . These filtering parameters are standard for SNP chip data [40–43] . The dataset contained 15 , 698 SNPs after this filtering . For SNP data , we ran four runs with the Bayesian program fastSTRUCTURE to estimate the number of genetic clusters and calculate ancestry fractions for each individual given K numbers of genetic clusters [44] . The results were visualized using DISTRUCT v . 1 . 1 . For comparison , we also used the maximum likelihood software Admixture 1 . 3 . 0 and the CV error method described in the software’s manual to estimate the number of genetic structures and visualize the ancestry fractions calculated for each individual [45] . PCA analyses were conducted in both Adegenet and PLINK and plotted in R v . 3 . 2 . 4 . We inferred demographic history and estimated relevant parameters using microsatellite data and Approximate Bayesian Computation methods [46] as implemented by the program DIYABC [47] . Four colonization scenarios were tested to determine if the current Californian populations are more likely to be the result of one or two introduction events ( Fig 2 ) . In the first scenario , Northern California populations originate from an invasion from South Central US , and Southern California populations from an invasion from the Southwest US . In the second scenario the origins are reversed; Northern California comes from Southwest and Southern California from South Central . The third scenario depicts just one invasion into California , and the fourth scenario is a neutral model in which all four populations branch from a common ancestor at the same time . We ran the analysis using two different datasets . First , to reduce the excess noise that can result from grouping disparate populations together , we ran the analyses with regional groups that were made up of 133 randomly chosen individuals from populations that were representative of the geographic regions , as identified in STRUCTURE ( Fig 3C ) and noted S1 Table . For example , San Mateo , Madera , and Fresno always clustered together , so they were chosen to represent Northern California ( Fig 3 ) . To assess whether we were biasing the analysis by excluding some populations in this first test , we ran the analysis again this time forming the four regional groups from 217 randomly selected individuals from all populations from the respective geographic region . In both cases , the number of individuals was chosen based on the number of individuals in the smallest of the four groups . For the DIYABC analyses , we first simulated 1 , 000 , 000 datasets for each scenario , resulting in 4 , 000 , 000 total simulated datasets . To determine which scenario was most supported by the data , we evaluated the relative posterior probability using a logistic regression on the 4 , 000 ( 1% ) simulated datasets closest to the observed dataset . To estimate demographic parameters , we chose scenario 1 and estimated posterior distributions of parameters taking the 1 , 000 ( 1% ) closest simulated datasets , after applying a logit transformation of parameter values . To evaluate confidence in the posterior probability of scenarios ( in the form of Type I and Type II errors ) , we used a logistic regression on 250 test datasets simulated for each scenario with the same values that produced the original dataset . Priors and parameters are provided in S3 and S4 Tables .
For microsatellites , 17 out of 2 , 241 ( 0 . 76% ) loci pairs were found to be in linkage disequilibrium , and 3 out of 360 ( 0 . 83% ) locus-population pairs were not in agreement with Hardy Weinberg equilibrium after Bonferroni correction for multiple comparisons . This is consistent with previous work indicating that these 12 microsatellites are single-copy and can be treated as independent loci for population genetic analyses [15 , 16] . Genetic diversity was significantly lower within CA populations than among other North American populations ( Table 2 and S1 Table ) . The mean observed heterozygosity +/- the standard deviation ( SD ) for CA sites was 0 . 45±0 . 088 , and the mean of all other sites included in analysis was 0 . 57±0060 ( Student’s t-Test , p = 0 . 0079 ) . This trend was largely due to Southern California populations; 5 out of the 10 populations with the lowest heterozygosity in this analysis were located in Southern California . Similarly , the mean estimated allelic richness for California , 3 . 05±0 . 64 , was lower than the mean of other populations , 3 . 95±0 . 51 ( Student’s t-Test , p = 0 . 0013 ) , due to Southern California . The mean allelic richness of the Southern California populations ( 2 . 62±0 . 41 ) was significantly lower than the mean from the Northern California populations ( 3 . 48±0 . 52 ) ( Student’s t-Test , p<0 . 0001 ) . Nine out of 10 of the analyzed sites with the lowest allelic richness were from California , and 8 of these were from Southern California . Among California populations , pairwise FST values ranged from 0 . 0010 to 0 . 42 ( S2 Table ) . Excluding Exeter as an outlier , pairs of Northern California sites had low mean FST values ( 0 . 060±0 . 051 SD ) . In contrast , Exeter and the Southern California sites had significantly higher mean FST values when paired with themselves ( 0 . 26±0 . 11 SD ) or with all the CA populations ( 0 . 26±0 . 10 ) ( Student’s t-Test , p<0 . 0001 ) . S1 Fig illustrates this pattern graphically and in the context of all analyzed North American populations . The IBD analyses on all the populations did not show a correlation between distance and FST values ( Spearman’s r = -0 . 19; p = 0 . 010; R2 = 0 . 036 ) , and the IBD test on just the populations from CA was not significant ( Spearman’s r = 0 . 11; p = 0 . 14; R2 = 0 . 011 ) . In regard to overwintering , the FST values between Madera 2015 and the two Madera 2013 populations is lower ( 0 . 01 and 0 . 047 ) than between Madera 2015 and any of the sites in the Central South populations ( range = 0 . 091–0 . 15 ) . The FST between San Mateo 2014 and San Mateo 2015 is higher ( 0 . 16 ) than between San Mateo 2014 and some of the Central South populations , such as New Orleans ( 0 . 13 ) . Bayesian clustering analysis for microsatellite data on all CA populations identified two primary clusters ( K = 2 ) , which divided the Northern California populations from the Southern California populations with the exception of Exeter and most of the Clovis individuals ( Fig 3A ) . At higher K values , STRUCTURE showed a high level of population differentiation between each of the southern California populations , Exeter , and the two Clovis populations ( Fig 3B and S2 Fig ) . In contrast , San Mateo , Madera , and Fresno populations always clustered together ( Fig 3B and S2 Fig ) . At higher Ks , Bayesian clustering analysis that included Northern CA , the South Central , and the Southeast consistently showed that San Mateo 2013 , San Mateo 2014 , Madera 2013 , and Madera 2014 formed a cluster that was separate from the South Central populations ( eg . S3 Fig ) . The results from these higher K values were also found with the SNP data , as described below . A PCA analysis using microsatellites from California populations found that the first Principal Component accounted for 13 . 17% of the variation and the second accounted for 9 . 71% . Using the same data for a DAPC analysis , the “find . clusters” command in Adegenet found the data could best be described by 12 clusters ( S4 Fig ) . The first axis on the DAPC plot corresponds with the north-south gradient and explains 27 . 26% of the total variance; the second axis highlights the uniqueness of Exeter and explained 20 . 21% of the total variance ( S4 Fig ) . In a DAPC plot using populations as priors , the first axis corresponds relatively well to the north-south gradient and explained 38 . 67% of the total variance , while the second axis explained 17 . 70% of the total variance ( S5 Fig ) . Analysis of the SNP data largely reinforced the microsatellite results . Seven genetic clusters were identified by fastSTRUCTURE and six by Admixture’s CV Error method of K selection on the final dataset of 15 , 698 SNPs ( Fig 4 and S6 Fig ) . San Mateo , Madera , and Fresno clustered together , and each of the other populations formed its own genetic cluster ( although Admixture did not distinguish between San Diego and Los Angeles ) ( Fig 4 and S6 Fig ) . The clusters observed in the PCA on the SNP data corresponded to those identified by Admixture ( Fig 5 ) . Using Adegenet , the first Principal Component explained 8 . 06% of the variation and was correlated with the north to south gradient , with the exception of Exeter ( Fig 5 ) . The second Principal Component accounted for 6 . 64% of the variation and highlighted the uniqueness of Garden Grove and Exeter . Including microsatellite data from all analyzed populations from this study ( Table 1 ) , the Bayesian clustering method implemented by STRUCTURE identified two ancestral groups ( K = 2 ) ( Fig 3C ) . Most of the Northern California populations clustered with the South Central US and Southeast US populations , while most of the southern California populations clustered with the Southwest US populations ( Fig 3C ) . The Bayesian clustering method implemented by STRUCTURE showed Northern California clustering with the South Central and Southeast regions , and Southern California clustering with the Southwest region . Since Gloria-Soria et al . showed that Houston or New Orleans was the likely origin of the Northern Californian populations [20] , we used a simulation to test the hypothesis that Northern California populations originated from South Central populations and that Southern California populations originated from Southwest populations . We ran the analysis first with individuals from populations that were representative of their regions ( Table 1 ) , and secondly without excluding any populations . Since the results are similar ( S3 and S4 Tables ) with one described exception , all results refer to the first analysis unless otherwise noted . Evaluation of the relative posterior probability of each of the four competing scenarios in Fig 2 supported Scenario 1 as the most plausible invasion scenario ( S3 Table ) , in which Northern California populations split from the Central South , and Southern California populations split from the Southwest populations ( p = 0 . 990 CI95%: 0 . 983–0 . 996 ) . Scenario 4 , the neutral model in which the four populations diverge from the same ancestor simultaneously , had the next highest posterior probability ( 0 . 0079 CI95%: 0 . 0019–0 . 0138 ) . The type I error rate was 0 . 14 , and the type II error rates under the three other scenarios were 0 . 16 , 0 . 11 , and 0 . 064 . The estimated time of divergence of the Central South from the Southwest populations was 4 , 200 generations ( approximately 420 years ) , and the 95% credible interval was 1 , 730–5 , 900 generations assuming 10 generations/year . The estimated time of divergence of Northern California populations from South Central populations was 292 generations or approximately 29 years ( 95% credible interval = 61–853 generations ) , and the estimated time of divergence of Southern California from the Southwest was 224 generations or approximately 22 years ( 95% credible interval = 39 . 9–774 ) . Full details are provided in S3 Table . The results from the second analysis ( in which no populations were excluded ) were mostly similar including a high support for Scenario 1 ( p = 0 . 9995 CI95%: 0 . 9991–0 . 9999 ) . The estimated time of divergence between Southern California and Southwest was approximately 35 years ( mean = 348 , 95% credible interval = 66 . 9–893 ) , and the estimated time of divergence between Northern California and the South Central/Southeast was approximately 8 years ( mean = 78 . 0 , credible interval = 15 . 6–414 ) . Full details are provided in S4 Table .
Our analyses suggest multiple introductions of A . aegypti into CA that came from at least two different regions in North America . As previously shown , the populations in Northern California likely originated from mosquitoes that were introduced from the South Central region of the US [20] . The results of this study also suggest that a second introduction event likely occurred from the Southwest/northern MX region , and that these mosquitoes gave rise to the current populations found in Southern California . Given the considerable distance between the Northern CA and Southern CA populations ( >275km from Exeter [E] to Los Angeles [F] ) and the recentness of the invasions , it is not surprising that the two groups maintain distinct signatures of their genetic ancestry . As more populations are discovered between Northern and Southern CA ( for example , Kern County ) , it would be interesting to add them to this analysis to identify whether the north-south break is clean or if the transition occurs as a cline . Identifying the area where the two clusters meet could eventually help us understand the factors leading to this genetic break . The DIYABC analysis using representative populations suggests that the Northern California lineage diverged from the South Central lineage 292 generations ago , estimated to be ~29 years , and that the Southern California lineage diverged from the Southwest/MX lineage ~22 years ago . These estimates have large credible intervals , so we cannot tell which invasion into California happened first . The lower bounds of the intervals containing 95% probability are both more than 3 years , so it seems likely the invasions occurred at least a year prior to the initial detection of A . aegypti in 2013 . In our second run of DIYABC , which did not exclude individuals from potential outliers ( Clovis and Exeter ) , a more recent time of divergence ( ~8yrs ) was estimated between Northern California and South Central/Southeast . This is more consistent with personal communications from CA vector control professionals who think it is unlikely the invasion could have occurred more than a year or two prior to initial detection . Invasion events are often accompanied by a bottleneck in population size and subsequent decrease in genetic diversity , especially allelic richness . Consistent with Gloria-Soria et al . [20] , we found that Northern California populations have similar levels of genetic diversity and allelic richness as other US populations . However , Southern California populations are less genetically diverse . This relatively low diversity is a possible signature of bottleneck ( s ) caused either by relatively recent founder effects and/or vector control measures that reduced A . aegypti population size . We observed significant population structure with genetic differentiation even among populations in close geographic proximity , particularly among Southern California populations . For example , Anaheim , Orange , Garden Grove , and Santa Ana are each less than 12km apart from the others , but they are genetically distinguishable ( S2 Fig ) . The dense highway system and highly discontinuous human habitats in Southern California could well cause A . aegypti to be broken into almost entirely isolated small local populations given the evidence suggesting this mosquito avoids crossing major roads/highways [48] . Similarly , Clovis and Fresno are less than 12km apart but genetically distinct ( Figs 3 and 4 ) . On the other hand , San Mateo is genetically indistinguishable from Madera and Fresno , despite the >200 km between them . Likely because of patterns like these , isolation by distance analyses did not explain the population structure in CA or throughout the regions sampled . The large pairwise FST values between Southern California sites may indicate limited gene flow among the populations , consistent with the relatively short active dispersal distance that has been found for A . aegypti [23] . The observations are consistent with the possibility that effective A . aegypti control in one locality may not be easily influenced by migration from neighboring localities . Further analysis taking into account both timing of invasion and connectivity through human transportation routes may help us understand the patterns we see in the structure of both Southern and Northern CA populations . In two cases , we analyzed CA populations from the same location from two different years: Madera 2013 and 2015 , and San Mateo 2013 and 2014 . We found strong evidence for overwintering in Madera and mixed results for San Mateo . The FST values between Madera 2013 and 2015 were lower than any of the FST values between Madera and populations from South Central US , suggesting this is a stable population and not one recolonized each year . The genetic diversity also changed very little: for example , the allelic richness was 3 . 5 in 2013 and 3 . 6 in 2015 . San Mateo 2013 and San Mateo 2014 did not follow these patterns , and the small sample size of San Mateo 2014 ( N = 7 ) could be a factor . It is unlikely that this change in genetic diversity is due to recolonization of the area by another northern California population because the mosquitoes were collected from the same confined area in 2013 and 2014 , and were not detected elsewhere in the county . In both cases the populations are always part of the same genetic cluster determined with Bayesian clustering ( e . g . Fig 3 ) , consistent with a previous study demonstrating that temporal differentiation does not obscure geographic structure in CA A . aegypti populations [16] . Strikingly , STRUCTURE analyses of North American populations at higher K values showed that San Mateo and Madera formed a genetic cluster separate from other Central South and Southeast populations ( S3 Fig ) . This temporal stability indicates that some populations in Northern California are stable and likely continuously breeding in situ , rather than being recolonized by their original source . In five cases , we have included multiple populations from the same city and the same years . Except for a few anomalies ( e . g . a subset of Clovis ) , these populations did not show genetic differentiation , suggesting no population structure within a city . Our paper is the first to combine SNP data and microsatellites to address the population structure and origins of the CA A . aegypti . We found the same genetic structure using both types of markers , which speaks to the robustness of our methods and results . Our results suggest the microsatellite markers , which are more cost-efficient , are sufficient for these types of analyses . However , we expect the SNP chip will continue to provide essential information in other situations , for example , at finer-scales , when fewer individuals are available , or when building phylogenies . That A . aegypti invaded CA multiple times , probably years before its first detection in 2013 , has important implications for vector control . It implies CA and other regions with a temperate climate may be more vulnerable to invasion than previously thought . Additionally , we find that CA A . aegypti populations near one another are often genetically distinct . A challenge to providing effective vector control is the potential for reinvasion in targeted regions . At least in Southern CA it appears that there is little evidence for extensive migration and gene flow among populations . The disparate genetic backgrounds in these A . aegypti populations may represent populations capable of responding differently to control measures such as pesticides , or perhaps these populations may have different vector competence for infectious pathogens . CA has one of the most extensive mosquito-monitoring systems in the US , so the possibility that A . aegypti was in CA years before detection may mean mosquito invasions have occurred elsewhere in the US but escaped notice . Understanding and accounting for the invasion dynamics of A . aegypti will continue to be essential for detecting new invasions , monitoring vector presence , and preventing disease outbreaks in California and other regions . | Infectious diseases transmitted by Aedes aegypti , also known as the yellow fever mosquito , are of growing concern in tropical and subtropical regions . Dengue and Zika incidences are increasing , and no vaccines are currently available . Here we investigate the origin of California A . aegypti and find that this mosquito likely entered California on multiple occasions , at least once from the South Central US region and once from the Southwest US/northern MX region . The evidence suggests that the first invasion event likely occurred some years before its initial detection in 2013 , despite California’s extensive and active surveillance program , implying that this invasive mosquito can go undetected . Understanding the invasion dynamics , gene flow , and population structure of A . aegypti can improve the monitoring of mosquitoes and prevent outbreaks of vector-borne disease . |
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Expression quantitative trait loci ( eQTL ) studies are an integral tool to investigate the genetic component of gene expression variation . A major challenge in the analysis of such studies are hidden confounding factors , such as unobserved covariates or unknown subtle environmental perturbations . These factors can induce a pronounced artifactual correlation structure in the expression profiles , which may create spurious false associations or mask real genetic association signals . Here , we report PANAMA ( Probabilistic ANAlysis of genoMic dAta ) , a novel probabilistic model to account for confounding factors within an eQTL analysis . In contrast to previous methods , PANAMA learns hidden factors jointly with the effect of prominent genetic regulators . As a result , this new model can more accurately distinguish true genetic association signals from confounding variation . We applied our model and compared it to existing methods on different datasets and biological systems . PANAMA consistently performs better than alternative methods , and finds in particular substantially more trans regulators . Importantly , our approach not only identifies a greater number of associations , but also yields hits that are biologically more plausible and can be better reproduced between independent studies . A software implementation of PANAMA is freely available online at http://ml . sheffield . ac . uk/qtl/ .
Genome-wide analysis of the regulatory role of polymorphic loci on gene expression has been carried out in a range of different study designs and biological systems . For example , association mapping in human has uncovered an abundance of cis associations that contribute to the variation of a third of all human genes [1] , [2] . In segregating yeast strains , linkage studies have revealed extensive genetic trans regulation , with a few regulatory hotspots controlling the expression profiles of tens or hundreds of genes [3] , [4] . Despite the success of such expression quantitative trait loci ( eQTL ) studies , it has also become clear that the analysis of these data comes along with non-trivial statistical hurdles [5] . Different types of external confounding factors , including environment or technical influences , can substantially alter the outcome of an eQTL scan . Unobserved confounders can both obscure true association signals and create new spurious associations that are false [6] , [7] . Suitable data preprocessing , or careful design of randomized studies are helpful measures to avoid confounders in the first place [8] , however they rarely rule out confounding influences entirely . It is also relatively straightforward to account for those factors that are known and measured . For example , it is standard procedure to include covariates such as age and gender in the analysis [9] , [10] . Similarly , the effect of populational relatedness between samples , a confounding effect that is observed or can be reliably estimated form the genotype data [11] , [12] , is usually included in the model . However other factors , including subtle environmental or technical influences , often remain unknown to the experimenter , but still need to be accounted for . Their potential impact has previously been characterized in multiple studies; for example Plagnol et al . [13] and Locke et al . [14] showed that virtually any aspect of sample handling can impact the analysis . Several computational methods have been developed to account for unknown confounding variation within eQTL analyses [2] , [6] , [7] , [15] , [16] . A common assumption these methods built on is that confounders are prone to exhibit broad effects , influencing large fractions of the measured gene expression levels . This characteristic has been exploited to learn the profile of hidden confounders using models that are related to PCA [2] , [6] , [15] . Once learnt , these factors can then be included in the analysis analogously to known covariates . Another branch of methods avoids recovering the hidden factors explicitly , instead correcting for the correlation structure they induce between the samples [7] , [16] . Here , the inter-sample correlation is estimated from the expression profiles first , to then account for its influence in an association scan using mixed linear models . Both types of methods have been applied in a number of studies . Advantages versus naive analysis include better-calibrated test statistics [16] and improved reproducibility of hits between independent studies [7] . Perhaps most strikingly , statistical methods to correct for hidden confounders have also been shown to substantially increase the power to detect eQTLs , increasing the number of significant cis associations by up to 3-fold [2] , [17] . While improved sensitivity to detect cis-acting eQTLs is an important and necessary step , we expect that even more valuable insights can be gained from those loci that regulate multiple target genes in trans . The interest in these regulatory hotspots has been tremendous in recent years , but limited reproducibility between studies has been a concern ( see for example the discussion in Breitling et al . [18] ) . Accurate correction for confounding factors is key to improve the reliability of these regulatory associations , however statistical overlap between confounding factors and true association signals from downstream effects can hamper the identification and fitting of confounders . For example , methodology that merely accounts for broad variance components , such as PCA , is doomed to fail . If the effect size of trans regulatory hotspots is large enough , they induce a correlation structure that is similar to the one caused by confounding factors . As a result , true trans regulators tend to be mistaken for confounders and are erroneously explained away . Here , we report an integrated probabilistic model PANAMA ( Probabilistic ANAlysis of genoMic dAta ) to address these shortcoming of established approaches . PANAMA learns a dictionary of confounding factors from the observed expression profiles . Unique to PANAMA is to jointly learn these factors while accounting for the effect of loci with a pronounced trans regulatory effect , thereby avoiding overlaps between true genetic association signals and the covariance structure induced by the learnt confounders . The statistical model underlying our algorithm is simple and computationally tractable for large eQTL datasets . PANAMA is based on the framework of mixed linear models , and combines the advantages of factor-based methods , such as PCA , SVA [6] or PEER [2] , [15] with methods that estimate the implicit covariance structure induced by confounding variation [12] , [16] . The model is fully automated and can be easily adapted to include additional observed confounding sources of variation , such as population structure or known covariates . We applied PANAMA to a range of eQTL studies , including synthetic data and studies from yeast , mouse and human . Across datasets , PANAMA performed better than previous methods , identifying a greater number of significant eQTLs and in particular additional trans regulators . We provide multiple sources of evidence that the associations recovered by PANAMA are indeed likely to be real . Most strikingly in yeast , the findings by PANAMA can be better reproduced between independent studies and are more consistent with prior knowledge about the underlying regulatory network . Finally , we also give insights into the limitations of current methods to account for confounders that help to understand the relationship between confounding variation , cis regulation and trans effects .
The statistical model underlying PANAMA assumes additive contributions from true genetic effects and hidden confounding factors . Briefly , this linear model expresses the gene expression of gene measured in individuals as the sum of weighted contributions from a set of SNPs as well as confounders , a mean term and a noise term ( See Figure 1a ) Neither the regression weights nor the profiles of the confounding factors are known a priori and hence need to be learnt from the expression data . Parameter inference in PANAMA is done in the mixed model framework [12] , [19] . In this hierarchical model , the regression weights of the hidden factors are marginalized out , yielding a covariance structure in a multivariate Gaussian model to capture the effect of confounders . Intuitively , the objective during learning in PANAMA is to explain the empirical correlation structure between samples shared across genes by the state of the hidden factors . In the presence of extensive trans regulation this approach leads to over-correction , running the risk of explaining away true genetic association signals . To circumvent this side effect , PANAMA also includes a subset of all SNPs in the model , resulting in a more complete covariance structure that satisfies an appropriate balance between explaining confounding variation and preserving true genetic signals ( Figure 1b , c ) . In this approach , the variance contribution of few major signal SNPs and the state of the hidden factors are then jointly estimated . Moreover , an appropriate number of hidden factors is determined automatically during learning . As a result , PANAMA is statistically robust and inference of hidden factors is feasible without manual setting of any tuning parameters . Additional observed covariates , if available , can also be included in the model; see Methods and the supplementary Text S1 for full details . The evaluation of methods to call eQTLs is difficult as reliable ground truth information is not available . Following previous work [2] , [20] , [21] , we have used synthetic data to assess and compare PANAMA with alternative approaches . To minimize assumptions we need to impose on the simulation procedure we created an artificial dataset that borrows key characteristics from a real eQTL study in yeast [4] ( See also Application to segregating yeast strains ) . In this approach , we first fit PANAMA to the original yeast eQTL data , thereby estimating the number of cis and trans associations , an empirical distribution of effect sizes , and finally the characteristics of confounding variation . Based on these estimates we recreated an in silico eQTL dataset using standard linear assumptions; see Text S1 for full details on the exact approach . To rule out possible biases of this dataset towards our method , we additionally considered a simulation setting when fitting the ICE model [7] to the real data for estimating simulation parameters ( see below ) . Given the synthetic eQTL study , we employed alternative methods to recover the underlying simulated associations . We compared PANAMA to standard linear regression ( LINEAR ) , ignoring the presence of confounders entirely , as well as SVA [6] , ICE [7] and PEER [2] , [15] , established and widely used approaches to correct for hidden confounders . For reference , we also compared to an idealized model with the simulated confounders perfectly removed ( IDEAL ) . First , Figure 2a and 2b show the respective number of significant cis and trans associations as a function of the false discovery rate ( FDR ) cutoff . To avoid overly optimistic association counts due to linkage disequilibrium , we considered at most a single cis association per gene and at most one trans association per chromosome for each gene . PANAMA found more cis associations than any other approach and retrieved the greatest number of trans associations among methods that correct for hidden confounders . Notably , the linear model appeared to find even more trans associations , however the majority of these calls were inconsistent with the simulated ground truth and were spurious false positives . The extent of false associations called by the linear model is also reflected in Figure 2c , which shows the receiver operating characteristics for each method . All approaches that correct for confounders performed strikingly better than the linear model . Among these , PANAMA was most accurate , achieving greater sensitivity than any other method for a large range of false positive rates ( FPR ) , approaching the performance of an ideal model ( IDEAL ) . Since some models , including SVA and PEER , allow to account for additional known covariates , we investigated their performance when adding the strongest genetic regulators as covariates . This procedure is mimicking the central concept of PANAMA using previous methods . However , comparative results ( Supplementary Figure S7 ) show that iterative learning of PANAMA still performs significantly better . Next , we studied the statistics of obtained p-values , checking for departure from a uniform distribution that either indicates inflation ( genomic control ) or deflation ( genomic control ) of the respective methods ( Figure 2d and Supplementary Figure S8 for corresponding Q-Q-plots ) . All methods except for ICE yielded an inflated p-value distribution . Notably , this observation also applies to the ideal model where the effect of confounders had been perfectly removed . Thus , in settings with sufficiently strong trans regulation , inflated statistics are not necessarily due to poor calibration because of confounders , but instead may occur as a consequence of an excess of true biological signals themselves . We also checked that calls by the various methods were not overly optimistic and artificially inflated . Indeed , false discovery rate estimates from all methods but the linear model were approximately in line with the empirical rate of errors when taking the ground truth into account ( Supporting Figure S1 ) , with PANAMA being the best calibrated method . We then repeated the same analysis on a broader range of simulated datasets , varying particular aspects of the simulation procedure around the parameters obtained from the fit to the real yeast data . Figure 2e shows the accuracy of alternative methods when reducing the extent of simulated trans regulation by subsampling from the set of initial trans effects . These results highlight that previous methods only work well in the regime of little trans regulation , while PANAMA provides for accurate calls for a wider range of settings . Similarly , Figure 2f shows results for strong trans regulation , now varying the extent of confounding factors from weaker to stronger influences . Again , PANAMA was found to be more robust than previous approaches , recovering true simulated associations with great accuracy irrespectively of the magnitude of simulated confounding . Finally , we investigated the impact of the exact of model used to fit the association characteristics to the initial yeast dataset . Supporting Figure S2 shows summary results for a second synthetic dataset fitted using ICE . As ICE tends to be the most conservative approach among the considered methods , the extent of trans regulation on this simulated data was severely reduced . As a consequence , the differences between methods were considerably smaller , however confirming the previously observed trends . Having established the accuracy of PANAMA in recovering hidden confounders , we applied PANAMA and the alternative methods to the primary eQTL dataset from segregating yeast strains [4] . These data cover a set of 108 genetically diverse strains that have been expression profiled in two environmental conditions , glucose and ethanol . First , we focused on the glucose condition , which has previously been expression profiled [3] , providing an independent study for the purpose of comparison . Figure 3a and 3b show the number of cis and trans associations for different methods as a function of the FDR cutoff . Again , we considered at most one association per chromosome to avoid confounding the size of associations with their number . In line with previously reported results [2] , [7] and the simulated setting ( Simulation study ) , the standard linear model identified fewer cis associations than methods that correct for confounding variation . The trends from the simulated dataset also carried over for trans associations , where the linear model called many more associations than methods that account for confounders , yielding an excess of regulatory hotspots ( See Supporting Figure S3 ) . It has previously been suggested that many of these are likely to be false; see for example the discussion in Kang et al . [7] . Among the methods that correct for confounding variation , PANAMA identified the greatest number of associations . Among the alternative methods , ICE appeared to be more sensitive in recovering cis associations while PEER and SVA retrieved a greater number of trans associations . Also note that models that account for confounding factors yielded slightly inflated p-value distributions ( Figure 3c , Supplementary Figure S9 ) , supporting that also in real settings , a certain degree of inflation may be caused by extensive trans regulation . Finally , supporting Figure S3 shows the number of associations called by different methods as a function of the genomic position . This summary of genome-wide eQTLs confirms that ICE is most conservative in detecting hotspots , whereas all other methods do find multiple trans bands . For comparison we also included a version of PANAMA that also corrects for the trans regulators that are accounted for while learning ( PANAMAtrans , see Methods and supporting Text S1 ) . PANAMAtrans yields near-identical results to ICE , which explains the differences and similarities between the two approaches , where PANAMA can be regarded as generalization of ICE . By accounting for pronounced regulators PANAMA circumvents the over-conservative correction of the ICE model . We successfully applied PANAMA to additional ongoing and retrospective studies . For example , on a dataset from inbred mouse crosses [23] , PANAMA identified a greater number of associations than other methods ( Supplementary Figure S5 ) . In contrast to the yeast dataset , the distribution of p-values on this dataset was almost uniform , suggesting that the extent of true trans regulation is lower . We also investigated parts of a dataset of the genetics of human cortical gene expression [24] . On chromosome 17 , methods that account for confounders identified more genes in associations than a linear model , with SVA and PANAMA retrieving the greatest number ( see supporting Figure S6 ) . Results on other four other chromosomes were similar ( data not shown ) . Finally , results of PANAMA applied to an RNA-Seq eQTL study on Arabidopsis [25] indicate that expression heterogeneity as accounted for by PANAMA is also present on expression estimates from short read technologies , which is consistent with previous reports in human RNA-Seq studies [26] . This suggests that statistical challenges due to confounding variation are not specific to a particular platform for measuring gene expression .
We have reported the development of PANAMA , an advanced statistical model to correct for confounding influences while preserving genuine genetic association signals . We have shown that this approach is of substantial practical use in a range of real settings and studies . The correction approach of PANAMA , for the first time , is able to not only find more cis eQTLs , but also greatly improves the statistical power to uncover true trans regulators . PANAMA finds a greater number of associations , and calls eQTLs that are more likely to be real , as validated by means of realistic simulated settings and an analysis of eQTL consistency between independent studies . Most notably , PANAMA identified several strong trans hotspots on yeast , out of which at least 40% could be reproduced on a replication dataset . There are several previous approaches to correct for confounding influences in eQTL studies . These methods can be broadly grouped into factor-based models like PCA , SVA [6] and PEER [2] , [15] , and approaches that employ a mixed linear model [7] , [16] , estimating a covariance structure that captures the confounding variation . An important reason why PANAMA performs well is the intermediate approach taken here , that is , learning a covariance structure within a linear mixed model ( LMM ) , but at the same time retaining the low-rank constraint which yields an explicit representation of factors . Moreover , PANAMA systematically exploits the flexibility provided by the representation in terms of covariance structures , jointly accounting for genetic regulators while estimating the confounding factors . Our approach is stable and robust , avoiding the need to first subtract off the genetic contribution greedily , as for example suggested and implemented in SVA [6] and PEER [2] , [15] . Although this is not the focus of this work , we have shown how our approach can be combined with additional measures to correct for observed sources of confounding variation , such as known covariates or populational relatedness . The utility of such measures has been illustrated in the joint analysis on data from two environmental conditions . A more specialized approach that is aimed at the combined correction for expression confounders and population structure has recently been proposed by Listgarten et al . [16] . This LMM-EH approach is methodologically related to what is done here , as the contribution from multiple sources of variation are combined within a single covariance structure . Importantly , the main contribution in PANAMA is an integrated model that does not include additional confounders but true genetic regulators . Unique to PANAMA , these regulators are jointly identified and accounted for during learning of the confounding factors . Our analysis shows , that this approach yields a significant improvement in the sensitivity of recovering trans associations and plausible regulatory hotspots . A tabular overview of the relation between alternative methods is shown in Supporting Table S2 . In conclusion , PANAMA is an important step towards exhaustively addressing common types of confounding variation in eQTL studies . The number of datasets that benefit from careful dissection of true genetic signals and confounders , as done here , is expected to rise quickly . Growing sample sizes and expression profiling in more than one environment allow for the estimation of more subtle confounding influences and at the same time provide the statistical power to detect many more trans effects than possible as of today .
If available , additional covariates can directly be included in the background covariance structure from Equation ( 2 ) ( 3 ) where denotes the covariance induced by these additional covariates and the corresponding scaling parameter . Examples for possible choices of this covariance include the covariance induced by a fixed covariate vectors , i . e . or a kinship matrix that accounts for the genetic relatedness ( see for example Kang et al . [12] and Listgarten et al . [16] ) . The most probable state of the latent variables and the hyperparameters can be identified via a straightforward maximum likelihood approach ( 4 ) for example employing a gradient-based optimizer . In practical applications of PANAMA , this model fitting ( Equation ( 4 ) ) is not carried out with the set of all genome-wide SNPs included in Equation ( 1 ) , because the number of weight parameters for each SNP would be prohibitive . Only those genetic regulators with strong effects on multiple genes do play a role during the estimation of hidden factors and thus need to be accounted for . Our inference scheme determines the set of relevant regulators in an iterative procedure . The number of hidden factors to be learnt , is not set a priori and instead is set to a sufficiently large value . During the optimization , the individual variance parameters for each factors , , automatically determine an appropriate number of effective factors , switching off unused ones . For full details of the algorithm and analysis of the robustness of this approach see Supporting Text S1 . Once the confounding-correcting covariance structure is determined from the maximum likelihood solution of Equation ( 4 ) , significance testing can be carried out in the framework of mixed linear models . The association between a SNP and gene to be tested is treated as fixed effect , allowing to construct a likelihood ratio statistics of the form ( 5 ) Here , the covariance matrix denotes the covariance structure explaining confounding variation , which is derived from the fitted PANAMA model . Computationally , the likelihood ratio tests ( Equation ( 5 ) ) can be efficiently implemented using recently proposed computational tricks [19] , allowing for application to large-scale genomic data ( Supporting Text S1 ) . In PANAMA , this correction covariance structure only accounts for the confounding factors , excluding the genetic regulators ( See Equation ( 2 ) ) In PANAMAtrans , also correcting for the trans factors , the covariance equals toFor computational efficiency we fix the covariance structure that is learnt from the full expression dataset upfront . The relative weighting of the covariance ( ) and the noise term ( ) are then adjusted on the background and null model ( Equation ( 5 ) ) for every single test carried out , using recent advances for efficient mixed model inference [19] . We used the yeast expression dataset from Smith et al . [4] ( GEO accession number GSE9376 ) , which consists of 5 , 493 probes measured in 109 segregants derived from a cross between BY and RM . The authors provided the genotypes , which consisted of 2 , 956 genotyped loci . An association was defined as cis if the location of the SNP and the location of the opening reading frame ( ORF ) of the gene were within 10 kb , and trans otherwise . In order to validate the associations found , we also used data from Brem et al . [3] ( GEO accession number GSE1990 ) , which consisted of 7 , 084 probes and 2 , 956 genotyped loci in 112 segregants . For the purpose of comparison , we defined cis associations in the same way as we did for the previous dataset . We used the data described in Schadt [23] , consisting of 23 , 698 expression measurements and 137 genotyped loci for 111 F mouse lines . We used the dataset from [24] ( GEO accession number GSE8919 ) , which consists of 14 , 078 transcripts and 366 , 140 SNPs genotyped on 193 human samples . We used data from Yeastract [22] , which contains information about the regulatory network between 185 transcription factors and 6 , 298 genes . Out of these 189 transcription factors , we selected the 129 TFs that had a polymorphism in the vicinity ( 10 kb ) of the coding region . | The computational analysis of genetical genomics studies is challenged by confounding variation that is unrelated to the genetic factors of interest . Several approaches to account for these confounding factors have been proposed , greatly increasing the sensitivity in recovering direct genetic ( cis ) associations between variable genetic loci and the expression levels of individual genes . Crucially , these existing techniques largely rely on the true association signals being orthogonal to the confounding variation . Here , we show that when studying indirect ( trans ) genetic effects , for example from master regulators , their association signals can overlap with confounding factors estimated using existing methods . This technical overlap can lead to overcorrection , erroneously explaining away true associations as confounders . To address these shortcomings , we propose PANAMA , a model that jointly learns hidden factors while accounting for the effect of selected genetic regulators . In applications to several studies , PANAMA is more accurate than existing methods in recovering the hidden confounding factors . As a result , we find an increase in the statistical power for direct ( cis ) and indirect ( trans ) associations . Most strikingly on yeast , PANAMA not only finds additional associations but also identifies master regulators that can be better reproduced between independent studies . |
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Epigenetic gene silencing plays a critical role in regulating gene expression and contributes to organismal development and cell fate acquisition in eukaryotes . In fission yeast , Schizosaccharomyces pombe , heterochromatin-associated gene silencing is known to be mediated by RNA processing pathways including RNA interference ( RNAi ) and a 3’-5’ exoribonuclease complex , the exosome . Here , we report a new RNA-processing pathway that contributes to epigenetic gene silencing and assembly of heterochromatin mediated by 5’-3’ exoribonuclease Dhp1/Rat1/Xrn2 . Dhp1 mutation causes defective gene silencing both at peri-centromeric regions and at the silent mating type locus . Intriguingly , mutation in either of the two well-characterized Dhp1-interacting proteins , the Din1 pyrophosphohydrolase or the Rhn1 transcription termination factor , does not result in silencing defects at the main heterochromatic regions . We demonstrate that Dhp1 interacts with heterochromatic factors and is essential in the sequential steps of establishing silencing in a manner independent of both RNAi and the exosome . Genomic and genetic analyses suggest that Dhp1 is involved in post-transcriptional silencing of repetitive regions through its RNA processing activity . The results describe the unexpected role of Dhp1/Rat1/Xrn2 in chromatin-based silencing and elucidate how various RNA-processing pathways , acting together or independently , contribute to epigenetic regulation of the eukaryotic genome .
In eukaryotic cells , DNA coils around histones to form nucleosomes , which are packaged into chromatin . Various post-translational modifications ( PTMs ) of histones , histone variants , and nucleosome remodeling factors confer distinct chromatin states on genes and facilitate the organization of large chromatin tracts into domains [1–3] . Euchromatic domains ( euchromatin ) contain actively transcribed genes and are enriched with hyperacetylated histones , while heterochromatic domains ( heterochromatin ) contain highly repetitive elements that are transcriptionally silenced and are associated with hypoacetylated histones [4–7] . In addition to its primary role in transcriptional gene silencing , heterochromatin is crucial for centromere-mediated chromosome segregation , cell fate determination , and the silencing of repetitive DNA elements [4] . In fission yeast , Schizosaccharomyces pombe ( S . pombe ) , its formation requires both histone hypoacetylation and histone H3 methylation at lysine 9 ( H3K9me ) , which provides a binding site for HP1 family proteins [8–10] . Heterochromatin is first nucleated at specific repetitive loci and subsequently spread for up to hundreds of kilobases ( kb ) into surrounding regions [4 , 11 , 12] . Once established , these silenced heterochromatic domains are heritable , and can be stably maintained through successive cell divisions [4 , 13 , 14] . Epigenetic silencing includes both transcriptional ( TGS ) and post-transcriptional gene silencing ( PTGS ) . In general , heterochromatin limits the access of RNA polymerase II ( RNAPII ) machinery to the DNA template and can therefore mediate transcriptional gene silencing ( TGS ) by preventing unwanted transcription from a given genomic region [15 , 16] . PTGS employs RNA processing machinery to rapidly degrade nascent RNAs to repress gene expression or to protect the genome from foreign genetic elements such as retroviral RNA or transposable DNA [17–20] . RNA processing machineries ensure the maturation and packaging of RNAs from longer precursors into mRNA/protein particles ( mRNPs ) before they are exported to the cytoplasm for translation [21] . Most of these processing events , such as addition of the 5’ cap , removal of introns , and polyadenylation at the 3’ end , occur while the RNA is still attached to RNAPII and chromatin , and are therefore referred to as transcription-coupled RNA processing events [22 , 23] . For example , RNA endocleavage at a polyadenylation ( polyA ) site is commonly required for transcriptional termination because the 5’ to 3’ exoribonucleolysis of the exposed 3’ fragment downstream of the polyA site facilitates RNAPII release from chromatin [24 , 25] . Besides their roles in RNA maturation and RNAPII termination , RNA-processing enzymes act as quality control systems , screening partly or fully transcribed products and degrading abnormal RNAs [22 , 26] . RNA processing pathways play an active role in epigenetic silencing , especially PTGS [27] , as many nuclear processes rely on the fine balance between RNA maturation and destruction to regulate gene expression [28] . The best understood RNA processing pathway in PTGS is the RNA interference ( RNAi ) -mediated formation of heterochromatin at centromeric regions in S . pombe [27 , 29 , 30] . In RNAi , RNAPII transcripts originating from repetitive DNA regions are converted to double-stranded RNA by the RNA-Dependent RNA Polymerase Complex ( RDRC ) [31] . They are then processed by Dicer into small interfering RNAs ( siRNAs ) [32 , 33] and loaded onto the Argonaute-containing RNA Induced Transcriptional Silencing ( RITS ) complex , which targets the repeat regions through the homology of the siRNA sequence [34] . RITS associates with chromatin by direct interaction with H3K9me [35] , then recruits the Clr4 complex to initiate chromatin remodeling [36–39] . Recently , several studies reported an RNAi-independent RNA-processing pathway in heterochromatin assembly at the centromeric region and some heterochromatic islands in euchromatic regions [40–43] . This new pathway is mediated by the exosome complex , which degrades unwanted RNAs via its 3’-5’ exoribonuclease activity [26] . Although both RNAi and exosome pathways are RNA-mediated and involved in processing long noncoding RNAs ( ncRNAs ) into small RNAs , how the exosome pathway contributes to heterochromatin assembly is not well understood . In addition , it is not known whether other RNAi-independent RNA processing pathways participate in epigenetic silencing . Here we report a new epigenetic silencing pathway involving Dhp1 , a conserved 5’→3’ exoribonuclease , the ortholog of budding yeast Rat1 and metazoan Xrn2 known to promote termination of RNA polymerase II ( RNAPII ) transcription [44–46] . We show that Dhp1-mediated heterochromatic silencing is independent of Din1 , an ortholog of budding yeast Rai1 that has been shown to stabilize Dhp1/Rat1 exoribonuclease activity [47] . In addition to maintenance of gene silencing , Dhp1 contributes to de novo establishment of heterochromatin at the centromeres and the silent mating type region . It also plays a role in the transcriptional-dependent spreading of heterochromatin . Importantly , Dhp1 interacts with heterochromatic factors and its catalytic activity is required for its role in silencing . Further genetic analyses indicate that Dhp1 operates in a distinct pathway parallel to RNAi and the exosome to mediate heterochromatic gene silencing . Finally , RNAPII localization and transcriptome-wide maps of RNAs associated with RNAPII revealed that Dhp1 likely acts at the post-transcriptional level to affect gene silencing . We propose that , in addition to RNAi and exosomes , Dhp1 constitutes a distinct RNA-processing pathway that enforces post-transcriptional gene silencing across the fission yeast transcriptome .
Dhp1/Xrn2 is an essential gene required for transcriptional termination and RNA quality control [22 , 45 , 46] . A recent study reported that impairment of transcription termination is sufficient to induce the formation of heterochromatin at protein-coding genes by trans-acting siRNAs in S . pombe [48] . However , it is not clear whether impaired transcription termination would alter epigenetic silencing at the major heterochromatic regions such as centromeric regions and the mating type locus . We therefore tested whether silencing at these regions is affected in dhp1 mutant cells . Because its loss is lethal , we utilized a conditional temperature-sensitive ( ts ) allele , dhp1-1 , which codes a truncated carboxyl-terminal form of Dhp1 that is partially replaced by a ura4+ transgene and is lethal at 37°C ( dhp1-1>>ura4+ ) [47] . We generated an independent dhp1-2 mutant , which carries the same carboxylic terminal truncation but is replaced with a nourseothricin resistance gene ( NatN2 ) ( S1 Table ) . This allele has a less severe ts phenotype compared to dhp1-1 , and is not lethal at 37°C ( S1A Fig ) . dhp1-2 is not fused with ura4+ , allowing us to investigate the silencing of the centromeric region by analyzing the expression of a ura4+ reporter gene inserted at the outer centromeric region ( otr∷ ura4+ ) ( Fig 1A , top panel ) . Wildtype cells carrying the reporter otr∷ura4+ grew well on counter-selective medium containing 5-Fluoroorotic Acid ( 5-FoA ) indicating otr∷ura4+ was silenced ( Fig 1A , bottom panel ) . The growth of dhp1-2 was greatly inhibited in the presence of 5-FoA indicating defective silencing of the reporter gene . Surprisingly , din1-null ( din1Δ ) cells do not have a severe growth or centromeric silencing defect , suggesting that Dhp1 is involved in a silencing pathway separate from its Din1-related activity ( Fig 1A , bottom panel ) . To further examine the observed silencing defect , an ade6+ reporter gene was inserted into the silenced mating type region ( Fig 1A , top panel ) . We can easily observe the silencing status of the ade6+ based on color; cells that cannot express the normal level of the reporter gene accumulate a red pigment due to blocked adenine biosynthesis . In wildtype cells , the reporter gene is silenced by heterochromatin , resulting in red/sectored colonies on low adenine media at 30°C . Cells lacking Clr4 , the sole histone H3K9 methyltransferase in S . pombe [49] , form white colonies due to loss of heterochromatin ( Fig 1A ) . Similar to clr4Δ , dhp1-2 but not din1-null ( din1Δ ) cells form white colonies , indicating a silencing defect at the mating type locus unique to the dhp1 mutant . Since dhp1-1 has a more severe silencing defect than dhp1-2 as evaluated by silencing assay and quantitative Reverse Transcription Polymerase Chain Reaction ( qRT-PCR ) ( S1 Fig ) , the rest of our studies focused on using the dhp1-1 allele . We next assessed whether loss of reporter gene silencing in the dhp1 mutant is correlated with the expression of heterochromatic repeats . Transcript analysis by qRT-PCR revealed substantially unregulated expression of repeats associated with pericentromeric heterochromatin and the silent mating type locus in dhp1 but not din1 mutant cells ( Fig 1B ) . Further analysis by expression profiling using a tiling microarray on both DNA strands showed increased expression throughout both heterochromatic regions in dhp1-1 , well above the increase observed in din1Δ cells ( Fig 1C ) . These results indicate that Dhp1 plays a previously unrecognized , Din1-independent function in epigenetic silencing . To avoid potential pleotropic effects caused by dhp1 mutation , we performed all our experiments at 30°C . This is a permissive temperature for dhp1-1 , in which silencing defects but no obvious growth deficiency are observed ( Fig 1A ) , suggesting that most of transcription-related functions of Dhp1 are retained . In addition , we carefully analyzed the transcriptional levels of all known heterochromatic factors in dhp1-1 cells using expression data . All coding transcripts of these proteins are affected less than 1 . 6-fold ( a typical threshold difference for microarray data ) compared to that of wildtype ( S2 Table ) . Further , a plasmid-borne wildtype copy of dhp1+ rescues the ts phenotype of dhp1-1 ( S2A Fig ) and a diploid heterozygous strain carrying a wildtype and a dhp1-1 allele showed the same phenotype as a wildtype diploid strain ( S2B and S2C Fig ) , demonstrating that dhp1-1 has no dominant negative effects . Altogether , these findings suggest that the loss of heterochromatic silencing in the dhp1 mutant is likely a direct consequence of impaired function of Dhp1 at heterochromatin rather than reduced transcription of heterochromatic factors . The silencing defect in dhp1-1 is unexpected because impaired transcription termination would reduce RNAPII transcription and the subsequent release of the RNA from the site of transcription , which may enhance the assembly of heterochromatin through induction of RNA-mediated chromatin modification such as H3K9 methylation ( H3K9me ) [48 , 50] . In addition , many reported Dhp1 functions are associated with Din1 , which contributes to the generation of the proper substrates for Dhp1’s exoribonuclease activity [46 , 51] . Because we did not observe a silencing defect in din1Δ cells , we wondered whether Din1 , like Dhp1 is involved in transcription termination . According to the “Torpedo model” [24 , 25] , Dhp1-mediated exonucleolysis of the cleaved 3’ fragment downstream of the mRNA polyA site facilitates RNAPII release from chromatin . Deficiency in Dhp1/Din1 will cause RNAs to accumulate at the 3’ end of genes , due to an RNAPII transcription termination defect [24 , 25] . To confirm this reported role of Dhp1/Din1 , we analyzed the transcriptomes of dhp1-1 and din1Δ cells at euchromatic regions . We detected a genome-wide increase of RNA levels at the 3’ end of genes compared to wildtype in both mutants , with a larger fraction of genes exhibiting transcription termination defects in dhp1-1 ( S3 Fig ) . While the role of Dhp1 is more dominant than that of Din1 , these results support earlier studies indicating that both Dhp1 and Din1 participate in RNAPII transcription termination . As an interacting protein of Rat1/Xrn2 , Rtt103 also contributes to transcription termination in yeast and humans [52–54] . Rhn1 , the S . pombe ortholog of Rtt103 , has a reported role in the suppression of meiotic mRNAs during vegetative growth [54] . However , whether it plays a role in heterochromatic silencing has not been reported . To further examine whether defective transcription termination is crucial for Dhp1-mediated epigenetic silencing , we compared the expression of repeat elements in wildtype and rhn1Δ cells using qRT-PCR and found that , like Din1 , loss of Rhn1 did not cause a silencing defect ( S4 Fig ) . Taken together , our data argue that Dhp1 plays a novel role in epigenetic silencing , which cannot be explained by its established function in transcription termination . Cells with mutations in factors that contribute to epigenetic silencing often exhibit defects in chromosome segregation as determined by their sensitivity to the microtubule-destabilizing drug thiabendazole ( TBZ ) [55] . Because heterochromatin formation has been linked to centromere function in various organisms including S . pombe [56–58] , we tested whether the dhp1 mutants are sensitive to TBZ , which would indicate impaired function of centromeric heterochromatin , resulting in a chromosome segregation defect . As expected , deletion of clr4 abolishes heterochromatin and causes severe TBZ sensitivity ( Fig 2A ) . Our assay clearly shows that dhp1 , but not din1 , mutant cells are sensitive to TBZ , suggesting a chromosome segregation defect specific to dhp1 mutants ( Fig 2A ) . To further examine the role of Dhp1 in chromosome segregation , we sporulated wildtype and mutant h90 strains to follow the segregation of chromosomes in tetrads using a fluorescence-based analysis ( S5 Fig ) . To sporulate , two haploid cells with opposite mating types conjugate to form a zygote which then enters meiosis . During meiosis , cells undergo two consecutive rounds of chromosome segregation . A normal meiosis results in an ascus in which each of four spores contain relatively equal amounts of DNA ( DAPI dots ) . Abnormal meiotic segregation within a tetrad will show an uneven distribution of DAPI staining in each spore , resulting in less than or greater than four dots . We found that meiotic chromosome segregation is severely perturbed in the dhp1-1 , but not in din1Δ cells , with nearly 50% of tetrads containing abnormal numbers of DAPI dots ( ≤ 3 or ≥ 5 ) ( S5 Fig ) . To determine whether the chromosome segregation defect seen in the dhp1 mutant is linked to its role in epigenetic silencing at major heterochromatic domains , we assessed the status of H3K9me-associated heterochromatin by a Chromatin-Immunoprecipitation ( ChIP ) assay . Although no reduction of H3K9me2 was seen at the endogenous repetitive regions ( Fig 2D and 2E ) , the levels of H3K9me2 at the reporter genes embedded in these regions were substantially reduced at these loci in cells deficient in dhp1 ( Fig 2B and 2C ) . Loss of din1 has no negative effect on the enrichment of the H3K9me mark at either the endogenous repetitive regions or the reporter genes ( Fig 2B–2E ) . These results suggest that Dhp1’s role in chromosome segregation is linked to its requirement to maintain functional heterochromatin at the centromeres . We next wondered whether Dhp1 interacts with heterochromatic proteins , which would support a direct role of Dhp1 in facilitating heterochromatin assembly . We purified Dhp1 and Din1 through two-step affinity purification ( S6 Fig ) and identified the co-purified proteins by mass spectrometry analysis ( S3 Table ) . Strains used for purification carry a functional Dhp1 or Din1 fused with FTP , a modified TAP tag comprising a protein A motif and a FLAG tag separated by a TEV protease cleavage site . Since Dhp1 and Din1 are associated with transcribing RNAs and chromatin , we performed all purifications in the presence of Benzonase to avoid indirect protein-protein interactions mediated by nucleic acids . Din1 is the major Dhp1-interacting protein as recovered Din1 peptides were found to be approximately 50% as abundant as those of the bait protein , Dhp1 . As expected , Dhp1 also co-purified with many RNAPII-related factors , consistent with its role in transcriptional termination . In particular , it is associated with several heterochromatic proteins , including Clr4 methyltransferase complex ( ClrC ) subunit Rik1 and exosome subunit Rrp6 . These data are consistent with interactions recently identified in a parallel study [59] . Notably , these heterochromatic proteins were not present in those fractions when Din1 was used as the bait , supporting a distinct role of Dhp1 in heterochromatic formation . Our data indicates that Dhp1 interacts with heterochromatic proteins and is likely directly involved in heterochromatin assembly . Multiple pathways are utilized to initiate epigenetic silencing including both RNA and DNA sequence-dependent mechanisms [9 , 60 , 61] . Several studies have shown that in S . pombe , both RNAi and the exosome contribute to the initiation of silencing at the centromere by processing RNAs transcribed from repetitive regions [13 , 41 , 61] . We next sought to determine whether Dhp1 also contributes to this process through examination of reporter gene expression following the reintroduction of functional clr4+ into dhp1-1 clr4Δ double mutant cells ( Fig 3A ) . Deletion of clr4 results in the abolition of H3K9me and the loss of heterochromatin . However , reintroduction of functional clr4+ is sufficient for de novo heterochromatin formation as previously reported [50] ( Fig 3B ) . One of the key members in RNAi machinery , Dcr1 , is the sole Dicer-family endoribonuclease in S . pombe [62] . dcr1Δ cells lose the ability to initiate heterochromatin formation de novo at repeat regions [13] . Consistent with previous findings , silencing at the centromeric region cannot be efficiently established without Dcr1 [13] . Reintroduction of clr4+ into clr4Δ cells shows a complete alleviation of TBZ sensitivity , while clr4+ reintroduction into dcr1Δ clr4Δ double mutant cells has no effect ( Fig 3B ) . Additionally , complementation of clr4+ has little effect on the relative expression of centromeric- and mating type locus-specific repeats in dcr1Δ clr4Δ cells , however silencing of these repeats is fully resumed in clr4Δ single mutants ( Fig 3B ) . Reintroduction of clr4+ in dhp1-1 clr4Δ double mutant cells partially resumed the silencing at the centromeric region as indicated by qRT-PCR ( Fig 3B ) . At the mating type locus , silencing is barely restored in dhp1-1 clr4Δ , having 10-fold more expression than dhp1-1 alone ( Fig 3B ) . H3K9me2 ChIP analysis further demonstrated that without functional Dhp1 , H3K9me2 is partially re-established at the centromeric region , but only a low level of H3K9me2 can be found at the mating type locus after complementation of clr4+ ( Fig 3C and 3D ) . These results indicate that Dhp1 is essential for efficient de novo heterochromatin assembly at peri-centromeres and the silent mating type locus . Spreading of heterochromatin from the nucleation sites enables the establishment of a heterochromatin domain spanning many kbps [12] . Although the mechanism is poorly understood , it depends on the oligomerization of chromatin modifiers such as HP1 and Tas3 , a RITS component , and the actions of Swi6-recruited histone deacetylases ( HDACs ) on adjacent nucleosomes [63–66] . While the polymerization of chromatin modifiers indeed constitutes a major part of heterochromatin spreading , a role for RNAPII in transcription-mediated spreading is currently being explored . The effect of spreading on the silencing of reporter genes inserted into centromeric repeat regions has been shown to vary with position relative to the RNAPII promoter; reporter genes downstream of the promoter are more effectively silenced than those inserted upstream [67 , 68] . While the molecular details remain unclear , transcription-mediated spreading appears to require transcription of the 3’ untranslated region as well as degradation of these transcripts by RNAi machinery [67] . Decreased enrichment of H3K9me at the reporter genes in dhp1-1 suggests that Dhp1 may be involved in the spreading of H3K9me ( Fig 2B and 2C ) . Because of its role in transcriptional termination , we next examined whether Dhp1 is required for RNAi-dependent spreading of heterochromatin , which partially relies on RNAPII transcription [68] . To this end , we adopted a spreading assay [13 , 69] ( Fig 4A ) . Nucleation of heterochromatin at cenH is dependent on RNAi and the cenH sequence itself [13 , 69] . Inserting the cenH sequence into a euchromatic locus causes ectopic establishment followed by spreading of heterochromatin and subsequent silencing of proximal genes [13] . By coupling ectopic cenH with an adjacent reporter gene ( ade6+ ) , we can directly observe the effects of spreading of silencing to the proximal reporter gene ( Fig 4A ) . In the wildtype background , about 35% of cells form pink/sectoring colonies , indicating the spreading of heterochromatin assembled at ectopic cenH to the ade6+ reporter gene . Cells lacking ago1 , a critical factor in RNAi form white colonies at 100% efficiency showing that the ade6+ reporter gene cannot be silenced . This is consistent with previous results indicating that RNAi machinery is required for the transcriptional-dependent spreading of heterochromatin assembled at cenH region [13 , 68] . Similar to ago1Δ , no pink colonies were formed in dhp1-1 background ( Fig 4B ) . H3K9me2 ChIP using multiple primers along ade6+ and its surrounding regions shows moderate reduction of this heterochromatic mark in dhp1-1 cells compared to wildtype cells ( Fig 4C ) . Altogether , these results suggest that Dhp1 plays a role in the transcription-related spreading of the H3K9me mark . While RNAi is critical to the establishment and spreading of heterochromatin , it is dispensable for the maintenance of a previously assembled chromatin state at the mating type locus and sub-telomeric regions [64 , 70] . To investigate the role of Dhp1 in heterochromatin maintenance , we introduced the dhp1 mutation into cells that lack part of the K region in the mating type locus , but continue to repress a proximal ade6+ reporter gene ( KΔ∷ade6+off ) ( Fig 5A ) . Deleting the K region results in loss of heterochromatin establishment within the mating type locus , but because heterochromatin is stably inherited through cell division , derepression is rarely seen without concomitant loss of the maintenance machinery [71] . Loss of maintenance machinery will result in derepression of ade6+ ( KΔ∷ade6+off will switch to KΔ∷ade6+on ) . While the molecular mechanisms which mediate maintenance remain unclear , Clr4 and Swi6 have been implicated [13 , 38 , 72] . We detected partial loss of repression of ade6+ in dhp1-1 , an intermediate phenotype between wildtype and swi6Δ ( Fig 5B and 5C ) . In the wildtype background , nearly 80% of cells form dark red colonies and only about 20% of cells form pink colonies . Unlike swi6Δ , which form 100% white colonies , about 75% of dhp1-1 cells form pink colonies , although no dark red colonies were ever observed . We further analyzed the ade6+ RNA level by qRT-PCR ( Fig 5D ) . Indeed , we observed a more than 20-fold increase the amount of ade6+ transcripts in dhp1-1 cells compared to that of wildtype cells . Consistent with previous studies , loss of Swi6 abolishes the enrichment of H3K9me2 at KΔ∷ade6+ ( Fig 5E ) [72] . Interestingly , mutation of dhp1 does not reduce this histone modification at the same region ( Fig 5E ) , suggesting that Dhp1 plays a role in effective maintenance of epigenetic silencing downstream of H3K9me . We consistently observed a stronger silencing defect in dhp1-1 at the mating type locus than the pericentromeric region ( Figs 1–3 ) . RNAi is known to play a major role in silencing centromeric repeats but only partially contributes to silencing at the mating type locus [64 , 65] . These results suggest that Dhp1-mediated silencing might be distinct from that of RNAi . To test this , we combined dhp1-1 with a deletion of ago1 , the sole Argonaute protein in S . pombe [62] , and analyzed the silencing defect at the centromeric region and the mating type locus by qRT-PCR . Consistent with previous findings , loss of Ago1 caused an upregulation of centromeric repeat transcripts ( Fig 6A ) and did not show an obvious silencing defect at the mating type locus ( Fig 6B ) . Whereas dhp1-1 exhibited a modest increase in transcription at the centromere and the mating type locus , a double mutant dhp1-1 ago1Δ showed a large increase beyond the cumulative effects of either single mutation ( Fig 6B ) , indicating that Dhp1 contributes to heterochromatic silencing in a pathway parallel to RNAi . Our conclusion was also supported by the expression data shown in Fig 3B: we consistently observed more relative expression of repeats in dhp1-1 dcr1Δ double mutant cells compared to that of dcr1Δ or dhp1-1 single mutant cells from both the centromeric region and mating type locus , further indicating an RNAi-independent role of Dhp1 . Many transcripts degraded by RNAi are also targets of Rrp6 [42] , the catalytic subunit of the nuclear exosome required for rapid elimination of cryptic unstable transcripts ( CUTs ) [73–75] . Its RNA degradation activities act in parallel with RNAi to promote heterochromatin assembly [43 , 50] . Since Dhp1 is an exoribonuclease and plays an independent role from RNAi , we next wondered whether Dhp1 has overlapping function with Rrp6 in the silencing of repeat elements . Indeed , qRT-PCR showed that dhp1-1 rrp6Δ double mutant cells have stronger silencing defects at both the centromeric region and mating type locus ( Fig 6C and 6D ) , although the effect is less than that observed in dhp1-1 ago1Δ . We next investigated whether the accumulated silencing defects in double mutants of dhp1 with ago1Δ or rrp6Δ are resultant from additive deficiencies of H3K9me2 . ChIP experiments show that , except for dhp1-1 ago1Δ at the centromeric repeats , none of the double mutants exhibit further reduction of H3K9me2 compared to single mutants ( Fig 6C and 6D ) , suggesting the role of Dhp1 in epigenetic silencing does not rely on H3 K9 methylation at repeat regions . Notably , combining dhp1-1 and rrp6Δ mutations enhances H3K9me both at the centromeric region and the mating type locus ( Fig 6C and 6D ) . A recent study reported that Rrp6 is required for RNAPII termination at specific targets [73] . Our observation of enhanced H3K9me occurring in dhp1-1 rrp6Δ double mutant cells suggests that transcription termination defects impair RNAPII transcription and favor the induction of RNA-mediated chromatin modification such as H3K9 methylation . In dhp1-1 rrp6Δ , the compounded silencing defect must be overcompensating for the increased silencing effect of the additively enhanced H3K9me ( S7 Fig and discussion ) . Collectively , these results show that the Dhp1-mediated silencing mechanism is independent of both RNAi and the exosome , and is likely downstream of H3K9me . Dhp1 is a conserved 5’-3’ exoribonuclease [44 , 46] . Previous studies of Xrn1 in Kluyveromyces lactis ( K . lactis ) indicated that switching the acidic aspartate at position 35 or glutamate at position 178 to neutral residues , such as alanine ( D35A ) or glutamine ( E178Q ) , completely abolished enzymatic activity [76] . In S . pombe , Dhp1D55 and E207 are conserved residues corresponding to K . lactis Xrn1D35 and E178 ( Fig 7A ) . To test whether the RNA processing activity of Dhp1 is important for its role in epigenetic silencing , we generated plasmids carrying a copy of dhp1 with both D55 and E207 mutated ( dhp1-D55A E207Q ) , which abolishes the catalytic activity of Dhp1 . A plasmid carrying a wildtype allele of dhp1+ can rescue both the ts phenotype and the silencing defect of dhp1-1 analyzed by dilution assays ( Fig 7B ) and qRT-PCR ( Fig 7C and 7D ) , suggesting that the wildtype allele can completely complement the C-terminal truncated form of dhp1 , further supporting that there is no dominate negative effect of dhp1-1 . However , the plasmid carrying the catalytic mutant could not rescue the ts phenotype ( Fig 7B ) or the silencing defect of dhp1-1 at the centromeric region and the mating type locus ( Fig 7C and 7D ) , indicating that the catalytic activity of Dhp1 is essential for its role in epigenetic silencing . In S . pombe , epigenetic silencing requires cooperation between the TGS and PTGS pathways [15 , 62 , 64] . As a classic example of PTGS , RNAi allows processing of RNAs transcribed from these regions to facilitate or reinforce heterochromatin assembly in a RNAPII-dependent manner [15 , 64] . In this process , siRNAs maintain the feedback loop and propagate heterochromatin . RNAPII activity is required for generating precursors of siRNA and thereby is crucial for heterochromatin assembly [68 , 77] . Additionally , RNAPII may have a more direct role in epigenetic silencing because mutation of RNAPII subunits , splicing factors , and RNA processing machineries impair heterochromatin [68 , 77–79] . TGS relies on heterochromatin which is mediated by histone modifications that recruit silencing effectors [4] . In addition to the H3K9 methyltransferase Clr4 and HP1 family proteins , HDACs are critical mediators of all three phases of heterochromatin formation [80–82] . Especially , deletion of class II HDACs clr3 or sirtuin sir2 cause marked reduction of H3K9me across the centromeric regions and mating type locus [80 , 82] . To gain further insight into the function of Dhp1 in TGS or PTGS , we compared the localization of Mit1-Myc , one of the core subunits of SHREC ( Clr3 complex ) at the centromeric regions and the mating type locus in wildtype , dhp1-1 , din1Δ , and clr4Δ cells ( Fig 8A and 8B ) . Mit1-Myc is a fully functional allele of Mit1 , and has been employed in previous studies [80] . Unlike clr4Δ , which abolishes the localization of Mit1 , dhp1-1 does not show any reduction of Mit1 localization at these regions ( Fig 8A and 8B ) , indicating that the localization of SHREC is not reduced . Next , we combined dhp1-1 with either clr3Δ or sir2Δ , and examined the silencing of repeat regions in wildtype , single and double mutant cells ( S8 Fig ) . RT-PCRs show that all double mutant cells have enhanced silencing defects compared to single mutants suggesting overlapping functions between Dhp1 and these HDACs ( S8 Fig ) . These results suggest that Dhp1 likely has a major role in PTGS and acts in a distinct pathway parallel to SHREC-mediated TGS . To further investigate the role of Dhp1 in TGS or PTGS , we analyzed the relationship between Dhp1 and RNAPII in heterochromatin formation . First , we attempted to combine dhp1-1 with rpb7-G150D , which carries a mutation on the RNAPII subunit Rpb7 and has a specific defect in centromeric pre-siRNA transcription [68] . Surprisingly , combined mutation of dhp1-1 with rbp7-G150D is lethal , suggesting the presence of a compensatory mechanism between Dhp1 and RNAPII to ensure proper regulation of the transcriptome . We next combined dhp1-1 with rpb2-m203 , a mutant of the second largest subunit of RNAPII [77] . This mutation does not affect the global transcriptional activity of RNAPII [77] . Instead , it specifically influences the generation of siRNA [77] . Our data indicates that Dhp1 plays a role in a pathway parallel to RNAi in the silencing of repetitive regions ( Fig 6 ) . Therefore , the Dhp1-mediated silencing defect is unlikely to be linked through rpb2-m203 . Indeed , rpb2-m203 dhp1-1 double mutant cells are viable , and have a stronger silencing defect at the centromere region than either single mutant ( S9 Fig ) , indicating independent , parallel functions in epigenetic silencing . Heterochromatic regions commonly exclude RNAPII as a mechanism of TGS , but PTGS mechanisms occur downstream of RNAPII recruitment . We wondered whether , like RNAi and the exosome , Dhp1 plays a major role in PTGS . Therefore , RNAPII inclusion or exclusion from chromosomal regions will serve as an indicator to elucidate the function of Dhp1 in transcriptional and/or post-transcriptional actions . We mapped RNAPII occupancy in wildtype and dhp1-1 by ChIP using clr4Δ as a control ( Fig 8C and 8D ) . Loss of Clr4 completely abolishes heterochromatin , thereby shows a strong TGS defect as indicated by dramatically increased RNAPII occupancy at the repetitive regions . However , no difference of RNAPII occupancy was observed between dhp1-1 and the wildtype control at repetitive regions , suggesting the role of Dhp1 is not in TGS , but rather PTGS ( Fig 8C and 8D ) . Decreased transcription termination demonstrated in dhp1 mutants may reduce the level of available RNAPII complexes for initiation of transcription and could mask the true extent of silencing . Additionally , protracted RNAPII association at a given locus due to stalling might confound ChIP results . To ensure that the true activity of RNAPII was measured , we performed a genome-wide survey of RNAPII targets using Cross-linking and analyses of cDNA ( CRAC ) in wildtype and dhp1-1 cells ( Fig 9 ) . This assay mapped the genome-wide distribution of RNAPII and also monitored the RNAPII complexes actively synthesizing RNAs [83] ( Fig 9A ) . A genome-wide study is necessary in this case as Dhp1 may serve distinct roles in euchromatin and heterochromatin , as genome-wide expression profiling suggested ( Fig 1 and S3 Fig ) . In euchromatic regions , defects in terminating RNAPII transcription caused by the dhp1 mutation led to an accumulation of unreleased RNAPII complexes at the 3’end of genes in dhp1-1 ( Fig 9B ) . However , the same phenotype was not observed in clr4Δ cells , indicating that loss of clr4 causes no transcription termination defect . In heterochromatic regions , although clr4Δ dramatically enhanced RNAPII-RNA associations at the centromeric region and mating type locus compared to that of wildtype cells due to complete loss of TGS and partial loss of PTGS , such a difference was not detected upon mutation of dhp1 ( Fig 9C ) . Given the fact that mutation of dhp1 leads to substantial upregulation of repeat transcripts ( Fig 1C ) without reduction of H3K9me at repetitive regions ( Figs 2 and 6 ) , and only marginally affects RNAPII occupancy and its association with repeat transcripts ( Figs 8C , 8D and 9 ) , the results support a primary role of Dhp1 in PTGS .
In spite of the crucial role for RNAi in heterochromatin assembly , heterochromatin is not completely abolished in RNAi mutants indicating that other pathways are involved [50 , 65 , 84] . These pathways are mediated by DNA-binding factors , RNA or RNAi-independent RNA processing factors [50 , 61] . In Arabidopsis , the flowering repressor gene FLC is thought to provide links between RNA processing activities and chromatin regulation in gene silencing [85] . In S . pombe , recent studies reveal the nuclear exosome , which governs RNA quality control and ensures the elimination of unwanted RNAs , exists as an RNAi-independent silencing mechanism [42 , 43 , 50 , 61] . Co-activators of the exosome , including TRAMP and MTREC , which help to recognize and degrade its substrates , are also connected to epigenetic silencing without affecting H3K9me , thereby play a major role in PTGS [61 , 86] . Additional studies on Triman , a 3’-5’ exonuclease in S . pombe , show that it generates Dicer-independent primal RNAs and is required for initiation of heterochromatin assembly via a mechanism requiring Ago1 [87] . In this study , we described a novel pathway involving Dhp1 , a conserved RNA 5’ to 3’ processing enzyme that contributes to PTGS ( Fig 10A ) . We propose that three RNA processing activities , RNAi , the exosome , and Dhp1/Xrn2 degrade repetitive transcripts to mediate the post-transcriptional gene silencing of repeat transcripts ( Fig 10B ) . Heterochromatin assembly is a dynamic process with distinct steps [4] . It is nucleated at genomic regions containing highly repetitive DNA elements and spread to surrounding regions [88] . Its structure is recaptured during DNA replication and maintained through cell division [14] . Silencing factors often participate at discrete step ( s ) rather than throughout the process . In particular , RNA-mediated silencing pathways are often required to nucleate heterochromatin formation [9] . Once silencing is established , these factors are dispensable [13]; the heterochromatic state persists in the absence of the initial stimulus . For example , at the mating type locus of S . pombe , RNAi machinery cooperates to nucleate heterochromatin assembly but is dispensable for its inheritance [13] . The re-establishment assay clearly indicates that Dhp1 is indispensable for efficient establishment of silencing at heterochromatic repeat regions ( Fig 3 ) . RNAi is well-known as the major nucleation pathway at centromeric regions but not at the mating type locus [64 , 65] . Interestingly , dhp1-1 ago1Δ double mutant cells have cumulative defects at the mating type locus indicating separate functions of these two pathways ( Fig 6 ) . Unlike Triman , which requires Argonaute to be loaded on longer RNA precursors [87] , Dhp1 has an Argonaute-independent role , although we cannot rule out the possibility that the slicer activity of Ago1 may also contribute to the generation of the substrates for Dhp1 . Since RNAi itself can initiate heterochromatin formation , we observed re-establishment of heterochromatin at repetitive elements in dhp1-1 cells following clr4+ complementation , although the restoration was not complete ( Fig 3 ) . These results suggest that Dhp1 plays a unique but overlapping role in heterochromatin nucleation in concert with RNAi . It is possible that the Dhp1-mediated degradation of heterochromatic repeat transcripts is required for de novo assembly of heterochromatin through recruiting silencing effectors , similar to RITS [36 , 38 , 39] . It is also possible that the processing activity of Dhp1 is involved in generating the primary small RNAs that contribute to initiation of epigenetic silencing as suggested for the role of the exosome in heterochromatin assembly [61] . In addition to defective nucleation , the H3K9me mark in dhp1 mutants is reduced in the reporter genes embedded at the repetitive regions , suggesting a spreading defect ( Fig 2B and 2C ) . The assay analyzing the spreading of H3K9me from an ectopic nucleation center to the surrounding regions indicates that Dhp1 facilitates the spreading of the heterochromatic mark ( Fig 4 ) . Although it is unclear how transcription-mediated spreading of heterochromatin occurs , it is possible that impaired transcription termination in the dhp1 mutant affects the rate of histone turnover during transcription and thereby impedes the spreading of H3K9me . In addition to its role in initiation and spreading , we provide evidence to show that Dhp1 functions in the maintenance of pre-established silencing ( Fig 5 ) . How does Dhp1 function in the maintenance of silencing ? It is known that heterochromatin maintenance relies on the binding of Swi6 and Clr4 to methylated H3K9 , which facilitates recapitulation of the specific chromatin configuration following DNA replication [38 , 70] . In addition , Swi6 and HP1 proteins work as binding platforms , recruiting other histone modifiers and with the factors that are involved in replication-coupled heterochromatin assembly , such as chromatin assembly factor 1 ( Caf1 ) [65 , 80 , 89 , 90] . Although the levels of H3K9me2 at the repeat regions are not decreased upon mutation of dhp1 , the dynamic binding of Swi6 could still be affected . In addition to H3K9me , Swi6 is also reported to bind “repellent” RNAs that antagonize the heterochromatic silencing [91] . Thus , Dhp1-mediated elimination of RNA may facilitate the dynamic binding of Swi6 to heterochromatin , and thereby ensure the maintenance of the silenced chromatic domains . Although the centromeric regions and the mating type locus are assembled by heterochromatin , they occupy different chromosomal contexts and use distinct strategies to target heterochromatin [4] . Notably , the effects in double mutants of dhp1 with ago1Δ or rrp6Δ are different at centromeres and the mating type region ( Fig 6 ) . At the centromeric region , RNAi is the major pathway [64] . Therefore , as expected , the ago1Δ single mutant exhibits a severe silencing defect and decreased H3K9me mark ( Fig 6 and S7 Fig ) . Compared to the already radically impaired silencing phenotype in the ago1Δ single mutant , the dhp1-1 ago1Δ double mutant shows even higher levels of repeat transcripts and lower levels of H3K9me2 ( Fig 6 ) , suggesting that transcripts produced from repeat regions in RNAi-deficient cells , are likely targets of Dhp1 . In contrast , without Rrp6/exosome , RNAi machinery is still functional . Therefore , we only observe a moderate silencing defect at the centromeric region in the dhp1-1 rrp6Δ double mutant ( Fig 6 ) . At the mating typing locus , at least three pathways initiate heterochromatin assembly and target H3K9me [4 , 27] . It is not surprising that the dhp1-1 ago1Δ double mutant maintains a high level of H3K9me2 ( Fig 6 ) ; other pathways may compensate for loss of function for both Dhp1 and RNAi at the mating type locus [4] . In addition , transcription termination defects caused by rrp6 and dhp1 mutation may contribute to the increased level of H3K9me seen at both centromere and mating type locus ( S7 Fig ) . Interestingly , cells containing the dhp1 mutation consistently show a stronger silencing defect at the mating type locus even in the presence of higher levels of H3K9me ( Figs 1–3 and 6 and S7 Fig ) , suggesting that Dhp1-mediated silencing occurs primarily downstream of H3K9me , likely as a mechanism of PTGS . In S . pombe , TGS and PTGS are intertwined . In TGS , heterochromatin greatly limits the access of RNAPII , allowing only a low level of transcription from highly repetitive DNA regions . RNAs transcribed from these regions are subject to PTGS by RNAi machinery , in which they are processed into siRNAs in order to feedback on chromatin to facilitate the assembly and propagation of heterochromatin [27 , 88] . The silencing defect in dhp1-1 is unexpected considering that compromised transcription termination would weaken RNAPII transcription and delay the release of RNA from the site of transcription , which may then enhance the assembly of heterochromatin mediated by RNA as suggested by previous studies [48 , 50] . To elucidate whether Dhp1 plays a major role in TGS , we used ChIP analysis to map H3K9me and SHREC ( Figs 2 , 8A and 8B ) , which have well-studied functions in TGS at repeat regions . If Dhp1 plays a role in TGS , we would expect to observe reduced enrichment of H3K9me and SHREC at the endogenous repetitive regions in dhp1-1 . No reduction of enrichment occurred however for either H3K9me2 or SHREC at endogenous repetitive regions in dhp1 mutants , suggesting that the role of Dhp1 in gene silencing is primarily associated with PTGS rather than TGS ( Figs 2 , 8A and 8B ) . We further investigated RNAPII occupancy and the levels of actively transcribing RNAPII at repeat regions in wildtype and mutant cells using ChIP and CRAC respectively ( Figs 8C , 8D and 9 ) . A role in TGS for Dhp1 would be suggested by increased RNAPII occupancy occurring at repetitive regions in dhp1-1 , as RNAPII in the context of impaired TGS would associate more frequently with heterochromatic transcripts . In contrast , no increase would implicate a role for Dhp1 in PTGS . Our RNAPII ChIP results clearly show no difference between dhp1-1 and wildtype , implicating a PTGS role for Dhp1 ( Figs 8C and 8D ) . Additionally , we showed that the catalytic activity of Dhp1 is required for its role in epigenetic silencing , providing strong evidence to support that the RNA processing role of Dhp1 is associated with PTGS ( Fig 7 ) . Although our results pinpoint the primary role of Dhp1 in epigenetic silencing through PTGS , completely discounting a function of Dhp1 in TGS is a challenge as Dhp1/Rat1/Xrn2 has well-established activity that is linked to RNAPII . RNAPII transcription and its associated activities are required for heterochromatin assembly . As a result , loss of silencing was reported to correlate with defective RNAPII transcription [68 , 77 , 92–94] . Is the RNAPII-linked function of Dhp1 related to epigenetic silencing ? In agreement with reported termination defects upon mutation of Dhp1 and Din1 , our expression profiling showed accumulation of 3’ untranslated transcripts at many genes in these mutants ( S3 Fig ) . To execute its function in RNAPII transcription termination , Dhp1/Rat1 exonucleases target the downstream fragments produced by cleavage at the polyA site during 3’ end processing [44–46] . The processed mRNAs are packed into nuclear RNA transporting cargos and exported to the cytoplasm for translation . Since this action of Dhp1/Rat1 in transcription termination lies downstream of mRNA processing and packaging , defects of Dhp1/Rat1 are unlikely to dramatically influence the amount and the quality of coding mRNAs . Indeed , at least at the permissive temperature , we did not observe significant alterations of coding transcripts in the dhp1 mutant ( S2 Table ) . Rather , the remarkable differences observed in the transcriptome were seen at non-coding regions ( S3 Fig ) . Recently , Rat1 in budding yeast was reported to maintain the balance of RNAPII CTD phosphorylation , and therefore plays a role in transcription elongation [95] . This finding suggests that Rat1 may have more complex roles in transcription than previously thought . In addition , neither loss of Rnh1 nor Din1 causes growth defects or silencing defects as seen in the dhp1 mutant ( Figs 1 and 2 and S4 Fig ) , raising the question about which role of Dhp1 , transcription or RNA quality control , is essential for cell growth and silencing . In this study , we indeed observed higher enrichment of H3K9me2 at the endogenous repetitive regions in the dhp1 mutant ( Figs 2 and 6 ) . This observation is in agreement with a study showing that an impaired Paf1 complex is sufficient to induce RNAi-mediated epigenetic silencing in trans at euchromatic loci , likely through its termination defect [48] . While a parallel study reported significant reductions of H3K9me2 in dhp1 mutants at all major heterochromatic regions [59] , we only observed reduced H3K9me at reporter genes but not at the repeat regions . It is likely that the discrepancies in the H3K9 methylation data are due to differences in culturing conditions . To minimize the pleiotropic impacts caused by dhp1 mutation and avoid the antagonistic effect of high temperature ( 37°C ) for heterochromatin formation [96] , we collected data at a permissive condition ( 30°C ) without shifting cell cultures to 37°C , the restrictive condition applied in the parallel study [59] . Therefore , our results are more likely to accurately represent the true effect of Dhp1 in epigenetic silencing . In addition , we provided evidence to show that Dhp1-mediated silencing is independent of RNAi ( Fig 6 ) . Overall , the role of Dhp1 in epigenetic silencing at major heterochromatic regions cannot be explained by its known function in transcriptional termination . It is possible that RNAPII may couple repeat transcription with its degradation by Dhp1 . A second possibility is that RNAPII may help “discriminate” noncoding pericentromeric repeat RNAs from general pre-mRNAs so that the former can be degraded by Dhp1 . The basis for this selection may be the aberrant ( double-stranded or abnormally capped ) structure of the transcribed RNA . Alternatively , the chromatin structure of the transcribed repeat region may somehow determine the fate of the transcripts , feeding into RNAi- , exosome- , or Dhp1-mediated silencing . The catalytic activity of Dhp1 is required for its role in epigenetic silencing ( Fig 7 ) . By what mechanism are the substrates for Dhp1-mediated silencing produced ? Due to the strong conservation of the active site , it is likely that the mechanisms of Xrns are very similar [97] . The crystal structure of Drosophila XRN1 indicates that substrates are limited to 5’ monophosphate RNAs because larger structures , such as m7G Cap or triphosphorylated RNAs , do not fit into the pocket [98] . Hence , the RNA pyrophosphohydrolase activity of Din1 seems necessary for the generation of monophosphorylated RNA substrates for Dhp1 , especially for decapping and RNA quality control . However , only Dhp1 , not Din1 , is essential for viability and epigenetic silencing . In addition , unlike Dhp1 , Din1 and its orthologs are not widely conserved [47 , 99] . Since Din1 is not essential and is not necessary for epigenetic silencing , an endoribonuclease or an extra RNA pyrophosphohydrolase likely produces the substrates for Dhp1 in silencing . In yeast , abnormal pre-mRNAs are degraded rapidly from both 5’ and 3’ ends by Rat1/Xrn2 and the nuclear exosome , respectively , with the exosome playing a dominant role [100] . In human cells , XRN2 appears to be more crucial for degradation of abnormal pre-mRNAs than the exosome [101] . Given the fact that Xrn2 is conserved from yeast to humans , our results may yield insights broadly applicable to the gene silencing field , including mammals . Dhp1/Xrn2 may represent a more generalized mechanism of an RNA-based form of silencing . Future studies identifying additional Dhp1/Xrn2 interacting proteins may help to address these questions .
S . pombe strains used in this study are listed in S1 Table . Cells were cultured using standard procedures for growth and manipulation [102] . Epitope-tagged and deletion mutant strains were engineered using standard PCR methods as described previously [103] . Double mutants were constructed via genetic crossing followed by tetrad dissection . For dilution assay , liquid cultures were diluted in series ( 1:10 ) and plated using a pin transfer tool on YEA media ( Rich , N/S ) , low adenine YE media , or YEA media containing either 20 μg/ml TBZ or 850 μg/ml 5-FoA . All cultures were grown at 30°C ( or 37°C where indicated ) . The strains used for cross-linking and analyses of cDNA ( CRAC ) carry a carboxyl-terminal HTP-tagged subunit of RNAPII , Rpb2-HTP . An HTP tag contains a 6X- His epitope and a protein A epitope separated by a Tobacco Etch Virus ( TEV ) protease cleavage site [83] . Strains carrying either KΔ∷ade6+off or KΔ∷ade6+on were isolated and saved as previously described [71] . To generate pdhp1+ , a PCR fragment amplified using oligos Dhp1-BamHI-Fw and Dhp1-Pst1-RV contains a wildtype dhp1+ gene including promoter , open reading frame , and 5’ and 3’ untranslated regions . The PCR fragment was digested by BamHI and PstI and ligated into a pREP41 digested with the same restriction enzymes ( BamHI/PstI ) . After BamHI/PstI digestion , pREP41 lost its nmt promoter . The resulting pdhp1+ expresses the wildtype dhp1+ driven by its endogenous promoter . pdhp1D55A E207Q was generated using a QuickChange Site-Directed mutagenesis kit ( Stratagene ) based on pdhp1+ . pclr4+ is a plasmid carrying a DNA fragment containing a wildtype clr4+ driven by its endogenous promoter as previously described [50] . Total RNA was prepared using the MasterPure Yeast RNA Purification Kit ( Epicentre ) . First-strand cDNA was produced with M-MLV Reverse Transcriptase ( Promega ) using site-specific primers following manufacturer protocols . Real-time PCR was performed on a 7500 Fast Real-Time PCR System ( Applied Biosystems ) with SYBR Select Master Mix ( Applied Biosystems ) . First-strand cDNA synthesis without reverse transcriptase was performed for negative controls . At least two biological repeats were performed for all experiments . Statistical analysis was performed using a student’s t test ( two-tailed distribution ) . Error bars represent standard error of mean ( s . e . m ) . Primers are listed in the S4 Table . Mating-type switching-competent ( h90 ) mid-log phase cells ( wildtype , dhp1-1 , or din1Δ ) were plated on solid sporulation medium ( SPA ) . Cells grew at 30°C for 6 hr , then switched to 37°C for 2 hr , and finally finished the sporulation at 30°C for 12hr . Cells were washed 3X with water . Ten microliter cells in water were spread on a glass slide , and fixed by heat at 70°C . The slides were then covered by 5μl of mounting buffer with DAPI ( VECTOR , H1500 ) and 13mm coverslips . The stained cells were imaged by a confocal microscope . Sample preparation for the expression array and array design were reported previously [104] . The expression profiling is performed as previously described [105] . The composite plot was generated using GenomicRanges R-package ( version 1 . 20 . 5 ) , from the high-resolution part of the microarray ( 2320 genes ) . The genes were aligned at the transcriptional termination site TTS ( S . pombe 2007_April annotation ) and the geometric means of the ratios ( Mutant/wt ) were plotted . Flag-TEV-protein A ( FTP ) -tagged purification and mass spectrometry were performed as previously described [105] . ChIP experiments were performed as described previously using antibodies against histone H3 ( di-methyl K9 ) ( Abcam , Ab1220 ) , RNAPII ( Abcam , Hab5408 ) , or Myc ( Santa Cruz , A-14 ) [106] . Real-time PCR was performed on a 7500 Fast Real-time PCR System ( Applied Biosystems ) with SYBR Select Master Mix ( Applied Biosystems ) . At least two biological repeats were performed for all ChIP experiments . Statistical analysis was performed using a Student’s t test ( two-tailed distribution ) . Error bars represent s . e . m . In vivo CRAC was performed as described with modifications [83] . Two-liter yeast cultures were grown to an OD600≈2 at 30°C . Cells were harvested by centrifugation and cell pellets were resuspended in 2 . 5L Phosphate-Buffered Saline ( PBS ) followed by UV-irradiation in a “Megatron” UV-cross-linker ( 254 nm ) for 3 min before cells were pelleted and frozen in liquid nitrogen . The pellets were then lysed by grinding in liquid nitrogen ( Resch , MM400 ) and resuspended in 10 ml of 1x TN150 lysis buffer ( 10x TN150: 0 . 5 M Tris-HCl ( pH 7 . 8 ) , 1 . 5 M NaCl , 1% NP-40 ) . Extracts were clarified by centrifugation ( 10 min at 4000 rpm and 45 min at 15 , 000 rpm at 4°C ) and incubated with 150 μl of equilibrated IgG Sepharose beads ( GE Healthcare ) for 1h at 4°C . After two washes with TN1000 buffer ( 100 mM Tris-HCl ( pH 7 . 8 ) , 2 M NaCl , 0 . 2% NP-40 ) and two washes with TN150 lysis buffer , the beads were incubated with GST-TEV protease for 2h at 16°C . The TEV eluates were collected by centrifugation and incubated with 10U of Turbo DNase ( Ambion ) for 8 min at 37°C followed by incubation with RNase Cocktail Enzyme Mix ( Ambion; 0 . 005 U RnaseA , 0 . 2 U Rnase T1 ) for 2 min at 37°C . Guanidine-HCl ( 0 . 4g ) was dissolved in 500 μl of TEV eluates . NaCl and Imidazole were added to final concentrations of 300 mM and 10 mM , respectively . Samples were incubated with 50 μl of nickel agarose beads ( Macherey-Nagel ) over night at 4°C . All washes , alkaline phosphatase treatment and 3’ linker ligation were carried out as described except that 40U T4 RNA ligase 2 truncated K227Q ( NEB ) was used instead of T4 RNA ligase . The beads were incubated in 80 μl phosphorylation mix ( 16 μl 5x PNK buffer ( 250 mM Tris-HCl ( pH 7 . 8 ) , 50 mM MgCl2 , 50 mM β-mercaptoethanol ) , 200 mM ATP ( Sigma , A6559 ) , 20U T4 polynucleotide kinase ( NEB ) , 80U RNase Inhibitor ) for 40 min at 37°C . For the ligation of the 5’ linker the beads were resuspended in 80 μl of 5’ ligation mix ( 16 μl 5x PNK buffer , 80U RNasin , 40U T4 RNA ligase , 100 pmol 5’linker , and 80 mM ATP ) and incubated at 16°C . After two washes with wash buffer II ( 50 mM Tris-HCl ( pH 7 . 8 ) , 50 mM NaCl , 10 mM Imidazole , 0 . 1% NP-40 ) the material was eluted with elution buffer ( 10 mM Tris ( pH 7 . 8 ) , 50 mM NaCl , 150 mM Imidazole and 0 . 1% NP-40 ) . The final eluate was incubated with 2 M EDTA , 20 μl 20% SDS and 100 μg proteinase K ( Ambion AM2548 ) for 2 hours at 50°C and the RNA was extracted using Phenol-Chloroform followed by ethanol precipitation . Reverse transcription with SuperScript III was performed following the manufacturer’s instructions ( Invitrogen ) followed by RNase H ( NEB ) digest ( 10U ) for 30 min at 37°C . The cDNA was amplified and the PCR-product was purified with the Agencourt AMPure XP PCR purification beads ( Beckman Coulter ) following the manufacturer’s instructions . The quality of the library was verified with the Bioanalyzer 2100 ( Agilent ) and the Agilent High Sensitivity DNA Kit ( Agilent ) . The amplified library was subject to high-throughput sequencing at BGI-Hong Kong Co . Ltd . The datasets were mapped to the mating type locus of S . pombe ( 41249 bp from chromosome 2 ) and to the full genome of S . pombe ( ASM294v1 . 17 ) using Tophat2 software . ( Tophat-2 . 0 . 14; [107] ) . Downstream data analysis was performed using R Bioconductor packages . The mean coverage over mRNA loci was normalized to 20 in the datasets . The difference plot was generated from all protein coding ORFs ( 5115 genes ) , aligning them at the annotated transcriptional termination site ( TTS ) ( S . pombe EF2 annotation ) . The plot is showing the ratio between the normalized Mutant/wt coverage . Two biological duplicates have been performed for genome-wide analysis for wildtype and dhp1-1 strains . All microarray and CRAC data sets are available at NCBI GSE77291 , GSE77289 and GSE77290 . | Epigenetic mechanisms regulate when , where , and how an organism uses the genetic information stored in its genome . They are essential to many cellular processes , such as the regulation of gene expression , genome organization , and cell-fate determination . They also govern growth , development , and ultimately human health . Heterochromatin constitutes silenced chromatic domains , in which gene silencing occurs through epigenetic mechanisms . RNA processing pathways , such as RNA interference ( RNAi ) and the exosome , are known to mediate the silencing of genes via degradation of unwanted or aberrant transcripts . In this study , we describe a new RNA processing mechanism in epigenetic silencing using fission yeast , a premier model for studying these processes . With genetic , cell biology , and genomic approaches , we uncovered a previously unrecognized function of Dhp1 , a highly conserved 5’-3’ exoribonuclease and ortholog of budding yeast Rat1 and metazoan Xrn2 . We show that Dhp1 mediates a novel RNA processing mechanism in epigenetic silencing which occurs independently of both RNAi and the exosome . Our results clarify how multiple RNA processing pathways are involved in the regulation of eukaryotic gene expression and chromatin organization . |
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Ebola and other filoviruses pose significant public health and conservation threats by causing high mortality in primates , including humans . Preventing future outbreaks of ebolavirus depends on identifying wildlife reservoirs , but extraordinarily high biodiversity of potential hosts in temporally dynamic environments of equatorial Africa contributes to sporadic , unpredictable outbreaks that have hampered efforts to identify wild reservoirs for nearly 40 years . Using a machine learning algorithm , generalized boosted regression , we characterize potential filovirus-positive bat species with estimated 87% accuracy . Our model produces two specific outputs with immediate utility for guiding filovirus surveillance in the wild . First , we report a profile of intrinsic traits that discriminates hosts from non-hosts , providing a biological caricature of a filovirus-positive bat species . This profile emphasizes traits describing adult and neonate body sizes and rates of reproductive fitness , as well as species’ geographic range overlap with regions of high mammalian diversity . Second , we identify several bat species ranked most likely to be filovirus-positive on the basis of intrinsic trait similarity with known filovirus-positive bats . New bat species predicted to be positive for filoviruses are widely distributed outside of equatorial Africa , with a majority of species overlapping in Southeast Asia . Taken together , these results spotlight several potential host species and geographical regions as high-probability targets for future filovirus surveillance .
After more than 40 years , the natural reservoirs of viruses in the genus Ebolavirus remain elusive . Accumulating indirect evidence during this time points to bats as primary suspects because several species have been found positive for filovirus antibodies ( S1 Table ) . Some of these species have also been confirmed as natural reservoirs for another filovirus , Marburg virus [1 , 2] . Three bat species demonstrate the ability to replicate ebolavirus following experimental inoculation [3] , and ebolavirus RNA has been discovered in three , naturally infected species [4] . In contrast to other surveyed mammal species ( great apes , duiker ) , there is little evidence of filovirus-induced morbidity in bats [1] . Such asymptomatic infections make bats more likely to be natural reservoir candidates for ebolaviruses than , for example , great apes ( gorilla and chimpanzee ) , which suffer mortality rates exceeding those observed in human populations [5] . Effective surveillance in the countries most frequently affected by ebolaviruses ( e . g . , Uganda , Democratic Republic of Congo [6] , and the countries affected by the recent outbreak in West Africa [7] ) is hampered by the incredible diversity of species over such a large geographical area . For example , West Africa is recognized as one of the most species-rich regions on Earth , with a large number of endemic species typically present in low densities [8 , 9] . Moreover , there is pronounced seasonality in regions affected by ebolaviruses with wet and dry seasons contributing to fruiting phenology and water availability that combine to influence the movement ecology , breeding , and birth pulses in a number of species , including bats [10–12] . Though wildlife surveillance to date surpasses 30 , 000 individuals collected from hundreds of species , we have yet to isolate live ebolavirus from any African wild species . What other species might be natural hosts of filoviruses in the wild ? To answer this question , we applied a machine learning approach to mine patterns in data on the world’s bat species . Here , we report an intrinsic trait profile that distinguishes seropositive bat species from all others with an estimated 87% accuracy . We identify a rank order of particular bat species whose trait profiles suggest a high probability that they could also be permissive to filovirus infection , and geographic regions where numerous of these potentially novel filovirus hosts co-occur to highlight surveillance targets of candidate reservoir species .
For all 1116 bat species , we collected life history , physiological and ecological traits from PanTHERIA [13] , a species-level database of the world’s mammals ( S2 Table ) . We calculated 3 additional , derivative traits from basic morphological and demographic variables: post-natal growth rate ( weaning body mass/neonatal body mass ) ; relative age to sexual maturity ( sexual maturity age/maximum longevity ) ; relative age at first birth ( age at first birth/maximum longevity ) . We added bat family as a series of 18 binary variables to explore the likelihood of taxonomic clustering among carriers . We calculated species density , defined as the richness of mammal species found within a species’ geographic range ( as reported in IUCN [14] ) divided by the total geographic range area for each bat species ( n/km2 ) . We compiled published data on diet and activity patterns [15]; torpor and migratory behavior [16]; and mass-corrected production ( the mean mass of offspring produced per year , normalized by adult body size [15] . Bat species names were standardized using Wilson and Reeder 2005 [17] . Each bat species was assigned a binary code according to its current status ( 0 –not currently known to carry a filovirus; 1 –published evidence; S1 Table ) . For this binary response variable , we applied generalized boosted regression [18–20] , a type of machine learning that seeks to maximize classification accuracy ( in this case , discriminating reservoir status among 1116 bat species ) by learning the patterns of features that distinguish between bats that have tested positive for filoviruses from all other species . Machine learning is particularly well-suited to comparative studies because it does not assume an underlying data distribution [21] , and explanatory power is unaffected by collinearity , hidden interactions , and non-random patterns of missing data common in ecological data sets ( e . g . , those that arise through sampling bias , or when species share similar trait values as a result of phylogenetic relatedness ) [22 , 23] . The model-free approach of machine learning algorithms like generalized boosted regression trees enables superior predictive accuracy based on patterns inherent in data themselves rather than based on a priori assumptions about underlying ecological processes or simple parametric relationships , in proportion to the quantity of information contained in the data . Boosted regression trees generate a series of recursive binary splits for randomly sampled predictor variables . Each successive tree is built using the residuals of the previous best-performing tree as the new response variable . Thus , an ensemble of linked trees is generated where each tree achieves increasingly more accurate classification based on randomly selected variables . In our analyses , we repeated the tree building process several thousand times to create an ensemble classification model of up to 5000 trees . Datasets were partitioned into training ( 80% of all 1116 species ) and test sets ( the remaining 20% ) prior to analysis . We applied 10-fold cross-validation during model building to prevent over-fitting , and permutation procedures to generate relative importance scores for each predictor variable ( S3 Table , which also summarizes tuning parameters , performance metrics ( AUC ) , and complete trait profiles ) . To calibrate performance , we conducted randomized bootstrapped permutation analysis of the species labels ( 500 permutations ) , a procedure referred to as target shuffling in business analytics . We calculated a baseline mean AUC for these permutations ( 0 . 6 ) and corrected our test AUC ( originally AUC = 0 . 97 ) by this baseline to arrive at our corrected test AUC of 0 . 87 = 0 . 97- ( 0 . 60–0 . 5 ) . To investigate the sensitivity of our results to errors and permutations in the covariates , we randomly removed 1% , 5% , 10% , 15% , and 20% of trait values , refit the model , and calculated the Spearman rank-order correlation between scores obtained using the corrupted data and those of our original analysis . This exercise showed the algorithm to be extremely adept at identifying the relative risk among bat species ( rank order ) with up to 5% of data removed ( ρ = 0 . 99 ) and very good with up to 20% of data removed ( ρ = 0 . 90 ) ( S4 Table ) . In our analysis , “unknown” carriers ( 1095 species ) were designated “non-carriers” , labeled as 0 . In the absence of repeated experimental inoculations , a large number of individuals of each species must be sampled before consensus can be reached that a given species is unable to harbor infection . Thus , we adopted this more conservative designation–essentially presence vs . background–to align with our aim of developing models whose baseline classification performance will continue to improve with future discoveries of new filovirus-positive species . Intrinsic features that reflect life history and biology are less susceptible to sampling biases than epidemiological data–for example , public heath and research expenditures are unlikely to influence a species’ age to sexual maturity , or other similar life history features . However , to control for any potential effect of sampling bias on our results , we tallied the number of primary literature citations in the Web of Science ( WOS ) for each bat species in our dataset as a proxy for study effort . Citation count was within the top dozen variables important for predicting filovirus-positive status , but it had low relative importance for prediction accuracy ( S2 Table ) . Removing WOS hits from the analysis did not alter the rank order of variables most important for predicting filovirus-positive bats , confirming that while some filovirus-positive bat species may be better studied than others , studied-ness did not bias the trait profiles generated by our modeling approach . Analyses were performed using the gbm package [19] in R [24] . To identify hot spots of filovirus carriers , we mapped the geographic ranges of all known filovirus-positive bat species ( S1 Table ) , as well as new filovirus carriers in the 90th percentile of model predictions ( S3 Table ) . We also provide maps for species comprising the 95th and 99th percentiles ( S1 Fig ) . All geographic ranges were obtained from the IUCN database of terrestrial mammals [14] and compiled in ArcGIS [25] .
From peer-reviewed primary literature , we identified 21 out of 1116 ( ~1 . 9% ) total extant bat species to have tested positive for any filovirus by means of any diagnostic ( i . e . , either serological or molecular assays ) . Approximately half of these species ( n = 11 ) are fruit bats belonging to Family Pteropodidae ( the Old World fruit bats ) , and the other half are primarily insectivorous bats from 4 families ( S1 Table ) . Although fruit bats comprise only about 16% of global bat biodiversity ( 186/1116 extant species ) , we estimate 5 times as many fruit bat individuals have been sampled for filoviruses compared to insectivorous bats ( S2 Table ) , which corroborates on a global scale the surveillance bias recently reported for ebolaviruses in bats of Africa [26] . Using 57 variables describing the biology , life history , ecology , taxonomy , and biogeography of all bat species ( S2 Table ) , our model predicted filovirus-positivity with 87% accuracy , and revealed a trait profile that distinguishes filovirus-positive species from other bats ( Fig 1 , S5 Table ) . In general , filovirus-positive bat species tend to have neonates that are larger at birth and wean at a larger size compared to other bats . This tendency to produce larger offspring was not an artifact of large adult body size . Rather , filovirus-positive bats produce greater biomass for their body size compared to other bat species ( the production variable [27] , Fig 1 ) . The majority of bats have 1 litter per year with a single pup in each litter , but some populations support a second litter in some years ( notably among the Vespertilionidae , the most speciose Family of insectivorous bats , and the Pteropodidae , the Old World fruit bats ) . We found that filovirus-positive species disproportionately display this tendency to have more than a single litter ( pup ) per year [28] . We also observed a bimodal pattern in sexual maturity age for filovirus-positive species , a pattern we conjecture may arise from small species ( insectivorous bats ) displaying earlier ages of sexual maturity compared to the large species ( fruit bats ) in the tropics where reproductive rates of non-hibernating bats decrease with body size [29] . Filovirus-positive species also display a tendency to live in larger population groups ( roosts ) compared to other bats . While group-living affords many benefits , costs of group living include increased pathogen transmission [30] and conspicuousness to predators , including human hunters [31] . Thus , it is possible that species living in large , conspicuous roosts are displaying compensatory effects of faster reproductive rates ( earlier age to sexual maturity [32] or more offspring per year [29] ) in response to increased extrinsic mortality risks conferred by hunting pressure . Overall , our results suggest that even though bats are constrained to a relatively slow life history strategy ( i . e . , long-lived with few offspring per year ) compared to similarly sized mammals , filovirus-positive bat species are those whose life history pace is at the leading edge of these constraints . In addition to traits that may enable bats to be more permissive to filovirus infection at the cellular level [33 , 34] , a life history profile reflecting faster reproductive rates may increase the likelihood of infection persistence through the more rapid replenishment of susceptible young [1 , 28] . Beyond intrinsic fitness components , our analyses revealed that filovirus-positive species exhibit larger geographic ranges containing higher mammal species richness per square kilometer than other bats ( species density , Fig 1 ) . Even after correcting for geographic range size , filovirus-positive bat species overlap with a greater diversity of mammal species per square kilometer , a finding that recapitulates a scientific consensus that there are likely to be multiple natural reservoirs supporting filoviruses such as Zaire ebolavirus [35] . This result corroborates independent studies within Africa predicting the environmental niche of ebolaviruses to span primary tropical rainforest ( continuous tropical rainforests as well as gallery rainforests , which occur along riparian and transitional zones ) [36–38] . But , in a departure from previous studies , our analysis identified several hotspots outside Africa where up to 25 predicted filovirus host species overlap in geographic range ( Figs 2 and 3; S1 Fig ) . Geographic ranges of filovirus-positive bat species are concentrated in sub-Saharan Africa and Southeast Asia , spanning a total of 133 countries ( Fig 2a ) . There is a conspicuous lack of surveillance in the western hemisphere , and to our knowledge there are no published studies reporting the results ( positive or negative ) of filovirus surveillance efforts in North , Central , or South America [26] . Novel bat carriers predicted by our model ( i . e . , those in the top 10% ) are much more widely distributed than expected , with predicted species occurring across Southeast Asia , and Central and South America ( Fig 2b; S3 Table ) . The predictions in the Americas are intriguing because , while New World bats may exhibit the appropriate traits , biogeographical processes may prevent filoviruses from existing in these regions . Indeed , homologous copies of VP35-like and NP-like gene integrations were found in both Old World and New World species of Myotis bats [39] . If filoviruses are discovered in bat species in the Americas , this would call into question the age of the Filoviridae , which , through whole genome analyses , have been estimated to share common ancestry 10 , 000 years ago [40] . Analyses of integrated elements in mammalian genomes , however , suggest filoviruses may be much older [41] . Among the 112 species comprising the 90th percentile probability there are 9 Myotis species ( S3 Table ) . Among these , Myotis ricketti tested seropositive and Myotis fimbriatus tested seronegative for Reston ebolavirus in China [42] . Diagnostic tests of the remaining 7 species have , to our knowledge , never been reported at the species level . A majority of the newly predicted filovirus carriers overlap in Southeast Asia ( Fig 3 ) , with notable hotspots occurring in regions of Thailand , Burma , Malaysia , Vietnam , and northeast India . A recent study reports the negative results of a large survey testing 500 individuals of Pteropus lylei for ebolavirus across 10 roosting sites in Thailand . This study was designed with enough statistical power to detect ebolavirus prevalence as low as 6% [43] . Our model ranked this particular fruit bat species behind 195 other bat species in its probability of filovirus-seropositivity . In particular , it is preceded by three other species commonly found in Thailand–Pipistrellus tenuis , Eonycteris spelaea , and Megaderma lyra , which rank 3rd , 5th , and 7th in a global list of unsurveyed bats predicted to be seropositive ( S3 Table ) . Future surveillance efforts may be streamlined by prioritizing filovirus-testing by species displaying the strongest trait similarities with known filovirus-positive species . Despite numerous suitable bat hosts and ongoing discoveries of novel filoviruses in this region ( e . g . , [44] ) , there are comparatively few reports of disease outbreaks in Asia . Though pigs were identified as possible reservoirs of Reston ebolavirus through routine investigation of syndromic disease , there have been no reports of human disease in this region . One outstanding question for future work is to investigate why there are so few spillover events reported for human and wildlife populations in Southeast Asia compared to equatorial Africa . Whether outbreaks are indeed occurring but on a smaller or less easily detectable scale ( e . g . , as in Ethiopia [45] ) , or whether filovirus strains in this region are fundamentally less virulent to their host species , sorting the competing hypotheses about why filovirus infection dynamics in Africa differ from those in Asia will begin with more targeted surveillance of candidate reservoir species . | Preventing future outbreaks of ebolaviruses in humans and other vulnerable animal populations will require identifying the natural reservoirs of filoviruses . Accumulating indirect evidence points to certain bat species as prime suspects . To guide the search for natural filovirus reservoirs , we mined intrinsic biological data on the world’s bat species to determine what features best predict filovirus hosts compared to bats at large . We report a suite of traits that distinguishes seropositive bat species from all others with an estimated 87% accuracy . We also identify several bat species not currently known to be filovirus hosts whose trait profiles indicate should be surveillance targets . Geographic regions where numerous potential filovirus hosts co-occur ( potential filovirus hotspots ) suggest that filovirus distribution and diversity may be greater than previously thought . |
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Japanese encephalitis ( JE ) , caused by a mosquito-borne flavivirus , is endemic to the entire south-east Asian and adjoining regions . Currently no therapeutic interventions are available for JE , thereby making it one of the most dreaded encephalitides in the world . An effective way to counter the virus would be to inhibit viral replication by using anti-sense molecules directed against the viral genome . Octaguanidinium dendrimer-conjugated Morpholino ( or Vivo-Morpholino ) are uncharged anti-sense oligomers that can enter cells of living organisms by endocytosis and subsequently escape from endosomes into the cytosol/nuclear compartment of cells . We hypothesize that Vivo-Morpholinos generated against specific regions of 3′ or 5′ untranslated regions of JEV genome , when administered in an experimental model of JE , will have significant antiviral and neuroprotective effect . Mice were infected with JEV ( GP78 strain ) followed by intraperitoneal administration of Morpholinos ( 5 mg/kg body weight ) daily for up to five treatments . Survivability of the animals was monitored for 15 days ( or until death ) following which they were sacrificed and their brains were processed either for immunohistochemical staining or protein extraction . Plaque assay and immunoblot analysis performed from brain homogenates showed reduced viral load and viral protein expression , resulting in greater survival of infected animals . Neuroprotective effect was observed by thionin staining of brain sections . Cytokine bead array showed reduction in the levels of proinflammatory cytokines in brain following Morpholino treatment , which were elevated after infection . This corresponded to reduced microglial activation in brain . Oxidative stress was reduced and certain stress-related signaling molecules were found to be positively modulated following Morpholino treatment . In vitro studies also showed that there was decrease in infective viral particle production following Morpholino treatment . Administration of Vivo-Morpholino effectively resulted in increased survival of animals and neuroprotection in a murine model of JE . Hence , these oligomers represent a potential antiviral agent that merits further evaluation .
The genus Flavivirus is composed of more than 70 different closely related species [1] . Many flaviviruses are arthropod-borne and causes significant human diseases . Among these , the four serotypes of dengue virus ( DENV ) , yellow fever virus ( YFV ) , West Nile virus ( WNV ) and Japanese encephalitis virus ( JEV ) are categorized as emerging global pathogens [2] . JEV is a mosquito-borne , positive sense , single stranded RNA virus , responsible for frequent epidemics of encephalitis , predominantly in children , in most parts of Southeast Asia and adjoining regions . It is the causal factor for 30 , 000–50 , 000 cases of encephalitis occurring every year and accounts for about 10 , 000 deaths annually with serious neurological squeal in the survivors [3] . JEV has been expanding its ‘geographical footprint’ into previously non-endemic regions and with several billion people at risk , Japanese encephalitis ( JE ) represents an internationally emerging concern in tropical and sub-tropical countries . Currently three types of JE vaccine are in use- the inactivated mouse-brain derived , the inactivated cell-culture derived and the live attenuated cell-culture derived . However , there are limitations for their usage in terms of availability , cost and safety [4] . At present , chemotherapy against JEV is largely supportive and not targeted towards the virus . A lot of avenues has been explored in the past and are also being currently tried , so as to develop a safe and effective molecule that would be able to prevent the virus from replicating within the host . The JEV genome is approximately 11 kb in length that carries a single long open reading frame ( ORF ) flanked by a 95-neucleotide 5′ untranslated region ( 5′ UTR ) and a 585-neucleotide 3′ UTR . The ORF encodes a polyprotein which is processed by viral and cellular proteases into three structural and seven non structural proteins [5] , [6] . The 5′ and 3′ UTRs of the JEV genome contain conserved sequence elements and can form conserved stem loop structure . 5′ UTR contain secondary structures which are required for the formation of translation pre-initiation complex [7] . JEV requires long range RNA-RNA interaction between 5′ and 3′ regions of its genome for efficient replication; one such interaction occurs between a pair of 10 complementary nucleotides , located in coding sequence for the capsid protein at 136–146 nucleotides from 5′ end of the genome , and 3′ cyclization sequence , commonly denoted as 3′CSI ( 3′ conserved sequence I ) located at 104–114 nucleotides from 3′ end of the genome [8] , [9] . The 3′CSI is highly conserved across members of JEV serocomplex , indicating the possibility that RNA elements within the 5′ and 3′ UTRs in JEV genome are essential for its replication . Anti-sense oligonucleotides have been shown to be effectively used as therapeutic agents against viral infection . In one such study siRNA generated against the cd loop-coding sequence in domain II of the viral Envelope protein ( which is highly conserved among all flaviviruses because of its essential role in membrane fusion ) has been found to protect against lethal encephalitis [10] . Similarly siRNAs has also been generated against various nonstructural proteins of JEV and were found to be effective in inhibiting viral replication [11] , [12] . Anti-sense approach has also been employed to inhibit flaviviral replication by generating anti-sense molecules against RNA elements within the 5′ and 3′ UTRs in flaviviral genome . In one such approach , DNAzyme against 3′ UTR of JEV genome has be found to be effective in controlling virus infection in a murine model [13] . Under the same approach but with different kind of anti-sense oligonuleuotide called Morpholino , flaviviral replication has been inhibited in cultured cells as well as in animal models [14] , [15] . Morpholino oligomers are single stranded DNA analogues containing same nitrogenous bases as DNA but joined by backbone consisting of morpholine rings and phosphorodiamidate linkages [16] . For efficient delivery into cells these Morpholino are often conjugated with arginine rich peptide [17] . However , in the current study we have used a different type of Morpholino oligomer called Vivo-Morpholino against 3′CSI and one of the secondary structures present in 5′ UTR of the JEV genome . Vivo-Morpholino are specialized type of non-peptide Morpholino oligomers , conjugated with a new transport structure that provides effective delivery into a wide variety of tissues in living animals , thereby raising the possibilities of their use as therapeutic agents . The transporter comprises of a dendritic structure assembled around a triazine core which serves to position eight guanidinium head groups in a conformation effective to penetrate cell membranes . Vivo-Morpholinos have also been shown to effectively enter and function within cultured cells [18] . Vivo-Morpholinos are also cost effective , non immunogenic , and stable under physiological conditions as compared to other types of Morpholinos . This study was designed to evaluate whether the use of Vivo-Morpholinos as therapeutic agents , is possible in an experimental model of JE . We intend to show that these specifically designed Vivo-Morpholinos are effective in countering the viral load in the body , thereby imparting significant protection to the animals that were infected with a lethal dose of JEV .
All animal experiments were approved by the institutional animal ethical review board named “Institutional Animal and Ethics Committee of National Brain Research Centre” . The animal experiment protocol approval no . is NBRC/IAEC/2007/36 . Animals were handled in strict accordance with good animal practice as defined by Committee for the Purpose of Control and Supervision of Experiments on Animals ( CPCSEA ) , Ministry of Environment and Forestry , Government of India . Vero cells ( a kind gift from Dr . Guruprasad Medigeshi , Translational Health Science and Technology Institute , Gurgaon , India ) and Neuro2A ( obtained from National Centre for Cell Science , Pune , India ) cells were grown in DMEM ( Dulbecco's modified Eagles medium , supplemented with 10% fetal bovine serum ( FBS ) and antibiotics . The GP78 strain of JEV was propagated in suckling BALB/c mice and their brains were harvested when symptoms of sickness were observed . A 10% tissue suspension was made in MEM ( minimum essential medium ) , followed by centrifugation at 10 , 000× g and finally filtered through a 0 . 22 µ sterile filter [19] . JEV was titrated by plaque formation on Vero cell monolayer . Vero cells were seeded in six-well plates to form semi-confluent monolayer in about 18 h . Cell monolayer were inoculated with 10-fold serial dilutions of virus samples made in MEM containing 1% FBS and incubated for 1 h at 37°C with occasional shaking . The inoculum was removed by aspiration and the monolayers were overlaid with MEM containing 4% FBS , 1% low-melting-point agarose and a cocktail of antibiotic–antimycotic solution ( Gibco , Invitrogen Corporation , Grassland , NY , USA ) containing penicillin , streptomycin , and amphotericin B . Plates were incubated at 37°C for 72–96 h until plaques became visible . To allow counting of the plaques , the cell monolayer was stained with crystal violet after fixing the cells with 10% formaldehyde . All Vivo-Morpholino ( MO ) oligos were commercially procured from Gene Tools LLC , ( Philomath , OR , USA ) . MOs were designed to be complementary to sequences in the JEV ( GP78 strain ) genome , as shown in Table 1 . These oligonucleotides targeted specific regions in the 3′ and 5′ UTRs of the JEV genomic RNA ( Figure 1 ) . A 21 base scrambled MO of random sequence ( SC-MO ) was used as a negative control in all the experiments . All MO sequences were screened with BLAST ( http://www . ncbi . nlm . nih . gov/BLAST ) against primate and murine mRNA sequences and the SC-MO was additionally screened against all flaviviral sequences . All MOs were procured in 300 nanomole quantities as a liquid of 0 . 5 mM stock ( approximately 4 mg/mL ) in buffered saline . They were diluted with sterile 1× PBS to achieve desired concentrations , and stored at 4°C as aliquots . Five to six weeks old BALB/c mice of either sex were randomly distributed into 5 groups- Sham , JEV-infected , JEV-infected and treated with scrambled Morpholino ( JEV+SC-MO ) , JEV-infected and treated with Morpholino against viral 3′ conserved region ( JEV+3′ MO ) and JEV-infected and treated with Morpholino against secondary structure in the 5′UTR of viral RNA ( JEV+5′ MO ) . Initially each group contained 8 animals . Animals belonging to all groups except Sham were infected with 3×105 plaque forming units ( PFU ) of JEV ( GP78 strain ) and that day was considered as day zero [20] . Animals of Sham group received equal volume of filtered MEM . Starting from 3 h post infection on day zero , 100 µg of SC-MO , 3′ MO and 5′ MO , diluted in 0 . 1 mL of sterile 1× PBS ( corresponding to 5 mg/kg body weight ) , were administered to animals belonging to JEV+SC-MO , JEV+3′ MO and JEV+5′ MO groups respectively , once per day , for 5 consecutive days . Animals belonging to the Sham-treatment group received equal volumes of sterile 1× PBS only . Survivality of animals in each group following JEV infection and Morpholino treatment were monitored daily upto 15 days post JEV infection ( or till their death , whichever was earlier ) . Toxicity of the Morpholinos in mice was evaluated by weight loss and abnormal behavioral & clinical observations ( including tremors , ruffled fur , hunching , ataxia , gait abnormalities ) , in a masked manner to minimize bias [14] , [20] . Mouse cytokine bead array ( CBA ) kits were used to quantitatively measure cytokine levels in mouse whole-brain lysates . 50 µL of bead mix containing a population of beads with distinct fluorescence intensities that have been coated with capture antibodies for different cytokines , and 50 µL of whole-brain lysates were incubated together , along with equal volume of phycoerythrin ( PE ) -conjugated detection antibodies , for 2 h at room temperature , in dark . The beads were then washed and re-suspended in 300 µL of supplied 1× wash buffer . The beads were acquired using Cell Quest Pro Software in FACS Calibur and analyzed using BD CBA software ( Becton Dickinson , San Diego , CA ) . Standard curve was prepared by incubating 50 µL of supplied mouse inflammation standards with 50 µL of bead mix and PE-conjugated detection antibodies [21] . Protein concentrations of whole brain lysates were estimated by Bradford method . Sample volumes containing 20 µg of protein were electrophoresed on polyacrylamide gel and transferred onto nitrocellulose membrane . After blocking with 7% skimmed milk , the blots were incubated overnight at 4°C with primary antibodies against JEV E-glycoprotein ( Abcam , USA ) , and JEV NS5 ( a kind gift from Dr . Chun-Jung Chen , Taichung Veterans General Hospital , Taichung , Taiwan ) , iNOS ( Upstate-Chemicon , USA ) , HSP-70 , SOD-1 ( Santa Cruz Biotechnology , CA , USA ) , TRX ( AB Frontiers , Korea; a kind gift from Dr . Ellora Sen , NBRC ) , phospho NFκB , phospho ERK1/2 , total ERK1/2 and phosphoP38 MAP kinase ( Cell Signaling , USA ) at 1∶1000 dilutions . After extensive washes with PBS–Tween , blots were incubated with appropriate secondary antibodies conjugated with peroxidase ( Vector Laboratories , CA , USA ) . The blots were again washed with PBS–Tween and processed for development using chemiluminescence reagent ( Millipore , USA ) . The images were captured and analyzed using Chemigenius , Bioimaging System ( Syngene , Cambridge , UK ) . The blots were stripped and reprobed with anti-β-tubulin ( Santa Cruz Biotechnology , USA ) to determine equivalent loading of samples [22] . For immunohistochemical staining , brains from scarified animals were excised following repeated transcardial perfusion with ice-cold saline and fixed with 4% paraformaldehyde . Twenty micron thick cryosections were made with the help of Leica CM3050S cryostat and processed for immunohistochemical staining to detect presence of JEV antigen in the brain and to label activated microglia . Sections were incubated overnight at 4°C with mouse anti-JEV antigen ( Nakayama , 1∶250 ) ( Chemicon , CA , USA ) and rabbit anti-Iba-1 ( 1∶ 500; Wako , Osaka , Japan ) , respectively . After washes , slides were incubated with FITC-conjugated anti-mouse or anti-rabbit secondary antibodies ( Vector laboratories Inc . Burlingame , USA ) and following final washes , sections were sections were cover slipped after mounting with 4′-6-diamidino-2-phenylindole ( DAPI , Vector laboratories Inc . ) . The slides were observed under Zeiss Axioplan 2 fluorescence microscope and Zeiss Apotome microscope ( Zeiss , Gottingen , Germany ) , respectively [21] . Cryosections of brain from Sham-treated , JEV-infected and JEV-infected and MO treated animals were rinsed in de-ionized water followed by incubation with the thionin dye . The excess dye was washed off and the slides were immersed in alcohol-dioxane ( 1∶1 ) solution for differentiation . After two changes in xylene the slides were mounted with DPX and observed under a Leica 4000 DB light microscope ( Leica Microsystems , USA ) [23] . The level of ROS produced within brain tissue of each treatment groups were measured by the cell permeable , non-polar , H2O2-sensitive probe 5 ( and 6 ) -chlromethyl-20 , 70-dichlorodihydrofluoresceindiacetate ( CM-H2DCFDA; Sigma , USA ) . CM-H2DCFDA diffuses into cells , where its acetate groups are cleaved by intracellular esterases , releasing the corresponding dichlorodihydrofluorescein derivative . Subsequent oxidations of CM-H2DCFDA yields a fluorescent adduct dichlorofluorescein that is trapped inside the cell . Brain homogenates were treated with 5 µM solution of CM-H2DCFDA followed by incubation in dark at room temperature for 45 min and then the relative fluorescence intensity were measured with the help of Varioskan Flash multimode reader ( Thermo Electron , Finland ) at excitation 500 nm and emission 530 nm . The fluorescence intensity of intracellular CM-H2DCFDA is a linear indicator of the amount of H2O2 in the cells . The measured mean fluorescence intensity was then normalized to equal concentrations of protein in each sample [23] . Nitric oxide released from brain homogenates following MO treatment was assessed using Griess reagent as described previously . Briefly , 100 µL of Griess reagent ( Sigma , St . Louis , USA ) was added to 100 µL of brain homogenate and incubated in dark for 15 min . The intensity of the color developed was estimated at 540 nm with the help of a Benchmark plus 96-well ELISA plate reader ( Biorad , CA , USA ) . The amount of nitrite accumulated was calculated ( in µM ) from a standard curve constructed with different concentrations of sodium nitrite [21] . Mouse neuroblastoma cells ( N2a ) were plated in five 60 mm plates at a density of 5×105 cells/plate , and were cultured for 18 h . After 6 h in serum free DMEM , cells were either mock-infected with sterile 1× PBS or infected with JEV at multiplicity of infection ( MOI ) of 5 . After 1½ h , cells were washed twice with sterile 1× PBS to remove non-internalized virus . Three of the four plates that were infected with JEV , were treated with SC-MO , 3′ MO and 5′ MO at 10 µM concentrations and all plates were incubated for 24 h in serum free media . After two washes with 1× PBS , cells were first fixed with BD cytofix solution ( BD Biosciences ) for 15 min and permeabilized by resuspending in permeabilization buffer ( BD Cytoperm plus; BD Biosciences ) and incubated at 25°C for at least 10 min . Cells were then washed twice in wash buffer ( PBS containing 1% bovine serum albumin ) then resuspended in wash buffer at 1×106 cells per 100 µL . Primary antibody ( JEV Nakayama strain; Chemicon , USA ) were added in 1∶100 dilutions and incubated for 30 min at 25°C . The cells were washed with wash buffer and pelleted by centrifugation followed by incubation with FITC conjugated secondary antibody for 30 min . After final wash with wash buffer , cells were resuspended in 400 µL FACS buffer and analyzed on a FACS Calibur . The percentage of population of JEV-positive cells was calculated after gating the populations on a Dot plot using Cell Quest Pro Software ( BD Biosciences ) . Statistical analysis was performed using SIGMASTAT software ( SPSS Inc . , Chicago , IL , USA ) . Data were compared between groups using one-way analysis of variance followed by post hoc test . Differences upto p<0 . 05 were considered significant .
MO treatment conferred significant protection to mice following JEV infection . The survival of mice following JEV infection was dramatically increased with treatments of both 3′ and 5′ MO . Approximately 90% of all the animals that were treated with 3′ MO survived as compared to 75% survival of those animals that were treated with 5′MO , post infection with JEV ( Figure 2A ) . Infection with JEV was accompanied with distinct symptoms and weight loss whereas treatments with both 3′ and 5′ MO post JEV infection , prevented animals from suffering . Not much considerable changes in the average body weights of JEV-infected animals treated with both 3′ and 5′ MO were observed when compared to animals belonging to JEV and JEV+ SC-MO groups showing significant reductions in their body weights 6 days post infection ( Figure 2B ) . The symptoms associated with JE in murine model were observed on daily basis and scores were attributed accordingly . The animals that had most symptoms received the highest scores . It was observed that 3′ and 5′ MO treated animals scored lesser than those belonging to the JEV-infected or JEV+SC-MO groups ( Figure 2C ) . To assess whether the MOs has any effect on reduction of viral load in brain , homogenized brain samples from all the treatment groups were subjected to plaque assay as described in materials and methods section . Number of PFU/mL of the brain homogenate was found to be significantly higher in both JEV and JEV+SC-MO groups when compared to Sham ( p<0 . 001 ) . Viral PFUs were found to be significantly reduced following 3′ MO and 5′ MO treatment when compared to only JEV-infected or JEV+SC-MO group ( p<0 . 001 ) ( Figure 3A ) . To further validate the results obtained from the plaque assay , immunoblot for some of the JEV-specific proteins were performed . The expression of NS5 , a non structural protein of JEV , was significantly increased in JEV and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) , but its level were found to be significantly reduced after both 3′ and 5′ MO treatments when compared to JEV-infected group ( p<0 . 01 ) . Similarly , E glycoprotein level showed significant increase in JEV-infected and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) which were then drastically reduced following 3′ and 5′ MO treatments ( p<0 . 01 ) ( Figure 3B–D ) . Immunostaining of brain sections showed greater presence of JEV antigen in JEV-infected and JEV+SC-MO groups , whereas 3′ and 5′ MO treatments resulted in lesser presence ( Figure 3E ) . To further characterize the inhibitory effects of MO on JEV-induced neuronal death , brain sections from all the treatment groups were subjected to thionin staining . Numerous healthy cells were seen in sections obtained from Sham , JEV+3′ MO and JEV+5′ MO groups when compared to sections belonging to only JEV-infected or JEV+SC-MO groups which contained numerous unhealthy/dying neurons with altered morphology ( Figure 4A ) . Microglial activation and increased proinflammatory cytokine production are the hallmarks of JEV infection [24] . To see whether MO treatment helps in vitiation of these effects , immunostaining for microglial specific marker Iba-1 was performed in brain sections of all treatment groups . In brain sections of JEV and JEV+SC-MO groups the number of activated microglia with characteristic morphology , appeared to be more frequent when compared to sections belonging to Sham , JEV+3′ MO and JEV+5′ MO groups ( Figure 4B ) . CBA performed to check the proinflammatory cytokines levels in the brain homogenates obtained from different treatments showed that levels of MCP-1 , IFN-γ , TNF-α , and IL-6 were found to be significantly increased in both JEV and JEV+SC-MO groups when compared to Sham infected groups ( p<0 . 01 ) . The elevated levels of these proinflammatory cytokines were drastically reduced with 3′ and 5′ MO treatments ( p<0 . 01 ) ( Figure 4C–F ) . Increased oxidative stress in CNS is a major outcome of JEV infection [20] . To evaluate whether MO treatment of mice resulted in abrogation of oxidative stress following JEV infection , we measured ROS and NO levels in brain homogenate obtained from all treatment groups . Two fold increases were observed in the ROS levels in the brain samples of JEV and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) , significant reduction in the ROS levels were observed in JEV+3′ MO and JEV+5′ MO groups when compared to only JEV-infected groups ( p<0 . 01 ) . Although ROS levels has decreased in JEV+3′ MO group when compared to JEV group , it remained significantly higher than that of Sham ( p<0 . 01 ) ( Figure 5A ) . Superoxide dismutase 1 ( SOD-1 ) and Thioredoxin ( TRX-1 ) are the proteins associated with oxidative stress . SOD-1 levels were found to be elevated approximately 2- and 3-fold in JEV-infected and JEV+SC-MO groups respectively when compared to Sham ( p<0 . 01 ) . Its levels in JEV+3′ MO and JEV+5′ MO groups were reduced significantly when compared to JEV-infected group ( p<0 . 01 ) . TRX-1 levels were also found to be increased significantly in JEV-infected and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) but it were significantly reduced in brain samples obtained from JEV+3′ MO and JEV+5′ MO groups when compared to only JEV-infected groups ( p<0 . 01 ) ( Figure 5B , D&E ) . HSP-70 is a heat shock protein that has been associated with intracellular stress . Significant twelve fold increases in the levels of HSP-70 were observed in JEV and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) , this drastic increases in the levels of HSP-70 in JEV and JEV+SC-MO groups were reduced in JEV+3′ MO and JEV+5′ MO groups ( p<0 . 01 ) ( Figure 5B&C ) . JEV infection leads to increased nitric oxide ( NO ) production in CNS [25] . Significant two fold increases were seen in the NO levels in brain samples obtained from JEV-infected and JEV+SC-MO groups when compared to those obtained from Sham ( p<0 . 01 ) . NO levels subsequently got down to significantly lower levels following 3′ and 5′MO treatments ( p<0 . 01 ) ( Figure 5F ) . Immunoblot analysis showed nearly 8-fold increases in levels of iNOS in JEV-infected and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) . iNOS levels showed significant decreases in JEV+3′ MO and JEV+5′ MO groups when compared to only JEV-infected groups ( p<0 . 01 ) ( Figure 5G&H ) . Western blot analysis demonstrated a significant inhibition in the expression of different stress related proteins whose levels were elevated following JEV infection . Upon MO treatments there were approximately 4-fold increases in the levels of pNFκB in JEV and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) . The levels of pNFκB were found to be significantly reduced in JEV+3′ MO and JEV+5′ MO groups when compared to only JEV-infected groups ( p<0 . 01 ) ( Figure 6A&B ) . Phospho p38 MAPK levels also showed significant 3-fold increases in JEV and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) , its levels were also found to be reduced significantly following treatment with 3′ and 5′ MO when compared to only JEV-infected groups ( p<0 . 01 ) ( Figure 6A&C ) . Both phospho ERK1 and ERK2 levels were found to be significantly increased in JEV and JEV+SC-MO groups when compared to Sham ( p<0 . 01 ) . The levels of phospho ERK1 and ERK2 showed considerable decreases in JEV+3′ MO and JEV+5′ MO groups when compared to only JEV-infected groups ( p<0 . 01 ) ( Figure 6A&D ) . To assess whether MO has any effect on viral load in vitro N2a cell lysates from all the treatment groups were subjected to plaque assay . PFU/mL of the cell lysates was found to be significantly higher in both JEV and JEV+SC-MO groups when compared to mock-infected cells ( p<0 . 01 ) . Viral loads were found to significantly reduced in both JEV+3′ MO and JEV+5′ MO groups when compared to only JEV-infected group ( p<0 . 01 ) ( Figure S1A ) . To further ascertain the results obtained from plaque assay , intracellular staining of JEV antigen in N2a was performed and number of JEV-positive N2a cells was then counted by flow cytometry . Only 16% and 9% of the total gated cells were found to be positive for JEV antigen in JEV+3′ MO and JEV+5′ MO groups respectively as compared to 30% in JEV-infected group , and 34% in JEV+SC-MO group ( Figure S1B ) .
Use of anti-sense molecules for targeted inhibition of viral replication has been under investigation for quite sometime . Though the application of these molecules has raised the possibilities of their future use as novel therapeutic agents , there are many issues regarding their effectiveness in terms of their stability and delivery to targeted cells . Recent studies are involved in developing techniques to minimize or eliminate these issues so that anti-sense therapy can be employed to a wide variety of intractable diseases such as splice-modifying genetic defects and viral diseases . The role of various anti-sense molecules in the inhibition of replication of JEV has been reported with positive outcomes [10] , [11] , [12] , [13] , [26] . Morpholino oligomers are single stranded anti-sense molecules that exert their action by steric blocking of complementary RNA . Unlike other types of anti-sense oligonucleotides , Morpholinos provide all the desired properties of stability , nuclease resistance , high efficacy , long-term activity , water solubility , low toxicity , and exquisite specificity . Morpholino oligomers has been used previously for the inhibition of flaviviral replication [14] , [27] including JEV [15] though all of them has utilized peptide based Morpholinos . The peptide based Morpholinos contain delivery moiety evolved from natural peptides whose active components are 6–9 arginine residues in a bio-available 6-aminohexanoic-spaced structure [28] . However , these arginine-based peptides are not commercially available for research purposes and their greatest efficacies have been in delivering Morpholinos to the cytosol of tissues like liver [29] or leaky muscle [30] , which would be considered as easily deliverable . As a result , the reach of peptide based Morpholinos into a wide spectrum of tissues remains questionable [18] . Also , owing to the peptidic nature , degradation of the peptide portion of the conjugates was found to be time and tissue dependent [31] . Furthermore , the applications of the arginine-rich peptide transporters are limited due to their high cost , scalability and stability . Added to that are the risks of immune responses against the peptides which limits repeated administrations for diseases requiring long-term treatment [32] . To minimize the problems encountered by the peptide-conjugated Morpholinos , octaguanidinium dendrimer-conjugated Morpholino oligomers have been developed that are commonly referred to as Vivo-Morpholino ( MO ) . These custom-sequence anti-sense molecules have been reported to enable Morpholino applications in adult animals . MO was our choice of anti-sense molecule as this enabled us to test the specifically designed oligonucleotides in both animal as well as cell culture models . Though ‘outstanding’ results have been reported to be achieved by intravenous ( i . v . ) administration of the MO , we preferred the intraperitoneal route via which modest systemic delivery can be achieved . This was so done because brain has been reported to be an ineffective tissue when MOs are administered i . v . [33] , though there is no direct evidence showing that MOs can cross blood brain barrier , when administered via other routes . According to the manufacturer's ( Gene Tools LLC ) instructions the maximum suggested dosage in mammals is 12 . 5 mg/kg in a 24 hour period . Our aim was to determine the minimum dose at which our desired effects could be achieved . Initially we had chosen two doses of 5 mg and 10 mg per kg body weight ( b . w . ) of the animals . We found that there was no significant difference between the survival rate of JEV-infected and MO treated mice in either dose ( data not shown for 10 mg/kg b . w . ) . The survival rate was approximately 90% in those JEV-infected animals that were treated with 3′ MO and 75% in animals treated with 5′ MO . Thus we decided to proceed with the 5 mg/kg b . w . dose for all subsequent experiments . Plaque assay from the brain homogenates of animals of all groups revealed that the number of infective viral particle production was dramatically reduced following 3′ and 5′ MO treatment . The 3′ MO was generated against the 3′ CSI region of the JEV genome that interacts with 5′ CS region located in coding sequence for capsid protein at 136–146 nucleotides from 5′ terminal of the genome . This interaction results in cyclization of JEV genome that is necessary for its efficient replication . The 5′ MO was targeted towards one of the secondary structures of the 5′ UTR that are required for the formation of translation pre-initiation complex . Blocking of these two sites in the JEV genome leads to the most likely effect , i . e . inhibition of replication and translation of viral genome . This was further corroborated by the decrease in the expressions of viral proteins ( NS5 , E glycoprotein and general flaviviral envelop protein ) in the brain . Flaviviral NS5 is known to possess guanylyltransferase activity that helps in the synthesis of methylated cap structure at the 5′ end of the viral genome that plays a crucial role in the translation and stability of mRNAs [34] . The JEV E glycoprotein is believed to be involved in viral adhesion and entry into host cells , hemagglutination , cellular tropism , viral virulence , and the induction of protective immune responses [35] . Decreased expression of these proteins indicates that viral replication and production of new infective viral particles are inhibited due to the MOs . Immunohistochemical staining for viral antigen also provided visual confirmation of the fact that JEV antigen was detected at much lower amounts in the brain following MO treatment . However , these data does not prove that MOs directly inhibit infective viral particle production in the brain itself , as it cannot be conclusively stated whether the MOs can reach brain . These data merely suggests that the number of replication-competent infective JEV in the brain was significantly reduced , which subsequently leads to neuroprotection . It is well known that JEV infection causes microglial activation . Activated microglia releases an array of chemical mediators that are detrimental for the neurons in brain [24] . Since there was reduction in the production of infective viral particles following 3′ and 5′ MO treatments , we studied the effect on microglial pathophysiology in mouse brain . Our results show that there was significantly reduced number of activated microglia in the brain sections of both 3′ and 5′ MO-treated animals as compared to only JEV-infected or JEV-infected and SC-MO treated animals . Since there were little or no activation of microglia , proinflammatory cytokine levels in the brain were found to be significantly downregulated . Histochemical staining also revealed that neuronal population and morphology remained largely unaffected in 3′ and 5′ MO-treated animals' brains as compared to only JEV-infected or JEV-infected and SC-MO treated animals . Generation of ROS with the generation of oxidative damage has been implicated in neurodegenerative diseases and in the degradation of nervous system functions and are also reported to increase following JEV infection [22] . Increase in ROS levels initiates various responses within the cell , including damage to proteins , DNA and lipid [36] . In this study , ROS levels were found to be many-fold increased in JEV-infected or JEV-infected and SC-MO treated animals that were then found to be counteracted by the treatment of 3′ and 5′ MO . The levels of stress related proteins such as SOD-1 , HSP-70 and TRX where also found to be positively modulated following 3′ and 5′ MO treatment . NO is a known antagonist of JEV . It has been shown that NO inhibits JEV infection by preventing viral replication [37] . In our study NO levels were increased in the brain in response to JEV infection possibly due to the upregulation of inducible nitric oxide synthase ( iNOS ) . Treatments with 3′ and 5′ MO caused a decrease of NO to basal levels as observed in Sham-treated animals . Activation of pNFκB regulates apoptotic genes , especially the TRAF1 and TRAF2 , and thereby checks the activities of the caspases , which are central to most apoptotic processes . JEV is known to activate pNFκβ via a PI3K-dependent pathway in the brain of infected animals , which is associated with apoptosis [38] . JEV infection has also been shown to activate stress kinases , which in turn results in activation of ERK1/2 , and p38 MAPK pathway leading to apoptotic death of neurons [39] . In accordance with the established results , here also we found that there was similar activation pattern of these molecules in JEV-infected and JEV-infected and SC-MO treated animal brain samples . Treatment with the MOs resulted in abrogation of those changes that led to greater survivality of brain neurons as observed by histochemical staining . Activation of p38MAPK is also related to the transcriptional activation of proinflammatory genes in the brain [40] . Thus the decrease in phophoP38 MAPK levels correlates with the decreased levels of proinflammatory cytokine levels in obtained from the brain . To confirm the anti-viral and neuroprotective property of the MOs observed in in vivo models , cultured neuroblastoma cells were infected with JEV , followed by MO treatment . Though the MOs are specifically developed for in vivo studies , they are also known to be taken up by cells in culture conditions [18] . There was a significant decrease in viral titer in samples obtained from the cells that were treated with 3′ and 5′ MOs as compared to either JEV-infected or JEV-infected and SC-MO treated cells , as revealed by plaque assay . This data was supported by FACS analysis following intracellular staining for JEV antigen . This study was undertaken to determine the antiviral and neuroprotective efficacy of Vivo-Morpholinos in an experimental model of JE so that it can be considered as a therapeutic agent in the near future . There have been studies regarding the anti-JEV effects of other types of Morpholino oligomers though none of them are yet to be considered for therapeutic purposes . This is the first study that investigates the role of Morpholino oligomers specially designed for effective delivery into live animal models . Generally , the i . p route of administration of any drug is preferred in animal studies over any other routes . However , the efficacy of these antisense molecules needs to be checked by administering through other applicable routes , as i . p . administration in humans is uncommon , though not unheard of . The amounts of oligomers required and the route of administration in this study marks these molecules as practicable therapeutic agents in JE , though further studies are required before these can be recommended for clinical trials . | Japanese encephalitis ( JE ) is caused by a flavivirus that is transmitted to humans by mosquitoes belonging to the Culex sp . The threat of JE looms over a vast geographical realm , encompassing approximately 10 billion people . The disease is feared because currently there are no specific antiviral drugs available . There have been reports where other investigators have shown that agents that block viral replication can be used as effective therapeutic countermeasures . Vivo-Morpholinos ( MOs ) are synthetically produced analogs of DNA or RNA that can be modified to bind with specific targeted regions in a genome . In this study the authors propose that in an animal model of JE , MOs specifically designed to bind with specific region of JE virus ( JEV ) genome , blocks virus production in cells of living organisms . This results in reduced mortality of infected animals . As the major target of JEV is the nerve cells , analysis of brain of experimental animals , post treatment with MOs , showed neuroprotection . Studies in cultured cells were also supportive of the antiviral role of the MOs . The potent anti-sense effect in animals and lack of obvious toxicity at the effective dosage make these MOs good research reagents with future therapeutic applications in JE . |
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The repair of DNA double-strand breaks must be accurate to avoid genomic rearrangements that can lead to cell death and disease . This can be accomplished by promoting homologous recombination between correctly aligned sister chromosomes . Here , using a unique system for generating a site-specific DNA double-strand break in one copy of two replicating Escherichia coli sister chromosomes , we analyse the intermediates of sister-sister double-strand break repair . Using two-dimensional agarose gel electrophoresis , we show that when double-strand breaks are formed in the absence of RuvAB , 4-way DNA ( Holliday ) junctions are accumulated in a RecG-dependent manner , arguing against the long-standing view that the redundancy of RuvAB and RecG is in the resolution of Holliday junctions . Using pulsed-field gel electrophoresis , we explain the redundancy by showing that branch migration catalysed by RuvAB and RecG is required for stabilising the intermediates of repair as , when branch migration cannot take place , repair is aborted and DNA is lost at the break locus . We demonstrate that in the repair of correctly aligned sister chromosomes , an unstable early intermediate is stabilised by branch migration . This reliance on branch migration may have evolved to help promote recombination between correctly aligned sister chromosomes to prevent genomic rearrangements .
Homologous recombination ( HR ) is a mechanism of DNA double-strand break repair ( DSBR ) that is conserved from bacteria to humans [1] . It involves resection of the broken DNA ends to generate single-stranded DNA overhangs , coated in a recombinase , which search the genome for homologous sequences and catalyse a reaction termed strand-invasion [2] . The product of strand-invasion is a joint molecule ( JM ) , containing multiple DNA duplexes and frequently comprised of D-loops and Holliday junctions ( HJs ) , also referred to as 3-way and 4-way DNA junctions , respectively . From the JM , DNA synthesis is established to restore the genetic information lost as a result of the break . Once synthesis is complete , the JM is resolved to generate the recombinant products of repair . When strand-invasion occurs between DNA sequences that are not fully homologous , such as between regions of repetitive DNA located on the same or different chromosomes , gross chromosomal rearrangements can occur . In higher organisms , where repetitive sequences are known to make up a substantial proportion of the genome , gross chromosomal rearrangements are associated with cancer [3] , [4] , [5] . This suggests that mechanisms exist for ensuring the correct pairing of sister chromosomes during HR . In order to gain further insight into the mechanism of HR , it is necessary to be able to detect different intermediates of repair as they are formed in live cells . To achieve this , it is desirable to work with a system for generating a site-specific DNA double-strand break ( DSB ) that can be efficiently repaired by HR with an unbroken sister chromosome . Such a system was described in 2008 in Escherichia coli [6] . This system uses an inducible hairpin endonuclease , SbcCD , to cleave a DNA hairpin that forms on the lagging-strand template following replication of a 246 bp interrupted palindrome that has been inserted into the chromosomal lacZ gene ( Figure S1 ) . Despite the fact that E . coli has a single origin of chromosomal DNA replication , this cleavage reaction generates a two-ended DSB at lacZ ( Figure 1A ) implying that cleavage occurs post-replication [6] . We distinguish the two sides of the break as origin-proximal ( OP ) and origin-distal ( OD ) , also labelled OP and OD in all relevant figures ( Figure S1 ) . The DSB was shown to be efficiently repaired by RecBCD-mediated HR ( Figure 1B ) [6] . In order to accumulate intermediates of repair generated by this system , it is necessary to prevent their resolution . In E . coli , the proteins RuvABC and RecG have been implicated in resolving intermediates of HR . HJs are branch migrated by RuvAB and resolved via cleavage mediated by RuvC [7] , [8] , [9] , [10] . Due to a strong synergistic effect of mutations in the ruv and recG genes in the efficiency of conjugational recombination , P1 transduction and survival following exposure to ionizing radiation and ISceI-mediated DSBs , a functional overlap of these proteins has been proposed , suggesting that RecG may also be implicated in resolving HJs [11] , [12] . Throughout this paper we use the term resolution in its general sense of converting a molecule containing HJs to one without ( i . e . resolution can be by branch migration , DNA replication , or cleavage ) . In support of a role of RecG in resolution , in vitro experiments have shown that both RuvABC and RecG process the same synthetic DNA junctions [13] , [14] . Additionally , in vivo suppression of ruv mutations , by expression of the cryptic HJ resolvase RusA , also requires RecG [15] . Furthermore , the Mycobacterium tuberculosis RecG homologue , MtRecG , was shown to process similar branched DNA junctions in vitro [16] . However , it is important to note that many different roles for RecG have been proposed in the literature . Early work has shown that RecG antagonises RecA-mediated strand-exchange [17] , [18] . This was puzzling given that RecG promotes recombination and led to the proposal that RecG might facilitate RecA-mediated strand-exchange from a 3′ invading substrate while antagonising strand-exchange from a 5′ invading substrate [17] , [19] . In subsequent work , it has been argued that RecG catalyses replication fork reversal following UV irradiation [20] and prevents over-replication caused by replication fork collision , by converting 3′ to 5′ single-strand flaps [21] , [22] , [23] , [24] . Whether or not these proposed activities relate to the synergy of recG and ruv mutations has not been clear , and the diverse consequences of a single recG mutation , as well as the ability of the purified protein to process many different substrates , have generated a complex picture of RecG's biological role . Using the palindrome-based system for inducing DSBR between sister chromosomes , we analyse the intermediates of repair accumulated in the absence of the ruv and recG genes to elucidate their function during DSBR and gain further insight into the precise mechanism of repair . We show that RuvABC is the main HJ branch migration and resolution complex in E . coli and that RecG is required for the formation of HJs , by converting 3-way DNA junctions ( D-loops ) to 4-way DNA junctions ( HJs ) . We go on to show that in the absence of both RuvAB and RecG , DNA is lost at the breakpoint due to an inability of a ΔruvAB ΔrecG mutant to catalyse branch migration . We conclude that branch migration , catalysed by either RuvAB or RecG , is essential for stabilising intermediates of DSBR by promoting the conversion of 3-way DNA junctions into 4-way DNA junctions , a conclusion that can explain the synergistic behaviour of ruv and recG mutants . We propose that this mechanism for stabilising intermediates favours DSBR reactions that occur between correctly aligned sister chromosomes , thus serving as a mechanism for ensuring correct pairing of sisters and , in turn , accurate repair of DSBs .
ruv and recG mutants have been shown to be sensitive to DNA damage and this sensitivity is exacerbated in ruv recG double mutants [11] , [12] . In accordance with these studies , DNA damage induced by SbcCD-mediated cleavage of a palindrome caused a loss of viability in single ΔruvAB or ΔrecG mutants that was severely exacerbated in the double ΔruvAB ΔrecG mutant ( Figure 2 and Figure S2 ) . Presumably , the decrease in viability is a consequence of the accumulation of toxic DNA repair intermediates that would normally be processed by these proteins . To detect these hypothetical repair intermediates and determine their structures , constructs containing three repeats of the crossover hotspot instigator , Chi ( χ ) , were integrated 1 . 5 kb either side of the palindrome in order to enrich for recombination intermediates in close proximity of the DSB ( Figure 1C ) . Subsequently , DNA from strains containing these constructs was isolated , digested with restriction endonucleases , and separated by two-dimensional ( 2D ) agarose gel electrophoresis; a useful technique for distinguishing between 3-way DNA junctions and 4-way DNA junctions ( Figure 3A ) . Three fragments surrounding the DSB were detected using radioactive probes ( Figure 3B ) . All membranes were exposed for the same amount of time and intermediates were quantified relative to linear DNA ( Figure 3C and S3 ) . As shown in Figures 3CII , an increase in intermediates was detected in ΔruvAB , ΔrecG and ΔruvAB ΔrecG mutants specifically in conditions in which DSBs were induced ( DSB+ ) relative to a very low background of spontaneous intermediates detected in the absence of induced breaks ( DSB− ) ( Figure S3 ) . A ΔruvAB mutant , accumulated a significant amount of 4-way junctions , presumably HJs , when DSBs were induced ( Figure 3CI; red arrows and 3CIII ) . This was not the case when DSBs were induced in either ΔrecG or ΔruvAB ΔrecG mutants ( Figure 3CI and 3CIII ) . As 4-way junctions accumulated in a ΔruvAB mutant but not in a ΔruvAB ΔrecG mutant , this suggests that RecG cannot simply be required for the resolution of 4-way junctions and must be required for their formation; presumably by catalysing the conversion of 3-way to 4-way junctions , an activity that has been reported for RecG in vitro [19] , [20] , [25] , [26] . It is interesting to note that the analysis of the ΔruvAB mutant reveals the existence of preferred configurations of branched DNA , which are seen as spots on the 2D gels ( Figure 3CI ) . The placement of these spots is reproducible suggesting that they reflect DNA structures that accumulate in preference to others . Further work is required to determine what these structures are and how they are formed . Spots on the 4-way junction spike may reflect asymmetrically placed single HJs or double HJs and spots on the 3-way junction arcs may reflect positions of preferential single-strand invasion or pausing of DNA synthesis . However , these 3-way junction spots do not simply correlate with the expected positions of single-strand invasion predicted by the positions of Chi ( χ ) sites . As 3-way junctions are expected to form early in the reaction via strand invasion , as well as later during re-synthesis of the broken DNA , further work is required to understand their provenance . 2D agarose gel electrophoresis is only suitable for analysing small chromosomal fragments ( 2–7 Kb ) . In order to determine whether intermediates of repair could be located across larger regions of the chromosome , pulsed-field gel electrophoresis ( PFGE ) was used as it allows the separation of big fragments of DNA . Additionally , branched DNA does not run into a pulsed-field gel ( PFG ) , but remains trapped in the wells , and this allows it to be separated from its linear counterpart [27] . Plugs containing chromosomal DNA were digested to release three fragments surrounding the DSB ( yagV , lacZ , and araJ ) ( Figure 4A ) . The total amount of DNA detected in these fragments ( the sum of the signal from the gel and the well ) was normalised to a control fragment , of a similar size , located on the opposite side of the chromosome ( cysN ) to account for differences in loading between samples . Additionally , the proportion of DNA that was retained in the wells of the gels was also measured as this DNA included the branched intermediates of repair ( Figure 4B–E ) . In conditions of no DSBs ( lanes 1 , 2 and 3 for each probe ) , little DNA , of all the fragments probed , was retained in the wells ( Figure 4B–E ) . A similar result was obtained when DSBs were induced in a recombination proficient strain ( Figure 4B; lane 4 for each probe ) . Upon inducing DSBs in a ΔruvAB mutant , a large proportion of the lacZ fragment , containing the DSB , was detected in the well of the gel whereas little of the yagV and araJ fragments appeared to contain branched DNA ( Figure 4C ) . In a ΔrecG mutant , DSB induction resulted in a small amount of branched DNA in all three fragments ( Figure 4D ) . Unexpectedly , analysis of the DNA extracted from a ΔruvAB ΔrecG double mutant showed that when DSBs were induced , a significant amount of the DNA at the breakpoint ( lacZ fragment ) was lost ( Figure 4E ) . It should be noted here that this result explained the low yield of DNA in the 2D gel analysis of the ΔruvAB ΔrecG double mutant . The reader should be aware that the DNA species obtained from the ΔruvAB ΔrecG mutant visualised using 2D gel electrophoresis ( Figure 3 ) , represent the minority of molecules recovered when DSBs were induced in that background . The lacZ probe lies between the palindrome and the OP 1 . 5 Kb 3x χ array , in a region of DNA predicted to be degraded pre-RecBCD-mediated loading of RecA and strand-invasion . Therefore , loss of DNA in this region may suggest an inability of this mutant to initiate DNA synthesis associated with repair . However , a significant loss of DNA was also detected in the OD araJ fragment , which lies beyond the OD 1 . 5 Kb 3x χ array . This profile suggests that the loss of DNA observed may not be due to an inability to re-establish DNA synthesis , but due to an inability to form repair intermediates close to the DSB . Interestingly , in the OP yagV fragment , there was no loss of DNA but a dramatic accumulation of branched DNA . 2D agarose gel electrophoresis confirmed this accumulation of intermediates but revealed that there was still no bias towards the accumulation of either 3-way or 4-way DNA junctions when DSBs were induced , as was seen with the same mutant in the DNA remaining at the locus of the breakpoint ( Figure 3C and Figure 5 ) . A ΔruvAB ΔrecG mutant , shown to lose DNA at the site of a DSB , is both unable to branch migrate and resolve HJs . In order to determine which of these activities is required to prevent the loss of DNA observed , a ΔruvAB ΔrecG mutant was compared to a ΔruvC ΔrecG mutant . A ΔruvC ΔrecG mutant still retains RuvAB and should therefore be able to catalyse branch migration . However , RuvAB cannot resolve HJs in the absence of RuvC , so HJs should remain unresolved in this background . The ability of RuvAB to catalyse branch migration in the absence of RuvC was confirmed by PFGE ( Figure 6 ) . A significant amount of branched DNA was accumulated in the wells of the PFGs in ΔruvAB and ΔruvC mutants ( Figure 6C ) , consistent with the hypothesis that HJs are only resolved when all components of the RuvABC complex are present . However , the branched DNA accumulated in a ΔruvAB mutant was located within the lacZ fragment containing the DSB , while in a ΔruvC mutant , branched DNA was detected in all three fragments surrounding the break . This is indicative of RuvAB-mediated branch migration being active in the absence of RuvC . Once this was verified , PFGE was used to check whether a ΔruvC ΔrecG mutant lost DNA in response to DSBs and to compare this to DNA loss in a ΔruvAB ΔrecG strain ( Figure 7 ) . In order to detect DNA located OP of the DSB and beyond the point of initial RecBCD-mediated loading of RecA and strand-invasion , a new probe , codB , that binds 8 . 5 Kb OP to the 3x χ array , was designed ( Figure 7A ) . Between the breakpoint and the codB probe , as well as the 1 . 5 Kb 3x χ array , there is an endogenous χ site located 5 Kb from the breakpoint , in the cynX gene . Assuming a 20%–35% probability of χ site recognition , these four χ sites should be responsible for between 59% and 82% of strand-invasion events [28] , [29] , [30] . As shown in Figure 7 , DNA hybridising to the codB probe was lost in a ΔruvAB ΔrecG mutant when DSBs were induced , consistent with the hypothesis that intermediates of repair are not stable in this background . Interestingly , this loss did not occur in a ΔruvC ΔrecG mutant . These results imply that the loss of DNA observed in a ΔruvAB ΔrecG mutant is due to an inability to branch migrate intermediates of repair , rather than an inability to resolve HJs , and this results in the destabilisation of repair intermediates .
Due to a synergistic effect of mutations in the ruv and recG genes , it had originally been argued that these proteins may provide alternative pathways for resolving HJs . We have corroborated the observation that mutations in both ruvAB and recG result in enhanced sensitivity to DSBs compared to the respective single mutations when DSBs are induced by SbcCD-mediated cleavage of a palindrome ( Figure 2 and Figure S2 ) . However , analysis by 2D agarose gel electrophoresis of the DNA at the DSB has confirmed that this enhanced sensitivity was not accompanied by an accumulation of HJs ( 4-way DNA junctions ) ( Figure 3C ) . This result argues against the view that RuvABC and RecG are simply redundant because they provide alternative pathways to resolve HJs . 4-way DNA junctions were indeed accumulated close to the DSB in a ΔruvAB mutant , consistent with a role of RuvAB in processing HJs ( Figure 3C ) . However , these 4-way junctions were not accumulated in proximity to the DSB in a ΔruvAB ΔrecG mutant , arguing that RecG is required for their formation . The use of PFGE for studying intermediates of DSBR revealed why 4-way DNA junctions were not accumulated close to the DSB in a ΔruvAB ΔrecG mutant . In the absence of both RuvAB and RecG , DNA was lost at the site of the DSB . This was accompanied by an accumulation of branched DNA over 30 Kb away from the breakpoint ( Figure 4 and 5 ) . For the DNA in the lacZ locus to be lost , and for intermediates of repair to be present in the yagV fragment , the OP DNA end must be processed , by RecBCD , from the lacZ fragment to the yagV fragment . This is surprising as RecBCD will encounter eight endogenous χ sites ( as well as the OP 3x χ array ) in the region of the chromosome between the DSB and the yagV fragment and should induce RecA-mediated strand-invasion as a result [31] . This suggests that in a ΔruvAB ΔrecG mutant background , the products of RecA-mediated strand-invasion are not stable , which allows RecBCD to process a region of the chromosome that would not be processed in a wild type context . χ sequences around the E . coli chromosome are distributed asymmetrically to limit DNA end processing by RecBCD on the OP side of a DSB [32] . The asymmetry detected for OP accumulation of branched DNA and OD loss of DNA in a ΔruvAB ΔrecG mutant reflects this asymmetry of endogenous χ sequences , strengthening the hypothesis that the degradation is mediated by RecBCD . There are eight endogenous χ sites between the break and the OP yagV fragment that itself contains two χ sites and only one endogenous χ site between the break and the OD araJ fragment that contains no χ sites . We conclude that in a ΔruvAB ΔrecG mutant the products of strand-invasion are transient and non-productive for repair due to an inability to branch-migrate 3-way junctions and form 4-way junctions . This leads to the disruption of the 3-way junctions and the formation of a new DNA end for RecBCD to process . When the next χ site is recognised , a new event of strand-invasion is initiated , which is once again disrupted by a lack of branch migration activity . Over time , the broken chromosome is degraded . We propose that in ruvABC+ recG+ cells , when sister chromosomes are correctly aligned , branch migration is facilitated and this stabilises intermediates of repair by promoting the formation of 4-way DNA junctions . This favours the accurate repair of DSBs . This interpretation is supported by the observation that the frequency of ectopic recombination is increased in recG mutant strains in a chromosomal direct repeat deletion assay [33] , [34] , [35] and in recombination between chromosomal and plasmid homologies [36] . In the direct repeat assay , this is the case unless the replicative helicase is compromised [33] , [35] . The redundancy we observe in the stabilisation of JMs can explain the synergistic defect caused by ruv and recG mutations and this no longer necessitates the previously proposed redundancy in HJ resolution . However , redundancy at this stage cannot be excluded . Furthermore , if RuvABC and RecG do not provide alternative pathways for the resolution of HJs , such pathways must nevertheless exist otherwise recG and ruv mutations would be epistatic . This has led us to consider again the evidence that recG and ruv provide two pathways for HJ resolution . The strongest evidence in favour of this hypothesis is the observation that suppressors of the UV sensitivity of ruv mutations cause activation of the cryptic HJ resolvase , RusA , and this suppression requires RecG [15] . The simplest interpretation of this result is that the branch migration activity of RecG translocates HJs to positions where they are cleaved by RusA . However , RusA is not expressed in the absence of the activating mutation , rus , and no HJ resolvases other than RusA and RuvC have been discovered in E . coli [37] . Furthermore the requirement for recG in the suppression of ruv by rus can now simply be explained by the destabilisation of JMs that we observe in a recG ruvAB double mutant . If JMs are not formed , then they cannot be resolved by RusA . This leaves the question of whether there exists a pathway to resolve HJs that is an alternative to cleavage by RuvABC . The genetics argue that this is so . Ruv mutants are only modestly recombination defective but recG ruv double mutants are as defective as recA . This is synergy , not epistasis , arguing that the presence of RuvABC or RecG can provide alternative ways of successfully catalysing recombination . If synergy is explained by redundancy of RuvAB and RecG at the stage of JM formation and RuvABC provides a way to resolve HJs then there must also be a way to resolve HJs in the absence of RuvABC . What is this route ? The observation that HJ resolution in the absence of RuvABC leads to substantial yields of chromosome dimers [11] , [27] demonstrates clearly that this pathway can generate crossover products and excludes models such as double HJ dissolution by branch migration that would produce only non-crossovers . It has been suggested that new rounds of DNA replication initiated at the chromosomal origin can sometimes pass through HJs and generate the resolved chromosomes [27] . To explain the synergy of recG and ruv , given the assumption that the activities were redundant for HJ resolution , it was suggested that RecG might facilitate this reaction . However , the results presented here open up the possibility that the replication forks that manage to pass through HJs may do so without the help of RecG . It is clear from our work that HJs accumulate in a ruvAB mutant , implying that they persist long enough to be detected and the data shown in Figure 6 argue that JMs are not resolved before they can be branch migrated by RuvAB . These data are not well explained by an immediate role of RecG in HJ resolution but are compatible with a delay of resolution in the absence of RuvABC as predicted if resolution is mediated by the next round of DNA replication initiated at the chromosomal origin . Many functions have been proposed for RecG , including the resolution of Holliday junctions [11] , [12] , replication fork reversal following UV irradiation [20] , conversion of 3′ flaps to 5′ flaps in the termination of replication [21] , [22] , [23] , [24] , destabilisation of RecA promoted strand exchange [17] , [18] and stabilisation RecA-promoted strand exchange [17] , [19] . Our results clearly demonstrate the importance of the role of RecG , as an alternative to RuvAB , in stabilising RecA-promoted strand exchange in DSBR . Many models for the repair of DNA DSBs have been proposed over the years and these are reviewed in detail by Pâques and Haber [38] . Some of the models predict the formation of 4-way DNA junctions , from 3-way DNA junctions , and some do not . Most models for the repair of two-ended DSBs in Saccharomyces cerevisiae implicate invasion of one DNA end followed by DNA synthesis that uncovers a region of homology to induce an event known as second-end capture . This can be processed to generate a double HJ intermediate that has been detected in vivo in meiotic and mitotic cells [39] , [40] , an intermediate that may be resolved by branch migration or HJ cleavage ( Figure 8 – HJ resolution ) . Alternatively , the invading strands can be ejected and re-annealed , prior to the completion of the double-HJ structure , in a reaction known as synthesis-dependent stand-annealing ( Figure 8B – SDSA ) , a mechanism that has the advantage of not generating crossover outcomes . If strand-invasion were to occur at short regions of homology , such as repetitive elements , rather than at correctly aligned sister chromatids or homologous chromosomes , second-end capture may be disfavoured . If it does occur , and resection proceeds beyond the region of homology , resolution by SDSA would minimise genome instability by ensuring non-crossover outcomes [41] . In S . cerevisiae , during the repair of a two-ended DSB in which second-end capture is prevented , the invading end can be repaired by break-induced replication ( BIR ) ( see [42] for a recent review ) . BIR has been shown to involve multiple rounds of strand-invasion in the initial phase of the reaction , consistent with repair-intermediate instability [43] . Furthermore , BIR is mutagenic consistent with a D-loop migration mechanism in which short-lived mismatches are not corrected but , instead , are copied in a conservative mode of DNA replication [44] , [45] , [46] ( Figure 8C ) . These observations suggest that second-end capture plays an important role in promoting accurate repair of two-ended DSBs . Indeed , second-end capture prevents BIR and promotes gene conversion through the operation of a recombination execution checkpoint ( REC ) that senses the proximity and orientation of the two recombining ends before DNA synthesis is initiated . When such ends are sensed , as is the case with a two-ended DSB , accurate repair is ensured and the outcome is directed towards gene conversion [47] . In contrast to DSBR in eukaryotes , in E . coli , DSBR involves extensive DNA degradation followed by the re-establishment of replication forks via the PriA-DnaB pathway of replisome loading [2] , [48] , [49] . This is understood to result in the formation of converging replication forks that restore the DNA between the two recombining ends ( Figure 1B ) . Within this model of DSBR , the stabilisation of intermediates by second-end capture should not be possible . We suggest that branch migration is an alternative to second-end capture for stabilising an intermediate that can be then converted to a 4-way DNA junction . The stabilisation of recombination intermediates by branch migration , which we have observed , is expected to work equally well for two-ended and one-ended DSBs . On the other hand , the stabilisation of intermediates determined in some way by second-end capture , by definition , cannot operate at one-ended DSBs . These types of DSBs do arise endogenously from replication forks that run into replication fork barriers , single-stranded DNA nicks or gaps , and from cleavage of reversed forks , and are thought to be the most common type of break encountered by all cells [50] , [51] , [52] . As second-end capture cannot be implicated as a mechanism for stabilising the intermediates generated from the repair of one-ended DSBs , this raises the intriguing question of how they can be stabilised in eukaryotic cells . The repair of one-ended sister chromatid breaks is distinguished from inter-chromatid BIR by the requirement of Rad51 , Rad52 , Rad54 and Rad59 [53] but little is known about the pathway of repair including how early intermediates are stabilised . One possibility is that some one-ended breaks await the formation of a second end produced by the firing of a replication origin situated on the other side of the causative lesion ( i . e . a two-ended break is generated from the sum of two one-ended breaks occurring one on each side of the same inducing lesion ( such as a persistent single-strand gap ) ) . The mechanism discovered here presents a solution adopted by E . coli that is expected to work equally well at one-ended and two-ended breaks . Repair of a DSB by HR with a sister chromosome has evolved to be accurate , despite the fact that genomes contain regions of repetitive sequence that could act as substrates for incorrect pairing . Here we show that the E . coli proteins RuvAB and RecG do not simply provide alternative pathways for the resolution of HJs , as previously suggested , but play redundant roles in stabilising recombination intermediates between sister chromosomes .
All strains used are listed in the supporting information . See Table S1 for a list of strains , Table S2 for plasmids used in the construction of the strains , Table S3 for oligonucleotides used in the construction of the plasmids and protocols S1 and S2 for methods used in the construction of the strains and plasmids . Overnight cultures grown in 5 ml L-broth were diluted to an optical density ( OD600nm ) of 0 . 02 and grow at 37°C with agitation to an OD600nm of 0 . 2 . The PBAD-sbcDC construct was induced by adding 0 . 2% arabinose . If PBAD-sbcDC was to be repressed as well as induced , the culture ( OD600nm of 0 . 2 ) was split in two and either 0 . 5% glucose or 0 . 2% arabinose was added . Cultures were put back at 37°C to grow for 60 minutes . Cells were harvested at 4°C and washed 2X in TEN buffer ( 50 mM Tris , 50 mM EDTA , 100 mM NaCl , pH 8 . 0 ) . Cells were re-suspended in TEN buffer to an OD600nm of 80 ( for 2D agarose gel electrophoresis ) or an OD600nm of 4 ( for PFGE ) and mixed with an equal volume of 0 . 8% ( for 2D agarose gel electrophoresis ) or 2% ( for PFGE ) low melting point agarose ( Invitrogen ) prepared in TEN buffer equilibrated to 50°C . The agarose/cell mix was poured into plug moulds ( BioRad ) and allowed to set . Plugs were treated in NDS solution ( 0 . 5 M EDTA , 10 mM Tris , 0 . 55 M NaOH , 36 . 8 mM lauroyl sarcosine; pH 8 . 0 ) supplemented with 1 mg/ml of proteinase K ( Roche ) and put at 37°C overnight . Fresh NDS + proteinase K was added for a second overnight and plugs were stored at 4°C in fresh NDS . To digest , a plug was washed in 1X restriction buffer for 6 hours , replacing the buffer every hour . The plug was placed in fresh 1X restriction buffer , supplemented with the restriction enzyme and incubated at 37°C overnight with rocking . A plug digested with a restriction enzyme was run in the first dimension in 1X TBE ( 89 mM Tris-borate , 2 mM EDTA ) on a 0 . 4% ( w/v ) agarose gel and run at 1 V/cm for 26 hours at 4°C . The lane was sliced out , rotated 90° , and set in the second dimension agarose ( 1% in 1X TBE supplemented with 0 . 3 µg/ml ethidium bromide ) . The second dimension was run at 6 V/cm for 10 hours at 4°C . The DNA was transferred to a positively charged nylon membrane by Southern blotting and cross-linked using UV-light . A plug digested with a restriction enzyme was run on a 1% ultra high gel strength agarose ( AquaPor ) prepared in 0 . 5X TBE and run on a CHEF-DR II PFGE ( BioRad ) at 6 V/cm for 10 hours at 4°C . Switch time was set to 5–30 seconds with an inclusion angle of 120° . The DNA was transferred to a positively charged nylon membrane by Southern blotting and cross-linked using UV-light . DNA was detected using 32P α-dATP incorporated ( using Stratagene Prime-It II random primer labelling kit ) into a PCR fragment . Probes were hybridised to membranes overnight at 65°C in 10 ml of Church-Gilbert buffer ( 7% SDS , 0 . 5 M NaH2PO4 , 1 mM EDTA , 1% BSA ) . Membranes were washed at 60°C in 2X SSC ( 1X SSC: 0 . 15 M NaCl , 0 . 015 M Na-citrate ) , supplemented with 0 . 1% SDS , for 15 minutes and then 0 . 5X SSC , supplemented with 0 . 1% SDS , for 30 minutes . Labelled membranes were exposed to GE healthcare storage phosphor screens and scanned using a Molecular Dynamics Storm 860 phosphor imager scanner . Images were quantified using GE healthcare ImageQuant TL . See Table S3 for the oligonucleotides used in the generation of the probes . | Genetic recombination is critically important for the repair of DNA double-strand breaks and is the only repair mechanism available to the bacterium Escherichia coli . Repair requires that the appropriate location on an unbroken sister chromosome is recognised as a repair template , and this can be accomplished by a system that detects the presence of extensive DNA sequence identity . We show here that the two known branch migration activities of the cell , RuvAB and RecG , provide alternative mechanisms for stabilising early recombination intermediates . In their absence , broken DNA is extensively degraded at the site of the break consistent with abortion of recombination . It has previously been proposed that RuvABC and RecG can substitute for each other in the resolution of four-way Holliday junctions , whereas we show that they play a synergistic role in the formations of these junctions . Our results demonstrate that branch migration provides a mechanism capable of stabilising recombination intermediates when extensive DNA sequence homology is available , a reaction that may contribute to ensuring that repair occurs at an appropriate location on a sister chromosome . |
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Yeast Npl3 is a highly abundant , nuclear-cytoplasmic shuttling , RNA-binding protein , related to metazoan SR proteins . Reported functions of Npl3 include transcription elongation , splicing and RNA 3’ end processing . We used UV crosslinking and analysis of cDNA ( CRAC ) to map precise RNA binding sites , and strand-specific tiling arrays to look at the effects of loss of Npl3 on all transcripts across the genome . We found that Npl3 binds diverse RNA species , both coding and non-coding , at sites indicative of roles in both early pre-mRNA processing and 3’ end formation . Tiling arrays and RNAPII mapping data revealed 3’ extended RNAPII-transcribed RNAs in the absence of Npl3 , suggesting that defects in pre-mRNA packaging events result in termination readthrough . Transcription readthrough was widespread and frequently resulted in down-regulation of neighboring genes . We conclude that the absence of Npl3 results in widespread 3' extension of transcripts with pervasive effects on gene expression .
Budding yeast Npl3 comprises two RNA binding domains ( RBDs ) and a C-terminal domain that is rich is Arg , Gly , Ser and Tyr residues . This structure shows similarities to the SR ( Ser-Arg rich ) class of metazoan pre-mRNA binding proteins [1 , 2] . Genetic and biochemical analyses have implicated Npl3 in many processes , including pre-mRNA splicing , polyadenylation , mRNA export and cytoplasmic translation [3–7] , as well as R-loop prevention and chromatin modification [6 , 8] . Transcription termination of RNA polymerase II ( RNAPII ) occurs by polyadenylation-dependent and polyadenylation-independent pathways , correlated with whether the transcript is coding or non-coding ( reviewed in [9 , 10] ) . Termination of mRNAs , requires two complexes termed cleavage and polyadenylation factor ( CPF ) and cleavage factor ( CF ) . Together , the CPF and CF complexes facilitate cleavage of the nascent RNA strand and removal of the elongating polymerase , resulting in a polyadenylated RNA product . Two mechanisms have been reported for these processes , which are likely to occur in combination . In the ‘torpedo’ pathway , the nascent RNA molecule is cleaved at the polyA site and the released 3’ fragment of the transcript still bound by RNAPII is degraded by the 5’-3’ exonuclease Rat1 . This is proposed to then destabilize the polymerase complex . A second “allosteric” mechanism leads to the elongating polymerase being disengaged from the nascent transcript downstream of the polyA site due to , poorly understood , conformational changes concomitant with assembly of the CPF-CF complex . Notably , analyses on reporter constructs indicated that Npl3 can act as an anti-terminator , by antagonizing cleavage factor 1 ( CF1 ) binding and thus restricting the use of cryptic poly ( A ) sites [4 , 7 , 11] . In addition to mRNAs , RNAPII also transcribes several classes of non-protein coding RNAs ( ncRNAs ) and the majority of these terminate by polyadenylation-independent pathways . These ncRNAs include the small nucleolar RNAs ( snoRNAs ) , 73 of which function in yeast ribosome synthesis , four small nuclear RNAs ( snRNAs ) that form the core of the pre-mRNA spliceosome , as well as diverse long ncRNAs ( lncRNAs ) such as the cryptic unstable trancripts ( CUTs ) . The snoRNAs are processed from pre-snoRNAs that can be independently transcribed , cleaved from polycistronic transcripts , or excised from pre-mRNA introns . Independently transcribed snoRNAs , snRNAs and CUTs are all thought to predominately terminate via a pathway that requires RNA-binding by Nrd1-Nab3 complex and the Sen1 helicase ( together termed the NNS complex ) [12–19] . Termination of snoRNAs and CUTs by the NNS complex is associated with recruitment of the TRAMP and exosome complexes to the nascent RNA [14 , 20–22] . The TRAMP complex tags RNAs by the addition of a short 3' oligo ( A ) tail , and directs target RNAs to the nuclear exosome for degradation [23–26] . This can result in either complete degradation of the RNA , in the case of CUTs , or the processing of long precursor snoRNAs to the shorter , mature form [27] . However , some snoRNAs can also be terminated by mRNA 3’ cleavage factors , with [20 , 28] or without [29] subsequent polyadenylation . In addition , surveillance factors can influence termination , since loss of exosome activity leads to defects in NNS termination [30–33] . Moreover , gene-length correlates with the termination pathway used , probably via changes in the phosphorylation state of RNAPII [34 , 35] and/or histone H3 , lysine 4 trimethylation [36] , both of which can promote NSS termination . Prior data indicate that a proportion of RNAPII transcription events terminate early on protein-coding genes [37–40] . These promoter proximal ncRNAs or “sCUTs” [39] are oligoadenylated , presumably by the TRAMP complex [37] , and targeted for turnover by the nuclear surveillance machinery . To better understand the in vivo functions of Npl3 , we determined its RNA binding profile , and identified changes in RNA abundance and RNAPII association when the NPL3 gene is deleted . The absence of Npl3 resulted in transcriptional termination defects at diverse RNAs , with readthrough observed on large subsets of both mRNAs and ncRNAs . These termination defects appear to cause widespread changes in gene expression , both through inappropriate termination and through transcriptional interference at neighboring genes .
To identify direct RNA targets of Npl3 binding , we performed in vivo UV cross-linking and analysis of cDNAs ( CRAC ) [41] . The endogenous NPL3 gene was tagged with an N-terminal ProteinA-TEV-His6 ( PTH ) tag , retaining the intact , endogenous NPL3 promoter . This construct supported wild-type growth as the sole source of Npl3 ( S1A Fig ) , indicating that the fusion protein is functional . Yeast cells were UV irradiated while actively growing and PTH-Npl3 was isolated , Npl3-bound RNA fragments were purified , converted to a cDNA library and sequenced by next generation sequencing ( all sequence data are available from GEO under accession number GSE70191 ) . S1B Fig shows expression of the tagged protein and an autoradiogram of labeled , associated RNAs . Npl3 binding sites were most frequent on mRNAs , consistent with previous studies [40 , 42 , 43] , but were also identified on several classes of ncRNA , including rRNAs , tRNAs , snRNAs and snoRNAs , as well as lncRNAs , including CUTs , stable unannotated transcripts ( SUTs ) and other unannotated transcripts apparently derived from intergenic regions or antisense transcription . The distribution of Npl3 across RNA classes is shown for two independent CRAC experiments in Fig 1A . In both datasets , Npl3 binding was predominately on RNAPII transcripts . The distribution of Npl3 along transcripts showed distinct patterns for different classes of RNA . On mRNAs , Npl3 binding was highest in the 5’ end region ( Fig 1B ) , consistent with other recent RNA-crosslinking data [40] . A previous ChIP analysis , in which Npl3 is crosslinked to chromatin , found Npl3 enriched at 3' ends [6] . This apparent discrepancy may reflect differences in Npl3 binding at the 5' and 3' ends of genes , with direct RNA binding occurring predominantly at the 5' end , and stronger association with the transcription and processing complexes at the 3' end . Similar 5’ enrichment was reported for nuclear surveillance factors including Nrd1 , Nab3 and Mtr4 as well as for RNAPII , and has been proposed to reflect a substantial level of premature transcription termination [37 , 38 , 40] . As described above , these promoter proximal lncRNAs are oligoadenylated by the TRAMP complex , and we therefore mapped the association of Npl3 with RNAs carrying non-encoded oligo ( A ) tails [37] . Among RNA fragments recovered in association with Npl3 , 24–28% carried oligo ( A ) tails , depending on the individual CRAC experiment , indicating that Npl3 frequently binds across the junction between truncated mRNAs and oligoA tails . Note that the total fraction of Npl3 target RNAs that are oligoadenylated is likely to be higher , as only a small region of each transcript is sequenced . In contrast , only 4–4 . 7% of RNAs bound by RNAPII were oligoadenylated ( see below ) . Fig 1C shows the distribution of Npl3 bound hits containing oligo ( A ) tails across different RNA classes for two independent CRAC experiments . The distribution of oligo ( A ) tails in Npl3 target sequences was similar to the overall distribution of hits on mRNAs ( Fig 1D ) . This indicated that Npl3 is frequently bound to degradation substrates or intermediates , including prematurely terminated mRNAs , and suggests that it may function with surveillance factors to mediate early transcription termination and/or RNA degradation . Npl3 is known to be required for efficient splicing of ribosomal protein gene ( RPG ) pre-mRNAs [3] . Consistent with this , we found that Npl3 strongly accumulated on introns of these pre-mRNAs relative to other intron-containing pre-mRNAs ( S1C Fig ) . On non-RPG , intron-containing pre-mRNAs , the binding of Npl3 dropped sharply at the 5’ end of the intron . The lower recovery of introns relative to mature message indicates that Npl3 remains bound to mRNAs after splicing ( S1D Fig ) . The distribution of Npl3 over the CUT class of lncRNAs was similar to that observed for mRNAs with strong enrichment towards the 5' end ( Fig 1E ) , consistent with the proposal that initial cotranscriptional packaging of pre-mRNAs and lncRNAs is similar [37] . In marked contrast , Npl3 binding was enriched towards the 3' end of snoRNAs ( Fig 1F ) , suggesting a role in transcription termination and/or 3' end processing of these ncRNAs . Overall , our RNA binding site data suggest that Npl3 is involved in surveillance and/or transcription termination of both mRNAs and ncRNAs . Motif analysis did not identify a specific Npl3 binding site . We note , however , that the four most overrepresented 4-nucleotide motifs each contain a U-G sequence ( S1E Fig ) . Npl3 , and particularly RRM2 , was reported to show strong in vitro binding to U+G rich sequences including U-G dinucleotides [46] . To identify functional targets of Npl3 , we assessed the transcriptome-wide effects of the loss of the protein on steady-state RNA levels , using strand-specific tiling arrays . Npl3 was reported to be highly abundant ( 78 , 700 copies per cell ) [47] and has many different targets , which might show differential binding to residual Npl3 following depletion or relocation . We therefore analyzed the effects of deletion of the NPL3 gene . Tiling array analyses and RNAPII crosslinking were determined using two independent strains in which NPL3 was deleted immediately prior to the commencement of the experiments . Wild-type ( WT ) and npl3Δ strains were grown to logarithmic phase , RNA was extracted and reverse transcribed to make cDNA , which was then hybridized to tiling arrays . Normalized probe intensity data for all detected transcripts can be found in S1 Table . Total RNA was extracted from WT and npl3Δ yeast strains , and equal amounts of cDNA were hybridized to strand-specific tiling arrays . Differential expression analysis identified 1391 mRNAs with significantly altered expression ( adjusted p-value <0 . 05 ) , of which 1229 were decreased and 162 were increased ( Fig 2A and 2B ) . S2 Table shows differential expression analysis for all mRNAs , snoRNAs , CUTs and SUTs . The opposite effect was observed for CUTs , with 410 showing significantly increased expression , and only 8 showing significantly decreased expression ( Fig 2C and 2D and S2 Table ) . Increased expression was also observed for snoRNAs; 33 showed significantly altered expression , 31 of which were increased in the mutant strain ( Fig 2E and 2F and S2 Table ) . To gain an understanding of how lack of Npl3 might lead to a global decrease in mRNA abundance , we ranked all mRNAs by log2 fold change in the mutant compared to the WT strain , according to the differential expression analysis ( S2 Table ) . We then focused our analyses on the 30 most down-regulated genes in the npl3Δ strain , and examined their genomic environment ( Table 1 ) . As expected , the most down-regulated gene was NPL3 , which is absent from the genome and was discounted from the analysis . We found that 15/30 ( 50% ) of down-regulated genes reside in a convergent orientation with an expressed protein coding gene . A previous analysis found that only 6% of all yeast genes reside in convergent orientations in which both genes are expressed [48] . The proportion of convergent mRNAs with reduced expression in npl3Δ strains was therefore unexpectedly high . At 11 of the 15 convergent mRNA loci ( 73% ) , the down-regulated gene is adjacent to a gene that showed clear transcription readthrough , suggesting that their expression is blocked by transcriptional interference . An additional nine down-regulated mRNAs are convergent with an ncRNA that showed transcription readthrough . A further four down-regulated mRNAs are located in tandem with an upstream gene that shows readthrough , while seven mRNAs are apparently down-regulated by both tandem and convergent readthrough . Three of the 30 most down-regulated genes do not appear to be inhibited by convergent or tandem readthrough , or by intergenic transcription . Of these , YJR015W is seemingly down-regulated due to transcription changes over a local chromosome domain , since both upstream tandem genes are also down-regulated , while FMP48 and TPO4 are down-regulated by unknown mechanisms . Although mRNA expression was most frequently decreased in npl3Δ strains , several mRNAs were up-regulated . We examined the genomic environment for the top 30 up-regulated genes ( S3 Table ) . Eleven of these correspond to spliced ribosomal protein genes , and increased intron signal in the npl3Δ strain accounts for the differential expression . A further eleven up-regulated genes showed increased readthrough from upstream mRNAs or ncRNAs , suggesting that apparent increased expression is due to readthrough signal from the neighboring gene rather than specific up-regulation . The remaining eight genes ( HSP12 , DDR2 , HES1 , YDR124W , YML007C-A , ALP1 , PUG1 and YCL049C ) are apparently specifically up-regulated in npl3Δ strains . To investigate whether the gene expression changes observed are indeed due to transcriptional interference , we more closely analyzed two strongly down-regulated mRNAs: THO1 and PTC7 ( Figs 3 and S2 , respectively ) . In Fig 3A , panels II and III show tiling array expression data for two biological replicates of the npl3Δ strain ( upper ) and WT ( lower ) strains in a genome viewer format . Panels I and IV show corresponding data for the association of RNAPII with the nascent transcript as determined by UV-crosslinking and analysis of cDNAs ( CRAC; see below ) . Features on the Watson strand are shown above the chromosomal nucleotide numbers and features on the Crick strand are shown below . Apparent readthrough from the VHR2 gene is associated with strong down-regulation of THO1 , which encodes a nuclear pre-mRNA binding protein ( Fig 3A ) . Strand-specific reverse transcription ( RT ) , followed by qPCR confirmed that the VHR2 gene was indeed extended , and that the increased downstream expression was not a distinct transcription product ( Fig 3B ) . Quantification by RT-qPCR indicated that 3’ extended VHR2 is elevated ~5 fold , whereas THO1 expression is reduced ~5 fold . The approximate positions of RT primers and qPCR amplicons are shown by green arrows and red lines , respectively , in Fig 3A . Similar analysis of the UPF2-PTC7 region revealed that apparent readthrough from the UPF2 gene is associated with strongly reduced expression of PTC7 , encoding a Type 2C serine/threonine protein phosphatase ( PP2C ) ( S2A Fig ) . In this case , RT-qPCR quantification revealed ~10 fold elevated readthrough from UPF2 , associated with ~6 fold suppression of PTC7 expression ( S2B Fig ) . This suggests that transcription termination defects in the npl3Δ strain lead to changes in expression of surrounding genes . It remained possible that changes in RNA abundance for the npl3Δ strain observed in tiling array and RT-qPCR data might reflect reduced pre-mRNA surveillance and degradation rather than altered transcription . To discriminate between increased readthrough and RNA stabilization , we assessed changes in RNAPII occupancy following loss of Npl3 . To do this , we used CRAC to crosslink RNAPII to the nascent transcript , which provides genome-wide , strand-specific , nucleotide resolution mapping data in vivo in growing cells . The CRAC technique was applied using strains in which the largest subunit of RNAPII , Rpo21 , carried a C-terminal , His6-TEV-Protein A ( HTP ) tag , as recently described ( Milligan et al . , submitted ) . Tagged Rpo21 was well expressed in WT and npl3Δ strains , and was shown to crosslink efficiently to RNA ( S3A Fig ) . Total RNAPII occupancy across different classes of RNA was largely unchanged between the WT and npl3Δ strains ( S3B Fig ) . However , significant differences in the location of RNAPII were observed for individual genes . In Figs 3A and S2A , blue plots show Rpo21 occupancy in WT yeast and red plots show occupancy in npl3Δ . The density of RNAPII was highest at the 5’ ends of most protein-coding genes , consistent with published NET-seq data that maps the transcribing polymerase by sequencing 3' ends of associated nascent transcripts [38] , and with the distribution of pre-mRNA binding factors , including Npl3 ( [37 , 40] and Fig 1 ) . Differences in RNAPII occupancy at the two convergent loci are summarized in Figs 3C and S2C . RNAPII occupancy within the VHR2 ORF was comparable between the two strains , and RNA accumulation was very similar in the mutant and WT strains ( Fig 3A and 3C ) . However , in the CUT557 region immediately downstream , RNA accumulation was increased 4 . 7 fold while polymerase occupancy was increased 1 . 8 fold in npl3Δ . RNAPII crosslinking in the region between CUT557 and the downstream gene HOR2 was also elevated by 2 . 1 fold in the mutant , indicating that transcriptional readthrough extends into this region . HOR2 itself appears to be inhibited by transcriptional interference acting in tandem , as shown by decreased RNA accumulation ( to 30% of WT ) , and polymerase occupancy ( decreased to 50% of WT ) . The THO1 transcript is greatly reduced in npl3Δ ( to 10% of WT ) , with polymerase occupancy reduced to 20% of WT . Analysis of expression and RNAPII occupancy over the UPF2-PTC7 locus also confirmed UPF2 readthrough and PTC7 down-regulation ( S2A and S2C Fig ) . In addition , RNAPII density was decreased over the downstream PPE1 gene . This indicates that the transcriptional readthrough from UPF2 also inhibits expression of this tandem , flanking gene . Down-regulation of PPE1 can only be determined from the RNAPII occupancy data and is not evident from tiling array data as the PPE1 signal is obscured by the UPF2 readthrough signal . This demonstrates the difficulty in discriminating down-regulation due to readthrough in tandem . We conclude that transcriptional readthrough of multiple mRNA genes results in down-regulation of downstream convergent and tandem genes . To determine whether correctly processed and polyadenylated mRNAs are also produced from genes showing transcriptional readthrough , we analyzed the 3' end of UPF2 in WT and npl3Δ by cleavage with RNase H using an oligo hybridizing ~250 nt upstream of the UPF2 annotated 3’ end . Cleavage reactions were performed with the gene-specific oligo , with and without the addition of oligo ( dT ) to deadenylate the cleavage product ( S2D Fig ) . We observed substantially less mature polyadenylated UPF2 mRNA in the mutant ( lanes 1 and 2 , compared to 4 and 5 ) , but the adenylation pattern was apparently the same ( lane 2 compared to 5 ) . This indicates that cleavage and polyadenylation of UPF2 mRNA is reduced in the npl3Δ strain , but the location of the residual activity is unaltered . The tiling array data indicate that expression of the CYC1 gene is down-regulated in npl3Δ due to transcriptional readthrough from the convergent gene UTR1 ( Table 1 ) . CYC1 encodes cytochrome C and transcription is up-regulated on glycerol medium . WT and npl3Δ strains were grown in either glucose or glycerol medium and the level of CYC1 mRNA was quantified by RT-qPCR ( Fig 3D ) . On glucose medium CYC1 was reduced ~5 . 9 fold in npl3Δ relative to WT , validating the findings of the tiling array . However , CYC1 abundance was increased 4 . 1 fold when the npl3Δ strains were transferred to glycerol medium , resulting in an expression level close to WT . In contrast , the level of THO1 was not increased by transfer of the npl3Δ strain to glycerol medium ( Fig 3D ) . This demonstrates that CYC1 expression remains subject to specific transcription regulation in the absence of Npl3 . Npl3 was crosslinked to ncRNAs ( Fig 1 ) and the npl3Δ mutation altered the expression of ncRNAs including CUTs and snoRNAs ( Fig 2 ) , suggesting that the loss of Npl3 might also affect transcription termination on ncRNA genes . Previous work identified genes that are regulated by upstream CUTs , which inhibit transcription of the downstream mRNA , including the nucleotide biosynthesis factors ADE12 and URA2 [49] . In npl3Δ strains , CUT680 upstream of URA2 and CUT324/325 upstream of ADE12 were accumulated , accompanied by reduced expression of the downstream protein-coding gene ( Fig 4A–4C ) . Metagene analyses show increased polymerase density at the 3' ends of CUTs , and immediately downstream , in npl3Δ compared to WT ( Fig 4D ) . These data suggest that that Npl3 is required for normal termination of CUTs , and that without proper termination these normally unstable transcripts are not efficiently turned over by the nuclear RNA surveillance machinery . Inspection of microarray data revealed 3’ extensions for many snoRNAs in npl3Δ strains . All H/ACA and C/D box snoRNAs were included in the analysis and , strikingly , we observed extended 3’ ends for 46 of the 51 RNAPII transcribed , monocistronic snoRNA genes , and for all five polycistronic pre-snoRNA transcripts . One gene ( SNR13 ) could not be interpreted due to missing probes ( Tables 2 and S4 ) . Another , SNR52 , is the sole snoRNA transcribed by polymerase III , and is therefore terminated through a different pathway . This leaves just three RNAPII transcribed snoRNAs that do not show readthrough: U3B ( SNR17B ) , SNR63 and SNR85 . Metagene analyses of the Rpo21 CRAC data showed increased RNAPII association towards the 3' ends of all snoRNAs in npl3Δ strains ( Fig 5A ) . Examples of extended snoRNAs are shown in Figs 5 and S4 . The box C/D snoRNA snR60 is extended approximately 500 nt in npl3Δ and appears to terminate about 100 nt into the downstream UBX6 gene ( Fig 5B ) . The presence of extended snR60 was confirmed by northern blot ( Fig 5C ) . S4 Fig shows extension of the box H/ACA snoRNA snR3 , determined by tiling array , and RNAPII occupancy data ( S4A Fig ) and confirmed by RT-qPCR ( S4B Fig ) . Comparison of expression and RNAPII occupancy at this locus is shown in S4C Fig . The snR3 transcript appears to be extended greater than 1000 nt downstream with transcription proceeding through downstream , annotated CUT genes ( CUT221/222/223 ) . In some cases , extension of snoRNA genes was associated with strongly reduced expression of neighboring genes . As an example , SNR3 readthrough correlates with reduced expression of EFM3 ( S4A–S4C Fig ) . Some snoRNAs appear to be extended many kilobases , apparently utilizing the termination site of the next downstream protein gene . To confirm that snoRNA 3’ extensions result from transcriptional readthrough , we calculated “readthrough scores” for three snoRNAs ( SNR11 , SNR30 and SNR60 ) that appeared to be extended based on tiling array data , as well as SNR17B that did not appear to be extended . We calculated the sum of all RNAPII hits in the 500 nt 3’ flanking region , relative to the sum of all hits within the snoRNA sequence , and compared this ratio for the WT and npl3Δ strains . For the extended snoRNAs , Rpo21 hits in the 3’ flanking region hits were elevated 1 . 16 to 2 . 17 fold in npl3Δ , but reduced to 0 . 84 fold of the WT for SNR17B ( Fig 5D ) . Overall , the magnitude of RNAPII occupancy changes downstream of snoRNAs in npl3Δ relative to WT is much less than changes in expression . We suggest that the extended snoRNA transcripts predominately reflect defects in RNA surveillance rather than processing/maturation , as we found the abundance of mature snoRNAs to be comparable in the npl3Δ mutant and WT strains ( S4D Fig ) . Many snoRNAs harbor a cleavage site for the endonuclease Rnt1 ( RNase III ) positioned downstream of the mature 3’ end ( reviewed in [50] ) . Cotranscriptional cleavage by Rnt1 provides an entry site for 3’-exonuclease processing back to the mature 3’ end of the snoRNA , and also allows the 5’ exonuclease Rat1 to degrade the nascent transcript and terminate the transcribing polymerase [51–58] . We therefore predicted that snoRNAs possessing 3' Rnt1 cleavage sites would not exhibit readthrough in npl3Δ strains . Unexpectedly , however , there was no apparent correlation between readthrough transcription in the npl3Δ strain and the presence or absence of reported Rnt1 cleavage ( S4 Table ) . No extension was seen on any of the RNAPII transcribed snRNAs ( U1 , U2 , U4 or U5 ) in the npl3Δ strain ( Table 2 ) . It had appeared that snRNAs and snoRNAs utilize related termination pathways [59] and a recent study found extended forms of both snoRNAs and snRNAs in strains lacking Rrp6 [31] . Furthermore , as for snoRNAs , Rnt1 cleavage sites flank the U1 , U2 , U4 and U5 genes [50] . However , despite these apparent similarities , there are clear differences in their requirement for Npl3 . Strains lacking Npl3 show transcription readthrough on protein coding genes , on which termination generally requires the cleavage and polyadenylation machinery , and on ncRNA genes that are terminated by the Nrd1-Nab3-Sen1 ( NNS ) complex . The NNS complex is implicated in termination of CUTs , snoRNAs and some mRNAs and physical interactions have been reported between Npl3 and the NNS components [60 , 61] . We therefore investigated whether this complex is properly recruited in npl3Δ . RNA crosslinking by Nab3 was more efficient than by Nrd1 , so we focused our analyses on this protein . To assess recruitment of the NNS complex we applied the CRAC approach to Nab3-HTP . The npl3Δ strain expressing tagged Nab3 grows very slowly ( doubling time 6h ) , indicating a negative genetic interaction . However , Nab3-HTP was well expressed in npl3Δ and crosslinked to RNA with even greater efficiency than in the WT ( S5A Fig ) . Crosslinking of Nab3 to different RNA classes was similar in npl3Δ and WT strains ( S5B Fig ) . Nab3 , like Npl3 , binds strongly at the 5' ends of mRNA transcripts ( S5C Fig ) and showed a substantial frequency of non-templated oligo ( A ) tails ( 36% in two experiments ) consistent with active surveillance in this region . Inspection of the VHR2-THO1 convergent gene locus ( Figs 3 and 6A ) revealed strong peaks of Nab3 binding at the 5’ ends of VHR2 and THO1 , reflecting the role of NNS in early termination on protein coding genes . In the npl3Δ strain the peak at the 5’ end of VHR2 was unaltered , whereas the peak on THO1 was lost due to transcription interference . A peak of Nab3 towards the 3’ end of CUT557 presumably reflects the known role of NNS in CUT termination . Notably , this peak was increased when Npl3 is absent , corresponding with the increased CUT557 expression . We conclude that the VHR2-CUT557 readthrough transcripts are likely to be terminated by the NNS pathway rather than by the CPF-CF pathway . Nab3 binding across CUTs was strongly increased in npl3Δ , particularly around the 3' ends of these transcripts and at downstream sites ( Fig 6B ) . The increased binding of CUTs by Nab3 in npl3Δ was greater than the increased RNAPII association we observe in the mutant strain ( Fig 4D ) suggesting that it reflects not only increased expression of these ncRNAs , but additional non-productive recruitment of this surveillance factor to normal degradation substrates . On snoRNAs we observe a contrasting phenotype , with reduced Nab3 binding across the length of the transcript in npl3Δ strains ( Fig 6C ) . Decreased Nab3 association with snoRNAs may be related to the apparent processing defect , since the NNS complex helps promote 3’ maturation by recruitment of the exosome [27] . Overall our Nab3 binding data suggest that readthrough transcripts are targets of the NNS complex , demonstrated by increased binding of Nab3 in the extended region in npl3Δ compared to WT . In the mutant strain we see a shift in Nab3 binding away from processing targets ( snoRNAs ) onto surveillance targets ( CUTs and extended mRNAs ) . This might explain why the npl3Δ/Nab3-HTP strain displays a synergistic growth defect . Efficient recruitment of Nab3 is likely to be more critical in an npl3Δ strain , in which many surveillance targets are produced . Mild interference with recruitment due to the tag might therefore have a negative effect on growth in the npl3Δ background , despite giving no clear phenotype in the WT . Widespread termination defects result in genome-wide expression changes . We next used individual probe intensity data from the tiling arrays to calculate the level of readthrough genome-wide . Three windows were defined for each transcript: DN100 ( 100 nt immediately downstream of the transcript 3’ end ) , DN200 ( 200 nt , starting immediately downstream of DN100 ) , and TRAN ( spanning the entire transcript , except for the first and last 50 nt ) . Median expression values ( normalized probe intensities ) were calculated for each and a “readthrough score” equal to DN200 / TRAN was obtained for each gene in WT and npl3Δ strains . The readthrough scores obtained for the two strains were then used to calculate readthrough ratios , comparing readthrough in the npl3Δ mutant strain to that in WT yeast ( S5 Table ) . A ratio greater than 1 indicates higher readthrough in the npl3Δ mutant strain . All mRNAs , snoRNAs , CUTs and SUTs were considered , with the exclusion of transcripts less than 200 nt in length , or closer than 400 nt to an annotated Ensembl feature on the same strand . S6A Fig shows the distribution of readthrough across all genes in the npl3Δ strain . The dark and light blue lines show the distribution of readthrough ratios for two replicate experiments , alongside the null ratio where WT is compared to WT ( red ) . Strikingly , most genes show some level of readthrough in the npl3Δ strain . The number of genes showing significant readthrough ( false discovery rate = 0 . 05 ) ranged from 29% ( 1165/3961 ) to 37% ( 1468/3961 ) , depending on the experiment . We applied stringent filters ( see Bioinformatics section in Experimental Procedures ) and plotted readthrough ratios for genes passing all filters ( 2234 ) against gene expression ( Fig 7A ) ; 32% of genes showed significant readthrough ( marked red; FDR = 0 . 05 ) , demonstrating a requirement for Npl3 in the termination of a substantial proportion of all RNAPII genes . We observed no clear correlation between readthrough ratio and expression level . We ranked all 2234 genes by readthrough ratio ( S5 Table ) and compared polymerase occupancy around the 3' ends of genes with the highest readthrough rank ( top 200 ) and the control group with a low readthrough rank ( 1200 genes ) . We found that polymerase occupancy downstream of the 3' end is higher in high readthrough genes than low readthrough genes in WT yeast ( Fig 7B ) . This suggests that these genes show a tendency towards readthrough , even in the presence of Npl3 . This effect is more pronounced in the absence of Npl3 ( Fig 7C ) , with a greater accumulation of polymerase downstream of the 3' end of high readthrough genes . We next sought to identify factors that might discriminate high readthrough genes from low readthrough genes . We found that readthrough correlated weakly with gene length . Longer genes were more likely to show readthrough ( S6B Fig ) , consistent with a report showing preferential binding of Npl3 to longer genes [6] . To identify potential motifs , we compared the 3’ regions from all genes in the top and bottom groups based on the readthrough ranking . This identified UAUAUA and UAAAUA motif as strongly over-represented in low readthrough genes ( Fig 7D ) . UAUAUA is the binding site for the pre-mRNA 3’-end processing factor Hrp1 [62] and comparison of the locations of the UAUAUA motifs showed enrichment at the expected location upstream of the pA site in low readthrough genes ( Fig 7E ) . The enrichment of Hrp1 binding sites in genes that do not show readthrough in the absence of Npl3 strongly suggests that direct , efficient recruitment of Hrp1 can bypass the requirement for Npl3 in termination . Gene ontology analysis showed that genes with higher readthrough were enriched for plasma membrane proteins and functions in localization and/or transmembrane transport ( S6 Table ) . This suggests these genes are potentially co-regulated through transcription termination .
Npl3 is bound to all classes of RNAPII transcripts , with enrichment for oligoadenylated RNAs characteristic of nuclear surveillance targets . Deletion of NPL3 revealed its involvement in termination on diverse transcripts that had not appeared to share termination systems . These included many mRNAs and ncRNAs including the CUT class of lncRNAs and most snoRNAs . In contrast , no defects were seen for snRNAs , which have 3’ processing and termination pathways that appeared to closely resemble snoRNAs . Significant transcription termination defects were seen on approximately 30% of protein coding genes in npl3Δ strains . Readthrough was associated with widespread gene expression changes due to transcriptional interference at downstream genes . This likely reflects the disruption of nucleosome positioning and/or transcription factor binding caused by passage of RNAPII through the nucleosome free regions characteristic of yeast promoters . The precise number of genes that are inhibited by this mechanism is difficult to determine accurately . In the cases of the convergent genes highlighted in the text , the phenotype is clear because the transcripts lie on opposite strands . However , actively transcribed , convergent genes are quite rare in yeast , and transcriptional interference on tandem genes may be less evident . Downstream gene expression may appear unaffected on microarrays , despite generating little functional mRNA , with downstream signal representing extended upstream gene products . From the RNAPII CRAC data it appears that sense-orientated genes some distance from a site of readthrough can display the hallmarks of decreased expression . This was shown , for example , by the decreased RNAPII peak at the 5’ end of the HOR2 gene , located downstream of the extended VHR2 transcript ( Fig 3 ) . The widespread interference seen in the absence of Npl3 highlights the necessity for very efficient release of RNAPII at the 3’ ends of genes . In general , fold changes in RNAPII occupancy were less marked than changes in downstream transcript levels . This indicates that readthrough by a small number of polymerases can drastically alter the regulation of gene-expression . In the case of snoRNAs , it appears that low levels of transcription readthrough , as determined by accumulation of downstream RNAPII , result in high levels of extended transcripts . Normal snoRNA termination and processing require the NSS complex , which stimulates exosome recruitment [27] , and Nab3 association with snoRNAs was reduced in npl3Δ strains . These observations strongly indicate that loss of Npl3 also leads to defects in snoRNA 3’ processing and/or surveillance of 3’ extended species . The relative contributions of impaired snoRNA processing versus impaired surveillance in npl3Δ mutants is difficult to assess—as is the case for many substrates for nuclear surveillance/processing factors . Distinguishing the contributions of processing and surveillance is not generally feasible when the phenotype is accumulation of extended species at steady state , and will require the development of very fast , in vivo kinetic analyses . Termination defects seen in the absence of Npl3 were restricted to RNAPII . However , while diverse classes of RNAPII transcripts are affected , this was not the case for all transcripts of any class . To try to understand what determines this apparent variability in the requirement for Npl3 , we ranked protein-coding genes by their degree of readthrough ( readthrough ratio ) in the absence of Npl3 , and sought correlated features in protein coding genes . A notable correlation was with the elevated presence of consensus , UAUAUA binding sites for the mRNA 3’ cleavage factor Hrp1 in the 3’ regions of transcripts with low readthrough scores ( i . e . with low dependence on Npl3 for termination ) . We postulate that association of Hrp1 and/or other cleavage factors with the pre-mRNA is normally promoted by Npl3-mediated packaging , but this requirement can be alleviated by the presence of high-affinity RNA-binding sites . In contrast , competition between binding of Npl3 and pre-mRNA cleavage and polyadenylation factors including Hrp1 was previously reported for GAL reporter constructs [7 , 11] . This apparent anti-termination activity of Npl3 is the opposite of our general findings . However , it could readily be envisaged that on individual genes , Npl3 binding sites conflict with the association of specific factors . The GAL genes are not expressed under the conditions used in our analyses , making it difficult to determine whether these effects are also seen on the endogenous genes . Readthrough ratio was weakly correlated with gene length , with longer genes more likely to exhibit termination defects when Npl3 was absent . Preferential association of Npl3 with longer transcripts as been reported [6] , suggesting that these may show greater changes in pre-mRNA packaging in its absence . However , we saw no clear length dependence for Npl3 in termination on ncRNAs , which are generally shorter than mRNAs . Several distinct , but overlapping pathways for RNAPII termination are normally used by transcripts that are extended in the absence of Npl3 . On pre-mRNAs , recognition of the cleavage and polyadenylation site is linked to changes in the transcribing polymerase that make it prone to termination at downstream pause sites . This may involve Tyr1 dephosphorylation in the CTD by the Glc7 phosphatase that associates with the CPF-CF [63] . Loss of Tyr1P promotes binding of the cleavage factor Pcf11 , as well as Rtt103 , which in turn recruits the Rai1/Rat1 complex for the “torpedo” termination pathway . In contrast , termination of a wide range of ncRNA transcripts involves the Nrd1/Nab3/Sen1 ( NNS ) complex , which binds to the nascent transcript and to the RNAPII CTD with Ser5P modification , as well as the TRAMP nuclear surveillance complex and promoter proximal nucleosomes with H3K4 trimethylation ( [18 , 22 , 34 , 36 , 59] reviewed in [64] ) . Other termination mechanisms are initiated by co-transcriptional cleavage by the RNase III homologue Rnt1 [52 , 65] and by formation of a transcription elongation “roadblock” due to Reb1 binding on the DNA [66] . We found no correlation between known Rnt1 or Reb1 targets and transcription readthrough in npl3Δ strains . Binding of Nab3 to the CUT lncRNAs was increased in npl3Δ strains . A simple , potential explanation might be that the absence of the , normally very abundant , Npl3 protein frees binding sites that can be occupied by other factors , including Nrd1-Nab3 . However , the abundance and readthrough of CUTs were also increased in the absence of Npl3 , and this may contribute to the apparent changes in Nab3 association . We propose that loss of Npl3 results in aberrant RNP formation that still permits Nab3 recruitment , but binding may be non-productive . Npl3 was reported to directly stimulate RNAPII elongation and a mutant that disrupts this function , npl3-120 , resulted in improved termination . The slower RNAPII elongation rate in npl3-120 strains may enhance termination by increasing the time available for recruitment of 3' end processing factors such as Hrp1 . In contrast , an Npl3 mutant ( S411A ) that blocks a phosphorylation site was associated with impaired transcription termination [67] . This defect was proposed to arise from retention of the mutant Npl3 in association with the RNAPII CTD and the mRNA . However , the list of genes showing 3’ extension in Npl3S411A strains overlaps substantially with the genes showing RT in npl3Δ , indicating that Npl3 retention is not solely responsible for this phenotype . Of the 818 genes showing 3’ extension in Npl3S411A , 143 overlap with the 614 genes showing significant readthrough in npl3Δ ( p-value 7 . 1e-16 , Fisher’s exact test ) . Npl3 is a highly abundant RNA binding protein that participates in many processing events and associates with all nascent RNAPII transcripts . It seems probable that its absence will result in substantial changes in the nascent RNP structure . We speculate that such inappropriately packaged RNA is associated with downstream defects in transcription termination , reflected by changes in binding by the termination factor Nab3 , consequently impairing a remodeling event that promotes removal of the polymerase from the nascent transcript .
Yeast were grown in standard SD medium at 30°C unless otherwise stated . Strains and plasmids used are listed in S7 Table . All oligonucleotides used are listed in S8 Table . All yeast analyses were performed in strains derived from BY4741 ( MATa; his3Δ1; leu2Δ0; met15Δ0; ura3Δ0 ) or , in the case of N-PTH-NPL3 , BY4727 ( MATalpha; his3Δ200; leu2Δ0; lys2Δ0; met15Δ0; trp1Δ63; ura3Δ0 ) . N-PTH-NPL3 is a strain in which a sequence encoding a PTH ( proteinA-TEV-His ) tag was integrated at the 5' end of NPL3 , resulting in the formation of an N-terminally tagged protein utilizing the endogenous NPL3 promoter . As the protein is N-terminally tagged in this strain , the orientation of the tag is reversed , allowing the order of protein purification steps to be retained . Generation of this strain involved inserting a URA3 marker between the NPL3 promoter and the NPL3 ORF , and then replacing the URA3 marker with a sequence encoding the PTH tag . The second PCR , amplifying the PTH tag , was performed on a plasmid expressing N-PTH-NPL3 ( pRS415-NPL3-PTH ) , and amplified a region running from the start of the PTH tag to ~600 nt into the NPL3 ORF to increase integration efficiency . The CRAC procedure involves purifying protein/RNA complexes , where the RNA has been covalently UV crosslinked to the protein [41] . RNA-protein complexes are purified , and RNAs are partially digested to leave only the 'footprint' bound by the protein . Linkers are then ligated to both ends and the protein is removed by proteinase K digestion . RNAs are reverse transcribed and resulting cDNAs subjected to next generation sequencing using the Illumina platform ( Edinburgh Genomics ) . WT and npl3Δ yeast were grown to mid-log phase ( OD600 ~0 . 5 ) and cells were collected by brief centrifugation ( 3000 xg , for 5 min ) . Total RNA was isolated by a standard acidic hot phenol method and DNA was removed by treating with RNase-free DNaseI ( Turbo DNA-free kit; Ambion ) . Reverse transcription and array hybridizations were carried out as previously described [68] . Yeast cultures were grown to mid-log phase ( OD600 ~0 . 5 ) and cells were collected by brief centrifugation ( 3000 xg , for 5 minutes ) . Total RNA was isolated by a standard acidic hot phenol method and DNA was removed by treating with RNase-free DNaseI ( Turbo DNA-free kit; Ambion ) . Single stranded cDNA was generated using gene specific primers , designed to prime from the 3' end of the transcript ( to measure expression ) or from ~500 nt downstream ( to measure transcriptional readthrough ) . Reverse transcription reactions were performed using Superscript III ( Invitrogen ) . The expression level of individual transcripts was determined by quantitative PCR using SYBR green fluorescence for detection . Relative quantities were calculated using a standard curve made with known concentrations of genomic DNA , and were normalized to levels of ACT1 in each RNA sample . Total RNA was isolated by a standard acidic hot phenol method . For SNR60 readthrough analysis , equal amounts of RNA ( 10 μg ) were resolved on a 1 . 2% agarose gel in TBE buffer and transferred onto Hybond N+ nitrocellulose membrane overnight in 6x SSC . For detection of mature snoRNAs and RNase H cleavage assay products , samples ( 4 μg total RNA for snoRNA detection ) were resolved on an 8% acrylamide gel containing 8 . 3 M urea , in TBE buffer and transferred onto Hybond N+ nitrocellulose overnight in 0 . 5x TBE . Oligo probes were end labeled with [γ-32P] ATP and hybridized to the membrane overnight at 37°C in ULTRAhyb-Oligo ( Ambion ) . Signals were detected using a Fuji FLA-5100 . Samples ( 30 μg ) of RNA were annealed with 750 ng oligo-dT and/or 10 pMoles gene-specific oligo , heated to 65°C and allowed to cool slowly to 30°C . Samples were then incubated with 1 unit RNase H ( Roche ) at 30°C for 1 hour . Total extract from crosslinked CRAC samples were loaded onto 4–12% NuPAGE gels and Transferred onto Hybond C nitrocellulose membrane . Following blocking in 5% milk , the membrane was incubated first inn anti-TAP primary antibody ( 1:5000 overnight ) and then anti-rabbit secondary ( 1:10000 for 1 hour ) . Signal was visualized using the Licor Odyssey system . All sequence data are available from GEO under accession number GSE70191 . http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=gdivgqmivxcpzkp&acc=GSE70191 Sequencing data were processed and quality filtered using the fastx toolkit as previously described [37] . Processed reads were mapped to the Saccharomyces cerevisiae genome ( SGD v64 ) using Novoalign ( Novocraft ) with genome annotation from Ensembl ( EF4 . 74 ) , supplemented with non-coding sequences as previously described [37] . Reads mapping to different transcript RNA classes were determined using the pyCRAC package [17] ( Figs 1A , 1C , S3B and S5B ) . All analyses were performed using genome SGD v64 unless otherwise stated . The distribution of hits across transcripts of different classes was determined in several ways . Firstly , to examine the distribution of proteins at the 5' and 3' ends of mRNAs , hits within 300–900 nt windows aligned to the start ( TSS ) and end ( pA ) were plotted using published scripts [37] . The top 2000 bound mRNAs for each protein were included in the analysis and average distribution was plotted ( Fig 1B and 1D ) . A similar analysis was performed to assess binding at snoRNAs and CUTs ( Figs 1E , 1F , 4D , 5A , 6B and 6C ) . In this instance smaller windows were used and hits per million mapped reads were plotted , rather than average distribution . Reads were aligned to the TSS or 3' ends , with flanking regions included as shown . We included all snoRNAs in the analysis , but only included CUTs > 150 nt in length . Hits at introns were also plotted using this approach ( S1C and S1D Fig ) . As an alternative way to assess binding across transcripts , we used pyBinCollector from the pyCRAC package , which normalizes transcripts by length , dividing hits into a given number of bins ( S1E , S1F , S3C , S3D , S5C and S5D Figs ) . Rpo21 occupancy was calculated to determine transcriptional readthrough ( Figs 3C , 4C , 5D , S2C and S4C ) using pyPileup from the pyCRAC package , with default settings . Hits containing unencoded 3' oligoA tails of 2 of more were determined using a reported pipeline [37 , 69] . These hits were then mapped to transcript groups and plotted across RNA classes as described above ( Fig 1C and 1D ) . All microarray data are available in the ArrayExpress database ( http://www . ebi . ac . uk/arrayexpress ) , under accession number E-MTAB-3642 . Array data can also be visualized in a genome browser heat map format ( http://steinmetzlab . embl . de/tollerveyLabArray ) . Microarray data were aligned to SGD S . cerevisiae genome version ( SGD v57 ) . Normalization of microarray hybridizations was performed as previously described [70] and transcript boundaries shown are as published [71] . Differential expression analyses were carried out using the R-package , Limma [72] , controlling for the false discovery rate arising from multiple testing [73] . Five snoRNAs were not included in the differential expression analyses due to lack of transcript boundary information ( Fig 2 and S5 Table ) . These can , however , be viewed in the genome browser heat map . CRAC hit data were aligned to SGD S . cerevisiae genome version ( SGD v57 ) alongside tiling array expression data at individual loci ( Figs 3A , 4A , 4B , 5B , S2A and S4A ) . Hits were normalized for library size by plotting hits per million mapped reads at each nucleotide . Fig 6A shows CRAC data aligned to SGD v57 without array data . Readthrough scores were calculated for mono-exonic snoRNAs , mRNAs , CUTs and SUTs with coordinates previously defined [71] . Transcripts that are < 200 nt were excluded , as were transcripts < 400 bp upstream of another annotated transcript [71] or in Ensembl release 68 . The exception to this is when an mRNA has an annotated CUT or SUT immediately downstream of the 3' end . In some instances , these annotated ncRNAs appear to correspond to upstream mRNA readthrough , and therefore these mRNAs were not filtered out . Three windows were defined for each transcript: DN100 ( 100 nt immediately downstream of the transcript 3’ end ) , DN200 ( 200 nt , starting immediately downstream of DN100 ) , and TRAN ( spanning the entire transcript , except for the first and last 50 nt ) . The median normalized probe intensities ( in log2 space ) for each microarray sample were calculated for each window , although windows with < 8 probes were excluded . The readthrough score was then defined for each gene and each sample ( wild-type replicate 1 , wild-type replicate 2 , npl3-delta replicate 1 , and npl3-delta replicate 2 ) as the median intensity for DN200 , minus the median intensity for TRAN . The difference in npl3-delta and wild-type transcriptional readthrough was determined by calculating a readthrough ratio for each gene , defined as the readthrough score for npl3-delta minus the readthrough score for wild-type . Readthrough ratios were also calculated for wild-type replicate 2 versus wild-type replicate 1 , to provide an empirical null distribution and enable transcripts with a significant increase in readthrough for npl3-delta versus wild-type to be identified . The Benjamini–Hochberg procedure was used to control the false discovery rate at 0 . 05 . For this step , the two replicate experiments were treated separately , then a stringent list of genes with elevated readthrough obtained by intersecting the results from both replicates . A series of filters was used to exclude transcripts for which readthrough ratios may be inaccurate , either due to low expression or because of evidence of independent transcription initiation downstream . The following criteria were used: ( i ) there must be < 10 Cbc1 ( cap-binding complex protein 1 ) CRAC reads in the DN100 window , ( ii ) TRAN median probe intensity must be > -4 . 88 for wild-type and npl3-delta , ( iii ) for npl3-delta , the median probe intensity in the DN100 window must be > 70% that of the TRAN window , and ( iv ) the median probe intensity in the TRAN window for npl3-delta must be at least 70% that of the same window in the wild-type sample . For filters ( ii ) - ( iv ) , the mean of the two replicates was used . Plots of Pol II distribution in regions centered on transcript 3’ ends were obtained by taking the individual Pol II CRAC read distributions for each gene , linearly transforming each gene so that its maximum value was equal to 1 , and then summing at each nucleotide for the indicated set of genes ( either high or low readthrough groups ) . We observed that genes with the very lowest readthrough ranks had a spurious negative readthrough ratio due to having increased expression in the npl3Δ strain relative to WT . To limit the contribution of these genes , we took a larger number of genes for the low readthrough group ( 1200 compared to 200 ) . Npl3 binding sites were analyzed for enriched motifs by first filtering total reads to exclude low complexity sequences , as previously described [37] . The pyCRAC package [17] was used to calculate statistical overrepresentation scores for every possible k-mer ( S1D Fig ) using a previously described algorithm [69] . We used pyCRAC to calculate False Discovery rates ( FDRs ) and selected only reads forming clusters of 5 reads or more with an FDR < 0 . 05 for further analysis . Reads were further filtered to include only those with one or more T-C substitution , representing a site of crosslinking , and therefore predicted to indicate genuine binding sites with greater stringency . To identify sequence motifs that differentiate high- and low-readthrough genes , we considered the 1822 genes for which reliable readthrough scores could be established , and separated these genes into quartiles by their readthrough scores . 2234 genes were included in the genome-wide readthrough analysis , but the bottom 250 were excluded from the motif analyses as these were found to have spuriously low readthrough ratios resulting from increased expression in the npl3Δ mutant . Of the remaining 1984 genes , only those with well-defined polyA sites ( 1822 ) were included in the motif analysis . For each 6-mer nucleotide motif , we calculated the numbers of genes in each quartile that contained the motif within the region ( -80 to -20 nucleotides ) from the polyadenylation site ( polyA site ) . The polyA site was defined from Pab1 CRAC data as described [37] . We then identified the motifs that were significantly enriched in the low-readthrough genes , relative to high-readthrough genes , by calculating Z-scores as described [69] . To illustrate the localization of motifs relative the polyA site , we plotted the total coverage of UAUAUA motifs as a function of distance from the polyA site , separately for the top and bottom quartile of genes ranked by readthrough scores . | Npl3 is a yeast mRNA binding protein with many reported functions in RNA processing . We wanted to identify direct targets and therefore combined analyses of the transcriptome-wide effects of the loss of Npl3 on gene expression with UV crosslinking and bioinformatics to identify RNA-binding sites for Npl3 . We found that Npl3 binds diverse sites on large numbers of transcripts , and that the loss of Npl3 results in transcriptional readthrough on many genes . One effect of this transcription readthrough is that the expression of numerous flanking genes is strongly down regulated . This underlines the importance of faithful termination for the correct regulation of gene expression . The effects of the loss of Npl3 are seen on both mRNAs and non-protein coding RNAs . These have distinct but overlapping termination mechanisms , with both classes requiring Npl3 for correct RNA packaging . |
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Zika virus ( ZIKV ) and chikungunya virus ( CHIKV ) are highly pathogenic arthropod-borne viruses that are currently a serious health burden in the Americas , and elsewhere in the world . ZIKV and CHIKV co-circulate in the same geographical regions and are mainly transmitted by Aedes aegypti mosquitoes . There is a growing number of case reports of ZIKV and CHIKV co-infections in humans , but it is uncertain whether co-infection occurs via single or multiple mosquito bites . Here we investigate the potential of Ae . aegypti mosquitoes to transmit both ZIKV and CHIKV in one bite , and we assess the consequences of co-infection on vector competence . First , growth curves indicated that co-infection with CHIKV negatively affects ZIKV production in mammalian , but not in mosquito cells . Next , Ae . aegypti mosquitoes were infected with ZIKV , CHIKV , or co-infected via an infectious blood meal or intrathoracic injections . Infection and transmission rates , as well as viral titers of positive mosquitoes , were determined at 14 days after blood meal or 7 days after injection . Saliva and bodies of ( co- ) infected mosquitoes were scored concurrently for the presence of ZIKV and/or CHIKV using a dual-colour immunofluorescence assay . The results show that orally exposed Ae . aegypti mosquitoes are highly competent , with transmission rates of up to 73% for ZIKV , 21% for CHIKV , and 12% of mosquitoes transmitting both viruses in one bite . However , simultaneous oral exposure to both viruses did not change infection and transmission rates compared to exposure to a single virus . Intrathoracic injections indicate that the selected strain of Ae . aegypti has a strong salivary gland barrier for CHIKV , but a less profound barrier for ZIKV . This study shows that Ae . aegypti can transmit both ZIKV and CHIKV via a single bite . Furthermore , co-infection of ZIKV and CHIKV does not influence the vector competence of Ae . aegypti .
Zika virus ( ZIKV; family Flaviviridae , genus Flavivirus ) is a pathogenic arthropod-borne ( arbo ) virus that causes neurological disease in humans and congenital syndrome in newborns and infants [1] . In the 60 years after its discovery in 1947 , sporadic ZIKV infections were reported in African countries and in parts of Asia [2] . The first larger ZIKV virus outbreak was reported in 2007 on the Yap Islands of Micronesia after which the virus quickly spread to other countries in south-east Asia , such as French Polynesia in 2013 , and Cook Islands and Easter Island in 2014 [3] . In 2015 , there was a dramatic increase of reported ZIKV cases in South America , especially Brazil where over 200 , 000 cases of infection , six deaths and over 2 , 200 incidents of ZIKV associated congenital syndrome were reported [4] . Prior to the ZIKV outbreak in the Americas , flavivirus infections linked to congenital disease were rarely reported . However , a causal relationship between ZIKV infection in pregnant women and subsequent birth malformations , such as microcephaly , has now been confirmed [1 , 4 , 5] . The main vector for ZIKV transmission is the Aedes aegypti mosquito [6–9] , while Ae . albopictus [9–11] , Ae . vittatus [12] Ae . luteocephalus [12] , and Ae . hensilli [13] can transmit ZIKV in laboratory studies . Other mosquito-borne viruses that circulate concurrently with ZIKV in South America include chikungunya virus ( CHIKV; family Togaviridae , genus Alphavirus ) , dengue virus ( DENV; family Flaviviridae , genus Flavivirus ) and yellow fever virus ( family Flaviviridae , genus Flavivirus ) . In 2013 , CHIKV was introduced into South America via the Caribbean . Since then over 319 , 000 cases of infection and 135 deaths have been reported in South America [14] . CHIKV strains that circulate in the Americas are predominantly transmitted by Ae . aegypti mosquitoes [15] . Since CHIKV and ZIKV co-circulate in the same geographical regions , individuals can become co-infected with both viruses [16 , 17] . Co-infections of patients with ZIKV and CHIKV already occurred in South America [18–20] , some even reporting triple infection with ZIKV , CHIKV , and DENV [21–23] . Whether a single bite of Ae . aegypti can transmit both ZIKV and CHIKV simultaneously , or whether sequential bites of two infected mosquitoes are required for such co-infections in humans , remains unclear . Here we designed a dual-colour immunofluorescence assay that can concurrently detect ZIKV and CHIKV infection in mammalian and mosquito cells . We analysed the effect of co-infections on virus growth kinetics in mammalian and mosquito cell lines . Furthermore , we studied the effect of co-infection with both ZIKV and CHIKV on the infection and transmission rates of both viruses in Ae . aegypti mosquitoes . Finally , mosquito transmission rates after an infectious bloodmeal and intrathoracic injections were compared to study the effects of the midgut and salivary gland barriers on co- and single-infections .
African green monkey kidney Vero E6 ( ATCC CRL-1586 ) cells were cultured in Dulbecco’s modified Eagle medium ( DMEM; Gibco , Carlsbad , CA , United States ) containing 10% fetal bovine serum ( FBS; Gibco ) , penicillin ( 100 U/ml; Sigma-Aldrich , Saint Louis , MO , United States ) , and streptomycin ( 100 μg/ml; Sigma-Aldrich ) ( P/S ) . Vero cells were cultured as monolayers in T25 cell culture flasks ( Greiner Bio-One , Kremsmünster , Austria ) at 37°C with 5% CO2 , and split every 3–4 days . Prior to infections , Vero cells were seeded in DMEM containing 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid ( DMEM-HEPES; Gibco ) supplemented with 10% FBS , penicillin ( 100 U/ml ) , and streptomycin ( 100 μg/ml ) , hereafter named DMEM-supplemented . Aedes albopictus C6/36 cells ( ATCC CRL-1660 ) were cultured in Leibovitz L-15 medium ( Gibco ) supplemented with 10% FBS , 2% tryptose phosphate broth ( Gibco ) , and 1% nonessential amino acids ( Gibco ) , hereafter named Leibovitz-complete . Aedes aegypti Aag2 cells were cultured in Schneider’s Drosophila medium ( Lonza , Basel , Switzerland ) supplemented with 10% FBS , hereafter named Schneider’s-complete . Both C6/36 and Aag2 cells were cultured as monolayers in T25 flasks at 28°C , and split every 3–4 days . All proceedings involving infectious virus were executed in the biosafety level 3 laboratory at Wageningen University & Research . An infectious clone derived chikungunya virus 37997 strain ( CHIKV37997 ) was used in all studies . To prepare the chikungunya virus 37997 infectious clone ( pCHIKIC-37997 ) , the 37997 structural cassette was produced synthetically with AscI/EcoRI overhangs ( Baseclear , Leiden , The Netherlands ) and cloned into the previously described CHIKV 37997 replicon CHIKrep-FlucEGFP to replace the Fluc-EGFP fusion gene [24] . CHIKV 37997 RNA was in vitro transcribed from 5 μg PacI ( New England Biolabs ( NEB ) , Ipswich , MA , United States ) linearized pCHIKIC-37997 using SP6 RNA polymerase ( NEB ) following the manufacturer’s protocol . Vero cells were seeded one day prior to infection in 6 well cell culture plates ( Greiner Bio-One ) until a confluency of ~80% was reached . The culture medium was replaced for Opti-Mem ( Gibco ) and 3 μl of in vitro transcribed RNA was transfected into Vero cells using 2 . 5 μl Lipofectamine 2000 ( Invitrogen , Carlsbad , United States ) . Four days post transfection the cell culture medium was harvested , centrifuged and stored at -80°C until further use ( P0 ) . In total , 500 μl P0 was used to inoculate a T75 flask ( Greiner Bio-One ) of C6/36 cells . Four days post infection ( dpi ) the cell culture medium was harvested ( P1 ) , centrifuged and the supernatant was stored in aliquots at -80°C . Virus titers were determined by end point dilution assay ( EPDA ) on Vero cells . Zika virus Suriname strain 011V-01621 ( ZIKVSUR GenBank accession number , KU937936 ) [5] , was obtained through the European Virus Archive Goes Global catalogue ( www . european-virus-archive . com/virus/zika-virus-strain-suriname-2016 ) as a P3 stock grown on Vero cells . ZIKV P4 was generated by inoculating a pre-seeded T75 flask of Vero cells with 250 μl ZIKVSUR P3 . The supernatant was harvested ( P4 ) at 2 dpi , centrifuged to remove cell debris , and the supernatant was stored in aliquots at -80°C . Virus titers were determined by EPDA on Vero cells . Vero cell suspensions were retrieved by detaching Vero cells from a T25 flask with 1 ml of Trypsin-EDTA ( Gibco ) , after which 4 ml of DMEM-supplemented was added . Virus stocks were thawed , vortexed and serial dilutions were made in DMEM-supplemented . Vero cell suspensions were diluted 1:4 with DMEM-supplemented and added to the virus dilutions in a 1:1 ratio . 10 μl of the inoculated dilutions was plated in 6-fold in micro-titer plates ( Nunc , Roskilde , Denmark ) . EPDAs of samples infected with one virus were scored at 3 dpi based on virus induced cytopathic effect ( CPE ) . EPDAs of co-infected samples were fixed with 4% paraformaldehyde and scored by immunofluorescence assay ( IFA ) at 3 dpi . Cell monolayers were seeded in 6-well plates and infected on the same day for C6/36 and Aag2 cells , or the next day for Vero cells . The cell culture fluid was removed and infections were performed at an MOI of 0 . 1 ( 5 . 7 × 104–2 . 2 × 105 TCID50 ) in standard culture media in a total volume of 1 ml . After 1 h the inoculum was removed and the monolayers were washed twice with 1 ml of Phosphate Buffered Saline ( PBS ) , before addition of 2 ml fresh culture medium . C6/36 and Aag2 cells were maintained at 28°C and Vero cells were maintained at 37°C and 5% CO2 . Samples of 100 μl were taken at 0 , 24 , 48 , 72 and 96 hours post infection ( hpi ) and stored at -80°C until titration by EPDA on Vero cells . Cells were fixed with 4% paraformaldehyde/PBS for 1–3 h . Monolayers were washed 3x with PBS , permeabilized by 10 min incubation in 0 . 1% SDS in PBS , and washed 3x with PBS . Monolayers were stained with α-CHIKV-E2 ( Rabbit Polyclonal; 1:5000; [25] ) and pan-Flavivirus α-E ( 4G2; Mouse monoclonal; 1:50 [26] ) in a 5% FBS solution dissolved in PBS for 1 h at room temperature ( RT ) . Cells were washed 3x with PBS and stained with goat-α-mouse-Alexa Fluor 568 ( 1:2000; Invitrogen ) and goat-α-rabbit-Alexa Fluor 488 ( 1:2000; Invitrogen ) for 1 h at 37°C . Monolayers were washed 3X with PBS and visualized using an Axio Observer Z1m inverted microscope ( Zeiss , Jena , Germany ) in combination with an X-Cite 120 series lamp . Vero cell monolayers were seeded in 96-wells plates one day prior to infection and infected at an MOI of 0 . 1 . At the indicated time-point , the medium was removed and replaced with 100 μl of passive lysis buffer ( Promega , Madison , Wisconsin , USA ) . Cells were lysed by 10 min incubation at RT and lysates were stored at -20°C until further use . Twenty-five μl of reconstituted CellTiter-Glo Reagent ( Promega ) was added to 25 μl cell lysate and incubated at RT in the dark for 10 min before measuring the luminescence using a FLUOstar OPTIMA microplate reader ( BMG Labtech , Ortenberg , Germany ) . Cell viability was calculated by normalizing the average luminescence of the sample to the averaged luminescence of the mock . In all experiments female Aedes aegypti mosquitoes ( Rockefeller strain , obtained from Bayer AG , Monheim , Germany ) were used . Larvae and adults were maintained at 27±1°C with 12:12 light:dark cycle and 70% relative humidity . Adult mosquitoes were provided with 6% ad libitum glucose solution . Human blood ( Sanquin Blood Supply Foundation , Nijmegen , The Netherlands ) was provided through Parafilm using the Hemotek PS5 feeder ( Discovery Workshops , Lancashire , United Kingdom ) . Female mosquitoes were kept together with males for 3 to 6 days in Bugdorm-1 insect rearing cages ( 30 x 30 x 30 cm , Bugdorm , Taiwan , China ) , before females were transferred to buckets ( diameter: 12 . 2 cm , height: 12 . 2 cm; Jokey , Wipperfürth , Germany ) and transported to the Biological Safety Level 3 facility for virus infection assays . One day before blood feeding , the glucose solution was replaced by water in order to stimulate blood feeding of Ae . aegypti females . Virus solutions were made by diluting the virus to the indicated titer in DMEM-supplemented for ZIKV , and Leibovitz-complete for CHIKV . Since CHIKV was grown on C6/36 cells and ZIKV on Vero cells , we compensated for differences in cell culture media by mixing 250 μl of virus solution with 250 μl conditioned media from cultured C6/36 cells for ZIKV and 250 μl conditioned media from cultured Vero cells for CHIKV , after which 500 μl human blood was added . The infectious blood meal was offered through Parafilm using the Hemotek PS5 feeder . Mosquitoes were allowed to feed for 1 h ad libitum in light conditions , at 24°C and 70% relative humidity ( RH ) . Mosquitoes were anesthetized with 100% CO2 , placed on a CO2 pad and fully engorged females were selected . Immediately after selection , a selection of mosquitoes was frozen at -80°C to determine the amount of virus ingested by the mosquitoes . Exposed mosquitoes were maintained at 28°C . The glucose solution was refreshed every 2–3 days until 14 dpi . Virus dilutions of 4 × 107 TCID50/ml were prepared by diluting the ZIKV and CHIKV virus stocks 1:1 with conditioned media taken from cultured C6/36 and Vero cells , respectively , to compensate for differences in growth media . Ae . aegypti mosquitoes were anesthetized with 100% CO2 and placed on a CO2 pad . Female mosquitoes were selected and injected with 69 nl of the prepared virus stock using a Drummond Nanoject II Auto-Nanoliter Injector ( Drummond Scientific , Broomall , PA , United States ) . Infected mosquitoes were maintained at 28°C . The glucose solution was refreshed every 2–3 days until 7 dpi . Fourteen days post blood meal or 7 days post injection mosquitoes were anesthetized with 100% CO2 , and placed on a CO2 pad . Mosquitoes that died within the 7 or 14 days incubation period were discarded . Mosquitoes were immobilized by removing their legs and wings with forceps . The proboscis of each mosquito was inserted into a 200 μl yellow pipet tip ( Greiner Bio-One ) containing 5 μl of a 1:1 solution of 50% glucose solution and FBS , for a minimum of 45 min . After salivation , the mosquito bodies were added to 1 . 5 ml Safe-Seal micro tubes ( Sarstedt , Nümbrecht , Germany ) containing 0 . 5 mm zirconium beads ( Next Advance , Averill Park , NY , United States ) . Each saliva sample was added to a 1 . 5 ml micro tube ( Sarstedt ) containing 55 μl DMEM-supplemented with additional Fungizone ( 50 μg/ml; Invitrogen ) , and Gentamycin ( 50 μg/ml; Life technologies ) , hereafter named DMEM-complete . Mosquito bodies and saliva samples were stored at -80°C until further processing . Mosquito bodies were taken from the -80°C freezer and immediately homogenized for 2 min at max speed in a Bullet Blender Storm ( Next Advance ) . Homogenized bodies were centrifuged briefly and resuspended in 100 μl DMEM-complete medium . The homogenate was blended again for 2 min at max speed using the Bullet Blender and centrifuged for 1 min at 14 . 500 rpm . Mosquito saliva samples were thawed at RT . Of each body homogenate or saliva sample , 30 μl was used to inoculate a Vero cell monolayer in a 96 wells plate . After 2–3 h the inoculum was removed and replaced with 100 μl DMEM-complete . For mosquitoes infected with a single virus , the wells were scored for virus induced CPE at 3 dpi . This method was validated by comparing results based on CPE with IFA for the first replicate of mosquitoes , which gave identical results . For mosquitoes that were infected with both viruses , the supernatant was removed and monolayers were fixed with 4% paraformaldehyde in PBS at 3 dpi after which the wells were scored by dual-colour IFA . Bodies and saliva samples of a selection of mosquitoes with a fully disseminated infection of ZIKV , CHIKV , or both , were titrated by EPDA . Kruskal-Wallis tests were used to test for differences between engorged viral titers , and final titers of mosquito bodies and saliva samples . If the outcome of a Kruskal-Wallis test was significant , differences among groups were further tested with Dunn’s tests and corrected with the Bonferroni correction for multiple comparisons . Infection and transmission rates were calculated , respectively , by dividing the number of female mosquitoes with infected bodies or with infected saliva by the total number of female mosquitoes in the respective treatment . Mosquitoes with infectious saliva , but uninfected body ( <1% ) , were excluded from the analysis . Differences in infection and transmission rates were tested with Chi-squared tests . Multiple comparisons were corrected with the Bonferroni correction . All statistical analyses were done with the statistical software package R [27] . Power analysis was performed to confirm adequate sample sizes for the vector competence studies using G*Power software ( Düsseldorf , Germany ) .
A dual-colour immunofluorescence detection assay was developed to investigate whether ZIKV and CHIKV can infect and replicate in the same mammalian or mosquito cell . Vero ( green monkey kidney , mammalian ) cells and C6/36 ( Ae . albopictus , mosquito ) cells were seeded as monolayers and inoculated with ZIKV , CHIKV , or co-inoculated and stained for viral antigens at 48 hpi ( Fig 1 ) . Distinctions between ZIKV and CHIKV infected cells were clear after immunostaining in both Vero and C6/36 cells , indicating that the dual-colour immunofluorescence assay can be used to score co-infected samples for the presence of ZIKV , CHIKV , or both ( Fig 1A & 1B ) . ZIKV infected Vero cells displayed localization of the envelope ( E ( pan-Flavivirus α-E ( 4G2 ) ) ) protein predominantly near the perinuclear regions / endoplasmic reticulum , corresponding to the replication and assembly sites of flaviviruses [28] ( Fig 1A ) . Most Vero cells in the CHIKV infected sample already lysed due to the strong CPE of CHIKV infection , as indicated by the presence of the E2 envelope protein on the remaining cell projections . Viable CHIKV infected cells showed localization of E2 near the cell boundaries , related to the assembly sites of CHIKV [29] ( Fig 1A ) . The localization of E and E2 was similar in C6/36 cells , with ZIKV-E mostly present near the endoplasmic reticulum surrounding the nucleus , and CHIKV-E2 near the cell membrane ( Fig 1B ) . Co-infection did not alter the localization of ZIKV-E nor CHIKV-E2 , indicating that these viruses can co-infect the same cell without obvious interference . These results show that ZIKV and CHIKV are intrinsically capable to co-infect and co-replicate in cells of their mammalian and insect host . Viral co-infections can influence the replication rate in mosquito cell lines and may affect transmission in vivo by the mosquito vector [30] . In order to assess whether ZIKV and CHIKV interfere with each other’s replication , the growth kinetics of ZIKV and CHIKV were determined during co- and single-infections in mammalian Vero , Ae . albopictus C6/36 , and Ae . aegypti Aag2 cells ( Fig 2 ) . Cells were infected at an MOI of 0 . 1 , washed , and culture fluid samples were collected at 0 , 24 , 48 , 72 , and 96 hpi , and titrated by EPDA . In Vero cells , ZIKV reached a peak titer of 8 . 0 × 107 TCID50/ml within 48 hpi ( Fig 2A ) , whereas CHIKV only reached a titer of 8 . 7 × 105 TCID50/ml at 24 hpi ( Fig 2B ) . During co-infection in Vero cells , the titer of ZIKV was approximately 3 logs lower at 48 hpi and 72 hpi as compared to single-infection , whereas the titer of CHIKV was not affected by co-infection with ZIKV . In C6/36 cells , ZIKV reached a relatively low peak titer of 9 . 6 × 105 TCID50/ml at 96 hpi ( Fig 2C ) , whereas CHIKV reached peak titers of 7 . 7 × 108 TCID50/ml at 48 hpi ( Fig 2D ) . Co-infection resulted in approximately 1 log lower titer of ZIKV at 72 and 96 hpi as compared to single-infection , whereas CHIKV replication was not seemingly affected by co-infection . In Aag2 cells , ZIKV reached a peak titer of 6 . 7 × 107 TCID50/ml at 96 hpi ( Fig 2E ) , indicating that ZIKV replicates better in Ae . aegypti as compared to Ae . albopictus cells ( compare Fig 2C with 2E ) . In contrast , CHIKV reached peak titers of 1 . 2 × 106 TCID50/ml at 48 hpi in Aag2 cells , and CHIKV titers rapidly decreased at later time points ( Fig 2F ) . This suggests that CHIKV replicates better in Ae . albopictus than Ae . aegypti cells ( compare Fig 2D with 2F ) . Importantly , co-infections in Aag2 cells did not significantly affect the replication of either ZIKV or CHIKV ( Fig 2E & 2F ) . To investigate whether the observed difference in growth kinetics of ZIKV during co- and single-infections in Vero cells was due to altered cell viability , a cell viability assay was performed ( Fig 3 ) . Signs of virus induced cytopathic effect were readily observed by bright field microscopy at 24 hpi in the CHIKV and co-infected cells , whereas ZIKV induced cytopathic effects were only observed after 48 hours ( Fig 3A ) . Additionally , the cell viability of CHIKV infected and co-infected Vero cells was decreased dramatically to 20% at 48 hpi , whereas ZIKV maintained high cell viability up to 48 hpi ( Fig 3B ) . These results suggest that the reduction of ZIKV titers during co-infection with CHIKV is due to the rapid and extensive CPE resulting from CHIKV-induced host-shut-off [31] , which interferes with ZIKV virion production . Moreover , co-infections did not affect the cell viability in both C6/36 and Aag2 cells until 96 hpi ( Cell viability: 80–100% ) . The viral infectious dose in the blood meal is known to have a strong effect on the mosquito infection rates of mosquito-borne arboviruses [32 , 33] . Therefore , we determined the dose-dependent infection and transmission rates of ZIKV and CHIKV in Ae . aegypti . Female Ae . aegypti mosquitoes were offered an infectious blood meal containing 2 . 0 × 105 , 2 . 0 × 106 or 2 . 0 × 107 TCID50/ml of ZIKV or CHIKV . When comparing single- with co-infections it is important that the infectious blood meals contain equal virus titers and that the mosquitoes engorge similar numbers of infectious particles . To ensure that the infectious blood meals were completely homogenized and to validate our viral dilution series we froze a selection of engorged mosquito bodies directly after the blood meal and determined the virus titer by EPDA . Indeed , mosquitoes infected with increasing doses of infectious virus in the bloodmeal had increasing titers in their bodies . The ingested virus titers of mosquitoes that were infected with the lowest dose were significantly lower than those infected with the two highest doses ( ZIKV: P < 0 . 01 , CHIKV: P < 0 . 01; Fig 4A ) , although mosquitoes infected with the two highest doses were not significantly different amongst each other ( P > 0 . 05 ) . The infection rates were determined at 14 dpi by infectivity assay of mosquito bodies and transmission rates by infectivity assay of saliva samples . Inoculation with 2 . 0 × 105 TCID50/ml in the blood meal resulted in an infection rate of 65 . 3% for ZIKV ( Fig 4B and Table 1 ) . Increasing the infectious dose to 2 . 0 × 106 or 2 . 0 × 107 TCID50/ml significantly increased the ZIKV infection rate to 92 . 2% and 100% ( P < 0 . 01 ) . For CHIKV , inoculation with 2 . 0 × 105 , 2 . 0 × 106 or 2 . 0 × 107 TCID50/ml resulted in infection rates of 47 . 9% , 66 . 7% , or 81 . 2% , respectively . Inoculation with the highest CHIKV dose resulted in significantly higher infection rates compared to the lowest dose ( P < 0 . 001 ) . These results indicate that the mosquito infectious dose for Ae . aegypti is higher for CHIKV than ZIKV . In the same set of experiments , the mosquito saliva was collected by forced salivation assay and scored for the presence of virus to calculate the transmission rates . Transmission rates of 34 . 7% for ZIKV and 10 . 4% for CHIKV were reached with an infectious dose of 2 . 0 × 105 TCID50/ml ( Fig 4C and Table 1 ) . With a viral titer in the blood meal of 2 . 0 × 106 or 2 . 0 × 107 TCID50/ml the transmission rates of ZIKV increased significantly to 68 . 6% and 68 . 3% ( P < 0 . 01 ) , whereas transmission rates for CHIKV of 5 . 9% and 21 . 2% were not significantly different as compared to the lowest dose ( P > 0 . 05 ) . To observe whether increasing infectious doses in the blood meal lead to higher viral titers in the mosquito bodies and saliva we titrated both the mosquito bodies and saliva samples , of mosquitoes with a fully disseminated ZIKV or CHIKV infection ( positive body and saliva ) at 14 dpi . Median ZIKV titers in mosquito bodies reached 2 . 0 × 107 , 2 . 0 × 107 , and 7 . 1 × 106 TCID50/ml for the respective inoculation doses of 2 . 0 × 105 , 2 . 0 × 106 , or 2 . 0 × 107 TCID50/ml ( Fig 4D ) . Median titers of mosquito bodies inoculated with 2 . 0 × 107 of ZIKV were significantly lower compared to median titers of mosquito bodies inoculated with the lower doses ( P < 0 . 05 ) , indicating that a higher infectious dose in the blood meal does not necessary lead to a higher viral load in the mosquito . Median CHIKV titers were approximately 1–3 logs lower than ZIKV titers and reached values of 6 . 0 × 105 , 5 . 0 × 104 , and 5 . 7 × 105 TCID50/ml for the respective inoculation doses of 2 . 0 × 105 , 2 . 0 × 106 , or 2 . 0 × 107 ( Fig 4D ) . No significant differences were found between the median titers of mosquito bodies inoculated with different doses of CHIKV ( P > 0 . 05 ) . In addition , median viral titers in virus-positive mosquito saliva samples were determined . All median viral titers of saliva samples were below the TCID50 detection limit , and no significant differences between saliva samples could be observed ( P > 0 . 05; Fig 4E ) . These results show that Ae . aegypti is a competent vector for both ZIKV and CHIKV . Moreover , the relatively high mosquito infectious dose for CHIKV indicates that a higher viral dose in the blood meal should be used to study the effects of ZIKV and CHIKV co-infections . Co-infections of arboviruses can affect their transmission potential by mosquito vectors and even exclude transmission of one virus [34 , 35] . To investigate the effect of co-infection on the infection and transmission of ZIKV and CHIKV , female Ae . aegypti mosquitoes were offered an infectious blood meal containing a dose of 2 . 0 × 107 TCID50/ml of ZIKV , CHIKV , or both . Titrations of engorged mosquito bodies that were immediately frozen after the infectious blood meal , showed that the mosquitoes ingested equal amounts of CHIKV and ZIKV in the single- and co-infections ( P = 0 . 24; Fig 5A ) . At 14 dpi , saliva was collected from the mosquitoes and the infection and transmission rates were determined by infectivity assay . ZIKV infection rates were 100% for the single-infection and 97 . 9% for the co-infection , which was not significantly different ( P = 1 . 00; Fig 5B & Table 2 ) . Similarly , no significant difference was found between infection rates of orally exposed mosquitoes to CHIKV in single- ( 81 . 2% ) and co-infection with ZIKV ( 85 . 4%; P = 1 . 00 ) . In both single- and co-infection , infection rates of mosquitoes orally exposed to CHIKV were significantly lower than those of mosquitoes exposed to ZIKV ( P < 0 . 05 ) . In total , 84 . 4% of mosquitoes that were simultaneously exposed to both viruses , were infected with both ZIKV and CHIKV . These results indicate that co-infection of ZIKV and CHIKV does not affect the infection rates in Ae . aegypti . Transmission rates of mosquitoes orally exposed to ZIKV were 68 . 3% for the single-infection and 72 . 9% for the co-infection , which was not significantly different ( P = 1 . 00; Fig 5C & Table 2 ) . For CHIKV , transmission rates were 21 . 2% for mosquitoes with a single-infection and 14 . 6% for mosquitoes with a co-infection , which was again not significantly different ( P = 1 . 00 ) . However , transmission rates of mosquitoes orally exposed to CHIKV were significantly lower than ZIKV exposed mosquitoes ( P < 0 . 001 ) . Importantly , 11 . 5% of mosquitoes that were simultaneously exposed to both viruses , had both ZIKV and CHIKV in their saliva , showing that Ae . aegypti can transmit both ZIKV and CHIKV via a single bite . In summary , these results show that simultaneous exposure can lead to concurrent transmission of both viruses without affecting the infection or transmission rates of ZIKV or CHIKV in Ae . aegypti . Although no effect of co-infection on the infection and transmission rates was observed , there might be an effect on the viral titers in either the mosquito body or saliva that could have an effect on virus transmission . Therefore , viral titers were determined at 14 dpi for both mosquito bodies and saliva samples , of mosquitoes with fully disseminated infections of ZIKV , CHIKV , or both . ZIKV median titers in mosquito bodies reached 7 . 1 × 106 after single- and 2 . 0 × 107 TCID50/ml after co-infection , whereas CHIKV reached titers of 5 . 7 × 105 after single- and 6 . 3 × 105 TCID50/ml after co-infection ( Fig 5D & Table 2 ) . Median titers of both ZIKV and CHIKV in saliva samples reached 1 . 0–4 . 6 × 103 TCID50/ml ( Fig 5E & Table 2 ) . Compared to single-infection , co-infection did not influence the titers of ZIKV or CHIKV in mosquito bodies or saliva ( P >0 . 05 ) . ZIKV titers were significantly higher than CHIKV titers in both mosquito bodies ( P < 0 . 01 ) and saliva ( P < 0 . 05; Fig 5D & 5E ) . These results show that co-infection with ZIKV and CHIKV does not affect the transmission potential of Ae . aegypti for either virus . Importantly , these experiments demonstrate for the first time that Ae . aegypti is intrinsically capable of transmitting ZIKV and CHIKV via a single bite . We observed high infection rates for ZIKV and CHIKV , but the transmission rates for CHIKV were notably lower as compared to ZIKV . This substantial difference in transmissibility of ZIKV and CHIKV by Ae . aegypti could be due to the presence of a midgut escape barrier , a salivary gland barrier or both . To discriminate between these two possibilities , female Ae . aegypti mosquitoes were intrathoracically injected ( to by-pass the midgut barriers ) with 2 . 8 × 103 TCID50 units of ZIKV , CHIKV or both viruses . After 7 days , mosquito saliva was collected and bodies and saliva were tested for presence of virus by infectivity assay . Injection with ZIKV , CHIKV , and both viruses resulted in all cases in 100% infection rates ( Fig 6A & Table 3 ) . Transmission rates of mosquitoes injected with ZIKV were similar for the single-infection ( 77 . 6% ) and after co-infection ( 68 . 8%; P = 1 . 00; Fig 6B & Table 3 ) . For CHIKV the transmission rates were also similar for the single-infection ( 22 . 9% ) and the co-infection ( 27 . 1%; P = 1 . 00 ) , again suggesting that ZIKV and CHIKV do not interfere . Transmission rates of mosquitoes intrathoracically injected with CHIKV were significantly lower compared to ZIKV ( P < 0 . 001 ) . In total , 20 . 8% of the mosquitoes that were simultaneously exposed to both viruses had both ZIKV and CHIKV in their saliva . Viral titers were again determined for mosquito bodies and saliva samples of mosquitoes with a fully disseminated infection , which were injected with ZIKV , CHIKV , or both viruses simultaneously . ZIKV reached mosquito body titers of 2 . 0 × 107 TCID50/ml after single- and 5 . 4 × 107 TCID50/ml after co-infections , whereas CHIKV reached titers of 2 . 0 × 106 after single- and 6 . 3 × 106 TCID50/ml after co-infection ( Fig 6C & Table 3 ) . Median titers of both ZIKV and CHIKV in saliva samples reached 1 . 0–4 . 2 × 103 TCID50/ml ( Fig 6D & Table 3 ) . Compared to single-infection , co-infection did not influence the titers of ZIKV or CHIKV in mosquito bodies and saliva ( P > 0 . 05; Fig 6C & 6D ) . The low transmission rates of CHIKV as compared to the high infection rates after both blood meal infection and intrathoracic injection ( Figs 5C & 6B ) , indicate the presence of a salivary gland barrier that prevents the virus from dissemination into the saliva . For ZIKV , the transmission rates after intrathoracic injections and blood meal infections are only slightly lower than the infection rates . This suggests that for ZIKV the salivary glands form a minor barrier for accumulation of infectious virus in the saliva .
Since the start of the global spread of ZIKV , this virus co-circulates with CHIKV in many parts of the world . There is an increase in the number of reports describing co-infections of ZIKV and CHIKV ( and also DENV ) in human patients , but the extent of co-infection in field-collected mosquitoes is not clear . The aim of this study was to assess whether the predominant vector of ZIKV and CHIKV in the Americas , Ae . aegypti , is able to transmit both viruses simultaneously , and whether co-infection may change the vector competence for either virus . Here we show that Ae . aegypti mosquitoes can indeed simultaneously transmit ZIKV and CHIKV via a single bite . Infection with both ZIKV and CHIKV did not result in lowered infection or transmission rates for either virus , although Ae . aegypti was shown to be a more efficient vector for ZIKV as compared to CHIKV . Finally , we show that Ae . aegypti mosquitoes have a salivary gland barrier for both CHIKV and ZIKV . Triple co-infections with ZIKV , CHIKV and DENV have already been reported in patients from Colombia and Nicaragua [21–23] and several cases of ZIKV and CHIKV co-infections in patients have been reported [18–20] . These observations indicate that a single mosquito could take a blood meal containing multiple arboviruses , potentially resulting in the transmission of different viruses simultaneously . Simultaneous transmission of alphaviruses and flaviviruses by Ae . albopictus has been reported for DENV and CHIKV [36] , and for DENV and Sindbis virus [34] . In another study , CHIKV and DENV co-transmission by either Ae . aegypti or Ae . albopictus only occurred after sequential blood meals , but not after simultaneous infection in a single blood meal [37] . Furthermore , co-infection of Sindbis virus and DENV greatly decreases both the infection and transmission rates of both viruses [34] . In contrast to these studies , our results clearly show that ZIKV and CHIKV do not interfere with each other in either their infection or transmission by Ae . aegypti . Our results show that ZIKV and CHIKV can replicate simultaneously in a single cell of both the mammalian host and mosquito vector . Furthermore , we show that ZIKV and CHIKV can simultaneously disseminate to the saliva of Ae . aegypti mosquitoes , indicating that co-infections do not strongly interfere with virus replication . The effect of co-infection of arboviruses on virus replication is still poorly understood . One explanation for interference between different viruses is superinfection exclusion , where infection of a primary virus excludes secondary infection with the same or a different virus [30] . The primary virus infection could induce the host-immune response , claim important cellular factors required for viral replication , or produce defective interfering particles , that suppress replication of a secondary viral infection . Alternatively , primary infection may lead to suppression of the mosquito’s antiviral responses , leading to enhanced infection of a secondary virus ( reviewed in [30] ) . Potentially , asynchronous co-infections of CHIKV and ZIKV in Ae . aegypti mosquitoes could result in detectable interference and may affect the infection and transmission rates . However , viral interference was observed in growth curves in Vero cells with co-infection resulting in decreased ZIKV titers . The interference in Vero cells is likely due to a decrease in cell viability as a result of CHIKV infection , leading to high cell death and , thus , less ZIKV production . In contrast to what we observed in mammalian cells , virus replication and cell viability were not affected when C6/36 or Aag2 cells were infected or co-infected by both viruses . The absence of detectable interference between the two viruses on virus replication in mosquito cell lines supports our findings on vector competence of Ae . aegypti mosquitoes infected with both viruses . CHIKV replicates in spherules at the plasma membrane [38] whereas flaviviruses replicate in perinuclear regions of the endoplasmic reticulum [39] , which may explain the lack of interference between both viruses . Furthermore , tampering of virus replication by defective interfering particles , which often contributes to viral interference , is less problematic with alpha- and flavivirus co-infections [40] . Potentially , co-infections of multiple flaviviruses ( e . g . DENV and ZIKV ) may have a stronger effect on vector competence . Our results support the role of Ae . aegypti as a competent vector for ZIKV and CHIKV . Previous vector competence studies reported infection rates between 70–100% in Ae . aegypti for ZIKV [6 , 9 , 41] . Infection rates reported here for the ZIKVSUR strain are in line with these findings with 100% infection after administering a high dose of 2 . 0 × 107 blood meal , and 65–90% with a ten- to hundred-fold lower dose of ZIKV in the blood meal . The transmission rates of 35–70% for the ZIKVSUR strain are higher than some previously reported transmission rates , which ranged between 10–30% for the American strains of ZIKV in Brazilian and Mexican Ae . aegypti mosquitoes [9 , 41] . However , studies with Australian and Poza Rica strain Ae . aegypti reported transmission rates between 70–85% [7 , 41] , which is more in the range of our findings . For the CHIKV37997 strain , the infection rates reported here ( 50–80% ) are higher than some previous reports on CHIKV vector competence , which range between 10–30% [42 , 43] . However , another comprehensive study that investigated the vector competence of ten different Ae . aegypti populations for CHIKV reported high infection and dissemination rates between 90–100% [15] . Transmission rates of CHIKV by Ae . aegypti mostly range between 40–60% with exceptions to some virus-vector strain combinations that report low transmission rates [8 , 15 , 43] . These findings confirm that vector competence is highly variable and dependent on the specific combination of viral strain and mosquito population . In order for an arbovirus to accumulate in the saliva it has to pass the midgut infection- and escape barriers and the salivary gland infection- and escape barriers [44] . A midgut barrier for CHIKV has previously been reported in Ae . aegypti [45] and Ae . albopictus [46] . Here , we report infection rates of up to 80% for CHIKV , suggesting that the midgut does not form a strong barrier against CHIKV infection in our Ae . aegypti colony . However , CHIKV transmission rates of maximum 20% after an infectious blood meal or intrathoracic injections indicate the presence of a mosquito salivary gland barrier . The presence of a salivary gland barrier is indicated by a low percentage of mosquitoes with a salivary gland infection when a larger percentage of mosquitoes reaches a disseminated infection . We determined transmission rates of CHIKV of maximum 21% after oral exposure while an 81% infection rate was observed , suggesting that the mosquito colony used has a strong salivary gland barrier to the CHIKV37997 strain . Additionally virus-positive saliva samples had a low viral titer for both ZIKV and CHIKV . Although we did not quantify the amount of saliva that the mosquitoes excreted , this suggests that there is indeed a salivary gland barrier for both viruses that prevents the accumulation of high viral titers in the saliva . We determined that the transmission rates after intrathoracic injections were similar to the transmission rates after an infectious blood meal at 7 dpi . Potentially CHIKV and ZIKV may require longer incubation periods between 7 and 14 days to successfully infect the salivary glands and reach the saliva . However , a salivary gland escape barrier has earlier been described for the CHIKV37997 strain , where only 60% of the infected mosquitoes had virus-positive saliva at 7 dpi [47] . It is surprising that ZIKV spread so rapidly from its original , natural range to territories in the Pacific ( Micronesia , Eastern Island , French Polynesia ) , and subsequently to South- and North-America between 2007 and 2015 [2] . Several hypotheses have been proposed for the rapid dissemination of ZIKV throughout the Americas . First , genetic changes of ZIKV strains could result in adaptations that make the strain that circulates in the Americas more virulent . Evidence for this comes from a comparative genomic study that indicated 15 amino acid substitutions in epidemic strains compared to pre-epidemic strains [48] . However , an African ZIKV isolate was shown to outcompete the American strain in vitro and in vivo suggesting that transmission of the epidemic strains is not enhanced [49] . Secondly , the presence of a large naïve and susceptible human population in combination with high densities of anthropophilic mosquitoes might have accelerated the spread of ZIKV . And thirdly , co-infection of mosquitoes with ZIKV and other arboviruses such as CHIKV and DENV or both may have a positive effect on the vector competence of mosquitoes , resulting in increased transmission rates and faster spread of the viruses . We now show for the first time that mosquito co-infections of ZIKV and CHIKV can indeed occur , without altering the vector competence of Ae . aegypti for either virus . Importantly , our results suggest that patients reported with ZIKV and CHIKV co-infections could have been infected with both viruses via the bite of a single Ae . aegypti mosquito . However , the proportion of co-infected mosquitoes in a population of Ae . aegypti mosquitoes is expected to be extremely small . We therefore consider it unlikely that co-infections of multiple arboviruses contribute to the rapid dissemination of ZIKV across the Americas . In summary , this study shows that Ae . aegypti can transmit ZIKV and CHIKV simultaneously by a single mosquito bite . Additionally , we show that Ae . aegypti has a higher vector competence for ZIKV than for CHIKV and that co-infections do not affect the vector competence . By comparison of infections via the blood meal with intrathoracic injections we show that Ae . aegypti has a strong salivary gland barrier for CHIKV and a minor salivary gland barrier for ZIKV . Finally , studies with cell lines show that ZIKV virus production is decreased in mammalian , but not mosquito cells , due to induction of cytopathicity by CHIKV . The outcomes of this research provide novel insights into the effects of co-infections on the transmission of arboviruses by mosquitoes . | Zika virus ( ZIKV ) and chikungunya virus ( CHIKV ) are highly pathogenic arthropod-borne viruses that present a serious health threat to humans . Since 2015 , both viruses circulate in the same geographical regions of the Americas and are predominantly transmitted by the Yellow Fever mosquito Ae . aegypti . There is a growing number of case reports of ZIKV and CHIKV co-infections in humans , but it is uncertain whether co-infection occurred via single or multiple mosquito bites . Therefore , we infected Ae . aegypti mosquitoes via an infectious blood meal with ZIKV , CHIKV , or both and scored the saliva of ( co- ) infected mosquitoes 14 days post infection for the presence of ZIKV , CHIKV or both . Ae . aegypti was competent for both viruses with transmission rates up to 73% ( ZIKV ) and 21% ( CHIKV ) . A substantial proportion of mosquitoes became saliva-positive for both viruses ( 12% ) , suggesting that Ae . aegypti can transmit both CHIKV and ZIKV via a single bite . Additionally , co-infections did not influence the infection or transmission rates of either ZIKV or CHIKV . Our results indicate that co-infection of CHIKV and ZIKV can lead to simultaneous transmission by the same mosquito in the field . |
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Reinforcement learning ( RL ) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices . An important problem , though , is how complex tasks can be represented in a way that enables efficient learning . We consider this problem through the lens of spatial navigation , examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL . Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location , revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics . Rather than localization , the key problem in learning is generalization between past and present situations , which may not match perfectly . Accordingly , although neural populations collectively offer a precise representation of position , our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons , which allows learning about rewards to generalize over longer distances given fewer training experiences . However , work on generalization in RL suggests the underlying representation should respect the environment's layout . In particular , although it is often assumed that neurons track location in Euclidean coordinates ( that a place cell's activity declines “as the crow flies” away from its peak ) , the relevant metric for value is geodesic: the distance along a path , around any obstacles . We formalize this intuition and present simulations showing how Euclidean , but not geodesic , representations can interfere with RL by generalizing inappropriately across barriers . Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields , which provides a new viewpoint on data concerning both spatial codes .
First , we used TD ( λ ) learning in three simple environments ( Figure 1A ) to test the ability of multiscale grid cell- and place cell-like basis sets to learn value functions in spatial RL ( see Materials and Methods ) . In order to verify the importance of generalization over long spatial scales , we compared learning with the modeled grid and place cell bases to a standard , tabular RL basis learning the same task . This is like a place cell basis using only a single , fixed scale of representation that is small with respect to the task-relevant distances . The simulated agent had to learn to navigate from a randomly chosen starting point to a goal state that contained a reward . To quantify performance , the number of steps needed to reach the reward was plotted as a function of the training trial . Although our key qualitative points are robust to changes in the free parameters ( simulations not shown ) , to ensure a fair comparison we optimized the learning rate ( a crucial free parameter ) separately for each condition ( i . e . basis function and gridworld ) to obtain its best performance . We additionally used the TD ( λ ) generalization of TD with a high value ( 0 . 9 ) of the eligibility trace parameter λ , since this provides another mechanism for learning to generalize along trajectories and might , in principle , help to compensate for the shortcomings of the tabular or Euclidean bases . As Figure 1B shows , the grid and place cell basis sets drastically quicken learning the value function compared to the tabular code , demonstrating the benefits of spatial generalization . Figure 2 illustrates the approximated value functions at different stages of learning and qualitatively shows the importance of generalization . In particular , the tabular basis does not take advantage of the spatial structure to generalize quickly and must learn each state's value separately from its neighbors by a slow process of TD chaining . Figure 2 also hints at a subtler problem of overgeneralization in Euclidean space . In particular , these grid and place cell basis functions tend to smear the value function across barriers , where it should change sharply ( arrows in Figures 2B and 2C , where the effects are most apparent ) . Because of this , value is underrepresented at states inside the walls ( i . e . locations closer to the reward , as in 2B ) and overrepresented on the other side of the barrier ( most visible in 2C ) . This distortion remains at asymptote and is likely not an artifact of insufficient experience . While this flaw does not notably degrade performance in these simple tasks , it can be detrimental when fine navigational precision is required . To demonstrate this , we tested the models in three environments that required the agent to navigate narrow halls or openings , and thus learn precise state value representations ( Figure 3A ) . Here , the grid cell and place cell basis functions performed poorly , and were outperformed by the tabular basis ( Figure 3B ) . Together , then , these simulations demonstrate that generalization due to spatial representations like those seen in the brain can help make reinforcement learning more efficient , but also that such generalization has drastic ( and , presumably , behaviorally unrealistic ) side effects , abolishing learning in tasks where paths are narrow . In general , as can be seen directly in the recursive definition of the value function , ( Equation 1 in Materials and Methods ) , the extent to which values are related between two states depends on how closely they are connected by the state-state transition probability function . Accordingly , work on value function approximation for reinforcement learning has proposed [14]–[17] that basis functions should be constructed to respect distance along the state transition graph . For instance , in temporal prediction tasks , value functions are smooth in time [61] . In a spatial task , the transition dynamics imply that states have similar values when they are near each other , but near as measured in geodesic ( along-path ) distance , rather than “as the crow flies” ( Euclidean distance ) . Formally , geodesic distance measures the number of steps along the transition graph needed to get from one state to another . A basis over geodesic distances would treat states separated by a boundary as comparatively far apart , enabling their values to be discontinuous , whereas the Euclidean basis used above ( and ubiquitously to characterize the spatial extent of place and grid fields ) would inappropriately treat them as adjacent . These considerations suggest that for efficacious representation of value functions over state space , the brain should adopt basis functions that are smooth along geodesic rather than Euclidean distances . In the open field there should be no difference between geodesic and Euclidean representations , since these metrics coincide there . However , if an environment has barriers , then Euclidean and geodesic firing fields will differ . The effect of such a difference should be to introduce geometric distortion into geodesic firing fields nearby obstacles , where geodesic and Euclidean metrics differ . Such a distortion can be characterized ( and indeed implemented ) by mapping the original Euclidean vector coordinates through an additional transform that accounts for geodesic distance . However , in the present work our goal is to investigate the brain's spatial representations through the lens of their downstream computations; thus , in contrast to much work on the hippocampal system [12] , [35] , [36] , [40] , [43] , [44] , [47] , [48] , [62] , [63] we do not focus on the “upstream” computations by which the grid or place representations ( or their hypothesized distortions ) are themselves computed from inputs . That is , we take geodesic or Euclidean representations as a given and focus our analysis on hypothesized learning that relies on entorhinal and hippocampal outputs . In particular , we modeled how basis functions would appear in environments with barriers , if they followed a geodesic metric , by evaluating Euclidean grid or place fields ( characterized by spatial grids or Gaussians ) over a new set of x–y coordinates , chosen such that their pairwise Euclidean distances approximated the states' geodesic distances ( see Materials and Methods ) . When viewed in the original Euclidean space , the effect of barriers is to produce geometric distortions , such as variations in grid orientation and firing field shapes ( Figure 4 ) . As one might expect , the basis functions tend not to cross walls and instead skirt along connected paths . We tested the geodesic bases in the environments that stressed importance of along-path generalization ( Figure 3A ) . As can be seen , the geodesic bases alleviated the poor learning caused by the indiscriminate generalization of their Euclidean counterparts ( Figure 3B ) . Since the geodesic grid cells and place cells generalize using the state transition graph , they learn at least as fast as the tabular TD control ( Figure 3B ) . Figure 5A–C depicts typical value functions at different stages of training using the geodesic basis functions ( 25 trials for Figure 5A–B , 50 trials for Figure 5C ) . Also note that both the Euclidean and geodesic bases used the same multiple granularities and tiling , with the sole difference the distance metric used . To test the role of multiple tilings in learning , we performed follow-up simulations for each of the six gridworlds using three different tiles bases . While the tile bases often learned faster than the tabular basis ( which one would expect ) , overall the geodesic bases tended to perform best ( data not shown ) . Together , these simulations demonstrate the representation benefits conferred by geodesic generalization , in particular how generalization along paths rather than across walls solves the problem of overgeneralization interfering with learning in the presence of obstacles . That the same qualitative results hold up using both grid-cell-like and place-cell-like representations points to their generality . In simulations not shown here , we also produced similar results using an overlapping tile code at a variety of single scales [8] , suggesting that the results relate to spatial generalization per se and not to the multiscale nature of the ( biologically inspired ) bases used here . The foregoing simulations suggest that to support efficient navigation , the brain's spatial representations should generalize according to a geodesic rather than a Euclidean metric . Of course , these two representations coincide in the open field , where most studies have been conducted . However , we believe our model's predictions are consistent with a number of studies where researchers recorded neurophysiological activity while rats foraged in environments containing barriers . Here we compare our model to examples from three studies [24] , [64] , [65] . Skaggs & McNaughton [64] recorded place cells as rats moved between two separate enclosures that were connected by a narrow corridor ( schematized in Figure 6 , top; cf . Figure 4 in [64] ) . Although this was not the major experimental question of the study , the narrow corridor provides a good test for our model's prediction that place fields should track along paths rather than ( as a Euclidean place field predicts ) across barriers . In the examples reproduced here , for instance , place cell spikes are almost exclusively confined to either the connecting corridor's entrance ( Figure 6A , left ) or the pathway between the two rooms ( Figure 6A , right ) . The spikes do not generalize across the walls separating parts of the environment , but instead appear to track along paths around them ( Figure 6 ) , even though a standard isotropic Gaussian place field over Euclidean coordinates would clearly not respect these barriers . The data are , however , similar to place field responses from the geodesic model in a similar environment ( Figure 6 , bottom ) . In another study [65] , a place field was first recorded in an open box and again after adding a barrier to the enclosure ( Figure 7A; cf . to Figure 8 in [65] ) . Recorded hippocampal place cell responses in the open field vanished immediately when the firing field was bisected by a wall [65] , Figure 6A . The geodesic model of neural spatial representation provides an elegant , intuitive account for why the place field disappears , whose graphical intuition is displayed in Figures 7A–B . In an environment without walls , one can think of the recorded place cell activity being measured over evenly spaced locations in 2D enclosure ( Figure 7A , left ) . Once a barrier is introduced that bisects the field , the nearby locations on adjacent sides of the wall are pulled apart , which changes the spacing between neighboring points compared to its Euclidean counterpart . Locations on either side of the wall are far , in geodesic terms , from each other , and from the center of a place field centered in the wall itself . As a result , a sinkhole is created that swallows the place field in the geodesic coordinate space , thus muting its activity ( Figure 7A–B ) . Similar results were also seen in a recent study of how place cell firing fields changed when mazes were reconfigured [66] . In particular , this work replicated the phenomenon of place fields diminishing or disappearing near newly introduced obstacles , and verified ( as in our simulations ) that such changes predominate near newly introduced obstacles . The study also demonstrates a rarer , complementary phenomenon whereby the introduction of obstacles caused firing to increase or even new place fields to appear , as verified in our simulations . In our model ( Figure 8 ) , increased firing is the flip side of responses diminishing for neurons coding “holes” in geodesic space; it occurs when geometric distortion “pushes” locations into areas previously off the map . Finally , Derdikman et al . [24] recorded from grid cells as a rat ran along a hairpin maze . Figures 1 and 2 from [24] show typical grid cell firing fields in an open field and again in a hairpin maze . The standard hexagonal pattern of responding is extremely distorted; instead , responses tend to track along the hallways but not to cross walls , and firing fields are similar between alternate arms . Grid cells simulated in the geodesic space share a number of these characteristics ( Figure 9 ) , though not ( as discussed below ) all of them . One limitation of the model is that it does not capture the repetitive place field firing observed by Derdikman et al . [24] .
Although researchers widely assume that reinforcement learning methods such as temporal difference learning subserve learned action selection in the brain [9]–[11] , it is less clear how tasks involving many structured states can be represented in a way that enables these methods to learn efficiently , due in large part to the curse of dimensionality . In computer science , stylized spatial navigation ( gridworld ) problems are the classic domain for studying this issue , since the state space is large but transparently visualized and manipulated [8] . Here we consider rodents' neural representations of spatial location from this perspective , treating them as basis functions for downstream reinforcement learning in high-dimensional state spaces and asking how well adapted they are to this role . Though previous modeling work has not extensively considered the constraints on the brain's location codes implied by this function , much work has more or less implicitly exploited the idea that unlike the tabular basis often assumed in simple RL , the spatial extent of place fields can help to cope with the curse of dimensionality by allowing learning to generalize between nearby locations [3] , [50] , [51] even over multiple scales [30] . The present study extends this idea to consider such generalization in light of work on efficient representation in machine learning [14]–[17] . These theoretical considerations , illustrated and verified by our simple simulation results , suggest that to enable efficient representation of value ( or other ) functions over space , grid and place fields should operate in a distorted geometry: generalizing according to geodesic ( on-path ) rather than Euclidean ( as-the-crow-flies ) distances . Although these two distance metrics coincide in the open field , they differ in the presence of boundaries . The geodesic metric predicts that grid and place fields should not spill across walls but should instead track along paths , and should also exhibit geometric distortions , such as altered grid orientation , near boundaries . We have reviewed data from a number of experiments that seem largely in accord with these predictions . It should be noted that these predictions are all at the neural level , and could be most directly tested quantitatively by simply examining whether neural firing is modulated more reliably with distances measured by either metric: e . g . , regressing distance ( computed according to either definition ) from a place field's center on firing rate . By contrast , since our argument is primarily one about learning efficiency ( which is difficult to quantify behaviorally , since it is affected by many factors ) , our model does not make categorical behavioral predictions . Our simulations ( Figure 3 ) demonstrate that simple TD models with Gaussian place fields ( like that of [3] ) can entirely fail to solve simple navigation problems involving narrow apertures or hallways . However , the fact that rats do not exhibit such problems of course does not by itself demonstrate that the brain adopts the same solution for this problem as the one we propose . Also , to focus on our main questions of interest , we omit many features that other models use to explain various behavioral phenomena of navigation , among them mechanisms for allocentric route-planning ( important for quick goal learning [3] and for planning shortcuts [67] ) and localization driven by combinations of cues and path integration [4] , [68] , both issues we discuss further below . The concept of geodesic generalization provides a formal perspective on spatial representation which is different from , but complementary to , much other work in this area . Whereas much experimental and theoretical work on the hippocampal formation concerns essentially sensory-side questions—how place or grid cells combine different sorts of inputs to produce their instantaneous representations , or to learn them over time—we attempt to isolate the downstream question of how the resulting representations serve downstream learning functions . To this end , we do not address the input-side question of how the hypothesized distorted spatial representations are themselves produced from more elementary inputs . We only assume , abstractly , that the basis functions are computed on the fly from a learned map of the barriers in the environment . In sparse environments such maps could easily be learned from observation in a single trial , and may implicate the “border cells” of entorhinal cortex [69] . All this leaves open the opportunity , in future work , for studying how the input- and output-side perspectives relate: whether the mechanisms studied by previous authors might be made to produce or approximate representations of the sort we envision . For instance , in the geodesic view , place fields tend to be unidirectional on the linear track [70] , [71] because the states of passing through them facing either direction are far apart in the state transition graph of a shuttling task . In input terms this more abstract relationship between states may be reflected in these situations being visually distinct [70] , [71] or anchored to a different prior reference point [72] . More generally , unlike idealized RL models [3] , [51] , theories of how place cells arise from sensory inputs ( e . g . via competitive learning [70] , [71] , or self-organizing maps [73] ) do not necessarily imply the isotropic Gaussian firing fields we criticize , and thus may also offer ( more mechanistic ) explanations for phenomena such as place fields not crossing walls . It remains to be seen to what extent such local learning rules can be massaged to produce maps that accord with the globally geodesic ideal . However , such unsupervised learning models tend to envision that representations are acquired incrementally over time , which stands in contrast to our assumption ( supported by data such as place field changes occurring immediately when barriers are added [65] ) that the geodesic basis is computed on the fly with respect to the current barrier locations . A different mechanism that could be useful in producing geodesic firing fields is the “arc length” cell posited by Hasselmo [63] , [74] , a circuit for computing along-path distance using oscillatory interference mechanisms related to those thought to be involved in grid formation . This mechanism has already been used to explain several examples of context-dependent firing of hippocampal neurons similar in spirit to the phenomena we consider here . The behavior of the entorhinal representation also raises interesting questions about the relationship between input- and output-side considerations . To start , it is often assumed that the place code is built up by linear combinations of grid cell inputs , e . g . by a sort of inverse Fourier transform [13] . In such a model , it can be shown ( and simulations , not shown , verify ) that place cells will inherit the geometry of their grid cell inputs . For this reason , we suggest that grid cells are likely to use a geodesic metric even if they do not directly serve as a basis for value function learning ( but only indirectly , as a basis for geodesic place cells ) . However , this exposes some tension between the output-side imperative of generalization for RL , which we have argued calls for geodesic distortions , and the input-side implication of the system in path integration ( i . e . tracking vector coordinates in a path-independent manner ) [35] , [37]–[40] , [47] , [75] , which is an inherently Euclidean operation . In this respect , the recent results of Derdikman et al . [24] showing distorted and fractionated grid fields in a hairpin maze seem difficult to reconcile with a global Euclidean path integrator ( since the hairpin barriers do not change the Euclidean coordinates ) , and at least qualitatively more in line with the geodesic view . One possible path toward reconciling these considerations is to consider a sort of hierarchical representation that treats the environment as a collection of rooms ( in the hairpin maze , hallways ) whose interrelationships are represented as by a geodesic graph , but with ( disjoint ) Euclidean representations maintained within each of them . This has resonance with multi-level navigation models from animal behavior ( e . g . [68] ) , with multiple map views of hippocampus [72] , and , also , mechanistically , with some of the more detailed aspects of the Derdikman [24] data that are not captured by our model . Most importantly , the Derdikman data suggest that the grid phase resets and “anchors” at left or right turns , producing similar patterns in alternating arms and suggesting a possible mechanism for separating adjacent hallways' representations . Such heuristics for grid resetting and anchoring ( and also stretching ) [24] , [34] may be able to produce a “good enough” approximation to the geodesic metric , at least in some environments , and have been examined in much more detail in more biologically detailed modeling of the task [38] . One sign of approximations is where they break down . In this respect , it is interesting that the rather extreme case of the hairpin maze results in badly fractionated downstream place fields as well [24] , a phenomenon not predicted by the exact geodesic model . Finally , unlike our full model , a resetting mechanism would not in itself seem to explain phenomena related to barriers within a room , such as those we illustrate in Figure 7 . A fuller understanding of these sorts of mechanisms demands additional research , both experimental and theoretical . Our simulations also demonstrate that the grid representation itself is a suitable basis for value function learning , even without an intermediate place cell representation . On one level , these results serve to underline the generality of our points about geometry and generalization , using a rather different basis . More speculatively , they point to the possibility that the grid representation might actually serve such a role in the brain , echoing other work on the usefulness of this Fourier-like basis for representing arbitrary functions [12] , particularly ( as also for standard uses of Fourier representations in engineering for compressing images and sounds ) smooth ones . However , although a few studies have demonstrated anatomical connections from the entorhinal cortex to striatum [55]–[57] , [76] , grid-like responses are less often reported in the deep layers that give rise to these subcortical projections ( though see [53] , [54] ) . Finally , although for simplicity and concreteness we have focused on the principles of value function generalization in the context of a particular task ( spatial navigation ) and algorithm ( TD ( λ ) learning ) , many of the same considerations apply more generally . First , across domains , in computational neuroscience , the need for ( temporally ) smooth basis functions been suggested to improve generalization also in learning about events separated in time rather than space [61] , though there is no obvious counterpart to the geodesic distance metric in this setting . Second , across algorithms , TD-like learning mechanisms also likely interact with additional ones in the brain , and the core considerations we elucidate about efficient generalization due to appropriate state space representations crosscut these distinctions . For instance , value functions may also be updated using replay of previously experienced trajectories ( e . g . , during sleep ) [28] , [51] . In models , this is typically envisioned to operate by the same TD learning rule operating again over the replayed experience [51] , [77] , and thus should imply parallel considerations of efficiency with respect to the number of replayed experiences required for convergence depending on the generalization characteristics of the basis . More distinct from these models , since the work of Tolman [67] it has been believed that spatial navigation may in part be accomplished by map-based route-planning processes that in RL terms correspond to model-based algorithms [78]–[82] rather than model-free algorithms like TD learning . These algorithms plan routes from a learned representation of the state transition matrix and rewards , typically using variants of the value iteration algorithm to compute state or action values . The core of this process is the iterative evaluation of Bellman's equation ( Equation 1 in Materials and Methods ) , the same equation sampled with each learning step of TD . Thus , there is reason to think that efficient value iteration ( here defined as fast convergence of the value function over iterations ) will analogously occur when the update is over state representations that provide better generalization over states at each step . In all , then , although we exemplify them in a highly simplified model , the principles of state representation for efficient reinforcement learning are quite general . Another issue arises when considering the present model in light of model-based RL . One of the hallmarks of model-based planning ( and the behavioral phenomena that Tolman [67] used to argue for it , albeit not subsequently reliably demonstrated in the spatial domain ) , is the ability to plan novel routes without relearning , e . g . to make appropriate choices immediately when favored routes are blocked or new shortcuts are opened . Interestingly , rather than by explicit replanning , some such behaviors could instead be produced more implicitly by updating the basis functions to reflect the new maze , while maintaining the weights connecting them to value . This is easy to demonstrate in the successor representation [16] , a model closely related to ours . To behave similarly , the present model would require additional constraints to ensure the basis functions corresponding to different mazes are interchangeable , but this would be one route toward explaining shortcut phenomena in this framework . More generally , because the present proposal uses a state transition model , implicitly , to generate a basis function that is then used with model-free learning [see also 16] , [83] , [84] , it resembles something of a cooperative hybrid of model-free and model-based techniques somewhat different from the competitive approaches suggested elsewhere [78] .
We simulate value function learning in a gridworld spatial navigation task in order to compare linear function approximation over several different spatial basis sets [8] . Our model learns to estimate the value function over states ( i . e . , positions in the grid ) , defined in the standard way as the expected future discounted reward: ( 1 ) To simplify notation , we omit the dependence of these quantities on the action policy throughout . The model learns approximations to these values by learning a set of N linear weights w1…N for N spatial basis functions φ1…N ( s ) defined over the entire state space . The estimated value is thus: ( 2 ) We use a simple temporal-difference algorithm with eligibility traces [8] , [85] to learn weights . Specifically , at each run upon visiting state s receiving reward r ( s ) and transitioning into state s′ , for each basis φi , weights wi are updated at each time step using the following algorithm: ( 3 ) This is just the version of the familiar TD ( λ ) rule for linear value function approximation , with free parameters α ( learning rate ) , λ ( trace decay rate ) , and γ ( discount factor ) . We tested the model in 20-by-20 ( M = 400 states ) gridworlds in which the agent could move in any of the four cardinal directions , unless a wall blocked such a movement . Agents were started at a random location ( i . e . state ) at each trial , and had to reach the goal state , which was the only state with a reward , r ( s ) = 1 . Individual trials ended when the agent reached the goal state , which was absorbing , or the maximum number of actions allowed , which was 500 . For simplicity , as described above the agent learns the value function over states and uses this to guide actions toward the goal , rather than directly learning the full Q-function over states and actions . This is because , in a spatial gridworld task , the state-action-state transition model is transparent , so we assume the agent evaluates the value of each action in a state as the value of the appropriate neighboring state [86] . Since the computation of Q involves a single step of what amounts to model-based lookahead , the approach is not as purely model-free as standard Q-learning or actor-critic algorithms . As with eligibility traces , we include this elaboration because it slightly improves generalization between states and actions , and might thus reduce the need for the sorts of basis-function-based generalization mechanisms we argue for . The agent chooses actions according to a softmax policy , i . e . , where actions unavailable ( due to walls ) are not considered and β is the inverse temperature that balances the amount of exploration and exploitation in action selection . For these simulations , the inverse temperature was fixed to β = 80 ( a factor calibrated to provide a reasonable explore/exploit balance in choice probabilities given the scale of the action values learned ) . To maintain such balance , because each gridworld had a different distance between the goal state and other states , for each environment the discount factor was scaled to γ = 0 . 9d/c so that each gridworld had the same value range . Here , d is the shortest maximum distance from any state to the goal , across all gridworlds tested , and c is the maximum interstate distance for a given gridworld ( range 26 to 105 states ) . In order to compare fairly the different basis functions , the learning rate α was chosen for each condition and each basis set to minimize the mean number of steps to termination over a fixed number of trials , using a grid search in the range [0 , 1] . All simulations and analyses were performed using Matlab ( Natick , MA ) . We compare the model's learning using several different linear basis sets . Each basis is an M ( states ) ×N ( basis functions ) matrix , with each column φi defining a function over the states . Bases were constructed as below , and lastly each row of the matrix was normalized by its L2 norm . This ensures that the learning rate parameter α in the update rule ( Equation 3 ) has a consistent interpretation ( as a fractional stepsize ) between different states and basis sets . | The central problem of learning is generalization: how to apply what was discovered in past experiences to future situations , which will inevitably be the same in some respects and different in others . Effective learning requires generalizing appropriately: to situations which are similar in relevant respects , though of course the trick is determining what is relevant . In this article , we quantify and investigate relevant generalization in the context of a particular learning problem often studied in the laboratory: learning to navigate in a spatial maze . In particular , we consider whether the brain's well-characterized systems for representing an organism's location in space generalize appropriately for this task . Our simulations of learning verify that to generalize effectively , these representations should treat nearby locations similarly ( that is , neurons should fire similarly when an animal occupies nearby locations ) —but , more subtly , that to enable successful learning , “nearby” must be defined in terms of paths around obstacles , rather than in absolute space “as the crow flies . ” These considerations suggest new principles for understanding these spatial representations and why they appear warped and distorted in environments , such as mazes , with barriers and obstacles . |
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Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus ( HIV ) and the Hepatitis C Virus ( HCV ) have been demonstrated to be informative of viral fitness . Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures . Here , considering multiple HIV and HCV proteins , we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins . Specifically , by introducing a robust statistical method based on sparse principal component analysis , we identify near-disjoint sets of collectively-correlated residues ( sectors ) having mostly a one-to-one association to largely distinct structural or functional domains . This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution , with each mode involving a sparse and localized network of mutational interactions . Moreover , individual inferred sectors of HIV are shown to carry immunological significance , providing insight for guiding targeted vaccine strategies .
HIV and HCV are the cause of devastating infectious diseases that continue to wreak havoc worldwide . Both viruses are highly variable , possessing an extraordinary ability to tolerate mutations while remaining functionally fit . While individual residue mutations may be deleterious , these are often compensated by changes elsewhere in the protein which restore fitness [1 , 2] . These interacting residues form compensatory networks which provide mutational escape pathways against immune-mediated defense mechanisms , presenting a major challenge for the design of effective vaccines [3] . The compensatory interaction networks that exist for HIV and HCV—and for other viruses more generally—are complicated and far from being well understood . Resolving these by experimentation is difficult , due largely to the overwhelming number of possible mutational patterns which must be examined . An alternative approach is to employ computational methods to study the statistical properties of sequence data , under the basic premise that the residue interactions which mediate viral fitness manifest as observable mutational correlation patterns . For HIV , recent analytical , numerical and experimental studies [4–7] provide support for this premise , indicating that these patterns may be seen as population-averaged evolutionary “footprints” of viral escape during the host-pathogen combat in individual patients . This idea has been applied to propose quantitative fitness landscapes for both HIV [8 , 9] and HCV [10] which are predictive of relative viral fitness , as verified through experimentation and clinical data . Fitness is a broad concept that is ultimately mediated through underlying biochemical activity . For both HIV and HCV , experimental efforts have provided increased biochemical understanding of the constituent proteins , leading in particular to the discovery of small and often distinct groups of residues with functional or structural specificity ( Fig 1 and S1 File ) . These include , for example , sets of protein residues lying at important structural interfaces , those involved with key virus-host protein-protein interactions , or those found experimentally to directly affect functional efficacy . An important open question is how these biochemically important groups relate to the interaction networks formed during viral evolution . Some insights have been provided for a specific protein of HIV [11] and HCV [12] . The main objective of these investigations was to identify potential groups of co-evolving residues ( referred to as “sectors” ) which may be most susceptible to immune targeting . In each case , a sector with potential immunological vulnerability was inferred , and this was found to embrace some residues with functional or structural significance . It is noteworthy that the employed inference methods were designed to produce strictly non-overlapping groups of co-evolving residues , which may hinder identification of inherent co-evolutionary structure and associated biochemical interpretations . In a parallel line of work , computational methods have been used to understand co-evolution networks for various protein families , with compelling results ( reviewed in [13] ) . Notably , for the family of S1A serine proteases [14] , a correlation-based method termed statistical coupling analysis ( SCA ) uncovered a striking modular co-evolutionary structure comprising a small number of near-independent groups of co-evolving residues ( again referred to as sectors ) , each bearing a clear and distinct biochemical association , in addition to other qualitative properties . Sectors have also been obtained for other protein families using SCA , and the functional relevance of these has been confirmed through experimentation [14–17] . A natural question is to what extent such modular , biochemically-linked co-evolutionary organization exists for the viral proteins of HIV and HCV ? This is not obvious , particularly when one considers the evolutionary dynamics of these viruses , which are complex and very distinct to those of protein families . Specifically , they involve greatly accelerated mutation rates , and are shaped by effects including intrinsic fitness , host-specific but population-diverse immunity , recombination , reversion , genetic drift , etc . The sampling process is also complicated and subject to potential biases , making inference of co-evolutionary structure difficult . In this paper , considering multiple proteins of HIV and HCV , we identify in each case a sparse and modular co-evolutionary structure , involving near-independent sectors . This is established by introducing a statistical method , which we refer to as “robust co-evolutionary analysis ( RoCA ) ” , that learns the inherent co-evolutionary structure by providing resilience to the statistical noise caused by limited data . Strikingly , the sectors are shown to distinctly associate with often unique functional or structural domains of the respective viral protein , indicating clear and well-resolved linkages between the evolutionary dynamics of HIV and HCV viral proteins and their underlying biochemical properties . Our results suggest that distinct functional or structural domains associated with each of the viral proteins give rise to quasi-independent modes of evolution . This , in turn , points to the existence of simplified networks of sparse interactions used by both HIV and HCV to facilitate immune escape , with these networks being quite localized with respect to specific biochemical domains . The insights provided by the inferred sectors also carry potential importance from the viewpoint of immunology and vaccine design , which we demonstrate for a specific protein of HIV .
By employing available sequence data , we investigated the co-evolutionary interaction networks for multiple HIV and HCV proteins . Our proposed approach RoCA resolves co-evolutionary structures by applying a spectral analysis to the mutational ( Pearson ) correlation matrices and identifying inherent structure embedded within the principal components ( PCs ) , which are representative of strong modes of correlation in the data . The RoCA algorithm is designed to be robust against statistical noise , which is a significant issue since the number of available sequences for each protein is rather limited , being comparable to the protein size ( Fig 2A ) . The RoCA method comprises two main steps . Similar to the previous PCA-based co-evolutionary methods [11 , 12 , 14] , the first step involves isolating PCs which carry correlation information from those which are supposedly dominated by statistical noise ( Fig 2B , top panel ) . The second and most significant step of RoCA provides robust estimates of the PCs ( Fig 2B , bottom panel ) selected in the first step . We developed an automated and suitably adapted version of a sparse PCA technique [19] , which is based on the standard orthogonal iteration procedure used to obtain the PCs of a matrix [20] . To summarize , this step ( Fig 2B , bottom panel ) involves: ( i ) a data-driven thresholding procedure applied to the PCs—designed based on ideas from random matrix theory—that distinguishes , for each PC , the significantly correlated residues from those residues whose correlations are consistent with statistical noise , and ( ii ) an iterative procedure that tries to robustly estimate the correlation structure between the selected residues across different PCs ( see Materials and methods for details ) . Based on the resulting PCs , the RoCA algorithm directly infers co-evolutionary sectors , representing groups of residues whose mutations are collectively coupled ( Fig 2C ) . We note that while many sparse PCA methods have been developed [19 , 21–24] and applied previously to problems in different fields ( e . g . , senate voting and finance [25] , network systems [26] , image processing [27] , and genome-wide association studies in cancer [28] ) , to our knowledge , RoCA is the first application of sparse PCA techniques to the co-evolutionary analysis of proteins . The automated robust estimation of PCs produced by RoCA ( Fig 2B , bottom panel ) bears an important distinction from previously proposed sectoring methods [11 , 12 , 14] which ( indirectly ) attempted to reduce the effect of statistical noise in the PCs using either visual inspection or an ad hoc thresholding procedure . Moreover , other than applying a suitable data-driven thresholding step to remove statistical noise , RoCA makes no structural imposition on the inferred sectors ( e . g . , enforcing the sectors to be non-overlapping as in [11 , 12 , 15] ) , and it is therefore designed to reveal inherent co-evolutionary networks as reflected by the data . We used RoCA for inferring the co-evolutionary structure of two proteins each of HIV and HCV which represent a good mix of structural ( HIV Gag ) , accessory ( HIV Nef ) , and non-structural ( HCV NS3-4A and NS4B ) proteins . For each viral protein , the RoCA method identified a small number of sectors ( Fig 3A and S1 Fig ) which together embraced a rather sparse set of residues ( i . e . , between 35%–60% of the protein; see S2 File for the complete list ) . In some cases the sector residues were localized in the primary sequence , while in others they were quite well spread ( Fig 3B and S1 Fig ) . Importantly , while each sector was identified from a distinct PC , they were found to be largely disjoint ( Fig 3A and S1 Fig ) . This suggests that the co-evolutionary structures are highly modular , with the different modules ( sectors ) being nearly uncorrelated to each other . In fact , further statistical tests demonstrated that the inferred sectors are nearly independent ( Fig 3C and S1 Fig ) . This identified modular co-evolutionary structure is in fact reminiscent of ‘community structure’ that has been observed in numerous complex networks , e . g . , metabolic , webpage , and social networks [29] . In such applications , the identified modules or communities have been shown to represent dense sub-networks which perform different functions with some degree of autonomy . For our co-evolutionary sectors , in line with previous studies on the fitness landscape of HIV [6–8] and HCV [10] , they appear to represent groups of epistatically-linked residues which work together to restore or maintain viral fitness when subjected to strong selective pressures during evolution ( e . g . , as a consequence of immune pressure ) . In light of this , one anticipates that the co-evolutionary sectors should afford an even deeper interpretation in terms of the underlying biochemical properties of the viral proteins , which fundamentally mediate viral fitness . To explore potential correspondences between the identified RoCA sectors and basic biochemical properties , we compiled information determined by experimental studies for each of the viral proteins . This consists of residue groups having prescribed functional or structural specificity ( see Fig 1; also S1 File for a more extensive list including small groups ) . These groups , which are seen to occupy sparse and largely distinct regions of the primary structure ( Fig 1 ) , are collectively referred to as “biochemical domains” . ( This should not be confused with the term “domain” , as classically used for a folding unit in structural biology and biochemistry . ) For each viral protein , structural domains were defined based on spatial proximity of residues in the available protein crystal structure; they include , for example , residues which lie on critical interfaces needed to form stable viral complexes , or those involved in essential virus-host protein-protein interactions . Functional domains , on the other hand , were typically identified using site-directed mutagenesis or truncation experiments , and they include groups of residues found to have a direct influence on the efficacy of specific protein functions . It is important to note , however , that while structural domains are typically clearly specified , functional domains are expected to be less so , due to experimental limitations . Results reported based on truncation experiments , for example , may comprise false positives . This is because the truncation experiment ( see [30 , 31] for specific examples ) typically involves a coarse procedure to predict the functionally important residues by removing different groups of contiguous residues from the protein , and studying the effect of each truncation on the protein function . This procedure may suggest a particular group of residues to be important for a protein function when only one or two residues may be critical . Thus , the remaining residues in the reported important group would be false positives . Despite potential limitations of the compiled biochemical domains , contrasting these domains with the RoCA sectors ( for all four viral proteins ) revealed a striking pattern , with most sectors showing a clear and highly significant association to a unique biochemical domain ( Fig 4 ) . This is most marked for the HIV Gag protein , where there is a one-to-one correspondence . These observations carry important evolutionary insights . Not only are the co-evolutionary networks of both HIV and HCV proteins modular , but the modules ( sectors ) seem to be intimately connected to distinct biological phenotype . Our results suggest that the fundamental structural or functional domains of these viral proteins spawn quasi-independent co-evolutionary modes , each involving a simplified sparse network of largely localized mutational interactions . The observed phenomenon is seemingly a natural manifestation of immune targeting against residues within the biochemical domains , since escape mutations at these residues likely lead to structural instability or functional degradation , necessitating the formation of compensatory mutations to restore fitness . We investigated whether our main findings could also be revealed by other sectoring methods . We first considered a method that we proposed previously based on classical PCA [12] , that sought to identify groups of collectively-correlated viral residues which may be susceptible to immune targeting . Note that this PCA-based method [12] is very similar to the method introduced in [11] , mainly differing in the procedure to form sectors from PCs; specifically , the method in [11] formed sectors from visual inspection of PC biplots , whereas an automated procedure was applied in [12] ( see S1 Text for implementation details ) . Moreover , while the PCA-based method [12] was used to study only a specific HCV protein , it is general in its application ( similar to the method presented in [11] and the proposed RoCA method ) and can be used to infer co-evolutionary sectors for the viral proteins under study ( S1 Text ) . An important feature of the PCA method [12] ( and also [11] ) was the imposition of a structural constraint in the inferred sectors , enforced to be disjoint , which may compromise its ability to infer natural co-evolutionary structure ( S1 Text ) . Despite the imposed zero inter-sector-overlap constraint , sectors produced by the PCA-based method [12] for the studied viral proteins tended to be larger than RoCA sectors ( S3 Fig ) , and they collectively embraced a larger set of residues ( covering 40%–80% of the protein ) . Comparing the sectors inferred by this method with those obtained using the RoCA method revealed that they included a mix of residues from multiple RoCA sectors ( Fig 5A ) , a fact that was also reflected in the biochemical associations of the sectors , where much of the resolved ( unique ) sector/domain associations shown by RoCA ( Fig 4 ) were indeed no longer revealed ( Fig 5C ) . We found that these key differences were attributed to the sensitivity of the PCA-based approach to sampling noise , as reflected by the noisy and significantly overlapping principal components ( Fig 5B ) . This was corroborated with a ground-truth simulation study , through which the ability to infer co-evolutionary structure was tested in synthetic model constructions ( S2 Text ) . The RoCA method resolved all the individual ( true ) sectors with high accuracy , whereas our previous method [12] inferred comparatively large sectors , which often included false positives and merged residues from different true sectors ( S3 Fig ) . We also tested other co-evolutionary methods , which tended to return very different results to RoCA , and generally revealed little biochemical association for the studied viral proteins ( S5 Fig ) . Most notable is the limited biochemical association of sectors identified using the benchmark SCA method [14] ( S5 Fig ) which has shown much success in resolving co-evolutionary structure for certain protein families [15 , 32] . Aside from the noise sensitivities shared by both SCA and classical PCA-based methods ( S5 Fig ) , the surprising disparity in this case appears due to the weighted covariance construction employed by SCA ( as opposed to the Pearson correlation ) which , while apparently suited to the analysis of certain protein families data [14–17] , does not seem suitable for identifying the co-evolutionary structure in the considered HIV and HCV proteins ( see S3 Text for details ) . In the following , we provide details on the biochemical associations of the identified RoCA sectors for each of the four viral proteins . Our main results carry potential immunological significance , which may provide useful input for vaccine design . To demonstrate this , we considered the HIV Gag protein , and contrasted the inferred RoCA sectors with the epitope residues targeted by T cells of HIV “long-term non-progressors” ( LTNP ) and “rapid progressors” ( RP ) . LTNP correspond to rare individuals who keep the virus in check without drugs , whereas RP are individuals who tend to progress to AIDS in less than 5 years ( compared to the population average of 10 years [56] ) . Clinical studies of HIV-infected cohorts have demonstrated a high correlation between possession of specific human leukocyte antigen ( HLA ) alleles with the disease outcome ( LTNP or RP ) [57 , 58] . The information of the epitopes targeted by the T cells in HIV-infected individuals with these specific HLA alleles was obtained from the Los Alamos HIV Molecular Immunology Database ( http://www . hiv . lanl . gov/content/immunology ) and is presented in S1 Table . Our analysis revealed that LTNP elicit immune responses strongly directed towards residues in sector 3 , whereas RP elicit responses against residues in sector 2 ( Fig 7 ) . Recalling the sector biochemical associations ( Figs 4 and 6 ) , these observations seem to promote the design of T-cell vaccine strategies which target sector residues lying on the p24 intra hexamer interfaces , while avoiding targeting residues on the p24-SP1 interface . In the former case , such targeting seemingly compromises viral fitness by disrupting the formation of stable HIV capsid [35] , which appears quite difficult to restore through compensatory mutations . In contrast , restoring fitness costs associated with destabilization of the p24-SP1 interface appears less difficult . These results were contrasted against a previous analysis of HIV Gag [11] , in which an inferred sector based on a classical PCA approach ( a slight variant of the approach [12] , discussed earlier ) was also found to associate with LTNP . The residues defining this immunologically important sector were directly extracted from [11] . Analyzing the biochemical association of the residues in this sector ( similar to the analysis done in Fig 4 ) revealed a significant association ( P < 0 . 05 , Fisher’s exact test ) with the p24 intra-hexamer interface ( as pointed out in [11] ) , but also with the p24-SP1 interface ( see S6 Fig for details ) . Hence , while reaffirming the importance of targeting interfaces within p24 hexamers , different conclusions were established regarding p24-SP1 , suggesting that this interface should be targeted , rather than avoided . This important distinction arises as a consequence of the methodological differences between RoCA and the previous PCA-based methods [11 , 12] , as discussed previously . By integrating our observations with population-specific HLA allele and haplotype information , candidate HIV immunogens eliciting potentially robust T cell responses can be proposed [11 , 12] . A more detailed investigation along these lines , as well as broadening the analysis to other viral proteins , is planned to be carried out in future work .
Characterizing the co-evolutionary interactions employed by HIV and HCV is an important problem . These interactions reflect the mutational pathways used by each virus to maintain fitness while evading host immunity . However , they are not well understood and pose a significant challenge for vaccine development . By applying statistical analysis to the available cross-sectional sequence data , we showed that for multiple HIV/HCV proteins the interaction networks possess notable simplicity , involving mainly distinct and sparse groups of interacting residues , which bear a strikingly modular association with biochemical function and structure . Essential to unraveling this phenomena was the introduction of a robust inference method . Our approach is particularly well suited for the “internal” proteins of chronic viruses such as HIV and HCV that are subjected to broadly directed T cell responses . For such proteins , and for HIV in particular , recent experimental and computational work has provided evidence that the population-averaged mutational correlations are reflective of intrinsic interactions governing viral fitness . This was shown to be a consequence of multiple factors which influence the complex evolutionary dynamics of HIV , including the extraordinary diversity of HLA genes in the human population which place selective pressure on diverse regions of the protein , thereby promoting wide exploration of sequence space , in addition to the tendency of mutations to revert upon transmission between hosts [4–6] . An additional important evolutionary factor is that of recombination , which introduces diversity through template switching during viral replication . A consequence of recombination is that it breaks mutational correlations between residues that are distant in the primary structure . That is , higher rates of recombination should lead to shorter-range correlations and vice-versa . Thus , the recombination involved in HIV and HCV evolution [59] may consequently distort the mapping between biochemical domains and the inferred sectors , and possibly result in inference of multiple distinct sectors associated with the same biochemical domain . However , such an effect of recombination is not evident from our results . Nonetheless , the effect of high recombination rate of HIV as compared to HCV [59] seems to be reflected in the separation among residues involved in the inferred sectors . Specifically , the HIV protein sectors are quite localized , with a median separation in the primary structure of up to 140 residues ( sector 6 of HIV Gag ) , while those of the HCV proteins are well separated with a median separation of up to 480 residues ( sector 1 of HCV NS3-4A ) . In general , the predicted sectors primarily comprise residues within the corresponding biochemical domains and a few other residues which are close in either primary or tertiary structure . However , these sectors also include a small proportion of residues which are distant from those within the respective biochemical domains ( S7 Fig ) and thus , appear to influence the associated structure or function by an allosteric mechanism . Such long-range interactions have been reported to play a role in maintaining viral fitness and facilitating immune evasion [60–62] . Allosteric interactions have also been observed in the co-evolutionary sectors of different protein families obtained previously with the SCA method [14–17] . The identified sectors for each viral protein together comprise between 35%–60% of the total residues in the protein ( Fig 3A and S1 Fig ) . This is consistent with the sparse sectors of co-evolving residues observed in different protein families using the SCA method [14–17] . However , one may ask about the role of non-sector residues , i . e . , those not allocated to any sector . Similar to the observations in other proteins [14–17] , our analysis suggests that non-sector residues evolve nearly independently , with associated biochemical domains being impacted only by individual mutations at these residues . Our co-evolutionary analysis is based on a binary approximation of the amino acid sequences , with mutants distinguished from the most frequent amino acid at each position ( see Materials and methods for details ) . This is a reasonable approximation due to the high conservation of the studied internal viral proteins ( S8 Fig ) . For other viral proteins which are comparatively less conserved , like surface proteins HIV Env and HCV E1/E2 , a similar co-evolutionary analysis may require refinement of such approximation by incorporating the information of amino acid identities of different mutants . This is a worthwhile problem to be pursued in a future study , in particular given the relevance for vaccine design of surface proteins , as these are a major target of neutralizing antibodies . We point out however that multiple HIV and HCV clinical studies have also demonstrated strong correlation of a broadly-directed cellular ( T-cell-based ) immune response against internal proteins with HIV control [57 , 58] and spontaneous HCV clearance [63 , 64] . These reports suggest that , for HIV and HCV , the immune response against internal proteins ( similar to those studied in this work ) may be just as important as—if not more important than—the antibody response against surface proteins . While our analysis has focused primarily on viral proteins , the proposed RoCA approach is general and may be applied more broadly , provided that the studied proteins are reasonably conserved . As an example , we computed sectors for the S1A family of serine proteases and compared these with results obtained previously with the SCA algorithm [14 , 15] . Similar to SCA , RoCA yielded three co-evolutionary sectors which had statistically-significant associations with distinct phenotypic properties; namely thermal stability , enzymic activity , and catalytic specificity ( S9 Fig ) . We point out however that the very notion of a “sector” as defined previously for SCA [14–17] has some differences to that of the RoCA sectors . Specifically , while for the HIV/HCV proteins in this work we employed an unweighted Pearson correlation measure and the inferred sectors were interpreted as simply groups of correlated residues; for the protein families [14–17] , SCA involved a conservation-weighted correlation measure and thus the inferred sectors represented groups of not only correlated but also conserved residues ( see S3 Text for details ) . For serine proteases , the relatively higher statistical significance of biochemical association for SCA sectors ( S9 Fig ) suggests that using a measure that also incorporates conservation may be useful for identifying biologically important residues in this case . Nonetheless , the RoCA sectors produced for the serine proteases , based on an unweighted Pearson correlation measure , further attest to the importance of residue interactions in mediating fundamental protein functions . For the HIV/HCV viral proteins under study , the relation between the biologically important residues ( reflected by the biochemical domains ) and conservation was not clearly apparent ( S10 Fig ) . In fact , a significant and particularly surprising aspect of our analysis is the substantial extent to which the correlation patterns , with no regard for conservation , encode information regarding qualitatively distinct phenotypes including structural units—virus-host and virus-virus protein interactions—and functional domains . The identified sectors may therefore also be seen as predictors of important biochemical domains . For each of the four viral proteins under study , there is at least one sector with unknown biochemical significance . Subsequent experimentation , such as mutagenesis experiments targeted at the identified sector residues , could therefore provide new insight which furthers the current understanding of HIV and HCV . Particularly interesting is the poorly understood NS4B protein of HCV , for which any biochemical activity underpinning the leading two sectors—representing the strongest co-evolutionary modes—have yet to be resolved .
The sequence data for HIV-1 clade B Gag and Nef was obtained from the Los Alamos National Laboratory HIV database , http://www . hiv . lanl . gov/ . We restricted our analysis to drug-naive sequences and any sequence marked as problematic on the database was excluded . To avoid any patient-bias , only one sequence per patient was selected . After aligning the sequences based on the HXB2 reference , they were converted to a N × M amino acid multiple sequence alignment ( MSA ) matrix , where N denotes the number of sequences and M denotes the number of amino acid sites ( residues ) in the protein . The downloaded sequences may include a few outliers due to mis-classification ( e . g . , sequences assigned to an incorrect subtype or clade ) in the database . Such outlying sequences were identified and removed using a standard PCA clustering approach ( see [11] for details ) . This yielded N = 1897 and N = 2805 sequences for HIV Gag and Nef , respectively . Moreover , the fully conserved and problematic residues ( with blanks or gaps greater than 12 . 5% ) were eliminated , resulting in M = 451 variable residues for Gag and M = 202 for Nef . Similarly , the sequence data for HCV subtype 1a NS3-4A and NS4B was downloaded from the Los Alamos National Laboratory HCV database , http://www . hcv . lanl . gov/ . The downloaded HCV sequences were then aligned based on the H77 reference and converted to the amino acid MSA . Applying the above-mentioned pre-processing resulted in N = 2832 sequences for NS3-4A and N = 675 sequences for NS4B , with an effective length of M = 482 for NS3-4A and M = 190 for NS4B . The processed amino acid MSA matrix A = ( Aij ) was converted into a binary matrix B , with ( i , j ) th entry B i j = { 0 if A i j is the consensus amino acid at residue j , 1 otherwise . ( 1 ) Thus , ‘0’ represents the most prevalent amino acid at a given residue and ‘1’ represents a mutant ( substitution ) . This is a reasonable approximation of the amino acid MSA , given the high conservation of the internal viral proteins under study ( S8 Fig ) . The binary sequences in B are generally corrupted by the so-called phylogenetic effect , which represents ancestral correlations . A comparatively large eigenvalue is observed in the associated correlation matrix due to these phylogenetic correlations [11 , 12] . Following previous ideas ( for details , see Sections 3 and 4 in the Supporting Information of [11] and Section 2 in the Appendix of [12] ) , such effects are reduced using standard linear regression . The resulting data matrix , denoted by B ^ , was the base for computing the correlations used to infer sectors . Specifically , we computed the M × M sample correlation matrix , along with its spectral decomposition , given by C ≜ V - 1 2 S V - 1 2 = ∑ k = 1 M λ k q k q k ⊺ . ( 2 ) Here , S is the sample covariance matrix with entries S i j = 1 N ∑ k = 1 N ( B ^ k i - B ¯ i ) ( B ^ k j - B ¯ j ) where B ¯ i = 1 N ∑ k = 1 N B ^ k i is the sample mean , while V is a diagonal matrix containing the sample variances , i . e . , Vii = Sii , and λk and qk are the kth-largest eigenvalue of C and its corresponding eigenvector , respectively . The superscript ⊺ denotes vector transposition . We introduced an approach based on robust PCA methods to accurately estimate the PCs ( i . e . , the leading eigenvectors ) of the correlation matrix , which were then directly used to identify sectors . In particular , we considered the iterative thresholding sparse PCA ( ITSPCA ) method which , in short , is a combination of the standard orthogonal iteration method [20] , used to compute the eigenvectors of a given matrix , and an intermediate thresholding step which filters out noise in the estimated PCs . However , the original ITSPCA method was not directly applicable to our correlation-based sectoring problem , since it was designed primarily for covariance matrices , and it involved a variance-dependent coordinate pre-selection algorithm which is no longer suitable . As such , for RoCA , we developed a version ( called Corr-ITSPCA , see Algorithm 1 ) which is appropriately adapted to operate on correlation matrices , and we designed automated methods for tuning the relevant parameters; specifically , the number of significant PCs α and the noise threshold γk . Algorithm 1 Corr-ITSPCA Method Inputs: 1 . Correlation matrix of size M × M , C; 2 . Number of PCs to be estimated , α; 3 . Noise threshold , γk , k = 1 , 2 , ⋯ , α . Output: Robust estimates of the PCs , pk , given as columns of the M × α matrix P = Q ( ∞ ) , where Q ( ∞ ) denotes Q ( i ) at convergence . 1: Initialization: i = 1; 2: Initial orthonormal matrix of size M × α , Q ( 0 ) = Qα; here Qα is a matrix whose columns are the α leading eigenvectors of C , i . e . , Qα = [q1 q2 ⋯ qα] . 3: repeat 4: Multiplication: T ( i ) = ( T ℓ k ( i ) ) = C Q ( i - 1 ) ; 5: Thresholding: T ^ ( i ) = ( T ^ ℓ k ( i ) ) , with T ^ ℓ k ( i ) = T ℓ k ( i ) 1 { | T ℓ k ( i ) | > γ k } , where 1{E} is the indicator function of an event E; 6: QR Factorization: Q ( i ) R ( i ) = T ^ ( i ) ; 7: i = i + 1; 8: until convergence Such automated design is crucial to obtain accurate results , as these parameters control respectively the number of sectors that we infer and the number of residues included in each sector . Note that this is a principled design approach , as opposed to an ad hoc approach considered previously by the authors to uncover vaccine targets against the NS5B protein of HCV [65] . These parameters are designed as follows: In Figs 3 and 6 , we used heat maps to illustrate the computed sample correlation matrix C . As discussed above , the sample correlations were generally corrupted by statistical noise due to the finite number of available sequences . Thus , for a better visualization and , in particular , to appreciate the strong correlations within the inferred sectors , the sample correlation matrix was cleaned from statistical noise by thresholding the sample eigenvalues in such a way that the significant α spectral modes ( Eq ( 3 ) ) were kept unaltered , while the remaining eigenvalues ( which do not appear to contribute genuine correlations ) were collapsed to a constant . Specifically , the cleaned sample correlation matrix was obtained as C ^ * = ∑ k = 1 M λ k * q k q k T , ( 11 ) where λ k * = { λ k if k ≤ α , ζ otherwise , with ζ a constant value such that the trace of C ^ * remained normalized ( equal to M ) . Note that C ^ * is not a standard correlation matrix as C ^ k k * ≠ 1 . A standardized version was then computed as C ^ = D - 1 2 C ^ * D - 1 2 , ( 12 ) where D is a diagonal matrix with D k k = C ^ k k * , and used to depict the cleaned correlations as a heat map . We introduced a metric called “normalized entropy deviation ( NED ) ” to quantify the extent to which two groups of residues are statistically independent of each other . The NED between two sectors i and j is defined as NED inter ( i , j ) = ( H s i + H s j ) - H s i ∪ s j H s i ∪ s j , ( 13 ) where si is a set comprising the five residues with largest correlation magnitude of sector i and H s i is the entropy of si computed from the binary MSA matrix . Specifically , this entropy is computed over all κ = 1 , 2 , ⋯ , 2 # ( s i ) combinations of the residues in set si as follows H s i = - ∑ κ = 1 2 # ( s i ) f κ ln f κ , f κ > 0 ( 14 ) where fκ is the frequency of the combination κ in the MSA and # ( si ) is the cardinality of set si . In theory , if two given sectors are perfectly independent , the sum of the entropies of the individual sectors must be equal to the entropy of both sectors taken together , resulting in NEDinter = 0 . In practice however , a small non-zero value of NEDinter is expected due to finite-sampling noise , even if the sectors are independent . We obtain an estimate of it by constructing a null case , where the entries of the MSA corresponding to the sets si and sj are randomly shuffled in such a way that any correlation between the two sets is essentially eliminated , while the correlations between residues in an individual set remain unaltered . Using Eq ( 13 ) , NEDinter is computed for 500 such randomly shuffled realizations of the MSA and the average value ( referred to as NEDrandom ) represents the null ( lower ) reference value for NEDinter which is expected if the two sectors are independent . Substantial deviations from NEDrandom should reflect correlation between the sectors . In order to quantify the extent of such deviations in a clearly correlated case , we computed an upper reference NEDintra , obtained when the residues in both sets si and sj belong to the same sector . It is defined as NED intra ( i , j ) = max ( ( H s i + H s i ′ ) - H s i ∪ s i ′ H s i ∪ s i ′ , ( H s j + H s j ′ ) - H s j ∪ s j ′ H s j ∪ s j ′ ) , ( 15 ) where s i ′ is the set comprising the five residues with largest correlation magnitude of sector i with the residues in si excluded . The HLA alleles associated with LTNP and RP were reported in [58] . A list of HIV Gag epitopes that are presented by HLA alleles and targeted by T cells of either HIV LTNP or RP was compiled using the data from the Los Alamos HIV Molecular Immunology Database ( http://www . hiv . lanl . gov/content/immunology ) and is presented in S1 Table . | HIV and HCV cause devastating infectious diseases for which no functional vaccine exists . A key problem is that while individual mutations in viral epitopes under immune pressure may substantially compromise viral fitness , immune escape is typically facilitated by other “compensatory” mutations that restore fitness . These compensatory pathways are complicated and remain poorly understood . They do , however , leave co-evolutionary markers which may be inferred from measured sequence data . Here , by introducing a new robust statistical method , we demonstrated that the compensatory networks employed by both viruses exhibit a remarkably simple decomposition involving small and near-distinct groups of protein residues , with most groups having a clear association to biological function or structure . This provides insights that can be harnessed for the purpose of vaccine design . |
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Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables , under the implicit assumption that natural selection imposes correlations between phenotypes , environments and genotypes . In practice , observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation . In theory , improved estimation of these forces could enable more powerful detection of loci under selection . Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis ( GWAS ) . Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana , we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables . Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation .
The genetic basis of environmental adaptation in natural and agricultural populations is a topic of growing interest and urgency . Conventionally , the search for adaptive genes involves testing for associations of genomic markers with either ecologically relevant traits measured in common garden experiments [1] [2] [3] [4] or with environmental variables [5] [6] [7] [4] [8] . These two approaches reflect the assumption that traits , environment and genotype are correlated due to natural selection , as is indeed expected under local adaptation [9] [10] [11] . In practice , observations and measurements are subject to error and may not accurately reflect the actual variables involved in adaptation [6] . At best therefore , empirical data on traits and environment provide independent approximations of the parameters defining ecological adaptation , offering limited power to detect causative genes when used in isolation . An obvious improvement would be to combine both types of data to better approximate the adaptive process . One example is to identify the most probable selective forces from a set of environmental variables based on their correlation with traits of interest and use these variables in association mapping , as was done recently in Arabidopsis thaliana [7] . Although attractive , the reliance on single variables means that this method cannot account for more complex relations between traits and the environment and makes limited use of the independent information provided by trait and environmental data . An alternative approach , which we explore here , is to extract information from ecological data by modeling traits as a function of multiple environmental variables [12] [13] and to use the resulting trait prediction , conjointly with the observed trait , in a bivariate analysis of genetic association . The reasoning behind this idea is as follows . We start from the usual assumption that individuals from different geographic locations express location-specific , genetically determined trait values that are optimal with respect to some combination of environmental conditions in their native habitat . Furthermore , as in other studies on environmental association , we assume that clinal variation in selective forces causes corresponding differences in gene frequencies across the landscape . Under these assumptions , the value of a trait and its defining selective environment can be treated as two correlated aspects of an individual’s phenotype with a shared genetic basis . In the same way , observed variation in an adaptive trait and a function of environmental variables explaining part of this variation can be treated as two genetically correlated characteristics that are effectively repeated measurements of the underlying selective environment . As has been shown for other genetically correlated traits , such repeated measurements may be combined to increase the power to detect common causative loci by testing for genetic associations with both traits simultaneously using a multi-trait mixed model ( MTMM ) [14] [15] . We propose that testing for genetic loci with an effect on both observed and predicted traits provides more power to detect genes of adaptive significance than mapping on individual traits or environmental variables separately . In addition , environmentally predicted traits may be used in univariate association mapping to map adaptive loci in individuals for which only environmental data is available . We will refer to these two applications of predicted traits as bivariate- and univariate Environmentally predicted Trait Mapping ( ETM ) throughout the paper . We demonstrate the potential of bivariate ETM by computer simulations and evaluate its performance using phenotypic and high-density SNP data from a published association study on flowering time in Arabidopsis thaliana [1] . Flowering time is known to affect fitness in A . thaliana [16] and shows strong geographic variation [17] , making it an ideal trait for our purposes . Moreover , its genetic and molecular basis is well understood [18] [19] . We compare the power of bivariate ETM to recover known flowering genes to that of conventional univariate association methods using single traits or environmental variables . In addition , we use univariate ETM to map flowering genes in individuals without available phenotypic data [7] , an approach that may offer potential for allele mining germplasm collections for adaptive variation .
ETM first models the observed phenotype as a function of environmental data , producing a combination of the environmental variables which we call the predicted phenotype . The trait prediction model is fit on the set of accessions for which both phenotypic and environmental data are available , but the resulting prediction can be extended to the accessions for which there are only environmental data . In case of non-constant prediction , bivariate ETM then performs multitrait association mapping on the observed and predicted phenotype , using all available accessions . In univariate ETM we perform single trait association mapping for the accessions with missing phenotypic data . As proof of concept , we simulated a simple scenario in which an adaptive trait is modeled as a linear function of a random subset of ten out of 30 simulated environmental variables ( Materials and Methods ) . The frequency of the causative SNP was set to be a monotone function of the true adaptive trait . The observed trait was then defined as the sum of a SNP effect and polygenic and residual noise . Four trait prediction methods were implemented: linear model ( LM ) prediction with backward variable selection , elastic nets ( EN ) [20] , random forests ( RF ) [21] and canonical correlation analysis ( CCA ) [22] . For comparison , we also performed bivariate analysis using the trait and the most correlated environmental variable , as well as univariate GWAS on the trait alone . Bivariate mapping was performed both using a test for a common marker effect ( ‘common’ ) and a test whether there is any marker effect ( ‘full’ ) , described in the Materials and Methods ( see also [14] ) . We first simulated a scenario where the heritability is 0 . 5 and the causative SNP explains 5% of the phenotypic variance; correlations between true and observed environmental variables was set to 0 . 8 . For both types of tests , bivariate ETM using predicted traits shows a clear gain in power over univariate mapping ( Fig 1 ) . Bivariate analysis using the environmental variable most correlated to the observed trait performs well in the test for any marker effect , but poorly when testing for a common marker effect , especially at lower significance thresholds . For the four prediction methods the two types of tests perform similarly . Using the test for a common marker effect , CCA showed the highest increase in power ( e . g . 0 . 80 at a −log10 ( p ) threshold of 5 , versus 0 . 64 for univariate mapping ) . Other methods perform similarly with power ranging between 0 . 68–0 . 73 at the same threshold , and achieving larger gains over univariate mapping at higher −log10 ( p ) thresholds . There is a clear relationship across simulated traits between the significance of ETM and correlation between the predicted trait and the simulated true adaptive trait ( S1 Fig ) : ETM is most powerful for simulations where this correlation is large . At lower prediction accuracy the difference with univariate p-values decreases , thus giving smaller differences in power at low −log10 ( p ) thresholds . Similar differences between methods are observed in 8 additional scenarios with heritabilities 0 . 2 , 0 . 5 and 0 . 8 and the causative SNP explaining 2% , 5% and 10% of the phenotypic variance ( S2a–S2i Fig ) . As expected , the advantage of ETM increases for larger proportions of variance explained . In S2a–S2i Fig we also compared bivariate ETM with univariate mapping on the predicted traits , the latter having lower power for most prediction methods , except for low heritabilities . For CCA , univariate mapping also performs well for higher heritabilities . Next , we modified the scenario of Fig 1 in the following ways: by lowering the correlations between true and observed environmental variables to 0 . 5 ( S3 Fig ) , by introducing genetic correlations between the trait and some of the environmental variables ( S4 and S5 Figs ) , and by removing the association between the environmental variables and the causative SNP ( S5 and S6 Figs ) . In the first case , the larger measurement errors in the observed environmental variables leads to a decrease in power of ETM , which however is still more powerful than univariate mapping ( S3 Fig ) . We then performed simulations where the polygenic component of the trait is correlated with the environmental variables defining the true adaptive trait , reflecting the presence of adaptive loci elsewhere on the genome . When the SNP explains 5% of phenotypic variance ( as in the main scenario ) , differences among methods become smaller , in particular between CCA and ETM with the correlated variable ( S4 Fig ) . When the SNP does not affect the phenotype , p-values appear randomly distributed on the unit interval ( S5 Fig ) , indicating that ETM adequately corrects for population structure . In our last scenario ( S6 Fig ) , neither the SNP under consideration nor the polygenic effect was related to the environmental variables . In this case ETM has lower power than univariate mapping , as the SNP is only associated with one the two variables . The largest loss in power then occurs in the test for a common effect , while also the test for any marker effect is affected due to less degrees of freedom [14] . Given the similar performance of the two tests we chose to present all subsequent results for the common marker effect only . We consider this test to be conceptually more appropriate for the detection of loci associated with both the observed trait and its selective environment , which are expected to be positively correlated . We used the statistical methods described above to predict flowering time variation among 149 Arabidopsis thaliana accessions [1] , using public data for 61 environmental variables ( S1 File ) . These predictions will be used in bivariate and univariate ETM below . As expected [23] [17] , flowering time is strongly correlated with variables related to latitude such as day length , potential evapotranspiration and temperature ( S7 Fig ) . Spring and summer day length are most correlated with flowering time [7] , each explaining 40% of variation compared to 29% for latitude itself . The importance of these variables is reflected in the trait predictions ( S8–S11 Figs ) , where day length is among the most important variables for all prediction methods . The contribution of other variables varies between methods , with the LM and RF prediction assigning relatively high importance to precipitation variables not strongly correlated with latitude ( S8 and S10 Figs ) . The highest correlation between the predicted trait and any single environmental variable , summer day length in all cases , ranges between 0 . 71–0 . 84 for LM , RF and CCA but is notably higher for EN ( r = 0 . 98 ) ( S8–S11 Figs ) . The EN-predicted trait may therefore offer little advantage over day length when used in bivariate ETM . Notwithstanding the differences between methods , trait predictions are highly correlated among themselves ( r = 0 . 78–0 . 88 ) and with the observed trait ( r = 0 . 84 ( CCA ) to r = 0 . 68 ( EN ) ) , suggesting that ETM performance will be similar for different prediction methods . For the different methods , we measured the cumulative success in recovering 240 known flowering genes ( S2 File ) as a function of the number of evaluated candidate genes . We thereby assume that GWAS results are used to create a list of candidate SNPs or genes of a certain length as a basis for further evaluation ( see S12 Fig for recovery as a function of p-values for comparison ) . SNPs were sorted by increasing p-value and candidates were defined as genes overlapping with or being closest to any of the top 2000 SNP positions , evaluated successively in order of significance ( approximately 1% of all SNPs ) . We compared univariate association mapping on observed flowering time , bivariate ETM and bivariate analysis using the most correlated trait ( Summer day length ) . Significance of enrichment was calculated as the probability of recovering the observed number of flowering genes by chance ( see Materials and Methods ) . All methods result in significant enrichment but recover only a modest number of genes , yielding 27 flowering genes at most ( Fig 2 , left ) . Maximum significance of enrichment ranged from 5 ⋅ 10−3 to 4 . 9 ⋅ 10−6 and was achieved after evaluating varying numbers of genes ( Fig 2 , right ) . Bivariate ETM outperforms univariate trait mapping over the entire range , with a maximum difference in recovery of 9 flowering genes at 621 evaluated genes ( Fig 2 , left ) . ETM based on LM and CCA trait prediction performs particularly well , with high and sustained recovery and peaks of maximum significance of enrichment of 4 . 9 ⋅ 10−6 and 1 . 3 ⋅ 10−5 respectively . Overall , the recovery curves for EN prediction and summer day length are similar , as expected based on the high correlation between the two variables . For all prediction methods ETM p-values showed some inflation , which also occurred in univariate mapping of the predicted traits , the individual environmental variables and to a lesser extent the observed trait ( S13–S14 Figs ) , and therefore does not appear to be an artifact of our method . Inflation largely disappeared in univariate analyses with a multi-locus mixed model [24] ( S15 Fig ) , suggesting that inflation is due to large effects of a small number of loci , insufficiently captured by the kinship matrix . Considering the top 400 candidate genes for each method , univariate mapping on observed flowering time recovers 2 flowering genes within the first 16 , with probabilities of 7 . 2 ⋅ 10−3 , but the total of 4 recovered genes does not represent a significant enrichment ( p = 4 . 1 ⋅ 10−1 ) . Bivariate ETM , by contrast , recovers 9–13 flowering genes within the first 400 candidates ( p = 5 . 6 ⋅ 10−3 − 2 . 6 ⋅ 10−5 ) , with all prediction methods providing higher enrichment than summer day length ( 7 genes , p = 4 . 5 ⋅ 10−2 ) . The four types of bivariate ETM all recover the genes SVP , GA1 , DFL2 , LDL1 , SPA2 , FPF1 , DOG1 , within the first 400 candidates ( Table 1 ) . The latter four genes are only recovered by univariate mapping after considering at least 100 additional genes . Although different bivariate ETM analyses identify different sets of genes , overlap is relatively high . Considering the top 400 candidate genes of each prediction method , an average of 249 ( 220–282 ) genes is shared between prediction methods ( Fig 3 ) , compared to an average of 199 between bivariate ETM and univariate mapping . Bivariate ETM and standard association mapping thus recover different genes . These differences are unlikely to be due to chance , as shown by the fact that bivariate ETM ( LM prediction ) with a simulated trait equally correlated with the observed trait ( i . e . r = 0 . 81 ) identifies only 5 unique genes compared to univariate association mapping ( Fig 3 ) . Environmental prediction of trait values offers the possibility of association mapping when phenotypic data is incomplete . Traits of interest can be predicted across geographic space using geographic information and association mapping may then be performed on any set of georeferenced individuals for which genotypic data are available . Fig 4 shows geographic maps of predicted flowering time obtained by the four different prediction methods . Although the importance of latitude is evident , in all cases the predicted trait surface clearly reflects the effect of variables that are not strongly correlated with latitude . We compared the performance of univariate ETM to that of ( univariate ) association mapping on summer day length and latitude , for a dataset of 478 genotyped and georeferenced accessions for which no flowering time data was available and whose range of predicted trait values did not exceed that observed for the 149 phenotyped individuals . Recovery of known flowering genes is somewhat lower compared to bivariate ETM ( Fig 5 ) . Although performance is only slightly higher compared to random permutations , maximum enrichment is significant in all cases . Differences in performance between methods are small , but ETM has higher recovery and enrichment within the first 400 genes compared to mapping the two environmental variables individually . Within these top 400 candidates , SVP , CRP , SPA2 , DOG1 , PIE1 and FRI are recovered by more than one method ( Table 2 ) and for each , ETM with LM prediction requires fewer candidate genes to be evaluated compared to mapping the two environmental variables , although the best prediction method differed between genes . FRI is a well studied , major flowering locus in A . thaliana[23] [25] , which together with the gene FLC affects the latitudinal cline in flowering time [26] [17] [27] [28] . FLC ranks 617 and 627 using RF and day length respectively , but is not recovered at all by latitude . The relatively weak recovery of FLC , FRI , SVP and DOG1 with latitude is surprising since all have been reported to show allelic variation with latitude [29] . This suggests that predicted traits used in ETM may be better correlated with the underlying gene frequency at these loci than latitude itself . We confirmed this by estimating the geographic frequencies of the SNP distinguishing the two functional haplotypes at FLC and FRI [16] and of the SNPs with the lowest p-values at SVP and DOG1 , and correlating these to the different variables including latitude ( Fig 6 ) . In each case , the best trait prediction ( i . e . yielding highest r2 with SNP frequency ) has a higher correlation with SNP frequency than either summer day length or latitude . In fact , our data provides no evidence for a latitudinal trend for either FRI or FLC , while the correlation with predicted flowering time is weak but significant ( p < 1 ⋅ 10−9 ) .
We have explored the use of environmentally predicted traits for genome-wide mapping of genes underlying adaptive trait variation . This is basically an extension of the concept of phenotype to include the environment . That idea is not new , in the sense that it has been implicit in most studies relating environment to gene frequency . The novelty of our approach lies in the fact that this extension is made explicit and is used in conjunction with the observed trait of interest to obtain a better approximation of the selection gradient responsible for trait variation . Although this may seem counter-intuitive at first , its merit becomes apparent when considering that information on correlated environmental variables can be used to reduce the effect of experimental error in the same way as correlated traits can [30] [14] [31] . We thereby take advantage of so-called latent variables , which are factors indirectly related to the trait of interest and that are generally considered a source of spurious associations [32] . Although selective forces determining trait variation may sometimes shape allele frequencies at non causal loci ( e . g . those affecting an unmeasured adaptive trait ) , independent estimates of these selective forces can at the same time help to find true associations , particularly when combined with the trait itself . Bivariate ETM is designed to detect genes whose frequencies correlate with selective forces that have shaped a trait of interest . These are likely to affect the target trait directly , although they may also be genes affecting correlated adaptive traits . In our case an average of 87% of the top 2000 SNPs for bivariate ETM had p-values below 0 . 05 for flowering time itself . Since our primary aim is to find genes related to adaptation however , any gene that is affected by the same selective environment is of interest , regardless of its causal relation to the trait . The success of this approach does require that traits and the environment provide complementary estimates of underlying selective forces , something that may not always be the case . The result that enrichment for known flowering genes is higher for bivariate ETM than for univariate mapping on the trait itself , and that this is not observed for randomly simulated variables with the same correlation to the observed trait , suggests that predicted and observed traits indeed complement each other . One thing to observe , is that our definition of recovery as the closest gene to a detected SNP , deviates from Atwell et al . ’s decision to consider SNPs within 20kb of their candidate genes [1] . Our criterion was chosen to avoid calling multiple genes per evaluation position and reflects the fact that in the Arabidopsis genome , LD is estimated to decay within 10kb on average [33] . Another application of environmental trait prediction is the mapping of adaptive genes in individuals with missing phenotypic information . It offers potential for mining the growing genomic data available for many species without the need for complete phenotypic data , and exploiting the wealth of publicly available geographic and environmental data . Our results on mapping flowering genes in unphenotyped individuals are encouraging in the sense that more genes are found than expected at random . On the other hand , the improvement achieved over single environmental variables such as latitude is rather modest . This probably reflects the fact that environmental variables related to latitude are the dominant selective agents affecting flowering time , making it hard to improve over the use of well chosen single environmental variables . Nonetheless , at several genes with known association with latitude , estimated gene frequencies are more strongly correlated with predicted flowering time than with latitude . This observation provides evidence that mapping on predicted traits has the potential of producing more relevant association results than single environmental variables chosen a priori . In conclusion , we have provided evidence that integrating environmental and phenotypic data can improve our ability to map genes of adaptive significance . We have thereby explored several statistical methods for modeling traits as a function of the environment . We do not consider our results conclusive with respect to the best prediction method and more work remains to be done in that respect . Alternatives such as sparse multivariate methods [34] may be worth exploring . In addition , it is conceivable to integrate prediction into the MTMM step of our approach , and target the combination of environmental variables with the highest genetic rather than phenotypic correlation . This however implies an optimization problem for which no algorithms currently seem to be available . Alternatively , bivariate MTMM could be replaced by multivariate MTMM , including all environmental variables individually ( as well as the observed trait ) , but state-of-the art approaches [15] currently cannot perform GWAS on more than 10 traits . Another issue is that of inflation , which may affect the distribution of p-values in any GWAS study due to confounding of the polygenic background with population structure [35] [36] or the occurrence of large effect loci [24] . Although we adopt the standard MTMM approach of correcting for population structure by a marker-based kinship matrix it is clear that for traits like flowering time there is a certain degree of residual inflation . The fact that inflation for most traits was adequately controlled in a univariate multi-locus mixed model ( MLMM ) , suggests there is scope for the development of a multi-locus version of MTMM . In terms of application , it will be interesting to test the added value of our approach for traits that are more weakly correlated with known environmental factors , such as is the case for disease or drought resistance . We hope that the present work may serve as a first step in moving adaptation mapping beyond the traditional univariate analysis of traits and environmental variables and towards a more integrated use of all available data .
We used two datasets from two highly cited examples of trait association and environmental association in A . thaliana [1] [7] . The first set consisted of 199 phenotyped accessions of which we retained 149 individuals with available Eurasian geographic coordinates and no missing data for any of the included traits . We reduced data on flowering time measured at 10 , 16 and 22 degrees Celsius to a single principal component explaining 90 percent of total variation , which was used in all subsequent analyses , unless stated otherwise . The second set consisted of 948 georeferenced accessions , sampled across Eurasia , of which we excluded 39 accessions with non-terrestrial coordinates . High-density Single Nucleotide Polymorphism ( SNP ) data , using the Affymetrix 250K SNP-tiling array was available for both studies [29] . SNP positions and gene annotations were based on version 10 of the Arabidopsis genome annotation ( TAIR10 ) . A list of 240 mapped candidate genes for flowering time was obtained from [1] and [37] , complemented with a subset of genes derived from the list of known Arabidopsis flowering genes available from the Prof . Coupland lab ( MPIPZ , Cologne , Germany; https://www . mpipz . mpg . de/14637/Arabidopsis_flowering_genes ) . SNP positions with the highest frequency differentiation at functional variants of the flowering genes FLC and FRI were identified based on 85 accessions for which functional haplogroups were available [16] . We compiled georeferenced climatic , soil and vegetation data from a variety of public sources ( S1 File ) , resulting in a final set of 61 environmental variables with a spatial resolution ranging from 0 . 5 to 50 km . Remote sensing data were mosaicked , time averaged and converted to GIS raster layers with custom R scripts , using functions from the programs cdo [38] , MRT [39] and the package Raster [40] . Average day length for different seasons was calculated from latitude [41] . Visualization of geographic data and assignment of environmental variables to sample locations was done using the QGIS software [42] . Estimates of continuous allele frequencies across the landscape were produced using the program SCAT [43] . Our ETM procedure can be summarized as follows . First we predict the observed phenotype as a function of environmental data . Below we describe four possible prediction methods , but in principle any method can be used here . Provided this prediction is not constant we then perform bivariate GWAS on the observed and predicted phenotype ( bivariate ETM ) , or univariate GWAS on the predicted phenotype alone ( univariate ETM ) . In the case of bivariate ETM , we consider the test for a common marker effect ( details given below ) , but the test for any marker effect is possible as well . For all methods ( bivariate/univariate ETM , univariate mapping ) SNPs were ordered by their significance and the 2000 SNPs with lowest p-values were considered as candidate SNPs . We assigned each of these SNPs to the gene ( s ) overlapping with its position or to the closest gene in the case of non-genic SNPs . This criterion differs from that used by Atwell et al . ( 2010 ) [1] , who assigned genes within a 20kb window around each SNP as candidates . Our criterion was designed to minimize the number of genes evaluated per SNP , without requiring arbitrary decisions on relevant window size ( See S16 Fig for a comparison of results using different criteria ) . We counted how many out of the 240 known flowering genes were recovered as a function of the number of unique genes considered when going down the ordered list of candidate genes . At each point , enrichment was calculated as the hypergeometric probability of finding ( at least ) the number of unique flowering genes , given the number of genes evaluated so far , the total of flowering genes ( 240 ) and the total of 29 , 477 genes assigned to any of the SNPs . We simulate traits and environmental variables for a fixed set of n = 300 accessions taken from the regmap , of which we randomly selected 100 Swedish , 100 French , 50 German and 50 Czech accessions . Each simulation consists of k = 30 simulated environmental variables and 1 simulated trait . Each simulation starts by drawing a Gaussian n × k matrix XT , containing the true ( unobserved ) environmental variables at the locations of origin of the accessions . XT specifies what we will call the true environment . First we randomly draw a subset S ⊂ {1 , … , k} , containing s = 10 environmental variables , which will later form the environmental gradient . We will use the notation XT ( S ) for the submatrix of XT with columns defined by S . To model confounding with population structure , the variables in XT contain polygenic components , such that their heritabilities are 0 . 5 . Specifically , XT is the sum of Genv and Eenv , which are drawn from zero mean matrix variate normal distributions ( see e . g . [15] ) . Genv is simulated together with the column ( n × 1 ) vector Gtrait , such that ( Genv , Gtrait ) is matrix variate normal with column covariance matrix VG and row covariance given by a marker-based kinship matrix K . Gtrait is the polygenic signal in the observed trait yO ( defined below ) . VG is the ( k + 1 ) × ( k + 1 ) covariance matrix of ( Genv , Gtrait ) . The off-diagonal elements of VG are chosen such that for each pair of variables in S , the genetic correlation is 0 . 5 . Also the genetic correlations between environmental variables from the complement of S are set to 0 . 5 , while it is zero for all variables j ∈ S and j′ ∈ Sc . The correlation between Gtrait and the columns of Genv ( S ) is either 0 or 0 . 5 . In the latter case , this reflects the assumption that Gtrait is to a certain extent adaptive . The correlation between Gtrait and the columns of Genv ( Sc ) is always 0 . The row and column covariance matrices of Eenv are both diagonal . Given the outcome of XT we then simulate XO , the observed environmental variables , by adding random Gaussian errors with variance chosen as to achieve a correlation of 0 . 80 , for each corresponding pair of columns in XT and XO . We then define the environmental gradient as yT = βXT ( S ) , where β1 , … , βs are drawn independently from a uniform distribution on the interval [−1 , 1] . For simplicity we assume that yT is the ( unobserved ) adaptive phenotype , although more complex relations between environmental gradients and phenotypes can be expected in nature . The vector f of causal allele frequencies at each simulated location , is defined as f ( y T ) = e λ y T / ( 1 + e λ y T ) with λ = 3 , and hence has a correlation of 1 with yT . A corresponding genotypic vector g is formed by sampling a single allele for each location from a Bernoulli distribution with probability f . Finally , we simulate the vector of observed phenotypes yO = βsnp g + Gtrait + Etrait , where βsnp represents the SNP-effect on the trait , Gtrait is the polygenic effect defined above , and Etrait is residual noise . We performed the following sets of 2000 simulations: The main set ( Fig 1 ) , where βsnp and the variance of Etrait are chosen such that the SNP explains 5% of the phenotypic variance , while Gtrait and Etrait explain respectively 45% and 50% , i . e . the heritability of the observed trait is 0 . 5 . The correlations between Gtrait and Genv ( S ) are set to 0 . In S2a–S2i Fig , we repeated the simulations from the main set , for heritabilities of 0 . 2 , 0 . 5 and 0 . 8 , and the causal SNP explaining 2% , 5% and 10% of the phenotypic variance . In S3 Fig , we repeated the simulations from the main set , lowering the correlations between true and observed variables to 0 . 5 . In S4 Fig , we repeated the simulations from the main set , the correlations between Gtrait and Genv ( S ) being 0 . 5 . In S5a and S5b Fig , we repeated the simulations from the main set , the correlations between Gtrait and Genv ( S ) being 0 . 5 . Additionally , the SNP effect ( βsnp ) was set to 0 , and Gtrait explained 50% of the variance . In S6 Fig , we repeated the simulations from the main set , but sampled the vector g of SNP scores randomly from independent Bernoulli ( 0 . 5 ) distributions , i . e . independent of any environmental variable . In all cases , ETM p-values from simulations yielding constant trait predictions were set to their corresponding univariate GWAS p-values . | Finding genes involved in adaptation to the environment has long been of interest to evolutionary biologists and ecologists . Most commonly , researchers look for loci whose differences in allelic state correlate with differences in a particular trait or environmental variable such as temperature . The implicit assumption behind such methods is that natural selection by the environment will shape variation in adaptive traits through associated changes in allele frequencies . This means that both environmental and phenotypic variation are relevant for detecting adaptive genes , although we have incomplete knowledge of how the two types of variation relate to adaptation . Here we present a method that aims to identify adaptive genes by combining phenotypic and environmental data . We first predict trait variation from a set of environmental variables as a way to extract the most biologically relevant information from the environment and then look for genes associated with both the predicted and observed trait . Using simulations and published data from the model plant Arabidopsis thaliana , we show that this approach may find adaptive genes more effectively compared to existing methods . We also demonstrate that predicted traits can be used to identify relevant loci in individuals for which no phenotypic data is available . |
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Identifying naturally-occurring neutralizing antibodies ( NAb ) that are cross-reactive against all global subtypes of HIV-1 is an important step toward the development of a vaccine . Establishing the host and viral determinants for eliciting such broadly NAbs is also critical for immunogen design . NAb breadth has previously been shown to be positively associated with viral diversity . Therefore , we hypothesized that superinfected individuals develop a broad NAb response as a result of increased antigenic stimulation by two distinct viruses . To test this hypothesis , plasma samples from 12 superinfected women each assigned to three singly infected women were tested against a panel of eight viruses representing four different HIV-1 subtypes at matched time points post-superinfection ( ∼5 years post-initial infection ) . Here we show superinfected individuals develop significantly broader NAb responses post-superinfection when compared to singly infected individuals ( RR = 1 . 68 , CI: 1 . 23–2 . 30 , p = 0 . 001 ) . This was true even after controlling for NAb breadth developed prior to superinfection , contemporaneous CD4+ T cell count and viral load . Similarly , both unadjusted and adjusted analyses showed significantly greater potency in superinfected cases compared to controls . Notably , two superinfected individuals were able to neutralize variants from four different subtypes at plasma dilutions >1∶300 , suggesting that their NAbs exhibit elite activity . Cross-subtype breadth was detected within a year of superinfection in both of these individuals , which was within 1 . 5 years of their initial infection . These data suggest that sequential infections lead to augmentation of the NAb response , a process that may provide insight into potential mechanisms that contribute to the development of antibody breadth . Therefore , a successful vaccination strategy that mimics superinfection may lead to the development of broad NAbs in immunized individuals .
Multiple studies have demonstrated the potential of HIV-specific neutralizing antibodies ( NAbs ) to protect against infection using nonhuman primate models [1] , [2] . However , it remains unclear how to elicit a NAb response of sufficient breadth and potency to protect humans against diverse circulating HIV-1 variants , which can differ by several orders of magnitude in neutralization sensitivity [1] , [2] . Therefore , investigating naturally-occurring antibody responses that can neutralize viruses across the major viral subtypes remains a major focus of research [3] . In the past few years , multiple HIV-specific broadly neutralizing monoclonal antibodies have been isolated from HIV-infected individuals with elite neutralizing activity [4]–[8] . This subset of individuals comprises about 1% of chronically-infected individuals and are considered elite neutralizers based on their ability to potently neutralize viruses from multiple subtypes [9] . The collection of broad monoclonal antibodies identified to date , which were isolated more than a decade after initial HIV-1 infection in some cases , have undergone extensive somatic hypermutation , a process that would be difficult to mimic with a HIV-1 vaccine [2] , [10] . Also , these monoclonal antibodies have been isolated from individuals who were presumably infected with a single HIV-1 strain , although in most cases , the possibility of superinfection ( SI ) was not addressed . Within singly infected populations , NAb breadth has been positively associated with viral diversity [11] . Therefore , individuals infected with multiple HIV-1 strains as a result of SI by a second source partner may generate broadly NAbs in response to stimulation from both viruses . Initially , it was hypothesized that SI resulted from a weak NAb response that was unable to protect the individual from reinfection . A small study of three SI cases and three viral strains provided some support for this model [12] . However , in a larger study using a panel of 16 viruses from a number of different subtypes , Blish et al . showed no significant differences in the NAb breadth or potency in six superinfected cases immediately before acquisition of the second virus compared to 18 singly infected controls at matched time points [13] . In this study , where the focus was on correlates of protection from SI , the NAb repertoire and breadth developed in the years following SI were not examined . In the past year , two studies have provided evidence of a broadening of the NAb response after SI . In a South African individual that became superinfected 13–15 weeks post-initial infection , broad and potent responses were detected 3 years post-initial infection ( 32 of 42 heterologous viruses neutralized , some at plasma dilutions >1∶10 , 000 ) [14] . However , it was not possible to determine whether SI is typically associated with a broad NAb response or if this single case was merely coincidental . Powell et al . aimed to address this question by measuring the difference in NAb responses from four superinfected cases and 23 singly infected controls against seven regional primary isolates from Cameroon and two subtype B viruses [15] . They showed that the average change in NAb breadth and potency between pre-SI and post-SI evaluations for superinfected cases was significantly greater than that of non-superinfected controls [15] . While this analysis provides some evidence that superinfected individuals develop broader NAb responses than singly infected individuals , the results have to be interpreted in the context of a number of limitations in the cohort studied , including an imbalanced distribution of cases and controls where matching was based on two groups of controls rather than an equal ratio of individuals . More significantly , since seroconversion dates were unknown for many individuals , the control groups were assigned based on the time each case had participated in the study , rather than how long they had been infected . Therefore , time since infection , which has been strongly associated with the development of NAb breadth [16]–[18] , could not be accounted for in this analysis . Furthermore , due to the small number of superinfected cases , the investigators were unable to control for potential confounding factors such as CD4+ T cell count and viral load , which also impact NAb breadth [11] , [16]–[19] . Nonetheless , these cases are intriguing and highlight a need for a controlled study with greater numbers of superinfected cases with appropriately matched controls . We have previously identified 12 superinfected individuals from a cohort of high-risk women in Mombasa , Kenya by comparing partial env and gag sequences over a 5-year period beginning with initial infection [20]–[22] . This cohort includes individuals initially infected by viruses from multiple subtypes ( A , C , and D ) , and subsequently reinfected by intra or intersubtype viruses . Here we describe the results from our nested cohort study in which we tested the hypothesis that superinfected individuals develop broader and more potent NAb responses compared to non-superinfected individuals as a result of increased antigenic stimulation by two distinct viruses . Our findings illustrate that SI leads to an augmentation of the NAb response and thus , provides significant support for SI as a useful model for studying the development of the NAb response to diverse HIV-1 antigens .
The 12 cases of SI demonstrated considerable heterogeneity with respect to the temporal occurrence and virologic factors related to their superinfections ( Figure 1 ) [20]–[22] . Some women were superinfected soon after initial infection , while others became superinfected much later during chronic infection ( range: ∼2 months to 5 years post-initial infection ) , with the median occurrence at 1 . 72 years post-initial infection . Eight individuals ( 60% ) experienced an increase in viral load after SI; in three cases the increase was very small ( <0 . 5 log10 copies/ml ) , while in the remaining five the mean change was 1 . 08 log10 copies/ml . Similar numbers of women had inter and intrasubtype superinfections based on env and gag sequences , and all women were infected with at least one subtype A virus , the dominant subtype in Kenya [23] . Based on the longitudinal analyses previously described [20]–[22] , the superinfecting strain persisted in combination with the initial virus in seven ( 58% ) of the women , whereas it appeared to have largely replaced the initial virus in the other five ( 42% ) ( Figure 1 ) . Three non-superinfected individuals , selected from a pool of women identified as singly infected in prior screens [20]–[22] , were matched to each superinfected case by the initial infecting virus subtype , time post-initial infection , and sample availability . NAb breadth and potency were analyzed both pre and post-SI . The pre-SI time point for each superinfected case and her matched controls varied in relation to initial infection depending on the timing of the SI event . The post-SI time point was evaluated at a single time point for all individuals an average of 5 years post-initial infection , when all 12 cases had been superinfected for at least 1 year . The single post-SI time point enabled us to draw comparisons across the entire cohort after the development of the NAb response to both the initial and superinfecting viruses , yet before the onset of overt immunodeficiency [16] , [24]–[26] . Superinfected women had significantly lower mean viral loads than singly infected women pre-SI ( Log10VL: 4 . 24 vs . 4 . 79 , respectively; p = 0 . 034 ) , but were comparable post-SI ( Log10VL: 4 . 78 vs . 4 . 89 , respectively; p = 0 . 699 ) . Both groups also had similar mean CD4+ T cell counts post-SI ( 370 vs . 380; p = 0 . 886 ) . Insufficient CD4+ T cell count data were available for the cohort at the time points prior to SI for analysis . Superinfected women had unprotected sex on average 22% of the time compared to 33% of the time for non-superinfected women ( p = 0 . 175 ) and both groups had similar numbers of partners per week ( 0 . 66 vs . 0 . 49 , respectively; p = 0 . 069 ) . All individuals in this study were ARV naïve at the time points examined . Eight viruses from subtypes A , B , C , and D that exhibit varying degrees of neutralization sensitivity to monoclonal antibodies as well as pooled plasma derived from HIV-1 infected individuals in Kenya were used to measure NAb breadth and potency both pre and post-SI ( Table 1 ) . Seven of the eight viruses were isolated from individuals during acute infection , and the majority were Tier 2 variants [27] . To evaluate whether harboring two viruses compared to a single virus influences the development of NAb breadth and potency , we first tested all superinfected cases and matched controls at a time point post-SI , approximately ∼5 years post-initial infection ( median time after initial infection: 5 . 01 years , Range: 2 . 8–8 . 1 years ) ( Figure 2 ) . Overall , the Tier 1 viruses that were neutralization sensitive ( SF162 and Q461d1 ) had the highest median IC50 values ( 606 and 583 , respectively ) for the cohort as a whole , while the median IC50s for the six Tier 2 viruses were all below 300 . Geometric mean IC50s averaged across the entire panel were significantly different between superinfected cases and singly infected individuals ( 326 . 19 vs . 193 . 33 , respectively; p = 0 . 038 ) . Furthermore , differences in neutralization potency between individual superinfected cases and matched controls are evident , most notably with the more neutralization resistant viruses such as Q769 . b9 and Q259 . d2 . 26 ( Figure 2 ) . Breadth and potency scores were calculated by normalizing the IC50 of each plasma-virus pair to the cohort median IC50 , as described [13] . The mean NAb breadth score of the 12 superinfected women was 5 . 75 , while the mean breadth score of the 36 non-superinfected women was 3 . 42 ( Figure 3A ) . Superinfected women had , on average , 1 . 68 ( CI: 1 . 23–2 . 30 , p = 0 . 001 ) times greater breath than non-superinfected women ( Table 2 ) . Similarly , the mean potency score in superinfected women was higher than singly infected women ( 17 . 25 vs . 11 . 84 , respectively ) ( Figure 3B ) , and superinfected women had 1 . 46 ( CI: 1 . 03–2 . 06 , p = 0 . 033 ) times greater potency than the non-superinfected group ( Table 2 ) . In order to determine whether the greater NAb breadth exhibited by superinfected women was independent of any effect of the NAb breadth developed prior to the SI event , we assessed the NAb responses elicited by the initial virus prior to SI in each individual ( Figure 4 ) . Viruses that were neutralization sensitive ( SF162 and Q461d1 ) had the highest median IC50 values ( 158 and 130 , respectively ) , followed by two Tier 2 viruses , DU156 . 12 and QD435 . 100M . a4 ( 108 and 61 , respectively ) . The cohort median IC50s was 50 for the other four Tier 2 viruses , with only a few individuals able to neutralize these viruses at greater than 50% at the lowest dilution tested . Overall , the geometric mean IC50s averaged across the entire panel were not significantly different between superinfected cases and singly infected controls ( 98 . 16 vs . 86 . 02 , respectively; p = 0 . 378 ) . The mean NAb breadth score for superinfected women was 4 . 30 , but only 2 . 80 for non-superinfected women ( Figure 3C ) . Superinfected women had , on average , 1 . 50 ( CI: 1 . 05–2 . 14 , p = 0 . 03 ) times greater breath than non-superinfected women at the matched pre-SI time points . However , upon adjusting for contemporaneous viral load , which differed between the two groups and is a correlate of NAb breadth [16] , [19] , the difference in breadth at this time point was attenuated and no longer statistically significant ( RR = 1 . 45 , CI: 0 . 97–2 . 16 , p = 0 . 067 ) . We observed comparable mean potency scores between superinfected and singly infected women ( 14 . 39 vs . 13 . 93 , respectively ) ( Figure 3D ) , that were not significantly different in our univariate analysis ( p = 0 . 447 ) , nor after adjusting for contemporaneous viral load ( p = 0 . 195 ) . We performed a multivariate analysis to examine the relationship between SI status and the NAb response while adjusting for breadth/potency scores pre-SI as well as contemporaneous viral load and CD4+ T cell count . We found that none of these factors individually had a major impact on the association between SI and NAb breadth post-SI , as our estimate of 1 . 68 remained statistically significant with estimates ranging between 1 . 56 to 1 . 70 ( Table 2 ) . Adjusting simultaneously for all three variables similarly did not substantially change the original estimate ( RR = 1 . 51 , CI: 1 . 01–2 . 25 , p = 0 . 040 ) ( Table 2 ) . The multivariate analysis for NAb potency post-SI after adjusting for these same three variables yielded a higher estimate , changing from 1 . 46 to 1 . 68 , illustrating that the association between SI and potency is stronger once these variables are accounted for . We next performed the same analysis with a modified breadth scoring method previously used by Simek et al . and the Center for HIV-1 AIDS Vaccine Immunology ( CHAVI ) for Protocol 008 [9] . Using this method , we similarly found that superinfected individuals demonstrated greater breadth than singly infected individuals post-SI ( mean scores: 0 . 75 vs . 0 . 57 , respectively; p = 0 . 002 ) ( Table S1 ) . Pre-SI breadth also showed differences between superinfected and non-superinfected individuals ( mean scores: 0 . 46 vs . 0 . 34 , respectively; p = 0 . 019 ) , but this difference did not remain significant after adjusting for contemporaneous viral load ( p = 0 . 435 ) . To determine if our results were sensitive to the exclusion or inclusion of a particular virus in the panel , we performed a stepwise sensitivity analysis where we assessed breadth after removing one virus at a time and also with various combinations of viruses from the original 8-virus panel ( e . g . only subtype As , only viruses with a particular neutralization profile , such as resistance to b12 or pooled plasma ) . Overall , the results were similar to our original finding , with estimates from regression analysis ranging from 1 . 36 to 1 . 91 , all of which represent statistically significant differences between the two groups ( Table S1 ) . In addition , estimates using percent neutralization at a fixed dilution of plasma ( 1∶100 , 1∶200 or 1∶400 ) , as was used in prior studies [9] , [15] , [28] , yielded similar results with statistically significant point estimates ranging from 1 . 55 to 1 . 70 ( Table S2 ) . Moreover , breadth scores calculated from the percent neutralization at a single dilution were also highly correlated with the breadth scores based on IC50s calculated from full neutralization curves with all six serial dilutions ( Spearman's rho range: 0 . 76–0 . 88 , all with p = <0 . 0005 ) ( Table S3 ) . To determine whether SI led to a rapid enhancement of the NAb response , we analyzed longitudinally banked plasma samples from the two women with the broadest responses post-SI ( QA013 and QB850 ) against the same 8-virus panel described above , beginning with the time point available following the initial detection of SI ( Figure 5 ) . We found that QA013 , who was superinfected ∼11 months after her first infection , experienced a boost in NAb activity immediately following SI ( pre-SI geometric mean ( GM ) IC50 = 118 , range: 50–308 , ∼0 . 6 years post-SI GM IC50 = 299 , range: 79–1073 ) . Her response continued to increase in potency , with a GM IC50 of 567 at ∼4 . 2 years post-SI ( range: 224–1311 ) . QB850 , who was superinfected ∼2 months after her first infection , at first displayed modest cross-subtype activity ∼1 year post-SI ( GM IC50 = 146 , range: 65–428 ) before gradually developing elite activity around ∼2 . 2 years post-SI ( GM IC50 = 451 , range: 168–2268 ) , which was ∼2 . 3 years post-initial infection . Overall , these data demonstrate that QB850 and QA013 developed cross-subtype neutralization within 1 year following SI , with two different trajectories that both led to elite NAb activity . To further assess the breadth of the response of these two individuals , additional viruses were tested , with an emphasis on subtypes that were represented by a single virus in our screen ( subtypes B and D ) . There are limited options for subtype D viruses , but we chose a neutralization resistant variant from the Mombasa cohort , QB857 . Three subtype B viruses JR-CSF , 6535 . 3 and CAAN5342 . A2 , were chosen based on their use in a prior screen to define elite neutralizers [9] . Both QA013 and QB850 plasma samples from 5 years post-initial infection neutralized all four of these viruses , with IC50s ranging from 72–810 . The IC50 values we observed for QA013 and QB850 against the subtype B viruses ( JR-CSF: 207 and 163 , 6523 . 3: 322 and 810 , CAAN5342 . A2: 127 and 106 , respectively ) were all comparable or above the cohort GM IC50s observed in the elite neutralizer screen [9] . However , it was not possible to directly compare all of our IC50s to the top 1% of elite neutralizers identified in that study because the IC50 values for those individuals against these viruses were not presented .
In this study , we tested the ability of antibodies present in superinfected and singly infected women to neutralize a spectrum of circulating HIV-1 variants and thus , discern whether antigenic stimulation by two viruses compared to one has an effect on the subsequent NAb response . Our results demonstrate that the NAb response of superinfected women is significantly broader and more potent than that of singly infected women when compared at matched time points . These data suggest that SI elicits a substantial enhancement of the NAb response with regards to cross-reactivity in the years following reinfection . This conclusion is supported by our analysis controlling for NAb breadth/potency prior to SI and clinical measures that are associated with NAb breadth such as CD4+ T cell count and viral load [11] , [16]–[19] . Therefore , the study of superinfected individuals may yield additional insights into the development of broad and potent NAb responses to diverse HIV-1 antigens . The majority of superinfected individuals exhibited breadth and potency scores that were greater than the average of their matched controls post-SI . Among the SI cases , the three women with the most potent responses ( QB850 , QA013 , QC885 ) experienced intersubtype superinfections that were characterized by persistence of both the initial and superinfecting viruses . While we attempted to parse out which factors relating to SI may be influencing the generation of a broad response , we were unable to do so conclusively due to a limited sampling of 12 cases ( data not shown ) . However , the characteristics of these three women suggests that continuous stimulation by two distinct viruses from different subtypes may be critical to the induction of broad NAbs . Still , other factors , including the antigenic nature and replication potential of the infecting viruses , may also be important . Conversely , we found a minority of superinfected individuals ( QB045 , QC858 , QD022 ) exhibited breadth and potency scores that were lower than that of the average from their matched controls . As all three had a CD4+ T cell count >400 post-SI , it is unlikely that lower breadth and potency simply reflected a lack of T cell help that could arise at terminal stages of infection . Some potentially informative features of these cases are that two of the three ( QB045 and QD022 ) are the only individuals in the cohort who became superinfected late after initial infection ( >4 years post-initial infection ) , suggesting that perhaps the timing or duration of either or both infections may be critical to the development of NAb breadth . Moreover , in QD022 and QC858 the superinfecting variant became dominant as the initial virus could no longer be detected post-SI , at least at levels captured by Sanger sequencing , suggesting that continued antigenic stimulation by both infecting viruses may be important in augmenting the NAb response . Additional studies that include deep sequencing of viruses in tissue and plasma would be one starting point to further explore this hypothesis . In one individual , QA413 , multiple Tier 2 viruses were neutralized prior to SI , while the Tier 1 viruses ( SF162 and Q461d1 ) were not . This may suggest that there are unique epitopes common to the two Tier 1 viruses that are not present in the Tier 2 viruses or vice versa . In the case of Q461d1 , the envelope is highly sensitive to neutralization because of conformational changes that expose multiple epitopes [29] . However , we have found that Q461d1 is relatively insensitive to neutralization by the PGT-type antibodies that recognize a quaternary structure that includes an Asn at amino acid position 160 ( unpublished ) . SF162 is also not recognized by PG9 and certain PGT antibodies because it encodes a Lys at position 160 [4] , [8] . Interestingly , the virus that initially infected QA413 encodes an Asn at position 160 , while the superinfecting virus encodes a Lys , perhaps suggesting that PGT-like antibodies could contribute to the antibody response in this woman . Mapping studies to define the epitope specificity will be needed to understand the unusual pattern of virus neutralization observed in QA413 pre- and post-SI . In this study , we examined breadth and potency after SI against a panel of heterologous viruses . In a prior study , the responses to autologous viruses were examined in five of these individuals [13] . Interestingly , one of the elite neutralizers , QA013 , developed very high titers against her superinfecting virus , with IC50 values >1 , 800 ( range: 1 , 000–25 , 000+ ) to all four SI variants cloned at ∼5 years post-SI ( 6 . 3 years post-initial infection ) . The other four superinfected individuals , who did not develop elite responses , had autologous responses that ranged from <100 to 10 , 000 . There was insufficient data to determine if there was an association between autologous and heterologous responses . It is difficult to directly compare the NAb responses of the superinfected women to broad neutralizers identified in other studies because there is no standard for quantifying NAb breadth against HIV-1 . We addressed this issue by using a diverse panel of viruses weighted towards Tier 2 variants and several alternate breadth scoring methods , which showed that no single virus drove the association observed between SI and NAb breadth and that our conclusion is the same irrespective of the scoring method used . Simek et al . previously defined elite activity as “the ability to neutralize , on average , more than one pseudovirus at an IC50 titer of 300 within a clade group and across at least four clade groups [9] . ” While we were unable to completely satisfy these criteria since our 8-virus panel included only single variants of subtypes B and D , we still found that the two superinfected individuals ( QA013 and QB850 ) with the broadest responses could neutralize at least two viruses in subtypes A and C , as well as both single viruses tested from subtypes B and D at an IC50 titer greater than 300 , supporting the characterization of these individuals as elite neutralizers . Notably , plasma antibodies from QB850 neutralized viruses from all four subtypes tested at IC50 values greater than 600 , more than 2-fold higher than the bar set for elite neutralizers [9] . Furthermore , the responses of these two women were greater than those found in a similar screen of 70 singly infected women in this cohort at ∼5 years post-initial infection [11] , with some observed IC50 titers against Tier 2 viruses 6-fold more potent than those of the top 10% of the singly infected women ( data not shown ) . Although we had limited opportunities to directly compare the responses of the superinfected individuals studied here and individuals identified as elite neutralizers by Simek et al . in the IAVI cohort , we did find that the SI cases had IC50 values that were either comparable or above the cohort GM IC50 for the three viruses tested in common between the studies . Together , these data suggest that 2 of the 12 individuals from our SI cohort developed NAbs that exhibit elite activity . This is a remarkable fraction ( 17% ) of individuals with elite activity , although because of differences between screening methods , it is difficult to compare this to the 1% of presumably singly infected elite neutralizers reported previously by Simek et al . In a prior study that examined four cases of SI , a greater increase in NAb breadth post-SI among superinfected individuals compared to singly infected individuals was also observed [30] . However , it is hard to compare their data with our own , as the previous study used randomly chosen primary isolates for measuring breadth in a cohort with unknown seroconversion dates . It is also interesting to note that this previous study observed a decrease in viral load in three of the four superinfected cases studied at a time point post-SI , two of which had undetectable viral loads . Such a significant drop in viral load has not been previously reported for cases of SI [31] . In contrast , we observed an increase or no change in viral load in the majority of superinfected women examined . This is consistent with the observation that viral load is highly correlated with NAb breadth [11] , [16] . However , after adjusting our breadth score analysis for contemporaneous viral load , the estimate of 1 . 68 was unchanged , while adjusting for this variable caused an increase in our estimate for differences in potency . This would imply that breadth in these superinfected cases alone cannot be explained entirely by an increase in viral load following SI , but rather suggests that stimulation from antigenically distinct viruses may contribute to the development of potency . Finally , we found evidence of cross-subtype breadth , including detectable neutralization of viruses from four different subtypes in the two women with elite NAb responses within 1 year of their SI . Because both of these individuals were superinfected soon after their initial infection , these cross-subtype responses arose relatively soon after HIV-1 seroconversion . Indeed , by 2 years after their initial infection , both women had antibodies capable of neutralizing 7 of the 8 primarily Tier 2 viruses tested . Recent studies suggest that cross subtype breadth is rare before 2 years post-infection in individuals who are presumably singly infected [17] , [32] . Our findings raise the interesting possibility that some of the individuals identified as having broad responses in prior screens may have been superinfected . A few caveats to these findings must be considered . First , there may have been potential for misclassification of singly infected women , due to our limit of detection or if recombination occurred between the initial and superinfecting strains in HIV-1 genomic regions outside of gag and env [21] . However , this misclassification would be expected to decrease our ability to detect differences between superinfected and singly infected women , making it more likely that the true association between SI and NAb breadth is stronger than what we observed . Also , we cannot exclude the possibility that there are other factors involved with the development of the broad responses in some of the superinfected women . This study reveals an unexplored source of naturally-occurring broadly NAbs , and represents a highly relevant approach to inform vaccine strategies in three key ways . First , studies on this and other SI cohorts may provide additional support for immunizing with particular combinations of different HIV-1 strains could be an effective vaccine approach [33]–[37] . Given that the greatest breadth was observed in cases of intersubtype SI , the use of Env immunogens from different subtypes may be optimal . Second , the NAbs and viruses isolated from members of this cohort may hold important clues to antigenic determinants capable of eliciting cross-reactive antibodies that can protect against multiple subtypes of HIV-1 . Third , longitudinal studies of superinfected individuals who develop broad and potent NAb responses , such as those identified here , may foster an understanding of the mechanism leading to the elicitation of breadth . Of particular interest is the observation that two elite neutralizers developed broad and potent responses soon after infection by a second HIV-1 strain , suggesting that the processes that lead to the development of broad HIV-specific NAbs are accelerated by a second infection . If SI ultimately leads to the rapid capacity of the overall NAb response to recognize diverse circulating HIV-1 variants , a successful vaccination strategy that mimics natural SI may lead to the development of broad NAb in immunized individuals .
The University of Washington's , University of Nairobi and Fred Hutchinson Cancer Research Center's Institutional Review Boards approved the study . Written informed consent was provided by all study participants . The individuals in this study represent a subset of a prospective cohort of HIV-1 negative high-risk women from Mombasa , Kenya [38]–[40] who have a defined date of infection based on approximately monthly HIV-1 serology and subsequent retrospective RNA testing of banked plasma from time points prior to seroconversion as described [41] . Twelve superinfected individuals were identified from a previous screen of 56 women from this cohort that compared partial env and/or gag sequences amplified from peripheral blood mononuclear cells ( PBMCs ) from the first visit after the detection of seroconversion and a visit during chronic infection ∼5 years later [20]–[22] . Briefly , single copy PCR was performed and the sequences from multiple independent PCRs ( median of 7 ) were examined at each time point . Two regions of the HIV-1 genome were examined: envelope V1-V5 ( ∼1 . 2 kb ) and gag p17 ( ∼700 bp ) . Individuals identified as potential SI cases were further analyzed to verify SI and determine the interval when it occurred . All cases of SI as well as the approximate timing of SI were determined by phylogenetic analysis and allele-specific PCR tailored to the sequences in each individual . For this study , three singly infected women from the 56 tested for SI in the prior study [20]–[22] , were matched to each of the 12 cases of SI according to initial infecting viral subtype and sample availability at time points approximately 5 years post-initial infection ( post-SI ) and 1 year prior to SI ( pre-SI ) . In cases where samples close to 5 years post-initial infection were not available for the SI case , the closest available sample post-SI was selected and the timing of sample selection was similar for the controls ( Figure 2 ) . Other than sample availability , the assignment of controls was random , and was performed using random number generation . Viral loads were determined by Gen-Probe and were available for all women , and CD4+ T cell counts were documented beginning in 1998 and were available for 17 women prior to SI and 45 women post SI [42]–[44] . All women were HIV-1 infected through heterosexual contact [38] , and none reported using antiretroviral therapy during follow-up for this study . The panel of eight viruses was chosen to include multiple subtypes with varying neutralization sensitivities to a pool of HIV+ plasma from the Mombasa cohort and monoclonal antibodies ( i . e . b12 , 4E10 [45] , [46] ) . The viruses used , and their subtypes in parenthesis , were: SF162 ( B ) [47] , Q461d1 ( A ) [45] , Q842 . d16 ( A ) [45] , QD435 . 100M . a4 ( D ) [45] , DU156 . 12 ( C ) [48] , QC406 . 70M . f3 ( C ) [46] , Q259 . d2 . 26 ( A ) [45] , Q769 . b9 ( A ) [45] . Plasma was also tested against an envelope from Simian Immunodeficiency Virus ( SIV ) , SIVmne CL8 [49] , to ensure that the neutralization observed against the virus panel was HIV-1-specific . The two elite neutralizers identified in the study were also tested against JR-CSF ( B ) [50] , 6535 . 3 ( B ) [51] , CAAN5342 . A2 ( B ) [51] , and QB857 . 23I . B3 ( D ) [46] . Pseudoviruses were made by cotransfecting 293 T cells with each of the cloned viral envelopes listed above and a full-length subtype A proviral clone with a partial deletion in envelope ( Q23Δenv ) , as previously described [52] . Briefly , an equimolar ratio of envelope-to-provirus plasmid was added to Fugene-6 transfection reagent ( Roche , Indianapolis , IN ) and then incubated with 4 million 293 T cells for 12 hours . The media was changed at 10 hours . After a total of ∼48 hours post-transfection , supernatants were harvested and filtered through a 0 . 22 um Steriflip Filter Unit ( Millipore , Billerica , MA ) to remove cellular debris . The resulting pseudoviruses were screened for infectivity on TZM-bl cells , a HeLa-derived reporter cell line that expresses high levels of CD4 , CCR5 , and CXCR4 as well as B-galactosidase under the transcriptional control of HIV-LTR [53] . Infectious titers were determined by serially diluting viruses 10-fold , adding 20 , 000 TZM-bl cells in growth media containing 20 ug/mL DEAE-dextran per well , and incubating at 37°C for 48 hours . The cells were then fixed and stained for B-galactosidase activity and infected cell foci were enumerated visually . The TZM-bl neutralization assay was used to quantify NAb breadth as previously described [45] . Briefly , 500 infectious pseudovirus particles , as determined by the infectious titer described above , were incubated in duplicates with 2-fold serial dilutions of plasma for 1 hour , beginning with an initial concentration of 1∶100 , before 10 , 000 TZM-bl reporter cells per well were added . Each plasma-virus combination was tested in duplicate and the assay was repeated twice . Infection levels were determined by B-galactosidase activity after 48 hours using a chemiluminescent readout . The IC50 , or reciprocal plasma dilution at which 50% of the virus is neutralized , for each plasma-virus pair was calculated using linear interpolation from the neutralization curve . In this assay , a plasma sample was considered to be below the detectable limit of neutralization for a given virus if the lowest dilution ( 1∶100 ) did not show >50% neutralization . With this criterion , plasma samples that showed neutralization below the limit of detection were designated an IC50 value of 50 , the midpoint between our starting dilution ( 1∶100 ) and 0 . Each round of assays included HIV-negative plasma and a HIV-positive plasma pool from 30 HIV-1 infected individuals in Kenya between 1998–2000 [45] serving as negative and positive internal controls , respectively . If a run showed neutralization of the negative control virus ( SIVmne CL8 ) greater than the limit of detection , we considered that a failed run and repeated the assay . Results from two independent experiments were averaged on the log scale before calculating breadth and potency scores . A composite breadth score for each plasma-virus pair was derived for each woman by comparing the IC50 for her plasma to the cohort median IC50 for each virus as described [13] . The cohort median IC50 was based on all IC50s from the individuals in the study , and this represented the unique neutralization sensitivity of that virus . If the IC50s for the given plasma-virus pair was greater than the cohort median IC50 , then individuals were given a score of 1 , while those below were scored as a 0 . The overall breadth was then a composite score for each individual against all eight viruses in the panel , with a maximum score of 8 and a minimum of 0 . We also analyzed our dataset using the percent neutralization at the first three dilutions in our assay ( 1∶100 , 1∶200 , 1∶400 ) and applied the same breadth scoring method . Potency was calculated by dividing the IC50 value for a given plasma-virus combination by the cohort median IC50 for that virus . The overall potency was then a composite score for each individual against all eight viruses in the panel . A method described in Simek et al , was also applied to the data . In this method , for each virus , the average log-transformed titer is calculated for a given sample then scaled to yield a value between 0 . 0 and 1 . 0 [9] . As a minor modification to take into account the varying sensitivities in our virus panel , we divided the average by the maximum value for the cohort . An average of the scaled averages across viruses was computed as a final breadth score for each individual . Breadth scores were analyzed using conditional Poisson regression , while log transformed potency scores were analyzed using linear regression generalized estimating equation ( GEE ) . These models were appropriate given the observed mean-variance relationships of the breadth and potency scores . The breadth scoring method described by Simek et al . was analyzed using GEE . All models assessed by GEE used an identity link , exchangeable working correlation structure , and robust standard errors . We used a paired t-test to compare viral load , CD4+ T cell count and geometric mean IC50s between superinfected and non-superinfected individuals . Spearman's rank correlation was used for comparing the results from our final model and single dilution analysis . Two-tailed P values of 0 . 05 or less were considered to indicate significance in all statistical tests . Analyses were performed using STATA statistical software ( version 11 , StataCorp , College Station , TX ) . | A broad and potent antibody response is considered essential for an effective HIV-1 vaccine that will protect against diverse circulating strains . Consequently , there is great interest in both the host and viral factors that impact the development of the neutralizing antibody ( NAb ) response in natural HIV-1 infections . HIV-infected individuals who become superinfected with a second virus from a different source partner represent unique cases for studying the antibody response , as superinfection reflects exposure to different HIV-1 antigenic variants , and hence may provide insight into the development of broadly NAbs . In support of this model , we show here that superinfected individuals develop broader and more potent NAb responses than singly infected individuals , a result that is likely due to the increased antigenic stimulation from two viruses compared to one . Our findings remained unchanged after controlling for other factors that have been shown to influence the NAbs response , such as CD4+ T cell count and viral load . This study demonstrates that superinfection yields antibodies that have the capacity to recognize diverse circulating HIV-1 variants . Therefore , further characterization of these superinfected individuals' NAb responses could lead to novel insights into pathways that elicit broadly NAbs . |
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Nutrigenomics investigates relationships between nutrients and all genome-encoded molecular entities . This holistic approach requires systems biology to scrutinize the effects of diet on tissue biology . To decipher the adipose tissue ( AT ) response to diet induced weight changes we focused on key molecular ( lipids and transcripts ) AT species during a longitudinal dietary intervention . To obtain a systems model , a network approach was used to combine all sets of variables ( bio-clinical , fatty acids and mRNA levels ) and get an overview of their interactions . AT fatty acids and mRNA levels were quantified in 135 obese women at baseline , after an 8-week low calorie diet ( LCD ) and after 6 months of ad libitum weight maintenance diet ( WMD ) . After LCD , individuals were stratified a posteriori according to weight change during WMD . A 3 steps approach was used to infer a global model involving the 3 sets of variables . It consisted in inferring intra-omic networks with sparse partial correlations and inter-omic networks with regularized canonical correlation analysis and finally combining the obtained omic-specific network in a single global model . The resulting networks were analyzed using node clustering , systematic important node extraction and cluster comparisons . Overall , AT showed both constant and phase-specific biological signatures in response to dietary intervention . AT from women regaining weight displayed growth factors , angiogenesis and proliferation signaling signatures , suggesting unfavorable tissue hyperplasia . By contrast , after LCD a strong positive relationship between AT myristoleic acid ( a fatty acid with low AT level ) content and de novo lipogenesis mRNAs was found . This relationship was also observed , after WMD , in the group of women that continued to lose weight . This original system biology approach provides novel insight in the AT response to weight control by highlighting the central role of myristoleic acid that may account for the beneficial effects of weight loss .
The main function of adipose tissue ( AT ) is to store excess energy as triglycerides and to release non-esterified fatty acids ( FAs ) for other tissues during periods of energy demand . AT also releases numerous peptidic/proteic and lipidic factors with signaling functions [1–3] . Obesity is characterized by an excess fat mass with deleterious health consequences . AT expansion results in dysfunctional non-esterified FA release and imbalance in production of anti/pro-inflammatory mediators [4] . Most of the obesity-related metabolic disturbances are reversible with weight loss [5] . However in obese individuals , weight fluctuations are frequent since individuals involved in dieting-induced weight loss are often unsuccessful at long last [6 , 7] . Adaptations occurring in AT during dietary weight management programs remain unclear especially regarding weight control after dieting [8] . The FA composition of AT reflects balance between exogenous FAs from food , triglyceride hydrolysis/synthesis and FA synthesis from glucose-derived acetylCoA , so-called de novo lipogenesis ( DNL ) . Studies on FA composition of AT during weight control trials are scarce [9 , 10] . Low 16:1 ( cis-9 ) ( palmitoleic acid ) and 14:1 ( cis-9 ) ( myristoleic acid ) may predict favorable weight control outcome [11] . Omics , especially transcriptome studies , have proved great potential in clarifying the role of AT biology with respect to response in weight controlling trials [12] . However , analyses based on single omics often do not provide enough information to understand biology . The integration of multiple omics may give a better understanding of a biological system as a whole . Global network-based approaches authorize multiple datasets analyses and carry the advantage of highlighting functionally related pathways and biological entities of potential relevance as hubs [13] . Networks are valuable models to dissect complex traits [14] . However , integrative analysis of datasets of different data types raises the issue of different scales of the multiple datasets . In gene expression networks , clusters are more robust than individual interactions [15] . Multivariate statistical approaches were recently developed to jointly analyze omics datasets , dealing with high dimension and using variable selection [16] . The present study aimed at revealing the characteristics of AT biological networks relevant to clinical traits during a long-term dietary intervention ( DI ) including calorie restriction and ad libitum follow-up after weight loss . Studies on human AT gene expression or lipidomic profiles from a systems biology point of view have only been reported at baseline [17 , 18] but not during DI . Network modeling has recently been applied using metagenomic , plasma and AT inflammatory markers to predict weight changes during stabilized weight loss [19] . To our knowledge , no study has jointly investigated AT lipidic and gene expression profiles , especially during long-term DIs . Here , the global AT networks were computed using FAs , mRNA levels , clinical risk factors and biochemical markers according to weight changes in the same individuals . Our purpose was to identify common as well as differential signatures with relationship to bio-clinical factors . The identification of novel AT features associated with weight regulation may influence our understanding of weight control and authorize new advances in obesity management .
Ethics statement . The samples investigated in this paper were collected from 2006 to 2007 during the DiOGenes study , a pan-European randomized DI trial which was approved by the ethics committees of each of the 8 European centres participating to the program ( registration no . NCT00390637 ) . Written informed consent was obtained from each patient according to the local ethics committee of the participating countries: 1 , Medical Ethics Committee of the University Hospital Maastricht and Maastricht University , The Netherlands; 2 , The Committees on Biomedical Research Ethics for the Capital region of Denmark , Denmark; 3 , Suffolk Local Research Ethics Committee , UK; 4 , University of Crete Ethics Committee , Greece; 5 , the Ethics Commission of the University of Potsdam; 6 , Research Ethics Committee at the University of Navarra , Spain; 7 , Ethical Committee of the Institute of Endocrinology , Czech Republic; 8 , Ethical Committee to the National Transport Multiprofile Hospital in Sofia , Bulgaria . Study design . The data presented in this paper are part of those collected during the DiOGenes study ( contact information at www . diogenes-eu . org ) The DiOGenes project investigated the effects of diets with different content of protein and glycemic index on weight-loss maintenance and metabolic and cardiovascular risk factors after a phase of calorie restriction , in obese/overweight individuals . The trial protocol and supporting CONSORT checklist are available as supporting information; see S1 Protocol and S1 Checklist . Healthy overweight ( body mass index ( BMI ) ≥27 kg/m2 ) individuals , aged <65 years were eligible for the study . Exclusion criteria were BMI 45 kg/m2 , liver or kidney diseases , cardiovascular diseases , diabetes mellitus ( type 1 or type 2 ) , special diets/eating disorders , systemic infections/chronic diseases , cancer within the last 10 years , weight change >3 kg within the previous 3 months , and other clinical disorders or use of prescription medication that might interfere with the outcome of the study . A detailed description of inclusion and exclusion criteria has been published previously [20] . BMI was calculated by dividing weight in kilograms by the square of height in meters . Waist circumference was measured between the bottom of the ribs and the top of the hip bone . A detailed description of the DiOGenes intervention trial and main outcomes can be found in previous core publications [20–22] . Briefly , after the first clinical investigation day ( baseline ) , eligible individuals followed an active weight loss phase of 8-week low calorie ( 3 . 3–4 . 2 MJ/d ) diet ( LCD ) using commercial meal replacements ( Modifast , Nutrition et Santé ) . The individuals with ≥ 8% of initial body weight loss during LCD were randomized into one of five ad libitum weight maintenance diets ( WMD ) for 6 months: 4 diets combining high and low protein content with high and low glycemic index of carbohydrates , and a control low fat ( 25–30% energy ) diet according to National dietary guidelines on healthy diets [22] . During WMD , the individuals were provided dietary instruction as described in [22] . Dietary intake was assessed at screening , 4 weeks after the beginning and at the end of WMD . The subjects were asked to complete a 3-day weighed food record , including 2-week days and 1 weekend day . Dietary records were validated by a nutritionist . Clinical investigations including anthropometric measures ( height , weight , waist circumference , body composition ) , blood pressure measurements , fasting blood sampling , and subcutaneous AT biopsies were performed at baseline ( BAS ) and at the end of each phase . All procedures were standardized between the 8 study centers across Europe [21] . Fig . 1 displays the organizational flowchart through the trial protocol and the individuals’ selection from the DiOGenes cohort for the present study . Patients and adipose tissue study . Biopsy samples were stored at -80°C until total RNA and FA extractions . The lipid fraction was extracted from the fat cake produced during total RNA extraction using gas chromatography as described in [11] . The list of FA extracted from the lipid fraction is presented in S1 Table . After RNA extraction the mRNA levels of a panel of 221 genes selected from previous published and unpublished DNA microarray analyses on limited number of individuals as described in [23] was assessed using high throughput real-time PCR as described in [24] . S2 Table describes these genes according to biological pathways and the biological function of the protein encoded . The list includes 68 genes previously shown as markers of subcutaneous AT from obese insulin resistant subjects with metabolic syndrome [25] , 65 genes described as markers of subcutaneous AT from lean individuals [25] , 33 genes selected from previous caloric restriction induced weight loss studies [26 , 27] , 27 markers of weight changes after caloric restriction [28] , and 28 unpublished predictors of weight change to distinguish between those subjects that will regain weight after LCD from those that will succeed weight maintaining based on the AT transcriptome at baseline or after the caloric restriction phase . These genes encoded proteins involved in various pathways such as metabolism ( 47 . 5% of the transcripts ) , immune response ( 19 . 5% ) , transport ( 4 . 5% ) , cell and tissue structure ( 3 . 6% ) , signal transduction ( 2 . 3% ) and response to stress ( 1 . 4% ) . A subgroup of the DiOGenes cohort was selected based on the availability of the FA and gene profiling quality data . Here , among the 214 individuals with both AT gene expression and FA content available at all steps of the DI , i . e . BAS , LCD and WMD , only premenopausal women were studied ( n = 135 ) . After LCD , the women were classified a posteriori into 3 separate groups according to weight changes during WMD , calculated by subtracting body weight at LCD to body weight at WMD . Subjects who experienced a weight loss or a weight regain greater or equal to 2 kg during WMD were classified as weight losers ( WL ) ( n = 45 ) or weight regainers ( WR ) ( n = 51 ) , respectively . Individuals with weight change of less than 2 kg were classified as stable weight ( WS , n = 39 ) . Data availability statement . Raw and processed RT-qPCR data files were deposited at the Gene Expression Omnibus depository and are available under series accession number GSE60946 . Other data data are available upon request . Data were first analyzed by multivariate statistical methods using principal component analysis to detect center or diet group biases and mean-centered transformed if needed . Gaussian distribution of data was tested using the Kolmogorov–Smirnov test and log transformed adequately . Differences in clinical data , mRNA and FAs between BAS , end of LCD and end of WMD were tested using one-factor repeated measure ANOVA with Bonferroni post-hoc test . The differences between each group ( WL , WR and WS ) at each step of the DI were tested with one-factor ANOVA and Bonferroni post-hoc test . Fatty acids and gene expression data were controlled for multiple testing by using Benjamini-Hochberg P value correction ( q-value ) [29] . Analyses were performed with SPSS Statistics 17 . 0 software ( SPSS Inc . , Chicago , Ill ) . The network analysis was performed as illustrated in Fig . 2: for BAS , the end of LCD and the 3 groups at the end of WMD ( WR , WL and WS ) , a system model was designed using a global network . The network was built using a 3 step approach . A first step consisted in inferring a network in each set of variables ( bio-clinical , FAs and mRNA level ) using a sparse Graphical Gaussian Model ( GGM , [30] ) . This model is based on the assumption that , in each set of variables , the distribution of the variables , ( Xj ) j = 1 … p’ is Gaussian N ( 0 , ∑ ) and that the observations obtained for all individuals are independent and identically distributed . The method then unravels the conditional dependency structure of the variables , i . e . , defines a network whose edges correspond to positive or negative partial correlations P ( Xj , Xj’| ( Xk ) k≠j , j’ ) Using a maximum likelihood approach , the method performs an edge selection , simultaneously to the estimation of partial correlations . Unlike simple correlation , partial correlation is a mean to assess direct correlations between pairs of variables , independently of the other variables and is thus closer to a causality relation than simple correlation . The number of selected edges was chosen according to the description given in step 3 below . A second step consisted in inferring a network between each pairs of two different sets of variables among bio-clinical , FAs and mRNA level sets . To do so , we used the approach that was proven successful to infer a gene/phenotype network in [31]: regularization canonical correlation analysis ( CCA , [16 , 32] ) . The additional regularization constraint was used to deal with the large number of variables as compared to the number of observations . The number of selected edges was chosen according to the description given in step 3 below . A third step consisted in merging the 3 networks obtained in the first step with the 3 networks obtained in the second step . As the number of variables in the 3 datasets was very different ( from 15 bio-clinical variables up to 221 gene expressions ) , a naive strategy consisting in estimating the selected edges in each set of variables ( or in each pair of two sets ) in a same manner would have led to give too much importance on the largest set of variables , i . e . , to the gene expression dataset . The number of selected edges was thus adjusted to be equal to the number of nodes in each set ( or pair of sets ) of variables , leading to smaller densities for the largest networks . The first step of the analysis was performed using the R package glasso ( cran . r-project . org/web/packages/glasso ) and the second step using the R package mixOmics ( http://perso . math . univ-toulouse . fr/mixomics ) . Global network analysis . To stress out the macro-structure of the network , a spin-glass model and simulated annealing were used to maximize the modularity quality measure [33] and obtain a vertex clustering [34] for all 5 networks . The significance of the clustering was assessed using a permutation test as described in [35]: the clustering was declared significant if the obtained modularity was larger than the maximum modularity found over 100 random graphs with the same degree distribution than the graph under study . Random graphs with identical degree distributions were generated using a permutation of the edges as justified by [36] . Sub-network analysis . Significance of the betweenness within a cluster was assessed using a permutation test to check if the betweenness was significantly high regarding the node’s degree in its cluster . A significant result ( p<0 . 05 ) indicates a node more central than expected in the graph and a non-significant ( p≥0 . 05 ) result indicates a node which centrality is expected for the node’s degree . For nodes with a high degree ( so-called hubs ) , a non-significant result does not however indicate that the node is not important within its cluster: its importance is already acknowledged by its many connections with the other nodes . But , provided its degree , it is not particularly central . Significant betweenness was thus used as a measure of importance of the hubs in the networks ( even though hubs were systematically investigated , it provided an additional information on the node’s critical role ) . The permutation test was performed in a way similar to the modularity test: the highest betweenness over 100 random graphs with the same degree distribution was compared to all observed betweenness . The nodes with an observed betweenness in the top 5% were declared significant . The network analysis ( node clustering and betweenness calculation ) was performed with the R package igraph ( igraph . org; [37] ) . Finally , clusters with identical central nodes in two different networks were tested for the significance of the number of common nodes using a Fisher exact test with the set of all variables as reference: pairs of clusters with a p-value smaller than 5% in the Fisher exact test are those that have a larger number of common nodes than what was expected by random chance only . Sub-graphs ( clusters ) were laid out using force-based algorithms in Gephi 0 . 8 . 2 software ( gephi . org , [38 , 39] ) . Nodes’ sizes indicate degree , i . e . , the number of edges adjacent to the node . Nodes with the largest degrees , called hubs , were systematically extracted . Nodes’ colors and font size indicate betweenness centrality , a measure that counts how often a node appears on shortest paths between two other nodes in the network . Therefore , betweenness centrality indicates nodes that are the most likely to disconnect the network if removed . The variables are connected by an edge only if they have been selected by the sparse estimation . Edge thickness is proportional to the strength of the correlation ( CCA ) or of the partial correlation ( GGM ) but should only be compared for a given set of estimation ( i . e . , partial correlation strength between two pairs of genes can be compared but should not be compared to correlation between a gene and a FA or a bio-clinical parameter ) . The biological functions represented by mRNAs from each cluster were searched using Ingenuity Pathways Analysis ( IPA ) software version 7 . 5 ( Ingenuity Systems , Redwood City , CA ) . The significance of canonical pathways was tested using the Fisher Exact test with the set of 221 genes as reference . Data were controlled for multiple testing by using Benjamini-Hochberg P value correction .
Baseline anthropometric and clinical characteristics of the 135 women are displayed in Table 1 . After the end of LCD , individuals were a posteriori classified into 3 groups according to weight changes during WMD . To ensure that there was no striking between group difference at baseline and after LCD , bio-clinical variables , gene expression and FA profiles were also analyzed a posteriori according to weight control classification . At baseline , women from WL group had higher weight and BMI than those from WS groups ( Table 2 ) . Weight loss induced by LCD was similar in the 3 groups even though mean weight in WL group remained higher than in WS group after the LCD . Plasma adiponectin was higher at baseline in WL group compared to WR and WS groups . During LCD , there was no intergroup difference in bio-clinical changes . All parameters improved except plasma fructosamine and adiponectin . S3 Table displays the anthropometric and clinical characteristics at the end of the weight maintenance phase according to weight control group and by randomization arm . During WMD , women from WL group lost 7 . 0 ± 0 . 4 kg compared to the end of LCD and those from WR group regained 5 . 0 ± 0 . 4 kg . Adiponectin improved during WMD only in WL group . There was no difference regarding age , center ( data not shown ) or distribution of the 5 WMD dietary arms between groups ( S3 Table ) . There was no intergroup difference in changes in dietary intake along DI ( S1 Fig . ) . A bunch of 221 mRNA ( S2 Table ) selected from previous AT investigations using microarrays was quantitatively assessed using RT-qPCR . Among these genes , 155 genes were down-regulated during LCD . The most representative pattern was a down-regulation during LCD and up-regulation during WMD . The most regulated genes in the 3 groups , SCD and FASN , encoded enzymes for different steps of FA synthesis , stearoyl CoA desaturase and fatty acid synthase , respectively ( S2 Fig . ) . S3 Table displays the AT changes in FA composition . At baseline , in WL group , AT had higher percentages of polyunsaturated FAs ( PUFAs ) and lower saturated FAs ( SFAs ) and mono unsaturated FAs ( MUFAs ) compared with other groups . SFAs and MUFAs exhibited the most representative changing course during DI . During LCD , in WL group , 2 SFAs ( 12:0 and 14:0 ) and 2 MUFAs ( 14:1 ( cis-9 ) and 16:1 ( cis-9 ) ) AT content decreased . Three other MUFAs ( 18:1 ( cis-9 ) , 20:1 ( cis-11 ) , 16:1 ( cis-7 ) ) , and 4 PUFAs , including 20:4 ( cis-5 , 8 , 11 , 14 ) , increased . Altogether , after WMD , the AT from WL and WS groups had similar FA profile than after LCD . In the WR group , the FA content returned to baseline values . The greatest changes were an increase in 12:0 , and 14:1 ( cis-9 ) and a decrease of 18:1 ( cis-9 ) percentages . S3 Fig . displays SCD activities assessed using 14:1 ( cis-9 ) /14:0 , 16:1 ( cis-9 ) /16:0 and 18:1 ( cis-9 ) /18:0 ratios and showed no between group difference at baseline and after LCD . At the end of WMD , 14:1 ( cis-9 ) /14:0 and 16:1 ( cis-9 ) /16:0 , but not 18:1 ( cis-9 ) /18:0 , were higher in WR compared to WL group . Network inference was performed using the 3 step inference method ( see Materials and Methods ) at baseline , at the end of LCD , and in the 3 groups at the end of WMD , resulting in 5 global networks . Then , to stress out the macro structure of the network , a vertex clustering was performed . All 5 clustering performed on the 5 global networks were found to have a significantly high modularity , proving the relevance of the sub-graphs ( clusters ) . At baseline . Before LCD , among 14 clusters detected , 9 displayed more than 6 vertices . Insulin , waist circumference and 18:1 ( cis-9 ) were central nodes of 3 of the clusters containing at least 2 types or variables ( bio-clinical , FAs or mRNAs ) ( Fig . 3 ) . Insulin was the variable with most significant betweenness centrality ( p-value = 0 . 03 ) among the most central nodes of the 3 clusters . The insulin-centered cluster contained plasma glucose and mRNA encoding proteins involved in “Adhesion and Diapedesis” as major canonical pathway according to IPA analysis . This included various cytokines ( CCL2 , CCL18 ) and metalloproteases ( MMP9 , MMP19 ) with positive correlation to fasting insulin . Most of these genes were negatively linked to 18:0 and positively linked to 16:1 ( cis-9 ) . The module whose hubs were waist circumference ( degree: 27; p-value of waist circumference betweenness centrality = 0 . 39—not significant ) and HDL ( betweenness centrality p-value = 0 . 36—not significant ) showed respectively positive and negative correlations with genes involved in an “Immune Response” gene expression IPA signature ( CD163 , CCL3 , CCL19 , C1QC , C2 , IL10 and FCGBP ) . Adiponectin was negatively connected to part of these immune response genes and 18:1 ( cis-11 ) . Among genes negatively connected to waist circumference were AZGP1 and GPD1L , whose lower expression in AT from metabolic syndrome ( MetS ) individuals was previously described [24] . The most significant mRNA signature of the module organized around 18:1 ( cis-9 ) ( degree: 38; betweenness centrality p-value = 0 . 85—not significant ) was “Fatty Acid Biosynthesis” . Transcripts of this path included all desaturases ( SCD , FADS1 and FADS2 ) , ALDH6A1 and ACSL1 . Like AACS and LPIN1 , two other transcripts involved in lipid metabolism , all transcripts but ALDH6A1 were positively and negatively connected to 14:1 ( cis-9 ) and 18:1 ( cis-9 ) , respectively . Effect of an 8-week active weight loss . After LCD , vertex clustering detected 10 modules of which 7 had more than 6 nodes . Fig . 4 displays the 4 modules with at least 2 types of variables . Hubs were 14:0 ( degree: 49; betweenness centrality p-value < 0 . 01 ) , waist circumference ( degree: 38; betweenness centrality p-value: 0 . 98—not significant ) , 14:1 ( cis-9 ) ) ( degree: 10; betweenness centrality p-value < 0 . 01 ) , and 18:2 ( cis-9 , 12 ) ( degree: 34; betweenness centrality p-value: 0 . 38—not significant ) . The 14:0 centered module also contained adiponectin as central node . The most significant mRNA signature was “Growth Hormone Signaling” . The transcripts from this signature ( GHR , IGF1 , IRS1 , and MAPK3 ) were all positively connected to 3 saturated FAs , i . e . 12:0 , 14:0 or 18:0 as well as to adiponectin . The module with waist circumference as hub also included BMI as high degree ( 35 ) and high centrality node . The most significant mRNA signature was “Adhesion and Diapedesis” . Transcripts from this signature ( CCL2 , CCL3 , CCL18 , CCL19 , FN1 and MMP19 ) were all positively connected to waist circumference , except MMP19 . CCL3 was positively connected to waist circumference whereas GPD1L and AZGP1 were negatively connected to this abdominal adiposity marker . In this module , 16:1 ( cis-9 ) was negatively connected to GPD1L and positively to anthropometric parameters , plasma triglycerides and insulin . The module with highest degree node 14:1 ( cis-9 ) encompassed genes involved in “Fatty Acid Biosynthesis” ( SCD , FADS1 and FADS2 ) as well as SLC2A4 , FASN , SREBP1 , PNPLA2 and PNPLA3 in a positive manner . Of note , all of these genes were significantly down-regulated during LCD . The 18:2 ( cis-9 , 12 ) with highest degree node mostly contained transcripts with negative relationship to this FA . These transcripts included those encoding proteins involved in triglyceride metabolism ( LIPE , DGAT1 , DGAT2 and AGPAT1 ) . After 6 months weight maintenance diet . Vertexes classification was performed and the most important heterogeneous clusters with more than 6 nodes are presented in Figs . 5 and 6 . Since WS group showed intermediary phenotype , we focused on WR and WL groups . Individuals regaining weight . Of 12 modules , classification detected 5 heterogeneous clusters of interest . As displayed in Fig . 4 , the systolic blood pressure ( degree: 34; betweenness centrality p-value = 0 . 05 ) and waist circumference ( degree: 32; betweenness centrality p-value = 0 . 33—not significant ) hubs showed negative relationship of these nodes with a “Sucrose , Serotonin and Adrenalin Degradation” IPA signature made of ADHFE1 , ALDOB , ALDOC , C2 and MAOA . These central nodes were also negatively connected to AZGP1 and GPD1L and positively to IL10 . The module converging on fructosamine ( degree: 10; betweenness centrality p-value = 0 . 12—not significant ) showed no FA but a “Growth Hormone Signaling” mRNA signature that included IRS1 , FGF2 , IGF1 and GHR , the 2 former transcripts being significantly up-regulated during WMD in the WR group and the latter positively connected to fructosamine via FGF2 . In the module organized around 14:1 ( cis-9 ) ( degree: 16; betweenness centrality p-value = 0 . 62—not significant ) the most significant mRNA signature was “Cancer Signal” mainly represented by transcripts up-regulated during WMD , i . e . CCND1 , CYCS , E2F4 , ITGB2 , and MAPK3 . 16:1 ( cis-9 ) was another hub ( degree: 12 ) positively connected to 14:1 ( cis-9 ) . Two modules were with saturated FAs as hubs . The first one focused on 18:0 ( degree: 20; betweenness centrality p-value = 0 . 09—not significant ) and contained a large array of poly-unsaturated FAs plus 18:1 ( cis-9 ) which amount significantly decreased during WMD , exclusively in WR group . The most significant mRNA signature was “Adhesion and Diapedesis” ( CCL18 , CCL19 , CCL3 and IL1RN ) . The second cluster was organized around 14:0 ( degree: 42; betweenness centrality p-value = 0 . 66—not significant ) which was positively connected to 16:0 and 12:0 . The most significant mRNA signature was “Angiogenesis Inhibition by TSP1” , especially VEGFA , an mRNA up-regulated during WMD and positively connected to 14:0 , and MMP9 with negative relationship to 14:0 . The SCD , FADS2 , ELOVL5 and SREBP1 transcripts involved in DNL were positively connected to 12:0 , 14:0 or 16:0 . Individuals with continued weight loss . Of 11 modules , the 3 heterogeneous clusters are presented in Fig . 5 . The 14:1 ( cis-9 ) centered module ( degree: 21; betweenness centrality p-value = 0 . 01 ) encompassed genes involved in DNL , i . e . AACS , FASN , SCD , FADS1 , FADS2 and ELOVL5 . All were positively correlated to 14:1 ( cis-9 ) . The AACS , SCD , FADS1 and ELOVL5 mRNA levels increased during WMD . The most complex path was based on waist circumference ( degree: 38; betweenness centrality p-value = 0 . 23—not significant ) and incorporates BMI , weight , C reactive protein ( CRP ) and 20:4 ( cis-5 , 8 , 11 , 14 ) as nodes with high centrality . AZGP1 and GPD1L were negatively connected to waist circumference . The most significant mRNA signature was “Complement Adhesion and Diapedesis” . This included CCL3 , CCL18 and CCL19 , C1QA , C1QB and C1QC that displayed significantly decreased mRNA levels during WMD and positive correlation with waist circumference . All FAs were n-6 with positive relationship to waist circumference , weight and BMI . Especially , the 20:4 ( cis-5 , 8 , 11 , 14 ) had positive correlation to CRP . The module organized around 18:1 ( cis-11 ) ( degree: 17; betweenness centrality p-value = 0 . 11—not significant ) contained low density lipoproteins , cholesterol and adiponectin but showed no enriched mRNA signature .
Both lipids and transcripts ( as frames for protein synthesis ) are important components of AT biology . To identify interactions between these molecular species we investigated the networks of AT esterified FAs and mRNAs together with bio-clinical data in obese women according to weight changes along a longitudinal DI . The present study is the first to jointly investigate gene expression and lipidome from the same biopsy of AT in such a large number of obese individuals . Networks are useful models to investigate a set of relations between variables . In particular , network clusters in gene networks are more robust , i . e . , less influenced by measurement noise , than each individual relation [15 , 40] . In the present case , the strength of the relations between the different sets of variables ( e . g . , the strength of the relation between two transcripts or the strength of the relation between a transcript and a FA level ) have very different scales . This caveat is controlled using a non-global inference approach , in order to have a global model of the interactions between all sets of variables . The regularized CCA has previously been used in combination with sparse partial least squares regression to investigate AT transcriptionally coordinated paths correlated with PUFA intake during the LIPGENE study [17] . Here , we used a 3-step inference method to infer a global model using 3 datasets: first , inferring a network in each dataset using a sparse GGM; second , inferring a network between each pairs of two different sets of variables using regularized CCA; third , merging the 3 networks obtained during the first step with the 3 networks resulting from the second step . As the numbers of variables in the 3 datasets were very different ( from 15 bio-clinical variables up to 221 mRNA levels ) , a simple strategy consisting in estimating the selected edges in each set of variables ( or in each pair of two sets ) in a same manner would have led to give too much importance on the largest set of variables , i . e . , to the gene expression dataset . The number of selected edges was thus adjusted to be equal to the number of nodes in each set ( or pair of sets ) of variables , leading to smaller densities for the largest networks . To improve the significance of our findings , systematic statistical tests were performed to test the significance of the betweenness centrality of the nodes compared to their degrees . Significance of nodes indicates that , given their degrees , they have a betweenness larger than expected and are thus significantly central in their clusters . Our study showed both constant and specific biological signatures in response to different weight control phases relevant to distinct metabolic features . We focused on body weight changes and especially according to weight control 6 months after calorie restriction . The present combination of network inference and node clustering enabled to draw a picture of transcript-FA-bioclinical variables interactions at each step of the longitudinal dietary intervention , leading to highlight the unexpected pivotal position of myristoleic acid ( 14:1 ( cis-9 ) ) . This FA was linked to DNL transcripts during active and continued weight loss . It is to be noted that , after WMD , the WR group merely displayed specific AT signatures never found at baseline or during weight loss . The most striking invariable feature was the presence of waist circumference as central node along all steps of the DI . To check similarity between all clusters with waist circumference as hub , paired comparison of the number of common nodes between clusters was performed between baseline cluster and either LCD , or WL group , or WR group cluster . The p-values of these tests were all < 0 . 001 , indicating a high similarity between nodes’ composition of the clusters having waist circumference for hub . Waist circumference is the most prominent clinical risk factor involved in MetS [41] . A persistent positive link with the macrophage inflammatory protein 1α ( CCL3 ) and negative with the adipokine α2-glycoprotein 1 ( AZGP1 ) and the enzyme glycerol-3-phosphate dehydrogenase 1-like ( GPD1L ) mRNA levels was found at baseline , after active weight loss and at 6 months of the weight control follow-up in WL and WR groups . Variants in GPD1L are associated with risk of sudden death in patients with coronary artery disease [42] . AZGP1 is a lipid mobilizing factor with putative role in insulin resistance as mRNA and protein were low in AT of type 2 diabetes patients and circulating AZGP1 protein inversely correlated with BMI and waist-to-hip ratio [43] . The chemokine CCL3 is up-regulated with insulin resistance in AT [44] . These genes are at top rank of the MetS signature described in AT from obese individuals [24] . The relationship between these transcripts and the major component of MetS suggests that they could be used as biomarkers for risk stratification of type 2 diabetes or cardiovascular disease in obese individuals , alone or combined to bio-clinical related factors . At baseline , fasting plasma insulin was the most significant central vertex among all modules . This cluster exhibited an immune signature , all transcripts of the Adhesion and Diapedesis pathway being positively connected to insulin . On the other hand , insulin was negatively connected to stearic acid and transcripts encoding factors involved in lipid metabolism ( CIDEA ) [45] , especially lipolysis ( GPR109A and ABDH5 ) [46] , and SIRT1 . The SIRT1 gene encodes a histone deacetylase that regulates various metabolic pathways and regulate lipids and glucose metabolism [47] . Besides the positive relationship between immune cells content in AT in the etiology of insulin resistance [48] , this cluster indicates that , in obese women , the higher is the insulin level at fasting , the lower is the lipid metabolism signaling in AT . After LCD induced weight loss , 3 modules focused on FAs . One was organized around linoleic acid , an essential FA that is highly represented ( >30% ) in the commercial hypocaloric meals provided during LCD ( data not shown ) . However , linoleic acid ( 18:2 ( cis-9 , 12 ) ) content of fat pads was unchanged compared to baseline . Indeed , there is minimal deposition of dietary fat into AT during periods of negative energy balance [9] . Myristic acid ( 14:0 ) was the most central vertex of a module along with lauric ( 12:0 ) and stearic acids ( 18:0 ) . Adiponectin , which is an adipocytokine with anti-inflammatory and insulin sensitive properties [49] was another central vertex . Myristic acid and adiponectin were both positively connected between each other and to insulin signaling or insulin-like transcripts ( IRS1 and IGF1 ) . The biological role of myristic acid remains poorly explored . Fatty acylation of signaling proteins play key roles in regulating cellular structure and function . Among the various myristoylated proteins are numerous signal transducing proteins [50] . In the present study , there was a statistically significant decrease in myristic acid triglycerides AT content during LCD , indicating a mobilization from lipid droplet that might provide non esterified myristic acid within the adipose cell . Whether such available myristic acid indeed does acylate signal transduction proteins is a question of particular interest . Six months after the end of LCD , the AT from women that continued to lose weight ( WL group ) also displayed two modules organized around FAs , myristoleic acid and vaccenic acid . Vaccenic acid amount is low in AT ( <2% ) . It comes from palmitoleic acid elongation . There was no significant change in AT vaccenic acid content during the dietary intervention . Its steadiness in AT from individuals continuing to lose weight indicates that this FA was poorly mobilized during weight loss . A positive correlation between vaccenic plasma TG content and insulin resistance has been shown in men [51] . Whether there is a similar link with AT triglycerides deserves attention even though no direct relationship with glucose homeostasis parameters appears in the present module . When considering active weight loss and continued weight loss after calorie restriction , a remarkable feature was the presence of myristoleic acid connected to an array of genes involved in FA synthesis , especially DNL enzymes and desaturases ( FASN , SCD , FADS1 and FADS2 ) . Like palmitoleic ( 16:1 ( cis-9 ) ) and oleic acids ( 18:1 ( cis-9 ) ) , myristoleic acid is a product of desaturation by SCD ( from myristic acid ) . It is a minor AT FA ( <0 . 5% of total FA content ) that is not provided by food . Surprisingly , in the present study it is an important focal node , which AT content decreased during LCD and remained stable at the end of WMD , except in WR group . Moreover , at the end of WMD , in WR group and in relation to SCD gene expression in AT , an increased SCD activity ( assessed by 14:1 ( cis-9 ) /14:0 ratio ) was observed that could be due to a positive regulation of SCD transcription by saturated FAs [52] . In this group , 14:0 and 16:0 were focal nodes and positively connected to SCD . The SCD activity is known to be associated with triglyceride accumulation [53] but its beneficial effect on insulin sensitivity remains controversial [52] . Control of SCD expression and DNL are coordinated . SCD is tightly regulated by saturated FAs and poly-unsaturated FAs as linoleic acid , SREBP-1c and carbohydrate response element binding protein ( ChREBP ) α and β [52] . ChREBP isoforms were not included in the series of mRNA quantified here but SREBP1 was positively connected to myristoleic acid after LCD . This is in agreement with the transcriptional activation of SCD by SREBP1c [52] . In contrast to liver where DNL is considered deleterious , DNL occurring in fat depots may provide beneficial health effects since it produces lipid species with bioactivities distinct from those of lipids predominantly derived from diet [2 , 54] . Strategies to enhance DNL specifically in AT may provide new therapies for metabolic and cardiovascular diseases [55–57] . The presence of a DNL signature with acute ( LCD ) and continuing ( WMD ) weight loss is in line with the enhanced differentiation potential of preadipocytes observed after calorie restriction [58] . In the present study , myristoleic acid might be an interesting marker of DNL and SCD activity in AT . Its persistence in AT triglycerides despite fat mass loss may constitute a hallmark of beneficial adipogenesis after weight loss . Last , AT from WR group showed a salient hyperplastic attribute with 3 modules encompassing genes involved in cell proliferation , angiogenesis , or growth factor signal transduction . Of note , the former cluster exhibited two mono-unsaturated FAs as central nodes with no link to genes involved in FA metabolism . The angiogenic signature was mainly due to VEGFA ( mean fold change during WMD = 1 . 9±0 . 4 ) that encodes an essential proangiogenic factor in AT [59] . The latter was organized around fructosamine , which is a serum marker of poor long-term glycemic control , as a hallmark of the deleterious effect of energy store repletion . The positive link of fructosamine to a series of transcripts- TWIST1 that encodes a transcription factor abundantly expressed in adipocytes [60] , which is positively correlated to insulin sensitivity [61] and SPTAN1 , a transcript encoding an insulin responsive α-fodrin involved in the glucose transporter GLUT4 translocation in adipocytes [62]-related to glucose homeostasis and beyond insulin signaling ( IRS1 , IGF1 , FGF2 and GHR ) may seem counterintuitive . Growth hormone shares protein anabolic properties with insulin . On the other hand , fasting insulin and glucose are part of another module which displays an immune signature ( Adhesion and Diapedesis ) , emphasizing the link between adipose tissue inflammatory status and insulin resistance [48] . The link between weight regain and proliferative patterns was previously shown using transcriptomic in a small subset of individuals from the same trial [28] . The present study indicates AT hyperplasia in individuals failing weight maintenance despite continued energy restriction . Altogether , no cluster showed a lipid metabolism signature in this group . Stearic acid was the hub of a module with the immune response signature . This FA was negatively connected to unfavorable bio-clinical parameters ( body fat mass , fasting plasma insulin , triglycerides and CRP ) and positively to beneficial ones ( adiponectin and HDL ) . This suggests that the highest is lipid droplet stearic acid content , the better is metabolic status . The cluster with systolic blood pressure and waist circumference as hubs displayed an amine degradation signature . Levels of noradrenaline associate with obesity and cardiovascular risk [63] . Systolic blood pressure was negatively correlated to most variables , including AZGP1 and GPD1L described above except diastolic blood pressure . This parameter was negatively correlated to waist circumference as well as BMI . This feature was different from the one observed in the weight loss group where waist circumference was positively correlated to weight and BMI . This emphasizes the predominant role of waist circumference , compared to blood pressure , in metabolic syndrome compared to blood pressure . The present investigation shows several limitations . Only women were investigated; as a preeminent effect of sex on AT gene expression was previously shown [24 , 64] . Also , we studied fat from the subcutaneous abdominal region and we cannot extrapolate our findings to other subcutaneous , gluteo-femoral or visceral fat depots . Last , we performed unsupervised learning using GGM . This approach uses partial correlations and differs from relevance networks that use direct correlations and thus provide a strong but sometimes biased measure of the dependence between variables . Bayesian networks that lead to directed acyclic graphs ( DAG ) could provide a clue on causal relationships but some knowledge information has to be provided a priori . In the present networks , edges do not represent simple correlations but between variables dependencies . Using GGM , interpretation is not causality but only a matter of strong and direct statistical association . Nodes with highest betweenness centrality represent variables whose fine tuning might greatly impact the level of the other connected variables . To conclude , this approach has linked a characteristic structure of AT network to a slimmed phenotype thereby suggesting myristoleic acid as main lipidic biomarker for DNL and SCD activity . The anabolic signature unique to individuals with unsuccessful weight control suggests detrimental tissue hyperplasia . This initial analysis provides a valuable starting point for more in-depth investigation of the implication of myristoleic acid in weight loss . | Obesity is an excess fat mass leading to metabolic diseases . Dietary management is a conventional strategy to promote weight loss . As energy buffering , in the form of esterified fatty acids , and secretory organ , the adipose tissue has a pivotal role in obesity and its related complications . A comprehensive insight of adipose tissue response during and after calorie restriction might improve obesity management . Modern nutrition research study the impact of diet on health by combining multiple datasets to provide an holistic view of tissue physiopathology . To identify significant clusters of fatty acids , transcripts or bio-clinical parameters related to weight change along calorie restriction and subsequent weight follow-up in obese individuals , the issue of different datasets integration must be resolved . Here , we implemented an innovative multistep approach to infer multi-data networks and compare clusters of network components . This original strategy highlighted an unexpected central role of a minor adipose tissue fatty acid , myristoleic acid , which is not provided by food . Its link to transcripts encoding enzymes from a pathway converting glucose into fat that mediates favorable metabolic effects makes myristoleic acid a key factor of the positive impact of fat mass reduction . |
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Bacterial metabolism has been studied primarily in liquid cultures , and exploration of other natural growth conditions may reveal new aspects of bacterial biology . Here , we investigate metabolic changes occurring when Escherichia coli grows as surface-attached biofilms , a common but still poorly characterized bacterial lifestyle . We show that E . coli adapts to hypoxic conditions prevailing within biofilms by reducing the amino acid threonine into 1-propanol , an important industrial commodity not known to be naturally produced by Enterobacteriaceae . We demonstrate that threonine degradation corresponds to a fermentation process maintaining cellular redox balance , which confers a strong fitness advantage during anaerobic and biofilm growth but not in aerobic conditions . Whereas our study identifies a fermentation pathway known in Clostridia but previously undocumented in Enterobacteriaceae , it also provides novel insight into how growth in anaerobic biofilm microenvironments can trigger adaptive metabolic pathways edging out competition with in mixed bacterial communities .
Bacteria rapidly adapt to changes in available resources and environmental fluctuations due to their remarkable ability to finely tune their physiology and metabolism [1 , 2] . Although bacterial adaptations have been primarily studied in liquid cultures composed of free-swimming planktonic cells , exploration of a broader range of growth conditions can reveal new aspects of bacterial biology [3] . One of the most common bacterial lifestyles corresponds to surface-attached communities called biofilms , in which high cell density , reduced diffusion and physico-chemical heterogeneity are associated with extensive physiological changes [4 , 5] . Biofilm bacteria display different properties compared to planktonic bacteria , and biofilms have long been considered potential reservoirs of unknown functions , contributing to their ability to thrive on surfaces present in natural and anthropic environments [6] . It was , for example , hypothesized early on that the study of physiological adjustments occurring during biofilm formation would provide insight into potentially uncharted metabolic pathways . However , due to difficulties associated with metabolic profiling in this complex environment , metabolic changes during biofilm growth are still poorly characterized . In the present study , we filtered through the wealth of molecules produced by mature Escherichia coli biofilms by restricting our analysis to comparison of volatile metabolites emitted from biofilm and planktonic cultures . We demonstrate that hypoxic conditions prevailing within biofilms induce high production of 1-propanol via a threonine fermentation pathway previously undocumented in Enterobacteriaceae , which confers a strong competitive advantage over bacteria unable to express this pathway . This study therefore demonstrates that investigation of metabolic changes associated with biofilm growth provides novel insights into the extent of bacterial metabolic potential and of bacterial adaptation to local microenvironments .
We combined headspace solid-phase microextraction ( HS-SPME ) with gas chromatography mass spectrometry ( GC-MS ) to analyze volatile compounds emitted by E . coli biofilms formed in microfermenters . Under these conditions , bacteria growing at the periphery of biofilms are exposed to oxygen present in the medium , while inner biofilm bacteria are exposed to a range of microaerobic to anaerobic conditions . We then compared volatile compounds detected in biofilms to those produced by planktonic bacteria grown aerobically ( S1 Fig ) . While these analyses revealed several differences , the most striking HS-SPME/GC-MS signal present in biofilm but absent in planktonic conditions corresponded to the volatile compound 1-propanol ( Fig 1A ) . Although low level of 1-propanol is produced by some Clostridium strains [7] , E . coli is not known to naturally produce this molecule , we therefore hypothesized the existence of a new E . coli metabolic pathway activated under conditions created by biofilm growth . Lack of 1-propanol production in aerobically grown planktonic cultures suggested that low oxygen conditions prevailing in biofilms might play a key role in E . coli 1-propanol production . Consistently , we observed 1-propanol signals in planktonic cultures grown in LB medium under fully anaerobic and microaerobic conditions , provided that the oxygen concentration did not exceed 0 . 7% ( Fig 1B ) . Conversely , when biofilms were formed in highly aerobic biofilm microfermenters , we did not detect 1-propanol ( Fig 1C ) . These results indicated that hypoxic conditions prevailing inside biofilms lead to 1-propanol production . Consistently , we showed that a mutation in the fnr gene encoding the primary transcriptional regulator mediating transition from aerobic to anaerobic growth [8] strongly reduced 1-propanol production ( S2 Fig ) . Engineering approaches used to induce 1-propanol production in E . coli often rely on the expression of heterologous alcohol dehydrogenases from various microbial sources [7 , 9] . We therefore hypothesized that at least one of the endogenous alcohol dehydrogenases of E . coli could contribute to 1-propanol production in biofilm . We tested the role of E . coli FucO , EutG , AdhE , YqhD and GlpQ alcohol dehydrogenases and determined that a mutation in the gene adhE , encoding a polymeric enzyme involved in ethanol production in E . coli and induced in anaerobic conditions [10] [11] , resulted in a marked reduction in 1-propanol production ( Fig 2A and S3 Fig ) . Lack of 1-propanol production observed in E . coli ΔadhE mutant could be restored in biofilm formed by E . coli ΔadhE complemented by pCANadhE ( S4 Fig ) . We next aimed to identify metabolic pathway constituents and necessary carbon sources for 1-propanol production . We observed that no 1-propanol could be detected in biofilms formed in LB supplemented with glucose ( Fig 2A ) , indicating that 1-propanol production is subjected to catabolic repression , nor in biofilm formed in M9 glycerol minimal media ( Fig 2B ) . However , supplementation of this medium with amino acids showed that L-threonine ( threonine ) , but not valine , serine or glycine , triggered 1-propanol production ( Fig 2B ) , as confirmed by NMR analysis ( S5 Fig ) . Consistently , growth in LB medium containing 0 . 4% threonine led to a highly significant AdhE-dependent increase in 1-propanol production ( Fig 2C ) . Finally , we monitored the steady-state level of incorporation of exogenous 13C-labeled threonine into 1-propanol in biofilm formed in M9 glycerol minimal medium . We observed strong isotope incorporation into 1-propanol , while we did not detect any significant isotope dilution by other unlabeled carbon sources ( Fig 3 ) . This demonstrated that all 1-propanol produced under these conditions originated from threonine degradation , which confirmed that threonine is the precursor of the 1-propanol pathway . Our results showed that 1-propanol can be directly produced in non-genetically-modified E . coli in a natural hypoxic microenvironment generated during biofilm growth . We observed that extension of biofilm cultures from 16 h to 48 h in biofilm microfermenters fueled with glycerol minimal medium supplemented with 0 . 4% threonine led to an increasingly strong 1-propanol signal ( S6A Fig ) . This suggested that increasing biofilm biomass correlated with increased 1-propanol production . Moreover , the 1-propanol signal produced by 24 h biofilm strongly increased when using amino-acid-rich media such as TB , a medium containing 4 times more yeast extract than LB ( S6B Fig ) . These results suggest that 1-propanol production could increase after prolonged biofilm growth in threonine-rich medium . Consistently , quantification of the amount of 1-propanol accumulated in the effluent of 48 h biofilm grown in TB or in TB supplemented with 0 . 4% threonine increased from 1 . 25 ± 0 . 15 g/L to up to 4 . 5 ± 0 . 34 g/L . These results therefore indicate that the 1-propanol yield could be optimized using amino-acid-rich unrefined media and E . coli biofilms as a production platform . In E . coli , threonine has been shown to degrade into the end products acetyl-CoA , glycine , propionate , L-isoleucine and methylglyoxal , but not 1-propanol ( Fig 4A ) . However , inactivation of genes involved in the first step of the known E . coli threonine degradation pathways , ltaE , yiaY , kbL , ilvA and tdcB , showed that only strains harboring a mutation in the threonine dehydratase gene tdcB impaired 1-propanol production in biofilm ( Fig 4B and S1 Table ) . The lack of 1-propanol production observed in E . coli ΔtdcB mutant could be restored in biofilm formed by E . coli ΔtdcB complemented by pCANtdcB ( S4 Fig ) . tdcB is part of the tdc operon , which is negatively regulated by catabolic repression and is involved in anaerobic uptake and degradation of threonine to propionate ( Fig 4A ) [12] . We tested the contribution of the other genes coding for enzymes involved in aerobic ( pflB , ptA and ackA ) and anaerobic ( tdcC , tdcE , ptA and tdcD ) propionate production , and showed that their inactivation did not affect 1-propanol production , except for tdcC ( encoding a threonine uptake transporter ) and tdcE ( encoding a 2-ketobutyrate formate-lyase ) ( S1 Table and S7A and S7B Fig ) . QRT-PCR gene expression analysis confirmed the strong induction of tdcBDE genes in planktonic anaerobic conditions ( 1 , 500-fold ) and biofilms ( 300-fold ) grown in LB medium ( S8A and S8B Fig ) . AdhE catalyzes successive reduction of acetyl-CoA to acetaldehyde and the latter compound to ethanol [11] . We hypothesized that the promiscuous AdhE enzyme , which is also induced in biofilm conditions ( S8C Fig ) , could carry out successive reduction of propionyl-CoA into propionaldehyde ( coenzyme-A-dependent aldehyde dehydrogenase activity of AdhE ) , and then , reduction of propionaldehyde into 1-propanol ( alcohol dehydrogenase activity of AdhE ) ( Fig 4C ) . Consistent with the existence , in E . coli , of a metabolic pathway branching out from the propionate pathway at the level of propionyl-CoA , we observed AdhE-dependent , increased production of 1-propanol in biofilms upon supplementation of LB medium with propionaldehyde ( S9A Fig ) . We also observed concomitant increased production of ethanol in a tdcB mutant ( S9B Fig ) , suggesting that blocking conversion of threonine into 1-propanol redirects AdhE metabolic activity towards ethanol synthesis . We next examined conservation of this newly uncovered threonine degradation pathway among bacterial taxa . We determined that co-occurrence of homologs of adhE , tdcB and tdcE genes required for threonine degradation into 1-propanol can be identified in many bacteria , with the strongest homology found in Enterobacteriaceae ( S10 Fig ) . Consistently , all tested E . coli isolates naturally produced 1-propanol in biofilms , while several other Enterobacteriaceae species , including Shigella flexneri , Salmonella enterica sv . Enteritidis and Citrobacter rodentium , also produce 1-propanol in anaerobic , but not in aerobic planktonic cultures ( Fig 5 ) . Since E . coli K-12 cannot use 1-propanol as a carbon source , and exposure to 1-propanol did not display a detectable phenotype ( S11 Fig ) , what could be the function of this threonine degradation into 1-propanol ? Considering that reduction of propionyl-CoA into 1-propanol involves two successive steps of re-oxidation of reduced nicotinamide adenine dinucleotide ( NADH ) into NAD+ ( Fig 4C ) , we hypothesized that degradation of threonine into 1-propanol recycles NADH into NAD+ , a key co-factor playing a major role in central metabolism [11] . Indeed , analysis of the NADH/NAD+ ratio under biofilm and planktonic anaerobic growth conditions showed that bacteria exhibit an increased NADH/NAD+ ratio in ΔtdcB and ΔadhE mutants , but not in ΔtdcD mutants ( Fig 4D and S12A , S12B and S12C Fig ) . Hence , while production of 1-propanol from threonine was reported in some Clostridia strains , it also corresponds to a previously undescribed native fermentation pathway in E . coli , contributing to intracellular redox balance in conditions of energy starvation in the absence of oxygen . To investigate the biological consequences of threonine fermentation in E . coli , we performed competition experiments between E . coli WT and a ΔtdcB ( no fermentation of threonine into 1-propanol ) or a ΔtdcD mutant ( threonine fermentation into 1-propanol ) , either in biofilm or planktonic anaerobic conditions . Whereas the tested strains did not display any growth defect in monoculture ( Fig 6A and 6B ) , we observed a 90% fitness reduction in the ΔtdcB mutant in competition experiments against WT when grown either in biofilm or planktonic anaerobic conditions ( Fig 6A , 6B and 6C ) , whereas the tdcD mutant displayed no growth nor fitness defect ( Fig 6B ) . Similarly , a ΔtdcB mutant display fitness reduction compared to WT , in competition experiments against Klebsiella pneumoniae ( Fig 6D ) , a strain that does not ferment threonine into propanol . The fitness defect of a ΔtdcB mutant correlates with a consistent growth lag in planktonic anaerobic conditions compared to a wild-type or unaffected ΔtdcD mutant , ( Fig 6E and S13 Fig ) . In contrast , a tdcB mutant did not display any growth lag ( Fig 6F ) or fitness cost when competition experiments were performed in aerobic planktonic culture conditions ( Fig 6G ) , demonstrating the particular relevance of this novel pathway during anaerobic and biofilm growth . These results demonstrate that the threonine-to-propanol fermentation pathway contributes to provide a competitive advantage in mixed anaerobic communities .
We show that native production of 1-propanol is not restricted to a few anaerobic bacteria , but naturally occurs from threonine degradation under hypoxia in E . coli and other Enterobacteriaceae . The link between 1-propanol production and threonine catabolism was previously reported in Clostridium sp . strain 17cr1 , which produces low amounts of 1-propanol ( less than 70 mg/L ) by an uncharacterized pathway [13] . Moreover , engineered E . coli strains can produce 1-propanol upon reduction of 2-keto-butyrate formed by the aerobic threonine degradation pathway [14] . In E . coli , we show here that anaerobic reduction of propionyl-CoA into propanal and 1-propanol by alcohol/aldehyde dehydrogenase AdhE constitutes a native alternative to anaerobic degradation of threonine into propionate and ATP synthesis by enzymes encoded by the tdc operon [12] . Although 1-propanol can be produced in anaerobic planktonic cultures , this metabolic capacity might have been overlooked due to the low biomass reached in these conditions . In contrast , we hypothesize that hypoxia spontaneously developing in biofilms enables a large bacterial biomass to be exposed to optimal conditions , inducing high production of 1-propanol . 1-propanol is an important industrial solvent and a major component of resins , the chemical synthesis of which requires a laborious two-step process involving catalytic hydroformylation of ethylene to produce propanal , and consecutive hydrogenation of propanal into 1-propanol [15–17] . Hence , improvement in renewable biological production of 1-propanol in metabolically engineered E . coli strains carrying heterologous genes of varying microbial origin recently gained significant attention [18 , 19] . We determined that the yield of 1-propanol spontaneously produced in continuous flow biofilms in amino-acid-rich media could reach up to 5 g/L in the biofilm effluent , a yield close to levels obtained using engineered strains grown in discontinuous fed-batch cultures ( <10 g/l ) [7] . Achieving industrial productivity using bacterial biomass immobilization in biofilm reactors still presents many important obstacles and challenges [20] . However , our results suggest that bioproduction of 1-propanol from a native , and therefore robust , metabolic pathway induced in E . coli biofilms could alleviate the need for establishing synthetic pathways in genetically modified organisms , and might constitute an alternative to current 1-propanol chemical synthesis . Fermentation is a central metabolic process that has been thoroughly investigated in Enterobacteriaceae in a variety of planktonic culture conditions , leading to production of lactate , ethanol , acetate , formate , citrate , succinate , hydrogen and carbon dioxide . However , our study reveals that , in addition to these classical fermentation products , E . coli and other Enterobacteriaceae can also produce 1-propanol upon reduction of threonine and reoxydation of NADH into NAD+ , a staple of the fermentation process . The ability to ferment threonine as well as aromatic and branched-chain amino acids via the Stickland reaction is particularly used by anaerobic bacteria such as Clostridia [21–23] . This reaction is characterized by oxidation of one amino acid coupled with reduction of another amino acid [24] . However , we did not observe any stimulation of 1-propanol production upon supplementation with various amino acids , suggesting that threonine-to-propanol fermentation does not correspond to a bona fide Stickland reaction [25] . We show that reduction of threonine contributes to cellular redox balance by restoring the intracellular oxydized NAD+ pool , which may play an important role in E . coli’s ability to cope with anaerobic environments . Consistently , lack of threonine reduction into 1-propanol in a tdcB mutant leads to a remarkable 90% fitness loss in competition with the wild type strain . This decreased fitness could be attributed to a marked growth lag , enabling depletion of limited nutrient resources by the competing wild type strain . In the context of biofilm formation , maintaining redox balance is likely to be an essential metabolic process . Consistently , in absence of threonine fermentation , E . coli biofilm bacteria can use another fermentation pathway to recycle consumed NADH into NAD+ , as shown by increased ethanol fermentation observed in a tdcB mutant ( S9B Fig ) . However , these pathways generally rely on the availability of glucose or other oxidized sugars , which , unless produced by costly gluconeogenesis , may not be generally abundant in E . coli nutritional environment . By contrast , the amino-acid fermentation pathway described in this study could confer Enterobacteriaceae the ability to maintain redox balance and edge out competition with other bacteria , using amino-acids produced by cell lysis and proteolysis inside biofilms . Another of such amino-acid rich environment could correspond to biofilm-like anaerobic gut environments , in which glycosylated mucins abundantly secreted in epithelial mucus contain up to 40% serine and threonine [26 , 27] . However , in vivo mouse colonization competition experiments between E . coli WT and tdcB mutants did not reveal any significant colonization defect ( S14 Fig ) , which might simply reflect the fact that the competitive fitness advantage provided by the propanol pathway cannot be relevantly tested in largely herbivorous mice . Identification of a widespread metabolic response to biofilm and anaerobic conditions expands the range of known E . coli metabolites , opening perspectives of biofilm-based approaches for harnessing bacterial metabolic potential . Our study also further supports the notion that mining of the biofilm mode could provide insight into new aspects of bacterial physiological adaptations to local microenvironments .
Bacterial strains used in this study are listed in S2 Table . E . coli mutants listed in S1 Table are from the Keio Collection [28] and each mutation was introduced into the E . coli TG1 strain by P1 vir phage transduction . Each mutant was confirmed by PCR . All experiments were performed in: lysogeny broth ( LB ) containing as amino acid sources 1% peptone and 0 . 5% yeast extract; Terrific broth ( TB ) containing 1 . 2% peptone , 2 . 4% yeast extract; or M9 glycerol 0 . 4% minimal medium containing no amino acid source . These media were supplemented with kanamycin ( 50 μg/ml ) when required and incubated at 37°C . When needed , 0 . 4% ( wt/vol ) L-threonine ( indicated as threonine throughout the text ) , glycine , serine or valine was added to the cultures . All media and chemicals were purchased from Sigma-Aldrich . Growth under anaerobic and microaerobic conditions was performed in a C400M Ruskinn anaerobic-microaerophilic station on multi-position magnetic stirrers XT35 . 1 ( Roth Sochiel ) at 37°C . Continuous-flow biofilm microfermenters containing a removable glass spatula were used as described in [6] ( see also https://research . pasteur . fr/en/tool/biofilm-microfermenters/ ) in one of the following methods: Biofilm microfermenters were inoculated by placing the spatula in a culture solution adjusted to OD600 = 1 ( containing 5 . 108 bacteria/ml ) for 5 min . The spatula was then reintroduced into the microfermenter . Flow rate was then adjusted ( 60 ml/h ) so that total time for renewal of microfermenter medium was lower than bacterial generation time , thus minimizing planktonic growth by constant dilution of non-biofilm bacteria . Total RNA was extracted from three independent samples using the Qiagen RNeasy mini-kit . DNase treatment on 3 mg of RNA was carried out twice with the Ambion Turbo DNA-free kit . All samples were checked for residual genomic DNA contamination with the TM1 and TM2 primer pair ( S3 Table ) , and were considered DNA-free if no amplification was detected at <38 cycles . The RNA concentration was measured with a NanoDrop and RNA quality was checked by gel electrophoresis . cDNA synthesis was carried out with 2 μg of RNA in a volume of 50 μl using the Bio-Rad iScript cDNA synthesis kit . Primers for quantitative real-time PCR were designed using the Primer3 online tool ( http://simgene . com/Primer3 ) and are listed in S3 Table . Amplicon sizes were confirmed by gel electrophoresis . cDNA levels were analyzed by EvaGreen detection in a Bio-Rad CFX96-1000 light cycler using the Bio-Rad SoFast EvaGreen Supermix ( 20 μl final volume ) , with 200 nM of each primer . Melting curves were checked to confirm that a single PCR product had been amplified . All quantitative real-time PCR reactions were carried out in quadruplicate for each sample in 96-well plates with simultaneous no-template controls . Relative quantification of gene expression levels was determined with the Delta Delta CT method [29] using 16S ( rssH ) , ihfb , opgG and hcaT genes as reference genes . E . coli TG1 was grown in M9 glycerol 0 . 4% minimal medium supplemented with 0 . 2% L-threonine . The culture sample was collected after 48 h , centrifuged and the supernatant was frozen at -20°C prior to NMR analysis . 1D and 2D NMR spectra were recorded on a Bruker Avance III 800 MHz instrument ( Bruker , Bremen , Germany ) , equipped with a 5 mm QPCI ( 1H , 13C , 31P and 15N ) cryoprobe . Supernatants ( 120 μl ) were mixed with 40 μl of 1 mM 2- ( trimethylsilyl ) propionic-2 , 2 , 3 , 3-d4 acid ( TSP-d4 ) solution in D2O as an internal intensity and chemical shift standard , without further sample pretreatment . Data were acquired and processed using TOPSPIN 3 . 0 software . E . coli biofilm was grown on M9 glycerol 0 . 4% minimal medium , supplemented with a mixture ( 1:1 ) of unlabeled L-threonine and [U-13C]L-threonine , in which the total concentration of L-threonine was 2 g/L . After 48 h of E . coli TG1 growth in the microfermenter at 37°C , the flux was stopped , and 15 h later , the biomass of the biofilm was recovered and centrifuged and the supernatant frozen until NMR analysis; Threonine 13C-enrichment was measured by NMR to be 44 . 5% ± 0 . 2% . The occurrence of a 13C atom resulted in splitting of 1H resonance due to 1H-13C coupling into 1H-12C ( central peak ) and 1H-13C ( “satellite” peaks ) signals . The percentage of 13C incorporated into the carbon position was measured by the area of 1H-13C signals relative to total resonance area . Specific 13C-enrichments on C2 ( 41 . 4% ± 0 . 6% ) and C3 ( 42 . 4% ± 0 . 2% ) of 1-propanol indicated the absence of significant isotopic dilution of 13C -labeled threonine when converted into 1-propanol . Insert: carbon positions in 1-propanol . Extraction of NADH and NAD+ was carried out according to the method described in [30] . Pellet samples of anaerobic planktonic culture or biomass of the biofilm were centrifuged at 16 , 000 x g for 1 min . Supernatant was removed and pellets were resuspended in 300 μl of 0 . 2M NaOH ( for NADH extraction ) or 0 . 2M HCl ( for NAD+ extraction ) . These extracts were incubated for 10 min at 50°C and then for 10 min on ice . While vortexing , 300 μl of 0 . 1M HCl ( for NADH ) or 0 . 1M NaOH ( for NAD+ ) was added dropwise to neutralize the solutions . Samples were then centrifuged for 5 min at 16 , 000 x g . Supernatants were transferred to fresh tubes and stored at -80°C until quantification . Relative or absolute NADH or NAD+ levels were quantified using an enzyme cycling assay adapted for measurement in a microtiter plate [31] . A master reagent mix was prepared with 1 x bicine buffer ( 1 . 0 M pH 8 ) , 3 x water , 1 x 40 mM EDTA , 1 x 100% ethanol , 1 x 4 . 2 mM thiazolyl blue and 2 x 16 . 6 mM phenazine ethosulfate . The reagent mix was warmed to 30°C , and then 90 μl aliquots were dispensed into individual wells of a 96-well microtiter plate; 5 μl of standard or sample were added to each well and the plate was incubated for approximately 10 min at 30°C . Then , the cycling reaction was started by the addition of 5 μl of alcohol dehydrogenase ( Sigma no . A-3263 ) prepared at 347 units/ml in 0 . 1M bicine ( pH8 . 0 ) . The microtiter plate was incubated at 30°C , contents were mixed by brief shaking and absorbance was measured every 60 s using a TECAN Infinite M200 PRO at 570 nm , i . e . the spectral peak of thiazolyl blue that increases upon reduction . Slopes arising from plots of absorbance at 570 nm over time were generated for NADH and NAD+ standards , as well as for all samples . Standard curves were used to calculate absolute concentrations in μM , and values were normalized to the optical density of the original cell culture sample . Volatile compounds emitted by bacterial planktonic or biofilm cultures were determined using an analytical approach coupling headspace solid phase microextraction ( HS-SPME ) with gas chromatography and mass spectrometry ( GC-MS ) [33] [34] . Preparation of biofilm extracts for HS-SPME/GC-MS analysis was as follows . After 24 h of culture at 37°C , biofilm biomass that had formed on the spatula was scraped off , put in a 10 ml headspace vial and frozen until HS-SPME/GC-MS analysis ( see S1 Fig ) . 1-Propanol was purchased from Sigma-Aldrich ( Saint Quentin Fallavier , France ) . Ultra-pure water was produced using a Direct-Q UV 3 system ( 18 . 2 MΩ/cm ) from Millipore ( Molsheim , France ) ; 75 μm carboxen-polydimethylsiloxane ( CAR-PDMS ) fiber was from Supelco ( Sigma-Aldrich , Saint Quentin Fallavier , France ) was used for SPME . The fiber used was conditioned prior to performing analyses by inserting them into the GC injector at 280°C for 10 min . For each HS-SPME analysis , equivalent planktonic or biofilm bacterial biomass resuspended into 1 ml of planktonic or biofilm medium was introduced into 10 mL SPME vials . The fully automated HS-SPME procedure was as follows . First , the vial was equilibrated at 60°C for 6 min; then , the SPME fiber was placed into the head-space of the sample vial for extraction and maintained at 60°C for 30 min . At the end of extraction , the SPME fiber was introduced directly into the GC injector ( desorption ) for 10 min at 280°C in split mode ( ratio1:2 ) . GC-MS analyses were performed on an Agilent 7890A gas chromatograph coupled with an Agilent detector 5975C inert XL MSD mass spectrometer ( Agilent Technologies , Les Ulis , France ) . The device is equipped with an MPS autosampler from Gerstel ( RIC , Saint-Priest , France ) that enabled fully automated HS-SPME analyses . The column used was a non-polar ( methyl 95%-phenyl 5% ) fused silica capillary column CP-SIL 8CB-MS ( 30 m x 0 . 25 mm with 1 μm film thickness ) obtained from Agilent Technologies ( Les Ulis , France ) . Helium was used as carrier gas in constant flow mode at 1 mL/min . The injector temperature was 280°C; injection mode was in split mode with a split ratio of 1:2 . The temperature program was 40°C , held for 3 min and then raised to 60°C at 2°C/min , increased to 300°C at 20°C/min and held for 3 min ( run 28 min ) . The transfer line temperature to the MS detector was set at 280°C . A mass spectrometer was used with the positive electronic ionization ( EI ) source ( 70 eV ) heated to 230°C and the MS quad at 150°C . Acquisition was simultaneously performed in scan and SIM ( single ion monitoring ) modes . Scan acquisition was made from m/z 20 to 250 . For SIM acquisition , the m/z fragments selected as characteristic fragment ions of 1-propanol were 31 ( CH2 = OH+ ) and 59 ( CH3-CH2-CH2O+ ) . Identification of volatile organic compounds was performed by matching their recorded mass spectra with standard mass spectra from the National Institute of Standards and Technology ( NIST , ver 2 . 0f , rev . 2010 ) . The identity of each volatile compound was also confirmed by comparing their retention time and mass spectra with those of pure standard compounds after GC-MS analysis . The yield of 1-propanol in biofilm was determined either by resuspending 24 h biofilms formed on the internal microfermenter directly in microfermenter medium , or by resuspending 24 h biofilms for 15 or 24 h or 48 h after stopping medium flow , to allow accumulation of 1-propanol . The 1-propanol yield in anaerobic planktonic culture was tested by direct sampling of culture medium . After centrifugation , 20 ml of biofilm resuspension or planktonic culture supernatant were sent to Aromalyze ( Quetigny , France http://www . aromalyse . fr ) . All samples were homogenized by vigorous stirring prior to analysis , and an aliquot of 0 . 5 ml was transferred into a 20 ml glass vial with a magnetic cap equipped with a PTFE/silicon septum containing 7 . 5 ml of deionized water and 2 . 5 g of NaCl ( 99% , Aldrich ) . As internal standard , an aqueous solution of 1-propanol-d7 ( 98atom% d , CDN Isotopes ) at known concentration was added . All samples were analyzed by HS-SPME/GC-MS carried out at 40°C for 30 min using a carboxen-polydimethylsiloxane ( CAR-PDMS ) fiber ( Supelco ) on a CombiPAL . Fiber desorption was performed in the injector unit of the chromatograph in splitless mode . GC-MS analyses were carried out on a Shimadzu 2010 chromatograph coupled with a Shimadzu QP2010+ mass spectrometer . The capillary column used was an Rtx-624 ( Restek ) with stationary phase cyanopropylphenylated ( 6% ) dimethyl polysiloxane ( 94% ) . The carrier gas was helium . Oven programming was as follows: 30°C for 5 min , 10°C/min to 150°C , 20°C/min to 300°C . Data acquisition was done in scan mode ( m/z = 29 to 100 , electron impact , 70eV ) . A blank sample ( water replacing sample aliquot ) was analyzed after each sample to exclude cross-contamination . All samples were analyzed in duplicate; several arbitrarily chosen samples were analyzed as triplicates . In all cases , the coefficient of variation on the calculated 1-propanol content was < 1 . 5% . Quantification was done using a signal of 1-propanol-d7 ( isotope dilution analysis ) and ions 60 for 1-propanol and 67 for 1-propanol-d7 . Paired or unpaired Student t-test analyses were performed using Prism 6 . 0 for Mac OS X ( GraphPad Software , Inc . ) . Each experiment was performed at least 3 times . * p<0 . 05; ** p<0 . 01; *** p<0 . 001 . | Whereas Escherichia coli does not naturally produce the 1-propanol unless subjected to extensive genetic modifications , we show that this important industrial commodity is produced in hypoxic conditions inside biofilms . 1-propanol production corresponds to a native threonine fermentation pathway previously undocumented in E . coli and other Enterobacteriaceae . This widespread adaptive response contributes to maintain cellular redox balance and bacterial fitness in biofilms and other amino acid-rich hypoxic environments . This study therefore shows that mining complex lifestyles such as biofilm microenvironments provides new insight into the extent of bacterial metabolic potential and adaptive bacterial physiological responses . |
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Flexible or innovative behavior is advantageous , especially when animals are exposed to frequent and unpredictable environmental perturbations . Improved cognitive abilities can help animals to respond quickly and adequately to environmental dynamics , and therefore changing environments may select for higher cognitive abilities . Increased cognitive abilities can be attained , for instance , if environmental change during ontogeny triggers plastic adaptive responses improving the learning capacity of exposed individuals . We tested the learning abilities of fishes in response to experimental variation of environmental quality during ontogeny . Individuals of the cichlid fish Simochromis pleurospilus that experienced a change in food ration early in life outperformed fish kept on constant rations in a learning task later in life—irrespective of the direction of the implemented change and the mean rations received . This difference in learning abilities between individuals remained constant between juvenile and adult stages of the same fish tested 1 y apart . Neither environmental enrichment nor training through repeated neural stimulation can explain our findings , as the sensory environment was kept constant and resource availability was changed only once . Instead , our results indicate a pathway by which a single change in resource availability early in life permanently enhances the learning abilities of animals . Early perturbations of environmental quality may signal the developing individual that it lives in a changing world , requiring increased cognitive abilities to construct adequate behavioral responses .
The ability of adapting to changes in the environment is an important driving force of evolution , as recognized already by Darwin in his famous quote: “It is not the strongest of the species that survives…it is the one that is the most adaptable to change” [1] . Animals may adapt by altering their behavior , physiology , or morphology . The construction of behavioral responses is thought to be the fastest and most flexible way of adapting to new situations . Animals often have to deal with new situations for which they must devise novel or flexible solutions [2] . Field observations and laboratory studies showed that the advantages of novel or altered behaviors increase with the complexity of the environment ( reviewed in [3] , [4] ) . This suggests that frequent and unpredictable environmental changes may select for increased cognitive abilities allowing animals to meet these challenges by constructing adequate behavioral responses . In mockingbirds , for example , the complexity of songs is assumed to reflect their cognitive abilities , and species inhabiting areas with a low predictability of climatic patterns show more elaborate song displays than species in stable environments [5] . On the level of the individual , environmental instability can be encountered by plastic trajectories of the development of cognitive abilities . Environmental fluctuations early in life are known to enhance the behavioral flexibility of animals with regard to predator avoidance strategies [6] , [7] , feeding performance [7] , and social behavior [6] , [8] . A possible explanation for these behavioral effects is that variable environments evoke repeated neural stimulations resulting in faster and better learning [7] . Several studies showed that neural stimulation over longer periods by exposing animals to enriched environments ( e . g . , [9] , [10] ) can enhance brain development [5] , [11] , for example through an increased synaptic density [12] , and can lead to improved learning abilities and memory capacity [12] . A food manipulation experiment indicated that a single change of diet can constrain neural development if later cognitive abilities are traded against the benefits of a compensatory growth response [7] , [13] . On the contrary , an environmental change early in life should be expected to favor enhanced cognitive development , if this early perturbation signals the developing individual that it lives in a more variable environment . In response to this signal animals should develop increased cognitive abilities , which help them to construct adequate behavioral responses to the environmental challenges . An experimental evaluation of this hypothesis has been hitherto lacking . Individuals of the African cichlid Simochromis pleurospilus live in a stable environment , but parts of the population experience a habitat shift around maturation [14] . If increased cognitive performance confers a fitness advantage when shifting habitats , we should expect that improved cognitive abilities can be triggered in S . pleurospilus by experimentally varying their juvenile environmental quality . We tested this prediction by investigating how the performance in a learning task was influenced by different juvenile feeding regimes in S . pleurospilus . Fish were fed either on a stable high or a stable low food ration , or rations were switched from low to high or vice versa . We trained the fish to associate a visual cue with food and tested how often they selected the positive stimulus . We tested their cognitive performance twice , at the end of the juvenile period and 1 y later when the fish were adults . We adhere to the broad definition of “cognition” as comprising “all mechanisms that invertebrates and vertebrates have for taking in information through the senses , retaining it , and using it to adjust behavior to local conditions” [15] .
The tests of learning abilities yielded similar results in juveniles ( J ) and adults ( A ) . Neither the amount of food received before the switch ( J: p = 0 . 62 , A: p = 0 . 53 ) nor after the switch ( J: p = 0 . 21 , A: p = 0 . 12 ) influenced the number of correct choices significantly . The interaction between early and late food treatment was significant ( J: p = 0 . 029 , A: p = 0 . 005 , Table 1 ) however , demonstrating that fish that had experienced a switch in feeding regime outperformed those fed constant rations . This effect is independent of the direction of the diet change ( high-to-low or low-to-high; Figure 1 ) . Alternatively , learning ability might be affected by the average amount of food during the juvenile period . In that case we should expect the learning performance to increase linearly with food ration or , if an optimal food level exists , to follow a dome-shaped relationship . To test for this possibility , we determined the average amount of food each fish consumed relative to its own body mass . However , the number of correct choices was not related to mean relative food ration , neither with a linear ( GLzM: J: p = 0 . 59 , A: p = 0 . 86 ) nor with a quadratic predictor ( GzLM: J: p = 0 . 26 , A: p = 0 . 90 ) . During the test of juvenile cognitive abilities the animals of different treatment groups differed in body size ( overall difference: ANOVA , df = 3 , F = 103 . 88 , p<0 . 001; differences between individual groups: Tukey's hsd test , all p<0 . 001 , Figure 2A ) and in the latency times to approach the stimulus ( ANOVA , df = 3 , F = 7 . 81 , p<0 . 001; differences between individual groups: Tukey's hsd test , all p<0 . 01 , Figure 2B ) . These differences had disappeared by the time the fish were tested for a second time during adulthood ( size: ANOVA: df = 3 , F = 0 . 61 , p = 0 . 61 , Figure 2C; latency time: ANOVA: df = 3 , F = 0 . 38 , p = 0 . 77 , Figure 2D ) .
Juveniles differed in size across the treatment groups as they were subject to different feeding rations during the tests . NHH and SLH fish received near ad libitum food and were presumably satiated , whereas NLL and SHL fish experienced a food shortage most likely resulting in a stronger motivation of these two groups to approach the test apparatus . This is reflected in substantial differences in the times to leave the shelter and to approach the choice apparatus . A differential motivation cannot explain our results however , as in this case the learning performance should differ between NHH/SLH and NLL/SHL fish . Moreover , these differences in latency time had disappeared , when testing the fish the second time during adulthood . Potential motivational differences were now eliminated as the size differences had vanished and all fish received the same food rations . Poor early nutrition can adversely affect neural development [16]–[18] and it can have a negative impact on song learning in birds [19] , [20] and intelligence quotients in humans [21]–[23] . Also this factor cannot explain our findings as NHH fish did not perform better than NLL fish . We assume that the low-food ration was sufficiently high to sustain normal neural development , as in a previous study [14] , [24] S . pleurospilus raised on the NLL ration developed and reproduced normally . Body size is amongst the traits under strongest selection [25] . Juvenile fish should have high incentives to grow fast , since the number of potential gape-size limited predators decreases exponentially with increasing body size [26] . Fast growth can have negative effects , however ( reviewed in [27] ) , including a negative influence on cognitive performance . In zebra finches , birds that had the highest rates of compensatory growth after experiencing a period of a reduced food ration performed worst in a subsequent learning task [13] . This effect might result from a trade-off between investment in growth versus neural development [28] , [29] or from prolonged stress due to increased foraging activity leading to chronically elevated levels of corticosterone , which in turn can adversely affect neural development [30] . If compensatory growth had affected the learning performance of S . pleurospilus , NHH fish should have outperformed those groups that had not started on a high-food diet and that exhibited compensatory growth in our experiment ( all fish reached similar sizes at the time when adults were tested , but NLL fish took longer to do so than SLH fish ) . If the brain was especially vulnerable to negative effects of compensatory growth during the juvenile period , then the fish that experienced a switch from a low to a high ration ( SLH; highest degree of compensatory growth ) should perform worst . The opposite was the case , however , as SLH fish outperformed NLL fish . To ensure that the learning experience of juveniles did not influence the subsequent learning performance of adults [31] , we performed a test series , which confirmed that the fish did not remember the conditioned cue of the juvenile test series before starting the second series . Other fish species have been shown to forget learned foraging techniques already within 2 d [32] , whereas the tests of juveniles and adults in our experiment were more than 1 y apart . We are therefore confident that we tested independent learning abilities of the fish in both test series rather than memory effects . Twelve individuals , which replaced fish that had died until the onset of the second test series and which had not been tested as juveniles , slightly outperformed the previously tested fish in the learning tests ( Table 1; see Material and Methods for details ) . Possibly fish tested 1 y before may still have associated the test apparatus with a food reward , as apparently they were less afraid of the test apparatus ( shorter latency times until approach; see Material and Methods ) . They may therefore have been less attentive to the type of stimulus cue during the training phase than previously untested fish . These fish approached the apparatus more cautiously and hence may have had more time to associate the cue with food . The effect of previous learning experience was the same across all treatment groups . Environmental conditions during development may trigger changes in morphology , physiology , or behavior , which can confer an adaptive advantage later in life if an animal remains in these conditions [33] . The main mechanisms proposed to explain such plastic responses to environmental cues involve repeated stimulation , for example , through physical exercise facilitating muscle development or by early neural stimulation through environmentally enriched raising conditions , which enhances cognitive abilities later in life [7] , [12] , [34] . But neither environmental enrichment nor training through repeated neural stimulation can explain our findings , as the sensory environment was kept constant during ontogeny and resource availability was changed only once . Our results rather show that already a single event—a change of food ration—during early ontogeny triggers learning ability possibly indicating the existence of a novel pathway of plastic neural development . It has been hypothesized that changing environments improve learning abilities , which consequently may allow animals to behave more adequately and flexibly [7] . Our results support this hypothesis by showing that environmental change can indeed directly affect learning abilities , independently of motivational differences between individuals . Changing environments experienced early in ontogeny can greatly improve the flexibility of behavior [6]–[8] . If such effects result partially from better learning abilities induced by early environmental change , these studies elucidate the manifold possible consequences of improved learning abilities , which extend to a wide range of behavioral contexts . The life history and ecology of S . pleurospilus suggests that the improvement of cognitive abilities in response to environmental change is adaptive . S . pleurospilus are algae grazers and hence depend upon the primary production of turf algae , which is influenced mainly by light intensity and a suitable substrate such as rocks and stones [35] . Algae productivity decreases exponentially with depth [35] . While juvenile S . pleurospilus inhabit the shallow regions of the lake with the highest algae intensity and some fish stay there throughout adulthood , other fish start to settle in deeper water around maturation [14] . These fish should benefit from increased cognitive abilities , as they have to cope with entirely different nutritional conditions . Improved cognition may help them to find and remember patches of high-quality turf algae ( reviewed in [36] ) , while those fish remaining stationary in the natal habitat do not necessarily require a better cognitive performance . Hence our findings suggest that habitat shifts can make these animals smarter . More generally , animals forced to cope with environmental changes as caused , for example , by anthropogenic perturbations of their habitats may benefit from improved cognitive abilities induced by these perturbations when forced to adjust to the new conditions . In conclusion we show for the first time that a single change in food availability early in life can enhance life-long learning abilities . Hence our study provides experimental support for the hypothesis that selection favors higher cognitive abilities in unpredictable or changing environments [5] , [9] . It also suggests a mechanism of how animals can acquire better abilities to cope with such environments: an environmental switch early in ontogeny may enhance learning ability persistently .
Simochromis pleurospilus is a maternally mouthbrooding cichlid of the subfamily Tropheini endemic to Lake Tanganyika , East Africa . It lives along the rocky shores of the lake where it feeds on epilithic turf algae . S . pleurospilus reproduces all year round and adult males defend small , adjoining territories of 2–4 m2 where females visit them to spawn . Juveniles and females are non-territorial and use large home ranges . After spawning females leave the male territory immediately and care for the clutch on their own [24] . Approximately 28 d after spawning , the young are independent . Juveniles and adults live sympatrically , but juveniles are confined to the shallow areas between 0 . 5 and 2 m depth , whereas adults often disperse to feed in greater depth between 1 and 12 m ( [14] , A . Kotrschal & B . Taborsky submitted ) . We raised 130 fishes in separate 20-l Plexiglas tanks , each equipped with a layer of sand , a flower-pot half for shelter , and an internal biological filter ( see [24] for details on experimental set-up ) . The experimental fish were derived from seven clutches of different females , and siblings were proportionally distributed over all treatments . We exposed the fish to two different feeding conditions in early and late adolescence , respectively , using a full-factorial design . Fish either received ( 1 ) a high food ration both in early and late life ( abbreviated as NHH , where “N” stands for “Not switched”; n = 40 ) ; ( 2 ) a low food ration both in early and late life ( NLL , n = 40 ) ; ( 3 ) a high food ration in early life , switched to a low food ration in late life ( SHL , where “S” stands for “Switched”; n = 22 ) ; or ( 4 ) a low food ration in early life , switched to a high food ration in late life ( SLH , n = 22 ) . Diet switches were performed either at 77 d ( i . e . , after the first third of the juvenile period; SLH: n77d = 11; SHL: n77d = 11 ) or at 133 d of age ( i . e . , after the second third of the juvenile period; SLH: n133d = 11; SHL; n133d = 11 ) . We had switched diets at two different ontogenetic stages to enhance the chances to capture a potential sensitive period when a change in resource availability affects cognitive abilities . As in the learning trials fish switched at day 77 did not perform differently from fish switched at day 133 , neither as juveniles ( GzLM; SLH: 77 d versus 133 d: χ2 = 2 . 1 , p = 0 . 15; SHL: 77 d versus 133 d: χ2 = 0 . 3 , p = 0 . 57 ) nor as adults [SLH: 77 d versus 133 d: χ2 = 0 . 09 , p = 0 . 77; SHL: 77 d versus 133 d: χ2 = 2 . 3 , p = 0 . 13 ( details of model see section Statistical Analysis ) ] , we combined the data of early and late switched fish resulting in four treatment groups: NHH , NLL , SLH , and SHL . Fish were fed 6 d a week with standardized agarose cubes containing an amount of Tetramin flake food corresponding to 12% ( near ad lib ) or 4% of mean body weight plus 5% Spirulina algae . All fish of a treatment group received the same food ration , which was based on the mean body mass of fish within this group . We adjusted the food rations to increasing mean body weights every 14 d . We stopped adjusting the rations to body weight in NHH fish at 189 d , because they no longer depleted the food cubes . We continued to adjust the ration for the NLL , SLH , and SHL fish until day 259 when they reached the same body size as NHH fish . Thereafter all fish were kept on the same food ration . We measured lengths and weights of fish every 3 wk . Standard lengths were read from a measuring board with a 1 mm grid and were estimated to the nearest 0 . 5 mm by eye . Weights were read to the nearest 1 . 0 mg from an electronic balance . All measurements were taken before the daily feeding and done by the same person ( A . K . ) . We first trained the animals to associate a certain visual cue with food and thereafter determined the number of correct decisions made when presenting the cue . We did the first test series in the juvenile phase shortly before maturation ( J ) at an age of 172 d ( ±10 d ) when the fish still received different food rations and differed in body size between treatments . The second test series was done 1 y later in adults ( A ) at an age of 585 d ( ±10 d ) , when all fish were fed the same rations and were of similar size . Each fish was tested in its individual raising tank . Twelve juveniles were excluded , as they were used in a pilot study after which we adjusted the testing protocol . Furthermore 12 fish never left their shelter within 12 min , yielding a final sample of 66 fish for the juvenile test series ( NHH = 19 , NLL = 19 , SHL = 14 , SLH = 14 ) . One year later some fish that had been tested previously had died in the meantime . Therefore we added 12 previously untested individuals to increase sample sizes . We used all SHL , SLH , and NLL fish still alive and 30 NHH fish for the second test run . Five adults refused to take food from the test apparatus and eight adults never left their shelter within 12 min , resulting in a sample size of 77 fish for the adult test series ( NHH = 30 , NLL = 25 , SHL = 8 , SLH = 14 ) . Overall 46 fish participated in all 6 juvenile trials , and 69 fish participated in all 10 adult trials . The mean rate of participation was 5 . 3 ( ±1 . 4 SE ) times out of 6 in juveniles and 9 . 7 ( ±1 . 0 SE ) times out of 10 in adults . Although juvenile NLL and SHL fish participated more often than NHH and SLH fish ( ANOVA: F = 4 . 63 , p = 0 . 005 ) , this did not bias the results because the statistical model accounts for participation rate ( see Statistical Analysis ) . Adult fish of all treatment groups participated at a similar level ( ANOVA: F = 1 . 04 , p = 0 . 38 ) . Since not all fish participated in every trial we used binary probit-link generalized linear models ( GzLM ) to analyze the cognitive performance with the total number of correct choices as the dependent variable and the number of times the fish participated as the independent variable [37] . We included food ration in early adolescence ( “early food treatment” ) and in late adolescence ( “late food treatment” ) as fixed factors . Twelve adults that had not been tested as juveniles took on average 70 s longer to enter the choice area ( Mann-Whitney U: Z = −2 . 19 , p = 0 . 028 ) , but they outperformed those fish already tested as juveniles ( GzLM , χ2 = 6 . 11 , p = 0 . 013 ) . As the latter effect occurred across treatment groups ( indicated by an absence of a significant interaction between treatment group and previous test experience , GzLM: χ2 = 352 , p = 0 . 84 ) , we included previous test experience ( yes or no ) in the model of adult learning performance . To examine whether the amount of food per se influenced the likelihood of correct choices , we tested if a positive , a negative ( linear predictor ) , or a dome-shaped ( quadratic predictor ) relationship exists between these two variables . We determined the amount of food received by each individual by calculating the percentage of food mass contained in the food pellets relative to the body mass of individuals using data from our tri-weekly body mass measurements . We then took the mean of these values during the entire juvenile period ( i . e . , until week 30 ) as a measure of food consumed by individual fish . All analyses were done with SPSS 17 . 0 , SPSS Inc . , Chicago . | Animals with higher cognitive abilities should be better capable of producing new , modified , or innovative behaviors as this ability could allow them to cope better with unpredictable environmental changes . Changing environments may hence select for higher cognitive abilities . Similarly , changing conditions during ontogeny can cause plastic responses , helping individuals to adapt to their current environment . In this study , we have used the cichlid fish Simochromis pleurospilus to show experimentally that individuals subjected to a change in food ration early in life ( i . e . , low to high or vice versa ) outperform fish kept on constant rations in a learning task later in life . Remarkably , this result was independent of the direction of the implemented change or the average amount of food each fish received , and the results in the juvenile stage did not change in adulthood . Our results suggest that a single environmental change early in life might enhance cognitive abilities in animals . |
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Combination antiretroviral therapy ( cART ) reduces HIV-associated morbidities and mortalities but cannot cure the infection . Given the difficulty of eradicating HIV-1 , a functional cure for HIV-infected patients appears to be a more reachable short-term goal . We identified 14 HIV patients ( post-treatment controllers [PTCs] ) whose viremia remained controlled for several years after the interruption of prolonged cART initiated during the primary infection . Most PTCs lacked the protective HLA B alleles that are overrepresented in spontaneous HIV controllers ( HICs ) ; instead , they carried risk-associated HLA alleles that were largely absent among the HICs . Accordingly , the PTCs had poorer CD8+ T cell responses and more severe primary infections than the HICs did . Moreover , the incidence of viral control after the interruption of early antiretroviral therapy was higher among the PTCs than has been reported for spontaneous control . Off therapy , the PTCs were able to maintain and , in some cases , further reduce an extremely low viral reservoir . We found that long-lived HIV-infected CD4+ T cells contributed poorly to the total resting HIV reservoir in the PTCs because of a low rate of infection of naïve T cells and a skewed distribution of resting memory CD4+ T cell subsets . Our results show that early and prolonged cART may allow some individuals with a rather unfavorable background to achieve long-term infection control and may have important implications in the search for a functional HIV cure .
HIV-1 infection is normally characterized by sustained viral replication and a progressive loss of CD4+ T cells , leading to AIDS . Combined antiretroviral therapy ( cART ) suppresses viral replication and drastically reduces morbidity and mortality [1] . However , cART does not eradicate infected cells [2] , and plasma viremia generally rebounds quickly after treatment is discontinued [3] . The existence of a few HIV-infected patients who spontaneously controlled HIV replication to undetectable levels for many years ( HIV controllers [HICs] ) suggests that a functional HIV cure or remission might be possible . However , how or whether other patients can achieve an HIC-like status is unclear . Emerging evidence shows that early treatment has long-term benefits [4] . Treatment initiation during primary HIV-1 infection ( PHI ) rather than during chronic HIV-1 infection ( CHI ) may i ) further reduce residual viral replication [5] , ii ) limit viral diversity [6] and viral reservoirs [7] , iii ) preserve innate immunity and T and B cell functions [8] , [9] , [10] , and iv ) accelerate immune restoration [11] . Most relevant studies show that CD4+ T cell counts are higher and that viral rebound occurs later ( and at a lower level ) after the discontinuation of treatment that began during PHI compared with treatment that began during CHI [12] , [13] . Although in most cases , these advantages wane soon after treatment interruption [14] , the existence of individuals in whom the viral load remains undetectable for several years after the interruption of prolonged therapy that was initiated very early after infection ( post-treatment controllers [PTCs] ) was reported by our group in 2010 [15] . These individuals hold important clues in the search for a functional HIV cure . Here , we have identified and characterized a group of 14 PTCs . We analyzed whether PTCs shared parameters that have been associated with spontaneous control of viremia in HICs , to explore whether the efficient control of infection in PTCs may indeed be derived from early treatment . In addition , we explored the level and distribution of the PTCs' latent viral reservoir in the blood . Indeed reaching functional cure will likely require reducing not only the size but also the distribution of the HIV reservoirs , particularly among the CD4 T cells with long lifespan or important clonogenic properties , as naïve and central-memory T cells ( TCM ) .
We studied 14 HIV-1-infected patients with durable viral control following the interruption of effective cART that was initiated during PHI ( PTCs ) . The patients' characteristics are reported in Table 1 and Figure 1 . All 14 patients were diagnosed with PHI in the late 1990s or early 2000s . Twelve patients had a symptomatic primary infection . During PHI ( 1 . 6 [1 . 1–2 . 1] months estimated after initial exposure ) , the PTCs had higher viral loads ( median 5 . 0 log HIV-1 RNA copies/ml ) and lower CD4+ T cell counts ( median 502 cells/µl; Table 1 ) compared with the 8 patients in the ANRS PRIMO cohort who subsequently exhibited spontaneous control of viremia ( median 3 . 0 log HIV-1 RNA copies/ml of plasma and 794 CD4+ T cells/µl at PHI ( 2 . 2 [1 . 7–3 . 5] months estimated after exposure , p = 0 . 11 for the delay when compared to PTC ) [16] ( Figure 2 ) . In contrast , PTC values during PHI were similar to those of patients in the ANRS PRIMO cohort who did not control their infection afterwards ( 5 . 1 log HIV RNA copies/ml and 517 cells/µl; Figure 2 ) . The PTCs received standard cART ( Table 1 ) available at the time , and their viral load became undetectable within a median of 3 months ( 0 . 5 to <8 months ) after treatment began ( Figure 1 ) . The median cART duration was 36 . 5 months , and the plasma viral load was no longer detectable after the first undetectable sample during treatment . During the treatment period , all PTCs except two ( OR2 , with high CD4+ T cell counts of 955 cells/mm3 at PHI , and OR3 , who was infected through a blood exposure accident and for whom no available CD4+ T cell counts were available before therapy ) experienced an increase in their CD4+ T cell counts between PHI and treatment interruption ( median 502 and 927 cells/mm3 , respectively , p<0 . 001 , n = 13 ) . Following the interruption of cART , viral control persisted for a median of 89 months , and the CD4+ T cell counts remained stable ( the median final CD4+ T cell count was 837 cells/mm3 , p = 0 . 58 , n = 14 ) . Eight PTCs had viral loads below the detection limit in all available samples after treatment interruption , whereas occasional increases were recorded for the other six patients ( Figure 1 and Table 1 ) . We then compared specific parameters among the PTCs , HICs , patients with uncontrolled viremia ( viremics [VIRs] ) and patients receiving cART ( [ARTs]; see methods ) . Protective HLA class I alleles ( HLA-B*27 and B*57 ) have been consistently found to be overrepresented in cohorts of HICs [17] , [18] , [19] who spontaneously control HIV-1 infection . One of the PTCs had one HLA-B*57 allele and two PTCs had one HLA-B*27 allele . However , in contrast to the HICs from the ANRS HIV controller cohort , we found no overrepresentation of HLA-B*27 or HLA-B*57 in our PTC group compared with the general French population [20] ( www . allelefrequencies . net; Figure 3A , Tables S1 and S2 ) . Furthermore , the risk alleles HLA-B*07 and HLA-B*35 [17] were highly prevalent in the PTC group ( 29% of all HLA-B alleles ) , but they were underrepresented in the HIC group ( p<0 . 001 ) . Three and five of the 14 PTCs carried one HLA-B*07 allele and one HLA-B*35 allele , respectively . Three PTCs carried HLA-B*3501 ( OR1 , OR2 and OCP ) , whereas the other two ( KPV and MWP ) carried the HLA-B*3503 allele , which is associated with a more rapid progression to AIDS [21] . We and others have shown that most HICs have high frequencies of highly efficient HIV-1-specific CD8+ T cells [22] , [23] . In fact , the elevated number of HIV-specific CD8+ T cells producing IFN-γ in the HICs was comparable to that in viremic patients ( VIR ) ( Figure 3B ) . In contrast , we found that the PTCs had very weak HIV-specific CD8+ T cell responses during the viral control period . On average , the level of these responses in the PTCs were similar to that found in treated patients ( ART ) , during both PHI and CHI ( not shown ) , and were much lower than in viremic patients and HICs ( Figure 3B ) . Indeed , HIV-specific CD8+ T cell responses were even barely detectable in some PTCs ( Table S1 ) . We then examined the capacity of CD8+ T cells from the PTCs to suppress ex vivo the HIV-1 infection of autologous CD4+ T cells , as we recently showed that this test distinguishes the effective CD8+ T-cell responses found in many HICs from the ineffective responses in other patients [22] . The HIV-suppressive capacity of CD8+ T cells from the PTCs was poor ( median decrease in p24: 0 . 39 log , Table S1 ) , comparable with the capacity of cells from viremic patients ( 0 . 55 [0 . 43–1 . 00] , p = 0 . 28 ) and treated patients ( 0 . 28 [0 . 12–0 . 86] , p = 0 . 88 ) and far weaker than that observed in the HICs ( 1 . 63 [0 . 62–3 . 22] , p<0 . 001 ) ( Figure 3C ) . Of note , the capacity of CD8+ T cells from the PTCs to suppress HIV-1 infection was still weaker than that of the subset of HICs that did not bear the HLA-B*27 or B*57 alleles ( 1 . 55 [0 . 71–3 . 28] , p = 0 . 002 , n = 29 ) . We then examined the activation status of CD4+ and CD8+ T cells from the PTCs by evaluating the expression of HLA-DR and CD38 . Because of the low or undetectable frequency of HIV-specific cells that were detected using tetramers in these individuals , the analyses were limited to the total cell population ( Figures 3D and S1 ) . HLA-DR and CD38 expression , both separately and in combination , were very weak in the PTCs during the period of viral control without therapy and similar to that observed in patients on cART , as expected within the context of very low viremia [24] . These results contrasted with the strong HLA-DR expression observed on CD8+ T cells from the spontaneous HIV controllers ( Figure 3D ) [22] , which has also been reported by others [25] . Overall , the PBMC-associated HIV-1 DNA levels in the PTCs during the infection control ( median 1 . 71 log copies/106 PBMC , Table 1 ) were similar to those in the HICs and much lower than those in patients with uncontrolled PHI or CHI or patients who started treatment during CHI [26] , [27] . Sequential PBMC-associated HIV-1 DNA levels since PHI were available for 6 of the PTCs . In these PTCs , the HIV-1 DNA levels had declined strongly at or just before the treatment interruption ( median 2 , 389 and 116 HIV-1 DNA copies/106 cells at PHI and before treatment interruption , respectively; p = 0 . 031; Figure 4A ) . The last available value , at a median of 6 years after the cART interruption , tended to be even lower ( 39 HIV-1 DNA copies/106 cells , p = 0 . 063; Figure 4A ) . Sequential PBMC-associated HIV-1 DNA levels were measured after treatment interruption for 8 PTCs ( Figure 4B ) . The HIV DNA levels remained stable after cART discontinuation in two PTCs and a positive slope was observed for OR3 , which is likely related to detectable viral replication at low levels in the last few years for this patient . In contrast , HIV DNA levels continued to progressively decline over the years in the five other PTCs in the absence of treatment . Thus , the PTCs had an extremely small viral blood reservoir , which in some cases continued to decline after long-term treatment interruption . We quantified the distribution of the HIV reservoir among various sorted lymphocyte populations of live peripheral cells available from 11 PTCs ( Figure 4C ) . The HIV DNA was detectable in the PBMCs from 7 out of 11 PTCs and in total purified CD4+ T cells from 6 out of 10 PTCs ( from whom enough cells were recovered ) . The results were either reported as the actual HIV DNA copy numbers/million cells or as an estimated value calculated as 50% of the detection threshold value when HIV DNA was not detected . As expected , the HIV DNA was 9-fold higher in the CD3+CD4+ T cells than in the total PBMCs ( median 2 . 3 versus 1 . 3 log HIV DNA copies/million cells ) . Among the CD4+ T cells , activated CD25+69+HLA-DR+ CD4+ T cells were significantly more infected than resting CD4+ T cells ( 2 . 8 versus 2 log HIV DNA copies/million cells , p = 0 . 01 ) . In contrast , the CD3−CD4+ monocytes were minimally infected , with the total cell-associated HIV DNA level detectable in only 2 out of 10 samples ( estimated median 2 . 3 log HIV DNA copies/million monocytes ) . We also analyzed the reservoir distribution among the resting naïve ( TN ) , central memory ( TCM ) , transitional memory ( TTM ) and effector memory ( TEM ) CD4+ T cell subsets from 11 PTCs ( Figure 4C ) . Cell-associated HIV DNA was detected in only 2 out of 11 samples in the resting naïve CD4 T cells ( TN ) ( median 1 . 6 log HIV DNA copies/million TN , p = 0 . 001 ) and was lower than in resting memory CD4+ T cell subpopulations . In contrast , all resting memory CD4 T cells contained comparable levels of cell-associated HIV DNA ( 2 . 5 , 2 . 4 and 2 . 3 median log HIV DNA copies/million in TCM , TTM and TEM cells , respectively ) . To assess the presence of an inducible virus and the true nature of this HIV reservoir , we used anti-CD3 and anti-CD28 in the presence of IL-2 and IL-7 to stimulate the sorted resting CD4+ T cell subpopulations of 7 PTCs from whom an adequate number of cells was recovered ( Figure 5 ) . We observed a time-dependent virus production upon in vitro stimulation in 5 of the 6 sorted resting TCM , TTM and TEM subsets that were analyzed . We detected virus production from at least one T cell subset from each of the 7 tested patients . The failure to detect HIV production reflected the low number of HIV-infected cells added at baseline ( a median of 6 . 5 HIV DNA copies in non-producing samples versus 97 HIV DNA copies when HIV RNA production could be detected ) . In line with the TN cells' extremely low infection levels , virus production in these cells was observed in only 2 out of 5 PTCs samples tested . The stimulation of a higher number of resting TN cells with IL-7 alone triggered virus production in 3 TN samples , despite undetected TN-associated HIV DNA in 2 cases ( Figure 5 ) . We then compared the HIV reservoir distribution among the PTCs' resting CD4 T cell subsets to those of the HICs , whose total blood cells had similar low levels of HIV DNA . No differences were observed between the PTCs' resting CD4 T cell subsets' infection levels and those of the HICs ( 1 . 6 , 2 . 7 , 2 . 6 and 2 . 2 median log HIV DNA copies/million in the TN , TCM , TTM and TEM cells from HIC , respectively ) , except that the HIV DNA was undetectable in the TN cells from 9 out of 11 PTCs but only 4 out of 8 HICs ( Figure 6A ) . To calculate each subset's contribution to the HIV reservoir , we evaluated the frequency of the resting CD4 T cell subsets in the blood ( Figure S3 ) . The predominance of the TTM subset in the PTCs drove the major contribution of this subset to the PTCs' resting CD4 T cell HIV reservoir ( median 54% ) . This contribution of the TTM subset was significantly higher than that of the TCM which contributed to only 22% , the TEM ( 13% ) , and the TN subset which contributed very minimally to the resting HIV reservoir ( 6%; Figure 6B ) . In contrast , both TCM and TTM subsets contributed equally to the HIV reservoir in the HICs , as has been reported for other HIV-infected patients [28] , [29] . Overall , such long-lived cells as the TN and TCM cells contributed very minimally to the PTCs' total HIV reservoir in resting CD4 T cells , which might have contributed to the gradual shrinking of the reservoir in some PTCs for whom the TTM subset was also the main contributor to the HIV reservoir ( Figure S4 ) . PTCs may represent between 5 and 15% of patients with early cART interruption [15] , [30] , [31] . To better understand this phenomenon , we estimated its frequency of occurrence within the French Hospital Database on HIV ( FHDH ANRS CO4 ) ( http://www . ccde . fr/main . php ? main_file=fl-1309272043-794 . html ) . Between 1997 and 2011 , 3 , 538 patients were included in the FHDH within 6 months of PHI . Among those , 1 , 013 patients were treated within 6 months post-infection , and 756 patients continued treatment for at least one year . Of those , only 70 patients with a viral load >50 copies/mL prior to treatment interrupted cART while their viral load was <50 copies/mL and with at least one viral load measurement recorded after treatment interruption . The mean number of viral load in the first three years post treatment interruption was 8 with a median delay of 3 months between 2 measurements . To estimate the probability of maintaining virological control , we used Kaplan-Meier estimates and defined loss of control as either 2 consecutive viral loads >50 copies/mL or 1 viral load >50 copies/ml , followed by cART resumption ( Figure 7 ) . The probability of maintaining viral control at 12 months was estimated as 15 . 3% [4 . 4–26 . 3] , and it was identical at 24 months post-cART interruption .
Numerous efforts have been aimed to achieve a functional cure for HIV infection that would allow treatment to be stopped altogether . We studied 14 patients in whom viral replication was controlled to undetectable levels for several years after the discontinuation of cART . These PTCs with long-term virological remission may hold important clues about a possible functional cure for HIV . The 14 PTCs presented in this study maintained lasting control of viremia after the interruption of prolonged therapy that began early during PHI . We found that most PTCs were readily distinguishable from spontaneous HICs in many respects . In many cases , spontaneous control seems to start very soon after HIV infection [16] , [32] , and most HICs have lower-than-normal viral loads during PHI [16] . In contrast , the PTCs had a more severe primary infection with higher viral loads and were frequently symptomatic , which may have prompted the early treatment in some cases . These observations are consistent with the generally unfavorable HLA genotypes of the PTCs . In particular , the risk alleles HLA B*35 and HLA-B*07 , rarely observed in the HICs [17] , were highly prevalent among the PTCs . Furthermore , two PTCs carried the HLA-B*3503 allele , which is associated with accelerated disease progression and impaired HIV-specific T cell function [33] . We cannot rule out the possibility that spontaneous control may have been masked in some cases by early therapy initiation . In particular , it might be possible that some potential HICs who lacked protective HLA alleles were more prone to have higher viral loads in primary infection and , hence , more likely to initiate therapy . However , other differences were observed between the PTCs and the HICs during the chronic phase of infection . In particular , the PTCs had a low frequency and quality of HIV-specific CD8+ T cell responses . Although some HICs do not exhibit strong HIV-specific CD8+ T cell responses [34] , [35] , the overall differences between the HICs and PTCs in our study were striking , even when the HICs carrying the protective HLA B*27 and B*57 alleles were excluded from the analyses . Finally , the PTCs were characterized by a lower CD8+ T cell activation status compared with the HICs . The 5 to 15% of PTCs observed among the patients in the FHDH ANRS CO4 study and in other studies [15] , [30] , [31] appears higher than the proportion of HICs with spontaneous viral control in patients followed from primary infection [16] , [36] . Therefore , our results strongly suggest that the infection control in the PTCs was not achieved spontaneously and was favored by the early onset of therapy . Because the interruption of long-term cART initiated early during PHI is not recommended , only a very small proportion ( ∼2% ) of the patients in the FHDH experienced such an interruption , which may explain the rarity of PTCs worldwide . It is also important to consider that the 14 PTCs studied here had exhibited infection control without therapy for a very long period , and they may differ from PTCs with a shorter period of control [31] . The control of viremia following treatment interruption was associated with very low HIV blood reservoirs in the PTCs . This observation , together with similar observations in the HICs [26] , suggests that limiting the pool of infected cells is crucial for the successful control of viral replication in the absence of therapy . In PTCs , the early cART initiation and the lengthy treatment period likely played an important role in reducing the reservoirs [7] , [37] . Interestingly , five PTCs experienced a progressive decline in their viral reservoir after treatment interruption , which is one of the goals in the search for an HIV cure . However , very small HIV reservoirs do not guarantee infection control off therapy [38] . A key additional element might be a low reservoir distribution in cell subsets with long lifespan as naïve and central-memory T cells . Indeed we found that the cell subsets of all the PTCs analyzed ex vivo carried very low levels of HIV DNA . In particular , long-lived resting CD4+ T cells from the PTCs provided a minor contribution to the total HIV reservoir . Naïve CD4+ T cells were poorly infected , and overall the presence of the virus in these cells could not be detected ( via DNA or viral replication ) in 40% of the samples . This extremely low reservoir in PTCs' naïve cells contrasts with the massive infection detected at the end of the first month after initial infection with a median of 3 log copies HIV-DNA/million naïve cells ( C . Bacchus and A . Cheret , personal communication ) , as also reported a year after initial infection in the absence of treatment , although the naïve cells contained a log lower level of cell-associated HIV DNA than other memory subsets [39] . These discrepancies suggest that early therapeutic intervention is extremely efficient at decreasing those very long-lived reservoirs . Central memory CD4+ T cells also contributed very weakly to the HIV reservoir because of a skewed resting CD4+ T cell subset distribution with a large proportion of shorter-lived transitional memory cells . The skewed distribution of the resting CD4+ T cells observed in the PTCs is also found in uncontrolled early infection ( our own unpublished results ) , further indicating that early therapeutic intervention strongly contributed to the nature of the viral reservoir in these individuals . The TCM cells have been shown to be heavily infected a year after infection , and the main contributor to the total HIV reservoir in patients treated during chronic infection [28] . Similarly weakly differentiated memory CD4 T cells were shown to contain the majority of the HIV reservoirs in untreated chronically infected patients [40] . In contrast , we recently reported a protection of TCMs that contributed less to the total HIV reservoir in long-term non progressors carrying HLA-B*27 or B*57 alleles [29] , and TCM protection has also been observed in the nonpathogenic SIV infection of sooty mangabeys [41] . Altogether our results suggest that a functional cure would most likely require reducing both the size and the distribution of the HIV reservoirs , particularly among those resting CD4 T cells with a long lifespan or important clonogenic properties , such as naïve and central memory T cells . Early therapy may also limit viral diversity and offer protection of innate and specific immunity from the deleterious effect of chronic immune activation . However , it remains unclear why only a limited fraction of patients is able to control the infection after therapy interruption , and a study of the effectors of control in PTCs is underway . In addition , mechanisms that diminish the susceptibility of host cells to HIV-1 infection [26] and protect long-lived cell types [42] have been implicated in the control of HIV/SIV infection and pathogenicity in humans and nonhuman primates and may favor infection control after treatment interruption in some individuals . Finally , it is also possible that properties of the viruses infecting the PTCs studied , along with potential limitation of viral diversity by early institution of cART may play a role in the phenotype reported . We are currently addressing these questions . Arguments against cART initiation during PHI include the potential for long-term toxicity , the development of resistant viruses and the cost . However , new antiretroviral drugs are well tolerated , highly effective and associated with excellent compliance , strongly reducing the risk of resistance [43] . In addition , early treatment initiation improves survival [4] and reduces the risk of HIV-1 transmission [44] . Here , we show that in some HIV-infected individuals with symptomatic primary infection and no favorable genetic background , off-therapy viral control for several years may be associated with a very early and prolonged antiretroviral treatment . These findings argue in favor of early cART initiation and open up new therapeutic perspectives for HIV-1-infected patients .
All of the subjects provided their informed written consent to participate in the study . The CO6 PRIMO , CO15 ALT and CO18 HIV controller cohorts are funded and sponsored by ANRS and were approved by the ethics review committees of Ile de France III , VI and VII , respectively . The institutional review board of Institut Pasteur and Pitié-Salpêtrière Hospital ( Paris , France ) also approved the study protocol . The VISCONTI study was funded by ANRS ( EP47 ) , sponsored by Orléans Regional Hospital and approved by the Tours ethics review committee . The post-treatment controllers ( PTCs ) were defined as patients who initiated cART within 10 weeks of PHI and whose plasma HIV RNA levels remained less than 400 copies/mL for at least 24 months after cART interruption . Primary infection was defined as symptoms associated with an incomplete HIV-1 Western blot and a positive p24 antigen test or detectable plasma HIV RNA , and/or seroconversion documented by a positive HIV antibody test that was preceded by a negative test less than 3 months before . Fourteen PTCs were included in this study . Four had been identified in a previous study [15] , six were recruited from the ANRS CO6 PRIMO cohort of patients diagnosed during PHI [31] , and four were recruited from patient follow-up at Hôpital de la Croix Rousse in Lyon , CHRU Gui de Chauliac in Montpellier , and CHU de Saint Louis in Paris , France . The HIV controllers ( HICs ) were patients from the ANRS CO15 and CO18 cohorts who had been infected for more than 5 years , were naïve of antiretroviral treatment and whose last 4 consecutive plasma HIV RNA values were less than 400 copies/ml . Viremic ( VIR ) patients were defined as patients who were HIV-1-infected for more than 6 months , were not receiving antiretroviral therapy and had HIV-1 plasma viral loads greater than 7500 RNA copies/ml . cART-treated individuals ( ARTs ) were HIV-1-infected patients whose viral load had been less than 50 RNA copies/ml of plasma for at least 6 months on cART initiated either on PHI or CHI . The subjects were serologically HLA-typed using complement-mediated lymphocytotoxicity testing ( InGen One Lambda , Chilly Mazarin ) . High-definition genotyping of the HLA-B*35 alleles was conducted by direct exon sequencing . Interferon ( IFN ) -γ secretion by HIV-specific CD8+ T cells was quantified ex vivo with an ELISPOT assay [22] . For each subject , the optimal peptides ( 2 µg/mL ) corresponding to known optimal CTL epitopes derived from the HIV-1 Env , Gag , Pol and Nef proteins were tested , depending on the results of the HLA typing . The method used to assess the CD8+ T cells' capacity to suppress an ex vivo HIV-1 infection of autologous CD4+ T cells has been thoroughly previously described [45] . The following antibodies were used: CD8-APC-H7 or -PerCPCy5 . 5 ( SK1 ) , CD3-APC or -APC-H7 ( SK7 ) , HLA-DR-PE-Cy7 ( L243 ) and CD38-PerCPCy5 . 5 ( HIT2 ) ( BD Biosciences ) . The cells were fixed and analyzed with a FACSCanto I flow cytometer ( BD Bioscience ) . PBMCs that were cryopreserved and stored in liquid nitrogen and had more than 80% viability after thawing were sorted as live monocytes ( CD3−CD4+ ) and activated and resting CD3+CD4+ T cells on a 5-laser FACS ARIA II cell sorter ( Becton Dickinson ) on the CyPS platform ( UPMC ) , after staining with the following combination: Live-Dead Fixable Aqua ( Life Technologies ) , CD3-Pacific Blue , CD4-AlexaFluor700 , CCR7-PE Cyanine7 ( 3D12 ) , CD27-APC , CD69-FITC and HLA DR-FITC ( BD Pharmingen ) , CD45RA-ECD and CD25-FITC ( Beckman Coulter ) . The resting CD4 T cells ( CD25−CD69−HLADR− ) were further sorted into the following categories: naïve ( TN , CD45RA+CCR7+CD27+ ) , central memory ( TCM , CD45RA−CCR7+CD27+ ) , transitional memory ( TM , CD45RA−CCR7−CD27+ ) , and effector memory ( TEM , CD45RA−CCR7−CD27− ) cells ( Supplementary Figure S2 ) . The collected cell numbers varied from 0 . 01 to 2 million cells among subsets and patients , and the purity of the sorted subsets was greater than 90% . The data were analyzed using Flowjo software ( Treestar ) . The total cell-associated HIV DNA was quantified using ultrasensitive real-time PCR ( Biocentric , Bantol , France ) in the PBMC , monocyte and CD4 T cell subsets , as previously described ( ANRS assay [46] ) . The entire HIV DNA extract was tested in two to four PCRs . The results are reported as either the actual HIV DNA copy numbers/million cells or as an estimated value calculated as 50% of the detection threshold value when the cell HIV DNA was lower than the threshold . The thresholds varied according to the available cell numbers and were calculated for each assay [47] . A first fraction of sorted resting CD4+ TN , TCM , TTM and TEM subsets from 7 PTCs was tested for the total cell-associated HIV DNA level ( see above ) . A second fraction of the same samples was cultured in variable numbers in 10% FCS supplemented RPMI 1640 medium for 13 days after stimulation at Day 0 with anti-CD3/anti-CD28 plus IL-2 ( Roche , 5 µg/ml ) and human recombinant IL-7 ( Cytheris , 1 ng/ml ) or with human recombinant IL-7 alone . At Days 3 , 6 , 8 , and 10 , half of the supernatants were removed to quantify the HIV RNA , and IL-2 and IL-7 were added . The viral production kinetic in the supernatants was measured using real-time PCR HIV RNA quantification ( Biocentric , Bandol , France ) . The viral production capacity of each subset was expressed as the ratio between the HIV RNA copies in the supernatants at a given day of culture and the level of cell-associated HIV DNA of each subset measured at Day 0 of culture . The Kruskal-Wallis nonparametric test was used to compare continuous variables between groups . A Wilcoxon matched-pairs signed rank sum test was used to compare variations in values ( CD4+ T cell counts , HIV DNA levels ) over time or to compare cell subsets in the sorting experiments . The allele frequencies in the different groups of patients were compared using Fisher's exact test . A Kaplan-Meier estimate was used to assess the probability of post-treatment control in patients who discontinued early cART . All values given in the text are medians and ( range ) or [IQR] . The SigmaStat 3 . 5 software ( Systat Software Inc . -SSI , CA ) or SAS software package , Version 9 . 2 ( SAS Institute , Cary , NC , USA ) was used for all analyses . | There is a renewed scientific interest in developing strategies allowing long-term remission in HIV-1-infected individuals . Very rare ( <1% ) patients are able to spontaneously control viremia to undetectable levels ( HIV controllers , HICs ) . However , the possibility to translate their mechanisms of control to other patients is uncertain . Starting antiretroviral therapy during primary infection may provide significant benefits to HIV-infected patients ( i . e . reduction of viral reservoirs , preservation of immune responses , protection from chronic immune activation ) . Indeed , we have observed that some HIV-infected patients interrupting a prolonged antiretroviral therapy initiated close to primary infection are able to control viremia afterwards . We present here 14 of such post-treatment controllers ( PTCs ) . We show that PTCs have achieved control of infection through mechanisms that are , at least in part , different from those commonly observed in HICs and that their capacity to control is likely related to early therapeutic intervention . We found that PTCs were able , after therapy interruption , to keep , and in some cases further reduce , a weak viral reservoir . This might be related to the low contribution of long-lived cells to the HIV-reservoir in these patients . Finally , we estimated the probability of maintaining viral control at 24 months post-early treatment interruption to be ∼15% , which is much higher than the one expected for spontaneous control . |
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The apicomplexans are a large group of parasitic protozoa , many of which are important human and animal pathogens , including Plasmodium falciparum and Toxoplasma gondii . These parasites cause disease only when they replicate , and their replication is critically dependent on the proper assembly of the parasite cytoskeletons during cell division . In addition to their importance in pathogenesis , the apicomplexan parasite cytoskeletons are spectacular structures . Therefore , understanding the cytoskeletal biogenesis of these parasites is important not only for parasitology but also of general interest to broader cell biology . Previously , we found that the basal end of T . gondii contains a novel cytoskeletal assembly , the basal complex , a cytoskeletal compartment constructed in concert with the daughter cortical cytoskeleton during cell division . This study focuses on key events during the biogenesis of the basal complex using high resolution light microscopy , and reveals that daughter basal complexes are established around the duplicated centrioles independently of the structural integrity of the daughter cortical cytoskeleton , and that they are dynamic “caps” at the growing ends of the daughters . Compartmentation and polarization of the basal complex is first revealed at a late stage of cell division upon the recruitment of an EF-hand containing calcium binding protein , TgCentrin2 . This correlates with the constriction of the basal complex , a process that can be artificially induced by increasing cellular calcium concentration . The basal complex is therefore likely to be a new kind of centrin-based contractile apparatus .
The phylum Apicomplexa contains ∼5 , 000 species of obligate intracellular protozoan parasites , many of which are important human or animal pathogens . Toxoplasma gondii is the most common cause of congenital neurological defects in humans , and an agent for devastating opportunistic infections in immunocompromised patients . Plasmodium falciparum , the most lethal form of malaria , kills more than a million people every year . The pathogenesis of the diseases that these parasites cause absolutely depend on their ability to replicate . In the absence of massive , uncontrolled expansion of the parasite population , the infections are benign . Thus , the understanding of parasite growth and division is crucial for developing effective therapies . The apicomplexan parasite cytoskeletons provide the framework for organellar replication and partition , and are essential for parasite survival and replication , therefore are attractive potential drug targets [1–3] . In addition to their importance in pathogenesis , the apicomplexan parasite cytoskeletons are marvelous structures [4–7] . Thus understanding the cytoskeletal biogenesis of these parasites is also of general interest for cell biology . In spite of the diversity in their host species , host selectivity , and the diseases they cause , the apicomplexan parasites share similar basic cell biology . In particular , all apicomplexans are obligate intracellular parasites . Most of them replicate inside a specialized parasitophorous vacuole within the host cell in an unusual process where the daughter cytoskeletons preform de novo prior to cytokinesis [1–3 , 8] . The daughter cytoskeletons contain several distinct sets of microtubules that are likely involved in different aspects of parasite motility and division [7 , 9 , 10]; the Inner Membrane Complex ( IMC ) , formed of flattened vesicles with a regular intramembranous particle lattice likely associated with the cortical microtubules [5 , 11–13]; a set of proteins weakly homologous to intermediate filaments underlying the IMC [2 , 14]; and a cytoskeletal apical complex that is closely associated with the membrane-bound invasion organelles [6 , 15 , 16] . During parasite division , the membrane bound organelles ( including the nucleus , ER , Golgi apparatus , mitochondrion , apicoplast and a collection of secretory organelles such as rhoptries and micronemes ) will be replicated and partitioned into the growing daughter cytoskeletons , until the end of the cytokinesis when the daughters bud out of the mother and take over mother's plasma membrane [1 , 2] . Compared with other compartments of the parasite body , much less was known about the architecture of the basal end of the parasite . We and others found that the basal end of the parasite contains a specialized compartment with distinct morphology and molecular composition [16 , 17] . It is constructed together with the rest of the daughter cytoskeleton , first as ring-like structures capping the growing ends of the daughters , which then constrict and eventually cap the basal pole of the adult parasite . Because of its distinct localization , organization and molecular composition , we used the term “basal complex” to represent this distinct compartment [16] . In adult parasites , the basal complex occupies the basal gap of the IMC , which , together with the underlying filamentous network , encloses the entire parasite body except for the extreme apical and basal ends ( Figure 1A and 1B ) ( the gap at the apical end of IMC is occupied by the cytoskeletal apical complex , an intricate assembly that includes the conoid , a tubulin-based molecular machine that does not utilize conventional microtubules; three polar rings; and two intra-conoid microtubules [Figure 1A and 1C] [6 , 7] ) . The basal complex is separated by more than 1 . 5 μm from another set of major cytoskeletal elements , the cortical microtubules , which emanate from the most posterior of the three polar rings and extend ∼2/3 of the length of the parasite body ( Figure 1A and 1C ) . The basal complex contains several distinguishable regions organized along its anterior-posterior axis , and is composed of substructures defined by different protein markers , including TgMORN1 , TgCentrin2 and TgDLC , a dynein light chain of T . gondii [16] . Interestingly , these basal complex proteins are also components of the apical complex and the centriole/spindle pole assembly ( Figure 1A–1D ) . The similarity in protein composition among these structurally and spatially distinct cytoskeletal assemblies is particularly intriguing given the de novo nature of the construction of the apical and the basal complex , because the centriole/spindle pole assembly are the only structures inherited by the daughter parasites during cell division , and the centrioles are the only cytoskeletal structure in T . gondii that can self-replicate , thus capable of propagating the structural information for building a new cytoskeleton to the daughters . Much of this study therefore focuses on the spatial-temporal coordination among the origination of the basal complex , the duplication of the centriole/spindle pole assembly , and the construction of the daughter cortical cytoskeleton . ( Throughout this paper , “the cortical cytoskeleton” is used to refer to the entire framework of cytoskeleton elements that aligns the parasite body [i . e . cortical microtubules , the IMC and the filamentous network underlying the IMC] except for the apical and the basal complexes [Figure 1A] ) . The basal complex is a new defining feature of T . gondii , and likely to be conserved in other important apicomplexan parasites [16 , 17] . To understand how this compartment comes into being and develops with the rest of the daughter cytoskeleton is crucial for elucidating how polarity is established in these parasites . It should be noted that the daughter cytoskeletons are built afresh , and the mother's body axis is apparently not used in the polarity determination of the daughters , since each daughter axis is more or less randomly oriented with respect to the mother and the other daughter [1 , 2] . Due to the limited spatial and temporal resolution of previous studies , and the fact that appropriate specific markers were not known , the precise sites and timing of basal complex initiation and maturation are not known . This study focuses on the key organizational changes of the daughter basal complex with respect to the centriole replication cycle as well as the maturation of the daughter parasite cortical cytoskeleton using high resolution wide-field deconvolution light microscopy . It reveals that the daughter basal complex is initiated in the vicinity of the centrioles before the establishment of the daughter cortical cytoskeleton , and that its initiation and construction are likely to be independent of the structural integrity of the daughter cortical cytoskeleton . The constriction of the basal complex that begins at a late stage of cell division correlates with the recruitment of a typical EF-hand containing calcium binding protein , TgCentrin2 , to its posterior compartment , which reveals the compartmentation and polarity of the basal complex in the developing daughter . Further , this constriction can be artificially induced by elevated intracellular calcium concentration . The timing of the recruitment , the localization , and the molecular characteristics of TgCentrin2 make it the most plausible candidate for driving the constriction of the basal complex during its maturation , and the basal complex in T . gondii is likely to be a new kind of centrin-based contractile apparatus .
Two previously identified basal complex components in T . gondii , TgMORN1 and TgCentrin2 [16] , occupy two distinct compartments in the basal complex of mature parasites . The TgMORN1 compartment is shaped into a cone that forms the main body of the basal complex ( Figure 1A–1E ) , whereas TgCentrin2 is concentrated at the posterior tip of the basal complex ( Figure 1D and 1F ) . Both TgCentrin2 and TgMORN1 are also components of the apical complex . In addition , they are localized to the spindle pole and the centrioles , respectively . The spindle pole and the centrioles are juxtaposed to each other during interphase ( Figure 1B–1D ) , but well separated after the daughter cortical cytoskeletons have formed in the mother [7 , 16 , 18 , 19] ( The slight displacement between the spindle pole and the centrioles in each interphase cell is a true spatial displacement , not an artifact of mis-registration induced by lens chromatic aberration or optical misalignment between the GFP and mCherryFP filters , because the shift between the red and green fluorescence of a multi-color 0 . 2 μm bead is clearly much smaller and below the resolution limit of the microscope [Figure S1] ) . The first sign of cell division is the migration of the centriole to the basal pole of the nucleus , where it replicates ( Nishi M , Hu K , Murray J , Roos D , manuscript submitted ) . The replicated centrioles sandwich the spindle pole ( Figure 2 ) , which at this point still appears as one spot . Surprisingly , ring structures containing TgMORN1 are observed forming around the duplicated centrioles even before the separation of the future apical and basal regions of the daughter parasites ( Figure 3 ) . These rings are at the outer edges of the initially planar aggregations of daughter cytoskeletal elements that will later become the daughter cortical cytoskeletons ( Figure 4 ) . The TgMORN1 rings are therefore likely to be the precursor of the future daughter basal ring complex . Two other components of the mature basal complex in adult parasites , TgCentrin2 ( Figure 3B ) and TgDLC ( data not shown ) , however , are not found in these early ring structures . How do these ring structures originate ? Will they really become the basal complex in the daughters ? To address these questions , I followed TgMORN1 distribution together with the daughter cortical cytoskeleton construction and centriole duplication in live parasites expressing EGFP-TgMORN1 and mCherryFP-Tg α1-tubulin ( TgTubA1 ) ( Figure 5; Video S1 ) . The TgMORN1 ring first appears as small extra masses outside the spindle pole , and is located close to the recently duplicated centrioles , which lie on each side of the spindle poles ( t = 10 min ) . 20–30 min later ( t = 30–40 min ) , the fluorescence of mCherryFP-TgTubA1 in the centriole spot increases , likely correlated with the initial assembly of the conoid in the apical complex , of which TgTubA1 is a major component . At this point , the ring-like nature of the TgMORN1 containing structure becomes apparent , and it is centered around the centriole/conoid mass ( t = 40 min ) . At t = 60 min , the centriole/conoid assemblies move apically above the plane of the TgMORN1 ring with the extension of the cortical microtubules , and the recruitment of TgMORN1 to the apical complex becomes clear . At this point the future apical and basal complexes are separated far enough to make it clear that the TgMORN1 rings formed at the beginning of the cell division are indeed precursors of the daughter basal complexes . The TgMORN1 rings remain at the basal ends of the daughter cortical cytoskeletons as the daughters grow ( t = 70–110 min ) . How is the basal complex able to remain at the constantly growing ends of the daughter cortical cytoskeletons ? Studies in mammalian cells have shown that microtubule plus-end ( MT-plus-end ) binding proteins probably maintain their position at the growing ends of microtubules by rapid association and dissociation [20] . Fluorescence Recovery After Photobleaching ( FRAP ) analysis of daughter basal complexes in T . gondii reveals constant protein exchange between the daughter basal complex and the cytoplasm ( Figure 6 ) . Although it is difficult to calculate an exact t1/2 for the fluorescence recovery because of the noise introduced by constant fluctuation of the basal complex position due to daughter cell movement , the recovery is clearly underway by ∼90 sec after photobleaching . This result indicates that the basal complex is intrinsically a dynamic “cap” , thus suggesting a mechanism similar to microtubule association of MT-plus-end binding protein is possibly involved in retaining the basal complex at the growing ends of the daughter cortical cytoskeletons . However , the growth of the daughter cortical cytoskeleton is likely not to be the pre-requisite for the protein exchange in the basal complex , as the fluorescence in the mature basal complex also partially recovers after photobleaching ( Figure S2 ) . The daughter cortical cytoskeleton and the basal complex appear to grow in concert during cell division . Is the construction and growth of the basal complex , a seemingly “downstream” structure , dependent on the structural integrity of the daughter cortical cytoskeleton ? To address this question , the construction of the basal complex was tracked in living parasites whose cortical microtubule extension and the daughter cortical cytoskeleton formation were severely disrupted by treating with oryzalin , a plant herbicide that binds to T . gondii α-tubulin and inhibits the construction of the spindle and the cortical microtubules , but not the centriole replication , during daughter formation ( Figure 7; Video S2 ) [9 , 10 , 21] . As expected , the mother's conoid , cortical microtubules , and basal complex are not affected by the oryzalin treatment , and the overall morphology of the mother cell remains normal until the distorted daughters attempt to bud . Daughter cortical microtubules , however , completely fail to appear . Despite the inhibition of the formation of functional daughter cortical cytoskeleton , the initiation of TgMORN1 ring formation proceeds normally ( Video S2 , 33 and 48 min ) . Furthermore , complete TgMORN1 rings are formed ∼30 min after the initiation ( Video S2; Figure 7 , t = 60 min ) , enlarge to ∼1 . 2 μm ( similar to the diameter of the basal complex in untreated daughters with extending cortical cytoskeletons [cf . Figure 5 , t = 70 min] ) , and retain their ring morphology till “budding” , at which point the organization of the basal complex becomes unclear due to the distorted parasite morphology . The initiation , construction and maintenance of the daughter basal ring complex are therefore independent of the structural integrity of the daughter cortical cytoskeleton . The organization of a growing daughter ring complex is quite different from the basal complex of an adult parasite . The basal complex of growing daughters is an annulus without any clear polarity ( e . g . Figure 5 , t = 70–110 min ) . The mature basal complex in adult parasite , however , is a conical structure , closed at one end , compartmentalized , and stratified along its anterior-posterior axis ( cf . Figure 1 ) . When is the polarity and compartmentation of the basal complex established ? The basal complex starts to constrict before cytokinesis and the constriction continues after cytokinesis when the daughters take over the mother's plasma membrane , thus closing the basal cap in the mature parasite ( cf . Video S1 ) [16 , 17] . Because TgCentrin2 resides in only the most constricted region of the basal complex in the adult parasite ( cf . Figure 1D ) , I investigated the relationships among the recruitment of TgCentrin2 , the constriction of the basal complex and the establishment of the polarity of the basal complex , by examining TgCentrin2 localization at several different stages of cell division . Interestingly , although TgCentrin2 is hardly detectable in the basal ring complex earlier during cell division ( cf . Figure 3B and Figure S3 ) , it is clearly localized to the daughter basal complex as a ring at a late stage when the daughter basal complex appears to be constricted ( Figure 8A ) . Its ring-like localization in the basal complex is also pronounced during cytokinesis when the daughters start to take over mother's plasma membrane ( Figure 8B ) , and after cytokinesis when the basal complex is still open at both ends ( Figure 8C ) . In all cases , the TgCentrin2 basal ring is located to the posterior of the TgMORN1 ring ( Figures 8 and 9 ) . The compartmentation and polarization of the basal complex are thus revealed upon the recruitment of TgCentrin2 prior to the closure of the basal complex . Like the TgMORN1 compartment , the TgCentrin2 basal compartment also undergoes significant constriction , from a ∼1 . 0 μm ring to a diffraction limited spot ( Figures 8 and 9 ) . Consistent with the involvement of centrin homologs in calcium sensitive contractile apparatus in other systems [22–24] , the constriction of the TgCentrin2 basal compartment can be artificially induced in daughter parasites at a late stage of cell division when the intracellular calcium concentration is elevated by treatment with calcium ionophore , A23187 ( Figure 10; Video S3 ) .
The results in this study clearly show that although the basal ring complex later becomes the distal end of the daughter cortical cytoskeletons , it is one of the first cytoskeletal structures assembled rather than the last . In addition , the daughter cortical cytoskeleton is unlikely to provide a structural base or template for the initiation of the basal complex , as the initial construction of the basal complex is largely unaffected when the cortical microtubule construction and the structural integrity of daughter IMC complex is abolished by oryzalin treatment . How is a macromolecular assembly like the basal complex built from scratch ? Although untemplated de novo assembly of huge macromolecular assemblies certainly can occur ( e . g . , T4 phage or other large viral particles ) , templated construction proceeding from an inherited “seed” seems to be the rule for most large structures in eukaryotes . Interestingly , the initiation of the basal complex spatially and temporally coincides with the replication of the self-duplicating cytoskeletal organelle- the centrioles , which makes the centrioles a particularly attractive candidate for providing the structural information to initiate the de novo assembly of the basal complex . However , it is also clear that the centriole itself is unlikely to be continuously responsible for the maintenance of the basal ring complex , as the diameters of the rings grow up to 1 μm , much larger than the size of the centrioles while they still surround the centrioles at an early stage of cell division . Thus if the centrioles play a role in the initiation and construction of the basal ring complex , other structures associated with it probably serve as intermediary . Future high resolution EM experiments will be essential to elucidate structural connections between the centrioles and the basal ring complex . TgMORN1 sometimes is seen to form long fibers in the cytoplasm , suggesting that this protein might have the propensity of interacting with itself and possibly form polymeric structures [16] . It is thus conceivable that the basal ring structure could be a product from a TgMORN1 polymer constrained into a ring form through its interaction with other proteins in the basal complex and/or the IMC . This , of course , is an extremely crude guess based on the scanty experimental data available . The final answer to this question will have to come from in vitro reconstitution experiments after we know enough about the protein composition and protein-protein interactions within the basal complex . Compared with that in daughter parasites under construction , the basal complex of fully mature parasites has constricted by ∼30%–40% . Much of the constriction occurs during the post-cytokinesis phase , as the basal complex is still a ∼1 μm ring when the cytokinesis has completed ( As a reference , the diameter of the mother mitochondria is around 0 . 3–0 . 4 μm [25–27] ) . What drives this constriction ? Actin/myosin containing contractile rings drive the cytokinesis of mammalian cells and yeast . However , although a type XIV Myosin , TgMyoC , was found in the basal complex [28] , so far available evidence does not support the involvement of the actin-myosin apparatus in basal complex constriction , as Gubbels et al . reported that cytochalasin D treatment did not seem to affect the TgMORN1 distribution [17] . A family of EF-hand containing calcium binding proteins , the centrins , underlie another type of contractile apparatus: calcium-sensitive contractile fibers associated with the algal flagella and basal body apparatus [22–24 , 29 , 30] . In this study , I found that TgCentrin2 , one of the four centrin homologs in T . gondii , is recruited to a ring structure at the basal end of the daughter parasites before the onset of cytokinesis , and shrinks to a small spot at the basal tip of the adult parasites . The focused localization of TgCentrin2 at the distal portion of the basal complex also correlates with greater constriction of this region in the adult parasites . Furthermore , a treatment that elevates the intracellular calcium level induces the constriction of the TgCentrin2 basal ring . TgCentrin2 also contains multiple potential phosphorylation sites in its EF hand domains , which could be the key in regulating this contraction process , as centrin dephosphorylation accompanies calcium-flux induced centrin-fiber contraction in algae [31] . TgCentrin2 , therefore , is recruited at the right time , to the right place , and possesses the right molecular characteristics to drive the closure of the basal complex at its posterior end during its maturation . Future experiments exploring conditions affecting the assembly and function of centrin contractile fiber , such as proton concentrations , and other intracellular signals will be crucial to test the role of TgCentrin2 in the basal complex constriction . The basal complex migrates distally away from the apical end of the daughter parasite as the daughter cortical cytoskeleton grows . There are at least two phases to this movement . Before cytokinesis , the basal complex lies at the ends of both the daughter IMC and the cortical microtubules . After cytokinesis , however , it migrates further distally and becomes clearly separated from the cortical microtubules . Therefore although it is plausible that the directional growth of cortical microtubules drives the movement of the basal complex away from the apical pole of the growing daughter before cytokinesis [17] , there is not yet any evidence to suggest a causal relationship between the basal complex distal migration and cortical microtubule growth , and the later distal movement of the basal complex during post-cytokinesis growth of the newly emerged parasites is clearly independent of microtubule growth . On the other hand , the distal migration of the basal complex is in synchrony with the growth of the IMC complex throughout the daughter construction , the directional growth of the IMC complex ( the mechanism of which is yet to be elucidated ) is therefore more likely to act as the driving force for the daughter basal complex migration . To summarize , the daughter basal complex in T . gondii is initiated close to the duplicated centrioles , and its construction is independent of the structural integrity of the daughter cortical cytoskeleton . The daughter basal complex is a dynamic cap , whose compartmentation and polarity is revealed upon the recruitment of TgCentrin2 to its posterior end during late stages of cell division . The stage specific recruitment of TgCentrin2 , its localization to the most constricted region of the basal complex , and the calcium sensitive nature of the basal complex contraction make TgCentrin2 an attractive candidate for driving the closure of the basal complex , which thus is likely to be a new centrin-based contractile apparatus . This study extends our knowledge of the origination , dynamics and coordination of the growth of distinct compartments in the daughter cytoskeletons of T . gondii . These issues are not only important for understanding , and eventually manipulating the cell biology of the apicomplexan parasites , but also of interest to the cell biology field in general , where the rules for the construction of macromolecular assemblies and polarity determination are hotly sought after .
T . gondii tachyzoites ( strain RH ) were cultivated in human foreskin fibroblast ( HFF ) cells , and transfected as previously described [32] . For each transfection , 1/3 of the RH parasites from a T12 . 5 flask culture ( ∼1 × 107 ) were transfected by electroporation with 30 μg of plasmid DNA and allowed to infect a fresh monolayer of host cells . EGFP-TgCentrin2 , EGFP-TgIMC4 , EGFP-TgMORN1 expressing parasites are clonal stable transgenic cell lines , and were cultured with 1μM pyrimethamine selection [16] . pmin-mCherryFP-TgMORN1 was constructed by replacing the TgDLC/BglII-AflII fragment in pmin-mCherryFP-TgDLC with the TgMORN1 BglII-AflII fragment from pmin-EGFP-TgMORN1 [16] . pmin-mCherryFP-TgDLC was constructed by replacing the EGFP/NheI-BglII fragment in pmin-EGFP-TgDLC [16] with mCherryFP/NheI-BglII fragment from ptub-mCherryFP-EGFP . The architectures of all pmin-XFP-TgGeneX plasmids are identical . ptub-mCherryFP-EGFP and ptub-mCherryFP-TgtubA1 were kind gifts from Dr John Murray at University of Pennsylvania . Fixed cells were prepared and observed as previously described [2 , 16 , 33] . Mouse monoclonal antibody anti-IMC1 was a kind gift from Dr . Gary Ward ( University of Vermont ) and it was detected with secondary antibody goat anti-mouse IgG Alexa350 ( #A21049 , Invitrogen-Molecular Probes , 1:1000 dilution ) . For live-cell imaging and time-lapse microscopy , parasites were inoculated into a sub-confluent layer of HFF cells grown in phenol red free DMEM ( #21063 , Invitrogen-Gibco ) with 10% heat-inactivated bovine calf serum in a 35mm plastic dish with #1 . 5 glass coverslip bottom ( MatTek #P35G-1 . 5–14-C , MatTek #P35G-1 . 5–20-C ) . After infection , the medium was changed to DMEM+1% heat-inactivated Fetal Bovine Serum ( FBS ) . Immediately before imaging , the medium was changed to phenol red free CO2 independent medium ( custom order , SKU#: RR050058 , Invitrogen-Gibco ) with 10%FBS and 2mM glutamine ( #25030 , Invitrogen-Gibco ) , 1mM sodium pyruvate ( #11360 , Invitrogen-Gibco ) and 100unit/ml antibiotics and antimycotics ( #1172 , Invitrogen-Gibco ) . The dish was then equilibrated in the humidified microscope environmental chamber [34] for 1–2 hours before imaging . 3D image stacks were collected at z-increments of 0 . 3 μm ( fixed samples ) , or 0 . 3–0 . 5 μm ( live samples ) on an Applied Precision Delta Vision workstation based on an Olympus IX-70 inverted microscope , using a 100× NA 1 . 35 oil immersion lens with immersion oils at refractive indexes of 1 . 524 ( 37°C , ambient humidity ) , 1 . 522 ( 37°C , 70% humidity in the chamber ) , or 1 . 518 ( room temperature , ambient humidity ) . Sedat Quad- ET ( #89000 , Chroma Technology Corp . ) and GFP/mCherry-ET ( #89021 , Chroma Technology Corp . ) filter sets were used for all the imaging in this paper . For estimating mis-registration induced by lens chromatic aberration or optical misalignment between the GFP and mCherryFP filters , 0 . 2 μm Tetraspeck beads ( #T7280 , Invitrogen-Molecular Probes ) attached to a #1 . 5 coverslip were mounted to a slide with a 0 . 12mm spacer ( #S24735 , Invitrogen-Molecular Probes ) in dH2O and imaged at 37°C with the GFP/mCherry ET filter set , using 100× NA 1 . 35 oil immersion lens and immersion oil at refractive index of 1 . 522 . Deconvolved images were computed using the point-spread functions and software supplied by the manufacturer . All fluorescent images in the figures , except for the gray scale images in Figure 6 and Figure S2 , are maximum intensity projections of deconvolved 3D stacks . The gray scale images in Figure 6 and Figure S2 are non-deconvolved single optical planes . The brightness and contrast of images used in the final figures were optimized for color prints . To avoid confusion , a consistent coloring for each T . gondii protein has been adopted throughout the remainder of this paper . This pseudo-color assignment will be maintained regardless of the actual wavelength bands used for detections of fluorescence . TgMORN1 is always colored green , IMC1 is always colored blue , and other molecules are always colored red . FRAP experiments were performed on the same imaging system equipped with a 10mW 488 laser connected to the microscope via a fiber optic . The laser was run at 50% power , using one 50 msec pulse for photobleaching . Pre and post bleaching images were collected using the Photokinetics module integrated in the Applied Precision Softworx software . About 3 × 107 transgenic parasites ( harvested from one T12 . 5 flask culture ) expressing EGFP-TgCentrin2 or EGFP-TgMORN1 were washed , resuspended in 15 μl DPBS ( #14190 , Invitrogen-Gibco ) and absorbed to nickel grids ( 5 μl parasite suspension/grid ) at room temperature for 1 hour . Parasites were then permeabilized in 0 . 5% TritonX-100 in DPBS for 20–25 min , and fixed for 15 min with 3 . 7% formaldehyde in DPBS , and washed 3 × 5 min with DPBS , followed by 10-min blocking in 5% BSA + 0 . 1% fish gelatin . Free aldehyde groups were blocked by incubation with 50mM glycine in DPBS for 15 min ( pH 7 . 5 ) followed by 15-min incubation with 0 . 1% NaBH4 in DPBS . Grids were washed twice with DPBS; blocked again with 5% BSA + 0 . 1% fish gelatin in DPBS for 30 min; washed 2 × 5 min with incubation buffer ( 0 . 8%BSA , 0 . 1% fish gelatin in DPBS plus 10mM NaN3 ) ; incubated for ∼2 h with primary antibody at room temperature by inverting the grids on 20 μl drops of primary antibody ( #A11122 , rabbit anti-GFP polyclonal , Invitrogen-Molecular Probes , diluted 1:800 in incubation buffer ) ; washed 6 × 5 min in incubation buffer; inverted on 15 μl drops of secondary antibody solutions ( anti-rabbit IgG conjugated with 1 . 4 nm gold , Nanoprobes , Yaphank , New York , United States , diluted 1:160 in incubation buffer ) and incubated for ∼19 h at 4 °C; then washed with incubation buffer as follows: 3 × 1 min , 2 × 10 min , 4 × 5 min; and finally washed 5 × 1 min with DPBS . The samples were then post-fixed 5 min with 1% glutaraldehyde in DPBS and washed with distilled water 3 × 5 min . Silver enhancement was carried out using the HQ silver enhancement kit ( Nanoprobes ) by floating grids on mixtures of the initiator , activator , and modulator for 2 min in a light-tight chamber , then washing briefly with dH2O once , followed by 2 × 5 min wash . Grids were negatively stained using 2% phosphotungstic acid ( pH 7 . 0 ) . After taking a set of pretreatment images , 0 . 625 μl 10mM oryzalin diluted in 500 μl CO2 independent medium with 10% FBS ( prewarmed to 37°C in the imaging chamber ) was added immediately to the dish containing 2 . 0 ml medium on the microscope stage . The final concentration of oryzalin was 2 . 5 μM . Imaging of the drug treated parasites started ∼30 min after the addition of oryzalin and continued for 10–12 hours . A23187 treatment experiments were conducted under both 37°C and room temperature conditions , which yielded similar results . The induction of the basal complex constriction however occurred much faster at 37°C , which made it difficult to capture the intermediate images . The result from a room temperature experiment was thus used for this paper ( Figure 10 ) . After taking a set of pretreatment images , 0 . 85 μl 5mM A23187 ( dissolved in DMSO ) diluted in 220 μl CO2 independent medium with 10% FBS was added to the dish containing 1 . 5 ml medium on the microscope stage , which gave a final concentration of A23187 at ∼2 . 5μM . After ∼3 . 5 minutes , additional 1 . 15 μl 5mM A23187 ( dissolved in DMSO ) diluted in 280 μl CO2 independent medium with 10% FBS was added to the dish , which gave a final concentration of A23187 at ∼5μM , and DMSO concentration at ∼0 . 1% . Images were taken at 15 second intervals for the first 105 seconds of acquisition and at ∼30 second intervals for the rest of the experiment . The free calcium concentration in CO2 independent medium +10%FBS was ∼3 . 5mM , determined by Eriochrome Black T dye assay described below . To 1 . 0 ml ammonia buffer ( 0 . 0214% NH4CL+ 0 . 73%NH4OH in dH2O ) , 5 μl Eriochrome Black Dye solution ( 0 . 25% Eriochrome Black T and 2 . 26% hydroxylamine hydrochloride ( NH2OH-HCL ) in dH2O ) was added . 1 . 0 ml of CO2 independent medium +10%FBS was then added to the mixture , which turned the blue-green solution to purple . After adding 35 μl 0 . 1M K2EGTA drop-wise to the solution , the purple color turned back to blue-green due to the chelation of the free calcium by K2EGTA , revealing that there was ∼3 . 5mM free calcium in CO2 independent medium +10%FBS . The result was confirmed by titrating 1 ml 3 . 5mM CaCl2 with 0 . 1M K2EGTA using the same procedure .
List of Tigr_final numbers for genes and proteins mentioned in the text are: TgMORN1 ( 583 . m05359 ) ; TgCentrin2 ( 50 . m03356 ) ; TgCentrin1 ( 50 . m00033 ) ; T . gondii α1 –tubulin ( TgTubA1; 583 . m00022 ) ; TgIMC4 ( 44 . m00031 ) ; T . gondii dynein light chain ( TgDLC; 41 . m01383 ) . The sequences are available for downloading at http://www . toxodb . org/toxo/ . | Toxoplasma gondii is one of the most prevalent parasites in warm-blooded animals and a highly important human pathogen . It is the most common cause of congenital neurological defects in humans and also causes devastating opportunistic infections in immuno-compromised patients . Many of its 5 , 000 relatives in phylum Apicomplexa are also important human or animal pathogens , including Plasmodium sps , which kill more than a million people every year . The pathogenesis of the diseases that these parasites cause absolutely depend on their ability to replicate , which in turn completely depends on the proper assembly of the parasite cytoskeletons . Here I probe how the basal complex , a novel cytoskeletal compartment contained within the basal end of T . gondii , is assembled during daughter cell formation of this parasite . I found that the daughter basal complex is one of the first cytoskeletal structures assembled during T . gondii cell division . In addition , the basal complex is likely to be a new kind of centrin-based contractile apparatus , as its polarization is first revealed upon the recruitment of a calcium binding protein , TgCentrin2 , which correlates with the constriction of the basal complex , a process that can be artificially induced by increasing cellular calcium concentration . |
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Rapid development of complex membranous replication structures is a hallmark of picornavirus infections . However , neither the mechanisms underlying such dramatic reorganization of the cellular membrane architecture , nor the specific role of these membranes in the viral life cycle are sufficiently understood . Here we demonstrate that the cellular enzyme CCTα , responsible for the rate-limiting step in phosphatidylcholine synthesis , translocates from the nuclei to the cytoplasm upon infection and associates with the replication membranes , resulting in the rerouting of lipid synthesis from predominantly neutral lipids to phospholipids . The bulk supply of long chain fatty acids necessary to support the activated phospholipid synthesis in infected cells is provided by the hydrolysis of neutral lipids stored in lipid droplets . Such activation of phospholipid synthesis drives the massive membrane remodeling in infected cells . We also show that complex membranous scaffold of replication organelles is not essential for viral RNA replication but is required for protection of virus propagation from the cellular anti-viral response , especially during multi-cycle replication conditions . Inhibition of infection-specific phospholipid synthesis provides a new paradigm for controlling infection not by suppressing viral replication but by making it more visible to the immune system .
The positive strand RNA ( ( + ) RNA ) viruses of eukaryotes universally assemble their RNA replication machinery in association with specialized membranous domains , featuring unique lipid and protein composition [1–3] . It is hypothesized that membranes may facilitate replication by increasing local concentration of the viral proteins , providing a scaffold for assembly of the multi-subunit replication complexes , and/or by hiding the dsRNA replication intermediates from cellular sensors of infection [4] . To trick the infected cells into building new membranous structures , viruses have to reorganize the complex network of cellular pathways controlling lipid synthesis , catabolism and membrane trafficking . Yet , in spite of the central role of the membranous replication organelles in the life cycle of ( + ) RNA viruses , our knowledge about the mechanistic details of their formation in most viral systems is very limited , and the experimental evidence supporting their importance for specific replication steps is scarce . Picornavidae is a family of small non-enveloped ( + ) RNA viruses of vertebrate hosts , and the number and diversity of known picornaviruses is rapidly increasing . Among picornaviruses are important human and animal pathogens such as poliovirus , etiological agent of poliomyelitis; Coxsackie viruses , associated with type I diabetes , myocarditis , and dilated cardiomyopathy; rhinoviruses , the main cause of the common cold; foot and mouth disease virus , the major concern for animal husbandry worldwide; and others [5–9] . Poliovirus , a representative of the Enterovirus genus , is the prototype member of the Picornaviridae family . Its genome RNA of ~7500 nt is directly translated in a cap-independent manner into one polyprotein ( ~200 KDa ) which undergoes a cascade of proteolytic cleavages generating a dozen of mature peptides and intermediate cleavage products . Proteins encoded in the P2-P3 region of the viral genome as well as the corresponding cleavage intermediates are responsible for genome replication , while the P1 region codes for capsid proteins . Poliovirus infection results in rapid dramatic reorganization of the cellular membrane architecture . Within a few hours post infection , new membranous structures harboring the viral replication complexes fill the cytoplasm . The 3D reconstruction of picornavirus replication organelles show that during the active RNA replication stage of infection , they are formed by tightly associated convoluted single-walled membranous compartments which later undergo transition into double-membrane vesicles [10–12] . Electron microscopy studies of cells infected with diverse picornaviruses demonstrate the virtually identical appearance of the replication membranes , at least at certain stages of infection , strongly suggesting a common mechanism behind their formation [13 , 14] . Based on the superficial morphological features of the replication membranes observed in thin section electron microscopy images , it was previously proposed that hijacking the normal cellular membrane metabolism pathways such as the secretory pathway and/or autophagy could be responsible for the membrane remodeling in infected cells . However , the accumulated evidence argue against such straightforward interpretation . First , picornaviruses vary greatly in their sensitivity to pharmacological or genetic manipulations of these pathways [13 , 15 , 16] . Second , it was recognized that the elements of the secretory pathway are involved in imparting the unique characteristics of the replication membranes such as their enrichment in phosphatidylinositol-4 phosphate and activated small GTPases of the Arf family , rather than in the structural development of these replication platforms [17–20] . Third , the activation of autophagy appears important for the maturation of infectious virions , but not for RNA replication , at least for poliovirus and related viruses [15 , 21 , 22] . It was previously established that in cells infected with poliovirus and encephalomyocarditis virus , a picornavirus from the Cardiovirus genus , synthesis of phospholipids is activated , and the newly-synthesized phospholipid molecules are found in the membrane fraction associated with the viral RNA-dependent RNA polymerase activity , suggesting that they may contribute to the development of the replication membranes [23–25] . However , the significance and the mechanisms of the infection-specific activation of phospholipid synthesis remained unknown . We previously demonstrated that in picornavirus-infected cells , long chain acyl-CoA synthetase activity is rapidly upregulated and is associated with the highly increased rate of long chain fatty acid ( FA ) import and their re-routing from triglyceride ( TG ) to phosphatidylcholine ( PC ) synthesis . Similar changes in long chain FA metabolism were observed in different cell types infected with diverse picornaviruses , indicating that they constitute a universal attribute of picornavirus infection [26] . Here , we further investigated the mechanism of infection-specific activation of phospholipid synthesis and its role in the development of the viral replication organelles . We demonstrate that activation of lipolysis of neutral lipids in lipid droplets , but not import or de novo synthesis , supplies the bulk of long chain FAs for infection-specific phospholipid production . We show that the key enzyme in phosphatidylcholine synthesis , CTP-phosphocholine-cytidyl transferase alpha ( CCTα ) translocates from the nuclei of infected cells and associates with membranes of the viral replication complexes . Inhibition of PC synthesis disrupts the normal structural development of the replication organelles . In the absence of a tight membranous matrix , the first round of viral replication can proceed normally , but viral propagation in multiple rounds of infection is severely compromised . Cellular sensors of infection are activated stronger , and viral replication becomes more sensitive to the anti-viral response if synthesis of structural phospholipids is inhibited . Thus , our research establishes an important role of lipid droplets in picornavirus infection and provides a novel paradigm for controlling viral infections not by targeting the viral replication per se but by making it more visible to the host defense mechanisms .
We previously showed that infection-specific activation of phospholipid synthesis does not depend on transcription of cellular genes [27] . Together with the rapid shut-off of cellular mRNA translation in poliovirus-infected cells [28] , this suggests that the activation of phospholipid synthesis must depend on post-translational regulation of the cellular enzymes already present before infection . Earlier studies of activation of PC synthesis in poliovirus-infected cells proposed that the rate limiting reaction activated upon infection is the synthesis of CDP-choline , a substrate for phosphocholine head group transfer to diacylglycerol; however , the mechanism of such activation has not been established [29] . The human genome contains two genes coding for CTP-phosphocholine-cytidyl transferase ( CCT ) enzymes , which can synthesize CDP-choline . CCTα is ubiquitously expressed , while CCTβ has a restricted tissue-specific expression pattern [30 , 31] . Due to the presence of N-terminal nuclear localization signal , CCTα is partitioned between the nuclear depot of inactive enzyme and the cytoplasmic pool containing the activated form [32 , 33] . To directly observe if CCTα activity can guide the changes in lipid metabolism similar to those found upon infection , we overexpressed a CCTα fused with a red fluorescent protein ( CCTα-RFP ) in HeLa cells and monitored incorporation of a fluorescent long chain FA analog Bodipy C4/C9 , into cellular structures . Bodipy C4/C9 mimics long chain FAs with C18 backbone and can be either incorporated in neutral lipids and stored in lipid droplets , or metabolized into membrane phospholipids . We previously validated it as a convenient tool to study membrane synthesis in infected cells [26 , 34] . HeLa cells were transfected with a CCTα-RFP-expressing plasmid and the next day , Bodipy C4/C9 was added to the incubation medium for 1h ( Fig 1A ) . For direct comparison , HeLa cells were infected with poliovirus at an MOI of 10 and Bodipy C4/C9 was added to the incubation medium of infected cells for 1 h at 4 h p . i . ( Fig 1B ) , similar to the experiments described in [26 , 34] . Microscopic examination of cells revealed that most of the RFP-CCTα signal was concentrated in the nuclei , as was previously described for CCTα [35] , thus the fusion recapitulated behavior of the native protein . Even if the bulk of overexpressed CCTα was confined in the nuclei , this was sufficient to significantly increase the activity of the enzyme because the incorporation of Bodipy C4/C9 was much stronger in cells expressing CCTα-RFP ( Fig 1A , yellow arrows ) , and in those cells the signal of the fluorescent FA was distributed in the cytoplasm , reflecting its partitioning to the membrane phospholipids . At the same time , in cells that did not express CCTα-RFP , the signal of Bodipy C4/C9 was found in lipid droplets ( Fig 1A , blue arrows ) . Similarly , in mock-infected cells , Bodipy C4/C9 signal was localized in lipid droplets , but in infected cells the incorporation of the fluorescent FA was much stronger , and it was redistributed in the cytoplasmic membranes ( compare Fig 1B mock- and polio-infected cells ) . Thus , overexpression of CCTα phenotypically recapitulates changes in the long chain FA metabolism observed during poliovirus infection . Next , we investigated what happens to the endogenous CCTα upon poliovirus infection . Immunostaining revealed that in mock-infected HeLa cells , the enzyme was mostly localized in the nuclei , with some scattered spots around the cytoplasm ( Fig 2A ) . In poliovirus-infected cells , however , nuclear CCTα signal was weaker than that in the cytoplasm , showing that the enzyme is translocated from the nucleus and associates with some cytoplasmic structures ( Fig 2A ) . CCTα staining in infected cells occupied the same cellular area as the viral replication organelles , visualized by staining of a viral antigen 3A , although there was no direct co-localization between CCTα and the viral protein ( s ) ( Fig 2A ) . To understand what viral proteins are responsible for CCTα translocation we expressed fragments of the poliovirus polyprotein in a replication-independent manner using a vaccinia-T7 expression system [36] . In HeLa cells expressing poliovirus proteins from 2A to 3D , we observed a significant translocation of CCTα from the nuclei , similar to that in infected cells ( Fig 2B , white arrows ) . In cells expressing poliovirus proteins from 2B to 3D , CCTα localization was almost exclusively nuclear , similar to that in cells transfected with an empty vector ( Fig 2B , blue arrows ) , even though the 2B-3D construct was expressed at a somewhat higher level than 2A-3D ( Fig 2C ) . 2A is a protease and it was previously shown to be responsible for disruption of the barrier function of nuclear envelope [37 , 38] . To see if the protease activity of 2A is important for CCTα translocation , we expressed a 2A*-3D polyprotein fragment with 2A containing a mutation in the catalytic triad . Both 2A-3D and 2A*-3D constructs were expressed at a similar level , and the lack of catalytic activity of 2A* was confirmed by the inhibition of cleavage of the cellular translation initiation factor eIF-4G , a well-known 2A-dependent process ( Fig 2C ) . Like in the cells expressing 2B-3D polyprotein fragment , CCTα was almost exclusively confined to the nuclei if 2A was inactivated ( Fig 2B ) . Thus , poliovirus infection induces massive translocation of CCTα from the nucleus to the cytoplasm and this process requires proteolytic activity of 2A . To analyze the dynamics of CCTα translocation during the time course of infection , we performed western blot analysis of lysates from HeLa cells infected with an MOI of 50 PFU/cell of poliovirus ( so that all the cells are infected simultaneously ) , treated or non-treated with digitonin before the lysis . Treatment with digitonin removes cholesterol from the membranes , thus making the cholesterol-rich plasma membrane permeable while leaving cholesterol-poor membranes of intracellular organelles , including the nuclear envelope , relatively intact . Western blot revealed CCTα signal in two bands ( Fig 3A ) , apparently corresponding to the phosphorylated and dephosphorylated forms ( Fig 3A , arrow ) of the protein observed previously [39 , 40] . It is believed that the dephosphorylated protein represents a more active form of the enzyme with a higher membrane affinity and increased sensitivity to the activation signals [41] . As infection progressed , there was a redistribution between the two forms of CCTα with the higher-running phosphorylated form disappearing , while the lower- running , dephosphorylated form was accumulating ( Fig 3A , compare lane 1 , mock , with lanes 2–4 , infected ) . Digitonin treatment did not significantly affect the total recovery of CCTα from the mock-infected cells , confirming that most of the protein is confined within the nuclei ( Fig 3A , compare lanes 1 and 5 ) . In the lysates from digitonin-treated infected cells , however , even at 2 h p . i . there was a significant reduction in the amount of recovered CCTα which was not detected at all in digitonin-treated infected cells at later time points ( Fig 3A , lines 6 , 7 , and 8 ) . Staining of the membrane for a soluble viral protein 3D , an RNA-dependent RNA polymerase , demonstrated that it was significantly lost upon digitonin treatment; some amount of 3D recovered from digitonin-treated cells is likely incorporated in membrane-associated replication complexes ( Fig 3A and 3D panel , compare lanes 3 , 4 and 7 , 8 ) . At the same time , the membrane-associated viral proteins 2C and 2BC were recovered similarly from both digitonin-treated and non-treated cells , confirming that treatment conditions preserved intracellular membranes ( Figs 3A and 2C panel , compare lanes 3 , 4 and 7 , 8 ) . The immunofluorescence data ( Fig 2A ) suggested that CCTα was interacting with some cytoplasmic structures in infected cells , however since the protein was lost after digitonin treatment , this interaction was not very strong . We hypothesize that this behavior may reflect activation of CCTα known to be accompanied by its translocation from the nucleus and transient association with membranes [42–44] . To see if CCTα is recruited to the replication complexes we performed a co-immunoprecipitation assay . HeLa cells were infected with polioviruses that had an HA tag either in 2A or in 3A protein with an MOI of 10 PFU/cell ( Fig 3B ) . Because poliovirus RNA is translated into one polyprotein which undergoes proteolytic processing , HA tags will be found not only in 2A or 3A , but also in multiple intermediate cleavage products ( Fig 3C , HA panel ) . As controls we used either cells infected with wild type ( wt ) poliovirus , or cells transfected with a plasmid expressing an HA-tagged long chain acyl-CoA synthetase 3 ( ACSL3-HA ) , an enzyme responsible for activation of long chain FAs and involved in the lipid metabolism [45] . Both HA-tagged viruses replicated to the same level as the wt control as evidenced by similar expression of the viral 2C protein in all samples ( Figs 3C and 2C panel , input ) . The cells were lysed at 6 h p . i . and processed for immunoprecipitation with anti-HA antibodies , and the recovered material was analyzed with anti-CCTα antibodies . CCTα was recovered only in co-IP samples from cells infected with 2A- or 3A-tagged polioviruses but not from cells infected with wt poliovirus or expressing HA-tagged ACSL3 ( Fig 3C , CCTα panel ) . We also detected a viral protein 2C , a known component of the poliovirus replication complex , in both co-IP samples from cells infected with HA-tagged viruses , thus confirming the specificity of co-IP conditions and indicating that CCTα is a part of multi-subunit replication complexes including viral and cellular proteins ( Figs 3C and 2C panel , co-IP ) . To see if CCTα is directly responsible for activation of membrane synthesis upon infection , we knocked down its expression using siRNA . siRNA-treated cells were infected with poliovirus at an MOI of 10 PFU/cell and at 4 h p . i . the cells were provided with a fluorescently-labeled long chain FA analog Bodipy C4/C9 . Quantitation of the fluorescent signal of the FA incorporated into cellular lipids demonstrated significant activation of lipid synthesis in infected cells treated with control siRNA , similar to that described previously [26] . In the cells treated with CCTα-specific siRNA , the increase in lipid synthesis almost disappeared ( Fig 3D ) , while the viral replication was not affected as evidenced by the similar accumulation of the viral proteins in both samples ( Fig 3D , western blots ) . The detailed investigation of the role of phospholipid synthesis in poliovirus infection is described further in the paper . It should be noted that due to its predominantly nuclear localization , CCTα has a long turnover period , and the siRNA-knockdown was not complete , which likely explains the residual activation of the lipid synthesis upon infection ( Fig 3D , western blots ) . Collectively , these data suggest that poliovirus infection induces strong post-translational activation of the cellular pool of CCTα , accompanied by its translocation from the nuclei and association with the replication membranes , which is likely necessary to sustain massive upregulation of phospholipid synthesis . Activation of phospholipid synthesis requires an increased supply of long chain FAs . The mammalian cells can obtain them from three major sources: import from the incubation medium ( serum ) [46 , 47]; de novo synthesis via fatty acid synthase ( FASN ) -dependent pathway [48 , 49]; or hydrolysis of triglycerides and cholesterol esters stored in lipid droplets [50] . We previously demonstrated that activation of phospholipid synthesis does not require presence of serum in the incubation medium [27] , indicating that membrane synthesis upon infection could be sustained by internal cellular resources . To evaluate the relative contribution of FAs from the FASN-dependent de novo synthesis and those released from lipid droplets , we blocked them individually and assessed the activation of PC synthesis upon infection . The activity of FASN was blocked by orlistat , a compound that inhibits the thioesterase domain of the multifunctional enzyme [51] . Activity of all lipid droplets-associated lipases can be efficiently blocked by diethylumbelliferyl phosphate ( DEUP ) [52–56] . In some cell types , microautophagy ( lipophagy ) can also significantly contribute to turnover of neutral lipids stored in lipid droplets [52] . This activity is sensitive to inhibitors of lysosome acidification such as bafilomycin [57] . To quantitate the activation of PC synthesis , we adopted a method of propargylcholine labeling of phospholipids described in [58] . Cells metabolize this compound similarly to choline and incorporate it into newly-synthesized phospholipids . Subsequent click-chemistry reaction between the alkyne group of propargylcholine with an azide derivative of a fluorescent dye allows quantitative measurement of phospholipid synthesis . First , we evaluated the effect of the inhibitors of FA fluxes on a single round of poliovirus replication upon infection of HeLa cells with an MOI of 50 PFU/cell . In these conditions , none of the treatments ( incubation of cells with DEUP , orlistat , or bafilomycin ) had significant effect on the viral propagation ( S1A Fig ) , thus permitting a direct comparison of the contribution of each source of long chain FAs in infection-specific upregulation of phospholipid synthesis . In infected cells incubated in the presence of orlistat , we observed the same level of upregulation of propargylcholine incorporation as in control cells , thus ruling out the significant contribution of newly synthesized FAs into the overall balance of phospholipid synthesis ( Fig 4A and 4B ) . On the other hand , incubation of infected cells in the presence of DEUP severely inhibited activation of phospholipid synthesis , indicating that lipid droplets provide the main source of material to support membrane biogenesis in infected cells ( Fig 4A and 4B ) . Since bafilomycin did not have an inhibitory effect on activation of membrane synthesis ( S1B Fig ) , we conclude that activity of lipid droplets-associated lipases , but not lipophagy is responsible for the release of FAs from lipid droplets in infected cells . To directly observe if long chain FAs from the neutral lipids stored in lipid droplets are used in the development of the poliovirus membranous replication organelles , we incubated HeLa cells for 1 h before infection in the presence of a fluorescent long chain FA analog Bodipy C4/C9 . After this incubation , the medium containing the fluorescent FA was removed , and the cells were extensively washed before infection to permit observation of redistribution of already incorporated fluorescent FA upon infection ( Fig 5A ) . As expected , in uninfected cells this molecule is preferentially incorporated into neutral lipids and is targeted to lipid droplets ( Fig 5B ) . In mock-infected cells , the fluorescence was still mostly exclusively confined to lipid droplets by 6 h of incubation ( Fig 5C ) . In cells infected with poliovirus at an MOI of 10 PFU/cell , we observed a massive translocation of fluorescence from lipid droplets into the perinuclear region , characteristic of localization of poliovirus replication complexes ( Fig 5C , red arrows ) , confirming that long chain FAs released from lipid droplets indeed support the development of the replication organelles . Note that some infected cells still show fluorescent signal residing in lipid droplets ( Fig 5C , blue arrows ) . Thus , our data show that neutral lipids stored in lipid droplets are mobilized upon poliovirus infection and sustain activation of membrane synthesis . To monitor the utilization of endogenous lipids in lipid droplets , we infected HeLa cells with poliovirus at an MOI of 10 PFU/cell and stained them with Bodipy 493/503 , a lipid droplet-specific dye , at 6 h p . i . In mock-infected cells multiple lipid droplets were detected in virtually every cell as bright green dots , while in infected cells the amount of lipid droplets was significantly lower , and in many cells the typical dot-like staining of lipid droplets was not detected at all ( Fig 5D , red arrows ) . Rather , the Bodipy 493/503 signal was increased in the perinuclear area , likely reflecting massive development of the replication membranes . Quantitative analysis confirmed a significant reduction of lipid droplets per cell from about nine in mock-infected control to less than two in infected cells , suggesting that many lipid droplets are completely exhausted upon infection ( Fig 5D ) . Mobilization of neutral lipids stored in lipid droplets depends on lipid droplet-associated lipases , such as adipocyte triglyceride lipase ( ATGL ) , hormone sensitive lipase ( HSL ) , and monoglyceride lipase ( MGL ) which release FAs from the glycerol backbone of triglycerides , with HSL being also active on cholesterol esters , reviewed in [59] . We monitored recruitment of these lipases to lipid droplets upon infection . We did not see any significant association of MGL signal with lipid droplets in either infected or control cells ( S2A Fig ) . The HSL signal in mock-infected HeLa cells was mostly localized in the nuclear area , with about 15% of cells having lipid droplets-associated signal ( Fig 5E ) . A significant nuclear signal of HSL in HeLa cells is consistent with the previously reported data of a nucleus-associated pool of HSL in the epithelial cells of mammalian female reproductive tract [60] as well as with the antibody manufacturers data . On the other hand , almost 80% of cells infected with 10 PFU/cell of poliovirus at 3 h p . i . demonstrated multiple bright HSL-positive cytoplasmic dots reflecting recruitment of the enzyme to lipid droplets ( Fig 5E , red arrows and inset ) . We also observed a significant recruitment of ATGL to lipid droplets upon infection , although it was less pronounced than HSL and was detected at 3 h p . i . in about 20% of infected cells compared to less than 5% in mock-infected control ( S2B Fig ) . Thus , poliovirus infection induces strong recruitment of major lipases controlling utilization of neutral lipids to lipid droplets , which is likely responsible for the increasing supply of long chain FAs required to feed the activated phospholipid synthesis in infected cells . To understand the role of activation of membrane biogenesis in poliovirus infection , we started from analyzing the effects of its inhibition on the first cycle of viral replication . To block new membrane synthesis , the cells were pre-incubated before infection in a choline-free medium to exhaust the endogenous choline pool . In conditions of cell culture , synthesis of all major structural phospholipids is regulated by the availability of choline , which cannot be synthesized by cells and has to be provided in the medium . Choline is first incorporated into PC , which in turn can be converted into phosphatidylserine and phosphatidylethanolamine , reviewed in [61 , 62] . To validate the choline depletion approach , we monitored the incorporation of a fluorescent long chain FA analog Bodipy C4/C9 . HeLa cells seeded at a low density were incubated for ~72 hours in a choline-free medium so that they could divide and grow , thus exhausting the intracellular choline depot for new membrane synthesis . Importantly , choline deprivation did not affect the overall cell viability for up to 4 days ( S3 Fig ) , in accordance with previous observations showing that cells can tolerate choline deprivation for prolonged period of time [63 , 64] . On the third day , choline-deprived cells were infected with poliovirus at an MOI of 10 PFU/cell , and were either incubated in a choline-free or a choline-supplemented medium after infection . At 5 h p . i . , the media in both samples were supplemented with Bodipy C4/C9 for 1 h ( Fig 6A ) . As can be seen in Fig 6B , incorporation of the fluorescent FA analog into cellular structures of infected cells incubated in choline-free or choline supplemented media was drastically different . In a choline-supplemented medium , the fluorescence was distributed in perinuclear rings , typical of localization of poliovirus membranous replication organelles , indicating activation of new membrane synthesis . In infected cells incubated in choline-free medium , however , the fluorescence was confined to lipid droplets , showing that the flux of long chain FAs was directed almost exclusively toward synthesis of neutral lipids . Thus , choline depletion prevented activation of membrane synthesis , as predicted . This result also confirms that the switch from neutral to phospholipid synthesis upon picornavirus infection should be attributed to activation of CCTα and increasing PC synthesis , and not to inhibition and/or degradation of neutral lipid synthesizing enzymatic machinery . At the same time , the level of virus replication was unaffected in a choline-free medium , as evidenced from the western blot showing accumulation of the viral proteins 2C and 2BC and the viral titer ( Fig 6C ) . Sometimes we observed a slight suppression of viral protein accumulation in choline-depleted cells , but that did not translate into a statistically significant difference of the virus titer by the end of infection ( S1C Fig ) . We also analyzed if choline deprivation may affect the first replication cycle if the cells are infected at a low MOI . HeLa cells pre-incubated in a choline-free medium for 72 h were infected with an MOI of 0 . 5 or 0 . 05 PFU/cell , and were incubated after infection in choline-free or choline-supplemented media for 6 h . There was no significant difference in the virus propagation in either condition ( Fig 6D ) , confirming that the first cycle of infection proceeds similarly in choline-deprived or choline-supplemented cells . Next , we compared the dynamics of synthesis of infectious poliovirus in one replication cycle in cells incubated in a choline-depleted or a choline-supplemented medium . We separately analyzed accumulation of progeny virions inside the cells and their release into extracellular medium , as it is likely that inhibition of membrane synthesis could affect the process of non-lytic picornavirus release described earlier [65–67] . HeLa cells pre-incubated in a choline-free medium for ~72 hours were infected with poliovirus at an MOI of 10 PFU/cell , and were incubated after infection in either a choline-free or a choline-supplemented medium . Extracellular and intracellular virus were collected at 2 , 4 and 6 h p . i . We did not observe any significant effect of membrane synthesis on either intracellular virus accumulation or on the amount of the virus recovered from the medium ( Fig 6E ) . Thus , activation of membrane synthesis is not essential for the first round of poliovirus replication . The ability of poliovirus to replicate equally well in conditions of activated and inhibited phospholipid synthesis allowed for direct investigation on whether the new membrane synthesis or remodeling of pre-existing cellular membranes supports structural development of poliovirus replication organelles . HeLa cells were pre-incubated for ~72 h in a choline-free medium , infected with 10 PFU/cell with poliovirus , and were incubated after infection in either a choline-free or a choline-supplemented medium until 4 h p . i . For comparison , a control sample was prepared with cells that did not undergo choline deprivation treatment and were maintained in a serum-supplemented complete medium . In these conditions ( choline-free , choline-supplemented , and complete medium with serum ) , accumulation of the viral proteins was similar ( S1C Fig ) , confirming that inhibition of PC synthesis did not negatively affect viral replication . Infected cells incubated in a complete serum-containing medium demonstrated typical development of the poliovirus replication organelles with large clusters of tightly associated heterogeneous membranous compartments appearing in the perinuclear region of the cells ( Fig 7 , control ) . The cells infected upon choline depletion and incubated in a choline-free medium post infection revealed detached vesicles or tubules sparsely distributed in the cellular cytoplasm , often found close to the ER tubules ( Fig 7 , choline- ) . Elongated membranous compartments , likely to be dilated ER tubules were also present ( Fig 7 , arrows ) . Addition of choline largely restored the complexity of the membranous replication structures , although their appearance was somewhat different compared to the cells that did not undergo choline deprivation treatment . Membranous compartments in choline-supplemented cells were smaller and more loosely associated , and contained more elongated tubular-like structures ( Fig 7 , choline+ ) . Thus , activation of phospholipid synthesis , not the remodeling of the pre-existing membranes , is responsible for the massive growth of the replication organelles . EM images of the replication structures formed in conditions of the inhibited membrane synthesis suggested that the viral replication complexes should be less protected and more accessible from the cytoplasm . To test this assumption , we investigated the accessibility of a viral antigen 2B , present in several proteins in the replication complex , in an immunofluorescence assay . When immunostaining was performed in conditions of thorough permeabilization of membranes with 0 . 2% Triton X100 , infected cells incubated in a choline-supplemented medium demonstrated typical continuous perinuclear distribution of the viral antigen , while in cells incubated without choline , viral-specific signals were scattered in separate foci throughout the cytoplasm , consistent with the EM data ( Fig 8A , triton panel ) . In conditions of mild permeabilization with 0 . 02% saponin , which leaves the membranes relatively intact , the overall appearance of the viral replication structures in cells incubated in choline-free medium was similar to those permeabilized with Triton X100 , indicating that membranes were not concealing the replication sites . On the other hand , in cells incubated in choline-supplemented medium , the antibodies upon mild permeabilization could access only the periphery of the perinuclear cluster of the replication membranes , leaving in many cells a dark protected area around the nucleus ( Fig 8A , saponin panel , arrows ) . To further characterize the effect of inhibition of membrane synthesis on the development of replication organelles , we monitored distribution of dsRNA , an intermediate product in the replication cycle of ( + ) RNA viruses , as well as redistribution of GBF1 and PI4KIIIβ , two components of the host secretory pathway known to be recruited to poliovirus replication complexes [18 , 20] . As expected , both cellular proteins were found to similarly redistribute in cells depleted of or supplemented with choline , since protein-protein interactions are unlikely to be affected by inhibition of membrane synthesis ( S3 Fig ) . The distribution of dsRNA , however was significantly different . In cells incubated in a choline-supplemented medium , the dsRNA signal was found in large perinuclear blobs , reflecting normal development of the replication membranes , while in the absence of choline , dsRNA was mostly concentrated in a tight circle around the nuclear envelope ( Fig 8B ) . These data imply that activation of membrane synthesis is important for proper development of the replication organelles , and that interfering with phospholipid synthesis at any step of the metabolic pathway should have similar phenotype in infected cells . Indeed , when we inhibited the hydrolysis of neutral lipids in lipid droplets with DEUP [52–56] , thus blocking the supply of long chain FAs , we observed similar defects in the development of the replication organelles as in cells deprived of choline . In the EM images of infected cells incubated in the presence of DEUP , the replication organelles appeared scattered throughout the cytoplasm and did not fuse into a tight perinuclear cluster . Likewise , in an immunofluorescent assay a viral antigen 2B was distributed in separate dots rather than in continuous perinuclear rings as in control cells ( S4 Fig ) . To biochemically assess the difference in accessibility of the viral proteins in conditions of activated and inhibited membrane synthesis , HeLa cells were pre-incubated in the absence of choline for ~48 h , infected with poliovirus at an MOI of 10 PFU/cell , and incubated after infection in either a choline-free or a choline-supplemented medium . At 4 h p . i . , the cells were treated with digitonin , a mild detergent permeabilizing plasma membrane but leaving the intracellular membranes relatively intact , similar to the experiment previously shown on Fig 3A , and after permeabilization the cells were treated with proteinase K . Fig 8C demonstrates that while the level of the viral proteins 2B and 2C accumulated in infected cells was the same regardless of the presence of choline ( Fig 8C , lanes 1 and 2 ) , the proteins in cells incubated in the absence of choline were more accessible to proteinase K treatment ( Fig 8C , lanes 3 and 4 ) . Thus , activation of phospholipid synthesis is important for the development of the replication organelles and defines their morphological characteristics , affecting the accessibility of the viral replication complexes . The exposure of the replication complexes to the cytoplasm in conditions of inhibited membrane synthesis suggests that the cellular sensors of infections may also be activated stronger and/or earlier . One of the major triggers of antiviral response to picornavirus infection is dsRNA , an intermediate of viral genome RNA replication [68] . Recognition of dsRNA by several specialized cellular sensors activates a network of protective pathways . Signaling cascades activated by retinoic acid inducible gene ( RIG ) -like receptors ( RLR ) RIG-I and MDA5 , and toll-like receptors ( TLR ) 3 and 7 , converge on two major branches . One is phosphorylation of transcription factors IRF3/7 , which induce expression of interferons α/β . The other is phosphorylation and degradation of Iκβ , resulting in release of transcriptionally active subunits of NF-κβ responsible for activation of expression of pro-inflammatory cytokines , reviewed in [69–71] . Activation of protein kinase R ( PKR ) by dsRNA induces phosphorylation of a number of substrates including eukaryotic initiation factor 2 α-subunit ( eIF2α ) leading to inhibition of translation of cellular and viral RNAs [72] . To see if infection-activated membrane synthesis is important for the cellular recognition of infection , we monitored phosphorylation of IRF3 , degradation of Iκβ , and phosphorylation of eIF2α . HeLa cells were pre-incubated in a choline-free medium for ~72 hours , infected with poliovirus at an MOI of 10 PFU/cell , and incubated after infection in either a choline-free or a choline-supplemented medium for 6 h . As expected , suppression of membrane synthesis did not affect viral replication in these conditions , as evidenced by similar accumulation of the viral antigen 2C ( Fig 9A and 9B ) . The level of eIF2α phosphorylation was similar in both conditions , suggesting that PKR-dependent pathways are not sensitive to the inhibition of membrane synthesis , at least in these conditions ( Fig 9A ) . Similarly , we did not observe any significant differences in the level of degradation of Iκβ ( S5A Fig ) . At the same time , phosphorylation level of IRF3 detected by western blot was at least two times stronger in infected cells incubated in the absence of choline at 6 h p . i . ( Fig 9B ) , indicating that the membranous scaffold of the replication organelles may be important for suppression of at least some branches of the cellular anti-viral signaling . IRF3 is a transcription factor controlling the initial steps of the anti-viral response , and even minor changes in its activation may significantly affect expression of multiple genes through subsequent signaling and amplification steps . To compare expression of cellular genes involved in the antiviral response in conditions of permitted and inhibited membrane synthesis , we used a qPCR panel profiling transcripts of 84 genes . In infected cells incubated without choline , we observed a statistically significant increase of transcription of several genes , including IL6 and IL8 , as well as components of NFκB and AP-1 transcriptional machinery involved in the inflammatory signaling ( S5B and S6 Figs ) . This suggests that membrane synthesis modulates cellular response to poliovirus infection , which may have important implications in a natural host . The one-cycle replication experiments , while very informative about the biochemical facets of viral replication , do not fairly reflect the natural infection conditions . In an animal host , infection begins in a few cells from the original virus inoculum , and the virus has to spread to other cells in the body in multiple cycles of replication , virus release , and new infections , accompanied by the mounting of the host anti-viral defenses . To see if activation of membrane synthesis may be important for the virus spread in multiple rounds of infection , we pre-incubated HeLa cells for ~72 h in choline free conditions and infected them with poliovirus at MOIs of 0 . 1 or 0 . 01 PFU/cell . After infection , the cells were incubated in a choline-free or a choline-supplemented medium for another 24 h to allow multiple cycles of infections ( poliovirus replication cycle in HeLa cells lasts about 6 h ) . By the end of the experiment , the total virus yield was around two orders of magnitude lower if membrane synthesis was inhibited ( Fig 9C ) . The virus yield in different experiments varied from being ~1 . 5 to 3 logs lower in choline-deprived than in choline-supplemented cells , likely reflecting the level of the inhibition of phospholipid synthesis . The strong dependence of poliovirus propagation in multiple cycles of infection on activation of membrane synthesis indicates that the cells may mount an efficient anti-viral response and/or that the infection becomes more sensitive to the cellular defense mechanisms if the viral replication complexes are not protected by the membranes . To test if inhibition of membrane synthesis increases the sensitivity of poliovirus propagation to pre-activated cellular anti-viral program , HeLa cells were pre-incubated for ~60 h in a choline-free medium , and treated overnight with 20 units of universal type I interferon . Such conditions of interferon treatment are very mild and the cells did not show any signs of toxicity . At the same time , expression of interferon-inducible genes was clearly activated ( S3B Fig ) . After the interferon treatment , the cells were infected with poliovirus at MOIs of 0 . 1 or 0 . 01 PFU/cell , and were incubated for 24h post infection in either a choline-free or a choline-supplemented medium without interferon . Importantly , in mock-infected cells the presence or absence of choline in the medium did not change expression of interferon-inducible genes ( S5C Fig ) . The infected cells incubated in the presence of choline , in spite of pretreatment with interferon , could support a relatively high level of poliovirus replication ( around 1E7 TCID50/ml ) , consistent with a previous report that normally enterovirus replication is not very sensitive to interferon treatment [73] . At the same time , virus propagation was almost completely inhibited in cells incubated in a choline-free medium ( 1E2-1E3 TCID50/ml ) ( Fig 9D ) . Thus , activation of membrane synthesis impedes recognition of dsRNA by at least some cellular sensors of infection and permits viral replication in conditions of pre-activated anti-viral response , likely constituting an important component of the virus survival strategy .
The rapid development of the membranous replication structures is one of the longest known but still enigmatic cellular manifestations of picornavirus infection . In cells infected with poliovirus or a related Coxsackie B3 virus , the virus-induced membranes appear as early as 2 h p . i . at the ER-Golgi interface and continue to grow throughout the replication cycle , transitioning from sponge-like membranous clusters to assemblages of double membrane vesicles [11 , 74–76] . The unique morphology of the replication organelles implies that mechanism ( s ) of their formation and/or their composition are different from those supporting membrane architecture in non-infected cells . These novel membranous structures , which may occupy most of the cytoplasmic space by the end of infection , are known to harbor actively replicating viral RNA , and thus are generally referred to as replication organelles . Later in infection , progeny virions are found both inside and outside the membranous vesicles , and the data suggest that the double membrane vesicles accumulated by the end of infection may facilitate virion maturation and spread [22 , 65 , 77 , 78] . Still , our understanding of the mechanistic contribution of the membranous matrix in the viral life cycle is mostly speculative . Development of membranous replication complexes is the hallmark of infection of all ( + ) RNA viruses of eukaryotes , suggesting that membrane association of the RNA replication and/or virion assembly machinery provides specific benefits in the cytoplasmic environment . On the other hand , it cannot be excluded that massive production of membranes is related to the cellular antiviral response aimed at blocking accessibility of the cellular translational apparatus or other metabolic resources necessary for virus propagation . Our goals in this study were to understand the mechanism ( s ) underlying the rapid development of the replication organelles , as well as to identify steps in the viral life cycle sensitive to the inhibition of their growth . Membranes in mammalian cells consist of three major structural phospholipids: PC accounts for more than 50% of total phospholipid content , phosphatidylethanolamine ( PE ) for 20–45% , and phosphatidylserine ( PS ) for 3–15% , depending on cell type [79] . Cells obtain long chain FAs necessary for synthesis of the hydrophobic part of these molecules from three major sources–import of lipids from the extracellular medium ( serum ) , FASN-dependent de novo synthesis , or hydrolysis of neutral lipids stored in lipid droplets . We demonstrated here that poliovirus-infected cells , at least in cell culture conditions , rely almost exclusively on lipid droplets for supply of long chain FAs for upregulation of membrane synthesis . Lipid droplets are dynamic cellular organelles that store neutral lipids , mainly triglycerides and cholesterol esters . The neutral lipid core is wrapped in a phospholipid monolayer , and diverse proteins associate with lipid droplets permanently or transiently , balancing the synthesis and hydrolysis of lipids , depending on cellular metabolic demands , reviewed in [50] . Lipid droplets previously have been shown to serve as platforms for structural protein processing and virion assembly of hepatitis C virus and some other flaviviruses , a group of ( + ) RNA viruses , but it is not known if they contribute to the infection-specific changes of lipid metabolism in flavivirus-infected cells [80] . Interestingly , hydrolysis of cholesterol esters stored in lipid droplets was shown to be important for cholesterol enrichment of the replication organelles of human rhinovirus A16 , but not other related picornaviruses [81 , 82] . Thus , diverse ( + ) RNA viruses seem to rely on these cellular organelles at some steps of their life cycles , but whether they share a requirement for lipid droplet-derived long chain FAs for the structural development of the replication organelles remains to be established . We observed recruitment of two major lipases , HSL and ATGL , to lipid droplets in infected cells , which likely explains strong activation of lipolysis and exhaustion of lipid droplets upon infection . Further research is required to understand whether viral proteins are engaged in direct interactions with the lipases or if they activate cellular signaling cascades leading to their recruitment and activation . Our data contradict with previous reports that FASN activity may be important for picornavirus infection [83 , 84] . The discrepancy is likely due to the nature of FASN inhibitors used in previous studies , cerulenin and C75 . Both molecules target the ketoacyl synthase domain of FASN resulting in accumulation of cytotoxic malonyl-CoAs , which can inhibit viral replication non-specifically [85–87] . In our study we used orlistat , an anti-obesity drug that inhibits pancreatic lipases , and was later discovered also to irreversibly inhibit the thioesterase domain of FASN , which does not result in accumulation of cytotoxic intermediates of FA synthesis [51] . The conclusions about the role of FASN in different viral systems , based only on the effect of cerulenin and similar compounds should be taken with caution . How can poliovirus efficiently manipulate cellular lipid synthesizing machinery with very limited genetic resources ? Poliovirus infection inactivates transcription and translation of cellular genes [88] , thus the metabolic changes in infected cells must rely on post-translational regulation of the enzymes present before infection . In most mammalian cell types , the bulk of PC , the major structural phospholipid , is synthesized through the Kennedy pathway with transfer of the phosphocholine group from CDP-choline to diacylglycerol . On the other hand , diacylglycerol can be converted to a triglyceride molecule upon attachment of the third long chain FA moiety . Such utilization of diacylglycerol in either phospholipid or triglyceride synthesis allows balancing the lipid homeostasis and retargeting the flux of long chain FAs towards membrane synthesis or storage , reviewed in [89] . It has been suggested that production of CDP-choline is the rate-limiting step for activation of PC synthesis upon poliovirus infection [29 , 90] . CDP choline is generated by CCTα , which in non-infected cells is largely localized inside the nuclei , and such confinement is important for controlling its activity . The active form of the enzyme is found in the cytoplasm where its activity is fine-tuned by phosphorylation status and binding to certain lipids , reviewed in [44 , 91] . In infected cells , we observed a rapid translocation of CCTα from the nuclei to the cytoplasm , accompanied by dephosphorylation of the enzyme , which is consistent with strong activation of CCTα . The massive translocation of CCTα to the cytoplasm depends on the proteolytic activity of the viral protease 2A , a key enzyme responsible for degradation of the nucleo-cytoplasmic barrier in poliovirus-infected cells [38 , 92] . The co-IP results suggest that at least a portion of CCTα is associated with the replication membranes and interacts with the viral proteins , which may also directly control its activity . The massive release of CCTα from the nuclear depot in infected cells would redirect diacylglycerol to PC synthesis and therefore drive the increasing rate of mobilization of neutral lipids in lipid droplets to replenish the exhausted diacylglycerol pool . Supporting this model are our observations that in non-infected cells , overexpression of CCTα is sufficient to redirect the flux of long chain FAs from neutral lipids to phospholipid synthesis , and that depletion of choline , which blocks the CCTα-dependent PC synthesis , leads to restoration of neutral lipid synthesis in infected cells . Given the activating effect of free long chain FAs and diacylglycerol on CCTα activity [42 , 93 , 94] , hydrolysis of lipids stored in lipid droplets and CCTα-driven synthesis of PC would engage in a self-amplifying loop , driving ever-increasing production of PC and massive extrusion of new membranes of the replication organelles . The translocation of CCTα into the cytoplasm and its association with the membranes may not only be responsible for activation of membrane synthesis but could also contribute to the development of enigmatic convoluted tubular morphology of the replication organelles . Binding of CCTα has been shown to remodel membranous surfaces into elongated tubules of diverse diameters in an in vitro system , which was attributed to the amphipathic helix present in the C-terminal part of the enzyme [95] . Activation of phospholipid synthesis is observed in cells infected with diverse ( + ) RNA viruses , including picornaviruses [23 , 25 , 96] , suggesting that it is an important component of infection . Nevertheless , propagation of poliovirus in one cycle replication experiments in choline-deprived cells was almost indistinguishable from that in control conditions . At the same time , inhibition of phospholipid synthesis had a dramatic effect on the structural development of the replication organelles . Instead of typical clusters of convoluted membranes , the mid-cycle replication organelles in cells that could not synthesize phospholipids consisted of scattered vesicles and elongated ER tubules , resembling structures normally observed very early in infection [74 , 97] . These data are strikingly similar to those described in a recently published report that demonstrated that inhibition of a lipid trafficking pathway blocks replication organelle development but does not significantly affect one cycle replication of Coxsackie B3 virus , another enterovirus [98] . Resilience of replication of diverse ( + ) RNA viruses to the changes of morphology and/or composition of the replication membranes has been documented in many systems . Replication complexes of flock house virus , an insect virus , could be retargeted from mitochondria to the ER [99] , and replication complexes of brome mosaic virus , a plant pathogen , could efficiently function either in the context of membrane invaginations or flat membranous sheets [100] . However , such plasticity of replication was registered in highly artificial settings , such as model replication in yeast , or in in vitro systems . One should keep in mind that animal viruses , including picornaviruses , have to survive and spread among hosts with fully functional innate and adaptive immunity . Thus , it is likely that many of the aspects of virus-cell interactions have evolved to ensure successful propagation and spread of the virus in natural conditions , rather than to merely support biochemical reactions of the replication of viral genome . Indeed , the defects of the development of the replication organelles in the absence of phospholipid synthesis , well tolerated in one cycle of replication , led to the collapse of viral propagation in multiple cycles of replication . Clearance of infection by an animal host ultimately depends on the ability of infected cells to detect and communicate their status by expression of an array of signaling molecules . The window when cells could efficiently mount antiviral response to picornavirus infection could be rather short , because these viruses rapidly inactivate cellular transcription and translation [88] . Thus , prevention of infection-induced phospholipid synthesis likely not only extends this period , but also makes the infected cells more sensitive to the effectors of antiviral response . Accordingly , the experiments with mild permeabilization of membranes demonstrated increased accessibility of the viral replication complexes , in the absence of a protective membranous matrix . It has been reported recently that membranous replication organelles of hepatitis C virus hide the replication complexes from the cellular sensors of infection [101] . Thus , the protective function of the membranous replication structures emerges as a strategy shared by diverse ( + ) RNA viruses . The inevitable reliance of diverse viruses on the same elements of cellular phospholipid synthesizing machinery to support infection-specific membrane synthesis offers multiple targets that can be exploited for broad anti-picornavirus therapeutics .
Human cervical carcinoma HeLa cell line was obtained from Dr . Ehrenfeld , NIH . The cells were maintained in DMEM , high glucose modification , supplemented with 10% heat-inactivated fetal bovine serum . For choline deprivation studies , the cells were seeded overnight in a 12-well plate in serum-supplemented DMEM at 140000cells/well; the next day they were washed with balanced Earle solution and incubated for ~48 or 72 hours in balanced Earle solution supplemented with MEM amino acid mix and L-glutamine . Upon infection , choline-deprived cells were incubated in the same solution , supplemented in corresponding samples with 25 μM choline chloride . Poliovirus type I Mahoney strain was propagated in HeLa cells . For experiments performed with choline-deprived cells , virus for inoculum was purified by CsCl gradient , essentially as described in [102] , and resuspended in TE buffer ( 10mM Tris-HCl , pH 8 . 0 containing 1mM EDTA ) . Infectious virus was quantified by either plaque assay on HeLa cell monolayer covered by an agarose-solidified medium and expressed in this case like PFU/ml , or by infection of HeLa cells grown in 96 well plates; in this case , the titer is expressed as concentration of inoculum inducing CPE in half of the wells ( TCID50/ml ) , calculated by Karber’s formula [103] . For infection , the cell monolayer was washed once with balanced Earle solution , and the virus diluted to the desired MOI in balanced Earle solution buffered with 50μM HEPES , pH 7 . 3 , was incubated with cells at room temperature for 30 min on a rocking platform . After adsorption , the cells were supplemented with the desired medium and incubated at 37C for the indicated time p . i . For collection of extracellular virus , the incubation medium was collected prior to freezing of the cells . For total virus collection , cells were frozen with the incubation medium . The virus was released from cells by three freeze-thaw cycles . Poliovirus with HA antigen insertions into 2A or 3A sequences were described in [27] and [104] , respectively . Both 2A-HA and 3A-HA viruses have replication kinetics similar to the wt and were propagated and quantified the same way as the wt virus . pCCTα-RFP was constructed by cloning the CCTα-coding sequence purchased from DNASU plasmid depository ( clone ID HsCD00515560 ) into pmRFP-N1 vector ( Clontech ) . Plasmids pTM-2A-3D , pTM-2B-3D , and pTM-2Amut-3D were described previously [26] . Plasmid pcDNA3-ACSL3-HA was generously provided by Dr . Joachim Füllekrug , University of Heidelberg , Germany . DNA transfections were performed with Mirus 2020 reagent according to manufacturer’s recommendation . Bodipy 500/510 C4/C9 ( a fluorescent long chain fatty analog ) , Bodipy 493/503 ( lipid droplets stain ) , Alexa-488 azide , and cell click chemistry kit were from Molecular Probes ( Thermo Fisher Scientific ) . Cell culture media and supplements were from Thermo Fisher ( GIBCO brand ) . Propargyl choline was synthesized as described in [58] . Digitonin was from Calbiochem . Triton-X100 was from Promega . Saponin , DEUP , Orlistat and bafilomycin were from Sigma Aldrich . Formaldehyde , glutaraldehyde , and cacodylate buffer were from Electron Microscopy Sciences . Recombinant universal type I interferon was from PBL Interferon Source . Mouse monoclonal anti-poliovirus 2C and 2B were described in [105] . Rabbit polyclonal anti-polio 3D antibodies were developed by Chemicon using recombinant 3D protein as immunogen . Rabbit monoclonal anti-CCTα , anti-eIF2α , anti-eIF2α ( phospho Ser51 ) , anti-IRF3 , anti-IRF3 ( phospho Ser396 ) , anti-HSL , anti-ATGL , anti-STAT1 , anti-Viperin , anti-ISG15 and anti-HA antibodies used in western blots and immunofluorescence were from Cell Signaling . Mouse monoclonal anti-dsRNA antibodies were from English and Scientific Consulting Kft . Mouse monoclonal anti-GBF1 antibodies were from BD Biosciences; rabbit anti-PI4KIIIβ were from EMD Millipore . Mouse monoclonal antibodies against Iκβ were a kind gift from Dr . John Patton ( University of Maryland ) . Alexa dyes conjugated antibodies were from Molecular Probes ( Thermo Fisher ) ; secondary HRP-conjugated antibodies were from Amersham . Mouse monoclonal anti-HA antibody used for co-IP was from Santa Cruz Biotechnology . Co-IP was performed using Classic IP/Co-IP kit ( Pierce ) according to the manufacturer’s protocol . Briefly , HeLa cells grown on six well plate were infected with 2A-HA or 3A-HA polioviruses at an MOI of 10 PFU/cell . Control cells were transfected with a plasmid expressing ACSL3-HA , or infected with a wt poliovirus at an MOI of 10 PFU/cell . At 6 h p . i . ( ~24 h post transfection with the ACSL3-HA expressing plasmid ) , the cells were harvested in 750 μl of IP lysis buffer supplemented with a Protease Inhibitor Cocktail ( Sigma-Aldrich ) . Lysates were clarified by low speed centrifugation and protein concentration was determined using Bradford reagent ( Bio-Rad ) . The amount of lysates corresponding to 1 mg of total protein was mixed with 4 μg of mouse monoclonal anti-HA antibody ( Santa Cruz Biotechnology ) in a total volume of 500 μl of IP lysis buffer and incubated with rotation during 2 hours at room temperature . Then , pre-washed protein A/G magnetic beads from the kit were added , and the samples were incubated with rotation for one more hour . The beads were collected with a magnetic stand and washed three times with IP lysis buffer . Bound proteins were eluted with elution buffer provided in the kit . Metabolic targeting of long chain FA was monitored using Bodily 500/510 C4/C9 , a C18 backbone long chain FA analog with incorporated fluorescent group essentially as described in [106] . Briefly , the cells were incubated in medium supplemented with 0 . 4 μM of the fluorescent FA analog for 1 h either pre-infection , or at the indicated times post infection or post transfection . The cells were fixed with 4% formaldehyde in PBS and processed for microscopy observations or quantitation of the fluorescent signal using Tecan Infinite M1000 plate reader . For labeling of newly-synthesized phospholipids the cells were incubated in balanced Earle solution supplemented with 100 μM of propargylcholine for 1 hour at the indicated time p . i . Immediately after the incubation with propargylcholine , the cells were fixed with 4% formaldehyde in PBS for 20min , washed with PBS for 3 times and processed for click-chemistry labeling with Alexa 488 azide using Click-it cell reaction buffer kit . Florescence was quantified with Tecan Infinite M1000 plate reader . The cells were incubated with 5 μM of Bodipy 493/503 ( lipid droplets stain ) for 15 min in PBS . 5 mM stock solution of Bodipy 493/503 was prepared in DMSO . HeLa cells were grown on 12-well plate and were incubated after poliovirus infection for the indicated periods p . i . For permeabilization , the cells were washed once with KHM buffer ( 110 mM K-acetate , 2 mM MgCl2 , 20 mM HEPES-KOH , pH 7 . 4 ) and incubated for 5 min in 50 μg/ml fresh digitonin solution in KHM ( KHM buffer without digitonin for control cells ) at room temperature . After permeabilization , the cells were washed twice with KHM and lysed with mild lysis buffer ( 0 . 1 M Tris-HCl pH 7 . 8; 0 . 5% Triton-×100 ) supplemented with protease inhibitors cocktail ( Sigma-Aldrich ) . The lysate cleared by low-speed centrifugation was used for western blot analysis . Previously validated siRNA targeting human CCTα ( GGCUUCACGGUGAUGAACG ) and control non-targeting siControl siRNA were from Dharmacon . HeLa cells were plated at 10000 cells/well in a 96 well plate and transfected with siRNA with Dharmafect 1 transfection reagent ( Dharmacon ) according to manufacturer’s recommendations . After 72 hours of incubation with siRNA , the cells were infected with poliovirus and assessed for activation of membrane synthesis using incorporation of the fluorescent long chain FA analog Bodily 500/510 C4/C9 . Purified recombinant vaccinia virus expressing T7 RNA polymerase ( VT7-3 [36] ) was a gift from Dr . Ioannis Bossis , University of Maryland . HeLa cells were transfected with pTM- based plasmids coding for fragments of polio cDNA under transcriptional control of T7 RNA polymerase promoter and translational control of EMCV IRES respectively , with Mirus 2020 DNA transfection reagent and simultaneously infected with 10 PFU/cell of the vaccinia-T7 virus . The next day , cells grown on glass cover-slips were fixed with 4% formaldehyde in PBS and processed for microscopy analysis . The assessment of expression of 84 genes involved in the cellular anti-viral response was performed using RT2 Profiler PCR Array ( Qiagen ) according to manufacturer’s recommendations . Briefly , cellular mRNA was isolated using RNAeasy kit ( Qiagen ) and cDNA was synthesized using RT2 First Strand Kit ( Qiagen ) . Quality of the isolated RNA and the lack of genomic DNA contamination was confirmed using RT2 RNA QC PCR Array ( Qiagen ) . qPCR data were normalized to average data of housekeeping gene transcripts ( beta actin , glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) , and hypoxanthine phosphoribosyltransferase 1 ) , and analyzed using the ΔΔCt method . The cells grown on coverslips in 12 well plates were fixed with 4% formaldehyde in PBS for 20 min and washed for 3 times with PBS . For regular permeabilization assays , the cells were incubated for 5 min in 0 . 2% Triton X100 in PBS followed by 1 h incubation in 3% membrane blocking agent ( Amersham ) in PBS . The same blocking solution was used for dilution of primary and secondary antibodies . For mild permeabilization assay primary and secondary antibodies were diluted in 0 . 02% saponin in PBS containing 5% fetal bovine serum as a blocking agent . The cells were incubated with all antibodies for one hour . Processed coverslips were mounted on slides using Fluoromount-G medium ( Electron Microscopy Sciences ) . Images were taken using either Zeiss Axiovert 200M fluorescent or LSM 510 confocal microscope . Cells grown in 12 well plates on glass coverslip were fixed with 2 . 5% glutaraldehyde /4% paraformaldehyde in 0 . 1 M sodium cacodylate buffer and processed for transmission EM imaging at the University of Maryland School of Medicine core facility . Digital images were processed using Adobe Photoshop software . All changes were applied equally to the whole image , and the same parameters were applied to images from the same experiment . The number of lipid droplets per cell was calculated using Fiji distribution of ImageJ software ( NIH ) , with Analyze Particle module . Western blot signals were quantified using Image Studio software ( Li-Cor ) . For statistical calculations , at least 100 cells from different fields , or 3 western blot membranes from independent experiments were analyzed for each data point . Statistical significance was calculated by two-tailed unpaired t-test using GraphPad Prizm software package . | The cellular pathways hijacked to support viral replication may provide a promising class of targets for anti-viral therapeutics , which will be effective against broad groups of viruses relying on the same cellular pathways , and will likely be refractory to the development of resistance since cellular factors are not subject to selection . All ( + ) RNA viruses share the requirement for cellular membranes to assemble replication complexes . Here we investigated the mechanism underlying the massive membrane remodeling in poliovirus-infected cells . Our results demonstrate reorganization of the cellular lipid synthesizing machinery upon infection and identify lipid droplets as the organelles supporting the structural development of the replication membranes . Moreover , we show that inhibition of the infection-specific phospholipid synthesis renders virus propagation much more vulnerable to the cellular anti-viral defenses , providing a new direction for the development of anti-viral therapeutics . |
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The diagnosis of canine echinococcosis can be a challenge in surveillance studies because there is no perfect gold standard that can be used routinely . However , unknown test specificities and sensitivities can be overcome using latent-class analysis with appropriate data . We utilised a set of faecal and purge samples used previously to explore the epidemiology of canine echinococcosis on the Tibetan plateau . Previously only the purge results were reported and analysed in a largely deterministic way . In the present study , additional diagnostic tests of copro-PCR and copro-antigen ELISA were undertaken on the faecal samples . This enabled a Bayesian analysis in a latent-class model to examine the diagnostic performance of a genus specific copro-antigen ELISA , species-specific copro-PCR and arecoline purgation . Potential covariates including co-infection with Taenia , age and sex of the dog were also explored . The dependence structure of these diagnostic tests could also be analysed . The most parsimonious result , indicated by deviance-information criteria , suggested that co-infection with Taenia spp . was a significant covariate with the Echinococcus infection . The copro-PCRs had estimated sensitivities of 89% and 84% respectively for the diagnoses of Echinococcus multilocularis and E . granulosus . The specificities for the copro-PCR were estimated at 93 and 83% respectively . Copro-antigen ELISA had sensitivities of 55 and 57% for the diagnosis of E . multilocularis and E . granulosus and specificities of 71 and 69% respectively . Arecoline purgation with an assumed specificity of 100% had estimated sensitivities of 76% and 85% respectively . This study also shows that incorporating diagnostic uncertainty , in other words assuming no perfect gold standard , and including potential covariates like sex or Taenia co-infection into the epidemiological analysis may give different results than if the diagnosis of infection status is assumed to be deterministic and this approach should therefore be used whenever possible .
An efficient and adequate diagnosis is at the core of effective surveillance , control and elimination programmes . For the effectiveness of such programmes , knowledge about test accuracies is indispensable , since even very accurate diagnostic tests might occasionally provide false positive and false negative test results . To diagnose canine echinococcosis , a number of tests are used including arecoline purgation , copro-antigen tests and detection of the presence of the parasite using a PCR analysis of the faeces ( reviewed by [1] ) . Arecoline purgation , a well-established technique of high specificity , has frequently been used in the past . However , it is a laborious and potentially hazardous procedure and has been reported to show poor sensitivity [2] . Hence , alternative methods have been developed for the routine diagnosis of Echinococcus infection in dogs and other canids . These tests include copro-antigen ELISA and copro-PCR techniques , but they cannot be considered a gold standard . Only a necropsy of dogs followed by the sedimentation and parasite-counting technique can be considered close to a perfect gold standard , i . e . , a gold standard with 100% sensitivity and 100% specificity . However , due to ethical reasons , this procedure cannot be used on the routine surveillance of dogs , since it would involve killing of a large number of affected as well as non-affected dogs . Even on a smaller scale , sacrificing dogs for the purpose of diagnostic test evaluation would potentially be impossible in a Buddhist country . In the absence of a perfect gold standard in surveillance studies , the accuracy of new or alternative tests cannot be estimated robustly and without bias by comparing such test results of new or alternative results against an imperfect gold standard . For example , in the case of a well-established test with a sensitivity of less than 100% , samples which are falsely classified as negative by such a gold standard test , might be correctly detected as positive by a more sensitive alternative test , thus leading to a biased –in this case too low- estimate of the specificity of the alternative test . Given the absence of a perfect gold standard , however , test accuracies can be estimated robustly using latent-class analysis [3] . In this context , latent refers to the idea that the true disease status for each animal is unknown and needs to be estimated from the data . Hui and Walter proposed a model in which two tests with unknown test accuracies are applied to individuals from two populations to estimate sensitivities and specificities as well as prevalences . Their model can be extended to any combination of tests ( R ) and populations ( S ) as long as the condition of S≥R/ ( 2R-1 – 1 ) is satisfied [3] . The Hui-Walter model relies on several assumptions , which if violated , may result in unreliable estimates [4] . The first assumption is that the tested individuals are divided into two or more populations with different prevalences . The second assumption is that sensitivities and specificities are constant across different populations . The third assumption is that test results are conditionally independent given the true disease status . Echinococcus infections in dogs vary in parasite abundance and/or prevalence with age [5] . Diagnostic tests which are based on the detection of the parasite might be correlated if the number of parasites found affects the sensitivities of these tests [6] . Although covariance terms for conditional test dependency in the Hui-Walter latent-class model [7]–[10] , have been included in a number of analyses that used a Bayesian approach , models using covariates to adjust for factors which might affect the sensitivity and specificity in different populations are scarce [11] . This contrasts with classic risk factor studies , where the outcome prevalence is routinely adjusted for covariates or confounders . In the case of infections with Echinococcus , covariates may include age or co-infection with other parasites such as Taenia spp . Whereas the classical Hui-Walter model is based on the assumption of different populations which differ in their prevalences , in practice , it could be difficult to justify the splitting of one population into sub-populations . The separation of a population into different “prevalence populations” based on a factor which might interact with one of the tests ( e . g . , age or co-infection with another pathogen ) is questionable [4] , since sensitivities and specificities might not be constant in these different populations . Including a covariate instead offers in addition the assessment if this covariate is significantly associated with the prevalence and this association can be quantified in terms of an odds ratio . The aims of this study were to obtain test accuracy and prevalence estimates for the diagnosis of Echinococcus granulosus and Echinococcus multilocularis in dogs in a highly endemic district of Sichuan province on the eastern Tibetan plateau . Three different tests were used for the diagnosis of E . granulosus and E . multilocularis infections in dogs , i . e . a genus-specific copro-antigen test , two species-specific copro-PCRs ( one for each species of Echinococcus ) , and arecoline purgation . The results of these diagnostic tests on this population of dogs were used to estimate the diagnostic sensitivities and specificities of the tests using latent-class analysis . Age , sex and Taenia spp co-infection have been integrated as covariates in the latent-class models and their effects on the true prevalence have been assessed . The types of tests used and the nature of the data collected allowed for a full latent-class analysis . The use of covariates in the analysis and appropriate prior assumptions on the specificity of arecoline purgation enabled us to explore the dependence structure of these tests in this population of dogs . In addition , because we had parasite abundance data from the results of arecoline purgation , we were able to explore the hypothesis that the intensity of infection with Echinococcus spp affected the diagnostic sensitivity of copro-antigen ELISA and copro-PCR tests .
A total of 365 dogs from a highly Echinococcus-endemic region of the Eastern Tibetan Plateau in the People's Republic of China were sampled . Full details of the study animals and study area can be found in previous publications [12] , [13] . Dog fecal samples were collected , and dogs subsequently received treatment , if their owners consented . Because the sampling was non-invasive , no prior ethical permission was sought . A table with data on test results classified according to Taenia co-infection is available in the supplementary online file ( Table S1 ) . A Bayesian approach was used to obtain estimates for the test accuracies of the three tests . Initial analyses with non-informative priors as beta distributions ( 1 , 1 ) were used for all parameters , except for the faecal counts of adult parasites following purge , where the specificity was set at 1 . This was justified as all purge positive samples had been confirmed morphologically through microscopic examination . Conditional dependencies between tests were assessed by separately examining the impact of each of the 4 covariance terms . In the case of three tests with unknown sensitivities and specificities , three pairs of covariance terms are possible ( between tests 1 and 2 , tests 1 and 3 and tests 2 and 3 ) for both sensitivity and specificity . Fixing one test specificity to 1 results in two covariance terms becoming obsolete , since if one test has a specificity = 1 , then the test specificities of the two other tests must be conditionally independent from the first test . Models allowing for age , sex or Taenia spp co-infection to be a covariate for prevalence were tested . In addition it was possible to examine the performance of the tests by fixing the specificity of PCR to 1 instead of and/or in addition to fixing the specificity of purge to 1 . Model selection was performed by using the deviance- information criterion ( DIC ) [17] . The DIC is used as a criterion for goodness of fit of the model . Smaller DIC , with a difference of at least 2 indicate a better fit of the model . For each model , the first 20 000 iterations were discarded as burn-in and the next 50 000 iterations were used to parameterize the model . Multiple chains were run from different initial starting points and checked for convergence . Models were fitted with the software JAGS ( http://mcmc-jags . sourceforge . net/ ) version 2 . 2 . 0 , the software R ( R , 2010 ) and the package coda . The model code is given in the supplementary online material ( Text S1 ) . To explore the possibility that the intensity of Echinococcus infection affected the diagnostic sensitivity of other tests we also undertook the analysis after reclassifying the results of the arecoline purgation . Therefore two further analyses were undertaken . When purge results indicated that the intensity of infection was less than 20 parasites , the purge results were classified as negative and the analysis repeated . For the second analysis reclassification was undertaken when purge results indicated a parasite intensity of between 1 and 99 parasites .
The estimated test accuracies given as posterior means and their corresponding 95% credible intervals are presented in table 1 and the posterior density distributions in the figures S1 , S2 , S3 , S4 . In a Bayesian context , the results are given as posterior density or probability distributions which reflect , given the data and prior information , what would be the most probable parameter values . The reported results have the lowest DIC of a number of competing model estimates . A better model fit was obtained by including a covariance term for a conditional dependence between the sensitivities of the copro-antigen ELISA and the copro-PCR for E . multilocularis . The true prevalence of E . multilocularis infection in this population of dogs was estimated at 15 . 3% ( 95% credible intervals 10 . 3–21 . 8% ) and the prevalence of E . granulosus was estimated at 11 . 1% ( 95% credible intervals 6 . 7–20 . 1% ) without Taenia co-infection included as a covariate in the model . Taenia co-infection was a significant covariate with both E . multilocularis infection ( odds ratio 2 . 06 , 95% credible intervals 1 . 07–3 . 9 ) and E . granulosus infection ( odds ratio 6 . 32; 95% credible intervals 2 . 8–15 . 2 ) ( figures 1 and 2 ) . The prevalence of E . multilocularis in Taenia test-negative dogs was estimated at 12 . 2% ( 95% credible intervals 7 . 6–18 . 9% ) , and in Taenia test-positive dogs was estimated at 22 . 3 ( 95% credible intervals 8 . 2–47 . 7% ) . The prevalence of E . granulosus in Taenia test-negative dogs was estimated at 4 . 1% ( 95% credible intervals 1 . 9–8% ) , and in Taenia test-positive dogs was estimated at 21 . 1% ( 95% credible intervals 5 . 1–56 . 9% ) . Changing the cut-off for being classified as positive in the purge from at least one parasite detected to at least 20 or 100 parasites did affect the estimates of the sensitivities of the three tests differentially . The sensitivity of the purge decreased by a maximum of 13 . 9% ( E . multilocularis ) and 33 . 6% ( E . granulosus ) . The sensitivity of the PCR decreased less by maximally 6 . 9% for E . multilocularis and 4 . 5% for E . granulosus . In contrast to this , the sensitivity of the copro-antigen ELISA increased by approx . 10% for both E . granulosus and E . multilocularis . The specificities for both Echinococcus species for the ELISA remained virtually the same and for the PCR decreased marginally by 6 . 6% for E . multilocularis and 2 . 9% for E . granulosus . Figures S5 and S6 show the effect of increasing the cut-off for a faecal sample to be classified as positive from at least 1 to at least 20 parasites detected . Increasing the cut-off leads to an increase in the posterior distribution of the sensitivity by approx . 10% . Results including the corresponding intervals are presented in table 2 . Fixing the specificity = 1 of the copro-PCR instead or in addition to the specificity of the purge did not affect the specificity of the copro-antigen ELISA . However , the sensitivities of the copro-antigen ELISA and the purge decreased ( 10 to 50% ) ( table 3 ) . However , the deviance-information criteria indicated that this model was a poorer fit than allowing the specificity of the copro-PCR to vary . Sex was not a significant covariate in any analysis indicating the true prevalence of Echinococcus spp infection did not vary between male and female dogs ( data not shown ) .
This study on the diagnosis of canine echinococcosis has used latent-class modeling to estimate the true prevalence and the diagnostic test performances of three tests for each Echinococcus spp . Arecoline purgation is a test that has been widely used in the past such as in the Echinococcus-elimination campaign in New Zealand [18] and for some transmission studies in central Asia [2] , [19] . One previous study that used latent-class analysis suggested that the sensitivity of arecoline purgation was poor , perhaps as low as 38% and 21% for the diagnosis of E . granulosus and E . multilocularis infection respectively [2] . In the present study , the best fitting model ( covariance with Taenia infection ) suggested the sensitivity of arecoline purgation was much higher ( table 1 ) with a sensitivity of over 75% . The specificity of the PCR test for the diagnosis of both parasitic infections converged on a lower value in the present study then in [2] where it was estimated as being 93% and 100% respectively . The two copro-PCR tests were not the same: the former study relied on egg isolation followed by PCR whereas the present studies omitted the egg isolation stage . However , it is important to reconcile these major differences . In the former study , there were only two tests used and the ability of the dog to roam as opposed to being tied all the time was a significant covariate . If the PCR test is fixed with a specificity of 100% , then the performance of the arecoline purgation drops markedly and is more similar to the values described in [2] ( table 3 ) . This indicates that the estimates of the performance of the arecoline seem to be highly dependent on the models' ability to classify PCR positives as true positives or allow for some false positives . The latter are important as there are a number of animals in both studies that are purge negative but copro-PCR positive . Indeed when the PCR tests were first developed by [15] , the specificity was estimated at 100% . However this estimate was based on a sample of 10 dogs from non-endemic areas . In a naturally infected population false positive PCR results may occur due to coprophagia of faeces containing Echinococcus eggs . Thus eggs ingested in this manner might passage the intestine resulting in a positive PCR result indistinguishable from a result generated from parasite material coming from an established infection . It should also be considered that the diagnostic performance may vary with the population of dogs . For example , the mean number of E . multilocularis parasites recovered from each dog in the present study is 131 ( 95% CI 62–375 ) [13] , [14] . This is significantly higher than the mean number of 65 ( 95% CI 22–123 ) parasites recovered by purgation from the study by Ziadinov et al [2] that reports the much lower sensitivity of purgation . It is therefore possible that the dog populations from the two studies had substantially different parasite abundancies . In addition , when we reclassified the diagnostic test results as being only positive if there were at least 20 or 100 parasites , arecoline purgation was considerably less sensitive . Thus a higher mean abundance in the Tibetan population of dogs compared to the Kyrgyzstan population of dogs might also partly explain the considerable variation in the sensitivity of arecoline purgation between the two studies . When we reclassified the diagnostic test results as being only positive if there were at least 20 or 100 parasites , the sensitivity of the copro-antigen ELISA increased by approx . 10% for both Echinococcus species . This might be explained by the ELISA performing better with higher parasite abundance in faecal samples . Previous studies have suggested that the sensitivity of copro-antigen increases as the intensity of infection increased [20] . There is little variation in the sensitivity of the PCR test regardless of which scenario is studied , indicating a sensitivity of approximately 89% for the diagnosis of E . multilocularis and 84% for the diagnosis of E . granulosus . This appears to be somewhat more sensitive than the test described in [2] . However , the previous test could only diagnose patent infections as it relied on prior egg isolation from the faeces . For patent infections the two tests are more comparable with the previous test able to detect an estimated over 87% and 72% for E . granulosus and E . multilocularis respectively . In another study with three tests based on antigen detection , DIC was also used as model selection criteria and indicated the same “best” models as likelihood ratio tests [21] . This analysis failed to find age of dog as a significant covariate and hence concluding that prevalence of Echinococcus infection does not vary significantly with age . This is consistent with the previous analysis [12] , although age may affect abundance of E . granulosus in this group of dogs [13] . Relatively lower parasite abundances in older canids has been suggested to be a result of exposure to the parasite and immunity to reinfection or by variations in infection pressure with age [5] and changes in abundance may not accompanied by changes in prevalence . The significance of Taenia as a covariate is to be expected as dogs are infected with both Taenia and Echinococcus spp through predation and local prey species are infected with metacestodes from both genera of parasites . In the previous analysis of this set of purge and faecal samples , significant correlations of abundance of Taenia and Echinococcus spp were found [12] . However , the present analysis failed to identify dog sex as a significant covariate . This is in contrast to a previous analysis of the same data using logistic regression and assuming that the results of arecoline purgation were definitive [12] which suggested that the prevalence in male dogs was higher than in females . The present study examined dog sex as a covariate in the latent-class analysis of diagnostic test performance and hence included the sensitivity of the arecoline purgation in the analysis . Thus a number of dogs in the previous study would have been misclassified and would have affected the results of the regression analysis . Techniques are now becoming available to incorporate the latent but unknown infection status in regression analysis [22] and these should be used where possible to avoid reaching inappropriate conclusions about the possible significance of covariates in epidemiological studies . In conclusion the results of this study demonstrate how the unknown true prevalence of Echinococcus spp in dogs can be estimated if a number of diagnostic tests are used in parallel with a suitable covariate structure . It also demonstrates that an identical diagnostic test may have a considerable difference in performance between different study populations . Sensitivity and specificity are population-dependent [23] and the terminology of “intrinsic diagnostic test characteristics” implying that these are “constant and universally applicable” across populations should be discouraged [24] . Thus multiple tests should ideally be used routinely in the population of interest if no perfect gold standard is available . In contrast to ( formerly ) used approaches like Kappa tests to assess agreement of test results beyond chance , Bayesian latent-class approaches are more suitable to model the prevalence and associated influencing factors in a robust way . Finally using the true prevalence rather than the test prevalence may give different results with regard to the importance of determinants ( such as Taenia in the case of this data set ) which are associated with infection . This is due to misclassification errors following false positive or false negative test results when using test results in a deterministic manner . | Dogs are a key definitive host of Echinococcus spp; hence , accurate diagnosis in dogs is important for the surveillance and control of echinococcosis . A perfect diagnostic test would detect every infected dog ( 100% sensitivity ) whilst never giving a false positive reaction in non-infected dogs ( 100% specificity ) . Since no such test exists , it is important to understand the performance of available diagnostic techniques . We used the results of a study that used three diagnostic tests on dogs from the Tibetan plateau , where there is co-endemicity of E . granulosus and E . multilocularis . In this study opro-antigen ELISA and copro-PCR diagnostic tests were undertaken on faecal samples from all animals . The dogs were also purged with arecoline hydrobromide to recover adult parasites as a highly specific but relatively insensitive third diagnostic test . We used a statistical approach ( Bayesian latent-class models ) to estimate simultaneously the sensitivities of all three tests and the specificities of the copro-antigen and copro-PCR tests . We also analysed how some determinants of infection can affect parasite prevalence . This approach provides a robust framework to increase the accuracy of surveillance and epidemiological studies of echinococcosis by overcoming the problems of poor diagnostic test performance . |
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Low adherence to multidrug therapy against leprosy ( MDT ) is still an important obstacle of disease control , and may lead to remaining sources of infection , incomplete cure , irreversible complications , and multidrug resistance . We performed a population-based study in 78 municipalities in Tocantins State , central Brazil , and applied structured questionnaires on leprosy-affected individuals . We used two outcomes for assessment of risk factors: defaulting ( not presenting to health care center for supervised treatment for >12 months ) ; and interruption of MDT . In total , 28/936 ( 3 . 0% ) patients defaulted , and 147/806 ( 18 . 2% ) interrupted MDT . Defaulting was significantly associated with: low number of rooms per household ( OR = 3 . 43; 0 . 98–9 . 69; p = 0 . 03 ) ; moving to another residence after diagnosis ( OR = 2 . 90; 0 . 95–5 . 28; p = 0 . 04 ) ; and low family income ( OR = 2 . 42; 1 . 02–5 . 63: p = 0 . 04 ) . Interruption of treatment was associated with: low number of rooms per household ( OR = 1 . 95; 0 . 98–3 . 70; p = 0 . 04 ) ; difficulty in swallowing MDT drugs ( OR = 1 . 66; 1 . 03–2 . 63; p = 0 . 02 ) ; temporal non-availability of MDT at the health center ( OR = 1 . 67; 1 . 11–2 . 46; p = 0 . 01 ) ; and moving to another residence ( OR = 1 . 58; 95% confidence interval: 1 . 03–2 . 40; p = 0 . 03 ) . Logistic regression identified temporal non-availability of MDT as an independent risk factor for treatment interruption ( adjusted OR = 1 . 56; 1 . 05–2 . 33; p = 0 . 03 ) , and residence size as a protective factor ( adjusted OR = 0 . 89 per additional number of rooms; 0 . 80–0 . 99; p = 0 . 03 ) . Residence size was also independently associated with defaulting ( adjusted OR = 0 . 67; 0 . 52–0 . 88; p = 0 . 003 ) . Defaulting and interruption of MDT are associated with some poverty-related variables such as family income , household size , and migration . Intermittent problems of drug supply need to be resolved , mainly on the municipality level . MDT producers should consider oral drug formulations that may be more easily accepted by patients . Thus , an integrated approach is needed for further improving control , focusing on vulnerable population groups and the local health system .
Leprosy control is based on early diagnosis , treatment , and cure , aiming at the elimination of sources of infection and of sequels in affected individuals . Similar to other countries , in Brazil leprosy control measures are integrated into general public health care , thus facilitating access to affected individuals and reduction of disease-related stigma [1] . Interruption and defaulting of multidrug therapy against leprosy ( MDT ) are still important obstacles of disease control in many endemic countries , with consequences for both patients and the control programs: low adherence is responsible for potentially remaining sources of infection , incomplete cure , and irreversible complications , and in addition may lead to multidrug resistance [2] . In Brazil , the number of patients defaulting treatment was reduced from 3 , 148 individuals in 2002 to 529 in 2009 ( with approximately 49 , 000 and 37 , 500 new cases , respectively ) [3] . The causes leading to low adherence and non-compliance to MDT are diverse and may include socio-economical , cultural , psychosocial , behavioral , drug-related and disease-related factors , as well as health service-related aspects [2] , [4]–[9] . For example , a recent study from India identified stigma as the most common reason given by defaulters , but failed to detail data and to compare these factors with non-defaulters [4] . In Paraíba State in the northeast of Brazil , defaulting of MDT was associated with regular alcohol use , but not with clinical characteristics [5] . However , that study involved only 13 patients who defaulted , as compared to 28 patients finishing treatment regularly . Here we present - as part of a major epidemiological investigation in 78 municipalities in Brazil - population-based data to further investigate factors associated with interruption and defaulting of MDT in a hyperendemic area .
Tocantins State is located in the central savannah region of Brazil ( Figure 1 ) . The state has been created in 1988 and has a total population of 1 , 3 million ( 2009 ) , distributed throughout 139 municipalities; 83% of the municipalities have less than 10 , 000 inhabitants . Tocantins is hyperendemic for leprosy: in 2009 , a total of 1 , 345 new cases were notified , and the detection rate was 88 . 54/100 . 000 inhabitants . The present study is part of a major epidemiological investigation performed in 79 municipalities of northern Tocantins . These municipalities are at highest risk for leprosy transmission , according to a recent cluster analysis performed by the Brazilian Ministry of Health ( Figure 1 ) [10] , [11] . The target population included all individuals newly diagnosed with leprosy from 2006–2008 , living and notified as leprosy cases in these municipalities . We excluded the municipality of Araguaína from the present analysis , the biggest city in the region with about 120 thousand inhabitants . Araguaína has a leprosy reference clinic and shows different characteristics , as compared to the other smaller municipalities that share mainly rural characteristics . These results will be published elsewhere . We also excluded patients who moved to municipalities outside the endemic cluster , suffered from mental disability or who have shown other characteristics that impeded an interview , such as individuals under the influence of alcohol . Relapsed leprosy cases were also excluded . Individuals who had died after diagnosis were not included in data analysis . The 78 Municipal Health Secretariats were informed by the Tocantins' State Health Secretariat about the study and the timeframe when the team would perform field visits for data collection . Previous to field visits , the target population was identified in the database of the National Information System for Notifiable Diseases ( Sistema de Informação de Agravos de Notificação – SINAN ) . In the municipalities , the patients' charts and the local notification records were first reviewed regarding clinical variables ( clinical form , operational classification , disability grade at diagnosis , mode of case detection , date of diagnosis , date of release from treatment and date of last appearance at health center for treatment ) . If in the local records patients were identified that had not been notified , we included them in the target population . Then , affected individuals were invited by community health agents to be interviewed at the local health care center . If individuals did not present at the health care center , we performed home visits accompanied by local community health agents . Data were obtained at this occasion according to a previously defined framework , using pre-tested structured questionnaires . The framework comprised of four blocks of independent variables possibly associated with the outcomes: 1 . Socio-demographic block ( gender , age , marital status , education , residence area , number of rooms , number of persons per household , household income , migration ) ; 2 . Disease-related block ( clinical form of disease , operational classification , disability grade , leprosy reaction , adverse events to MDT , difficulty swallowing MDT drug ) ; 3 . Health service-related block ( mode of case detection , non-availability of MDT drugs , distance to health care center , perceived difficult access to health care center ) ; 4 . Knowledge , attitudes and practices block ( alcohol consumption , information of peer persons regarding disease , knowledge on leprosy and cure ) . Data were collected from September to December 2009 . To reduce inter-observer bias , all questionnaires where applied by two previously trained field investigators ( OAC , ARO ) who were supervised during the entire study . Data from patients' charts were collected by another two investigators ( KH , FW ) . Extensive pre-tests were performed under supervision . Data were entered twice , using Epi Info software version 3 . 5 . 1 ( Centers for Disease Control and Prevention , Atlanta , USA ) and cross-checked for entry-related errors . Answers to open-ended questions were grouped according to similarities and categorized for bivariate analysis . Open-ended questions included information on clinical characteristics for definition of leprosy reaction and adverse events; and questions on knowledge , attitudes and practices . Data analysis was done using STATA version 9 ( Stata Corporation , College Station , USA ) . As the number of individuals defaulting MDT was relatively low , two separate bivariate analyses were performed , with two different outcomes based on the non-attendance of patients at treatment centers: Variables were first analyzed and presented in a bivariate manner . Odds ratios and their respective 95% confidence intervals are given . We applied Fisher's exact test to estimate significance of the difference of relative frequencies . Continuous and discrete variables were not normally distributed and thus compared applying the Wilcoxon rank sum test for unmatched data . Unconditional logistic regression analysis using backward elimination was then performed to calculate adjusted odds ratios for the independent association between 1 ) interruption of; and 2 ) defaulting MDT , and the respective explanatory variables . Results of both analyses are presented separately . In addition to sex , age and leprosy form ( PB/MB ) which we used as adjusting variables throughout multivariate analysis , variables with a p value<0 . 25 in the Fisher's exact test were entered into the initial regression models , and then backward elimination was run . To remain in the model , a significance of p<0 . 05 was required . Variables were checked for collinearity . Confounding and interaction between variables were also investigated by stratification and by constructing 2×2 tables . All variables that remained in the final models are presented , and odds ratios were adjusted for all other variables in the respective model . The study was approved by the Ethical Review Board of the Federal University of Ceará ( Fortaleza , Brazil ) and by the Ethical Review Board of Lutheran University of Palmas ( Tocantins , Brazil ) . Permission to perform the study was also obtained by the Tocantins State Health Secretariat , the State Leprosy Control Program and the municipalities involved . Informed written consent was obtained from all study participants after explaining the objectives of the study . In the case of minors , consent was obtained from a caretaker . Interviews were always performed separately to guarantee strict privacy , and the diagnosis of leprosy was not given to family members or other community members , in case the patient had not revealed the diagnosis . If any leprosy-associated pathology was observed during the interview or during clinical examination ( data of clinical examination to be published elsewhere ) , participants were referenced to the responsible health care service .
Of the target population of 1635 individuals from 78 municipalities , 936 ( 57 . 2% ) from 74 municipalities were included in data analysis; one municipality did not diagnose a single case of leprosy in the study period , and another three municipalities had few cases , but no participants were included ( non-consent or not encountered ) . Twelve patients refused to participate in the study . We excluded another 13 ( five under of influence of alcohol that impeded an interview; four convicted; three severely sick who were hospitalized; and one due to advanced age ) . In addition , 674 were not encountered even after home visits , were not known at the local health centers , or had moved to another city outside the cluster . For the analysis of interruption of MDT 130 individuals were excluded ( 92 did not have information about date of the beginning of treatment or last date of supervised monthly dose in the health care center , and 38 were classified as MB leprosy with treatment started <13 months before data collection ) . Thus , data analysis regarding defaulting included 936 , and regarding interruption 806 individuals . Information from patients' charts was available in 894 of cases . Of the total of 936 individuals , 491 ( 52 . 5% ) were males; the age ranged from 5 to 98 years ( mean = 42 . 1 years; standard deviation: 18 . 8 years ) . Two-hundred and twenty-five ( 24 . 0% ) were illiterate . Median monthly family income was R$ 465 ( about 270 USD at the time of the study; interquartile range: R$ 300–R$ 900 ) . In total , 497 ( 55 . 6% ) were classified as PB leprosy , and 395 ( 44 . 1% ) as MB . We identified 28 ( 3 . 0% ) patients who defaulted MDT; 16 defaulters were included by reviewing the SINAN data information system , and an additional 12 locally in the patients' charts . Only 5 individuals were in the both databases . In total , 147/806 ( 18 . 2% ) interrupted MDT . Factors associated with interruption of MDT are detailed in Table 1 . Moving to another residence after diagnosis and living in a small residence were significantly associated with interruption . In addition , disease- and health service-related variables ( difficulty in swallowing MDT drug; temporal non-availability of MDT drugs ) were significantly associated with an increased chance of interruption of treatment ( Table 1 ) . Interestingly , disease-related factors such as the clinical form , presence of leprosy reactions or occurrence of adverse events to MDT did not play a significant role . Figure 2 depicts the frequency of interruption of MDT , stratified by age groups and gender . In general , the 16–30 year-olds showed the highest chance of interruption , as compared to all other age groups together ( OR = 1 . 84; 95% confidence interval: 1 . 20–2 . 77; p = 0 . 04 ) . This effect could be mainly attributed to the 16–30 year-old males , who showed the highest frequency of interruption ( 34 . 4% ) , roughly a two-fold difference to females of the same age group ( 17 . 6%; p = 0 . 01; Figure 2 ) . Logistic regression analysis identified temporal non-availability of MDT drugs at the health care center as an independent risk factor for treatment interruption ( Table 2 ) . An increased number of rooms per household ( as an indicator for wealth ) was identified as an independent protective factor . Bivariate analysis of factors associated with defaulting MDT is depicted in Table 1 . Several socio-economic variables ( number of rooms per household; moving to another residence after diagnosis; family income ) were significantly associated with defaulting ( Table 1 ) . Similar to interruption of MDT , disease-related factors did not play a significant role . Health service variables did also not show any significant association . In logistic regression analysis , we identified the number of rooms per residence as a factor independently associated with defaulting , with a protective odds ratio of 0 . 67 for each additional room in the household ( Table 2 ) , but no other factors .
Low adherence to drugs is in general a major obstacle in the control of infectious diseases that require prolonged treatment , such as leprosy and tuberculosis . Our comprehensive population-based study shows that poverty , behavior , drug-related and service-related factors were associated with adherence to MDT , hampering leprosy control in a hyperendemic area in Brazil , and suggest evidence-based actions for improving control measures . It is widely believed that understanding and behavior of patients in relation to drug compliance are largely influenced by their socio-economic condition and level of knowledge; socio-economic factors were previously suggested to influence adherence to MDT [5] , [7] , [13] . Even though family income as a direct indicator of poverty was not significantly associated with low adherence ( but with defaulting ) , number of rooms was identified as an independent risk factor in both bivariate and multivariable analyses . Poverty and its consequences , similar to other neglected tropical diseases , has been shown to be associated with leprosy in general [14] , and our results reflect this complex interaction of causation leading to higher risk of disease in underprivileged populations . In addition , population movements are usually associated with socio-economic conditions in Brazil . In our study , people who had moved to another residence were more vulnerable for low adherence . These people may lose their bonds with community health workers and other health professionals of the primary health care centers , besides other factors that change in life when moving to another place . Similar findings have been made in India and southeast Brazil , where treatment interruption due to migration has been reported [15] , [16] . In the case of tuberculosis , moving to another district with subsequent change of health unit was also shown to increase the risk of defaulting treatment in Uganda [17] . On the other hand , changing residence due to leprosy was clearly not a factor that played a role in our study ( data not shown ) . Interestingly , the frequency of defaulting MDT was relatively low , as compared to other settings [2] , [4] , [13] , [18] , [19] , with a rate of only 3% . In Tocantins , the defaulting rate was 47% in 2005 , but was reduced drastically in subsequent years [20] . This may reflect the success of efforts made in the last years by Tocantins's health services . In fact , the Brazilian national and state leprosy control programs have put a major effort in improving the decentralized primary health care services , with 90% population coverage of the Family Health Program in Tocantins . As another consequence , variables related to health services seemed to play a minor role for defaulting in our study , despite the identification of temporary shortage of drugs as a significant risk factor for interruption of MDT . We have shown previously that the patients of this area answered most commonly to an open-ended question about the reason for interrupting MDT with temporary shortage of drugs at the health care center , but median time of interruption was only 15 days which indicates that this operational issue was usually resolved quickly [21] . In fact , these logistical problems occurred mainly on the municipality level , as MDT provided by the State Leprosy Control Program to the municipalities did not suffer any shortage in the study period ( A . C . F . , unpublished observation ) . In other countries and settings , where leprosy control programs are not yet well established , such as in northern Mozambique , Nigeria and Sudan , health-service related factors play a more crucial role [4] , [7] , [18] , [19] , [22] . Our data also indicate that in a setting with an established leprosy control program , clinical variables are of minor importance for low adherence to MDT . In case of leprosy reactions , for example , the primary health care services and the reference centers seem to be prepared to cope with the situation . Similarly , previous studies from northeast Brazil , the Philippines and Nepal suggested that clinical data such as type of leprosy , occurrence of reactions or disability grading at diagnosis would not play a significant role in the given context [2] , [5] , [23] . Difficulty in swallowing drugs was previously suggested as a factor associated with low adherence to MDT [2] . Considering also the long course of treatment , this shows the need for the search of new formulations that may be better accepted by patients . Studies from other parts of the world , mainly from the South Asian and Southeast Asian sub-regions , identified other risk factors for low adherence . For example , in the Philippines adverse events were given by the patients as the most important reason ( 40% ) for defaulting [2] . People in Assam ( India ) who defaulted treatment mentioned loss of occupational hours when going to the health care center ( 33 , 1% ) , adverse events ( 26 , 0% ) and social stigma ( 18 , 1% ) as the most common reasons [13] . About 10 years ago , these factors were identified in a qualitative study from Espírito Santo State in Brazil [16] . Since then , Brazilian control programs have improved considerably , e . g . by performing health education on adverse events and leprosy reactions , by training health care professionals and by improved access of the users to the primary health care system . The results of our study reflect these efforts and highlight the differing situation in other countries . Available evidence on the influence of demographic variables on adherence to treatment is contradictory . Similar to the study from the Philippines [2] , demographic data such as gender , age and civil status were not associated with low adherence in our study population . In contrast , in endemic regions of Nepal and India , more males than females completed treatment , and illiteracy was also significantly associated with low treatment compliance [9] , [13] . However , both studies had some methodological problems , and analysis of data is limited . Interestingly , our study showed highest interruption rates in young males , when data were stratified by gender . This indicates that factors are multifaceted and that in this case , young males , who are generally known to show insufficient health care behavior , should be considered a vulnerable group for low adherence . In fact , the Brazilian Ministry of Health has taken into consideration the special needs of the male population and recently launched an integrative program focusing on male gender issues [24] . Similar to leprosy , tuberculosis needs prolonged treatment and has also shown to reveal problems regarding adherence . Improving adherence to treatment against leprosy can thus be expected to have positive impact also on other diseases , such as tuberculosis . In fact , the factors associated with low adherence to tuberculosis are similar . For example , in Ethiopia , the occurrence of adverse events to tuberculosis treatment was found to be a significant risk factor for defaulting , whereas knowledge about duration of treatment was protective and increased the odds of terminating treatment [25] . A study from Nepal identified distance to health care services and low knowledge on disease and its treatment as risk factors for non-adherence to tuberculosis directly observed short-course ( DOTS ) [26] . An ancillary finding was the detection of incomplete patients' charts and registries in many cases . We detected in total 128 leprosy cases that were not included in the national SINAN database for notifiable diseases , and a considerable number of cases of abandonment from treatment , which had not been registered as such in SINAN . In addition , only in 72 . 1% ( 645/894 ) information on degree of disability at diagnosis was available in the patients' charts . The quality of patients' records and datasets has improved in the past years , but there is still a clear need for more complete data sets and patient charts , as suggested recently in a study performed in northeast Brazil [27] . Though being a population-based study performed in a considerable number of municipalities in a leprosy hyperendemic region , our study is subject to limitations . First , the number of defaulters , as a result of the ongoing leprosy control measures , has been reduced significantly in the past years , and we included only 28 patients who defaulted treatment . This hampered statistical analysis to some degree . Second , non-participation bias , mainly of those who abandoned treatment , may have played a role . Thus , we performed an additional analysis using a less stringent criterion for compliance: interruption of treatment , based on the duration of treatment . However , this analysis did not take into account adherence to drugs taken at home , but was based on appearance at the health care centers for the monthly supervised dose , which should be taken into account in the interpretation of results . Finally , incomplete patients' charts and subsequent missing data hampered analysis regarding clinical variables in some cases . On the other hand , integration of local primary health care professionals and of the State and Municipal Leprosy Control Programs reduced non-participation bias . We conclude that in an area in Brazil where leprosy control actions are well established , adherence to MDT is a result of a complex interaction between different socio-cultural , service-related , drug-related and economical factors . Intermittent problems of drug supply need to be resolved , mainly on the municipality level . MDT producers should consider oral drug formulations that may be more easily accepted by patients . An integrated approach is needed to further improve adherence and other aspects of leprosy control , such as early diagnosis , including the stakeholders involved: patients and their families , health care professionals , and policy makers [6] , [28] , [29] . Improved adherence to MDT will further improve the leprosy control programs and in addition minimize the risk of possibly upcoming drug resistance . | Leprosy is still a public health problem in Brazil , and low adherence to multidrug therapy against leprosy ( MDT ) is an important obstacle of disease control . This may lead to remaining sources of infection , incomplete cure , complications , and multidrug resistance . We performed a study in 78 municipalities in central Brazil , and interviewed leprosy-affected individuals . In total , 3% of patients defaulted , and 18 . 2% interrupted MDT . Risk factors for interruption of treatment include: reduced number of rooms per household ( OR = 1 . 95; p = 0 . 04 ) ; difficulty in swallowing MDT drugs ( OR = 1 . 66; p = 0 . 02 ) ; temporal non-availability of MDT drugs at health center ( OR = 1 . 67; p = 0 . 01 ) ; and moving residence after diagnosis ( OR = 1 . 58; p = 0 . 03 ) . Defaulting MDT was significantly associated with: reduced number of rooms per household ( OR = 3 . 43; p = 0 . 03 ) ; moving to another residence ( OR = 2 . 90; p = 0 . 04 ) ; and low family income ( OR = 2 . 42; p = 0 . 04 ) . Our study shows that defaulting and interruption of MDT against leprosy are associated with some poverty-related variables such as family income , household size , and migration . Intermittent problems of drug supply need to be resolved , mainly on the municipality level . MDT producers should consider drug formulations that are more easily accepted by patients . An integrated approach is needed for further improving control , focusing on most vulnerable population groups and the local health system . |
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It is estimated that 190 million individuals are at risk of blindness from trachoma , and that control by mass drug administration ( MDA ) is reducing this risk in many populations . Programs are monitored using prevalence of follicular trachoma disease ( TF ) in children . However , as programs progress to low prevalence there are challenges interpreting this indirect measure of infection . PCR and sero-surveillance are being considered as complementary tools to monitor low-level transmission , but there are questions on how they can be most effectively used . We use a previously-published , mathematical model to explore the dynamic relationship between TF and PCR throughout a control program and a sero-catalytic model to evaluate the utility of two cross-sectional sero-surveys for estimating sero-conversion rates . The simulations show that whilst PCR is more sensitive than TF at detecting infection , the probability of detecting at least one positive individual declines during an MDA program more quickly for PCR than for TF ( for the same sample size ) . Towards the end of a program there is a moderate chance of a random sample showing both low PCR prevalence and higher TF prevalence , which may contribute to the lack of correlation observed in epidemiological studies . We also show that conducting two cross-sectional sero-surveys 10 years apart can provide more precise and accurate estimation of epidemiological parameters than a single survey , supporting previous findings that whilst serology holds great promise , multiple cross-sections from the same community are needed to generate the most valuable information about transmission . These results highlight that the quantitative dynamics of infection and disease should be included alongside the many logistical and practical factors to be considered in designing a monitoring and evaluation strategy at the operational research level , in order to help subsequently inform data collection for individual country programs . Whilst our simulations provide some insight , they also highlight that some level of longitudinal , individual-level data on reinfection and disease may be needed to monitor elimination progress .
Trachoma is targeted for elimination as a public health problem by 2020 by the World Health Organization . At the global level there has been a high degree of programmatic success in terms of control [1] , as the established intervention strategies have been highly effective in a large proportion of endemic districts . There do , however , remain a number of districts , primarily in Ethiopia , where disease and infection remain persistent and endemic , despite long-term intervention programmes [2 , 3] . Irrespective of a district or region’s current elimination status robust surveillance systems must be able to effectively monitor overall programmatic success , confirm elimination as well as re-emergence [4] , however the appropriate choice of diagnostic and sampling strategy is unlikely to be uniform when trying to address each of the three aforementioned surveillance questions . Currently polymerase chain-reaction ( PCR ) testing of eye-swabs and clinical examination for inflammation are the most established diagnostic tools for monitoring trachoma surveillance within the key indicator group of 1-9 year olds [5] , although the vast majority of programmatic decisions are currently made based only on TF prevalence . However , an increasing number of studies are looking to assess the value of ‘alternative indicators’ ( serology and PCR for trachoma surveillance ) , as it has been suggested that other factors may cause TF-like symptoms making it difficult to ascertain at low TF prevalence levels whether what is being observed is truly TF . Current epidemiological data suggests that following a period of intervention within a community the relationship between PCR and TF prevalence within the community becomes non-linear [6] and the results from the two diagnostics no longer correspond well with one another . Therefore it can be challenging and unclear how to interpret and explain such data in a programmatic setting [6] . As global prevalence of trachoma continues to decline it becomes increasingly challenging to identify and confirm TF cases and the cost of training graders becomes more expensive [7] , therefore sero-surveillance for trachoma is also currently being evaluated as more long-term tool to monitor low-level transmission and re-emergence ( in addition to PCR ) [8 , 9] . For sero-surveillance to be informative for understanding re-emergence it is first important to understand how serology relates to transmission intensity , and the duration of time which individuals in the population remain sero-positive , in order for us to understand what future sero-prevalence in the community will be post-elimination . As programs approach the elimination phase and non-linearity in diagnostic outcomes become apparent or the utility of new surveillance tools needs to be evaluated , well-designed operational research is required before country specific programme surveillance recommendations can be provided . In this study , we provide two suggestions on how future data for trachoma surveillance could be collected in order to help provide insights into the dynamics of disease as population prevalence declines to help guide monitoring and evaluation . Here we evaluate how the proportion of TF and PCR positive individuals changes over the course of an intervention period and during re-emergence to assess if , or how , this impacts our probability of detecting infection or disease within a community . We assess whether these variations can be explained by the differences in the proportion of people in each state that would test positive with each of the different diagnostic tools . With our findings we suggest the types of data that could be collected to fully elucidate and understand the differences in prevalence patterns observed in these data . We then use simulated serological data to assess the identifiability of key epidemiological parameters from single and multiple cross-sections sampling a range of different age groups . Through this we advise on the optimal range of age groups to sample from in order to estimate the sero-conversion and sero-reversion rates for the population and for the key indicator group of 1-9 year olds .
We simulated prevalence data within a single community of 3 , 000 individuals ( 1/3rd of which were assumed to be aged 1-9 years , denoted N1 ) [10 , 11] to assess the probability of identifying TF and PCR positive individuals . To simulate data we used an age-structured ordinary differential equation ( ODE ) transmission model . We used a previously validated model structure that was identified as the most parsimonious and appropriate model when fitting to a single cross-section of age-specific PCR and TF prevalence data [12] . We used the framework of the classic SEIR model structure , with slightly different notation to indicate the different infection states for trachoma Fig 1 . Individuals were susceptible to infection in the ( S ) state , exposed and incubating in the ( E ) state , who would test PCR positive , infected and infectious ( ID ) with detectable TF and who would also test PCR positive and those who remained diseased but were no longer infectious to others ( D ) ( TF positive only ) , individuals in the D state were susceptible to re-infection with a reduced probability . Those who were re-infected then returned to the AI state ( both PCR and TF positive ) [12] . For each endemicity we simulated 3 annual rounds of MDA with azithromycin distributed to the whole community , assuming 80% coverage and a treatment efficacy of 85% [13] . The baseline values of the model parameters are presented in Table 1 . The code for the model is available as a supplementary file . We used the transmission model to generate prevalence data at different sampling intervals to obtain the proportion of individuals PCR and TF positive at any point in time . The first scenario considered that the sampling was conducted at 6 monthly intervals over the course of 3 annual treatment rounds and we evaluated the probability ( Pi ) of detecting at least 1 TF and/or PCR positive individual . The sample size used at each sampling time point was fixed across the 3 year period . The probability of identifying a PCR positive individual in a given sample collected at time i was the proportion of the population who we would expect to be PCR positive: P i P C R = E i + A I i N 1 ϕ ( 1 ) Where ϕ is the sensitivity of the assay . The probability of detecting at least one PCR positive individual was given by: 1 - ( 1 - P i P C R ) N s a m p l e ( 2 ) where Nsample was the sample size , which was used , unless otherwise stated , 50 children [11] . Only AI and D state individuals test positive for TF therefore the probability of detecting a TF positive individual was: P i T F = A i + D i N 1 ψ ( 3 ) where ψ is the sensitivity of the diagnostic test for TF [17] . We note that sensitivity is a difficult parameter to quantify , particularly for TF , additionally it may reduce as local and global prevalence declines . The probability of detecting a single positive individual was similar to the expression for PCR above ( Eq 2 ) . For the second scenario we simulated the model to endemic equilibrium for a range of TF prevalence levels ( between 6% and 50% ) and assessed after 3 rounds of annual MDA what the probability of detecting at least 1 PCR and TF positive individual was at the end of the intervention period only . For the final time point we also simulated sampling Nsample individuals from a population of individuals with this prevalence of PCR or TF , to demonstrate the range of possible outcomes which one would expect if the dynamics followed the transmission model ( i . e . some correlation between PCR and TF positivity ) to evaluate the range of outcomes that occur by chance . Lastly , we assessed the probability of detecting at least 1 positive individual in a situation where infection and disease were re-emerging within the community two years post-intervention . It has been reported that when only one sero-prevalence cross-section is available it can be challenging to estimates key parameters such as the sero-conversion rate ( λ ) and the sero-reversion rate ( ρ ) simultaneously [18] . This is because with only one cross-section is not always possible to distinguish between a scenario where people sero-convert and sero-revert quickly vs one where they sero-convert and sero-revert slowly , as both scenarios can provide comparable fits to a single cross-sectional dataset . As such , it is typically more preferable to have more than one cross-section from the sample population in order to distinguish between these two competing hypotheses . We simulated sero-prevalence data for individuals aged 1-60 years within a community exposed to trachoma . We simulated 2 cross-sectional surveys , one pre and one post-intervention where in the post intervention data we assumed an 80% reduction in transmission occurred 10 years ago . We assumed that no individuals in the population were sero-positive as a result of exposure to any other pathogens , only trachoma . We fitted sero-catalytic models to data from both cross-sections simultaneously and also to each cross-section individually to assess how the precision and accuracy of the estimates was impacted by fitting to 1 vs 2 cross-sections . When fitting the 2 cross-sections together and the post-intervention only cross-section we estimated 4 parameters the: sero-conversion rate ( λ ) , sero-reversion rate ( ρ ) , the proportional drop in transmission ( γ ) and the time at which the drop in transmission occurred ( Tc ) . Sero-negative individuals become sero-positive at a rate λ and sero-positive individuals become sero-negative at a rate ρ [19] . Thus the proportion of sero-positive individuals within the cross-section collected is determined by the following: d P d t = λ ( t ) ( 1 - P ) ρ P ( 4 ) Where in a model that assumes a change in transmission at an instantaneous point in time λ is defined as follows: λ ( t ) = { λ 0 t < T c λ c ≥ T c ( 5 ) For the pre-intervention dataset we only estimated 2 parameters λ and ρ . We also estimated epidemiological parameters from data collected from only 1-9 year olds ( the key indicator group for surveillance ) , from both cross-sections simultaneously and individually . We then assessed how sampling an additional age group as well the current indicator group impacted the accuracy and precision of parameter estimation . We henceforth define accuracy in terms of parameter estimation as how close the paramater estimate was to the true simulated value , and precision as the narrowness of the credible intervals ( CrI ) for the estimate of any given parameter .
We considered a community with a true endemic disease prevalence of 20% ( 16% infection prevalence ) . Following a single round of treatment , the prevalence of PCR detectable infection dropped much more quickly than the prevalence of TF ( Fig 2a ) . Thus , declines in TF prevalence lagged behind the changes observed for PCR . This was consistent for all three rounds of MDA ( Fig 2a ) . Consequently , true TF prevalence was consistently higher than true PCR prevalence within the period evaluated . The proportion of individuals that were prevalent by any diagnostic test ( Fig 2b ) prior to MDA commencing showed that 6% of exposed individuals would have tested PCR-only positive , 67% would have tested PCR and TF positive , while 27% would have tested TF-only positive ( Fig 2b ) . Once the intervention had begun the ratio of individuals in different infection and disease states altered ( Fig 2b ) . As the intervention period progressed the proportion of individuals that tested PCR-only positive fell from 6% to 3 . 5% , while the proportion of individuals that tested both PCR and TF positive declined substantially from 67% to 41% . By contrast the proportion of TF-only positive people increased markedly from 27% to 55% ( Fig 2b ) . The large decline in the overall proportion of people PCR positive helps to explain the marked reduction in the probability of detection for PCR positive individuals ( Fig 2c ) and the slower decline in the probability of detection of TF positive individuals ( Fig 2c ) . As such , the opportunity to identify individuals who were both TF and PCR positive was most likely to occur when the ratio of PCR to TF positive individuals were similar in the population , which was most likely to occur at endemic equilibrium . Once sampling time point 5 was reached we saw a slight indication that re-emergence may be occurring ( Fig 2a ) . For sampling point S5 in comparison to sampling point S4 there was a marked increase in the proportion of individuals that tested PCR and TF positive ( 41% to 63% , Fig 2b ) and a decrease in the proportion of individuals that tested TF-only positive ( 55% to 30% , Fig 2b ) —these differences were reflected in the increase in probability of detection for PCR , but only a minor increase in the probability of detection of TF positives ( Fig 2c ) . When sampling 50 individuals at each time point , we found that with the exception of time point S4 the expected median prevalence was consistently higher for TF than PCR ( Fig 2c ) , however the variance in the expected TF prevalence was larger than for PCR . Additionally , the lack of overlap in the prevalence estimates over the intervention period suggested that at multiple times during the intervention period it is possible that people will test positive with one diagnostic but not the other ( Fig 2c ) . This coupled with a marked reduction in the probability of detection as prevalence declines suggests non-linearity in the results from different diagnostics is not unexpected . Following 2 years of MDA cessation in the community we considered the dynamics of detection during a potential resurgence ( Fig 3a ) . As re-emergence continued the rate at which TF prevalence increased was faster than that of PCR prevalence , this was because only few people test PCR-only positive , but there was an increase in the number of people who test positive PCR and TF as well as TF positive only . This was likely to be because when prevalence begins to increase gradually the rate of re-infection in the TF only state is low , due to an initial low force of infection in the community . Assessing the proportion of individuals by diagnostic state when re-emergence first began , 5 . 5% of exposed individuals would have only tested PCR positive , 67% would have tested PCR and TF positive , while 27% would have tested TF positive only ( Fig 3b ) . As re-emergence continued to occur across the first 4 sampling time intervals the proportion of TF only positive individuals was consistently higher than PCR and TF positive individuals , at sampling time point 4 , 41% of individuals tested PCR and TF positive , while 55% of individuals were TF positive only . In contrast , at sampling time point 5 the ratio of individuals in different diagnostic states was more comparable to that seen in the community prior to MDA being implemented ( Fig 2b ) where 63% of individuals tested PCR and TF positive and 30% of individuals were only TF positive ( Fig 2b ) . As re-emergence continued the probability of detection with both diagnostics increased , the probability of detection increased at a similar rate as time progressed for both tests ( Fig 3c ) , in contrast to the results seen when prevalence was declining during the MDA programme where the probability of detecting PCR positive individuals declined much more rapidly than TF positive individuals ( Fig 2c ) . The variance in the estimated PCR and TF prevalences were slightly higher for PCR detectable infection , whilst the variance in the estimated prevalence of TF and PCR overlapped at some sampling time points , this was not consistent for all sampling points . Highlighting that in a low prevalence re-emergence setting it’s possible we would find individuals PCR and or TF positive ( Fig 3c ) . In the pre-MDA setting at higher levels of TF prevalence the overall proportion of PCR and TF positive individuals was much higher than at lower levels of endemic prevalence . When TF prevalence was 50% , 74 . 5% of infected individuals were PCR and TF positive , but when TF prevalence was 10% , 64% of individuals were TF and PCR positive ( Fig 4a ) . Here the proportion of TF-only positive individuals increased from 20% , to 32% when endemic prevalence was 50% , in comparison to 10% ( Fig 4a ) . Whilst when TF prevalence was 30% the ratio of individuals in each diagnostic state was more comparable to when TF prevalence was 50%: ( 6 . 8% , 70% , 22% ) ( Fig 4a ) . At high levels of endemic prevalence we would expect the greatest proportion of individuals in the population to be both TF and PCR positive because individuals in the TF only state will be continuously re-infected . However , at lower levels of prevalence the rates of re-infection are not as high , resulting in a higher proportion of TF-only positive individuals . Across all TF prevalence levels post-MDA a comparable ratio of individuals in each diagnostic state to the pre-MDA levels was observed , although for a number of initial prevalence levels the proportion of PCR only positive individuals was slightly higher than at endemic equilibrium . For example , the proportion of PCR positives only increased from 7 . 6% to 9 . 7% ( Fig 4b ) . For lower levels of endemic prevalence post-MDA we observed a slight decrease in the proportion of individuals PCR and TF positive , and a small increase in the proportion of individuals who would test only TF positive—for an endemic TF prevalence of 10% the proportion of individuals TF and PCR positive dropped from 64% to 54 . 5% and the proportion of TF only positive individuals increased from 31% to 38% ( Fig 4b ) . Our simulations have suggested that the expected proportion of individuals detectable as both PCR and TF positive declines as the overall prevalence in the community declines , typically as prevalence declines a higher proportion of individuals become TF-only positive . Additionally , the probability of detecting an individual as PCR positive during an intervention period declines much more quickly than for TF , this difference in the probability of detection may also help to explain disparities in the reported prevalence of infection and disease as transmission declines when surveys are conducted . To understand more clearly what is happening when we observe non-linearity in prevalence in 1-9 year olds by PCR and TF surveillance we would need individual level data on PCR and TF prevalence , this would enable us to see whether the proportion of PCR and TF positives in the data is comparable to the ratios that the model predicts . At both high and low levels of transmission the simulations above suggest that the true underlying PCR and TF prevalence levels do correlate with one another . At high levels of infection and transmission PCR and TF prevalence correlate with one another due to rapid rates of reinfection occurring , ensuring that PCR and TF prevalence correlate well with one another . As prevalence declines although the true underlying prevalence’s may correlate at low prevalence sampling noise can play an important role , leading to some samples being collected in which TF prevalence is much higher than PCR prevalence ( Fig 5 ) . For these simulations , both TF and PCR sensitivity mean that prevalence is usually underestimated ( the coloured dots are down and to the left of the black dot indicating true prevalence ) . If sensitivity declines as prevalence continues to fall , then this discrepancy will be larger . Fitting a 2 parameter model to pre-intervention cross-sectional data the median estimates of λ and ρ were lower than the true values of the simulated data: 0 . 04 and 0 . 02 vs 0 . 10 and 0 . 05 , however the credible intervals included the true value ( Fig 6 ) . Fitting the post-intervention dataset in isolation the estimate of λ was close to the true value ( 0 . 13 vs 0 . 10 ) , however the credible intervals were much wider than when the two cross-sections were fitted simultaneously . The median estimate of γ was lower than the true value , with much wider credible intervals in comparison to when 2 cross-sections were fitted together ( Fig 6 ) . Estimates of the ρ and Tc were similar to the true values , however the precision of the estimates were less than when 2-cross sections were fitted simultaneously ( Fig 6 ) . Fitting 2 cross-sections to data from only 1-9 year olds ( 300 samples ) the median estimated λ was similar to the true estimate ( 0 . 12 vs 0 . 10 ) , Tc was also estimated relatively accurately . The estimate of γ was much lower than the true value 0 . 09 ( CrI: 0 . 01-0 . 28 ) , but the credible intervals did include the true value . The estimate of ρ was higher than the true value ( 0 . 09 vs 0 . 05 ) , but the wider credible intervals still included the true value . Fitting to pre-intervention period data from 1-9 year olds , estimates of λ and ρ were much lower than the true values and the credible intervals did not include the true values , estimates were also much lower than when a single cross section for the full dataset was fitted to , suggesting that fitting to a small cross-section of the population is not sufficient to accurately estimate these parameters . For the post-intervention data in 1-9 year olds λ was over-estimated and the credible interval range was large , much wider than when the full single cross section was evaluated . Estimates of ρ were similar when 1-9s were evaluated as when the full cross-section was , however this is likely to have traded off with the estimate of ρ . The median estimate of gamma was below the true value but similar to when all data was fitted to for the single post-intervention dataset , whilst Tc was above the true value . Therefore overall for the pre-intervention data estimates of λ and ρ were markedly different to the full dataset and when the two cross-section were fitted together . For the post-intervention data estimates of ρ and Tc were similar to when the single full cross section was fitted to and not too dissimilar from when 2 cross-sections were fitted together . However the estimate of λ was much higher in 1-9s in comparison to the full single cross section and gamma was similar to when both cross-sections were fitted together , but lower than when all the data was evaluated . When we fitted only 1-9 year olds the precision and accuracy of the estimated parameters was lower than when the all-age data was fitted to , therefore we evaluated whether sampling an additional age group outside of the current indicator group containing the same total number of samples could help improve the precision and accuracy of the parameter estimates . When including an additional age group outside of the current indicator group , for λ the precision and accuracy of the estimate when 20-30 year olds were also sampled was much improved , and the median estimate of 0 . 095 was very close to the true value of 0 . 10 . The precision of the estimated value of γ was generally poorer than when only 1-9s were evaluated , however when a second group was also fitted to the accuracy of the estimate to the true value was much better than when only 1-9s were fitted to . Including age ranges above 30 years slightly reduced the precision of the estimate in comparison to when 10-20 or 20-30 year olds were included . Incorporating an additional age group up to 50 years of age helped improve the precision and accuracy of the estimated value of ρ , highlighting the value of sampling outside of the current indicator group for more precise parameter estimates . The most precise and accurate estimates of ρ were obtained when individuals 20-30 years were in the sample as well . Tc was also most accurately and precisely estimated when 20-30 year olds were included in the sample .
Inconsistencies in the observations from PCR and TF samples can make interpretation of trachoma surveillance data challenging [6] . The similarity observed between PCR and TF prevalence that breaks down as prevalence declines is currently not well understood or fully explained . In this article we have presented a possible explanation as to how these observations in surveillance data may be occurring . Through evaluating the proportion of individuals that would be present in each diagnostic state in the community with a dynamic model we have shown that as prevalence declines within a community the proportion of individuals PCR only or PCR and TF positive declines and a higher proportion of the PCR or TF positive population are only TF positive . These changes in the proportion of people that would test positive in each diagnostic state impact the diagnostic test results , making the proportion of TF and PCR positives less similar . The dynamics of transmission also mean that as prevalence declines the probability of detecting at least 1 positive individual by PCR with a fixed sample size declines much more rapidly than with TF ( assuming a fixed sensitivity of the diagnostic over time ) . We note that an individual-based modelling approach would be needed to fully explain the observations seen in surveillance data . Importantly , individual-level diagnostic data from low prevalence settings would help us to understand whether the proportions of PCR and TF positives align with those predicted by the model . Individual level data is essential for testing the assumptions in this model and providing guidance on sampling strategies for PCR use in routine surveillance . For sero-surveillance we have shown that much more accurate and precise parameter estimates can be inferred when 2 cross-sections are fitted to in comparison to 1 . Particularly for the pre-intervention cross-section , we clearly saw how estimates of λ and ρ could be traded off with one another causing imprecise estimation [18] . When only fitting to data from 1-9 year olds the accuracy and precision of the parameter estimation was reduced in comparison to fitting to the all-age data . However , through the inclusion of one additional age-group we were able to improve the precision and accuracy of all parameter estimates when fitting 2 cross-sections simultaneously . In this situation it appeared that the inclusion of 20-30 year olds as well as 1-9 year olds had the most substantial impact on improving parameter estimation precision and accuracy . Therefore in terms of helping to quantify epidemiological parameters more accurately in the future we would suggest that at least 2 cross-sections be collected from the same community and that an age-group outside of the 1-9 year old group also be sampled . This will ensure that both ρ and the force of infection ( determined by λ ) are estimated more accurately and with less uncertainty . For a number of NTDs the issue of systematic non-compliance/adherence to treatment has been reported and the potential issues it may present to elimination evaluated , ie . Treatment coverage within the community is not random . However , for the purposes of this study when modelling treatment we have assumed coverage is random . If individuals in the community systematically miss treatment then they may remain a reservoir source of infection , helping to ensure on-going transmission . However , for trachoma in particular little to no epidemiological data has been presented to suggest that systematic non-compliance is occurring during MDA rounds , and generally the coverage level is reported to be at least at the target level of 80% , as such , currently no data are available to indicate to what extent systematic non-compliance may be occurring . Despite our assumption of random coverage we do not expect a large impact at these coverage levels for the qualitative conclusions , unless it is quite extreme . However , if those being treated are the same as those being tested , and there were a group who were consistently not treated or measured , that would be more of a problem for the discussion posed here . Also , at these coverage levels , systematic non-compliance becomes a particular issue when non-adherence to treatment is correlated with infection risk , ie if those more at risk of infection continually miss treatment then they are more likely to remain a reservoir source of infection in the community . If this is the case in the communities we have evaluated , we would be more likely to see faster rates of re-emergence of infection but the qualitative observations and diagnostic outcomes reported in the study would be unlikely to change . The are a number of limitations to the study . Firstly , for both diagnostic tests we assumed 100% specificity [17] , if this assumption were relaxed we would expect an increase in the proportion of overall positives , leading to a possible over-estimate of the prevalence , and thus increasing the probability of detection with each diagnostic . However , despite modelling a slightly higher proportion of positives in the population in comparison to what may be true we would not expect the qualitative form of the relationships observed to be altered . Secondly it is possible that the sensitivity and specificity of the PCR and TF diagnostics may alter over time as prevalence declines [6] , whereas we have only considered a single fixed value . Again , it is likely that this assumption would not alter the qualitative relationships observed here , but potentially the magnitude . As prevalence declines it becomes more challenging to detect both infection and disease , therefore we would expect the true probability of detection to potentially be even lower . Furthermore we would expect the noise around the low prevalence estimates to increase substantially [20] . Thirdly , in the sero-surveillance work , we chose to illustrate the importance of a second cross-section over only one , with an assumed reduction in transmission compared with 10 years previously , as we felt this was a case which would illustrate the point most effectively . However , as we approach an era of elimination for trachoma it is becoming increasingly unlikely that the opportunity will arise to conduct 2 surveys 10 years apart in time , therefore the question becomes how frequently should surveys be conducted in order to help accurately estimate the sero-reversion rate . This crucially depends on the rate of antibody decay , which is not yet known . However with exploratory simulation it may be possible to get a better idea on how frequently surveys should be conducted in order to estimate this . Lastly , in settings where urogenital infection is high such as the South Pacific [21] , individuals may also test sero-positive to anti-trachoma antigens as a result of exposure to urogenital chlamydia . This can complicate the estimation of the sero-reversion and conversion rates for exposure due to trachoma , and would potentially need to be accounted for if sero-surveillance data was being collected in individuals past the age of sexual debut in populations with a high incidence of urogenital chlamydia infection . PCR and sero-surveillance are important potential tools for trachoma surveillance , which may offer additional opportunities for understanding transmission dynamics as incidence declines . This paper highlights some of the links between these dynamics and potential survey design . However , there are , of course , many logistical constraints which would need to be considered before they were implemented widely in routine surveillance . From this study we highlight 2 key recommendations for future data collection for trachoma surveillance , in order to understand low-level transmission dynamics in greater detail as population prevalence declines . Firstly , individual-level diagnostic data from low prevalence settings would help us to understand whether the proportions of PCR and TF positives align with those predicted by the model . Secondly , we clearly highlight that for sero-surveillance more accurate and precise parameter estimates can be inferred when 2 cross-sections are fitted to in comparison to 1 . We would therefore recommend at least 2 cross-sectional serological surveys being conducted several years apart in order to improve the estimation of epidemiological parameters from serological data . | Trachoma is a bacterial infection , which , with repeated infections over time , can lead to blindness . The WHO is aiming to eliminate trachoma as a public health problem by 2020 , however at low prevalence levels the relationship between infection and disease prevalence is non-linear , making the interpretation of data from the two diagnostic tests challenging . However , it is hard to know if this is an expected outcome or a biological inconsistency . Sero-surveillance is being considered as an additional tool to understand transmission when infection and disease prevalence data provide different information . We highlight , through mathematical modelling , that a lack of strong correlation between infection and disease prevalence data at low levels of transmission seen in epidemiological data is not unexpected and demonstrate that multiple sero-surveillance surveys should be conducted from at least 2 different age groups in order to accurately estimate epidemiological parameters that will help to monitor low-level transmission . |
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Cyclic paroxysm and high fever are hallmarks of malaria and are associated with high levels of pyrogenic cytokines , including IL-1β . In this report , we describe a signature for the expression of inflammasome-related genes and caspase-1 activation in malaria . Indeed , when we infected mice , Plasmodium infection was sufficient to promote MyD88-mediated caspase-1 activation , dependent on IFN-γ-priming and the expression of inflammasome components ASC , P2X7R , NLRP3 and/or NLRP12 . Pro-IL-1β expression required a second stimulation with LPS and was also dependent on IFN-γ-priming and functional TNFR1 . As a consequence of Plasmodium-induced caspase-1 activation , mice produced extremely high levels of IL-1β upon a second microbial stimulus , and became hypersensitive to septic shock . Therapeutic intervention with IL-1 receptor antagonist prevented bacterial-induced lethality in rodents . Similar to mice , we observed a significantly increased frequency of circulating CD14+CD16−Caspase-1+ and CD14dimCD16+Caspase-1+ monocytes in peripheral blood mononuclear cells from febrile malaria patients . These cells readily produced large amounts of IL-1β after stimulation with LPS . Furthermore , we observed the presence of inflammasome complexes in monocytes from malaria patients containing either NLRP3 or NLRP12 pyroptosomes . We conclude that NLRP12/NLRP3-dependent activation of caspase-1 is likely to be a key event in mediating systemic production of IL-1β and hypersensitivity to secondary bacterial infection during malaria .
Every year , approximately 250 million people are infected with Plasmodium , contributing to significant social and economic instability in the developing countries around the world [1] . One of the main physiological responses to Plasmodium infection is the paroxysm – characterized by cycles of sharp peaks of high fever accompanied by chills and rigors , which coincide with the release of parasites from synchronized infected red blood cells [2] , [3] . Parasite components , such as DNA bound to hemozoin [4] , [5] and glycosylphosphatidylinositol ( GPI ) anchors [6] , trigger the production of proinflammatory cytokines , including interleukin-1 beta ( IL-1β ) , via activation of Toll-Like receptors ( TLRs ) [7] . Furthermore , malaria sepsis [8] leads to an exquisite sensitivity to secondary bacterial infections , in particular non-typhoidal salmonellosis , that often associate with severe disease [9]–[12] . Hence , a better understanding of the mechanisms involved on this inflammatory stage during malaria is critical for the clinical management and prevention of severe disease . TLRs are only one family of the receptors required for the release of active IL-1β , as cleavage of pro-IL-1β by caspase-1 also requires activation of Nod-Like Receptors ( NLRs ) [13] , [14] . Upon stimulation , the respective NLRs oligomerize and recruit pro-caspase-1 directly via a N-terminal caspase recruitment domain ( CARD ) homotypic interaction ( CARD-CARD ) ( e . g . , CARD-containing NLRs such as NLRP1 or NLRC4 ) or indirectly via the adaptor molecule called apoptosis-associated speck-like protein containing a caspase recruitment domain ( ASC ) , as is the case of NLRP3-inflammasome [13] . The inflammasome assembly culminates on activation of caspase-1 , and consequent release of the active form of IL-1β . NLRP3 containing inflammasome is activated in response to a large range of insults , such as pathogens , bacterial RNA , and crystal structures [15]–[17] . The NLRP12 was the first NLR shown to associate with ASC and to form an active IL-1β-maturing inflammasome [18] . This receptor was initially placed as a negative regulator of inflammation [19] , [20] , but it was also shown to be involved in periodic fever of cryopyrinopathies [21] , and to mediate host resistance to Yersinia pestis [22] . Here , we asked what are the molecular steps required for and the physiological role of inflammasome assembly during malaria sepsis . Our results indicate that symptomatic Plasmodium infection triggers inflammasome formation and caspase-1 activation via an intricate process that requires several inflammatory mediators as well as NLRP3 and NLRP12 . Furthermore , we found that the malaria-primed monocytic cells produce deleterious amounts of IL-1β when exposed to a second microbial challenge , being an important component of the overwhelming inflammatory response observed during bacterial superinfection .
The Plasmodium chabaudi AS rodent model was used to evaluate the in vivo activation of inflammasome . The microarray analysis of splenocytes from C57BL/6 at 6 days post-infection demonstrates enhanced expression of various genes from the inflammasome pathway , including Casp1 and Il1b ( Figure 1A ) . Consistently , the FLICA assay , which employs the fluorescent probe FAM-YVAD-FMK and Western blot , indicate that infection with P . chabaudi is sufficient to promote caspase-1 activation ( Figures S1A and 1B ) . Immunoblots evidenced enhanced expression and cleavage of pro-caspase-1 in spleens from P . chabaudi infected mice ( Figures S1B , S1C and 1C ) . The FLICA assay also revealed that macrophages ( CD11b+F4/80+ ) and dendritic cells ( DCs ) ( CD11c+MHC-II+ ) are the main source of active caspase-1 ( Figure 1B ) in the spleens from infected mice . We also observed a high frequency of macrophages and DCs undergoing inflammatory cell death ( pyroptosis ) , as defined by damage of cell membranes evaluated by DNA-7AAD staining and augmented cell size associated with active caspase-1 ( Figure 1B ) . Other splenic cell subsets did not express active caspase-1 or were undergoing pyroptosis during P . chabaudi infection ( Figure S1D ) . Importantly , macrophages and DCs from mice deficient for Asc ( ASC−/− ) and Casp1 ( Casp-1−/− ) were negative for both active caspase-1 and pyroptosis markers during P . chabaudi infection ( Figures 1B ) . Similar results were obtained when we used splenocyte lysates in immunoblots to detect active caspase-1 ( Figures 1C ) . We also evaluated the role of caspase-1 activation in host resistance to P . chabaudi . C57BL/6 , ASC−/− and Casp-1−/− mice injected with 105 P . chabaudi infected erythrocytes displayed similar parasitemia beginning at day 5 , peaking at days 7–8 days , and completely resolved at 25 days post-infection ( Figure S2A ) . No lethality was observed until the end of the experiment . Leuckocytes from mice and humans infected with Plasmodium are hyper-responsive to TLR agonists [23] . This hyperresponsiveness does not occur at all phases of infection . For example , as we show in Figure S2B , mice that are infected with P . chabaudi were hyperresponsive to small doses of TLR ligands , such as lipopolysaccharide ( LPS ) , during the acute phase of the disease ( day 7 ) . This hyperresponsiveness was no longer observed when mice were challenged with low dose LPS 4 weeks after infection . Based on this and other studies [24] , [25] , we hypothesize that bacterial superinfection is a common co-factor for severe malaria . Thus , we used a challenge with low dose of bacterial LPS to mimic secondary bacterial infection and to evaluate the role of inflammasome and IL-1β in this process . C57BL/6 mice infected with P . chabaudi produced low , but significant levels of IL-1β , when compared to uninfected controls ( Figures 1D and S2B ) . Surprisingly , these low levels of circulating IL-1β were produced in an ASC and Caspase-1-independent manner ( Figure 1D ) . In contrast , production of IL-1β , which was extremely high in infected C57BL/6 mice challenged with LPS , was severely impaired in ASC−/− or Casp-1−/− mice ( Figure S2B and Figure 1D ) . In order to check whether the active caspase-1+ cells were the main sources of active IL-1β , highly purified CD11b+ and CD11c+ cells from infected mice were stimulated with LPS and IL-1β measured thereafter . Both CD11b+ as well as CD11c+ cells produced high levels of IL-1β ( Figure 1E ) . The in vivo proinflammatory priming promoted by Plasmodium infection is partially dependent on TLR9 activation by parasite DNA [4] , [23] , [26] . Consistently , expression of various inflammasome genes was no longer enhanced in Myd88-deficient mice ( MyD88−/− ) infected with P . chabaudi ( Figure 1A and Table S1 ) . Accordingly , we did not observe active caspase-1 , and pyroptotic features in macrophages and DCs from MyD88−/− mice infected with P . chabaudi ( Figure 1C and 1F ) . As expected , no systemic IL-1β was detected in sera of infected MyD88−/− mice challenged or not with LPS ( Figure 1G ) . Furthermore , the data presented in Figure S3A show reduced expression of pro-caspase-1 in Tlr9-deficient mice ( TLR9−/− ) . In view of this reduced level of basal expression of the pro enzyme , it is not surprising that expression levels of its active form were decreased in TLR9−/− mice infected with P . chabaudi . Likewise , the levels of circulating IL-1β were partially affected and survival rates increased in infected TLR9−/− mice challenged with the low LPS dose ( Figures S3B and S3C ) . Finally , we found that priming with CpG immunostimulatory oligonucleotides mimics P . chabaudi infection leading to enhanced susceptibility to LPS challenge , but does not induce lethality when used to challenge P . chabaudi infected mice ( Figure S3D ) . The levels of circulating interferon gamma ( IFN-γ ) , tumor necrosis factor-alpha ( TNF-α ) and IL-1β in sera of P . chabaudi infected mice , challenged or not with LPS , are presented in Figure S2B . Very low levels of IFN-γ , TNF-α , and IL-1β were detected in sera from uninfected mice challenged with low dose LPS . The hyperresponsiveness to LPS was observed in the first two weeks , but not at 28 days post-infection ( Figure S2B ) . IFN-γ was shown to be a key mediator of inflammatory priming in febrile malaria patients and in mice infected with P . chabaudi [23] . Furthermore , TNF-α , a critical cytokine in malaria pathogenesis [27] , [28] was shown to mediate expression of inflammasome components and pro-IL-1β [29] , [30] . Indeed , even upon LPS challenge , active IL-1β was not produced in either Ifng or Tnfrsf1a-deficient mice ( IFN-γ−/− ) and ( TNFR1−/− ) respectively , when infected with P . chabaudi ( Figure 2A ) . Normal activation of caspase-1 was observed in macrophages and DCs from TNFR1−/− , but not from IFN-γ−/− mice ( Figures 2B , 2C and S4 ) . In contrast , both IFN-γ and TNF-α were required for expression of pro-IL-1β by spleen cells of infected mice challenged with LPS ( Figures 2D and S4 ) . We next evaluated the requirement of specific NLRs for caspase-1 activation . The P2X7 receptor ( P2X7R ) which senses extracellular ATP , opens a cation-specific channel that alters the ionic environment of the cell [31] culminating on NLRP3-inflammasome assembly under certain conditions [32] , [33] . Here we found that P2X7R is necessary for caspase-1 activation during in vivo infection with P . chabaudi ( Figure 3A ) . Consistently , NLRP3 and NLRP12 were required for activation of caspase-1 , systemic production of IL-1β and pyroptosis ( Figures 3B , 3C and 3D ) . Other cytosolic receptors , i . e . NLRC4 and absent in melanoma 2 ( AIM2 ) were not essential for IL-1β release ( Figure 3D ) . The levels of circulating IL-1β in sera of C57BL6 , NLRP3−/− , NLRP12−/− , ASC−/− as well as Casp-1−/− infected mice , but no challenged with LPS , were not statically different ( Figure 3D ) . The infected Il1r-deficient mice ( IL-1R−/− ) mice are partially resistant to the LPS challenge , despite of the sustained levels of active caspase-1 , IL-1β and TNF-α ( Figures 4A and 4B ) . Importantly , in the different mouse lineages infected with P . chabaudi , we observed a striking correlation of high circulating IL-1β , but not necessarily active caspase-1 , and lethality induced by LPS ( Table 1 and Figure 3D ) . Consistently , treatment with IL-1 receptor antagonist ( IL-1RA ) prevented lethality of P . chabaudi infected mice challenged with LPS ( Figure 4C ) . These results were validated in P . chabaudi infected mice challenged with sub-lethal cecal ligation puncture ( CLP ) ( Figure 4D ) , a classic model for bacterial sepsis , as well as peroral infection with Salmonella typhimurium ( Figure 4E ) . In both cases , bacterial superinfection leaded to rapid lethality that was associated with high circulating levels of IL-1β . Furthermore , mortality of co-infected mice was delayed or prevented by treatment with the IL-1RA . The loads of bacteria translocation were similar in co-infected mice treated or not with IL-1RA ( Figures 4F and 4G ) . We next studied caspase-1 activation and IL-1β release in peripheral blood mononuclear cells ( PBMCs ) from patients undergoing febrile malaria . Two different subsets of monocytes were closely examined: CD14+CD16− or CD14dimCD16+ monocytes ( Figure 5A and S5A ) . Active caspase-1 was constitutively expressed in CD14+CD16− cells from healthy individuals , as previously described [34] . Nevertheless , the frequency of CD14+CD16− cells was augmented on average 3 fold in P . vivax malaria patients . The frequency and intensity of caspase-1 expression , indicated by median fluorescence intensity ( MFI ) , in different monocyte subsets from healthy and P . vivax infected individuals before and after treatment are shown in Figure 5A . Expression of active caspase-1 in association with membrane damage and augmented cell size was also observed in CD14dimCD16+ monocytes ( Figure S5A ) . The representative histograms presented in Figure S5A were obtained from counter plot gates shown in Figure 5A and illustrate the results for active caspase-1 , membrane damage and cell size change from one malaria patient before and after treatment , and a healthy donor . Other cell populations found in PBMCs were negative for active caspase-1 and pyroptosis markers ( Figure S5B ) . We also observed increased cleavage of caspase-1 ( p10 ) in PBMCs from either P . vivax or P . falciparum malaria patients ( Figures 5B , 5C and S5C ) . In addition , LPS-induced release of IL-1β is highly augmented in PBMCs from the same patients undergoing malaria sepsis ( Figure 5B and 5C – bottom panels ) . Furthermore , we observed enhanced expression of inflammasome genes in PBMCs from P . falciparum malaria patients ( Figure S5D and Table S2 ) . As observed in the rodent malaria model ( Figure 2 ) , IFN-γ-priming of primary human monocytes mimics in vivo infection with Plasmodium and augments expression of pro-caspase-1 , pro-IL-1β as well as IL-1β release induced by LPS stimulation ( Figure S5E ) . The nature of malaria-induced inflammasome was further explored by performing a crosslinking assay and confocal analyses . We observed an augmented multimerization of ASC ( Figure 6A ) in PBMCs from P . vivax infected individuals . Additionally , confocal microscopy indicated that inflammasome specks contained either NLRP3 ( green ) or NLRP12 ( red ) , were present in 7 and 10% of monocytes from P . vivax malaria patients , respectively ( Figure 6B and 6C ) . There was no co-localization of NLRP3 and NLRP12 specks . We did not detect monocytes containing NLRC4-inflammasome , and AIM2 specks appeared in a low frequency , ∼0 . 25% of monocytes from infected individuals . No specks were detected in monocytes from uninfected healthy donors ( Figure 6C ) . Figure S6 provides controls of the confocal analysis .
Paroxysm , an acute fever accompanied by chills and rigors , is a hallmark of Plasmodium infection [3] , [35] . While the physiological role of fever is controversial , it can aid in host defense , delaying the growth of pathogens with strict temperature preferences [36] . In fact , prior to the discovery of antibiotics , deliberate infection with P . vivax was used to induce high fever and eliminate infection with Treponema pallidum in neurosyphilis patients [37] . However , fever is also associated with various pathological processes , such as respiratory distress , anemia , and neurological manifestations that cause morbidity and mortality in malaria [3] , [35] . These clinical manifestations are related to the intensity of the systemic inflammatory response , yet the basic details of malaria-induced cytokinemia are not understood . Overall , our results indicate that Plasmodium infection primes innate immune cells leading to the oligomerization of inflammasomes containing , ASC , NLRP3 and NLRP12 , resulting in activation of caspase-1 and the production of copious amounts of IL-1β upon a second TLR activation . An immediate consequence of this inflammatory priming is a drastic reduction in the threshold to septic shock-like syndrome caused by secondary bacterial infection . Although there is a controversy about the role of different TLRs in the pathogenesis of malaria [38]–[41] , various studies indicate that the initial cytokine storm during malaria is driven by TLR activation . Initial studies suggested that GPI anchors were the main P . falciparum molecules responsible for eliciting the production of proinflammatory cytokines during malaria [6] . However , recent studies indicate that parasite derived DNA containing both immunostimulatory CpG and AT-rich motifs , is instead the main force driving the cytokine storm during malaria sepsis [4] , [5] , [42] . Indeed , mice bearing non-functional TLR9 or MyD88 , or treated with a TLR7/TLR9 antagonist display a less pronounced inflammatory response and attenuated pathology during experimental malaria [23] , [26] , [38] , [41] . Moreover , mutations in TLR2 , TLR9 and Mal/TIRAP appear to affect the outcome of human disease [24] , [25] , [43] , [44] . Importantly , infection with Plasmodium in humans leads to a proinflammatory priming and hyperresponsiveness to microbial products [23] , [45]–[47] . This enhanced ability to respond to microbes during immunosurveillance protects the host against infectious insult , but the innate immune response is the classic “double-edged sword” . Thus , the proinflammatory priming means that the innate immune system can overreact to secondary infection , leading to a septic shock syndrome with clinical manifestations . It is noteworthy that malaria is often associated with bacterial infections [12] . Furthermore , a recent study highlights that the chance to develop severe malaria is elevated 8 . 5 fold in children with bacteremia [9] . Similarly , bacterial and viral infections may also act as co-factors for severe P . vivax malaria [10] , [35] , [48] . We propose that inflammasome and IL-1β [35] , [49] , [50] are important components of the proinflammatory priming and the exquisite sensitivity to superinfection during malaria . Here , we demonstrate that both in mouse and human malaria expression of inflammasome genes , caspase-1 activation and pyroptosis are induced in phagocytic cells . As a consequence , during Plasmodium infection the threshold for LPS sensitivity is decreased in at least 100 folds , as compared to non-infected control mice [51] , [52] . Previous studies have shown that 1 . 0 mg of LPS induces the production of high IL-1β levels and lethality [52] , [53] . In our model , challenge with 10 µg of LPS did not promoted or augmented caspase-1 activation in either uninfected or infected mice , respectively . Hence , Plasmodium infection is sufficient to induce inflammasome assembly and caspase-1 activation , but requires a challenge with a low dose of LPS to induce expression of pro-IL-1β and release of high levels of its active form . Our studies in the P . chabaudi mouse model demonstrate that expression of inflammasome genes; pro-caspase-1 as well as pro-IL-1β are all dependent on intact Myd88 function . The MyD88 role on the process is independent of the IL-1 receptor signaling , as macrophages and DCs from P . chabaudi-infected IL-1R−/− mice show normal levels of caspase-1 activation and pyroptosis . In fact , we found that TLR9 explains , in part , the role of MyD88 on caspase-1 activation . As previously reported [54] , treatment with CpG oligonucleotides mimics the P . chabaudi infection making the mice more susceptible to septic shock . Curiously , challenge of infected mice with CpG oligonucleotides did not result in lethality . As CpG DNA and LPS preferentially target DCs and monocyte/macrophages , respectively [55] , our data suggest that the nature of the ligand and differential expression of TLRs are important determinants on priming and cytokine production by Plasmodium infected mice challenged with LPS . We hypothesize that by targeting TLR9 on DCs , Plasmodium infection elicits the IL-12 production , and consequent IFN-γ-dependent inflammatory priming [23] . Priming with IFN-γ mediates caspase-1 activation through the induction of pro-caspase-1 expression , and the lack of either IFN-γ or TNFR1 results in impaired expression of pro- IL-1β . Hence , LPS , but not CpG , stimulates the production of high levels of TNF-α and pro-IL-1β culminating on the release of deleterious amounts of active IL-1β by IFN-γ-primed monocytes/macrophages . An alternative explanation is that by inducing Type I IFN production by DCs , Plasmodium infection inhibits the excessive production of IL-1β [56] , [57] , and prevents lethality induced by TLR9 activation . Another important requirement for caspase-1 activation in rodent malaria was the purinergic P2X7 receptor that under certain conditions mediates NLRP3-inflammasome activation [32] , [58] . Indeed , in vitro experiments reported that synthetic hemozoin induces inflammasome formation , activation of caspase-1 and release of IL-1β by macrophages via NLRP3 [59]–[62] . Here , we demonstrated an in vivo requirement of both NLRP3 and NLRP12 for inflammasome formation and caspase-1 activation during in vivo infection with P . chabaudi . We also report that symptomatic infection with either P . falciparum or P . vivax leads to enhanced expression of inflammasome related genes and caspase-1 activation . Notably , we notice an in vivo assembly of NLRP3 and NLRP12 specks and ASC oligomerization in febrile malaria patients . A diagram detailing the different steps required for inflammasome assembly during malaria sepsis , and release of copious amounts of IL-1β during bacterial superinfection is presented in Figure 7 . It is noteworthy that periodic fever is a main symptom of cryopyrinopathies , a human inflammatory disorder that is associated with mutations in both NLRP3 and NLRP12 genes [21] . These patients have less severe symptoms when treated with IL-1R antagonist [63] . However , the relevance of this process during in vivo Plasmodium infection is controversial . While Dostert and colleagues [59] demonstrated a partial protection to experimental cerebral malaria in NLRP3−/− mice , other studies reported that this pathological process occurs independent of NLRP3 , ASC , Caspase-1 , IL-1R , IL-1β , and IL-18 [62] , [64] . Furthermore , a study in the P . chabaudi model shows that caspase-12 ( but not caspase-1 ) , modulates cytokine responses and development of acquired immunity [65] . Thus , our results indicate that bacterial superinfection overcome the regulatory role of caspase 12 . Our results demonstrate that P . chabaudi infection triggers low levels of IL-1β release in an inflammasome-independent manner . Nevertheless , the parasite-induced NLRP3 and NLRP12 inflammasomes play a key role in the release of high IL-1β levels and hypersensitivity to LPS during malaria sepsis . Importantly , the lethality induced by low dose LPS , peroral infection with S . typhimurium , or sublethal CLP in P . chabaudi infected mice was prevented or delayed by treatment with IL-1R antagonist . Relevant to our findings is the recently published study demonstrating that malaria impairs host resistance to Salmonella infection [11] . They propose that induction of heme oxygenase −1 ( HO-1 ) by Plasmodium infection limits the generation of reactive oxygen species ( ROS ) , an important mechanism of host resistance to Salmonella infection [66] . Another consequence of the decreased ROS generation during malaria would be the uncontrolled activation of caspase-1 and release copious amounts of active IL-1β by phagocytes , as previously reported in patients with chronic granulomatous disease ( CGD ) [67]–[69] . Similarly to murine malaria , extremely high levels of IL-1β are produced by PBMCs from P . vivax or P . falciparum malaria patients exposed to bacterial components [23] , [47] . Importantly , the bacterial load in mice undergoing malaria sepsis was not different from mice treated with IL-1RA , suggesting that the acute lethality caused by bacterial superinfection is due to the deleterious inflammatory response . Overall , our data argue that the IFN-γ , TNF-α and MyD88 role on hypersensitivity to septic shock during malaria is , at least in part , mediated by inflammasome-dependent release of IL-1β . In conclusion , years of research on malaria pathogenesis have funneled into the consensus that the clinical manifestations are often a result of the excessive activation of the innate immune cells . Recent reports have emphasized the important role of bacterial infections as co-factors for severe disease . Here we report that Plasmodium-induced NLRP3 , NLRP12 , ASC containing inflammasomes and caspase-1 activation , which are important events for the overwhelming IL-1β response and morbidity observed in bacterial superinfection during malaria sepsis .
The study with Plasmodium infected patients and healthy controls was approved by the Ethical Committee of Research ( CEP ) from the Research Center of Tropical Medicine ( CEP-CEPEM 096/09 ) ; the Brazilian National Committee of Research ( CONEP/Ministry of Health – 15653 ) ; as well as the Institutional Research Board from the University of Massachusetts Medical School ( UMMS ) ( IRB-ID11116_1 ) . Informed written consent was obtained before enrollment of all subjects ( Plasmodium infected patients and healthy control ) . All experiments involving animals were in accordance with guidelines set forth by the American Association for Laboratory Animal Science ( AALAS ) and with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) and the Federal Law 11 . 794 ( October 8 , 2008 ) . All protocols developed for this work were approved by the Institutional Animal Care and Use Committee ( IACUC ) at the UMMS ( ID - 2371-12 ) , ( ID - 1369-11 ) and also were approved by the Council of Animal Experimentation of Oswaldo Cruz Foundation ( CEUA protocol 38/10-3 ) . LPS O55:B55 from E . coli , nigericin and RIPA buffer were obtained from SIGMA . All mAbs used in flow cytometry , as well as 7AAD and PI were purchased from Ebiosciences . Active caspase-1 detection kit was obtained from ImmunoChemistry Technologies , LLC ( catalog no . 98 ) . Human ( SC-515 ) and mouse ( SC-514 ) anti-caspase-1 ( p10 ) , ASC ( SC-22514-R and SC-271054 ) primary antibodies were obtained from Santa Cruz Biotechnology and anti-actin ( A2066 ) was purchased from SIGMA . Anti-caspase-1 ( p20 ) was donated by Genentech . Anti-NLRP3 ( ab4207 and ab17267 ) , -NLRP12 ( ab64928 and ab57906 ) , -NLRC4 ( ab99860 ) , secondary Anti-rabbit IgG Texas Red ( ab6800 ) , secondary anti-goat IgG Texas Red ( ab6883 ) , secondary anti-goat IgG FITC ( ab6881 ) , and secondary anti-mouse IgG FITC ( ab7057 ) were purchased from Abcam . The secondary antibodies used in western blots were purchased from KPL . Ficoll-Paque from GE Healthcare , and the crosslinker disuccinimidyl suberate [70] was obtained from Thermo Scientific . Protease inhibitor ( EDTA free ) was purchased from Roche and RPMI and DMEM from Gibco . Cytokine ELISA kits and Cytometric Bead Array were obtained from R&D Systems and BD Biosciences , respectively . Flow cytometry mAbs for mouse were from BD Biosciences: CD11c-PE ( cat-557401 ) , CD11b-PE ( cat-557397 ) , CD3-FITC ( cat-553062 ) , B220-APC ( cat-561880 ) , and from Ebioscience: MHC-II-PercpCy5 ( cat-15-5321-82 ) , and F4/80-APC ( cat-17-4801-82 ) , CD4-PE ( cat-12-0041 ) , CD8-PE ( cat-12-008-81 ) , and NKG2d-PE ( cat-12-5872 ) . Flow cytometry mAbs for human were from BD Biosciences: CD16-FITC ( cat-555969 ) , CD14-APC ( cat-561708 ) , CD19-FITC ( cat-555412 ) ; and from Ebioscience: CD3-FITC ( cat-55332 ) , CD4-PE-Cy5 ( cat-555348 ) , CD8-PE-Cy5 ( cat-555368 ) , CD1c-APC ( cat-17-0015 ) , CD123-PercpCy5 . 5 ( cat-45-1239 ) , CD303-FITC ( cat-11-9818-42 ) , CD56-PE ( cat-9012-0567 ) . Patients with acute febrile malaria were seen in the outpatient malaria clinic in the Tropical Medicine Research Center in Porto Velho , Brazil . Patients infected with P . falciparum received a fixed dose of the artemeter ( 20 mg ) and lumefetrine ( 120 mg ) combination four times a day for three days . Patients infected with P . vivax were treated with chloroquine ( 150 mg ) every 8 hours for three days and primaquine ( 15 mg ) in a single dose per day for two weeks . Up to 100 cc of blood was obtained immediately after confirmation and differentiation of Plasmodium infection by a standard peripheral smear; and 30–40 days after therapy with confirmed parasitological cure by PCR . Non-infected subjects living in Porto Velho were also included in the study . The knockout mice , ASC−/− , NLRP3−/− , and NLRP12−/− mice were generated by Millennium Pharmaceuticals and backcrossed 8–11 generations to C57BL/6 background . MyD88−/− and NLRC4−/− mice were provided by S . Akira and R . Flavel . AIM2−/− mice were provided by K . Fitzgerald . C57BL/6 , IL-1R−/− , TNFR1−/− , P2X7R−/− and IFN-γ−/− mice were purchased from Jackson Laboratories . The caspase-1 knockout mice used in this work was originally provided by Dr . Flavell from Yale University School of Medicine . All mice used in experiments were 8–12 weeks of age and bred in isolated conditions in the animal house at CPqRR or at UMMS Animal Facility . The Plasmodium chabaudi chabaudi AS strain was used for experimental infections . This strain was kept in our laboratory as described elsewhere [71] . Briefly , P . chabaudi strain was maintained in C57BL/6 mice by passages once a week . For experimental infection mice were injected i . p . with 105 infected red blood cells and parasitemia followed every three days . Although animals exhibit signs of disease , lethal infection is uncommon . The course of parasitemia in WT mice was similar to that reported in other studies [23] , [72] , . For sub-lethal sepsis induced by CLP , mice were anesthetized , incision made on the anterior abdomen , cecal ligated and punctured two times with a 22-gauge needle . Bacterial load in exudates from the peritoneal cavity and blood 24 hours after CLP was evaluated on Mueller-Hinton agar dishes [74] . For Salmonella infections mice were inoculated intragastrically with Salmonella enterica serovar Typhimurium ( ATCC 14028 ) ( 108 cfu ) . Three days after infection , mice were euthanized , liver aseptically collected , weighed , and homogenized in sterile PBS ( 1∶10 , w/v ) . One hundred µl aliquots of serial decimal dilutions of liver homogenates and blood were plated onto MacConkey agar [75] . PBMCs were isolated from whole blood on Ficoll-paque Plus ( GE Healthcare ) . Cells were then plated into 96-well cell culture plates at a density of 2×105 in DMEM containing 10% FCS and 10 µg/ml ciprofloxacin . Supernatants were collected 24 hours after stimulation and used to determine the levels IL-1β . Monocytes were purified from PBMCs of P . vivax infected patients and healthy donors by using a kit based on immunomagnetic negative selection from StemCell Technologies ( catalog number 19058 ) . PBMCs from acutely infected patients were stained with combinations of the following mAbs: monocytes ( CD14/CD16 ) , T lymphocytes ( CD3+/CD4+ or CD3+/CD8+ ) , B lymphocytes ( CD19 ) , myeloid DCs ( CD1c+/CD19− ) , plasmacytoid DCs ( CD123+/CD303+ ) and NK cells ( CD3−/CD56+ ) . Splenocytes from infected mice were stained with combinations of the following mAbs: macrophages ( CD11b+/F4/80+ ) , DCs ( CD11c+/MHC-II+ ) , T lymphocytes ( CD3+/CD4+ or CD3+/CD8+ ) , B lymphocytes ( B220+ ) and NK cells ( NKG2d+ ) . To each sample , FLICA reagent and PI ( or 7AAD ) were added as indicated . The data were acquired using a LSRII cytometer . Ripa buffer ( 250 µl ) plus protease inhibitor cocktail from Roche were added to a pellet containing 4×107 splenocytes or PBMCs . After 15 minutes on ice , lysates were centrifuged at 13 , 000 g for 20 min at 4°C . The supernatants were separated in a 15%-acrylamide SDS-PAGE , transferred onto nitrocellulose membranes . The membranes were incubated with caspase-1 or pro-IL-1β specific antibodies , and then revealed with HRP-conjugated antibody and the ECL system from Amersham ( Bucks , UK ) . PBMCs were resuspended in a hypotonic solution ( 10 mM Hepes - pH 7 . 9 , 1 . 5 mM MgCl2 , 10 mM KCl , 0 . 2 mM PMSF , 0 . 5 mM DTT , protease inhibitor cocktail Roche ) , incubated on ice for 15 minutes , homogenized ( Kontes 22 mm ) and centrifuged for 8 minutes at 10 , 000 g . The pellets were resuspended in 500 µl of CHAPs buffer ( 20 mM HEPES-KOH - pH 7 . 5 , 5 mM MgCl2 , 0 . 5 mM EGTA , 0 . 1% CHAPs , 0 . 1 mM PMSF , and protease inhibitor cocktail from Roche ) and centrifuged for 8 minutes at 10 , 000 g . Finally , the pellets were resuspended in 200 µl of CHAPs buffer , 4 µl of a 100 mM DSS stock solution to a final concentration of 2 mM , and incubated for 30 min in the dark . The oligomers were resolved on a 12% SDS-PAGE and visualized by immunoblotting with an anti-ASC antibody ( SC-22514-R ) . Cells were fixed with paraformaldehyde 4% , permeabilized using Triton X-100 and stained with anti-NLRP3 ( FITC ) , anti-NLRP12 ( Texas Red ) , NLRC4 ( Texas Red ) and anti-AIM2 ( Texas Red ) . Images were acquired using a Zeiss LSM510 Microscope and analyzed by ImageJ software . Dual color images were acquired by consecutive scanning with only one laser line active per scan to avoid cross-excitation . Measurements of mouse cytokines were performed using commercially available ELISA Duoset kits from R&D Systems . The ranges of detection are 15 . 6–1000 pg/ml for IL-1β; and 31 . 2–2000 pg/ml for TNF-α and IFN-γ . Human IL-1β was detected by ELISA kit from Ebioscience in a range of 4 to 500 pg/ml . For mouse experiments we used splenocytes from 3 C57BL/6 and 3 MyD88−/− mice at 6 days p . i . with P . chabaudi or uninfected . Gene expression was accessed by microarray analysis using a gene chip from Affymetrix ( ∼23 , 000 transcripts ) . Genes were clustered by Tiger Multi Experiment Viewer software using the fold increase value obtained by the reason of the signal intensity values from infected vs . non-infected mice . Differences in gene expression between the 2 conditions were considered significant if p<0 . 05 as defined by unpaired t test . Detailed methodological and analysis for human microarrays are presented in Table S1 . Expression Omnibus ( http://www . ncbi . nlm . nih . gov/gds/ ) accession numbers are GSE35083 for mouse and GSE15221 for human microarrays . All data were analyzed using Graphpad Prism 5 . 0 Software . Cytokine measurements from human PBMCs were analyzed using two-tailed student's t test . Mann-Whitney testing was used for non-parametric analysis when data did not fit a Gaussian distribution . A p value≤0 . 05 was considered to be statistically significant . | Together Plasmodium falciparum and P . vivax infect approximately 250 million individuals , reaping life of near one million children every year . Extensive research on malaria pathogenesis has funneled into the consensus that the clinical manifestations are often a consequence of the systemic inflammation . Importantly , secondary bacterial and viral infections potentiate this inflammatory reaction being important co-factors for the development of severe disease . One of the hallmarks of malaria syndrome is the paroxysm , which is characterized by high fever associated with peak of parasitemia . In this study we dissected the mechanisms of induction and the importance of the pyrogenic cytokine , IL-1β in the pathogenesis of malaria . Our results demonstrate the critical role of the innate immune receptors named Toll-Like Receptors and inflammasome on induction , processing and release of active form of IL-1β during malaria . Importantly , we provide evidences that bacterial superinfection further potentiates the Plasmodium-induced systemic inflammation , leading to the release of bulk amounts of IL-1β and severe disease . Hence , this study uncovers new checkpoints that could be targeted for preventing systemic inflammation and severe malaria . |
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The mammalian odorant receptor ( OR ) repertoire is an attractive model to study evolution , because ORs have been subjected to rapid evolution between species , presumably caused by changes of the olfactory system to adapt to the environment . However , functional assessment of ORs in related species remains largely untested . Here we investigated the functional properties of primate and rodent ORs to determine how well evolutionary distance predicts functional characteristics . Using human and mouse ORs with previously identified ligands , we cloned 18 OR orthologs from chimpanzee and rhesus macaque and 17 mouse-rat orthologous pairs that are broadly representative of the OR repertoire . We functionally characterized the in vitro responses of ORs to a wide panel of odors and found similar ligand selectivity but dramatic differences in response magnitude . 87% of human-primate orthologs and 94% of mouse-rat orthologs showed differences in receptor potency ( EC50 ) and/or efficacy ( dynamic range ) to an individual ligand . Notably dN/dS ratio , an indication of selective pressure during evolution , does not predict functional similarities between orthologs . Additionally , we found that orthologs responded to a common ligand 82% of the time , while human OR paralogs of the same subfamily responded to the common ligand only 33% of the time . Our results suggest that , while OR orthologs tend to show conserved ligand selectivity , their potency and/or efficacy dynamically change during evolution , even in closely related species . These functional changes in orthologs provide a platform for examining how the evolution of ORs can meet species-specific demands .
Odorant receptors ( ORs ) expressed at the cell-surface of olfactory sensory neurons ( OSNs ) in the main olfactory epithelium detect chemical cues in the proximate environment . The ability to detect these cues is crucial for survival of an individual and a species; odorants can signify favorable or toxic food sources , mating preferences , predators and habitat [1] , [2] . The ecological niche that an animal inhabits is directly associated with the OR repertoire in each species [3] , but we do not know how the functional OR repertoire evolves to maximize an animals' fitness . As a first step to understand the functional evolution of ORs , it is essential to compare OR function in related species , paying attention to the evolutionary relationship of each tested receptor . Gene orthology is a key concept in evolutionary and functional genomics . Here we define orthologs as genes derived from a single ancestral gene that diverged since a speciation event; this is in contrast to paralogs , which are genes related via gene duplication [4] , [5] ( Figure S1 ) . Orthologous genes typically perform equivalent–if not identical—functions , especially when comparing closely related species , while paralogs are thought to be more divergent in function [4]–[7] . Indeed , this is a key assumption in a wide variety of biological research , as it allows research from model organisms to translate into health interventions in humans [7] . Comparisons of sequenced genomes show that many orthologous genes can be identified between divergent species , but the functional equivalency of the vast majority have not been experimentally tested [4] . The OR repertoire suffered extensive gains and losses of genes between species , resulting in a significant decline in the number of putatively functional ORs in primate species when compared to the OR repertoires in other mammals [8]–[12] . Comparisons of high-coverage primate genomes revealed that the size of the functional OR repertoire and percentage of pseudogenized ORs is quite similar between human , chimpanzee ( Great Ape ) and rhesus macaque ( Old World Monkey ) [12] , [13] . However , between humans and chimpanzees , approximately 25% of the OR repertoire exists in only one species [13] , suggesting adaptation to differing environments to meet species-specific demands [3] , [14] , [15] . Importantly , many ORs have clear orthologs in closely related species [13] . Sequence similarity of ORs is often used as a proxy for functional variability [9] , [16]–[19] , but this assumption remains largely untested [9] , [20] due to the paucity of functional data matching ORs with ligands . While full-length sequence comparison provides insight into the evolutionary relationship of ORs , it is thought to have less predictive value about the binding sites of these receptors [9] . Man et al . ( 2004 ) proposed a set of 22 amino acids important for ligand binding under the assumption that orthologs will have more similar odor specificities than paralogs , showing a greater conservation in amino acids residues at odor-binding sites than across the entire coding region [9] , [17] . Several studies have examined changes in ligand selectivity and sensitivity of OR orthologs , but these studies were limited to a single receptor set [17] , [21]–[23] . It is still unclear whether or not this change in sensitivity of orthologs is restricted to a specific family of ORs or is a more general phenomenon across all OR orthologs . To understand how the olfactory system has evolved and how the human OR repertoire was shaped , we must identify the functional changes of orthologous ORs between species . With the development of a high-throughput in vitro assay for OR function , we are now able to directly test how well OR sequence similarity predicts function [24]–[26] . Here we conduct the first multi-receptor comparison of ligand selectivity and sensitivity of OR orthologs in primates and rodents and , further , ask if orthologs respond to a common ligand more often than OR paralogs from the same subfamily .
Starting with a set of human ORs previously matched to at least one ligand ( deorphaned ORs ) [27] , we searched the chimpanzee and rhesus macaque genomes for putatively functional orthologous gene sets where there was no evidence of gene duplication in either species ( one-to-one ortholog ) [13] . We identified 18 ORs that have putatively functional orthologs: 12 orthologous trios ( in all three species ) , five human-chimp duos that lack orthologs in macaque and one human-macaque duo that lacks a chimp ortholog . Additionally , we identified 17 mouse-rat ortholog sets where there is at least one known ligand for the mouse OR ( for sequences of ORs used in functional experiments , see Table S1 ) [27] , [28] . Using the similarity of amino acid properties [29] , we constructed a tree of all 390 putatively functional human ORs; the 18 orthologous primate sets used for analysis are highlighted on the tree ( Figure 1A ) . These ORs represent Class I and II receptors , seven of the 13 families described by Hayden et al . ( 2010 ) [3] and contain human ORs shown to be both broadly and narrowly tuned to odors [27] . Our mouse-rat orthologs also cover both Class I and II ORs and represent 17 of the 228 families described by Zhang and Firestein ( 2002 ) [18] ( Figure 1B ) . This suggests that our set of ORs is not significantly biased towards any particular family of ORs . Using the Jukes-Cantor model of nucleotide substitution rates [30] , comparison between orthologs are consistent with the expected phylogeny between species . Human-chimp orthologs are the most similar , followed by human-macaque and chimp-macaque orthologs , with mouse-rat orthologs being the most divergent ( Figure S2A , Table S2 ) . We used Grantham's distance to compare amino acid similarity of the entire open reading frame ( ORF ) among ortholog sets [29] . Our results show our OR sets are not biased to ORs with highly similar amino acid substitutions ( Figure S2B , Table S2 ) . Additionally , we compared the Grantham's distance of the 22 amino acids predicted to be involved in ligand binding by Man et al . ( 2004 ) [17] . We found that all human-chimp orthologs are identical at these 22 positions , while six human-macaque orthologs and two mouse-rat orthologs differ , but the amino acid substitutions are fairly conservative ( Table S2 , Figure S3A ) . Previous literature suggests there is evidence for both positive and purifying selection in the OR repertoire , so to determine if our OR sets broadly represent genes evolving under different selective pressures , we calculated the ratio of nonsynonymous to synonymous substitutions ( ω or dN/dS ) for our 18 orthologous primate sets and for each of the putatively functional 259 human-chimp or 152 human-macaque 1∶1 orthologs from Go and Niimura ( 2008 ) [13] ( Figure S4 ) . In the absence of selective pressure , the rates of synonymous substitutions per synonymous site ( dS ) are equal to that of nonsynonymous substitutions ( change the resulting amino acid ) per nonsynonymous site ( dN ) , thus , ω = dN/dS = 1; ω<1 suggests evidence of purifying selection and ω>1 indicates evidence of positive selection acting on a gene [31] . The distributions of ω are significantly different between human-chimp and human-macaque ( median human-chimp ω = 0 . 608; median human-macaque ω = 0 . 319; z = −9 . 61 , p<0 . 001 , Wilcoxon Rank Sum ) ( Figure S4 ) . All human-macaque gene pairs have ω<1 , while the human-chimp gene pairs show a wide distribution of ω values ( for branch-test and branch-site tests , see [13] ) . The median ω value of mouse-rat orthologs was 0 . 124 , consistent with previous literature [32] . To determine if gene orthology accurately predicts the functional properties of orthologs , we expressed each OR in a heterologous cell system , using a cyclic adenosine monophosphate ( cAMP ) -mediated luciferase reporter gene to assay the function [26] . We tested each orthologous OR set against a panel of chemically diverse odors to compare their ligand selectivity and responses . We chose a panel of 42 chemically diverse odors to represent most of “odor space” using a method described previously [27] , [33] , and tested these chemicals in triplicate at 100 µM ( Figure S5 , Table S3 ) . Within an OR set , the response of orthologs across the panel of odors was consistent , but with differences in the overall magnitude of the response of an OR ( negative values on the y-axis indicate an odor elicited an inhibitory response ) ( Figure 2 , Figure S6 ) . For example , human OR2W1 responded to 12 ligands , while chimp OR2W1 responded to the same 12 odors but with a diminished magnitude . In some instances the response of the human and mouse ORs could not be used to predict the OR function in other species , as the ligands tested did not activate the orthologous receptors . To address concerns that there is a species-specific interaction between ORs and variants of the Receptor Transporting Protein-1 short form ( RTP1S , the accessory protein necessary for functional expression of ORs at the cell surface ) we tested the functional consequence of swapping human and mouse versions of RTP1S with four human and four mouse ORs [25] , [26] , [34] . With the tested ORs , our data did not support the idea that mouse RTP1S was the most efficient for trafficking only mouse ORs and human RTP1S was the most efficient at trafficking the human ORs ( F ( 7 , 80 ) = 1 . 03 , p = 0 . 416 , 2-way ANOVA ) ( Figure S7 ) . To determine how well sequence similarity among orthologs can predict the function of ORs , we plotted the relationship of Jukes-Cantor distance ( J-C , nucleotide ) , Grantham's distance ( amino acid similarity ) using the entire ORF or 22 amino acid positions predicted to be involved in ligand binding , and ω ( dN/dS ) versus the functional distance of the orthologous sets . Here we define the functional distance as the correlation between OR responses across the 42 odor panel , where a receptor responded to three or more odors . Jukes-Cantor and pairwise ω values do not correlate with functional distance ( J-C , rs = 0 . 14 , p = 0 . 36; ω , rs = 0 . 18 , p = 0 . 24 , Spearman's correlation ) ( Figure 3A , 3C ) . Amino acid similarity using the ORF has a correlation to functional distance ( rs = 0 . 38 , p = 0 . 01 , Spearman's correlation ) and amino acid similarity using predicted binding residues is similar but slightly less significant ( rs = 0 . 32 , p = 0 . 04 ) ( Figure 3B , Figure S3B ) . Additionally , we did not find a correlation between sequence similarity and the number of ligands that activate each OR ( ω , rs = 0 . 02 , p = 0 . 92; J-C , rs = 0 . 10 , p = 0 . 49; Grantham ORF , rs = 0 . 03 , p = 0 . 83; Grantham 22AA , rs = 0 . 02 , p = 0 . 87 , Spearman's correlation , data not shown ) . Finally , the removal of primate OR sets that lack orthologs in one of the three primate species ( five human-chimp duos and one human-macaque duo ) from our analysis did not change the overall conclusions when comparing Jukes-Cantor , Grantham and ω values to the functional distance , suggesting that inclusion of these data does not bias our results . We next wanted to examine the functional changes in sensitivity of orthologs to individual ligands by testing each ortholog across a range of concentrations . We selected a single odor for a given OR to construct a representative dose-response curve , fit the data to a sigmoid curve , and then compared the response of the human allele against the primate orthologs and the mouse allele with the rat ortholog using an extra sum of squares test . Looking at both the potency ( EC50 ) and efficacy ( dynamic range ) of each OR to a particular ligand , an orthologous pair was classified as either indistinguishable , hyper/hypo functional ( one OR had both a lower potency and efficacy ) , or undefined ( orthologs were different but potency and efficacy did not change concordantly ) ( Figure S8 ) . Within each OR set , we saw dramatic differences in the overall potency and efficacy to a particular ligand ( Figure 4 , Figure S9 ) . For example , human and chimp OR8K3 orthologs are indistinguishable in their response to ( + ) -menthol ( Extra sum of squares test , F ( 3 , 36 ) = 0 . 13 , p = 0 . 944 ) , but human and chimp OR8K3 are hypofunctional in comparison to macaque OR8K3 when tested with the same ligand ( Extra sum of squares test , human to macaque F ( 3 , 36 ) = 15 . 16 , p<0 . 001 , chimp to macaque F ( 3 , 36 ) = 17 . 40 , p<0 . 001 ) ( Figure 4C , Table S4 ) . Macaque OR10G7 and human OR10G7 are indistinguishable in response to eugenol ( Extra sum of squares test , F ( 3 , 36 ) = 0 . 97 , p = 0 . 418 ) , but chimp OR10G7 is hypofunctional to human ( Extra sum of squares test , F ( 3 , 36 ) = 54 . 54 , p<0 . 001 ) and macaque ( Extra sum of squares test , F ( 3 , 36 ) = 84 . 82 , p<0 . 001 ) ( Figure 4B , Table S4 ) . Additionally , mouse and rat ORs showed differential responses to a given ligand ( Figure 4D , 4E ) . If we define functional differences as changes in potency and efficacy of a common ligand , comparison of our set of human ORs to primate orthologs revealed functional differences 87% of the time , while mouse-rat orthologs differed 94% of the time ( Figure 5A–5D ) . If our human ORs are randomly compared to other human alleles of the same OR , their dose-response curves are functionally different 25% of the time ( Mainland et al . , unpublished ) . In other words , sequence variation within the human population does not alter receptor function as much as sequence variation between orthologs in closely related species . To further address the question of how well orthology predicts function , we compared the response of orthologous ORs from closely related species to that of orthologs from more distant species and to ORs that are classified in the same subfamily based upon sequence similarity . For our sets of primate orthologs , we identified the putative human-mouse ortholog , as defined by the reciprocal ‘best-hit’ with >80% amino acid identity , and human OR paralogs—members of the same subfamily [11] , [35]—and tested these receptors against a common ligand . Comparison of sequences using Neighbor-Joining phylogenetic analyses showed that our primate orthologs are most similar to the human reference OR , while the mouse best-hit ORs are more distantly related . Human paralogs have a unique relationship for each OR group ( Figure S10 ) , but are generally less related than the primate orthologs . In sum , our ortholog and paralog assignment is congruent with speciation and gene duplication events . Overall , we find that orthologs respond to a common ligand 82% of the time while human OR subfamily members respond to a common ligand 33% of the time . Species-specific comparison of orthologs showed human-chimp orthologs respond to a common ligand 93% ( 14/15 ) of the time , human-macaque 67% ( 8/12 ) , and human-mouse ( 10/12 ) 83% . Using the above criteria to define changes in function to a given ligand , we again find significant differences in the potency and efficacy of each OR within a group ( Figure 6 , Figure S11 , Table S5 ) . For example , human OR5K1 and mouse ortholog mOR184-3 respond to eugenol methyl ether ( human to mouse F ( 3 , 39 ) = 21 . 59 , p<0 . 001 , undefined ) but human OR5K1 is hyperfunctional to both chimp and macaque OR5K1 orthologs . None of the three human 5K family paralogs respond to this ligand ( Figure 6A , Table S5 ) . Human OR8D1 is hyperfunctional to chimp and macaque OR8D1 orthologs , while human paralogs 8D2 and 8D4 do not respond ( Figure 6B , Table S5 ) . Mouse ortholog mOR171-22 is hypofunctional to OR8D1 ( F ( 3 , 42 ) = 873 . 69 , p<0 . 001 ) while mOR171-9 does not respond . From our analysis , orthologs respond to a common ligand more often than OR paralogs of the same subfamily , albeit with differences in sensitivity , suggesting that OR paralogs in the same subfamily may show distinct ligand selectivity . Orthologs were more similar than paralogs when measuring Grantham's amino acid similarity using both the entire ORF and the 22 predicted binding residues ( z = −6 . 61 , p<0 . 0001 ORF; z = −7 . 35 , p<0 . 0001 22AA , Wilcoxon Rank Sum ) ( Figure 7A , Table S6 , Figure S12 ) . Orthologs that responded to the same odor as the human reference OR were not significantly different from orthologs that did not respond when comparing amino acid similarity of both the ORF and the 22 predicted binding residues ( z = 0 . 89 , p = 0 . 37 ORF; z = 1 . 22 , p = 0 . 22 , 22AA , Wilcoxon Rank Sum ) ( Figure 7B ) . This suggests that amino acid similarity did not accurately predict OR function among orthologs . While amino acid similarity of the ORF did not predict the response of paralogs ( z = −1 . 47 , p = 0 . 14 , Wilcoxon Rank Sum ) , the amino acid similarity of the 22 predicted binding residues was significantly different , with responding paralogs being more similar in sequence ( z = −3 . 54 , p<0 . 0004 , Wilcoxon Rank Sum ) ( Figure 7B lower panel , Table S6 ) . Our data suggest that comparing the 22 residues involved in ligand binding is better than the entire ORF when predicting the response of OR paralogs . To determine if individual differences in receptor activity are influenced by the amount of receptor at the cell surface , we assessed cell surface expression of each OR [36] , [37] . Using fluorescent immunocytochemistry in live cells , we measured the Cy3 signal intensity of each ortholog and paralog in our receptor set and compared them against the human counterpart . Within each set of receptors , we found cell-surface signal levels did not predict the potency of the OR to a single ligand ( Figure 8 , Figure S13 ) . For example , human , chimp and macaque OR2W1 are similar in their receptor tuning with differences in response magnitude ( Figure 2A , 2B ) and have differences in EC50 values to a common ligand , allyl phenyl acetate , while human paralogs OR2W3 and OR2W5 do not respond to the common ligand ( Figure 4A , Figure 8A ) . Surface labeling of human OR2W1 was not significantly different from either orthologs or paralogs ( Figure 8B , 8C , Table S7 ) , consistent with the idea that surface expression levels of ORs do not predict sensitivity of ORs . No OR surface expression results in no response to known ligands [34] . However , ORs with very intense surface staining are not necessarily responsive to a common ligand , nor are they the most sensitive to that ligand if they do respond . ORs with very few detectable receptors at the surface still showed functional activity , suggesting receptor amount does not dictate response ( Figure S13 ) .
Here we showed that OR orthologs are similarly tuned within an OR set , but that dramatic differences in efficacy and potency to a common odor are frequent . These functional changes are not specific to the primate lineage where significant gene loss has impacted the size of the OR repertoire and a decline in the relative importance of the olfactory system is commonly assumed [8] , [10] , [13] , [14] , [38] , as we see similar changes in rodent orthologs . Comparison of primate orthologs , more distantly related orthologs ( human-mouse ) and human OR paralogs of the same subfamily suggest that orthologs respond to a common ligand more often than other subfamily members . This idea is consistent with the approach used by Man et al . ( 2004 , 2007 ) comparing residues conserved in orthologs and differing in paralogs to predict 22 amino acid residues involved in ligand binding [9] , [17] . In our example of human OR8D1 and mouse orthologs mOR171-22 and mOR171-9 , we see that mOR171-22 responds to a common ligand while mOR171-9 does not ( Figure 6B ) . Comparison of the predicted binding residues shows that mOR171-22 is identical to human OR8D1 at all 22 sites , while mOR171-9 differs at one position ( Figure S12 , Table S6 ) ; each receptor has an overall amino acid identity of 85% to the human ortholog . While the amino acid similarity using the ORF or the 22 predicted binding residues did not predict OR ortholog response , the amino acid similarity of the 22 predicted binding residues was significantly different for paralogs that responded to a common ligand , ( z = −3 . 54 , p<0 . 0004 , Wilcoxon Rank Sum ) ( Figure 7B lower panel , Table S6 ) . Thus , the identification of a true ‘functional ortholog’ must be supported by both bioinformatics and experimental approaches . A significant portion of mammalian ORs are orphan receptors , although progress has been made in matching individual OR-ligand interactions [9] , [27] . Not all ORs with an intact ORF are necessarily functional . Similarities between coding sequences are used to predict functionality in lieu of real experimental data , thus , ORs grouped into the same subfamily are thought to share similar functional properties based upon sequence homology [10] , [38]–[40] . This has also led to the idea that OR orthologs from different species will maintain the same olfactory capabilities [9] , [16] . We have taken the approach of using an evolutionary analysis to examine the relationship of OR-ligand interactions . Several studies have looked at changes in ligand selectivity and sensitivity of OR orthologs using a single receptor . One functional study identified 18 ligands that activate human paralogs OR1A1 and OR1A2 . Human OR1A1 and mouse ortholog Olfr43 shared 9 common odors; twelve amino acids thought to influence ligand binding properties overlap with the prediction from Man et al . ( 2004 ) [17] , [21] . Krautwurst et al . ( 1998 ) showed that the orthologous mouse I7 and rat I7 receptors show changes in the fine-tuning of ligand selectivity , with mouse I7 preferring heptanal to octanal , and the reverse was shown for the rat ortholog [22] . Zhuang et al . ( 2009 ) showed that OR7D4 orthologs from many primate species differed in potency and efficacy to a common ligand [23] , but the question remained whether these functional differences extended to many ORs or if OR7D4 was a special case . Androstenone and androstadienone , the steroid ligands for OR7D4 , are found in human male sweat , urine and semen , and have extreme perceptual differences in the human population [41] , [42] . Additionally , these odorous steroids have been linked to changes in physiological response in both males and females , making them a unique case [43] , [44] . Our data suggest that many ORs show dynamic functional changes during evolution , thus OR7D4 is not the special case . In the case of many mammalian OR orthologs , we show that amino acid changes have dramatic functional consequences on the OR . While the ligand selectivity across OR orthologs is similar , there are changes in the magnitude of response; there are also frequent functional changes in the potency and efficacy of response to a common odor . We find that orthologs respond to a common odor more often than paralogs ( 82% versus 33% , respectively ) . Species-specific comparison of orthologs shows human-chimp orthologs responding to a common odor 93% ( 14/15 ) of the time , human-macaque orthologs 67% ( 8/12 ) of the time and human-mouse orthologs 83% ( 10/12 ) of the time , again with differences in potency and efficacy . While sequence comparison predicts human-macaque orthologs to be more similar than human-mouse best-hit pairs , it is interesting that human-mouse orthologs respond to a common ligand more often . While our results raises a possibility of accelerated functional changes in the macaque lineage , further investigation with more ORs and additional macaque species will be necessary . However , this result must be interpreted with caution , as our sample size may not extrapolate to a larger data set and our assignment of human-mouse orthologs is based upon the mutual best-hit assignment in BLAST . The use of best-hit to define our human-mouse orthologs may result in an OR pair representing a one-to-many relationship , in contrast to the more closely characterized one-to-one relationship among primate and rodent orthologs . However , investigating the phylogenetic relationship of our orthologs does reflect overall predicted speciation events , supporting the idea that human-macaque comparisons should be more functionally conserved ( Figure S10 ) . One caveat in our study is that we do not examine within-species variation; we are using only one allele from one animal in a particular species and using these data to represent the complexity of OR response . Though we do not know functional variation within non-human species , a study comparing human alleles of the same OR shows much lower frequency of functional differences ( 25% ) when comparing response to a common ligand ( Mainland et al . , unpublished ) , suggesting within species variation would account for minor fraction of functional differences in our analysis . In the future , it would be very interesting to address the within-species allelic variation from primates and rodents to examine how sequence variation within species impacts OR function in comparison with what is seen in the human population . Aside from ORs , there are several examples where the functions of orthologous genes are not equivalent [5]–[7] . First , the FOXP2 transcription factor that plays a role in speech and language in humans , is highly conserved from mice to humans , differing at only 3 amino acid positions; despite the similarity , the genetic substitution of mouse Foxp2 with the human ortholog results in differences in ultrasonic vocalizations and affects dopamine levels , dendrite morphology , gene expression and synaptic plasticity of medium spiny neurons in the basal ganglia [45]–[47] . Second , a functional comparison of rhodopsin genes from 35 vertebrate species and 11 reconstructed ancestral genes revealed that each rhodopsin receptor has a specific wavelength of maximal absorption that can be related to the environmental changes of an organism's habitat [48] . There are also studies elucidating changes in ligand selectivity of nuclear hormone receptors using ancestral reconstruction [49]–[51] , showing changes in ligand preference , sensitivity and general function over time . Human and chimpanzee bitter taste receptors at the TAS2R38 locus show changes in potency to the known ligand PTC ( phenylthiocarbamide ) when tested in a heterologous system [52] , raising the possibility that bitter receptors might also show dynamic functional evolution in closely related species . One concern is that our in vitro system does not mimic the in vivo olfactory sensory system and that the expression of primate receptors is systematically misrepresented . Our data suggest that human RTP1S is capable of trafficking ORs from different species and that these receptors can couple to the canonical signaling pathway , sometimes outperforming the human version of the OR . We can also interchange human and mouse versions of RTP1s and do not see a pattern of species-specific RTP-OR interactions ( Figure S7 ) . While our set of human ORs tends to be hyperfunctional in comparison to primate orthologs , it does not mean that human ORs as a whole are necessarily more functional . Our selection process for OR orthologs began with previously deorphaned human ORs , thus , we would expect all human receptors to show a response while the chimp and macaque orthologs are variable . An additional concern is that codon bias between species may alter the expression levels , leading to variability that would not exist in the in vivo system . Our data include two human-chimp orthologs ( OR5P3 and OR8K3 ) that differ at the nucleotide level but have identical amino acid sequence . For these pairs of OR orthologs , the response is indistinguishable across 42 ligands and within a single odor ( Figure 4C , Figure S6 , Figure S9 ) . This suggests that sequence variation at the nucleotide level does not impact the results in our heterologous expression system . Cell surface expression levels of individual ORs do not appear to dictate the changes in potency of a given receptor in our assay , suggesting the functional changes are an inherent property of the receptor itself ( Figure 8 , Figure S13 ) . While this assay may not provide the resolution for low-levels of OR expression , our data is consistent with the idea that cell-surface expression level does not correlate with OR function , though a minimum level of cell-surface expression , facilitated by the accessory proteins , is required [25] , [26] , [34] , [42] . It is important to note that for ORs that do not have known ligands , a lack of response to a given odor does not mean these ORs are nonfunctional . Rather , it is possible that these ORs have acquired a new functional activation by different odors not used in our assay . In addition to ligand binding to the ORs , it is plausible that differences in G-protein coupling or receptor recycling do exist between these receptors and should be investigated in the future . There are several studies that have shown the reliability of this in vitro system to predict in vivo function and odor perception . Comparison of patch-clamp recordings of olfactory sensory neurons expressing the mouse receptor SR1 to heterologous cells transiently expressing SR1 showed similar patterns of activation to a panel of odors; however , the response from the heterologous system did appear less sensitive than the intact olfactory sensory neurons [53] . In another study , variants of human OR7D4 were shown to respond differently to the ligands androstenone and androstadienone in a heterologous system , and these differences translated to perceptual differences to the odors in the human population [42] . However , the in vitro system is not perfect . Our in vitro assay lacks many components of an in vivo olfactory system , including odorant binding proteins , a mucosal layer , intracellular molecules , and sniffing behaviors . The failure of a specific OR to respond to any of the tested odorants must be interpreted with caution , since it may reflect a failure of the OR to function in our assay rather than a lack of sensitivity to the tested odorant . Taken together , probing the functional differences of OR genes across species using an in vitro system is likely to provide useful information in understanding the evolution of the OR family . Comparisons of high-coverage sequenced genomes show that orthologous relationships of genes between divergent species can be identified for a majority of genes [4] , [7] . The idea that the identified function of a gene is upheld for orthologs of that gene across species is a widely accepted assumption for the progress of bioinformatics , as most sequenced genes may never be subjected to functional experimentation; for many examples in closely related species , this idea of equivalent function is upheld [4] , [54]–[61] . In the multi-gene family of ORs , it appears that even with a clear 1∶1 evolutionary relationship of orthologs between closely related species , that functional equivalency , in terms of efficacy and potency , is limited . To further understand functional changes of ORs , it will be necessary to test the functional properties of individual mammalian ORs from many species to determine if any ORs orthologs have undergone changes in ligand selectivity . Hayden et al . ( 2010 ) showed that the olfactory subgenome in different species is directly associated with the habitat in which the animal exists [3] . Additionally , a comparison of fruit fly Drosophila melanogaster with mosquito Anopheles gambiae OR repertoires suggests ecology has shaped the repertoires and that odorants are differentially encoded in a way consistent with ecological niches of each organisms; the two species show different coverage of a chemically defined odor space , tuned to their food-seeking preferences [62] . In the case of rhodopsin orthologs from vertebrate species , the change in functional wavelength adapted to the environment of the organism [48] . While we do not know if these OR functional changes have an impact on the behavior of an individual or species , we can speculate that the OR repertoire in each species has adapted to meet niche- and species-specific demands .
Starting with a list of human and mouse ORs with previously identified ligands [27] , we identified orthologous genes between human , chimpanzee and rhesus macaque [13] and between mouse and rat [28] . Putative human-mouse OR orthologs were defined as the reciprocal best-hit to the human reference OR with a >80% amino acid identity . Selection of additional human OR subfamily members ( paralogs ) were based upon receptors already cloned and available in our library . ORs were amplified from genomic DNA ( Coriell Cell Repositories ) using Phusion polymerase ( New England Biolabs ) and subcloned into a mammalian expression vector , pCI ( Promega ) , containing the first 20 amino acids of human rhodopsin ( Rho-tag ) . Each receptor was sequenced using a 3100 or 3730 Genetic Analyzer ( ABI Biosystems ) . Analysis of sequence variation was conducted in MATLAB . Evolutionary distance of the nucleotide sequences for each ortholog pair was calculated using the Jukes-Cantor model [30] and the amino acid comparisons were made using Grantham's scale [29] . The 22 amino acid alignment was conducted in Seaview using the ClustalW2 alignment method . The pairwise dN/dS ( ω ) was determined using the Nei-Gojobori 1986 method , based on the Jukes-Cantor model [63] The additional sequence data for ORs used in Figure S4 originated in Go and Niimura [13] , who originally conducted this pairwise analysis using the modified Nei-Gojobori method and additionally assessed selection pressure using branch-test and branch-site test for ORs . Neighbor-Joining trees were built in Seaview . Dual-Glo Luciferase Assay System ( Promega ) was used for the luciferase assay as previously described [26] . Rho-tagged ORs ( 5 ng/well ) were transfected into the Hana3A cell line in 95-well plate format ( Thermo Scientific ) along with the human receptor trafficking protein , RTP1S [25] ( 5 ng/well ) , pRL-SV40 ( 5 ng/well; Promega ) , CRE-luciferase ( 10 ng/well; Stratagene ) and muscarinic acetylcholine receptor ( M3 ) [24] ( 2 . 5 ng/well ) . Luminescence was measured using a Polarstar Optima plate reader ( BMG ) . First , all luminescence values were divided by the Renilla Luciferase activity to control for transfection efficiency and cell viability in a given well . Normalized luciferase activity was calculated by the formula ( LN-Lmin ) / ( Lmax-Lmin ) , where LN is the luminescence of firefly luciferase in response to the odorant , Lmin is the minimum luciferase value on a plate or set of plates , and Lmax is the maximum luciferase value on a plate or set of plates . Data was analyzed using GraphPad Prism 5 . 0 and MATLAB . 42 odorants that quantitatively span chemical space were chosen using a method previously described [27] , [33] . Briefly , we generated 20 physicochemical descriptors that predict 62% of the variance in mammalian OR responses [27] for 2683 commonly used odorants . We then divided the 2683 odorants into 42 clusters using k-means clustering . For each cluster , we selected the odorant closest to the centroid of the cluster among odorants that are previously shown to activate at least one OR . If no such ligand was present in the cluster , we selected the odorant closest to the centroid of the cluster to maximize structural diversity . Each orthologous set and a vector control ( Rho-pCI ) were tested against each odorant at 100 µM ( except androstenone , which was applied at 10 µM ) and compared to a no odor control; each comparison was performed in triplicate and statistical significance was assessed by a t-test with a correction for multiple comparisons ( 2-tailed t-test , α = 0 . 05/42 ) . The human and mouse ORs were used as the reference OR and chimpanzee , rhesus macaque and rat orthologs were the variant ORs . The order of odors is the same within a set of OR orthologs , but is different across ORs . Odors are listed in Table S3 . Dose-response curves were constructed using a single odor at concentrations ranging from 10 nM to 10 mM for the OR-odor pairs for each orthologous set . Each concentration was tested in triplicate and a vector-only control ( Rho-pCI ) was included for each odorant . The odors for dose responses were chosen before we determined the responses to the comprehensive set of 42 odors , thus , we did not always choose the best ligand for dose response curves , although the chosen ligands always robustly activated at least one of the tested orthologs . We tested all the orthologs against this panel of 42 odors and since we did not find changes in ligand selectivity among orthologs , we did not go back to test the best ligands for dose-responses . The dose-response data were fit to a sigmoid curve and the resulting data were fit with a 3-parameter logistic model . An odorant was considered an agonist if the 95% confidence intervals of the top and bottom parameters did not overlap , the standard deviation of the fitted log EC50 was less than 1 log unit , and the extra sums-of-squares test confirmed that the odorant activated the receptor significantly more than the vector-only transfected control . For each pair of ORs , we determined if one model fit the data from both ORs better than two separate models using the extra sums-of-squares test . A pair of ORs is classified as hyper/hypofunctional if one OR in the pair had both a higher EC50 ( lower efficacy ) and a lower potency ( dynamic range , or top-bottom ) . A pair of ORs was undefined if the potency and efficacy showed discordant changes . Dose-response curve images were graphed in GraphPad Prism 5 . 0 and further analyzed in MATLAB . For dose-response curves , data was baslined and normalized to the maximum response across a set of ORS ( Figure 4 , Figure 6 ) . In Figure S9 , the left column was normalized to the human or mouse OR variant response to show the differences in receptor basline activity and the identical data in the right column was baselined and normalized to the maximum response across the set of receptors . Classification of ORs using the above criteria was coducted in MATLAB . Hana3A cells were maintained in minimal essetial medium ( Sigma ) containing 10% fetal bovine serum ( Sigma ) ( M10 ) , 500 µg/ml peniciilin-streptomycin ( Invitrogen ) and 6 µg/ml amphotericin B ( Sigma ) [34] . Live-cell surface staining was done as previously described [25] , [36] , [37] . Briefly , Hana3A cells were seeded on poly-d-lysine coared glass coverslips in 35 mm dishes and transfected with 1000 ng OR , 250 ng RTP1S , and 50 ng of EGFP to control for transfection efficiency . 24-hours post-transfection , primary incubation was carried out at 4°C using mouse monoclonal antibody anti-rhodopsin 4D2 ( provided by R . Molday ) diluted 1∶100 in M10 containing 15 mM NaN3 and 10 mM HEPES ( Invitrogen ) for 45 min . Cells were washed in Hanks' balanced salt solution containing containing 15 mM NaN3 and 10 mM HEPES ( Invitrogen ) , followed by secondary incubation with Cy3-conjugated donkey anti-mouse IgG ( Jackson Immunologicals ) for 30 min at 4°C , fixed in 1% paraformaldehyde and later mounted in Mowiol . Slides were analzyed on a Zeiss Axioskop2 microscope at 40x oil lens and images were captured using QImaging Retiga 2000R camera and QCapture Pro 6 . 0 software . For ORs being compared , staining was performed in parallel and pictures were taken with the same exposure time , brightness and contrast . Images were anaylzed in Adobe Photoshop . Cy3 intensity was measured as integrated density ( grey value mean X area ) and quantifed for background levels and cell-surface expression . Background was subtracted from the cell-surface values and the average and S . E . M . were calculated for each receptor . Cy3 intensity was then compared to the human OR in each OR set using a student's t-test ( p<0 . 05 ) . | The mammalian odorant receptor repertoire has been subjected to significant gene duplication and gene loss between species , presumably to adapt to the environment of an organism . However , even in distantly related species , a clear orthologous relationship exists for many genes . While ligands have been identified for several ORs , many of these receptors remain uncharacterized , especially in species other than human and mouse . Due to this paucity of functional data , it is assumed that ORs with similar sequence share functional characteristics . Here we investigate the functional evolution of OR orthologs—genes related via speciation—and OR paralogs—genes related via a duplication event—to provide insight as to how this large gene family has evolved . We show that OR orthologs have similar ligand selectivity to a panel of odors but differ in response magnitude . Additionally , orthologs respond to a common ligand more often than human OR paralogs , but there are vast differences in the potency and efficacy of individual receptors . This result stresses the broad importance of combining evolutionary genomics and molecular biology approaches to study gene function . |
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The HCV NS5A protein plays multiple roles during viral replication , including viral genome replication and virus particle assembly . The crystal structures of the NS5A N-terminal domain indicated the potential existence of the NS5A dimers formed via at least two or more distinct dimeric interfaces . However , it is unknown whether these different forms of NS5A dimers are involved in its numerous functions . To address this question , we mutated the residues lining the two different NS5A dimer interfaces and determined their effects on NS5A self-interaction , NS5A-cyclophilin A ( CypA ) interaction , HCV RNA replication and infectious virus production . We found that the mutations targeting either of two dimeric interfaces disrupted the NS5A self-interaction in cells . The NS5A dimer-interrupting mutations also inhibited both viral RNA replication and infectious virus production with some genotypic differences . We also determined that reduced NS5A self-interaction was associated with altered NS5A-CypA interaction , NS5A hyperphosphorylation and NS5A subcellular localization , providing the mechanistic bases for the role of NS5A self-interaction in multiple steps of HCV replication . The NS5A oligomers formed via different interfaces are likely its functional form , since the residues at two different dimeric interfaces played similar roles in different aspects of NS5A functions and , consequently , HCV replication . In conclusion , this study provides novel insight into the functional significance of NS5A self-interaction in different steps of the HCV replication , potentially , in the form of oligomers formed via multiple dimeric interfaces .
Hepatitis C virus ( HCV ) is a main causative agent associated with chronic liver diseases including chronic hepatitis , cirrhosis and hepatocellular carcinoma [1 , 2] . It is an enveloped , positive-stranded RNA virus belonging to the genus hepacivirus within the flaviviridae family [3] . A single polyprotein translated from the viral genome encodes structural proteins , including core , E1 , and E2 at the N-terminal domain followed by the viral assembly accessory proteins p7 [4 , 5] and NS2 [6–10] . The C-terminal domain encodes five different nonstructural proteins including NS3 , NS4A , NS4B , NS5A and NS5B , which comprise viral replicase complexes [11] and regulate viral assembly [12–17] . NS5A associates with membrane through its N-terminal amphipathic helix ( AH ) domain [18] . Following the AH domain are three major domains called domain I ( DI ) , DII , and DIII . These domains are separated by two low-complexity sequences ( LCS ) called LCSI and LCSII . In general NS5A-DI and DII were shown to play roles in HCV RNA replication [19–21] , and DIII was associated with virus particle assembly [16] . NS5A is a phosphoprotein expressed as a hypophosphorylated form , which is further phosphorylated to a hyperphosphorylated form [22 , 23] . The clusters of highly conserved residues at LCSI served as a target of casein kinase I-α ( CKI-α ) -mediated hyperphosphorylation , and blocking this inhibited the NS5A localization to the lipid droplets ( LD ) -associated , low-density membranes and impaired infectious virus production [17] . The casein kinase II-mediated NS5A-DIII phosphorylation was also shown to affect HCV particle assembly by regulating NS5A and core interaction [24 , 25] . Highly effective anti-HCV therapies are composed of different combinations of antiviral compounds targeting viral enzymes , such as NS3/4A protease and NS5B polymerase , and NS5A [26] . Since NS5A lacks enzyme activity , NS5A inhibitors were discovered via high throughput screening ( HTS ) of chemical libraries by using HCV replicon systems [27 , 28] . NS5A inhibitors are one of the most potent antivirals to date , inhibiting NS5A function during HCV replication at a picomolar range in cell culture-based systems ( reviewed in [29] ) . NS5A inhibitors impaired HCV RNA replication by preventing the formation of double membrane vesicles ( DMV ) that constitute HCV RNA replication factories [30–32] . NS5A inhibitors also blocked intracellular HCV particle assembly [33] . Both AH and DI domains of NS5A were indispensable for DMV formation [19] . Therefore , it is no surprise that NS5A inhibitors , which were shown to inhibit DMV formation , target these two domains , as evidenced by the accumulation of drug-resistant mutations in these areas [34] . Interestingly , many NS5A inhibitors are bivalent ( have two identical pharmacophores ) , and the bivalent form of compounds such as the iminothiazolidinone-based compound from Bristol-Myers Squibb ( BMS-824 ) are much more potent than the corresponding monovalent forms [27 , 35] . These observations fueled the speculation that bivalent NS5A inhibitors target the dimeric form of NS5A . In fact , multiple lines of evidence from different studies support that NS5A functions as a dimer or multimer . These include its dimeric crystal structures [36–38] and the detection of NS5A-NS5A interaction in vitro by using purified proteins [20 , 39] or in vivo in the NS5A ectopic expression system [40 , 41] and HCV-replication system [30 , 42] . In addition , Lim et al . showed that mutations introduced to four cysteine residues in NS5A ( C39 , C57 , C59 and C80 ) , shown to be involved in Zinc binding and required for HCV RNA replication [43] , inhibited purified NS5A self-interaction [20] . However , while these studies clearly demonstrated the correlation between NS5A dimerization and HCV RNA replication , the exact roles of NS5A dimerization in HCV life cycle is still unclear . In this study , we performed a structure-function analysis of highly conserved , surface-exposed NS5A-DI residues located at two different dimer interfaces , predicted from NS5A-DI crystal structures , on NS5A protein interactions and HCV replication . Our data indicate that these dimer interface residues are involved in NS5A self-interaction in Huh-7 cells and that NS5A self-interactions through these residues are critical for different steps of HCV replication by regulating NS5A hyperphosphorylation , subcellular localization and interaction with host protein CypA .
The genotype 1b ( gt1b ) NS5A-DI crystal structures depicted in Fig 1 , designated as 1ZH1 and 3FQM , suggested that the NS5A self-interaction occurs via at least two different interfaces [36 , 37] . To determine the relevance of these two dimeric forms of NS5A in its self-interaction in cells , we performed alanine-scanning mutagenesis of selected dimer-interface residues in full- length NS5A derived from gt1a ( H77 strain ) and gt2a ( JFH1 strain ) and determined the impact of these mutations on NS5A self-interaction . NS5A-DI residues 36 , 37 and 38 were chosen since they provide a continuous surface patch involved in 1ZH1-specific dimer interaction ( Fig 1A , see also S1A Fig showing the 1ZH1 residue interaction networks for detail ) . Residues 112 and 148 were selected since they could form a salt bridge at the 3FQM-specific dimer interface ( Fig 1B , see also S1B Fig showing the 3FQM residue interaction networks for detail ) . As shown in Fig 1C , residues 38 serine ( 38S ) , 112 arginine ( 112R ) and 148 aspartic acid ( 148E ) are completely conserved among 672 HCV sequences derived from all genotypes deposited in the Los Alamos HCV database . Residue 36 encodes major variant phenylalanine ( F ) or minor variant leucine ( L ) . Residue 37 encodes hydrophobic residues , including valine ( V ) , leucine ( L ) , phenylalanine ( F ) or isoleucine ( I ) . We determined the NS5A self-interaction by using a mammalian two-hybrid system as previously described [40 , 41] ( Fig 2 ) . In brief , NS5A was fused to the GAL4 DNA-binding domain ( GAL4/BD ) in pBIND vector and the herpes simplex virus VP16 activation domain ( VP16/AD ) in pACT vector . Then , these two vectors plus a third vector , pGL4 . 3[luc2P/Gal4UAS/Hygro] , encoding five GAL4 binding sites upstream of the firefly luciferase ( F-Luc ) gene , were transfected to Huh-7 cells . Two days following the transfection of these plasmids , cells were lysed to detect NS5A self-interaction efficiency by measuring F-Luc activity normalized by that of Renilla reniformis luciferase ( R-Luc ) expressed from a pBIND vector to adjust transfection efficiency . Under these experimental conditions , stronger protein-to-protein interaction resulted in higher F-Luc/R-Luc ratio . These ratios derived from the basal vectors ( pACT and pBind ) and the known interacting-pair [pACT-MyoD ( myogenic regulatory protein ) and pBind-ID ( negative regulator of myogenic differentiation ) ] were used as a protein-protein interaction negative ( - ) control and positive ( + ) control , respectively ( Fig 2A ) . Robust interaction was detected between a wild-type ( wt ) NS5A pair derived from a gt1a HCV H77 ( designated as H-wt ) , evidenced by the higher F-Luc/R-Luc value from H-wt pair than that from the ( + ) control . Interestingly , NS5A from a gt2a HCV JFH1 ( designated as J-wt ) showed an even stronger self-interaction than did H-wt ( Fig 2A ) . Next , we determined the impact of alanine mutations in the residue lining the 1ZH1 and 3FQM NS5A dimer interfaces , including 36A/37A/38A ( mutation group a ) and 112A/148A ( mutation group b ) , respectively . These H77 NS5A mutants , designated as H-a or H-b , showed a significantly reduced NS5A-NS5A interaction compared to that of H-wt ( Fig 2B ) . The mutants having both mutations , designated as H-ab , showed a stronger reduction in NS5A self-interaction ( Fig 2B ) . Similarly , JFH1 NS5A-NS5A interaction was also significantly reduced when these mutations were introduced separately or combined ( Fig 2C ) . In summary , these data suggest that NS5A from gt1a H77 and gt2a JFH1 form dimers/oligomers via at least two different dimeric interfaces in Huh-7 cells . To narrow down the critical residues involved in H77 NS5A self-interaction , we introduced individual alanine mutations to residues 36 , 37 , 38 , 112 or 148 in H77 NS5A two-hybrid vectors and designated them as H-36A , H-37A , H-38A , H-112A or H-148A , respectively . Then the effects of these individual mutations on NS5A self-interaction were determined by measuring the dual luciferase ( F-Luc and R-Luc ) activities from Huh-7 cells transfected with these plasmids as described above . As shown in Fig 3A , all mutations , with the exception of 38A , significantly reduced NS5A self-interaction down to the levels between ~10 to 50% of wt H77 NS5A-NS5A interaction efficiency on average . Interestingly , the 38A mutation significantly enhanced NS5A self-interaction ( ~30% above wt level on average ) . We speculate that 38A mutation altered the non-covalent interactions of a residue 157 arginine ( 157R ) , which was a wt 38S interactor , inadvertently stabilizing the NS5A inter-molecular interaction ( see S1 Fig ) . Next , we determined the effect of mutations modulating NS5A self-interaction on infectious gt1a H77D replication [44] , following electroporation of wt and mutant H77D RNAs to Huh-7 cells , then , determining the expression of viral proteins and RNAs at different time points post electroporation by western blot and quantitative RT-PCR analyses ( Fig 3B and 3C ) . Results indicated that altered H77 NS5A self-interaction inhibited viral replication ( Fig 3B and 3C ) . In detail , the 37A and 148A mutations caused moderate-to-substantial reductions in viral protein expression and RNA replication ( Fig 3B and 3C ) . The 36A and 112A mutations completely blocked viral replication as evidenced by undetectable viral protein expression and the lack of viral RNA increase over the replication-defective H77 mutant AAG [45] ( Fig 3B and 3C ) . The 38A mutation , which moderately enhanced NS5A self-interaction , also reduced HCV RNA replication ( Fig 3C , left panel ) . These data suggest that optimal mode of H77 NS5A self-interaction is critical for efficient gt1a virus RNA replication . Alternatively , 38A mutation could have disrupted other functions of H77 NS5A unrelated to NS5A self-interaction , or altered overall conformation of NS5A , resulting in H77D replication defect . To better understand the exact role of H77 NS5A residue 38 in HCV RNA replication , we have tried to obtain the revertant of H77D/38A mutant . However , our multiple attempts to obtain H77D/38A revertant were unsuccessful . Next , to test the effect of H77 NS5A self-interaction-altering mutations on infectious virus production , we determined the intracellular and extracellular infectivity titers following electroporation of H77D RNA having these individual mutations to Huh-7 cells . As expected , replication-defective H77D/36A or H77D/112A mutants showed no evidence of virus production ( Fig 3E ) . Other mutants , including H77D/37A , H77D/38A and H77D/148A showed severely impaired virus production ranging between ~10- to ~1 , 000-fold reductions in intracellular infectivity and ~3- to ~100-fold reductions in extracellular infectivity compared to those of the wt at the 72-h time point ( Fig 3E ) . None of the virus-producing H77 NS5A mutants impaired the secretion of virus particles , since the percentage of intracellular infectivity per total ( intracellular plus extracellular ) infectivity was similar between the wt H77D and these mutants ( Fig 3F ) . Supporting this , the ratio of extracellular and intracellular HCV RNA was similar between the wt H77D and its virus-producing NS5A mutants ( Fig 3D ) . On the other hand , virus particle-assembly efficiency was significantly reduced in these H77 NS5A mutants , since the relative levels of total infectivity per total HCV RNA in these mutants were significantly lower than that from wt H77D ( Fig 3G ) . Next , we performed a density gradient centrifugation of extracellular virus present in the cell culture supernatant to determine whether H77 NS5A mutations , including 37A , 38A and 148A , impaired the H77D infectivity by modulating virus particle density or specific infectivity . The results shown in Fig 4 indicate that the density distributions of wt H77D and three different NS5A mutant viruses are similar , since the relative percentages of HCV RNA and infectivity present in different density fractions are almost identical between the wt and mutants ( Fig 4B and 4D ) . We also did not detect any significant alteration in the specific infectivity of virus particles between the wt and mutants calculated as a ratio of infectivity per HCV RNA at different density fractions ( Fig 4E ) . Interestingly , the majority of infectious particles ( >95% ) from both wt and mutants banded at a density between 1 . 060 and 1 . 200 g/cm3 , centering around at 1 . 100 g/cm3 ( Fig 4B ) , whereas the peak of specific infectivity was detected at a density of ~1 . 060 g/cm3 ( Fig 4E ) . These data indicate that a small fraction of low-density gt1a virus particles is more infectious than the majority of virus particles , consistent with previous reports [46 , 47] . In summary , these results suggest that H77 NS5A self-interaction is critical for infectious particle-assembly efficiency , but has little impact on specific infectivity and density of infectious virus particles as well as their egress efficiency . We introduced individual alanine mutations to residues 36 , 37 , 38 , 112 or 148 in JFH1 NS5A two-hybrid vectors and designated them as J-36A , J-37A , J-38A , J-112A and J-148A , respectively . We transfected these two-hybrid plasmid sets for each mutant to Huh-7 cells to determine the impact of these individual mutations on JFH1 NS5A self-interaction . It is interesting that most of the mutations introduced individually to JFH1 NS5A showed relatively moderate defects in NS5A self-interaction , retaining between ~60 to 75% of wt JFH1 NS5A self-interaction on average , except for an 36A mutation , which caused severe defects in NS5A self-interaction ( ~20% of wt interaction ) . Unlike the 38A mutation in H77 NS5A , which increased NS5A self-interaction , the same mutation in JFH1 NS5A reduced NS5A self-interaction . In the structural perspective , the key inter-domain interactor of wt residue 38S in H77 NS5A is 157R , which provides both van der Waals and hydrogen-bond interactions to 38S ( S1A Fig ) . However , in JFH1 , the corresponding 38S interactor is 157 glutamine ( 157Q ) , which shows substantial physico-chemical differences from 157R . We speculate that different properties of residue 157 in H77 and JFH1 contributed a genotype-specific difference in the intermolecular interaction phenotypes of 38A mutation . Also contrary to 112A mutation in H77 NS5A , which caused severe defects in NS5A self-interaction , the equivalent mutation in JFH1 NS5A did not impair NS5A self-interaction in cells ( compare Figs 3A and 5A ) . The wt residue 112R in H77 NS5A is involved in a network of electrostatic interactions ( “salt bridge” pattern ) with the residues 48 arginine ( 48R ) and 148 glutamic acid ( 148E ) in the partnering NS5A resulting in intra/inter-molecular 48R-148E-112R interaction network ( S1B Fig ) . We speculate that the disruption of this electrostatic network mediated by the 112A mutation was substantial enough to impair the intermolecular H77 NS5A self-interaction . However , since the residue 48 alanine ( 48A ) in JFH1 NS5A does not support a similar kind of interaction network , it is reasonable to assume that a different kind of , genotype-specific interaction network surrounding the residue 112 may have negated the impact of 112A mutation on JFH1 NS5A self-interaction . Alternatively , R112 residue in JFH1 NS5A may not contribute to NS5A self-interaction unlike the same residue in H77 NS5A ( see discussion ) . To determine the effect of these individual JFH1 NS5A mutations on viral RNA replication , we introduced them to H77-JFH1 chimeric HCV ( HJ3-5 ) encoding JFH1 NS3-NS5B proteins [6 , 48] ( Fig 5B ) and then analyzed viral protein expression and HCV RNA levels following electroporation of these RNAs to Huh-7 cells . The results of these experiments could be summarized as follows . First , viral RNA replication was undetectable for HJ3-5/36A mutant ( Fig 5B and 5C ) . Thus , a 36A mutation in NS5A , which impaired both H77 and JFH1 NS5A self-interaction , blocked both H77 and JFH1 RNA replication ( Figs 3 and 5 ) . We also detected reduced replication of an HJ3-5/148A mutant , similar to the case of an H77D/148A mutant . The relatively moderate impact of 148A mutation on HJ3-5 replication , compared to its more severe effect on H77D replication , correlates with its weaker impact on self-interaction of JFH1 NS5A compared to that of H77 NS5A ( compare Figs 3 and 5 ) . Overall , these data indicate that JFH1 NS5A self-interaction is also critical for JFH1 replicase-mediated viral RNA replication . Second , both 37A and 38A mutants moderately increased the relative levels of intracellular HCV RNA compared to that of wt HJ3-5 at 72 h post electroporation ( Fig 5C , left panel ) . These results may indicate that these two mutations in JFH1 NS5A potentially enhanced HCV RNA replication . However , additional data indicate that this may not be the case . For example , we detected much lower levels of extracellular HCV RNAs from these mutants compared to those from the wt HJ3-5 ( Fig 5C , right panel ) . In addition , relative extracellular/intracellular HCV RNA ratios from these mutants were over 10-fold lower than that from the wt at the 72-h time point ( Fig 5D ) . Based on these data , we believe that decreased viral RNA secretion , rather than enhanced viral replication , has caused the relatively high levels of intracellular 37A or 38A mutant RNA accumulation . Third , an 112A mutation in JFH1 NS5A completely blocked HCV replication despite having no effect on its self-interaction . At a first glance , these data seem to contradict to the potential role of NS5A self-interaction in HCV replication However , previous study determined that HCV replication defect caused by 112A mutation could be attributed to the inhibition of NS5A-RNA interaction and dysregulation of NS5A’s role in HCV translation [49] . Fourth , the 37A , 38A and 148A mutations impaired the JFH1 NS5A hyperphosphorylation ( Fig 5B ) . This phenotype was also observed from the corresponding H77 NS5A mutants , although the hyperphosphorylation efficiency of H77 NS5A was quite low compared to that of JFH1 NS5A ( compare Figs 3B and 5B , see also [50 , 51] ) . In summary , these data indicate that JFH1 NS5A self-interaction also plays an important role in JFH1 replicase-mediated RNA replication , despite some genotype-specific differences . As expected from reduced viral RNA replication , the HJ3-5/148A mutant showed significantly reduced intracellular and extracellular infectivity during the entire time course of experiments ( Fig 5E ) . In the case of 37A or 38A mutants , while they also showed significantly lower intracellular and extracellular titers at the 24-h time point , by 48 h , their titers rapidly caught up with those from the wt HJ3-5 . However , by the 72-h time point , extracellular infectivity from these two mutants was significantly lower than that from the wt , while intracellular infectivity remained similar to that of the wt ( Fig 5E ) . The extracellular/intracellular RNA ratios of the 37A , 38A and 148A mutants were also significantly lower than that of wt HJ3-5 at the 48- and 72-h time points ( Fig 5D ) . In addition , the percentages of intracellular infectivity per total infectivity of all three mutants were significantly higher than that of the wt at 72 h ( Fig 5F ) . These results suggested a decreased egress of these mutant viruses compared to the wt virus . Interestingly , none of these mutants affected virus particle-assembly efficiency , since the relative ratios of total HCV infectivity per total HCV RNA were similar between wt and these mutants ( Fig 5G ) . Overall , it is remarkable that same mutations introduced to highly conserved residues 37 , 38 and 148 in NS5A showed genotype-specific effects on virus production in that H77 NS5A mutants impaired virus assembly , not viral egress , but JFH1 NS5A mutants impaired viral egress , not virus assembly ( Figs 3 and 5 ) . Next , we determined the density and specific infectivity of extracellular HJ3-5 wt as well as those of 37A , 38A and 148A mutant viruses by performing density gradient centrifugation . In general , comparable density distribution patterns were detected between wt HJ3-5 and mutant viruses , as judged from the relative density distributions of viral RNA and infectivity ( Fig 6 ) . However , the percentage of wt HJ3-5 in high-density fractions ( >1 . 201 g/cm3 ) was higher than those of mutants . In fact , ~12% of viral RNA from wt HJ3-5 was detected in these high-density fractions compared to ~3% from each of the JFH1 NS5A mutants ( Fig 6D , right panel ) . However , infectivity of wt HJ3-5 in these high-density fractions was low accounting for less than 2% of total infectivity ( Fig 6B , right panel ) . These results suggest that a significant portion of poorly infectious , high-density immature particles might have been secreted from wt HJ3-5-replicating cells , probably due to highly efficient virus egress ( Fig 5F , see discussion ) . On the other hand , relative titers of mutant viruses at low-density fractions ( <1 . 059 g/cm3 ) were 6- to 7-fold higher than those of the wt HJ3-5 ( Fig 6B , right panels ) . Due to this , the specific infectivity of mutant viruses at low-density fractions was relatively higher than that of the wt HJ3-5 ( Fig 6E ) . These results suggest that too efficient virus egress might have negative impact on infectious virus maturation ( see discussion ) . The interaction between NS5A and CypA is critical for HCV RNA replication [52] . Since most of the NS5A self-interaction mutants , especially those derived from gt1a H77D , significantly impaired HCV RNA replication , we asked whether reduced NS5A self-interactions in these mutants might have impaired NS5A-CypA interactions , resulting in decreased HCV RNA replication . To measure the interaction between NS5A and CypA quantitatively , we used a checkmate assay as this method successfully measured the interaction between NS5A and CypA in the previous study [41] . As shown in Fig 7A , the level of interaction between H77 NS5A and CypA was comparable to that of the positive ( + ) control . Interestingly , interaction between JFH1 NS5A and CypA was stronger than that between H77 NS5A and CypA ( Fig 7A ) . Next , we determined the interaction between CypA and H77 NS5A dimer-interface mutants . The results showed that the mutations that significantly reduced NS5A-NS5A interaction , including H-36A , H-37A , H-112A and H-148A ( Fig 3A ) , also significantly impaired the NS5A-CypA interaction ( Fig 7B ) . These results indicate that effective H77 NS5A self-interaction is critical for H77 NS5A and CypA interaction . These data also suggest that reduced NS5A-CypA interaction in H77 NS5A mutants was responsible for defective viral RNA replication ( Fig 3C ) . The NS5A self-interaction-enhancing H-38A mutant did not significantly reduce the NS5A-CypA interaction ( Fig 7B ) , which correlates with the relatively moderate effect of this mutation on viral RNA replication ( Fig 3C ) . The results of NS5A dimer-interface mutations on the interaction between JFH1 NS5A and CypA could be summarized as follows . First , the J-36A mutant that showed the most significant defect in JFH1 NS5A self-interaction ( Fig 5A ) also had the most substantial defect in the JFH1 NS5A and CypA interaction ( Fig 7C ) , which correlates nicely with the undetectable level of HJ3-5/36A RNA replication ( Fig 5C ) . Second , both J-37A and J-38A did not show any significant effect on JFH NS5A-CypA interaction ( Fig 7C ) , which also correlates with their relatively minor effects on NS5A self-interaction and viral RNA replication ( Fig 5A and 5C ) . Third , despite having no impact on JFH1 NS5A self-interaction ( Fig 5A ) , 112A mutation impaired the JFH1 NS5A-CypA interaction ( Fig 7C ) , suggesting that the role of J112R on NS5A-CypA interaction could differ mechanistically from other residues involved in NS5A self-interaction . Fourth , the J-148A mutant showed a reduced NS5A-CypA interaction ( Fig 7C ) , correlating with reduced J-148A self-interaction ( Fig 5A ) and impaired replication of the HJ3-5/148A mutant ( Fig 5C ) . In aggregate , these results suggest that NS5A self-interaction contributes to NS5A-CypA interaction , and that the impaired viral RNA replication observed from the majority of NS5A self-interaction-defective mutants could be due to a defective NS5A-CypA interaction . Hyperphosphorylation of NS5A was shown to contribute to infectious HCV production by regulating NS5A recruitment to low-density membranes in the vicinity of lipid droplets ( LD ) and facilitating NS5A-core interaction [17] . Since NS5A hyperphosphorylation , as well as infectious virus production , were reduced in NS5A self-interaction mutants , we asked whether these phenotypes were caused by impaired NS5A subcellular localization and/or its interaction with core protein . To facilitate the detection of the NS5A in an immunofluorescence assay and the NS5A-core interaction in a co-immunoprecipitation assay , we used HJ3-5/NS5AYFP , which encodes YFP-tag within the NS5A-DIII and is capable of virus production ( Fig 8A ) [6] . First , we confirmed that NS5A dimer interface mutations in HJ3-5/NS5AYFP also impaired NS5A hyperphosphorylation and virus production ( Fig 8A ) . To determine the NS5A subcellular localization , Huh-7 cells electroporated with either wt HJ3-5/NS5AYFP or its 37A , 38A and 148A mutants were subjected to confocal imaging analysis following a LipidTOX deep-red lipid staining to detect the LD . As shown in Fig 8B , we frequently detected a tight association between wt NS5A ( measured by YFP fluorescence ) and LD . However , in the case of NS5A mutants , a majority of NS5A was detected as the distinct foci in the cytoplasm without the tight LD association ( Fig 8B ) . In fact , significantly lower degrees of NS5A-LD co-localization were calculated from the mutants compared to those from the wt , based on Pearson’s correlation measurements derived from the confocal images obtained from ~30 different cells ( with the means of Pearson’s correlation coefficients for wt equaling 0 . 5206 versus 0 . 2598 , 0 . 2424 and 0 . 3245 for 37A , 38A and 148A mutants , respectively ) ( Fig 8C ) . The NS5A-core interaction was measured by two different methods: NS5A-core co-localization and co-immunoprecipitation ( co-IP ) . NS5A-core co-localization was determined by performing confocal imaging analysis following immunostaining of core by using core-specific antibody in cells replicating HJ3-5/NS5AYFP ( Fig 9A ) . As shown in Fig 9B , a strong degree of co-localization was detected between wt NS5A and core ( with a mean of Pearson’s correlation coefficient equaling 0 . 7726 ) . However , lesser degrees of co-localization between these two proteins were detected from the 37A , 38A and 148A mutants ( with the means of Pearson’s correlation coefficients equaling 0 . 5742 0 . 6564 and 0 . 6100 , respectively ) ( Fig 9B ) . Next , we determined the NS5A-core interaction by performing a GFP-pull down assay . As shown in Fig 9C , compared to wt , NS5A mutants showed reduced NS5A and core co-IP efficiency ( ~50% lower than wt ) expressed as a ratio of co-IP-core level per immunoprecipitated ( IP ) -NS5AYFP . These NS5A-core co-IP results correlate well with their co-localization data ( Fig 9A and 9B ) and indicate that NS5A-core interaction in NS5A mutants was reduced compared to that in wt . These results indicate that NS5A self-interaction regulates subcellular localization of NS5A and NS5A-core interaction . Since previous study showed the core-dependent recruitment of NS5A to LD-associated membranes [53] , it is possible that NS5A self-interaction is critical for its interaction with core , which then promotes NS5A localization to LD-associated membranes . Alternatively , NS5A self-interaction promoted the NS5A localization to LD-associated membranes , consequently enhancing the interaction between NS5A and core at these membranes . Interestingly , all three NS5A mutants defective in self-interaction also reduced the core localization to the LD ( Fig 9D ) . These results are consistent with recent study by Yin et al . , which showed the reduced core localization to the LD by using other NS5A mutants ( V67A or P145A ) , also defective in NS5A self-interaction [42] . To better understand the role of NS5A self-interaction in HCV replication , we attempted to isolate revertants with primary- or second-site mutations that could rescue viral replication . This was done by continuously sub-culturing the Huh-7 cells electroporated with H77D or HJ3-5 encoding different NS5A interface mutations and monitoring the viral replication every 3 days . Among mutants that showed no evidence of transient replication , including 36A and 112A mutants in H77D or HJ3-5 background , only the HJ3-5/36A mutant showed strong evidence of viral replication and infectious virus production by day 10 post electroporation of this viral RNA to Huh-7 cells . Sequencing of the entire coding region of secreted HJ3-5/36A-derived revertant collected at 28 days post-electroporation cell culture supernatants reveled a single mutation in JFH-1 NS5A at residue position 36 to valine ( 36V ) . This was not a wt reversion since the wt JFH1 NS5A residue in this position is a phenylalanine . Since , JFH1 NS5A/36A was defective in NS5A self-interaction ( Fig 5A ) and NS5A-CypA interaction ( Fig 7C ) , which , we believe , has caused defective HJ3-5/36A replication ( Fig 5B and 5C ) , we asked whether the 36V mutation is capable of restoring all of these defects associated with NS5A/36A mutation . To answer this question , we determined the effect of 36V mutation on the efficiency of JFH1 NS5A self-interaction and NS5A-CypA interaction by using checkmate assays as described above . As shown in Fig 10A and 10B , the 36V mutation in JFH1 NS5A significantly restored the 36A mutation-mediated impairments in NS5A self-interaction and NS5A-CypA interaction . Next , to verify that the 36V mutation in JFH1 NS5A was indeed responsible for the emergence of a replicable revertant from HJ3-5/36A mutant , we introduced the 36V mutation to HJ3-5 . The results shown in Fig 10C to 10F indicate that HJ3-5/36V substantially restored viral RNA replication and virus production . HJ3-5/36V also showed reduced virus secretion compared to wt HJ3-5 ( Fig 10E and 10G ) without affecting virus assembly efficiency ( Fig 10H ) . It is also notable that NS5A hyperphosphorylation in the HJ3-5/36V mutant was significantly lower than that in wt HJ3-5 , probably due to only a partial restoration of NS5A self-interaction by a 36V mutation in NS5A ( Fig 10A and 10C ) . In fact , all of these replication phenotypes of HJ3-5/36V revertant strikingly resemble those of HJ3-5/37A and HJ3-5/38A mutants . Overall , these results verified the concept that JFH1 NS5A self-interaction is critical for the NS5A-CypA interaction and NS5A hyperphosphorylation , which contribute to efficient HCV RNA replication and virus secretion . Next , we determined the density and specific infectivity of extracellular HJ3-5/36V . Overall , density profiles of viral RNA and infectivity of this mutant closely resembled to other replication-competent , NS5A self-interaction defective NS5A mutants , including 37A , 38A and 148A mutants ( compare Figs 6 and 11 ) , reflecting the fact that 36V mutant is a partial revertant showing significantly lower NS5A self-interaction than wt ( Fig 10A ) . Accordingly , similar to above three mutants , the relative titers of low-density ( <1 . 059 g/cm3 ) 36V revertant were ~10 folds higher than those of the wt HJ3-5 ( compare Figs 6B and 11B right panels ) . Due to this , the specific infectivity of 36V mutant viruses at low-density fractions was also higher than wt HJ3-5 ( Fig 11E ) , similar to other mutants . Interestingly , however , proportions of 36V mutant RNA and infectivity , respectively , detected at high density fractions ( >1 . 201 g/cm3 ) were much higher than those of other mutants , but comparable to those of wt HJ3-5 ( compare Fig 6B and 6D and Fig 11B and 11D , right panels , see below and also Discussion ) . To determine the effect of 36V mutation on NS5A subcellular localization , we introduced this mutation to HJ3-5/NS5AYFP , and then confirmed that HJ3-5/NS5AYFP /36V mutant is defective in NS5A hyperphosphorylation and virus production compared to wt , similar to the phenotypes of HJ3-5/36V ( Fig 12A and 12B ) . Confocal imaging analysis revealed that a majority of NS5AYFP/36V was detected as the distinct foci in the cytoplasm without the tight LD association ( Fig 12C ) , which is consistent with low degree of NS5AYFP and LD co-localization based on Pearson’s correlation measurements ( Fig 12D ) . Also we detected reduced degree of NS5AYFP and core co-localization as well as core-LD association ( Fig 12F and 12H ) . These NS5AYFP/36V phenotypes resembled those of other NS5AYFP mutants shown in Figs 8 and 9 , probably because they shared a common defect in NS5A hyperphosphorylation , which was shown to facilitate NS5A localization to the LD as well as NS5A and core co-localization [17] . However , uniquely to 36V mutant , we consistently detected small fraction of large NS5A foci , co-localizing with LD and core ( Fig 12C and 12E ) . Also , we detected wt level pull-down of core by NS5AYFP/36V ( Fig 12G ) , which , apparently , is contradictory to the reduced degree of NS5AYFP and core co-localization detected from this mutant compared to wt ( Fig 12F ) . Based on these data , we propose that 36V mutation in NS5A may have enhanced its affinity to core , partially compensating its LD localization defect caused by its defective hyperphosphorylation , resulting in fraction of NS5A recruitment to LD in core-NS5A interaction dependent manner . We speculate that LD-localized NS5A/36V may have contributed to near wt level of high-density particles detected from HJ3-5/36V mutant ( Fig 11B and 11D , right panels ) . However , further study will be needed to verify this point .
HCV NS5A is a multifunctional protein involved in both viral RNA replication and virus production [24 , 54 , 55] . Our study provided mechanistic insights regarding the roles of , crystal structure-defined , NS5A dimer interface residues in these two critical steps in the HCV life cycle . Specifically , our data revealed that these residues regulate NS5A self-interaction , NS5A-CypA interaction , NS5A hyperphosphorylation , NS5A localization to LD and NS5A-core interaction , promoting HCV replication and infectious HCV production . Among three domains of NS5A , DI plays a major role in NS5A self-interaction [39] . Currently three independent crystal structures of NS5A-DI , two from gt1b ( Con1 strain ) and one from gt1a ( H77 strain ) , are available [36–38] . While their monomeric structures were similar to each other with an average Cα RMSD ( root-mean-square deviation ) equal or less than 1 Å [37 , 38] , four distinct dimeric forms were detected in different crystal packing conditions , including the two forms from gt1b shown in Fig 1 [36–38] . Interestingly , these NS5A dimeric forms are not mutually exclusive but have a potential to form NS5A oligomers via multiple different interfaces [37 , 38] . Our data shown in Fig 2B support this possibility , since combining mutations located at two different interfaces additively reduced the level of NS5A self-interaction . Importantly , the fact that NS5A mutations located at different dimeric interfaces exhibited similar phenotypes , including their effects on NS5A hyperphosphorylation , NS5A-CypA interaction and NS5A subcellular localization , and , consequently , viral RNA replication and virus assembly/egress ( Figs 3 to 9 ) , strongly suggests the cooperative roles of different dimeric interactions within the same complexes . From a functional point of view , NS5A oligomerization is desirable for its role in promoting the formation of DMV [31 , 32] , which are sites of HCV RNA replication . In addition , the NS5A oligomerization model may be the best way to explain the high potency of NS5A inhibitors , which corresponds to one molecule of NS5A inhibitor impacting ~ 50 , 000 molecules of NS5A as in the daclatasvir example [56] , and a synergistic activity of different NS5A inhibitors in re-sensitization of drug-resistant NS5A variants [56] . The structure of gt2a JFH1 NS5A is currently unknown . However , a substantial NS5A-DI sequence difference exists between gt2a JFH1 and gt1b Con1 ( 69% amino acid homology ) . Thus , it was remarkable that two-to-five mutations introduced to JFH1 NS5A-DI residues located at positions corresponding to two different gt1b NS5A-DI dimer-interfaces significantly inhibited its self-interaction at the levels similar to those of gt1a H77 NS5A-DI ( compare Fig 2B and 2C ) , since gt1a H77 NS5A-DI is more homologous to gt1b NS5A-DI in sequence ( 82% amino acid homology ) and structure ( average Cα RMSD of 0 . 57 Å ) [36–38] . Interestingly , the impacts of individual NS5A-DI dimer-interface mutations on NS5A self-interaction were different between H77 and JFH1-derived NS5A ( Figs 3A and 5A ) . This difference was most apparent for R112A mutation , since this mutation severely impaired H77 NS5A self-interaction , while same mutation had no effect on JFH1 NS5A self-interaction . These results suggest that some intermolecular NS5A residue interactions might vary in these two HCV isolates due to differences in near neighbor residues . Supporting this interpretation , our preliminary data indicate that R112 residue in JFH1 NS5A may not participate in NS5A self-interaction , since neither ( similarly charged ) R112K nor ( oppositely charged ) R112E mutations affected this interaction ( S2 Fig ) . On the contrary , JFH1 NS5A self-interaction was severely impaired by E148R mutation , but unaffected by E148D mutation ( S2 Fig ) . These results suggest that E148 residue in JFH1 NS5A is involved in NS5A self-interaction via salt bridge formation , similar to E148 residue in H77 NS5A , but with different residue ( s ) instead of R112 . However , JFH1 NS5A-DI structure determination will be necessary to identify the exact residues involved in NS5A self-interactions at its dimer interface ( s ) . The NS5A mutation-mediated impairment in NS5A self-interaction correlated with HCV RNA replication-defects driven by either H77 NS5A or JFH1 NS5A-containing viral replicases ( Figs 3 and 5 ) . These results suggest that NS5A self-interaction is critical for HCV RNA replication regardless of HCV genotypes . Interestingly , we detected a strong correlation between NS5A self-interaction and NS5A-CypA interaction that was shown to be critical for HCV RNA replication [52] ( Figs 3 , 5 and 7 ) . Since the mutations we tested are located at NS5A-DI rather than at NS5A-DII that encodes the CypA interacting domain [57 , 58] , a direct role of these mutations in disrupting NS5A-CypA interaction is unlikely . Also , two mutations in NS5A ( D316E/Y317N ) , which conferred the CypA-independent HCV replication phenotype [58] , did not affect H77 NS5A self-interaction and slightly reduced JFH1-NS5A interaction ( S3A Fig ) . These results support that NS5A self-interaction is driving NS5A-CypA interaction , and not vice versa . Interestingly , NS5A-CypA interaction was detected only from GAL4/BD-CypA and VP16/AD-NS5A pairs and not in reverse configuration ( S4 Fig ) . We believe that these data support our interpretation that NS5A oligomers may interact with CypA , since we could easily envision a soluble VP16/AD-NS5A , not a DNA-bound GAL4BD-NS5A , forming a CypA-binding-competent oligomer . Now , how could NS5A self-interaction affect NS5A-CypA interaction ? We propose that NS5A self-interaction may modulate the orientation of CypA-binding region in NS5A-DII , likely within the context of oligomeric NS5A structure , allowing efficient CypA binding . Previous study by Lim et al . showed that mutating zinc-binding cysteines to alanine ( C to A ) within NS5A-DI ( C39A , C57A , C59A and C80A ) disrupted NS5A self-interaction and HCV RNA replication by using bacterially expressed and purified proteins [20] . These data support the role of NS5A self-interaction in HCV replication . However , NS5A-CypA interaction was not affected by any of eleven C to A mutations within full length NS5A ( including the four zinc binding residues mentioned above ) , regardless of their impact on NS5A self-interaction [20] . These findings are different from our data , which showed positive correlation between NS5A self-interaction and NS5A-CypA interaction . We speculate that potential difference in NS5A oligomeric states between current and previous experimental systems might have altered the availability of CypA-interacting region in NS5A-DII for CypA binding leading to different outcomes . Our data indicated that impaired gt1a H77 NS5A self-interaction resulted in virus particle assembly defects ( Fig 3G ) , while that of gt2a JFH1 NS5A resulted in virus secretion defects ( Fig 5F ) . Although this genotypic difference in regard to assembly versus secretion is difficult to understand , we could envision that more than 10 folds higher viral replication from HJ3-5 ( encoding JFH1 NS5A ) , compared to H77D ( encoding H77 NS5A ) , could have altered the relative roles of NS5A self-interactions in viral assembly/egress processes . Alternatively , H77 NS5A and JFH1 NS5A contributed to HCV assembly/egress via intrinsically different mechanisms . Regardless , this genotypic difference was not due to the chimeric nature of HJ3-5 , which encode H77 core to NS2 in the background of JFH1 , since NS5A dimer interface mutations introduced to full length JFH1/QL showed exactly same virus secretion defect as did HJ3-5 mutants ( S5 Fig ) . Interestingly , while H77 NS5A mutants showed no effect on virus density or specific infectivity ( Fig 4 ) , JFH1 NS5A mutants increased the proportion of low-density , high-infectivity particles ( Fig 6 ) . It seems paradoxical that the specific infectivity of JFH1 NS5A mutants was higher than that of wt considering the reduced overall infectivity of mutants . However , it is possible that slowed viral egress in JFH1 NS5A mutants allowed their enhanced lipidation , resulting in low-density , highly infectious viruses , while this type of slow maturation of wt virus was relatively decreased due to efficient virus secretion ( Fig 5D and 5F ) . Interestingly , LD localization of JFH1 NS5A dimer interface mutants was reduced , indicating that virus maturation into low-density particles may not strictly depend on a tight association of NS5A with the cytoplasmic LD . Alternatively , efficient LD localization of NS5A in wt HJ3-5-replicating cells could have enhanced virus secretion at a level to over-saturate the cells’ capacity for normal virus maturation , consequently , forcing the significant portion of viral particles to quickly egress as immature forms ( Fig 6 ) . It is important to note that not all viral RNA secreted to the supernatant of HCV replicating cells is associated with infectious virus . In fact , Gastaminza et al . showed that HCV replicating cells released the low-density particles , including the exosome-like large vesicles , and high density particles , most likely representing non-enveloped core particles , in addition to majority of intermediate density particles corresponding to enveloped HCV particles [59] . Accordingly , low infectivity ( relative to viral RNA ) of both high- and low-density particles detected in our study could be attributed to these defective-particles , including exosomes and non-enveloped core particles . In this context , it is interesting to note that relative proportion of secreted , minimally infectious , high density viral particles from 36V revertant was significantly higher than those from 37A and 38A mutants ( compare Figs 6 and 11 ) , despite that most of other phenotypes of 36V revertant were similar to those of 37A and 38A mutants consistent with their similarly defective NS5A self-interaction . The second phenotypic difference between 36V revertant and these other mutants was the affinity of core and JFH1 NS5A , which was unchanged in the 36V revertant , but reduced in the 37A and 38A mutants , compared to that of wt ( compare Figs 9C and 12G ) . Based on these data , we propose that high-affinity interaction between core and NS5A , independent from NS5A self-interaction , could promote the secretion of defective , high-density core particles . Previously Miyanari et al . demonstrated that two different triple alanine mutations introduced to the residues 99–101 and 102–104 within NS5A-DI reduced NS5A localization to LD , establishing the role of NS5A-DI on NS5A localization to the LD [53] . Subsequently , Masaki et al . showed that NS5A hyperphosphorylation promote its LD localization [17] . Now , our data suggest that NS5A dimer-interface residues in NS5A-DI contribute to NS5A localization to the LD by regulating NS5A hyperphosphorylation ( Figs 5 and 8 ) . Interestingly , our extended study by using HCV polyprotein expression system indicate that all of NS5A dimer interface mutants that we tested , including 36A , 37A , 38A , 112A and 148A , regardless of their impact on NS5A self-interaction , could impair JFH1 NS5A hyperphosphorylation ( S6 Fig ) . These data suggest that NS5A self-interaction per se may not be sufficient to promote its hyperphosphorylation . A recent study by Ross-Thriepland and Harris [23] indicated that JFH1 NS5A-DI residue 146 serine ( 146S ) is a target of phosphorylation and mutating this residue to phosphomimetic aspartic acid ( 146D ) led to decreased NS5A hyperphosphorylation . Since 146S is located near the 3FQM dimer interface in the vicinity of the 112R-148E salt-bridge , the authors predicted that phosphorylation at 146S might potentially regulate NS5A dimerization [23] . However , our data showed that 146A or 146D mutations did not significantly affect H77 or JFH1 NS5A self-interaction ( S3B Fig ) . These data indicate that 146D mutation impaired NS5A hyperphosphorylation without affecting NS5A self-interaction similar to the phenotypes of 112A mutation ( Fig 5A and S6 Fig ) . Consistent with these data , the NS5A inhibitors also reduced NS5A hyperphosphorylation [60] , yet they did not affect NS5A self-interaction [40] . Intriguingly , NS5A inhibitors were implicated to affect intermolecular NS5A conformation [56 , 61] , and phosphorylation of NS5A residue 146 has a potential to alter local dimeric conformation [23] . Thus , these data may indicate that hyperphosphorylation of NS5A is dependent on its specific conformation that allows its interaction with kinases [such as casein kinase I-α ( CKI-α ) ] involved in this process [17] . Accordingly , we propose that only a defined conformation of NS5A , mostly likely within the oligomeric complexes , permits the access of kinases to NS5A LCSI domain resulting NS5A hyperphosphorylation [17 , 62] , and disturbing the kinase-accessible conformation of NS5A either by different NS5A mutations or treatment with NS5A inhibitors impairs NS5A hyperphosphorylation . It is possible that all or some of our mutants may have impacted HCV RNA replication and virus assembly by altering NS5A conformation or other functions of NS5A , in addition to altering NS5A self-interaction-mediated functions . However , the ultimate proof supporting the role of NS5A self-interaction in HCV RNA replication and virus production was provided by a 36 valine ( 36V ) revertant mutation in JFH1 NS5A , which replaced the original alanine mutation that conferred a severe defect in JFH1 NS5A self-interaction . This JFH1 NS5A/36V mutation , not only significantly restored the NS5A self-interaction , but also restored NS5A-CypA interaction , and HJ3-5/36V RNA replication and infectious virus production . Interestingly , 36V mutation introduced to H77 NS5A also partially enhanced NS5A self-interaction as well as an NS5A-CypA interaction ( S7 Fig ) . These data support the notion that 36V mutation in JFH1 NS5A was indeed selected to rescue the NS5A self-interaction-defect caused by 36A mutation . However , the H77D/36V mutant did not show detectable level of viral RNA replication . The exact reason for impaired replication of H77D/36V is unclear . However , it is interesting to note that H77 NS5A residue F36 points toward lipid phase in the 1ZH1 structure ( Fig 1A ) , suggesting that F36 may contribute to NS5A and membrane interaction . Based on this , we speculate that 36V mutation may have failed to restore the F36’s additional function at the NS5A-membrane interface and , as a consequence , could not rescue H77D replication . This potential , additional role of F36 residue in H77D replication may also explain the reason H77D/36A mutant could not replicate when 148A mutant showed low level replication ( Fig 3C ) , despite their similar NS5A self-interaction defects ( Fig 3A ) . As illustrated in Fig 13 , we propose that high-order NS5A oligomerization stabilizes NS5A conformation at local domains , including the DII and LCSI , which would allow them to interact with host proteins including CypA [63 , 64] and CKI-α [17 , 65] . The CypA-induced modulation of NS5A would then promote DMV formation , which will harbor HCV replication complexes [19 , 30–32] . This early event will be followed by NS5A hyperphosphorylation by CKI-α [17 , 22 , 66] , which would be promoted by interaction between NS5A and other HCV nonstructural proteins in the replication complexes as demonstrated in previous reports [67 , 68] . Then the hyperphosphorylated NS5A ( in the replication complexes ) will move to low-density membrane domains near LD [53] , interact with core protein , and promote HCV assembly and/or virus egress [17] . A recent pulse-chase imaging study by Wang and Tai strongly supports this scenario , since they determined that the NS5A-associated organelles ( replication complexes ) are continuously generated de novo and NS5A in the aged version of these organelles tend to associate with LD , accompanied with increases in NS5A hyperphosphorylation [66] . In conclusion , our study provided novel insights indicating that NS5A may function as oligomers formed via multiple dimeric interfaces to promote HCV RNA replication and virus production . It is likely that NS5A functions requiring its oligomerization make it an excellent target of highly potent inhibitors [29] . Although NS5A inhibitors did not perturb NS5A dimerization per se [20 , 40 , 69] , it is suggested that they might have modulated its conformation [70] or higher-order NS5A oligomerization state [56] . In the future , understanding the detailed mechanistic function of NS5A oligomers during HCV replication , combined with determining the exact mode of action of NS5A inhibitors , will provide insights into understanding HCV replication mechanisms and for improving/identifying potent antivirals against HCV and other agents that utilize proteins functioning in an equivalent manner .
The construction of H77D and HJ3-5 was described previously [44 , 48] . To generate the pairs of pACT and pBind vectors expressing full-length NS5A from two different genotypes , H77 and JFH1 NS5A sequences were PCR amplified from H77D and HJ3-5 with the primer sets introducing SgfI and PmeI restriction enzyme sites at their N- and C-terminus , respectively , and then cloned into pFN10A ( ACT ) Flexi vector and pFN11A ( BIND ) Flexi Vector digested with SgfI and PmeI enzymes ( Promega , WI , USA ) . NS5A mutations were introduced by using the QuikChange II XL site-directed mutagenesis kit ( Agilent Technology , Santa Clara , CA ) . The sequences of regions manipulated within each plasmid were verified by DNA sequencing . Other plasmids used for vector control ( pACT and pBind ) , positive controls ( pACT-MyoD and pBind-ID ) and pGL4 . 3[luc2P/Gal4UAS/Hygro] were provided via a Checkmate/Flexi Mammalian Two-Hybrid System ( Promega , WI , USA ) . Huh-7 cell lines used in this study including Huh7 . 5 ( a clonal cell line of Huh-7 , kindly provided by Dr . Charles M . Rice at Rockefeller University [71] ) and FT3-7 ( a clonal cell line of Huh-7 as described in [72] ) were maintained in Dulbecco's modified Eagle medium ( DMEM ) ( Invitrogen , Carlsbad , CA ) containing 10% fetal bovine serum ( Invitrogen , Carlsbad , CA ) at 37°C in a 5% CO2 atmosphere . The interactions between NS5A-NS5A or NS5A-CypA were evaluated by using a Checkmate Mammalian Two-Hybrid System ( Promega , WI , USA ) . In brief , pACT and pBIND based plasmids along with the pGL4 . 3[luc2P/Gal4UAS/Hygro] reporter plasmid were co-transfected into FT3-7 cells by using TransIT-LT1 ( Mirus , Madison , WI ) reagent according to the manufacturer’s instructions at a ratio of 3 μl transfection reagent per 1 μg of plasmid DNA . At 48 h post transfection , cell lysates were prepared to assess the firefly and Renilla luciferase activities by using a Dual-Luciferase Reporter ( DLR ) Assay System ( Promega , WI , USA ) and GloMax DISCOVER instrument ( Promega , WI , USA ) according to the manufacturer’s instructions . HCV RNA was transcribed in vitro from linearized HCV cDNA by using the T7 MEGAscript Kit ( Life Technologies , Carlsbad , CA ) and purified by using an RNeasy RNA isolation kit ( Qiagen , Valencia , CA ) . In brief , 5 x 106 FT3-7 cells were mixed with 10 μg of HCV RNA in a 4-mm cuvette and pulsed once at 270 V and 950 μF by using a Gene Pulser System ( Bio-Rad , Hercules , CA ) . Electroporated cells were transferred into 12-well plates for HCV RNA analysis and or 6-well plates or 6 cm dishes for virus titration and protein analysis . Extracellular and intracellular HCV titers in clarified cell culture supernatants and 4-cycle , freeze-thaw cell lysates harvested from FT3-7 cells at different time points post-electroporation of HCV RNA were determined by performing an HCV core antigen immunfluorescence assay as described before [73] . HCV RNA in cell culture supernatants and gradient fractions ( see below-density gradient ultracentrifugation ) was harvested by using a QIAamp viral RNA mini kit ( Qiagen , Valencia , CA ) . Cell-associated HCV RNA was harvested by using an RNeasy RNA isolation kit ( Qiagen , Valencia , CA ) . To quantitate the level of HCV RNA , a real-time RT-PCR assay was performed by using a QuantiNova Probe RT-PCR Kit ( Qiagen , Valencia , CA ) and a CFX96 real-time system ( Bio-Rad , Hercules , CA ) with custom designed primer probe sets ( Sense primer: HCV84FP , 5’-GCCATGGCGTTAGTATGAGTGT-3’; antisense primer: HCV 303RP , 5’-CGCCCTATCAGGCAGTACCACAA-3’; and probe: HCV146BHQ , FAM-TCTGCGGAACCGGTGAGTACACC-DBH1 ) . Briefly , 10 μl 2x QuantiNova Probe RT-PCR Master Mix , 1 μl each of 20 μM sense- and antisense primers , 0 . 16 μl of 20 μM HCV-specific probe , 0 . 2 μl of 100x QuantiNova Probe RT Mix , 4 μl of template RNA and RNase-free water were combined to make 20 μl reaction mixtures . HCV RNA was reverse transcribed for 10 min at 45°C followed by 5 min incubation at 95°C to activate PCR polymerase , then PCR was performed for 30 cycles of 95°C for 5 seconds ( denaturation ) and 60°C for 30 seconds ( annealing and extension ) . Cell lysates were prepared in 1% CHAPS in PBS lysis buffer containing 1x protease- and phosphatase inhibitor cocktail mix ( GenDEPOT , Katy , TX ) , separated by SDS-PAGE and transferred onto PVDF membranes . The membrane was blocked and probed with primary antibodies to core protein ( 1:2 , 000 dilution of C7-50 , Thermo Scientific , Rockford , IL ) , NS3 ( 1:2 , 000 dilution of 9-G2 , ViroGen , Watertown , MA ) , NS5A [1:15 , 000 dilution of 9E10 ( kindly provided by Dr . Charles M . Rice at Rockefeller University ) or 1:2000 dilution of 2F6 , BioFront Technologies , Tallahassee , FL] , and NS5AYFP ( 1: 2000 dilution of anti-GFP , Life Technologies ) and tubulin ( 1:7000 dilution , EMD Millipore , Billerica , MA ) . Protein bands were visualized by incubating the membranes with IRDye Secondary antibodies ( Li-Cor Biosciences , Lincoln , NE ) , followed by imaging with an Odyssey infrared imaging system ( Li-Cor Biosciences , Lincoln , NE ) . Approximately 1 . 5 x 107 FT3-7 cells were electroporated with in vitro-transcribed RNAs and seeded into 175cm2 flasks . The HCV containing cull culture supernatants were collected from 48 to 72 h for every 4–6 h , pooled and centrifuged to remove cell debris . The clarified supernatants were loaded onto Centricon Plus-70 ( Millipore , Germany ) , concentrated by centrifugation at 3 , 500 x g at 4°C and subjected to discontinuous Optiprep gradient centrifugation ( 60 , 45 , 30 , and 15% ) for 16h at 120 , 000 x g at 4°C in a SW55Ti rotor ( Beckman , Indianapolis , IN ) . Each of 450 μl fraction was collected by aspiration from the top of the gradient and analyzed to determine its density , infectivity and amounts of HCV RNA as described above ( see also [12] ) . Cell lysates were prepared in 1 ml of lysis buffer [0 . 5% Triton X-100 , 10mM Tris-HCl ( pH 7 . 5 ) , 150mM NaCl] containing 1x protease- and phosphatase inhibitor cocktail mix ( GenDEPOT , Katy , TX ) and incubated on ice for 1h . Cell lysates were incubated with anti-GFP magnetic beads ( Miltenyi Biotech , Auburn , CA ) for 1h at 4°C with gentle mixing and applied to μ columns . Magnetic beads were washed 4 times each with lysis buffer , and wash buffer I ( 150mM NaCl , 1%NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 50mM Tris-HCl , pH 8 . 0 ) , respectively , followed by one wash with buffer 2 ( 20mM Tris-HCl , pH 7 . 5 ) . Bound immune complexes were eluted from columns by applying a preheated SDS sample buffer . HCV RNA electroporated cells were plated on 8-well chamber slides ( BD Bioscience , Bedford , MA ) at a density of 1x104 cells per well . Two to three days later , the slides were washed with PBS , fixed with 4% formaldehyde for 20 min at room temperature , and permeabilized with 0 . 2% Triton X-100 in PBS for 10 min , then incubated overnight at 4 oC with anti-core monoclonal antibody ( 1:2 , 000 dilution of C7-50 , Thermo Scientific , Rockford , IL ) , followed by Alexa Fluor 405-conjugated goat anti-mouse antibody ( 1:1000 dilution , Invitrogen , Carlsbad , CA ) for 1 h . Lipid droplets were stained with HCS LipidTOX deep red neutral lipid stain ( 1:1000 dilution , Molecular Probes Inc , Eugene , OR ) . The slides were examined with an Olympus FluoView FV10i confocal microscope ( Olympus America Inc , Waltham , MA ) . Pearson’s coefficient was obtained by using FV10i-ASW 4 . 2 viewer software . Student’s t-test ( unpaired ) was performed by using GraphPad Prism version 6 software to determine the significance in differences between paired values . A P value less than 0 . 05 was considered statistically significant . | HCV NS5A is a multifunctional protein involved in both viral RNA replication and infectious virus production , and is a target of one of the most potent antivirals available to date . However , the mode of action of NS5A inhibitors is still unclear due to the lack of mechanistic detail regarding NS5A functions during HCV life cycles . In this study , we have provided evidence that surface-exposed NS5A residues involved in two different dimeric interactions in crystal structures are indeed involved in NS5A self-interactions in cells . We also showed that these NS5A residues play critical role in HCV RNA replication and infectious virus production by regulating NS5A hyperphosphorylation , its subcellular localization and its interaction with host protein CypA . Overall , our data support the functional significance of “NS5A oligomers” formed via multiple interfaces in HCV replication . We speculate that the NS5A inhibitors exploited the NS5A oligomer-dependent functions during HCV replication , rather than targeting individual NS5A , which consequently resulted in their high potency . |
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RIG-I-like receptors ( RLRs: RIG-I , MDA5 and LGP2 ) play a major role in the innate immune response against viral infections and detect patterns on viral RNA molecules that are typically absent from host RNA . Upon RNA binding , RLRs trigger a complex downstream signaling cascade resulting in the expression of type I interferons and proinflammatory cytokines . In the past decade extensive efforts were made to elucidate the nature of putative RLR ligands . In vitro and transfection studies identified 5′-triphosphate containing blunt-ended double-strand RNAs as potent RIG-I inducers and these findings were confirmed by next-generation sequencing of RIG-I associated RNAs from virus-infected cells . The nature of RNA ligands of MDA5 is less clear . Several studies suggest that double-stranded RNAs are the preferred agonists for the protein . However , the exact nature of physiological MDA5 ligands from virus-infected cells needs to be elucidated . In this work , we combine a crosslinking technique with next-generation sequencing in order to shed light on MDA5-associated RNAs from human cells infected with measles virus . Our findings suggest that RIG-I and MDA5 associate with AU-rich RNA species originating from the mRNA of the measles virus L gene . Corresponding sequences are poorer activators of ATP-hydrolysis by MDA5 in vitro , suggesting that they result in more stable MDA5 filaments . These data provide a possible model of how AU-rich sequences could activate type I interferon signaling .
The retinoic acid inducible gene I ( RIG-I ) -like receptor ( RLR ) proteins are key players in innate immunity and act by recognizing viral RNA ( vRNA ) in the cytosol . The RLR family consists of the members retinoic acid inducible gene I ( RIG-I ) , melanoma differentiation associated protein 5 ( MDA5 ) , and laboratory of genetics and physiology 2 ( LGP2 ) [1]–[3] . In vitro studies have shown that RIG-I and MDA5 recognize the majority of viruses in a complementary manner . While many negative-strand RNA viruses like rabies and influenza viruses are predominantly sensed by RIG-I , picornaviruses are predominantly recognized by MDA5 . The observed preferences are , however , unlikely to be exclusive and the exact role of LGP2 still needs to be investigated [4]–[9] . In case of MDA5 , a minor contribution to recognition of measles , rabies , vesicular stomatitis and Sendai virus has been reported [10]–[13] . The RLR proteins belong to the DExD/H-box ATPases sharing a central ATP-dependent helicase domain and a C-terminal regulatory domain ( RD ) that is responsible for initial RNA binding . In addition , RIG-I and MDA5 possess N-terminal tandem caspase activation and recruitment domains ( CARDs ) that are responsible for downstream signaling transduction [2] , [14] , [15] . Several crystal structures of RIG-I have shown that , in the absence of virus , the protein exists in an auto-inhibited state where the RD domain folds back to the CARDs , thereby shielding them from the cytosol . Upon viral infection and initial vRNA binding , the protein undergoes large conformational changes leading to the interaction with the mitochondrial associated signaling protein ( MAVS ) [16]–[19] . This leads to the activation of a downstream signaling cascade and finally to the induction of type I interferon ( IFN ) expression and the establishment of an anti-viral state . Although the exact nature of RLR ligands is not yet fully understood , several studies report that RIG-I preferentially binds to relatively short ( between 25 to 1000–2000 bp ) 5′-triphosphate double-stranded RNAs ( 5′-triphosphate dsRNA ) like those of Sendai virus ( SeV ) defective interfering ( DI ) particles [20]–[23] . In contrast , MDA5 seems to have a preference for long ( more than 1000–2000 bp ) dsRNA stretches [24] , [25] . Upon binding to dsRNA , MDA5 is thought to cooperatively form polar helical filaments leading to association with MAVS and activation of the downstream signaling cascade [26]–[28] . Viruses have developed numerous strategies to evade the immune system . For instance , viruses of the paramyxovirus family ( e . g . measles , parainfluenza , Sendai and Nipah viruses ) encode V inhibitor proteins that specifically bind to MDA5 and LGP2 , but not always to RIG-I [29]–[31] . By determining the structure of MDA5 in complex with parainfluenza virus V-protein , we previously showed that the viral protein unfolds the ATPase domain of MDA5 . This leads to the disruption of the MDA5 ATP-hydrolysis site and prevents RNA bound MDA5 filament formation [32] . One of the remaining key questions in this field is how RLR proteins are able to distinguish between self and non-self RNA in the cytosol . Recently , several studies showed that 5′-triphosphate RNA is not the only RNA ligand for RIG-I . Specific poly U/C-rich regions within certain viral genomes seem to contribute to efficient recognition by the protein [33] , [34] . In case of MDA5 , it is not known which features of vRNA are required in order to induce an immune response . Expression of subgenomic and subgenic RNA from parainfluenza virus 5 ( PIV5 ) indicated that MDA5 recognizes a specific region within the L mRNA [35] . For picornaviruses , it is speculated that MDA5 binds to long dsRNA that represents replicative intermediates composed of the positive genome and the negative antigenome [36] . These studies were , however , based on in vitro transfection experiments and it has so far not been possible to isolate a natural RNA ligand for MDA5 directly from virus-infected cells . In this study we combined different methods , including RNA-protein crosslinking and deep sequencing , to investigate in vivo RNA ligands for RLR proteins from virus-infected cells . Based on the crosslinking we were able to co-purify immunostimulatory RNA in a RIG-I and MDA5 dependent manner from measles virus ( MeV ) -infected cells . Deep sequencing and bioinformatics analysis revealed that RIG-I and MDA5 bind RNA of positive polarity originating from the L gene of the MeV genome . In addition , RIG-I binds to the 5′ ends of genomic and antigenomic RNAs , which probably represent 5′-triphosphate RNA , and are therefore not recognized by MDA5 . Furthermore , we showed that RIG-I , but not MDA5 , binds RNA of negative polarity , indicating that MDA5 does not efficiently recognize the MeV genome . Based on bioinformatics analysis , we observed a correlation between MDA5-enriched RNA sequences and the AU content and this was confirmed by in vitro transcription assays . In summary , we report the isolation of MDA5-associated RNA from virus-infected cells and the discovery of in vivo occurring activating viral RNA ligands for MDA5 .
Several in vitro studies showed that MDA5 preferably recognizes long dsRNA stretches [24] , [25] . However , it is still unclear if the protein has a preference for specific RNA sequences . The main reason for this may lie in the weak interaction between the protein and its ligand resulting in very poor RNA levels that co-purify from MDA5 immunoprecipitates . In order to address this problem , we established an RNA-protein crosslinking approach adapted from the PAR-CLIP ( Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation ) methodology [37] . With this approach , we intended to improve RNA recovery from RLR immunoprecipitates in the context of a viral infection . For validation of the method , we compared the crosslinking approach with a conventional pull-down technique previously used for the identification of SeV DI particles as potent RIG-I inducers [20] . We infected A549 human lung carcinoma cells with SeV at a high multiplicity of infection ( MOI ) in the presence of 4-thiouridine ( 4SU ) and allowed infection to occur over 24 h . A part of the cells was then exposed to 365 nm UV light and endogenous RIG-I was immunopurified ( Figure 1a ) . The recovered RNA was isolated and subjected to quantitative PCR ( qPCR ) analysis and immunoactivity experiments . The data indicate that treatment of cells with 4SU and exposure to 365 nm UV light lead to a reduction of immunostimulatory activity of RIG-I-associated RNA to 50% ( Figure 1b ) . However , the results of qPCR analysis showed that the crosslinking approach yields a quantitatively improved RNA recovery , with an increase of 50% in SeV DI particles in comparison to the non-crosslinking approach ( Figure 1c and d ) . Furthermore , we confirmed that treatment of cells with the photoreactive nucleoside does not affect cell viability or virus replication ( data not shown ) . Taken together , our data indicate that the crosslinking technique is a promising tool to study in vivo occurring RNA ligands for RLR proteins . Next , we validated the crosslinking approach on cells that were infected with a variety of viruses , including negative-stranded ( − ) RNA viruses ( MeV [38] and rabies [39] ) and positive-stranded ( + ) RNA viruses ( Encephalomyocarditis virus ( EMCV [40] ) and Mengo virus [41] ) . In all cases , we infected A549 cells at an MOI of 1 . 0 in the presence of 4SU . Cells were crosslinked 24 h post infection ( hpi ) and RIG-I and MDA5 were immunopurified . The recovered RNA was subjected to immunoactivity experiments . Based on the data , we concluded that immunoactive RNA was co-purified in a RIG-I- and MDA5-dependent manner from MeV-infected cells . This induction was significant in comparison to the negative control ( Figure 2 ) . In the case of RIG-I-associated RNA , we obtained an immunostimulatory effect that was 2600-fold higher in comparison to the control . For MDA5 , we observed an 800-fold induction . The data show that the approach yields RIG-I- and MDA5-specific immunoactive RNA from MeV-infected cells in a RIG-I- and MDA5-dependent manner . Although we detected significant immunostimulatory activity for RLR-associated RNAs from MeV-infected cells , the experimental set up is currently unsuitable for the isolation of RLR RNA ligands from the other viruses ( Figure S1 ) . The reason for this may lie in the heterogeneity and the need for precise timing of viral replication cycles or in the efficiency of 4SU incorporation and crosslinking . Utilization of this technique for other viruses may require adjustment of parameters , such as the time points of 4SU addition , crosslinking and harvesting after infection . Based on the above-mentioned results , we focused our studies on MeV , which belongs to the order of Paramyxoviridae . MeV has a single-stranded RNA genome of negative polarity consisting of 15 , 894 nucleotides . It comprises six non-overlapping genes , which are flanked by small terminal non-coding regions known as leader ( le ) and trailer ( tr ) sequences . These sequences serve as promoter regions during viral replication and transcription [42] , [43] . While the replication of the genome and antigenome is performed in a continuous process , viral transcription is carried out in a sequential manner , giving rise to an mRNA gradient declining in the 3′ to 5′ direction ( Figure S2 ) , as previously published [44] . Since ( − ) RNA virus polymerases eventually fail in transcription termination , they generate , in addition to monocistronic mRNAs , numerous alternative RNA species including read-through transcripts , such as leader-N , bi- or tricistronic mRNAs [45] . Furthermore , replication can give rise to abortive replication products and DI RNA with large internal deletions or copy-back genomes [46] . Due to the complex RNA composition of a virus-infected cell , the analysis of specific RNA ligands for RLR proteins is challenging . In order to shed light on the exact nature of RIG-I and MDA5-associated RNAs derived from MeV-infected cells , we performed a deep sequencing analysis on isolated RNA species from co-immunopurifications with antibodies against endogenous RIG-I and MDA5 . As a control , we used an antibody against GFP ( GFP protein was not present ) . The MeV strain used for the studies presented here was a recombinant measles virus rescued from cDNA with the exact sequence of the Schwarz vaccine strain ( Genbank AF266291 . 1 ) [38] . Obtained sequences were mapped to the MeV antigenome and the relative abundances of these sequences between RIG-I pull-down , MDA5 pull-down , and GFP pull-down were compared . Analysis of the reads showed that RIG-I and MDA5 bind to similar regions within the L gene-derived RNAs . In addition , RIG-I , but not MDA5 , binds to RNAs derived from the 3′ and the 5′ ends of the MeV genome ( Figure 3a and b ) . These regions probably represent le or trRNA generated in the course of replication or transcription . Additionally , internal genomic and antigenomic sequences found in the pull-downs could potentially originate from MeV DI particles [46]–[51] . To address this question , we performed a PCR analysis of RLR libraries in which we specifically amplified copyback DI RNA of MeV [47]–[49] . Indeed , we detected copyback DI particles not only in the RIG-I pull-down but also within RNA recovered from MDA5 immunoprecipitates ( Figure S3 ) . We did not find DIs in the GFP control pull-downs . Consistent with previous work , the higher copy numbers of reads indicate that RIG-I binds MeV RNA with higher affinity than MDA5 [11] . This observation is in good agreement with the increased immunostimulatory activity of isolated RNA from RIG-I pull-down samples in comparison to MDA5 . Regarding the immunostimulatory activity , RIG-I-associated RNA gives a 4-fold higher induction in comparison to MDA5-associated RNA ( Figure 3d and e ) . Based on the protocol used for cDNA library preparation , sequencing reads could be separated according to their strand orientation . During cDNA synthesis , adaptors were specifically ligated to the 3′ or 5′ ends , thereby keeping the information of strand specificity during the deep sequencing run . Separation of sequences revealed remarkable differences between both protein immunoprecipitations . RIG-I associated RNA sequences of positive polarity , which represent either antigenomic RNA or mRNA transcripts , are enriched in regions close to the 5′ end of the viral antigenome ( leader ) but also in distinct regions within the L gene . In contrast , sequences of negative polarity , representing the viral genome , are exclusively enriched in the 5′ end of the genome ( trailer region ) and in regions of the L gene ( Figure 4a ) . Analysis of MDA5-associated RNA revealed that sequences of positive polarity were enriched within the L gene originating from similar regions as ( + ) RNA from the RIG-I library ( Figure 4b ) . In contrast to RIG-I , however , MDA5 did not bind to RNA sequences comprising the 5′ end of the antigenome or leader RNA . Comparison of ( − ) RNA from RIG-I and MDA5 libraries further revealed that , in contrast to RIG-I , MDA5 did not enrich sequences of negative polarity , including trailer sequences . According to the analysis of strand specific enrichment , it appears that MDA5 does not bind vRNA of negative polarity that represents the MeV genome . Furthermore , the data evidently rule out the possibility that MDA5 recognizes RNA duplexes of ( + ) and ( − ) RNA that might represent replication intermediates , as previously suggested for a positive-strand RNA virus [36] . In fact , the result suggests that MDA5 binds ( + ) RNA that could either represent mRNA or the MeV antigenome . To further validate the specificity of the accumulation of RIG-I and MDA5-associated RNA , we calculated specific read enrichments [52] of the RLR libraries compared to the control library ( Figure S4 ) . Enrichment ( greater than 2× compared to the control library ) of RIG-I-associated RNA of positive polarity can be found across the whole genome , whereas only few reads of negative polarity are enriched within the N and L segment . In contrast , enriched sequences of MDA5-associated RNA are exclusively present within the L segment of positive polarity , whereas no specific enrichment was observed for ( − ) RNA . Based on the data , we observed a good correlation between the deep sequencing analysis and enrichment calculations , indicating that distinct regions within the MeV genome are indeed specifically enriched in a RIG-I- and MDA5-dependent manner in comparison to the control . To independently validate the relative amount of RLR-associated RNA , qPCR amplification was performed . The obtained copy read numbers were normalized to the GFP negative control in order to compare the genomic segments in the RIG-I and MDA5 samples ( Figure 5a ) . Analysis of relative abundances confirmed that RIG-I specifically enriches sequences from the 3′ and 5′ regions of the MeV genome , representing either antigenome or viral mRNA . Interestingly , the analysis showed that RIG-I-associated RNA from the genomic 3′ end most likely represents leader read-through transcripts or abortive replication products and not N mRNA . In MDA5 pull-downs , RNA was enriched in the case of the L mRNAs and partly in the case of H mRNAs , while no relevant copy numbers were obtained at other genomic positions . This is in good agreement with the results of the deep sequencing analysis , indicating that MDA5 indeed recognizes RNA originating from the L gene of the MeV genome . Furthermore , comparison of the relative copy numbers between RIG-I and MDA5 revealed remarkable differences between both proteins . The relative abundances in the RIG-I sample were up to 40-fold higher in comparison to MDA5 . This observation again indicates that RIG-I has a higher affinity for MeV RNA sequences in comparison to MDA5 . Our conclusion is further supported by immunoactivity experiments , where the relative immunostimulatory activity of RIG-I-associated RNA was 20-fold higher in comparison to MDA5 ( Figure 5b and c ) . To elucidate the exact nature of sequences enriched by RIG-I and MDA5 immunoprecipitations , we conducted a bioinformatics analysis . For this , the complete genome was divided into fragments of size 201 nt with a shifting window of 5 nt . Each sequence was folded in silico ( RNAfold [53] ) and several RNA primary and secondary structure features were analyzed . The analyzed parameters were set in relation to the mean coverage of sequencing reads from RIG-I and MDA5 pull-down experiments . Heat scatter plots indicate that sequences rich in AU correlate with a high mean coverage of sequencing reads in both the RIG-I ( cor = 0 . 273 , cor = 0 . 334 ) and MDA5 ( cor = 0 . 358 , cor = 0 . 348 ) libraries ( Figure 6a and b ) . These data suggest that RIG-I and MDA5 preferably bind to AU-rich sequences originating from the viral genome . Although we further analyzed a variety of secondary structure parameters , including paired nucleotides and bulges , we did not see any other relevant correlation with the mean coverage of sequencing reads ( Figure S5 and Figure S6 ) . To further confirm the obtained sequencing data , we generated 17 single-stranded , 200 nucleotide long in vitro transcripts ( IVTs ) covering different regions of the MeV antigenome ( Table S1 ) . RNAs were double-dephosphorylated in order to ensure that 5′-triphosphate groups were removed . For immunoactivity experiments IVTs were transfected into 293T ISRE-FF reporter cells . The stimulatory effect revealed a correlation of high read numbers from deep sequencing analysis and high stimulatory activity of the IVT sequences ( Figure 7 ) . According to the immunostimulatory experiment , we observed increased immunostimulatory activities for transcripts 8 , 9 , and 12 ( Figure 7a ) . These transcripts correspond to regions at the 5′ end of the L gene , which is also the region with the highest copy numbers of reads ( Figure 3 ) . In general , IVTs representing regions within the L gene have higher immunostimulatory activity in comparison to the upstream genomic segments . This is in good agreement to the deep sequencing analysis . Furthermore , calculated Pearson correlations showed that the best correlation between maximal numbers of sequencing reads and the immunostimulatory activity of RNA transcripts can be found in the MDA5 sequencing data ( cor = 0 . 526 ) , while RIG-I and GFP samples showed less correlation ( cor = 0 . 369 and cor = 0 . 217 ) ( Figure 7b ) . In order to find a possible explanation for the different immunostimulatory potentials of IVTs , several characteristics of the transcripts were analyzed in silico . The obtained data revealed that the immunostimulatory potential correlates with the AU content of IVTs ( cor = 0 . 599 ) ( Figure 7d ) , which is consistent with the results from the deep sequencing analysis . Visualization of transcripts on an Agilent bioanalyzer RNA chip indicates that no higher-order structures due to the sequence composition were formed that might explain differences in immunostimulatory activity ( data not shown ) . In order to get a more general conclusion about the contribution of the AU content to the immunostimulatory potential of RNAs , in vitro transcripts from Mengo virus ( Table S3 ) were tested for their immunostimulatory activity . The transcripts were generated according to the protocol for MeV RNA sequences . We again observed a correlation ( cor = 0 . 583 ) of the AU content of the tested sequences and their immunostimulatory potential ( Figure S 7a and b ) . These data are consistent with the in vitro analysis of MeV RNA sequences indicating that the AU composition of RNA might play a general role in activating RLR signaling . Finally , we asked whether the ATP hydrolysis activity of MDA5 correlates with the immunostimulatory potential of the tested IVTs . We measured the ATP hydrolysis rate of recombinant mouse MDA5 in the presence of RNA transcripts ( Figure 7 and Figure S8 ) and observed a negative correlation between the maximum number of sequencing reads in the MDA5 library and the ATP hydrolysis rate ( cor = −0 . 414 , Figure 7c ) . Analysis of the in vitro data revealed that AU-rich sequences lead to a decrease in ATP hydrolysis activity of MDA5 ( cor = −0 . 445 ) . Furthermore , the ATP hydrolysis rate negatively correlates with the immunostimulatory potential of RNA transcripts ( cor = −0 . 426 ) ( Figure 7d ) . This result suggests that the ATPase hydrolysis activity of MDA5 is not correlated to the binding and the immunostimulatory potential of the RNA transcripts and could therefore provide a model of RNA recognition by the protein . The data are consistent with previous work on MDA5 filament formation upon dsRNA binding [26] , [27] . In structural and biophysical studies , Berke et al showed that ATP hydrolysis by MDA5 causes filaments to disassemble , perhaps by inducing translocation along the RNA or triggering a conformational change in the protein . According to our data , this may explain the observed inverse correlation between the immunostimulatory activity of IVTs and their potential to induce the ATPase activity of MDA5 .
Until now , in vivo RLR ligands were poorly understood and a naturally occurring MDA5 ligand could only be purified indirectly by immunoprecipitation of LGP2:RNA complexes from virus-infected cells overexpressing LGP2 [54] . By applying a combination of RNA-protein crosslinking , immunoprecipitation of endogenous proteins and RNA deep sequencing analysis , we were able to investigate RLR-associated RNA from MeV infected cells . We compared our results to the empty GFP antibody control resembling a previously published immunoprecipitation strategy [20] . Our approach reveals that MDA5 preferentially binds measles virus RNA of positive polarity , whereas RIG-I additionally binds to ( − ) sense RNA within the trailer region as well as in the adjacent L gene . We propose that enriched RNA of positive polarity most likely represents mRNA species , since antigenomic RNA is only generated during replication and is immediately packed into nucleocapsids [55]–[57] . For Mononegavirales , these RNA-protein complexes are considered inaccessible for cytoplasmic proteins [55] , [58] and might not be ligands for RLR proteins unless they become released . We show that , unlike MDA5 , RIG-I binds ( + ) sense RNA originating from not only the L genomic segment , but also from the 3′ end of the MeV genome , which could be either le-N read-through transcripts or abortive replication products comprising 5′-triphosphate ends [45] , [46] . Furthermore , we hypothesize that RIG-I specific enriched RNA of negative polarity represents abortive replication products also having 5′-triphosphate ends [20]–[23] . Additionally , 5′-copyback DI sequences combining vRNA of positive and negative polarity were found both in RIG-I and MDA5 immunoprecipitates and may contribute to recognition [49] . Bioinformatics analysis and in vitro transcription experiments revealed a correlation between AU content and read coverage of the obtained sequences or IVTs , respectively . As shown before [59] , this indicates that RNA rich in AU could serve as a putative ligand for RIG-I and MDA5 , or in a secondary manner lead to a specific structure that is recognized by both proteins . The slightly weaker correlation of RIG-I associated sequences with their AU content compared to MDA5 bound RNAs could be explained by additional sequences or triphosphate RNAs recognized by RIG-I that originate from regions less rich in AU . Interestingly , ATP hydrolysis assays performed with recombinant MDA5 and RNA transcripts indicate that the AU content of RNA negatively correlates with the ATP hydrolysis rate of the protein . This inverse correlation between the immunostimulatory potential of RNAs and their capability to stimulate ATP hydrolysis by MDA5 lets us speculate that the ATPase activity might not be necessary for , or even interfere with , the immunoactivity of RNA ligands . Although this observation disagrees with recent findings about the role of ATP hydrolysis in RIG-I oligomerization on 5′-triphosphate dsRNA [60] , we assume that MDA5 and RIG-I differ markedly in their mechanical activation and the role of ATP hydrolysis . Our data is supported by results suggesting that MDA5 filament formation is abrogated in an ATP-sensitive manner . By electron microscopy ( EM ) analysis it was shown that MDA5 filaments disassemble in the presence of ATP , indicating that ATP hydrolysis triggers the translocation of the protein along the dsRNA molecule or reduces the binding affinity , thereby interfering with downstream signaling [26] , [27] . In light of the available data in the literature we therefore hypothesize that the ATPase activity of the MDA5 helicase domain contributes to substrate specificity by detaching the protein from low affinity substrates . To further test this hypothesis we generated RIG-IE373Q and MDA5E444Q , which are mutated in the “Walker B” ATP hydrolysis motif [61] , slowing down or abrogating the ATP hydrolysis activity of the proteins , while preserving formation of ATP complexes . Overexpression of these mutant proteins from transfected plasmids showed a dramatic increase in their immunostimulatory potential in the absence of any viral ligands in comparison to expressed wild-type MDA5 ( Figure S9 ) . Furthermore , pull-down studies with the RIG-I Walker B mutant revealed an increase in the amount of recovered RNA while their immunostimulatory potential decreased ( data not shown ) . The increased immunostimulation of ATPase deficient RLRs is consistent with the model that RNAs that lead to a reduced ATP-hydrolysis rate are more proficient in immunostimulation , possibly by stabilizing RLR∶RNA complexes . The negative correlation between AU-rich sequences and the ATP hydrolysis rate suggests that MDA5 binds AU-rich RNA in preference to GC-rich RNA . This would lead to a stronger interaction between RNA and MDA5 and result in a higher immunostimulatory signal . In order to test this hypothesis , we performed binding assays with MDA5 and IVTs but we were not able to demonstrate differences in the binding affinities between the different transcripts that might support this theory ( data not shown ) . Finally , we speculate that RNA ligands for RLR proteins could be divided into two classes . The first class would comprise RNA molecules originating from the 5′-triphosphate ends of the genome or antigenome . These molecules could be generated in the course of read-through transcription and abortive replication [45] , [46] and could therefore represent preferred ligands of RIG-I , as shown previously [20] . The second class of RNA molecules could be recognized by both receptor proteins . Our data suggest that recognition of these RNAs might occur through the AU composition of sequences [34] . This second class might also prominently include defective interfering ( DI ) particles generated during MeV replication . For MDA5 , however , our deep sequencing data show that the ( − ) strand portion of the DIs is either relatively short or the fraction of DIs binding to MDA5 is magnitudes lower than the binding to L derived ( + ) sense RNAs and therefore not easily detectable during sequencing . A more detailed analysis of the deep sequencing data is currently ongoing in order to shed more light on the complex nature of the DIs involved . It will be interesting to see what types of RNA associate with RIG-I and MDA5 during infections with different viruses and to what extent the AU composition and DI generation contributes to RNA recognition in these types of viruses . In particular , the finding that both RIG-I and MDA5 localize to AU rich regions suggests partially overlapping roles in detection of different viruses . The specificity of RIG-I and MDA5 for certain viruses may lie not only in the detection of 5′-triphosphate by RIG-I , but also in the heterogeneity of viral evasion strategies [62] . Our findings support a model for the recognition of AU-rich sequences by RIG-I and MDA5 from MeV-infected cells . Consistently , we find a similar correlation for in vitro transcribed RNA from the Mengo virus genome . In general , the data support previous experiments indicating that MeV is mainly recognized by RIG-I , while MDA5 seems to play a minor role [4] , [5] , [13] , [63] . It could be possible that RIG-I initially recognizes le-N read-through transcripts or abortive replication products containing 5′-triphosphate ends , leading to the activation of the signaling cascade . In a second round of recognition , RIG-I and MDA5 then recognize viral transcripts that are rich in AU . To further test this hypothesis , time dependent experiments need to be carried out . One feature of the applied crosslinking technique is the introduction of specific T to C transitions at the interaction sites of 4SU-labeled RNA and the protein upon UV light exposure [37] . By identifying these point mutations in the deep sequencing data , one can exactly pinpoint the RNA sequences that interact with the protein of interest . However , our bioinformatics analysis did not reveal significant enrichment of T to C mutations , which could be explained by the rather low incorporation efficiency of the photoreactive nucleoside into viral RNA , consistent with the low incorporation level of 4SU into host RNA . Nevertheless , by increasing the incorporation efficiency in future studies , the identification of point mutants could further narrow down the precise binding sites of RLRs . In summary , our approach provides a first insight into the molecular basis of vRNA derived from MeV interaction with MDA5 in living cells and reveals a preference for binding of AU-rich regions originated from ( + ) -sense RNA of the L gene . In vitro , these RNA molecules appear to be a poorer stimulator of the ATPase activity of MDA5 , and result in more stable MDA5 filaments and support better downstream signaling .
Infection experiments were carried out in A459 human lung carcinoma cells . HEK 293T ISRE-FF reporter cells ( stable expression of firefly luciferase under the control of an interferon stimulated response element ) were used for interferon stimulation luciferase reporter gene assays . All cells were maintained in Dulbecco's Modified Eagle Medium supplemented with 2 mM L-glutamine , 1% Penicillin-Streptomycin and 10% FBS ( all purchased from Invitrogen ) . Viruses used for infections were recombinant measles virus with a sequence identical to the vaccine strain Schwarz ( AF266291 . 1 . ) , Sendai virus , Sendai virus defective interfering particles H4 ( kindly provided by Dominique Garcin , Geneva , Switzerland ) , Mengo virus strain pMC0 ( kindly provided by Anne Krug , TU Munich , Germany ) and EMCV . Primary antibodies to human MDA5 ( AT113 ) and RIG-I ( Alme-1 ) were purchased from Enzo Life Science ( Loerrach , Germany ) . Antibody to GFP ( ab1218 ) was obtained from Abcam ( Cambridge , UK ) . Secondary antibodies were supplied by GE Healthcare ( Buckinghamshire , UK ) . A549 cells were infected with virus with an MOI of 1 . 0 in the presence of 400 µM 4SU . Infection was allowed to proceed for 24 h and living cells were washed with PBS ( 10 mM phosphate , 137 mM NaCl , 2 . 7 mM KCl , pH 7 . 5 ) and exposed to 1 J/cm2 365 nm UV light using a photocrosslinker ( Vilbert Lourmat ) . Cells were harvested and incubated in Nonidet P-40 lysis buffer ( 50 mM HEPES , 150 mM KCl , 1 mM NaF , 10 µM ZnCl2 , 0 . 5% NP-40 , 0 . 5 mM DTT , protease inhibitor , pH 7 . 5 ) for 10 min on ice . The lysate was cleared by centrifugation and endogenous proteins were immunoprecipitated for 4 h with the respective antibodies ( 1 µg/mL ) bound to protein G Dynabeads ( Life Technologies ) . The beads were washed five times with high-salt wash buffer ( 50 mM HEPES , 500 mM KCl , 0 . 05% NP-40 , 0 . 5 mM DTT , protease inhibitor , pH 7 . 5 ) and incubated with proteinase K ( Thermo Scientific ) for 30 min at 55°C . The RNA was isolated by phenol/chloroform/isoamylalcohol extraction and subjected to further analysis . A549 cells were infected with MeV with an MOI of 1 . Cells were harvested 24 hpi . Total RNA was isolated according to manufacturer's protocol of the RNeasy Protect Mini Kit ( Qiagen ) and subjected to Illumina deep sequencing . Immunoactivity experiments were carried out in 24-well plates . 2 . 5×105 HEK 293T ISRE-FF reporter cells were transfected with 250 ng of recovered RNA , 500 ng in vitro transcripts or 500 ng plasmid DNA using Lipofectamine 2000 ( Invitrogen ) according to manufacturer's protocol . After 24 h incubation , cells were subjected to immunoactivity experiments using the Dual-Glo luciferase assay system ( Promega ) according to manufacturer's instructions . The luciferase activity was determined in a 96-well plate reader . Significance of differences in luciferase activity between samples were determined via an unpaired t-test . Isolated RNA was prepared for Illumina sequencing using the mRNA-Seq library preparation kit ( Epicentre ) according to manufacturer's protocol . To remove ribosomal RNA species from the sequencing libraries a Ribo-Zero rRNA removal kit ( Epicentre ) was used . Quality of RNA-Seq libraries was validated on a DNA1500 chip for the Bioanalyzer 2100 ( Agilent ) . Sequencing was performed on the Illumina Genome Analyzer in the Gene Center sequencing facility ( LAFUGA ) . Obtained sequences were processed with the FASTX toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/ ) in order to remove adapter sequences and reads with PHRED scores below 30 . Remaining sequences were mapped to human and viral genomes by utilization of the Bowtie algorithm [64] , allowing maximal one mismatch per unique read . The Bowtie sequence alignments were converted with SAMtools [65] to pileup format , which was subsequently used for further data analysis . Relative sequence abundances were analyzed between RLR pull-down samples and the GFP control . Specific read enrichments were calculated by determining the relative sequence abundance at each position on the genomic segment and calculating the average of the RLR/GFP ratios over a dynamic window of 200 reads . Relative sequence abundances with log2 ratios above +1 were defined as significantly enriched in the RLR library . RNA secondary structure prediction from measles virus genome or in vitro transcripts was performed by utilization of RNAfold from the ViennaRNA package [53] using standard parameter settings . For this purpose , the genome was divided into 201 nt fragments with a shifting window size of 5 nt . The sequences were folded in silico and the linear relationship between different data sets was quantified with the Pearson correlation coefficient . DNase treatment of the immunoprecipitated RNAs and qPCR was performed as previously described [66] . The primer pairs used for quantification were identical to those published [67] . For cDNA synthesis a random hexanucleotide mix was used ( Roche ) . Full length MeV vac2 cDNA with a known concentration was used for standard generation . Copy number values obtained for MDA5 and RIG-I were normalized to the control GFP . Specific primers for reverse transcription ( Roche transcriptor transcriptase ) and the subsequent PCR ( Biozym Phusion Polymerase ) were adapted from Calain et al [47] . PCR products were analyzed on agarose gels and stained with ethidium bromide . Templates were generated for in vitro transcription in a PCR adding the T7 promoter sequence ( TAATACGACTCACTATA GGG ) to the 5′ end of the desired MeV or Mengo virus genomic fragment , respectively ( for oligonucleotides see Tables S2 und S4 respectively ) . PCR products were subsequently purified on agarose gels . RNA was transcribed using the Ambion Megashortscript T7 Kit according to the manufacturer's protocols . The reaction was incubated overnight at 37°C and RNA was precipitated using LiCl at −20°C for 30 minutes . Afterwards , RNA was subjected to triphosphate digestion using FastAP ( Fermentas ) according to the manufacturer's instructions and purified on denaturing 8 M urea/10% polyacrylamide gels at 25 mA constant current . Gel slices containing RNA were incubated overnight with 450 µL probe elution buffer ( 0 . 5 M ammonium acetate , 1 mM EDTA , 0 . 2% SDS ) . Eluted RNA was isolated by phenol/chloroform/isoamylalcohol extraction and precipitated with ethanol . ATPase hydrolysis activity was determined using [γ-P32] ATP . Mouse MDA5 was purified as described previously [32] and 1 . 6 µM of protein was preincubated with 80 nM in vitro transcribed RNA for 10 min at room temperature . The reaction was initiated by addition of ATPase hydrolysis buffer ( 20 mM HEPES , pH 7 . 5 , 150 mM NaCl , 1 . 5 mM MgCl2 , and 2 mM DTT ) containing 2 mM ATP and 0 . 2 µCi [γ-P32] ATP . The hydrolysis rate was monitored over 1 h and analyzed by thin layer chromatography ( TLC ) . Sequences encoding full-length human RIG-I with N-terminal FLAG-tag and full-length human MDA5 with N-terminal FLAG-tag were cloned into pcDNA5 FRT/TO ( Invitrogen ) . Mutants ( FLAG-RIG-I E373Q and FLAG-MDA5 E444Q ) were generated by site directed mutagenesis with PfuUltra ( Agilent ) . | RIG-I-like receptors ( RLRs ) are helicase-like molecules that detect cytosolic RNAs that are absent in the non-infected host . Upon binding to specific RNA patterns , RLRs elicit a signaling cascade that leads to host defense via the production of antiviral molecules . To understand how RLRs sense RNA , it is important to characterize the nature and origin of RLR-associated RNA from virus-infected cells . While it is well established that RIG-I binds 5′-triphosphate containing double-stranded RNA , the in vivo occurring ligand for MDA5 is poorly characterized . A major challenge in examining MDA5 agonists is the apparently transient interaction between the protein and its ligand . To improve the stability of interaction , we have used an approach to crosslink MDA5 to RNA in measles virus-infected cells . The virus-infected cells were treated with the photoactivatable nucleoside analog 4-thiouridine , which is incorporated in newly synthesized RNA . Upon 365 nm UV light exposure of living cells , a covalent linkage between the labeled RNA and the receptor protein is induced , resulting in a higher RNA recovery from RLR immunoprecipitates . Based on next generation sequencing , bioinformatics and in vitro approaches , we observed a correlation between the AU-composition of viral RNA and its ability to induce an MDA5-dependent immune response . |
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Aging in Caenorhabditis elegans is characterized by widespread physiological and molecular changes , but the mechanisms that determine the rate at which these changes occur are not well understood . In this study , we identify a novel link between reproductive aging and somatic aging in C . elegans . By measuring global age-related changes in the proteome , we identify a previously uncharacterized group of secreted proteins in the adult uterus that dramatically increase in abundance with age . This accumulation is blunted in animals with an extended reproductive period and accelerated in sterile animals lacking a germline . Uterine proteins are not removed in old post-reproductive animals or in young vulvaless worms , indicating that egg-laying is necessary for their rapid removal in wild-type young animals . Together , these results suggest that age-induced infertility contributes to extracellular protein accumulation in the uterus with age . Finally , we show that knocking down multiple age-increased proteins simultaneously extends lifespan . These results provide a mechanistic example of how the cessation of reproduction contributes to detrimental changes in the soma , and demonstrate how the timing of reproductive decline can influence the rate of aging .
Aging in C . elegans is characterized by stereotyped physiological changes over the course of its three week lifespan . Animals become infertile early in life , followed by a decline in locomotion and the degeneration of many organs , such as the intestine and the muscle [1–3] . Other tissues overproliferate with age; for example , aged worms have increased cuticle thickness , accumulation of yolk protein in the body cavity , masses in the germline , and ectopic neuronal branching [3–6] . Some of these cases of hypertrophy may be related to unchecked protein production or accumulation . However , the upstream causes of these changes and the factors that determine their timing are not yet well understood . One way to understand the molecular causes of aging is to use unbiased approaches to profile changes that occur with age , and identify the upstream regulators of these changes . Transciptional profiling has been used to identify genes that change expression during C . elegans aging at the RNA level [7–9] . This has allowed the identification of transcription factors that bind and regulate these age-regulated genes . Many of these transcription factors themselves change expression with age and can modulate lifespan when their expression is reduced or increased [7 , 10–12] . However , the mechanisms that induce changes in transcription factor expression , and therefore determine the rate and timing of their change , are generally not known . Relative to the aging transcriptome , the proteome of aging animals has been less well characterized . Assessing changes in the aging proteome directly is important because changes in RNA abundance are not always predictive of downstream protein abundance changes [13 , 14] . In addition , aging has been shown to involve dysregulated protein homeostasis , including reduced protein synthesis and protein folding capacity , and increased proteome insolubility and protein damage [15–21] . Previous studies of the aging proteome in C . elegans have identified a large number of proteins that aggregate with age [17–19] . In addition , there are large scale changes in soluble protein abundance in old animals [19 , 22] . However , the causes of these changes , the factors influencing their timing , and their effect on lifespan remain unclear . Here , we identify a novel mechanistic link between reproductive aging and somatic aging in C . elegans . Using mass spectrometry-based proteomics , we identify a previously uncharacterized group of secreted proteins that localize to the adult uterus and dramatically increase in abundance with age . We show that this accumulation is partially driven by the termination of reproduction , a very early event in the aging process . Finally , we show that knocking down multiple age-increased proteins simultaneously extends lifespan . Our results suggest that the cessation of reproduction contributes to changes in the post-reproductive animal that are detrimental for survival , and indicate that the timing of reproductive decline can influence the rate of somatic aging .
In order to interrogate changes in the proteome with age , we isolated protein from young ( day 4 ) and old ( day 13 ) adult C . elegans and measured relative protein levels by isotopic labeling by reductive dimethylation and liquid chromatography tandem mass spectrometry [23] . Worms were grown on 5-fluoro-2’-deoxyuridine ( FUDR ) to inhibit progeny production , and strained through a 40 μm pore nylon mesh each day to remove any contaminating eggs and larvae . We chose day 4 for the young sample to ensure that it would be relatively free from contamination by embryos . Day 13 was chosen as the old sample because worms of that age show clear signs of age-related deterioration [1 , 3]; however , a majority of the population is still alive at that time ( 82±7% surviving; S4 Table ) . We performed three biological replicates of this aging time course . We identified 3159 proteins in total , and 1796 proteins in at least two of the three biological replicates ( S1 Table ) . Of the 1796 proteins that we quantified in at least two replicates , 53 significantly change abundance with age by rank-product analysis at a 10% false discovery rate ( FDR ) [24] . Forty of these proteins increase in abundance with age , and 13 decrease ( Fig 1A , S2 Table ) . We used gene set enrichment analysis on the list of 1796 proteins to determine which classes of proteins tended to change with age . Consistent with previous studies of the aging proteome in C . elegans [19 , 22] , extracellular proteins were strongly enriched for increasing with age ( FDR<10−4 ) and ribosomal proteins were enriched for decreasing with age ( FDR<0 . 01; S3 Table ) . Furthermore , we found that proteins that increased or decreased in abundance in two previous studies of the aging proteome generally changed in the concordant direction in this study , even if they did not reach our threshold for statistical significance ( S1A and S1B Fig ) . Previous work also defined a set of proteins that become increasingly insoluble with age [17 , 18] . We asked whether loss of solubility might underlie the changes in soluble protein abundance measured in our study . Aggregation of a specific protein in old age could reduce the amount of that protein that is soluble , leading to an apparent decrease in its abundance . However , neither proteins that increased abundance in our study nor those that decreased abundance were enriched for becoming age-insoluble ( S1C Fig ) . None of the 13 proteins that significantly decreased abundance in our data were age-insoluble in both datasets . Another way to test whether protein aggregation drives changes in the soluble proteome is to ask whether age-insoluble proteins tend to decrease in abundance in the soluble proteome . To do this , we compared the distribution of age-related changes in abundance of insoluble proteins ( 601 insoluble proteins from [17] and 185 insoluble proteins from [18] ) to age-related changes in abundance of all 1796 proteins in our experiment . There was no substantial difference between these two sets , indicating that increasing protein insolubility with age does not substantially deplete the soluble protein pool for most proteins ( S1D Fig ) . This result is consistent with recent findings demonstrating that only a small fraction of the total protein pool becomes age-insoluble for abundant proteins [19] . In order to assess underlying mechanisms responsible for changes in protein abundance in old age , we next asked whether proteins that change in abundance with age also show concordant changes in RNA expression during aging . We compared changes in protein abundance of the 53 significantly changed proteins to changes in their respective transcript levels , as measured in a previously generated DNA microarray expression dataset [7] ( Fig 1B ) . We found that proteins that decrease abundance with age also generally decrease at the RNA level . Of the 9 proteins that significantly decreased abundance and were also represented in the microarray dataset , 6 had significantly lower transcript levels with age , a 12-fold enrichment over expectation ( p<10−5 by Fisher’s exact test ) . 8 of the 9 proteins had a lower RNA levels regardless of significance . This result suggests that most of our observed decreases in protein abundance in old age can be explained by decreases in their corresponding transcript levels . However , there was no such concordance for proteins that increased abundance with age . Of the 32 proteins that increase abundance in old age and are also covered in the microarray expression dataset , 14 had significantly decreased RNA expression and just 4 had significantly increased RNA expression with age . This result suggests that most proteins that increase abundance with age in this study do not do so because of corresponding changes in the RNAs that encode them , and instead are likely to increase abundance for a different reason . Thus , we chose to focus our further analysis on the 40 proteins that increase abundance with age . Using gene set enrichment analysis , we found that extracellular proteins tend to increase in abundance with age . In total , 249 of the 1796 proteins covered in our experiment ( 14% ) are predicted to be secreted . However , 34 out of 40 ( 85% ) of the proteins that significantly increase abundance with age are putative secreted proteins , a 6-fold enrichment over expectation ( p<10−23 by Fisher’s exact test; Fig 2A ) . Furthermore , we compared the distribution of changes in abundance with age for all 1796 proteins covered by our experiment to the 249 proteins that are predicted to be secreted and found that the set of secreted proteins tend to increase abundance with age ( p < 10−27 by Kolmogorov-Smirnov test; S2A Fig ) . This trend holds true even for the 215 proteins that remain after filtering out the 34 proteins that are both significantly increased and secreted ( p < 10−17 by Kolmogorov-Smirnov test; S2A Fig ) . These results indicate that secreted proteins generally increase abundance during C . elegans aging , even though many of these individual proteins do not meet our threshold for statistical significance . To identify possible shared regulation between proteins that increase in abundance with age , we asked whether these proteins are also coexpressed during development . We examined the expression pattern of the transcripts encoding the proteins that increase in abundance with age in early embryos , late embryos , the four larval stages , and day 1 adult worms using data generated by the modEncode consortium [26] . We observed that many of these 40 proteins were expressed specifically in day 1 adults or L4 ( late larvae ) stage , and not expressed in earlier developmental stages ( Fig 2B ) . To compare the developmental expression pattern of the proteins that increase abundance with age to all proteins , we performed hierarchical clustering of all 1796 proteins detected in this study on the relative RNA expression in each developmental stage . From this , we identified a cluster that represented transcripts specifically expressed in the day 1 adult ( Fig 2C , blue rectangle ) . In this cluster of 95 proteins , 17 significantly increase in abundance between day 4 and day 13 , an 8-fold enrichment over expectation ( p<10−11 by Fisher’s exact test; Table 1 ) . The observed adult-specific expression pattern is not because these 17 genes are expressed at extremely high levels in adults . Rather , these genes have low RNA expression levels in the embryo and larval stages , and are expressed in young adults at similar levels to that of transcripts encoding the background set of proteins analyzed in this study ( S2D Fig ) . We next asked whether adult-specific proteins show a general trend towards increasing abundance with age . The entire group of 95 adult-specific proteins tends to increase with age compared to all 1796 proteins in this study ( p < 10−10 by Kolmogorov-Smirnov test; S2B Fig ) . This trend holds true for the 78 proteins that remain after filtering out the 17 proteins that significantly increase in abundance with age ( p<10−4 by Kolmogorov-Smirnov test; S2B Fig ) . This suggests that proteins that begin to be expressed on day 1 of adulthood tend to continue to increase in abundance between day 4 and day 13 of adulthood . Of the 17 proteins that significantly increase in abundance with age and are adult-specific , all are known or predicted to be secreted . Notably , there is a significant overlap between adult-specific proteins and predicted secreted proteins ( 55 proteins , a 4-fold enrichment over expectation , p < 10−24 by Fisher’s exact test ) . These 55 proteins are strongly shifted towards increasing abundance with age , even though most are below our significance threshold ( p < 10−18 by Kolmogorov-Smirnov test; S2C Fig ) . In summary , these results suggest that adult-specific and secreted proteins may define a class of proteins that tend to increase abundance during C . elegans aging . We hypothesized that this class of secreted , adult-specific , and age-increased proteins might have a common mechanism for their increase in abundance with age and chose to study some members of this group in more detail . Most of 17 proteins in this group have no known molecular function ( Table 1 ) . One exception is the yolk protein VIT-6 , one of 5 vitellogenin proteins in C . elegans . Vitellogenins are produced in the intestine , secreted into the body cavity , and taken up by the germ cells in order to provide nourishment for the developing embryos . They are known to accumulate post-reproductively in the body cavity with age [3] . In order to learn more about the uncharacterized members of this group of proteins , we tagged five members with a fluorescent protein at their C-terminus , preserving the entire upstream and downstream elements of the gene ( named ULE-1 to ULE-5 , as explained below ) . ULE-3 and ULE-4 were tagged with eGFP , and ULE-1 , ULE-2 , and ULE-5 were tagged with the photoconvertible fluorescent protein Dendra2 . Surprisingly , we found that all five tagged proteins localized predominantly in the uterus around the embryos ( Fig 3A ) . For four of the five reporters , there was no visible fluorescence associated with the embryo after it had been laid , suggesting that these reporters are localized to the luminal space outside of the embryos within the uterus . For the fifth reporter ( ULE-5::Dendra2 ) , fluorescence remained associated with the boundary of the egg after it had been laid , suggesting that this protein is either part of the eggshell or is inside the egg ( Fig 3B ) . Because of the unique expression pattern of these reporters , we named these proteins Uterine Lumen-Expressed ( ULE ) 1–5 . As expected from their RNA profiles , all five tagged proteins have only weak or absent expression before the L4 stage; ULE-2 and ULE-4 show expression in the region of the developing uterus in L4 animals , while the other three are not visible in this region until adulthood , when embryos are present . Fluorescence expression of the ULE-2 , ULE-3 , ULE-4 and ULE-5 reporters is limited to the uterus , while ULE-1::Dendra2 is visible in some cells of the hypodermis and tail in approximately 20% of transgenic worms ( S3 Fig ) . There is no visible expression of any of the five uterine protein reporters in young adult male worms . To verify the proteomic results and identify age-related changes in localization pattern , we examined changes in expression between day 4 and day 13 animals ( the same days as used to generate the aging proteome data ) for all five uterine protein reporter lines . As expected , all five reporters increase abundance substantially with age ( 3 to 14-fold; p<0 . 001 by Student’s t-test for all five reporters ) . In old worms , the fluorescent reporters completely fill the uterus , which becomes large and distended ( Fig 3C ) . Even in old worms , fluorescent protein expression is confined to the uterus and is not visible in other tissues or within the body cavity . To confirm that the observed increase in fluorescence was due to reporter expression and not increasing autofluorescence , we treated all five reporter lines with RNAi against the tagged gene and examined the expression at day 4 and day 13 . We found that RNAi against each ULE gene decreases expression of its reporter at least 3-fold at both day 4 and day 13 for all five lines ( S4B Fig; p<0 . 001 by Student’s t-test for all five reporters at both timepoints ) . Furthermore , we observe that autofluorescence in the uterus increases only 30% in non-transgenic control animals between day 4 and day 13 ( S4A Fig ) , suggesting that the 3-14-fold increase in ULE reporter expression that we observe cannot simply be explained by increasing autofluorescence . Finally , as we used FUDR to inhibit progeny production in the proteomics discovery experiment , we asked whether the use of FUDR affected uterine protein reporter expression with age . We observed a small but significant increase in ULE-2::Dendra2 expression at day 4 ( 70% increase; p<0 . 01 by Student’s t-test ) and a small but significant decrease in ULE-5::Dendra2 expression at day 13 ( 20% decrease; p<0 . 05 by Student’s t-test ) . There was no change in expression of any of the other reporters at either timepoint . All five reporters increased significantly with age in the absence of FUDR , suggesting that FUDR does not have a substantial effect on uterine protein expression dynamics ( S4C Fig ) . In summary , these results indicate that we have identified a previously uncharacterized group of uterine proteins that dramatically accumulate with age . To determine which cells produce these secreted uterine proteins , we blocked protein secretion using RNAi against two genes encoding components of the COPII secretory vesicle coat complex , sar-1 and sec-23 . RNAi against either sar-1 and sec-23 has been previously shown to prevent protein secretion and trap fluorescent reporters in the cells that normally secrete them [27] . After 24 hours of RNAi treatment , fluorescent protein was only visible in the uterine cells ( toroidal cells surrounding the uterus ) in the ULE-1 , ULE-2 , and ULE-4 reporter lines ( Fig 4A and S5A Fig ) . For the ULE-5 reporter line , fluorescence was predominantly visible in the spermatheca adjacent to the uterus ( Fig 4B ) . We were not able to identify the cells secreting the fifth reporter ( ULE-3 ) , because GFP fluorescence was too dim to assess changes in expression pattern after sar-1 and sec-23 RNAi . We do not observe fluorescence in any other tissue of the worm in animals treated with sar-1 and sec-23 RNAi , suggesting that these uterine proteins are produced by the cells of the uterus or spermatheca and secreted locally into the uterine space . To determine whether the presence of germ cells or fertilized eggs are required for uterine proteins to be expressed , we examined the effect of glp-1 RNAi on our five fluorescent reporter strains . Loss of glp-1 activity leads to sterility due to a failure of mitotic proliferation of the germline [28] . We observed that all five of these reporters were still expressed in the uterus of young adult glp-1 RNAi treated animals , even in the absence of germ cells or developing embryos ( Fig 4C ) . In addition , we asked whether sperm or oocytes specifically were required for uterine protein production by assessing uterine protein reporter expression in worms treated with fog-2 and mog-5 RNAi . fog-2 is required for spermatogenesis in hermaphrodites and RNAi against fog-2 leads to a feminization of the hermaphrodite germline [29 , 30] . mog-5 is required for the switch from spermatogenesis to oogenesis in the hermaphrodite germline , and loss of mog-5 leads to masculinization of the germline [31] . Similar to our results with glp-1 RNAi treated animals , we observed that all five uterine protein reporters were expressed in the uterus of young adult fog-2 and mog-5 RNAi treated animals ( S5B Fig ) . Together , these results indicate the neither sperm , oocytes or the germline are required for expression of the ULE proteins . Finally , we examined expression of the genes encoding the 17 adult-specific , age-increased proteins in glp-4 ( bn2 ) animals ( which have few to no germ cells ) using a previously generated DNA microarray dataset [32] . We found that none of the transcripts encoding these 17 proteins have significantly different expression levels in glp-4 worms compared to controls . This result shows that the presence of germ cells , fertilized eggs , or developing embryos is not necessary for expression of these 17 proteins . Together with the data on secretion mutants , these data suggest that uterine proteins are produced by the somatic cells of the adult worm and secreted into the uterine lumen . However , it remains possible that these proteins could be subsequently transported inside the embryo while it is in the uterus , particularly in the case of the ULE-5 reporter that remains associated with the embryo after it has been laid . Increased abundance of a protein in old age could be caused by a decreased rate of protein removal . To test whether the removal rate of uterine proteins changes with age , we used the ULE-1 and ULE-2 reporters tagged with the photoconvertible fluorescent protein Dendra2 to examine the dynamics of these proteins in young and old worms . Dendra2 is a monomeric fluorescent protein that can be irreversibly converted from a green to a red form by exposure to short wavelength light [33] . In unconverted worms , Dendra2 is green with essentially no background red fluorescence . After photoconversion , a population of red protein initially appears and declines with time as photoconverted protein is degraded or otherwise removed ( Fig 5A ) . The level of red fluorescence can be monitored over time , allowing one to measure the rate of protein loss . Fusion proteins tagged with Dendra2 have been previously used to determine protein half-lives in tissue culture cells [34] . In order to interrogate the dynamics of Dendra2 reporters for ULE-1 and ULE-2 , we photoconverted Dendra2 in day 2 and day 12 adults , re-imaged these photoconverted worms at several time points after conversion , and measured the decrease in red fluorescence intensity . We chose to examine day 2 adults as the young timepoint so that we could assess the effect of reproduction on uterine protein removal . We also imaged unconverted worms at the same time points to measure changes in total protein levels during the course of our experiment . Comparing the rate of protein loss to the change in total protein allows us to infer whether there is new protein production during the time span of the experiment . Photoconverted red proteins were rapidly lost in young adult animals , with a half-life of less than 4 hours for ULE-2::Dendra2 , and approximately 6 hours for ULE-1::Dendra2 . However , in old animals , the half-life was greater than 36 hours for both Dendra2 reporters ( Fig 5B and 5C ) . Two replicate experiments yielded nearly identical results regarding age-related changes in the dynamics of ULE-2::Dendra2 protein loss ( S6 Fig ) . These results indicate that the removal rate of ULE-1::Dendra2 and ULE-2::Dendra2 protein is substantially slower in old animals than in young . In young animals , the total levels of both ULE-1::Dendra2 and ULE-2::Dendra2 increased by approximately 50% over the course of the 12 hour experiment ( Fig 5D ) . This increase in total protein level indicates that proteins are being rapidly synthesized in young worms . Together , these results show that ULE-1 and ULE-2 accumulate with age because protein production exceeds protein removal in young worms , and because protein removal rate substantially decreases in old worms . Why are uterine proteins removed slowly in old animals ? One hypothesis is that the egg-laying process itself clears these proteins from the uterus in young worms . Since post-reproductive worms cannot lay fertilized eggs , they may fail to effectively remove uterine proteins . One of our five fluorescent reporters , ULE-5::Dendra2 , is localized to the eggshell and is visible on eggs that have been laid ( Fig 3B ) . We were unable to observe fluorescence outside the worm body after egg laying for the other four tagged uterine proteins . However , it is possible that they are not bound to the eggshell , so that even if they are excreted during egg-laying they may diffuse away too quickly to be observed . If egg-laying or another aspect of progeny production is the major means of clearing age-increased uterine proteins , we hypothesized that extending the reproductive period of the animal might reduce their age-related accumulation . Self-fertile hermaphrodites lay eggs for the first 4 days of adulthood , at which time they are depleted of sperm . Mating hermaphrodites to males extends their reproductive period until day 8 of adulthood [35] . Therefore , mating to males allowed us to test whether continued reproduction could prevent the accumulation of uterine proteins in post-reproductive animals . We compared the expression of the five uterine protein reporters between day 5 self-fertile hermaphrodites ( past the reproductive period ) and day 5 mated hermaphrodites ( producing progeny ) to assess whether post-reproductive worms had higher levels of uterine protein than reproductive worms at the same age . At day 5 , mated hermaphrodites had approximately 50% the level of uterine protein reporter expression as unmated hermaphrodites for all five reporter lines ( p<0 . 05 by Student’s t-test; Fig 6A ) . Fluorescent reporter expression increased 2 . 5 to 12-fold between day 1 and day 5 in unmated hermaphrodites , indicating that the increase in uterine protein abundance begins early in adult life . In mated animals , this increase in protein abundance was substantially blunted , though not completely abolished . A second way to test whether the accumulation of uterine proteins can be delayed by extending reproduction is to use genetic perturbations that extend the reproductive period . RNAi of the genes daf-2 and moma-1 extends reproductive span , though to a lesser degree than mating [36] . RNAi treatment of daf-2 but not moma-1 also significantly extends lifespan [36] . We tested the effects of RNAi against these two genes on protein accumulation on the five uterine protein reporters . RNAi against daf-2 significantly reduced the accumulation of all five uterine protein reporters at day 5 compared to control worms at day 5 ( p<0 . 05 by Student’s t-test ) . RNAi against moma-1 significantly reduced the levels of all the uterine protein reporters at day 5 except ULE-5::Dendra2 , which had reduced expression but was not statistically significant ( p = 0 . 09 by Student’s t-test; S7 Fig ) . Neither gene knockdown had an effect on uterine protein reporter expression at day 1 . Together with the mating results , these results indicate that prolonging reproductive lifespan reduces uterine protein accumulation early in life . Conversely , if egg-laying or another aspect of progeny production is responsible for removing uterine proteins in young worms , we would expect that worms that do not produce progeny would accumulate uterine proteins more quickly than wild-type animals . To test this , we compared the levels of our five uterine protein reporters in worms treated with empty vector or glp-1 RNAi at day 1 ( young reproductive ) , day 5 ( young post-reproductive ) , and day 13 ( old ) . We found that all five reporters had significantly higher levels of reporter expression in glp-1 worms at day 1 and day 5 , and two were also significantly higher at day 13 ( p<0 . 05 by Student’s t-test; Fig 6B ) . These results indicate that uterine protein accumulation does occur more rapidly in worms that have never reproduced , and supports the hypothesis that reproduction is required for uterine protein removal in young worms . Finally , to test more directly whether uterine proteins are removed by egg-laying , we asked whether the vulva is required for uterine protein removal in young worms . Eggs are normally laid through the vulva , and vulvaless mutant worms cannot lay any eggs . Therefore , if uterine proteins are removed by egg-laying in young worms , they should not be removed in young worms lacking a vulva . To test this possibility , we treated worms expressing either ULE-1::Dendra2 or ULE-2::Dendra2 with lin-39 RNAi , which produces vulvaless worms [37] . We converted the tagged protein from green to red early on day 1 of adulthood ( when most worms had only 1–2 fertilized eggs ) , and reimaged the worms 4 hours later for ULE-2 and 6 hours later for ULE-1 to measure the degree of protein removal . These timepoints were chosen because our previous data showed that at least 50% of red protein would be removed in wild-type worms . At these early timepoints , none of the lin-39 RNAi treated worms had internal hatching of progeny . Any lin-39 RNAi worm that was not vulvaless was discarded . 100% of control worms laid eggs in the 4 or 6 hours after photoconversion . As in our previous data , we observed that approximately 75% of the red protein was removed in control worms in 4 hours for ULE-2::Dendra2 and 6 hours for ULE-1::Dendra2 expressing worms ( Fig 6C ) . However , no significant amount of red protein was removed in worms treated with lin-39 RNAi at the same time points . This suggests that in young worms , the predominant source of protein removal is by egg-laying ( or another means of exit through the vulva ) . In summary , the results of the mating , reproductive mutants , glp-1 mutants , and vulvaless mutants all indicate that uterine proteins are removed by egg-laying in young adults , and that the cessation of egg-laying contributes to the accumulation of uterine proteins with age . Next , we asked whether the accumulation of proteins that increase in abundance with age is detrimental for lifespan . First , we reduced the expression of each of the 53 proteins that significantly changed abundance with age using RNAi starting at day 1 of adulthood and measured the resulting lifespan . We repeated this screen for the 17 age-increased , adult-specific proteins , this time starting the RNAi treatment at L1 , so that the dsRNA against the gene would be present as soon as it became expressed . None of these single knockdowns showed a repeatable increase in lifespan ( S4 Table ) . However , we identified a group of adult-specific and secreted proteins , several of which are expressed in the uterus , that all increase in abundance with age . If the negative effect of protein accumulation is caused by this group as a whole , it may be necessary to reduce the levels of several proteins simultaneously in order to have an effect on lifespan . We chose to reduce the expression of several uterine proteins , as well as one other age-increased protein that is highly expressed ( far-6 ) . ULE-4 , ULE-5 and FAR-6 have the highest levels of RNA [26] and protein ( this study ) in young adults ( not including vit-6 , which was excluded because vitellogenins are already known to have a detrimental effect on lifespan [38] ) . We attempted to express a GFP tagged version of far-6 to determine if it was expressed in the uterine lumen , but we found that overexpression of this gene is toxic . ULE-2 and ULE-3 also relatively abundantly expressed , so that knockdown of these proteins might help alleviate overall accumulation of uterine proteins in the uterus . We confirmed the effectiveness of the double , triple , and quadruple RNAi by examining the expression of the relevant uterine protein reporters after RNAi . Double , triple , and quadruple RNAi treatment substantially and significantly reduced the expression of the relevant reporters , although the RNAi knockdown effect in experiments involving two , three , and four simultaneous RNAi clones was reduced compared to the effect of single RNAi treatments ( S8 Fig ) . We did not observe any defects in brood size , survival of embryos , or eggshell permeability in worms treated with ule-4 , ule-5 , and far-6 RNAi ( S9 Fig ) . We observed a 10–20% increase in median lifespan in worms treated with RNAi against ule-4 , ule-5 , and far-6 in three biological replicates ( p<0 . 01 by log rank test for all three replicates; Fig 7A and S4 Table ) . Reducing the expression of only ule-4 and ule-5 produced a 10–15% increase in lifespan in two out of three replicates ( p<0 . 01 by log rank test; S4 Table ) . Reducing the expression of ule-3 , ule-4 , and ule-5 simultaneously did not have a significant effect on lifespan in two biological replicates ( Fig 7B and S4 Table ) . RNAi against ule-3 , ule-4 , ule-5 , and ule-2 simultaneously increased lifespan by 15–20% in two biological replicates ( p<0 . 01 by log rank test; Fig 7B and S4 Table ) . These results suggest that the accumulation of individual uterine proteins is not detrimental for lifespan , but that the cumulative accumulation of multiple age-induced proteins or uterine proteins has a modest negative effect on lifespan . Finally , to test whether increased expression of uterine proteins is sufficient to reduce lifespan , we generated overexpression lines for each of the five uterine proteins that carried extra copies of the protein tagged with either GFP or Dendra2 on an extrachromosomal array . We measured the lifespan of two independent overexpression lines for each of the five uterine proteins and observed a 20–30% reduction in lifespan in all lines compared to a transgenic control line ( p<0 . 01 by log rank test; Fig 7C and S4 Table ) . This result is consistent with the possibility that accumulation of uterine proteins is detrimental for lifespan .
In this study , we identify a novel link between reproductive senescence and somatic aging in C . elegans . By assessing changes in protein abundance with age in an unbiased manner , we identify a previously uncharacterized class of proteins that accumulates with age in the adult uterus . The increase in uterine protein abundance in old animals is at least partially driven by age-induced infertility , which is a very early event in the aging process . Extending the reproductive period delays the accumulation of uterine proteins , and the presence of a vulva is required for the effective removal of uterine proteins in young animals . Therefore , the mid-life cessation of egg-laying likely contributes to the accumulation of uterine proteins with age . Finally , we show that simultaneous knockdown of multiple uterine proteins extends lifespan . These results suggest that the cessation of reproduction contributes to changes in the post-reproductive animal that are detrimental for survival . Our findings provide a mechanistic link between aging of the reproductive system and aging of the soma . Many evolutionary theories of aging posit that the force of natural selection declines after the end of reproduction , because survival of the old parent provides little to no selective advantage for its offspring [39] . As a result , processes that are beneficial to the animals during development and reproduction may become dysregulated late in life [39 , 40] . Uterine proteins are produced at high levels in young animals and are removed during the reproductive process . In old worms , the cessation of reproduction stops the removal of uterine proteins , leading to their accumulation with age . Though knockdown of single uterine proteins does not have an effect on lifespan , we show that knockdown of multiple uterine proteins simultaneously leads to a small but reproducible increase in lifespan . This suggests that high cumulative levels of uterine proteins is modestly detrimental for survival . Despite this negative effect on the soma , it is likely that there is insufficient selective pressure in a post-reproductive animal to evolve a new outlet for these proteins and prevent their accumulation . The accumulation of uterine proteins with age is conceptually similar to the post-reproductive accumulation of vitellogenins ( yolk proteins ) . In young worms , these proteins are taken up by oocytes and are essential for viability of progeny . After reproduction ceases , the vitellogenins accumulate to toxic levels in the body cavity of the worm [3 , 38] . In C . elegans , the age-related decline of the elt-3 transcriptional circuit is another example of late life dysregulation of processes that are beneficial to the young animal . In young worms , elt-3 is vital for function of the skin whereas in old worms , loss of elt-3 function limits lifespan [7] . In mammals , the cell cycle regulator p16Ink4a increases expression with age in many human and mouse tissues and promotes cellular senescence [41 , 42] . The regenerative capacity of many tissues declines with age , and reducing p16Ink4a expression can ameliorate these declines in many cell types [43–45] . A key difference between the uterine and yolk proteins and other examples of late life dysregulation of gene expression is that the timing of reproductive decline provides a mechanism for their accumulation . In many cases , the upstream causes of age-related changes in gene expression are not known , and therefore it is not clear what determines the kinetics of their age-related decline . For uterine and yolk proteins , the timing of their accumulation is tied to reproductive senescence . This provides a mechanistic link between the rate of aging of the germline and that of the soma and predicts that variation in the reproductive period ( either experimentally or naturally occurring ) could have a direct effect on somatic aging . Although the specific uterine proteins that we identify are not clearly conserved in mammals , the concept that the reproductive period has a direct role in specifying the rate of aging of the soma is likely to apply to other species as well . What is the function of uterine proteins in young animals ? ule-1 was previously identified in mass RNAi screens to be required for normal progeny production [46 , 47] , and we confirmed in this study that knockdown of ule-1 produces a small reduction in brood size . However , knockdown of ule-4 , ule-5 , and far-6 simultaneously ( which extends lifespan ) had no effect on fertility or eggshell integrity . However , as these uterine proteins are expressed at very high levels in young animals and are actively produced over the reproductive period , it is likely that they do indeed have a function that remains to be discovered . It is possible that they are required redundantly for reproduction , and we did not achieve a sufficient degree of knockdown to observe a strong effect . It is also possible that they serve some non-reproductive function that we did not assay , or are beneficial only in particular environmental situations that are not recapitulated in the lab environment . Many previous studies have examined potential trade-offs between reproductive capacity and somatic maintenance . One such study found that higher levels of progeny production in mated worms is a biomarker of aging and correlates with a longer lifespan [48] . This is consistent with the possibility that prolonged reproduction can be beneficial for lifespan . However , removing the entire germline extends lifespan , indicating that the presence of the germline generally accelerates aging of the soma [49–52] . It is unlikely that the longevity of germline-less mutants is mediated by uterine proteins , as we find that they are still expressed in animals lacking germ cells . Furthermore , removing the entire reproductive system ( germ cells and somatic gonad , which includes the uterine cells and spermatheca that secrete uterine proteins ) should eliminate uterine protein accumulation but does not increase lifespan [49] . Therefore , these observations indicate that the germline and somatic gonad have effects on lifespan independent of the effect of accumulation of uterine proteins . Previous studies of uterine morphology during aging have observed a large increase in the size of the uterus in old worms [4 , 53] . Much of this uterine hypertrophy can be attributed to the presence of uterine tumors , which are predominantly masses of DNA caused by the endoreduplication of unfertilized oocytes [4 , 53 , 54] . However , McGee et al ( 2012 ) also observed an increase in acellular material associated with the uterine masses , which is consistent with our observation that there is a general accumulation of extracellular protein in the uterus with age . In addition to the five uterine proteins that we characterize in this study , we find that secreted proteins and proteins expressed specifically in adult animals generally increase in abundance with age . Several previous studies of the aging proteome in C . elegans also found that extracellular proteins increase in abundance with age , though the mechanism for this increase is unclear [19 , 22] . A study that characterized newly synthesized proteins in young and old worms showed that extracellular proteins are enriched for having increased synthesis with age [22] . Therefore , some extracellular proteins might increase in abundance due to increased production , even as the protein synthesis rate declines with age generally [19 , 20 , 55] . Furthermore , it is likely that many of the additional secreted and adult-specific proteins that increase in abundance with age are also expressed in the uterus and increase by the same mechanism as the five that we examined in detail . We created fluorescent reporter proteins for five of 17 significantly age-increased , secreted , and adult-specific proteins , and found that all five are expressed in the uterine lumen . This suggests that there is a large class of extracellular uterine proteins in C . elegans that remain to be identified . In summary , we found that uterine proteins accumulate in post-reproductive animals in part because of reproductive senescence itself . Therefore , the accumulation of uterine proteins is an example of an aging process that is at least partially driven by an intrinsic property of the animal ( the length of the reproductive period ) , rather than extrinsic processes such as molecular damage . In addition , the post-reproductive accumulation of uterine proteins is a mechanistic example of how the timing of reproductive decline can in part determine the rate of somatic aging . It is likely that the duration of the reproductive period , a trait under strong selection , is an important determinant of lifespan in C . elegans as well as other species .
For the proteomic analysis of aging , worms were grown on NGM plates supplemented with 30 mM 5-fluoro-2’-deoxyuridine ( FUDR ) to inhibit progeny production . During the reproductive period , worms were strained through a 40 μm pore nylon mesh each day to remove any contaminating eggs and larvae and transferred to new plates . Dead worms were manually removed from day 13 plates by picking before the worms were harvested for protein extraction . On day 4 and day 13 of adulthood , we collected approximately 10 , 000 worms into an equal volume of modified RIPA buffer ( 10 mM sodium pyrophosphate , pH 7 . 4 , 150 mM NaCl , 1% Triton X-100 , 1% sodium deoxycholate , 0 . 1% SDS , 2 mM EDTA ) with protease inhibitors ( Roche Complete , EDTA-free , #04693159001 ) . The resulting pellet was frozen in liquid nitrogen and thawed three times , and manually disrupted by mortar and pestle until no large fragments of worm remained . The resulting samples were sonicated 3 times for 10 seconds each time on medium amplitude using a Diagenode Biorupter ( UCD-200 ) . The lysates were centrifuged at 2500xg for 30 minutes at 4°C to remove insoluble material and cell debris . We performed 3 biological replicates of this aging timecourse . Protein lysates from three pairs of young and old biological replicates were precipitated by the addition of saturated trichloroacetic acid to 15% final concentration . The resulting protein pellet was washed five times with ice cold excess acetone . Proteins were resuspended in 8 M urea , 100 mM NaCl , 25 mM Tris , pH 8 . 2 in the presence of protease inhibitor cocktail ( Roche Complete , EDTA-free , #04693159001 ) . Proteins were reduced by the addition of dithiothreitol ( DTT ) to 5 mM final concentration and incubation at 50°C for 30 min . Cysteines were alkylated by the addition of iodoacetamide to 14 mM final concentration and incubation at room temperature for 1hr in the dark . The alkylation reaction was quenched by increasing the final concentration of DTT to 10 mM . After diluting the samples to 1 M urea , proteins were proteolytically digested with 5 ng/ml trypsin ( Sequencing Grade Modified Trypsin , Promega , #V5111 ) . Enzymatic digestion was quenched by the addition of trifluoroacetic acid to 0 . 1% final concentration , pH < 2 . The resulting peptides were desalted using Sep-Pak C18 columns ( Waters , # WAT023590 ) . Peptides were dried down and resuspended in 1 M HEPES , pH 7 . 5 . Peptides were chemically labeled by reductive dimethylation of lysine residues and N-termini as previously described [23] . Peptides derived from young worm lysates were light-labeled ( +28Da ) by reaction with 4% d0-formaldehyde and 600 mM sodium cyanoborohidride for 10 minutes at room temperature . Peptides derived from old worm lysates were heavy-labeled ( +34Da ) by reaction with 4% formaldehyde- d2 and 600 mM sodium cyanoborodeuteride for 10 minutes at room temperature . Each reaction was repeated and then quenched by the addition of 10% formic acid to pH 3 . Quenching was aided by bath sonication for 1 hour . Light- and heavy-labeled pairs were mixed one-to-one and subsequently desalted using Sep-Pak C18 columns . Light- and heavy-labeled peptide mixtures were then chromatographically fractionated using strong cation exchange chromatography on an Agilent 1200 high-performance liquid chromatography instrument ( Agilent Technologies ) . Peptides were loaded on to a Polysulfoethyl A column ( Poly LC Inc . , #209SE502 ) in buffer A ( 7 mM KH2PO4 , 30% Acetonitrile , pH 2 . 65 ) and eluted with a gradient of 0–25% buffer B ( 7 mM KH2PO4 , 350 mM KCl , 30% Acetonitrile , pH 2 . 65 ) for 29 minutes , 25–100% buffer B for 5 minutes , and 100% buffer B for 5 minutes . Twelve fractions were collected and desalted using Sep-Pak C18 columns . Peptides from each fraction were analyzed on an LTQ Velos Orbitrap mass spectrometer ( Thermo Fisher Scientific ) coupled to an Agilent 1100 high performance liquid chromatography pump ( Agilent Technologies ) and a MicroAS autosampler ( Thermo Scientific ) . Approximately 10% of each fraction was loaded on to a 17cm fused silica microcapillary column ( 100um inner diameter ) with an in-house pulled tip ( ~5 um inner diameter ) packed with C18 reversed-phase resin ( Magic C18AQ , Michrom Bioresources ) . Peptides were eluted into the mass spectrometer’s nanospray ionization source via a two-step gradient of 7–25% buffer B ( 2 . 5% water and 0 . 1% formic acid in acetonitrile ( v/v ) ) in buffer A ( 2 . 5% acetonitrile and 0 . 1% formic acid in water ( v/v ) ) over 60 minutes followed by a second phase of 25–45% buffer B over 20 minutes . The mass spectrometer collected 10 ion-trap MS/MS spectra per data-dependent cycle . Raw data acquired by the mass spectrometer were converted to the mzXML format , and MS and MS/MS data were extracted using in-house software ( [56 , 57]; software available by request ) . Spectra were analyzed using the Sequest ( version 27 , revision 12 ) algorithm and the Uniprot C . elegans protein sequence database using the target-decoy strategy [58] . Search parameters included tryptic cleavage , two missed cleavages allowed , peptide mass tolerance of 50 ppm , static carboxyamidomethylation of cysteine residues ( +57 . 02146 Da ) , differential oxidation of methionine residues ( +15 . 99491 Da ) , static demethylation of lysine residues and N-termini ( +28 . 03230 Da ) , and differential modification of lysine residues and N-termini ( +6 . 037660 Da ) . Peptide-spectrum matches were determined with an estimated false discovery rate <1% and quantification of light- and heavy-labeled peptides was performed by the Vista quantitative analysis tool [56] . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD002432 . The resulting heavy/light ratios for each protein were log2 transformed and median centered to control for inexact mixing . Therefore , proteins that changed in this experiment are those that changed relative to all proteins , rather than on an absolute per worm basis . Only proteins that were identified in at least two of the three replicates were considered for further analysis ( 1796 proteins ) . Proteins that significantly changed across replicates were identified using the rank product algorithm as previously described [24] . The false discovery rate corresponding to each rank product was calculated by 10 , 000 random permutations of the ranks for each of the three replicates . At a 10% false discovery rate , 53 proteins significantly changed in abundance with age . Gene set enrichment analysis was performed as previously described [59] , using C . elegans gene ontology terms as gene sets and including annotations inferred electronically . False discovery rates were calculated using 1000 permutations of the data . Only those gene ontology terms that were enriched with an FDR<0 . 05 are reported as significant . To analyze the developmental expression pattern of each protein , we used stage-specific RNA-seq data generated by the ModEncode consortium [26] . RPKM values were row normalized to obtain the relative expression of each gene in each stage ( independent of the absolute level of expression of that gene ) . Hierarchical clustering of the developmental expression pattern of all proteins was performed in MATLAB ( MathWorks Incorporated , R2010b ) with the clustergram function using the “correlation” distance metric . All C . elegans strains were handled and maintained as described previously [60] . Worms were maintained and all experiments were performed at 20°C . The N2 strain was used for all experiments unless otherwise stated . GFP and Dendra2 reporter constructs and overexpression lines were generated by cloning the complete coding sequence and introns , 5’ and 3’ UTRs , and 600–1000 base pairs of upstream and 200–1000 base pairs of downstream flanking sequence ( generally to start or end of the adjacent gene ) of each protein of interest into the pCFJ350 or pCFJ352 vector [61] . These vectors also contain the C . briggsae unc-119 gene as a transgenic marker . We inserted either eGFP or Dendra2 into the C-terminus of the protein directly upstream of the stop codon by Gibson assembly [62] . Transgenic worms were produced by microinjection of the resulting plasmid into the unc-119 ( ed3 ) background to form extrachromosomal arrays . A list of all of the strains used in this study and the primers used to clone each gene can be found in S4 Table . All RNAi experiments were carried out on NGM plates supplemented with 100 ug/mL ampicillin , and 1 mM IPTG ( 2 mM IPTG if using FUDR ) . Plates were seeded with 10x concentrated overnight cultures of E . coli expressing the appropriate RNAi clone or control . Most RNAi clones were obtained from the Ahringer RNAi library [63] and sequenced to verify proper insertions . HT115 ( de3 ) E . coli carrying the L4440 empty vector plasmid were used as a control for all experiments . For some proteins of interest , the corresponding RNAi clone was not present in the Ahringer RNAi library , did not grow , or was incorrect when sequenced . For these proteins , we constructed RNAi vectors by cloning a 500–1500 base pair fragment of the gene into the L4440 vector at the EcoRV restriction site . The resulting plasmids were transformed into the HT115 ( de3 ) strain of E . coli . A list of these strains and the primers used to clone the inserts can be found in S4 Table . When multiple RNAi clones were used simultaneously in an experiment , the cultures were mixed such that the final concentration of E . coli was the same as the single RNAi clone and control conditions . Synchronized worms at the relevant age were immobilized on slides with 1 mM levamisole and imaged on a Zeiss Axioplan fluorescent microscope . All conditions that are quantitatively compared were imaged on the same day using the same microscope and image capture settings . Representative images of conditions that are not quantitatively compared may have been taken using different settings in order to best display the expression pattern . The average pixel intensity in the uterus of each worm was quantified using ImageJ [64] and background subtracted using the average pixel intensity of at least two representative background areas outside the worm . Regions of interest were selected manually . Late L4 worms of the relevant reporter strain were placed on plates seeded with sar-1 , sec-23 , or empty vector RNAi . Worms were imaged 24 hours later on a Leica TCS SP8 confocal microscope using the 20x objective at 1024x1024 pixels . Images were obtained as Z stacks at 1 . 04 μm intervals . The top and bottom of the worm ( first and last image in the stack ) were selected manually . Images are displayed as either as a maximum projection of all the images in the Z-stack , or just the center image in the Z-stack . For the mating experiment , synchronized adult worms of the relevant reporter strain were either maintained as self-fertile hermaphrodites , or placed with an equal number of N2 males on day 1 of adulthood . Total worm number was kept the same on self-fertile and mating plates ( for example , 50 hermaphrodites on the self plates and 25 hermaphrodites and 25 males on the mating plates ) . Males were removed two days later on day 3 of adulthood , and both mated and self-fertile hermaphrodites were transferred to fresh plates . Mating success was judged by the presence of male progeny on the plate and the continued production of fertilized eggs at day 5 . Self-fertile worms were imaged at day 1 and day 5 of adulthood , and mated worms at day 5 . For the Dendra2 conversion experiments , worms were mounted on 7% agarose pads using 0 . 2% tricaine and 0 . 02% levamisole in M9 as anesthetic . Coverslips were sealed to the slide with petroleum jelly . Worms were mounted on individual slides and recovered to individual plates after imaging so that the data from each worm could be analyzed separately . Dendra2 was converted by scanning single worms with a 405 nm 50 mW diode laser at 100% output for 60 s using a 20x objective at 1024x1024 pixels . Converted and unconverted worms were imaged on a Zeiss Axioplan microscope using the GFP filter to visualize the green form of the protein and the TRITC filter to visualize the red form . Worms were imaged immediately after conversion and then recovered to individual seeded NGM plates at 20°C . At the indicated timepoints after conversion , individual worms were re-mounted on slides and re-imaged following the same procedure as above , and then returned to seeded plates . Microscope and imaging settings were kept constant between imaging timepoints . Both converted worms and unconverted worms that had otherwise been treated identically were imaged at each timepoint . Any worm that died during the course of the experiment was not considered in the final analysis . The red expression values for each worm were background subtracted using the average red expression in unconverted worms as background . For each worm at each timepoint , the background subtracted red expression value was normalized to that same worm’s expression right after conversion ( time 0 ) . Therefore , a relative expression value of 1 means that the worm’s expression is the same as at time 0 , while a relative expression value of 0 means that the worm’s expression is equal to the background red expression level in unconverted worms . Absolute levels of red or green protein are not directly comparable between young and old worms , as the images were taken using different settings to best capture the full range of pixel intensities between converted and unconverted worms . Therefore , all comparisons are relative to expression at time 0 for each condition . Lifespan assays were performed as previously described [65] . All lifespan assays were carried out at 20°C . Worms were maintained on NGM plates supplemented with 30 mM FUDR to inhibit progeny production . In experiments using RNAi , 100 ug/mL ampicillin and 2 mM IPTG were used to select for and induce RNAi bacteria . Animals that died due to internal progeny hatching or bursting were censored . Animals were scored as dead if they failed to respond to prodding by a pick . Significance of lifespan experiments was determined using the log-rank test [66] . Synchronized L1 worms were placed on NGM plates seeded with the appropriate RNAi clone or empty vector control . At the L4 stage , individual unmated hermaphrodites were placed on a new RNAi or control plate and transferred every 24 hours until day 4 of adulthood . The number of eggs laid by each worm was counted each day . 48 hours later , the percent of eggs that had hatched and developed to L4 larvae stage was counted . Mixed stage embryos that had been laid by day 1 adult worms raised on the indicated RNAi clone since L1 were stained with 20 ng/mL 4’ , 6-diamidino-2-phenylindole ( DAPI ) in an isotonic buffer ( 150 mM KCl , 5 mM HEPES , pH 7 . 5 ) , as previously described [67] . | To understand the process of aging at the molecular level in C . elegans , we measured changes in protein abundance with age , determined whether these age-related protein changes lead to dysfunction in old animals , and have elucidated one of the upstream pathways responsible for these aging changes . We found that egg-laying in young worms permits removal of a novel class of proteins present in the uterus . When the reproductive period ends , the removal of uterine proteins stops , causing them to accumulate to toxic levels . This shows that the timing of reproductive decline influences the rate of somatic aging . The concept that the reproductive period has a direct role in specifying the rate of aging of the soma likely applies to other species as well . |
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Apolipoprotein E ( apoE ) is a forefront actor in the transport of lipids and the maintenance of cholesterol homeostasis , and is also strongly implicated in Alzheimer’s disease . Upon lipid-binding apoE adopts a conformational state that mediates the receptor-induced internalization of lipoproteins . Due to its inherent structural dynamics and the presence of lipids , the structure of the biologically active apoE remains so far poorly described . To address this issue , we developed an innovative hybrid method combining experimental data with molecular modeling and dynamics to generate comprehensive models of the lipidated apoE4 isoform . Chemical cross-linking combined with mass spectrometry provided distance restraints , characterizing the three-dimensional organization of apoE4 molecules at the surface of lipidic nanoparticles . The ensemble of spatial restraints was then rationalized in an original molecular modeling approach to generate monomeric models of apoE4 that advocated the existence of two alternative conformations . These two models point towards an activation mechanism of apoE4 relying on a regulation of the accessibility of its receptor binding region . Further , molecular dynamics simulations of the dimerized and lipidated apoE4 monomeric conformations revealed an elongation of the apoE N-terminal domain , whereby helix 4 is rearranged , together with Arg172 , into a proper orientation essential for lipoprotein receptor association . Overall , our results show how apoE4 adapts its conformation for the recognition of the low density lipoprotein receptor and we propose a novel mechanism of activation for apoE4 that is based on accessibility and remodeling of the receptor binding region .
Apolipoprotein E ( apoE ) is a member of the superfamily of exchangeable apolipoproteins . It mediates cellular uptake of cholesterol-rich lipoproteins by acting as a high affinity ligand for cell surface receptors belonging to the low-density lipoprotein ( LDL ) receptor family [1] . An imbalance in cholesterol homeostasis increases the risk for cardiovascular diseases and is also linked to neurodegenerative disorders [2 , 3] . Therefore , the receptor binding property of apoE stresses its importance in the transport of lipids and metabolism of cholesterol both within the plasma and the central nervous system [4 , 5] . In blood plasma , the receptor mediated uptake and endocytosis of apoE-containing lipoproteins lowers the overall levels of circulating lipoproteins , explaining the anti-atherogenic effect of apoE [6] . In the brain , although apoE is involved in lipid redistribution and neuronal growth and repair , the presence of the ε4 allelic form of the apoE gene also represents the most significant genetic risk factor of developing Alzheimer’s disease [7] . An abnormal trafficking of lipids and cholesterol by apoE4 is among the pathogenic mechanisms that are proposed to contribute to the susceptibility of ε4 carriers for Alzheimer’s disease [8 , 9] . ApoE is a ~34 kDa protein composed of 299 amino acids . Single point variations at positions 112 and 158 distinguish the three main isoforms of apoE: apoE2 ( Cys112 , Cys158 ) , apoE3 ( Cys112 , Arg158 ) and apoE4 ( Arg112 , Arg158 ) [10] . These sole amino acid substitutions result in structural differences between these isoforms [11] and marked effects on their lipid binding abilities [12] , providing grounds to explain their different physiological role ( s ) in cardiovascular and Alzheimer’s diseases [13] . In the lipid-free state , all three apoE isoforms possess two independently folded structural domains linked by a protease sensitive loop [14] . The N-terminal ( NT ) domain ( res . 1 to 191 ) comprises an elongated four-helix bundle that contains the binding region to the members of the LDL receptor family on the fourth helix [15] . The C-terminal ( CT ) domain ( res . 210 to 299 ) presents the major lipid binding region [16] and is particularly challenging to study , as it is involved in the oligomerization of apoE in the absence of lipids [17] . Several mutations had to be introduced in the CT domain to generate a stable monomeric protein leading to the so far only available full-length high resolution three-dimensional ( 3D ) structure of a lipid-free apoE protein . In this structure , the CT domain variant contains three α-helices folded upon the NT domain conferring a globular shape to apoE [18] . Upon binding to lipid particles , apoE undergoes a large conformational conversion to accommodate and stabilize the lipids through its amphipathic α-helices , allowing thereby their trafficking in the circulation [19] . Additionally , lipid binding induces apoE to adopt a biologically active conformation that is a prerequisite for the binding of lipoproteins to cell surface LDL receptors and their internalization [1] . Analysis of reconstituted discoidal phospholipid-apoE particles ( rHDL , more recently termed nanodisk ) presented a major step forward towards a structure of lipid-bound apoE [19] . These particles mimic in vivo nascent high density lipoproteins ( HDL ) in shape , size , density and functional properties [20] . It was demonstrated that in these systems , the α-helices of apoE are oriented perpendicularly to the acyl chains of the lipids and the apolipoprotein molecules circumscribe the edge of the discoidal particles [21–23] . Lipid-binding also triggers the elongation of NT domain helix 4 which was proposed to represent a key lipid-induced conformational change allowing for the recognition of apoE by LDL receptors [24 , 25] . However , the conformation adopted by apoE molecules at the surface of these discoidal particles remains an open question . While it is accepted that the CT domain adopts an extended α-helical structure [19 , 22 , 23] , the conformation of the NT domain has not converged towards a single model . Based on calorimetry measurements , it was proposed that the four-helix bundle opens to expose the hydrophobic faces of the amphipathic helices towards the lipids and that further reorganization of helices occurs , triggered by lipid binding [26] . Although this bundle opening was suggested to ultimately lead to a fully extended conformation of apoE that wraps around the entire circumference of the lipid bilayer of the disc [22] , several studies have indicated that apoE adopts a hairpin structure for which distinct hinge localizations were proposed [27–29] . Supported by low resolution X-ray density and electron paramagnetic resonance ( EPR ) measurements , an alternative model was developed . In this case , even though apoE also folds in a hairpin structure , the hydrophobic faces of apoE helices are suggested to interact with each other , while the polar faces contact the phospholipids leading to ellipsoidal lipoparticles [30 , 31] . Despite two decades of intensive structural studies , a consensus on the conformation of lipidated apoE has not yet been reached . With the aim of deciphering the molecular structure adopted by apoE at the surface of rHDL in solution , we designed an approach where complementary low resolution structural data were combined with 3D structural modeling ( Fig 1 ) . We decided to focus our present work on rHDLs containing only apoE4 , considering the prevalent role of this isoform in Alzheimer’s disease [8 , 9] . Experimental data were primarily generated from chemical cross-linking ( XL ) coupled to mass spectrometry ( -MS ) which produces covalently connected pairs of peptides that provide a set of distance restraints between cross-linked residues on the native protein , enabling low resolution models to be elaborated . XL-MS has seen significant progress recently [32–37] and has been successfully applied to a large number of protein complexes [38–41] . The distance restraints from our intramolecular XLs , together with additional experimental data obtained in this work and information from the literature were then used in our hybrid molecular modeling approach . Two alternative models of lipidated apoE4 were validated by our XL-MS results and assessed by molecular dynamics simulations . Our resulting models represent the most detailed structures obtained so far on full-length apoE4 associated to rHDL and they provide unprecedented insight into the active structure of apoE4 . Taken together the data allowed us to propose a novel molecular mechanism that explains how apoE is recognized by the members of the LDL receptor family .
ApoE can bind to lipoproteins of variable sizes and shapes due to its conformational flexibility [42] . To obtain detailed information on lipidated apoE4 conformation , it was desirable to obtain highly homogeneous lipoproteins , in order to stabilize a uniform apoE4 conformation . For the preparation of such rHDL , we used the cholate dialysis method [43] and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) as a source of phospholipids . The initial lipid:protein molar ratio was optimized to enhance homogeneity of apoE4/POPC particles by following the resulting rHDL migration on native PAGE ( S1 Fig ) . At an initial apoE4/POPC molar ratio of 1:110 , a single population was apparent , displaying a Stokes diameter of ~105 Å ( Fig 2A ) . A single population was also detected by gel filtration of apoE4/POPC rHDL ( Fig 2B ) . Finally , quantification of the protein content indicated that the apoE4/POPC reconstitution allowed recovering up to ~50% of the protein initially engaged in rHDL . These reconstitution parameters allowed us to prepare highly homogeneous apoE4/POPC rHDL particles in a reproducible manner . To provide detailed input data for the structural modeling of lipid-bound apoE4 , we extensively characterized their composition and shape . Quantification of the concentration of lipid by phosphorus assay revealed that each particle contained about 200 POPC molecules . Chemical XL with large excess of bis ( sulfosuccinimidyl ) suberate ( BS3; molar ratio BS3:apoE4 200:1 ) followed by SDS-PAGE analysis resulted in a single apoE4 dimer band at approximately 70 kDa , indicating that two molecules of apoE4 were present on each apoE4/POPC rHDL . Infrared measurement revealed a sharp peak for the amide I band ( 1700–1600 cm-1 ) centered at 1652 cm-1 characteristic of α-helical structures [44] ( Fig 2C ) . An α–helical content of approximately 60% was estimated by curve-fitting of the amide I band . ApoE4/POPC rHDL were next characterized by both negative staining ( NS ) and cryo-transmission electron microscopy ( TEM ) . Most representative class-averages of NS-TEM images revealed that apoE4/POPC rHDL mainly appeared to be of circular shape with a diameter of 115 ± 10 Å ( S2A Fig ) . To determine the overall shape of the rHDL particles in hydrated state and to avoid possible artifacts due to sample drying and heavy metals on the observed shape , we visualized the apoE4/POPC rHDL by cryo-TEM ( S2B Fig ) . The homogeneity of the apoE4/POPC reconstitution enabled us to perform single particle analysis . Two-dimensional averages identified both top and side views of the rHDL particles ( Fig 2D ) . These projections displayed a diameter similar to the one previously measured in NS-TEM and a thickness of 50 ± 10 Å , in good agreement with the expected thickness of a POPC bilayer [45] . NS- and cryo-TEM images therefore strongly support a discoidal shape for the apoE4/POPC rHDL . These particles will further be designated as apoE4 nanodiscs in this work . The apoE4 protein conformation at the surface of nanodiscs was investigated by XL-MS using the homobifunctional disuccinimidyl suberate ( DSS ) cross-linker that reacts with primary amino groups ( Lys residues and protein N-termini ) . The extended Cα-Cα distance for lysine pairs that can be cross-linked by DSS is usually considered to have an upper limit of about 30 Å [46 , 47] . An equimolar mixture of light DSS ( DSS-H12 ) and heavy DSS ( DSS-D12 ) was used , providing a unique isotopic signature to cross-linked peptides and facilitating their detection and identification by MS [48 , 49] . The apoE4 molecules at the surface of the nanodiscs were cross-linked with 8 moles of DSS for 1 mole of apoE4 . The low DSS/apoE4 molar ratio was chosen to minimize the risk of disturbing the structure adopted by apoE4 in the nanodiscs . The resulting species were isolated by SDS-PAGE revealing two bands of comparable intensity at approximatively 35 and 70 kDa , which were assigned to cross-linked monomeric apoE4 and cross-linked dimeric apoE4 , respectively ( Fig 3A ) . To generate exclusively unambiguous intramolecular XL products , the monomeric apoE4 band at 35 kDa was processed by in-gel digestion with trypsin and analyzed by liquid chromatography MS/MS . The resulting fragment ion spectra were analyzed using the dedicated software pipeline xQuest/xProphet [46 , 48] . 27 cross-linked peptides were identified for monomeric apoE4 ( S1 Table ) , which corresponded to 22 unique Lys-Lys distance restraints ( Fig 3B and Table 1 ) . Evaluation of the intramolecular XL data set obtained for the apoE4 nanodiscs revealed that both CT and NT regions of the protein are covered by the ensemble of XLs , with 11 out of the 12 apoE Lys residues involved in at least one XL . The XLs can be classified into two main categories . The first , and largest , group comprises XLs that were formed between Lys residues located in the NT domain and the CT domain ( Fig 3B and 3C , dotted lines ) . The second group contains pairs of Lys residues belonging to the NT domain only , the vast majority connecting different helices forming the four-helix bundle adopted by apoE in its soluble form ( Fig 3B and 3D , dashed lines ) . Topological information on the conformation of lipidated apoE4 could be deduced from the distance restraints derived from the XL data . The distribution and number of intramolecular XLs between Lys residues of the NT and CT domains were inconsistent with a completely extended conformation of apoE4 at the surface of the nanodiscs . They rather suggested a hairpin conformation ( Fig 3C ) . Besides , the scattering and number of intra-NT domain XLs were indicative of a relatively compact state of the NT helix bundle ( Fig 3D ) . Once the in-depth experimental characterization of the nanodiscs was achieved , we set out to generate a model of apoE4 bound to rHDL . To do so a two-step procedure was set up ( Fig 1 ) : first , monomeric conformations of apoE4 were constructed by molecular modeling using experimental data to guide the modeling process . Then , dimer assemblies of these monomer structures were wrapped around an explicit lipid disc and the evolution over time of these systems was investigated by molecular dynamics simulations . In a first modeling approach , we directly used all the intramolecular XLs as long and medium-range distance restraints so as to generate a structural model of monomeric lipidated apoE4 . However , this attempt was unsuccessful as the ensemble of XLs restraints could not generate any concluding structures that would fit the experimental characterization of the nanodiscs ( shape and size ) . From this first approach , it appeared evident that the ensemble of XL data would not be satisfied by a single ultimate model , hinting at the presence of at least one alternative conformation . We therefore devised a second approach in which here-acquired structural data were rationalized in the light of current knowledge on lipidated apoE to narrow the range of conformational states apoE4 could adopt at the surface of nanodiscs ( Fig 1 ) . They were implicitly included in sets of constraints for the structure generation ( S1 Text and S2 Table ) . First , to fulfill the hairpin conformation suggested by the NT-CT spatial proximity , evidenced by our XL-MS data ( Fig 3C ) , we inserted a hinge , allowing the CT domain to fold back along the NT domain . To determine the apex of the hairpin , we tested three different hinge positions in the non-structured portions of apoE4 connecting the NT and CT domains ( res . 164 to 168 , 186 to 193 , or 201 to 208 ) . Although our XL data pointed toward a relatively compact conformation of the NT domain ( Fig 3D ) , we conjectured that an apoE4 NT domain conformation completely folded as in solution would be hardly compatible with a receptor active conformation , as it is commonly accepted that opening of the NT bundle upon lipid interaction is a prerequisite for exposure of NT helix 4 containing the region involved in recognition of LDL receptors [19] . Therefore , based on literature [18 , 22 , 28 , 29] , we decided to partially open the NT four-helix bundle by unfurling the turn in between NT helices 3 and 4 and aligning these two helices with the CT domain in a hairpin conformation by using a zipping procedure . Second , we maintained NT helices 1 to 3 bundled together by applying a zipping procedure between NT helices 2 and 3 , thus promoting their spatial proximity to comply with the XL-MS data and to place NT helix 2 outside of the implicit lipid disc . On the other hand , due to the lack of XL data for NT helix 1 , which does not contain any Lys residue , preventing us to rule on its position , we chose to keep this helix in contact with NT helix 2 by using the distances and angles from the NMR study of full length mutated apoE3 [18] . A partially opened state comprising a NT three-helix bundle with NT helix 4 detached was hence generated . Finally , we imposed a curvature to adapt the conformation of apoE4 molecules to the experimental discoidal shape of the nanodiscs and applied a distance constraint to move the flexible CT end outside of the nanodisc . Validation of our models by the XL data revealed that , from the structures generated with the three different hinge positions , the model featuring a hairpin structure containing the hinge formed by res . 186 to 193 best matched the XL pattern , satisfying 12 out of 22 XLs ( Table 1 ) . This model was named “opened hairpin” model ( Fig 4A ) and the selected hairpin apex placed NT helices 3 and 4 in juxtaposition to the CT domain , in good agreement with 6 XLs ( out of 11 ) formed between these two domains ( Table 1 ) . Nevertheless , the opened hairpin model left out 10 XLs that failed to comply with its structure . These non-satisfied XL were either intra NT domain ( helices 2/3 connected to helix 4 ) or NT-CT domains XL ( helices 2/3 connected to a different region of the CT domain ) ( Table 1 ) . Careful inspection of the opened hairpin model suggested that these XLs were likely to be satisfied if the NT domain adopted a four-helix bundle . We thus constructed a second monomeric apoE4 model , using the same hinge region ( res . 186 to 193 ) but adjusted the constraint list ( S2 Table ) to retain a compact state of the NT domain bundle . Remarkably , in this second model , named “compact hairpin” model in the following ( Fig 4B ) , 19 out of the 22 identified XLs were validated ( Table 1 ) . The three non-satisfied XLs involved a subset of the NT-CT links ( helices 2/3 with res . 262 and 282 of the CT domain ) that otherwise supported the opened hairpin model . The two conformations proposed here may therefore represent distinct states of lipidated apoE4 that dynamically co-exist in solution . Both the opened and compact hairpin monomeric models were dimerized in either a head-to-head or head-to-tail orientation . They were wrapped around a solvated POPC disc producing four different molecular systems ( S1 Text and S3 Fig ) . In all 4 setups , the final number of lipids contained in the nanodisc is in good agreement with the experimental values we measured , providing a first validation of our models before we further studied their dynamic behavior using molecular dynamics simulation . In the first nanoseconds of the trajectories , the amphipathic α-helices were observed to rearrange so as to more efficiently protect the hydrophobic acyl chains of the lipids located at the edge of the nanodiscs from the solvent . By adjusting their α-helical segments contacting the lipids , two apoE molecules are able to accommodate the number of lipids contained in each lipoprotein particle and match the average diameters of the nanodiscs as they were observed in this study by native PAGE ( Fig 2A ) , NS- and cryo-TEM ( Fig 2D and S2 Fig ) . Further , our 75-ns long trajectories highlighted that the lipid structures kept their disc shape in all cases ( Fig 5A and S4 Fig ) and the majority of the XLs remained satisfied at the end of our simulations ( S3 Table ) . The α–helical content at the end of the simulations calculated with DSSP [50] ranged between 51% and 66% in good agreement with the 60% estimated from our infrared measurements . No significant differences could be evidenced between the systems featuring either a head-to-head or head-to-tail apoE dimer and we therefore could not discriminate between both orientations . However , during the trajectories local changes in the secondary structure were observed in some regions of the protein . Remarkably , a short stretch ( res . 164 to 168 ) at the end of NT helix 4 switched from a random coil to an α-helical conformation and remained α-helical for the rest of the simulation in one of the monomers in all models ( Fig 5B ) . This structural change , close to the binding region to LDL receptors , promoted an extension of helix 4 resulting in a long amphipathic helix spanning res . 131 to 180 ( Fig 5B ) . Furthermore , upon this change Arg172 , known to be involved in the recognition of LDL receptors [51] and other upstream basic residues , also known to interact with the receptor [52] , underwent a reorientation leading to their respective alignment ( Fig 5B ) . Comparison of the solvent accessibility of these residues in our two models ( S5 Fig ) indicated that , while most residues binding to the LDL receptors featured a low accessibility in the compact hairpin model , they really pointed into the solvent in the opened hairpin model regardless of the dimer arrangement . Therefore , although both conformations may co-exist in solution , they may exert variable binding activities towards receptor recognition with the opened hairpin model representing the active conformation of lipidated apoE4 .
The XL-MS distance restraints obtained here from the cross-linked monomeric apoE4 molecules argued against a model where apoE could adopt a completely extended structure surrounding the nanodisc , with two molecules of apoE running along each other in a ‘double-belt’ organization as was proposed previously [22] . A large subset of our intramolecular XL data rather inferred a hairpin fold of lipidated apoE as previously proposed in other studies [29 , 31] . Alike previous models of full-length apoE , our XLs implied the hinge of the hairpin to be situated in the unstructured region connecting the NT and CT domains but with subtle differences resulting in significant structural and mechanistic implications . Specifically , in the so far most detailed Xray/EPR model of lipidated apoE4 [30 , 31] , the hinge is situated at res . 162 to 169 ( vs res . 186 to 193 here ) and suggested to bring in close proximity regions that are known to be important for the interaction with LDL receptors , the region spanning res . 134 to 150 and Arg172 . However , due to the hinge location in this model , the α-helical extension of NT helix 4 , suggested to be essential for receptor binding activity [24 , 25] , is no longer possible . This hinge location was also not supported here , as the model we built with the hinge on res . 164 to 168 only satisfied 9 out of the 22 identified XLs . In spite of the difference in hinge localization , spatial proximity of significant pairs of residues could be reconciled between our and previous hairpin models . For instance , for apoE4 , the spatial proximity of two residues , Arg61 and Glu255 , that are proposed to form a salt bridge promotion the interaction between NT and CT domains in the lipid-free form [60] , was confirmed to be maintained in the lipid-bound state in discoidal particles [29] . The proximity of these residues was also preserved here , thanks to the partially closed conformation of the NT domain . Further , a significant number of EPR constraints [31] were also validated in our models , including the intramolecular spatial proximity of residues 76/77 with residues 239/241 that were established in our study to be intramolecular by the selection of the monomeric band for in-gel digestion ( Fig 3A ) . The here-produced hairpin models thus allow at the same time both spatial proximity of recognized pairs of important residues in CT and NT regions and the opportunity for the extension of helix 4 needed for the recognition of LDL receptors ( Fig 5B ) . However , a limitation of our study is that , in the current setting , we did not specifically discriminate between intra- and intermolecular cross-links within homodimeric apoE proteins and the respective organization of the two apoE molecules on the lipid particle could therefore not be deduced . The head-to-head and head-to-tail dimerizations , as presented in S3 Fig , therefore remain to a certain degree speculative . Two alternative models , featuring three or four bundled amphipathic helices from the NT domain , were constructed that together satisfied the ensemble of XL derived spatial restraints ( Fig 4 and Table 1 ) . The compact hairpin model features a NT four helix bundle laid along the CT domain and interacting with the lipids only via helix 4 . In this conformation , helix 4 , that contains essential residues for recognition of the members of the LDL receptor family [52] , was shielded from the solvent by helix 3 ( Fig 4B and S5 Fig ) . In the opened hairpin model , the turn between helices 3 and 4 was unfurled , in agreement with previous studies [18 , 28] , and allowed an opening of the bundle with NT helix 3 now interacting with the lipids . This partial opening of the bundle was sufficient to expose helix 4 to the solvent ( Fig 4A and S5 Fig ) . The opening movement from the compact to the opened hairpin model therefore provides us with a possible regulatory mechanism of apoE4 lipoproteins ( Fig 6A and 6B ) . In contrast to earlier studies that indicated that the interaction of the NT domain with the lipids would engage an open and active conformation of the receptor binding region [18 , 26] , our models strongly suggest that , in both the open and compact state , the NT domain of apoE is associated with lipids at the surface of the nanodisc . The domains outside the lipid disc in the compact hairpin model ( S3C and S3D Fig ) are not clearly resolved by NS-TEM ( S2 Fig ) . Heterogeneity in the disc size , dynamic structure of NT domain switching between compact and open conformation , and small size of the folded domain preclude its visualization by single particle technique based on averaging of projections of individual aligned particles . Moreover NT helix 1 that was considered in our modeling as part of the NT helix bundle despite the absence of structural data could instead adopt a more extended conformation . Each of these two cases would then contribute to decrease the compactness of the NT portion that may be observed by NS-TEM . We speculate that both the open and compact hairpin model co-exist in a dynamic equilibrium where the different forms could concurrently be captured by our XL experiments . Further , we propose that in presence of the receptor this equilibrium is shifted to the opened hairpin model , the model that represents the state accessible to LDL receptors , and therefore allows us to draw a mechanism of accessibility of the LDL receptor binding region ( Fig 6A and 6B ) . A relatively small structural change , observed in all models during the molecular dynamics trajectories , elongated helix 4 and connected it with a subsequent small helix spanning res . 169 to 180 , leading to the formation of a 50 residue-long amphipathic helix ( res . 131 to 180 ) ( Fig 5B ) . This helix extension upon lipidation has already been proposed experimentally by NMR and EPR [24 , 25] and it was suggested to act as a molecular switch that stably anchors the receptor binding region on the lipid surface or/and correctly positions residue 172 with other basic residues ( in the region 136 to 150 ) known to be required for an optimal interaction with the LDL receptors [51 , 52] . These receptors share highly conserved structural domains , including ligand-binding domains containing cysteine-rich ligand binding type-A ( LA ) repeats . For the LDLr , the most prevalent member of this family of receptors , it is now well established that among the 7 LA repeats , LA5 is essential for binding of apoE lipoproteins [61] and that the pair LA4-LA5 is sufficient to bind apoE in rHDL [62] . The residues known to interact with the LDLr on apoE [51 , 52] span a too large region to be recognized by a single LA repeat of the LDLr and would thus allow for the binding of the two LA repeats to the same apoE molecule as we proposed earlier based on the lipid-bound structure of an apoE-derived peptide [25] . In addition , our study showed that the elongation of NT helix 4 upon lipidation led to a reorganization of the LDLr binding residues that could promote their binding to the LDLr LA4-LA5 repeats ( Fig 5B ) . To support this hypothesis , we performed docking assays in which LA4 and LA5 were docked individually to such an elongated NT helix 4 ( S4 Table ) . The results confirmed that the distance between the two docked modules was in agreement with the long loop between LA4 and LA5 repeats ( S6 Fig ) . This distance is unique between this pair of LA repeats [63] , highlighting its importance in lipidated apoE recognition . Contrary to the soluble form of apoE4 , the elongation of NT helix 4 conferred upon lipidation would therefore represent an additional prerequisite for binding to LDL receptors ( Fig 6D ) . In summary , our data advocate that several requirements need to be met to provide a fully receptor-active apoE ( Fig 6D ) : lipid binding , exposure of the receptor binding region and elongation of NT helix 4 . We speculate that the here proposed compact hairpin model is a stable conformation co-existing with the active , receptor-competent open structure , explaining why these two alternative conformations could be trapped in the XL-MS experiments . Both conformations may therefore be part of a regulation mechanism of apoE function at the surface of lipids . Our work represents a building stone towards a better understanding of the strong anti-atherogenic effect of apoE and the models we are proposing could prove useful in the study of lipidated apoE in totally different contexts , such as understanding its role in Alzheimer’s disease . Overall our hybrid approach , compatible with the presence of lipids , results in 3D structures of lipidated apoE4 that represents the most comprehensive model of the active form of apoE4 to date and might be applied to the study of other ( membrane ) proteins where such complementary low resolution structural data are available .
Unless otherwise stated , all chemicals were obtained from Sigma-Aldrich at the highest purity available . Water was double-distilled and deionized using a Milli-Q system ( Millipore ) . The human full-length apoE4 gene fused to a self-cleavable intein tag and a chitin binding domain cloned into a pTYB2 vector was a kind gift of Dr . Vasanthy Narayanaswami ( University of Long Beach , California , U . S . A . ) . ApoE4 was expressed in a T7 expression strain of Escherichia coli ( ER2566 ) in 2xYT medium by the addition of isopropyl β-D-thiogalactopyranoside . Pelleted cells were resuspended in buffer A ( 20 mM HEPES , 500 mM NaCl , 1 mM EDTA , pH 8 ) , supplemented with 0 . 5% ( v/v ) triton-X-100 ( TX100 ) and anti-proteases ( cOmplete EDTA-free protease inhibitor cocktail , Roche ) , and apoE4 was released by high-pressure homogenizer . The protein was purified following standardized protocols previously described for intein-labelled proteins [64] . Briefly , the clarified cell lysate was loaded onto chitin beads ( Impact system , New England Biolabs ) equilibrated with 5 column volumes ( CV ) of buffer A containing 0 . 5% ( v/v ) TX100 and incubated at 4°C during 1 h on a rotating wheel . The flow through was discarded and the beads were washed with 10 CV of buffer A containing 0 . 3% ( v/v ) TX100 . ApoE4 was released by incubation of the chitin beads with buffer A containing 30 mM dithiothreitol ( DTT ) at 4°C during 40 h and finally eluted with 3 CV of buffer A containing 5 mM DTT . ApoE4 was then buffer exchanged against buffer B ( 20 mM ammonium bicarbonate , pH 8 ) with PD-10 desalting column ( GE healthcare ) and lyophilized overnight . Prior to utilization , lyophilized apoE4 was solubilized in buffer C ( 20 mM HEPES , 150 mM NaCl , pH 7 . 4 ) containing 6 M guanidine-HCl and further purified by size exclusion chromatography on a Superose 6 matrix ( GE Healthcare ) eluted with buffer C containing 4 M guanidine-HCl . Fractions containing apoE4 were pooled together and dialyzed against buffer B during 48 h at 4°C . ApoE4 concentration and purity were assessed by absorbance at 280 nm and SDS-PAGE . ApoE4 rHDL were formed using 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC , Avanti polar lipids ) following a modified version of the protocol initially developed for apoA-I by Matz and Jonas [43] . POPC solubilized in chloroform was dried under nitrogen and resuspended to a concentration of 20 mg/ml in buffer C . Sodium cholate was added at a POPC:sodium cholate molar ratio of 1:2 and the mixture was sonicated for 1 h with vortexing every 15 min . ApoE4 was added to the mixture at different molar ratio and incubated overnight at 4°C on a rotating wheel . Sodium cholate was eliminated by dialysis during 24 h against buffer B ( 3 buffer exchange ) . Samples were purified on a Superose 6 matrix eluted with buffer C . Fractions containing apoE4 rHDL were pooled together and concentrated by filtration up to an apoE4 concentration of 0 . 5 mg/ml ( Vivaspin 6 , 50 K MWCO , Sartorius ) . The homogeneity and the size distribution of the rHDL were both assessed by blue native PAGE ( S1 Fig ) while their apoE4 and POPC content was evaluated by measuring the concentration of proteins and lipids by absorbance at 280 nm and phosphorus assay [65] , respectively . Blue native PAGE ( 3 . 5–13% ) electrophoresis was realized following a procedure described in [66] using HMW Native Marker Kit ( GE Healthcare ) for protein standards . SDS-PAGE ( 8% ) was realized according to [67] using prestained protein ladder ( Fermentas ) as molecular weight size marker . Both blue native PAGE and SDS-PAGE were revealed using Coomassie blue staining . Infrared spectroscopy was performed in attenuated total reflection mode and infrared spectrum was recorded on an Equinox 55 spectrophotometer ( Bruker Optics ) . Measurement was made at room temperature by spreading 2 μL of apoE4/POPC rHDL solution ( 0 . 5 mg/ml ) on the surface of the internal reflection element made of a diamond crystal . Excess water was removed under nitrogen flow . The spectrum represents the mean of 256 spectra recorded at a 2 cm-1 resolution . Data were analyzed using Kinetics software ( SFMB , Brussels , Belgium ) and processed for baseline correction and subtraction of the water vapor contribution . Curve-fitting on the non-deconvoluted spectrum was performed to determine the global secondary structure content of a protein . The proportion of a particular structure is computed to be the sum of the area of all the fitted bands having their maximum in the frequency region where that structure occurs divided by the total area of the amide I band between 1700 and 1600 cm-1 . They were chosen by the program on the basis of the shape of the most deconvoluted spectrum ( α-helices and random coil absorb at 1637–1662 cm-1 , turns at 1662–1682 cm-1 and β-sheets at 1613–1637 cm-1 and 1682–1689 cm-1 ) . TEM data were collected at a nominal magnification of 60 , 000 and pixel size 1 . 9 Å/pix on a JEM-1400 ( JEOL ) operating at 120 kV with a LaB6 filament and equipped with a CMOS TemCam-416 4016x4016 camera ( TVIPS ) . For NS-TEM , grids were glow discharged system and 2 μl of sample at concentration of 0 . 01 mg/ml was applied and stained with uranyl formate . Images were collected at defocus between 1 and 2 μm . A total of 6 , 766 particles were picked manually . The set of particles was first classified with multiple cycles of k-means classification and multi-reference alignment using SPARX with the exclusion of particles less representative . In complement , a second set of classifications was performed using EMAN2 [68] resulting in 25 class-averages corresponding to 1188 symmetric top-view particles . For cryo-TEM , a frozen-hydrated grid was prepared by blotting 2 μl of sample ( 1 mg/ml ) on a Quantifoil holey carbon-film-coated 400-mesh copper grids and plunge-frozen . From a total of 660 images , with an average dose of 41 electrons/Å2 , 29 , 134 particles were carefully manually selected . The particles were classified using EMAN2 . The final representative class-averages were calculated from 10 , 249 particles . XL-MS analysis was carried out essentially as described elsewhere [49] . Briefly , an 8-fold molar excess of DSS ( Creative Molecule Inc . ) over apoE4 concentration was added to the apoE4 nanodiscs . The mixture was incubated for 30 min at 37°C and the XL reaction was quenched by the addition of ammonium bicarbonate to a final concentration of 50 mM for 10 min at room temperature . The products resulting from the XL reaction were separated by SDS-PAGE and visualized with Coomassie blue staining . Bands containing the cross-linked species of interest were sliced from the gel into cubes of 1 mm3 , transferred into protein low binding tubes ( Eppendorf ) and submitted to in-gel digestion [69] using trypsin ( Promega ) . MS analysis was carried out on a Thermo Orbitrap Elite mass spectrometer ( Thermo Scientific ) and data analysis was performed using xQuest [48] . False discovery rates ( FDR ) and delta score ( deltaS ) of cross-linked peptides were assigned using xProphet [46] . Cross-linked peptides that were identified with an assigned FDR below 5% and a deltaS below 95% were selected for this study ( S1 Table ) . In all cases the FDR , which denotes the false-discovery rate as calculated by xProphet [46] , was equal to zero . All selected XLs were further analyzed by visual inspection in order to ensure good matches of ion series on both cross-linked peptide chains for the most abundant peaks . Lys146 was not detected as a XL site . This most likely resulted from trypsin digestion producing a dipeptide which is too short to be considered by our applied XL-MS method in order to ensure good matches of cross-linked peptides . Based on intramolecular XLs and information on the shape/organization of the apoE4 nanodiscs , the structure of an apoE4 monomer surrounding an implicit POPC disc was modeled with CNSsolve [70 , 71] . The modeling procedure is described in Supporting Information S1 Text . All molecular dynamics calculations were performed in the isothermal-isobaric ensembles at 300 K with the program NAMD2 . 9 [72] . The CHARMM 27 force-field [73 , 74] with CMAP corrections [75] was used for protein , water and ions and a united atom force field [76] described the lipid molecules . The protocols of the molecular dynamics simulations and of the molecular dynamics trajectory analysis are described in the Supporting Information S1 Text . LDLr LA repeats LA4 ( res . 179–214 ) and LA5 ( res . 121–167 ) , as extracted from the first and representative conformation in the NMR structure ( PDB code 2LGP ) [77] , were docked individually to a part of apoE4 ( res . 125–185 ) containing the elongated helix 4 extracted from one molecule of apoE4 in the head-to-head opened hairpin model using the Haddock web server [78] . LA4/LA5 repeats were docked individually ( and not together ) as the loop between the two is highly flexible . In accordance to an earlier docking study [79] the LA5 module was docked to apoE4 using four unambiguous constraints ( S4 Table ) . For the docking of the LA4 module to apoE4 ambiguous constraints were used in accordance to mutational data [51] and structural data of LA4 repeat bound to other proteins [80–82] ( S4 Table ) . For the individual dockings of LA5/apoE4 and LA4/apoE4 , 4 and 40 poses were obtained respectively . Each possible combination of LA4/LA5 repeats pairing was screened for the distance between the backbone carbon atom of residues Y167 ( LA4 ) and F179 ( LA5 ) . The shortest distance between these residues is about 35 Å ( S6 Fig ) , a value in good agreement with the loop length between LA4 and LA5 repeats ( 12 residues ) . | Among the proteins involved in the transport of lipids and their distribution to the cells , apolipoprotein E ( apoE ) mediates the internalization of cholesterol rich lipoproteins by acting as a ligand for cell-surface receptors . In the central nervous system , while apoE is the major cholesterol transport protein , a dysfunction of apoE in the transport and metabolism of lipids is associated with Alzheimer’s disease . A molecular understanding of the mechanisms underlying the receptor binding abilities of apoE is crucial to address its biological functions , but is so far hindered by the dynamic and complex nature of these assemblies . We have designed an original hybrid approach combining experimental data and bioinformatics tools to generate high resolution models of lipidated apoE . Based on these models , we can propose how apoE adapts its conformation at the surface of lipid nanoparticles . Further , we propose a novel mechanism of regulation of the activation and receptor recognition of apoE that could prove valuable to interpret its role in Alzheimer and apoE-related cardiovascular diseases . |
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This genome-scale study analysed the various parameters influencing protein levels in cells . To achieve this goal , the model bacterium Lactococcus lactis was grown at steady state in continuous cultures at different growth rates , and proteomic and transcriptomic data were thoroughly compared . Ratios of mRNA to protein were highly variable among proteins but also , for a given gene , between the different growth conditions . The modeling of cellular processes combined with a data fitting modeling approach allowed both translation efficiencies and degradation rates to be estimated for each protein in each growth condition . Estimated translational efficiencies and degradation rates strongly differed between proteins and were tested for their biological significance through statistical correlations with relevant parameters such as codon or amino acid bias . These efficiencies and degradation rates were not constant in all growth conditions and were inversely proportional to the growth rate , indicating a more efficient translation at low growth rate but an antagonistic higher rate of protein degradation . Estimated protein median half-lives ranged from 23 to 224 min , underlying the importance of protein degradation notably at low growth rates . The regulation of intracellular protein level was analysed through regulatory coefficient calculations , revealing a complex control depending on protein and growth conditions . The modeling approach enabled translational efficiencies and protein degradation rates to be estimated , two biological parameters extremely difficult to determine experimentally and generally lacking in bacteria . This method is generic and can now be extended to other environments and/or other micro-organisms .
In the era of “omics” , systems biology has emerged with the availability of genome-wide data from different levels , i . e . genome , transcriptome , proteome , metabolome [1] , [2] . This approach aims at integrating omics data , mainly through computational and mathematical models [3] , [4] so as to decipher biological systems as a whole [5] . The integration of transcriptomic and proteomic results is a huge challenge by itself . The literature usually exploits these two approaches as complementary tools and does not often provide a correct confrontation of the two datasets . Until now , only a few researchers , mainly interested in yeast physiology [6] , [7] , have been working on this aspect and the results typically revealed modest correlations between those two datasets [8]–[10] . These weak correlations between transcript and protein levels can be the consequence of the involvement of post-transcriptional regulations [11] , such as translation control and protein degradation as evidenced by Brockmann et al . [12] . Translation regulations are believed to be involved in protein level control but are generally studied at the level of controlling specific molecular mechanisms and not at the genome scale [13]–[15] . Although polysome profile analysis allows translation efficiencies to be experimentally determined for the various transcripts simultaneously , this technique has been only rarely used and almost exclusively for S . cerevisiae [16] . Protein stability can also influence intracellular protein level and the correlation between transcript and protein [10] , [17] , [18] . However protein stability is rarely studied at the genome scale and data are only available for S . cerevisiae [19] , [20] . Finally , the rate of protein disappearance due to protein dilution by cellular growth is also potentially involved in protein level modifications but this physical phenomenon is generally neglected . More generally , even if translation efficiency , protein degradation and dilution rate can all influence protein levels , these parameters are not usually studied simultaneously . The role of each parameter in a whole cellular adaptation process has not been elucidated and it is not clearly known today which parameter is preponderant and if the control is constant or not when environmental conditions are modified . The aim of this study was to analyse the control of intracellular protein level taking into account all the parameters of this control , in a prokaryotic organism , the model of lactic acid bacteria , Lactococcus lactis . To achieve this purpose , transcriptomic and proteomic analyses were performed with cells from the same culture . Transcriptomic data were already available [21] and the corresponding proteome measurement was performed . The whole protein related processes including translation , dilution rate and protein degradation were modelled , and , since biological data were obtained at steady state , equations describing the protein levels equilibrium were solved . This modeling approach allowed translation efficiency and protein degradation to be estimated and the relative involvement of all the various parameters of protein control to be analysed .
L . lactis was grown in continuous culture at different growth rates in the conditions previously described [21] and samples were taken for both transcriptome and proteome analysis at three dilution rates , i . e . 0 . 09 , 0 . 24 and 0 . 47 h−1: the lowest growth rate ( μ = 0 . 09 h−1 ) was chosen as reference . Despite the small size of the L . lactis genome ( 2310 genes [22] ) , a total of 346 different proteins were quantified corresponding to 308 different proteins measured in each repetition for each of the 3 steady states . Among these proteins , 193 showed differential profiles in response to a growth rate increase: 88 with reduced level and 105 with higher level . All the proteins displaying a significant level of modification for at least one of the dilution rates are listed in Table 1 . In accordance with what has previously been found with transcriptome analysis [21] , increased levels of proteins related to biogenesis were observed when the growth rate was increased , i . e . proteins related to transcription ( GreA , NusA , QueA , RpoA ) , translation and more specifically ribosomal proteins ( GatA , GatB , RplE , RplI , RplJ , RplK , RplM , RplN , RpmE , RpsA , RpsF , RpsT ) , enzymes related to fatty acid and phospholipid metabolism ( AccA , AccD , FabD , FabF , FabG1 , FabH , FabZ1 , HmcM , ThiL , YdiD , YscE ) , two proteins involved in cell division ( FtsY , FtsZ ) , and some proteins associated with purine , pyrimidine , nucleoside and nucleotide metabolism ( Add , Adk , Apt , DeoB , GuaA , GuaC , Hpt , NrdE , PydA , PyrC , PyrE , RmlA , RmlB , Upp ) . Proteome profiles differed between the various stress-related proteins . On one hand , the two chaperones DnaK and GroEL , the superoxide dismutase associated to oxygen stress SodA , and DpsA , were found in higher quantity , while on the other hand , the cold shock associated protein CspE , ClpC and the adaptation related peroxidase Tpx , had decreased levels in response to growth rate increase . Besides those opposite punctual regulations , other proteins encoding important functions involved in stress protection such as ATPases or peptidases ( excepting PepP ) , were present at constant levels , independently of the growth rate . This lack of general tendency observed here at proteomic level was also observed at transcriptomic level [21] . In contrast , a wide down-regulation of genes involved in stress protection was observed in yeast when growth rate was increased [23] , [24] . Finally , one can notice that the two single phage-related proteins measured in those proteomics experiments showed significantly reduced levels at high growth rate . This last observation can be connected with the previously described massive down regulation of the expression of phage-related genes [21] . Transcriptomic and proteomic analyses were performed with cells collected simultaneously from the same fermentor; thus data can be strictly compared . Proteins and their corresponding transcript levels were compared individually . Transcriptomic data were already available [21] but were nevertheless re-processed so as to obtain concentrations rather than abundances ( see Materials and methods ) . For proteomic data , concentration and abundance values are expected to be similar ( see Materials and methods ) . For each growth rate , transcriptomic and proteomic mean values with their standard deviations are given in supplementary data ( Table S1 ) . The mRNA/protein ratios were not constant for the different genes since , at a given growth rate , data were spanned among five orders of magnitude ( Figure 1C ) . These variations were linked both to protein and mRNA changes though protein concentrations were globally more spanned than mRNA concentrations ( 4 and 2 log of magnitude respectively; see figure 1A and 1B ) . mRNA/protein ratios were compared between two conditions using the lowest growth rate ( 0 . 09 h−1 ) as reference ( Figure 1D ) and they globally increased with the growth rate . This tendency was confirmed when we analyzed similarly data corresponding to the maximum growth rate of 0 . 88 h−1 . These last data , also available in our group , were obtained in batch culture during the exponential growth phase , since this high growth rate could not be reached in continuous culture without any wash out of the cells from the chemostat [25] . What normally occurs in bacterial cells is the transcription of genes into mRNA , which are then translated into proteins that can be either diluted by growth or degraded ( Figure 2 ) . Hence , protein concentration is determined not only by translation rates but also by dilution and degradation , therefore the following balance equation can be written: ( 1 ) Rates of mRNA translation or protein degradation/dilution are assumed to be not constant and related to mRNA or protein concentration respectively . Biological rates were expressed as first order kinetics of their substrate concentration as previously postulated in L . lactis [26] , [27] but also in yeast strains [19] , [28] . Such modeling approach at the genomic scale is rare in the literature and dynamic experimental data allowing more elaborated kinetics to be hypothesized are not available . Making more complex those rate expressions would thus not make sense today . Dilution constant corresponds to the growth rate ( μ ) , degradation constant ( k″ ) is proportional to protein half-life ( t1/2 = ln ( 2 ) /k″ ) and k′ represents the translation efficiency . At steady state , the various concentrations are expected to remain constant , the time derivative of the protein concentration is equal to zero and the previous equation can be simplified and reorganized as follows: ( 2 ) In the chemostat cultures at the various growth rates , cells are at steady state; similarly , during the exponential growth phase , cells are physiologically stable and are also considered to be at steady-state [29] . The previous observation , establishing a relationship between mRNA/protein ratios and the growth rate for these four steady states ( see above ) , is in accordance with this last equation ( 2 ) . 171 different mRNA and protein couples were available in each repetition of the various steady states ( intersection of 308 couples in the 3 chemostat steady states and 191 in the batch ) . For only a few proteins were probes missing on the microarray; hence it was not possible to rebuild these couples . In order to estimate translation efficiency and protein degradation rate , the best mathematical solution to the equation ( 2 ) was sought , using numerical estimations performed on Matlab . The k′ and k″ values were postulated to be positive , in accordance with biological reality . Different solutions with k′ and/or k″ constant , directly or inversely proportional to μ were investigated . Estimation of the best fitting solution was based on the least square criterion [30] . For the 171 couples , the mean sum of the squared residuals ( difference between a ratio and its estimation ) associated to every combination are given in Table 2 . Considering the lower mean sum of the squared residuals , the best solution was obtained when both k′ and k″ were proportional to 1/μ ( k′ = α/μ and k″ = β/μ ) . Hence the equation ( 2 ) could be written as follows: ( 3 ) The mRNA/protein ratios were thus linked to the growth rate ( μ ) through a polynomial function of order two ( μ2 ) , which is consistent with the visual observation of the various curves ( not shown ) . For each mRNA–protein couple the reliability of the two estimated constants α and β was evaluated by their associated R2 . All regression coefficients are listed in Table 3 . The mean linear coefficient ( R2 ) associated to this model was 0 . 83±0 . 04 . Finally , the consistency of our modeling approach was checked when removing the data of the batch exponential growth phase from the analysis . A high mean R2 of 0 . 77±0 . 12 was still obtained using chemostat data exclusively . On the contrary , data not at steady state coming from other growth phases in batch cultivation could not be included . Indeed when taking into account mRNA/protein values during growth deceleration or during stationary phase , R2 was strongly affected and dropped to 0 . 21±0 . 03 and 0 . 24±0 . 04 respectively . The model ( 1 ) states that translation rate is proportional to the concentration of mRNA species which assumes that translation is mRNA-limited . An alternative hypothesis , would be the saturation of the ribosome with mRNA , as previously postulated in E coli [31] , which implicitly suggests competition of any specific mRNA with all others to be the determining factor in synthesis of the corresponding protein . We have thus tested the model ( 2 ) with mRNA abundances rather than concentration values . The modeling approach was robust since similar k′ and k″ dependencies to the growth rate ( k′ = α/μ and k″ = β/μ ) were obtained ( Table 2; data in italic ) . However the expression of individual protein/mRNA as a function of μ2 ( 3 ) had generally lower R2 ( only 48 couples with R2≥0 . 90 compared to 130 with concentrations ) , indicating the modeling approach with abundances was less satisfactory than with concentrations . In order to carry on our modeling approach , the data corresponding to mRNA concentrations were filtered and only couples with R2≥0 . 90 ( 130 ) were retained for further analyses ( Table 3 ) . It could be noticed that among the 41 eliminated couples , 15 displayed non monotonous evolutions of their mRNA/protein ratios against μ2 and 20 had very low mRNA/protein ratios , which were thus more sensitive to errors . The k′ and k″ coefficients were numerically calculated for each protein and in each growth condition from the values of α and β ( Table 2 ) by the relation k′ = α/μ and k″ = β/μ . Value distributions ( Figure 3 ) demonstrate the wide variability of k′ and k″ among proteins but also between growth conditions . The k″ decreases when growth rate increases which is consistent with the general idea that protein degradation is high in stationary phases [32] . Protein half-lives were calculated and median values of t1/2 were respectively 23 , 61 , 119 and 224 min for 0 . 09 , 0 . 24 , 0 . 47 and 0 . 88 h−1 . These values are in the same order of magnitude as those obtained recently for S . cerevisiae ( mean 43 min at a growth rate of 0 . 1 h−1 [19] ) . Like k″ , the translation efficiency k′ is also expressed as 1/μ function , indicating that , when the degradation process increases at low growth rate , the translation efficiency is also increasing in order to attenuate this negative biological effect . Due to the restricted size of the dataset but also to the non-uniform distribution of detected proteins in the various functional categories , it was not possible to use statistical tests to rigorously determine functional enrichments in extreme values of k′ or k″ . However , among the 15 genes that are translated the most efficiently ( highest α values in Table 3 ) , one can notice the over-representation of genes involved in major cellular processes: the Tig chaperone [33] and proteins involved in replication ( HslA , which can unwind DNA and plays a role in its supercoiling , [34] ) , and translation ( ribosomal proteins: RplA , RplF , RplK , RplN , RpsT ) . Carbon metabolism is also represented by 7 proteins ( GapB , EnoA , FbaA , Pmg , Pyk , TpiA , Ldh ) , all belonging to glycolysis which is the major metabolic pathway for energy production in L . lactis . The extremely stable proteins correspond to null values of β , and consequently k″ , were represented by a group of 26 proteins . Remarkably , half of them were related to stress responses: ClpE protease , PepC and PepP peptidases , three reductases that are usually linked to oxidative stress ( AhpC , TrxB1 , YpjH ) , but also MurF , involved in parietal structure , YtgH , which is homolog to Staphylococcus aureus alkaline stress protein [35] , YtaA and YahB two hypothetical protein sharing homologies with E . coli universal stress protein Usp [36] , YuhE , whose E . coli homologue is involved in copper resistance [37] , and two cysteine desulfurases ( YeiG and YseF ) whose corresponding genes in E . coli are involved in oxygen and copper stress responses [38] . Moreover , those extremely stable proteins are rather in the last third for translation efficiency . Thus L . lactis may limit degradation of stress-related proteins so as to maintain a minimal pool ready to use in case of emergency , which is biologically relevant . Biological determinants of translation efficiency and protein stability were investigated through correlation studies . Correlations providing a Spearman coefficient ( RSpearman ) with associated p-values lower than 0 . 05 were considered as significant . The codon adaptation index ( CAI ) positively correlates with k′ ( RSpearman = 0 . 57 ) . Since CAI directly reflects translation efficiency during the elongation step [39] , this result validates our translation efficiency estimations . Translation efficiency is also tightly related to the amino acid composition of proteins . A negative correlation of k′ was obtained with tyrosine , cysteine , histidine , aspartic acid and isoleucine frequencies while lysine and alanine richness had a positive influence ( Table 4 ) . The amino acids the most used have a positive influence on k′ whereas those with a negative effect are the less frequent ones ( Table 4 ) . The single exception is for isoleucine , but since it is the limiting nutrient it is not surprising to find it negatively correlated with translation efficiency , despite its high frequency in L . lactis proteins . This amino acid bias , together with the codon bias ( CAI ) , shows that translation efficiency is strongly dependent of the gene sequence . This optimized functioning state is probably the result of a long evolutionary process . Finally it was found that translation efficiency is affected by protein length: the longer the protein , the more k′ decreases ( RPearson of −0 . 18 ) . This negative correlation with length has already been reported for yeast [19] and can possibly be explained by a decrease of the ribosome density on long mRNA as previously shown for S . cerevisiae [40] . The only apparent correlation emerging for protein degradation constants k″ is a negative influence of cysteine richness ( Table 4 ) . Degradation and dilution by growth are both involved in protein disappearance and are competitive reactions . The degradation and dilution constants , k″ and μ , can be directly compared . The k″ is higher than μ at low growth rate but becomes lower after a critical value of 0 . 39 h−1 ( Figure 4 ) . The role of the degradation may thus be major at low growth rate while dilution may become the main phenomenon at fast growth . More generally , variations in protein concentration between two conditions can be related to changes in the three rates: protein synthesis , degradation and dilution . In order to better understand this regulatory node and identify which are the major controls , the quantitative involvement of the different actors was analyzed . Regulation coefficients corresponding to the protein level control were estimated with a method based on the one developed on S . cerevisiae [41] , [42] . Derivation of equation ( 2 ) leads to the following relationship: ( 4 ) The term of the equation ( 4 ) represents translation control on protein concentration and is called ρt while the term − , named ρd , includes both the dilution and the degradation and represents protein control by disappearance . ρt and ρd were estimated for each growth rate interval ( between 0 . 09 and 0 . 24 h−1; between 0 . 24 and 0 . 47 h−1; between 0 . 47 and 0 . 88 h−1 ) . The values of ρt were used to elucidate the nature of the control and are given in supplementary data ( Table S2 ) . If ρt≤0 , protein disappearance is the major controlling mechanism; if ρt≥·1 , it is translation; and if 0·< ρt·< ·1 , the control of protein is shared . The nature of the control for a given protein and its strength differed in the various the growth rate intervals . However a constant control by disappearance was observed for 6 proteins distributed all over the metabolism ( Als , GreA , LplL , PyrC RplQ , ThrC , see Table 3 for associated functions ) . Inversely the unknown protein YpdC was the single one constantly controlled by translation process . Independently of the growth rate , protein levels are mostly controlled by disappearance ( Table 5 ) . Translation control strongly decreases , and protein level control becomes less specific and more and more shared with increasing growth rates . Similar conclusions were valid when ρd was used for the control analysis instead of ρt ( data not shown ) .
The comparison of mRNA and protein ratios revealed a strong heterogeneity among genes but also for a given gene , at different growth conditions . Variability among genes has recently been reported for the model yeast S . cerevisiae but these ratios remained constant between the two studied conditions , i . e . a rich and a poor media [43] . Though lacking in this publication , the maximum growth rates of S . cerevisiae were estimated to be 0 . 46 and 0 . 35 h−1 respectively in a rich and a poor media ( Parrou J . L . , personal communication ) . Thus it is postulated that the growth rate difference between these two conditions was too small to induce changes in mRNA/protein ratios . The combination of two modeling approaches , one based on biological knowledge and the other on experimental data fitting , has enabled translation efficiency and protein degradation rate to be determined for each protein , phenomena which have been shown to be protein specific and growth rate dependant . The positive correlation of translation efficiency with codon bias in L . lactis is consistent with the results obtained for the yeast , though translation efficiencies have been calculated differently [12] . The presence of genes related to major cellular processes essential for growth were marked among the best translated . This finding corroborates what was found in archaebacteria for ribosomal proteins [44] . In L . lactis , the growth-rate dependant variations in translation efficiency are probably not related to changes in the amount of intracellular ribosomes if the constant ratio between mRNA and ribosomal RNA ( see Material and methods ) is taken into account . However one has to bear in mind that rRNA does not necessarily means assembled and/or active ribosomes . It is known for example that E . coli ribosome activity can be modulated by the inter-conversion between a functional 70S and a dimerized 100S inactive form [45] . To resolve this question , it will be necessary to investigate genome-wide ribosomal activity via polysome distribution which would provide key information to decipher the regulatory processes controlling translation . Polysome profile technology is already available for yeast but may be difficult to adapt to bacteria due to the co-localisation of transcription and translation in the cytoplasm . Protein half-lives for the whole genome have never been determined nor estimated in any bacteria and data are only available in the literature for S . cerevisiae . However studies disagreed in terms of average half-life values: 31 h for Pratt et al . against 43 min for Belle et al . [19] , [20] . Those differences could be explained by methodological reasons since one study used pulse chase experiments [20] whereas the other one consisted in a direct measurement of each epitope-tagged proteins [19] . In our study , for L . lactis , protein median half-lives ranged from 23 to 224 min . These low values are in good agreement with most recent values obtained for S . cerevisiae [19] and indicate that protein degradation is considerably more rapid than was once believed . Degradation rates in L . lactis were negatively correlated to cysteine content in proteins . In yeast , stable proteins were previously found to have a higher valine density whereas unstable ones are enriched in serine [19] . It is difficult to strictly compare those results since amino acid bias may be species specific and reflect the particularities of proteases involved in protein degradation . The negative correlation with cysteine could nevertheless be related to the potential formation of disulfide bridges known for stabilizing proteins [46] . The current work also revealed the presence of stress related proteins among the most stable . This last observation differs from results obtained in yeast indicating that ribosomal proteins and enzymes from amino-acids metabolism have the higher half-life [19] . This high stability of stress protein together with the lack of global transcriptional stress response observed in L . lactis when the growth rate is changed clearly underlines differences of stress adaptation mechanisms between the two micro-organisms . Protein degradation exerts a major role in the cellular adaptation process since protein half-live data depend on the growth rate ( 1/μ function ) . Moreover , the degradation rate is even higher than dilution rate at low growth rate ( Figure 4 ) . Considering that protein degradation is an ATP consuming process [47] , high protein degradation at slow growth rate may contribute to the increase of maintenance energy that is generally observed in such conditions [48] . Like protein degradation , translation efficiency is also increased at slower growth rates . Effects of translation efficiency and protein degradation are thus antagonist and this mode of regulation is probably dedicated to attenuate biological changes . Inversely , proteins with the lowest degradation rates also corresponded to low translation efficiencies . The analysis of the regulation involved in the control of protein concentrations demonstrated that it is not constant in the different ranges of growth rate . At low growth rates , disappearance seems to be the main controlling mechanism , which could be attributed to high degradation rate . At high growth rate , the control becomes more complicated with some proteins regulated at the level of synthesis , disappearance or both ( shared control ) . This increased complexity is consistent with cells approaching their maximum growth performance . With this modeling approach , we have estimated translational efficiencies and protein degradation rates . These two biological parameters are extremely difficult to measure experimentally and have even never been previously determined in bacteria . The method was based on an in depth comparison of proteome and transcriptome data and was developed with the small genome bacterium L . lactis on a limited number of mRNA - protein couples ( 171 ) . It will be possible in the future to broaden these couples since other proteomic methodologies , such as the APEX technology [8] , allow more proteins to be detected . The approach remains generic and can be applied to all microorganisms . Modelling equations were solved because steady-states cultures were used: chemostat fermentation technology enabling steady states to be studied has thus proved to be a powerful tool to understand microbial physiology . We have demonstrated that bacteria exert a sharp control on intracellular protein levels , through a multi-level regulation involving three growth rate dependant actors: translation , dilution and degradation . Here , the growth rate was changed via chemostat cultures , but such growth rate modifications are also encountered in nature when cells have to face new environments . In this case , the adaptation process involves growth rate adaptation as well as other specific metabolic adaptations . It remains to be determined how the protein control is exerted in such natural environment .
Lactococcus lactis ssp . lactis IL1403 , whose genome has been entirely sequenced [22] , was grown as previously described [21] . Briefly , three different growth rates have been studied , namely 0 . 09 , 0 . 24 and 0 . 47 h−1 during anaerobic chemostat cultures ( under nitrogen atmosphere and regulated pH ) on a chemically defined medium limited by isoleucine concentration . For each steady-state , samples have been harvested in at least quadruplicate with a minimum delay of five doubling time between each sampling . Transcriptomic data ( geo platforms GSE10256 [21] for chemostat culture and GSE12962 for batch exponential phase ) were already available . Briefly , these transcriptomic analyses had been obtained with a constant amount ( 10 µg ) of total RNA ( mRNA , ribosomal RNA and transfer RNA ) labeled by retro-transcription ( 33P ) and hybridized on nylon membrane as previously described [49] . Three independent biological repetitions were used . These transcriptomic data had been normalized by all spots' mean intensity and thus corresponded to mRNA abundances . They were reprocessed here in order to calculate mRNA concentrations with the method previously described [49] . Raw data were first standardized by the all spots' mean intensities of the reference membrane ( and not with its proper membrane ) in order to eliminate the bias of the radioactivity level between the various repetitions and then corrected by total RNA concentration in order to take into account changes in intracellular RNA yield in the cells . Consistent with previous results [50] , this yield increased significantly with the growth rate in L . lactis ( 3 . 58±0 . 39 , 4 . 92±0 . 52 , 7 . 34±0 . 28 and 11 . 06±0 . 23 g for 100 g cell dry weight at μ = 0 . 09 , 0 . 24 , 0 . 47 and 0 . 88 h−1 respectively ) . Since the amount of RNA to perform transcriptomic analysis is maintained constant in order to avoid retro-transcription labelling bias , these RNA yield changes are completely hidden by the technology . The total raw intensity of the membrane without any normalisation represents the amount of mRNA in the RNA sample used for transcriptomic analysis ( 10 µg ) . This total intensity was constant at each growth rate and lower than the saturation threshold ( mean value of 1660±584 , 1462±383 , 1389±366 , 1474±367 , 1496±425 at μ = 0 . 09 , 0 . 24 , 0 . 47 and 0 . 88 h−1 respectively ) . Thus , it can be deduced that the ratio mRNA/total RNA was constant and assuming that ribosomal RNA is the major component of total RNA we can postulate that the fraction mRNA/ribosomal RNA is independent of the growth rate . For each condition , three repetitions were performed with independent cultures , extractions and electrophoresis . Bacteria were harvested from the cultures and cell pellets were washed twice with ice-cold 200 mM Na-phosphate , pH 6 . 4 and re-suspended in 4 ml of 20 mM Na-phosphate buffer , pH 6 . 4 , 1 mM EDTA , 10 mM tributylphosphine , a cocktail of protease inhibitors ( P8465; Sigma Aldrich , St Louis , MO ) 20-fold diluted and catalase 40 U/ml ( C3155; Sigma Aldrich , St Louis , MO ) to limit isoform formation . The cell suspension ( approximately 35 units of optical density at 600 nm [OD600]/mL , ) was transferred to the pre-cooled chamber of a BASIC Z cell disrupter ( Celld , Warwickshire , United Kingdom ) and was subjected to a pressure of 2 , 500 bars . The suspension was centrifuged at 5 , 000×g for 20 min at 4°C to remove unbroken cells and large cellular debris . The supernatant was collected and centrifuged at 220 , 000×g for 30 min at 4°C . The total protein concentration in the resulting supernatant ( cytosolic fraction ) was determined with the Coomassie protein assay reagent ( Pierce , Rockford , IL ) using bovine serum albumin as standard and was included between 1 and 2 mg/mL . The cytosolic fraction was aliquoted and stored frozen at −20°C . R2 calculations and equations resolution were perform with MATLAB software . Correlations were estimated using R free statistical software to calculate Spearman rank correlation coefficient and the associated p-value . | This work is in the field of systems biology . Via an in-depth comparison of proteomic and transcriptomic data in various culture conditions , our objective was to better understand the regulation of protein levels . We have demonstrated that bacteria exert a tight control on intracellular protein levels , through a multi-level regulation involving translation but also dilution due to growth and protein degradation . We have estimated translational efficiencies and protein degradation rates by modeling . These two biological parameters are extremely difficult to measure experimentally and have not been previously determined in bacteria . We have found that they are growth rate dependent , indicating a fine control of translation and degradation processes . We have worked with the small genome bacterium Lactococcus lactis on a limited number of mRNA-protein couples but keeping in mind that this approach could be extended to other micro-organisms and biological phenomena . We have exhibited that mathematical modeling associated to experimental steady-states cultures is a powerful tool to understand microbial physiology . |
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Phlebotomine sand flies are blood-sucking insects that can transmit Leishmania parasites . Hosts bitten by sand flies develop an immune response against sand fly salivary antigens . Specific anti-saliva IgG indicate the exposure to the vector and may also help to estimate the risk of Leishmania spp . transmission . In this study , we examined the canine antibody response against the saliva of Phlebotomus perniciosus , the main vector of Leishmania infantum in the Mediterranean Basin , and characterized salivary antigens of this sand fly species . Sera of dogs bitten by P . perniciosus under experimental conditions and dogs naturally exposed to sand flies in a L . infantum focus were tested by ELISA for the presence of anti-P . perniciosus antibodies . Antibody levels positively correlated with the number of blood-fed P . perniciosus females . In naturally exposed dogs the increase of specific IgG , IgG1 and IgG2 was observed during sand fly season . Importantly , Leishmania-positive dogs revealed significantly lower anti-P . perniciosus IgG2 compared to Leishmania-negative ones . Major P . perniciosus antigens were identified by western blot and mass spectrometry as yellow proteins , apyrases and antigen 5-related proteins . Results suggest that monitoring canine antibody response to sand fly saliva in endemic foci could estimate the risk of L . infantum transmission . It may also help to control canine leishmaniasis by evaluating the effectiveness of anti-vector campaigns . Data from the field study where dogs from the Italian focus of L . infantum were naturally exposed to P . perniciosus bites indicates that the levels of anti-P . perniciosus saliva IgG2 negatively correlate with the risk of Leishmania transmission . Thus , specific IgG2 response is suggested as a risk marker of L . infantum transmission for dogs .
Leishmania infantum ( syn . Leishmania chagasi ) is a protozoan parasite that causes zoonotic leishmaniasis , including the life-threatening visceral form , occurring also in the Mediterranean Basin . Parasites are transmitted by the bite of infected phlebotomine sand flies to dogs , the major host and the main domestic reservoir for human visceral leishmaniasis , or to humans . The clinical forms of canine leishmaniasis range from asymptomatic to lethal ( reviewed in [1] , [2] ) . Nonetheless , all seropositive infected dogs , including those without any clinical signs , can serve as a source of infection for sand flies in endemic areas [3] , [4] . The major vector of canine leishmaniases in Mediterranean countries , including Italy , is Phlebotomus perniciosus [5] , [6] . Control programs for human visceral leishmaniasis caused by L . infantum are primarily aimed at preventing sand flies from feeding on dogs to reduce Leishmania transmission among dogs and humans ( reviewed in [1] , [2] ) . Measuring the exposure of dogs to sand fly bites is important for estimating the risk of L . infantum transmission . Recently , it was demonstrated that experimental exposure of dogs to Lutzomyia longipalpis bites elicits the production of specific anti-saliva IgG which positively correlates with the number of blood-fed sand flies [7] . Therefore , monitoring canine IgG levels specific for sand fly saliva could indicate the intensity of exposure to sand fly bites . Such a monitoring technique would be useful for evaluating the need for , and effectiveness of , anti-vector campaigns [7] , [8] . Exposure to sand fly bites as well as immunization with sand fly saliva or its compounds elicits in naive hosts protection against Leishmania infection under laboratory conditions ( reviewed in [9] ) . It is widely accepted that the protective effect is mediated by CD4+ Th1 cellular response and characterized by increased production of IFN- γ , which activates macrophages to kill Leishmania parasites ( reviewed in [10] ) . Recently , it was shown that protective effect elicited by inoculation of Lutzomyia longipalpis recombinant proteins in dogs was associated with production of IFN-γ by CD3+ CD4+ T cells and by dominance of IgG2 antibodies [11] . In this study we described the anti-saliva IgG response in dogs experimentally exposed to P . perniciosus under laboratory conditions and those naturally exposed in an endemic focus of L . infantum . We also tested the association between the anti-saliva IgG subclasses and the levels of IFN-γ in Leishmania infantum-seropositive and -seronegative dogs . Additionally , we characterized the major P . perniciosus salivary antigens recognized by sera of experimentally and naturally bitten dogs .
A colony of Phlebotomus perniciosus was reared under standard conditions as described in [12] . Salivary glands were dissected from 4–6 day old female sand flies , placed into 20 mM Tris buffer with 150 mM NaCl and stored at −20°C . Twelve laboratory dogs , beagles , were housed and handled in the Bayer Animal Health GmbH animal facility ( Leverkusen , Germany ) . Dogs were sedated and individually exposed to approximately 200 P . perniciosus females as described in [7] , [13] . Twenty hours after exposure , sand flies were collected and microscopically examined to assess the ratio of blood-fed females . In two independent experiments , two groups of three dogs each were used . Dogs in groups 2 and 4 wore insecticide-impregnated collars that were administrated 8 days before the first sand fly exposure , for a reduction of sand fly bites . In comparison , dogs in groups 1 and 3 remained without any repellent or insecticide application during the whole study . Therefore , dogs in groups 1 and 3 are hereafter defined as high-exposed ( HE ) and the dogs in groups 2 and 4 as low-exposed ( LE ) . Dogs were exposed to sand fly bites once a week for five consecutive weeks . For the detailed numbers of blood-fed females see Table 1 . Blood samples were collected throughout the study according to the following schedule: before the first exposure ( week 0 , pre-immune serum ) , during the sand fly sensitization ( weeks 1–5 ) , and weekly after the last exposure for 5 weeks ( weeks 6–10 ) . Twenty nine mixed-breed young dogs ( from 90 to 145 days old ) and eleven laboratory reared beagles ( 120 days old ) were enrolled in the trial . All animals were housed in a private open-air shelter in Putignano ( Bari province , Apulia , Italy ) , where P . perniciosus is the most abundant phlebotomine sand fly species [14] . All dogs were vaccinated against common dog pathogens and dewormed as described in [15] . The canine antibody response against P . perniciosus saliva was studied at the beginning ( March 2008 ) and at the end ( November 2008 ) of the sand fly season . In parallel , at four intervals ( March , July , November 2008 and March 2009 ) dogs were tested for L . infantum infection status by serological , cytological and molecular methods . All dogs were L . infantum negative at the beginning of the trial ( March 2008 ) , which was proved by all three diagnostic methods used . Leishmania-positive dogs were defined by positive anti-L . infantum serology and , in a subset of seropositive dogs ( 4 out of 18 ) , the infection was confirmed by PCR or cytology . For details on the diagnostic methods , see [15] , [16] . Considering the long incubation period of canine leishmaniasis and the occurrence of sand flies exclusively during the summer season ( from June to October ) [14] , dogs with anti-Leishmania seroconversion in March ( 2009 ) are presumed to have become infected during the previous season ( 2008 ) . Dogs that were seronegative for L . infantum at all four screening intervals were included in the Leishmania-negative group . Anti-P . perniciosus IgG , IgG1 and IgG2 were measured by enzyme-linked immunosorbent assay ( ELISA ) as described in [7] with some modification . Briefly , microtiter plates were incubated with 6% ( w/v ) low fat dry milk in PBS with 0 . 05% Tween 20 ( PBS-Tw ) . Canine sera were diluted 1∶200 or 1∶500 in 2% ( w/v ) low fat dry milk/PBS-Tw . Secondary antibodies ( anti-dog IgG , IgG1 , or IgG2 from Bethyl laboratories ) were diluted and incubated as previously described [7] . Absorbance was measured at 492 nm using a Tecan Infinite M200 microplate reader ( Schoeller ) . The cut-off value ( IgG = 0 . 145; IgG1 = 0 . 126; IgG2 = 0 . 165 ) was determined as less than two times the standard error of the mean of the absorbance of pre-immune serum . Phlebotomus perniciosus salivary gland homogenate from 5-day-old sand fly females were separated by SDS-PAGE on a 10% gel under non-reducing conditions using the Mini-Protean III apparatus ( BioRad ) . Separated proteins were blotted onto a nitrocellulose ( NC ) membrane by Semi-Phor equipment ( Hoefer Scientific Instruments ) and blocked with 5% ( w/v ) low fat dry milk in Tris-buffered saline with 0 . 05% Tween 20 ( TBS-Tw ) . Strips of NC membrane were incubated with canine sera diluted 1∶50 ( experimentally bitten dogs ) or 1∶25 ( naturally bitten dogs ) in TBS-Tw for 1 hour . The strips were then washed three times with TBS-Tw and incubated with peroxidase-conjugated sheep anti-dog IgG ( Bethyl Laboratories ) diluted 1∶3000 in TBS-Tw . The chromogenic reaction was developed using a solution containing diaminobenzidine and H2O2 . For mass spectrometric analysis , salivary glands from 5-day-old P . perniciosus females were homogenized by 3 freeze-thaw cycles . Samples were dissolved in non-reducing sample buffer and electrophoretically separated in 10% polyacrylamide SDS gel . Proteins within the gels were visualized by staining with Coomassie Blue G-250 ( Bio-Rad ) . The individual bands were cut and incubated with 10 mM dithiothreitol ( DTT ) and then treated with 55 mM iodoacetamid . Washed and dried bands were digested with trypsin ( 5 ng Promega ) . The alpha-cyano-4-hydroxycinnamic acid was used as a matrix . Samples were measured using a 4800 Plus MALDI TOF/TOF analyzer ( AB SCIEX ) . Peak list from the MS spectra was generated by 4000 Series Explorer V 3 . 5 . 3 ( AB SCIEX ) without smoothing . Peaks with local signal to noise ratio greater than 5 were picked and searched by local Mascot v . 2 . 1 ( Matrix Science ) against a database of putative salivary protein sequences derived from a cDNA library [17] . Database search criteria were as follows – enzyme: trypsin , taxonomy: Phlebotomus , fixed modification: carbamidomethylation , variable modification: methionine oxidation , peptide mass tolerance: 80 ppm , one missed cleavage allowed . Only hits that scored as significant ( p<0 . 05 ) are included . The data from experimentally bitten dogs obtained by ELISA were subjected to GLM ANOVA and Scheffe's Multiple Comparison procedure to analyse differences in kinetics of antibody response between HE and LE dogs at all sampling points . The non-parametric Wilcoxon rank sum test for differences in medians was used for comparison of anti-P . perniciosus IgG , IgG1 , IgG2 and IgG1/IgG2 ratios between Leishmania-seropositive and -seronegative dogs . The non-parametric Wilcoxon signed-rank test for differences in medians was used for comparison of antibody increases between March and November blood samples in naturally bitten dogs . For correlation tests we used the non-parametric Spearman rank correlation matrix . For all tests statistical significance was regarded as a p-value less than or equal to 0 . 05 . All statistical analyses were performed using NCSS 6 . 0 . 21 software . Relative risk ( the probability of the developing the disease occurring in the group exposed to the risk factor versus a non-exposed group ) , attributive risk ( absolute effect of exposure to the risk factor ) and ODDS ratio ( odds of an event occurring in the exposed group to the odds of it occurring in non-exposed group ) were calculated for dogs from the field study to find out the relationship between the levels of anti-P . perniciosus saliva antibodies and leishmaniasis incidence as described in [18] . Low level of specific antibodies ( lower than the cut-off value ) was determined as the risk factor and the confidence interval for relative risk was calculated as described in [19] . Phlebotomus perniciosus: DQ153102; DQ154099; DQ150622; DQ150621; DQ192490; DQ192491; DQ153100; DQ153101; DQ153104; DQ150624; DQ150623; DQ150620; DQ153105 . Lutzomyia longipalpis: AF132518 .
To investigate the kinetics of antibody response against anti-P . perniciosus saliva , two groups of experimentally bitten dogs , low-exposed ( LE ) and high-exposed ( HE ) , were followed for 10 weeks . Five weekly experimental exposures to P . perniciosus bites led to increased levels of anti-saliva specific IgG , IgG1 and IgG2 in both LE and HE groups . No anti-saliva antibodies were detected in any pre-immune dog sera tested . In HE dogs , anti-P . perniciosus antibody levels increased significantly ( p<0 . 05 ) in comparison to the pre-immune sera after the second ( IgG; IgG2 ) and third exposure ( IgG1 ) ( Figure 1A–C ) . Anti-saliva IgG and IgG2 developed with similar kinetics; rapidly increased after the third exposure , and gradual increase until week five ( the last exposure ) , followed by a steady decrease to the end of the study . Anti-saliva IgG1 increased rapidly between weeks three and five and persisted at elevated levels until the end of the study . In LE dogs , anti-P . perniciosus antibody levels increased significantly ( p<0 . 05 ) in comparison to the pre-immune sera after the fourth ( IgG; IgG2 ) and sixth exposure ( IgG1 ) ( Figure 1A–C ) . Similar to HE dogs , kinetics of anti-P . perniciosus IgG and IgG2 in LE dogs was detected at peak levels on week five followed by a rapid decrease . Conversely , IgG1 was measured at peak levels on week six and persisted at elevated quantities to the end of the study ( Figure 1A–C ) . All HE dogs produced significantly higher levels of anti-P . perniciosus IgG ( p = 0 . 0001 ) , IgG1 ( p = 0 . 0032 ) and IgG2 ( p = 0 . 0003 ) compared to LE dogs throughout the study ( Figure 1A–C ) . A positive correlation was detected between number of blood-fed female sand flies and the levels of canine anti-P . perniciosus IgG ( r = 0 . 75 , p<0 . 0001 ) , IgG1 ( r = 0 . 74 , p<0 . 0001 ) and IgG2 ( r = 0 . 72 , p<0 . 0001 ) ( Figure 1D–F ) . Overall , sera of experimentally bitten dogs produced higher concentrations of specific IgG2 compared to specific IgG1 ( data not shown ) . To determine the anti-P . perniciosus saliva antibody levels and the seasonal changes in specific antibody response , canine sera were screened at the beginning and at the end of the sand fly season , March and November , respectively . Incidence of leishmaniasis in dogs naturally exposed to sand flies was high , 18 out of 40 ( 45% ) were found anti-L . infantum seropositive ( 0/40 in March 2008; 0/40 in July 2008; 5/40 in November 2008; 13/40 in March 2009 ) . In March , higher levels of anti-P . perniciosus IgG and IgG2 ( compared to cut-off value ) were detected in about 55% and 10% of dog sera , respectively , while IgG1 levels were comparable to pre-immune sera ( Table 2 ) . In November , elevated levels of specific IgG were found in 87 . 5% , IgG2 in 72 . 5% and IgG1 in 45% of the 40 enrolled dogs ( Table 2 ) . In both groups of dogs , Leishmania-positive and Leishmania-negative , specific IgG , IgG1 and IgG2 levels significantly increased during the sand fly season ( Figure 2A–C ) . Leishmania-positive and Leishmania-negative dogs did not statistically differ in IgG and IgG1 production ( Figure 2A , B ) ; however , a significant difference was found in IgG2 levels ( Figure 2C ) . Indeed , Leishmania-positive dogs revealed significantly lower anti-P . perniciosus IgG2 at the beginning ( p = 0 . 047 ) and at the end ( p = 0 . 05 ) of sand fly season ( Figure 2C ) . Negative correlation was found between the levels of anti-P . perniciosus saliva IgG2 and the risk of Leishmania transmission , supported well by epidemiological parameters: relative risk = 2 . 6 ( 95% confidence interval: 0 . 66; 10 . 63 ) ; attributive risk = 1 . 6; and ODDS ratio = 10 . Sera of all naturally bitten dogs showed significantly higher levels of specific IgG2 compared to specific IgG1 ( data not shown ) . Moreover , the IgG1/IgG2 ratio differed between Leishmania-positive and -negative dogs; Leishmania-positive dogs revealed higher IgG1/IgG2 ratio , although the difference was statistically significant only at the beginning of sand fly season ( p = 0 . 039 ) ( Table 2 ) . Furthermore , higher levels of IFN-γ were detected in sera of Leishmania-negative dogs throughout the study but with no statistically significant difference ( Figure S1 ) . Phlebotomus perniciosus salivary antigens were studied using sera of naturally and experimentally bitten dogs . Pre-immune sera of experimentally bitten dogs did not recognize any of the salivary proteins by Western blot analysis ( Figure 3 ) . Sera of experimentally exposed dogs produced 11 bands on a salivary gland Western blot with approximate molecular weights of 75 , 50 , 42 , 40 , 38 , 34 , 33 , 29 , 27 , 23 and 14 kDa ( Figure 3 ) . The molecular weights of salivary antigens recognized by canine sera were similar in all dogs tested with the exception of the 23 and 27 kDa protein bands ( recognized only by some sera ) . The salivary gland antigens most intensely recognized by the sera of all experimentally bitten dogs had molecular weights of 42 , 38 , 33 and 29 kDa . Sera of naturally bitten dogs with both negative and positive anti-L . infantum serology reacted with up to 9 protein bands of 50 , 42 , 38 , 34 , 33 , 29 , 27 , 23 and 14 kDa . All naturally exposed dogs tested in both groups recognized similar salivary antigens and the most intensive reactions were detected with the 42 and 33 kDa salivary antigens . Mass spectrometry revealed that the main antigens recognized by sera of bitten dogs were salivary endonuclease ( 50 kDa - DQ154099 ) , yellow proteins ( 42 kDa - DQ150622; 40 kDa - DQ150621 ) , apyrases ( 38 kDa - DQ192490; 38 kDa - DQ192491; 33 kDa - DQ192491 ) , antigen-5 protein ( 29 kDa - DQ153101 ) , D7 proteins ( 27 kDa - DQ153104; 23 kDa - DQ150624; 23 kDa - DQ150623 , and proteins of the SP-15 like protein family ( 14 kDa - DQ150620; 14 kDa - DQ153105 ) ( Table 3 ) .
Canine antibody response against P . perniciosus saliva was studied in dogs bitten by sand flies under well-defined laboratory conditions as well as in dogs from an endemic focus of visceral leishmaniasis in Italy . In experimentally bitten dogs we observed a significant increase in production of specific IgG , IgG1 and IgG2 in the course of 10 weeks and a positive correlation was found between the levels of specific antibodies and the number of blood-fed females P . perniciosus . Anti-saliva specific IgG and IgG2 developed with similar kinetics and correspond well with previous results [7] in dogs experimentally bitten by Lutzomyia longipalpis . While in sera of healthy dogs , IgG1 and IgG2 usually occur in comparable concentrations [20] , IgG2 prevailed in sera of bitten dogs in our study as well as in dogs experimentally bitten by L . longipalpis [7] , [11] . In our field trial , we detected the increase in number of anti-P . perniciosus saliva seropositive dogs as well as in the amount of specific antibodies in dog sera as the sand fly season progressed . Statistically significant increases in production of specific IgG , IgG1 and IgG2 were observed in both Leishmania-positive and Leishmania-negative dogs at the end of sand fly season . Interestingly , Leishmania-positive dogs revealed significantly lower anti-P . perniciosus saliva IgG2 compared to Leishmania-negative dogs and the IgG1/IgG2 ratio was significantly higher in Leishmania-positive dogs . These data may suggest either that dogs with low IgG2 levels were at the higher risk of becoming Leishmania-infected or that Leishmania infection decreases the production of IgG2 in bitten dogs . Considering the IFN-γ levels in canine sera , that were shown to positively correlate with the protective Th1 immune response [11] , it seems that the first hypothesis is more feasible . Although , the difference in IFN- γ production between Leishmania-negative and Leishmania–positive dogs was not statistically significant . Published data from field studies suggests that humoral immune responses against sand fly saliva vary between hosts with cutaneous and visceral forms of leishmaniases ( reviewed in [9] , [21] ) . In foci of cutaneous leishmaniases caused by L . tropica and L . braziliensis , the levels of specific anti-sand fly saliva antibodies in humans positively correlated with the risk of Leishmania transmission [22] , [23] . In contrast , in foci of visceral leishmaniasis caused by L . infantum , levels of human anti-sand fly saliva antibodies positively correlated with anti-Leishmania DTH ( delayed-type hypersensitivity ) and thus with protection against potential infection [24] , [25] . So far , those studies have been performed only in humans . In canids , several studies showed presence of anti-sand fly saliva antibodies in sera from endemic areas in Brazil [8] , [26] , [27] , however our study is the first describing the association with canine leishmaniasis . Canine sera recognized more than eleven P . perniciosus antigenic bands by Western blot and the most intense reaction was often observed against a 42 kDa band . Mass spectrometry identified the 42 kDa band as a single protein belonging to the Yellow protein family ( DQ150622 ) . Previously , another Yellow protein of 47 . 3 kDa ( AF132518 ) was reported as the major antigen recognized by sera of dogs bitten by L . longipalpis in the field [26] . The recombinant L . longipalpis Yellow proteins ( rLJM11 and rLJM17 ) prepared in mammalian expression system kept their antigenicity and were successfully used to screen dog sera from Brazil [27] , predicting similar features for Yellow protein of P . perniciosus . All canine sera tested recognized additional three major antigens of the 38 , 33 and 29 kDa; the 38 and 33 kDa proteins are apyrases and the 29 kDa antigen represents the antigen 5-related protein family . These four antigens ( 42 , 38 , 33 and 29 kDa ) are promising candidates as markers of sand fly exposure . In conclusion , we confirmed that levels of antibodies against sand fly saliva positively correlate with the number of blood-fed sand flies and therefore , monitoring canine antibody response to specific sand fly salivary proteins may evaluate the need for , and effectiveness of , anti-vector campaigns . Moreover , this is the first study demonstrating relationship between the anti-sand fly saliva antibodies and the status of L . infantum infection in dogs . The levels of anti-P . perniciosus IgG2 in dogs naturally bitten by this sand fly species negatively correlate with the anti-Leishmania seropositivity . Thus , for dogs living in endemic area specific IgG2 response against saliva of the vector is suggested as a risk marker of L . infantum transmission . | Leishmania infantum is the causative agent of zoonotic visceral leishmaniasis in the Mediterranean Basin and Phlebotomus perniciosus serve as the major vector . In the endemic foci , Leishmania parasites are transmitted mostly to dogs , the main reservoir host , and to humans . We studied the canine humoral immune response to Phlebotomus perniciosus saliva and its potential use as a marker of sand fly exposure and consequently as a risk marker for Leishmania transmission . We also characterized major salivary antigens of P . perniciosus . We demonstrated that under laboratory conditions , the levels of anti-P . perniciosus saliva antibodies positively correlated with the number of blood-fed sand flies and therefore , may be used to evaluate the need for , and the effectiveness of , anti-vector campaigns . In parallel , we studied sera of dogs naturally exposed to P . perniciosus in highly active focus of canine leishmaniasis in Southern Italy . Specific antibodies against P . perniciosus saliva were significantly increased according to the ongoing sand fly season . Moreover , the levels of anti-P . perniciosus antibodies in naturally bitten dogs negatively correlated with anti-Leishmania seropositivity . Thus , for dogs living in endemic areas , specific antibody response against saliva of the vector is an important marker for estimating the risk of Leishmania transmission . |
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The fibrinogen ( Fg ) binding MSCRAMM Clumping factor A ( ClfA ) from Staphylococcus aureus interacts with the C-terminal region of the fibrinogen ( Fg ) γ-chain . ClfA is the major virulence factor responsible for the observed clumping of S . aureus in blood plasma and has been implicated as a virulence factor in a mouse model of septic arthritis and in rabbit and rat models of infective endocarditis . We report here a high-resolution crystal structure of the ClfA ligand binding segment in complex with a synthetic peptide mimicking the binding site in Fg . The residues in Fg required for binding to ClfA are identified from this structure and from complementing biochemical studies . Furthermore , the platelet integrin αIIbβ3 and ClfA bind to the same segment in the Fg γ-chain but the two cellular binding proteins recognize different residues in the common targeted Fg segment . Based on these differences , we have identified peptides that selectively antagonize the ClfA-Fg interaction . The ClfA-Fg binding mechanism is a variant of the “Dock , Lock and Latch” mechanism previously described for the Staphylococcus epidermidis SdrG–Fg interaction . The structural insights gained from analyzing the ClfANFg peptide complex and identifications of peptides that selectively recognize ClfA but not αIIbβ3 may allow the design of novel anti-staphylococcal agents . Our results also suggest that different MSCRAMMs with similar structural organization may have originated from a common ancestor but have evolved to accommodate specific ligand structures .
Staphylococcus aureus is a Gram-positive commensal organism that permanently colonizes 20% of healthy adults and transiently colonizes up to 50% of the general population [1] . For many years , S . aureus has been a major nosocomial pathogen causing a range of diseases from superficial skin infections to life-threatening conditions , including septicemia , endocarditis and pneumonia [1] , [2] . Within the last decade a dramatic increase in the number of invasive infections caused by community-acquired S . aureus have been recorded in otherwise healthy children and young adults [3] , [4] . This outbreak together with the continued increase in antibiotic resistance among clinical strains underscores the need for new prevention and treatment strategies [1] . A detailed characterization of the molecular pathogenesis of S . aureus infections may expose new targets for the development of novel therapeutics . Several staphylococcal virulence factors have been identified including capsule , surface adhesins , proteases , and toxins ( reviewed in [5] , [6] , [7] , [8] ) . One of these virulence factors is the MSCRAMM ( microbial surface components recognizing adhesive matrix molecules ) clumping factor A ( ClfA ) . ClfA is the major staphylococcal fibrinogen ( Fg ) binding protein and is responsible for the observed clumping of S . aureus in blood plasma [9] , [10] . Essentially all S . aureus clinical strains carry the clfA gene [11]; ClfA is a virulence factor in a mouse model of septic arthritis [12] and in rabbit and rat models of infective endocarditis [13] , [14] , [15] . ClfA generates strong immune responses and has shown potential as a vaccine component in active and passive immunization studies . In one study , mice vaccinated with a recombinant ClfA segment containing the Fg-binding domain and subsequently challenged with S . aureus showed significantly lower levels of arthritis compared to mice vaccinated with a control protein [12] . In another study , mice passively immunized with polyclonal or monoclonal antibodies against the ClfA Fg-binding domain were protected in a model of septic death [16] . The humanized monoclonal antibody , Aurexis® , has a high affinity for ClfA and inhibits ClfA binding to Fg [17] . Aurexis is currently in clinical trials in combination with antibiotic therapy for the treatment of S . aureus bacteremia [18] . Thus ClfA is a viable target for both vaccine and therapeutic strategies . ClfA belongs to a class of cell wall-localized proteins that are covalently anchored to the peptidoglycan [5] , [19] , [20] . Starting from the N-terminus , ClfA contains a signal sequence followed by the ligand-binding A region composed of three domains ( N1 , N2 , and N3 ) , the serine-aspartate repeat domain ( R region ) , and C-terminal features required for cell wall anchoring such as the LPXTG motif , a transmembrane segment and a short cytoplasmic domain [21] , [22] , [23] . A crystal structure of a Fg-binding ClfA segment ( residues 221–559 ) which includes two of the domains ( N2N3 ) demonstrates that each domain adopts an IgG-like fold [24] . This domain architecture was also determined from the crystal structure of the ligand binding segment of SdrG from Staphylococcus epidermidis , an MSCRAMM that binds to the N-terminal region of the Fg β-chain [25] . A dynamic mechanism of Fg binding termed “Dock , Lock and Latch” ( DLL ) has been proposed for SdrG based on a comparison of the crystal structures of SdrG N2N3 as an apo-protein and in complex with a synthetic peptide mimicking the targeted site in Fg [25] . In the SdrG DLL model , the apo-form of the protein adopts an open conformation that allows the Fg ligand access to a binding trench between the N2 and N3 domains . As the ligand peptide docks into the trench , a flexible C-terminal extension of the N3 domain is redirected to cover the ligand peptide and “lock” it in place . Subsequently the C-terminal part of this extension interacts with the N2 domain and forms a β-strand complementing a β-sheet in the N2 domain . This inserted β-strand serves as a latch to form a stable MSCRAMM ligand complex . ClfA binds to the C-terminus of the Fg γ-chain [10] , [23] and a synthetic 17 amino acid peptide corresponding to this region was shown to bind to ClfA . Interestingly , the A-region of the staphylocccal MSCRAMM FnbpA protein also binds to the same region in Fg [23] . Moreover residues in this Fg segment are also targeted by the platelet αIIbβ3 integrin [26] , [27] , [28] and a recombinant form of ClfA has been shown to inhibit platelet aggregation and the binding of platelets to immobilized Fg [10] , [29] , [30] . The current study was undertaken to characterize the interaction of ClfA and Fg to define in detail the binding of the C-terminus of Fg's γ-chain and to explore if compounds can be constructed that antagonize the ClfA-Fg interaction but does not affect the Fg interaction with the platelet-integrin αIIbβ3 .
In previous studies , a segment of ClfA composed of residues 221–559 was shown to bind to the C-terminal end of the human Fg γ-chain [10] . We designed , based on structural similarities with SdrG , a smaller ClfA construct ( 229–545 ) predicted to be composed only of the N2 N3 domains and showed that ClfA229–545 retained the Fg-binding activity . To identify specific residues in Fg that are important for binding to ClfA229–545 , a panel of peptides ( Fig . 1A ) based on the Fg γ-chain sequence 395–411 ( referred to as γ1–17 ) were synthesized in which each position was sequentially substituted with an alanine residue ( alanines 11 and 14 were changed to serines ) . These peptides were tested as inhibitors in solid-phase binding assays , using a peptide concentration giving about 50% inhibition by the wild-type peptide . Peptides γ1–17H6A , γ1–17H7A , γ1–17G10A , γ1–17Q13A , γ1–17A14S and γ1–17G15A were significantly less potent inhibitors than the native sequence suggesting that the Fg residues H6 , H7 , G10 , Q13 , A14 and G15 interact with ClfA ( Fig . 1B ) . Remarkably , peptides γ1–17A11S , γ1–17D16A and γ1–17V17A showed enhanced inhibition of ClfA binding to a recombinant form of residues 395–411 of the Fg γ-chain fused to a GST protein ( GST-Fg γ1–17 ) compared to a peptide with the wild-type sequence , indicating a higher affinity of the peptide variants for ClfA . The ability of ClfA229–545 to bind to the peptide containing the γ1–17D16A mutation was further characterized . In solid-phase assays , ClfA binds to immobilized GST-Fg γ1–17 fusion protein with a lower affinity ( Kd = 657 nM ) compared to the mutated GST-Fg γ1–17D16A ( Kd = 35 nM ) ( Fig . 1C ) . In solution , using isothermal titration calorimetry ( ITC ) assays , ( Fig . 1D ) , ClfA also binds with a lower affinity to the native γ1–17 peptide ( Kd of 5 . 8 µM ) compared to the mutant Fg γ1–17D16A ( Kd of 3 µM ) . Thus , although the apparent dissociation constants differ according to the assays used to estimate them , similar trends in affinity between the wild-type and the D16A mutation were observed . Our results showed that alanine substitution at the C-terminal but not in the N-terminal region of the peptide affected MSCRAMM binding suggesting that the ClfA binding site is located at the very C-terminus of the Fg γ-chain ( Fig . 1 ) . Results also show that certain amino acid changes in the γ1–17 sequence enhance ClfA binding compared to the wild-type Fg sequence indicating that the human Fg γ C-terminal 17 residues may not be the optimum ligand for ClfA . Analysis of the previously solved SdrG-Fg peptide complex crystal structure showed that only 11 out of the 18 peptide residues interacted with the MSCRAMM . Similarly , only a part of the 17-residue γ-chain segment may be required for binding to ClfA . In order to establish the minimum Fg peptide required for binding to ClfA229–545 , a series of N- and C-terminal truncations of the γ1–17D16A peptide were synthesized ( Fig . 2A ) . Truncations of 2 , 4 , 6 or 8 amino acids at the N-terminus of the Fg γ-peptide resulted in a reduced but detectable binding affinity when tested using ITC . There was a direct relationship between the length of the peptide and its affinity for ClfA . The smaller the peptide , the lower was the observed affinity for the MSCRAMM ( Fig . 2B ) . Thus , the N-terminal residues of the Fg peptide ( residues 1–8 ) are not critical for the interaction but may either contribute to or stabilize the binding of the peptide to ClfA . On the other hand , deletions of 2 or 4 residues from the C-terminal end of the γ1–17D16A peptide abolished binding . These results indicate that the C-terminal amino acids of Fg are critical for binding to ClfA and are in agreement with a previous report that showed that Fg lacking the C-terminal residues AGDV in the γ chain ( corresponding to residues 14–17 in the peptide ) or a Fg-variant that replaces the last four γ-chain residues with 20 amino acids lacks the ability to bind recombinant ClfA221–550 and induce S . aureus clumping [10] . The Fg binding mechanism of SdrG276–596 involves a transition from an open conformation , where the peptide binding trench between the N2 and N3 domains is exposed for ligand docking , to a closed conformation of the SdrG276–596 seen for the MSCRAMM in complex with the ligand peptide . The insertion of the N3 extension into the latching trench on N2 , which represents the last step in the dynamic DLL binding mechanism , stabilizes the closed conformation of SdrG237–596 [31] . A closed conformation of apo SdrG N2N3 , stabilized by introducing a disulfide bond between the end of the N3 latch and the “bottom” of N2 , no longer binds Fg [31] demonstrating that for SdrG an open conformation is required for the initial docking of the ligand peptide . To explore if the binding of ClfA to Fg is also dependent on a movement of the latch we constructed a ClfA protein containing two cysteine substitutions . The locations of the cysteine mutations were determined using computer modeling and by sequence alignment to corresponding mutations in SdrG [31] . The mutant ClfAD327C/K541C generated a stable , closed conformation form . This recombinant His-tag fusion protein was purified by Ni+ chelating chromatography; ion-exchange and gel permeation chromatography . The ClfAD327C/K541C open and closed conformation forms were examined by SDS-PAGE analysis ( Fig . 2C ) . Under non reducing conditions , the disulfide bonded closed form of ClfAD327C/K541C migrated faster on SDS-PAGE than its non-disulfide bonded open form . Presumably , under non-reducing conditions , closed conformation mutants are more compact and migrate faster on SDS-PAGE than open conformation constructs . Under reducing conditions , the disulfide mutant and the wild-type protein migrate at the same rate . Surprisingly , the closed conformation of the disulfide mutant ClfAD327C/K541C was able to bind Fg ( Fig . 2C ) . Elisa-type binding assays where Fg or GST Fg γ1–17 peptide were coated in microtiter wells and incubated with ClfA showed that the closed conformation ClfAD327C/K541C bound the ligand with a much lower apparent Kd ( 34 nM Fg; 20 nM GST-Fg γ1–17 ) compared to the wild-type ClfA229–545 ( apparent Kd 305 nM Fg; 222 nM GST-Fg γ1–17 ) ( Fig . 2D ) . These results demonstrate that an open conformation may not be required for Fg binding to ClfA and that Fg binding by ClfA involves a mechanism that is different from the DLL mechanism employed by SdrG . Crystallization screens were carried out with ClfAD327C/K541C in complex with several N-terminal truncations of the γ1–17D16A peptide that were shown to bind ClfA . Crystals of the stable closed conformation of ClfA229–545 in complex with several peptides were obtained , but structure determination was attempted for only the ClfA ( 229–545 ) D327C/K541C-γ5–17D16A peptide . The crystals of the ClfA-peptide complex diffracted to a 1 . 95 Å resolution . Two copies of the ClfA-peptide complex were found in the asymmetric part of the unit cell and are referred to as A∶C and B∶D . Although the 13 residue Fg γ5–17 chain synthetic peptide was used for crystallization , only 11 residues were identified completely in both copies of the complex . The two molecules of ClfAD327C/K541C ( A and B ) are nearly identical with rms deviation of 0 . 3 Å for 312 Cα atoms and 0 . 55 Å for backbone atoms . As observed in the apo-ClfA221–559 structure [24] , the ClfA ( 229–545 ) D327C/K541C N2 and N3 domains adopt the DE-variant IgG fold . The overall structure of the ClfAD327C/K541C peptide complex ( A∶C ) and the two different orientations of the complex are shown in Figure 3A and 3B respectively . The C-terminal extension of the N3 domain makes a β-sheet complementation with strand E of the N2 domain . This conformation is locked by the engineered disulfide bond as predicted by SDS-PAGE analysis ( Fig . 2C ) and confirmed by the crystal structure ( Fig . S1 ) . The two copies of the Fg γ-peptide molecules are nearly identical with rms deviation of 0 . 5 Å for 11 Cα atoms and 0 . 89 Å for backbone atoms . The interaction between the ClfAD327C/K541C and the peptide buries a total surface area of 1849 Å2 and 1826 Å2 in the A∶C and B∶D complex , respectively . The interaction of the peptide with the N2 domain is predominantly hydrophobic in nature , in addition to a few main-chain hydrogen bonds ( Fig . 3C ) . Interactions between the Fg peptide and the N3 domain are both hydrophobic and electrostatic with the electrostatic contribution coming almost entirely from the main chain-main chain hydrogen bonds due to the parallel β-sheet formation of the peptide with strand G of the N3 domain ( Fig . 3C ) . The side-chain interactions between the peptide and ClfA are predominantly hydrophobic . The 11 C-terminal residues of the Fg γ-chain peptide sequence that interact with ClfA are composed of only two polar residues , Lys12 and Gln13 . Side chain atoms of Lys12 point away and do not interact with the ClfA protein whereas Gln13 makes two hydrogen bonds with the main chain atoms of Ile384 in ClfA ( Fig . 3D ) . A water-mediated interaction is also observed between Gln13 of the peptide and Asn525 of ClfA . Tyr338 in the N2 domain and Trp523 in the N3 domain play an important role in anchoring the peptide molecule . Tyr338 and Trp523 are stacked with residues Gly15 and Gly10 , respectively . In addition , Met521 and Phe529 make hydrophobic interactions with Ala7 and Val17 , respectively . The C-terminal residues of the peptide Ala14 , Gly15 , Ala16 , and Val17 are buried between the N2–N3 domain interface with the terminal Val residue , presumably threaded through a preformed ligand binding tunnel after ClfAD327C/K541C adopted its closed conformation . A hydrogen bond is observed between Lys389 of ClfA and the C-terminal carboxyl group of the peptide ( Fig . 3D ) . Mutational studies showed that Tyr338Ala and Lys389Ala mutant ClfA showed significantly reduced binding to Fg [24] which corroborates with the structural results . Also an earlier study showed that E526A and V527S affected the binding [32] . The structure shows that these residues make main-chain interactions with the peptide ( Fig . 3C ) . These residues are critical for the anchoring the peptide ( Lock ) and redirection of the latch . The individual N2 and N3 domains in the apo-ClfA221–559 and the closed form of ClfAD327C/K541C are almost identical with rms deviations of 0 . 33 and 0 . 42 Å for molecule A and 0 . 35 and 0 . 42 Å for molecule B , but the relative orientation of the N2 and N3 domains are significantly different ( Fig . 4A ) . This difference affects the association of the N2 and N3 domains . In the apo conformation , the buried surface area between the N2 and N3 domains is 87 Å2 compared to 367 Å2 in the closed form of the ClfA ( 221–559 ) D327C/K541C-peptide complex . In the apo-ClfA221–559 , the C-terminal residues ( Ala528-Glu559 ) of the N3 domain fold back and do not interact with the N2 domain . Moreover the folded-back segment completely occupies the binding site ( Fig . 4B ) . Therefore , in the folded-back conformation , the ligand binding site appears not to be accessible to the peptide and thus this conformation appears to be inactive . It is presently unclear what the spatial arrangements of the N2N3 domains are in intact ClfA expressed on the surface of a staphylococcal cell . The two structures of these domains solved so far where one is active and the other inactive form suggests a possible regulation of ClfA's Fg binding activity by external factors . One such factor may be Ca2+ which has been shown to inhibit ClfA-Fg binding [32] . Alternatively , it is possible that the folded-back conformation ( which is a larger protein construct ) is only one of the many possible conformations adopted by the unbound protein . Molecular modeling shows that the two domains in the folded-back conformation could adopt an orientation similar to their orientation in the ClfA-peptide complex ( Fig . S2 ) . Most likely , the structural rearrangements responsible for the transition of ClfA from an open unbound to the closed bound form are complex and involve different intermediate forms . The major difference between Fg-binding to ClfA and SdrG is that the directionality of the bound ligand peptide is reversed ( Fig . 4C ) . The C-terminal residues of the ligand is docked between the N2 and N3 in ClfA and makes a parallel β-sheet complementation with strand G of the N3 domain , whereas in SdrG , the N-terminal residues of the ligand are docked between the N2 and N3 domains and form an anti-parallel β-sheet with the G strand . In both cases there are 11 ligand residues that make extensive contact with the MSCRAMM but with one residue shifted towards the N3 domain in ClfA . Of these 11 residues , 7 and 11 residues participate in the β-strand complementation of SdrG and ClfA , respectively . Although the peptide binding model of ClfA is different to that of SdrG , the inter-domain orientations of the two MSCRAMMS are very similar [25] . Superposition of 302 corresponding atoms in the N2 and N3 domains of ClfA and SdrG showed a small rms deviation of 0 . 65 Å indicating the high structural similarity between the two MSCRAMMS . Another striking difference is that ClfA does not require an open-conformation for ligand binding , whereas Fg can not bind to a stabilized closed conformation of SdrG . ClfA binds the C-terminal end of Fg and the last few residues of the γ-chain presumably can be threaded in to the binding pocket . In the SdrG-Fg interaction , the binding segment in Fg does not involve the seven N-terminal residues of the ligand and therefore an open conformation may be required for ligand binding . The C-terminus of Fg γ-chain , which is targeted by ClfA , is also recognized by the αIIbβ3 integrin in Fg induced platelet aggregation , a vital step in thrombosis [10] , [33] . The Fg γ-chain complex with αIIbβ3 structure is not available but structures of related complexes provide clues on how αIIbβ3 likely interact with Fg [34] . In addition , the crystal structure of the αvβ3 integrin in complex with an RGD ligand provided a structural model of a similar ligand-integrin interaction [35] . In this structure , the Asp ( D ) residue of the RGD sequence coordinates with the metal ion in the Metal Ion Dependent Adhesion Site ( MIDAS ) of the integrin and thus plays a key role in the interaction . The platelet specific integrin αIIbβ3 recognizes ligands with an RGD sequence or the sequence Lys-Gln-Ala-Gly-Asp-Val found in Fg [34] . Structural studies with drug molecules that antagonize the integrin-RGD or -Fg interaction showed that each of the drug molecules contains a carboxyl group moiety that mimics the aspartic acid and a basic group that mimics the Arg ( or Lys in the case of Fg ) in the ligand [34] . These results suggest that the Lys and Asp residues in the C-terminal γ-chain sequence are critical for the interaction with integrin . Interestingly , our studies have shown that these Lys and Asp residues in Fg are not critical for ClfA binding ( Fig . 1B ) . In fact , substitution of Asp with Ala ( γ1–17D16A ) results in a higher binding affinity . Absence of a strong interaction with Lys12 in the ClfA-peptide complex structure also correlates with the biochemical data , suggesting that Arg is not a key player in the ClfA-Fg interaction . In general , our studies show that K406 and D410 , which are essential for the platelet integrin αIIbβ3-Fg interaction , are dispensable for the ClfA-Fg interaction . To experimentally examine this proposed difference , the ability of the synthesized Fg WT γ1–17 and mutated peptides ( γ1–17D16A and γ1–17K12A ) to inhibit full length Fg binding to αIIbβ3 was analyzed by an inhibitory ELISA type assay ( Fig . 5 ) . The WT γ1–17 peptide completely inhibited the binding of full-length fibrinogen to αIIbβ3 whereas γ1–17D16A and γ1–17K12A weakly inhibited Fg binding to αIIbβ3 . These results clearly demonstrated that the γ1–17D16A and γ1–17K12A peptides bind weakly to platelet integrin and therefore could serve as specific antagonists of Fg-ClfA interaction . Based on the results presented here , we postulate that the mechanism of interaction between ClfA and Fg is a variation of the “Dock , Lock and Latch ( DLL ) ” model of SdrG binding to Fg . In the DLL model of binding , the apo-form of the SdrG is in an open conformation to allow the ligand access to the binding cleft . A closed conformation of SdrG is unable to bind Fg . In the ClfA model , we believe that the peptide may thread into the cavity formed in a stabilized closed configuration and therefore the ClfA-Fg binding mechanism could be called “Latch and Dock” . In the case of CNA , a collagen binding MSCRAMM from S . aureus , the collagen molecule binds to CNA through a “collagen hug” model [36] which represents yet another variant of the DLL binding mechanism . All three MSCRAMM-ligand structures determined so far , SdrG , CNA and the ClfA have different ligand binding characteristics and mechanisms , although the overall structures of the ligand binding regions of these MSCRAMMs are very similar . These observations suggest that an ancestral MSCRAMM has evolved along different paths to accommodate different ligands without greatly altering the overall organization of the proteins . The co-crystal structure of ClfA in complex with the C-terminal region of the γ-chain of Fg will allow the design of potent antagonist of the ClfA-Fg interaction . The Fg based peptide analogs that antagonize the ClfA-Fg interaction but not affect the αIIbβ3 integrin interaction could serve as a starting point to develop novel anti-staphylococcal therapeutic agents that do not affect the αIIbβ3 .
Escherichia coli XL-1 Blue ( Stratagene ) was used as the host for plasmid cloning and protein expression . Chromosomal DNA from S . aureus strain Newman was used to amplify the ClfA DNA sequence . All E . coli strains containing plasmids were grown on LB media with ampicillin ( 100 µg/ml ) . DNA restriction enzymes were used according to the manufacturer's protocols ( New England Biolabs ) and DNA manipulations were performed using standard procedures [37] . Plasmid DNA used for cloning and sequencing was purified using the Qiagen Miniprep kit ( Qiagen ) . DNA was sequenced by the dideoxy chain termination method with an ABI 373A DNA Sequencer ( Perkin Elmer , Applied Biosystems Division ) . DNA containing the N-terminal ClfA sequences were amplified by PCR ( Applied Biosystems ) using Newman strain chromosomal DNA as previously described [38] . The synthetic oligonucleotides ( IDT ) used for amplifying clfA gene products are listed in Table S1 . Cysteine mutations were predicted by comparing ClfA221–559 to SdrG ( 273–597 ) disulfide mutant with stable closed conformations [31] and by computer modeling . A model of ClfA in closed conformation was built based on the closed conformation of the SdrG-peptide complex [25] . The Cβ-Cβ distances were calculated for a few residues at the C-terminal end of the latch and strand E in the N2 domain . Residue pairs with Cβ-Cβ distance less than 3 Å were changed to cysteines to identify residues that could form optimum disulfide bond geometry . The D327C/K541C mutant was found to form a disulfide bond at the end of the latch . The cysteine mutations in ClfAD327C/K541C were generated by overlap PCR [39] , [40] . The forward primer for PCR extension contained a BamHI restriction site and the reverse primer contained a KpnI restriction site . The mutagenesis primers contained complementary overlapping sequences . The final PCR product was digested with BamHI and KpnI and was ligated into same site in the expression vector pQE-30 ( Qiagen ) . All mutations were confirmed by sequencing . The primers used are listed in Table S1 . E . coli lysates containing recombinant ClfA and GST-Fg γ-chain fusion proteins were purified as previously described [32] . PCR products were subcloned into expression vector pQE-30 ( Qiagen ) to generate recombinant proteins containing an N-terminal histidine ( His ) tag as previously described [10] . The recombinant ClfA His-tag fusion proteins were purified by metal chelation chromatography and anion exchange chromatography as previously described [23] . To generate recombinant ClfA229–545 and ClfA221–559 proteins , PCR-amplified fragments were digested with BamHI and KpnI and cloned into BamHI/KpnI digested pQE-30 . The primers used to generate the recombinant constructs are listed in Table S1 . The reactions contained 50 ng of strain Newman DNA , 100 pmol of each forward and reverse primers , 250 nM of each dNTP , 2 units of Pfu DNA polymerase ( Stratagene ) and 5 µl Pfu buffer in a total volume of 50 µl . The DNA was amplified at 94°C for 1 min , 48°C for 45 sec; 72°C for 2 min for 30 cycles , followed by 72°C for 10 min . The PCR products were analyzed by agarose gel electrophoresis using standard methods [37] and purified as described above . The ability of the wild-type ClfA229–545 and disulfide ClfA mutants to bind Fg was analyzed by ELISA-type binding assays . Immulon 4HBX Microtiter plates ( Thermo ) were coated with human Fg ( 1 µg/well ) in HBS ( 10 mM HEPES , 100 mM NaCl , 3 mM EDTA , pH 7 . 4 ) over-night at 4°C . The wells were washed with HBS containing 0 . 05% ( w/v ) Tween-20 ( HBST ) and blocked with 5% ( w/v ) BSA in HBS for 1 h at 25°C . The wells were washed 3 times with HBST and recombinant ClfA proteins in HBS were added and the plates were incubated at 25°C for 1 h . After incubation , the plates were washed 3 times with HBST . Anti-His antibodies ( GE Healthcare ) were added ( 1∶3000 in HBS ) and the plates were incubated at 25°C for 1 h . The wells were subsequently washed 3 times with HBST and incubated with goat anti-mouse-AP secondary antibodies ( diluted 1∶3000 in HBS; Bio-Rad ) at 25°C for 1 h . The wells were washed 3 times with HBST and AP-conjugated polyclonal antibodies were detected by addition of p-nitrophenyl phosphate ( Sigma ) in 1 M diethanolamine ( 0 . 5 mM MgCl2 , pH 9 . 8 ) and incubated at 25°C for 30–60 min . The plates were read at 405 nm in a ELISA plate reader ( Thermomax , Molecular Devices ) . For the inhibition assays , recombinant ClfA229–545 was pre-incubated with Fg γ peptides in HBS for 1 h at 37°C . The recombinant protein-peptide solutions were then added to plates coated with 1 µg/well GST fusion protein containing the native human Fg γ 395–411 sequence ( called GST-Fg γ1–17 ) and bound protein was detected as described above . If the peptide binds ClfA it would inhibit binding of the GST-Fg γ1–17 to the MSCRAMM . For αIIbβ3 inhibition assay , Immulon 4HBX Microtiter 96-well plates ( Thermo ) were coated with αIIbβ3 ( 0 . 25 µg/well ) in TBS ( 25 mM Tris , 3 mM KCl , 140 mM NaCl , pH 7 . 4 ) over night at 4°C . The wells were washed with TBS containing 0 . 05% ( w/v ) Tween-20 ( TBST ) . After blocking with 3% ( w/v ) BSA dissolved in TBS for 1 h at RT , 10 nM of full length Fg was applied in the presence of either WT γ1–17 , γ1–17D16A or γ1–17K12A peptides and plates were incubated at RT for another hour . The bound full length Fg was then detected by goat anti human Fg ( 1∶1000 dilution , Sigma ) antibody followed by horseradish peroxidase-conjugated rabbit anti-goat IgG antibody ( 1∶1000 dilution , Cappel ) . After incubation with 0 . 4 mg/ml of substrate , o-phenylenediamine dihydrochloride ( OPD , Sigma ) dissolved in phosphate-citrate buffer , pH 5 . 0 , bound antibodies were determined in an ELISA reader at 450 nm . The proteins , antibodies and peptides were diluted in TBST containing 1% ( w/v ) BSA , 2 mM MgCl2 , 1 mM of CaCl2 and MnCl2 . The wild-type and mutated peptides corresponding to the 17 C-terminal residues of the fibrinogen γ-chain ( 395–411 ) and truncated versions of this peptide ( listed in Figure 2A ) were synthesized as previously described and purified using HPLC [10] . The interaction between ClfA proteins and soluble Fg peptides was analyzed by Isothermal titration calorimetry ( ITC ) using a VP-ITC microcalorimeter ( MicroCal ) . The cell contained 30 µM ClfA and the syringe contained 500–600 µM peptide in HBS buffer ( 10 mM HEPES , 150 mM NaCl , pH 7 . 4 ) . All samples were degassed for 5 min . The titration was performed at 30°C using a preliminary injection of 5 µl followed by 30 injections of 10 µl with an injection speed of 0 . 5 µl/sec . The stirring speed was 300 rpm . Data were fitted to a single binding site model and analyzed using Origin version 5 ( MicroCal ) software . The ClfAD327C/K541C protein was purified as described earlier and concentrated to 30 mg/ml . The synthetic γ-chain peptide analogs , P16 and N-terminal truncations of P16 ( P16 -2Nt , P16 -4Nt and P16 -6Nt ) were mixed with the protein at 1∶20 molar ratio and left for 30 min at 5° C . This mixture was screened for crystallization conditions . Small needles of the ClfA/P16 -2Nt , -4Nt and -6Nt were obtained during initial search of the crystallization condition , but we could only successfully optimize ClfA/P16 -4Nt and ClfA/P16 -6Nt . Diffraction quality crystals were obtained by mixing 2 µl of protein solution with 2 µl of reservoir solution containing 16–20% PEG 8K , 100 mM succinic acid pH 6 . 0 . Crystals of ClfA/ P16 -4Nt were flash frozen with a stabilizing solution containing 20% glycerol . Diffraction data were measured on Rigaku R-Axis IV++ detector . A total of 180 frames were collected at a detector distance of 120 mm with 1° oscillation . Data were indexed , integrated and scaled using d*terk [41] . The crystals diffracted to 1 . 95 Å and the data statistics were listed in Table 1 . Calculation of the Matthews coefficient suggested the presence of 2 copies of the molecule in the unit cell of the triclinic cell . The structure was solved by molecular replacement ( MR ) with the program PHASER [42] using individual N2 and N3 domains of ClfA as search model . Solutions for the N3 domain were obtained for the two copies followed by the solutions of N2 domains . Data covering 2 . 5–15 Å were used for the molecular replacement solution . Electron density maps calculated during the initial rounds of refinement showed interpretable density for 11 out of 13 peptide residues in both the copies of the complex . Modeling building of the peptide and rebuilding of a few loop regions were performed using the program COOT [43] . A few cycles of ARP/WARP [44] were performed to improve the map and for the building of water model . After a few cycles of refinement using Refmac5 . 2 [45] , electron density was clear for only the backbone atoms for two remaining N-terminal residues of the peptide molecule D and one residue for peptide C . The final model of ClfA included residues 230–299 , 303–452 , 456–476 and 479–545 in molecule A and 230–438 , 440–476 and 479–542 in molecule B . The structure was refined to a final R-factor of 21 . 1% and R-free of 27 . 9% . Stereochemical quality of the model was validated using PROCHECK [46] . Molecular modeling studies were performed using InsightII software ( Accrelys Inc ) . Figures were made using RIBBONS [47] . The atomic coordinates and structure factors of the complex structure have been deposited in Protein data bank with accession number; 2vr3 . | Staphylococcus aureus ( S . aureus ) is a common pathogen that can cause a range of diseases from mild skin infections to life-threatening sepsis in humans . Some surface proteins on S . aureus play important roles in the S . aureus disease process . One of these bacterial surface proteins is clumping factor A ( ClfA ) that binds to the C-terminal region of one of the three chains of fibrinogen ( Fg ) , a blood protein that plays a key role in coagulation . We carried out biochemical and structural studies to understand the binding mechanism of ClfA to Fg and to define the residues in Fg that interact with ClfA . Interestingly , the platelet integrin , which is important for platelet aggregation and thrombi formation , also binds to the same region of Fg as ClfA . Despite the fact that the two proteins bind at the same region , the mode of recognition is significantly different . Exploiting this difference in recognition , we have demonstrated that agents could be designed that inhibit the ClfA–Fg interaction but do not interfere with the interaction of Fg with the platelet integrin . This opens the field for the design of a novel class of anti-staph therapeutics . |
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Adhesion of the Trypanosoma cruzi trypomastigotes , the causative agent of Chagas' disease in humans , to components of the extracellular matrix ( ECM ) is an important step in host cell invasion . The signaling events triggered in the parasite upon binding to ECM are less explored and , to our knowledge , there is no data available regarding •NO signaling . Trypomastigotes were incubated with ECM for different periods of time . Nitrated and S-nitrosylated proteins were analyzed by Western blotting using anti-nitrotyrosine and S-nitrosyl cysteine antibodies . At 2 h incubation time , a decrease in NO synthase activity , •NO , citrulline , arginine and cGMP concentrations , as well as the protein modifications levels have been observed in the parasite . The modified proteins were enriched by immunoprecipitation with anti-nitrotyrosine antibodies ( nitrated proteins ) or by the biotin switch method ( S-nitrosylated proteins ) and identified by MS/MS . The presence of both modifications was confirmed in proteins of interest by immunoblotting or immunoprecipitation . For the first time it was shown that T . cruzi proteins are amenable to modifications by S-nitrosylation and nitration . When T . cruzi trypomastigotes are incubated with the extracellular matrix there is a general down regulation of these reactions , including a decrease in both NOS activity and cGMP concentration . Notwithstanding , some specific proteins , such as enolase or histones had , at least , their nitration levels increased . This suggests that post-translational modifications of T . cruzi proteins are not only a reflex of NOS activity , implying other mechanisms that circumvent a relatively low synthesis of •NO . In conclusion , the extracellular matrix , a cell surrounding layer of macromolecules that have to be trespassed by the parasite in order to be internalized into host cells , contributes to the modification of •NO signaling in the parasite , probably an essential move for the ensuing invasion step .
Trypanosoma cruzi is the etiological agent of Chagas disease , an infectious disease affecting areas of poor socioeconomic development . The parasite infects a wide range of mammalian hosts , including humans , from which 7–8 million are infected and other 25 million are at risk of contamination [1] . T . cruzi trypomastigotes , the classical parasite infective form , invade almost all mammalian cells , including macrophages [2 , 3 , 4] , being exposed to nitrosative and oxidative stress during the life cycle [5 , 6 , 7] . The cytotoxic effect of •NO and its derivatives on pathogens such as T . cruzi is well known . In mammals and other organisms , the free radical •NO is endogenously synthesized by nitric oxide synthase catalyzing the conversion of L-arginine to L-citrulline [8] , a reaction that depends on heme , FAD , FMN and tetrahydro-L-biopterin ( BH4 ) as co-factors . •NO is highly reactive towards O2 , but reactions with biological molecules preferentially occur with •NO- derived species ( N2O3 , NO2• or ONOO- ) [9] . Biologically , •NO plays essential role in cell signaling , acting by two main mechanisms: ( i ) activation of guanylyl cyclase , yielding cGMP—the classical pathway; or ( ii ) acting in post-translational modifications such as S-nitrosylation and tyrosine nitration- the non-classical pathway [10 , 11] . Protein S-nitrosylation and tyrosine nitration affect the activity of many relevant targets of several biological processes [12 , 13] . Proteins are S-nitrosylated ( SNO ) by the addition of a nitroso group into a cysteine residue in a non-enzymatic process , dependent on the local nitric oxide concentration or by transnitrosylation , a key mechanism in •NO signaling ( acquisition of a •NO from another S-nitrosothiol ) [14 , 15 , 16] . Denitrosylation may occurs by nonenzymatic mechanisms or by the action of denitrosylases [17 , 18 , 19] . New targets of S-nitrosylation are being extensively described in different organisms due to the development of tools such as the traditional biotin-switch technique associated with proteomic analysis [20 , 21] . As an example , 319 putative S-nitrosylation targets , as well as enzymatic denitrosylating and transnitrosylating activities in Plasmodium falciparum were recently described [22] . Of note , P . falciparum lacks a NOS ortholog and probably produces •NO from a nitrate/nitrite chemical reduction pathway [23] . In contrast to S-nitrosylation , tyrosine nitration of proteins was classically regarded as an undesired byproduct of radical species with greater reactivity capable of oxidizing tyrosine to 3-nitro-tyrosine . However , tyrosine nitration of proteins occurs under physiological conditions , with an increment of 3-nitro-tyrosine in many physiopathological and aging processes . Protein nitration is mediated by free radical reactions , with the intermediate •Tyr reacting with •NO or •NO2 [24] . Not all proteins are nitrated , pointing out to the specificity of the modification . Only one or two specific protein residues are preferentially modified and a close relationship between protein tyrosine nitration and the presence of a transition metal has been made [rev . 25] . Although not well established as S-nitrosylation , evidence gathered in the past few years suggests that tyrosine-nitrated proteins regulate several biological processes , such as stress response in plants [26] , cytochrome c regulation [27] , protein degradation [28] , control of the redox environment [29] and PKC signaling [30] . There is a limited knowledge of •NO signaling in T . cruzi , as happens with other parasites [31] . S-nitrosylation or tyrosine nitration of T . cruzi proteins remains largely unexplored , despite the relevance of •NO and •NO-derived species produced by mammals in response to T . cruzi infection . In vitro treatment of cruzipain , the major T . cruzi papain-like cysteine proteinase [32] , with •NO donors led to inhibition of the enzyme activity [33] , but this modification has not been reported in vivo . Additionally , the putative signaling in response to the endogenous •NO formation is mostly unknown in T . cruzi . Biochemical evidence of NOS activity , with •NO donors leading to an increase in the cGMP concentration was described in T . cruzi extracts [34] . Probing with an anti-neuronal NOS antibody the enzyme was localized in the inner surface of cell membranes , cytosol , flagellum and apical extremity [35] . However , NOS and guanylyl cyclase orthologs seem to be lacking in the parasite genome . An adenylyl cyclase containing a putative guanylyl cyclase domain was suggested to be responsible for cGMP production [36 , 37] and a soluble dual-specificity phosphodiesterase ( TcrPDEC ) , capable of cleaving both cAMP and cGMP with similar Km ( 20–31 . 6 μM and 78 . 2 μM , respectively ) would be responsible for the degradation of cGMP [38 , 39 , 40 , 41] . The downstream effector of cGMP is assumed to be the cGMP-dependent protein kinase ( PKG ) . While the involvement of cAMP is relatively well known in biological processes , such as T . cruzi metacyclogenesis [40 , 42 , 43] and downstream proteins that interact with PKA were characterized [44] the knowledge of cGMP signaling is far from being understood . Although the role of cGMP signaling pathway is currently unresolved in T . cruzi and other kinetoplastids , the presence of cGMP-specific kinase in T . brucei [45] and Leishmania [46] points out to the presence of the pathway in these parasites . In addition to a structural role in tissues , the extracellular matrix ( ECM ) , a complex tridimensional structure composed of more than 300 proteins and glycoproteins [47] , is relevant for many cellular signaling pathways , including •NO signaling in mammalians . T . cruzi trypomastigotes bind to components of the extracellular matrix ( ECM ) , such as laminin , fibronectin , collagen , heparan sulfate , thrombospondin or galectin-3 , as an early event of the infection process of mammalian cells [2 , 3 , 48 , 49] . Despite this , the signaling pathways triggered in T . cruzi after adhesion to ECM or its components are less characterized . Adhesion to laminin or fibronectin leads to changes in the phosphorylation level of T . cruzi proteins , including paraflagellar rod proteins and tubulins , probably involving the ERK1/2 pathway [50] . Herein , the role of nitric oxide in post-translational modification of proteins as a consequence of trypomastigotes adhesion to ECM is focused . A decrease of the •NO signaling pathway , including S-nitrosylation and tyrosine nitration of proteins was observed in T . cruzi trypomastigotes upon adhesion to host cell-derived ECM , an essential event for mammalian host cell invasion . To our knowledge this phenomenon is described for the first time .
S-methyl-methanethiosulfonate ( MMTS ) , imidazole , L-arginine and L-citrulline , 3-isobutyl-1-methylxanthine ( IBMX ) , GTP , sulfanilamide , N- ( 1-naphthylethylenediamine dichloride , HgCl2 , S-nitrosoglutathione , neocupreine and also the antibodies: anti-alfa tubulin , anti-nitrosocysteine , anti-mouse FITC conjugated , anti-rabbit HRP conjugated were purchased from Sigma-Aldrich ( St . Louis , USA ) . Phosphoric acid and sodium hydroxide were acquired from Synth ( São Paulo , Brazil ) . Sodium dihydrogenphosphate was purchased from Merck ( Darmstadt , Germany ) . The antibody anti-nitro-tyrosine was obtained from Millipore ( Billerica , USA ) . Sepharose beads , anti-rabbit Alexafluor 555 conjugated and DAPI were acquired from Invitrogen ( Carlsbad , USA ) . EZ-link HPDP Biotin was from Thermo Scientific ( Waltham , USA ) . The L-[3H]-Arginine was from PerkinElmer ( Waltham , USA ) . T . cruzi epimastigotes , Y strain , were cultivated at 28°C in Liver infusion Tryptose ( LIT ) medium supplemented with 10% fetal bovine serum ( FBS ) , up to 107 epimastigotes per mL . [51] . T . cruzi trypomastigotes , Y strain , were maintained by infection in LLC-MK2 cells in DMEM supplemented with 2% FBS at 37°C and 5% CO2 . Five days after infection , trypomastigotes released into the medium were collected , washed in DMEM 2% FBS ( 10 , 000 x G for 12 minutes ) and resuspended to adequate cell density accordingly to the experiment [52] . Trypomastigotes ( 1x109/mL ) were incubated with 10 mg/mL ECM ( Gibco ) for 2 h , unless otherwise stated , at 37°C and 5% CO2 . After incubation , parasites were washed twice in PBS containing 5 mM NaF , 2 mM Na3VO4 , 50 μM Na β-glicerophosphate , 1 mM PMSF and protease inhibitor cocktail ( Sigma-Aldrich ) , and kept at -80°C until used . After incubation with ECM , parasites were centrifuged ( 10 , 000 x G , 5 minutes ) and the supernatant separated for nitric oxide quantification , as described by the manufacturer ( Measure-iT High-Sensitivity Nitrite Assay Kit , Invitrogen ) Nitric oxide synthase activity was determined by following the conversion of L[3H]-arginine to L[3H]-citrulline in T . cruzi cell lysates ( 5x108 ) , accordingly to the manufacturer ( Cayman ) , and as previously described [34] . The presence of [3H]-citrulline was confirmed by thin layer chromatography and radioactivity measurement of the spots , as described [34] . cGMP was measured in T . cruzi extracts ( 5x108 cells ) accordingly to the commercial EIA test Biotrak instructions ( GE Healthcare ) . Due to the intrinsic presence of extracellular matrix proteins in some of the experimental assays specific activity could not be calculated and , thus , results are expressed in total femtomoles produced . Total S-NO was quantified by the Saville-Griess method , as described elsewhere [53] . Briefly , the parasite pellet ( 109 ) was lysed in 20 mM Tris-HCl buffer , pH 7 . 4 , containing 0 . 1% Triton X-100 , 5 mM NaF , 2 mM Na3VO4 , 50 μM Na β-Glycerophosphate , 1 mM PMSF and protease inhibitor cocktail ( Sigma-Aldrich ) and centrifuged ( 10 minutes at 14 , 000 x G , 4°C ) . Twenty μL of supernatant were then added to 180 μL reaction buffer ( 57 mM Sulfanilamide , 1 . 2 mM N- ( 1-Naphthyl ) ethylenediamine dihydrochloride in PBS , pH 7 . 4 ) and the reaction started by the addition of 100 μM HgCl2 . After 30 minutes at room temperature and in the dark , the absorbance at 496 nm was measured . Controls without HgCl2 were included to account for NO already present in the sample . The amount of total S-NO was estimated against a standard curve with S-nitrosoglutathione . The parasite pellet was lysed in H2O: methanol ( 1:1 , v:v ) , centrifuged for 10 minutes at 14 , 000 x G , 4°C and the supernatant collected was dried in SpeedVac . The resulting dried pellet was resuspended with 200 μL MilliQ water and centrifuged ( 10 minutes at 14 , 000 x G , 4°C ) to remove impurities . The supernatant was analyzed by a capillary electrophoresis system ( model PA 800 , Beckman Coulter Instruments , Fullerton , USA ) , equipped with DAD detector and a temperature control device . Data acquisition and treatment were carried out by the vendor software ( 32 Karat Software version 8 . 0 , Beckman Coulter ) . A fused silica capillary ( Polymicro Technologies , Phoenix , USA ) of 50 . 2 cm total length , 40 . 0 cm effective length and 50 μm i . d . was used . The capillary was preconditioned as follows: 1 mol . L-1 NaOH ( 5 minutes/20 psi ) , MilliQ water ( 5 min/20 psi ) and background electrolyte ( BGE ) ( 5 minutes/20 psi ) . The BGE was comprised of 50 mmol . L-1 of sodium dihydrogenophosphate at pH 2 . 5 , adjusted with phosphoric acid . Samples were injected hydrodynamically by applying a 0 . 5 psi pressure during 2 s . The conditions applied during separation were voltage of 25 kV and detection at 200 nm . To quantify arginine and citrulline in the samples , analytical curves were constructed with background electrolyte the linear range of 50–200 mg . mL-1 and 1–50 mg . mL-1 , respectively . Imidazole was used as internal standard ( 50 mg . mL-1 ) . After incubation with ECM and subsequent washes , parasites were fixed in 2% paraformaldehyde for 15 minutes at room temperature , pelleted by centrifugation ( 5 , 000 x G for 5 minutes ) , resuspended in PBS and added to a cover glass and left to dry for 16 hours at room temperature . After permeabilization of the parasites ( PBS containing 1% BSA and 0 . 1% Triton X-100 for one hour at 37°C ) , anti-nitrosocysteine ( rabbit , 1:200 ) , anti-nitro-tyrosine ( rabbit , 1:500 ) or anti-alpha-tubulin ( mouse , 1:500 ) were added and incubated for 2 h at 37°C . After exhaustive washes with PBS containing 1% BSA , the correspondent secondary antibodies were added ( anti-rabbit Alexa 555 conjugated , 1:500; anti-mouse FITC conjugated , 1:100 ) , followed by one hour incubation at 37°C . After successive washes in PBS-1% BSA , the slides were mounted in a solution containing 50% glycerol , 50% milliQ H2O and 10 μg DAPI . The images were taken on an ExiBlue camera ( Qimaging ) coupled to a Nikon Eclipse E 600 optical microscope and deconvoluted using the software Huygens Essential ( Scientific Volume Imaging ) . Proteins from the parasite were extracted with Laemmli Buffer [54] without reducing agents . SDS polyacrylamide gel electrophoresis was performed in a 6–16% gradient polyacrylamide gel and transferred to a 0 . 45 μm nitrocellulose membrane for 16 hours at 15 V . The membrane was blocked in 5% BSA and incubated with the primary antibody ( anti-nitrosocysteine or anti-nitro-tyrosine , produced in rabbit , 1:2000 ) , washed thrice in PBS-0 . 1% Tween 20 , incubated with secondary antibody ( anti-rabbit conjugated with HRP , 1:8000 dilution ) , washed five times in PBS-0 . 1% Tween 20 and developed by electrochemiluminescence . Conversion of protein SNO to biotinylated groups was performed as described [20] . Briefly , parasites were lysed by sonication in 25 mM HEPES , 50 mM NaCl , 0 . 1 mM EDTA , 1% NP-40 , 5 mM NaF , 2 mM Na3VO4 , 50 μM Na β-glicerophosphate , 1 mM PMSF and protease inhibitor cocktail ( Sigma ) , then clarified by centrifugation at 14 , 000 x G for 10 min at 4°C . The supernatant was blocked in 250 mM Hepes-NaOH buffer , pH 7 . 7 , containing 1 mM EDTA , 0 . 1 mM Neocuproine , 2 . 5% SDS and 20 mM MMTS for 20 minutes at 50°C , protected from light . Proteins were then precipitated by the addition of six volumes of ice cold acetone for one hour at -20°C . After successive washes in 70% acetone , the precipitate was resuspended in 250 mM Hepes-NaOH buffer , pH 7 . 7 , containing 1 mM EDTA , 0 . 1 mM neocuproine , 1% SDS , 4 mM Biotin-HPDP and 1 mM sodium ascorbate . The mix was incubated for one hour at 25°C at dark . Proteins were then precipitated in ice cold acetone for one hour at -20°C and washed extensively in 70% acetone . Biotin-containing proteins were prepared for sequencing as described [21] Proteins were digested by trypsin ( sequencing grade 1:100 , mass/mass ) for 16 hours at 37°C and the reaction stopped by the addition of 0 . 5 mM PMSF . Then , biotin-containing proteins were pulled-down using streptavidin beads . After 2 hours incubation at room temperature under gentle shaking , the beads were washed thrice in 20mM Tris-HCl buffer pH 7 . 7 , containing 1 mM EDTA and 0 . 4% Triton X-100 . Proteins were eluted with 5 mM ammonium bicarbonate containing 150 μL of 50 mM 2-mercaptoethanol for 5 minutes . Sequencing was determined ( Veritas Life Sciences , Brasil ) using a LTQ-Orbitrap coupled to nLC-MS/MS . Acquired data were automatically processed by CPAS ( Computational Proteomics Analysis System ) [55] and only peptides with high quality were considered ( expected score <0 . 2 ) . The TryTripDB was used for protein search combining Esmeraldo-like , non-Esmeraldo-like and unassigned . For Mucin II validation , proteins were biotinylated as described above , ressuspended in Tris-HCl 20 mM and incubated at 12°C for 16 hours with streptavidin beads . After three successive washes , the beads were ressuspended in Laemmli buffer without reducing agents , incubated at 100°C for 5 minutes , followed by SDS-PAGE using a 6–16% gradient gel . The proteins were transferred to a nitrocellulose membrane and the immunoblotting was performed using anti-rabbit Mucin II antibodies ( 1: 500 in PBS- 5% BSA ) . Parasites were lysed by sonication using 100 μL RIPA buffer , containing 5 mM NaF , 2 mM Na3VO4 , 50 μM Na β-Glicerophosphate , 1 mM PMSF and protease inhibitor cocktail ( Sigma ) . Samples were diluted in 1 . 4 mL of 20 mM Tris-HCl buffer , pH 7 . 4 , containing the same concentration of inhibitors . After centrifugation at 14 , 000 x G for 10 minutes at 4°C , the supernatant was added to protein A- Agarose with the desired antibody or normal serum ( control ) or to covalent-linked antibody-Agarose ( anti-nitro-tyrosine-resin ) . After 16 h at 12°C , the beads were washed with 20 mM Tris-HCl buffer , pH 7 . 4 containing 0 . 1% Triton X-100 and the bound material eluted with Laemmli buffer without reducing agent . In the particular case of immunoprecipitation using covalent-linked anti-nitro-tyrosine antibodies , the elution was performed by incubation in 5 M acetic acid for 5 minutes . The eluted proteins were identified by a commercial facility ( Veritas Life Sciences , Brasil ) , as described above .
It was previously shown that T . cruzi is able to produce •NO via a putative tcNOS [34] although no NOS ortholog can be found on the parasite genome . The synthesis of •NO was then first confirmed in T . cruzi extracts of the non infective ( epimastigote ) and infective ( trypomastigote ) stages by measuring the conversion of 3H-L-arginine to 3H-L-citrulline by thin layer chromatography , as described [34] and the product of the reaction confirmed by capillary electrophoresis . Importantly , approximately 260 fold-enrichment in NOS activity was obtained after partial purification of the enzyme from epimastigotes , as described [34] . Even though the identity of the enzyme remains elusive , the possible involvement of •NO signaling during T . cruzi binding to ECM was pursued . Trypomastigotes were incubated with purified ECM for to 2 h and the amount of extracellular •NO was quantified ( Fig . 1A ) . The extracellular •NO concentration dropped 43% under this condition , in the same order of magnitude observed when trypomastigote extracts were incubated with 10 mM L-NAME , a NOS inhibitor ( Fig . 1A ) . The simultaneous incubation with ECM and L-NAME leads to an even higher inhibition of •NO production ( approximately 72% ) . The data strongly suggest that interaction of the parasites with ECM hinders •NO responses . Accordingly , 37% decrease in NOS activity was observed in parasite extracts previously incubated with ECM , as compared to parasites incubated in the absence of ECM under the same experimental conditions ( Fig . 1B ) . Partial or total inhibition of •NO production by 10 mM L-NAME or by boiling the cellular extracts for 10 minutes at 100°C , respectively , confirmed that the •NO measured is a product of an enzymatic activity ( Fig . 1B ) . The enzymatic activity was reduced to 9% by the addition of 50 mM L-NAME . Furthermore , changes in L-arginine/L-citrulline ratio upon incubation with ECM strengthen the evidence of declining NOS activity upon parasite adhesion to ECM ( Fig . 1C , D ) . Whereas Intracellular concentration of L-citrulline decreased 83% upon adhesion of trypomastigotes to ECM , no significant change was observed in the L-arginine levels . This could be attributed to the contribution of other metabolic routes , but it is important to note that T . cruzi lacks a pathway to convert citrulline to arginine ( i . e . arginase , an enzyme of the urea cycle , is absent ) [56] , which strongly suggests that the decline in the L-citrulline levels might be , at least in part , a consequence of NOS activity inhibition . Since biological signaling by •NO is primarily mediated by activation of guanylyl cyclase , the production of cGMP in trypomastigotes incubated or not with ECM was quantified ( Fig . 2 ) . The levels of cGMP production fell from 3 . 5 to 0 . 6 fmoles after adhesion of the parasite to ECM ( Fig . 2 ) . Taken together , the findings strongly suggest that parasite adhesion to ECM leads to inhibition in •NO production , consequently deactivating a classical •NO signaling pathway . To check whether parasite adhesion to ECM would modulate protein S-nitrosylation ( SNO ) and tyrosine nitration , immunological assays were performed employing anti-S-nitroso-cysteine and anti-3-nitro-tyrosine antibodies . Immunoblotting experiments reveal a time-dependent decrease of SNO in specific bands , mainly in the 37 kDa region , but also noticeable in protein bands at the 47 , 20 , 18 , 15 and 13 kDa regions ( Fig . 3 ) . Differently from SNO , the number of nitrated-proteins detected by anti-3-nitro-tyrosine was considerably less and differences in tyrosine-nitrated proteins were not significant at the first hour of the experiment ( Fig . 4 ) . However , the levels of tyrosine-nitrated proteins were extensively reduced at 2 h incubation , affecting proteins in the range of 10 to 37 kDa ( Fig . 4 ) . Likewise , the general decrease in SNO and nitrated-proteins can be observed by immunofluorescence microscopy . Paraformaldehyde-fixed T . cruzi trypomastigotes previously incubated with ECM for 2 h showed a significant decrease in the immune reaction for both S-nitrosylation ( Fig . 5 ) and tyrosine nitration of proteins ( Fig . 6 ) . Indeed , image pixels/spots quantification showed a reduction higher than 50% and around 30% in the immunoreaction for S-nitrosylated and for tyrosine nitration proteins , respectively , when parasites were incubated with ECM . In order to confirm the adhesion effect on protein S-nitrosylation , total S-nitrosylated proteins in T . cruzi extracts were quantified by the Saville-Griess method [53] . A pronounced decrease of 87% was observed in the total SNO trypomastigote proteins when parasites were incubated with ECM , as compared to the control ( Fig . 7A ) . As additional controls , parasites were also incubated in the presence or absence of 100 μM CysNO ( •NO donor ) or 100 μM cPTio ( •NO scavenger ) . As expected , increasing •NO availability led to an improvement in SNO , as well removing •NO from the system resulted in SNO decrease ( Fig . 7A ) . Also , addition of CYsNO to parasites incubated with ECM did not restore the levels observed for trypomastigotes incubated with CYsNO only , showing the predominance of the ECM effect . On the other hand , cPTio added to ECM-treated parasites reduced even more the amount of S-nytrosylated proteins . The different treatments did not affect parasite viability ( Fig . 7B ) . Preliminary data using the biotin-switch technique and mass spectrometry further confirmed the presence of S-nitrosylated proteins in T . cruzi . Although a low number of SNO proteins were detected , an even lower amount was present in ECM-incubated trypomastigotes , as predicted by the experiments herein described . Examples of putative modified proteins that have been identified under different conditions were: ( 1 ) calpain-like cysteine peptidase , retrotransposon hot spot protein , surface protease GP63 , trans-sialidase and mucin TcMUCII , in both untreated and ECM-incubated parasites; ( 2 ) fucose kinase , glycerophosphate mutase and kinesin K39 only in ECM-incubated parasites; ( 3 ) DGF-1 , fatty acid elongase and helicase only in untreated parasites . Additionally , 27 hypothetical S-nitrosylated proteins were detected in ECM-treated ( 7 ) or untreated ( 20 ) trypomastigotes . However , it must be emphasized that the presence or absence of a modification in a particular protein due to the incubation of the parasite with ECM needs validation in each case , due to the possibility of a nonspecific binding during the enrichment of the SNO proteins . The existence of S-nitrosylation in T . cruzi proteins was validated for mucin TcMUCII . SNO proteins were converted to biotin-containing proteins , pulled-down by streptavidin beads as described in Materials and Methods , and the biotinylated-proteins were subjected to immunoblotting using anti-rabbit Mucin II antibodies ( Fig . 8A ) . As a negative control , proteins prepared by the biotin switch method in the absence of ascorbate gave no reactivity with anti-mucin II antibodies ( Fig . 8A ) . A significant increase in the level of S-nitrosylation was detected in ECM-treated trypomastigotes in relation to the untreated parasites ( Fig . 8B ) and taking into account the protein loaded in each case . In relation to the tyrosine-nitrated modifications , immunoprecipitated proteins with anti-nitro-tyrosine antibodies were identified by nLC-MS/MS . A number of putative nitrated targets identified decreased in ECM-incubated trypomastigotes ( Table 1 ) . Hypothetical and ribosomal proteins comprise the majority of the sequences obtained in untreated trypomastigotes . Also , the majority of the identified proteins were detected in ECM- treated or untreated parasites . To confirm this post-translational modification , histone 2A , histone 4B , enolase , alpha-tubulin , beta-tubulin and paraflagellar rod proteins ( PAR ) were selected . Modified histones and tubulins were detected herein ( Table 1 ) , enolase was included since nitrated-enolase was already described in the literature [57] and PAR was chosen as a negative control of the method . The mentioned proteins were immunoprecipitated with commercial specific antibodies ( except for anti-PAR monoclonal antibody prepared in the laboratory [50] ) followed by Western blot developed with anti-nitro-tyrosine antibodies . An increase in the nitrosylation levels of enolase and histones 2A and 4 were observed after the incubation with ECM ( Fig . 9A , B ) . No changes in the nitration levels were observed when a similar experiment was performed using anti-alpha and beta-tubulin antibodies , while no reactivity was detected with paraflagellar proteins immunoprecipitated with anti-PAR monoclonal antibody ( Fig . 9C , D ) . The results have shown that in spite of the general down regulation of protein S-nitrosylation and nitration upon incubation of the parasites with ECM , there is a specific response for each protein , including the stimulation of the nitration levels of enolase and histones 2A and 4B . Taken together , the results herein presented strongly suggest that T . cruzi responds to the interaction with ECM by the involvement of •NO regulated pathways .
Nitric oxide is a key signaling molecule affecting many biological activities . Human parasites such as T . cruzi are exposed to the anti-parasitic •NO produced by the host but also to its own •NO . Since the interaction of T . cruzi trypomastigotes with ECM is an essential step during the infective process [2 , 48 , 49] , the in vitro model in the absence of host cells has been used to study the role of •NO in the parasite response to the interaction . ECM is a very dynamic structure and its relevance in •NO signaling was shown , for example , by thrombospondin-1 inhibition [58] of the •NO pathway in vascular cells . Additionally , the relevance of ECM to T . cruzi signaling was previously shown by changes in parasite protein phosphorylation levels [50] . Endogenous nitric oxide is predominantly produced in T . cruzi by enzyme catalysis , probably by NOS , as described [34] since the addition of the inhibitor L-NAME drastically reduces •NO production ( Fig . 1A , B ) . In addition to •NO , the reaction produces L-citrulline from the substrate L-arginine . Of note , a strong reduction of citrulline , but not of arginine , was measurable in ECM-incubated trypomastigotes , suggestive of an inhibited NOS activity , although other possibilities such as its utilization in another activated metabolic route could not be ruled out . Arginine , on the other hand , is a substrate for protein synthesis and a precursor of •NO and other important metabolites as phosphoarginine , an energy buffer synthesized in T . cruzi by arginine kinase . Due to its relevance , it was previously suggested that arginine concentration under different external conditions may be buffered by TcAAP3 , a specific permease [59] . Adhesion of trypomastigotes to ECM resulted in a remarkable inhibition not only on NOS activity but also in •NO and cGMP concentrations ( Fig . 1 , 2 ) . However , a direct correlation between •NO levels and cGMP concentrations is difficult to make since the amount of •NO that may activate cGMP synthesis in trypomastigotes is unknown , due to the fact that no typical guanylyl cyclase is present in the T . cruzi genome . Moreover , the putative cGMP synthetic activity of an ubiquitous adenylyl cyclase remains non characterized [37] . Additionally , cGMP concentrations would depend on its degradation by a soluble dual-specificity phosphodiesterase ( TcrPDEC ) [38 , 39 , 40 , 41] . Presumably , the downstream signal transmission may be dependent on a protein kinase A activated by cGMP , as described for T . brucei [45] and Leishmania [46] . The decrease in total nitration and S-nitrosylation levels of proteins as described here for ECM-incubated trypomastigotes probably reflects the lower level of nitrosative stress . Changes in S-nitrosylated proteins were easily noticed by Western blot experiments after 30 minutes incubation of the parasites with ECM , with a marked decrease after 2 h period time ( Fig . 5 ) and confirmed by immunofluorescence and decrease in the total SNO measured ( Figs . 5 and 7 ) . S-nitrosylation of proteins is a key player in diverse biological functions of •NO and is associated with processes such as apoptosis [60] and regulation of numerous signaling pathways , for example , PKC [61] and MAPK [62] . Of interest , S-nitrosylation has been associated with activation and desensitization of the human soluble guanylyl cyclase that possesses 37 cysteine residues ( review in [58] ) . To our knowledge , the only study describing S-nitrosylation in T . cruzi proteins used •NO-donors to investigate a possible role of the host derived •NO in the inhibition of cruzipain , a cysteine protease important for the parasite infection [33] . However , the relevance of S-nitrosylation in T . cruzi signaling was not further explored . Here proteins putatively S-nitrosylated in normal conditions and after interaction of the T . cruzi with the extracellular matrix were analyzed for the first time , using mucin II to validate the modification ( Fig . 8 ) . A number of other interesting targets were identified including some proteins already described as S-nitrosylated with relevant modification in function , such as Dual Specificity Phosphatase ( DUSP ) [62] , Serine-Threonine Protein Kinase [61] and HSP 90 [63] . Interestingly , phosphorylation levels in DUSP and Serine/Threonine Protein Kinase were also modified in ECM-incubated parasites [50] , but the possibility that both modifications are somehow related , as happens in other cases , has not been addressed . The results described point out to a possible role of the S-nitrosylation in T . cruzi signaling pathways and will be further explored . Tyrosine nitration is not as well understood as S-nitrosylation , although relevant processes seem to be modulated by this covalent modification , such as PKC signaling [30] and protein degradation [28] . The present study was able to identify some of the proteins previously described as potential nitration targets ( Table 1 ) , such as enoyl-CoA hydratase [64] , glyceraldehyde-3-phosphate dehydrogenase [65] , heat shock protein 70 [66] and histone 2A [67] . Furthermore , validation of the data for histone 2A , histone 4 , enolase and tubulins was achieved ( Fig . 8 ) . In the literature , for example , nitration of histones was associated with the induction of autoimmunity in systemic lupus erythematosus and rheumatoid arthritis [67] . Remarkably , a large number of 40 and 60 S-ribosomal proteins were modified by nitration , but whether this modification affects protein expression in T . cruzi remains to be elucidated . Nitration of ribosomal proteins was also described in native and differentiated PC12 cells , but tryptophan was identified as the modified amino acid [11] . In summary , the present work was able to confirm previous claims on the existence of enzymatic NOS activity in T . cruzi and demonstrated that the •NO classic signaling pathway is greatly inhibited in the presence of ECM regarding the synthesis of •NO and cGMP . Furthermore , numerous possible S-nitrosylation and tyrosine nitration targets have been identified , with a total decrease in the level of modified proteins upon interaction of the parasite with ECM . However , in spite of general down regulation of protein S-nitrosylation and nitration , the increase in the S-nitrosylation level of mucin II or in the nitration of enolase or histones 2A and 4 , in contrast to the constant nitration levels of alpha and beta-tubulins under any condition , point out to the specificity of the modification for each particular target . The biological relevance of each of these target modifications remains to be explored and may give clues to the function of each target in parasite internalization into host cells . In this regard it must be stressed that •NO availability appears to be essential for parasite motility [68] . Thus , it is tempting to speculate that adhesion of T . cruzi trypomastigotes to ECM , an obligatory path to reach the host cell , triggering decrease in •NO levels , may also decrease parasite motility , somehow facilitating its binding to the host cell plasma membrane prior to invasion . | Interaction of Trypanosoma cruzi with the extracellular matrix ( ECM ) is an essential step in the invasion of mammalian cells . However , the nature of the signaling triggered in the parasite is poorly understood . Herein the key role of nitric oxide in T . cruzi signaling is described , using an ECM preparation , in the absence of host cells . Inhibition of NOS activity , with the expected decrease in •NO production , as well as decrease in cGMP concentration were observed by the incubation of T . cruzi trypomastigotes with ECM . Additionally , lower levels of protein S-nitrosylation and nitration were detected . These post-translational modifications have been analyzed by biotin-switch and protein immunoprecipitation approaches coupled to mass spectrometry . The presence of both modifications was confirmed for specific proteins , as mucin II ( S-nitrosylation ) , histones , enolase and tubulins . To our knowledge , decrease in the •NO signaling pathway upon T . cruzi trypomastigotes adhesion to ECM , affecting both the canonical pathway ( •NO-soluble guanylyl cyclase-cGMP ) and protein S-nitrosylation and nitration is described for the first time in this parasite . |
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The most prominent developmental regulators in oocytes are RNA-binding proteins ( RNAbps ) that assemble their targets into ribonucleoprotein granules where they are stored , transported and translationally regulated . RNA-binding protein of multiple splice forms 2 , or Rbpms2 , interacts with molecules that are essential to reproduction and egg patterning , including bucky ball , a key factor for Bb formation . Rbpms2 is localized to germ granules in primordial germ cells ( PGCs ) and to the Balbiani body ( Bb ) of oocytes , although the mechanisms regulating Rbpms2 localization to these structures are unknown . Using mutant Rbpms2 proteins , we show that Rbpms2 requires distinct protein domains to localize within germ cells and somatic cells . Accumulation and localization to subcellular compartments in the germline requires an intact RNA binding domain . Whereas in zebrafish somatic blastula cells , the conserved C-terminal domain promotes localization to the bipolar centrosomes/spindle . To investigate Rbpms2 functions , we mutated the duplicated and functionally redundant zebrafish rbpms2 genes . The gonads of rbpms2a;2b ( rbpms2 ) mutants initially contain early oocytes , however definitive oogenesis ultimately fails during sexual differentiation and , rbpms2 mutants develop as fertile males . Unlike other genes that promote oogenesis , failure to maintain oocytes in rbpms2 mutants was not suppressed by mutation of Tp53 . These findings reveal a novel and essential role for rbpms2 in oogenesis . Ultrastructural and immunohistochemical analyses revealed that rbpms2 is not required for the asymmetric accumulation of mitochondria and Buc protein in oocytes , however its absence resulted in formation of abnormal Buc aggregates and atypical electron-dense cytoplasmic inclusions . Our findings reveal novel and essential roles for rbpms2 in Buc organization and oocyte differentiation .
Two major objectives of oocyte development are to produce haploid gametes through meiosis , and to prepare the ovulated egg for successful fertilization and early embryonic development . Unlike most developmental programs that are regulated by transcription factors , the developmental programs of oocyte maturation , egg fertilization , and early embryonic development take place while the oocyte and early embryonic genomes are transcriptionally silent ( reviewed in [1 , 2] ) . During this period , RNA-binding proteins ( RNAbps ) are the predominant post-transcriptional regulators that coordinate localization and translation of the RNA molecules encoding the proteins that govern processes essential to oogenesis and early embryogenesis . The RNAbp RNA-binding protein with multiple splicing , RBPMS , family is generally represented by two paralogs in vertebrates , RBPMS and RBPMS2 [3] . The RNA recognition motif of RBPMS family members contains two ribonuclear protein domains , RNP1 and RNP2 , which contain the 6–8 residue structural elements which bind to RNA [4–6] . RBPMS proteins associate with poly-adenylated mRNAs in vitro [7] , and PAR-CLIP followed by RNA sequencing identified the 3’UTR of target RNAs as the primary region to which RBPMS proteins bind ( ~ 35% ) , followed by intronic regions ( ~ 20% ) and coding sequence ( ~10% ) [3] . Interestingly , the association with intronic regions suggests that RBPMS proteins can interact with pre-mRNA , and indeed , RBPMS/RBPMS2 can shuttle between nuclear and cytoplasmic fractions [3] . In germ cells , RNAbps associate with RNAs into supramolecular complexes called RNPs ( ribonucleoproteins ) , which further aggregate into granules that are a hallmark feature of primordial germ cells ( PGCs ) , and oocytes of various stages ( reviewed in [8 , 9] ) . In primary oocytes , a transient structure called the Balbiani body ( Bb ) is a single , large , cytoplasmic aggregate of RNPs , scaffolding proteins , and other patterning molecules which indicates the future vegetal pole of the oocyte [10] . The RNAbp RNA-binding protein with multiple splicing ( Rbpms ) , or hermes in Xenopus , localizes to the Bb of frog and zebrafish oocytes [11 , 12] , where it interacts with Bucky ball protein ( called Velo1 in Xenopus ) , the only vertebrate gene known to be required for Balbiani body formation [13–16] . Zebrafish Rbpms2b also binds to the bucky ball transcript , which contains numerous predicted Rbpms2 RNA recognition elements within its introns and 3’UTR [14] . In spite of Rbpms2 localization to the Bb of oocytes and the presence of these important biochemical interactions , the function of Rbpms2 in oocyte development or Bb formation has not been well elucidated . In this work , we characterized the localization of wild-type and mutant Rbpms2 proteins to cellular RNA granules , including germ granules of PGCs , the Bb of oocytes , and granules within somatic cells . Rbpms2 localization to germ granules and the Bb of oocytes is dependent on its RNA binding domain . In zebrafish somatic cells , this domain is sufficient for granule localization , while the C-term domain promotes association with the bipolar spindle at the expense of granules . In HEK 293 cells , RNA binding is dispensable for granule localization , indicating Rbpms2 uses different domains to achieve its subcellular localization in diverse cell types . To investigate Rbpms2 functions , we generated zebrafish mutants disrupting the duplicated rbpms2 genes , rbpms2a and rbpms2b using Crispr-Cas9 mutagenesis . These analyses revealed that Rbpms2 is essential for oocyte development as rbpms2 double mutants ( hereafter rbpms2 mutants ) develop exclusively as fertile males . Zebrafish rbpms2 mutants have normal germline development until the onset of sexual differentiation , at which time rbpms2 mutants can initiate , but not complete , oocyte differentiation . Testis differentiation is unimpaired in rbpms2 mutants . Ultrastructural analysis revealed early oocytes of rbpms2 mutants can asymmetrically accumulate mitochondria like their normal siblings , but have large cytoplasmic inclusions that were not present in wild-type . Based on the presence but unusual localization of Bucky ball protein in early oocytes of rbpms2 mutants , we conclude that Rbpms2 is dispensable for Buc translation; however , Rbpms2 seems to be required for structural integrity of Buc aggregates . In addition , Rbpms2 has a Buc-independent function in ovary maintenance . This data reveals a novel and essential role for rbpms2 in oogenesis and Bucky ball organization within early oocytes .
In Xenopus , hermes is expressed in maturing oocytes , and hermes RNA and protein are both localized to the Balbiani body of primary oocytes [12] . Similarly , in primary zebrafish oocytes , the Xenopus anti-Hermes antibody detects a Bb-localized Hermes homolog [11 , 15] . Zebrafish have three Hermes homologs encoded in their genome: Rbpms ( hereafter Rbpms1 for clarity ) , and the similar proteins Rbpms2a and Rbpms2b , the products of rbpms2 gene duplication in zebrafish [17] . Additionally , Xenopus Hermes interacts with the Bb-localized Bucky ball homolog Velo1 [16] , as do all three protein products of the zebrafish Rbpms family ( Fig . S1M in S1 Supporting Information ) [14] . Phylogenetic analyses of RBPMS proteins have previously been described [3 , 18 , 19]; however , none directly compared zebrafish RBPMS proteins with Xenopus Hermes . Thus , we compared protein similarity of Hermes and the zebrafish Rbpms family , which revealed that zebrafish Rbpms2a/b proteins clustered more tightly together with Hermes , and thus likely represent the closest Xenopus Hermes homolog ( Fig . S1A in S1 Supporting Information ) [20 , 21] . Maternal RNA transcripts for rbpms1 , rbpms2a , and rbpms2b were all abundant in early embryonic stages prior to the maternal-zygotic transition , after which their levels were severely reduced ( Fig . S1B–E” in S1 Supporting Information ) . Examination of rbpms2 transcripts during zebrafish embryogenesis by whole-mount in situ hybridization ( ISH ) revealed that rbpms2a and rbpms2b were expressed in the embryonic heart , retina and pronephros , an evolutionarily conserved expression pattern similar to that previously reported for hermes homologs in other vertebrates , including Xenopus , chicken , and mouse ( Fig . S1H–K in S1 Supporting Information ) [7] . Low levels of rbpms1 expression were ubiquitous throughout most tissues of 24-48hpf embryos ( Fig . S1F–G in S1 Supporting Information ) . Next we examined rbpms1 and rbpms2a/b expression in zebrafish oocytes . We found that while all three transcripts were expressed in primary oocytes ( Fig 1A–1D and Fig . S1L in S1 Supporting Information ) , only rbpms2a transcripts were enriched within the Bb ( Fig 1A , arrow ) . In contrast , rbpms2b transcripts appeared to be enriched at the oocyte cortex or within the granulosa cell layer ( Fig 1C , arrowheads ) . RT-PCR on FACS-sorted GFP-positive granulosa and theca cells from Tg[cyp19a1a:GFP] transgenic zebrafish ovaries , revealed no rbpms2 expression in these cell populations [22] ( Fig 1E ) . However , because Cyp19a1a:GFP is only expressed in granulosa cells of stage II follicles or later [22] , we cannot exclude the possibility that rbpms2 RNAs are transiently expressed in granulosa cells of stage I follicles , then rapidly down-regulated prior to stage II . Finally , we tested whether both Rbpms2 proteins were expressed in primary oocytes ( stage I ) and localized to the Bb . We used two commercial antibodies raised against human Rbpms2 which we predicted would recognize both Rbpms2a and Rbpms2b based on protein similarity ( Fig . S2A in S1 Supporting Information ) . Stained ovaries from zebrafish mutants for rbpms2a ( Fig 1F and Fig . S3A , E in S1 Supporting Information ) or rbpms2b ( Fig 1I , Fig . S3B , F in S1 Supporting Information ) both exhibited staining of Rbpms2 in the Bb , likely representing the ohnolog protein , or alternatively a truncated mutant protein ( mutants described in next section ) . The absence of signal in rbpms2 double mutants indicates antibody specificity for Rbpms2s ( Fig . S3C in S1 Supporting Information ) . Rbpms2 protein in single mutant ovaries is localized to the Bb along with Bucky ball ( Fig 1F–1K ) . Thus , consistent with the expression of Xenopus Hermes , we conclude that both Rbpms2a and Rbpms2b proteins are localized to the Bb of zebrafish oocytes . We anticipated that rbpms2a and rbpms2b might function redundantly based on protein similarity ( 92% identity ) and their comparable gene expression profiles ( Fig 1 and Fig . S1 in S1 Supporting Information ) . Therefore , to study rbpms2 functions we generated zebrafish mutant lines disrupting both rbpms2a and rbpms2b using CRISPR/Cas9 mutagenesis [23] . After numerous unsuccessful attempts to mutagenize upstream regions of the rbpms2a and rbpms2b genes , we ultimately succeeded in mutating analogous regions of exon 5 in rbpms2a/2b using CRISPR sites predicted by the web-based ZiFiT targeting program ( https://crispr-cas9 . com/96/zifit-targeter-crispr-cas9/ ) ( Fig 2A and Fig . S2B , C in S1 Supporting Information ) . We recovered germline-transmitted mutant alleles for each rbpms2 gene . Sequencing of genomic regions as well as mutant cDNAs revealed that the isolated mutations were as follows: the rbpms2aae27 allele had an in-frame deletion of 9 amino acids , the rbpms2aae30 allele had a 15bp deletion and 17bp insertion resulting in a truncated protein ( Fig . S2B in S1 Supporting Information ) , and the rbpms2bae32 allele had a 20bp insertion resulting in a truncated protein ( Fig . S2C in S1 Supporting Information ) . Additionally , we characterized a splice-site mutation from the Sanger Institute’s Zebrafish Mutation Project [24] , allele rbpms2bsa9329 , which was found to contain a T>A point mutation in the 5’ splice site between exon 3 and 4 that causes in-frame skipping of exon 3 and is predicted to partially disrupt RNP1 ( Fig . S2C in S1 Supporting Information ) . We found that zebrafish embryos homozygous for mutations in rbpms2a or rbpms2b had no observable morphological differences from their wild-type siblings . Adult fish mutant for a single rbpms2 gene had no apparent phenotypes , and differentiated into fertile fish of either sex ( Fig 2B ) . Moreover , no overt maternal-effect , paternal-effect or maternal-zygotic phenotypes were observed in the progeny of single mutant adults for rbpms2 genes ( n>20 adult mutants per allele examined ) . To test the possibility that the rbpms2 genes were functionally redundant , we made rbpms2a;rbpms2b double mutants . At 3 days post fertilization ( d3 ) no phenotypes were observed when a single functional copy of rbpms2 was present; however , double mutant embryos for the truncation alleles ( rbpms2aae30/ae30;rbpms2bae32/ae32 ) or for the rbpms2a truncation allele and the rbpms2b exon-skipping allele ( rbpms2aae30/ae30;rbpms2bsa9329/sa9329 ) displayed cardiac edema phenotypes ( Fig 2C and 2D ) . This cardiac phenotype is consistent with the conserved expression of rbpms2 in the embryonic heart ( Fig . S1H–K in S1 Supporting Information ) [25 , 26] , as well as the previously published cardiac phenotypes caused by hermes/rbpms2 overexpression in Xenopus [7 , 25 , 26] . Consistent with previous findings that demonstrate Rbpms proteins function as dimers [6 , 7 , 27] , we found that zebrafish Rbpms2a and Rbpms2b can both homodimerize , and Rbpm2a can heterodimerize with Rbpms2b ( Fig . S6B in S1 Supporting Information ) . Thus , there is likely no requirement for heterodimers and no difference in Rbpms2a or Rbpms2b homodimer function since complete loss of a single rbpms2 gene is still sufficient for normal Rbpms2 function . Approximately half of the embryos with cardiac edema at d3 recovered by d5 and were raised to adulthood ( 47±18% , based on quantification of 71 total edematous embryos at d3 from 9 parental in-crosses ) ( Fig 2E–2G ) . In contrast , no cardiac edema phenotypes were observed in rbpms2aae27/ae27;rbpms2bsa9329/sa9329 double mutants or rbpms2a trans-het;rbpms2b double mutants ( rbpms2aae27/ae30;rbpms2bsa9329/sa9329 ) ( Fig 2B ) . This analysis suggests that the rbpms2aae27 allele retains sufficient activity to fulfill Rbpms2 functions; whereas , rbpms2aae30 , rbpms2bae32 , and rbpms2bsa9329 are loss-of-function mutations . Therefore , we focused subsequent analyses on the double mutants rbpms2aae30/ae30;rbpms2bae32/ae32 or rbpms2aae30/ae30;rbpms2bsa9329/sa9329 , hereafter referred to as rbpms2 DM ( double mutants ) or simply rbpms2 mutants . To examine the stability of the mRNA for the different rbpms2 alleles , we performed RT-PCR on RNA extracted from maternal-zygotic ( MZ ) mutant embryos at the 4-cell stage . This embryonic stage precedes zygotic genome activation; thus , the contributions of maternal transcripts from rbpms2a or rbpms2b single mutant mothers can be examined . We found that the rbpms2aae30 and rbpms2bae32 transcripts were detectable , but appeared to be less abundant than wild-type , indicative of potential nonsense mediated decay ( NMD ) ( Fig 3A , pink asterisks ) . This is consistent with descriptions of NMD in zebrafish , which does not typically lead to complete degradation of transcripts containing early stop codons [28] . The finding of NMD further supports the notion that rbpms2aae30 and rbpms2bae32 are likely null alleles . Mammalian RBPMS has previously been studied in HEK293 cells , where it has been demonstrated to localize to stress granules along with poly-adenylated RNAs and the stress granule marker G3BP1 [3] . Therefore , we used this established localization assay to examine the stability and activity of the Rbpms2 proteins encoded by the zebrafish mutant alleles . First , we examined the localization of the wild-type zebrafish Rbpms2 proteins in HEK293 cells using GFP protein fusions . We found that GFP-Rbpms2a and GFP-Rbpms2b were each predominately localized to small punctate structures resembling the previously described stress granules ( Fig 3B and 3D ) . Stable GFP fusion proteins for GFP-Rbpms2aae30 and GFP-Rbpms2bae32 were detected in HEK293 cells , indicating that the mutant RNAs and proteins were sufficiently stable to be visualized in this context; however , in contrast to the punctate localization of the WT proteins , the GFP-Rbpms2aae30 and GFP-Rbpms2bae32 proteins were diffusely cytoplasmic ( Fig 3C and 3E ) . GFP-Rbpms2bsa9329 exhibited an intermediate localization phenotype , displaying both diffuse GFP fusion protein as well as localization to somatic cell granules ( Fig 3F ) , suggesting this allele may behave as a hypomorphic reduction-of-function allele . Taken together these data indicate that the C-terminus is required for localization to granules in this somatic cell type . Next , we examined the localization of Rbpms2 fusion proteins in somatic cells of zebrafish embryos . Wild-type GFP-Rbpms2 was detected near the nucleus ( ~20% ) and associated with the centrosomes or spindle ( 9% ) , and as in HEK293 cells , GFP-Rbpms2 was localized to granules in 65% of somatic cells of the zebrafish embryo ( Figs 3G–3H and 4 1; n = 489 cells , 19 embryos ) . To determine the identity of these Rbpms2 positive granules , we examined endogenous markers , including the classical stress granule marker Tial-1 and the p-body marker Dcp2 , using antibodies previously validated in zebrafish [29] . We detected no overlap with Tial-1 ( Fig 3G; n = 172 cells , 8 embryos ) and partial overlap with Dcp2 ( Fig 3H; n = 317 cells , 11 embryos ) , indicating that the granules to which GFP-Rbpms2 localizes in zebrafish somatic cells are not stress granules , but that a small subset are GFP-Rbpms2 and Dcp2 positive granules . Therefore , we designate these heterogeneous GFP-Rbpms2 granules as granules of somatic cells . In most GFP-Rbpms2bae32 expressing cells the protein was detected in the nucleus , throughout the cells , and in granules ( 73% ) ( Figs 3J and 4; n = 232 cells , 8 embryos ) . In GFP-Rbpms2aae30 fewer cells with GFP granules were detected ( 43% ) . GFP-Rbpms2aae30 was also detected throughout the cytoplasm ( 33% ) or in the nucleus , or in a bipolar pattern likely representing the centrosomes ( 14% and 10% respectively ) ( Figs 3K and 4; n = 177 , 8 embryos ) . Strikingly , the sa9329 protein , which disrupts the RNA binding domain was not detected in granules , but strongly localized to the centrosomes/spindle ( Figs 3I and 4; n = 311 cells , 9 embryos ) . These data indicate that the RNA binding and C-terminal domains differentially contribute to Rbpms2 subcellular localization in somatic cells . To test if Rbpms2 proteins can localize to granules in the zebrafish germline , we used a commonly employed assay in which in vitro transcribed RNA encoding GFP-rbpms2a/2b was injected into fertilized zebrafish eggs . For many germ plasm RNAs and interacting proteins , the translated product of the exogenous RNA will localize to germ granules of primordial germ cells ( PGCs ) in 24-30hpf embryos ( reviewed in [1 , 30] ) . PGCs were marked by cytoplasmic expression of RFP-nanos3’UTR . Co-injection of RFP-nanos3’UTR and either wild-type protein , GFP-Rbpms2a or GFP-Rbpms2b , demonstrated that Rbpms2 fusions localize to germ granules of PGCs . ( Fig 5A , 5B and 5F ) . Next , we checked the germ granule localization capacity of the rbpms2 mutant alleles: GFP-Rbpms2aae30 , GFP-Rbpms2bae32 and GFP-Rbpms2bsa9329 . Like wild-type Rbpms2 proteins , all mutant proteins became restricted to PGCs ( Fig 5C–5E ) ; however , the GFP-Rbpms2aae30 mutant protein and the GFP-Rbpms2bsa9329 were cytoplasmic rather than enriched in granules ( Fig 5C , 5D and 5F ) . Similarly , the GFP-Rbpms2bae32 fusion protein was diffuse throughout the cytoplasm in 35% of expressing cells; whereas , in the majority of the cells ( 65% ) GFP-Rbpms2bae32 cells was both diffusely cytoplasmic and enriched in germ granules ( Fig 5D and 5F ) . The PGC-enrichment of the mutant GFP-Rbpms2 alleles suggests that the injected RNAs , or more likely the protein products , are capable of interacting with germ cell factors that stabilize them in germ cells . Because only Rbpms2bae32 retains partial ability to localize to germ granules this suggests that an intact RRM and sequences adjacent to the RRM are required but not sufficient for germ granule localization in PGCs . To further investigate the mechanisms that mediate localization to germ and somatic cell granules , we asked if Rbpms2 localization was dependent on its interaction with RNA . To test this , we constructed a mutant version of Rbpms2b , called GFP-rbpms2bΔRNP1 , that lacks seven amino acid residues of the RNP1 ( ribonuclear protein ) domain that are essential for making direct contact with the RNA molecule ( Fig 6A ) [4 , 5] . We injected in vitro transcribed GFP-rbpms2b and GFP-rbpms2bΔRNP1 to test if the encoded proteins were able to localize to germ granules of PGCs . Wild-type GFP-Rbpms2b was present in germ granules where it localizes with the endogenous germ granule component Vasa; however , GFP-Rbpms2bΔRNP1 did not localize and did not appear to be enriched in PGCs ( Fig 6C and 6D” ) . To determine if failure to localize to germ granules was due to instability of the mutant protein , we examined the injected embryos earlier , at 3-4hpf , and found that both WT and mutant proteins had robust observable GFP signals ( Fig 6G and 6H ) . Furthermore , when these constructs were transfected into HEK293 cells , both wild-type and Rbpms2bΔRNP1 could localize to somatic cell granules ( Fig 6I and 6J ) . Therefore , we determined that GFP-Rbpms2bΔRNP1 produces a stable protein; however , the protein lacking RNP1 cannot localize to the germ granules . Germ granules in PGCs are similar to the Bb of stage I oocytes , in that both are germline aggregates of RNP particles , and many germ plasm components are localized in both cell-specific structures ( reviewed in [1] ) . To determine fusion protein localization in oocytes , we constructed a transgenic zebrafish line expressing wild-type Rbpms2b under the ovary-specific buc promoter [14] , Tg[buc:RFP-rbpms2b] , and a transgenic line that harbors the same RNA-binding deficient mutation described above , Tg[buc:RFP-rbpms2bΔRNP1] ( Fig 6B ) . When we observed dissected oocytes from adult transgenic females , we found that the wild-type transgenic protein localized to the Bb similar to endogenous Rbpms2 . However , as in PGCs , RFP-Rbpms2bΔRNP1 was not localized , and RFP protein was not detectable in oocytes , although transcripts were detectable for both transgenes by in situ hybridization ( Fig . S4 in S1 Supporting Information and Fig 6E and 6F ) . The ability of Rbpms2 fusion protein to localize to germ granules or the Bb is likely independent of interaction with Bucky ball , since myc-Rbpms2bΔRNP1 , which does not accumulate in germ granules or the Bb can still immunoprecipitate with GFP-Buc in co-IP experiments ( Fig 6K ) . Thus , Rbpms2b requires the RNA-binding RNP1 domain to accumulate in and localize to RNP aggregates of the germline . It remains to be determined if RNP1 contributes to stabilization , efficient translation the protein , or both; however , this domain is dispensable for stability and granule localization in somatic cells such as HEK293 . This further suggests that the mechanisms used to localize Rbpms to granules are distinct between germline and soma . We raised rbpms2 mutants that had recovered from the edema phenotype in order to assess possible rbpms2 roles in reproductive development . The adult progeny ( >2 . 5 months ) of in-crosses between double heterozygous rbpms2aae30/+;rbpms2bsa9329/+ fish were genotyped , and rbpms2a or rbpms2b single mutants and double heterozygotes or heterozygote-mutants displayed typical 50/50 male-female sex ratios ( Fig 7A–7D and 7G ) . However , no rbpms2 DM adults were identified among 129 fish , although approximately 8 would be expected according to Mendelian genetics ( at a prevalence of 1:16 ) . To overcome a potential survival disadvantage of rbpms2 mutants , we sorted mutants based on their transient edema phenotype at d3 to determine if rbpms2 mutant adults could be recovered when reared separately from their wild-type siblings . Using this strategy , we recovered 33 rbpms2 mutants to adulthood , all of which were fertile males ( Fig 7E–7G ) . Fertility of rbpms2 mutant males was indistinguishable from wild-type males based on mating assays ( fertilization of eggs ) . Additionally , histological evaluation of mutant testes , through H&E staining and expression of a transgene [ziwi:GFP] that marks germ cells of both sexes [31] , revealed no differences from normal male siblings ( Fig 7C–7F ) . Undifferentiated spermatogonia were noted on the basement membrane of the testicular tubules , as well as differentiated spermatozoa within tubule lumens , indicating that progression of spermatogenesis is normal in rbpms2 mutants . Zebrafish will differentiate as males if germ cell numbers are diminished or oogenesis fails . Early in embryonic development , germline differentiation to the male fate can result when there are few or no PGCs [32–35] . To test the possibility that the all-male phenotype of rbpms2 mutants was caused by insufficient PGC numbers , we stained d3 rbpms2 mutants and their siblings for the germ cell marker Vasa [36] . PGCs were examined by confocal imaging of the lateral side followed by manual counting through the Z series . We found no significant difference in PGC numbers between non-mutant siblings and rbpms2 mutant embryos , with both groups having approximately 20 PGCs per side ( Fig 8A–8C ) . Next , we examined d21 bipotential gonads in the [ziwi:GFP] transgenic line and determined that development of primitive oocytes/gonocytes and meiotic initiation were comparable between rbpms2 mutants and their non-mutant siblings ( Fig 8D and 8E ) . Next , we examined rbpms2 mutants and siblings at d35 when sexual differentiation of the gonads has been initiated . In non-mutant siblings , we found readily distinguishable ovary tissue indicative of female differentiation with numerous stage I and II oocytes ( Fig 8F ) , or testis differentiation with a few gonocytes but mostly small spermatogonia-like cells ( Fig 8G ) . In contrast , rbpms2 mutant gonads were either wholly testis-like ( resembling gonads of their non-mutant male siblings at d35 ) ( Fig 8I ) , or were of mixed character with some spermatogonia-like cells , and also significant numbers of oocyte-like germ cells based on cell diameter and nuclear morphology ( Fig 8H ) . We quantified the size of [ziwi:GFP]-labeled oocytes at d35 and found that non-mutant oocytes reached average diameters of up to 57±5 μm ( n = 26 oocytes , 3 fish ) ; whereas , the largest rbpms2 mutant oocytes reached average diameters of up to 31±4 μm ( n = 23 oocytes , 4 fish ) . These diameters are consistent with rbpms2 mutants failing in oogenesis sometime during the growth phase of stage Ib oocytes ( which typically have diameters of 20–140 μm ) [37] . Consistent with this notion , ultrastructural examination of d21 gonads by transmission electron microscopy revealed no discernable differences in nuclear or cytoplasmic morphology of gonocytes ( n = 3 rbpms2 mutants , n = 4 wild-type ) ( Fig 9A and 9B ) . Taken together , these results suggest that intersex rbpms2 mutants initiate oogenesis , but are in the process of transitioning to the male phenotype , since ultimately all recovered rbpms2 mutants are fertile males . In zebrafish the transition to male fate has been reported to involve apoptosis of germ cells [38] . Consistent with this notion , disrupting the key regulator of apoptosis , Tp53 , can suppress loss of the female germline and the eventual male-only phenotypes of zebrafish mutants disrupting zar1 , brca2 or fancl [39–41] . These studies indicate that apoptosis of ovary-like cells of the bipotential gonad facilitates the transition to testis . Therefore , we reasoned that suppressing tp53-mediated apoptosis by genetically eliminating tp53 may support sustained development of oocytes in rbpms2 mutants . To investigate whether loss of tp53 could support oocyte development , we generated triple mutants with rbpms2 and tp53M214K mutations ( Berghmans et al . , 2005 ) and examined the gonads of rbpms2aae30/ae30;rbpms2 ae32/ae32;tp53M214K/M214K fish at d35 , when gonad differentiation into testis or ovary has occurred . At this stage , we detected no differences between the gonads of rbpms2 mutants and rbpms2 mutants lacking Tp53 ( n = 5 ) ( Fig . S5 in S1 Supporting Information ) , and more advanced stages of oogenesis were not recovered . Moreover , rbpms2;tp53 triple mutants developed exclusively as fertile adult males , like rbpms2 double mutants ( n = 3 ) . Based on this analysis , we conclude that loss of tp53 is not sufficient to support further ovary differentiation in rbpms2 mutants . Moreover , differentiation of rbpms2 mutants as males occurs by a mechanism that is independent of the p53-mediated apoptotic pathway . To examine the subcellular compartment of rbpms2 mutant oocytes more closely , we compared the ultrastructure of d35 gonads from wild-type females ( n = 2 ) and rbpms2 mutants containing oocyte-like germ cells ( n = 2 ) . We found both wild-type and rbpms2 mutant oocytes had accumulated asymmetric mitochondria on one side of the nucleus , a hallmark of early oocyte polarization and Bb formation ( Fig 9C and 9D , mitochondrial accumulation outlined in white ) [15 , 42] . In general , oocytes in the wild-type gonads tended to be larger; therefore , we focused our analysis on oocytes with similar diameters in both genotypes ( ranging from 20–40 μm ) ( Fig 9E ) . We also examined these oocytes for the presence of characteristic nuage: discrete areas of cytoplasm that appear as electron-dense , fibrous accumulations , closely associated with the nuclear envelope and mitochondria [43] . We found similar numbers of cytoplasmic nuage accumulations in wild-type and rbpms2 mutant oocytes ( Fig 9C , 9D and 9F , yellow arrows ) . However , rbpms2 mutant oocytes also contained atypical cytoplasmic inclusions with electron density distinct from nuage that excluded mitochondria ( Fig 9D , 9D’ and 9G ) ; atypical cytoplasmic inclusions >1μm were rarely observed in wild-type oocytes ( p = 0 . 01 , unpaired t-test ) . In rbpms2 mutants , cytoplasmic inclusions were often significantly large , with three of the eight analyzed oocytes containing structures measuring over 3μm across . Although these large electron dense structures are a feature of rbpms2 mutant oocytes , the molecular contents of these cytoplasmic bodies remain to be determined . We have previously shown that Rbpms2 C-terminus interacts with Bucky ball protein and buc transcripts ( Fig . S6A in S1 Supporting Information ) [14]; therefore , we assessed whether loss of Rbpms2 affects Bucky ball abundance or localization by examining Bucky ball protein in Rbpms2 mutants . As previously reported , no specific Buc staining was apparent in d35 testes , including in larger gonocytes of males ( Fig 10A ) , and Buc protein was expressed in pre-Bb stage oocytes at d35 ( Fig 10B ) [14] . Accordingly , no Buc staining was observed in d35 rbpms2 mutant gonads that had already undergone testis differentiation ( Fig 10C ) . Consistent with the notion that a subset of rbpms2 mutants initiated oogenesis as judged by this marker , we detected asymmetric Buc staining in germ cells of rbpms2 mutants that retained oocyte-like cells . As previously reported , the morphology of the Buc stained structures in non-mutant primary oocytes was compact and perinuclear ( Fig 10B and 10B’ white arrows ) . Interestingly , rbpms2 mutant oocytes had more dispersed Buc distribution that formed a ring-like structure ( Fig 10D and 10D’ white arrow heads ) . Although perinuclear localization of Vasa was detected , Vasa was also more dispersed in rbpms2 mutant oocytes ( Fig . S3 in S1 Supporting Information ) . Whereas another Balbiani body localized protein , Macf1/Mgn [44] was not detected in these early stage oocytes ( Fig . S3 in S1 Supporting Information ) . Therefore , Macf1 protein may only accumulate in more mature Balbiani bodies , consistent with its role in Balbiani body dispersal [44 , 45] . Thus , we conclude that Rbpms2 is not required for initial Buc or Vasa translation , but may play a role in localizing Buc protein or regulating Bb morphology .
Stress granules are ribonucleoprotein particles formed in the cytoplasm of somatic cells during stress responses that stall the initiation of protein translation; for example , stress granules form in response to environmental stress such as heat shock , translation-initiation blocking drugs , or in response to overexpression of RNA-binding proteins that inhibit translation ( reviewed in ( Buchan and Parker , 2009; Panas et al . , 2016 ) ) . Stress granules store mRNAs that are in the process of translation initiation , and typically contain numerous components of translation initiation complexes including poly ( A ) positive mRNAs , the 40s ribosomal subunit , numerous eukaryotic initiation factor proteins ( eIF2 , 3 and 4 ) , as well as RNA helicases , and other RNAbps ( reviewed in ( Buchan and Parker , 2009; Kedersha and Anderson , 2009 ) . Previous work on human RBPMS and RBPMS2 has found that these proteins partially overlap with poly ( A ) + mRNA and the stress granule marker G3BP1 ( GTPAse SH3-Domain Binding Protein/ Stress Granule Assembly factor 1 ) , consistent with a possible role in translational repression in the stress granule ( Farazi et al . , 2014 ) . We found that zebrafish fusion proteins containing wild-type GFP-Rbpm2a or GFP-Rbpms2b localize to punctate structures in the cytoplasm of HEK 293 and zebrafish somatic cells ( Fig 10A and 10B ) . In zebrafish somatic cells , these granules partially overlapped with the p-body marker Dcp2 but not the stress granule marker Tial-1 . Granule localization was disrupted for GFP-Rbpms2aae30 and GFP-Rbpms2bae32 , which are expressed in the cytoplasm of HEK 293 and in the cytoplasm and nucleus of zebrafish blastomeres . In HEK 293 cells , localization was only partially impaired in GFP-Rbpms2bsa9329 , which was not in granules but instead showed a striking localization to the centrosome in zebrafish somatic cells ( Fig 11A and 11B ) . Interestingly , the C-terminal part of Rbpms2 that is missing from GFP-Rbpms2aae30 and GFP-Rbpms2bae32 ( which have intact residues 1–107 and 1–116 , respectively ) has previously been shown to be dispensable for RNA-binding in electrophoretic mobility shift assays ( EMSAs ) ( Farazi et al . , 2014 ) , and for dimerization ( Sagnol et al . , 2014 ) . Furthermore , GFP-Rbpms2bsa9329 , which partially lacks RNA-binding residues coded for by the ( skipped ) third exon , and GFP-Rbpms2bΔRNP1 , which lacks all the RNA-binding residues of RNP1 , can still localize to granules in HEK 293 cells and to the centrosome of zebrafish somatic cells , where the wild-type protein can also be detected ( Fig 10A and 10B ) . Thus , the abnormal localization of the truncated mutant proteins GFP-Rbpms2aae30 and GFP-Rbpms2bae32 , is likely not due to inability to bind RNA or dimerize . This suggests that perhaps another protein interaction that is mediated through the C-terminus of RBPMS2 facilitates recruitment of these proteins to somatic cell granules . The germ granules of PGCs share many components and general features of somatic cell stress granules and p-bodies , but also contain RNAs and proteins that are unique to the germline ( reviewed in ( Voronina et al . , 2011 ) ) . Injection of exogenous GFP-tagged rbpms2a/b RNAs resulted in the localization of the translated proteins to the PGC germ granules . Localization of all three mutant proteins , GFP-Rbpms2aae30 , GFP-Rbpms2bae32 , and GFP-Rbpms2bsa9329 , was absent or severely reduced in germ granules although diffuse expression in the germ cell cytoplasm was robust ( Fig 11A and 11B ) . However , unlike in somatic cell granules , the lack of the RNP1 motif in GFP-Rbpms2bΔRNP1 prevented this mutant fusion protein from becoming enriched in PGCs ( Fig 10A and 10B ) . Based on this result , we speculate that interaction with RNA is likely required for enrichment or stabilization of Rbpms2 proteins to the PGC cytoplasmic compartment; however , localization within the granules depends on a separate mechanism . A similar phenomenon may operate for the localization and stability of Rbpms2 in the oocyte Balbiani body because the fusion protein coded for by the transgenic line [buc:RFP-rbpms2b] localizes to the Bb like the endogenous protein; however , the product of [buc:RFP-rbpms2bΔRNP1] transgenics does not localize to the Bb or yield detectable stable protein , despite comparable transcript expression from both transgenes ( Fig 11A and 11B ) . Therefore , Rbpms2 interaction with germ plasm RNAs may be required for efficient translation of rbpms2 RNA or stability of the protein in germ cells including primordial germ cells and the primary oocyte . We predicted that the zebrafish rbpms2a and rbpms2b , might function redundantly in the germline based on their overlapping expression domains , and their extremely similar protein sequences . Furthermore , Rbpms2a and Rbpms2b can both interact with Bucky ball protein in co-transfection and co-IP experiments . Thus , we strategically targeted both rbpms2 genes for Crispr-Cas9 mutagenesis . Indeed , single mutants for rbpms2a or rbpms2b had no discernable embryonic or adult phenotypes , supporting our prediction of functional redundancy between these genes . However , loss of both rbpm2a and rbpms2b resulted in cardiac phenotypes in zebrafish embryos , consistent with the reported role for hermes in embryonic heart development ( Gerber et al . , 2002; Gerber et al . , 1999 ) . Furthermore , loss of both rbpms2 genes resulted in defective oocyte differentiation , and a male only phenotype , confirming that these genes are likely redundant in their reproductive functions . Of the adult rbpms2a;rbpms2b double mutants ( rbpms2 mutants ) that escaped the embryonic cardiac phenotype , we could recover only fertile males . Due to the complex manner in which zebrafish sexual differentiation is regulated , we reasoned that this all-male phenotype was likely the result of a defect in an rbpms2-dependent aspect of germline development . Nonetheless , we observed comparable gonad development between rbpms2 mutants and their non-mutant siblings during early embryonic development ( d3 ) when mutants and siblings had similar numbers of PGCs , and later at the bipotential stage ( d21 ) when mutants and siblings had comparable germ cell morphologies and composition of meiotic-stage gonocytes . At the onset of sexual differentiation ( d35 ) , approximately half of the wild-type siblings will differentiate ovaries containing numerous primary oocytes , and the other half develop primitive testes containing numerous spermatogonia that are characteristically small in size , with high nuclear-to-cytoplasmic ratios , and present in large clusters . However , gonads of rbpms2 mutants do not complete differentiation as ovaries and instead were categorized as either testis-like gonads resembling those of their male siblings , or intersex gonads with some spermatogonia , but also many germ cells that morphologically resemble oocytes . Many of the rbpms2 mutant oocytes were significantly large , reaching diameters comparable to the oocytes of their siblings . Furthermore , rbpms2 mutants express the female-specific marker Bucky ball in their oocytes ( Bontems et al . , 2009; Heim et al . , 2014 ) . Importantly , it should be noted that the role of rbpms2 in promoting oogenesis is Bucky ball-independent , since zygotic buc mutants progress normally through oogenesis , and can become adult females with mature ovaries ( although buc mutant eggs are un-patterned ) ( Bontems et al . , 2009; Dosch et al . , 2004; Heim et al . , 2014; Marlow and Mullins , 2008 ) . In contrast , rbpms2 mutants initiate oocyte differentiation and maturation; however , mutant oocytes ultimately fail to complete differentiation and are lost in a tp53-independent manner , resulting in apparently normal spermatogenesis . Rbpms2 protein interacts with the Bucky ball protein and RNA transcript ( Heim et al . , 2014 ) ; therefore , we examined Bucky ball protein in rbpms2 mutant oocytes to determine if Bucky ball abundance or localization is Rbpms2-dependent . The presence and asymmetric pattern of Buc localization in rbpms2 mutant oocytes indicates that Rbpms2 is not required for translation of the bucky ball transcript . This is in agreement with other studies suggesting that Xenopus Hermes and human RBPMS proteins are likely to be translational repressors , rather than activators ( Farazi et al . , 2014; Song et al . , 2007 ) ; however , it is also possible that other RNA-binding proteins , like the related Rbpms ( 1 ) , act redundantly with Rbpms2 to promote buc translation . In wild-type oocytes prior to the Bb stage , Buc staining typically appears as a crescent abutting the oocyte nucleus and then coalesces to form a spherical Balbiani body ( Heim et al . , 2014 ) ( Fig 11C ) . However , in rbpms2 mutant oocytes we observed more diffuse Buc staining that formed a ring-like structure not observed in wild-type oocytes . Therefore , Rbpms2 may play a role in localizing Buc protein through its C-terminus , possibly by bringing Buc molecules into proximity to promote their oligomerization , or regulating Bb morphology by interactions with other Bb localized proteins , or by preventing translation of a protein that promotes Bb disassembly ( Fig 11C and 11D ) . The only protein known to promote Bb disassembly in zebrafish is Mgn/Macf1 [44 , 45]; however , we did not detect Mgn/Macf1 in the early stage oocytes that are present in rbpms2 mutants , although Vasa and Buc were detected . Thus , it seems unlikely that premature activation of Macf1/Mgn accounts for the dispersed Buc and Vasa in rbpms2 mutants . Examination of ultrastructure with TEM in primary oocytes determined that asymmetric mitochondrial localization or accumulation of nuage is not dependent on rbpms2; however , the presence of atypical cytoplasmic inclusions is significantly enhanced in the mutant population . It seems plausible that these electron dense structures might interfere with proper Bb coalescence; thus , Rbpms2 would limit a factor that is disruptive to Bb assembly . Identification and further characterization of additional Bb-localized components is needed to determine if this peculiar localization pattern is specific to Bucky ball protein , or reflects a pattern of exclusion of Bb components such as mitochondria and germ plasm by the large electron dense structures present in rbpms2 mutants . In summary , using transient and transgenic localization assays we identified unique domains of Rbpms2 that mediate its localization to subcellular structures of germ cells and somatic cells . We determined that an intact RNA binding domain is dispensable for Rbpms2 localization to granules of HEK 293 cells , and Rbpms2 subcellular localization to the centrosome in somatic cells of the zebrafish blastula , but is required for Rbpms2 association with germ granules in primordial germ cells and the Balbiani body of oocytes . Using CRISPR-Cas9 mutagenesis we showed that the two zebrafish Rbpms2 genes function redundantly in heart development and in oogenesis . Establishment of oocyte polarity and initial translation of Buc and Vasa proteins do not require Rbpms2 protein; however , Rbpms2 is required for normal Balbiani body structure and to prevent formation of aberrant cytoplasmic bodies . In addition to its role in preserving Balbiani body architecture , we discovered an independent and novel role for Rbpms2 in maintaining ovary fate . Further analyses are required to determine how Rbpms2 fits into the current framework of factors known to promote oogenesis .
Wild-type zebrafish embryos of the AB strain were obtained from pairwise matings and reared according to standard procedures [46] . Embryos were raised in 1X Embryo Medium at 28 . 5°C and staged as described [47] . All procedures and experimental protocols were in accordance with NIH guidelines and approved by the Einstein ( protocol #20140502 ) and ISMMS ( protocol # 17–0758 INIT ) IACUCs . The zebrafish rbpms2bsa9329 allele was obtained from the Sanger Institute’s Zebrafish Mutation Project [24] . The zebrafish transgenic reporter lines ziwi:GFP and cyp19a1a:GFP were obtained from Bruce W . Draper [22 , 31] . Zebrafish rbpms2aae30 and rbpms2bae32 mutants were made using CRISPR-Cas9-mediated mutagenesis as described [23] . Guide RNAs ( gRNA ) targeting the fifth exon of rbpms2a and rbpms2b were designed using the open-access ZiFiT Targeter website ( https://crispr-cas9 . com/96/zifit-targeter-crispr-cas9/ ) , resulting in the targeting sequences of 5’-GGGTGCAGGTTGGAAGGGTT-3’ for rbpms2a and 5’-GGGTGGATATTTGTGGGATT-3’ for rbpms2b . The rbpms2 targeting oligos were ligated into the gRNA expression vector pDR274 ( Addgene Plasmid #42250 ) . The gRNAs were synthesized using MAXIscript T7 Kit ( Life Technologies , AM1312M ) . The Cas9 RNA ( Addgene Plasmid #42251 ) was synthesized using mMESSAGE MACHINE SP6 Transcription Kit ( Life Technologies , AM1340 ) and Poly ( A ) Tailing Kit ( Life Technologies , AM1350 ) . 100ng of gRNA was co-injected with 300ng of cas9 RNA into the cytoplasm of 1-celled zebrafish embryos . T7 Endonuclease I assays ( NEB , M0302 ) and sequencing were used to confirm mutagenesis at d2-3 . Injected embryos were raised to adulthood and their progeny were screened to identify founders with germline mutations . Identified alleles were outcrossed to wild-type AB fish prior to incrossing . Genomic DNA was extracted from adult fins or single embryos using standard procedures [46] . The genomic region surrounding rbpms2aae30 was amplified using the primers 5’-GGGAAGCACCGCTTACAATA-3’ and 5’- TTTGACTCACATGGGTCTCG-3’ , followed by digestion of the wild-type strand with the enzyme BsurI ( ThermoFisher , FD0154 ) . The genomic region surrounding rbpms2bae32 was amplified using the primers 5’- GCGTGTAGTTTGTGTCCACC-3’ and 5’- TGTGGGCCGGAAACTTACAT-3’ , followed by digestion of the mutant strand with the enzyme EcoRV ( ThermoFisher , FD0304 ) . Finally , the genomic region surrounding rbpms2bsa9329 was amplified using the dCAPs primers 5’- CACTTATCAAGCTAACTTCAAAGCAGC-3’ and 5’- TGAAAGGGGACAAATAAGTCA-3’ , followed by digestion of the wild-type strand with the enzyme HpyF3I ( ThermoFisher , FD1884 ) . After 40 cycles of PCR at 60°C annealing , samples were digested for one hour using specified restriction enzyme . Digested PCR products were resolved using a 1 . 5% Ultrapure agarose ( Invitrogen ) and 1 . 5% Metaphor agarose ( Lonza ) gel . Genotyping for the tp53M214K allele was performed as previously described ( Berghmans et al . , 2005 ) . For whole mount ISH , embryos treated with PTU ( to prevent pigment formation ) were collected at specified stages , fixed in 4% paraformaldehyde overnight at 4°C , washed in PBS , and dehydrated in methanol . ISH was performed according to standard protocols [48] , except hybridization was performed at 65°C , maleic acid buffer ( 100mM maleic acid , pH 8 , 150mM NaCl ) was substituted for PBS during antibody incubations with alkaline phosphatase ( AP ) - conjugated anti-DIG antibodies ( Roche , 11093274910 ) , and BM Purple was used to develop the chromogenic reaction ( Roche , 1442074 ) . For fluorescent ISH in oocytes , whole ovaries were fixed as described above and the same ISH protocol was used until the point of chromogenic detection , at which point Fast Red Tablets ( Roche , 11496549001 ) dissolved in 2 ml 0 . 1M Tris-HCl was used for the fluorescent detection of AP enzyme activity . To generate antisense probes for rbpms2 transcripts , a linear template with T7 promoter was amplified from pcs2-rbpms2a and pcs2-rbpms2b plasmids using the primers: 5’-CAAACGCTGCGTCTGGAGT-3’ ( rbpms2a F ) , 5’-TAATACGACTCACTATAGGGCATGTCTCCACCTTTCA-3’ ( rbpms2a T7 R ) , and 5’-GCTAAGGCCAACACGAAGAT-3’ ( rbpms2b F ) , 5’-TAATACGACTCACTATAGGGCACTGGGCTACACTTC-3’ ( rbpms2b T7 R ) . For rbpms1 sense and antisense probes , rbpms1 amplicon ( see RT-PCR for primers ) was TOPO-cloned into pCR2 . 1 ( ThermoFisher , K450001 ) , and plasmids were linearized with HindIII ( ThermoFisher , FD0505 ) . Probes were in vitro transcribed with digoxigenin-UTP labeling kit ( Roche , 11175025910 ) . Whole-mount ISH of embryos was imaged using an Olympus SZ61 dissecting microscope with a high- resolution digital camera ( model S97809 , Olympus America ) and Picture Frame 2 . 0 software . Constructs used for transient expression assays and co-immunoprecipitation assays were made using the Gateway Recombination LRII Cloning Enzyme Mix ( Invitrogen , 11791 ) to insert the coding sequence of wild-type or mutant rbpms2a or rbpms2b into the destination vectors pCS-GFP Dest ( Addgene Plasmid #13071 ) or pCS-MT Dest ( Addgene Plasmid #13070 ) [49 , 50] . The coding sequences of rbpms2a and rbpms2b were amplified using Easy-A High Fidelity Taq polymerase ( 600400 , Agilent ) and the PCR fragments were TOPO cloned into pCR8/GW/TOPO ( K250020 , Invitrogen ) . Wild-type rbpms2a and rbpms2b were PCR-amplified from AB strain ovary cDNA and sequenced , while the mutant alleles rbpms2aae30 , rbpms2bae32 and rbpms2bsa9329 were PCR-amplified from cDNA from 4-cell stage maternal-zygotic ( MZ ) mutant embryos and sequenced , using the primers: 5’- ATGAGTCTGAAGTCAGATTCAGAGAC-3’ ( rbpms2a ATG F ) and 5’- TTAACAGAACTGACGTGATTTCC-3’ ( rbpms2a stop R ) , or 5’- ATGAGTGTCAAGTCCGACTC-3’ ( rbpms2b ATG F ) and 5’- TTAACAGAACTGTCGGGATTTCC-3’ ( rbpms2b stop R ) . The constructs used to generate stable transgenic lines ( Tol2-cmcl2GFP-bucP-mApple-rbpms2-3’UTR and Tol2-cmcl2GFP-bucP-mApple-rbpms2bΔRNP-3’UTR ) were made using multiple-fragment cloning with the Gateway Recombination LRII+ Cloning Enzyme Mix ( Invitrogen , 12538120 ) to insert the coding sequence and 3’UTR of wild-type rbpms2b into the Tol-2 destination vector pDestTol2CG2 ( Tol2 kit v1 . 2 #395 ) behind the bucky ball promoter ( p5E-bucP ) and mApple RFP sequence ( pME-mApple , Tol2 kit v2 . 0 #763 ) [14 , 49] . The rbpms2b coding and 3’UTR sequence was TOPO cloned using amplified products from ovary cDNA into pCR8 as described above using the same F primer ( rbpms2b ATG F ) and 5’-GCATTCCAATTTTAATACTATCATACAGTAGTTTCTTT-3’ ( rbpms2b 3’UTR R ) . pCS-GFP-rbpms2bΔRNP1 and Tol2-buc-mApple-rbpms2bΔRNP-3’UTR constructs were made using the QuikChange II Site-Directed Mutagenesis Kit ( Agilent , 200523 ) and mutagenesis primers: 5’-ATCAAGCTAACTTCAAAGGACAGTCGTTCTGGCGCT-3’ ( rbpms2bΔRNP1 F ) , and 5’-AGCGCCAGAACGACTGTCCTTTGAAGTTAGCTTGAT-3’ ( rbpms2bΔRNP1 R ) . The rbpms2bΔCterm constructs were made from pcs2-MT-rbpms2b using the QuikChange II Site-Directed Mutagenesis Kit ( Agilent , 200523 ) and mutagenesis primers 5’-GGACTCGTCCCAGCCTGGATTAAGATTCGAGCCTCTAGAAC-3’ ( rbpms2bΔCterm F ) and 5’-GTTCTAGAGGCTCGAATCTTAATCCAGGCTGGGACGAGTCC-3’ . For assays of localization in somatic cells and PGCs , GFP-Rbpms2 and RFPnos3UTR [51] plasmids were linearized and then transcribed using the mMessage mMACHINE SP6 Transcription Kit ( AM1340 , Invitrogen ) . For GFP-rbpms2 and RFPnos3UTR plasmids , 200pg of RNA was injected into 1-cell embryos . For analysis of somatic cells , injected embryos were fixed at sphere stage in 4%PFA , and for PGC analysis embryos were fixed at 30hpf . Antibody staining was performed as indicated below in the Immunofluorescence section . For whole-mount IF of embryos or ovaries , tissue was fixed in 4% paraformaldehyde overnight at 4°C , dehydrated in MeOH , and placed at—20°C . Two anti-Rbpms2 antibodies were used in this study , mαRbpms2 ( Abcam , ab169394 ) a mouse polyclonal raised to full-length human protein ( amino acids 1–209 NP_919248 ) , and rαRbpms2 ( abcam , ab170777 ) a rabbit polyclonal antibody raised to a peptide within amino acids 120–149 were diluted at 1:500 , Anti-Bucky ball y1165 at 1:500 [14] , and anti-GFP antibody ( Invitrogen , A10262 ) was used at 1:500 . Chicken anti-Vasa antibody was a gift of Bruce W . Draper and used at 1:1000 dilution . Rabbit anti-ACF7/Macf1 was used at 1:1000 [52] . Rabbit anti-DCP2 ( Novus Biologicals , NBP2-16109 ) and rabbit anti- TIAL-1 ( Novus Biologicals , NBP1-79932 ) were used at1:2000 as in [29] . Alexafluor488 , Alexafluor568 , CY3 , C5 ( Molecular Probes ) secondary antibodies were diluted at 1:500 . Images were acquired using a Zeiss Axio Observer inverted microscope equipped with Apotome or ApotomeII and a CCD camera , or Zeiss Live DuoScan ( line-scanning ) Confocal . Images were processed in ImageJ/FIJI , Adobe Photoshop and Adobe Illustrator . In zebrafish , Vasa protein localizes in a perinuclear ring of staining around each PGC nucleus that can be used much like a nuclear marker to identify and count individual cells . Z-series image stacks of one lateral half of embryonic gonads were obtained using a Zeiss Live DuoScan ( line-scanning ) confocal microscope , and cells in the stack were manually counted by analyzing the slices and nuclear morphology comprising each Z-stack with ImageJ/FIJI . Co-IPs were performed as previously described ( Heim et al . , 2014 ) . Briefly , sub-confluent HEK293 cells ( 1×106 ) were transfected with 3 μg pCS3-MT-Rbpms1/2a/2b , or the specified pCS3-MT-Rbpms2b mutation/truncation , and 3 μg pCS3-GFP-Buc or pCS3-GFP ( control ) with 3 to 1 ratio of polyethylenimine:DNA , overnight . IP was performed on supernatant from cell lysates ( including mRNA ) with 1 mg of anti-GFP 3E6 antibody ( A11120 , Invitrogen ) and Protein G magnetic beads ( S1430 , NEB ) . Precipitated proteins were separated by SDS-PAGE , and transferred to ImmobilonP ( Millipore ) . Membranes were blotted using 1:2000 anti-Myc ( 9E10 , Santa Cruz ) or 1:2000 anti-GFP ( 11814460001 , Roche ) , washed in TBST , then stained with 1:25 , 000 goat anti-mouse HRP ( 12–349 , Millipore ) prior to ECL detection ( GE Healthcare ) . To visualize granules in HEK293 cells , the same transfection protocol was used with pCS3-GFP-Rbpms wild type and mutant constructs , and the cells were plated onto cover-slipped dishes and photographed live . For semithin and ultrathin sections , samples were fixed with 2 . 5% glutaraldehyde , 2% paraformaldehyde in 0 . 1 M sodium cacodylate buffer , postfixed with 1% osmium tetroxide followed by 2% uranyl acetate , dehydrated through a graded series of ethanol and embedded in LX112 resin ( LADD Research Industries , Burlington VT ) . Ultrathin sections were cut on a Reichert Ultracut UCT , stained with uranyl acetate followed by lead citrate and viewed on a JEOL 1200EX transmission electron microscope at 80kv . Haematoxylin and Eosin ( H&E ) staining was performed as previously described ( Heim et al . , 2014 ) . Briefly , tissue was fixed in 4% paraformaldehyde overnight at 4°C , dehydrated in MeOH , and placed at—20°C . After paraffin embedding and sectioning onto slides , tissue was deparaffinized , stained with H&E , coated with Permount solution ( Fisher Scientific ) , coverslipped , and imaged using an AxioSkop2 microscope and AxioCam CCD camera . Total RNA was extracted from pooled embryos ( n = 20-30/stage ) or pools of 2–3 adult organs using Trizol ( Life Technologies , 15596 ) . cDNA was prepared with SuperScript III Reverse Transcription Kit ( Life Technologies , 18080–051 ) . RT-PCR was performed using the primers 5’-ATTCACCTCTAAACAGCCGGT-3’ and 5’-AGGCTAGGCTAATCATTACACTG-3’ for rbpms1 , 5’-ATGCGTTAAATGGCATCCGC-3’ and 5’-GTCCTCAGCATCTCTACCGC-3’ for rbpms2a , 5’-CAACGCATCTGAGCATGAAG-3’ and 5’-GATCCAGTCGCACTTTAAGGA-3’ for rbpms2b . The primers for ef1α , vasa and cyp19a1a are as described in [14] . FASTA protein sequences corresponding to the longest known alternative transcript for each of the zebrafish rbpms genes and Xenopus hermes were obtained from the Ensembl website ( www . ensembl . org ) and analyzed using ClustalW for multiple alignments and JalView software to visualize . Phylogeny tree is calculated for neighbor joining using percent identity . Aligned sequences include rbpms ( herein rbpms1 ) ( ENSDART00000127288 ) , rbpms2a ( 20ENSDART00000067514 ) , rbpms2b ( ENSDART00000006619 ) and xhermes ( ENSXETT00000024635 ) . | Oocyte development relies on posttranscriptional regulation by RNA binding proteins ( RNAbps ) . RNAbps form large multi-molecular structures called RNPs ( ribonucleoproteins ) that further aggregate into regulatory granules within germ cells . In zebrafish primary oocytes , a large transient RNP aggregate called the Balbiani body ( Bb ) is essential for localizing patterning molecules and germline determinants within oocytes . RNA-binding protein of multiple splice forms 2 , or Rbpms2 , localizes to germ granules and the Bb , and interacts with bucky ball , a key factor for Bb formation . We show that Rbpms2 requires RNA binding for localization within germ cells , and that the C-term and RRM contribute to Rbpms2 subcellular localization in distinct somatic cell types . To investigate Rbpms2 functions we mutated the duplicated zebrafish rbpms2 genes . Consistent with redundant functions , rbpms2a and rbpms2b gene expression overlaps , and single mutants have no discernible phenotypes . Although rbpms2a;2b double mutants have cardiac phenotypes , those that reach adulthood are exclusively fertile males . Genetic analysis shows that rbpms2 mutant oocytes are not maintained even when Tp53 , a regulator of cell death is absent . Initial oocyte polarity is established in rbpms2 mutants based on asymmetric distribution of Buc protein and mitochondria; however , abnormal Buc structures and atypical cytoplasmic inclusions form . This work reveals independent Rbpms2 functions in promoting Bb integrity , and as a novel regulator of ovary fate . |
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An increasing number of genes required for mitochondrial biogenesis , dynamics , or function have been found to be mutated in metabolic disorders and neurological diseases such as Leigh Syndrome . In a forward genetic screen to identify genes required for neuronal function and survival in Drosophila photoreceptor neurons , we have identified mutations in the mitochondrial methionyl-tRNA synthetase , Aats-met , the homologue of human MARS2 . The fly mutants exhibit age-dependent degeneration of photoreceptors , shortened lifespan , and reduced cell proliferation in epithelial tissues . We further observed that these mutants display defects in oxidative phosphorylation , increased Reactive Oxygen Species ( ROS ) , and an upregulated mitochondrial Unfolded Protein Response . With the aid of this knowledge , we identified MARS2 to be mutated in Autosomal Recessive Spastic Ataxia with Leukoencephalopathy ( ARSAL ) patients . We uncovered complex rearrangements in the MARS2 gene in all ARSAL patients . Analysis of patient cells revealed decreased levels of MARS2 protein and a reduced rate of mitochondrial protein synthesis . Patient cells also exhibited reduced Complex I activity , increased ROS , and a slower cell proliferation rate , similar to Drosophila Aats-met mutants .
A number of neurological diseases are associated with mitochondrial dysfunction . For example , mutations in the mitochondrial genome have been found in a wide range of disorders including Leber's Hereditary Optic Neuropathy ( LHON ) , Neuropathy , Ataxia and Retinitis Pigmentosa ( NARP ) , Mitochondrial myopathy , Encephalopathy , Lactic Acidosis and Stroke ( MELAS ) , Myoclonic Epilepsy associated with Ragged Red Fibers ( MERRF ) , Nonsyndromic Sensorineural Deafness ( NSSD ) , and Kearns-Sayre Syndrome [1] , [2] . All of these disorders cause some dysfunction of the nervous system . Aside from these mitochondrially encoded genes , there is a growing list of mitochondria-targeted nuclear genes that when mutated cause diseases . These include ( 1 ) components of the respiratory chain/assembly factors [3] , [4] , ( 2 ) genes required for mtDNA maintenance/replication [5] , [6] , ( 3 ) genes that regulate dNTP pools [7] , ( 4 ) genes that regulate mitochondrial morphology/cellular trafficking [8] , [9] , and ( 5 ) genes involved in mtDNA transcription and translation [10] . Mitochondria are critical for energy production and are intricately linked to numerous aspects of cellular function . For example , cell proliferation defects have been reported for several mitochondrial fly mutants [11] , [12] . It has been proposed that Complex I disruption results in reduced cell proliferation caused by the buildup of Reactive Oxygen Species ( ROS ) . ROS are short-lived oxygen radicals that are produced at low levels as a result of impaired electron transport . These ROS can react with proteins , lipids , and DNA resulting in major damage to the cell and its mitochondria [13] . Studies in Drosophila have provided insight into the function of numerous human disease genes [14] . Indeed , work on the fly homologue of the then newly discovered PARK2 gene responsible for Autosomal Recessive Juvenile Parkinson's Disease ( OMIM #600116 ) [15] provided compelling evidence that parkin mutations result in mitochondrial dysfunction and oxidative stress [16] , [17] , [18] , work that was subsequently confirmed in human cells [19] , [20] . Forward genetic screens have also been carried out to isolate genes that cause a neurodegenerative phenotype [21] , [22] . These forward genetic screens may allow us to identify novel genes and help us understand the cellular mechanisms required for neuronal survival . For example , the gene nmnat , whose loss has a strong neurodegenerative phenotype , encodes an important neuroprotective protein that may act as a chaperone [23] , [24] . Interestingly , one of its orthologues in mice has been shown to confer significant neuroprotective effects in several disease models [25] . We decided to reassess the phenotypes of numerous mutants that were isolated in a mosaic eye screen in which we screened for defective electroretinograms ( ERGs ) in mutant photoreceptors on chromosome arm 3R [24] , [26] , [27] . Here we report the isolation and characterization of the Drosophila mitochondrial gene Aats-met ( Aminoacyl-tRNA synthetase-methionine , NP_650348 . 1 ) . We show that a partial loss of Aats-met results in mitochondrial dysfunction and causes a severe and progressive neurodegenerative phenotype . We further show that rearrangements in its human homologue , MARS2 ( Methionyl Aminoacyl-tRNA Synthetase 2 , NP_612404 . 1 ) , are responsible for a human neurodegenerative disease named ARSAL , for Autosomal Recessive Spastic Ataxia with Leukoencephalopathy , or Spastic Ataxia type 3 ( SPAX3 , OMIM #611390 ) [28] .
We reexamined a collection of lethal mutants generated on chromosome 3R to identify mutations that cause a degenerative phenotype [26] . We induced large clones of homozygous mutant tissue in the eyes using the ey-FLP system and screened for flies with aberrant ERGs that significantly worsen with age as a readout for degeneration of photoreceptors [29] . As shown in Figure 1 , we isolated a lethal complementation group consisting of two alleles , HV and FB . Control flies exhibit an “on” transient ( black arrowhead ) upon a flash of light ( Figure 1A ) . A change in potential ensues ( arrow ) , which is followed by an “off” transient ( white arrowhead ) when the light is switched off . The HV and FB mutants produced ERGs with significantly reduced amplitudes ( double-headed arrow ) ( Figure 1B , D ) , suggesting a defect in phototransduction and synaptic transmission . As the flies age , the ERGs exhibit gradually smaller amplitudes in response to light ( Figure 1C , E ) . A less severe genetic combination of alleles that produces adult flies ( see below ) , HV/FB , have normal ERGs at 1 d of age , while 3-wk-old animals ( Figure 1F , G ) have severely affected ERGs . To map the HV and FB mutations we turned to meiotic recombination mapping with P-element lines [30] and deficiency mapping ( Figure S1A–B ) . This pinpointed a 120 Kb region with 18 candidate genes . One lethal mutation , a piggyBac ( PB ) transposon insertion [31] in an intron of the Aats-met gene ( Aats-metc00449 ) , failed to complement the lethality of the FB allele ( Figures 1K , S1B ) . Sequencing revealed that HV and FB affect the Aats-met gene: HV carries a c . 125T>A predicted to result in the missense mutation p . V42D , whereas FB carries a c . 671C>T predicted to result in the missense mutation p . S224L ( Figure 1L ) . Aats-met encodes the uncharacterized Drosophila mitochondrial methionyl-tRNA synthetase , with 44% identity and 75% similarity to its human orthologue MARS2 ( Figure 1L , M ) [32] . Complementation tests with the three alleles and a deficiency ( Df ( 3R ) Exel7321 ) indicate the following allelic series: Df>PB>FB>HV . Flies homozygous for HV or transheterozygous for HV and FB are semi-viable , although they exhibit reduced lifespans ( see below ) . To demonstrate that the phenotypes associated with the mutations are indeed caused by a defective Aats-met gene , we ubiquitously expressed the Drosophila Aats-met and human MARS2 cDNAs using the Gal4/UAS system in mutant backgrounds [33] . The fly and human cDNAs rescued the lethality associated with FB/Df and HV/Df , the strongest allelic combinations . Note that overexpression of these cDNAs in a wild-type background , ubiquitously or only in the eye , results in a wild-type ERG phenotype ( Figure 1J ) . Moreover , the ERGs of aged HV/Df rescued flies are normal ( compare Figure 1C with 1H–I ) , demonstrating that the mutations in Aats-met are indeed responsible for the lethality and ERG defects . These data also indicate that MARS2 and Aats-met are homologous genes as both rescue the Aats-met mutants . We also Flag-tagged the human MARS2 construct at the C-terminus and performed colocalization experiments with the mitochondrial reporter mito-GFP protein [34] in mitochondria of Central Nervous System neurons of 3rd instar larvae ( Figure 1N ) . Both proteins co-localize , indicating that MARS2 is indeed a mitochondrial protein . To assess whether a worsening of the ERG phenotype is due to progressive degeneration of photoreceptor neurons ( PRs ) in Aats-met mutant retina , we performed Transmission Electron Microscopy ( TEM ) of the retinas of flies of different ages . We focused our analysis on transheterozygous escapers ( HV/FB ) and clones of the PB allele . Both have normal ERGs ( Figure 1F ) , with no obvious developmental defects , and possess the correct number of photoreceptors per ommatidium in 1-d-old animals ( Figure 2A–C ) . They display no defects in their rhabdomeres , and the overall appearance of the PRs also appears normal . As shown in Figure 2D–E and 2G , the PRs and support cells ( glia ) progressively degenerate . By 2 wk of age , the PRs of HV/FB animals display more severe phenotypes , and some PRs are vacuolated ( arrowhead , Figure 2D ) . By 3 wk of age , most PRs are severely affected and many organelles are barely recognizable ( Figure 2E , G ) . Similarly , in mutant clones of the piggyBac ( PB ) , PRs are mostly normal at day 1 ( Figure 2C ) and become severely affected by 2 wk of age ( Figure 2F ) . In summary , different mutations cause a severe progressive degeneration of PRs and glia . A careful quantitative analysis of the TEM micrographs revealed some subtle defects in young animals . Indeed , the total mitochondrial area in mutant PRs is greater in 1-d- and 1-wk-old animals ( 2-wk-old animals were too severely affected to quantify ) ( Figure 2H ) . In addition , we also noted many grey spheres in the glia in mutants , indicating the presence of lipid droplets that are not observed in wild-type animals ( black arrowhead , Figure 2B , F ) . That these are indeed lipid droplets was confirmed with toluidine blue staining ( red arrows in Figure S1E–F ) , a possible indication of a fatty acid metabolism defect [35] . In summary , the electrophysiological and ultrastructural features indicate that the mutant photoreceptor neurons undergo progressive degeneration . HV/FB and HV/HV escapers are morphologically normal . They feed , walk , and mate , suggesting that their development and basic physiological features are relatively normal . They , however , have much shorter lifespans than wild-type flies ( Figure 2J ) and are unable to fly . In light of their inability to fly and shortened lifespans , we examined the indirect flight muscles of these flies . Interestingly , the myofibrils seem intact at 1 d of age ( Figure 3A , C ) , but the mitochondria are clearly aberrant: they are larger than normal ( Figure 3C–E ) . In 1-wk-old HV/FB flies , the myofibrils display defects ( arrowhead in Figure 3D ) , and the mitochondria are very large ( Figure 3D–E ) . At 2 wk of age the muscle is too fragmented to take TEM images . Hence , partial loss-of-function mutations in Aats-met impair longevity and mitochondrial morphology . We noted that HV/Df mutants die as late 3rd instars or small pupae , possessing small imaginal discs and larval brains ( Figures 1K , 4A–G ) . Despite their smaller size , mutant larval brains do not show any obvious differences in the immunostaining patterns and localization of neuronal and glial proteins like Elav , Bruchpilot , Fasciclin II , and Repo when compared to wild-type brains ( unpublished data ) . Mutant cells exhibit a proliferative disadvantage when compared to wild-type cells as the mutant clones are significantly smaller than their wild-type twin spots in wing imaginal discs ( Figure 4H–I ) . Moreover , anti-phosphoHistone 3 ( PH3 ) staining , a mitotic cell marker , is decreased by 23% in mutant clones when compared to wild type clones in wing imaginal discs ( Figures 4L and S2A ) , suggesting that cell proliferation is affected . However , cell growth does not seem to be significantly impaired based on staining against the cell membrane marker Dlg ( Figure 4J–K ) . We also observed no difference in the number of apoptotic cells between wild-type and mutant clones based on Caspase 3 staining ( Figure S2B–C ) , and ubiquitous overexpression of the anti-apoptotic protein P35 did not suppress the small larval brain phenotypes ( Figure S2D–G ) . In summary , these data strongly indicate that Aats-met affects cell proliferation but not cell growth and apoptosis in non-neuronal cells . A mitochondria-specific stress response ( UPRmt ) induced by the overexpression of a misfolded mitochondrial matrix protein in mammalian cells has been described [36] and confirmed to be present in C . elegans [37] . In C . elegans , many of the RNAi constructs found to activate the UPRmt correspond to mitochondrial translation factors [38] . Since Aats-met/MARS2 is a mitochondrial translation factor , and since the highly conserved mitochondrial chaperone Hsp60 is a good reporter of the UPRmt in C . elegans , we examined expression of Hsp60 [39] . We observe an elevation in Hsp60 levels in Aats-met mutant clones in the eye ( Figure S3A–B ) as well as in mutant clones in the wing imaginal discs ( Figure S3C–D ) . To determine if the cytoplasmic UPR is affected , we carried out immunohistochemical stainings with BiP/Hsc3 , which has been shown to be a reliable marker in flies for the cytoplasmic UPR [40] , [41] . Unlike Hsp60 , BiP/Hsc3 is not induced in mutant cells , indicating that the two UPR processes are uncoupled ( Figure S3E–F ) . To assess the functional consequence of mutations in Aats-met on oxidative phosphorylation , the rate of oxygen consumption of intact mutant mitochondria was measured in vitro by performing polarography [42] . In the presence of the Complex I–specific oxidizable substrates malate and glutamate , mutant mitochondria exhibit a decreased respiratory control ratio ( RCR ) , the ratio of state III ( ADP-stimulated O2 consumption rate ) to state IV ( ADP-limiting O2 consumption rate ) . The RCR for the most severe allelic combination ( FB/Df ) was significantly lower compared to control mitochondria , primarily due to a relative increase in the state IV rate , likely reflecting a partial uncoupling of oxidative phosphorylation in mutant mitochondria ( Figure 5A ) . Interestingly , the oxygen consumption rates in the presence of the Complex II–specific oxidizable substrate succinate are increased for Aats-met mutant ( FB/Df ) mitochondria compared to controls , while the RCRs remain preserved , possibly indicating a compensatory response ( Figure 5A , Table S1 ) . This is consistent with the finding in C . elegans of increased Complex II–dependent respiration activity when levels of various Complex I components are knocked down with RNAi [43] . Given that the mitochondrial genome encodes 13 polypeptides that are all components of the mitochondrial Electron Transport Chain ( ETC ) ( Table S3 ) , we investigated whether there is a respiratory chain deficiency . To directly assess the individual ETC complexes , enzyme activities of the individual respiratory chain complexes from purified and disrupted mitochondria were measured spectrophotometrically . We observed a significant decrease in Complex I activity ( Figure 5B , Table S2 ) . The partial deficiency of Complex I in mutant mitochondria is relatively mild given that 7 out of the 40 or more Complex I subunits are encoded in the mtDNA and are therefore dependent on mitochondrial protein translation ( Table S3 ) . It has been proposed that high levels of ROS ( primarily superoxide anion ) because of aberrant Complex I activity results in reduced cell proliferation ( Figure 4H–I ) , although low levels appear to promote proliferation [12] , [44] . Hence , we hypothesized that the reduced cell proliferation in Aats-met mutants may be caused by elevated levels of ROS . Since mitochondrial aconitase activity is highly sensitive to ROS [45] , [46] , we measured aconitase activity normalized to total protein levels and found it to be greatly reduced ( Figure 5C ) . Upon addition of a reducing agent , the aconitase activity is restored in the mutants , showing that aconitase is indeed more oxidized in the mutants than in the wild-type controls . One of the mutant phenotypes associated with loss of Aats-met in the eye is very similar to the loss of Pdsw , which affects Complex I [12] . Clones of Pdsw in the eye cause a glossy patch and reduce the eye size . As shown in Figure 5D , Aats-met mutant clones exhibit similar phenotypes . We therefore tested if these phenotypes can be suppressed by antioxidants and supplemented with food with the lipophilic/cell-permeable Vitamin E ( α-tocopherol ) and water-soluble N-acetylcysteine amide ( AD4 ) [47] . We scored the loss of the glossy patch and the number of ommatidia . As shown in Figure 5D–E , low levels of Vitamin E ( 20 µg/ml ) significantly improved eye morphology and size ( p<0 . 001 ) . In addition , the percentage of adult female escapers of the genotype HV/FB able to eclose at room temperature increased significantly with antioxidants , although this was not observed in males ( Figure 5F ) . Note that the doses of Vitamin E and AD4 used had no effect on wild-type eyes or eclosion rates ( unpublished data ) . We noted that the human orthologue of Aats-met , MARS2 , was located in a 3 . 33 Mb candidate interval on chromosome 2q33 . 1 . Some of the authors of this manuscript had previously mapped a neurologic disease named ARSAL to this interval [28] . ARSAL is found in a large cohort of French-Canadian families and is an autosomal recessive spastic ataxia frequently associated with white matter changes as detected by MRI [28] . To examine this region closer , we generated Single Nucleotide Polymorphism ( SNP ) haplotypes using the 300K Illumina SNP-array on selected families . This documented the existence of three different disease carrier haplotypes in French-Canadian ARSAL cases ( Figure S4 ) . Recombination events within families established a minimum candidate interval of 579 Kb ( rs16865262–rs7581202 ) ( black bar in Figure S4 ) , containing nine genes including MARS2 . MARS2 is a single exon gene that spans 3 , 528 bp of genomic DNA and encodes a 593 aa protein homologous to Aats-met [32] . Interestingly , no point mutations were uncovered by genomic or cDNA sequencing . The first mutation was identified by PCR in Family E and consists of a 268 bp deletion predicted to lead to a premature STOP codon at position 236 ( c . 681Δ268bpfs236X ) , referred to subsequently as Dup-Del ( Figure 6A ) . This deletion was confirmed by sequencing in nine patients from different families ( Tables S4 and S5 ) . As shown in Figure 6A , PCR amplification of MARS2 encompassing the first third of the coding sequence revealed the presence of a deleted fragment that segregates in ARSAL Family E ( arrow , E9 , E10 , E11 ) and can also be seen in the father ( E9 ) , who is an unaffected carrier . This deleted fragment is not observed in the mother ( E8 ) and in Family B members , who possess a different type of mutation in the MARS2 gene ( see below ) . The wild-type sequence of the MARS2 PCR products ( Figure 6B ) and DNA sequencing of the amplicon of compound heterozygous case E10 documents the deletion ( compare Figure 6C to 6B ) . This mutation was confirmed by oligonucleotide custom array Comparative Genomic Hybridization ( aCGH ) , as discussed below . Interestingly , the deletion is part of a complex duplication of MARS2 in these patients ( see below ) . In affected brothers E10 and E11 , the aCGH discriminated the presence of a duplication ( black lines/dots above the +2 copies green line in Figure 6D ) in both patients as well as a deletion ( red arrows in Figure 6D–E , compare to Figure 6I ) . Further evidence that mutations in MARS2 were causative came from the identification of a 300 bp insert in the coding sequence that segregated within Family C ( patients C6 and C8 in Figure 6F but not in Family B , which possesses a different mutation—see below ) . The insertion's sequence provided evidence of a complex 5′ mutation , since only a partial sequence of MARS2 was revealed ( Figure 6G , H ) . The presence of repetitive sequences at the 5′ end of MARS2 combined with a 250 bp GC-rich sequence immediately 5′ of the ATG hampered MARS2 full genomic sequencing . This region is 67% GC-rich and contains a 27 bp G/C stretch that is not polymorphic in controls ( [CGGGG]n in Figure 7A ) . The small size of the gene and the limited number of restriction sites prevented us from generating informative Southern blots to further investigate the breakpoints of rearrangements . Nevertheless , quantitative Southern Blot analysis using five additional restriction enzymes ( ApaI , NcoI , XhoI , KpnI , and HindIII ) confirmed the presence of the duplication ( unpublished data ) . Based on our Southern blots , we conclude that the MARS2 breakpoints are >15 Kb away from the wild-type copy of the MARS2 gene . In summary , the presence of two mutations in the MARS2 locus was documented using PCR and a Southern blot-based method . The nature of these two mutations and a third type of mutation ( e . g . , Family B ) is further documented below . To better define the rearrangements , we performed a series of experiments to identify MARS2 copy number variations ( CNVs ) . In order to circumvent the problem of low average SNP densities in the standard Illumina and NimbleGen CGHs , we designed a custom 845 Kb NimbleGen aCGH array encompassing MARS2 with an average probe density of 60 nucleotides ( nt ) to uncover small rearrangements . This high-resolution aCGH was performed on six cases from four families . Note that the MARS2 gene is surrounded by repetitive DNA , specifically Line 1 and Line 2 elements , but also AT- and TTTA-rich segments , as well as [CGGGG] repeats ( Figure 7A ) . Based on haplotype analysis ( Figure S4 ) , at least three duplication events have occurred in our ARSAL cohort ( Figure 7B–C ) . Indeed , evidence of MARS2 duplications was uncovered in all six cases that were tested by aCGH ( Figure 6D , E , I ) . The CGH data analysis established that the 268 bp deletion , described above as the c . 681Δ268bpfs236X mutation , is part of a duplication since most oligonucleotide probes covering the entire coding sequence of MARS2 have a log2 value ( Cy5/Cy3 ) of ∼0 . 5–1 . 0 ( Figure 6D–E ) , whereas compound heterozygous patients should have values of ∼0 . 2–0 . 5 . To determine whether these complex mutations were segregating in all families and were present in other ARSAL patients , we used seven pre-designed ABI-based Copy Number Assays . Four were located in the MARS2 coding region and one in each of the nearby genes PLCL1 , HSP60 , and COQ10 ( Table S4 ) . PLCL1 , HSP60 , and COQ10 do not exhibit CNVs , whereas MARS2 duplications were uncovered in all 54 ARSAL cases belonging to 38 families and were not found in 384 control chromosomes ( Table S5 ) . Similarly , a Brazilian patient with an ARSAL phenotype also carried a duplication ( patient 57 in Table S5 ) . We hypothesized that the duplications may affect MARS2 expression levels . Indeed , Northern blots show the expected mRNA size in all patients ( Figure S5A ) , but qPCR quantification assays revealed an increase in mRNA expression in two compound heterozygous and four homozygous ARSAL patients that carry the common duplication ( Figure 8A ) . In addition to the normal MARS2 mRNA band , we detected small mRNA fragments ( ∼500 bp ) in ARSAL cases but not in the controls ( Figure S5B ) . These bands are suggestive of mRNA instability or aberrant MARS2 mRNA products . Interestingly , PCR primer walking produced different amplicon lengths that are suggestive of microdeletions ranging from 1 bp to 33 bp in the 250 bp GC-rich 5′ region and interspersed L1-type repetitive elements ( Figure 7A ) . The numerous L1 and L2 elements suggest that the duplications were generated by Fork Stalling and Template Switching ( FoSTeS ) [48] . However , due to the repetitive nature of the DNA , we were unable to determine precisely where and in which orientation the MARS2 duplications were located . In summary , our mapping and CNV data convincingly show that the CNVs are responsible for the ARSAL mutations since none of the 384 non-affected individuals show a CNV in the MARS2 locus . In addition , the MARS2 rearrangements do not affect the expression of surrounding genes such as HSPD1 and PLCL1 as assessed by aCGH and quantitative PCR ( unpublished data ) . Further evidence of the rare nature of these mutations is the fact that no CNV events have been catalogued for the MARS2 region in the Database of Genomic Variants ( DGV ) track . Interestingly , a single Yoruba sequence clone from the Human Genome Structural Variation Project Discordant Clone End track was reported to be discordant from the reference sequence [49] . The discordant clone consists of a 726 bp sequence containing a 276 bp L2 element that mapped within the MARS2 coding sequence ( Figure 7A ) and shares the junction breakpoint seen in the ARSAL rearrangements . The CNVs , the quantitative Southern blots , and the Northerns indicate that the rearrangements alter both the dosage of the MARS2 gene and mRNA . Our CNV results and the presence of numerous local repetitive elements support the hypothesis that regional genomic instability has caused template switching during DNA replication ( FoSTeS ) ( modeled in Figure 7B–C ) [48] , [50] as well as recombination errors [48] , [51] , [52] . To explore the impact of the mutations on protein levels , control and ARSAL patient protein extracts were analyzed by immunoblotting with a mouse polyclonal antibody against the N-terminal end of human MARS2 . Despite increased levels of aberrant mRNA transcripts , we find a reduced level of MARS2 protein in all tested patients , ranging from 40%–80% of normal , using mitochondrial proteins encoded in the nucleus as loading controls ( Figure 8C , quantified in 8D ) . Importantly , carriers of the deletion ( but none of the other patients or controls ) produce the expected 24 kDa truncated protein in addition to the normal band ( black arrow in Figure 8C , Figure 7C ) . The level of the truncated MARS2 protein is at least three times higher than the level of the wild-type protein found in controls . The Western blot data combined with Northern blot data argue that some MARS2 transcripts are not translated , possibly because of a post-transcriptional regulatory event such as an RNA-mediated interference of translation ( Figure 7B ) . To test whether mutations in MARS2 affect mitochondrial translation , we pulse-labeled the mtDNA-encoded polypeptides in patient and control immortalized lymphoblast lines as previously reported ( Figures 8B , Table S6 ) [53] , [54] . Of the six patients tested , three showed a translation deficiency . These three patients are homozygous for the common mutation ( Dup1/Dup1 ) ( cases B4 , B5 , and P24 ) and correspond to the most severe cases diagnosed at the ages of 6 , 3 , and 9 , respectively ( Table S5 ) . Two patients with control levels of translation were compound heterozygotes for two different duplications ( EE41 , AA35 ) . These patients were clearly less severely affected and were diagnosed as adults at the ages of 36 and 26 , respectively . In addition , other clinical variables such as loss of walking ability ( Table S5 ) correlate with the extent of the translation defect in lymphoblasts . Despite the relative decrease of MARS2 levels , no effect on the steady-state levels of mitochondrial tRNAmethionine was uncovered ( Figure S6A–B ) , suggesting that the amino-acylation defect does not destabilize the cognate tRNA . To address if and how knockdown of MARS2 in cells affects translation of mitochondrial proteins , we reduced the levels of MARS2 in HEK293 cells with three different shRNAs ( Figure S6C ) . A severe knockdown ( SH-452 ) clearly affects mitochondrial protein translation ( Figure S6E ) , whereas a less severe reduction in MARS2 ( SH-152 ) does not cause an obvious reduction in mitochondrial protein translation when compared to wild-type controls . Similarly , overexpression of MARS2 had no effect on mitochondrial protein translation ( Figure S6D , F ) . Hence , unless the MARS2 protein level is reduced beyond a certain level , levels of mitochondrial translation are not obviously affected . We did not identify a significant difference in MARS2 protein levels between the patients of different genotypes , although most patients with the Dup1/Dup1 genotype have slightly lower MARS2 levels than the other patients ( unpublished data ) . Finally , consistent with our findings in Aats-met mutant flies , cultured patient fibroblasts displayed reduced Complex I activity , increased ROS levels , and concomitantly decreased cell proliferation rates ( Figure 8E–G ) . Finally , we performed an examination of the genotype-phenotype relationship using the age of symptom onset as a measure of the severity of the disease and noted that patients carrying the duplication-deletion tend to have an earlier onset ( Figure 8H ) .
ARSAL exhibits clear inter- and intrafamilial variability reminiscent of Friedreich Ataxia [28] , [65] , [66] . In the present study , we report a group of 54 affected French-Canadian cases belonging to 38 families with a mean age of onset of 24 . 4 ( 2–59 ) in which we uncovered complex genomic MARS2 rearrangements ( Table S5 , Figure 7B–C ) . The mutations are complex genomic MARS2 rearrangements that always include a gene duplication event . Duplications were found with similar breakpoints located in a GC-rich 5′ UTR sequence and in a 3′ non-coding region . The junctions created by the rearrangements are located outside the coding region of MARS2 or other known genes and do not disturb the expression of neighboring genes as demonstrated by CNV assays and quantitative PCR . The 3′ UTR of MARS2 also seems affected by putative disruptions of regulatory elements at the breakpoint junction ( Figure 7A ) . This duplication was neither detected in 384 controls , nor described in the structural variation database . Moreover , in all families for which we have affected and unaffected relatives available for genetic analysis , the presence of the rearrangement ( CNV ) segregated with the disease . These data strongly argue that mutations in MARS2 are the cause of ARSAL , and this in turn is supported by an increase in message levels of MARS2 mRNA , reduced levels of MARS2 protein , and a reduction in mitochondrially translated proteins and Complex I activity in patients . The high prevalence of repetitive sequences at both breakpoint junctions , including many long-interspersed elements ( LINES ) at the 5′ region of MARS2 , and several AT-rich repeat sequences are likely to have mediated the rearrangements ( Figure 7A ) [67] , [68] . Despite the increased mRNA levels , we observed decreased MARS2 protein levels . The increased mRNA levels may be due to the duplications of the gene as well as duplications of regulatory elements in the CpG island at the 5′ end of the MARS2 gene . Consistent with recent studies , analysis of the MARS2 genomic structure reveals a functional CpG island ( Figure 7A ) [69] , [70] . CpG islands act as constitutive promoters of housekeeping genes and are methylated to silence transcription [71] . These findings suggest that the MARS2 duplications may dysregulate transcription , possibly by affecting the size , composition , or methylation ability of these islands . The decrease in protein levels contrasts with the increase in message . The simplest hypothesis is that the gene duplications were caused by FoSTeS , and a small fragment of DNA encoding some of the 5′ or 3′ UTRs was inverted . This inverted segment may affect mRNA stability and/or translation of MARS2 via an RNAi-mediated mechanism . Indeed , FoSTeS has been shown to result in duplicated inverted segments [72] , [73] . Unfortunately , the highly repetitive nature of the DNA surrounding the MARS2 gene did not allow us to document this inversion . Our data suggest that decreased levels of Aats-met/MARS2 protein or protein function lead to a subtle reduction in mitochondrial translation in humans and problems with mitochondrial function in flies and humans . The partial loss of Aats-met protein seems to lead to the accumulation of misfolded proteins in mitochondria , triggering a mitochondrial Unfolded Protein Response ( UPRmt ) ( Figures S3A–D , S3G ) . Mutant flies and patient cells also exhibit abnormal mitochondrial physiology , most notably a rather surprisingly mild reduction in Complex I activity , as well as accumulation of ROS ( Figures 5A–C , 8D–E ) . The reduction in Complex 1 activity is consistent with the observation that 7 of the 13 mitochondrially encoded proteins are incorporated in Complex 1 . The brain tissue of Aats-met mutants contains lipid droplets that are almost never observed in wild type neurons and glia . Such an increase in lipid droplets , potentially related to a lipid metabolism defect , was also recently observed in a 12-y-old girl exhibiting progressive muscle degeneration and autoimmune polyendocrinopathy and was determined to have cosegregating mutations in MARS2's cognate tRNA , mitochondrial tRNAmethionine , as well as COX III [74] , as well as in patients with other mitochondrial diseases such as Leigh Syndrome , Alpers Disease , and Lethal Infantile Mitochondrial Disease [75] . Aats-met/MARS2 mutations do not solely affect neuronal function and survival . Indeed , severe allelic combinations affect cell proliferation , but not cell growth and apoptosis . These data are consistent with the role of increased levels of ROS in the activation of the G1-S checkpoint via the JNK signaling pathway , blocking cell cycle progression [12] . ROS has been shown to play a role in the regulation of the cell cycle , both in its promotion and blockage [44] . Importantly , several of the patient cell lines , similar to what was observed in flies , also exhibit reduced cell proliferation and increased ROS ( Figure 8F–G ) . The clinical features of ARSAL clearly argue that the neurons , glia , and muscles are more affected than other tissues or organs ( Table S5 ) [28] . Indeed , ARSAL patients exhibit ataxia , severe cerebellar and some cerebral atrophy , dystonia , and leukodystrophy . Flies that carry weak allelic combinations also exhibit a progressive demise of the muscles and brain , as can be seen in Figures 2 , 3 , and S1 . In both patient cells and flies we observe decreased levels of Complex I activity and increased levels of ROS . The ability to partially suppress the morphological defects in flies with various antioxidant compounds is noteworthy . Normally , ROS levels are tightly controlled and known to play important roles in signaling pathways , including the HIF-1α , JNK , NFκB , TNF-α , and NADPH Oxidase pathways [76] . The production of excessive levels of ROS may also play a prominent role in other neurodegenerative diseases [77] , [78] , [79] . Finally , Vitamin E deficiency as a cause of an ataxia ( AVED , OMIM #277460 ) further supports a role for ROS in hereditary cerebellar diseases [80] . In conclusion , mutations in Aats-met in flies or reduced levels of MARS2 protein in humans result in aberrant translation of the Respiratory Chain and concomitant production of ROS . These ROS are especially damaging to neurons , as evidenced by our finding that the ERG progression of the Aats-met mutants can partially be suppressed by antioxidants ( unpublished data ) . This ROS also has the effect of reducing cell proliferation , a phenotype that can also be suppressed by antioxidants ( Figure 5D–E ) . Our model is summarized in Figure S7 . It remains to be determined if antioxidants will prove beneficial for ARSAL patients .
All probands and family members underwent a detailed neurological examination by experienced neurologists . All medical records and imaging were reviewed . All families were of French-Canadian ancestry except for one Brazilian family . None of the families were known to be consanguinous . All MRIs were reviewed by J . L . This project was approved by the Institutional Ethics Committee of CRCHUM . Informed consent was obtained from all patients , all family members , and controls . Genomic DNA was extracted from blood or saliva using standard procedures ( Oragene , DNA Genotek ) . Mutagenesis of chromosome 3R was performed as described previously [26] . The genotypes of FB and HV are: y w; FRT82B Aats-metFB/TM3 and y w; FRT82B Aats-metHV/TM3 . P-element/deficiency mapping was performed as described [30] . The genotype of the Df stock is: y w; Df ( 3R ) Exel7321/TM3 , hs-hid [81] . The genotype of the piggyBac is: y w; FRT82B pBacc00449/TM3 , hs-hid [31] . The control strain used was y w; FRT82B isogenized . To generate mutant eye clones , y w eyFLP; FRT82B w+ cl/TM3 was crossed to y w; FRT82B Aats-metFB/TM3 and y w; FRT82B Aats-metHV/TM3 . Transheterozygous escapers were generated in large numbers by raising the larvae/pupae at 18°C . They were subsequently raised at room temperature and transferred and scored every 2–3 d for aging experiments . Heat-shock clones were generated using y w hsFLP; FRT82B ubi-GFPnls/TM6B . For rescue experiments , y w; Act5C-Gal4/CyO was used . The UAS-p35 stock used to inhibit apoptosis has been described [82] . Climbing assays were performed exactly as described [83] . Unless indicated , stocks were obtained from the Bloomington Drosophila Stock Center ( BDSC ) and are listed on FlyBase ( http://flybase . bio . indiana . edu ) . AD4 ( N-acetylcysteine amide ) and Vitamin E ( MP Biomedicals ) were dissolved in standard fly food . The same food batch without drug supplementation was used for the control . ERGs were recorded as described previously [26] . Images of eyes and pupae were taken with a MicroFire camera ( Optronics ) mounted on a Leica MZ16 microscope . TEM of photoreceptors was performed as described previously [24] . At least five animals were analyzed . Thick sections were prepared for inspection of sample integrity . For quantification , 18–20 photoreceptor cartridges for each genotype were analyzed . Thick sections of the optic lobe ( Figure S1 ) were visualized using a microscope ( Imager . Z1; Carl Zeiss , Inc . ) , camera ( AxioCam MRm; Carl Zeiss , Inc . ) , AxioVision release 4 . 3 software ( Carl Zeiss , Inc . ) , and the Plan-Apochromat 20× NA 0 . 75 lens . For sequencing , DNA from mutant larvae was sequenced ( Macrogen ) and analyzed ( DNAStar ) . The Aats-met cDNA ( DGC clone GH13807 ) and the human MARS2 cDNA ( Open Biosystems MHS4426-99239542 ) using iProof polymerase ( Bio-Rad ) and appropriate oligos ( with a Kozak sequence ) were subcloned into the pUAS-attB vector and injected into embryos containing the VK37 attP site [84] . Third instar larvae were homogenized in cold mitochondrial isolation buffer using a Dounce homogenizer ( Kontes ) , filtered through cheesecloth , and centrifuged at 150 G , then 9 , 000 G . Oxygen consumption of mitochondria was measured ( Clark microelectrode ( YSI Life Sciences ) ) , recorded ( PowerLab data recorder ) , and analyzed ( ADInstruments LabChart ) . Rates ( ng atomic oxygen/min/mg mitochondrial protein ) were expressed as percentage control activity . Polarography was performed for six independent mitochondrial isolations . For enzymology , 3rd instar larval mitochondria were sonicated as above . Spectrophotometric kinetic assays were performed ( monochromator microplate reader ( Tecan M200 ) ) . Complex I activity was determined by measuring NADH oxidation ( 340 nm ) , Complex II activity by measuring DCIP reduction ( 600 nm ) , Complex III activity by measuring CytC reduction ( 550 nm ) , Complex IV activity by measuring CytC oxidation ( 550 nm ) , and Citrate synthase activity by measuring DTNB reduction ( 412 nm ) coupled to acetyl-CoA reduction . All activities were calculated as nmoles/min/mg protein and expressed as percentage control . Six independent samples for each genotype were tested . The activity of mitochondrial aconitase was measured on the basis of conversion of citrate into α-ketoglutarate coupled with NADP reduction ( Sigma ) and was normalized for total protein [45] . Activity was measured in the native state and after “reactivation” by incubating mitochondria in ferrous ammonium sulfate for 5 min before performing the assay . In vitro labeling of mitochondrial translation products was performed as described previously [53] . Immunohistochemistry was performed as previously described [85] . Anti-BiP ( 1∶200 ) [41] , anti-Drosophila Hsp60 ( 1∶200 ) [39] , anti-Dlg ( 1∶50 ) [86] , anti-cleaved caspase 3 ( 1∶500 ) ( Cell Signaling ) , anti-PhosphoHistone 3 ( ab5176 ) ( 1∶1 , 000 ) ( Abcam ) , anti-Fasciclin II ( 1D4 ) ( 1∶10 ) [87] , anti-Elav ( 1∶500 ) ( 7E8A10 ) [88] , anti-Brp ( Nc82 ) ( 1∶100 ) [89] , and anti-Repo ( 8D12 ) ( 1∶10 ) [90] were used . Secondary antibodies conjugated to Cy3 , Cy5 , or Alexa 488 ( Jackson ImmunoResearch and Invitrogen ) were used at 1∶250 . For anti-PH3 quantification , homozygous FB clones were stained with anti-PH3 to mark cells undergoing DNA synthesis . The largest box possible was made of the disc , and PH3-positive cells were documented with red dots in heterozygous tissue or purple dots in the homozygous tissue . The area was then determined for both , and paired Student t tests were performed for each of five discs to compare the difference in the number of PH3-positive cells in homozygous tissue versus heterozygous tissue . A total of 20 pairs of clones and their twin spots for each genotype+temperature were measured . A SNP genome-wide scan with the Illumina HAP300 SNP chip was conducted at the Genome Quebec Innovation Center , McGill University ( Montreal , Canada ) on nine affected individuals and six non-affected family members . BeadStudio Software was used as an analysis tool for genotyping , homozygosity , and loss of heterozygosity analysis . Copy number analysis was performed using the PennCNV program . We used seven pre-designed ABI-based Copy Number Assays for human CNV screening; four were located in the MARS2 coding region , one in each coding sequence of the surrounding genes ( PLCL1 , HSPD1 , and COQ10 ) ( Table S4 ) . Each reaction was performed in quadruplicate on a 384-well PCR plate with the ABI Copy Number Reference Assay ( RNaseP ) . CopyCaller ( Applied Biosystems ) was used for data analysis , and all steps were done according to instructions . NimbleGen CGH-array was performed using a chr2 specific fine-tilling oligonucleotide ( HG18 CHR2 FT ) to detect chromosomal changes . The median probe spacing was ∼500 bp . Custom high-resolution NimbleGen's 12×135K CGH arrays ( 38 , 725 probes per array on Chr2 ) were designed to cover the entire 0 . 845 Mb surrounding MARS2 [91] , [92] . The median probe spacing was 1 bp . Primers were designed ( Table S4 ) using Primer 3 or ExonPrimer ( see URLs section below ) . Sequences were analyzed on an ABI3730 Genetic Analyzer ( Applied Biosystems ) . RNAs were treated with DNase I to avoid genomic DNA amplification . Reverse transcription was performed using 3 µg total RNA using random hexamers , OligodT , and Superscript III ( Invitrogen ) according to the vendor's protocol . We prepared cDNAs from total RNA and performed cDNA analysis by PCR with the primers as indicated in the manufacturer's protocol . Purified PCR fragments were subcloned into pCR II-TOPO TA cloning kit ( Invitrogen ) ( Table S4 ) . Quantitative real-time PCR experiments were performed using an ABI PRISM 7900 HT ( Applied Biosystems ) on genomic DNA and cDNAs . Transcript-specific primers were designed with Primer Express software ( Applied Biosystems ) . The PCR conditions and analysis of the obtained data were optimized using published protocols [93] , [94] . The cycle of threshold value ( Ct ) was normalized to the transcripts for the housekeeping genes β-globulin and GAPDH . We performed calculations as described previously [93] , [94] . Primer sequences are shown in Table S4 [95] . Cell lines were maintained under normal condition ( 37°C , 5% CO2 ) in standard culture media ( DMEM containing 10% FBS and 100 µg/ml Pen-Strep and 50 µg/ml gentamicine ) . RNA extraction was performed using TRIZOL ( Invitrogen ) . To measure the fibroblast cell proliferation rate , fibroblasts from the three control and seven patient cell lines were cultured in 12 well plates as described earlier . They were plated at the same pre-determined concentration ( 900 cells/ml ) using a hemocytometer as a guide and were counted using a Beckman Coulter Vi-Cell XR2 . 03 cell viability analyzer after 48 h and then quantified . An N-terminal mouse polyclonal antibody was obtained from Abnova ( MARS2-H00092935-Q01 ) and used at 1∶1 , 000 . We used antibodies against LRPPRC and SLIRP as loading controls . The LRPPRC polyclonal antibody was prepared by Zymed Laboratories ( #295–313 ) and used at 1∶3 , 000 . The polyclonal antibody against SLIRP was used at 1∶1 , 000 ( Abcam #ab51523 ) . Protein was extracted from cultured cells , and 20 µg were subjected to SDS-PAGE and transferred to nitrocellulose membranes ( Millipore ) . The blot was probed overnight at 4°C with the primary antibodies and then probed for 1 h at room temperature with anti-rabbit IgG-HRP secondary antibody ( 1∶10 , 000; Santa Cruz Biotechnology ) . We visualized proteins using ECL Western Blot detection reagent ( PerkinElmer ) . 10 µg of total RNA extracted from control and patient lymphoblasts were run on a 10% polyacrylamide gel containing 7 M urea , followed by transfer to Hybond N+ membrane ( GE Healthcare ) . Pre-hybridization and hybridization were carried out in EXPRESS-Hyb solution ( Clontech ) according to the manufacturer's instructions . The oligonucleotides used for the generation of the 32P-labeled probes had the following sequences: 5′-TGGTAGTACGGGAAGGGTATAACC-3′ for tRNA-Met and 5′-TGGTATTCTCGCACGGACTACAAC-3′ for tRNA-Glu . The commercial cDNA of MARS2 was digested by Xho1/Pst1 ( OriGene; SC100504 ) and oligonucleotides of complement and reversed MARS2 sequences . Southern blot analysis was performed to assess MARS2 genomic rearrangements . Southern blots were produced using standard protocol with control and mutation carrier DNA . The following restriction enzymes for DNA digestion were used: AflIII , ApaI , BamHI , BglII , HindIII , KpnI , NcoI , PstI , and XhoI . A cDNA probe was obtained from commercial human cDNA digested with XhoI/PstI ( OriGene; SC100504 ) . The blots were hybridized with a 32P-labeled MARS2 cDNA probe as described ( http://www . protocol-online . org/cgi-bin/prot/view_cache . cgi ? ID=2746 ) . Statistical analysis was performed using Excel ( Microsoft ) and Prism ( GraphPad ) . Except where otherwise mentioned , unpaired two-tailed Student t tests were used . Percentage protein similarity was determined using BlastP ( NCBI ) . We used sequences for MARS2 with accession numbers NM_138395 . 2 and NP_612404 . 1 . | Neurodegenerative diseases , as a group , are relatively common and often devastating to those who suffer from them . Key insights are emerging from the study of homologues of identified human disease-causing genes in model organisms such as fruit flies , worms , and mice . In this study , we used the fruit fly to identify novel neurodegeneration-causing mutations and identified the Aats-met gene , whose protein product is involved in mitochondrial translation . We found that mutations in this gene cause neurodegeneration , impaired mitochondrial activity , and elevated oxidative stress . We were able to attenuate these defects with antioxidants like Vitamin E . We also determined that unusual duplications in the homologous human gene , MARS2 , were responsible for a novel type of progressive ataxia found in some French Canadian families . Cells taken from these patients have many of the characteristic defects observed in flies , showing that the fly mutants can be used to further explore disease mechanisms and test potential treatments . |
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The long-term health outcome of prenatal exposure to arsenic has been associated with increased mortality in human populations . In this study , the extent to which maternal arsenic exposure impacts gene expression in the newborn was addressed . We monitored gene expression profiles in a population of newborns whose mothers experienced varying levels of arsenic exposure during pregnancy . Through the application of machine learning–based two-class prediction algorithms , we identified expression signatures from babies born to arsenic-unexposed and -exposed mothers that were highly predictive of prenatal arsenic exposure in a subsequent test population . Furthermore , 11 transcripts were identified that captured the maximal predictive capacity to classify prenatal arsenic exposure . Network analysis of the arsenic-modulated transcripts identified the activation of extensive molecular networks that are indicative of stress , inflammation , metal exposure , and apoptosis in the newborn . Exposure to arsenic is an important health hazard both in the United States and around the world , and is associated with increased risk for several types of cancer and other chronic diseases . These studies clearly demonstrate the robust impact of a mother's arsenic consumption on fetal gene expression as evidenced by transcript levels in newborn cord blood .
Arsenic is a ubiquitous environmental pollutant and a known human carcinogen [1] . Chronic arsenic exposure is an important public health hazard around the world , with millions of people exposed to drinking water with levels far exceeding the guideline of 10 μg/l established by the WHO . Exposure to arsenic-contaminated drinking water is alarmingly high in many countries , most notably Bangladesh , where >25 million people are chronically exposed to extreme arsenic levels . Arsenic contamination is also a significant health concern in the United States , with numerous public water supplies measuring above the WHO limit [2] . Epidemiological studies indicate that chronic arsenic exposure in drinking water is associated with increased risk of skin , bladder , lung , liver , and kidney cancer [1]; in 1987 , arsenic was classified as a Group 1 carcinogen by the International Agency for Research on Cancer . Although the mechanism of arsenic-induced carcinogenesis is not clearly established , it has been attributed to genotoxicity associated with reactive oxygen species [3] . Arsenic is also implicated in other human diseases such as vascular disorders , peripheral neuropathy , bronchiecstasis , and diabetes [1] . The long-term health consequences of prenatal arsenic exposure in human populations are pronounced , with increased mortality rates caused by prenatal and early childhood exposures [4] . The detrimental health impact of prenatal arsenic exposure has also been shown in rodent models where in utero arsenic exposure resulted in a striking carcinogenic response ( 5-fold increase in hepatocellular carcinomas ) among offspring; in utero arsenic exposure also changed the expression of genes involved in cell proliferation , stress , and cell–cell communication that are evident even when the offspring reach adulthood . These results have profound implications suggesting that in utero arsenic exposure may result in epigenetic changes that persist through the life of the organism , ultimately impacting health status . A landmark study in mouse models shows that , indeed , in utero exposures via the maternal diet can cause permanent gene expression changes in the offspring that affect susceptibility to disease in the adult [7] . Given the implications of prenatal exposure on human health and the known public health hazard of chronic arsenic exposure , we set out to establish the extent to which maternal arsenic exposure in a human population impacts newborn gene expression . Additionally , these studies were aimed at understanding exactly how arsenic affects biological systems and identifying genes that could be used as predictors , and therefore potential biomarkers , of prenatal arsenic exposure .
We set out to determine whether gene expression changes in a set of infants born to arsenic-exposed women versus unexposed women ( as judged by WHO guidelines ) could be used to predict arsenic exposure in a test population . For these analyses , two-class prediction was employed , where a training population was used to derive gene sets that were then tested as predictors of exposure in a separate population . The analyses were carried out in two phases: ( i ) where the training population was selected at random and the analyst “blinded” to arsenic exposure level in the test population and ( ii ) where all arsenic exposure levels of the population were revealed and used to define new training populations . The first training population comprised 13 newborn subjects selected at random from the 32 newborns ( Figure 1A ) . Specifically , RNA was extracted from cord blood of newborns 1–13 , and hybridized to whole human genome arrays ( Materials and Methods ) . To identify genes whose expression was associated with prenatal arsenic exposure , we used an approach that combined differential expression testing between the populations , plus a positive or inverse correlation of expression with increasing arsenic exposure ( Materials and Methods ) . From the 13 newborn subjects , we identified the first expression signature ( first gene set , Figure 1B ) composed of 170 genes ( Table S1 ) that differentiated the unexposed newborns ( subjects 1–6 ) from the arsenic-exposed newborns ( subjects 7–13 ) . This prenatal arsenic exposure expression signature of 170 genes was then used to predict prenatal exposure in the remaining population of 19 newborns ( subjects 14–32 ) . The percent accuracy of class prediction was determined post-analysis by revealing the arsenic exposure of the test population to the analyst . Expression of these 170 genes accurately predicted prenatal arsenic exposure in 15 of 19 ( 79% ) of the newborns ( Figure 1B ) . When the arsenic levels of the entire population were revealed , it became apparent that the first training population was composed of newborns with a wide range of exposure levels distributed over almost the entire range ( Figure 1B ) . We hypothesized that a training population based on extreme exposures might yield higher predictive capacity . To assess this , arsenic-associated genes were identified using newborns at the extremes of arsenic exposure ( i . e . , the lowest versus the highest exposures ) as the second training population ( Figure 1A , second training population ) . Six newborns comprised the low-exposure population ( subjects 1 , 14 , 15 , 2 , 16 , and 3 ) , and six newborns comprised the high-exposure population ( subjects 29 , 30 , 12 , 13 , 31 , and 32 ) ( Figure 1A ) . As with the first gene set , differential expression testing and correlation analysis identified an expression signature , this time composed of 38 genes ( Table S2 ) that differentiated infants born to mothers with very low and very high arsenic exposure levels ( Figure 1A ) . These 38 genes were used to predict arsenic exposure in the remaining test population of 20 newborns . Even though the gene set was smaller ( 38 versus 170 ) , prediction was just as high as that of the first gene set , with prenatal arsenic exposure accurately predicted in 16 of 20 ( 80% ) of the newborns ( Figure 1B , second test population ) . We next determined whether a training population derived from a combination of all of the training samples used to generate the first and second gene set would yield an expression signature with higher predictive capacity . This third training population was composed of nine unexposed newborns and 11 exposed newborns ( Figure 1A ) . Differential expression testing and correlation analysis identified an expression signature of 11 genes ( Figure 1B ) that could predict prenatal arsenic exposure in 10 of 12 ( 83% accuracy ) of the remaining newborn test population ( Figure 1B ) . It is noteworthy that with only 11 genes , the power of prediction is as high as the first and second gene sets . Many of the genes in the third gene set were represented in the gene sets derived from the first and second training populations . Specifically , five of the 11 were identified in the first gene set and all 11 were present in the second gene set ( Table 1 ) . Given the high predictive capacity of these 11 genes , we hypothesize that these are key genes involved in the prenatal response of babies to arsenic and represent potential biomarkers of arsenic exposure . The potential arsenic biomarker set is composed of transcripts for the CXL1 , DUSP1 , EGR-1 , IER2 , JUNB , MIRN21 , OSM , PTGS2 , RNF149 , SFRS5 , and SOC3 genes ( Table 1 ) . The dose response of expression level of each of the identified biomarkers is evident when plotted versus arsenic exposure across the population ( Figure S2 ) . Furthermore , to substantiate the association of the expression of the biomarkers with arsenic exposure , a multivariate model was employed ( Materials and Methods ) . The model was employed to determine significance of association of expression with two factors: ( i ) arsenic exposure and ( ii ) geographic source of samples ( Materials and Methods ) . Geographic source was determined to be a nonsignificant factor for the expression level of the biomarkers ( p = 0 . 11 ) , whereas arsenic exposure was determined to be a highly significant factor ( p = 1 . 3 × 10−9 ) . Furthermore , for the set of biomarkers , the two factors of arsenic exposure and geographic source were not associated ( p = 0 . 77 ) . Notably , associated molecular functions for the 11 gene products include stress response and cell cycle regulation . The zinc finger DNA binding transcription factor EGR-1 ( early growth response 1 ) is related to cell proliferation and is induced by mitogens such as EGF [13] . EGR-1 regulates both proinflammatory cytokine activation and p53 transcription [14 , 15] . Not surprisingly , as EGR-1 is known to activate cytokines , such signaling molecules are present in the arsenic biomarker gene set; namely , OSM ( oncostatin M ) , a member of the interleukin-6 ( IL-6 ) family of cytokines known to control cell cycle progression [16] , CXL1 ( chemokine ligand 1 ) , and SOC ( suppressor of cytokine signaling 3 ) . Additionally , DUSP1 ( dual specificity phosphatase 1 ) is involved in cell cycle regulation and is known to modulate cytokine expression [17 , 18] . An inflammation-activated acute phase response is indicated by the presence of the JUNB transcription factor , and IER2 ( immediate early response 2 ) transcripts in the biomarker set . For a more global assessment of the impact of prenatal arsenic exposure on fetal gene expression , all biological pathways modulated in response to arsenic exposure were identified by studying the ontology of all the genes differentially expressed between the exposed and unexposed newborns across the entire population . For these analyses , the entire newborn population was used ( the fourth population , Figure 1A ) to define the fourth gene set that was differentially expressed between the two populations: the 21 newborns whose mothers were exposed to arsenic and the 11 newborns whose mothers were unexposed . It should be noted that for this analysis of global changes between the populations , the requirement for correlation with increasing arsenic exposure was not imposed ( Materials and Methods ) . This analysis identified 447 genes differentially expressed between the two populations of newborns , of which 404 ( 90% ) were upregulated ( Figure 2A; Table S3 ) . Gene ontology enrichment analysis was performed to classify the genes modulated by prenatal arsenic exposure ( Materials and Methods ) . This analysis identified ten gene ontology categories that were significantly enriched in the list of 447 genes ( Table 2 ) . Among the gene ontology categories that are significantly enriched are immune and inflammatory response ( p < 0 . 001 ) ( Table 2 ) . As an alternative approach to determine if groups of genes with common function are differentially expressed between the two newborn populations ( arsenic exposed or unexposed ) , we have employed the knowledge-based Gene Set Enrichment Analysis ( GSEA ) ( Materials and Methods ) . GSEA identified significant enrichment ( false discovery rate [FDR] q-value < 0 . 01 ) of ten expression signatures with common biological function that are differentially expressed between the unexposed and exposed newborns . The groups of genes include three that represent stress-response signatures and three that represent tumor/cancer signatures ( Table 3 ) . The GSEA results also highlight that genes associated with estrogen receptor signaling are differentially expressed between the unexposed and exposed newborn populations ( Table 3 ) . We next determined whether known molecular interactions exist among the proteins encoded by the arsenic modulated transcripts . Of the 447 arsenic modulated transcripts , 285 gene products were identified in the Ingenuity knowledge base and overlayed with known human molecular interactions ( Materials and Methods ) . Among these proteins , we identified the presence of a large arsenic-modulated interacting network of proteins ( Figure 2B ) . Specifically , we identified a large interacting network comprised of 105 human proteins encoded by arsenic-modulated transcripts ( indicated as red and green nodes ) ( Figure 2B; Table S4 ) . The probability of finding 105 arsenic-modulated transcripts that encode for a protein network of this size by chance is p < 10−55 . Of the 105 proteins , 96 ( 91% ) had transcripts that were upregulated in response to arsenic exposure . Further analysis identified three highly significant ( p < 10−55 ) sub-networks embedded within the large interacting network ( Figure 3A–3C ) . The first sub-network centers around the nuclear transcription factor NF-κB and the pro-inflammatory interleukin 1 family member IL1-β ( Figure 3A ) . This network integrates two members of the potential biomarkers; namely , SOC3 and CXCL1 ( Figure 3A ) . Note that transcripts for all proteins directly associated with NF-κB in this sub-network are upregulated in infants born to arsenic-exposed mothers ( Figure 3A ) . The second sub-network integrates biomarker member DUSP1 with two stress-activated transcription factors; namely , signal transducer and activator of transcription ( STAT1 ) and hypoxia inducible factor-1 α ( HIF-1α ) ( Figure 3B ) . Transcripts for both STAT1 and HIF-1α were upregulated in infants with arsenic-exposed mothers ( Figure 3B ) . STAT1 is involved in cytokine signal transduction and is known to be activated by arsenic [19] . HIF-1α activation and resultant tumorigenesis has been linked to chronic arsenic exposure [20] . The third sub-network integrates four of the 11 potential arsenic biomarkers; namely , EGR-1 , OSM , PTGS2 , and JUNB ( Figure 3C ) . These arsenic biomarker gene products are highly integrated with proteins known to be involved in cell cycle regulation , including JUN and FOS , as well as stress-response proteins such as interleukin-8 ( IL-8 ) ( Figure 3C ) . An overlay of molecular processes represented in this sub-network highlights the finding that prenatal arsenic exposure modulates numerous biological processes including stress response , signal transduction , cell adhesion , and transcription ( Figure 3C ) . Using network analyses , we also established that there are known molecular interactions among the 11 potential arsenic biomarker genes . Eight of the 11 biomarker gene products ( exclusive of SFRS5 , MIRN21 , and RNF149 ) are highly integrated with tumor necrosis factor-α ( TNF-α ) , another proinflammatory cytokine ( Figure 3D ) . TNF-α is involved in the control of both cell proliferation and apoptosis [21] . Here , we identify TNF-α activation in newborn cord blood upon exposure to prenatal arsenic . In an effort to uncover potential regulatory mechanisms underlying the transcription of the arsenic-modulated gene sets , we performed transcription factor binding site analysis within the promoters of the arsenic-modulated genes ( Materials and Methods ) . Promoter region comparisons for the arsenic-modulated genes identified significant enrichment ( p < 0 . 05 ) for two transcription factor binding sites across all four gene sets . Specifically , binding sites for NF-κB and serum response factor ( SRF ) are enriched in all four arsenic-modulated gene sets ( Table 4 ) . Moreover , metal response element binding sites ( MREs ) for the metal-responsive transcription factor-1 ( MTF1 ) are enriched in three of the four gene sets ( sets 1 , 3 , and 4 ) ( Table 4 ) . The MTF1 binding site enrichment was highest for the third gene set with five of the 11 genes containing the MRE element ( Figure 3D ) . Notably , the enrichment for MTF1 in the second gene set only narrowly misses the enrichment p < 0 . 05 cutoff , at p = 0 . 054 ( Table 4 ) . MTF1 was shown to be activated upon arsenic exposure in animal models [23 , 24] . It is noteworthy that gene targets for a known arsenic-inducible transcription factor are found among the transcripts modulated in the cord blood of infants born to arsenic exposed mothers . As the unexposed samples utilized in this study were obtained from two different locations and could confound expression testing , we have used an alternative approach to substantiate the identified arsenic-induced pathways . Differential expression testing was performed between the cord blood of exposed and unexposed newborns from Ron Pibul ( Materials and Methods ) . These analyses identified 321 genes that were differentially expressed between the arsenic-unexposed and -exposed newborns ( Table S5 ) . Notably , a direct comparison of gene expression changes identified considerable overlap between the transcripts differentially expressed between the newborns from Ron Pibul and transcripts differentially expressed across the whole population ( fourth gene set ) ( Table S5 ) . To identify the biological pathways modulated by prenatal arsenic exposure , the proteins encoded by the 321 transcripts were analyzed for significant enrichment of molecular networks ( Materials and Methods ) . Three highly significant protein sub-networks ( p < 10−30 ) were identified ( Figure S3 ) . As with the network findings from the entire population of newborns , the networks identified here integrate proteins known to be involved in cell cycle regulation including JUN , as well as stress-response proteins such as interleukin-8 ( IL-8 ) , the pro-inflammatory interleukin 1 family member IL1-β , and hypoxia inducible factor-1 α ( HIF-1α ) ( Figure S3 ) . Furthermore , the NF-κB protein is integrated into the sub-networks and found to be activated in the cord blood of newborns exposed to arsenic within the Ron Pibul population ( Figure S3 ) . Finally , our analyses included comparisons of the gene expression changes identified in this study with arsenic-induced gene expression changes reported in the literature in mouse models as well as a separate arsenic-exposed human population . Our results were compared with ( i ) expression changes in livers of mice treated with arsenic [24] , ( ii ) expression changes identified in arsenic-induced tumors resulting from in utero exposures to arsenic in mice [6] , and ( iii ) expression changes in blood from a human population from Taiwan exposed to arsenic [25] . These comparisons identify overlap of similarly modulated transcripts in response to arsenic exposure that include: BCL6 ( B-cell CLL/lymphoma 6 ) , CD14 ( CD14 antigen ) , CXCL1 ( chemokine ligand 1 ) , EGR1 ( early growth response 1 ) , FOS ( v-fos FBJ murine osteosarcoma ) , FOSB ( FBJ murine osteosarcoma viral oncogene homolog B ) , GADD45B ( growth arrest and DNA damage inducible beta ) , IFNGR1 ( interferon gamma receptor 1 ) , IL1B ( interleukin 1 beta ) , IL1R1 ( interleukin 1 receptor 1 ) , JUN ( v-jun sarcoma virus oncogene ) , MAPK6 ( mitogen-activated protein kinase 6 ) , MT1X ( metallothionein 1X ) , RAD23B ( RAD23 homolog B ) , and TOP1 ( topoisomerase DNA 1 ) ( Tables S3 and S5 ) . These results highlight the modulation of stress related transcripts in both mice ( acute and in utero exposures ) and a separate adult human population in response to arsenic exposure .
In summary , class prediction algorithms identified gene expression signatures that predict arsenic exposure in a test population with about 80% accuracy . Notably , by integrating training populations with varied exposures , a highly predictive potential biomarker gene set composed of just 11 genes was identified . These genes are promising as genetic biomarkers for prenatal arsenic exposure . Currently , we cannot eliminate the possibility that the gene expression signatures identified here are not absolutely specific for arsenic; they may also be predictive of other environmental exposures , e . g . , exposure to other heavy metals . Nevertheless , this study underscores that there is a robust prenatal response that correlates with arsenic-exposure levels that could modulate numerous biological pathways including apoptosis , cell signaling , the inflammatory response , and other stress responses , and ultimately affect health status . Arsenic contamination of the drinking water in the Ron Pibul area of Thailand is representative of that seen in many other areas of South East Asia , most notably Bangladesh [9] , suggesting that prenatal exposures are likely to be endemic in these areas . Moreover , arsenic contamination of the Ron Pibul drinking water is roughly the same as that known to be present in many of the western United States [2 , 9] , suggesting that prenatal arsenic exposure may also be a problem in the United States . These data contribute to our understanding of biological responses upon arsenic exposure , and show that prenatal exposure in humans results in measurable phenotypic responses in the newborn .
The study was conducted in Bangkok and the Ron Pibul District of the Nakhon Sri Thammarat Province located in the southern peninsula of Thailand ( Figure S1 ) . Five villages in the Ron Pibul district were selected for the study location as they had been classified as high level arsenic contaminated areas , and arsenicosis had been reported there [8] . Arsenicosis has not been reported in Central Thailand , specifically Bangkok , where arsenic concentrations in water and soil have been determined to be very low [8] . The study subjects consisted of 32 pregnant women ( 20–40 y old ) . All subjects were healthy , pregnant volunteers undergoing vaginal childbirth without birth stimulation or anesthesia . Twenty-three pregnant women living in the Ron Pibul District and nine women living in Bangkok for at least 1 y were recruited for the study . Women from both sites were age , educational level , and socioeconomically matched . Questionnaires were administered to all participants to obtain personal information regarding residential history , health history and potential confounding factors , birth and pregnancy information ( number of births , abortions or complications ) , use of community drinking water and well water , plus water and food consumption habits . Cord blood samples were collected from January 2004 to December 2005 in the Ron Pibul Hospital ( Ron Pibul District ) and the Rajvithi Hospital ( Bangkok ) . This study was conducted according to the recommendations of the Declaration of Helsinki ( World Medical Association 1989 ) for international health research . All subjects gave written informed consent to participate in this study . Pregnant participants were asked to provide toenail samples during pregnancy for analysis of total arsenic concentration , which was determined by Inductively Coupled Plasma-Mass Spectrometry ( ICP-MS ) ( Agilent 7500c ) . After delivery , 2 . 5 ml of newborn cord blood was collected into a PAXgene Blood RNA ( Qiagen ) tube for study of gene expression . All cord blood samples were kept at −70 °C until analysis . Total RNA was isolated from 32 cord blood samples according to the PAX gene protocol and Qiagen RNA extraction kit . RNA was labeled using a globin reduction protocol ( Affymetrix ) and hybridized to HGU133 Plus 2 . 0 full genome human arrays in technical duplicate for a total of 64 arrays . Data were first normalized using Robust Multi-Chip Average ( RMA ) [32] and filtered for expressed transcripts across all arrays ( +2 standard deviations above mean background ) resulting in reduction of the probesets from the original 54 , 675 to 15 , 265 . A mean absolute expression value was calculated from technical duplicates of the arrays for all expressed transcripts . Differential gene expression and association with increasing arsenic concentration was calculated as follows . The samples comprising the training sets were separated into two groups based on arsenic exposure level . The two groups were unexposed ( maternal toenail <0 . 5 μg/g ) or exposed ( maternal toenail ≥0 . 5 μg/g ) . The two-class exposure designation is based on the WHO standards for exposure to arsenic of 10 μg/l arsenic . A mean toenail arsenic concentration of 0 . 5 μg/g corresponding to chronic consumption of drinking water at 10 μg/l arsenic was derived from two studies associating arsenic toenail concentration and drinking water in a population from Bangladesh [12] and the United States [11] . Differential expression was determined as a significant difference in the expression of a gene ( exposed versus unexposed ) where the average fold change was greater than +/−1 . 5 and p < 0 . 05 ( t-test ) . Additionally , significant association of gene expression and increasing arsenic level was determined by correlation measurements ( r2 ≥ +0 . 6 , r2 ≤ −0 . 6; p < 0 . 01 ) calculated using the linear regression model in S-PLUS 7 . 0 ( http://www . insightful . com ) . The two-class prediction model used for assessing arsenic exposure in test populations was Support Vector Machine , carried out in Gene Pattern Software ( version 2 . 0 . 1 ) ( http://www . broad . mit . edu ) . Multivariate analysis was performed as follows: the expression values ( Y ) for each gene were modeled using Y = β1 + β2 ars ( arsenic ) + β3 loc ( geographic location ) , where toenail arsenic concentration is a continuous variable and location is binary . Statistical significance was determined by subjecting β2 and β3 to t-statistics . A χ2 test for dependence ( association ) of the two factors ( e . g . , arsenic and geographic location ) was performed for the set of arsenic biomarkers . A Fisher's exact test was employed to determine overrepresentation of the biomarkers within the genes significantly associated with either geographic source or arsenic exposure ( p < 0 . 01 ) . Network analyses were performed using the Ingenuity software ( http://www . ingenuity . com ) . Gene ontology enrichment analysis was performed using GO Miner [33] . GSEA [34] was performed using the GSEA desktop software [35] , with a false discovery rate correction ( Benjamini-Hochberg ) employed . Microarray data have been deposited to the Gene Expression Omnibus repository . Transcription factor binding site analysis was performed using Expander software [36] and Genomatix software ( http://www . genomatix . de ) . For both analyses , Affymetrix probesets were linked to sequence data for regions 1 , 000 base pairs upstream and 200 base pairs downstream of the transcription start sites , and these were analyzed for significant enrichment of transcription factor binding sites . Significance ( p ≤ 0 . 05 ) was calculated where significance is the probability of obtaining an equal or greater number of sequences with a model match in a randomly drawn sample of the same size as the input sequence set .
Microarray data have been deposited to the National Center for Biotechnology Information ( NCBI ) Gene Expression Omnibus repository under Series Record GSE7967 ( http://www . ncbi . nlm . nih . gov/geo/ ) . | Arsenic is an environmental pollutant and known human carcinogen . Chronic exposure to arsenic-contaminated water is an important public health hazard around the world , including the United States , with millions exposed to drinking water with levels that far exceed World Health Organization ( WHO ) guidelines . Given the implications of prenatal exposure on human health and the known public health hazard of chronic arsenic exposure , this study was aimed at establishing the extent to which maternal arsenic exposure in a human population affects newborn gene expression . The authors show that prenatal arsenic exposure in a human population results in alarming gene expression changes in newborn babies . The gene expression changes monitored in babies born to mothers exposed to arsenic during pregnancy are highly predictive of prenatal arsenic exposure in a subsequent test population . The study establishes a subset of just 11 transcripts that captured maximal predictive capability that could prove promising as genetic biomarkers of prenatal arsenic exposure . Pathway analysis of the genome-wide response in the babies exposed to arsenic in utero indicates robust activation of an integrated network of pathways involving NF-κB , inflammation , cell proliferation , stress , and apoptosis . This study contributes to our understanding of biological responses to arsenic exposure . |
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Oct4 is a widely recognized pluripotency factor as it maintains Embryonic Stem ( ES ) cells in a pluripotent state , and , in vivo , prevents the inner cell mass ( ICM ) in murine embryos from differentiating into trophectoderm . However , its function in somatic tissue after this developmental stage is not well characterized . Using a tamoxifen-inducible Cre recombinase and floxed alleles of Oct4 , we investigated the effect of depleting Oct4 in mouse embryos between the pre-streak and headfold stages , ∼E6 . 0–E8 . 0 , when Oct4 is found in dynamic patterns throughout the embryonic compartment of the mouse egg cylinder . We found that depletion of Oct4 ∼E7 . 5 resulted in a severe phenotype , comprised of craniorachischisis , random heart tube orientation , failed turning , defective somitogenesis and posterior truncation . Unlike in ES cells , depletion of the pluripotency factors Sox2 and Oct4 after E7 . 0 does not phenocopy , suggesting that ∼E7 . 5 Oct4 is required within a network that is altered relative to the pluripotency network . Oct4 is not required in extraembryonic tissue for these processes , but is required to maintain cell viability in the embryo and normal proliferation within the primitive streak . Impaired expansion of the primitive streak occurs coincident with Oct4 depletion ∼E7 . 5 and precedes deficient convergent extension which contributes to several aspects of the phenotype .
Oct4 is a homeodomain-containing transcription factor ( TF ) of the POU family required for pluripotency in ES cells and preimplantation embryos [1] . It has been extensively characterized in ES cells , and established as a hub of the signaling network that maintains pluripotency [2]–[5] . Embryonically , Oct4 is present in the developing zygote and down-regulated somatically between E7 . 0 and E9 . 0 depending on the cell type ( see Supplementary ( S ) Figure ( Fig . ) S1 and S2 for detail ) [6] , [7] . After E9 . 0 of murine development Oct4 is restricted to the germline , persisting until maturation of type A to type B spermatogonia in the male germline , in contrast to the female gametic lineage where it is depleted during meiosis ( E14–16 ) before up-regulation as oocytes mature within primordial follicles [6] , [8]–[10] . Several regulators of Oct4 have been established in vivo . Oct4 is maintained through the early stages of embryonic development by intercellular Nodal acting in part through Smad2 [11] , [12] . Conversely , Cdx2 mediates repression of Oct4 in trophectoderm of the early blastocyst , while both Eomes and Gcnf mediate repression in the embryo after implantation [13] , [14] . Oct4 buffers the ICM against differentiation into trophectoderm ( the embryonic contribution to the placenta ) , but the proposal that Pou5f1 ( gene symbol for Oct4 ) emergence relates to evolution of the mammalian placenta [15] is not supported given that Pou5f1 evolved before the origin of amniotes [16] . It is unknown whether Oct4 has a conserved role , or any post-implantation function in murine somatic development . Pluripotent somatic cells persist until E7 . 5–8 . 5 based on teratogenesis experiments [17] , [18] and ∼E8 . 0 based on epiblast stem cell ( EpiSC ) derivation [19] , suggesting that Oct4 might continue to maintain pluripotency during this window of development . in vitro studies have also inferred many roles for Oct4 between the pre-streak and headfold stages , ∼E6 . 0–E8 . 0 , including regulating neural versus mesendoderm differentiation [20] , [21] as well as promoting cardiomyocyte [22] and neuronal differentiation [23] . However aside from maintaining the viability of primordial germ cells ( PGCs ) , Oct4's role in post-implantation development has not been characterized in vivo [1] , [2] , [24] , [25] . The extent of Oct4's function at the molecular level is also unclear . Physical interactions suggest Oct4 may have roles in chromatin modification , regulation of transcription , DNA replication and DNA repair as well as post-transcriptional modification , ubiquitination , and various other functions [2]–[4] , [26] , [27] . Oct4 both activates and represses transcription [28] . It binds thousands of sites in the ES cell genome , often co-occupying these sites with Sox2 , Nanog , Smad1 and Stat3 [5] . The majority of genes occupied by several of these transcription factors ( TFs ) are active in ES cells , but their binding does not ensure expression [5] . Since Oct4 protein normally persists in somatic cells until ∼E7 . 0–E9 . 0 but Pou5f1 null embryos arrest at E3 . 5 , we asked what role Oct4 had later in murine development , using a conditional system to deplete it ∼E7 . 5 . We show that Oct4 depletion ∼E7 . 5 results in craniorachischisis , random heart tube orientation , failed turning , defective somitogenesis as well as posterior truncation . The phenotype is not the result of a general delay in development , nor does it result from a failure in the pluripotency network present in the ICM . Depletion of Sox2 , another core member of the pluripotency network in an overlapping window of development does not phenocopy Oct4 depletion . Instead , Oct4 is required until ∼E7 . 5 to maintain cell viability in the embryo and proliferation in the primitive streak . In its absence , convergent extension is disrupted leading to several morphogenetic defects .
We used a conditional mutant of Oct4 to study its role after E3 . 5 when it is essential for development . We used floxed Pou5f1 alleles ( Oct4f ) [25] and a tamoxifen inducible recombinase ( CreERT2 ) that is ubiquitously expressed from the ROSA locus [29] . To establish the window of development during which embryos are sensitive to Oct4 depletion , we staggered the initial dose of tamoxifen with respect to embryonic maturity and administered a second supplementary dose 12 hrs later to enhance overall recombination efficiency . Oct4f/f;CreERT2+/− embryos administered tamoxifen ∼E8 . 0 and ∼E8 . 5 before analysis ∼E9 . 5 did not have a phenotype ( Table S1 , row A ( S1A ) , while tamoxifen administration ∼E7 . 5 and ∼E8 . 0 before analysis ∼E9 . 5 resulted in a partially penetrant phenotype ( Fig . S3; Table S1B ) . Unlike tamoxifen administration beginning ∼E7 . 5 or ∼E8 . 0 , all Oct4f/f; CreERT2+/− embryos induced ∼E6 . 0 and ∼E6 . 5 before analysis ∼E9 . 5 were amorphous , lacking structures aside from what resembled anterior neural head folds ( Fig . S4; Table S1C ) . Tamoxifen administration ∼E7 . 0 and ∼E7 . 5 also led to a fully penetrant phenotype ∼E9 . 5 ( Table S1D ) . E9 . 5 embryos administered tamoxifen ∼E7 . 0 and ∼E7 . 5 failed to turn , had severe posterior truncations , randomly oriented heart tubes , craniorachischisis ( open neural tube along its entire length ) as well as impaired somitogenesis ( Fig . 1A–C ) . Such animals are referred to as Oct4COND MUT in the remainder of this report . The phenotype is not a consequence of tamoxifen administration , leaky recombinase activity prior to tamoxifen administration , or associated with recombination of a single Pou5f1 allele: no Oct4f/f embryos induced ∼E7 . 0 , no uninduced Oct4f/f;CreERT2+/− embryos , nor any Oct4+/f;CreERT2+/− embryos induced ∼E7 . 0 had phenotypes ∼E9 . 5 ( Table S1E–G ) . Reducing the quantity of tamoxifen per dose administered ∼E7 . 0 or failure to administer the second dose ∼E7 . 5 led to incomplete penetrance of the Oct4COND MUT phenotype ( Table S1H–J ) : 80% , 40% and 0% of embryos ∼E9 . 5 exhibited the Oct4COND MUT phenotype when a single full , half , and quarter tamoxifen dose was administered ∼E7 . 0 ( Table S1H–J ) . This suggests reduced recombination with these lower tamoxifen doses . Collectively , these data support Oct4 depletion causing the Oct4COND MUT phenotype . To determine the time course of Oct4 depletion with this system , we compared Oct4 transcript and protein abundance between Oct4f/f and Oct4f/f;CreERT2+/− littermates administered tamoxifen ∼E7 . 0 . A single dose of tamoxifen was used to avoid a compound effect from a second dose . Relative Oct4 transcript abundance ( Oct4f/f;CreERT2+/−/Oct4f/f;CreERT2−/− littermates ) was significantly different 12 hrs after tamoxifen administration ( ATA ) ( Fig . 1D; Table S1K; F5 , 13 = 15 . 48 , p<0 . 05 1-way ANOVA , *p<0 . 05 , **p<0 . 01 Bonferroni posttest ) . The fraction of cells in which Oct4 was detectable by immunohistochemistry was lower 20 hrs ATA , which is ∼E7 . 5 ( Fig . 1E , Fig . S5A–D; Table S1L; F3 , 10 = 12 , p<0 . 05 1-way ANOVA , **p<0 . 01 Bonferroni posttest ) . A distinct primary antibody indicated that Oct4 protein was undetectable 24 hrs ATA in Oct4f/f; CreERT2+/− embryos ( Fig . 1F , G; Table S1L ) . Since penetrance of the phenotype is complete when tamoxifen administration begins ∼E7 . 0 , partial when tamoxifen administration begins ∼E7 . 5 , and the fraction of cells with detectable Oct4 protein reduced ∼20 hrs ATA ( following administration ∼E7 . 0 ) , these data indicate that Oct4 is required until ∼E7 . 5 . Oct4 depletion does not cause a global delay in development . Administering tamoxifen ∼E7 . 0 and ∼E7 . 5 to avoid partial penetrance , Oct4f/f;CreERT2+/− embryos were recovered in a ratio of 1∶1 with Oct4f/f littermates until E9 . 5 , but less frequently at E11 . 5 ( Fig . 2A; Table S1M–O ) . Features disrupted in Oct4COND MUT remained arrested in the mutants that persisted beyond E9 . 5 ( Fig . 2B , C ) , indicating that the Oct4COND MUT phenotype is not a global delay in development but disruption of select features . Indentation of the otic cup occurred and the branchial arches formed in Oct4COND MUT , events that normally occur by E9 . 0 . Forelimb buds also protruded in Oct4COND MUT as they normally do by E9 . 5 . Conversely , the neural tube normally closes rostrally between E8–9 and caudally by E9–10 ( we refer to caudal and rostral neural tube closure with respect to closure point 1 at the hindbrain cervical boundary throughout; see Figure 2D ) [30] , turning normally occurs by ∼9 . 0 and posterior extension normally reaches 21–29 somites by E9 . 5 in WT embryos . These events always failed at E9 . 5 when Pou5f1 excision was induced ∼E7 . 0 ( Fig . 1A–C; Table S1D; 26 . 5 versus 4 . 6 somites in Oct4f/f versus Oct4f/f;CreERT2+/− littermates ) . Additionally , heart tube orientation was randomized , 38 . 6% of Oct4f/f;CreERT2+/− had situs inversus while the orientation of 6 . 8% was ambiguous ( Table S1P; p>0 . 05 Chi-square test ) . The neuroepithelium of Oct4COND MUT embryos was also thicker in regions , particularly in the distal portion of the embryo ( Fig . S6A–C; Table S1D; F1 , 287 = 94 . 95 , p<0 . 05 2-way ANOVA , ***p<0 . 001 Bonferroni posttest ) . These data indicate that Oct4 is required for posterior extension , turning , heart tube orientation and neural tube closure ( NTC ) . Partial phenotype penetrance following tamoxifen administration ∼E7 . 5 was used to assess whether the cause of disrupted features in Oct4COND MUT embryos were related . Coincidence of features in litters with incomplete phenotype penetrance suggests related causation of the coincident features . Craniorachischisis and posterior truncation coincided in all 23 of the 36 embryos analyzed ( Fig . S2; Table S1B; p = 1 . 64E-10 , hypergeometric test ) . Conversely 2 turning defects in the 9 embryos where rostral NTC failed suggests independence of these processes , although the small number of embryos limits statistical power in this case ( Fig . S3; Table S1B; p = 0 . 72 , hypergeometric test ) . These data suggest independent requirements for Oct4 in closure at closure point 1/posterior extension and rostral NTC . Craniorachischisis occurs when closure at closure point 1 fails ( see Figure 2D ) . Convergent extension elongates the embryo in the anterior-posterior axis during gastrulation and neurulation , bringing the neural folds into opposition prior to adhesion at closure point 1 . Failed convergent extension results in broad midlines and enlarged notochord diameter as both narrow during convergent extension . Oct4COND MUT embryos exhibit broad neural plates ( Fig . 2H–J; Table S1D; F2 , 22 = 17 . 42 , p<0 . 05 2-way ANOVA , **p<0 . 01 Bonferroni posttest ) and enlarged notochord diameter ( Fig . S6D–F; Table S1D; p<0 . 05 , two-tailed student t-test ) . Concordance between posterior truncation and craniorachischisis , broadened neural plates , and broader notochords are consistent with deficient convergent extension . NTC rostral and caudal to closure point 1 occur by different mechanisms . Unlike the spinal region where expansion of paraxial mesoderm is not required for elevation and subsequent NTC , cranial NTC is initiated by expansion of underlying mesenchyme [30] . Mesenchyme density , including cranial mesenchyme , was reduced in Oct4COND MUT ( Fig . 2E–G; Table S1D; F1 , 13 = 54 . 59 , p<0 . 05 2-way ANOVA , *p<0 . 05 , ***p<0 . 001 Bonferroni posttest ) . Hence expansion of cranial mesenchyme that is required for cranial NTC is deficient in the absence of Oct4 . A requirement for Oct4 in extraembryonic tissue offers one possible explanation for the Oct4COND MUT phenotype: ∼E7 . 5 Oct4 is present in extraembryonic mesoderm , allantoic angioblasts as well as extraembryonic endoderm which promotes proliferation and organization of the primitive streak [6] , [31] . To test this possibility , Oct4+/+ Red fluorescent protein positive ( RFP+ ) ES cells were aggregated with tetraploid Oct4f/f;Z/EG+/−;CreERT2+/− embryos , where ES cells contribute to the embryo , and tetraploid cells generate trophectoderm and visceral endoderm [32] . In this scheme , tamoxifen administration will selectively remove of Oct4 from the tetraploid extraembryonic lineages . Tetraploid Oct4f/f;Z/EG+/−;CreERT2+/− embryos induced ∼E6 . 5 and ∼E7 . 0 supported development of WT ES-derived embryos to E9 . 5 ( Fig . 3A–C , E; Table S1Q ) . Embryos were dosed on this relatively early schedule to avoid false negatives that might result from altered timing of development associated with transferring embryos to pseudopregnant mothers . In practice transferred embryos synchronize with the maternal uterine environment [33] , suggesting false negatives for this reason are unlikely . Normal embryonic development after excision of Pou5f1 in trophectoderm and visceral endoderm suggests Oct4 is required in embryonic tissue . To identify non-autonomous effects of Oct4 depletion , we tested whether lineage-specific removal of Oct4 affected development of other tissues . Since Oct4 is present in the primitive streak , neuroepithelium and portions of mesoderm ∼E7 . 5 as well as mosaically in definitive endoderm ( Fig . S1 and S2 ) , a primary effect in one of these lineages might non-autonomously cause other aspects of the Oct4COND MUT phenotype [6] . To test this possibility , Oct4 was removed in the neuroepithelium using Sox1-Cre , which is expressed and catalytically active from ∼E7 . 5 [34]; in definitive endoderm using tamoxifen-inducible Foxa2mcm , which is expressed ∼E6 . 25 [35]; as well as in embryonic mesoderm using Brachyury ( Bry ) -Cre , which is expressed and catalytically active from ∼E6 . 25 [36] . Excision of Pou5f1 by lineage-specific recombinases ( Bry-Cre , Sox1-Cre or Foxa2mcm ) did not result in a phenotype or impact embryonic viability at E9 . 5 . Oct4f/f; Z/EG+/; lineage-specific Cre+/− embryos should reveal aspects of the Oct4COND MUT phenotype related to requirements for Oct4 within their respective expression domains or cause the embryo to resorb by E9 . 5 if development is more severely impacted than in Oct4COND MUT embryos . Recombination at the lacZ/enhanced GFP ( Z/EG ) locus yields GFP expression , so the Z/EG allele was incorporated to gauge recombination efficiency [37] . Based on the parental genotypes used in the cross ( Table S1R–T ) , a genotypic ratio where Oct4f/f; Z/EG+/−; lineage-specific Cre+/− embryos comprise ¼ of the progeny is expected if this genotype , where lineage-specific excision of Pou5f1 occurs , does not impact viability . Such embryos with no phenotype comprised ¼ of each litter ( Table S1R–T ) . To test whether the lineage-specific recombinases yielded false negative results due to infrequent biallelic excision , we assessed the development of embryos where one Pou5f1 allele was removed prior to recombinase expression . Even with this sensitized approach , Oct4Δ/f; Z/EG+/−; lineage-specific Cre+/− embryos with no phenotype comprised ¼ of the progeny at E9 . 5 . This genotypic ratio indicates that excision of Pou5f1 by these lineage-specific recombinases did not impact viability ( Table S1U , V ) . Since false-negatives may arise due to low recombination efficiency in this scheme , we used the GFP expression resulting from recombination at the Z/EG locus in Oct4f/+; Z/EG+/−; lineage-specific Cre+/− embryos as a proxy for recombination efficiency . By E9 . 0 Sox1-Cre and Bry1-Cre induced >95% and >51% recombination within their respective domains ( Fig . S7A–C; Table S1W–Y ) , while Foxa2mcm yielded <5% ( data not shown ) . However , prior to E8 . 0 when embryos are sensitive to Oct4 depletion , Sox1-Cre and Bry-Cre also yielded <5% recombination ( Fig . S7C; Table S1Z , AA ) [30] . Notably , the distribution of Oct4Δ/f; Z/EG+/−; Bry-Cre+/− cells did not appear altered ∼E9 . 5 ( Fig . S7D , E ) , suggesting that any effect Oct4 has on cell fate either coincides with lineage specification or precedes it . To investigate how recombination frequency influences phenotype penetrance in embryos where Pou5f1 is removed by lineage-specific recombinases , we generated diploid chimeras by aggregating WT and Oct4f/f;HisGFP+/−;CreERT2+/− morulas . The ubiquitously expressed fusion protein ‘HisGFP , ’ which is comprised of histone H2B and eGFP was used to mark transgenic cells [38] . Following tamoxifen administration ∼E6 . 5 and ∼E7 . 0 , we recovered 16 chimeras where contribution by Oct4f/f;HisGFP+/−;CreERT2+/− morulas ranged from 20–60% ( Table S1AB ) . 11 of these 16 embryos had no phenotype , while the remaining 5 chimeras had rostral NTC deficits ( Fig . 3D , E ) . This indicates that Oct4+/+ cells rescue the developmental deficiencies caused by Oct4−/− cells in mosaic embryos . Since efficient depletion of Oct4 is required for the Oct4COND MUT phenotype , the inefficient recombination of Bry-Cre , Sox1-Cre and Foxa2mcm during the window of development in which embryos are sensitive to Oct4 depletion does not resolve whether Oct4 is ubiquitously required ∼E7 . 5 , required only in unspecified progenitors , or necessary in a subset of specified lineages , such as in specified Oct4+Bry+ mesoderm . Since this data suggested that differences in the kinetics of Pou5f1 excision with lineage-specific recombinases and CreERT2 ( when tamoxifen is administered ∼E7 . 0 ) are responsible for the absence and presence of phenotypes following Pou5f1 excision , we tested whether expansion of specified lineages was affected in Oct4COND MUT embryos . Lineage-specified Bry+ and Sox2+ cells were present 48 hrs ATA in Oct4f/f;CreERT2+/− embryos ( Fig . S8A , B; Table S1AC ) . We quantified the fraction of phosphorylated Histone H3 ( PH3 ) + cells in specified lineages . The PH3+ fraction of neural or mesoderm cells ( Oct4f/f;CreERT2+/− versus Oct4f/f ) was the same ( Fig . S8C , Table S1AC ) . The data indicate that expansion of these specified lineages is not impacted by Oct4 depletion . To test whether disruption of the pluripotency network causes the Oct4COND MUT phenotype , we removed Sox2 using the same conditional approach [39] . Sox2 is a core component of the pluripotency network that complexes with Oct4 , co-occupies many genomic sites ( Oct4/Sox2 ) and is required for maintenance of Pou5f1 expression in ES cells . ES cells differentiate into trophectoderm when Sox2 is removed [40] , however the ability of Oct4 over-expression to rescue pluripotency in these cells suggests that the critical role of Sox2 in pluripotency is to maintain Pou5f1 expression [40] . Sox2 null embryos lack epithelial cells typical of the epiblast and have a later extraembryonic defect which does not permit development past E7 . 5 [41] . Following tamoxifen administration ∼E6 . 5 and ∼E7 . 0 to Sox2f/f;CreERT2+/− embryos [39] , hydrocephalus was evident in 11/20 Sox2f/f;CreERT2+/− and 2/20 others had kinked neural tubes ∼E9 . 5 ( Fig . 4A–C; Table S1AD ) . Thus Sox2 removal did not phenocopy Oct4 depletion ∼E7 . 5 . These data do not rule out partial compensation for loss of Sox2 by redundant factors , however between E7 . 0–E8 . 0 Oct4 and Sox2 only overlap spatially in anterior neuroepithelium ( compare Figure S1 , S2 and S9 ) [6] , [41] . The distinct phenotypes produced by depletion of Sox2 and Pou5f1 indicate that at least part of their functions do not overlap ∼E7 . 0–E8 . 0 , in contrast to ES cells . Oct4 is reported to bind 784–4234 genomic loci in ES cells depending on the methodology used to map binding sites [5] , [42] , [43] . To determine which targets might be contributing to the Oct4COND MUT phenotype , we measured gene expression changes that occurred coincident with Oct4 depletion ( ∼E7 . 5 ) and thereafter ( ∼E8 . 0 and ∼E8 . 5 ) . Oct4f/f;CreERT2+/− embryos were separated from Oct4f/f littermates by genotyping extraembryonic tissue , and differential expression assessed within litters with ≥3 CreERT2+/− and ≥3 CreERT2−/− embryos ( Table S1AE ) . RNA was extracted 24 , 36 and 48 hrs ATA , when Oct4 transcript abundance in CreERT2+/− embryos is <5% CreERT2−/− littermates ( Fig . S5A–D ) . 754 unique genes were differentially expressed ( p<0 . 01 ) at one or more of these three timepoints . To determine whether the differential expression following Oct4 depletion was a direct consequence of Oct4 loss at its genomic targets , we assessed whether Oct4's direct targets were enriched amongst up- or down-regulated genes as Oct4 both activates and represses transcription [28] . Systematic mapping of TF targets in early embryos is currently prohibitive [44] , so a genome-wide binding map of Oct4 in ES cells was used [5] . This particular genomic binding map , which is based on ChIP-seq data , was used because it offers more complete genomic coverage than target maps based on ChIP-chip data , and also contained the most extensive set of other TF binding maps for additional analysis ( alternatives include: [42] , [43] ) . Enrichment of TF binding targets from ES cells amongst differentially expressed genes after ∼E7 . 5 requires that binding sites be conserved between these stages . Oct4 binding sites from ES cells were enriched amongst up-regulated genes ( Fig . 5B ) , supporting conservation of the binding sites between ES and ∼E7 . 5–E8 . 5 embryos . Oct4 binding targets were also enriched when alternative datasets were analyzed . For comparison , with the aggregate of differentially expressed genes ( 24 , 36 and 48 hrs ATA ) , enrichment using hypergeometric tests were: p = 3 . 45E-11 [43] , p = 2 . 13E-08 [5] , and p = 7 . 36E-4 [42] . This suggest that the expression changes at these sites were a direct consequence of Oct4-mediated transcriptional regulation being removed after ∼E7 . 5 . Oct4 targets whose transcription is regulated by Oct4 in ES cells were differentially expressed coincident with Oct4 depletion ∼E7 . 5 . Lefty1 and Klf2 that are activated by Oct4 in ES cells decreased [45] , [46] , while Xist was notable among the most up-regulated genes following Oct4 depletion as it is repressed by Oct4 in ES cells [41] . An unbalanced male∶female ratio in the intra-litter comparisons , rather than Oct4 depletion , might explain the increase in Xist transcript abundance since embryos were not sexed in the microarray , however Quantitative ( Q ) -PCR on independent balanced comparisons confirmed that the increase related to Oct4 depletion . An intra-litter comparisons to match developmental stage , and inter-litter comparisons to reduce biological variance associated with comparing a small number of embryos both supported Oct4-mediated repression of Xist ∼E7 . 5: Xist was 3 . 20 times more abundant in the intra-litter comparison , and 2 . 85±0 . 76 s . e . m . more abundant in the inter-litter comparison of Oct4f/f;CreERT2+/−/Oct4f/f 24 hrs ATA ( Table S1AF ) . Enrichment for genomic targets of Oct4 is expected with this approach , but transcriptional activators of Oct4 and proteins that physically interact with it were also differentially expressed . Ligands that maintain Oct4 such as Nodal and Wnt3a [11] , [47] exhibit decreased transcript abundance coincident with Oct4 depletion ∼E7 . 5 , while transcriptional activators of Oct4 such as Sp1 [48] and Ago2 [49] exhibited increased transcript abundance , perhaps due to a feedback loop . Proteins that physically interact with Oct4 were also enriched amongst the genes up-regulated following Oct4 depletion ( see Table S2 for cofactor identities; p = 1 . 99E-08 24 hrs ATA , p = 1 . 64E-05 36 hrs ATA , p = 5 . 55E-07 48 hrs ATA enrichment using hypergeometric tests ) . Interestingly , we found considerable enrichment for Oct4 within genomic regulatory elements of these physical cofactors ( p = 5 . 34E-07 for 24 , 36 and 48 hrs ATA collectively using a hypergeometric test ) . This suggests that ∼E7 . 5 Oct4 directly represses expression of a subset of the genes it physically interacts with in ES cells and that its absence triggers positive indirect feedback of the expression of others . Collectively , these data suggest that several regulatory relationships of Oct4 are maintained between preimplantation development and ∼E7 . 5–8 . 5 . To test whether signaling networks other than direct targets of Oct4 might contribute to the Oct4COND MUT phenotype , we determined the transcriptional response that target sets bound by TFs other than Oct4 had to Oct4 depletion . The binding maps of 12 other TFs , and combination of several with Oct4 , were assessed for enrichment amongst the genes differentially expressed after Oct4 depletion ( Fig . 5A ) [5] . Targets of c-Myc and Smad1 were enriched amongst genes up-regulated after Oct4 depletion [5] . Unlike c-Myc , which does not cluster at binding sites with Oct4 in the genome , Oct4 facilitates the binding of Smad1 such that they overlap at a subset of sites [5] . However up-regulation of Smad1 targets after Oct4 depletion occurred at sites Smad1 occupies independent of Oct4 , indicating that enrichment of up-regulated Smad1 targets is not due to direct relief of Oct4-mediated repression at sites that the two co-occupy [5] . The enrichment of Smad1 targets amongst up-regulated genes that are not co-occupied by Oct4 are: p = 6 . 14E-06 24 hr ATA , p = 4 . 55E-03 36 hr ATA , p = 3 . 53E-09 48 hr ATA ( hypergeometric test ) . Like Oct4 , Smad1 has been implicated in both activation and repression of target genes [50] , consistent with a separate subset of Smad1 targets are de-repressed 24 hrs ATA . These data suggest that the absence of Oct4 yields a transcriptional environment conducive to target activation by c-Myc and Smad1 . Conversely , enrichment of co-occupied Oct4/Sox2 target sites amongst down-regulated genes ( Fig . 5C ) suggests that Oct4 participates in transcriptional activation of these ∼E7 . 5 and after . Since conditional removal of Sox2 and Pou5f1 do not phenocopy ( compare Figure 1A to 4A ) , Sox2 is either not essential for activation of these sites , which is consistent with data from ES cells [40] , or down-regulation of these targets does not contribute to the Oct4COND MUT phenotype . Oct4 binds thousands of sites in the genome , and it is unlikely that disruption of a single target gene causes the Oct4COND MUT phenotype . To relate molecular changes resulting from Oct4 depletion with the Oct4COND MUT phenotype , we determined which signaling pathways were disrupted coincident with Oct4 depletion and prior to the onset of the phenotype . Unsupervised clustering was used to assess the function of differentially expressed genes collectively . To discern primary effects of Oct4 depletion , we sub-setted for genes that are direct targets of Oct4 based on the ES binding maps [5] , clustered these ( Fig . 6A; Table S1AE ) , and then compared the clusters to global changes ( Fig . 6B; Table S1AE ) . 3 of the 4 pathways showing the strongest enrichment in the set of direct targets also showed significant enrichment in the global set . Coordinate regulation of additional genes that are not targets of Oct4 within the same pathways as those directly regulated by Oct4 , suggests amplification of the direct effects ( Fig . 6A , B; Table S1AE ) . QPCR on independent biological samples confirmed a subset of changes from the global expression analysis ( Fig . 6C , Table S1AG ) , supporting the reproducibility of the differential expression . Differential expression was then considered in relation to the Oct4COND MUT phenotype . The expression profiling suggested that decreased TGF-β signaling and increased nuclear import of NF-κB were primary effects as they occurred within hours of Oct4 depletion ( 24 hrs ATA ) amongst direct targets of Oct4 , while decreased Notch signaling and increased protein translation are other candidates that occurred later ( Fig . 5A ) . The node is required to coordinate left-right asymmetry , specification of definitive endoderm and somitogenesis [51] . Given these roles in development , we considered the possibility that Oct4 was required in node formation a candidate that might explain the situs inversus , defective somitogenesis and the posterior truncation ( via either endoderm specification or defective somitogenesis ) observed in Oct4COND MUT embryos . Gene expression changes following Oct4 depletion also suggested the possibility of node malformation: decreased Dll1 contributed to the ‘Notch signaling’ enrichment in the microarray and was confirmed by QPCR in separate litters ( Fig . 6C; Table S1AG ) . Decreased Dll1 following Oct4 depletion is relevant because loss of Dll1 was previously shown to disrupt node formation and cause defects in left/right asymmetry [52] . While these data were suggestive of a candidate mechanism underlying the Oct4COND MUT phenotype , the presence and appropriate localization of the node marker Chordin both 24 hrs ATA ( Fig . 7A , B , Table S1AC ) and 36 hrs ATA ( Fig . S10A , B; Table S1AC ) suggests that initial node specification occurs in Oct4COND MUT [53] . The disruption of left-right asymmetry is likely downstream of node specification , as transcript abundance of laterality specifiers that are asymmetrically distributed by the node during development is altered: Nodal , Dll1 , Lefty1 and Lefty2 are decreased while Hand1 and Hand2 are increased . These data do not support the Oct4COND MUT phenotype being caused by a failure in Notch-mediated node specification . Contraction of actin-myosin microfilaments contributes to the morphogenetic processes of turning and convergent extension . A decrease in ‘actin filaments’ ( p = 1 . 88E-07 ) following Oct4 depletion ( Fig . 6B; Table S1AE ) suggests that actin networks are affected by Oct4 depletion . The distribution of actin appeared altered 24 hrs ATA with phalloidin staining ( Fig . S10C , D; Table S1AC ) . Indeed the distribution of actin in Oct4f/f;CreERT2+/− embryos suggests that adhesion between anterior and posterior neuroepithelium in the distal portion of the embryo may contribute to thicker neuroepithelium in this regions and impaired embryonic morphogenesis . TGF-β signaling has also been implicated in several processes disrupted in Oct4COND MUT embryos: expansion of primitive streak [54] , patterning derivatives of the anterior primitive streak [55] , establishment of definitive endoderm [56] , maturation of the node [57] and left/right asymmetry establishment [58] , [59] . Unsupervised clustering indicates that Oct4 directly maintains TGF-β signaling ( Fig . 6A ) . TGF-β signaling through Smad2 competes with Smad1 for the co-activator Smad4 [60] , so up-regulation of Smad1 targets following Oct4 depletion may involve an increase in Smad1 , expansion of the domain of activated phosphorylated-Smad1 ( p-Smad1 ) , or diminished competition from TGF-β-Smad2 . Increased transcript abundance of Smad1 was confirmed by Q-PCR ( Fig . 5C; Table S1AG ) . The p-Smad1 domain also appears altered 24 hrs ATA ( Fig . 7C , D; Table S1AC ) . Variance in p-Smad1 introduced by differences in embryonic stage and ‘batch effects’ during detection prohibited making a statistically meaningful quantitative comparison of protein abundance between stage-matched Oct4f/f; CreERT2+/− and Oct4f/f embryos . Quantitative comparison with high-content image analysis software did suggest a difference in p-Smad1 abundance related to Oct4 depletion ( Fig . S11 ) , but this approach would require a considerable increase in sample size to test significance . These data suggest a direct effect of Oct4 depletion on diminished TGF-β signaling . Presence of Oct4 in the primitive streak ∼E7 . 5 ( Fig . S1 ) , impaired axial extension in Oct4COND MUT embryos and differential expression of TGF-β signaling that is essential for expansion of primitive streak [54] suggested an effect on its expansion . An effect on the primitive streak and consequently its derivatives might have broad relevance: cranial mesenchyme supports NTC , while mesendoderm facilitates posterior extension , somitogenesis and turning . The frequency of cells undergoing apoptosis ( Caspase-3+ ) in the Oct4COND MUT was increased ( Fig . 7I; Table S1AC ) , suggesting that diminished cell viability might contribute to the phenotype . Notably , the distribution of apoptotic cells throughout the embryo , including regions where Oct4 is not expressed , suggests that some apoptosis may be a secondary defect . Conversely , fewer cells proliferated indicated by phosphorylated histone H3 positive ( PH3+ ) in the primitive streak of embryos 24 hrs ATA ( Fig . 7G , H , J; Table S1AC ) . To confirm the localization of these effects , we divided embryos into three segments ( proximal anterior , distal and proximal posterior ) and quantified the abundance of transcripts regulating apoptosis and proliferation . To obtain sufficient material for comparison , CreERT2+/−;Oct4f/f samples 24 hrs ATA were compared to CreERT2+/−;Oct4f/f stage-matched samples from separate litters . While there was no difference in the transcript abundance of apoptosis regulators Bax and Bcl2 , a negative regulator of proliferation , Cdkn1c , which exhibited increased transcript abundance in the differential expression analysis was selectively increased in the posterior third of embryos coincident with the loss of Oct4 ( Fig . 7K; Table S1AH ) . These data indicate that ubiquitous Oct4 depletion leads to increased apoptosis and deficient proliferation in the primitive streak .
∼E7 . 5 , Oct4 is still present in the primitive streak , posterior visceral endoderm , several mesoderm derivatives , neuroepithelium as well as extraembryonic endoderm and mesoderm ( Fig . S1 ) [6] . Proliferation of the primitive streak decreases and apoptosis increases within the embryo coincident with Oct4 depletion ∼E7 . 5 , and by ∼E9 . 5 several morphogenetic processes are disrupted: turning , posterior extension , laterality and NTC all are affected , demonstrating that Oct4 is required for somatic development after implantation . Reduced proliferation in the primitive streak coincident with Oct4 depletion suggests that Oct4 might maintain potency ∼E7 . 5 as it does in the ICM [1] . EpiSC-derivation and teratoma assays support the persistence of pluripotent somatic cells ∼E8 . 0 , while lineage tracing indicates the presence of neuro-mesodermal progenitors ∼E8 . 0 [61] . However excision of pluripotency factors Sox2 and Oct4 ∼E7 . 0 do not phenocopy as their depletion in ES cells do [1] , [40] , indicating that the pluripotency network is altered between the ICM and ∼E7 . 5 . Differences in localization contribute: at the latest stage embryos are sensitive to Oct4 depletion and a proliferation deficit is evident in the primitive streak of Oct4COND MUT embryos ( ∼E7 . 5 ) , Sox2 transcript is limited to the chorion and anterior neuroectoderm ( Fig . S9 ) [41] . Neural-specific Sox2 excision results in enlarged lateral ventricles ∼E19 . 5 due to decreased proliferation of neural stem and progenitor cells [62] , suggesting that hydrocephalus in Sox2COND MUT embryos may result from insufficient expansion/thickening of the neuroepithelium . This might render the neuroepithelium more elastic and distended as a result of the positive fluid pressure in the neural lumen [63] , or precede the collapse or kinking of neural tubes that infrequently occurred . The differing phenotypes following depletion ∼E7 . 5 indicate that Sox2 is not required for Pou5f1 transcription or as a cofactor in the processes disrupted in Oct4COND MUT embryos . Oct4 promotes mesoderm as opposed to neural fate during ES differentiation [20] , as does XlPou91 ( the paralog in X . laevis ) in response to FGF [64] , [65] , suggesting that Oct4 depletion might divert mesoderm to neural tissue . Decreased expression of Tbx6 [66] and Wnt3a [67] whose loss is associated with diversion to ectopic neural tubes from paraxial mesoderm following Oct4 depletion is consistent with this possibility , as is thicker neuroepithelium of Oct4COND MUT embryos near closure point 1 . However this differential expression may not reflect altered specification per se , but altered proportions of the embryo associated with defective axial extension . Similarly , neuroepithelial thickening unrelated to cell fate divergence is common amongst mutants with NTC defects such that this is not a reliable indicator of fate changes [30] . Finally , the distribution of Oct4Δ/f; Z/EG+/−; Bry-Cre+/− cells did not appear altered . This suggests that any effect Oct4 has on cell fate either coincides with lineage specification or precedes it . An alternative to an effect on cell fate specification is that Oct4 promotes expansion of unspecified progenitors by driving the cell cycle . Reduced mesenchyme density , decreased proliferation in the primitive streak , increased Trp53 ( p53 ) expression and increased Cdkn1c expression in the Oct4COND MUT embryonic posterior all indicate that expansion of posterior progenitors is disrupted when Oct4 is depleted . The G1/S transition is effectively absent from ES cells , and binding of Oct4 to micro-RNAs that suppress inhibitors of the G1/S transition [68] may promote its bypass and limit the window for lineage-specific chromatin remodeling . Indeed , genes regulating ‘chromatin modification’ are up-regulated 24 hrs ATA coincident with reduced proliferation in the primitive streak ( cluster 1–295: p = 2 . 1E-04 and cluster 613–908: p = 3 . 7E-04 using hypergeometric tests ) . Finally , c-Myc activates G1/S checkpoint complexes [69] , [70] , suggesting that c-Myc may be required to promote G1/S transition when the G1/S checkpoint is established coincident with Oct4 depletion . Morrison and Brickman proposed that the evolutionarily conserved role of Oct4 might be facilitating expansion of progenitor populations during and after gastrulation based on work with paralogs: Pou2 in D . rerio and XlPou91 in X . laevis [64] . These D . rerio Pou2 mutants [71] and X . laevis embryos treated with morpholinos against XlPou91 share posterior truncations [64] . Since Pou5f1 arose by duplication of Pou2 [64] , these data support a conserved role for Oct4 in posterior extension , which in mice includes maintaining proliferation in the primitive streak .
All procedures were approved by the University of Toronto Animal Care Committee in accordance with the Canadian Council on Animal Care . Foremost , both euthanasia and surgery were minimized . When performed , stress was minimized to the greatest extent possible before rapid depressive action on the CNS during euthanasia . Minimally invasive surgeries were performed under anesthetic to achieve complete depression of feedback from the PNS and analgesic used for recovery . For staging , embryos were assumed to be 0 . 5 days post coitum at 1pm on the day a vaginal plug was found . This is 12 hrs after the midpoint of the 14 hr light/10 hr dark cycle we used , where the lights were shut off every night at 8 pm and came on every morning at 6 am . Given the relevance of staging to this set of experiments , it is important to note that use of vaginal plugs –as opposed to direct observation of conception– is accompanied by ±7 hrs of variability in embryonic staging and is inferred from the midpoint of the dark period in the light/dark cycle . Embryos were dissected in Dulbecco's PBS ( Gibco ) and immediately placed in either liquid nitrogen ( for microarrays and QPCR analysis ) or in 4% paraformaldehyde ( for sectioning and immunohistochemistry ) . Dissections for embryonic stages that are whole numbers ( e . g . E8 . 0 or E9 . 0 ) were performed between 9 and 11 pm , while those occurring 12 hrs apart from whole days post coitum ( e . g . E9 . 5 or E10 . 5 ) were performed between 12 and 2 pm . For the experiments assessing the timeframe of Oct4 depletion ( Fig . 1D–G , S5A–D ) , tamoxifen was administered at 9 pm±30 min , and dissections performed the indicated number of hours ATA , e . g . dissections for the time-point 3 hrs ATA were done at midnight ( 12 am ) . The following stocks were used in the study: CD1 ( Charles River ) , Oct4f/f [25] , lacZ/eGFP ( Z/EG ) [37] , B6 . Cg-Tg ( Hist1H2BB/Egfp ) 1Pa/J ( Histone H2B/eGFP fusion ‘HisGFP’ ) [38] , Bry-Cre [36] , Sox1-Cre [34] , Foxa2tm2 . 1 ( cre/Esr1* ) Moon/J [35] , Sox2f [39] , CreERT2 [29] . Individual embryos or the associated extraembryonic tissues were genotyped as originally described . Because a variety of experimental permutations were used in this project , the details of each permutation , including the mouse strains , genotypic ratios , tamoxifen administration regimen and other relevant features are provided on a separate row in Table S1 ( the relevant row is noted as the experiment is described where ‘S1 , row A’ is ‘S1A’ ) . Tamoxifen was administered according to the protocol optimized following CreERT2 development [29] . 99 mg of tamoxifen ( Sigma ) was dissolved by sonication in a solution of 100 ul of ethanol ( Sigma ) and 1 ml of peanut seed oil ( Sigma ) [29] . The solution was kept in a ∼50°C water bath during preparation and prior to administration to avoid precipitation . 50 µl doses of this solution were administered to pregnant mothers by oral gavage using a 250 µl gastight #1725 syringe ( Hamilton ) [29] . Because of the uncertainty associated with staging embryos with vaginal plugs ( ±7 hrs ) , the time-point ( s ) indicated for tamoxifen administration are approximations , and listed as such ( ∼ ) within the text to reflect this uncertainty . In practice , tamoxifen was given at 9pm±30 min ( ∼E6 . 0 , ∼E7 . 0 or ∼E8 . 0 ) or 9 am±30 min ( ∼E6 . 5 , ∼E7 . 5 or ∼E8 . 5 ) . The time-point ( s ) when tamoxifen was administered for each experimental permutation are listed in Table S1 as well as in the figure captions . The density of mesenchyme , frequency of apoptosis and proliferation , relative abundance of transcripts ( other than Oct4 ) , distance between neural folds and thickness of neuroepithelium were compared using 2-way ANOVAs . Depletion of Oct4 protein and transcript were compared with 1-way ANOVAs . F-values from the embryonic genotype's contribution ( Oct4f/f versus Oct4f/f;CreERT2+/− ) to variation are indicated except for Figure 7K and S6C where the intra-embryo segment contribution is reported ( e . g . difference between segments in the same embryo ) . Binding enrichment amongst differentially expressed genes and common causality of disrupted features in partially penetrant Oct4COND MUT embryos was assessed using hypergeometric tests . The thickness of notochords was compared using a two-tailed t-test . A threshold of p<0 . 05 was used for each test ( ANOVA , hypergeometric and t-test ) . Please see the Supplementary Methods ( ‘Text S1 , page 1’ ) for detail on how measurements of Oct4 protein depletion , mesenchyme density , neuroepithelium thickness , notochord thickness , distance between the neural folds , and the fraction of Ph3+ , Caspase-3+ and Oct4+ cells were taken ( ‘Basic Measurements’ ) . Images in Figure 1F , G; Figure 2 F , G , I , J; Figure 3A–D; Fig 7A–J; Figure S6A , B , D , E; Figure S7A , B , D , E; Figure S8A , B and Figure S10A–F were taken with a Zeiss Axio Observer , images of Figure S5A–D were taken with an Olympus Fluoview 1000 , images of Figure 2 B , C and Figure 4A–C were taken with an Olympus SZ61 , and images of Figure 1A–C; Figure 3SA , B and Figure S4A were taken with a Leica MZ16 FA stereomicroscope . Contrast of the images in Figure 3D , 4A and 4C was enhanced with Adobe Photoshop v12 . Oct4 staining was performed as described previously [6] . For all other immunohistochemistry , embryos were fixed in 4% PFA overnight at 4°C , sectioned at a thickness of 10 µm and primary antibodies applied overnight at 4°C at the following concentrations: Oct-3/4 1∶200 ( C-10 Santa Cruz ) , Chordin 1∶100 ( R & D Systems ) , p-Smad1 1∶400 ( Cell Signaling ) , Caspase-3 1∶500 ( Promega ) , Ph3 1∶500 ( Cell Signaling ) , Bry 1∶50 ( R & D Systems ) , Sox2 1∶50 ( R & D Systems ) . An antigen retrieval step of boiling the sample in 10 mM Sodium Citrate Buffer , pH 6 . 0 for 15 min was used for Oct-3/4 ( C-10 immunofluorescent ) and Chordin staining . Phalloidin staining ( Alexa Fluor , Life Technologies ) was performed according to the manufacturer's instructions . Hematoxylin and Eosin ( Sigma ) staining was performed according to the manufacturer's instructions . Different litters from those used in the microarray analysis were used to confirm changes in gene expression by QPCR . Please see Supplementary Methods ( ‘Text S1 , page 2 ) for assay details . Chimeras were produced as outlined in [72] , and contribution was assessed by semi-quantitative PCR . Please see Supplementary Methods ( ‘Text S1 , ’ page 2 ) for details . RNA was extracted with Trizol according to the manufacturer's instructions ( Invitrogen ) and sent to the UHN Microarray Centre ( Toronto , ON , Canada ) for fluor-labeling ( protocol GE2 v5 . 7 ) , microarray hybridization , and array scanning . Please see Supplementary Methods ( ‘Text S1 , ’ page 4 ) for additional detail and analysis methodology . Please see ‘Text S1 . ’ Please see ‘Text S1 . ’ Please see ‘Text S1 . ’ Please see ‘Text S1 . ’ Please see ‘Text S1 . ’ Please see ‘Text S1 . ’ | Embryogenesis is an intricate process requiring that division , differentiation and position of cells are coordinated . During mammalian development early pluripotent populations are canalized or restricted in potency during embryogenesis . Due to considerable interest in how this fundamental state of pluripotency is maintained , and the requirement of the transcription factor Oct4 to maintain pluripotency , Oct4 has been intensively studied in culture . However , it is not clear what role Oct4 has during lineage specification of pluripotent cells . Oct4 removal during lineage specification indicates that it is required in the primitive streak of mouse embryos to maintain proliferation . The consequences of Oct4 removal diverge from the consequences of removing another factor required for pluripotency between preimplantation development and early cell fate specification suggesting that the network Oct4 acts within is altered between these stages . |
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Decision making has been studied with a wide array of tasks . Here we examine the theoretical structure of bandit , information sampling and foraging tasks . These tasks move beyond tasks where the choice in the current trial does not affect future expected rewards . We have modeled these tasks using Markov decision processes ( MDPs ) . MDPs provide a general framework for modeling tasks in which decisions affect the information on which future choices will be made . Under the assumption that agents are maximizing expected rewards , MDPs provide normative solutions . We find that all three classes of tasks pose choices among actions which trade-off immediate and future expected rewards . The tasks drive these trade-offs in unique ways , however . For bandit and information sampling tasks , increasing uncertainty or the time horizon shifts value to actions that pay-off in the future . Correspondingly , decreasing uncertainty increases the relative value of actions that pay-off immediately . For foraging tasks the time-horizon plays the dominant role , as choices do not affect future uncertainty in these tasks .
Decision making has been studied with a wide array of tasks . Choices in many of these tasks either do not affect future choices or are modeled as if they do not affect future choices . For example , when asked to choose between gambles ( e . g . 50% chance of $20 or 100% chance of $11 ) , the choice in the current trial does not affect the gambles presented in the next trial , or the information on which one decides in the next trial . Correspondingly , even reinforcement learning tasks , where choices do affect the information that will be available for future choices , are often modeled using delta rule reinforcement learning ( DRRL ) or logistic regression , neither of which provides a normative description of the task . These modeling approaches assume that current choices should be driven entirely by past outcomes without considering how they will affect the future . Many interesting decision making problems , however , require consideration of how current choices will affect the future [1–7] . For example , there has been interest in the explore-exploit tradeoff [8–17] , information sampling [4 , 6] , and foraging [18 , 19] . Explore-exploit trade-offs exist in any real-world decision making context where one has to choose between continuing to exploit a known option , for example a familiar restaurant , vs . exploring an unknown or novel restaurant . Similarly , information sampling underlies many deliberative choice processes where one collects information before committing to a decision . For example , one might study product reviews or ratings before making a large purchase . These tasks require more sophisticated choice strategies because choices can be driven by future expected values . In other words , the best choice may not be the one that delivers the largest immediate reward . The best choice may lead to larger rewards in the future at the expense of smaller immediate rewards . Choices in these tasks can be modeled with markov decision processes ( MDPs ) . MDPs provide a general modeling framework , useful in tasks where the future depends upon what one chooses in the present . If one assumes that an agent is maximizing the expected ( discounted or undiscounted ) total reward , MDPs can be used to provide normative , or at least approximately normative , solutions to most current decision problems . While the choice behavior of subjects often deviates from normative behavior [4] , particularly in patient groups [5 , 20 , 21] , normative models are still important . Specifically , normative models identify the information on which decisions should be based , and the computations that must be carried out on that information . These two points can be conceptualized as the strategy optimal for the task . Further , normative models can be parameterized to fit the behavior of individual subjects [4 , 5 , 22] . This approach can provide insight into how subjects are deviating from the normative model and therefore it can suggest specific deficits or biases , as opposed to an overall change in task performance . Here , we used MDPs to model n-armed bandit , information sampling , and foraging tasks . Normative solutions to some of these tasks , to our knowledge , do not currently exist in the literature . There is , however , a long theoretical literature on binary bandit tasks [23 , 24] , and some foraging tasks have been modeled using the marginal value theorem [25] . For MDPs , the development of approximation techniques using basis functions has opened up the solution of a much larger class of problems than was tractable previously [26] . The normative solutions provide insight into the optimal strategies . We also used the models to examine several specific questions . For example , when is it useful to explore in a bandit task , and which features of the task can increase the value of exploring ? How can non-stationarity drive exploration ? Furthermore , once the tasks were mapped into the MDP framework we could examine their similarities and differences . This showed that decisions in all of these tasks pose a trade-off between immediate and future expected rewards . Further , we identified two factors that are important to this trade-off in these tasks . The first is uncertainty and the second is the time horizon . In bandit and information sampling tasks future expected values are relatively higher for options about which there is more uncertainty . When there is less uncertainty , action values are driven more by immediate expected reward . Further , uncertainty , and the value of exploring uncertain options is more valuable when the time horizon is longer . We also show that reward rate maximization in foraging tasks with an undiscounted , infinite time horizon is insensitive to travel delays to patches . In general with MDPs , infinite horizon undiscounted models are insensitive to finite delays to rewards .
When the environment is unknown , and model-free reinforcement learning ( RL ) is used to learn the environment [27] , exploration can be used to drive the RL algorithm to sample from the complete space of possible options . Here we deal with tasks where the environment is specified and MDPs ( or POMDPs ) can be used to calculate expected values for each state . Therefore heuristic exploration does not have to be used to make choices . Exploration , if it is defined as selecting options which have a smaller IEV but a larger action value , however , can still be optimal [27] . If an agent is maximizing total expected reward , an option with a smaller IEV can be selected if its FEV is relatively larger . Thus , immediate rewards can be foregone to obtain more total rewards over the relevant time horizon . We began by examining the explore-exploit trade-off in a stationary 2-armed bandit task , in which both bandits paid-off with the same fixed reward . The bandits varied , however , in the fraction of times they delivered a reward if chosen . In this case the explore-exploit trade-off affects the first few choices , before both targets have been sampled a few times . We modeled this as a finite state , finite horizon , undiscounted POMDP , where the information states were the number of times each bandit was chosen , Ci and the number of times each bandit was rewarded , Ri . This information state space is formed by the sufficient statistics for the two bandit processes . Transitions through the information state space occur after each choice and its associated outcome and they correspond to belief updates for the process . To examine the information state space for the bandit task more quantitatively , we can examine the distributions over expected future reward values generated in the task . Each of the bandit options is represented by a tree of possible outcomes ( Fig . 1A ) . Each node in the tree defines the information state ( i . e . Ri , Ci ) for that option . The information state can be used to estimate the underlying reward probability , q for each bandit option , where q is the hidden state of the system . As one of the options is sampled , the tree is traversed . With a binomial likelihood function and a beta ( α , β ) prior , the posterior over reward probability is given by pqri , ci∝pri , ciqpq p ( q|ri , ci ) ∝qri ( 1−q ) ci−riqα−1 ( 1−q ) β−1 p ( q|ri , ci ) ∝qri+α-11-qci-ri+β-1 The beta prior is the natural conjugate prior for the binomial likelihood function . Therefore , the prior can be interpreted as data . The posterior expected value is q|Ri , Ci = RiCi = α+riα+β+ci , where we have defined the actual choices and rewards as ri , ci , and the posterior choices and rewards as the data plus the prior Ri = ri + α , Ci = ci +α + β . If we start with a beta ( α = 1 , β = 1 ) prior we have posterior values of Ri = 1 , Ci = 2 for each bandit arm before any options have been sampled ( Fig . 1A ) . The possible posterior expected values are given by the nodes of the tree ( Fig . 1A ) . These nodes are also the immediate expected value for a choice , i . e . <rst , a> = RiCi , and these values also define the transition probabilities . Thus , if one is in state , Ri , Ci one transitions to Ri+1 , Ci+1 with probability pj = Ri+1 , Ci+1st = Ri , Ci , a = RiCi and one transitions to Ri , Ci+1 with probability pj = Ri , Ci+1st = Ri , Ci , a = 1-RiCi . This defines two of the terms on the r . h . s . of equation 2 ( ignoring the cost to sample ) . The other term on the r . h . s . of equation 2 is the utility of the next state , ut+1 . These utilities are recursively related to future utilities , ut+2 , etc . However , in the final trial , assuming a task where there are a finite number of trials and the number is known a-priori , there is no FEV because there will be no choices in the following trial . Therefore , utilities in the final trial , t = N , are given by the IEV , <rt ( st , a ) > . The IEV for each state that can exist in the final trial can be directly calculated from these information states . Once these are calculated , one can calculate the utilities for t-1 , and continue backwards until the utilities for the current trial can be calculated . This is the backwards induction algorithm ( [28]; see methods ) . When an option has not been sampled , any point in the tree can potentially be reached , although not under the optimal policy , and the distribution over reward probabilities is broad . This tree , therefore , represents the possible outcomes if one of the options is chosen repeatedly ( Fig . 1A ) . The state space for the task is , however , the product space over the nodes of two of these trees ( Fig . 1B ) , as it is constructed of all combinations of possible outcomes from each individual tree . When the FEV is calculated for one of the options , it is only calculated across the nodes in the full tree that are visited by the optimal policy . This is because the max operator in equation 1 is an expectation over the policy that optimizes choices in each state . Thus , when the FEV is calculated the expectation is taken over the portion of the product space ( Fig . 1B ) where the expected action value of an option is greater than the other option ( thick lines in Fig . 1B ) . The expectation is not computed over the dotted lines ( Fig . 1B ) because an optimal policy does not choose these actions . If we examine the distribution of reward probabilities over a representative finite horizon ( Fig . 1C ) we see that options which have been sampled less have higher expected values , when IEVs are each 0 . 5 . In this example it is less likely that one will encounter a reward probability ( q ) of 0 . 5 for options that have been sampled less , and more likely that one will encounter options that have a reward probability greater than 0 . 8 . This increased mass over higher reward probability nodes in the tree drives exploration in bandit tasks . As an example , we examined a scenario in which bandit option 1 was sampled 6 times , and rewarded three times ( Fig . 2A ) . ( Note that in this example the agent is not following the optimal policy . Rather we have defined choices and outcomes to illustrate action values under particular scenarios . ) The action value for option 1 exceeds the action value for option 2 during the first three trials while it is being rewarded . The FEV , however , of option 2 is larger than the FEV of option 1 , even in the first 3 trials , during which option 1 is being rewarded . After option 1 is not rewarded once , it becomes more valuable to sample option 2 ( i . e . Q ( s , 2 ) > Q ( s , 1 ) in trial 5 ) . After option 1 had been sampled 6 times and rewarded three times , its IEV is the same as option 2 , which had an expected value of 0 . 5 because of its prior . However , the action value ( IEV + FEV ) favors option 2 at this point ( i . e . trial 7 ) . If option 2 is then sampled 6 times and rewarded 3 times , the action values of the two options are again the same ( i . e . trial 13 ) . The exploration bonus ( here taken as the difference in FEV between the two options on trial 4 ) is also larger when the time horizon is longer ( Fig . 2B ) . This is because option 2 can be exploited for a longer time horizon if it is sampled and found to be better . When the first option chosen is rewarded , and it continues to be chosen and rewarded , the action value of the second option will not exceed the value of the first option ( Fig . 2C ) , given these finite time horizons . The exploration bonus is driven by three factors . Continuing on the example above , assume option 1 has been sampled and option 2 has not been sampled . First , there is uncertainty about option 2 ( i . e . the prior distribution over possible reward probabilities for unsampled options is broad , assuming a vague prior ) . Therefore , option 2 might be better than option 1 . If option 1 cannot be better than option 2 , because of the structure of prior knowledge , there is no exploration bonus . The second factor , as shown above ( Fig . 2B ) is the time horizon [17] . If the time horizon is too short one cannot obtain enough additional rewards when option 2 is found to be better than option 1 , to make up for the scenarios ( i . e . other episodes of the task ) when option 2 is found to be not as good as option 1 . This factor relies on the assumption that option 2 might be better than option 1 . Third , if option 2 is sampled and it is not as good as option 1 , then one can switch back to option 1 . On the other hand , if option 2 is better than option 1 , then one can stick with option 2 . This preference for the option which will be found to be better in the future , drives choices in the present via the max operator over action values in the utility equation ( equation 1 ) , which operates on the distribution of future outcomes via the embedded recursion . We next examined a novelty task [5 , 8 , 29] . This is a 3-armed bandit task similar in several ways to the 2-armed bandit task described above . The size of the reward is the same for each bandit option , but the probability of receiving a reward when each option is selected differs . In addition to this , however , choice options are replaced by novel choice options at stochastic intervals . Thus , after subjects accumulate experience with the current set of 3 bandit options for a period of time , one of the options is replaced by a novel option . These replacements are stochastic and not known in advance , but they are indicated to the subject . We modeled this task with an infinite horizon , finite state , discounted POMDP . Consistent with the 2-armed bandit , the information state is defined by Ri , Ci for each option . The full information state is now a product space across 3 trees ( Fig . 1A ) , so it is larger . To examine this task we considered a scenario similar to the one examined for the 2-armed stationary bandit . The action value of the chosen option ( option 1 ) increased while it was being rewarded in trials 1–3 ( Fig . 3A , for the choices and rewards see Fig . 3D; note that these actions are not chosen by the optimal policy . Rather they were chosen to illustrate the effect of experience with an option ) . The FEV also increased for all 3 options because of the overall increase in the expected reward in the environment ( Fig . 3C ) . However , similar to what was seen in the 2-armed bandit ( Fig . 2A ) , the FEV was larger for unexplored options ( Fig . 3B ) . Further , when option 1 was replaced , after each of the options had been chosen a few times , its FEV increased relative to the other two options ( Fig . 3B , trial 15 ) . Similarly , when option two was replaced on trial 20 , its FEV increased ( Fig . 3B ) . As with the 2-armed bandit , when the discount parameter was increased towards 1 ( Fig . 3E ) , the exploration bonus increased ( Fig . 3F ) . Thus , when a long time-horizon is available to exploit a novel option if it is found to be more valuable , the FEV for exploring that option increases . Every time a novel option is introduced , it is equivalent to resetting that option to the beta ( 1 , 1 ) prior , resetting it to the start of the tree ( Fig . 1A ) . Thus , uncertainty drives an exploration bonus as long as a sufficient time horizon is available to exploit the novel option if it turns out to be better than the alternative options available . Correspondingly , the substitution rate of novel options also affects the novelty bonus , by effectively limiting the time horizon ( Fig . 3F ) . If the substitution rate is high , one likely will have less time to exploit novel options that turn out to be good , before they are again replaced . To examine exploration in related bandit tasks , we used an infinite horizon , discounted , continuous state , POMDP to model a non-stationary two-armed bandit task [9] . The information state in this model is given by the mean and variance of the bandits , which are the sufficient statistics for the two processes . The bandits in this task returned continuous valued rewards ( e . g . 0–100 ) . The means of the returned values for each bandit were non-stationary in time , following independent , random walks that decayed to 50 . The actual reward earned on an individual trial was given by a sample from a Gaussian distribution with the current mean , and a standard deviation of 4 . The IEV is given by the estimated mean of each bandit . The utility depends on the estimated means of the two options ( Fig . 4A ) as well as the estimated variance of the options ( Fig . 4B ) . The effect of variance on utility also depends on the time-horizon ( Fig . 4B ) . The variance has a larger effect when the time horizon is longer . The effect of the variance of the utility can be understood in the framework developed above for the stationary bandit ( Fig . 1 ) . Specifically , when an option is not sampled its variance grows because of the nonstationarity of the underlying generative model , effectively driving it backwards in the tree ( Fig . 1A ) . On the other hand , when an option is sampled its variance decreases , effectively driving it forwards in the tree ( Fig . 1A ) . Thus , an option which has not been sampled for several trials becomes similar to a novel option , and it should be explored . We examined the choice sequence of the algorithm for some examples . If we consider an artificial case where the means are locked at 45 and 55 ( but the algorithm still assumes the means are non-stationary ) , and compare the sampling under two different discount parameters ( effective time horizons ) we see that the algorithm periodically samples the option with a smaller estimated mean , as its variance grows ( Fig . 4C ) . In addition , when the discount parameter is larger ( γ = 0 . 90 vs γ = 0 . 99 ) the algorithm samples more often , consistent with the larger difference in utility for a given standard deviation for larger discount parameters ( Fig . 4B ) . This can also be seen clearly in the action values ( Fig . 4E and F—Note that the algorithm stochastically sampled option 1 first in panel E and option 2 first in panel F , which gives rise to the initial downward vs . upward fluctuation ) . With the means fixed , the action values depend only on the variance of the two processes , if we ignore the decay of the process to 50 . When an option is sampled its variance decreases and its utility decreases , and when an option is not sampled its variance increases and its utility increases . The combination of these eventually drives the action value of the recently unsampled option to exceed the action value of the option currently being sampled ( Fig . 4E and F ) , and the option which has not been recently sampled is then sampled . This can be seen in example sequences drawn from the actual generative process as well ( Fig . 4G-H , the actual process values are identical for these two examples ) . In this case when the algorithm is modeled with a longer time horizon it samples more ( Fig . 4H ) . We next examined an information sampling task , often referred to as the beads or urn task [4 , 5 , 20 , 21 , 30] . In this task subjects are shown a sequence of beads drawn from one of two possible urns ( Fig . 5A ) . One of the urns has q orange beads and 1-q blue beads and the other has q blue beads and 1-q orange beads . After each bead is drawn subjects have three choices . They can either draw another bead from the urn , guess that beads are being drawn from the predominantly blue urn , or guess that beads are being drawn from the predominantly orange urn . Sampling another bead usually involves an explicit cost-to-sample . In other words , subjects are charged for collecting more information . In this task , the value of choosing an urn is given by the IEV , because no more samples are allowed after an urn is chosen so FEV is zero , whereas the value of sampling another bead is given by the FEV ( minus the cost-to-sample ) , because there is no reward if one does not try to infer the urn . Thus , this task explicitly sets up a trade-off between immediate and future expected rewards , and in this sense it is similar to the explore-exploit trade-off in bandit tasks . In most cases subjects are told that they can draw only up to a maximum number of beads and after the last bead is drawn they have to guess an urn . As such , the task can be modeled as a finite horizon , finite state , undiscounted , POMDP . The information state space is simpler than the state space in the bandit task , as it is given by a single tree ( Fig . 5B ) , where instead of rewards and no rewards , the state is given by the number of blue ( or orange ) beads that have been drawn , and the total number of beads drawn . These form the sufficient statistics for the process . As one draws beads , one works through the state space , similar to the situation with the bandit tasks . For example , the first 3 bead draws for the example sequence shown in Fig . 5C would go through the set of states shown ( Fig . 5B ) . Unlike the bandit task , this task was modeled with an uninformative prior on bead draws , because it is normally implemented by showing subjects one bead before asking them to decide [21] . The action values for guessing either of the urns or sampling again show that the value of guessing an urn increases as evidence for that urn increases ( i . e . more beads drawn of the corresponding color ) , and decreases as evidence for the urn decreases , in a cumulative fashion ( Fig . 5C-F ) . The value of sampling again is initially above the value of guessing an urn , but at some point it drops slightly below . Note that without a cost-to-sample ( C ( st , a ) = -0 . 005 in panels C-E and C ( st , a ) = -0 . 025 in panel F ) it is always best to sample all of the available beads . To examine the effect of the cost-to-sample , we calculated values for two costs , on identical sequences of bead draws ( Fig . 5E-F ) . When the cost was lower ( C ( st , a ) = -0 . 005; Fig . 5E ) , it was optimal to delay the decision until after the 11th bead was drawn , whereas when the cost was higher ( C ( st , a ) = -0 . 025; Fig . 5F ) it was optimal to decide after 2 beads . This task can be considered a pure exploration task: how long does one explore before committing to ( exploiting ) one of the choices ? This is similar to exploring a novel option for several trials , while always considering whether to switch back to the known option , or sticking with the novel option . As the certainty about which urn is being drawn from increases , picking an urn ( which will deliver an IEV ) , as opposed to drawing again ( which is valuable because of the FEV ) , becomes more valuable . The final tasks we considered were foraging tasks . Much like the tasks examined above , these tasks trade-off immediate and future expected values . Should one stay in the current patch whose resources are being depleted ( i . e . choose IEV ) or travel to a new patch ( i . e . choose FEV ) [19] ? Or , should one sample again ( i . e . choose FEV ) or commit to the current gamble on offer ( i . e . choose IEV ) [18] ? The state spaces for these tasks differ in a fundamental way from the state spaces in the bandit and information sampling tasks ( Fig . 6A and 7A ) . The state spaces for the foraging tasks are recursive . Stated another way , the state spaces for the foraging tasks do not represent learning or information accumulation . Learning or information accumulation are not recursive because you do not return to the same state ( technically , this is not completely accurate , as one can with some probability , return to a previous state in either the non-stationary bandit or the novelty bandit ) . Rather , in the foraging tasks the current state is provided to the animal and the animal does not have to estimate beliefs or distributions over states . Therefore these tasks are MDPs , as opposed to POMDPs where the state is hidden . In the foraging tasks one observes the state directly . In the patch leaving time task the subjects chose between staying in the current patch or traveling to a new patch [19] in each trial . The state relevant to choices is defined by the current amount of juice and the travel delay . If they stay in the current patch , they receive a ( slightly delayed ) reward , and the amount of reward that they will receive in the next trial if they again choose to stay in the current patch is decreased . If they choose to leave the current patch they have to wait for a known travel delay and they receive no immediate reward . The amount of reward that will be received in the new patch is reset to a fixed level and the travel time to the next new patch is sampled from the distribution of possible travel times ( Fig . 6A ) . The patch leaving time task was modeled as an infinite horizon , discounted MDP . The relevant state variables when a decision is made are given by the current travel delay and the current reward estimate ( Fig . 6A ) . From the model one can calculate the difference in action value for staying in the patch vs . leaving for another patch ( Fig . 6B and 6C ) . It can be seen that the longer the travel time , the longer one stays in the patch ( Fig . 6D ) , consistent with what was shown previously with heuristic models [19] . However , this effect only occurs for discount parameters less than 1 . The undiscounted model is insensitive to finite travel times ( Fig . 6E and 6F ) . This is because undiscounted infinite horizon MDPs are insensitive to finite time delays . Stated another way , if K is the mean first passage time to a state st = j and from state j one follows the optimal policy , then with an infinite horizon the value function can be written [26]: vNπs = limN = ∞1N∑t = 1K-1rst , asπ+limN = ∞1N∑t = KN-1rst , asπ From this it can be seen that actions taken prior to entering state j , at time K , do not matter . This is because the first sum is finite if the rewards are finite , and so it goes to zero in the limit . In the foraging task , if K is the time to get to the inter-trial interval ( ITI ) after choosing to travel , it doesn’t matter how long K is for finite K . The final task is a variant on standard foraging tasks . The state for this task is given by the current gamble pair on offer and the state space includes all the possible gamble pairs . In most foraging tasks a decreasing marginal reward in the current patch eventually drives the action value to leave the patch above the action value to stay in the patch , because leaving has a fixed expected value . This task , however , used a paradigm in which one samples , in each trial , two gambles from a set of six possible individual gambles ( Fig . 7A ) . The six individual gambles from which the pairs were drawn were shown for the current foraging bout and their reward values were known ( e . g . gamble 1 may have had a value of 12 points ) . In each round , a pair of gambles from the set of individual gambles was sampled ( 15 possible pairs assuming sampling without replacement from the 6 , and symmetry of gambles ) . For example , if the gambles for a given session were g1 … g6 , a subject might be shown in a single trial g3 and g5 . They then have to decide whether to engage with that offer pair , or sample again . If they sampled again , a new pair was drawn from the current set of six possible gambles ( perhaps g2 and g3 ) . Every time the subjects sampled again they also incurred a cost-to-sample . ( Note that a cost-to-sample is paid at the time of sampling , and it does not decrease the value of future gambles , in an MDP . ) If they decided to accept the offer , they moved to a decision stage . In this stage the probability that the reward associated with each gamble would be delivered was revealed , and this probability was randomly assigned to each gamble every time the decision stage was entered . The subjects had to choose one of the two gambles in the decision stage based on its magnitude and the associated probability . For example they might be choosing between p1g1 and p4g4 where pi is the probability that the subjects will receive reward gi if they choose that gamble in the decision stage . The agent then selects the gamble that has the highest expected value . The value of sampling again is given by the FEV . The FEV is not equal to the average values of the individual gambles . The FEV is the expected value of future draws ( see methods ) , plus the cost-to-sample . The time horizon is long , and many future samples could be drawn . However , the cost-to-sample decreases the value of future samples linearly with time , when viewed from the present decision . This can be compared to exponential discounting which exponentially decreases the value of future samples . With a sufficient time horizon the FEV is fixed . The task provided no explicit time horizon so we modeled it as a finite ( although long ) time horizon MDP . Therefore one simply samples until the IEV of the offered pair exceeds the ( constant ) FEV ( Fig . 7B ) . It is important to point out that sampling more in this foraging task , unlike the beads task , does not improve the IEV . In other words , the IEV does not necessarily increase with samples , although one can sample a pair with a better IEV . This is related to the state space of the problem . Additionally , without a cost-to-sample , the optimum strategy would be to sample until the pair with the highest value is drawn . The cost-to-sample creates a situation where choice of a gamble pair that is not the largest is optimal , because it may cost too much to obtain a better pair .
We began by examining the explore exploit trade-off in a two-armed bandit task , in which the reward amount for both options was the same , but they differed on the fraction of times that they were rewarded . Bandit tasks have been used to study learning in healthy and clinical populations [31 , 32] . In the first few trials there is value to sampling both options , and unsampled options have a larger FEV . This future expected value depends on three factors . First , the distribution over possible reward probabilities for the unsampled option is broad , given by the prior . Thus , the unsampled option may be more rewarding than the options which have been sampled . Second , if the unsampled option is sampled , and it is not as good as the other options , the subject can switch back to the other options . However , if the ( previously ) unsampled option is better than the other options , the subject can stick with it . Finally , the time horizon must be long enough to reap the rewards of investing samples in the novel option . Heuristically , one could consider the following approximate example . Assume that one has sampled one of two available options ( call it option 1 ) and found that it is being rewarded 70% of the time , and that one now has 100 more trials . One could then sample the alternative option ( option 2 ) 10 times . If option 2 is rewarded 80% of the time one could then stick with that option , gaining on average 80 rewards over the 100 trial horizon . If it is found that option 2 is only rewarded 20% of the time , then one could switch back to option 1 , gaining 0 . 2*10 + 0 . 7*90 = 65 rewards on average . If option 2’s ( i . e . all option 2’s that one encounters , in repeated plays of the task ) are either rewarded 80% of the time , or 20% of the time , the average reward with this simplified strategy will be 72 . 5 over the 100 trials , whereas it would only be 70 if one always stuck with option 1 . The 2 . 5 additional rewards on average is the exploration bonus . It depends on the possibility that the novel option is better than the current option , the fact that one will switch back to the alternative if it is better than the novel option , and having a sufficient time horizon . We also examined two other tasks which are extensions of the bandit task . Specifically , a non-stationary bandit [9] , and a novelty task [5 , 8 , 29 , 33] . In the non-stationary bandit the mean reward magnitudes of the two options follow independent random walks . When an option is sampled several times , a relatively accurate ( i . e . low variance ) estimate of its mean can be derived . However , when an option is not sampled , the distribution of its mean becomes broad . When a random walk is not observed for a period of time , the variance of its estimate grows linearly with time . One way to conceptualize this , relative to the stationary bandit , is to say that when an option has not been sampled for some time , it becomes like a new option , and there is value in exploring it . This is true of any tasks that have an underlying non-stationarity in the reward [34] . It is , however , variance in the estimate of the mean that drives the exploration bonus . When the variance gets large , the option might be better than the current options , and exploration is advantageous . Similarly in the novelty task , when a novel option is substituted for one of the options that has been sampled , the reward probability for the novel option is unknown , and therefore it is valuable to explore it . Next we examined the beads or urn task [4 , 5 , 20–22 , 30 , 35] . This is an information sampling task , similar in structure to other sampling tasks [36] . The POMDP model for this task only optimized choices in single trials with an explicit cost-to-sample . It did not optimize reward rates over multiple trials . Subjects are given the option to sample as much information as they would like , before guessing an urn . The choice to sample rests on the belief that the FEV of sampling is greater than the IEV of guessing an urn . In this respect , sampling is similar to exploring , as it is a choice in favor of the FEV , relative to the IEV . It differs from exploration , however , in that exploration in bandit tasks usually has some IEV . That is , choice of the unknown option in bandit tasks usually leads to some reward . This does not need to be true in general . In information sampling tasks , however , choosing to sample usually leads to zero IEV ( or a slightly negative IEV , given by the cost to sample ) . In this way , sampling is more similar to foraging . It is also worth pointing out that reaction time versions of perceptual inference tasks can be modeled within a framework that is equivalent to the approach used here to model information sampling [37 , 38] . Perceptual inference tasks , as well as many other choice tasks , are often modeled using a drift-diffusion framework , and it is assumed that when an evidence bearing particle crosses a threshold a decision is made . The “threshold” crossing is a choice to stop sampling . It is often inferred for drift diffusion models in perceptual inference tasks , on the basis of behavioral reaction times . But with an MDP the threshold can be calculated dynamically , on the basis of current levels of belief , costs-to-sample , and transition probabilities [38] . Thus , an optimal threshold can be inferred for any tractable task . The final tasks we considered were foraging tasks . These tasks also trade-off immediate vs . future expected values . The choice to forage leads to a zero IEV . The action value of choosing to forage is entirely an FEV . Foraging tasks differ from the tasks considered above , because their state spaces have a recursive structure and the state is observed , not inferred from information bearing observations . The tasks loop through their recursive state spaces over and over again . The choice is defined as a comparison between current and future stochastic offers . The current offer can be to stay in the patch and collect an approximately known , decreasing reward , or take the current pair of gambles that have been offered . The future stochastic offer can be explicitly calculated from the information given . It is either the value of a new patch , given the current travel time , or the expected value of the decision stage for the set of gambles that can be drawn from the current set . These average values are fixed with a sufficient time horizon . Therefore the strategy is to either stay in the current patch until the reward value drops below the value of leaving , or to sample gambles until the sampled gamble is worth more than the expected value of future samples . In foraging tasks there is generally no updating of distribution estimates , and therefore foraging differs fundamentally from exploration , in this respect . There are two important factors that drive choice preferences across these tasks . The first is uncertainty , and the second is the time horizon . Uncertainty affects these models in two ways . First , in bandit tasks when novel options are available , or equivalently when non-stationary options have not been explored for some time , the distribution of possible reward values is broad and uncertainty is high . Therefore , sampling the options a few times to learn about them is valuable , given a sufficient time horizon . The value of this uncertainty is driven by the future expected value . The sampling itself , however , decreases uncertainty about the options . When one learns that an option either is , or is not valuable , then one can act accordingly . Thus , increased uncertainty drives value through the FEV . Because we used models that maximize expected reward , uncertainty does not affect IEV . However , as uncertainty can lead to a larger FEV , decreasing uncertainty and therefore decreasing FEV increases the relative importance of IEV on the total action value . The same reasoning applies to the information sampling tasks . As long as uncertainty is high , the FEV is high . When uncertainty is decreased , however , the IEV of guessing an urn becomes larger . Interestingly , and in contrast to this , increasing uncertainty in temporal-discounting tasks actually decreases preference for delayed , larger rewards [5] . ( Temporal-discounting tasks are tasks in which subjects are offered a choice between an immediate smaller reward and a delayed larger reward . ) This is because of the state space of temporal-discounting tasks . One can model temporal discounting tasks using an MDP which , at each time step , includes the possibility of exiting the path to the reward and terminating in a state with no reward , with some probability . If this probability of terminating in a no reward state increases , it becomes less likely that one will get to the reward , for a fixed delay to the reward . Interestingly , this is thought to be a fundamental factor that drives crime [39] . Time horizon is also important . In infinite horizon problems the time horizon is controlled by the discount parameter . In bandit problems , the time horizon affects the relative value of exploration . In stationary problems the time-horizon affects the relative value of exploring novel or unknown options . Longer time-horizons , or discount parameters closer to 1 , increase the value of exploration . In non-stationary environments this relationship is more complex , as the non-stationarity limits the effective time-horizon of any policy . In foraging tasks , however , time horizon is also important . In undiscounted infinite horizon problems , travel times are irrelevant . If one has an infinite , undiscounted time horizon , then any finite travel time does not affect value . In the non-stationary bandit task , when the discount parameter approaches one , the algorithm samples options with lower means more often . As another example , consider the simplified MDP shown in Fig . 8 . The undiscounted , infinite horizon solution to this problem does not favor action 1 over action 2 [40] because the relative value of this initial transient reward will be zero in the infinite time limit . Methods such as sensitive discount optimality exist to deal with such situations , although these can only be applied to tractable state spaces [40] . However , a discounted MDP favors action 1 , in this case . This suggests that temporal discounting , in some form , is ubiquitous because it is always biologically ( or computationally ) relevant . Whether discounting is specifically exponential or hyperbolic , or takes on some other form is less the issue . More important is some sort of monotonic decrease in the value of future rewards with distance into the future . The explore-exploit trade-off is often modeled with heuristics . A strong criticism of heuristics is that they explain no more than they assume , and tell us no more than the data does [28] . Heuristics , however , can provide reasonable solutions to engineering problems , often provide insight into patterns in the data , and may better approximate behavior than normative models [15] . For example , recent work has explicitly examined the role of noisy vs . directed exploration , and found that human subjects use both directed and noisy exploration strategies [31] . In some cases , however , heuristics can be difficult to interpret . For example , the beta or inverse temperature parameter in delta-rule reinforcement learning ( DRRL ) is often thought to control the “explore-exploit” trade-off . This parameter can only control noise in choice processes , however , and standard implementations of DRRL do not turn this noise down as reward values are learned . Therefore , exploration cannot be differentiated from noise in the choice process using this parameter and poor learning looks like exploration . Several more sophisticated variants including Thompson sampling [41 , 42] and related algorithms [43] , however , decrease exploration with learning and can achieve minimal regret . In an MDP framework exploration need not be undirected or noisy . Exploration can be an intentional , directed , normative strategy if there is sufficient knowledge of the environment and the agent has sufficient computational resources . One does not necessarily explore so much as one learns or accumulates information ( bandit or information sampling tasks ) until the additional information indicates that an alternative choice is better . Every choice delivers some information , because one is always transitioning through states as choices deliver information in these tasks . Equivalently , leaving the current patch in a foraging task is an explicit calculation of the relative value of traveling to a new patch , the expected value of which is characterized by some probability distribution over patch values . It is possible that animals have relatively unsophisticated strategies for dealing with these issues . It seems likely , however , that they have developed at least a good approximation to the underlying normative utilities , at least in tasks that match the animal’s ecological nitch or on which the animals have extensive experience .
We modeled the tasks using markov decision processes with either observable ( MDP ) or partially observable ( POMDP ) states . Tasks were modeled as finite or infinite horizon , discrete time , and discounted ( i . e . with a discount parameter γ < 1 ) or undiscounted ( i . e . with a discount parameter γ = 1 ) as indicated in the manuscript . Some models also included a cost-to-sample . For discrete state models the utility , u , of a state , s , at time t is ut ( st ) = maxa∈Ast{ r ( st , a ) +C ( st , a ) +γ∑j∈Sp ( j|st , a ) ut+1 ( j ) } where Ast is the set of available actions in state s at time t , r ( st , α ) is the reward that will be obtained in state s at time t if action a is taken . The variable C ( st , α ) is a cost-to-sample , which may be zero . The summation on j is taken over the set of possible subsequent states , S at time t+1 . It is the expected future utility , taken across the transition probability distribution p ( j|st , α ) . The transition probability is the probability of transitioning into each state j from the current state , st if one takes action a . The γ term represents a discount factor . The terms inside the curly brackets are the action value , Q ( st , α ) = r ( st , α ) + C ( st , α ) + γΣj∈sp ( j|st , α ) ut+1 ( j ) , for each available action . For continuous state models the utility is utst = maxa∈Astrst , a+C ( st , a ) +γ∫Sp ( j|st , a ) ut+1 ( j ) dj All state integrals over continuous states were calculated with discrete approximations . Equations 1 and 2 assume a reward maximizing agent , through the max operator . For discrete state , finite horizon models with tractable state spaces , we used the backward induction algorithm to calculate utilities and action values [28] . This was done for the 2-armed stationary bandit , beads and sampling foraging tasks . With a finite horizon the final state delivers a reward , but no further actions are possible . Therefore , if we start by defining the utilities of the final states , we can work backwards and define the utilities of all previous states . Specifically , the algorithm proceeds as follows [40] , where N is the final state . 1 . Set t = N uN ( sN ) =r ( sN ) for all sN ϵ N . 2 . Substitute t-1 for t and compute utst = maxa∈Astrst , a+C ( st , a ) +γ∑j∈Sp ( j|st , a ) ut+1 ( j ) Set Ast , t* = argmaxa∈Astrst , a+C ( st , a ) +γ∑j∈Sp ( j|st , a ) ut+1 ( j ) 3 . If t = 1 stop , otherwise return to 2 . The non-stationary 2-armed bandit , novelty and patch-leaving foraging tasks were modeled as infinite horizon POMDPs or MDPs . The utilities were fit using the value iteration algorithm [40] . This algorithm proceeds as follows . First , the vector of utilities across states , v0 , was initialized to random values . We set the iteration index , n = 0 . Then computed: vn+1 = maxa∈Ast{ r ( s , a ) +γ∑j∈Sp ( j|st , a ) vn ( j ) } . ( 3 ) After each iteration we calculated the change in the value estimate , Δv = vn+1-vn , and examined either ||Δv||<∈ or span ( Δv ) <∈ . The span is defined as . span ( v ) = maxs∈S v ( s ) —mins∈S v ( s ) For infinite horizon undiscounted models the value continues to grow with iterations of equation 3 , but the spans converge [40] . This is because the final state values are the average costs per stage plus a differential . This only applied to the foraging patch leaving example with discount parameter equal to 1 . We only examined differential values , in that case , so the average cost per stage is subtracted out , because it is added to all states . We also used approximate methods for the non-stationary 2-armed bandit and the novelty task , as their state spaces were intractable over relevant time horizons . For these POMDPs we defined a basis , and then approximated the utility with v^ ( s ) =∑i=1Maiϕi ( s ) . ( 4 ) In all cases we used fixed basis functions so we could calculate the basis coefficients , ai using least squares techniques . We assembled a matrix Φi , j = ϕi ( sj ) , which contains the values of the basis functions for specific states , sj . We then calculated a projection matrix H = Φ ( Φ'Φ ) -1Φ' ( 5 ) And calculated the approximation v^ = Hv . ( 6 ) The bold indicates the vector over states , or the sampled states at which we computed the approximation . When using the approximation in the value iteration algorithm , we first compute the approximation , v^ . We then plug the approximation into the right hand side of equation 3 , vn+1 = maxa∈Astr ( s , a ) +γ∑j∈Sp ( j|st , a ) v^n ( j ) . We then calculate approximations to the new values v^n+1 = Hvn+1 . This is repeated until convergence . For basis functions we used piece-wise polynomials and/or b-splines [44] . For b-splines see [44] . For piecewise polynomials , the first basis functions are given by hi ( x ) = xi-1 . For an order K spline ( i . e . for cubic K = 3 ) , i goes from 1 to K+1 . In addition to these global polynomials , we also add hj ( x ) = ( x-tj ) K for the J knots , tj . Because all of the state spaces were multidimensional and the piece-wise polynomial basis varied between knots , we also had to compute products of the basis functions across dimensions . Computing the full tensor product basis space was usually intractable . It created a projection matrix that either could not be stored in memory or iteration over the very large projection matrix was so slow that the algorithm would not converge in a reasonable amount of time . Therefore we started with linear terms and added interaction terms of increasing order ( i . e . second order , third order , … ) until the approximation stopped improving . We did not find an improvement by going beyond the quadratic terms . Knot locations were explored systematically to find locations that led to good approximations . Approximations were checked in several ways . First , we plotted vn+1 vs . v^n to see that they were consistent after convergence , as well as checking the variance of the residual . Second , we added knots to see if the fit was improved . Third , we increased the order of the polynomial to see if the fit was improved . Cubic polynomials ( i . e . K = 3 ) were used in all cases . When the order was increased beyond cubic the value iteration often diverged . Finally , performance of the approximate inference MDP for the novelty task could be compared to a corresponding finite horizon model , at least for short time horizons to see if they made consistent predictions . For the novelty task , the numerics were easier to implement if we approximated the number of samples for each option ( N ) and the probability that it was rewarded ( p ) . We used a 3rd order B-spline basis . Knot locations for N were 0 and 150 , and the algorithm was optimized at ( using Matlab colon operator notation ) N = e0:54:5 and p = 0: 0 . 25: 1 . The N values were not integers , but this does not affect evaluation of the value function . Interactions up to second order were included . For the non-stationary two-armed bandit , the means were fit with a 3rd order B-spline , and the standard deviations were fit with a 2nd order piece-wise polynomial . This approach gave well-behaved value functions . The node locations for the means were given by -30 50 and 130 . The means were evaluated at 0 , 12 . 5 , 25 , 37 . 5 , 50 , 62 . 5 , 75 , 87 . 5 , 100 . The node locations for the standard deviations were given by 0 . 25 , 1 , 3 , 5 , and 15 . The standard deviations were evaluated at 0 . 5 , 1 , 2 , 3 , 4 , 5 , 7 and 14 . Interactions between all basis functions up to second order were included in the model . | Numerous choice tasks have been used to study decision processes . Some of these choice tasks , specifically n-armed bandit , information sampling and foraging tasks , pose choices that trade-off immediate and future reward . Specifically , the best choice may not be the choice that pays off the highest reward immediately , and exploration of unknown options vs . exploiting known options can be a normatively useful strategy . We characterized the optimal choice strategies across these tasks using Markov Decision Processes ( MDPs ) . The MDP framework can characterize optimal choice strategies when choices are affected by the value of future rewards . We found that uncertainty and time horizon have important effects on the choice strategies in these tasks . Specifically , in bandit and information sampling tasks , increasing uncertainty increases the value of exploring choice options that tend to pay off in the future , while decreasing uncertainty increases the value of choice options that pay off immediately . These effects are increased when time horizons are longer . Foraging tasks differ in that uncertainty plays a minimal role . However , time horizon is still important in foraging . Specifically , for long time horizons , travel delays to rewards become less relevant . |
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Dengue is endemic to the rural province of Kamphaeng Phet , Northern Thailand . A decade of prospective cohort studies has provided important insights into the dengue viruses and their generated disease . However , as elsewhere , spatial dynamics of the pathogen remain poorly understood . In particular , the spatial scale of transmission and the scale of clustering are poorly characterized . This information is critical for effective deployment of spatially targeted interventions and for understanding the mechanisms that drive the dispersal of the virus . We geocoded the home locations of 4 , 768 confirmed dengue cases admitted to the main hospital in Kamphaeng Phet province between 1994 and 2008 . We used the phi clustering statistic to characterize short-term spatial dependence between cases . Further , to see if clustering of cases led to similar temporal patterns of disease across villages , we calculated the correlation in the long-term epidemic curves between communities . We found that cases were 2 . 9 times ( 95% confidence interval 2 . 7–3 . 2 ) more likely to live in the same village and be infected within the same month than expected given the underlying spatial and temporal distribution of cases . This fell to 1 . 4 times ( 1 . 2–1 . 7 ) for individuals living in villages 1 km apart . Significant clustering was observed up to 5 km . We found a steadily decreasing trend in the correlation in epidemics curves by distance: communities separated by up to 5 km had a mean correlation of 0 . 28 falling to 0 . 16 for communities separated between 20 km and 25 km . A potential explanation for these patterns is a role for human movement in spreading the pathogen between communities . Gravity style models , which attempt to capture population movement , outperformed competing models in describing the observed correlations . There exists significant short-term clustering of cases within individual villages . Effective spatially and temporally targeted interventions deployed within villages may target ongoing transmission and reduce infection risk .
Dengue remains a major public health concern throughout global tropical and subtropical regions . An estimated 390 million people are infected by the mosquito-borne virus each year , of which 96 million develop symptomatic disease [1] . Thailand , like most countries in Southeast Asia , has experienced endemic dengue circulation of all four serotypes for decades [2] , [3] . An effective dengue vaccine remains elusive and intervention measures will continue to rely on mosquito control for the foreseeable future . These efforts include the detection and removal of potential oviposition sites , the spraying of insecticides , and potentially the future releases of Wolbachia-infected mosquitoes that have been shown to reduce the mosquitoes' ability to transmit dengue [4] . Effective use of these measures requires a good understanding of the spatial distribution of cases . Of particular use is an understanding of where other cases are likely to be found on detection of an index case . Characterizing the spatial dependence between dengue cases can also provide insight into potential mechanisms of disease spread . The home locations of individuals hospitalized with dengue in Bangkok have been shown to exhibit significant spatial dependence at distances of around a kilometer [5] . Such spatial structure suggests focal transmission events are driving viral dispersal in this large , super-urban population . The situation in rural areas , which make up the majority of the country , may be markedly different . Phylogenetic studies have shown widespread genetic and serotype diversity across the rural Thai province of Kamphaeng Phet with some clustering of lineages within villages [6] , [7] . In addition , cluster studies in the same region detected infected individuals within 15 days of an index case at distances of 100 m within villages [8] , [9] . However , the extent at which spatial dependence is observed in these areas is not known . Unlike continuously inhabited urban centers such as Bangkok , rural communities in Thailand tend to be separated by wide expanses of uninhabited farmland or forests . The distance between neighboring rural communities is typically far beyond the short flight range of the main dengue vector , Aedes aegypti [10] . For sustained transmission to occur between rural communities , movement of infected individuals is likely necessary . If human movement between neighboring communities were key to DENV dispersal in this region , we would expect short-term spatial dependence between cases occurring at between-community scales . Further , we would expect that patterns of population flows would correlate with the spatio-temporal location of infections . It has previously been shown that individuals tend to move to larger and closer communities [11]–[13] . Such population flows can be captured using gravity models that incorporate the size of populations and the distance between them . Similar approaches have previously been used in phylogenetic analyses to describe dengue viral flow in Vietnam [14]–[16] . Appropriate data necessary to describe the spatio-temporal patterns of dengue virus require , 1 ) a long time series , 2 ) availability of address data for patients , and proper diagnostics to confirm DENV infection . We used a unique dataset that meets all of these criteria: the geocoded home addresses of 4 , 768 individuals who were admitted to the provincial hospital in Kamphaeng Phet , Thailand over a fourteen-year period ( 1994–2008 ) . The objective of our study was to characterize the short-term spatial dependence between dengue cases , to quantify the correlation in the long-term epidemics experienced by different communities and to explore the ability of human movement models to describe the observed correlations .
Data were collected from existing records without personal data . The research components of this project received approval from the Ethical Research Committee of Faculty of Public Health , Mahidol University and U . S . Army Medical Research Materiel Command ( USAMC-AFRIMS Scientific Review Committee ) review and approval . Kamphaeng Phet is a largely rural province in northern Thailand with an area of 8 , 600 km2 ( Figure 1 ) [17] . It had a population of 797 , 000 people in the 2010 census , mainly residing in villages . The largest town in the province is the capital ( Mueang Kamphaeng Phet ) with 30 , 000 inhabitants . The landscape is dominated by rolling hills with large portions of the province covered by forests . Since 1994 , the Armed Forces Research Institute of Medical Sciences ( AFRIMS ) has conducted dengue surveillance at Kamphaeng Phet Provincial Hospital ( KPPPH ) . KPPH is the largest hospital in Kamphaeng Phet , located in the capital , and such receives referral cases as well as walk-in patients of all ages from throughout the province . For each suspected dengue case , DENV infection is confirmed using semi-nested RT-PCR and IgM/IgG ELISA . In addition , home address information is collected on each patient . We geocoded the home address down to the village level for each individual using detailed base maps of the region . Individuals from the same village were given the same coordinates ( Table 1 ) . To characterize the short-term spatial dependence between rural dengue cases , we used the statistic on all cases occurring outside the provincial capital [5] . This statistic estimates the probability of two cases occurring both within distances d1 and d2 and within a month of each other relative to the independent probabilities of observing two cases within d1 and d2 over the entire time series and of observing two cases within a month of each other over the whole study area . This approach therefore measures the interaction in time and space of cases and has previously been used to characterize the spatial dependence of dengue cases in Bangkok [5] . Where is the set of cases that occur both within a 30 day period and within d1 and d2 of case i; is the set of cases within d1 and d2 of case i over the entire time series and is the set of cases that occur within a 30 day period from case i over the study area . Importantly , as underlying spatial biases such as population density and hospital utilization rate differences impact both the numerator and the denominator in the same way , they do not bias our estimates of spatial dependence . We estimate as follows ( see [5] for details ) :We generated bootstrapped confidence intervals for by resampling the cases with replacement 500 times . Ninety-five percent confidence intervals were calculated from the 2 . 5% and 97 . 5% quantiles from the resulting distribution . Patterns of spatial dependence may have changed over the time period of the study . We therefore recalculated using cases from annual incremental five-year windows from between 1994 and 2008 . We explored whether any short-term spatial dependence between individual cases resulted in correlation in the epidemics experienced by different communities . In this analysis , to avoid excessively small numbers of cases per location over the entire time period , villages were grouped into clusters by placing a grid over the province . The distance between each grid point was 3 km and villages were assigned to the closest grid point . Only village clusters with at least 40 cases over the time series were used in the analysis . The population of each village cluster was extracted from LandScan data [18] . LandScan uses a combination of satellite imaging and census data to construct population estimates throughout the world . To make the epidemic curves between locations as comparable as possible , we down-sampled each epidemic curve ( to create “down-sampled curves” ) by randomly selecting 40 cases ( the minimum number of cases at within a village cluster ) with replacement from all the cases that occurred at that location . We calculated the Pearson correlation coefficient between all pairs of down-sampled curves . We calculated the loess curve of the relationship between the Euclidean distance and correlation between village cluster pairs . We repeated the down-sampling process 500 times and reported the mean of the resulting distribution . In addition , 95% confidence intervals for the loess curves were estimated from the 2 . 5% and 97 . 5% quantiles . We compared our estimate of the expected correlation by distance separating communities to a theoretical complete-synchrony scenario where there was no distance effect . The complete-synchrony distribution was generated by randomly reassigning the location of all cases , keeping the month in which they occurred fixed . The total number of cases within any location over the whole time series was unchanged . The resulting distribution is that expected under a scenario of complete synchrony of cases over the province . The mean and confidence intervals for the complete-synchrony distribution were calculated by repeating the process above in generating down-sampled curves , repeating each resampling event 500 times . There exist alternative measures of correlation . We explored the consistency of our findings to a different measure: the Spearman rank correlation coefficient . In this sensitivity analysis , we recalculated the correlation coefficients for both the observed data and the theoretical complete-synchrony scenario . Gravity models can be used to describe population flows [11]–[13] . Here we used them to explore their ability to explain the correlation in the epidemic curves between pairs of village clusters:where pop1 and pop2 are the populations of the two settlements and dist is the Euclidean distance between the two settlements . By log-transforming the equation , we can estimate the exponents α and β through linear regression:We used Akaike's Information Criterion ( AIC ) to compare the performance of the gravity model to an intercept only model and a univariate model incorporating Euclidean distance only ( Table 2 ) [19] . All of the models were performed using the correlation coefficients from each set of down-sampled curves ( 500 in all ) . We reported the mean coefficient across all sets of down-sampled curves for each model . In addition we calculated 95% confidence intervals using the 2 . 5% and 97 . 5% quantiles from the distribution of coefficient estimates . All analyses were conducted in R 2 . 15 . 2 [20] .
Between 1994 and 2008 , 4 , 768 dengue inpatients at KPPPH were successfully geocoded ( 93% of all cases ) ( Table 1 ) coming from 568 different villages ( Figure 1 ) . The provincial capital , where KPPPH was located , had 732 cases ( 15% of all cases ) . The mean age of cases was 11 . 0 years and 59% of cases suffered from the more severe hemorrhagic form of the disease ( Table 1 ) . On average , villages were separated by 1 . 4 km from their closest neighboring village . We characterized the short-term spatial dependence between the home locations of the cases presenting at KPPH using the φ ( d1 , d2 ) statistic . We found that cases were 2 . 9 times more likely ( 95% confidence interval of 2 . 7–3 . 2 ) to occur both within the same community and to be infected within the same month of each other than the independent probabilities of occurring within the same community over the entire study period and occurring within the same month across the entire province ( Figure 2 ) . This fell to 1 . 4 times ( 1 . 2–1 . 7 ) for communities separated by between 0 . 5 km and 1 . 5 km and to 1 . 2 times ( 1 . 1–1 . 3 ) for communities separated by 2 . 5 km −3 . 5 km . We observed significant spatial dependence , albeit at low levels , at distances up to 5 km . However , when we divided the entire time series into smaller subsets covering five year time periods only , there was a clear trend in the spatial extent of spatial dependence ( Figure S1 ) . Cases from the 1990s exhibit spatial dependence at larger distances than more recent cases . To explore whether short-term spatial dependence between individual cases resulted in similar patterns of disease observed between communities , we compared the correlation of the epidemic curves between communities by the distance separating them . We divided the villages into 24 village clusters with each village cluster having at least 40 cases over the 14 years . The locations of the village clusters are illustrated by the red dots in Figure 1 . The mean correlation in the monthly epidemic curves between all village cluster pairs was 0 . 19 , however , there existed substantial structure in the correlation: village clusters that were under 5 km apart had a mean correlation of 0 . 28 ( 95% confidence interval of 0 . 25–0 . 31 ) , whereas village clusters separated by between 20 km and 25 km had a mean correlation of 0 . 16 ( 95% confidence interval: 0 . 14–0 . 17 ) ( Figure 3 ) . We estimated that a ( theoretical ) scenario of complete synchrony across the entire province would result in a mean correlation of 0 . 32 , irrespective of distance between village clusters ( Figure 3 ) . This correlation was much less than 1 . 0 as there are fewer cases than locations for many time points resulting in occasional small peaks in the epidemic curves that were not matched across all locations . The correlation under full synchrony and the observed correlations looked very similar when the alternative Spearman rank correlation coefficient was used instead ( Figure S2 ) . We explored whether different statistical models could explain the observed correlation between community-pairs ( Table 2 ) . We found that univariate model incorporating only the Euclidean distance separating communities explained only 7% of the variance in the correlations ( Table 3 ) . Incorporating population sizes ( model 3 ) substantially improved the fit of the model although the majority of the variance remained unexplained ( R2 of 0 . 13 ) . Model 3 was also strongly favored by AIC [21] .
We have used a large dataset from a long time series with geocoded addresses to explore the spatial patterns of dengue cases in a rural region with endemic circulation . We have shown substantial short-term clustering of dengue cases within communities , consistent with transmission chains circulating at small spatial scales . We observed a large drop in the clustering of cases from within-community to between community scales . Our findings suggest that upon discovering an infected individual , there is a significant risk that other individuals from his or her village will also be infected . The removal of mosquitoes in that community could potentially reduce the risk of onward transmission . While lower than within-community estimates , significant short-term spatial dependence was nevertheless observed at inter-settlement scales . This observation is consistent with viral movements between neighboring communities , distances greater than the flight range of the dengue vector [10] . These findings point to a potential role for human movement in driving the spread of the virus . This was further supported by a clear reduction in the correlation in the epidemic curves between communities with increasing spatial separation between them . Gravity models are regularly used to describe human population flows [11]–[13] . Here a related formulation of gravity models that describes the correlation in the epidemic curves between communities was found to outperform competing models . This finding supports previous findings from gravity models fit to phylogeographic data from southern Vietnam [15] . Human movement has also been suggested to play a major role in the dengue epidemic in Iquitos , Peru [22] . Spatial correlation in ecological conditions ( e . g . , vector density ) or in behavioral factors ( e . g . the use of screens on windows ) between communities may also explain these observations . We cannot definitively differentiate between these potential explanations here . Further research using information on the infecting pathogen , such as serotype or genetic information could help disentangle these competing hypotheses . Our findings of focal patterns of disease support the results of previous cluster studies in the region [8] , [9] . In addition , a previous study in Bangkok observed short-term spatial dependence in the homes of hospitalized cases between 1995 and 1999 at distances up to around 1 km [5] . Overall , we observed spatial dependence at larger distances than in the Bangkok study although when we looked at 5-year subsets of the data , the spatial extent of clustering was shorter among more recent cases . Higher levels of movement across the province as a whole suppresses spatial dependence by promoting the global mixing of the population . Our observations are therefore consistent with increased movement across the province in more recent years . Mosquito control efforts are widely used throughout Southeast Asia and center on the use of insecticides . Insecticide fogging has been shown to temporarily reduce the number of mosquitoes in any location [23] . However , the ability of insecticides to reduce the risk of dengue infection remains unclear . Insecticide effectiveness may be limited by an inability to reduce mosquito density sufficiently or for a long enough period to prevent transmissions from viremic individuals . This is supported by a lack of a clear relationship between vector density and dengue transmission risk [24] . In addition , spraying may be too spatially restricted , allowing mosquitoes outside spray zones to rapidly repopulate fogged spaces . Finally spraying is sometimes only deployed in outdoor areas whereas Aedes aegypti mosquitoes tend to be found inside households . Estimating the impact of insecticides on dengue infection is difficult . The majority of dengue infections are not detected and the appropriate characteristics of control populations for any study are unclear . Nevertheless , further studies are needed to provide a sound evidence base for the widespread use of these measures . The study has some limitations . The mean correlation between the epidemics experienced by pairs of communities appeared low ( mean of 0 . 19 ) . However , this was only slightly less than expected if all cases at any time point were randomly distributed throughout the communities ( mean of 0 . 32 ) , resulting in synchronous epidemics . This low level of correlation occurs because of the small numbers of cases ( all the epidemic curves were down-sampled to only 40 cases ) . Even in the scenario of complete synchrony , tiny fluctuations were regularly present in the epidemic curve in one location and not in the curves of others , deflating correlation . These observations illustrate the problems in using the absolute correlation as a marker of similarity when many time points have no cases . Nevertheless , trends in correlation over distance and comparisons to a distribution expected under complete synchrony remain useful . Our data consists of cases that presented at hospital only . The majority of infections , however , result in asymptomatic or only mildly symptomatic . The spatial dependence between these infections may be different . We could only geocode individuals to the village level . We could not therefore explore spatial differences within any village . Future work using exact home locations may allow elucidation of finer scale spatial dependence between case homes . Finally , the relationship between gravity models fit to population flows directly and those fit to the correlation in epidemic curves may be complex and setting specific . Further work using simulated data may help provide insight into their relationship . In conclusion , cases of dengue appear highly spatially correlated within villages in rural Thailand; however , neighboring communities nevertheless appear to observe correlated epidemics . Human movement patterns may be a key driver of dengue dispersal in this region . Future studies that incorporate movement diaries or GPS trackers would help describe population flows and allow the development of mechanistic models for the dispersal of dengue . | Transmission of dengue virus has long been studied in Kamphaeng Phet , Northern Thailand , but how cases are related in time and space is still unclear , as is the role of human movement in generating these patterns . Because of these knowledge gaps , public health officials cannot make educated decisions on how to target vector control interventions and mechanisms of virus dispersal are not known . We mapped the homes of dengue cases admitted to the main hospital in the province capital from 1994–2008 and quantified the spatial correlation between them . We found an almost three times greater chance that cases from the same month came from the same village than expected , given the overall distribution of cases . Some clustering was also observed between cases in neighboring villages with the overall epidemics experienced by neighboring communities also more correlated than epidemics in villages farther apart . The short-term clustering observed within individual villages implies that effective spatially targeted interventions deployed within villages may reduce infection risk . As the distance between neighboring communities exceeds the typical flight range of the dengue vector , these findings also suggest a potential role for human movement in driving the wider spread of the virus . |
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Almost 60 years ago , Severo Ochoa was awarded the Nobel Prize in Physiology or Medicine for his discovery of the enzymatic synthesis of RNA by polynucleotide phosphorylase ( PNPase ) . Although this discovery provided an important tool for deciphering the genetic code , subsequent work revealed that the predominant function of PNPase in bacteria and eukaryotes is catalyzing the reverse reaction , i . e . , the release of ribonucleotides from RNA . PNPase has a crucial role in RNA metabolism in bacteria and eukaryotes mainly through its roles in processing and degrading RNAs , but additional functions in RNA metabolism have recently been reported for this enzyme . Here , we discuss these established and noncanonical functions for PNPase and the possibility that the major impact of PNPase on cell physiology is through its unorthodox roles .
In bacteria and eukaryotes , PNPase has an important function in RNA decay . PNPase catalyzes the processive degradation of single-stranded RNA in the 3ʹ to 5ʹ direction using inorganic phosphate as the nucleophile to attack the 3ʹ phosphodiester bond releasing a ribonucleoside diphosphate ( NDP ) . A metal cofactor , Mg2+ or Mn2+ , is required to stabilize the transition state of the phosphate during the reaction [30] . To degrade RNA efficiently , PNPase must bind a single-stranded stretch of RNA at least six nucleotides in length at the 3ʹ terminus [31 , 32] . PNPase subsequently degrades RNA in a stepwise motion , rapidly removing discrete segments of 6 to 7 nucleotides between short pauses [33] . A stem loop structure in an RNA substrate can act as a roadblock that halts degradation by PNPase . In vitro studies using a set of GC-rich RNA hairpins of varying length followed by single-stranded sequences demonstrated that a hairpin with a stem as short as seven base pairs inhibited degradation by PNPase [32] . However , PNPase also rapidly degrades some natural hairpins such as the Rho-independent terminators that end many sRNAs [34] . Thus , while very stable hairpins can block the exoribonucleolytic activity of PNPase , merely resulting in 3ʹ end trimming [35] , this enzyme can degrade many natural double-stranded RNAs provided that a single-stranded region is present at the 3ʹ end to initiate degradation . This balance between degradation and inhibition is important for maturation of some tRNA transcripts , in which PNPase removes Rho-independent terminators but stops short of the CCA determinants [36–39] . Likewise , structural features of the 16S rRNA preribosomal particle are likely important for preventing excessive 3ʹ trimming of the 16S rRNA by PNPase and other exoribonucleases during its maturation [40 , 41] . Crystal structures for PNPase from Streptomyces antibioticus [42] , E . coli [30 , 43] , Caulobacter crescentus [44] , and Homo sapiens [45] have been solved , and much of the substrate specificity and activity of PNPase can be assigned to distinct structural and organizational features of this enzyme . Each PNPase monomer consists of two RNase PH-like domains , which are separated by an α-helical domain and are followed by a KH and S1 domain ( Fig 1A ) . As a functional enzyme , PNPase is assembled into a torus-shaped trimer in which alternating RNase PH-like subunits and α-helical domains form a central ring from which the KH/S1 domains extend outward ( Fig 1B and 1C ) [30 , 42–44] . The first RNase PH-like domain contributes to RNA and NDP binding , and the second domain additionally possesses enzymatic activity [43 , 46] . The active site is positioned in a shallow groove along the inner rim of the trimer ( Fig 1D , panel iii ) , and a constriction point formed by the FFRR loop at the entrance of the core ring creates a pore that only allows access by single-stranded RNA ( Fig 1D ) [43] . Catalytic activity of the central ring is facilitated by the other domains; the α-helical domain appears to regulate access of phosphate or NDP to the active site [30 , 47 , 48] , and the KH and S1 domains each contribute to capturing and binding RNA substrates [44 , 49] . Additionally , the KH domain imparts RNA directionality through the interactions of the conserved GSGG loop with the RNA backbone [44] . Finally , the processivity of PNPase is attained through its ring-like structure that retains RNA substrates via multiple RNA-binding interactions , including hydrogen binding between the GSGG loops of the KH domains and the RNA phosphate backbone ( Fig 1D , panel i ) and base stacking interactions between the aromatic phenylalanines in the conserved FFRR loops and a ribonucleotide base ( Fig 1D , panel ii ) [44] . While PNPase can function independently as a 3ʹ to 5ʹ exoribonuclease , in bacteria , PNPase also serves as a component of an organized RNA degradation machine ( Fig 2A ) . Termed the degradosome , this multiprotein complex is responsible for bulk mRNA decay [50 , 51] . At the core of this RNA-degrading machine in gram-negative bacteria is the endoribonuclease RNase E , which initiates RNA decay . The essential N-terminal domain of RNase E contains the active site and additional features including the S1 domain and 5ʹ sensor pocket important for binding many RNAs ( reviewed in [52] ) . The C-terminal domain is required for formation of the RNA degradosome and contains binding sites for other proteins; in the canonical E . coli RNA degradosome , these proteins include the glycolytic enzyme enolase , the DEAD-box RNA helicase RhlB and PNPase [53–55] . However , the RNase E–based degradosome can vary in composition between species or even within the same organism depending on cellular conditions [56] . In C . crescentus , aconitase is exchanged for enolase , and RNase D was validated as a legitimate degradosome component [57 , 58] . Gram-positive bacteria have a similar RNA degradation machine that is likewise organized around a core endoribonuclease , in this case , RNase Y . Like RNase E , RNase Y has an unstructured C-terminal region with specific binding sites for PNPase , enolase , and an RNA helicase [59] . However , despite these functional similarities , the two proteins are evolutionarily distinct , belonging to different protein superfamilies [60] . Unlike RNase E , RNase Y features an N-terminal region with an RNA-binding KH domain and an active site–containing HD domain , and its C-terminal domain additionally interacts with 5ʹ to 3ʹ exoribonucleases [60 , 61] . PNPase and the RNA degradosome of the gram-negative bacterium E . coli have been studied in much detail . As part of the degradosome , PNPase cooperates with RNase E to degrade a specific set of mRNAs , many of which encode proteins involved in macromolecule biosynthesis or modification [50] . Additionally , within the degradosome , binding of RhlB and PNPase to the RNase E scaffold is necessary for the degradation of highly structured RNA sequences , termed repeated extragenic palindrome ( REP ) elements that are found in some mRNAs [62–64] . Although PNPase participates in mRNA decay as a component of the RNA degradosome , the enzyme primarily functions independent of this machine . This is evident by the number and distribution of PNPase and RNase E molecules in E . coli . PNPase is approximately three to five times more abundant than RNase E and is mostly distributed throughout the cytoplasm ( 69% of PNPase; Fig 2A ) , whereas the majority of RNase E ( 91% ) is located near or on the cell membrane [65] . Moreover , only a minority of E . coli PNPase trimers are bound to RNase E at any one time [53] . The independent function of PNPase is also evident by global gene expression profiling , which demonstrated that many mRNAs stabilized by the absence of PNPase are not significantly impacted by loss of the degradosome [50] . Furthermore , PNPase functions independently of the RNA degradosome in tRNA processing [37 , 38 , 66] , rRNA degradation [40 , 67] , and sRNA-mediated gene regulation [68] . In bacteria , the fully assembled 70S ribosome is made up of the small 30S subunit containing the 16S rRNA and the large 50S subunit consisting of the 23S rRNA and 5S rRNA . In E . coli and presumably most gram-negative bacteria , PNPase and RNase R perform routine rRNA quality control by degrading fragments of 16S and 23S rRNAs that might otherwise compete with mature rRNAs for ribosomal proteins and impair proper ribosomal assembly [69 , 70] . These rRNA fragments are generated by RNase E [70] , which may cleave rRNAs that cannot be assembled into functional ribosomes due to improper processing , damage , or overabundance relative to ribosomal proteins . In E . coli , PNPase was not required for rRNA decay induced by nutrient starvation [71] . In some bacteria such as the radiation-resistant D . radiodurans , PNPase mediates rRNA degradation in response to nutrient starvation with the assistance of the Ro sixty-related protein , Rsr ( Fig 2A ) [72 , 73] . Ro was originally identified in human cells as an autoantigen recognized by antibodies from lupus erythematosus patients [74] . Ro and its homologs , which are present in many vertebrates and in roughly 5% of bacterial genome sequences [75] , bind to structured noncoding RNAs called Y-RNAs [13 , 72 , 74 , 76 , 77] and are involved in rRNA decay [73 , 78 , 79] . In D . radiodurans , Y-RNA tethers Rsr to PNPase resulting in the formation of the “RYPER” complex ( Fig 2A ) that degrades structured RNA including the 5S , 16S , and 23S rRNAs [72 , 73] . Y-RNAs have a highly conserved structure that consists of an extended stem generated by pairing between bases at the 3ʹ and 5ʹ ends and a large internal loop that in many cases is decorated with two hairpins [80 , 81] . A conserved region within the Y-RNA stem binds to Rsr , whereas a region containing two hairpins , one resembling a T-arm of a tRNA , binds to the KH and S1 domains of PNPase [72 , 82] . The 3ʹ end of the misfolded rRNA appears to thread through the central pore of the toroid-shaped Rsr protein and into the central channel of PNPase , where it is degraded [72] . Although for many years it was largely assumed that PNPase only contributed to RNA processing and degradation in bacteria , it has become increasingly clear over the last decade that PNPase also plays an important role in regulating sRNA function and stability [68] . In bacteria , sRNAs range in size from 50 to 250 nucleotides and alter gene expression by sequestering regulatory proteins or by base-pairing with target mRNAs to modulate translation and transcript stability ( reviewed in [83 , 84] ) . Many sRNAs interact with RNA chaperones , such as FinO [85] , ProQ [86 , 87] , or the host factor for phage Qβ ( Hfq ) [88 , 89] , to facilitate this process . In the latter case , Hfq protects sRNAs from degradation by occluding an endoribonuclease cleavage site [90 , 91] and facilitates sRNA–mRNA annealing [92 , 93] . In E . coli and its close relative Salmonella Typhimurium , binding by Hfq dictates whether an sRNA is degraded or stabilized by PNPase . For Hfq-independent sRNAs such as CopA , CsrB , and CsrC , PNPase degrades these RNAs following initial cleavage by RNase E [94 , 95] . Even some Hfq-binding sRNAs such as SraL , RybB , and MicA are destabilized by PNPase [94 , 96]; however , PNPase degrades only the pool of MicA that is not bound by Hfq [97 , 98] . Indeed , PNPase binds and rapidly degrades several Hfq-binding sRNAs in vitro but only in the absence of Hfq [34] . In the presence of Hfq , PNPase instead forms a stable ternary complex with Hfq and sRNAs , and experimental evidence supports the existence of this complex in E . coli [34] . In vivo , PNPase stabilizes many Hfq-dependent sRNAs , and deletion of the gene encoding this RNase paradoxically results in reduced sRNA stability [34 , 68 , 97 , 98] . What is the role of PNPase in facilitating sRNA-mediated gene regulation ? In our speculative model , PNPase forms a complex with Hfq that is mediated by sRNAs ( Fig 2A ) . Within this complex , PNPase is unable to degrade the sRNA because its 3ʹ end is bound to Hfq . After sRNA–mRNA pairing , Hfq is released from the complex and in most cases each RNA is first cleaved by an endoribonuclease ( RNase E ) , followed by rapid degradation of the resulting sRNA and mRNA fragments by PNPase . In the absence of PNPase , specific mRNA fragments accumulate and go on to pair with additional sRNAs resulting in their cleavage by RNase E . By this mechanism , these mRNA fragments act to deplete the pool of specific sRNAs , resulting in decreased regulation of their mRNA targets . Given that Hfq-binding sRNAs in E . coli and other gram-negative bacteria regulate many physiological processes—including DNA repair [99 , 100] , motility [101–104] , biofilm formation [102 , 105–111] , and antibiotic resistance [112–114]—and that PNPase regulates sRNA stability and function , we postulate that the majority of the phenotypes associated with the loss of functional PNPase are due to its role in degrading or stabilizing sRNAs . There is already some evidence supporting this hypothesis . For example , the reduced rate of spontaneous mutation observed for an E . coli pnp deletion strain [115] may originate from reduced ArcZ sRNA levels , as disruption of the negative regulation of mutS by ArcZ also reduces the spontaneous mutation rate in E . coli [100] . Similarly , the role of PNPase in promoting biofilm formation may be due to its function in stabilizing Hfq-dependent sRNAs; recent studies have collectively shown that deletion of pnp , hfq , or genes encoding the sRNAs DsrA or ArcZ from E . coli each resulted in defects in biofilm formation [108 , 116] . Studies of the mammalian PNPase have been fraught with controversy , and many functions have been reported for the human PNPase ( hPNPase ) , including mitochondrial RNA import , processing , and decay and miRNA and mRNA degradation ( Fig 2B ) . Careful studies mapping the cellular location of hPNPase indicate that it mainly resides in the mitochondrial inner membrane space ( IMS ) located between the outer and inner membranes [117 , 118] . hPNPase is guided to the IMS via a mitochondrial targeting sequence that is cleaved off when it is translocated into the IMS [118] . Upon overexpression , hPNPase accumulates in other cellular compartments such as the cytoplasm [119] , but conditions in which the natively expressed hPNPase is found in this space have not been identified until recently . A newly published study revealed that natively expressed hPNPase can also be released into the cytoplasm upon mitochondrial outer membrane permeabilization during programmed cell death , whereupon hPNPase contributes to global apoptotic RNA decay by degrading mRNAs and polyadenylated noncoding RNAs ( Fig 2B ) [120] . Within the IMS , PNPase is a peripheral membrane protein that reportedly binds the 5S rRNA and the RNase P and RNase MRP RNAs to facilitate importation of these RNAs into the mitochondrial central space , or matrix [25] . This import function of PNPase did not appear to require its catalytic activity , and intriguingly , a 20-nucleotide stem loop structure found in the RNase P and MRP RNAs was sufficient for PNPase-mediated mitochondrial import [25] . In addition , Wang and colleagues [25] found that processing of polycistronic tRNA transcripts in vivo required the RNase P RNA . These results appear to conflict with previous work showing that the MRP RNA was undetectable [121] or at infinitesimal levels [122] in HeLa cell mitochondria , that only a very small number of RNase P RNA molecules were associated with the mitochondria of HeLa cells [122 , 123] , and that a reconstituted mitochondrial RNase P lacking an RNA component was functional in processing mitochondrial precursor tRNAs [124] . As argued by Wang and colleagues [25] , it is possible that RNase P exists in mammalian mitochondria in both the protein-only and H1 RNA-containing forms and that the RNA-containing form of RNase P is much less abundant . Both RNases serve critical roles in mitochondria , in which RNase MRP cleaves the RNA primers used for mitochondrial DNA replication and RNase P processes the large mitochondrial polycistronic transcripts that give rise to 22 tRNAs , 12S and 16S rRNAs , as well as 13 mRNAs encoding electron transport chain ( ETC ) components involved in oxidative phosphorylation ( reviewed in [125] ) , i . e . , the synthesis of ATP that is powered by the transfer of electrons from NADH or FADH2 to O2 . Likewise , a stable PNPase knockout in mouse embryonic fibroblasts resulted in the loss of both mitochondrial DNA and cellular respiration , supporting a role for PNPase in mitochondrial DNA maintenance [126] . The vital function of hPNPase in facilitating proper expression of the ETC components is further evidenced by the fact that knockdown of PNPase in HEK293 cells leads to impairment of the ETC and disruption of oxidative phosphorylation [117] . Additionally , several recent clinical reports demonstrate that patients suffering from hereditary hearing loss , delayed myelination , axonal neuropathy , and Leigh syndrome have mutations in PNPT1 , the gene encoding hPNPase [26–29 , 127] . In several of these reports , the authors provided evidence that the hPNPase encoded in these patients’ genomes contributes to a defect in oxidative phosphorylation and mitochondrial RNA import [27–29] . hPNPase also catalyzes mitochondrial RNA decay with assistance from the suppressor of Var 1 , 3 ( SUV3 ) RNA helicase [128–130] . The involvement of hPNPase in this process requires that it associate with the SUV3 helicase in the mitochondrial matrix . Consistent with some hPNPase binding SUV3 in the mitochondrial matrix , PNPase coimmunoprecipitated with SUV3 from mitochondrial cell extracts and foci of exogenously produced hPNPase and SUV3 colocalized with mitochondrial DNA and RNA [128 , 130] . Furthermore , knockdown of hPNPase in HeLa or T-Rex 293 cells resulted in stabilization of mitochondrial mRNAs [128 , 129] , and depletion of hPNPase or SUV3 led to accumulation of mitochondrial double-stranded RNA [131] . hPNPase also facilitates degradation of the c-myc mRNA [132 , 133] and miRNAs including miR-221 , miR-222 , and miR-106b in vitro and in vivo upon overexpression [134] . miRNAs are a class of sRNAs in humans that regulate gene expression by base-pairing with target mRNAs ( reviewed in [135] ) . However , to degrade these RNAs , hPNPase must reside in the cytoplasm , but this has only been shown to occur during apoptosis or upon exogenous overexpression in human cells [119 , 120 , 136] . Thus , the role of PNPase in degrading these RNAs may not be relevant under most physiological conditions . Aside from this potential degradation role , PNPase also appears to facilitate the import of miR-378 into mitochondria , resulting in down-regulation of the mt-ATP6 transcript and a reduction in ATP synthase activity [137] .
Although PNPase has been studied for over 60 years , new functions for this old enzyme have been recently uncovered . In bacteria , a novel function for PNPase in degrading some sRNAs and protecting others has been discovered [68 , 94 , 138] . Because each sRNA can potentially regulate hundreds of distinct transcripts , PNPase impacts many , if not most , regulatory circuits in bacterial cells . Therefore , we postulate that the vast majority of phenotypes associated with loss of PNPase function in bacteria are due to its role in mediating sRNA stability . Equally exciting were the discoveries that hPNPase mediates the importation of RNA into the mitochondrial matrix [25] and degrades mRNAs and polyadenylated noncoding RNAs upon release into the cytoplasm following mitochondrial outer membrane permeabilization during apoptosis [120] . Given the recent discovery that PNPase is critical for mitochondrial DNA maintenance [126] , hPNPase is vital to the proper replication and function of mitochondria and to human life . Considering that these recent discoveries of additional functions for PNPase were made after more than a half century of study , we expect to see many more exciting findings reported on this ancient enzyme in the years to come . | Widely distributed among bacteria and eukaryotes , including humans , polynucleotide phosphorylase ( PNPase ) is a critical enzyme in RNA metabolism that functions in most organisms as a 3ʹ to 5ʹ exoribonuclease . In bacteria , inactivation of the gene encoding PNPase results in a wide range of consequences , including impaired growth , diminished stress responses , and loss of virulence . In mammals , PNPase has an essential role in mitochondrial function . Mutations in the gene encoding the human PNPase ( hPNPase ) that reduce its activity can lead to hereditary hearing loss , encephalomyopathy , severe axonal neuropathy , delayed myelination , and Leigh syndrome . In this review , we highlight both the canonical and unorthodox activities that have been reported for PNPase . Specifically , we examine its role in bacterial mRNA and rRNA decay , RNA processing , and small regulatory RNA ( sRNA ) degradation and stabilization . Furthermore , we explore the recently reported findings on the function of hPNPase in mitochondrial RNA import and degradation and cytoplasmic mRNA and noncoding RNA decay . Despite being discovered more than six decades ago , we are still only beginning to grasp the breadth of mechanisms by which the enzymatic activities of PNPase contribute to cellular and organismal physiology . |
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The proper biological functioning of proteins often relies on the occurrence of coordinated fluctuations around their native structure , or on their ability to perform wider and sometimes highly elaborated motions . Hence , there is considerable interest in the definition of accurate coarse-grained descriptions of protein dynamics , as an alternative to more computationally expensive approaches . In particular , the elastic network model , in which residue motions are subjected to pairwise harmonic potentials , is known to capture essential aspects of conformational dynamics in proteins , but has so far remained mostly phenomenological , and unable to account for the chemical specificities of amino acids . We propose , for the first time , a method to derive residue- and distance-specific effective harmonic potentials from the statistical analysis of an extensive dataset of NMR conformational ensembles . These potentials constitute dynamical counterparts to the mean-force statistical potentials commonly used for static analyses of protein structures . In the context of the elastic network model , they yield a strongly improved description of the cooperative aspects of residue motions , and give the opportunity to systematically explore the influence of sequence details on protein dynamics .
Deciphering the motions that underlie many aspects of protein function is a major current challenge in molecular biology , with the potential to generate numerous applications in biomedical research and biotechnology . Although molecular dynamics ( MD ) hold a prominent position among computational approaches , considerable efforts have been devoted to the development of coarse-grained models of protein dynamics [1] . Besides their ability to follow motions on time scales that are usually not accessible to MD simulations , these models also give the possibility to better understand the general principles that rule the dynamical properties of proteins . The elegant simplicity of the elastic network models ( ENM ) certainly contributed to their popularity , and they have been successfully exploited in a wide range of applications [2]–[5] . In these models , the residues are usually represented as single particles and connected to their neighbors by Hookean springs [6] , [7] . The input structure is assumed to be the equilibrium state , i . e . the global energy minimum of the system . Common variants include the homogeneous ENM , in which springs of equal stiffness connect pairs of residues separated by a distance smaller than a predefined cutoff , and other versions in which the spring stiffness decays as the interresidue distance increases [8]–[10] . In all cases , the equations of motion can be either linearized around equilibrium , to perform a normal mode analysis of the system [11]–[13] , or integrated to obtain time-resolved relaxation trajectories [14] , [15] . Despite their many achievements , purely structural ENM also come with severe limitations . Notably , modeling the possible effects of mutations within this framework usually requires random local perturbations of the spring constants [16] , or a more drastic removal of links from the network [17] . A few attempts have been made to include sequence-specificity in the ENM by setting the spring constants proportional to the depth of the energy minima , as estimated by statistical contact potentials [18] , [19] . However , this approach cannot be extended to distance-dependent potentials , for they are not consistent with the ground hypothesis of the ENM , i . e . that all pairwise interaction potentials are at their minimum in the native structure . Other studies have led to the conclusion that the ENM behave as entropic models dominated by structural features , and that the level of coarse-graining is probably too high to incorporate sequence details [7] , [20] . Still , the chemical nature of residues at key positions can have significant effects on the main dynamical properties of a protein . Hinge motions [21] , for instance , obviously require some architectural conditions to be fulfilled , such as the presence of two domains capable of moving relatively independently . But the amplitude and preferred direction of the motion are most likely determined by fine tuning of specific interactions in the hinge region . In proteins subject to domain swapping , the hinge loops have indeed been shown to frequently include residues that are not optimal for stability [22] . The importance of the amino acid sequence has also been repeatedly emphasized by experimental studies of the impact of mutations on the conformational dynamics of proteins [23]–[25] . A major obstacle to the definition of accurate coarse-grained descriptions of protein dynamics lies in the highly cooperative nature of protein motions , which makes it difficult to identify the properties of the individual building blocks independently of the overall architecture of each fold . By condensing the information contained in a multitude of NMR ensembles , we build here a mean protein environment , in which the behavior of residue pairs can be tracked independently of each protein's specific structure . This methodology brings an efficient way of assessing coarse-grained models of protein dynamics and of deriving effective energy functions adapted to these models . In the context of the ENM , we identify a set of spring constants that depend on both the interresidue distances and the chemical nature of amino acids , and that markedly improve the performances of the model .
The mean-square fluctuations of individual residues ( MSRF ) have been extensively relied on to characterize protein flexibility and to evaluate coarse-grained models of protein dynamics [26] , in part because of their widespread availability as crystallographic B-factors . However , since the MSRF carry little information about the cooperative and anisotropic nature of residue motions , we propose to examine the dynamical behavior of proteins from the perspective of residue pairs rather than individual residues . Information about the fluctuations of interresidue distances is contained in the data of NMR experiments for numerous proteins , and will be exploited here . We define the apparent stiffness of a pair of residues in a protein : ( 1 ) where is the Boltzmann constant , the temperature , and the variance of the distance between residues and , in a structural ensemble representative of the equilibrium state . is defined up to a multiplicative factor , which corresponds to the temperature . We also introduce the uncorrelated apparent stiffness , to quantify the impact of the individual fluctuations of residues and on the fluctuations of the distance that separates them . This is achieved by using instead of in eq . 1 , where is computed after exclusion of all correlations between the motions of residues and ( see Methods ) . As illustrated in Figure 1 , can be quite different from one residue pair to another . Indeed , besides the impact of direct interactions , is also strongly dependent on the overall fold of the protein , and on the position of the pair within the structure . To remove the specific influence of each protein's architecture , we define the apparent stiffness in a mean protein environment : ( 2 ) where is one of 210 amino acid pairs , the discretized equilibrium distance between pairs of residues ( Å ) , the number of structures in the equilibrium ensemble of protein , and the number of residue pairs in protein . Pairs of consecutive residues were dismissed , so as to consider only non-bonded interactions . The mean protein environment is thus obtained by averaging over a large number of residue pairs in a dataset of different proteins ( see Methods ) . The influence of the distance separating two residues on the cooperativity of their motions can be investigated by considering amino acid types indistinctively in eq . 2 . Interestingly , follows approximately a power law , with an exponent of about −2 . 5 ( Figure 2 ) . Finer details include a first maximal value occurring for – distances between 5 and 5 . 5 Å , i . e . the separation between hydrogen-bonded residues within regular secondary structure elements , and a second around 9 Å , which corresponds to indirect , second neighbor , interactions . The high level of cooperativity in residue motions is well illustrated by the comparison of and its uncorrelated counterpart . Indeed , these two functions would take identical values if the variability of the distance between two residues could be explained solely by the extent of their individual fluctuations . In a mean protein environment , however , is about two orders of magnitude larger than at short-range , and the difference remains quite important up to about 30–40 Å . The comparison of values extracted from subsets containing exclusively small , large , all- , or all- proteins indicates that the content of the dataset has a remarkably limited impact on ( Figure S1 ) . This distance dependence can thus be seen as a general property of protein structures , a signature of protein cooperativity at the residue pair level . Of course , since is representative of a mean protein environment , deviations may occur for individual proteins , according to their specific structural organizations ( Figure S2 ) . The apparent stiffness is computed for each type of amino acid pair using eq . 2 , by considering only residue pairs separated by less than 10 Å . As shown in Figure 3A , the chemical nature of the interacting residues is a major determinant of their dynamical behavior . Unsurprisingly , Glycine and Proline appear as the most effective ingredients of flexibility . Pairs involving hydrophobic and aromatic amino acids tend to be considerably more rigid , with values up to 6 times larger . These differences originate in part in the individual propensities of different amino acids to be located in more or less flexible regions ( e . g . hydrophobic core vs . exposed surface loops ) . However , there is only a limited agreement between and ( Figure 3A–B ) : the correlation coefficient is equal to 0 . 71 , and spans a much wider range of values . Beyond individual amino acid preferences , the specifics of residue-residue interactions play thus a significant role in determining the extent of cooperativity in residue motions . The computation of the apparent stiffness of residue pairs in a mean protein environment provides an interesting tool to probe the dynamical properties of proteins . It also generates a very straightforward approach to assess the ability of coarse-grained models to reproduce accurately this general behavior . We focus here on four common variants of the residue-based ENM [27] , [28] , which differ only by the functional form of the spring constants . The dependence of on the interresidue distance is defined by two parameters: the cutoff distance , above which residues and are considered disconnected , and the exponent that determines how fast decreases with increasing distances: ( 3 ) where is the Heaviside function . The value of the temperature-related factor is obtained , for each protein independently , by fitting the predicted MSRF with the experimental ones . This ensures that the amplitude of the individual fluctuations of the beads in the network is on average equal to that observed in the corresponding NMR ensemble , and that the predicted values can thus be directly compared with those extracted from the NMR data . We consider the following models: , , , . These ENM variants were used to estimate the value of for each pair of residues in the 1500 proteins of our NMR dataset ( see Methods ) , and to subsequently compute and from eq . 2 . Strikingly , all ENM variants systematically predict values to be lower than the experimental ones , at least up to interresidue distances of 20–30 Å ( Figure 2 ) . These models overestimate thus the amplitude of pairwise fluctuations , relatively to the amplitude of individual fluctuations . For example , if two residues in a protein undergo highly correlated motions , the amount of thermal energy necessary to induce a moderate variance on the distance between them will generate high variances on their individual coordinates . Consequently , if the motions of the beads of the ENM are less coordinated , adjusting the scale of the spring constants to reproduce the amplitude of individual fluctuations leads to an overestimated variance on the interresidue distances , and thus to lower values . This problem is particularly apparent when is assumed to decrease proportionally to the square of the interresidue distance , in the . Although this model was shown to perform well in predicting MSRF values [10] , our results suggest that it negates almost completely the coordinated aspect of residue motions . Indeed , as shown in Figure 2 , the values predicted by this model are very close to those obtained from the experimental data after removal of the correlations between the motions of the different residues ( ) . This observation is consistent with the extremely short atom-atom correlation length characteristic of the , recently estimated on the basis of an X-ray structure of Staphylococcal nuclease [27] . The ENM is often considered as an entropic model , not detailed enough to include sequence information in a relevant way [7] , [20] . It is therefore hardly surprising that common ENM variants produce a poor description of the sequence specificities of protein dynamics . Individual amino acid preferences for more or less densely connected regions are responsible for some variety in the predicted values of ( Figure 3C–D ) . However , this variety is far from matching the one observed in the experimental data , as shown by a much narrower range of values , and a limited correlation coefficient with the experimental values , e . g . 0 . 64 for the and 0 . 62 for the ( Figure S3 ) . There is a much better agreement between the values predicted by the , and the experimental values of the uncorrelated apparent stiffness ( Figure 3B , D , correlation coefficient of 0 . 84 ) , which confirms that this model ignores the coordinated aspects of residue motions . Mean-force statistical potentials are commonly used to perform energetic evaluations of static protein structures [29]–[31] . These potentials do not describe explicitly the “true” physical interactions , but provide effective energies of interaction in a mean protein environment , in the context of a more or less simplified structural representation . Similarly , within the ENM framework , defines for each pair of residues an harmonic interaction potential . This potential is also effective in nature , accounting implicitly for everything that is not included in the model ( e . g . the surrounding water ) . Hence , we seek to identify the value of yielding the most accurate reproduction of the dynamical behavior of each type of pair in a mean protein environment , which is conveniently captured by the apparent stiffness . For that purpose , let us define as the energy of the elastic spring connecting two residues of type , in a mean protein environment: ( 4 ) where is the apparent stiffness extracted from the experimental data . is unknown and is expected to be different for different pair types . The knowledge of is thus not sufficient to estimate directly . However , from any approximate set of spring constants , we may build the ENM for all proteins in our dataset , to reproduce the mean protein environment , and compute for each pair type an estimated value of the apparent stiffness , , and bond energy , . Since the behavior of a given residue pair is highly dependent on its environment , we can make the assumption that is a relatively good approximation of , even if : ( 5 ) Indeed , if the spring stiffness of a residue pair is underestimated , it will also appear as less rigid in the ENM than in the experimental data . A more detailed discussion is given in Supporting Text S1 . From eqs . 4 and 5 , we devise thus an iterative procedure in which is updated at each step by confronting the predicted values of the apparent stiffness , , with the experimental ones , . It is expected to converge when , that is , when the predictions of the model agree with the experimental data: ( 6 ) We used this approach to derive , from the NMR data , four novel ENM variants: the distance-dependent dENM ; the sequence-dependent and , with a distance cutoff of 10 and 13 Å , respectively , and the sequence- and distance-dependent sdENM ( see Methods ) . Interestingly , the values for the 210 amino acid pairs in the are relatively well correlated with the corresponding contact potentials [30] , even though they result from totally different approaches ( Figure S4 ) . Some common general trends can be identified , e . g . hydrophobic contacts tend to be associated with both favorable interaction energies and large values ( Figure 4A ) . However , the overall correspondence remains limited , indicating that the determinants of protein rigidity and stability are related , but distinct . The distance dependence of in the dENM is remarkably similar to the power law that was previously obtained by fitting against a 1 . 5 ns MD trajectory of a C-phycocyanin dimer [8] ( Figure 4B ) , although our new model presents more detailed features . Notably , remains approximately constant up to interresidue distances of 5–6 Å , and then drops by about two orders of magnitude to reach a second plateau between 7 and 12 Å . The bootstrap estimates of the 90% confidence intervals displayed on Figure 4B underline the robustness of our derivation scheme , and indicate that the values determined here depend only marginally on the content of the dataset . The values of the sdENM are shown in Figure 4C , for a few amino acid pairs . This model not only combines the strengths of the sENM and the dENM , but also reveals the sequence specificity of the distance dependence . The D-R pair , for example , is almost as rigid as I-I at short distances consistent with the formation of a salt bridge , but almost as flexible as G-G at larger distances . There is of course a larger uncertainty on the determination of values , which is reflected by wider confidence intervals than those on in the dENM ( Figure 4B , C ) . This is due to the limited amount of available experimental data , and to the fact that the modelled dynamical behavior of a protein is obviously less sensitive to variations of the spring constant values for one type of amino acid pair , than for all amino acid pairs indistinctively . However , this uncertainty remains small enough to allow the identification of significant differences between the values determined for different types of amino acid pairs . In the example of Figure 4C , is consistently larger than over the whole range of inter-residue distances , whereas is significantly larger than at short-range ( 4–6 Å ) , and significantly smaller than at mid-range ( 6–12 Å ) . The sdENM yields a much more accurate reproduction of the dynamical behavior of residue pairs in a mean protein environment than the common ENM variants , as demonstrated by the good agreement between experimental and predicted values of ( Figures 5A , S5 ) , and ( Figure 5B ) . Beyond its performances in a mean protein environment , our new model also brings highly notable improvements with respect to previously described ENM variants when it is applied to the specific architecture of a given protein . This is illustrated by two examples , on Figure 6 . A more thorough assessment of the ability of the different ENM variants to capture the motions of individual proteins was performed on an independent dataset of 349 proteins . The correlation coefficient between predicted and observed MSRF ( ) has been widely used in the past but ignores the cooperativity inherent to protein dynamics , and presents other shortcomings . Therefore , we introduce a new measure ( ) that quantifies the relative error on the estimation of the variability of the distance between residue pairs , and is thus focused on the cooperative aspects of residue motions ( see Methods ) . Among the 4 previously described ENM variants , the is better at predicting the individual residue fluctuations ( Table 1 ) . Interestingly , the , with its simple cutoff distance , appears superior when it comes to the reproduction of cooperative motions ( ) . The new ENM variants based on our effective harmonic potentials present enhanced performances in comparison with the common models . In particular , the dENM reaches the same level of quality as the for individual fluctuations ( ) , but surpasses even the for the description of cooperativity ( ) . On the other hand , the impact of introducing sequence specificity can be examined by comparing with , and sdENM with dENM . It consists in a slight improvement of the correlation coefficient , and a pronounced decrease of the error , especially at short- ( 0–15 Å ) and mid- ( 15–30 Å ) range .
For the last decades , statistical potentials extracted from datasets of known protein structures [29]–[31] have played a critical role in static analyses of protein structures , with major applications including structure prediction , protein-protein docking , or rational mutant design . Our study demonstrates that a similar approach can be taken to derive effective energy functions that are specifically adapted to the coarse-grained modeling of protein dynamics . More precisely , in the context of the ENM , we exploited a dataset of 1500 NMR ensembles to determine the values of the spring constants that describe best the behavior of pairs of residues , as a function of both their chemical nature and the distance separating them . The success of our approach is attested by a drastic enhancement of the ability to accurately reproduce the cooperative nature of residue motions , with respect to previously described ENM variants . Moreover , a definite advantage of our method is that the effective parameters characterizing the strength of the virtual bonds are directly extracted from the experimental data without any a priori conception of their functional form . The fact that the distance dependence of the spring constants that we retrieve is quite similar to the power law , which was considered so far as underlying one of the best performing ENM variants [8] , [27] , also constitutes a major support to our approach . In our derivation scheme , the virtual bonds are parametrized so as to reproduce the behavior of amino acid pairs in a mean protein environment . The analysis of the ability of different models of protein dynamics to describe the motions of residues within this environment sheds an interesting new light on the properties of these models . In particular , our results indicate that previous ENM variants underestimate , sometimes dramatically , the rigidity of amino acid pairs at short- and mid-range . Our new model does however provide a much more accurate reproduction of the balance between short-range and long-range coordinated motions . This is arguably a crucial aspect when considering , for example , the consequences of localized alterations induced by ligand binding on signal transduction or global conformational changes , such as in ATP-powered molecular motors . Importantly , our results also demonstrate that the ENM does not have to be exclusively structural , and that sequence details may be allowed to play a major role in coarse-grained descriptions of protein dynamics . Thereby , this study paves the way towards comparative analyses of motions in proteins that share a similar structure but present differences in sequence . Such investigations will prove particularly interesting in the context of the rational design of ( modified ) proteins with controlled dynamical properties . On the other hand , the importance of orientational effects in protein dynamics has been underlined by both experimental and computational studies [5] , [7] , [32]–[36] . At the protein level , these effects are nicely illustrated by the strong anisotropy of a protein's response to applied external forces [33] , [34] , [36] . At the residue level , the anisotropy can be related to the directional variability of the packing density experienced by any given residue [5] , [35] . The accurate description of such orientational effects should benefit from the availability of sequence-specific models . Indeed , beyond the number of contacts established in each direction , the actual nature of these contacts can also have a substantial influence on the anisotropy of residue fluctuations . Although we focused here on residue-based elastic network models , our approach is not limited to this particular family , and can be readily implemented to use available dynamical data for the evaluation and optimization of other coarse-grained models of protein dynamics . Notably , the impact of chemical specificity on the dynamical behavior of residues should be even more accurately rendered by effective potentials based on a more detailed structural description .
We retrieved , from the Protein Data Bank [37] , ensembles of at least 20 models from solution NMR experiments , corresponding to monomeric proteins of at least 50 residues that present at most 30% sequence identity with one another . Entries under the SCOP classifications “Peptides” or “Membrane and cell surface proteins” were not considered . The presence of ligands , DNA or RNA molecules , chain breaks , non-natural amino acids , and differences in the number of residues per model were also grounds for rejection . These criteria led to the selection of 1849 distinct structural ensembles . A subset of 1500 ensembles was randomly selected for the main analysis , and the remaining 349 were used to assess the performances of the different ENM variants . Unfolded C- or N-terminal tails were automatically identified ( MSRF values larger than twice the average for all residues in the protein ) and removed from consideration . In each ensemble , the structure with the lowest root mean square deviation from the mean structure , after superposition , is chosen as representative and used to build the ENM . The network is built by considering each residue as a single bead , placed at the position of the corresponding atom in the input structure , and connecting neighboring beads with Hookean springs [6] , . The ENM variants considered here differ only by the form of the spring constant as a function of interresidue distance and of amino acid types . In all variants , bonded interactions are described by a larger value of , defined as ten times the value of for non-bonded interactions at a separation of 3 . 5 Å , averaged over all amino acid types . The potential energy of the network is given by: , where and are the instantaneous and equilibrium distances between residues and , respectively . By definition , the input structure corresponds to the global energy minimum , with . For a protein of residues , the Hessian of the system is the matrix of the second derivatives of with respect to the spatial coordinates of the residues . The eigenvalue decomposition of yields the covariance matrix of the spatial coordinates , which constitutes the output of the model: ( 7 ) where the sum is performed over the non-zero eigenvalues of , and are the corresponding eigenvectors . is a symmetrical matrix , constituted of submatrices : ( 8 ) where , , and correspond to the displacements of residue from its equilibrium position , along the three Cartesian coordinates . The predicted MSRF of residue is given by the trace of submatrix . For each pair of residues in a given protein , the experimental value of this variance is readily computed from the NMR data: ( 9 ) where is the number of structures in the NMR ensemble , the distance between the atoms of residues and in structure of protein , and the average distance over all structures . In the context of the ENM , values are estimated from the covariance matrix of the spatial coordinates , by standard statistical propagation of uncertainty: ( 10 ) where is the Jacobian of the distance as a function of the six spatial coordinates: ( 11 ) This estimation of takes into account the individual , anisotropic , fluctuations of both residues , as well as the correlations between their respective motions . It relies on the validity of the first order Taylor expansion of the distance as function of the coordinates in the vicinity of the average distance . We ensured that no systematic bias arose from this approximation ( Figure S6 ) . To quantify the impact of the individual motions of residues on their relative positions , we use eq . 10 to compute in an artificial construct where residue motions are not correlated . This is achieved by extracting the covariance matrix from the NMR data , and setting to zero all submatrices where . The values of the spring constants of the new ENM variants were derived from the dataset of 1500 NMR ensembles using eq 6 . For the dENM , and , the initial values of the spring constants were set equal to the experimental values of the apparent stiffness: or . Note that the values were computed by considering only residue pairs separated by a distance lower than the cutoff of 10 or 13 Å . For the sdENM , the values were set equal to the final values of the spring constants in the dENM , , for all amino acid types . A correction for sparse data was devised to ensure that tends to when the number of residue pairs of type is too small to obtain relevant estimations of . Instead of eq . 2 , we used the following definition to compute both the experimental and predicted apparent stiffness: ( 12 ) where , is the number of pairs of type in protein , and is the number of structures in the NMR ensemble of protein . The adjustable parameter can be understood as the number of occurrences of a residue pair , , that is needed to obtain a relevant estimation of . For a given type of residue pair , if , then no correction is necessary , and eq . 12 reduces to eq . 2 . On the contrary , if , then the data on pairs is considered too sparse to reliably estimate , and . We found that the value of has little impact on the overall quality of the model , as long as it is not too small ( ) , in which case aberrant values of are determined for some uncommon pairs , or too large ( ) , in which case the performances decrease because of a loss of information on sequence-specificity . The value of the parameter was set here to 500 . The values were rescaled after each iteration step , so that the average value of over all amino acid types is equal to 1 for pairs separated by a distance of 6 Å . Residue pairs of a given type for which ( after rescaling ) , were considered to establish no direct interaction: was set to 0 , and they were no longer considered in the iterative procedure . The performances of the new ENM variants after the first nine iteration steps are reported in Table S1 . The procedure converged rapidly for the dENM and the sdENM , and the final models were selected after 5 and 3 iteration steps , respectively . The sENM variants did not improve significantly with respect to the initial models ( ) , indicating that is a good approximation , contrary to . The procedure was thus stopped after one iteration step , for both the and the . To assess the robustness of the derivation scheme , and the sensitivity of the values determined for each type of residue pair to the content of the dataset , we calculated the bootstrap estimates of the 90% confidence intervals on , , and . For that purpose , the iterative procedure was repeated with 100 different datasets , each one consisting of 1500 NMR ensembles randomly picked , with replacement , from the original training dataset . All values , and the corresponding confidence intervals , are given in Dataset S1 . The ability of coarse-grained models to accurately describe protein dynamics is commonly evaluated by computing the Pearson correlation coefficient between predicted and experimental MSRF , , over all residues of a given protein: ( 13 ) where , for simplicity , was used instead of . There is indeed a direct relationship between the MSRF and the cristallographic B-factors: . and correspond thus here to the MSRF of residue extracted from the NMR data and predicted by the ENM , respectively . The scale of the predicted MSRF values depends on the scale of the spring constants , which are only defined up to a constant factor . This factor was determined , for each protein independently , by fitting the scales of the predicted and experimental MSRF , i . e . to ensure that: ( 14 ) Although it has been widely used in previous studies , is probably not the most adequate measure to evaluate the performances of coarse-grained models of protein dynamics . As pointed out previously [26] , [27] , it does indeed present several shortcomings: e . g . it is strongly affected by the presence of highly flexible regions , and does not account for possible flaws leading to an intercept of the regression line different from zero . Most importantly , the MSRF describe individual fluctuations but provide no information about the cooperative aspects of residue motions . The quality of the MSRF predictions gives thus no guarantee about the ability of the model to describe the cooperativity of protein dynamics . The provides an interesting example , for it performs quite well in predicting the MSRF but basically negates all cooperativity ( Figure 2 , Table 1 ) . Therefore , we introduce a new measure that exploits the information contained in the correlation matrix , to quantify the error on the estimation of the fluctuations of the interresidue distances: ( 15 ) where is the number of non-bonded residue pairs in protein , and are the experimental ( eq . 9 ) and predicted ( eq . 10 ) values of , respectively . is obtained after fitting the experimental MSRF with the predicted ones ( eq . 14 ) . The error is normalized by , which is the expected value of given the individual , anisotropic , fluctuations of both residues extracted from the NMR data , but neglecting all correlations between their respective motions . This normalization ensures that the contributions of the different pairs of residues are equivalent , and that the measure is not dominated by highly flexible regions . Both and are computed independently for each of the 349 proteins of our test set , and the average values are reported . We also report the short- ( ) , mid- ( ) , and long-range ( ) contributions to , obtained by considering only pairs separated by 0–15 Å , 15–30 Å , and more than 30 Å , respectively . | Decades of experimental evidence have underlined the fact that protein structures can hardly be considered as static objects . To understand how a protein achieves its biological purpose , it is therefore quite often necessary to unravel the complexity of its dynamical behavior . However , the definition of accurate and computationally tractable descriptions of protein dynamics remains a highly challenging task . Indeed , even though proteins are all built from a limited set of amino acids and local conformational arrangements , the specific nature of biologically relevant motions may vary widely from one protein to another , which constitutes a serious obstacle to the identification of common rules and properties . Here , instead of focusing on the study of a single protein , we adopt a more general perspective by condensing the information contained in a multitude of NMR conformational ensembles . This approach allows us to characterize the dynamical behavior of residues and residue pairs in a mean protein environment , independently of each protein's specific architecture . We describe how this analysis can be exploited to assess the performances of coarse-grained models of protein dynamics , to take advantage of existing experimental data for a more rational and efficient parametrization of these models and , ultimately , to improve our understanding of the intrinsic dynamical properties of amino acid chains . |
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Infection with the Gram-negative bacterium Burkholderia pseudomallei is an important cause of community-acquired lethal sepsis in endemic regions in southeast Asia and northern Australia and is increasingly reported in other tropical areas . In animal models , production of interferon-gamma ( IFN-γ ) is critical for resistance , but in humans the characteristics of IFN-γ production and the bacterial antigens that are recognized by the cell-mediated immune response have not been defined . Peripheral blood from 133 healthy individuals who lived in the endemic area and had no history of melioidosis , 60 patients who had recovered from melioidosis , and 31 other patient control subjects were stimulated by whole bacteria or purified bacterial proteins in vitro , and IFN-γ responses were analyzed by ELISPOT and flow cytometry . B . pseudomallei was a potent activator of human peripheral blood NK cells for innate production of IFN-γ . In addition , healthy individuals with serological evidence of exposure to B . pseudomallei and patients recovered from active melioidosis developed CD4+ ( and CD8+ ) T cells that recognized whole bacteria and purified proteins LolC , OppA , and PotF , members of the B . pseudomallei ABC transporter family . This response was primarily mediated by terminally differentiated T cells of the effector–memory ( TEMRA ) phenotype and correlated with the titer of anti-B . pseudomallei antibodies in the serum . Individuals living in a melioidosis-endemic region show clear evidence of T cell priming for the ability to make IFN-γ that correlates with their serological status . The ability to detect T cell responses to defined B . pseudomallei proteins in large numbers of individuals now provides the opportunity to screen candidate antigens for inclusion in protein or polysaccharide–conjugate subunit vaccines against this important but neglected disease .
Melioidosis is a serious infectious disease in Southeast Asia and Northern Australia caused by the soil-dwelling Gram-negative bacterium , Burkholderia pseudomallei [1] . In Northeast Thailand , the mortality rate for acute melioidosis remains high , approximately 50% , despite recent advances in antibiotic treatments . Serological evidence , based on the indirect hemagglutination assay ( IHA ) , suggests that 80% of people living in endemic areas have been exposed to B . pseudomallei , without showing clinical symptoms [1]–[3] . Recurrent melioidosis can also occur either as relapse after antibiotic treatment or re-infection [3] , [4] . B . pseudomallei is classified as a NIAID category B potential agent for biological terrorism [5] . The mechanism that enables the organism to avoid the bactericidal effects of the host immune response has never been fully understood , and there are no licensed vaccines . B . pseudomallei is able to disseminate throughout the body , invades non-phagocytic cells and replicates in phagocytes [6] , [7] . In mice , B . pseudomallei is a potent inducer of IFN-γ and IFN-γ inducing cytokines such as IL-12 , IL-18 and TNF in vitro and IFN-γ is essential for resistance in vivo via the activation of macrophages for both oxygen dependent and independent killing mechanisms [8] . In mice , NK cells and bystander CD8+ T cells provide innate production of IFN-γ [9] , while IFN-γ secreting , antigen-specific CD4+ T cells contribute to protection against primary infection with B . pseudomallei and following immunization with experimental vaccines in vivo [10] , [11] . In addition , murine models of vaccination with dendritic cells pulsed with heat killed B . pseudomallei in the presence of CpG oligodeoxynucleotides showed significant levels of protection [12] suggesting the role of specific T cells in host protection . In contrast , the mechanisms of cell-mediated immunity to B . pseudomallei in humans are poorly understood . IFN-γ , IL-12 , IL-18 and TNF are found in plasma samples from acute , septic melioidosis but the IFN-γ producing cells have not been well characterized [13] . Previous studies in small numbers of patients in northern Australia and Papua New Guinea who recovered from melioidosis have demonstrated evidence of T cell priming to B . pseudomallei , but the characteristics of the responding cell populations and the antigens recognized have not been defined [14] , [15] . Here , we analyzed a large cohort of individuals from the melioidosis endemic region of Thailand to identify the cellular sources of IFN-γ in response to whole B . pseudomallei and the bacterial ABC transporter proteins LolC , OppA and PotF which are T cell immunogens in mice and candidate vaccine antigens [16] , [17] . Peripheral blood cells from healthy individuals with serological evidence of exposure to B . pseudomallei and recovered melioidosis patients ( but not seronegative control subjects ) showed evidence of CD4 and CD8 T cell priming to both whole bacteria and purified B . pseudomallei antigens . Together with a prominent IFN-γ response from NK cells , these sources of IFN-γ may contribute to host resistance against melioidosis in the endemic setting .
Blood samples from 133 healthy donors who had no clinical history of melioidosis were collected at the Blood Bank , Khon Kaen University , Thailand . Another set of blood samples was obtained from patients and control subjects at Sappasithiprasong Hospital , Thailand for cellular studies by ELISPOT assay . Patients were defined as those who had recovered from melioidosis ( previously diagnosed by isolation of B . pseudomallei from blood or tissues ) and completed antibiotic treatment ( n = 36 ) . Non infected control subjects ( n = 21 ) were those who attended the hospital for non infectious reasons at the diabetic clinic and had no history of clinical melioidosis , and were matched for age , sex , occupation , the presence of diabetes as an underlying condition and lived in the same endemic area . In addition , 24 recovered melioidosis patients and 10 healthy control subjects were enrolled , using the same criteria , at Srinagarind Hospital , Thailand for cellular sources of IFN-γ , kinetics and memory cells assayed by flow cytometry . The subjects who had antibodies to B . pseudomallei at a titer of 1∶40 or greater by IHA were considered seropositive [15] , [18] . None of the subjects had any clinical sign or symptoms of any infection including HIV/AIDS at the time of blood collection . B . pseudomallei strain K96243 is a clinical isolate from Thailand and is the prototype genome sequence strain [19] . Whole heat-killed B . pseudomallei ( hkBp ) was prepared by heating the bacteria at 100°C for 20 minutes , washed twice with PBS pH 7 . 4 , aliquoted and stored at −80°C . The number of viable bacteria was determined by colony-forming counts and defined as colony-forming units ( CFU ) prior to heating . Recombinant B . pseudomallei ABC transporter proteins ( LolC , OppA and PotF ) were prepared as previously described [16] , [17] and used as test stimulators in this study . Phytohemagglutinin ( PHA ) ( Biochrom AG , Germany ) , human recombinant IL-12 , and IL-15 ( BD Biosciences , USA ) and a MHC class I-restricted T cell epitope control of pooled peptides of cytomegalovirus , Epstein Barr virus and influenza virus ( CEF ) were used as positive controls ( Mabtech , AB , Sweden ) . Recombinant protein from Francisella tularensis , FT1823 [20] was included as a non related protein/negative control . Peripheral blood mononuclear cells ( PBMCs ) from each subject were isolated from heparinized blood samples by density centrifugation on Ficoll-Hypaque and adjusted the number of cells as required prior to stimulation . In brief , 96-well PVDF-plates ( MSIP , Millipore ) were previously coated overnight with 15 µg/ml 1D1K anti-human IFN-γ at 4°C . Fresh PBMCs were added in duplicate wells at 5×105 PBMCs/well and each stimulator was added at the optimal concentration . After 42 hours , secreted IFN-γ was detected by adding 1 µg/ml biotinylated mAb 7-B6-1-biotin for IFN-γ for 3 hours and followed by 1 µg/ml streptavidin-alkaline phosphatase ( Mabtech , AB , Sweden ) prior to enumeration under a stereomicroscope . The responses were compared in the absence or presence of 0 . 3 µg/ml cyclosporin A ( CsA , Sigma , USA ) . Whole blood samples were firstly analyzed for complete blood count using an automatic machine ( Sysmex , Germany ) . The number of absolute lymphocytes was then adjusted to 9×105 lymphocytes/ml by diluting with completed RPMI medium ( 10% FBS supplement ) . The adjusted cells in 100 µl were added into 96 well culture plates and added up by another 100 µl of 2× concentration of stimulators and incubated at 37°C with 5% CO2 . Cultured cells were blocked with 10 µg/ml brefeldin A ( Sigma , USA ) for 3 hours prior to the end of the incubation time . Then washed and blocked with anti-CD16 ( BD Biosciences ) . The following antibodies were used for immune cell surface staining: FITC anti-CD4 , PE anti-CD8 or PE anti-CD56 ( BD Biosciences ) and Tricolor anti-CD3 ( Invitrogen , USA ) . In addition , cell surface markers for memory T cell phenotypes were included: FITC anti-CCR7 ( R&D systems , USA ) , PE anti-CD45RA ( Invitrogen ) and Tricolor anti-CD4 or CD8 ( Invitrogen ) . Isotype-matched control antibodies were used in each analysis . After 30 min of staining , followed by fixation with 10% paraformaldehyde-PBS overnight at room temperature , cells were then permeabilized by 0 . 12% saponin ( Sigma , USA ) for 15 min followed by APC anti-IFN-γ ( Invitrogen , USA ) for 30 min prior to analysis by FACScalibur with CELLQuest software ( BD Biosciences , USA ) . Statistical analysis ( one way-ANOVA , unpaired and paired t-test ) was performed using Graphpad Prism version 5 software ( GraphPad , San Diego , CA , USA ) . A P-value<0 . 05 was considered statistically significant .
To examine the cellular immune response to B . pseudomallei of healthy individuals living in Northeast Thailand , PBMCs of 133 donors from the Blood Bank at Khon Kaen University , Thailand were cultured with whole bacteria , recombinant B . pseudomallei ABC transporter proteins ( LolC , OppA and PotF ) or control stimulators and 42 hours later assayed for IFN-γ production by ELISPOT . We have previously shown in mice that several different cell types contribute to IFN-γ responses to B . pseudomallei in vitro; NK cells and bystander T cells produce IFN-γ indirectly via a cytokine mediated pathway which is not blocked by cyclosporin A ( CsA ) , whereas specific B . pseudomallei primed T cells respond via a CsA sensitive T cell receptor ( TCR ) dependent process [9] , [11] . To validate this approach in human peripheral blood , we initially compared the CsA sensitivity of cytokine ( IL-12+IL-15 ) , mitogen ( PHA ) or antigen specific IFN-γ responses in vitro in the presence or absence of CsA . Compared to medium alone controls , cells incubated with PHA or a pooled cocktail of established T cell reactive peptides from pathogens known to be present in the Thai population ( CMV , EBV and influenza ) showed strong IFN-γ responses which were inhibited in the presence of CsA ( Figure 1A; P<0 . 0001 , paired t-test ) . In contrast , the IFN-γ response to IL-12/IL-15 or the low but detectable background response observed in cells incubated with an irrelevant Francisella tularensis control protein were not CsA susceptible . Moreover , the results revealed that whole B . pseudomallei ( hkBp ) and three Bp-derived ABC transporter proteins ( LolC , OppA and PotF ) could induce IFN-γ responses via TCR independent ( innate ) and dependent ( specific ) pathways in healthy individuals in vitro ( Figure 1B ) . These IFN-γ responses were in a dose dependent manner ranging between 1×104–1×107 CFU/ml hkBp and 0 . 1–3 . 0 µg/ml of the 3 proteins ( data not shown ) . IHA has been widely used as routine serologic test for melioidosis with the threshold titer of 1∶40 in the endemic area indicating previous exposure to B . pseudomallei [15] , [18] . To investigate whether the magnitude of the cellular immune response correlated with evidence of exposure to B . pseudomallei by serology , 133 healthy donors were classified into five groups based on their B . pseudomallei antibody IHA titers ( as 1∶20 , 1∶40 , 1∶180 , 1∶160 and 1∶320 ( n = 6 , 19 , 60 , 45 and 3 , respectively; Figure 2 ) . The results revealed that the continual increase of the average values of specific ( CsA sensitive ) IFN-γ spots in response to B . pseudomallei and its proteins was significantly correlated with increasing antibody titers ( P<0 . 0001 , one way ANOVA ) . No such correlation was observed in the response to CEF vs . medium controls or for the innate ( CsA resistant ) IFN-γ spots to B . pseudomallei ( P>0 . 05 , one way ANOVA ) . Thus environmental exposure to B . pseudomallei in the endemic region of NE Thailand generates both T cell and B cell responses to B . pseudomallei in healthy individuals even in the absence of disease . To assess the extent of T cell priming in patients who had survived active infection , specific T cell responses to whole bacteria and recombinant proteins of B . pseudomallei were studied in 36 recovered melioidosis cases and 21 other patient control subjects from the same endemic region chosen on the basis of same age , sex and occupation with no history of clinical melioidosis but who were seropositive for B . pseudomallei exposure . The frequency of IFN-γ producing cells was significantly increased in recovered patients compared to seronegative control subjects ( data not shown ) but was similar to that observed in seropositive healthy individuals ( Figure 3A ) ( P>0 . 05 , unpaired t-test ) . However , there were some individuals who had no specific IFN-γ producing cells to B . pseudomallei above the background of medium control in both groups . Of note , IFN-γ levels as quantified by ELISA were significantly higher in recovered melioidosis patients than seropositive individuals ( Figure S1 ) . According to Figure 3B , the comparison of IFN-γ responses between patients who recovered from melioidosis with a history of localized infection ( n = 13 ) and severe sepsis ( n = 11 ) did not show any significant difference . These specific T cell responses declined over the time but remained detectable after 80 weeks ( Figure 3C ) . These results indicated that either whole B . pseudomallei or its proteins could trigger the cellular immune response following re-exposure to the microorganism in vitro up to 80 weeks post admission . Diabetes mellitus ( DM ) is a major risk factor for human melioidosis [21] , and only 4 cases of recovered melioidosis without DM were found in this study . Even though we observed no difference between IFN-γ responses of these groups , it remains inconclusive for the effect of diabetic condition on host T cell responses ( data not shown ) . In addition , recovered melioidosis patients with a history of recurrent infection ( n = 6 ) compared to those with a single disease episode ( n = 30 ) also showed no statistically significant difference ( data not shown ) ; suggesting that under these conditions IFN-γ responses do not differentiate between primary and recurrent melioidosis . To identify the cellular sources of IFN-γ responses to B . pseudomallei , whole blood samples from six seropositive healthy individuals and ten recovered melioidosis cases ( all with IHA antibodies 1∶40 or greater ) were restimulated with B . pseudomallei in the absence of CsA and analyzed by four-color flow cytometry . As shown from one representative of seropositive group , the small lymphocyte area was gated ( Figure 4A ) and analysis of IFN-γ+ cells showed that NK cells ( CD3−CD56+ ) , CD4+ T ( CD3+CD4+ ) and CD8+ T ( CD3+CD8+ ) cells all contributed to IFN-γ production to hkBp ( Figure 4B ) . A dominant contribution of CD4+ and CD8+ T cells on IFN-γ production was confirmed by significant reduction of specific IFN-γ ( CsA sensitive ) spots following depletion of CD3 , CD4 and/or CD8 cells ( Figure S2 ) . In addition , the mean fluorescent intensities ( MFI ) of intracellular IFN-gamma staining of CD3+ and CD3− ( NK ) cells of 4 recovered melioidosis cases were analyzed and revealed that the average MFI of IFN-gamma gated on CD3+ cells was significantly higher than CD3− ( NK ) cells ( P<0 . 05 , paired t-test ) ( Figure S3 ) . Together with our finding that the ELISPOT size of remaining IFN-γ+ cells after T cell depletion was very small suggests that innate ( CsA resistant ) cells produce less of this cytokine compared to specific T cells . The analysis of blood samples from 6 seropositive individuals and 10 recovered melioidosis cases showed background staining of total IFN-γ producing cells in medium alone at 0 . 03±0 . 01 and 0 . 3±0 . 09% ( mean±SE ) , respectively . The relative contribution of each cell type to the IFN-γ response to B . pseudomallei also varied according to the time point examined in culture after addition of the bacteria . NK cells appeared to respond more rapidly than T cells and significantly contributed to the production of rapid IFN-γ at 4 hours and decreased over time in both groups ( Figure 5 ) . On the one hand , the frequency of IFN-γ producing NK cells were significantly higher in seropositive healthy individuals than recovered melioidosis at 4 and 12 hours ( P<0 . 05 , unpaired t-test ) . On the other hand , there was a statistically significant difference of IFN-γ producing CD4+ T cells being higher in recovered melioidosis than the seropositive individuals at both time points and all three time points for IFN-γ producing CD8+ T cells . These results demonstrated the increasing contribution to IFN-γ production over the time from T cell subsets , particularly in the recovered melioidosis group . To investigate whether the rapid IFN-γ producing T cells in response to B . pseudomallei were memory T-cell phenotypes , immune subsets of human memory T cells were identified based on the cell surface expression of CD45RA and CCR7 [22] in 16 recovered melioidosis cases and 7 seropositive control subjects . The results demonstrated the percentages of IFN-γ producing memory T cells and the majority of memory CD4+ and CD8+ T cells of both groups were revealed as terminally differentiated T effector memory ( TEMRA ) cells which was significantly higher than the other memory phenotypes of effector memory ( TEM ) and central memory ( TCM ) cells of CD4+ and CD8+ T cells ( P<0 . 0001 , unpaired t-test ) ( Figure 6A ) . When the clinical histories of these 16 recovered melioidosis subjects were analyzed , distinctive patterns of memory T cell phenotypes were revealed . The memory T cells of the septicemic melioidosis group ( n = 12 ) were TEMRA significantly greater than TEM and TCM of both CD4+ and CD8+ subsets ( P<0 . 0001 , unpaired t-test ) . Interestingly , there was a trend of localized melioidosis group ( n = 4 ) showed stronger responses of TEMRA with small contribution of TCM cells and TEM cells , but it was not statistically significant ( Figure 6B ) .
Burkholderia pseudomallei is an important cause of community acquired sepsis and death in endemic regions of SE Asia and Northern Australia and is listed as potential bioterrorism threat . Yet despite its current and potential impact on public health our understanding of immune defenses against this pathogen are incomplete . B . pseudomallei is capable of extensive extracellular growth and abscess formation , but is also genetically adapted to survive and replicate within host cells [6] , [23] . It is killed by IFN-γ activated macrophages in vitro [24] , making cell mediated immunity a potentially important component of resistance . Here , a total of 224 individuals living in the endemic area of NE Thailand of varying immunological and clinical history for exposure to B . pseudomallei were examined for the magnitude and characteristics of their IFN-γ responses following restimulation of whole blood with whole bacteria or B . pseudomallei derived antigens in vitro . Northeast Thailand is the primary endemic focus of melioidosis in SE Asia and the majority of individuals show evidence of seroconversion from an early age [2] . To obtain an initial estimate of the diversity of the IFN-γ response in this setting , blood samples from 133 randomly selected individuals who had no clinical history of melioidosis were tested for reactivity to B . pseudomallei by IFN-γ ELISPOT . The majority showed clear induction of IFN-γ positive cells above that of medium alone controls in the presence of whole bacteria . Addition of cyclosporin A ( CsA ) which specifically inhibits T cell receptor-mediated but not cytokine mediated lymphocyte activation [9] , [11] , [25] showed this response was made up of both innate and adaptive IFN-γ responses . To further define the adaptive IFN-γ component , we compared the frequency of CsA sensitive IFN-γ producing cells against the antibody titer of each individual . Serological evidence of exposure to B . pseudomallei is clinically determined by an indirect hemagglutination ( IHA ) assay which mostly detects antibodies to conserved lipopolysaccharides and/or capsular polysaccharides and is useful in diagnosis of melioidosis particularly in non or low endemic areas [1] . Although the threshold IHA titer for serodiagnosis varies in different countries , in the Northeast Thai population; an IHA titer 1∶40 is considered to be indicative of previous exposure to B . pseudomallei in healthy individuals [15] , [18] . The frequency of specific , B . pseudomallei induced IFN-γ cells closely correlated with the serological status of the donor , whereas no such correlation was observed with control antigens derived from viruses known to be prevalent in the Thai population . Thus environmental exposure to B . pseudomallei induces concordant adaptive T and B cell responses as also seen in other examples of infection or vaccination [26] , [27] . In mice , IFN-γ is critical for survival of the infected host and NK cells , as well as both CD4+ and CD8+ T cells contribute to its production [9] , [10] . Using intracellular cytokine staining and specific cell depletion we found a similar situation in humans in which all three cell types produced IFN-γ in vitro , with their relative contribution differing according to the serological status of the host . An initial finding was that human NK cells were prominent producers of IFN-γ in vitro , providing some 80% of the IFN-γ positive cells in the first few hours of the culture period . This response was observed in both seronegative and seropositive individuals , was not inhibited by CsA and most likely represents an innate response to the bacterium presumably driven via the generation of IFN-γ inducing cytokines such as IL-12 , IL-15 and IL-18 in culture [9] , [28] . This observation may also explain the previous findings by Lauw et al of a rapid IFN-γ dependent induction of the chemokines Mig and IP-10 in whole blood cultures of healthy individuals in the presence of dead B . pseudomallei [29] . In seropositive individuals , this innate response was supplemented by the presence of IFN-γ positive CD4+ T cells and CD8+ T cells in both recovered melioidosis patients and asymptomatic healthy control subjects . A predominance of CD4+ T cells was observed from the peripheral blood of recovered melioidosis subjects . Haque A , et al . also reported that antigen-specific CD4+ T cells were important for the resistance against B . pseudomallei during the later phase of primary infection [10] . Clear evidence of priming of CD8+ T cells was also observed , presumably reflecting the cytoplasmic habitat of the bacterium within host cells [14] . These antigen specific T cells provided the majority of the total IFN-γ generated in culture as evidenced by their higher mean fluorescent intensities ( MFI ) of IFN-γ staining ( Figure S3 ) , larger ELISPOT sizes ( data not shown ) and by the significant effect of T cell depletion on the IFN-γ ELISPOT response . Of note , we have compared T cell responses by the production of IFN-γ vs . granzyme B by ELISPOT and the results showed high correlation of these 2 indicators in response to B . pseudomallei suggesting the importance of cytotoxic T cell response in melioidosis ( Figure S4 ) . However , the role of these cells to combat this intracellular pathogen requires further studies . We then used differential expression of CD45RA and CCR7 to characterize the IFN-γ producing T cells as either central memory ( CM ) , effector memory ( EM ) or a more recently described effector memory RA ( EMRA ) populations [30]–[32] . By these criteria , >80% of IFN-γ+ CD4+ T cells and >90% of IFN-γ+ CD8+ T cells reacting to B . pseudomallei were TEMRA cells , with the remaining minority being TCM and TEM cells . Thus , B . pseudomallei predominately induces ‘effector memory RA’ T cells in the peripheral blood that respond rapidly to repeated exposure to the microorganism as also reported with other pathogens such as human cytomegalovirus and human immunodeficiency virus [31] , [33] . There was a trend towards a greater contribution of TEM and TCM cells in patients with a history of septicemia compared to localized melioidosis but this did not attain statistical significance and further studies using larger cohort sizes are needed to confirm this . Several earlier reports established that exposure to B . pseudomallei primed human T cells for proliferation and secretion of the macrophage activating cytokine IFN-γ in vitro . However , these studies were restricted to small numbers of individuals in Northern Australia and Papua New Guinea and did not define the frequencies , memory phenotypes of the responding cell populations or the antigen specificity of these responses . The results presented here confirm and extend these findings to a larger sample size in the endemic region of Thailand . A consistent finding in all studies are that T cell responses were greater in seropositive versus seronegative individuals . With the larger group sizes provided in the Thai population we can go further and show that this also correlates with antibody titer , and not simply between antibody positive versus negative status . What is less clear is the relative strength of the cell mediated responses between seropositive healthy donors and recovered patients . Barnes et al found that lymphocyte proliferation and IFN-γ production was greater in seropositive healthy donors ( n = 8 ) than those recovered from infection ( n = 5 ) , arguing as in the case of tuberculosis , of impaired immunity in those who experienced clinical disease [13] . However , our results in the Thai population showed no difference in the frequencies of IFN-γ producing cells in the recovered melioidosis group versus seropositive healthy donors , although both were clearly greater than seronegative control subjects . In contrast , the amount of IFN-γ secreted ( as determined by ELISA ) and the frequency of high IFN-γ responders was greater in the recovered group suggesting an increased immune priming following a significant bacterial burden compared to healthy exposed control subjects . Of note , even with the larger sample sizes used here , IFN-γ responses were similar between individuals with and without diabetes , in patients with septicemic versus localized disease or in cases of recurrent versus single episodes of disease [4] . Given the high mortality of acute melioidosis and the problems of treatment , the development of an effective vaccine is an important but difficult task . This is needed to protect individuals living in endemic areas as well as in situations of accidental or purposeful exposure following a bioterrorism scenario . Experimental strategies using wild type bacteria of reduced virulence [34] , live attenuated mutants of B . pseudomallei [23] , [35] and killed whole cells [12] have all been attempted with varying success . However , one important approach requires identification of individual B . pseudomallei specific proteins , which are both immunogenic and protective , for inclusion in protein and/or polysaccharide sub-unit based vaccines [36]–[38] . To date , the number of B . pseudomallei proteins which have been defined as T cell immunogens in mice or humans is very limited [38]–[40] . In other pathogenic bacteria , ABC transporter proteins have roles in bacterial survival , virulence and pathogenicity , are immunogenic in humans and an increasing number are being considered as candidate vaccine antigens [41]–[43] . We have previously shown that three members of the bacterial ABC transporter family , LolC , PotF and OppA are immunogenic in mice and particularly in the case of LolC provide at least partial protection against lethal challenge with B . pseudomallei following immunization in adjuvant [17] . We now show that all three proteins are recognized by T cells from seropositive individuals and could be considered for future vaccine development . No T cell response was observed in B . pseudomallei seronegative individuals , arguing that these antigens are relatively specific for B . pseudomallei and priming is not the result of cross reactivity against other common bacterial infections in the community . In conclusion , we provide here the most extensive study to date of the human cell mediated immune response to B . pseudomallei and the first to define this aspect of immunity in Thailand , the major endemic focus of melioidosis in the world . Our data demonstrate that B . pseudomallei specific CD4+ T cells secreting IFN-γ are generated following exposure to the bacterium in the environment and the magnitude of this cellular response correlates with the serological status of the individual . Our findings that NK cells and CD8+ T cells also provide a potential source of IFN-γ , may help to explain the apparent lack of impact of HIV/AIDS on the incidence of melioidosis in Thailand . Our ability to detect specific T cell responses to defined B . pseudomallei proteins in large numbers of individuals now provides the opportunity to screen candidate antigens for inclusion in protein or protein-polysaccharide conjugate subunit vaccines against this important and emerging infection . | The Gram-negative bacterium , Burkholderia pseudomallei , is a public health problem in southeast Asia and northern Australia and a Centers for Disease Control and Prevention listed Category B potential bioterrorism agent . It is the causative agent of melioidosis , and clinical manifestations vary from acute sepsis to chronic localized and latent infection , which can reactivate decades later . B . pseudomallei is the major cause of community-acquired pneumonia and septicemia in northeast Thailand . In spite of the medical importance of B . pseudomallei , little is known about the mechanisms of pathogenicity and the immunological pathways of host defense . There is no available vaccine , and the mortality rate in acute cases can exceed 40% with 10–15% of survivors relapsing or being reinfected despite prolonged and complete treatments . In this article , we describe cell-mediated immune responses to B . pseudomallei in humans living in northeast Thailand and demonstrate clear evidence of T cell priming in healthy seropositive individuals and patients who recovered from melioidosis . This is the most detailed study yet performed on the cell types that produce interferon-gamma to B . pseudomallei in humans and the antigens that they recognize and the first to study large sample numbers in the primary endemic focus of melioidosis in the world . |
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Woolly mammoths ( Mammuthus primigenius ) populated Siberia , Beringia , and North America during the Pleistocene and early Holocene . Recent breakthroughs in ancient DNA sequencing have allowed for complete genome sequencing for two specimens of woolly mammoths ( Palkopoulou et al . 2015 ) . One mammoth specimen is from a mainland population 45 , 000 years ago when mammoths were plentiful . The second , a 4300 yr old specimen , is derived from an isolated population on Wrangel island where mammoths subsisted with small effective population size more than 43-fold lower than previous populations . These extreme differences in effective population size offer a rare opportunity to test nearly neutral models of genome architecture evolution within a single species . Using these previously published mammoth sequences , we identify deletions , retrogenes , and non-functionalizing point mutations . In the Wrangel island mammoth , we identify a greater number of deletions , a larger proportion of deletions affecting gene sequences , a greater number of candidate retrogenes , and an increased number of premature stop codons . This accumulation of detrimental mutations is consistent with genomic meltdown in response to low effective population sizes in the dwindling mammoth population on Wrangel island . In addition , we observe high rates of loss of olfactory receptors and urinary proteins , either because these loci are non-essential or because they were favored by divergent selective pressures in island environments . Finally , at the locus of FOXQ1 we observe two independent loss-of-function mutations , which would confer a satin coat phenotype in this island woolly mammoth .
Woolly mammoths ( Mammuthus primigenius ) were among the most populous large herbivores in North America , Siberia , and Beringia during the Pleistocene and early Holocene [1] . However warming climates and human predation led to extinction on the mainland roughly 10 , 000 years ago [2] . Lone isolated island populations persisted out of human reach until roughly 3 , 700 years ago when the species finally went extinct [3] . Recently , two complete high-quality high-coverage genomes were produced for two woolly mammoths [4] . One specimen is derived from the Siberian mainland at Oimyakon , dated to 45 , 000 years ago [4] . This sample comes from a time when mammoth populations were plentiful , with estimated effective population size of Ne = 13 , 000 individuals [4] . The second specimen is from Wrangel Island off the north Siberian coast [4] . This sample from 4 , 300 years ago represents one of the last known mammoth specimens . This individual comes from a small population estimated to contain roughly 300 individuals [4] . These two specimens offer the rare chance to explore the ways the genome responds to pre-extinction population dynamics . Nearly neutral theories of genome evolution predict that small population sizes will lead to an accumulation of detrimental variation in the genome [5] . Such explanations have previously been invoked to explain genome content and genome size differences across multiple species [6] . Yet , within-species comparisons of how genomes are changed by small effective population sizes remain necessarily rare . These mammoth specimens offer the unique opportunity for within-species comparative genomics under a 43-fold reduction in population size . This comparison offers a major advantage as it will be free from confounding biological variables that are present in cross species comparisons . If nearly neutral dynamics lead to an excess of detrimental variation , we should observe an excess of harmful mutations in pre-extinction mammoths from Wrangel Island . We use these two ancient DNA sequences to identify retrogenes , deletions , premature stop codons , and point mutations found in the Wrangel Island and Oimyakon mammoths . We identify an excess of putatively detrimental mutations , with an excess of stop codons , an excess of deletions , an increase in the proportion of deletions affecting gene sequences , an increase in non-synonymous substitutions relative to synonymous substitutions , and an excess of retrogenes , reflecting increased transposable element activity . These data bear the signature of genomic meltdown in small populations , consistent with nearly-neutral genome evolution . They furthermore suggest large numbers of detrimental variants collecting in pre-extinction genomes , a warning for continued efforts to protect current endangered species with small population sizes .
We identified all SNPs in each mammoth genome as well as one Indian elephant specimen , Maya , using GATK [7] . We identified all non-synonymous and synonymous changes relative to the L . africana reference genome ( https://www . broadinstitute . org/scientific-community/science/projects/mammals-models/elephant/elephant-genome-project ) using r3 . 7 annotations lifted over to L . africana 4 . 0 genome sequences . We observe a significant increase in the number of heterozygous non-synonymous changes relative to synonymous changes in the Wrangel island genome compared with Oimyakon ( χ2 = 68 . 799 , df = 1 , P < 2 . 2 × 10−16; S1 Table ) . There is also a significant increase in the number of homozygous mutations at non-synonymous sites relative to synonymous sites ( χ2 = 9 . 96 , df = 1 , P < 0 . 0016; S1 Table ) . We further observe an excess of premature stop codons in the genome of the Wrangel Island mammoth , with 1 . 8X as many genes affected . There are 503 premature stop codons in the Oimyakon genome ( adjusting for a 30% false negative rate at heterozygous sites ) compared with 819 in the Wrangel island genome ( Fig 1 , Table 1 ) . There are 318 genes that have premature stop codons that are shared across the two mammoths , and 357 genes that are truncated in both mammoths , including mutations that form at independent sites . A total of 120 of these genes have stop codons in the two mammoths as well as in Maya the Indian elephant , suggesting read through in the L . africana reference . Among truncated genes , there is a significant excess of olfactory genes and oderant binding receptors that appear to be pseudogenized with an EASE enrichment score of 9 . 1 ( S2 Table ) [8 , 9] . We observe 85 truncated olfactory receptors and 3 vomeronasal receptors as well as multiple signal transduction peptides compared with 44 olfactory receptors and 2 vomeronasal receptors pseudogenized in the mainland mammoth . It is possible that DNA damage in the archaic specimens could contribute to a portion of the observed stop codons . When we exclude A/G and C/T mutations , there is still a gross excess of premature stop codons , with 645 genes truncated in the Wrangel Island mammoth compared with 377 in the Oimyakon mammoth . Hence , the patterns are not explained solely by differential DNA damage in the two mammoths . Maya , the Indian Elephant specimen shows 450 premature stop codons , but 401 when A/G and T/C mutations are excluded . When putative damage to ancient DNA is excluded , Maya appears to house an intermediate number of premature stop codons , with a 6% increase compared to the Oimyakon mammoth . We identify 27228 deletions over 1 kb long in the Wrangel island genome , and 21346 ( correcting for a 0 . 5% false negative rate at heterozygous sites ) in the Oimyakon genome ( Table 1 ) . There are 6147 deletions ( 23% ) identified in the Wrangel Island mammoth that are homozygous ( ≤ 10% coverage ) compared with 5035 ( 24% ) in the Oimyakon mammoth . ( S3 Table ) . A total of 13 , 459 deletions are identified in both mammoth genomes ( S4 Table ) . Some 4813 deletions in the Wrangel Island mammoth and 4598 in the Oimyakon mammoth appear hemizygous but have stretches of zero coverage for at least 50% of their length . These sites may represent multiple independent homozygous deletions that cannot be differentiated via change point statistics . Alternatively , they might indicate smaller secondary deletions that appear on hemizygous haplotypes . Such secondary deletions are common when large loop mismatch repair attacks unpaired , hemizygous stretches of DNA [10 , 11] . The Wrangel Island Mammoth has sharply increased heterozygosity for deletions in comparison with the Oimyakon mammoth ( S3 Table ) . Some portion of the inflated heterozygosity for deletions in the Wrangel Island mammoth could be due to this difficulty in inferring genotypes in a high throughput setting . Alternatively , the effective mutation rate may have increased as fewer deletions were removed from the population via purifying selection , inflating θdel . It is also possible that there was an increase in the rate of deletions in the Wrangel Island lineage due to defective DNA repair mechanisms . An increase in non-homologous end joining after DNA breaks rather than double stranded break repair could putatively induce such a change in the deletion rate . Maya the Indian elephant shows a larger number of deletions than the Oimyakon mammoth , but with different character from the Wrangel Island mammoth . The bulk of these are derived from 22 , 954 hemizygous deletions ( S3 Table ) . Maya houses only 5141 homozygous deletions , similar to the mainland mammoth ( S3 Table ) . There is an increase in the number of hemizygous deletions that affect gene sequences , but only a modest increase in the number of homozygous deletions that affect gene sequences ( S3 Table ) . Competing pressures of higher Ne , longer time frames to accumulate mutations toward equilibrium frequencies , differences in mutation rates between the mammoths and elephants , differences in selective pressures , differences in the distribution of selective coefficients for deletions , different effective mutation rates due to different selective constraints , or differences in dominance coefficients might all contribute to differences in the number of deletions observed in elephants and mammoths . Additional samples would be necessary to determine the extent to which genetic declines may be influencing the diversity of deletions in modern Indian elephants . We currently have no basis for conclusions given this single sample , with no prior comparison . There is a significant difference in the size distribution of deletions identified in the two mammoth samples , with a mean of 1707 bp in Oimyakon and 1606 bp in the Wrangel mammoth ( Wilcox W = 304430000 , P < 2 . 2e − 16; Fig 2 ) . This difference could reflect either differences in DNA replication or repair mechanisms in the two mammoths , or altered selective constraints for different types of duplications . No significant difference is observed between the Wrangel island mammoth down sampled sequence data ( W = 2004400 , P = 0 . 3917 ) suggesting that the observed decrease in size is not due to differences in coverage . Some 1628 genes have deleted exons in the Wrangel Island mammoth compared to 1110 in Oimyakon ( Table 1 ) , a significant excess of genes deleted compared to expectations based on the number of deletions ( χ2 = 12 . 717 , df = 1 , P = 0 . 0003623 ) . Among these deleted genes , 112 in the mainland mammoth are homozygous compared to 133 homozygous exon deletions in the Wrangel Island Mammoth . Gene functions for affected genes in the Oimyakon mammoth include synapse functions , PHD domains , zinc fingers , aldo-keto metabolism , calcium dependent membrane targeting , DNA repair , transcription regulation , and development ( S5 Table ) . Gene functions overrepresented among deletions in the Wrangel Island mammoth include major urinary proteins , lipocalins , and pheromones , pleckstrins , transcription regulation , cell transport , DNA repair , chromatin regulation , hox domains , and development ( S5 Table ) . Among the genes deleted in the Wrangel Island mammoth , several have phenotypes of interest in other organisms . We observe a hemizygous deletion in riboflavin kinase RFK in the Wrangel Island mammoth , but normal coverage in the Oimyakon mainland mammoth ( S1 Fig ) . Homozygous knockouts of riboflavin kinase , essential for B2 utilization/FAD synthesis , are embryonic lethal in mice [12] . Finally , we identify a hemizygous deletion in the Wrangel island mammoth that would remove the entire gene sequence at the FOXQ1 locus ( S2 Fig ) . The alternative haplotype carries a frameshift mutation that disrupts the FOXQ1 functional domain . FOXQ1 knock-outs in mice are associated with the satin coat phenotype , which results in translucent fur but normal pigmentation due to abnormal development of the inner medulla of hairs [13] , with two independent mutations producing this phenotype [13] . FOXQ1 also regulates mucin secretion in the GI tract , a case of pleiotropic functions from a single gene [14] . If the phenotype in elephantids matches the phenotype exhibited in mice , this mammoth would have translucent hairs and a shiny satin coat , caused by two independently formed knock-out alleles at the same locus . These genes each have functions that are conserved across mammals , though there is no guarantee that they would produce identical phenotypes in other species . Retrogene formation can serve as a proxy for retrotransposon activity . We identify retrogenes that display exon-exon junction reads in genomic DNA . We observe 1 . 3X more retrogenes formed in the Wrangel island mammoth . The Wrangel Island mammoth has 2853 candidate retrogenes , in comparison with 2130 in the Oimyakon mammoth and 1575 in Maya ( Table 1 ) . There are 436 retrogenes that are shared between the two mammoths , though some of these could arise via independent mutations . This excess of retrogenes is consistent with increased retroelement activity in the Wrangel Island lineage . During retrogene formation , highly expressed genes , especially those expressed in the germline , are expected to contribute to new retrogenes . To determine the types of loci that had been copied by retrotransposons , we performed a gene ontology analysis using DAVID [8 , 9] . Functional categories overrepresented among candidate retrogenes include genes involved in transcription , translation , cell division/cytoskeleton , post translational modification , ubiquitination , and chaperones for protein folding ( S6 and S7 Tables ) . All of these are expected to be highly expressed during cell divisions or constitutively expressed , consistent with expectations that highly expressed genes will be overrepresented . Gene ontologies represented are similar for both mammoths ( S6 and S7 Tables ) . Although these retrogenes are unlikely to be detrimental in and of themselves , they may point to a burst of transposable element activity in the lineage that led to the Wrangel island individual . Such a burst of TE activity would be expected to have detrimental consequences , additionally contributing to genomic decline . Under nearly-neutral theory of genome evolution , detrimental mutations should accumulate in small populations as selection becomes less efficient [5] . This increase in non-neutral amino acid changes and premature stop codons is consistent with reduced efficacy of selection in small populations . We attempted to determine whether the data is consistent with this nearly-neutral theory at silent and amino acid replacement substitutions whose mutation rates and selection coefficients are well estimated in the literature . Under nearly neutral theory , population level variation for non-synonymous amino acid changes should accelerate toward parity with population level variation at synonymous sites . Given the decreased population size on Wrangel Island , we expect to observe an accumulation of detrimental changes that would increase heterozygosity at non-synonymous sites ( HN ) relative to synonymous sites ( HS ) in the island mammoth . Heterozygosity depends directly on effective population sizes . We observe HS = 0 . 00130 ± 0 . 00002 in the Wrangel Island mammoth , which is 80% of HS = 0 . 00161 ± 0 . 00002 observed in the Oimyakon mammoth ( Table 2 ) . The magnitude of the difference between HS in these two mammoths is 28 standard deviations apart , suggesting that these two mammoths could not have come from populations with the same effective population sizes . The specimens are well beyond the limits of expected segregating variation for a single population . To determine whether such results are consistent with theory , we fitted a model using PSMC [42] inferred population sizes for the Wrangel island mammoth , based on decay of heterozygosity of ( 1 − 1/2N ) t H0 . The observed reduction in heterozygosity is directly consistent theoretical expectations that decreased effective population sizes would lower heterozygosity to HS = 0 . 00131 . At non-synonymous sites , however , there are no closed-form solutions for how HN would decay under reduced population sizes . We observe HN = 0 . 000490 in the Wrangel Island Mammoth , 95% of HN = 0 . 000506 in the Oimyakon mammoth ( Table 2 ) . To determine whether such results could be caused by accumulation of nearly-neutral variation , we simulated population trajectories estimated using PSMC [42] . This trajectory shows ancient populations with Ne ≈ 104 , followed by a population decline prior to extinction . These numbers are slightly lower than previous estimates of ancestral Ne based on mitochondrial DNA [43] . We were able to qualitatively confirm results that population trajectories from PSMC with previously described mutation rates and selection coefficients can lead to an accumulation of detrimental alleles in populations . However , the magnitude of the effects is difficult to fit precisely . The simulations show a mean HS = 0 . 00148 and HN = 0 . 000339 in Oimyakon and HS = 0 . 00126 and HN = 0 . 000295 for the Wrangel Island Mammoth ( S3 Fig ) . In simulations , we estimate HN/HS = 0 . 229 both for the Oimyakon mammoth and directly after the bottleneck , but HN/HS = 0 . 233 in the Wrangel Island Mammoth at the time of the Wrangel Island mammoth . These numbers are less than empirical observations of HN/HS = 0 . 370 ( Table 2 ) . Several possibilities might explain the observed disparity between precise estimates from simulations versus the data . The simulations may be particularly sensitive to perturbations from PSMC population levels or time intervals . Similarly , selection coefficients that differ from the gamma distribution previously estimated for humans might lead to greater or lesser changes in small populations . Additionally , an acceleration in generation time on Wrangel Island is conceivable , especially given the reduced size of Wrangel Island mammoths [15] . Finally , positive selection altering nucleotide variation on the island or the mainland could influence diversity levels . Founder effects during island invasion sometimes alter genetic diversity in populations . However , it is unlikely that a bottleneck alone could cause an increase in HN/HS . There is no evidence in effective population sizes inferred using PSMC to suggest a strong bottleneck during Island colonization [4] . The power of such genetic analyses may be limited , but these results are in agreement with paleontological evidence showing no phenotypic differentiation from the mainland around 12 , 000 years ago followed by island dwarfism much later [15] . During glacial maxima , the island was fully connected to the mainland , becoming cut off as ice melted and sea levels rose . The timing of separation between the island and mainland lies between 10 , 000 years and 14 , 000 years before present [3 , 15–17] , but strontium isotope data for mammoth fossils suggests full isolation of island populations was not complete until 10 , 000-10 , 500 years ago [18] . Forward simulations suggest that hundreds of generations at small Ne are required for detrimental mutations to appear and accumulate in the population . These results are consistent with recent theory suggesting extended bottlenecks are required to diminish population fitness [19] . Thus , we suggest that a bottleneck alone could not produce the accumulation of HN/HS that we observe . E . maximus indicus specimen , Maya shows an independent population decline in the past 100 , 000 years , with current estimates of Ne = 1000 individuals ( S4 Fig ) . This specimen shows a parallel case of declining population sizes in a similar species of elephantid . Maya houses hemizygous deletions in similar numbers with the Wrangel Island Mammoth . However , the number of stop codons and homozygous deletions is intermediate in comparison with the Oimyakon and Wrangel mammoths ( Table 1 ) . It is possible that Indian elephants , with their recently reduced population sizes may be subject to similar accumulation of detrimental mutations , a prospect that would need to be more fully addressed in the future using population genomic samples for multiple individuals or timepoints and more thorough analyses .
Nearly-neutral theories of genome evolution have attempted to explain the accumulation of genome architecture changes across taxa [5] . Under such models , mutations with selection coefficients less than the nearly neutral threshold will accumulate in genomes over time . Here , we test this hypothesis using data from a woolly mammoth sample from just prior to extinction . We observe an excess of retrogenes , deletions , amino acid substitutions , and premature stop codons in woolly mammoths on Wrangel Island . Given the long period of isolation and extreme population sizes observed in pre-extinction mammoths on Wrangel Island , it is expected that genomes would deteriorate over time . These results offer genetic support for the nearly-neutral theory of genome evolution , that under small effective population sizes , detrimental mutations can accumulate in genomes . Independent analysis supporting a reduction in nucleotide diversity across multiple individuals at MHC loci suggests a loss of balancing selection , further support the hypothesis that detrimental variants accumulated in small populations [20] . We observe two independent loss-of-function mutations in the Wrangel Island mammoth at the locus of FOXQ1 . One mutation removes the entire gene sequence via a deletion , while the other produces a frameshift in the CDS . Based on phenotypes observed in mouse models , these two independent mutations would result in a satin fur coat , as well as gastric irritation [14] . Many phenotypic screens search for homozygous mutations as causative genetic variants that could produce disease . More recently , it has been proposed that the causative genetic variation for disease phenotypes may be heterozygous non-complementing detrimental mutations [21] . These data offer one case study of independent non-functionalizing mutations in a single individual , genetic support for independent non-functionalizing mutations at a single locus . Woolly mammoth outer hairs house multiple medullae , creating a stiff outer coat that may have protected animals from cold climates [22] ( though see [23] for alternative interpretations ) . Putative loss of these medullae through loss of FOXQ1 could compromise this adaptation , leading to lower fitness . One of the two specimens comes from Wrangel Island , off the northern coast of Siberia . This mammoth population had been separated from the mainland population for at least 6000 years after all mainland mammoths had died off . Prior to extinction , some level of geographic differentiation combined with differing selective pressures led to phenotypic differentiation on Wrangel island [15] . Island mammoths had diminished size , but not until 12 , 000 years ago when mainland populations had reduced and ice sheets melted [15] . One possible explanation for the poor fit of simulations is that generation time may have decreased . Previous work suggested a very high mutation rate for woolly mammoths based on comparisons between island and mainland mammoths . It is possible that an acceleration in generation times could cause the accumulation of more mutations over time , and that the real mutation rate is similar to humans ( 1 − 2 × 10−8 [24] rather than 3 . 8 × 10−8 [4] ) . Such changes would be consistent with island dwarfism being correlated with shorter generation times , and would explain the unusually high mutation rate estimate for mammoths based on branch shortening observed in [4] . We observe large numbers of pseudogenized olfactory receptors in the Island mammoth . Olfactory receptors evolve rapidly in many mammals , with high rates of gain and loss [25] . The Wrangel island mammoth has massive excess even compared to the mainland mammoth . Wrangel island had different flora compared to the mainland , with peat and sedges rather than grasslands that characterized the mainland [17] . The island also lacked large predators present on the mainland . It is possible that island habitats created new selective pressures that resulted in selection against some olfactory receptors . Such evolutionary change would echo gain and loss of olfactory receptors in island Drosophila [26] . In parallel , we observe a large number of deletions in major urinary proteins in the island mammoth . In Indian elephants E . maximus indicus , urinary proteins and pheromones ellicit behavioral responses including mate choice and social status [27] . It is possible that coevolution between urinary proteins , olfactory receptors , and vomeronasal receptors led to a feedback loop , allowing for rapid loss in these related genes . It is equally possible that urinary peptides and olfactory receptors are not essential and as such they are more likely to fall within the nearly neutral range [25] . Either of these hypotheses could explain the current data . Many factors contributed to the demise of woolly mammoths in prehistoric times . Climate change led to receding grasslands as forests grew in Beringia and North America and human predation placed a strain on already struggling populations [2] . Unlike many cases of island invasion , Wrangel Island mammoths would not have continuous migration to replenish variation after mainland populations went extinct . Under such circumstances , detrimental variation would quickly accumulate on the island . The putatively detrimental variation observed in these island mammoths , with the excess of deletions , especially recessive lethals may also have limited survival of these struggling pre-extinction populations . Climate change created major limitations for mammoths on other islands [28] , and these mammoths may have struggled to overcome similar selective pressures . Many modern day species , including elephants , are threatened or endangered . Asiatic cheetahs are estimated to have fewer than 100 individuals in the wild [29] . Pandas are estimated to have 1600 individuals living in highly fragmented territories [30] . Mountain Gorilla population census sizes have been estimated as roughly 300 individuals , similar to effective population sizes for pre-extinction mammoths [31] . If nearly neutral dynamics of genome evolution affect contemporary endangered species , detrimental variation would be expected in these genomes . With single nucleotide changes , recovered populations can purge detrimental variation in hundreds to thousands of generations , returning to normal genetic loads [19] . However , with deletions that become fixed in populations , it is difficult to see how genomes could recover quickly . The realm of back mutations to reproduce deleted gene sequences will be limited or impossible . Although compensatory mutations might conceivably correct for some detrimental mutations , with small effective population sizes , adaptation through both new mutation and standing variation may be severely limited [32] . Thus we might expect genomes affected by genomic meltdown to show lasting repercussions that will impede population recovery . All sequences are taken from publicly available sequence data in the ENA or SRA . Indian elephant specimens for previously published sequence data were handled by the San Diego Zoo .
We used previously aligned bam files from ERR852028 ( Oimyakon , 11X ) and ERR855944 ( Wrangel , 17X ) ( S8 Table ) [4] aligned against the L . africana 4 . 0 reference genome ( available on request from the Broad Institute—vertebrategenomes@broadinstitute . org; https://www . broadinstitute . org/scientific-community/science/projects/mammals-models/elephant/elephant-genome-project ) . We also aligned 33X coverage of sequencing reads for one modern E . maximus indicus genome Maya ( previously described as “Uno” ) using bwa 0 . 7 . 12-r1044 [33] , with parameters set according to [4] bwa aln -l 16500 -o 2 -n 0 . 01 . The E . maximus indicus sample , previously labeled in the SRA as “Uno” , is from Maya , a former resident of the San Diego Zoo wild-born in Assam , India , North American Studbook Number 223 , Local ID #141002 ( O . Ryder , personal communication ) . We were not able to use two other mammoth sequences are publicly available , M4 and M25 from Lynch et al . [34] . These sequences display abnormal PSMC results ( S4 Fig ) , high heterozygosity ( S5 Fig ) , and many SNPs with asymmetrical read support ( S6 Fig ) . The unrealistically high heterozygosity as well as abnormal heterozygote calls raise concerns with respect to sequence quality . For further description , please see Supporting Information . We used the GATK pipleine [7] v3 . 4-0-g7e26428 to identify SNPs in the aligned sequence files for the Oimyakon and Wrangel Island mammoths . We identified and realigned all indel spanning reads according to the standard GATK pipeline . We then identified all SNPs using the Unified Genotyper , with output mode set to emit all sites . We used all CDS annotations from cDNA annotations from L . africana r3 . 7 with liftover coordinates provided for L . africana 4 . 0 to identify SNPs within coding sequences . We identified all stop codons , synonymous substitutions , and non-synonymous substitutions for the Wrangel Island and Oimyakon mammoths at heterozygous and homozygous sites . We aligned all reads from the mammoth genome sequencing projects ERR852028 ( Oimyakon ) and ERR855944 ( Wrangel ) ( S8 Table ) against elephant cDNA annotations from L . africana r3 . 7 . Sequences were aligned using bwa 0 . 7 . 12-r1044 [33] , with parameters set according to [4] bwa aln -l 16500 -o 2 -n 0 . 01 in order to account for alignments of damaged ancient DNA . We then collected all reads that map to exon-exon boundaries with at least 10 bp of overhang . Reads were then filtered against aligned genomic bam files produced by Palkopoulou et al [4] , discarding all exon-exon junction reads that have an alignment with equal or better alignments in the genomic DNA file . We then retained all putative retrogenes that showed signs of loss for two or more introns , using only cases with 3 or more exon-exon junction reads . We calculated coverage depth using samtools [35] with a quality cutoff of -q 20 . We then implemented change point analysis [36] in 20 kb windows . Change point methods have been commonly used to analyze microarray data and single read data for CNVs [37–39] The method seeks compares the difference in the log of sum of the squares of the residuals with one regression line vs . two regression lines [36] . The test statistic follows a chi-squared distribution with a number of degrees of freedom determined by the number of change-points in the data , in this case df = 1 . We required significance at a Bonferroni corrected p-value of 0 . 05 or less . We allowed for a maximum of one CNV tract per window , with minimum of 1 kb and maximum of 10 kb ( half the window size ) with a 100 bp step size . We did not attempt to identify deletions smaller than 1 kb due to general concerns of ancient DNA sequence quality , limitations to assess small deletions in the face of stochastic coverage variation , and concerns that genotype calls for smaller deletions might not be as robust to differences in coverage between the two mammoths . Sequences with ‘N’s in the reference genome did not contribute to change point detection . We excluded all deletions that were identified as homozygous mutations in both mammoths and in E . maximus indicus specimen Maya , as these suggest insertion in the L . africana reference rather than deletion in other elephantids . To determine the effects that coverage differences would have on deletions , we downsampled the sequence file for the Wrangel Island mammoth using samtools to 11X coverage , using chromosome 1 as a test set . We observe a reduction in the number of deletions for chromosome 1 from 1035 deletions to 999 deletions , resulting in an estimated false negative rate of 0 . 5% at reduced coverage for deletions greater than 1 kb . Highly diverged haplotypes with greater than 2% divergence might prevent read mapping and mimic effects of deletions , but this would require divergence times within a species that are greater than the divergence between mammoths and L . africana . Mutations were considered homozygous if mean coverage for the region was less than 10% of the background coverage level . Otherwise it was considered to be heterozygous . These methods are high-throughput , and it is possible that multiple small homozygous deletions interspersed with full coverage sequences might mimic heterozygote calls . Whether such mutations might meet the conditions for significant change-point detection would depend on the deletion length , placement , and background coverage level . We identified SNPs that differentiate Mammoth genomes from the reference using samtools mpileup ( options -C50 -q30 -Q30 ) , and bcftools 1 . 2 consenus caller ( bcftools call -c ) . The resulting vcf was converted to fastq file using bcftools vcf2fq . pl with a mimimum depth of 3 reads and a maximum depth of twice the mean coverage for each genome . Sequences were then converted to psmc fasta format using fq2psmcfa provided by psmc 0 . 6 . 5-r67 . We then ran psmc with 25 iterations ( -N25 ) , an initial ratio of θ/ρ of 5 ( -r5 ) , and parameters 64 atomic time intervals and 28 free parameters ( -p “4+25*2+4+6” ) as was done in previous analysis of woolly mammoths [4] . Effective population sizes and coalescence times were rescaled using previously estimated mutation rates of 3 . 8 × 10−8 . Using the population size estimates from PSMC , we calculated the expected reduction in heterozygosity at synonymous sites according to ( 1 - 1 2 N ) t for each time period in PSMC output . We compared the number of deletions , number of premature stop codons , proportion affecting gene sequences , and number of putative retrogenes between the two mammoth genomes using chi squared tests . To determine expectations of sequence evolution at non-synonymous sites under population crash , we ran simulations using SLiM v . 2 . 0 population genetic software [40] . We modeled two classes of sites: neutral and detrimental . For detrimental mutations we used a gamma distributed DFE with a mean of -0 . 043 and a shape parameter of 0 . 23 as estimated for humans [41] , assuming a dominance coefficient of 0 . 5 and free recombination across sites . Mutation rates were set as 3 . 8 × 10−8 based on previously published estimates [4] . The trajectory of population sizes was simulated according to estimates from PSMC , omitting the initial and final time points from PSMC , which are often subject to runaway behavior . We then simulated the accumulation of HN/HS in the Wrangel Island Mammoths . Simulations were run with a burn-in of 100 , 000 generations . We simulated 460 replicates of haplotypes with 100 sites for each mutation class . To gather a portrait of functional categories captured by deletions , retrogenes , and stop codons , we identified all mouse orthologs based on ENSEMBL annotations for L . africana 3 . 7 for affected gene sequences . We then used DAVID gene ontology analysis with the clustering threshold set to ‘Low’ ( http://david . ncifcrf . gov/; Accessed April 2016 ) [8 , 9] . S2–S7 Tables include all functions overrepresented at an EASE enrichment cutoff of 2 . 0 . Full gene ontology data is included in Supplementary Information . | We observe an excess of detrimental mutations , consistent with genomic meltdown in woolly mammoths on Wrangel Island just prior to extinction . We observe an excess of deletions , an increase in the proportion of deletions affecting gene sequences , and an excess of premature stop codons in response to evolution under low effective population sizes . Large numbers of olfactory receptors appear to have loss of function mutations in the island mammoth . These results offer genetic support within a single species for nearly-neutral theories of genome evolution . We also observe two independent loss of function mutations at the FOXQ1 locus , likely conferring a satin coat in this unusual woolly mammoth . |
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Pseudomonas aeruginosa infection can be disastrous in chronic lung diseases such as cystic fibrosis and chronic obstructive pulmonary disease . Its toxic effects are largely mediated by secreted virulence factors including pyocyanin , elastase and alkaline protease ( AprA ) . Efficient functioning of the endoplasmic reticulum ( ER ) is crucial for cell survival and appropriate immune responses , while an excess of unfolded proteins within the ER leads to “ER stress” and activation of the “unfolded protein response” ( UPR ) . Bacterial infection and Toll-like receptor activation trigger the UPR most likely due to the increased demand for protein folding of inflammatory mediators . In this study , we show that cell-free conditioned medium of the PAO1 strain of P . aeruginosa , containing secreted virulence factors , induces ER stress in primary bronchial epithelial cells as evidenced by splicing of XBP1 mRNA and induction of CHOP , GRP78 and GADD34 expression . Most aspects of the ER stress response were dependent on TAK1 and p38 MAPK , except for the induction of GADD34 mRNA . Using various mutant strains and purified virulence factors , we identified pyocyanin and AprA as inducers of ER stress . However , the induction of GADD34 was mediated by an ER stress-independent integrated stress response ( ISR ) which was at least partly dependent on the iron-sensing eIF2α kinase HRI . Our data strongly suggest that this increased GADD34 expression served to protect against Pseudomonas-induced , iron-sensitive cell cytotoxicity . In summary , virulence factors from P . aeruginosa induce ER stress in airway epithelial cells and also trigger the ISR to improve cell survival of the host .
The Gram-negative bacterium Pseudomonas aeruginosa is an opportunistic pathogen that increases morbidity and mortality in chronic lung diseases , such as cystic fibrosis ( CF ) and chronic obstructive pulmonary disease ( COPD; GOLD stages III-IV ) ) [1–3] . P . aeruginosa often causes chronic infection due to its ease of developing antibiotic resistance and its ability to form biofilms in these patients . Furthermore , its survival in the host in the early stages of infection is supported by the secretion of toxins and virulence factors , including pyocyanin and its proteases elastase and alkaline protease ( AprA ) ( reviewed in [4 , 5] ) . Interestingly , their production appears to be lower in the later stages of infection [6 , 7] . Therefore , the specific role of these virulence factors in chronic infections is incompletely understood . Pyocyanin is a redox-active toxin that causes cellular senescence [8] , ciliary dyskinesia [9] , increased expression of IL-8 [10] and disruption of calcium homeostasis [11] in human lung epithelial cells . Pyocyanin inactivates α1-antitrypsin , thereby contributing to the protease-antiprotease imbalance found in CF lungs [12] , while P . aeruginosa elastase additionally cleaves many proteins of the extra-cellular matrix , including collagen , fibrinogen and elastin , and opsonin receptors , thus contributing to the invasion of bacteria into the lung parenchyma [13] . AprA is thought to modulate the host response and prevent bacterial clearance by degrading proteins of the host immune system , including TNFα and complement factors [14–16] . P . aeruginosa requires iron both for its respiration and for biofilm formation [17 , 18] . Competition with the host is fierce and so P . aeruginosa has evolved specific strategies to obtain iron [19] . It produces redox-active phenazine compounds to turn insoluble Fe3+ to the more soluble Fe2+ , siderophores to scavenge iron and receptors for the uptake of iron-siderophore complexes , proteases to degrade host iron-binding proteins , and bacteriocins to eliminate competitors ( reviewed in [19] ) . Moreover , iron availability regulates the production of virulence factors such as pyocyanin , AprA and exotoxin A [20] . The endoplasmic reticulum ( ER ) functions to fold secretory and membrane proteins and its quality control systems ensure that only properly folded proteins exit the organelle . Accumulation of incompletely folded proteins can impair ER homeostasis and induces “ER stress” , which activates intracellular signal transduction pathways collectively called the “unfolded protein response” ( UPR; Fig 1 ) . This response restores ER homeostasis by reducing the influx of new proteins into the lumen of the ER and by enhancing the organelle’s capacity to fold proteins; however , if the stress cannot be resolved then apoptotic cell death pathways are invoked ( reviewed in [21] ) . Three distinct sensors detect ER stress: protein kinase RNA ( PKR ) -like ER kinase ( PERK ) , inositol-requiring enzyme 1 ( IRE1 ) and activating transcription factor 6 ( ATF6 ) [21] . Early during ER stress , the kinase PERK phosphorylates eukaryotic translation initiation factor 2 on its alpha subunit ( eIF2α ) causing the inhibition of protein synthesis and thus preventing the load on the ER from increasing further [22–24] . In addition , this promotes the translation of specific mRNAs , for example that encoding the transcription factor ATF4 [25] . One important target of ATF4 is the transcription factor called C/EBP homologous protein ( CHOP ) , and both individually can trans-activate the GADD34 gene [26] . GADD34 is a phosphatase that selectively dephosphorylates eIF2α , completing a negative feedback loop and enabling the translation of other targets of the UPR [27] . In parallel , IRE1 initiates the unconventional splicing of the mRNA encoding X-box binding protein-1 ( XBP-1 ) [28] . Spliced XBP-1 mRNA encodes an active transcription factor that , in concert with ATF6 , induces expression of UPR genes , such as the chaperones GRP78 ( also known as BiP ) and GRP94 [28–30] . The phosphorylation of eIF2α is a point at which the responses to several forms of stress are integrated [31] . During ER stress , PERK phosphorylates eIF2α , but eIF2a can also be phosphorylated by PKR responding to double-stranded RNA during viral infection [32 , 33] , by GCN2 during amino acid starvation [25 , 34 , 35] , and by HRI during iron deficiency ( reviewed in [31] ) . For this reason , the events initiated by eIF2α phosphorylation have been termed the “integrated stress response” ( ISR; Fig 1 and [36] ) . Abnormal function of the ER has been implicated in the pathogenesis of many diseases , including diabetes mellitus , atherosclerosis , Alzheimer’s disease and cancer [21 , 37] . Remarkably , the ER also plays an important role during immune responses to infection and malignancy . For example , during bacterial infection , Toll-like receptor ( TLR ) activation triggers splicing of XBP1 mRNA , possibly in response to the increased biosynthesis of secreted inflammatory mediators , increasing the capacity for protein secretion and thus contributing to an augmented inflammatory response [38–40] . In addition , induction of GADD34 is required for cytokine expression during viral infection; however , in contrast to ER stress , pathogen-induced induction of GADD34 appears to be independent of CHOP [41 , 42] . Nevertheless , sustained activation of the UPR can impair the immune response by triggering cell death [26 , 43] . Previously , it has been shown that infection of airway epithelia or Caenorhabditis elegans with P . aeruginosa can elicit an UPR [39 , 44 , 45] . In worms , activation of the IRE1-XBP-1 branch of the UPR was dependent on p38 MAPK-signalling [39] , but it is unknown if this signalling response is conserved in humans . Moreover , it is unclear whether living bacteria are required for the induction of ER stress or if unidentified secreted factors are sufficient . In the present study , we set out to test the hypothesis that virulence factors secreted by P . aeruginosa trigger the UPR in human cells via the p38 MAPK pathway . We found that p38 MAPK signalling was required for the response of human epithelial cultures to bacterial conditioned medium and that the secreted factors pyocyanin and AprA contribute to the induction of ER stress . Furthermore , we showed that induction of the ISR target GADD34 is mediated by the iron-regulated kinase HRI and this induction protects the host against the toxic effects of P . aeruginosa .
Infection with live P . aeruginosa has previously been shown to induce the UPR in mouse macrophages and human immortalized bronchial epithelial cells [40 , 45] . To identify whether P . aeruginosa could induce the UPR in primary bronchial epithelial cells ( PBEC ) and whether living bacteria were necessary for this , we stimulated PBEC with filter-sterilised conditioned medium ( CM ) from P . aeruginosa strain PAO1 ( CM-PAO1 ) , containing secreted virulence factors without living bacteria . Treatment with CM-PAO1 induced ER stress in a time- and dose-dependent manner , as evidenced by a 9 . 9-fold increase of splicing of XBP1 mRNA ( p<0 . 01 ) , a 12 . 8-fold increase of CHOP mRNA ( p = 0 . 02 ) and a 16 . 2-fold increase of GADD34 mRNA ( p<0 . 05 ) after 8–12 hours ( Fig 2A and 2B ) . This was accompanied by an increase in phosphorylation of eIF2α and protein expression of GADD34 and GRP78 ( Fig 2C ) . This increase in phosphorylated eIF2α was accompanied by a decrease in global protein translation as assessed by puromycin incorporation in nascent proteins ( Fig 2D ) [46] . In line with previous reports [47–49] , CM-PAO1 gradually impaired epithelial integrity until the monolayer was completely disrupted after 24 hours . Although the epithelial layer was disrupted by CM-PAO1 ( as reported by trans-epithelial resistance; S1A Fig and visualised by light microscopy; S1B Fig ) , the cell membranes themselves remained intact as reported by exclusion of trypan blue stain ( S1B Fig ) . Infection of C . elegans with P . aeruginosa has been reported to cause splicing of XBP1 mRNA in a p38 MAPK-dependent manner [39] . To exclude the effects of donor variation and complex nutrient/growth factor requirement of primary cells , we tested whether exposure of 16HBE cells , a SV-40 transformed bronchial epithelial cell line , to P . aeruginosa conditioned medium would trigger phosphorylation of p38 MAPK and activate the UPR . We observed that CM-PAO1 caused prolonged phosphorylation of p38 MAPK in 16HBE cells up to 6 hours ( Fig 3A ) . We reasoned that the activation of p38 MAPK after 15 minutes might represent the activation of TLR signalling , since stimulation of HEK-TLR2 or HEK-TLR4 cells [50] with CM-PAO1 demonstrated robust TLR2 and TLR4 activation . The sustained activation was similar to that observed in C . elegans infected with Pseudomonas [39] , which suggests the importance of p38 MAPK in the induction of the UPR . To examine if p38 MAPK signalling was required for the ER stress response , we pre-treated 16HBE cells with an inhibitor of p38 MAPK ( SB203580 ) or an inhibitor of TAK1 ( 5Z-7-oxozeanol , better known as LL-Z1640-2 ) , a kinase upstream of p38 MAPK . We then exposed cells to CM-PAO1 and observed that both compounds markedly reduced activation of p38 by CM-PAO1 ( Fig 3B ) . In addition , both compounds reduced secretion of IL-8 in response to CM-PAO1 treatment ( Fig 3C ) . Of note , these compounds strongly inhibited splicing of XBP1 mRNA and abrogated the induction of CHOP and GRP78 mRNA ( Fig 3D ) . However , the induction of GADD34 was insensitive to the inhibitors ( Fig 3D ) suggesting the involvement of an additional pathway independent of CHOP . To prepare P . aeruginosa conditioned medium , cultures were grown for 5 days ( see Experimental procedures and [47] ) to a high optical density , at which quorum-sensing is activated in this strain , thus triggering the production of a variety of virulence factors among which the cytotoxic exoproduct pyocyanin . When pyocyanin levels in CM-PAO1 were measured , values up to 5 . 5 μg/ml ( 26 μM ) were detected ( Fig 4A ) , which were similar to values observed in sputum of CF patients colonised with P . aeruginosa [51] . We first wished to determine if pyocyanin was an important mediator of the observed ER stress response by CM-PAO1 . To this end , P . aeruginosa bacterial cultures were supplemented with iron to suppress pyocyanin production together with other iron-regulated factors ( Fig 4A ) . The conditioned medium prepared in this manner was significantly less efficient at triggering the splicing of XBP1 mRNA and at increasing expression of GRP78 mRNA ( Fig 4B ) . Surprisingly , CHOP mRNA was not significantly affected ( Fig 4B ) , whereas GADD34 mRNA induction was completely abrogated . These experiments provided only indirect support for the involvement of pyocyanin , since iron supplementation also affects production of other P . aeruginosa virulence factors and may also affect host cells . We therefore tested whether purified pyocyanin could induce ER stress in 16HBE cells . Treatment with purified pyocyanin caused dose-dependent splicing of XBP1 mRNA , induction of CHOP and GRP78 mRNAs and expression of GRP78 and GRP94 protein ( Fig 4C and 4D ) , maximal at 10 μM ( 2 . 1 μg/ml ) . In contrast , GADD34 mRNA continued to rise up to a maximum at ≥ 30 μM ( 6 . 3 μg/ml ) of pyocyanin ( Fig 4D ) . Once again , this suggested that induction of GADD34 in this system might not simply reflect activation by ER stress . As expected , pyocyanin potently induced secretion of IL-8 by 16HBE cells ( Fig 4E ) [10] . Since pyocyanin is a redox active toxin , we tested the effect of co-administration of the anti-oxidants N-acetylcysteine ( 10 mM ) and glutathione reduced ethyl-ester ( 10 mM ) for 24 hours . Both failed to ameliorate the ER stress response suggesting that pyocyanin caused ER dysfunction independent of causing oxidative stress [52 , 53] ( see online repository ) . Taken together , these observations suggested that conditioned medium of P . aeruginosa caused ER stress via multiple virulence factors , including pyocyanin . Furthermore , the induction of GADD34 appeared to involve an additional pathway independent of CHOP . Having found evidence for the involvement of multiple virulence mechanisms in the induction of ER stress , we next attempted to determine their identities . The P . aeruginosa AB toxin exotoxin A is known to cause translational attenuation by catalysing the ADP-ribosylation of elongation factor 2 ( EF2 ) [54] . We investigated whether purified exotoxin A could also induce ER stress , but detected no increase in spliced XBP1 , CHOP , GADD34 or GRP78 mRNA ( S2A Fig ) nor the phosphorylation of eIF2α ( S2B Fig ) . Next , to more broadly explore the involvement of other potential virulence factors , we made use of strains of P . aeruginosa that lacked specific toxic products: PAN8 , a lasB aprE double mutant , which is deficient in the production of elastase [55] and the secretion of AprA; PAN11 , an xcpR lasB mutant , which is deficient in the production of elastase and the secretion of all other substrates of the type II protein secretion system but still produces AprA; and PAO25 , a leu arg double mutant derivative of PAO1 and the direct parental strain of both mutants ( Table 1 ) . CM-PAO25 did not differ from CM-PAO1 in the content of all toxins measured ( S3A and S3B Fig ) and in inducing spliced XBP1 , CHOP , GADD34 and GRP78 mRNA ( S3C Fig ) . In spite of the aprE mutation , still traces of AprA were detected in the culture supernatant of the PAN8 strain ( Fig 5A ) , presumably due to cell lysis during the 5-days growth period . When 16HBE cells were incubated with CM-PAN8 ( lacking elastase and AprA ) , XBP1 mRNA splicing and induction of GRP78 mRNA were completely abolished , and only low induction of CHOP mRNA remained ( Fig 5B ) . In contrast , the response of 16HBE cells to CM-PAN11 ( containing AprA , but no elastase or other substrates of the type 2 secretion system ) was much less affected relative to CM-PAO1 treatment ( Fig 5B ) , indicating that the reduced response to CM-PAN8 is primarily due to the absence of AprA in this CM rather than to the absence of elastase . Indeed , stimulating 16HBE cells with purified elastase did not elicit an ER stress response within 24 hours ( see online repository ) . On the other hand , incubation with 10 nM purified AprA induced the splicing of XBP1 mRNA , and up-regulated CHOP and GRP78 mRNA ( Fig 5C ) . These experiments suggested that , in addition to pyocyanin , AprA also contributed to the induction of ER stress in 16HBE cells . We therefore next generated conditioned medium of a series of specific AprA and pyocyanin mutant strains to demonstrate the relative contribution of AprA and pyocyanin to the induction of ER stress . However , these experiments were inconclusive because the corresponding wild type strains did not induce sufficient ER stress ( see online repository ) . Remarkably , once again the induction of GADD34 mRNA followed a distinct trend from the other markers of ER stress . Particularly a lack of AprA ( in CM-PAN8 ) was correlated with an increased expression of GADD34 ( Fig 5B ) , whilst purified AprA did not induce GADD34 mRNA ( Fig 5C ) . This suggested that an unrelated mechanism regulated GADD34 induction by CM-PAO1 and that this might be independent of ER stress . To examine the involvement of ER stress-dependent and-independent responses to CM-PAO1 , we next made use of the specific inhibitor of IRE1 , 4μ8C , which blocks splicing of XBP1 mRNA during ER stress ( [56] and Fig 6 ) . Of note , this compound not only attenuated the splicing of XBP1 mRNA elicited by CM-PAO1 , but interestingly , it also attenuated the secretion of IL-8 by 16HBE in response to CM-PAO1 ( S4A Fig ) . During ER stress , the kinase PERK phosphorylates eIF2α , thereby activating the ISR . When Perk-/- mouse embryonic fibroblasts ( MEFs ) were exposed to CM-PAO1 , the induction of Gadd34 mRNA was unaffected , while the response to the ER stress-inducing agent tunicamycin ( Tm ) was abrogated ( Fig 7A ) . However , phosphorylation of eIF2α was required for the induction of Gadd34 mRNA in response to CM-PAO1 as demonstrated by the failure of the conditioned medium to induce Gadd34 mRNA in fibroblasts homozygous for the eIF2αAA mutation , which renders them insensitive to all eIF2α kinases ( Fig 7B ) . Moreover , ATF4 , a transcription factor translationally up-regulated upon phosphorylation of eIF2α , was essential for the induction Gadd34 mRNA by CM-PAO1 ( Fig 7C ) . As we have shown previously [26] , CHOP was only partially required for tunicamycin ( ER stress ) -induced expression of Gadd34 mRNA ( S4B Fig ) . The same was observed for CM-PAO1 , although it did not reach statistical significance ( S4B Fig ) . Interestingly , murine fibroblasts stimulated with CM-PAO1 failed to splice Xbp1 mRNA ( S4C Fig ) , suggesting that activation of IRE1 by CM-PAO1 may be less important in this cell type than in human epithelial cells . However , reassuringly , ISR-dependent signalling in response to pseudomonal toxins was preserved in these cells and , once again , expression of Chop mRNA was regulated via eIF2α and ATF4 . As had been observed for Gadd34 , Chop induction was independent of PERK , suggesting that in MEFs treated with CM-PAO1 , Chop was induced by a stimulus other than ER stress ( S4D–S4F Fig ) . We next examined which eIF2α kinase was responsible for activation of the ISR by CM-PAO1 . To this end , we made use of Pkr-/- , Gcn2-/- and Hri-/- MEFs [25 , 57 , 58] and observed a significant deficit of CM-PAO1 induction of Chop and Gadd34 mRNA in Hri-/- cells , suggesting the involvement of the iron-sensing kinase HRI ( Fig 7D–7F and S4G–S4I Fig ) . In contrast , although it has been suggested previously that GCN2 is involved in the stress response induced by P . aeruginosa in gut epithelial cells [59] , we observed no significant effect on the induction of Gadd34 mRNA in Gcn2-/- cells ( Fig 7E ) . We therefore went on to deplete either GCN2 or HRI in HeLa cells using two separate siRNA oligonucleotides for each gene and obtained similar results: whereas both siRNAs directed against HRI decreased induction of Gadd34 mRNA , one siRNA directed against GCN2 had no effect whereas the other even increased Gadd34 mRNA expression ( Fig 7H and S4J Fig ) . Whereas we cannot exclude the possibility that this increasing effect of one siRNA directed against GCN2 may result from putative off-target effects , we conclude that these data support a role for HRI rather than GCN2 . Since RPMI is an iron-poor medium , we reasoned that the CM-PAO1 would limit iron availability to epithelial cells , e . g . by the presence of siderophores [60] , which might activate HRI through depletion of iron from the culture medium . We therefore first evaluated the effect of iron depletion of the epithelial cell culture medium using deferoxamine ( DFO ) . DFO treatment resulted in a marked increase in the expression of the ISR and UPR related genes CHOP and GADD34 , whereas GRP78 and spliced XBP1 were not affected ( Fig 7H ) . This is line with selective activation of the ISR by iron depletion . We next confirmed the presence of the iron-chelating siderophore pyoverdine in the CM-PAO1 by the bright fluorescence of the medium upon exposure to UV light ( see online repository ) . To test the possible involvement of iron depletion in CM-PAO1-mediated Gadd34 induction , we supplemented the epithelial cell culture medium with iron , which indeed completely suppressed the induction of Gadd34 mRNA ( Fig 7I and S4K Fig ) . Taken together , these data demonstrate that CM-PAO1 induces splicing of XBP1 mRNA ( ER stress ) in human bronchial epithelial cells , while induction of GADD34 predominantly reflects an iron-dependent ISR mediated by the eIF2α kinase HRI . During chronic ER stress in cell and animal models of disease , the induction of GADD34 appears to mediate cellular toxicity [26 , 43] . In contrast , during the acute stress of SERCA pump inhibition by thapsigargin , GADD34 has been shown to be protective [61] . To test the role of ER stress-independent induction of GADD34 by exposure to CM-PAO1 , we made use of Gadd34ΔC/ΔC MEFs [61] , which lack GADD34 phosphatase activity . Cells expressing wild-type GADD34 were more resistant to the cytotoxic effects of CM-PAO1 compared with Gadd34ΔC/ΔC fibroblasts , as reported by the release of lactate dehydrogenase ( LDH ) ( Fig 8A ) . To confirm these findings , we repeated these experiments in HeLa cells expressing GADD34 from a tetracycline-responsive promoter . The induction of GADD34 with doxycycline significantly increased cell viability upon exposure to CM-PAO1 ( Fig 8B ) . When the cell culture medium of wild-type cells was supplemented with iron , the release of LDH was prevented ( Fig 8C , left panel ) . Iron supplementation was also observed to rescue cell viability reported by MTT assay ( Fig 8C , right panel ) . Taken together , these data suggest that the toxicity of CM-PAO1 is sensitive to iron and that HRI-mediated induction of GADD34 is protective in this context . Supplementation with iron relieves both the cytotoxicity and the requirement for induction of GADD34 .
It is known that a normal response to ER stress is required for an efficient innate immune response to bacterial infection [39] , but whether live bacteria are required for this has been unclear . In this study , we have shown that secreted virulence factors of P . aeruginosa cause ER stress in primary bronchial epithelial cells and in a cell line , and that this is mediated by TAK1 and phosphorylated p38 MAPK . In addition , we have identified GADD34 induction via an ER-stress independent ISR . We have demonstrated pyocyanin to be one of the factors eliciting these responses , while AprA contributes to the activation of the UPR . We were however unable to establish the relative contribution of pyocyanin and AprA to the activation of the UPR . In contrast , activation of the ISR with induction of GADD34 mRNA is most likely a response to reduced iron availability and may serve a cytoprotective role during exposure to conditioned medium of P . aeruginosa . In line with these observations , phosphorylation of p38 MAPK has previously been shown to be involved in the splicing of XBP1 upon infection with P . aeruginosa [39 , 45] , although the involvement of TAK1 upstream of p38 MAPK and its essential involvement in the activation of CHOP and GRP78 are novel findings . Interestingly , GADD34 , classically a downstream target of CHOP , was regulated independently of the TAK1-p38 MAPK pathway . The induction of GADD34 is only partially dependent on CHOP ( S4B Fig and [26] ) , but it is absolutely reliant on phosphorylation of eIF2α and ATF4 [26] . This is concordant with the recent description of a virus-induced “microbial stress response” mediated via the PKR/eIF2α/ATF4 pathway , which fails to induce CHOP , but potently induces GADD34 [41 , 42] . In contrast to the response of human airway epithelial cells , P . aeruginosa conditioned medium failed to cause splicing of Xbp1 mRNA in murine fibroblasts , suggesting that ER stress may not be a conserved feature of the cellular response to this insult . This is unsurprising , as induction of ER stress is known to be highly cell-type dependent [40] . In the absence of ER stress in the murine fibroblasts , the induction of Chop and Gadd34 suggests that activation of the ISR by the secreted virulence factors may be a more conserved response . Of note , in human bronchial epithelial cells , the induction of CHOP seems primarily subordinate to an ER stress-induced ISR , rather than the microbial stress response ( S7 Fig ) . Consequently , induction of CHOP was dependent on the TAK1-p38 MAPK pathway in those cells ( Fig 3D ) and its induction was only partially inhibited when bacterial cultures were supplemental with iron ( Fig 4B ) , in contrast to MEFs where Chop induction was dependent on HRI ( S4I Fig ) . Recent evidence suggests that bacterial components may function as triggers for the UPR . Flagellin has been shown to induce an atypical ER stress response in CF bronchial epithelial cells during live infection [45] , while N- ( 3-oxo-dodecanoyl ) homoserine lactone ( C12 ) has been observed to phosphorylate eIF2α and activate p38 MAPK [62] . We have now shown that at least two secreted virulence factors , pyocyanin and AprA , also contribute to this ER stress response to Pseudomonas . More research has to be done to assess the involvement of ( other ) individual virulence factors . High concentrations of pyocyanin also mediated an ER stress-independent , ISR-dependent induction of GADD34 ( Fig 4E ) . We were able to identify a crucial role for iron availability and for the iron-sensing kinase HRI in this response , although we cannot fully exclude a role for the kinase GCN2 that has been previously implicated in responses to Pseudomonas spp [59] . Of note , it is possible that the protective effect of GADD34 is unrelated to its ability to dephosphorylate p-eIF2alpha . Interestingly , AprA was not involved in the induction of the ISR response but rather appeared to dampen it , since considerably higher GADD34 expression was observed when conditioned medium of the aprE mutant PAN8 was used to stimulate the cells ( Fig 5B ) . Among other possibilities , an explanation for this observation could be that AprA present in the conditioned medium of the wild-type strain partially degrades HRI , a possibility that warrants further investigations . The discovery of this ER-independent ISR may plausibly offer novel potential therapeutic targets . It has been shown recently that spliced XBP1 is required for C12-mediated apoptosis [62] . Remarkably , exposure of cells to C12 does not itself trigger the splicing of XBP1 mRNA suggesting that basal levels of XBP1 splicing are both necessary and sufficient for this response . Moreover , the transcriptional activity of spliced XBP1 does not appear to be required for this cell death , indicating that the spliced XBP1 protein may have additional , as yet unidentified , activities . C12 appears able to trigger the ISR in an ER stress-independent matter , although the mechanism for this remains to be determined . It would be interesting to determine if C12 can activate HRI . Chronic elevation of GADD34 in ER stress can mediate cellular toxicity [26] , but GADD34 has been shown to be protective during the acute stress of SERCA pump inhibition with thapsigargin , which depletes the ER of calcium [61] . As with thapsigargin , P . aeruginosa has been associated with altered ER calcium signalling [38 , 44] . It is therefore of interest that expression of GADD34 reduced cell toxicity and increased cell survival upon iron deficiency caused by treatment with conditioned medium from P . aeruginosa . It has been shown that lungs of cystic fibrosis patients lack the ability to induce GADD34 [45] , which might plausibly lead to increased cytotoxicity or altered innate immunity due to Pseudomonas infection of the lungs of CF patients . However , future in vivo studies are required to confirm the observed cytoprotective effect of GADD34 induction during Pseudomonas infections . In summary , secreted virulence factors of the PAO1 strain of P . aeruginosa , including pyocyanin and AprA , are sufficient to elicit an ER stress response but the relative contribution of these virulence factors remains to be investigated . In contrast to these virulence factors , our findings strongly suggest that iron depletion mediated by the secretion of siderophores causes an ER stress-independent ISR . The induction of GADD34 by this may serve to ameliorate the toxic effects of P . aeruginosa conditioned medium .
All strains used in this study are listed in Table 1 . CM was prepared as described previously with slight modifications [47] . Briefly , overnight bacterial cultures in Luria Broth were inoculated 1:50 into RPMI 1640 ( Gibco , Life Technologies , Breda , the Netherlands ) and incubated at 37°C shaking at 200 rpm . After 5 days , the cultures were centrifuged and supernatants were filter-sterilized through 0 . 22 μm pore-size filter ( Whatman , Dassel , Germany ) to obtain CM . Pyocyanin and AprA levels in CM were measured as described previously [63 , 64] . PBEC were obtained from tumour-free resected lung tissue by enzymatic digestion as described previously [65] . 16HBE cells ( passage 4–15; kindly provided by Dr . D . C . Gruenert , University of California , San Francisco , CA , USA ) were cultured in MEM ( Invitrogen ) supplemented with 1 mM HEPES ( Invitrogen ) , 10% ( v/v ) heat-inactivated FCS ( Bodinco , Alkmaar , the Netherlands ) , 2 mM L-glutamine , 100 U/ml penicillin and 100 μg/ml streptomycin ( all from BioWhittaker ) . All MEFs were maintained as described previously [23 , 26 , 36 , 66 , 67] . HEK-TLR2 and HEK-TLR4 [50] were a kind gift from Prof . Dr . M . Yazdanbakhsh ( Leiden University Medical Center , the Netherlands ) . HeLa cells were transfected for 6 hours with two different ON-TARGETplus Human EIF2AK1 siRNA ( GCACAAACUUCACGUUACU and GAUUAAGGGUGCAACUAAA ) and knockdown was assessed 48 hours after transfection ( S5 Fig ) . GADD34-N1-eGFP ( kind gift form S . Shenolikar , Duke-NUS Graduate Medical School Singapore , Singapore ) was excised with BglII and NotI and ligated into pTRE2-hyg plasmid ( Clontech Laboratories , Mountain View , CA , USA ) digested with BamHI and NotI . HeLa Tet-On advanced cells ( Clontech Laboratories ) were transfected with the pTRE2-hyg_GADD34-eGFP plasmid and selected with 600 μM hygromycin to generate a stable cell line conditionally expressing GADD34-GFP ( S6 Fig ) . Positive cell clones were visualised by GFP expression in response to 1 μg/ml of doxycycline . Once identified , expanded and characterized , these clones were maintained in DMEM ( Sigma ) supplemented with 10% FBS and antibiotics ( 100 U/ml penicillin G , 100 μg/ml streptomycin , 200 μg/ml G418 and 200 μM hygromycin ) . Expression of GADD34 was typically induced using 1 μg/ml doxycycline ( Sigma ) for 24 hours . Cells were exposed at 80–90% confluence for 24 hours ( unless stated otherwise ) to CM-PAO1 ( 1 in 5 dilution , unless stated otherwise ) , pyocyanin ( 1–30 μM ) , ammonium iron ( III ) citrate ( 100 μM; Fe3+ ) , exotoxin A ( 1–10 ng/ml ) , AprA ( 10 nM ) , elastase ( 16–64 μg/ml ) and/or DFO ( 1–100 nM ) as indicated ( all from Sigma ) . Puromycin ( 10 μg/ml; Sigma ) was added 30 minutes before harvesting . Thapsigargin ( 100 nM; Sigma ) , TNFα and IL-1β ( both 20 ng/ml; Peprotech , Rocky Hill , NJ ) were used as positive controls . The compounds SB203580 ( 10 nM; Sigma ) and 5Z-7-oxozeanol ( also called LL-Z1640-2; 100 nM; TebuBio , Heerhugowaard , the Netherlands ) were added 30 minutes before stimulation for the inhibition of p38 MAPK and TAK1 , respectively . The specific IRE1-inhibitor 4μ8C ( 30 μM ) [56] was a kind gift from Prof . Dr . D . Ron , University of Cambridge . Cells were lysed in buffer H ( 10 mM HEPES , pH 7 . 9 , 50 mM NaCl , 500 mM sucrose , 0 . 1 mM EDTA , 0 . 5% ( v/v ) Triton X-100 , 1 mM PMSF , 1X Complete protease inhibitor cocktail ( Roche Applied Science , Mannheim , Germany ) ) supplemented with phosphatase inhibitors ( 10 mM tetrasodium pyrophosphate , 17 . 5 mM β-glycerophosphate , and 100 mM NaF [25 , 27] ) for detection by antibodies directed against phospho-eIF2α ( Cell Signaling Technology , Danvers , MA , USA ) , eIF2α ( gift from Prof . Dr . D . Ron ) , KDEL ( Enzo Life Sciences ) , GADD34 ( ProteinTech , Chicago , IL , USA ) , puromycin ( Millipore , Billerica , MA , USA ) , β-actin and GAPDH ( CellSignalling ) , or in sample buffer ( 0 . 2 M Tris-HCl pH 6 . 8 , 16% [v/v] glycerol , 4% [w/v] SDS , 4% [v/v] 2-mercaptoethanol , 0 . 003% [w/v] bromophenol blue ) for detection by antibodies directed against ( phospho- ) p38 MAPK ( both CellSignalling ) . The proteins in the samples were separated using a 10% SDS-PAGE gel and transferred onto a nitrocellulose membrane . After blocking with PBS containing 0 . 05% Tween-20 ( v/v ) and 5% skimmed-milk ( w/v ) , the membrane was incubated overnight with the primary antibody ( 1:1000 ) in TBS with 0 . 05% Tween-20 ( v/v ) and 5% BSA ( w/v ) at 4°C . Next , the membrane was incubated with HRP-labelled anti-mouse or anti-rabbit antibody ( Sigma ) in blocking buffer for 1 hour and developed using ECL ( ThermoScientific ) . Total RNA was isolated using Qiagen RNeasy mini kit ( Qiagen/Westburg , Leusden , the Netherlands ) . Quantitative reverse-transcriptase polymerase chain reaction ( qPCR ) was performed as described previously [68] using the primer pairs as defined in Table 2 . Relative mRNA concentrations of RPL13A and ATP5B ( GeNorm , PrimerDesign Ltd . , Southampton , UK ) were used as housekeeping genes for human genes and Actb ( β-actin ) and Sdha for mouse genes to calculate normalized expression . IL-8 was measured using commercially available ELISA kit according to manufacturer’s instructions ( Sanquin , Amsterdam , the Netherlands ) . LDH release was measured with a LDH-cytotoxicity colorimetric assay kit following manufacturer’s instructions ( Biovision , Milpitas , CA , USA ) . Thiazolyl blue tetrazolium bromide ( MTT; Sigma ) was dissolved in a 5 mg/ml stock concentration in sterile water and cells were incubated with a 1:10 dilution for 2 hours at 37°C . Next , the water-insoluble formazan formed from MTT in viable cells was dissolved in isopropanol for 10 min before the absorbance was read at 570 nm wavelength . Epithelial barrier function was measured using ECIS ( Applied Biophysics , Troy , NY , USA ) as described previously [69] . Resistance was measured at 1000 Hz and cells were stimulated with CM-PAO1 when the resistance was stable . Results are expressed as mean ± SEM . Data were analysed using one- or two-way analysis of variance ( ANOVA ) and corrected with the Bonferroni post-hoc test . Differences with P-values <0 . 05 were considered to be statistically significant . | Pseudomonas aeruginosa causes a devastating infection when it affects patients with cystic fibrosis or other chronic lung diseases . It often causes chronic infection due to its resistance to antibiotic treatment and its ability to form biofilms in these patients . The toxic effects of P . aeruginosa are largely mediated by secreted virulence factors . Efficient functioning of the endoplasmic reticulum is crucial for cell survival and appropriate immune responses , while its dysfunction causes stress and activation of the unfolded protein response . In this study , we found that virulence factors secreted by P . aeruginosa trigger the unfolded protein response in human cells by causing endoplasmic reticulum stress . In addition , secreted virulence factors activate the integrated stress response via a parallel independent pathway . Both stress pathways lead to the induction of the protein GADD34 , which appears to provide protection against the toxic effects of the secreted virulence factors . |
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In eukaryotic cells , most mRNAs are exported from the nucleus by the transcription export ( TREX ) complex , which is loaded onto mRNAs after their splicing and capping . We have studied in mammalian cells the nuclear export of mRNAs that code for secretory proteins , which are targeted to the endoplasmic reticulum membrane by hydrophobic signal sequences . The mRNAs were injected into the nucleus or synthesized from injected or transfected DNA , and their export was followed by fluorescent in situ hybridization . We made the surprising observation that the signal sequence coding region ( SSCR ) can serve as a nuclear export signal of an mRNA that lacks an intron or functional cap . Even the export of an intron-containing natural mRNA was enhanced by its SSCR . Like conventional export , the SSCR-dependent pathway required the factor TAP , but depletion of the TREX components had only moderate effects . The SSCR export signal appears to be characterized in vertebrates by a low content of adenines , as demonstrated by genome-wide sequence analysis and by the inhibitory effect of silent adenine mutations in SSCRs . The discovery of an SSCR-mediated pathway explains the previously noted amino acid bias in signal sequences and suggests a link between nuclear export and membrane targeting of mRNAs .
In eukaryotes , mRNAs are synthesized and processed in the nucleus before they are transported through the nuclear pores into the cytoplasm , where they are translated into proteins . Nuclear export of most mRNAs is mediated by the conserved transcription export ( TREX ) complex that is comprised of the Tho complex , UAP56 , and Aly . In vertebrates , the TREX components are recruited to the 5′ end of newly synthesized transcripts by the combined action of the 5′ cap binding complex , CBP80/20 , and factors that are loaded during the splicing of the intron closest to the 5′ cap [1–4] . Once assembled , the TREX complex recruits the heterodimer TAP/p15 as an export factor [5 , 6] . TAP interacts with nucleoporins directly [7–9] or through the factor Rae1 [10 , 11] and may thus allow bound transcripts to enter and eventually pass through the nuclear pores . It remains unclear how mRNAs exit the pores on the cytoplasmic side , but RNA helicases , such as Dbp5 , may be involved [12 , 13] . Although many details of the export mechanism remain to be clarified , it seems clear that the efficient export of most mRNAs requires both splicing and a functional cap . Not all mRNAs follow this canonical export pathway . In higher eukaryotes , transcripts coding for cyclin D [14] and other regulators of cell division [15] use elements in the 3′ untranslated region ( UTR ) as well as the cap binding protein eIF4E to engage the exportin protein Crm1 . A Crm1-dependent pathway is also used for the export of the intron-containing RNA genome of the human immunodeficiency virus ( HIV ) [16] . In macrophages , the export of interferon-induced transcripts is sensitive to the levels of Nup96 , a component of the nuclear pore , whereas other transcripts are insensitive [17] . In Saccharomyces cerevisiae , the export of some mRNAs requires either the Aly ortholog Yra1p or the TAP ortholog Mex67p , but not both [18] . Transcripts coding for heat-shock proteins , such as Hsp70p , are also exported by an Aly-independent pathway that is stimulated under stress conditions [19] . Together , these data indicate that there may be distinct export pathways for certain classes of mRNAs , which may be related to the different functions of the final translation products . One specialized class of mRNAs codes for secretory proteins . These mRNAs are often translated by ribosomes that are targeted to the endoplasmic reticulum ( ER ) membrane . As a result , the mRNAs also become membrane-bound . In addition , it is possible that these mRNAs associate with the ER membrane in a ribosome-independent manner by interacting with RNA-binding proteins [20–22] . These proteins may be loaded onto the mRNAs in the nucleus , during their passage through the nuclear pores , or in the cytoplasm . Thus , the membrane localization of these mRNAs may require factors that mediate nuclear export and distribution in the cytoplasm , which are distinct from factors used by mRNAs translated on free ribosomes in the cytoplasm . A major characteristic of a secretory protein is a hydrophobic signal sequence close to its N terminus . The signal sequence is first recognized by the signal-recognition particle ( SRP ) as it emerges from the translating ribosome and is then transferred into the protein-conducting channel formed by the Sec61p complex [23–25] . In most cases , the signal sequence is cleaved during or shortly after the polypeptide is transferred into the ER . Although the major requirement for a signal sequence is a stretch of at least 6–7 hydrophobic amino acids , there appear to be other , poorly defined properties that may distinguish different signal sequences [26 , 27] . For example , some signal sequences require auxiliary translocation components [28 , 29] or are sensitive to the drug cotransin [27 , 30] . In addition , signal sequences have other unexplained characteristics . For example , it has been noted that signal sequences in humans tend to be rich in leucine and poor in isoleucine [31] , despite the fact that these two amino acids have similar hydrophobicities [32] and would thus be expected to function equally well to promote translocation . This leucine/isoleucine bias is not seen in prokaryotes . Another puzzling feature is that signal sequences are often conserved across species to a higher degree than expected [33] . These observations raise the possibility that not only the signal sequence per se , but also the encoding nucleotide sequence ( signal sequence coding region or SSCR ) may have a function . We report here on the nuclear export of mRNAs coding for secretory proteins . We used microinjection of mRNAs and DNAs as well as transfection of DNA to demonstrate that the SSCR can promote the export of mRNAs that cannot use the conventional pathway because they lack an intron or a functional cap . By using large-scale sequence analysis , we found that vertebrate SSCRs have a low content of adenine , a feature that may in part be responsible for the preference of leucine versus isoleucine in signal sequences . Consistent with these observations , the incorporation of silent adenine mutations within the SSCR inhibits its nuclear export activity . Even the export of a natural RNA containing introns is facilitated by its SSCR . The discovery of an SSCR-mediated mRNA export pathway thus explains the previously noted amino acid bias in signal sequences and suggests a link between nuclear export and membrane targeting of mRNAs .
To study the nuclear export of mRNA coding for a secretory protein , we used a model mRNA that is derived from a fragment of the fushi tarazu ( ftz ) gene . The original construct contains an intron and was previously used to monitor mRNA splicing and nuclear export in Xenopus oocytes [1 , 34] . The construct was modified by adding a Kozak consensus sequence to allow efficient expression in mammalian cells . Sequences encoding FLAG and hemagglutinin ( HA ) epitopes were included at the 5′ and 3′ ends of the open reading frame ( ORF ) , respectively , to monitor translation of the mRNA . Because the intron contains in-frame stop codons ( Figure 1A; asterisks ) , the HA epitope will only be synthesized if the mRNA is spliced . To target the translation product to the ER , we attached an SSCR derived from the mouse major histocompatibility complex ( MHC ) class 2 molecule H2-K1 . The final construct is called t-ftz-i ( Figure 1A and Figure S1 ) . We first tested the translation of the t-ftz-i mRNA in vitro . When translated in reticulocyte lysate , a polypeptide of 13 kDa was generated , consistent with the size expected from the location of the in-frame stop codon in the intron ( Figure 1B , lane 3 ) . When the intron was deleted , the resulting mRNA ( t-ftz-Δi ) gave rise to a 17-kDa translation product ( lane 4 ) , again in agreement with the expected size of the ORF . With the original ftz constructs that lacked translation initiation signals , no translational products were detected ( lanes 1 , 2 ) . When t-ftz-i mRNA was microinjected into nuclei of NIH 3T3 fibroblasts , it was efficiently spliced within 15 min , as shown by reverse-transcriptase ( RT ) -PCR ( Figure 1C; lanes 2–4 ) . As expected , no detectable splicing was observed when t-ftz-i was microinjected into the cytoplasm ( lane 5 ) . Next , we tested the translation of t-ftz-i transcripts that were microinjected into the nuclei of NIH 3T3 fibroblasts . Nuclear injection was confirmed by co-injecting fluorescently labeled 70-kDa dextran , which is too large to passively cross the nuclear pores ( Figure 1D and 1E; see insets ) . After 4 h , the FLAG and HA epitopes could be detected by immunofluorescence microscopy in over 90% of the injected cells ( Figure 1D and 1E ) . In addition , both epitopes co-localized with the ER resident protein TRAPα ( Figure 1D and 1E ) , indicating that the translation product was translocated into the ER . The protein is probably not secreted efficiently , because it contains only a fragment of ftz and therefore may not be properly folded . When t-ftz-i transcripts were injected into the cytoplasm , the expression of the FLAG but not of the HA epitope was observed ( unpublished data ) , as expected from the presence of the unspliced intron . Both the FLAG and HA epitopes were expressed when t-ftz-Δi transcripts were injected into nuclei or cytoplasm ( unpublished data ) . To monitor the nuclear export of t-ftz-i mRNA , transcripts were microinjected into nuclei of NIH 3T3 cells . The cells were fixed at various time points , and the localization of the injected RNA was probed by fluorescence in situ hybridization ( FISH ) . We optimized the FISH procedure , omitting harsh acid and ethanol treatments , such that intracellular morphology was largely maintained . We estimate that 20 , 000 to 50 , 000 transcripts were injected per cell , which is a small number compared with the total number of transcripts in a typical mammalian cell ( 400 , 000 to 850 , 000 molecules ) [35] . Again , fluorescent 70-kDa dextran was co-injected to identify nuclear-injected cells ( Figure 2 , insets ) . Immediately after injection , the transcripts were confined to the nucleus . Over time , however , the majority of t-ftz-i transcripts ( ∼80% ) accumulated in the cytoplasm ( Figure 2A ) . Quantitation showed that the half-time of mRNA export was ∼15 min ( Figure 2C ) , similar to previous estimates [36 , 37] . About 50% of the mRNA molecules remained intact after 4 h , as shown by the total level of FISH signal ( Figure 2D ) . Surprisingly , when we injected t-ftz-Δi mRNA into nuclei , we also observed efficient export into the cytoplasm ( Figure 2B; quantitation in Figure 2C ) , despite the fact that the absence of an intron should have prevented the recruitment of TREX components and thus nuclear export [2 , 4] . The kinetics of mRNA export was about the same for transcripts containing or lacking the intron ( Figure 2C ) . In contrast to a previous report [38] , a large fraction of the intron-less transcripts remained stable over the time of analysis ( Figure 2D ) . To exclude the possibility that the nuclear export of the intron-less transcript is caused by the introduction of exogenously synthesized mRNAs , we tested the export of mRNA after its synthesis in the nucleus . To this end , we microinjected plasmids containing the t-ftz-Δi or t-ftz-i genes into NIH 3T3 nuclei . After 30 min , the RNA Polymerase II inhibitor α-amanitin was added to inhibit further transcription , and then the distribution of mRNA over time was monitored using FISH . Immediately after α-amanitin addition , most transcripts were in the nucleus , but ∼20% were already found in the cytoplasm . Over time , the nuclear fraction was efficiently exported to the cytoplasm ( Figure 2E and 2F ) with a rate that was slightly lower than that of microinjected RNA . As before , we observed only minor differences between intron-containing and intron-lacking t-ftz transcripts ( Figure 2F ) . Pretreatment of cells with α-amanitin 5 min prior to DNA microinjection completely inhibited mRNA synthesis , as assayed by FISH ( unpublished data ) . From these results , we conclude that export of an intron-lacking mRNA can occur efficiently , regardless of whether or not export is coupled to transcription . Our results are in apparent contradiction to previous observations showing that ftz constructs lacking introns are not exported from nuclei of Xenopus oocytes [1] . A major difference to the transcripts tested in Xenopus oocytes is the presence of an SSCR in t-ftz-Δi . We therefore tested whether the SSCR was required to promote nuclear export of this mRNA . Indeed , a transcript that lacked the SSCR , and thus encoded a cytoplasmic version of ftz ( c-ftz-Δi ) , remained in the nucleus 30 min after injection ( Figure 2G , quantitation in Figure 2C ) , a time during which most of the control t-ftz-Δi mRNA was exported . At later time points , much of the c-ftz-Δi mRNA was degraded ( Figure 2D ) . Because c-ftz-Δi mRNA was stable when injected directly into the cytoplasm ( unpublished data ) , it is unlikely that nuclear-injected c-ftz-Δi mRNA was exported and then rapidly degraded in the cytoplasm . The addition of an intron into c-ftz-Δi mRNA ( resulting in c-ftz-i mRNA ) restored nuclear export and stability of the mRNA ( Figure 2H , quantitation in Figure 2C and 2D ) . A fraction of the exported c-ftz-i transcripts accumulated in stress granules ( unpublished data ) . Together , these experiments suggest that either an SSCR or an intron can serve as a nuclear export signal . To exclude the possibility that the SSCR-mediated export pathway is a peculiarity of NIH 3T3 cells , we microinjected mRNA precursors into the nuclei of COS-7 cells . As before , t-ftz-i mRNA containing both the splicing and the SSCR signals—as well as t-ftz-Δi and c-ftz-i mRNAs , which each contain only one of the two signals—were exported efficiently into the cytoplasm ( Figure 3A and Figure S2; quantitation in Figure 3B ) . In contrast , c-ftz-Δi mRNA lacking both signals was not exported ( Figure 3B and Figure S2 ) . Again , the c-ftz-Δi mRNA was significantly less stable than the other mRNAs ( Figure 3C ) . These data suggest that the SSCR-mediated nuclear export pathway may be present in many mammalian cell types . In COS-7 cells , we were able to visualize the targeting of the mRNAs to the ER membrane . All mRNAs containing an SSCR gave a typical reticular staining in the cytoplasm and co-localized with TRAPα , a marker of the ER membrane ( Figure 3A and Figure S2 ) . To test whether other SSCRs could promote mRNA export , we replaced the SSCR of the MHC class 2 molecule H2-K1 with that of human insulin in the t-ftz-Δi construct ( ins-ftz-Δi , see Figure S3 ) . When injected into COS-7 cell nuclei , ins-ftz-Δi mRNA was efficiently exported ( Figure 3B and Figure S2 ) . In fact , the export kinetics was faster than with t-ftz-Δi mRNA ( Figure 3B ) . Again , the mRNA was targeted to the ER membrane ( Figure S2 ) . Taken together , these results indicate that the SSCR-mediated pathway may be quite general . Finally , we performed transfection experiments in COS-7 cells with plasmids coding for t-ftz and c-ftz mRNAs containing or lacking an intron . The steady-state distribution of the mRNAs between the nucleus and cytoplasm was determined by FISH ( Figure S4 , quantitation in Figure 3D ) . The SSCR-containing mRNAs ( t-ftz-i and t-ftz-Δi ) were mostly found in the cytoplasm , indicating that they are efficiently exported from the nucleus . A significant fraction of mRNAs lacking an SSCR ( c-ftz-i and c-ftz-Δi ) were found in the nucleus , despite the fact that one contained an intron . In the cytoplasm , the SSCR-containing ftz constructs were targeted to the ER membrane , whereas c-ftz constructs partially co-localized with TIA-1 ( Figure S4 ) , a marker of stress granules [39] . These data confirm the presence of an SSCR-mediated mRNA export pathway and suggest that , at least under certain conditions , it may be more efficient than the splicing-mediated export pathway . Next we characterized SSCR-mediated mRNA export , using t-ftz-Δi mRNA that contains the SSCR signal but lacks an intron . When this mRNA was injected into nuclei of NIH 3T3 cells that had been depleted of ATP by treatment with azide [40] , no export into the cytoplasm was observed ( Figure 4A ) . This treatment did not compromise the viability of the cells as judged by phase microscopy or immunostaining of the microtubule network ( unpublished data ) . Blocking the nuclear pores by pre-injecting wheat germ agglutinin ( WGA ) [41 , 42] also inhibited the nuclear export of microinjected t-ftz-Δi mRNA ( Figure 4B ) . Thus the export of t-ftz-Δi mRNA requires energy and functional nuclear pores . As expected , nuclear mRNA export was unidirectional . When transcripts were injected into one of the nuclei of a binucleated cell , 80% of the mRNA was transported into the cytoplasm , but no fluorescence was detected in the uninjected nucleus ( Figure S5 ) , indicating that there is no re-uptake of exported mRNA . Next we attempted to define the components required for t-ftz-Δi mRNA export . To test whether the export factor TAP is required , we took advantage of the viral constitutive transport element ( CTE ) , which binds to and sequesters TAP [43] . Nuclear export of t-ftz-Δi mRNA was inhibited in cells pre-injected with CTE RNA ( Figure 4C , arrow indicates a cell pre-injected with CTE , quantitation is shown in Figure 4D ) . In contrast , cells that were not pre-injected with CTE ( Figure 4C , arrowhead ) , or cells pre-injected with control buffer ( Figure 4D ) , exported t-ftz-Δi mRNA . Previously , it was demonstrated that short mRNAs use the TAP-independent snRNA export pathway [44] , but our results demonstrate that the t-ftz-Δi transcripts exclusively follow the TAP-dependent pathway used by most mRNAs . We then tested the involvement of the TREX complex . To deplete the TREX complex , we incubated HeLa cells with small interfering RNAs ( siRNAs ) against the TREX component UAP56 and its ortholog URH49 , a treatment that has been shown to inhibit the majority of mRNA export in mammalian cells [45] . After one day of RNA interference ( RNAi ) treatment , UAP56 levels were effectively reduced ( Figure 4E ) . Although siRNA-treated cells were compromised for the export of c-ftz-i transcript , these cells exported both t-ftz-Δi and t-ftz-i mRNA ( Figure 4F and 4G ) , suggesting that the SSCR-mediated export pathway is less sensitive to TREX complex depletion than the splicing-mediated pathway . In some cells depleted of UAP56/URH49 , t-ftz-Δi and t-ftz-i transcripts became enriched in the nuclear rim , perhaps due to a pleiotropic effect that inhibits the movement of mRNAs from the nuclear pores into the cytoplasm . Cells treated with siRNA for two days showed even more nuclear rim localization of t-ftz-Δi and t-ftz-i transcripts , and their export was inhibited . Although c-ftz-i mRNA was also not exported , it only accumulated in the nucleoplasm ( unpublished data ) , supporting the idea that different export pathways are used . As expected , the export of the intron-lacking RNA t-ftz-Δi was not reduced when a component of the exon junction complex ( EJC ) , the RNA helicase eIF4AIII , was depleted by RNAi ( Figure 4G and Figure S6 ) . However , even the export of the intron-containing mRNAs t-ftz-i and c-ftz-i remained unaffected , in agreement with previous results that indicated that the EJC is not a main factor in recruiting TREX [3 , 46] . Finally , we tested whether the SSCR-mediated export requires a 5′ end cap , as in splicing-dependent export [3] . Because uncapped transcripts were rapidly degraded after microinjection into NIH 3T3 nuclei ( unpublished data ) , we used mRNAs that were capped with the cap analogs ApppG or trimethyl-2 , 3 , 7-GpppG ( 3mGpppG ) . These modified caps do not associate with the cap binding complex CPB80/20 [47] , which is essential for the subsequent recruitment of the TREX complex [3] and for efficient splicing [47] . Capping of t-ftz-Δi mRNAs with the cap analogs did not inhibit nuclear export ( Figure 4H ) . In addition , the mRNAs were as stable as transcripts containing the natural cap , methyl-7-GpppG ( mGpppG ) ( Figure 4I ) . In contrast , the c-ftz-i transcripts that use the splicing-dependent pathway were not exported when capped with ApppG , as expected ( Figure 4H ) . Thus , we conclude that unlike the splicing-mediated mRNA export , the SSCR-mediated pathway does not require a natural cap . In principle , the SSCR nuclear export signal could either be an RNA element or its translated amino acid sequence , the signal sequence . To distinguish between these possibilities , we altered five nucleotides within the SSCR such that three encoded hydrophobic residues within the core of the signal sequence were changed to arginines ( 3R-ftz , see Figure S3 ) . The mutated signal sequence should no longer target the translation product to the ER membrane . Upon injection into the nuclei of COS-7 cells , 3R-ftz-Δi mRNA was exported with a kinetics similar to that of t-ftz-Δi ( Figure 5A; quantitation in Figure 5B ) . As expected , the mRNA was no longer targeted to the ER , and instead was distributed diffusely in the cytoplasm ( Figure 5A ) . Its translation product was targeted to mitochondria ( unpublished data ) , possibly because the signal sequence was converted into an amphipatic helix typical of mitochondrial targeting sequences [48] . To further ensure that the translation product of the SSCR did not determine mRNA export from the nucleus , we altered the ORF of the SSCR . We added a nucleotide at the beginning of the sequence and deleted a nucleotide at the end , such that the coding region following the SSCR remained unchanged . The altered SSCR codes for a less hydrophobic sequence . The frame-shifted ftz transcript ( fs-ftz-Δi , Figure S3 ) was again efficiently exported into the cytoplasm ( Figure 5A and 5B ) . As expected , it was not targeted to the ER ( Figure 5A ) , and its translation product remained in the cytoplasm ( unpublished data ) . Transcripts that lacked a translation start codon at the beginning of the SSCR ( UUG-ftz-Δi , see Figure S3 ) were also efficiently exported ( Figure 5B and Figure S2 ) . To further confirm that translation of the t-ftz-Δi transcript was not required for its export , cells were pretreated with pactamycin , an inhibitor of translation initiation [49] , before being microinjected . Although translation was effectively inhibited after 15 min of drug treatment , as assayed by 35S-methionine incorporation ( unpublished data ) , an even longer pretreatment with pactamycin ( 20 min ) did not inhibit t-ftz-Δi mRNA export or affect t-ftz-Δi stability ( Figure 5C–5E ) . Interestingly , after 1 h of pactamycin pretreatment , mRNA export was inhibited and nuclear rim staining was seen , as in cells depleted of the TREX components UAP56 and URH49 , which suggests that in both conditions , the synthesis of a factor required for the movement of the mRNA from the nuclear pores into the cytoplasm is impaired . From these experiments , we conclude that the nuclear export signal of the SSCR does not require translation and is thus likely an RNA element . To identify features in SSCRs that might function as nuclear export signals , we performed a large-scale sequence analysis of various genomes . We confined our analysis to the first 69 base pairs ( bp ) following the initiator methionine codon , a length that covers most SSCRs . We used the annotation in Ensembl and PSORTdb to classify genes into SSCR-containing and SSCR-lacking ORFs in genomes ranging from bacteria to humans . As an additional control , we analyzed 69 bp in the central region of each ORF , which should reflect the overall base composition of the coding region . This analysis showed that the SSCRs in all eukaryotes have a marked deficiency of adenines , in contrast to those in bacteria ( Figure 6A ) . Consistent with this observation , eukaryotic SSCRs have long nucleotide stretches devoid of adenines ( no-A tracts ) ( Figure 6B ) . The no-A tracks were longer in vertebrates than in invertebrates . Several factors account for the adenine deficiency in SSCRs . First , hydrophobic amino acids , which are enriched in the signal sequence , have codons that contain few adenines . Another factor is bias among amino acids with similar biochemical properties , such that amino acids encoded by codons with fewer adenines are used . This is illustrated by leucine , which has few adenine-containing codons , and isoleucine , which has at least one adenine in each of its codons . As previously noted , human signal sequences have significantly more leucine residues than equally hydrophobic isoleucine residues [31] . We confirmed this leucine-versus-isoleucine bias for SSCRs in all vertebrates analyzed ( Figure 6C ) . A similar analysis was performed on positively charged amino acids that are frequently found in the amino acid sequence preceding the hydrophobic core of a signal sequence . Arginine is encoded by codons containing relatively few adenines , whereas lysine is encoded by AAA and AAG , and arginines were significantly more frequent than lysines in vertebrate , but not invertebrate , SSCRs ( Figure 6D ) . In total , about 15% to 25% of the adenine deficiency in SSCRs in vertebrates is caused by selection between similar amino acids for those encoded by codons with lower adenine content ( Figure S7 ) . The third factor that contributes to the adenine deficiency is a bias toward codons lacking adenine for amino acids that are encoded by multiple codons ( e . g . , CUC versus CUA ) . This is illustrated by the usage of codons for leucine and serine . In vertebrates , the adenine-lacking codons for both amino acids are more frequently used than the ones containing adenines ( Figure 6E and 6F ) . The bias against adenine-containing codons in vertebrate SSCRs could also be seen when the analysis was extended to all amino acids that are encoded by both adenine-lacking and -containing codons ( Figure 6G ) . In total , bias between synonymous codons accounts for an additional 15% to 25% of the adenine deficiency in vertebrates ( Figure S7 ) . In humans , the bias between similar amino acids and between synonymous codons together account for almost half of the total adenine deficiency ( Figure S7 ) . Our analysis suggests that over the course of evolution , there was a strong selection against adenines that was likely caused by some requirement of the nucleotide sequence , rather than of the encoded amino acid sequence . It should be noted that the two SSCRs that were tested experimentally , completely follow the rules established in the large-scale sequence analysis . The longest no-A track in the SSCRs derived from H2-K1 and insulin were 35 and 40 nucleotides , respectively . Both SSCRs contain leucines and arginines , but no isoleucines or lysines , and both have a high bias against adenine-containing codons . To test whether the bias against adenines within SSCRs is important for promoting mRNA export , we mutated seven nucleotides of the SSCR that were derived from the MHC class 2 molecule H2-K1 to adenines , without altering the encoded amino acid sequence ( 7A-ftz-Δi , see Figure S3 ) . Upon injection into COS-7 cell nuclei , the export of 7A-ftz-Δi mRNA into the cytoplasm was significantly less efficient than that mediated by the original SSCR ( Figure 7A and 7B ) . The nuclear export of an intron-containing version ( 7A-ftz-i ) was not inhibited by the mutations ( Figure 7A and 7B ) , which is consistent with the expectation that this transcript can use the splicing-dependent pathway . The exported 7A-ftz-Δi and 7A-ftz-i mRNAs were targeted to the ER ( Figure 7A ) and resulted in the expression of ER-bound t-ftz protein ( unpublished data ) , indicating that translation of the mRNAs was largely unaffected by the silent mutations . The mutations also did not grossly affect the stability of the mRNAs ( Figure 7C ) . The nuclear export of 7A-ftz-Δi mRNA was also significantly less efficient than that of t-ftz-Δi mRNA when tested in transfection experiments with plasmids coding for these RNAs ( Figure 3D and Figure S4 ) . In fact , the export was as inefficient as with RNA lacking an SSCR altogether , indicating that the adenine mutations severely affected the SSCR-export signal . In addition , a portion of the cytoplasmic fraction of the 7A-ftz-Δi mRNAs was localized to stress granules , as determined by co-staining with the stress granule marker TIA-1 ( Figure S4 ) . An inhibitory effect of adenines on the nuclear export function of an SSCR could also be demonstrated for human insulin mRNA . We first used intronless transcripts , generated by in vitro transcription of human insulin cDNA . Upon injection into COS-7 cell nuclei , insulin mRNA with a wild-type SSCR was efficiently exported into the cytoplasm , whereas mutant mRNA , in which five silent adenine mutations were introduced into its SSCR , was significantly delayed ( Figure 7D and 7E ) . Again , the mutations did not significantly affect the stability of the mRNAs ( Figure 7F ) . To address whether the SSCR contributed to the export of a physiologically transcribed and spliced mRNA , we microinjected plasmids that contained the insulin gene with its two introns under the control of the CMV promoter ( insulin-2i ) . After 30 min , transcription was blocked with α-amanitin , and nuclear export of the newly synthesized transcripts was followed over the course of 2 h . The rate of mRNA export was significantly decreased when silent adenine mutations were incorporated into the SSCR of intron-containing or -lacking mRNAs ( 5A-insulin-2i and 5A-insulin-Δi ) ( Figure 7G ) . On the other hand , deletion of the introns ( insulin-Δi ) had no effect . All the tested transcripts were stable over the tested time course ( Figure 7H ) . From these experiments we conclude that a functional SSCR can enhance the export of mRNAs , regardless of whether they contain or lack introns . These results provide evidence that the SSCR-mediated pathway operates within the context of a natural gene .
Here we describe the surprising discovery of a nuclear export pathway in mammalian cells that appears to be specific for mRNAs coding for secretory proteins . We found that mRNAs lacking an intron or functional cap , which cannot use the canonical , splicing-dependent pathway , can efficiently be exported into the cytoplasm by means of a signal in the SSCR . The signal is an RNA element that is deficient in adenines . Thus , the SSCR not only codes for the hydrophobic amino acid sequence that targets the translation product to the ER membrane , but also functions at the nucleotide level to promote the export of the mRNA from the nucleus . The SSCR even enhances the export of natural transcripts containing introns . Like the splicing-dependent pathway , the SSCR-mediated export pathway requires the export factor TAP , but it is less dependent , or perhaps even independent , of the TREX complex . Our sequence analysis suggests that the SSCR-mediated pathway may exist in all vertebrates . Why would mRNAs coding for secretory proteins use a separate nuclear export pathway ? One possibility is that nuclear export of these mRNAs might be coupled with some downstream event in the cytoplasm . For example , it is possible that the mRNAs emerging from the nuclear pores are initially associated with proteins that keep them translationally silenced and promote their distribution in the cytoplasm . These factors could be distinct from those used by mRNAs coding for cytoplasmic proteins . For example , factors associated with SSCR containing transcripts could have affinity for ER membrane proteins or molecular motors . The recent observation of an association of the TAP homolog , NXF2 , with kinesin lends support to the idea that the export and cytoplasmic distribution of mRNAs may be coupled [50] . Our own data show that in transfection experiments in which the steady-state distribution of mRNAs was investigated , transcripts with defective or lacking SSCRs were not only inefficiently exported but also often accumulated in cytoplasmic stress granules , indicating that their normal cytoplasmic distribution was disrupted ( Figure S4 ) . How nuclear mRNA export would be coupled with downstream events remains to be elucidated . It is also possible that the export of mRNAs coding for secretory proteins is regulated differently from other mRNAs in certain situations . However , we have not found any changes of SSCR-mediated mRNA export upon accumulation of misfolded proteins in the ER ( unpublished data ) . In yeast , a block in secretion results in the relocalization of certain nuclear pore components to the cytoplasm and leads to the inhibition of protein transport between the nucleus and the cytoplasm [51 , 52] . Conceivably , this mechanism may also provide a feedback signal between the demand of secretory protein synthesis in the cytoplasm and the nuclear export of the corresponding SSCR-containing mRNAs . The mechanism by which SSCRs are recognized and trigger nuclear mRNA export also remains to be investigated . A clear nucleotide motif that is common among all SSCRs is not obvious . One possibility is that a negative export regulator would bind to all adenine-containing , non-SSCR sequences . However , we favor models in which either the paucity of adenines per se is recognized by an export-mediating protein with a similar RNA-binding specificity as the Muscleblind family of proteins [53] , or that the adenine-lacking segment of an SSCR folds into a conformation that would recruit an export factor . Regardless of the precise signal , its position at the 5′ end of the mRNA would allow this part of the mRNA to emerge into the cytoplasm first , in the same direction of mRNA export used in the splicing-dependent pathway [3 , 54] . Although many SSCR-containing mRNAs also contain introns , the 5′ end localization of the SSCR signal might allow it to recruit factors to the newly synthesized transcript before the introns are synthesized and thereby overrule the splicing-dependent signals . Our data indicate that the SSCR recruits TAP , likely without involvement of TREX . One possibility is that the serine/arginine-rich ( SR ) proteins serve as adaptors for TAP binding . SR proteins associate with mRNAs during transcription and/or splicing . They are required for splicing [55] , can associate with TAP [56 , 57] , and are involved in the export of intron-lacking histone H2A mRNA [58] . We have not been able to prevent SSCR-dependent mRNA export by pre-injection of an SR antibody that inhibits splicing [55] ( unpublished results ) , but further work is required to test the possible role of the SR proteins in SSCR-mediated export . Our analysis shows that the SSCRs have a nucleotide bias that cannot be explained solely by the encoded amino acid sequence . Although these results indicate that the nucleotide sequence itself is important , there may be additional variability of signal sequences at the level of amino acids , as suggested by experiments in which different signal sequences responded differently to the accumulation of unfolded proteins in the ER [27] . One would assume that the SSCR-mediated mRNA export pathway operates in all eukaryotic cells , but our large-scale sequence analysis showed a clear bias against adenine-containing codons only in vertebrate SSCRs . A slight bias against adenines was seen in lower eukaryotes , but it is possible that SSCRs have additional properties that are found in all eukaryotes , for example , a common folded structure . The same argument might explain the absence of any obvious adenine bias at the 5′ end of genes coding for membrane proteins that lack a signal sequence ( unpublished data ) , even though the corresponding mRNAs also need to be targeted to the ER and would likely take the same pathway as those coding for secretory proteins . Obviously , the surprising discovery of an SSCR-dependent export pathway for mRNAs raises a large number of interesting questions that need to be addressed in the future .
For RT-PCR , total RNA was extracted from injected cells and analyzed using M-MLV reverse transcriptase ( Invitrogen ) and HiFi Taq polymerase ( Invitrogen ) according to the manufacturer's protocol . PCR reactions were carried out using gene-specific primer pairs , Ftz-127F and Ftz-444R ( see Figure 1A ) , and for controls , we used mouse 18S rRNA-448F and mouse 18S rRNA-926R . The ftz constructs were modified to generate t-ftz-i ( Figure 1A and Figure S1 ) , t-ftz-Δi , and their derivatives ( Figure S3 ) . For in vivo mammalian expression experiments , t-ftz constructs were digested with HindIII and XhoI , and then ligated into the pcDNA3 expression vector . Human insulin cDNA was amplified from a cDNA library and the insulin gene was amplified from HeLa genomic DNA using gene-specific primer pairs digested with HindIII and XhoI , and then ligated into the pcDNA3 expression vector . Insulin was modified with PCR primers to generate 5A-insulin ( see Figure S3 ) . In vitro transcription was carried out using the T7 mMESSAGE mMACHINE transcription kit containing excess cap ( Ambion ) . To synthesize mRNAs with cap analogues , the reaction was carried out with 35 mM ApppG or 3mGpppG ( New England Biolabs ) ; 10 mM ATP , UTP , and CTP; and 1 mM GTP . Transcripts were poly-adenylated using Poly ( A ) tailing kit ( Ambion ) , generating poly ( A ) tails of 200–300 nucleotides . mRNA purification was carried out using MEGAclear kit ( Ambion ) . mRNA was then precipitated with 150 mM potassium acetate ( pH 5 . 5 ) and 2 . 5 volumes 100% ethanol . mRNA was resuspended in injection buffer ( 100 mM KCl , 10 mM HEPES , pH 7 . 4 ) . mRNAs were translated in a TnT reticulocyte lysate system in the presence of 35S-methionine ( Promega ) . NIH 3T3 fibroblasts were maintained in DMEM supplemented with 10% calf serum . HeLa and COS-7 cells were maintained in DMEM supplemented with 10% fetal bovine serum . Cells were plated overnight on 35-mm-diameter dishes with glass coverslip bottoms ( MatTek Corp . ) . For RNAi experiments , HeLa cells were transfected with siRNA directed against human UAP56 and URH49 [45] or eIF4AIII [59] . 24 and 48 h post transfection , cells were either plated on 35-mm dishes ( for microinjections ) or collected to assess protein levels by SDS-PAGE and Western blot with rabbit anti-UAP56 serum [4] , rabbit anti-eIF4AIII [59] , or rabbit anti-CBP80 serum [3] . For DNA transfections , cells were transfected with DNA and lipofectamine ( Invitrogen ) using the manufacture's protocol . Microinjections were performed as previously described [60] . mRNA was microinjected at 200 μg/ml along with fluorescein isothiocyanate ( FITC ) –conjugated 70-kDa dextran ( 1 mg/ml; Invitrogen ) . Insulin mRNA was heated to 70 °C for 10 min prior to injections . DNA was injected at 50 μg/ml along with FITC-conjugated 70-kDa dextran , and translation was inhibited with 50 μg/ml α-amanitin ( Sigma ) . For export inhibition experiments , cells were first microinjected with WGA ( 3 mg/ml; Sigma ) or CTE RNA ( 200 μg/ml ) along with cascade-blue–conjugated 10-kDa dextran ( Invitrogen ) , and then incubated for 30 min at 37 °C prior to mRNA microinjection . For azide treatments , microinjected cells were washed three times with Dulbecco's modified PBS ( D-PBS; 10 mM phosphate , pH 7 . 4 , 140 mM NaCl , 3 mM KCl , 0 . 8 mM CaCl2 , 0 . 7 mM MgCl2 ) and incubated in D-PBS supplemented with 10 mM azide ( Sigma ) or 10 mM D-glucose ( Sigma ) . For pactamycin treatments , cells were incubated in DMEM ( 10% fetal bovine serum ) with 200 nM pactamycin 20 min before microinjections . Microinjected cells were washed with D-PBS , fixed in 4% paraformaldehyde ( Electron Microscope Sciences ) in D-PBS , and permeabilized with 0 . 1% Triton X100 ( Peirce ) in PBS . For immunostaining , fixed samples were first incubated with primary antibodies ( rabbit polyclonal against TRAPα [61] , goat polyclonal antibody against TIA-1 [Santa Cruz Biotechnology] , 12CA5 monoclonal antibody against HA [Roche Applied Sciences] , and M2 monoclonal antibody against FLAG [Sigma] ) diluted 1:200 in immunostain solution ( PBS , 0 . 1% Triton X100 , 2 mg/ml RNAse free BSA; Ambion ) for 30 min , washed three times with PBS , and incubated with various Alexa-conjugated secondary antibodies ( Invitrogen ) , diluted 1:200 in immunostain solution . For FISH , fixed cells were washed with 50% formamide in 1X SSC ( 150 mM NaCl , 15 mM NaCitrate , pH 7 . 10 ) and then incubated overnight at 37 °C in 200-ml hybridization buffer ( 50% formamide , 100 mg/ml dextran sulphate , 0 . 02 mg/ml RNAse free BSA , 1 mg/ml Escherichia coli tRNA , 5 mM VRC , 1X SSC ) containing 30–50 ng oligonucleotide probe ( GTCGAGCCTGCCTTTGTCATCGTCGTCCTTGTAGTCACAACAGCCGGGACAACACCCCAT for ftz , GGTCCTCTGCCTCCCGGCGGGTCTTGGGTGTGTAGAAGAAGCCTCGTTCCCCGCACACTA for insulin ) labeled at their 5′ end with Alexa-546 ( Integrated DNA Technologies ) . Cells were washed in five times with 50% formamide in 1X SSC and images were captured using an EM-CCD Camera , Model C9100–12 ( Hamamatsu ) on an inverted microscope ( 200M , Carl Zeiss ) using Metamorph software ( Molecular Devices Corporation ) . Unaltered 14-bit images were quantified in Metamorph and analyzed in Excel ( Microsoft ) . For each image the area ( A ) and the average intensity ( I ) of each injected nuclei ( n ) and cell body ( b ) were recorded . For the background intensity , the average intensity of an un-injected cell ( u ) was used . The cytoplasmic fluorescence was equal to ( Ab ) ( Ib – Iu ) – ( An ) ( In – Iu ) . The ratio of cytoplasmic/total fluorescence equals [ ( Ab ) ( Ib – Iu ) – ( An ) ( In – Iu ) ]/[ ( Ab ) ( Ib – Iu ) ] . For figure production , the contrast and brightness of the aquired 14-bit micrographs were adjusted to optimize the ability to view the fluorescence . The resulting images were converted to 8-bit files using Metamorph . Nucleotide sequences were downloaded from Ensembl Biomart ( http://www . biomart . org/index . html ) and the National Center for Biotechnology Information ( NCBI ) ( E . coli . and Bacillus subtilis ) . Signal sequence containing proteins were determined by annontation in PSORTdb ( E . coli and B . subtilis ) and Ensembl Biomart . A Perl script ( Protocol S1 ) was written to count the nucleotide content , amino acid content , and codon content , of the first 69 nucleotides , middle 69 nucleotides ( offset toward the start to maintain frame as needed ) , and last 69 nucleotides . The script also determined the longest no-adenine and one-adenine tracks completely contained in the first 69 nucleotides . Tabulated results were examined in Excel to determine nucleotide content , codon bias , and amino acid bias . Percent of adenine bias explained by codon bias ( e . g . , CUC versus CUA ) and similar amino acid bias ( e . g . , isoleucine versus leucine ) was calculated by comparing the number of adenines in non–signal sequence containing proteins to the number of adenines found in signal sequence containing proteins normalized for the number of proteins .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for genes investigated in the paper are as follows: H2-K1 ( GI: 14972 ) , TAP ( GI: 10482 ) , UAP56 ( GI: 7919 ) , URH49 ( GI: 10212 ) , and eIF4AIII ( GI: 9775 ) . The GenBank accession number for human insulin cDNA is GI: 3630 . | In eukaryotic cells , precursors of messenger RNAs ( mRNAs ) are synthesized and processed in the nucleus . During processing , noncoding introns are spliced out , and a cap and poly-adenosine sequence are added to the beginning and end of the transcript , respectively . The resulting mature mRNA is exported from the nucleus to the cytoplasm by crossing the nuclear pore . Both the introns and the cap help to recruit factors that are necessary for nuclear export of an mRNA . Here we provide evidence for a novel mRNA export pathway that is specific for transcripts coding for secretory proteins . These proteins contain signal sequences that target them for translocation across the endoplasmic reticulum membrane . We made the surprising observation that the signal sequence coding region ( SSCR ) can serve as a nuclear export signal of an mRNA that lacks an intron or functional cap . Even the export of an intron-containing natural mRNA was enhanced by its SSCR . The SSCR export signal appears to be characterized in vertebrates by a low content of adenines . Our discovery of an SSCR-mediated pathway explains the previously noted amino acid bias in signal sequences , and suggests a link between nuclear export and membrane targeting of mRNAs . |
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Transcription and replication of the influenza A virus ( IAV ) genome occur in the nucleus of infected cells and are carried out by the viral ribonucleoprotein complex ( vRNP ) . As a major component of the vRNP complex , the viral nucleoprotein ( NP ) mediates the nuclear import of the vRNP complex via its nuclear localization signals ( NLSs ) . Clearly , an effective way for the host to antagonize IAV infection would be by targeting vRNP nuclear import . Here , we identified phospholipid scramblase 1 ( PLSCR1 ) as a binding partner of NP by using a yeast two-hybrid ( Y2H ) screen . The interaction between NP and PLSCR1 in mammalian cells was demonstrated by using co-immunoprecipitation and pull-down assays . We found that the stable overexpression of PLSCR1 suppressed the nuclear import of NP , hindered the virus life cycle , and significantly inhibited the replication of various influenza subtypes . In contrast , siRNA knockdown or CRISPR/Cas9 knockout of PLSCR1 increased virus propagation . Further analysis indicated that the inhibitory effect of PLSCR1 on the nuclear import of NP was not caused by affecting the phosphorylation status of NP or by stimulating the interferon ( IFN ) pathways . Instead , PLSCR1 was found to form a trimeric complex with NP and members of the importin α family , which inhibited the incorporation of importin β , a key mediator of the classical nuclear import pathway , into the complex , thus impairing the nuclear import of NP and suppressing virus replication . Our results demonstrate that PLSCR1 negatively regulates virus replication by interacting with NP in the cytoplasm and preventing its nuclear import .
Influenza A virus ( IAV ) , a single-stranded , negative-sense RNA virus with an eight-segmented genome , is the causative agent of influenza in many animal species , including humans . Inside the virion , all eight viral RNA ( vRNA ) segments bind to the three RNA polymerases ( polymerase basic protein 2 , PB2; polymerase basic protein 1 , PB1; and polymerase acidic protein , PA ) and are encapsidated by the nucleoprotein ( NP ) to form viral ribonucleoprotein ( vRNP ) complexes [1] . The vRNP complex is the essential functional unit for the transcription and replication of the IAV genome [2] . Electron microscopy of isolated vRNPs has shown that both ends of the vRNA interact with each other to form a circular or supercoiled structure and that the RNA polymerase interacts with both ends of the vRNA segment [2–4] . The rest of the vRNA is encapsidated by the NP protein with approximately 24 nucleotides per molecule [5] . A prominent feature of the IAV life cycle is that the transcription and replication of the viral genome occur in the nucleus of infected cells [6 , 7] . During the early phase of virus infection , after completion of endocytosis and uncoating , the vRNP complex is released into the cytoplasm and is translocated to the nucleus , which is mediated by the nuclear localization signals ( NLSs ) of the NP protein [8] . Two amino acid sequences have been identified as NLSs for the NP protein: an unconventional NLS in the N-terminus ( residues 3 to 13; NLS1 ) [9 , 10] , and a bipartite NLS ( residues 198 to 216; NLS2 ) [11] . The unconventional NLS appears to be the major determinant for NP nuclear import [12] . NP relies on the classical nuclear import pathway to enter the nucleus of infected cells . In this pathway , importin α functions as an adaptor by recognizing NLS sequences in cargo proteins and associating with the importin β receptor [13 , 14] . Through a process that involves multiple rounds of interaction between importin β and nucleoporins of the nuclear pore complex ( NPC ) , the trimeric importin α/β-cargo complex translocates into the nucleus [15] . NP interacts with various isoforms of importin α , including importin α-1 , -3 , -5 , and -7 [10 , 16 , 17] . Previous studies have shown that the nuclear import of vRNP and newly synthesized NP to the nucleus of infected cells is a crucial step in the IAV life cycle [12 , 18] . In addition to the central role played by importin α/β in modulating the nuclear transport of NP , host proteins could also be involved , such as α-actinin-4 , Hsp40 , or MOV10 , which may promote or inhibit this active process [17 , 19 , 20] . However , the detailed mechanism that regulates the migration of vRNP complexes and newly produced NP into the nucleus remains obscure , and the identification of the potential host factors involved is not yet complete . Phospholipid scramblase 1 ( PLSCR1 ) was first identified in erythrocyte membranes , where it was activated under conditions of elevated calcium , resulting in disruption of phospholipid asymmetry across the plasma membrane [21] . Its function in remodeling the distribution of plasma membrane phospholipids in mammalian cells is still controversial because increase in PLSCR1 expression and gene depletion of PLSCR1 can occur in response to calcium without affecting the transmembrane movement of phospholipids [22–24] . In addition to its unresolved role as a scramblase , PLSCR1 appears to be involved in multiple biological processes . Several studies have shown that PLSCR1 plays a critical role in cellular maturation and terminal differentiation: PLSCR1 expression is markedly increased during the terminal differentiation of the monocytic and granulocytic lineages of hematopoietic precursor cells [25–27] , and gene deletion of PLSCR1 in mice was found to impair the differentiation of hematopoietic precursor cells into mature granulocytes in response to select hematopoietic growth factors [24] . Although initially identified as a transmembrane protein , PLSCR1 also contains a nonclassical NLS and can be imported into the cell nucleus [28] . The nucleus-localized PLSCR1 can directly bind to the promoter region of the inositol 1 , 4 , 5-triphosphate receptor type 1 gene ( IP3R1 ) to enhance its expression [29 , 30] , and can also interact with angiogenin ( ANG ) in the nucleus to positively regulate rRNA transcription [31] . Another important function of PLSCR1 is as an effector of the interferon ( IFN ) signaling pathway . PLSCR1 interacts with Toll-like receptor 9 ( TLR9 ) and regulates its trafficking from the endoplasmic reticulum ( ER ) to the endosomal compartment in plasmacytoid dendritic cells ( pDCs ) [32] , which is an important step in IFN production in pDCs . PLSCR1 harbors an IFN-stimulated response element in its first exon [23] , is induced by IFN-α , -β , and -γ [23 , 33 , 34] , and can enhance the expression of a subset of IFN-stimulated genes ( ISGs ) in response to IFN-β treatment to inhibit the replication of vesicular stomatitis virus ( VSV ) and encephalomyocarditis virus ( EMCV ) [33] . PLSCR1 mediates IFN-α-induced protection against staphylococcal α-toxin [35] , is a main effector of IFN-γ-mediated antiviral activity against Hepatitis C virus ( HCV ) [34] , and can also inhibit the replication of Hepatitis B virus ( HBV ) and human T-cell leukemia virus type-1 ( HTLV-1 ) [36 , 37] . In the present study , we discovered that the interaction between IAV NP and cellular PLSCR1 occurs in both transfected and infected mammalian cells . Importantly , overexpression of PLSCR1 significantly suppressed IAV replication , whereas siRNA knockdown or CRISPR/Cas9 knockout of PLSCR1 expression increased the virus titer , thereby demonstrating that PLSCR1 is a host restriction factor for IAV infection . We further found that PLSCR1 inhibited NP nuclear import and caused retardation of the virus life cycle . Strikingly , PLSCR1 formed an integrative complex with NP and different members of the importin α family , which inhibited the incorporation of importin β into the complex and impaired the import of NP via the nuclear import pathway .
To identify host cellular proteins that interact with influenza virus NP protein , we employed the yeast two-hybrid system to screen a cDNA library generated from a mixed human cell culture ( A549 , HEK293T , THP-1 , and U251 ) as described previously [38] . The full-length NP protein from A/Anhui/2/2005 ( AH05 , H5N1 ) was used as bait . Putative positive clones were obtained after selection on QDO/X/A ( Ade/–His/–Leu/–Trp/X-a-Gal/AbA ) plates . After growing the putative positive clones in DDO ( SD/−Leu/−Trp ) medium , plasmids were isolated and sequenced to identify the potential NP interactants . One specific clone from this screen was found to contain the full-length open reading frame of PLSCR1 ( GenBank accession no . NM_021105 ) . The interaction between PLSCR1 and NP was then retested by yeast co-transformation , as described in the Materials and Methods . As shown in Fig 1 , PLSCR1 specifically interacted with NP in yeast . To further examine the PLSCR1-NP interaction , we performed co-IP experiments . HEK293T cells were transfected with V5-tagged WSN NP and Flag-tagged PLSCR1 , individually or in combination . Cell lysates were immunoprecipitated with an anti-V5 mAb , followed by western blotting with rabbit pAb against V5 or the Flag tag ( Fig 2A ) . Flag-tagged PLSCR1 was coimmunoprecipitated with V5-tagged NP of A/WSN/33 ( WSN , H1N1 ) virus when they were coexpressed , but not in the absence of WSN NP , indicating that PLSCR1 interacts with influenza NP in mammalian cells . When a reverse co-IP experiment was performed with an anti-Flag mAb , V5-tagged WSN NP was also coimmunoprecipitated with Flag-tagged PLSCR1 ( Fig 2B ) , further demonstrating the specificity of the NP-PLSCR1 interaction . The PLSCR1-NP interaction was also confirmed in a GST pull-down assay . WSN NP was pulled down by GST-PLSCR1 , but not by GST alone ( right panel , Fig 2C ) . Similarly , PLSCR1 was only pulled down by GST-WSNNP ( right panel , Fig 2D ) . A key function of influenza NP protein during the virus life cycle is to encapsidate viral RNA to form the vRNP complex in preparation for transcription , replication , and packaging [39] . To examine whether the interaction between PLSCR1 and NP is dependent on the RNA-binding activity of NP , we performed a co-IP assay with cell lysates that were first treated with 100 μl of RNase A/T1 ( Fig 2E ) . Flag-PLSCR1 was still coimmunoprecipitated with V5-WSNNP , indicating that the interaction between PLSCR1 and NP did not rely on the RNA binding activity of NP . We performed an additional co-IP experiment in A549 cells that were mock infected or infected with WSN virus at an MOI of 5 . At 6 h post infection ( p . i . ) , cell lysates were immunoprecipitated with a rabbit pAb against PLSCR1 , followed by western blotting with a rabbit anti-PLSCR1 pAb for the detection of PLSCR1 and a mouse anti-NP mAb to reveal the presence of NP ( Fig 2F ) . The results showed that WSN NP interacted with PLSCR1 during the natural viral infection . We then attempted to define the region of NP that was critical for its binding with PLSCR1 . We generated five truncated NP constructs ( NP1-80 , NP1-162 , NP1-271 , NP1-351 , and NP268-498 ) , which were fused to the C-terminus of GST , and then examined their interaction with PLSCR1 in HEK293T cells . We found that all five truncated versions of NP were well expressed , although there were differences in their expression levels ( Fig 2G ) . The pull-down assay showed that NP1-271 and NP1-351 exhibited strong binding to PLSCR1 . In contrast , the two short N-terminal NP mutants , NP1-80 and NP1-162 , almost lost their ability to interact with PLSCR1 . Further , the interaction between the C-terminal NP mutant , NP268-498 , and PLSCR1 was also dramatically decreased compared with that of NP1-271 and NP1-351 . These results indicate that neither the N-terminal nor the C-terminal region of NP is critical for its interaction with PLSCR1; rather , the middle region of NP is likely involved in the interaction with PLSCR1 . To study the role of the PLSCR1-NP interaction during the virus life cycle , we analyzed the effect of upregulating PLSCR1 on virus replication . We transduced A549 cells with a retrovirus encoding PLSCR1 to establish a stable PLSCR1-overexpressing cell line or with an empty retrovirus as a control cell line . As expected , PLSCR1 expression at both the mRNA and protein level was increased in PLSCR1-overexpressing cells compared with the empty retrovirus-transduced control cells ( Fig 3A and 3B ) . The control and PLSCR1-overexpressing A549 cells were infected with WSN virus at an MOI of 0 . 1 . Culture supernatants were collected at different timepoints after infection and titrated on MDCK cells . Strikingly , PLSCR1 overexpression led to a 20- to 100-fold decrease in virus titers at 12–48 h p . i . ( Fig 3C ) . Similar reductions in virus titers were observed for influenza viruses AH05 ( H5N1 ) ( Fig 3D ) , A/Anhui/1/2013 ( H7N9 ) ( Fig 3E ) and A/Fuzhou/1/2009 ( H1N1 ) ( Fig 3F ) at both 24 h and 48 h p . i . In a separate experiment , we examined NP and PLSCR1 expression in WSN virus-infected cells at timepoints between 0 and 48 h p . i . ( Fig 3G ) . In control A549 cells , viral NP expression was abundant at 12 h p . i . , and remained high until 48 h . In clear contrast , less NP was detected at 24 h p . i . , with somewhat more detected at 48 h p . i . in PLSCR1-overexpressing A549 cells . Moreover , the expression of PLSCR1 remained unchanged in virus-infected PLSCR1-overexpressing A549 cells , whereas in control A549 cells , the expression of PLSCR1 was upregulated at 6 h p . i . , increased at 12 h p . i . , and remained elevated at 48 h p . i . Together , these data indicate that endogenous expression of PLSCR1 is strongly induced by influenza virus infection , and stable overexpression of PLSCR1 significantly inhibits NP expression and virus replication . We further analyzed the effect of PLSCR1 downregulation on IAV infection by means of small interfering RNA ( siRNA ) -mediated silencing . Real-time PCR and western blotting confirmed that the expression of PLSCR1 was significantly reduced in specific siRNA-treated A549 cells but not in cells treated with scrambled siRNA ( Fig 3H and 3I ) . PLSCR1 downregulation had no major effect on cell viability as measured by a luminescent cell viability assay ( Fig 3J ) . A549 cells treated with siRNA targeting PLSCR1 or with scrambled siRNA were infected with WSN virus . Culture supernatants were collected at 24 and 48 h p . i . and titrated on MDCK cells . As shown in Fig 3K , knockdown of PLSCR1 by specific siRNA increased the virus titer compared with that in scrambled siRNA-treated A549 cells . We further generated a PLSCR1-KO HEK293T cell line by using the CRISPR/Cas9 system . The knockout of PLSCR1 was confirmed by western blotting with a rabbit anti-PLSCR1 pAb ( Fig 3L ) . The PLSCR1-KO HEK293T or control cells were infected with WSN virus at an MOI of 0 . 1 , and the supernatants collected at 24 and 48 h p . i . were titrated on MDCK cells . As shown in Fig 3M , the titers of WSN virus in PLSCR1-KO HEK293T cells were dramatically increased compared with those of the control cells . Together , these data demonstrate that PLSCR1 negatively regulates IAV replication via its interaction with NP . The cellular distribution of NP during the virus life cycle was investigated by using a time-course experiment in both PLSCR1-overexpressing and empty retrovirus-transduced control A549 cells infected with WSN virus at an MOI of 5 . In the control A549 cells , NP had clearly accumulated in the nucleus of approximately 45% of cells at 4 h p . i ( Fig 4A and 4C ) . By 6 h p . i . , the percentage of cells with NP in the nucleus had increased to 54% . In addition , NP localized at both the edge of nucleus and the cytoplasm of 25% of the infected cells , an indication of vRNP export from the nucleus . At 8 h p . i . , the distribution of NP was mixed , with approximately 37% , 32% , and 14% of cells showing clear nuclear localization , simultaneous localization at both the edge of the nucleus and the cytoplasm , and exclusive cytoplasmic distribution , respectively . At 10 h p . i . , the newly synthesized vRNP complex was largely exported from the nucleus into the cytoplasm , as indicated by the cytoplasmic distribution of NP in 60% of the cells . NP was also primarily localized close to the cytoplasmic membrane in 15% of the cells . By 12 h p . i . , the percentage of cells with NP distributed close to the cytoplasmic membrane was 90% , indicating that vRNP export was largely complete and active assembly and budding were underway . The endogenous PLSCR1 was predominantly localized in the cytoplasm of the control A549 cells throughout the observation period from 4 to 12 h p . i ( Fig 4A ) . The colocalization of NP and PLSCR1 appeared in cells with obvious cytoplasmic distribution of NP at 8 h p . i . At 10 and 12 h p . i . , a large amount of newly synthesized vRNP complex was visualized in the cytoplasm or close to the cytoplasmic membrane , where obvious colocalization of NP and PLSCR1 was observed . In comparison with the control A549 cells , the virus life cycle was significantly delayed in the PLSCR1-overexpressing A549 cells ( Fig 4B and 4C ) . At 4 h p . i . , NP did not accumulate in the nucleus of any of the visualized cells , suggesting that the nuclear import of the vRNP was inhibited by the overexpressed PLSCR1 . At 6 h p . i . , only approximately 6% of cells showed clear nuclear accumulation of NP . At 8 h p . i . , NP had clearly accumulated in the nucleus of approximately 16% of cells , and roughly 3% of cells had NP at both the edge of the nucleus and the cytoplasm . At 10 h p . i . , NP showed clear nuclear localization , simultaneous localization at both the edge of the nucleus and the cytoplasm , and exclusive cytoplasmic distribution in 25% , 5% , and 2% of cells , respectively . At 12 h p . i . , the NP distribution pattern was similar to that at 10 h p . i . except that the percentages of cells showing clear nuclear localization , simultaneous localization at both the edge of the nucleus and the cytoplasm , and exclusive cytoplasmic distribution were further increased to 31% , 15% , and 6% respectively . As in the control A549 cells , PLSCR1 was almost exclusively localized in the cytoplasm of the PLSCR1-overexpressing cells ( Fig 4B ) . In addition , co-localization of NP and PLSCR1 was detected in the cytoplasm of cells exhibiting considerable vRNP export at 10 and 12 h p . i . Taken together , these data demonstrate that overexpression of PLSCR1 significantly inhibits the nuclear import of the vRNP complex , and causes dramatic retardation of the virus life cycle . At the early timepoints ( i . e . , 4 and 6 h p . i . in control cells , and 4 , 6 , and 8 h p . i . in PLSCR1-overexpressing cells ) , co-localization of NP and PLSCR1 was not observed in the cytoplasm , most likely because of the relatively low abundance of the vRNPs in the cytoplasm prior to their import into the nucleus . We attempted to determine whether the interaction between NP and PLSCR1 could directly inhibit the import of NP into the nucleus . To this end , we transfected A549 cells with a pCAGGS-WSNNP construct together with either pCAGGS-PLSCR1 or the empty vector . The localization of NP and PLSCR1 was visualized at 20 h post-transfection . As shown in Fig 4D , NP clearly accumulated in the nucleus of cells without exogenous PLSCR1 expression . In contrast , NP was predominantly retained in the cytoplasm and colocalized with PLSCR1 when PLSCR1 was substantially overexpressed . These results further confirm that PLSCR1 inhibits the import of NP into the nucleus . We next validated the inhibitory effect of PLSCR1 on the nuclear import of NP with a cell fractionation experiment . The PLSCR1-overexpressing or empty retrovirus-transduced control A549 cells were infected with WSN virus at an MOI of 5 . At 6 h p . i . , the infected cells were lysed . The cytoplasmic and nuclear fractions were separated and subjected to western blotting . As shown in Fig 4E , the marker proteins GAPDH and LaminB1 were only detected in the cytoplasm and nucleus , respectively . PLSCR1 almost exclusively localized in the cytoplasm in both PLSCR1-overexpressing and control cells . A considerable amount of NP was detected in both the nucleus and the cytoplasm of the control cells . In contrast , NP was primarily detected in the cytoplasm and was only weakly detected in the nucleus of the PLSCR1-overexpressing cells . We further investigated the inhibitory role of PLSCR1 on the nuclear import of incoming vRNPs by treating PLSCR1-overexpressing A549 cells or control A549 cells with cycloheximide ( CHX ) to inhibit protein synthesis . The treated cells were infected with WSN virus at an MOI of 5 , and were separated into nuclear and cytoplasmic fractions at 2 h p . i . , followed by western blotting to detect the NP in the nuclear and cytoplasmic fractions . We found that most of the NP was detected in the nucleus of the control A549 cells; however , in PLSCR1-overexpressing A549 cells , most of the NP was detected in the cytoplasm and NP was only weakly detected in the nucleus ( Fig 4F ) . Since the only source of NP protein was from the incoming vRNPs under CHX treatment , this experiment demonstrates that PLSCR1 directly inhibits the nuclear import of incoming vRNPs . Collectively , these results demonstrate that the expression of PLSCR1 suppresses the nuclear accumulation of NP/vRNP , thus inhibiting the virus life cycle . We hypothesized that viral RNA transcription and replication would be impaired due to the retention of vRNP and NP in the cytoplasm caused by PLSCR1 expression . To test this hypothesis , we transfected HEK293T cells with specific siRNA targeting PLSCR1 or with scrambled siRNA for 48 h . Western blotting analysis showed that specific siRNA treatment indeed downregulated the expression of PLSCR1 ( Fig 5A ) . The siRNA-treated cells were then transfected with protein expression constructs of the RNP complex proteins ( PB2 , PB1 , PA , and NP ) , along with a reporter plasmid containing the terminal coding and noncoding sequences from the NS segment and the luciferase gene driven by the human RNA polymerase I promoter and terminator . Forty-eight hours later , the luciferase activity of the cell lysates was measured to reveal the RNP activity . We found that the RNP activity was increased by approximately 16-fold when the expression of PLSCR1 was knocked down by specific siRNA compared with that in scrambled siRNA-treated cells ( Fig 5B ) , indicating that the endogenous PLSCR1 inhibited the transcription and replication of the viral genome . To further determine the steps of viral transcription and replication that were affected by PLSCR1 expression , we infected the PLSCR1-overexpressing and empty retrovirus-transduced control A549 cells with WSN virus at an MOI of 5 . At 6 and 10 h p . i . , vRNA , mRNA , and cRNA derived from segment 5 were measured by quantitative reverse transcription PCR ( RT-qPCR ) . At both timepoints , the levels of all three species of viral RNA were found to be significantly decreased in the PLSCR1-overexpressing cells compared with those in the control cells ( Fig 5C and 5D ) . Among the three species of viral RNA , the reduction in the vRNA level was the most sizeable . This finding could indicate that vRNA synthesis occurred after the synthesis of the mRNA and cRNA , and reductions in the synthesis of mRNA and cRNA would lead to an accumulative defect in the amplification of the vRNA species . The nuclear import of influenza NP protein can be regulated via its phosphorylation status [9 , 40] . We therefore determined whether the effect of PLSCR1 on the import of NP is achieved by modulating NP phosphorylation . We infected either PLSCR1-overexpressing or empty retrovirus-transduced control A549 cells with WSN virus at an MOI of 5 . At 6 and 8 h p . i . , the NP and PLSCR1 expression levels in the infected cells were determined by western blotting ( Fig 6A ) . In PLSCR1-overexpressing cells , the level of PLSCR1 remained relatively constant between the two timepoints , whereas the expression of viral NP protein was increased at 8 h compared with that at 6 h p . i . In contrast , in control A549 cells , the increase in PLSCR1 expression was more obvious than that of NP between the two timepoints . We then performed an immunoprecipitation experiment by using an anti-NP mAb to reveal the level of total NP , an anti-p-Ser mAb to determine the level of serine-phosphorylated NP , and an anti-p-Tyr mAb to detect the level of tyrosine-phosphorylated NP . At 6 h p . i . , NP was clearly serine- and tyrosine-phosphorylated in control cells , and the extent of NP phosphorylation was further increased at 8 h p . i . In PLSCR1-overexpressing cells , NP phosphorylation was barely detectable at 6 h p . i . , the timepoint when the expression of total NP was only weakly detected . In contrast , a considerable amount of NP was phosphorylated at 8 h p . i . In general , NP was less phosphorylated at both timepoints in the PLSCR1-overexpressing cells compared with the control cells . However , the proportion of phosphorylated NP of the total NP , as indicated at 8 h p . i . , was similar between the PLSCR1-overexpressing cells and the control cells . These data indicate that overexpression of PLSCR1 does not affect the phosphorylation status of the viral NP protein . Two phosphorylation sites in NP , S9 and Y10 , are highly conserved among all influenza A viruses [41] . Mutations that abolish these two sites have been shown to significantly reduce virus replication [40 , 41] . Here , we examined the growth properties of a phosphorylation mutant , S9A/Y10F , in PLSCR1-overexpressing A549 cells . As shown in Fig 6B , we observed an additive effect of the inhibitory role of PLSCR1 overexpression and that of mutation of key NP phosphorylation sites on virus replication . The replication of wild-type WSN virus was significantly inhibited in PLSCR1-overexpressing A549 cells compared with control cells ( Fig 6B ) . Moreover , the replication of the phosphorylation mutant S9A/Y10F was decreased further in PLSCR1-overexpressing cells than in control cells . These results indicate that regardless of the phosphorylation status of the NP residues S9 and Y10 , PLSCR1 overexpression consistently reduced virus replication , implying that the inhibitory role of PLSCR1 on virus replication is not played by influencing the phosphorylation status of NP . PLSCR1 can potentiate the antiviral activity of IFN when exogenous IFN is present [33] . We therefore investigated the possibility that PLSCR1 indirectly inhibits virus infection by stimulating the IFN pathway . To this end , we measured the protein level of Mx1 , a key antiviral effector protein of the IFN pathway , in both PLSCR1-overexpressing A549 cells and control A549 cells . As shown in Fig 6C , Mx1 expression was not detectable in either PLSCR1-overexpressing cells or control cells when they were not treated with IFN-α . In contrast , IFN-α treatment efficiently induced the expression of Mx1 protein in both types of cells . However , no difference in the level of Mx1 expression was observed between PLSCR1-overexpressing cells and control cells when they were treated with IFN-α . Therefore , the overexpression of PLSCR1 did not result in observable changes in the expression of Mx1 relative to the control cells . We also determined the luciferase activity of HEK293T cells transfected with an ISRE luciferase reporter gene , together with a PLSCR1 expression construct or an empty vector ( Fig 6D ) . We found that the overexpression of PLSCR1 did not increase the expression of the ISRE luciferase reporter gene compared with that of the PLSCR1-non-overexpressing cells . Together , these results demonstrate that the inhibitory role of PLSCR1 in influenza virus replication does not involve stimulating the IFN pathways . Importin α plays an important role in the nuclear import of proteins [42] . PLSCR1 has been reported to interact with importin α [28] . We therefore attempted to determine whether the interaction between PLSCR1 and NP interferes with the complex formation between NP and importin α , thus preventing the nuclear import of NP through the classical nuclear import pathway . To this end , we transfected HEK293T cells with V5-tagged NP and Myc-tagged importin α proteins , together with gradually increasing amounts of Flag-tagged PLSCR1 construct . At 48 h post-transfection , cell lysates were immunoprecipitated with an anti-Myc mAb , followed by western blotting with rabbit pAbs against Myc , V5 , or the Flag tag to detect importin α , NP , and PLSCR1 , respectively . As shown in Fig 7A , V5-tagged NP was coimmunoprecipitated with Myc-tagged importin α1 when they were coexpressed , but not in the absence of Myc-tagged importin α1 , indicating that NP interacts with importin α1 in mammalian cells . When increasing amounts of Flag-tagged PLSCR1 construct were co-transfected with V5-tagged NP and Myc-tagged importin α1 , the expression level of PLSCR1 also gradually increased in the cell lysates . More PLSCR1 was detected in the importin α1 immunoprecipitates as the amount of transfected PLSCR1 construct increased from 0 . 2 to 0 . 6 μg . Significantly , V5-tagged NP and Flag-tagged PLSCR1 were simultaneously co-immunoprecipitated with Myc-tagged importin α1 , and the amount of NP coimmunoprecipitated with importin α1 was not reduced when the amount of transfected PLSCR1 was increased . This result indicates that the presence of PLSCR1 did not affect the formation of the complex between NP and importin α1; rather , a trimeric complex of PLSCR1 , NP , and importin α1 was formed . We then performed similar co-IP experiments with PLSCR1 , NP , and other members of the importin α family . V5-tagged NP and Flag-tagged PLSCR1 were coimmunoprecipitated with Myc-tagged importin α3 ( Fig 7B ) , importin α5 ( Fig 7C ) , or importin α7 ( Fig 7D ) , and the gradually increased expression of PLSCR1 did not reduce the interaction between NP and these members of the importin α family . To validate this finding , we included the host factor MOV10 as a control in the co-IP experiment , because MOV10 has been shown to compete with importin α to interact with NP [17] . We found that co-expression of MOV10 reduced the amount of NP coimmunoprecipitated with importin α1 , indicating that MOV10 indeed inhibited the interaction between importin α and NP ( Fig 7E ) . In contrast , the expression of PLSCR1 did not affect the binding between importin α and NP . These data clearly indicated that the NP protein bound by MOV10 was no longer bound by importin α , but the NP protein bound by PLSCR1 could still bind to importin α . Taken together , these results demonstrate that PLSCR1 forms an integrative three-subunit complex with NP and importin α . Thus , our results demonstrated that PLSCR1 forms a complex with NP and importin α and causes cytoplasmic retention of NP . We then performed another co-IP experiment to further reveal the underlying mechanism . HEK293T cells were transfected with plasmids expressing V5-WSNNP , Myc-importin α1 , importin β , or together with Flag-PLSCR1 . The cell lysates were immunoprecipitated with a mouse anti-Myc mAb , and the bound proteins were detected by western blotting with rabbit pAb against V5 , Myc , Flag tag , or importin β . Strikingly , PLSCR1 expression significantly reduced the amount of importin β in the immunoprecipitates ( Fig 7F ) , indicating that the formation of the complex of PLSCR1 , NP , and importin α1 blocked the access of importin β , the key mediator of the classical nuclear import pathway , to the complex , thereby inhibiting the nuclear import of NP via the classical nuclear import pathway and suppressing virus replication .
The vRNP complex is responsible for the transcription and replication of the influenza viral genome in the nucleus of infected cells [16 , 18 , 43] . In addition to the three polymerase subunits with one copy of each , most of the vRNP complex is encapsidated by the viral NP protein [44 , 45] . As the most abundant protein in the virus particle , except for M1 [46] , NP inevitably becomes the main target of the host defense system . In this study , we identified the host cellular protein PLSCR1 as an interacting partner of the NP protein by using yeast two-hybrid screening . We demonstrated that NP and PLSCR1 interact in both transfected and infected mammalian cells . Western blotting analysis showed that PLSCR1 did not affect the phosphorylation status of NP . Instead , we found that PLSCR1 formed a complex with NP and members of the importin α family , inhibited nuclear import of vRNP/NP , and thereby suppressed virus genome transcription and replication and negatively regulated the propagation of different influenza virus subtypes . The active nuclear import of the vRNP complex is mediated by an interaction between NP and importin α through the classical nuclear import pathway [10 , 16 , 17] . Host factors are reported to be involved in this active process . One such factor , Hsp-40 , has been shown to be required for the efficient association between NP and importin α , thus promoting the nuclear localization of vRNP complex [20] . In contrast , MOV10 was found to disrupt the binding between NP and importin α , thereby causing the retention of NP in the cytoplasm and a reduction in virus replication [17] . In the present study , we found that the nuclear import of the vRNP complex was significantly retarded in virus-infected PLSCR1-overexpressing A549 cells compared with empty retrovirus-transduced control cells . Furthermore , NP clearly accumulated in the nucleus of cells that were not transfected with the PLSCR1 construct , whereas NP was predominantly retained in the cytoplasm and colocalized with PLSCR1 when PLSCR1 was significantly overexpressed by transfection . These results demonstrate that PLSCR1 inhibited the import of the NP/vRNP complex into the nucleus . Interestingly , we found that the inhibitory effect of PLSCR1 on the nuclear import of the NP/vRNP complex was not achieved by impairing the interaction between NP and importin α . Instead , NP , PLSCR1 , and importin α formed a stable complex , which inhibited the interaction between importin α and importin β . Taken together , our findings favor a model in which influenza virus NP , derived from the newly synthesized NP or incoming vRNP , is bound by the heterodimeric import receptor , importin α/importin β , in the cytoplasm and is transported into the nucleus; in the presence of PLSCR1 , the complex formed among NP , PLSCR1 , and importin α in the cytoplasm prevents importin α from forming a functional nuclear import receptor complex with importin β , thereby suppressing the nuclear import of NP ( Fig 8 ) . We speculate that the simultaneous binding of two molecules by importin α may overload this nuclear import adaptor , or alter its structural property , thereby affecting its ability to interact with importin β . As an IFN-inducible gene , PLSCR1 can enhance the antiviral activity of IFN by increasing the expression of IFN-stimulated genes [33] . In the presence of exogenous IFN-β , PLSCR1 has been shown to increase its antiviral activity against VSV and EMCV [33] . PLSCR1 has also been shown to inhibit HBV replication by reducing the synthesis of viral proteins , DNA replicative intermediates , and viral RNAs [36] . In addition to these indirect antiviral activities , PLSCR1 can directly bind to the Tax protein of HTLV-1 to reduce its transactivation activity by altering the subcellular distribution and homodimerization of Tax [37] . In the present study , we found that PLSCR1 directly binds to the NP protein of influenza virus and inhibits the nuclear import of NP/vRNP , thus demonstrating a new direct antiviral role for PLSCR1 . The localization of PLSCR1 is directly correlated with its function . Because it was initially identified as plasma membrane protein , its role in regulating the movement of plasma membrane phospholipids was intensively studied [22 , 47 , 48] . Moreover , cell surface-localized PLSCR1 can bind to the envelope proteins E1 and E2 of HCV and serve as an attachment factor for HCV entry [49] . As a result , downregulation of PLSCR1 expression inhibits HCV entry and infection . PLSCR1 can also be imported into the nucleus by the importin α/β import pathway [28] , where it can bind to genomic DNA or nuclear proteins to perform different functions [29 , 31 , 50] . Yet , we found that PLSCR1 was localized predominantly in the cytoplasm of both PLSCR1-overexpressing and empty retrovirus-transduced control A549 cells infected with influenza viruses . The accumulation of PLSCR1 in the cytoplasm would likely enable it to efficiently participate in interactions with both importin α and viral NP , and effectively inhibit the nuclear import of vRNP/NP and virus replication . Mutations of NP phosphorylation sites can reduce the binding affinity between NP and various members of the importin α family , resulting in the inhibition of nuclear import of NP and a reduction in virus replication [40] . In the present study , we found that the replication of influenza virus in PLSCR1-overexpressing A549 cells decreased the overall expression of viral NP protein . However , the extent of NP phosphorylation was similar between the PLSCR1-overexpressing cells and empty retrovirus-transduced control A549 cells . In addition , the replication of the NP phosphorylation mutant S9A/Y10F was further decreased in PLSCR1-overexpressing cells , thereby demonstrating an accumulative inhibitory effect on influenza virus propagation . Together , these results suggest that the effect of PLSCR1 on virus replication does not involve modulating the phosphorylation status of NP . In summary , here we demonstrated that PLSCR1 is an interacting partner of the influenza NP protein . This interaction appears to downregulate virus replication since overexpression of PLSCR1 resulted in a significant reduction in virus titer in cell cultures of different virus subtypes , whereas siRNA knockdown or CRISPR/Cas9 knockout of PLSCR1 expression increased virus replication . Importantly , PLSCR1 inhibited the nuclear import of vRNP/NP , thus causing retardation of the virus life cycle . Moreover , we revealed that the mechanism by which PLSCR1 regulates influenza virus replication involves the formation of a complex with viral NP and importin α , which inhibits the incorporation of importin β into the complex and suppresses the nuclear import of NP . Collectively , our data suggest that PLSCR1 is an important host restriction factor against influenza virus .
HEK293T ( ATCC CRL-3216 ) , A549 ( ATCC CCL-185 ) , and MDCK ( ATCC PTA-6500 ) cells were cultured in DMEM ( Life Technologies , Grand Island , NY ) containing 10% fetal bovine serum ( FBS , Sigma-Aldrich , St . Louis , MO ) , in F12K ( Life Technologies ) with 10% FBS , or in MEM ( Life Technologies ) containing 5% newborn calf serum ( NCS; Sigma-Aldrich ) , respectively . All media were supplemented with 100 units/ml penicillin and 100 μg/ml streptomycin ( Life Technologies ) . All cells were cultured at 37°C with 5% CO2 . A/Anhui/2/2005 ( AH05 , H5N1 ) and A/Anhui/1/2013 ( AH13 , H7N9 ) were grown in 10-day-old embryonated chicken eggs . A/WSN/33 ( WSN , H1N1 ) and A/Fuzhou/1/2009 ( FZ09 , H1N1 ) were propagated in MDCK cells cultured in MEM containing 0 . 3% bovine serum albumin ( BSA , Sigma-Aldrich ) and 0 . 5 μg/ml L-1-tosylamide-2-phenylmethyl chloromethyl ketone ( TPCK ) -treated trypsin ( Worthington , Lakewood , NJ ) . All experiments with H5N1 and H7N9 viruses were conducted within the enhanced animal biosafety level 3 ( ABSL3+ ) facility in the Harbin Veterinary Research Institute ( HVRI ) of the Chinese Academy of Agricultural Sciences ( CAAS ) , which is approved for such use by the Ministry of Agriculture of China and the China National Accreditation Service for Conformity Assessment . The yeast two-hybrid screen for protein-protein interactions was performed by using the matchmaker yeast two-hybrid system ( Clontech , Mountain View , CA ) as previously described [38] . NP of AH05 ( H5N1 ) was constructed in pGBKT7 , fused to the C-terminus of the GAL4-binding domain ( BD ) , and used as bait . cDNAs prepared from a mixed human cell culture comprising A549 , HEK293T , THP-1 ( ATCC TIB-202 ) , and U251 ( Type Culture Collection of the Chinese Academy of Sciences , Shanghai , China ) were cloned into pGADT7 , fused to the GAL4-activation domain ( AD ) , and used as prey . The yeast strain Y2HGold was transformed with the pGBKT7-AH05NP bait by using lithium acetate , and was then mated with the Y187 strain transformed with the pGADT7-based cDNA library . Transformants were selected on plates with synthetically defined medium lacking adenine , histidine , leucine , and tryptophan ( SD/–Ade/–His/–Leu/–Trp ) ( quadruple dropout medium , QDO ) . The recovered colonies were grown on QDO plates containing 5-bromo-4-chloro-3-indolyl-α-d-galactopyranoside ( X-α-Gal ) and aureobasidin A ( AbA ) ( SD/–Ade/–His/–Leu/–Trp/X-a-Gal/AbA , QDO/X/A ) . Blue colonies were selected and cultured in medium lacking leucine and tryptophan ( SD/−Leu/−Trp ) ( double dropout medium , DDO ) . Plasmids were purified and sequenced to identify the potential cellular interactants with NP . To eliminate false-positive interactions , the bait and prey plasmids were cotransformed into the Y2HGold strain . Cotransformation of pGADT7-T ( AD-T ) with pGBKT7-p53 ( BD-p53 ) into Y2HGold served as a positive control , and cotransformation of AD-T with pGBKT7-Lamin ( BD-Lam ) served as a negative control . Human PLSCR1 , importin β and the open reading frames ( ORFs ) of PB2 , PB1 , PA , and NP derived from WSN virus were cloned into the mammalian expression vector pCAGGS ( a gift from Dr . Yoshihiro Kawaoka , University of Wisconsin-Madison ) . GST-tagged PLSCR1 and WSN NP were constructed in pCAGGS with a GST tag at the N-terminus . Plasmids pCAGGS-V5-WSNNP , pCAGGS-Flag-PLSCR1 and pCAGGS-Flag-MOV10 were generated by inserting the ORF of WSN NP , PLSCR1 and MOV10 fused with the V5 or Flag tag sequence at the N-terminus into the pCAGGS vector . Truncation mutants of GST-tagged WSN NP were generated by using a PCR approach and were cloned into the pCAGGS vector . pQCXIN-PLSCR1 was constructed by inserting the PLSCR1 ORF into the pQCXIN vector ( Clontech ) . WSN NP S9A/Y10F , containing two mutations in the full-length NP gene , was generated by using a Fast Mutagenesis System ( Transgen , Beijing , China ) and was cloned into the pHH21 vector ( a gift from Dr . Yoshihiro Kawaoka , University of Wisconsin-Madison ) . pHH21-SC09NS F-Luc , used to produce negative-sense RNA containing 176 bases of the 3’ end of the NS vRNA derived from A/Sichuan/1/2009 ( SC09 , H1N1 ) , a firefly luciferase , a stop codon ( TAA ) , and 179 bases of the 5’ end of SC09NS vRNA , was constructed by using the PCR method . The full-length ORFs of human importin α1 , importin α3 , importin α5 , and importin α7 were cloned into pCAGGS with a Myc tag at the N-terminus . All plasmid constructs were confirmed by sequencing . Mouse anti-NP monoclonal antibody ( mAb ) and rabbit anti-NP polyclonal antibody ( pAb ) were prepared in our laboratory by using conventional methods . The following primary antibodies were purchased from commercial resources: rabbit anti-V5 pAb ( AB3792 , Merck Millipore , Darmstadt , Germany ) ; rabbit anti-GAPDH pAb ( 10494-1-AP ) , rabbit anti-importin β pAb ( 10077-1-AP ) , rabbit anti-LaminB1 pAb ( 12987-1-AP ) , rabbit anti-Mx1 pAb ( 13750-1-AP ) and rabbit anti-PLSCR1 pAb ( 11582-1-AP ) from Proteintech ( Wuhan , China ) ; mouse anti-actin mAb ( sc-47778 ) , mouse anti-p-Ser mAb ( sc-81514 ) and mouse anti-p-Tyr mAb ( sc-508 ) from Santa Cruz ( Dallas , TX ) ; mouse anti-Flag mAb ( F3165 ) , mouse anti-Myc mAb ( M4439 ) , mouse anti-V5 mAb ( V8012 ) , rabbit anti-Flag pAb ( F7425 ) and rabbit anti-Myc pAb ( C3965 ) from Sigma-Aldrich . The secondary antibodies used in the western blotting were DyLight 800 goat anti-mouse IgG ( H+L ) ( 072-07-18-06 ) and DyLight 800 goat anti-rabbit IgG ( H+L ) ( 072-07-15-06 ) , purchased from KPL ( Gaithersburg , MD ) ; the secondary antibodies used in the confocal microscopy were Alexa Fluor 488 donkey anti-rabbit IgG ( H+L ) ( A21206 ) and Alexa Fluor 633 goat anti-mouse IgG ( H+L ) ( A21050 ) obtained from Life Technologies . To examine the interaction of proteins in transfected cells , HEK293T cells were transfected with the indicated plasmids by using the Lipofectamine LTX and Plus Reagents ( Invitrogen , Carlsbad , CA ) . To determine the interaction of proteins during natural viral infection , A549 cells were mock infected with PBS or infected with WSN virus at an MOI of 5 . Cell lysates were prepared at 48 h post-transfection for transfected HEK293T cells or at 6 h p . i . for infected A549 cells . Briefly , the cells were washed twice with cold PBS and lysed with IP buffer ( 25 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 1% NP-40 , 1 mM EDTA , 5% glycerol; Pierce , Rockford , IL ) containing complete protease inhibitor cocktail ( Roche Diagnostics GmbH , Mannheim , Germany ) for 30 min on ice and then centrifuged at 12 , 000 rpm at 4°C for 10 min . The supernatants were mixed with the respective primary antibodies , rocked overnight at 4°C , mixed with Protein G-Agarose beads ( Roche ) and rock for 6–8 h . The beads were washed four times with wash buffer ( 25 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 1 mM PMSF ) . The bound proteins were then boiled in 2 × SDS sample buffer , separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) , and detected by western blotting . HEK293T cells grown in 10-cm dishes were individually transfected with 10 μg of each plasmid ( pCAGGS , pCAGGS-GST , pCAGGS-GST-PLSCR1 , pCAGGS-NP , pCAGGS-GST-NP , or pCAGGS-PLSCR1 ) by using the Lipofectamine LTX and Plus Reagents . At 48 h post-transfection , cells were solubilized with 0 . 8 ml of IP buffer . Then , 300 μl of the cleared lysates from cells transfected with pCAGGS-GST , pCAGGS-GST-PLSCR1 , or pCAGGS-GST-NP was mixed with 40 μl of Glutathione Sepharose 4 Fast Flow ( GE Healthcare , Pittsburgh , PA ) and rocked for 1 h at 4°C . After three washes with wash buffer , 300 μl of the cleared lysates from cells transfected with non-GST expressing constructs ( i . e . , pCAGGS , pCAGGS-NP , or pCAGGS-PLSCR1 ) was added and incubated for 2 h at 4°C . After three washes , the bound proteins were separated by SDS-PAGE . GST , GST-PLSCR1 , or GST-WSNNP proteins in the eluates were detected by Coomassie blue ( CB ) staining , and non-GST tagged NP and PLSCR1 proteins were detected by western blotting . Protein samples fractionated by SDS-PAGE were transferred onto nitrocellulose membranes ( GE Healthcare ) . Membranes blocked with 5% skim milk in PBST were incubated overnight at 4°C with appropriately diluted primary antibody in PBST containing 2% BSA . After incubation with DyLight 800 goat anti-mouse IgG ( H+L ) and DyLight 800 goat anti-rabbit IgG ( H+L ) , blots were visualized by using an Odyssey infrared imaging system ( Li-Cor BioSciences , Lincoln , NE ) . The AmphoPack-293 packaging cell line ( 631505 , Clontech ) cultured in 10-cm dishes was transfected with either retroviral construct pQCXIN-PLSCR1 or with the empty pQCXIN vector by using Lipofectamine LTX and Plus Reagents . At 48 h post-transfection , viral supernatants from the transfectants were collected and used to transduce A549 cells cultured in 6-well plates . Forty-eight hours later , the transduction was repeated to enrich for transductants . The confluent transduced cells were split and cultured in medium supplemented with 1000 μg/ml G418 for selection . The surviving cells were individually cloned in 96-well plates , propagated , and examined for PLSCR1 overexpression by quantitative reverse-transcription PCR ( RT-qPCR ) and western blotting . To study the effect of PLSCR1 overexpression on influenza virus replication , we used WSN ( H1N1 ) , AH05 ( H5N1 ) , AH13 ( H7N9 ) or FZ09 ( H1N1 ) to infect the PLSCR1-overexpressing cells or the empty retrovirus-transduced control A549 cells at an MOI of 0 . 1 . Supernatants were collected at the indicated timepoints after infection and virus titers were determined by means of plaque assays on MDCK cells [38] . siRNA targeting PLSCR1 ( 5’-GCGGAAGAUACUGAUUGCU-3’ ) or scrambled siRNA ( Genepharma , Shanghai , China ) at a concentration of 30 nM was transfected into A549 cells seeded in 12-well plates by using the Lipofectamine RNAiMAX transfection reagent ( Invitrogen ) . Forty-eight hours later , the knockdown efficiency was checked by means of RT-qPCR and western blotting . To study the effect of PLSCR1 knockdown on the growth of influenza virus , the WSN virus was used to infect siRNA-treated A549 cells at an MOI of 0 . 1 . Supernatants were collected at 24 and 48 h post-infection ( p . i . ) , and the virus titers were determined by means of plaque assays on MDCK cells . Cell viability was determined by using the CellTiter-Glo kit ( Promega , Madison , WI ) as described previously [38] . Briefly , A549 cells seeded in opaque-walled 96-well plates were transfected with siRNA targeting PLSCR1 or with scrambled siRNA at a concentration of 30 nM . At 48 h post-transfection , 100 μl of CellTiter-Glo reagent was added directly into each well and incubated with the cells for 10 min on a shaker to induce cell lysis . The luminescence was measured with a GloMax 96 Microplate Luminometer ( Promega ) . PLSCR1-KO HEK293T cells were established using the CRISPR/Cas9 system . The PLSCR1 gene target sequence , 5’–CAGGATATAGTGGCTACCCT– 3’ ( to target exon 4 ) , was inserted into the guide RNA ( gRNA ) expression cassette of the pX330 vector [51] , which also contains an expression cassette of Cas9 . Six micrograms of the pX330 plasmid containing the PLSCR1 target sequence was then transfected into HEK293T cells with TransIT-LT1 ( Mirus , Madison , WI ) . The transfected cells were trypsinized 24 h later into single cells , which were diluted and inoculated into 96-well plates for colony formation . Each colony was individually propagated into 24 well-plates , and the knockout of PLSCR1 expression was confirmed by western blotting . The PLSCR1-KO HEK293T or control cells were infected with WSN virus at an MOI of 0 . 1 . Supernatants were collected at 24 and 48 h p . i . , and virus titers were determined by means of plaque assays on MDCK cells . A549 cells seeded in glass-bottom dishes were transfected with the indicated plasmids by using the Lipofectamine LTX and Plus Reagents . PLSCR1-overexpressing cells or empty retrovirus-transduced control A549 cells were infected with WSN virus at an MOI of 5 . At 20 h post-transfection or 4 , 6 , 8 , 10 , and 12 h p . i . , cells were fixed with 4% paraformaldehyde ( PFA ) in PBS for 1 h , and permeabilized with 0 . 5% Triton X-100 in PBS for 30 min . The permeabilized cells were blocked with 5% BSA in PBS for 1 h , and then incubated with primary antibodies ( mouse anti-NP mAb , 1:500; rabbit anti-PLSCR1 pAb , 1:1000 ) for 2 h . The cells were washed three times with PBS and incubated with the secondary antibodies ( Alexa Fluor 488 donkey anti-rabbit IgG ( H+L ) , 1:10000; and Alexa Fluor 633 goat anti-mouse IgG ( H+L ) , 1:10000 ) for 1 h . After four washes , the cells were incubated with DAPI ( 4’ , 6-diamidino-2-phenylindole , Thermo Fisher Scientific , Waltham , MA ) for 15 min to stain the nuclei . Images were acquired by using the Leica SP2 confocal system ( Leica Microsystems , Wetzlar , Germany ) . HEK293T cells were treated with either siRNA specifically targeting PLSCR1 or with scrambled siRNA ( 30 nM ) for 48 h , and were then cotransfected with the four protein expression plasmids of the RNP complex from WSN ( pCAGGS-PB2 , pCAGGS-PB1 , pCAGGS-PA , and pCAGGS-NP; 1 μg of each ) , the construct pHH21-SC09NS F-Luc ( 0 . 1 μg ) , and an internal control pRL-TK ( 0 . 1 μg ) . At 48 h post-transfection , cell lysates were prepared by using the dual luciferase reporter assay system ( Promega ) , and the luciferase activities were measured on a GloMax 96 microplate luminometer ( Promega ) . HEK293T cells grown in 24-well plates were transfected with the ISRE-Luc reporter plasmid ( 0 . 25 μg ) , pRL-TK control plasmid ( 0 . 02 μg ) , and the pCAGGS-PLSCR1 or empty pCAGGS plasmid ( 0 . 25 μg ) for 20 h . The luciferase activity of the transfected cells was determined by using the dual-luciferase reporter assay . PLSCR1-overexpressing cells or empty retrovirus-transduced control A549 cells grown in 12-well plates were left untreated or treated with 100 U/mL of IFN-α ( Sigma-Aldrich ) for 24 h . Cell lysates were then prepared and subjected to western blotting with a rabbit anti-Mx1 pAb to determine the expression level of Mx1 protein . The mutant WSN virus WSN NP S9A/Y10F , which possesses two mutations in the viral NP protein , was generated by use of reverse genetics as described previously ( 34 ) . Briefly , the eight plasmids for the synthesis of viral RNA ( vRNA ) and the four supporting plasmids to express the PB2 , PB1 , PA , and NP proteins were transfected into HEK293T cells with the Lipofectamine LTX and Plus Reagents . At 48 h post-transfection , the transfection supernatant was harvested and used to infect MDCK cells to produce stock viruses . To ensure that the mutant virus contained the desired mutation , vRNA was extracted from the stock viruses using a QIAmp viral RNA mini kit ( QIAGEN , Valencia , CA ) , reverse transcribed into cDNA with Superscript III reverse transcriptase ( Invitrogen ) , and amplified by PCR with gene-specific primers . The complete NP segment was sequenced by using an ABI 3500xL genetic analyzer ( Applied Biosystems , Carlsbad , CA ) . The PLSCR1-overexpressing or empty retrovirus-transduced control A549 cells grown in 12-well plates were infected with wild-type WSN virus or the NP mutant , WSN NP S9A/Y10F , at an MOI of 0 . 1 . The cells were incubated with F-12K medium containing 0 . 3% BSA at 37°C . Virus-containing supernatant was harvested at the indicated timepoints and was subjected to plaque assays on MDCK cells to determine the virus titer . To quantify the level of PLSCR1 mRNA , total RNA was extracted from PLSCR1-overexpressing A549 cells or siRNA-treated A549 cells at 48 h post-transfection by using an RNeasy kit ( QIAGEN ) . The first-strand cDNA was generated with oligo ( dT ) primer using Superscript III reverse transcriptase . Real-time PCR was conducted using SYBR premix Ex Taq II ( TaKaRa , Dalian , China ) and 0 . 4 μM PLSCR1 primers according to the manufacturer’s instructions . Relative RNA quantities were determined by using the comparative cycle-threshold method , with cellular GAPDH serving as the endogenous reference and empty retrovirus-transduced A549 control cells or scrambled siRNA-treated cells serving as the control . The PLSCR1-overexpressing or empty retrovirus-transduced control A549 cells grown in 6-well plates were infected with WSN virus at an MOI of 5 . Total RNA was extracted by using an RNeasy kit at 6 h and 10 h p . i . Relative quantities of viral NP genomic RNA ( vRNA ) , complementary RNA ( cRNA ) and mRNA were determined by qRT-PCR as described previously [52] . Relative RNA quantities were determined with GAPDH serving as the endogenous reference . The PLSCR1-overexpressing or empty retrovirus-transduced control A549 cells grown in 6-well plates were infected with WSN virus at an MOI of 5 . At 6 h p . i . , the cells were separated into nuclear and cytoplasmic fractions by using NE-PER Nuclear and Cytoplasmic Extraction Reagents ( Pierce ) according to the manufacturer’s procedure . The amount of NP and PLSCR1 in each fraction was determined by western blotting with a rabbit anti-NP pAb and a rabbit anti-PLSCR1 pAb , respectively . LaminB1 and GAPDH , nuclear and cytoplasmic fraction markers , respectively , were detected by western blotting with a rabbit anti-GAPDH pAb and a rabbit anti-LaminB1 pAb , respectively . In another experiment , PLSCR1-overexpressing or control A549 cells grown in 6-well plates were pretreated with 50 μg/mL CHX ( Sigma-Aldrich ) for 1 h , and then infected with WSN virus at an MOI of 5 . The virus-infected cells were maintained in culture medium containing CHX for 2 h , and were then subjected to cell fractionation and western blotting as described above . Unless otherwise indicated , all experiments were performed at least three times; data from representative experiments are shown . Data were statistically analyzed by using the Student’s t test . A mean difference was considered statistically significant if the P value was < 0 . 05 . | Influenza viral RNA is encapsidated by three polymerase proteins and the NP protein to form the vRNP complex , which is transported to the nucleus of infected cells for viral transcription and replication . The active nuclear import of the vRNP complex is mediated by the interaction between NP and importin α through the nuclear import pathway . Because the interactions between NP and the components of the nuclear import pathway are indispensable in mediating the nuclear import of the vRNP complex , the host has evolved mechanisms to antagonize influenza virus infection that target this crucial step . In this study , we identified PLSCR1 as an interacting partner of the influenza NP protein . We found that PLSCR1 negatively regulates influenza virus replication by inhibiting the nuclear import of the NP/vRNP complex . Importantly , we found that PLSCR1 did not disrupt the interaction between NP and importin α . Instead , NP , PLSCR1 , and importin α formed a stable complex that blocked the interaction between importin α and importin β , thereby inhibiting the import of NP/vRNP complex through the nuclear import pathway . Our findings provide an example of a host restriction factor binding simultaneously to a nuclear import adaptor and to a cargo protein to inhibit the import of that cargo into the nucleus . |
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Several critical events dictate the successful establishment of nascent vasculature in yolk sac and in the developing embryos . These include aggregation of angioblasts to form the primitive vascular plexus , followed by the proliferation , differentiation , migration , and coalescence of endothelial cells . Although transforming growth factor–β ( TGF-β ) is known to regulate various aspects of vascular development , the signaling mechanism of TGF-β remains unclear . Here we show that homeodomain interacting protein kinases , HIPK1 and HIPK2 , are transcriptional corepressors that regulate TGF-β–dependent angiogenesis during embryonic development . Loss of HIPK1 and HIPK2 leads to marked up-regulations of several potent angiogenic genes , including Mmp10 and Vegf , which result in excessive endothelial proliferation and poor adherens junction formation . This robust phenotype can be recapitulated by siRNA knockdown of Hipk1 and Hipk2 in human umbilical vein endothelial cells , as well as in endothelial cell-specific TGF-β type II receptor ( TβRII ) conditional mutants . The effects of HIPK proteins are mediated through its interaction with MEF2C , and this interaction can be further enhanced by TGF-β in a TAK1-dependent manner . Remarkably , TGF-β-TAK1 signaling activates HIPK2 by phosphorylating a highly conserved tyrosine residue Y-361 within the kinase domain . Point mutation in this tyrosine completely eliminates the effect of HIPK2 as a transcriptional corepressor in luciferase assays . Our results reveal a previously unrecognized role of HIPK proteins in connecting TGF-β signaling pathway with the transcriptional programs critical for angiogenesis in early embryonic development .
Vascular morphogenesis is controlled by an intricate interplay of extrinsic factors and their downstream signaling mechanisms [1] , [2] . At the early stage of vascular development , several critical events dictate the successful establishment of nascent vasculature in yolk sac and in the developing embryos . These include aggregation of angioblasts to form the primitive vascular plexus , followed by the proliferation , differentiation , migration , and coalescence of endothelial cells [2] , [3] . Subsequently , branching morphogenesis and arteriovenous specification further facilitate the maturation of an interconnecting and fully functional network of blood vessels to provide nutrients to the entire organism [4] . Many of the mechanisms that govern the normal vascular development can also be recapitulated in angiogenesis that occurs during disease conditions , including tumorigenesis , metastasis , stroke , and tissue repair after injury [1] , [5] . Transforming growth factor–β ( TGF-β ) represents a family of highly conserved cytokines that have profound effects in regulating epithelial–mesenchymal transition ( EMT ) , vascular morphogenesis , and cellular and organismal functions during development and in disease conditions [6]–[8] . Indeed , genetic analyses in mouse and human have shown that mutations involving components of the TGF-β signaling pathway affect many aspects of vascular morphogenesis during development and in adult life [9] . For instance , loss-of-function analyses of TGF-β1 , TGF-β type I receptor ALK1 or ALK5 , or TGF-β type II receptor ( TβRII ) in mouse reveal a distinct role of each of these signaling components in regulating the proliferation , differentiation , and survival of endothelial cells and smooth muscle cells . These analyses further indicate that the outcome of the deletion involving different components of the TGF-β signaling pathway can be cell context-dependent . Furthermore , the timing of targeted deletion and the presence of genetic modifiers can also affect the phenotypic manifestations [7] . With respect to the roles of TGF-β signaling in endothelial functions , TGF-β type I receptors ALK1 and ALK5 have been shown to have opposite effects , with ALK1 contributing to the proliferation and migration of endothelial cells and ALK5 inducing the maturation of blood vessels [10] , [11] . While the underlying mechanisms for distinct effects of ALK1 and ALK5 are still unclear , it is possible that the signaling downstream of the TGF-β type I receptors may diverge due to the involvement of Smad and non-Smad-dependent mechanisms that regulate the transcription of angiogenesis-related genes [12] . Homeodomain interacting protein kinase 2 ( HIPK2 ) is a transcriptional cofactor in the downstream of TGF-β/BMP signaling pathway [13]–[17] . Interestingly , loss of HIPK2 reduces cellular responses to TGF-β during neuronal development and in mouse models of renal fibrosis [13] , [17] . While mice lacking HIPK1 show no detectable defects [18] , simultaneous loss of HIPK1 and HIPK2 leads to severe growth retardation and early embryonic lethality [19] , [20] . Although the study by Aikawa and colleagues has implicated vascular defects in Hipk1−/−;Hipk2−/− double mutants [20] , the detailed mechanism responsible for the phenotypes remains unclear . It is also unclear if HIPK1 and HIPK2 can cooperatively regulate TGF-β signaling and thereby contribute to the angiogenesis during early embryonic development . Here , we show that HIPK1 and HIPK2 cooperatively suppress the expression of angiogenic genes that are critical for endothelial proliferation and adherens junction formation . Loss of HIPK1 and HIPK2 leads to a marked up-regulation of VEGF and MMP10 , and early embryonic lethality due to excessive proliferation and poor adherens junction formation in the endothelial cells . Consistent with these results , siRNA knockdown of Hipk1 and Hipk2 results in similar phenotype in human umbilical vein endothelial cells ( HUVECs ) . Furthermore , endothelial cell-specific deletion of TβRII results in phenotypes similar to those in Hipk1−/−;Hipk2−/− mutants . The mechanism of HIPK1 and HIPK2 involves their interaction with HDAC7 to suppress MEF2C-mediated transcriptional activation of Mmp10 and Vegf . Importantly , the activity of HIPK critically depends on the TGF-β-TAK1 mechanism , which promotes the phosphorylation of HIPK2 on a highly conserved tyrosine residue in the kinase domain . Together , these results provide novel insights into the role of HIPK1 and HIPK2 in the signal transduction mechanism downstream of TGF-β and the transcriptional control of angiogenic gene expression during the critical stages of vascular morphogenesis .
To determine if HIPK1 and HIPK2 cooperatively regulate gene expression , we analyzed vascular development in Hipk1−/−;Hipk2−/− mutants . In contrast to the previous report [20] , CD31 ( PECAM-1 ) staining in the yolk sacs of E9 . 5 Hipk1−/−;Hipk2−/− mutants showed an excessive growth of endothelial cells , with reduced avascular areas , reduced vascular branch points , increased fragment length , and a significant increase in BrdU incorporation ( Figure 1A–E , H ) . Similar vascular phenotypes , including increase in endothelial cell proliferation and vascular density , were also detected in the endothelial cells in the head and trunk regions of E9 . 5 Hipk1−/−;Hipk2−/− ( Figure 1F–H ) . Electron microscopy further revealed that the adherens junctions in the endothelial cells of Hipk1−/−;Hipk2−/− mutants were significantly smaller and showed reduced density per unit area compared to those in control ( Hipk1+/−;Hipk2+/+ ) ( Figure 1I–K ) . Despite these defects , the endothelial cells in Hipk1−/−;Hipk2−/− mutants showed no evidence of disruption or disorganization , and blood cells remained confined within the vessels with no evidence of vascular leakiness ( Figure 1I–I' ) . Another prominent phenotype in Hipk1−/−;Hipk2−/− mutants was the absence of blood vessel growing into the neural tubes ( Figure 1G–G' ) , which may have contributed to the increase in cell death and reduced proliferation in the neural progenitors in Hipk1−/−;Hipk2−/− mutants [19] . To investigate the molecular bases of the Hipk1−/−;Hipk2−/− mutant phenotype , we used the CodeLink Mouse Whole Genome Bioarrays to characterize gene expression profiles in E9 . 5 control ( Hipk1+/−;Hipk2+/+ ) , Hipk1−/−;Hipk2+/+ , Hipk1+/−;Hipk2−/− , and Hipk1−/−;Hipk2−/− embryos . Unsupervised hierarchical clustering analyses of all genes showed that the transcriptomes of Hipk1−/−;Hipk2+/+ embryos were more similar to that of control ( Hipk1+/−;Hipk2+/+ ) , whereas the profiles of Hipk1+/−;Hipk2−/− were more similar to Hipk1−/−;Hipk2−/− embryos ( Figure S1A ) . Consistent with this , Gene Ontogeny and KEGG pathway analyses indicated that only a very small number of genes in Hipk1−/−;Hipk2+/+ embryos showed altered expression patterns . In contrast , the number of affected genes in each pathway showed a progressive increase from Hipk1−/−;Hipk2+/+ , Hipk1+/−;Hipk2−/− , to Hipk1−/−;Hipk2−/− mutants ( Figure S1B ) . Together , these results supported the idea that HIPK1 and HIPK2 regulated target genes expression in a cooperative and interdependent manner . Given the role of HIPK2 in the TGF-β-BMP signaling pathways [13] , [14] , we next asked if the concomitant loss of HIPK1 and HIPK2 could affect the expression of TGF-β-BMP downstream targets . Consistent with this idea , a number of TGF-β target genes were either up- or down-regulated in Hipk1−/−;Hipk2−/− embryos ( Table S1 ) . These included genes related to vascular development ( e . g . , Pai-1 ) [21] or cell cycle regulation ( e . g . , Cdkn2c , Cyclin E2 , Pcna ) ( Figure 2A and Figure S1C ) [22]–[24] . Remarkably , further analyses of the HIPK1/2 targets revealed several additional potent angiogenic genes , including Mmp10 , Vegfa , Angiogenin 2 , Nkx2 . 5 , Gata-6 , and PECAM-1 ( CD31 ) , that were drastically up-regulated in Hipk1−/−;Hipk2−/− mutants ( Figure 2A ) . Indeed , immunohistochemistry using antibodies specific for VEGF-A , MMP10 , or PAI-1 confirmed that these proteins were up-regulated in the endothelial cells of E9 . 5 Hipk1−/−;Hipk2−/− embryos ( Figure 2B ) . In support of these results , qRT-PCR on Vegf , Pai-1 , and Mmp10 showed that the up-regulation of these genes was much more drastic in Hipk1−/−;Hipk2−/− mutant , but modest in Hipk1−/−;Hipk2+/+ or Hipk1+/−;Hipk2−/− single mutants ( Figure S1D ) , further supporting the cooperative role of HIPK1 and HIPK2 in the transcription of these targets . To further investigate the mechanisms of HIPK1/2 , we focused on the transcription of Mmp10 and Vegf because of their well-established functions in angiogenesis [2] , [25] . Previous studies indicate that MEF2C promotes the transcription of Mmp10 by binding to the upstream promoter . Interestingly , transcriptional corepressor HDAC7 suppresses MEF2C-dependent activation of Mmp10 and that loss of HDAC7 leads to severe vascular phenotype and embryonic lethality similar to those in Hipk1−/−;Hipk2−/− mutants [25] . Since HIPK proteins have been implicated as transcriptional corepressors , we reasoned that HIPK1 and HIPK2 might suppress the transcription of Mmp10 through its participation in the transcriptional complex involving HDAC7-MEF2C . Due to the role of HIPK2 in the TGF-β signaling pathway [13] , [15] , it is possible that HIPK1/2 may regulate Mmp10 gene expression through Smad-dependent mechanisms . Alternatively , HIPK1/2 may function downstream of TGF-β downstream kinase , TAK1 , which regulates vascular development during early embryogenesis [26] . Within the 1 kb upstream regulatory sequences of the Mmp10 gene , we identified one Smad-binding element ( SBE ) site in position −221 to −215 , close to the previously reported MEF2 recognition motif ( TAAAATA ) ( position −80 to −73 ) ( Figure 2C ) . Interestingly , however , unlike MEF2C , Smad2/3/4 by itself did not activate the transcriptional activity of Mmp10-Luc reporter ( Figure S2 ) . Rather , Smad2/3/4 modestly suppressed both wild-type Mmp10-Luc reporter and Mmp10-Luc mutating the SBE site ( Mmp10-mSBE-Luc ) ( Figure S2 ) , suggesting that the inhibitory effects of Smad2/3/4 on Mmp10-Luc reporter were most likely nonspecific . Furthermore , the presence of TGF-β did not change these results ( Figure S2 ) . In contrast to Smad2/3/4 , MEF2C showed similar effects in promoting the transcriptional activity of wild-type Mmp10-Luc and Mmp10-mSBE-Luc , whereas mutating the MEF2-binding elements in Mmp10-luciferase reporter completely abolished the effects of MEF2C on this reporter ( Figure 2D ) [25] . These results supported the idea that the SBE site in the promoter of Mmp10 was dispensable for MEF2C-mediated regulation of Mmp10 gene expression , and that HIPK2 may regulate Mmp10 transcription via MEF2C-dependent mechanism . Consistent with its role as a transcriptional corepressor , HIPK2 showed a dose-dependent suppression of MEF2C-mediated activation of the Mmp10-Luc reporter ( Figure 2E ) . The corepressor effects of HIPK2 required its kinase activity since the kinase inactive mutant HIPK2-K221A failed to suppress MEF2C-dependent activation of Mmp10-Luc reporter . Furthermore , the corepressor activity of HIPK2 required the protein–protein interaction domain ( amino acids 582–898 ) because HIPK2 mutant protein lacking the C-terminal sequence from amino acid 898 to 1189 ( HIPK2-Δ898 ) could still suppress Mmp10-Luc reporter , whereas further deletion from amino acid 582 to 1189 ( HIPK2-Δ582 ) completely abolished the corepressor effects of HIPK2 ( Figure 2E ) . Similar to HIPK2 , HIPK1 could also suppress the MEF2C-dependent activation of Mmp10-Luc reporter . Although HIPK1 by itself was less effective compared to HIPK2 ( unpublished data ) , HIPK1 and HIPK2 showed additive effects in suppressing the Mmp10-Luc activity ( Figure 2F ) . To further characterize the transcriptional corepressor effects of HIPK2 , we used siRNA to knock down the endogenous Hipk2 expression in HEK293T cells and showed that lowering HIPK2 levels resulted in further up-regulation of MEF2C-mediated activation of Mmp10-Luc activity without affecting the levels of MEF2C ( Figure 2G and Figure S3 ) . Together , these results supported the novel role of HIPK1 and HIPK2 as transcriptional corepressors in MEF2C-mediated activation of Mmp10 expression . To determine if MEF2C and HIPK2 can also regulate the transcription of Vegf , we identified a potential MEF2 binding site in the Vegf locus ( position −2679 to −2672 ) and generated a luciferase reporter that contained 4 . 5 Kb promoter sequence of Vegf gene ( Vegf-Luc ) ( Figure 2H and Figure S4 ) . Using similar approaches , we showed that MEF2C could indeed activate Vegf-Luc activity . Interestingly , MEF2C-mediated activation of Vegf-Luc could be suppressed by HIPK2 in a dose-dependent manner . Similar to the results from Mmp10-Luc , mutating the MEF2 binding element in Vegf-Luc reporter almost completely abolished the effects of MEF2C and HIPK2 ( mVegf-Luc , Figure 2H ) . Furthermore , HIPK1 and HIPK2 also showed additive effects in suppressing the Vegf-Luc activity ( Figure 2I ) . Although the effect of HIPK2 on Vegf-Luc reporter was not as robust as in Mmp10-Luc , these results were consistent with the previous results that the transcriptional controls of Vegf expression are a tightly regulated process such that loss of one Vegf allele or a slight increase in Vegf expression could result in marked abnormalities in angiogenesis during early embryonic development [27] , [28] . To further characterize the role of HIPK2 in the transcriptional control of Mmp10 expression , we expressed MEF2C and HIPK2 in HEK293T cells and used co-immunoprecipitation ( co-IP ) to show that HIPK2 could indeed be detected in a complex with MEF2C ( Figure 3A , upper panels ) . In addition , similar co-IP experiments using protein lysates from wild-type mouse embryonic fibroblasts ( MEF ) also showed that the endogenous HIPK2 proteins could be detected in a complex with MEF2C ( Figure 3A , bottom panel ) . Consistent with the requirement of HIPK2 kinase activity in the transcriptional control of Mmp10 ( Figure 2E ) , the protein complex formation between kinase-inactive HIPK2-K221A and MEF2C was significantly reduced compared to wild-type HIPK2 ( Figure 3A ) , whereas the MEF2C protein levels were comparable in cells expressing wild-type HIPK2 and kinase inactive HIPK2-K221A . The trace amount of MEF2C detected in the complex with HIPK2-K221A showed smaller molecular mass , suggesting that HIPK2 may affect the posttranslational modifications of MEF2C ( Figure 3A ) . Indeed , treatment of alkaline phosphatase abolished the upward shift of MEF2C by HIPK2 ( Figure S5 ) , supporting the idea that the stable complex formation between HIPK2 and MEF2C required phosphorylation of MEF2C . To further characterize the involvement of HIPK2 and MEF2C in the regulation of Mmp10 gene expression , we performed chromatin immunoprecipitation ( ChIP ) assays using native chromatin extracts from HUVEC and showed that endogenous MEF2C , HIPK1 , and HIPK2 proteins were bound to the MEF2 site on the Mmp10 promoter ( Figure 3B ) . Similar results could also be detected in mouse brain microvascular endothelial ( bEnd . 3 ) cells ( unpublished data ) . Given that HDAC7 suppresses MEF2-mediated expression of Mmp10 [25] , we reasoned that HIPK2 might interact with the HDAC7-MEF2 transcriptional corepressor complex . Indeed , co-IP results using protein lysates from HEK293T cells overexpressing HIPK2 , HDAC7 , and MEF2C showed that HIPK2 and HDAC7 could each be detected in protein complexes with MEF2C ( Figure 3C ) . Interestingly , however , the interaction between HIPK2 and MEF2C appeared to be reduced , but not completely eliminated , by the increasing amount of HDAC7 . Conversely , the interaction between HDAC7 and MEF2 could also be reduced by the progressive increase in HIPK2 ( Figure 3C ) . These results suggested that the recruitment of transcriptional corepressor complex to MEF2C might depend on the equilibrium between HIPK2 and HDAC7 [29] . Indeed , increasing the level of HIPK2 led to a progressive suppression of MEF2C-mediated activation of Mmp10-Luc reporter activity in the presence of HDAC7 ( Figure 3D ) . To further determine if the corepressor activity of HIPK2 was dependent on HDAC7 , we used a HDAC7 mutant that lacked the MEF2 interacting domain ( HDAC7-ΔMEF ) and therefore could not suppress MEF2-mediated transcription [25] . Interestingly , HIPK2 could suppress Mmp10-Luc activity in the presence of HDAC7-ΔMEF , suggesting that the transcriptional corepressor activity of HIPK2 could be independent of HDAC7 ( Figure 3D ) . Consistent with these results , HIPK2 continued to suppress MEF2-mediated Mmp10-Luc activity in HEK293T cells in which the endogenous HDAC7 expression was reduced by siRNA ( Figure S6 ) . Similarly , HDAC7 could still suppress the Mmp10-Luc reporter activity in HEK293T cells treated with Hipk2 siRNA ( Figure 3E ) . Several previous studies have indicated that HIPK2 and TAK1 cooperatively regulate the transcriptional activity of c-Myb through phosphorylation and proteasome-dependent degradation in the Wnt-1 signaling pathway [30] , [31] . Since both TAK1 and HIPK2 have been implicated in the downstream of TGF-β [13] , [32]–[34] , we postulated that the transcriptional corepressor activity of HIPK2 might be further regulated by TAK1 in response to TGF-β . Consistent with this idea , co-IP assays showed that the presence of TAK1 and TGF-β enhanced the interaction between MEF2C and HIPK2 ( Figure 3F ) . Moreover , the presence of TAK1 and TGF-β enhanced the corepressor effects of HIPK2 on MEF2C-mediated activation of the Mmp10 luciferase reporter ( Figure 3G ) . Consistent with these results , co-IP assays in HUVEC cells detected protein complex formation among endogenous HIPK2 , TAK1 , and MEF2C under normal growth conditions . Such interactions can be further promoted by treatment with TGF-β in HUVEC cells ( Figure 3H ) . The observation that mice lacking TAK1 exhibit severe vascular phenotype similar to Hipk1−/−;Hipk2−/− mutants [26] supports the idea that the protein complex involving HIPK2 and TAK1 may regulate TGF-β–dependent control of angiogenesis . To further characterize the role of HIPK2 in TGF-β signaling pathway , we performed immunoprecipitation–in vitro kinase ( IP-IVK ) assays and found that , under normal growth condition , HIPK2 showed a basal level of γ-32P-ATP incorporation . The addition of TGF-β further promoted the γ-32P-ATP incorporation in HIPK2 by 2- to 3-fold within 30′ to 1 h after treatment and remained higher than basal level for 24 h ( Figure 4A ) . This effect was completely abolished in kinase-inactive HIPK2-K221A mutants or by TGF-β type I receptor ALK5 inhibitor SB431542 ( Figure 4A , B ) . Since TAK1 has been shown to directly interact with TGF-β receptors [32] , [34] , we reasoned that the signal transduction from TGF-β to HIPK2 could induce a sequential activation of TAK1 and HIPK2 kinase activity through protein complex formation . Indeed , co-IP assays showed that TAK1 and HIPK2 formed a protein complex , and that the TAK1-HIPK2 complex formation could be further enhanced by TGF-β treatment ( Figure 3F , H ) . These results were further supported by immunofluorescent confocal microscopy showing that TGF-β treatment promoted co-localization of HIPK2 and phospho-TAK1 in the nucleus of HUVEC cells ( Figure S7 ) . However , co-IP using TGF-β receptor antibodies showed protein complex formation between TGF-β receptors and TAK1 , but not between TGF-β receptors and HIPK2 ( unpublished data ) . In addition to the interaction between TAK1 and HIPK2 , our results showed that TAK1 could also activate the kinase activity of HIPK2 . This effect was further enhanced by the treatment with TGF-β ( Figure 4C ) . Surprisingly , expression of the dominant negative TAK1 ( TAK1-DN ) , which carried a point mutation in the highly conserved lysine residue ( K63W ) in the kinase domain and therefore lacked kinase activity [35] , led to a marked reduction in the HIPK2 protein level and HIPK2 kinase activity , even in the presence of TGF-β ( Figure 4C ) . The effect of TAK1-DN on HIPK2 protein level appeared to be mediated by proteasome-dependent degradation since treatment with proteasome inhibitor MG-132 restored the level of HIPK2 protein in cells expressing TAK1-DN and further increased HIPK2 protein in cells expressing wild-type TAK1 ( Figure 4D ) . The robust effects of TGF-β-TAK1 on HIPK2 phosphorylation raised the possibility that TGF-β could induce phosphorylation on specific amino acids in HIPK2 and thereby influence its transcriptional corepressor effects . Examinations of the amino acid sequence in the activation loop of the kinase domain of HIPK2 revealed a region from positions 346 to 371 that were highly conserved in HIPK1 , HIPK2 , and HIPK3 and among other species ( Figure 5A , B ) . Since phosphorylation in the tripartite Ser-Thr-Tyr residues in positions 359 , 360 , and 361 of HIPK2 are similar to those identified in the activation loop of other MAP kinases [36] , [37] , we reasoned that TGF-β or TAK1 might promote phosphorylation on these amino acids in HIPK2 . To address this , we mutagenized each of these amino acids and found that replacing S359 or T360 with a neutral amino acid did not affect the ability of HIPK2 to incorporate γ-32P-ATP ( Figure 5C ) . In contrast , replacing Y361 with phenylalanine drastically reduced the ability of mutant HIPK2 ( HIPK2-Y361F ) to incorporate γ-32P-ATP upon activation by TGF-β or TAK1 ( Figure 5C , D ) . To further confirm that TGF-β-TAK1 promotes the phosphorylation of HIPK2 on Y361 , we used a phospho-Y361–specific antibody ( HIPK2-P-Y361 ) in Western blot analyses with cell lysates from HIPK2-TAK1–expressing HEK293T cells treated with or without TGF-β ( Figure 6A ) . Similar to the results in Figure 5 , we showed that , under normal growth conditions , HEK293T cells exhibited a steady-state level of HIPK2 phosphorylation on Y361 , which could be further promoted by TGF-β ( Figure 6A ) . In contrast , cells expressing HIPK2-Y361F mutant proteins showed no evidence of phosphorylated proteins that could be recognized by this antibody ( Figure 6A ) . Interestingly , treatment with TGF-β inhibitor SB431542 completely abolished the effects of TGF-β , but did not affect the basal phosphorylation level of HIPK2-P-Y361 in HUVEC cells . These results suggested that additional TGF-β–independent mechanism ( s ) might regulate the basal phosphorylation of HIPK2-P-Y361 ( Figure 6B ) . To characterize the functional consequence of TGF-β–induced phosphorylation of HIPK2 on Y361 , we performed Mmp10-Luc assays using wild-type HIPK2 and mutant HIPK2 with specific point mutation in the tripartite S359 , T360 , or Y361 . Whereas HIPK2-S359A and HIPK2-T360A dose-dependently suppressed MEF2C-dependent activation of Mmp10-Luc just like wild-type HIPK2 , this suppressor effect was completely abolished in HIPK2-Y361F ( Figure 6C ) . These results were also confirmed in the HUVEC cells ( Figure 6D ) . Together , these results indicated that TGF-β and TAK1 control the expression of angiogenic genes ( e . g . , Mmp10 ) by activating transcriptional corepressor HIPK2 via phosphorylation on a highly conserved tyrosine residue in the kinase domain . The results that HIPK2 can be activated by TAK1 in the TGF-β signaling pathway raised the possibility that endothelial cell-specific deletion of TGF-β signaling may result in phenotypes and perturbations in gene expression similar to those in Hipk1−/−;Hipk2−/− mutants . To test this , we generated conditional mutants that lacked TβRII in the endothelial cells by crossing the TβRIIfl allele with the Tie2-Cre , which targets recombination in the endothelial cells as early as E7 . 5–8 . 5 in the developing embryos and yolk sacs [38] . Similar to Hipk1−/−;Hipk2−/− mutants , the Tie2-Cre;TβRIIfl/fl mutants showed severe vascular defects and were lethal by E11 . 5–12 . 5 . Analyses of the E9 . 5 Tie2-Cre;TβRIIfl/fl mutant embryos showed a significant increase in the number of CD31+ endothelial cells in the trunk vasculature and in the developing endocardium ( Figure 7A , B ) . The endothelial cells in Tie2-Cre;TβRIIfl/fl mutants exhibited increases in BrdU incorporation ( Figure 7C ) . Remarkably , qRT-PCR analyses of the mRNA from the E9 . 5 Tie2-Cre;TβRIIfl/fl mutant embryos showed misregulations of TGF-β targets and angiogenesis genes similar to those seen in the Hipk1−/−;Hipk2−/− mutants ( Figure 7D ) . To further determine if loss of HIPK1 and HIPK2 or perturbations in TGF-β signaling recapitulates the vascular phenotype in Hipk1−/−;Hipk2−/− and Tie2-Cre;TβRIIfl/fl mutants , we established in vitro angiogenesis assays using HUVEC cells cultured in growth-factor–reduced Matrigel to determine if siRNA knockdown of HIPK1 and HIPK2 ( siHipk1/2 ) or TGF-β type I receptor ALK5 ( siTβRI ) could affect vascular development in vitro . Our results indicated that HUVEC cells treated with control siRNA formed an intricate network of capillary-like structures ( Figure 7E ) . In contrast , those treated with siRNA for Hipk1/2 or TβRI showed poorly developed capillary-like structures and an increased propensity to form clusters of cells ( Figure 7F–H ) , with a significant increase in BrdU incorporation ( Figure 7I–L ) . In addition to the Matrigel in vitro angiogenesis assays , we also examined the effects of TGF-β and HIPK1/2 in regulating the expression of Mmp10 and Vegf genes and cellular proliferation in HUVEC cells . Using qRT-PCR , we showed that siRNA knockdown of Hipk1/2 or TβRI led to up-regulations of Mmp10 and Vegf mRNA levels in HUVEC cells ( Figure 7M ) . In contrast , treatment of TGF-β suppressed the Mmp10 and Vegf mRNA levels in HUVEC cells ( Figure 7N ) . Interestingly , reducing HIPK1 and HIPK2 using siRNA blocked the ability of TGF-β to suppress the expression of Mmp10 and Vegf ( Figure 7N ) . Similar to these results , TGF-β–induced suppression of cellular proliferation in HUVEC cells , measured by BrdU incorporation , could also be blocked by siRNA knockdown of Hipk1/2 ( Figure 7O ) . Thus , the results from Hipk1−/−;Hipk2−/− mutants , Tie2-Cre;TβRII conditional mutants , the in vitro angiogenesis , and qRT-PCR assays in HUVEC cells supported the idea that the TGF-β–HIPK1/2 signaling pathway regulates a common set of target genes that are critical for angiogenesis during early embryonic development ( Figure 8 ) .
Perturbations to the TGF-β signaling mechanisms are known to have serious impacts on cardiovascular development in mice and in human diseases [7] , [9] . The manifestations of mouse mutants with targeted deletion in TGF-β signaling components , however , are quite complex and , in some instances , seemingly conflicting . One possible contributing factor to such complexity is that different TGF-β receptors can trigger multiple , divergent downstream signaling via Smad and non-Smad-dependent mechanisms [12] , [39] . In addition , the temporal and context-dependent effects of TGF-β on different cell types in the vasculature can further contribute to the final phenotypic outcomes [7] . TGF-β is known to either promote or antagonize endothelial proliferation and migration during vasculogenesis . Although the disparate outcomes of TGF-β are likely due to the differences in how TGF-β type I receptors ALK1 and ALK5 transduce its downstream signals , the exact mechanisms downstream of these receptors are not entirely clear [10] , [11] . Our results reveal a previously unrecognized mechanism involving the cooperative role of HIPK1 and HIPK2 in the downstream of TGF-β–TAK1 signaling pathway that regulates the expression of a number of potent angiogenic genes during early embryonic development ( Figure 8 ) . First , based on the morphological analyses and gene expression profiling in Hipk1−/−;Hipk2−/− mutants , and the results from siRNA knockdown of Hipk1 and Hipk2 in Matrigel angiogenesis assays using HUVEC cells ( Figures 1 , 2 , and 7 ) , our data indicate that HIPK1 and HIPK2 act cooperatively to regulate a set of angiogenic genes , including Mmp10 and Vegf , that are critical for the early stage of vascular development . This is further supported by a series of in vitro biochemical assays that validate HIPK2 and HDAC7 as important transcriptional corepressors that regulate the expression of Mmp10 and Vegf ( Figures 2 and 3 ) . Consistent with these results , EM analyses also show that the endothelial cells in Hipk1−/−;Hipk2−/− mutants exhibit defects in the adherens junction formation similar to those described in Hdac7−/− mutants ( Figure 1I–K ) [25] . While HIPK2 and HDAC7 have synergistic effects in suppressing the transcription of Mmp10 , each can work independently to suppress MEF2C-mediated gene expression . Surprisingly , the effect of HIPK2 and HDAC7 in MEF2C-mediated transcriptional control of Mmp10 expression seems to depend on a delicate balance of protein–protein interaction in the transcriptional complex because increasing abundance of HIPK2 can reduce the presence of HDAC7 in complex with MEF2C and vice versa . One possible explanation for the antagonistic effect of HIPK2 and HDAC7 is that both may compete for the same or similar binding site in MEF2C , which can reach equilibrium as more HIPK2 or HDAC7 are recruited to the complex . This is particularly appealing because the transcriptional machinery involves dynamic assembly of large protein complexes that include transcriptional corepressors , such as HIPK2 and HDAC7 [29] . Alternatively , and not mutually exclusive , it is possible that HIPK2 and HDAC7 may cross-regulate each other through posttranslational modifications , such as phosphorylation or acetylation , which are likely to change the equilibrium of transcriptional complex formation . The role of HIPK2 as a transcriptional corepressor of MEF2C proteins is further supported by the protein complex formation between MEF2C and HIPK2 in HEK293T cells . Such protein complex formation between endogenous HIPK2 and MEF2C can also be detected in wild-type MEF and HUVEC cells ( Figure 3 ) . Interestingly , the interaction between HIPK2 and MEF2C seems to require the kinase activity of HIPK2 because significantly fewer MEF2C proteins are detected in a complex with kinase inactive HIPK2-K221A . Furthermore , the MEF2C proteins that do interact with HIPK2-K221A have lower molecular mass compared with those in complex with wild-type HIPK2 , suggesting that HIPK2 may posttranslationally modify MEF2C and thereby inhibits the transcriptional activity of MEF2C . In support of this idea , alkaline phosphatase treatment reduces the HIPK2-induced high molecular mass migration of MEF2C in SDS-PAGE ( Figure S5 ) . Although there is no evidence that MEF2C is a direct phosphorylation substrate for HIPK2 , it is possible that HIPK2 may activate other protein kinases , such as Cdk5 and GSK3β [40] , [41] , to phosphorylate MEF2 and thereby promote the pro-differentiation function of MEF2 in endothelial cells . One remarkable finding from this study is the identification of TGF-β and TGF-β–activating kinase 1 ( TAK1 ) as upstream mechanisms that regulate the interaction between HIPK2 , HDAC7 , and MEF2C ( Figures 3 and 4 ) . These results indicate that TAK1 have two distinct roles in regulating HIPK2 functions . First , using immunoprecipitation–in vitro kinase ( IP-IVK ) assays , we show that both TGF-β and TAK1 can activate HIPK2 by phosphorylating the tyrosine on position 361 ( Y361 ) , a highly conserved residue among all HIPK members in the activation loop of the kinase domain ( Figure 5 ) . These results are further verified using a phospho-HIPK2 specific antibody , HIPK2-P-Y361 ( Figure 6 ) . Strikingly , HIPK2 with a tyrosine-to-phenylalanine mutation ( HIPK2-Y361F ) on this amino acid completely loses its ability to suppress MEF2C-dependent transcriptional activity ( Figure 6 ) . Second , and quite unexpectedly , we discover that kinase inactive TAK1 blocks HIPK2 function by promoting the degradation of HIPK2 through proteasome-dependent mechanisms ( Figure 4 ) . Consistent with these results , treatment with TAK1 inhibitor 5Z-7-Oxozeaenol also promotes HIPK2 degradation in HEK293T cells ( Y . S . , unpublished observations ) . These results suggest that , in the absence of signal from TGF-β–TAK1 , dephosphorylated HIPK2 proteins may undergo rapid turnover via proteasome pathway ( Figure 8 ) . Alternatively , kinase inactive TAK1 may alter intracellular transport of HIPK2 and promote proteasome-mediated degradation of HIPK2 . Given the closely interconnected functions between TAK1 and HIPK2 , it is perhaps not surprising that loss of TAK1 results in early embryonic lethality due to defects in vascular morphogenesis similar to those in Hipk1−/−;Hipk2−/− mutants [26] . While our results highlight the robust effects of HIPK1 and HIPK2 as corepressors in the MEF2C-dependent transcriptional activation of angiogenic genes , there are several indications that HIPK proteins may have broader functions in regulating the outcome of TGF-β signaling . For instance , HIPK2 has been shown to serve as a transcriptional coactivator in the Smad2/3/4-SBE reporter assays and in JNK-mediated functions , which critically regulate the decision of survival and apoptosis in dopaminergic neurons and in tumor cells , respectively [13] , [16] . In addition , HIPK2 can also function as a corepressor in Ski-dependent suppression of BMP-Smad1/4-induced transcriptional activation [15] . Given the complexity of TGF-β signaling mechanisms , it is possible that the final outcomes of HIPK2 functions will likely be context-dependent . In support of this view , loss of HIPK1 and HIPK2 leads to down-regulation of several genes critical for the control of cell cycle progression ( Figures 2 and 8 ) . Although the magnitudes of reduction in these genes are not as drastic as the up-regulation of angiogenic genes , many of these genes have been well-documented to be the transcriptional targets in the canonical TGF-β–Smad pathway ( Figure 8 and Table S1 ) [42] . It will be interesting to determine if HIPK1 and HIPK2 may regulate the transcriptional control of these target genes , thus establishing these kinases as novel mediators connecting the Smad and non-Smad signaling pathways downstream of TGF-β . Finally , the gene expression data in Hipk1−/−;Hipk2−/− mutants also reveal a significant , albeit modest , down-regulation of Alk1 , Alk5 , and Hdac7 transcripts . While it is unclear if HIPK1 and HIPK2 can also directly regulate the transcription of these genes , based on the well-characterized functions of these genes , their down-regulation could certainly amplify the vascular defects in Hipk1−/−;Hipk2−/− mutants .
The Hipk1−/− and Hipk2−/− mutant mice have been described previously [18] , [43] . The Tie2-Cre and the floxed TGF-β type II receptor ( TβRIIfl ) mice were generously provided by Dr . Rong Wang and Dr . Harold Moses , respectively [38] , [44] , [45] . Animal care was approved by the Institutional of Animal Care and Use Committee and followed the NIH guidelines . Embryonic day ( E ) 9 . 5 and E10 . 5 embryos and yolk sacs were fixed at 1% PFA in PBS for 2 h , cryoprotected in 15% sucrose for 30 min , and then in 30% sucrose for 30 min . Tissue sections were incubated with primary antibodies overnight and with secondary antibodies for 1 h . To label the cells in S-phase of cell cycle , pregnant mice were injected intraperitoneally with BrdU ( 50 mg/kg body weight , BD Bioscience ) and sacrificed 2 h later . To detect BrdU+ endothelial cells , tissue sections were incubated with the CD31 antibody . Afterward , the tissue sections were fixed in 4% PFA for 30 min and then treated with 2N HCl at 37°C for 30 min . After three washes with Borax solution , the tissue sections were incubated with primary antibody against BrdU overnight , and then incubated with Alexa Fluor-conjugated secondary antibody for 1 h . For whole-mount immunofluoescent staining , E9 . 5 embryos and yolk sacs were fixed in 4% PFA in PBS overnight at 4°C , washed four times in PBS at 4°C , and blocked overnight at 4°C in 5% goat serum , 0 . 1% Triton X-100 in PBS . They were then incubated in rat anti-CD31 antibody ( 1∶500; Mec13 . 3; BD Biosciences ) overnight at 4°C , washed in PBT overnight at 4°C , and incubated in Alexa Fluor 488 Goat Anti-Rat IgG ( 1∶1 , 000 ) for embryos and Alexa Fluor 555 Goat Anti-Rat IgG ( 1∶2 , 000 ) for yolk sacs . To determine the number of endothelial cells in S-phase of the cell cycle , tissue sections were double labeled with anti-CD31 ( 1∶20; Cat No . 550274; BD Biosciences ) and anti-BrdU ( 1∶500; MAB3222; Millipore ) . Immunohistochemistry using PAI1 antibodies required antigen retrieval , in which the tissue sections were incubated in 10 mM sodium citrate buffer at 100°C for 30 min . Sample preparations and image capture for electron microscopy were described previously [14] . Neurolucida was used to determine the avascular area , fragment length ( length of a vessel before it branches ) , and branch points in the yolk sacs . Individual avascular areas were manually traced and then added up to get the total avascular area per frame using “contour mapping” option in Neurolucida ( MicroBrightField ) . Individual fragment lengths were measured with each fragment length separated by a different colored line . Fragment lengths were then averaged to get the average fragment length per frame [46] . Total RNA was extracted from embryos using PicoPure RNA Isolation Kit ( Arcturus ) and used as a template for reverse transcriptase with MessageAmp II-Biotin enhanced Kit ( Ambion ) . Microarray analysis was performed using CodeLink Mouse Whole Genome Bioarray ( Applied Microarrays ) . The microarray data have been deposited in Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) , accession number GSE39253 . The RNA from HEK293T cells , HUVEC cells , or MEF was isolated by Trizol reagent ( Invitrogen ) and used as a template for reverse transcriptase with random hexamer primers ( Invitrogen ) . Primer sequences for specific genes are available in Table S2 . HEK293T cells were purchased from ATCC and MEFs was reported previously [47] . Both cell lines were cultured in DMEM growth medium with 10% fetal bovine serum ( Hyclone ) . HUVEC cells were maintained in EGM-2 medium ( Lonza Walkerville Inc . ) . For immunostaining , cells were plated on gelatin-coated glass coverslips , fixed in 4% PFA , and stained with appropriate primary antibodies as described previously [43] , [47] . siRNA oligonucleotides for Hipk1 ( Cat No . sc39048 ) , Hipk2 ( Cat No . sc39050 ) , Hdac7 ( Cat No . sc35546 ) , or TGF-β type I receptor ( TβRI ) ( Cat No . sc40222 , specific for ALK5 ) were purchased from Santa Cruz Biotechnology , Inc . and used at a concentration of 30 pM to transfect HEK293T or HUVEC cells using Lipofectamine 2000 ( Invitrogen ) . Two days after transfection , cells were harvested either for RNA isolation or for luciferase activity measurement . RT-PCR and Western blots were performed multiple times with comparable results . Primer sequences for PCR were provided in Table S2 . Luciferase assays were performed using the dual-luciferase assay system ( Promega ) [13] , [43] , [47] . The luciferase reporter activity was measured using the dual-luciferase system on a luminometer ( Turner Designs ) . Relative luciferase activity was reported as a ratio of firefly over Renilla luciferase readouts . The Mmp10-luciferase reporters , HDAC7 constructs , and myc-tagged MEF2C construct were gifts from Dr . E . Olson [25] . The Vegfa-luciferase reporter contained 4 , 512 bp to 1 bp of the mouse Vegfa gene , subcloned into pGL4 . 10[Luc2] vector ( Promega ) . The Vegfa-luciferase construct that contained mutations in the MEF2 binding site ( mVegfa-luc ) was generated using the QuikChange II Site-Directed Mutagenesis kit ( Stratagene ) . Whole-cell lysates were collected from HEK293T cells 24 h after transfection in lysis buffer containing 50 mM HEPES ( pH 7 . 4 ) , 50 mM NaCl , 0 . 1% Tween 20 , 20% glycerol , and 1× protease inhibitor cocktail ( Roche Molecular Systems ) with brief sonication . The same amount of supernatants was incubated overnight at 4°C with different primary antibody and then incubated with Protein A/G Plus Agarose beads for 3 h at 4°C . Immune complexes were washed in buffers containing 50 mM HEPES ( pH 7 . 4 ) , 300 mM NaCl , 0 . 2 mM EDTA , and 1% NP-40 and analyzed on SDS/PAGE . For in vitro kinase assays , cells were treated with DMSO or 10 ng/ml TGF-β 24 h after transfection , and then whole-cell lysates were collected in lysis buffer . Immune complexes were washed with kinase buffer ( 25 mM Tris-HCl , pH 8 . 0 , 10 mM MgCl2 ) , and then incubated with 1 mM ATP and 5 µCi of γ-32P-ATP ( Perkin Elmer ) for 3 h at room temperature . The resin beads were then washed with 10 nM Tris-HCl ( pH 7 . 5 ) and the proteins eluted with 25 µl SDS loading buffer . Phosphorylation of HIPK2 on Y361 was confirmed by HIPK2-P-Y361 specific antibody ( Thermo Scientific , Cat No . PA5-13045 , 1∶500 dilution ) in Western blots using HEK293T cell lysates . ChIP assays were performed as described [47] . Briefly , HUVEC or bEnd . 3 cells were fixed with 4% PFA and treated with SDS lysis buffer . After shearing with a sonicator and contrifugation , the supernatant of cell lysates were used for immunoprecipitation with different antibodies . The DNA–protein–antibody complexes were isolated using antibodies for HIPK1 ( p-16 , sc-10289 ) , MEF2C ( e-17 , sc-13266 ) ( Santa Cruz Biotechnology ) , or HIPK2 ( ab28507 , Abcam ) . The complexes were washed with buffers , and the DNA were eluted and purified . Primer sequences were available in Table S2 . HUVEC cells were cultured in EBM-2 medium containing serum and endothelial cell supplements ( EGM2 ) according to the manufacturer's instructions ( BD Biosciences ) . The siRNA-mediated knockdown was performed when the cells reached 80% confluence . For in vitro angiogenesis assays , HUVEC cells were trypsinized 48 h after transfection , and reseeded onto Matrigel-coated plate in the presence of EGM2 medium . After 18 h , vascular formation was assessed and photographed under a Nikon TE2000-U microscope with 4× objective . For BrdU incorporation assays , HUVECs were seeded onto gelatin-coated coverslips in 24-well plates , and incubated with BrdU ( 10 µM ) for 2 . 5 h . Data were analyzed by two-tailed Student's t test . Values were expressed as mean ± S . E . M . Changes were identified as significant if the p value was less than 0 . 05 . | An essential step during early embryonic development is to establish elaborate vascular networks that provide nutrients to ensure the proper growth of the embryos . This process , known as angiogenesis , requires coordinated regulation of cell proliferation , migration , and differentiation in endothelial cells , which provide the inner-most linings of blood vessels . It is well accepted that transforming growth factor–β ( TGF-β ) and its downstream signal pathways are required to regulate endothelial cell growth , but the exact mechanisms remain poorly characterized . Using mouse genetics and in vitro angiogenesis assays , we show that transcriptional cofactors in the homeodomain interacting protein kinase ( HIPK ) family are activated by TGF-β to control the expression of target genes that regulate proliferation and adherent junction formation in endothelial cells . Our study also identifies a highly conserved tyrosine residue in HIPK proteins that is required to transduce TGF-β signal . These results provide new insights into the mechanism of TGF-β signaling in angiogenesis , and how this process may be exploited to develop therapeutic targets that control angiogenesis during development and in disease conditions . |
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Chlamydia trachomatis is the most common cause of bacterial sexually transmitted infection , responsible for millions of infections each year . Despite this high prevalence , the elucidation of the molecular mechanisms of Chlamydia pathogenesis has been difficult due to limitations in genetic tools and its intracellular developmental cycle . Within a host epithelial cell , chlamydiae replicate within a vacuole called the inclusion . Many Chlamydia–host interactions are thought to be mediated by the Inc family of type III secreted proteins that are anchored in the inclusion membrane , but their array of host targets are largely unknown . To investigate how the inclusion membrane proteome changes over the course of an infected cell , we have adapted the APEX2 system of proximity-dependent biotinylation . APEX2 is capable of specifically labeling proteins within a 20 nm radius in living cells . We transformed C . trachomatis to express the enzyme APEX2 fused to known inclusion membrane proteins , allowing biotinylation and purification of inclusion-associated proteins . Using quantitative mass spectrometry against APEX2 labeled samples , we identified over 400 proteins associated with the inclusion membrane at early , middle , and late stages of epithelial cell infection . This system was sensitive enough to detect inclusion interacting proteins early in the developmental cycle , at 8 hours post infection , a previously intractable time point . Mass spectrometry analysis revealed a novel , early association between C . trachomatis inclusions and endoplasmic reticulum exit sites ( ERES ) , functional regions of the ER where COPII-coated vesicles originate . Pharmacological and genetic disruption of ERES function severely restricted early chlamydial growth and the development of infectious progeny . APEX2 is therefore a powerful in situ approach for identifying critical protein interactions on the membranes of pathogen-containing vacuoles . Furthermore , the data derived from proteomic mapping of Chlamydia inclusions has illuminated an important functional role for ERES in promoting chlamydial developmental growth .
Chlamydia trachomatis is an obligate intracellular bacterium that infects mucosal epithelial cells of the endocervix and conjunctiva . It infects millions of people every year and is the etiological agent of ocular trachoma [1 , 2] . Although C . trachomatis infections are effectively treated with antibiotics , the majority of infections are asymptomatic and go untreated [3] . The consequences of long term infection can be severe , especially in chronically infected women that are at risk for developing pelvic inflammatory disease , ectopic pregnancy , or infertility as a consequence of infection [4] . Chlamydiae undergo a biphasic developmental cycle , characterized by transitions between infectious elementary bodies ( EB ) and metabolically active reticulate bodies ( RB ) [5 , 6] . During infection , an EB attaches to a host cell and internalizes into a vacuole called the inclusion . Within the inclusion , EB–RB conversion and replication proceed , ultimately followed by asynchronous conversion to EB and exit from the host cell . Chlamydial growth within host cells is critically dependent on recruitment of host proteins to the inclusion membrane early during infection , and on extracting nutrients from the host cell . The mechanisms responsible for these processes are not well understood . The streamlined genome of Chlamydia necessitates a dependency on the host cell for many nutrients , yet some of these molecules cannot freely permeate the inclusion membrane and import systems have not been identified [7] . In addition , while much is known about chlamydial manipulation of host signaling , very little is known about the molecular processes necessary for Chlamydia to obtain nutrients from the host [6 , 8] . The inclusion membrane represents the major interface through which chlamydiae manipulate host cell function . In accordance with this , different chlamydial species encode 50–70 type III secreted inclusion membrane ( Inc ) proteins that are predicted to localize to the inclusion membrane during the chlamydial developmental cycle [9–14] . The Inc family is a signature genetic feature of chlamydiae; however , their lack of sequence similarity with proteins from other bacteria has largely precluded bioinformatic prediction of molecular functions and potential host binding partners . Two major proteomic studies in recent years have greatly advanced our knowledge of candidate host proteins associated with the inclusion membrane and with specific Inc proteins [15 , 16] . These studies also highlighted molecular interactions that occur between the inclusion membrane and the retromer complex [15 , 16] . A comprehensive understanding of inclusion membrane modifications , and the host proteins recruited to the inclusion , has not been realized . Even less is known regarding the temporal dynamics of these interactions over the 48-72-hour C . trachomatis developmental cycle , and the factors that are critical for inclusion biogenesis . Molecular analysis of early inclusions has been particularly elusive due to their small size . Previous proteomic efforts provided major new insight into the inclusion membrane proteome; however , they were unable to characterize protein compositions of early inclusions or identify temporal protein associations . The development of techniques to investigate the inclusion membrane proteome under native conditions , at multiple stages of infection , would accelerate the discovery of Chlamydia–host interactions . To this end , we used the APEX2 system of proximity-dependent biotinylation in C . trachomatis , as a flexible tool for exploring host-pathogen interactions . APEX2 has been tested in Chlamydia by microscopy and western blot , and shown to be able to biotinylate proteins on the inclusion membrane when fused to inclusion membrane proteins IncF , IncA , or a truncated IncA [17] . APEX2 is an ascorbate peroxidase that catalyzes a reaction between biotin-phenol and hydrogen peroxide , forming a phenoxyl radical that rapidly forms a covalent bond with a nearby amino acid [18 , 19] . The labeling radius of APEX2 is less than 20 nm , and the reaction is carried out in living cells for only one minute; this allows highly spatially and temporally resolved biotinylation of proteins in situ . In combination with mass spectrometry , APEX2 has been used to map the mitochondrial matrix , outer membrane , and inner membrane space , as well as the endoplasmic reticulum ( ER ) membrane and several other subcellular locations of mammalian cells [19–22] . Recently , APEX2 was used to study how protein interactions with G-protein-coupled receptors change after activation , highlighting its experimental utility for studying protein interaction dynamics [23] . We engineered a C . trachomatis strain that contained an Inc protein fused to APEX2 , under the control of an inducible promoter . Using this strain , we expressed Inc-tagged APEX2 on the inclusion membranes of cells infected with C . trachomatis at three stages of growth . Subsequent quantitative mass spectrometry defined the proteomes of inclusion membrane proximal proteins at early , middle , and late stages of chlamydial development . Analysis of proteomic data for early inclusions showed a significant enrichment of endoplasmic reticulum proteins , in particular factors with established roles in the regulation of ER exit sites ( ERES ) . We detail the recruitment of specific ERES factors to C . trachomatis inclusions , and we demonstrate that functional ERES are important for chlamydial developmental growth .
We sought to develop the APEX2 system for identifying inclusion interacting proteins , with the primary goal of determining how these interactions evolve during Chlamydia infection of host cells . Previous mass spectrometry studies of the inclusion membrane were either done in the absence of infection , or only at a later stage of infection after mechanical manipulations [15 , 16] . These studies also used techniques that required lysing open the host cells and pulling down Incs or whole inclusions in an in vitro environment , which may have disrupted weaker , more transient protein-protein interactions . With the recent development of a transformation system for Chlamydia , a wide range of techniques are now possible [24 , 25] . Leveraging this advance , we infected cells with a C . trachomatis L2/434 strain engineered to express flag-tagged Inc-APEX2 fusion proteins , to enable the labeling of inclusion membrane interacting proteins in live cells , at multiple times during infection . The Inc-APEX2 fusion protein localized to inclusion membranes , with APEX2 exposed in the host cell cytosol . The APEX2 system is highly sensitive , has defined the proteomes of cellular compartments refractory to other techniques , and APEX2 enzymatic function was shown to remain intact when tagged to a chlamydial Inc protein [17 , 18 , 21] . We transformed C . trachomatis to express different Inc proteins ( IncB , IncA , IncC , InaC , and CT223 ) tagged to flag-APEX2 and tested protein biotinylation levels by western blot , and flag-APEX localization by immunofluorescence microscopy . Although IncB , IncC , and CT223 are known to localize to microdomains in the inclusion membrane , when overexpressed and fused to APEX2 they often localized around the entire inclusion membrane ( S1 Fig ) . There is no quantitative data in the literature about the frequency of microdomains , so it is unknown if the overexpression is truly causing a difference in localization . IncC-APEX2 was seen in smaller patches of the inclusion membrane in some inclusions and distributed around the inclusion membrane in others ( S1 Fig ) . IncB-APEX2 and CT223-APEX2 did not appear to be exclusively in microdomains; CT223-APEX2 was observed both on the inclusion membrane as well as inside bacteria , while IncB-APEX2 was exclusively observed on the inclusion membrane ( S1 Fig ) . These results may be different than the localization seen by overexpression of flag-tagged Incs , where IncB-flag , CT223-flag , and IncC-flag localized to microdomains; however , without side-by-side comparison of both constructs it is difficult to assess if the localizations were altered by APEX2 [14] . It is possible that APEX2 interfered with endogenous binding partners of these Incs , or expression levels were different than in a previous study [14] . IncA-APEX2 was not secreted very efficiently , but this appears to be an issue with our construct , as IncA-APEX2 has been previously tested [17] . To determine whether overexpression had a negative effect on Chlamydia growth we infected HeLa cells with Chlamydia strains expressing Inc-APEX2 fusions and induced expression with 1 ng/mL anhydrotetracycline ( ATc ) at the start of infection , then measured inclusion diameter at 24 hours post infection ( hpi ) . Strains expressing IncA-APEX2 and IncB-APEX2 resulted in the largest inclusions as measured by diameter; IncA-APEX2 was not secreted efficiently so we did not go forward with this strain , but IncB-APEX2 had a clear localization around the inclusion membrane ( S1C Fig ) . We also tested if IncB-APEX2 overexpression affected endogenous Inc localization using immunofluorescence microscopy . By comparing uninduced or IncB-APEX2 expressing C . trachomatis we did not observe any differences in IncA localization , and microdomains marked with CT223 appeared normal ( S2 Fig ) . There was a slight decrease in inclusion diameter with the IncB-APEX2 Chlamydia when grown in the presence of ATc , indicating overexpression had some detrimental effects on inclusion growth ( S2C Fig ) . Since IncB-APEX2 distributed evenly around the inclusion membrane and did not cause expansion of endogenous CT223 positive microdomains , we deemed it the most suitable whole inclusion membrane probe , rather than identifying microdomain-specific proteins [14 , 26] . To further ensure that the results of IncB-APEX2 were generalizable , we performed preliminary mass spectrometry analysis with the two Incs that gave the highest amount of biotinylation by western blot ( IncB and CT223 ) . We determined that IncB-APEX2 identified more host proteins when overexpressed , in concordance with the western blot data , and IncB-APEX2 detected all the proteins that were found by the CT223-APEX2 fusion with the exception of two proteins . This fits with previous uses of APEX2 , where the proteomic results were localization-specific , rather than representing binding partners of the protein fused to APEX2 . We therefore chose to focus on IncB-APEX2 for the rest of our experiments . In subsequent mass spectrometry experiments IncB-APEX2 identified the two proteins that were identified by CT223-APEX2 in the first trial , confirming that these proteins were not unique to CT223 . Furthermore , IncB is constitutively expressed during infection , ensuring that any chaperones necessary for proper secretion will be present at any time points tested [26–28] . For mass spectrometry experiments we used C . trachomatis L2/434 transformed with a tetracycline inducible plasmid encoding IncB-APEX2 ( Fig 1A ) . At specific time points , biotin-phenol and hydrogen peroxide were added to catalyze protein biotinylation , and resulting labeled proteins were pulled down using streptavidin and identified by mass spectrometry ( Fig 1B ) . We confirmed that protein biotinylation required IncB-APEX2 expression , biotin phenol , and hydrogen peroxide ( Fig 1C ) . Furthermore , the profile and depth of labeled proteins in cells infected with the IncB-APEX2 expressing C . trachomatis strain were distinct from cells infected with a strain expressing an untagged APEX2 that was not secreted ( Fig 1C ) . Staining of infected , labeled cells with a fluorescent streptavidin probe demonstrated that biotinylated proteins were enriched on inclusion membranes at 8 , 16 and 24 hours post infection ( hpi ) ( Fig 1D ) . Biotinylated proteins at 8 hpi manifested as distinct foci which were always outside of the bacterial outer membrane , indicating that IncB-APEX2 is being secreted . HeLa cells were infected with IncB-APEX2 C . trachomatis , with 1 ng/ml anhydrotetracycline added at the start of infection to induce expression [29] . At 8 , 16 , or 24 hpi , infected cells were incubated with biotin-phenol for 30 min , and protein biotinylation was catalyzed by the addition of hydrogen peroxide for 1 minute . Following reaction quenching , cells were immediately pelleted and frozen for further processing . To control for endogenously biotinylated proteins , control cells were infected and processed in parallel to the biotinylated samples , except no hydrogen peroxide was added so any biotinylated proteins present were not due to the APEX2 reaction . For each infection time point , biotinylated proteins were prepared from six biological replicates and six controls for enrichment and analysis by mass spectrometry . Mass spectrometry of IncB-APEX2-labeled samples identified 452 unique host proteins and 15 chlamydial proteins across the three time points analyzed . The presence of these proteins exhibited notable dynamics over the times tested , for example some only present at a single time point , and others maintained throughout infection ( Fig 2A ) . Among host proteins recruited to C . trachomatis inclusions , 89 were significantly enriched at 8 hpi , 178 proteins at 16 hpi , and 396 proteins at 24 hpi . There were 37 proteins that maintained enrichment at all three time points ( Fig 2A ) . Of the 15 chlamydial proteins labeled by IncB-APEX2 , 11 were annotated as Incs ( Fig 2B , Incs highlighted in green ) . Another , CT610 ( CADD ) , does not have a canonical Inc structure but has been shown to be secreted with an inclusion membrane localization [30] . Of the remaining three Chlamydia proteins , two are found in high abundance in the bacteria and thus may be due to labeling of pre-secreted IncB-APEX2 . A complete list of proteomic data is contained in S1 Table . To develop an understanding of the general roles of proteins identified by IncB-APEX2 labeling , and to confirm that our approach labeled pathways and cellular components that Chlamydia is known to interact with , we analyzed proteomic data against annotation databases . Pathway overrepresentation analysis was performed using InnateDB with KEGG pathways , and the representation of subcellular locations in the data set was determined using the Human Protein Atlas database [31–33] . KEGG pathway analysis showed that many of the identified proteins were associated with cellular pathways known to play roles during Chlamydia infection , including ‘bacterial invasion of epithelial cells’ and endocytosis ( Fig 2C ) . Proteins associated with ER processing were significantly enriched at all three time points . Proteins involved in glycolysis and the pentose phosphate pathway were significantly enriched at 8 hpi . Analysis of subcellular localization annotations for IncB-APEX2 labeled proteins revealed a general spatial context consistent with the known perinuclear residence of the inclusion ( Fig 2D and 2E ) [8] . Ontology analysis indicated that early in C . trachomatis infection , at 8 hpi , inclusions acquired proteins normally localized to the cytosol , nucleus , plasma membrane , ER , and vesicles . Growth and maturation of the inclusion , at 16 and 24 hpi , was accompanied by a sustained enrichment of proteins associated with early inclusions , as well as an emergence of interactions with cytoskeleton associated proteins: actin , microtubules , centrosomes , the MTOC , and intermediate filaments . Although there are limitations to the accuracy of annotations in KEGG or localization databases , the pathway overrepresentation analysis highlights expected enrichments in the IncB-APEX2 dataset , as well as potential new associations that could be relevant to Chlamydia growth . We used the STRING database to identify known molecular interactions between identified proteins , and R to plot all proteins with at least one interacting partner ( Fig 3A ) . This allowed analysis of the potential recruitment of multiprotein interaction complexes to the C . trachomatis inclusion . Highly interconnected protein communities were identified using the Louvain method of community detection . From this analysis , major protein networks emerged , encompassing innate immune signaling , the nuclear membrane , vesicular traffic , the cytoskeleton , nucleoside metabolism , and ER chaperones . Temporal analysis of these interactions revealed a dramatic recruitment of glycolytic proteins to early inclusions , for example aldolases , transketolase , pyruvate kinase , glucose-6-phosphate isomerase , and peroxiredoxin ( Fig 3B ) . Inclusion maturation was marked by a significant expansion of protein networks related to the MTOC and centrosome , innate immune signaling , proteasome regulation , and clathrin assembly ( Fig 3B ) . Network analysis also allowed identification of host factors that may potentially interact with known host–Inc interactions , for example VAPA associates with IncV [34] and the STRING database has evidence that VAPA associates with S10AG and S10AE . Because C . trachomatis inclusions at 8 hpi are so small , validation of host protein recruitments by fluorescence microscopy is difficult . We reasoned that proteins associated with early inclusion membranes might play important roles in mediating the biogenesis of the inclusion . To validate the functional participation of inclusion-associated proteins towards Chlamydia developmental growth , we performed RNA interference ( RNAi ) on 64 of the 89 proteins present in the 8 hpi interaction dataset . HeLa cells were grown in a 96 well plate format , transfected with siRNA oligonucleotides for 48 h , and infected with C . trachomatis L2 to determine the impact of protein depletion on bacteria growth . At 48 hpi , EB were harvested from siRNA treated cells and analyzed for inclusion forming units ( IFU ) on fresh cells . Knockdown of 16 proteins ( 25% ) resulted in a >1 . 5-fold change in infectious progeny formation , as compared to the mean IFU of the plate and a nontargeting siRNA control ( Fig 4A ) . A change of 1 . 5-fold was highlighted due to use in a previous siRNA screen [35] . As a positive control , depletion of MAP1LC3B resulted in decreased C . trachomatis IFU , consistent with reported findings [35] . Overall , 10 protein knockdowns resulted in a >1 . 5-fold decrease in IFU ( Fig 4A top ) , and 6 protein knockdowns led to a >1 . 5 fold increase in IFU ( Fig 4A bottom ) . We verified knockdown by qRT-PCR for the targets with at least 1 . 5-fold difference in IFU ( S3 Fig ) . The knockdown efficiency was over 75% for 11 of the 16 targets , so some of the knockdowns in this screen may be underestimating the effect of depletion on IFU due to low knockdown efficiency . Proteins whose depletion resulted in decreased IFU were frequently targets identified by IncB-APEX2 at multiple stages of Chlamydia development ( Fig 4A ) . In contrast , many of the proteins whose knockdown led to increased IFU production were more transiently recruited to inclusions , with enrichments primarily detected at early inclusions ( Fig 4A ) . Collectively , the RNAi data show IncB-APEX2 identified proteins at the inclusion membrane that functionally impacted chlamydial growth . Our mass spectrometry data provided an opportunity to synthesize it with those generated by previous studies , to develop a more comprehensive understanding of the protein networks recruited to the inclusion membrane . First , we compared our IncB-APEX2 data set to two previous mass spectrometry experiments that conducted analysis on purified inclusions ( labeled as ‘inclusion-MS’ in Fig 4B ) [16] , and affinity purified Incs expressed in 293 cells ( labeled as ‘AP-MS’ in Fig 4B ) [15] . Furthermore , proteins were manually cross-referenced against the literature to identify evidence-based reports of proteins recruited to inclusions or inclusion membrane proteins ( labeled with PMID in Fig 4C ) . Overall , 16 proteins were identified by all three MS approaches to interact with inclusions , and the present study now provides temporal data for when these interactions occur during host cell infection ( Fig 4B , upper half ) . Ten of the 16 proteins were previously reported to be recruited to inclusion membranes by immunofluorescence microscopy ( Fig 4C ) . The remaining 6 proteins identified by all 3 MS approaches–ASPH , LRRF1 , LRC59 , TMOD3 , CLINT1 , and BAP31–represent priority targets for further study in the context of Chlamydia infection . IncB-APEX2 labeling contained 60 additional proteins that were identified on 24 hpi inclusions by inclusion-MS ( S1 Table ) [16] . Thirteen additional proteins from IncB-APEX2 data correlated with Inc specific data obtained from AP-MS , thus adding in situ context to previously described molecular interactions ( Fig 4B , lower half ) [15] . Finally , IncB-APEX2 identified 31 proteins previously shown by microscopy to be in close proximity to inclusions during infection ( Fig 4C ) . This substantiates the efficacy of the APEX2 approach and additionally provides important new temporal information for how these host targets are dynamically recruited to the inclusion membrane by Chlamydia . IncB-APEX2 proteomic analysis revealed a significant enrichment of ER associated proteins near inclusion membranes throughout infection . These findings are consistent with previous reports of membrane contacts between inclusions and the ER [36 , 37] . IncB-APEX2 data showed that ER protein associations were present near early inclusions , at 8 hpi , and additionally highlighted an enrichment of markers for ER exit sites ( ERES ) . ERES are specialized subdomains of the ER from which COPII coated vesicles bud and traffic to the ER-Golgi intermediate compartment ( ERGIC ) [38] . Sec16 , TANGO1 , TFG , and peflin , were identified on inclusion membranes by IncB-APEX2 , with TANGO1 present at all three time points ( S1 Table ) . Sec16 is thought to act as a critical ERES scaffold in mammalian cells and is capable of stabilizing COPII subunits at ERES membrane regions [39] . TANGO1 recruits cargo to ERES through interactions with Sec16 and cTAGE5 [40 , 41] , and TFG promotes COPII uncoating of vesicles prior to fusion with the ERGIC [42] . In support of an intimate association between ERES and inclusions , five members of the p24 family , proteins which regulate ERES organization and are packaged into COPII vesicles [43] , were identified by inclusion-MS and AP-MS [15 , 16] . To validate these findings , we used immunofluorescence microscopy to investigate the spatial relationship between the ERES marker Sec16 and the inclusion membrane . In uninfected cells , Sec16 was distributed in a normal pattern throughout the cytoplasm in small punctae , with a dense cluster of Sec16 in the perinuclear region ( Fig 5A ) . In C . trachomatis infected cells , Sec16 redistributed in a punctate pattern around the inclusion at 24 hpi ( Fig 5A ) . We quantified the redistribution of the dense perinuclear cluster of Sec16 around the inclusion membrane using CellProfiler to identify the dense areas of Sec16 and determine how far around the inclusion they are distributed ( S4A Fig ) [44] . The mean fraction of the inclusion perimeter surrounded by clustered Sec16 was 0 . 577 ( SD = 0 . 210 ) . About 50% of inclusions had at least 70% or more of the perimeter surrounded by clustered Sec16 . Several Sec16 punctae closely abutted inclusion membranes defined by IncA ( Fig 5A , inset ) . Sec16 was also recruited to inclusions at 14 hpi ( S5 Fig ) . We further examined the ectopic distribution of Sec16 in live cells using HeLa cells transfected with Sec16-GFP and infected with a C . trachomatis strain expressing mCherry and observed a similar association of Sec16 with inclusions ( S6 Fig ) . Next , we tested whether the COPII coat protein Sec31 associated with inclusion membranes in a similar manner , and as would be expected for functional ERES . Like Sec16 , Sec31 was redistributed around inclusion membranes , with some Sec31 foci appearing to overlap with the inclusion membrane ( Fig 5B , S5 Fig ) . We quantified Sec31 clusters in the same way as Sec16 , and the mean fraction of the perimeter surrounded was 0 . 66 ( SD = 0 . 247 ) , with 56% of inclusions having at least 70% of the perimeter surrounded by Sec31A clusters ( S4B Fig ) . Together , these data demonstrate that structural and regulatory ERES proteins are recruited to inclusion membranes . It is unclear if these ERES proteins are on the ER , on COPII vesicles , or on the inclusion membrane . The localizations of ERES , ERGIC , and cis Golgi network , are closely related in mammalian cells . Secretory cargo in COPII-coated vesicles leave the ER at ERES , quickly fuse with the ERGIC , and then are packaged into COPI-coated vesicles for transport to the Golgi . To check if the Golgi was necessary for the distribution of ERES to Chlamydia inclusions , we resolved the localization of the Golgi and ERES in infected cells with or without Brefeldin A ( BFA ) treatment ( S7 Fig ) . In untreated cells , the general localization of Sec31 and Golgi were similar , although the individual components did not colocalize . After BFA treatment , the Golgi was dispersed , whereas Sec31 remained distributed in close proximity to inclusions . We next tested if functional ERES were required for Chlamydia infection , by using a specific inhibitor of ERES export , FLI-06 [45 , 46] . FLI-06 is a cell permeable , reversible inhibitor of ERES cargo loading and the early secretory pathway . The molecular target of FLI-06 target is unknown; however , the compound has been shown to prevent cargo recruitment to ERES . Any cargo already recruited is secreted , but no new cargo can be loaded into exit sites . First , we investigated how the localization of Sec16 and Sec31 were affected by incubation of C . trachomatis infected cells with FLI-06 . Treatment of infected cells for 4 h with FLI-06 , from 20–24 hpi , abrogated the recruitment of both of these ERES proteins to inclusions , resulting in diffuse localization similar to that seen in uninfected cells treated with FLI-06 ( Fig 6A , S8 Fig ) . Similar effects were observed with live cells expressing Sec16-GFP ( S5 Fig ) . The number of Sec31 punctae that overlapped with IncA were counted and confirmed that treatment with FLI-06 resulted in significantly reduced COPII coat protein recruitment to inclusion membranes ( Fig 6B ) . These data indicate that Sec16 and Sec31 recruitment to inclusions are a functional consequence of COPII vesicle formation , and not a byproduct of their proximity to ER membranes . To explore this outcome further , we tested the effect of FLI-06 on chlamydial developmental growth . C . trachomatis infected cells were treated with three concentrations of FLI-06 at distinct stages of infection: 2 . 5 , 18 , 24 , and 40 hpi ( Fig 7A ) . Following treatment , the effects of FLI-06 on primary infection and infectious progeny formation were determined by measuring inclusion diameter and IFU , respectively , at 48 hpi for all treatment groups ( Fig 7B and 7C ) . We measured a significant , dose-dependent reduction in inclusion diameter for infected cells treated starting at 2 . 5 or 18 hpi ( Fig 7B , S9 Fig ) . Inhibitory effects were most pronounced with 10 μM of FLI-06 , a concentration previously shown to block the recruitment of VSVG cargo to ERES [45 , 46] . The effects of FLI-06 were reversible , as treatment of infected cells at 18 hpi followed by washout at 24 hpi resulted in a recovery of inclusion diameter at 48 hpi ( Fig 7B ) . For cells treated with 10 μM FLI-06 at 2 . 5 hpi , fully formed inclusions at 48 hpi were rarely observed , indicating that early stages of Chlamydia and inclusion growth are reliant on COPII vesicle production from ERES . Two to 10-fold lower concentrations of FLI-06 resulted in dose-dependent phenotypes , thus strengthening the support that the effects of FLI-06 were biological . The impact of FLI-06 on C . trachomatis IFU formation was more pronounced . Treatment of infected cells with 10 μM FLI-06 at 2 . 5 hpi resulted in no infectious progeny production , and a 99 . 8% or 95 . 6% reduction in Chlamydia IFU was observed in cells treated with 10 μM FLI-06 at 18 or 24 hpi , respectively ( Fig 7C ) . Similar to the effects on primary infection , the impact of FLI-06 on Chlamydia growth was dose dependent . Cells pulsed with FLI-06 from 18–24 hpi partially recovered from the treatment , though IFU was reduced by 57 . 0% compared to untreated cells; this indicates that replication was reduced even during a 6-hour treatment . No effect on IFU production occurred when FLI-06 was applied to cells at 40 hpi , indicating that FLI-06 is not directly toxic to Chlamydia and that the effects of ERES on chlamydial inclusions were not on EB viability or infectivity . The potent effects of ERES disruption on inclusions at 18 hpi , when inclusions contain mostly RB , were consistent with an inhibitory effect that most heavily impacts RB growth . The application of FLI-06 at 18 hpi prevented the population of bacteria at that stage from converting into infectious EB by 48 h . Taken together , the data demonstrate that early C . trachomatis inclusions acquire vital cellular components from ERES-derived COPII vesicles for their complete developmental growth . Since FLI-06 blocks secretion from ERES , it has severe effects on downstream steps of the secretory pathway and disrupts the Golgi [45] . Chlamydia is known to acquire sphingomyelin and cholesterol from the trans Golgi network . To test whether Golgi disruption contributed to the effects of FLI-06 on chlamydial growth , we used a chemical inhibitor with a different mechanism from FLI-06 . Sodium phenylbutyrate ( 4PBA ) has been used for many years to relieve ER stress by preventing accumulation of misfolded proteins within the ER . Recently it was shown that the secretion of misfolded proteins during 4PBA treatment happens through decreased selectivity of COPII cargo [46] . 4PBA binds the p24 family of proteins , which provide cargo specificity for COPII vesicles , concentrating secreted proteins into vesicles while excluding ER resident proteins [47] . COPII vesicles produced in cells treated with 4PBA show dramatically increased levels of ER resident proteins , along with decreased levels of normal COPII cargo [47] . We repeated the same experiments done with FLI-06 using 4PBA instead and saw similar results on both inclusion diameter and IFU ( Fig 7D and 7E ) . When 4PBA was added very early during infection , inclusions were significantly smaller ( Fig 7D ) and almost no viable EB were produced ( Fig 7E ) . Consistent with 4PBA having a less severe effect on ERES compared to FLI-06 , IFU of cells treated at 18 or 24 hpi with the highest dose of 4PBA was 79 . 5% or 59 . 25% reduced , respectively , much less severe than cells treated with FLI-06 at the same times . IFU was not affected for cells treated with 4PBA from 18–24 hpi then washed off , or cells treated starting at 40 hpi , supporting that 4PBA is not directly toxic to Chlamydia . Since 4PBA allows protein secretion and still affects Chlamydia growth , this indicates that the effects of FLI-06 were unlikely to be a nonspecific effect of blocking protein secretion of the ER or Golgi , and COPII cargo regulation is important for the Chlamydia developmental cycle . When FLI-06 or 4PBA were added early during infection , the inclusion diameter and IFU correlated well , with both being significantly lower than the control . When added at 24 hpi , however , both inhibitors did not significantly affect inclusion diameter , yet IFU for FLI-06 was reduced by 95 . 6% and for 4PBA was reduced by 59 . 25% ( Fig 7C and 7E ) . We tested whether the reduction in IFU was due to fewer bacteria present , or if it resulted from disrupted RB-EB conversion . We infected cells with C . trachomatis; at 24 hpi we added 10 μM FLI-06 , 5 mM 4PBA , or 0 . 5 μg/mL chloramphenicol , then incubated until 48 hpi , extracted genomic DNA and used qPCR to measure Chlamydia genome copy number ( Fig 7F ) . As expected , chloramphenicol inhibited Chlamydia replication and caused a 67 . 7% reduction in the number of bacterial genomes present , compared to the control . FLI-06 caused a 40 . 4% reduction in genome copy number , and 4PBA genome copy number was not significantly different from the control . This indicates that adding FLI-06 at 24 hpi caused some reduction in bacterial replication , but the drastic reduction in IFU must at least in part be due to reduced bacterial infectivity after treatment . 4PBA had a less severe effect on IFU , and the genome copy number supports that replication was not significantly reduced when added at 24 hpi , but there is a reduction in bacterial infectivity . Given other known effects of 4PBA , it is possible that its effect on Chlamydia growth was due to decreased ER stress rather than dysregulation of COPII cargo . Because FLI-06 blocks secretion from the ER , it is also possible that ER stress was induced during FLI-06 treatment and resulted in a detrimental effect on Chlamydia growth . We checked if ER stress is induced during C . trachomatis infection under normal conditions , or after 4PBA or FLI-06 treatment . We compared ER stress in uninfected or Chlamydia-infected HeLa cells at 24 hpi after a 4 hour treatment with FLI-06 , 4PBA , or thapsigargin , a well described inducer of ER stress [48] . ER stress was assessed by mRNA expression of CHOP , BIP , and spliced XPB1 . No significant differences were found between ER stress levels in infected cells and uninfected cells for all conditions tested ( Fig 7G ) . Although CHOP , BIP , and sXBP1 expression were increased in FLI-06 treated cells , this increase was not statistically significant . Thapsigargin was able to induce much higher expression of CHOP , BIP , and sXBP1 . Overall , these data indicate that it is unlikely that 4PBA or FLI-06 are acting by altering the levels of ER stress in infected cells . C . trachomatis inclusions form membrane contact sites with the ER , and whether ERES are recruited by the same mechanism is unclear [36 , 37] . Previously , the lipid transfer protein CERT was shown to be a key component of ER membrane contact sites with inclusions [36 , 49]; however , CERT did not colocalize with ERES markers ( Fig 8A ) , indicating that ERES are likely an additional type of ER–inclusion interaction . Since FLI-06 and 4PBA act on the ER , it is possible they could indirectly affect Chlamydia growth by reducing contacts between the inclusion membrane and ER . To test this , we overexpressed fluorescent protein tagged CERT or the ER resident protein PDI and saw no difference in their recruitment to inclusion membranes after treatment with FLI-06 or 4PBA ( Fig 8A and 8B ) . We quantified the overlap between IncA and CERT or PDI using Manders’ coefficients and there were no significant reductions in CERT or PDI overlap with IncA following treatment with 4PBA or FLI-06 , compared to DMSO ( S10 Fig ) [50] . BFA was included as a control to ensure any effects were due to the ER rather than post-ER trafficking steps . CERT recruitment to the inclusion membrane is thought to facilitate non-vesicular ceramide trafficking , whereas the Golgi provides a vesicular route for ceramide uptake into the inclusion . To ensure that these inhibitors did not act through downstream effects on the Golgi , we evaluated Golgi morphology in infected cells treated with 4PBA , FLI-06 , or BFA from 20–24 hpi . As expected , the Golgi marker GM130 was distributed around the inclusion in control cells and those treated with 4PBA , while cells treated with BFA and FLI-06 had faint , diffuse GM130 staining ( S11 Fig ) . Although the Golgi was not disrupted by 4PBA , it is possible that there is still an effect on the acquisition of Golgi derived vesicles by Chlamydia . To test this , we used fluorescently labeled NBD-C6-ceramide as a marker for sphingomyelin uptake into the inclusion , as described previously [51] . HeLa cells were infected with C . trachomatis and grown for 22 hours . Cells were then treated with either 10 μM FLI-06 , 5 mM 4PBA , or 3 μg/mL BFA for 1 hour , then incubated for 30 minutes with NBD-ceramide , followed by 1 . 5 hours back-exchange in the presence of ERES inhibitors or BFA . Fluorescence microscopy was used to assess mean fluorescence of the inclusions with different treatments . Similar to previous studies , treatment with BFA reduced ceramide uptake by 58 . 0% ( Fig 8C ) . FLI-06 reduced uptake by about the same amount , 52 . 9% , indicating that although FLI-06 disrupts the Golgi , it does not affect chlamydial ceramide acquisition any more than BFA . Since BFA has no effect on IFU , it is unlikely that the reduction in IFU seen following FLI-06 treatment is due to the disruption of the Golgi . This is further supported by 4PBA having no discernible effect on ceramide uptake ( Fig 8C ) . We next sought to determine the specific ERES and COPII associated proteins necessary for providing factors critical for chlamydial growth . Using RNAi , we knocked down the expression of Sec16A and TANGO1 , proteins which are required for efficient COPII transport [41] . We additionally knocked down cTAGE5 and Sec12; Sec12 is the guanine exchange factor for Sar1 GTPase , which in turn regulates COPII vesicle formation [52] . Sec12 also interacts with cTAGE5 at ERES , and it has been shown that cTAGE5 can recruit Sec12 to COPII budding sites [53] . Bet3 , a key component of the TRAPP complex that functions in COPII vesicle tethering and fusion at the ERGIC , was also knocked down [54] . HeLa cells were transfected with siRNA oligos and incubated for 48 hr prior to infecting with C . trachomatis . At 48 hpi , cells were lysed and chlamydial IFU was quantified on fresh HeLa monolayers . Knockdown of Sec12 and cTAGE5 resulted in a 49 . 1% and 58 . 4% reduction in IFU , respectively ( Fig 9A , S12 Fig ) . Simultaneous knockdown of both Sec12 and cTAGE5 reduced IFU by 85 . 2% . Surprisingly , Sec16A and TANGO1 depletion had no effect on IFU . Bet3 disruption also had no effect on IFU; however , this result was expected since Bet3/TRAPP functions downstream of ERES and COPII vesicle formation , as a tethering complex on the ERGIC and cis-Golgi . ERES proteins typically exhibit similar subcellular localizations , and we used immunofluorescence microscopy to determine if Sec12 had a similar distribution as other key COPII components during Chlamydia infection . Sec12 colocalized with Sec31 in infected cells ( Fig 9B ) , and both proteins remained closely associated after treatment with FLI-06 or 4PBA ( S13 Fig ) . Sec12 also overlapped with IncA in distinct punctae , similar to the overlap seen with Sec31 or Sec16 and IncA ( Fig 9C ) . While it is unclear why Sec12 and cTAGE5 depletion affect IFU while Sec16 and Tango1 have no effect , it seems that Chlamydia may require a specific cargo or function of ERES rather than general COPII vesicular trafficking . It is also possible that there is redundancy in the roles of Sec16 and Tango1 during infection . Future efforts will need to resolve whether the basis of this interaction is to provide COPII vesicular cargo to inclusions , or to allow Chlamydia to interfere with an ERES-mediated process critical for infection .
Establishment and maintenance of the inclusion is critical to Chlamydia’s ability to infect and grow within host cells . Bioinformatic analysis of the C . trachomatis genome predicts over 50 type III secreted Inc transmembrane proteins [7 , 9 , 11]; however , little is known about the broad spectrum of host proteins that are recruited to the inclusion membrane . Recent proteomic studies have provided a major , initial snapshot of proteins that comprise mature inclusions [16] and that associate with exogenously expressed Inc proteins [15] . However , we still lack an understanding of host factors recruited to inclusions in their endogenous host cell setting , how the inclusion membrane interactome changes throughout the developmental cycle , and in particular what signaling pathways are critical for inclusion biogenesis . To advance knowledge in these important unexplored areas , we developed the APEX2 proximity-dependent biotinylation platform for Chlamydia , to allow type III mediated expression of Inc proteins , fused to APEX2 , on the inclusion membrane . Using this approach , we obtained proteomic data for the dynamic recruitment of 452 host proteins to C . trachomatis inclusion membranes; this work strengthens existing proteomic datasets and additionally provides new insight into the Chlamydia–host interactions that shape the chlamydial intracellular inclusion niche [15 , 16] . APEX2 proximity dependent proteomics represents a powerful tool for investigating host–pathogen interactions . Previous work has demonstrated the efficacy of tagging IncA and IncF with APEX2 [17] , and we urge the field to exploit this system to accelerate our understanding of the molecular functions of chlamydial type III secreted proteins . A major finding of this study was the population of proteins assembled on early inclusions , as this subset is predicted to contain proteins important for regulating processes that shape inclusion biogenesis . Our data show that early inclusions , containing only a few bacteria , were enriched in proteins associated with the early secretory pathway and distinct cellular processes . In accordance with the need of Chlamydia to scavenge nutrients and energy from the host cell , proteins important for glycolysis were identified , including aldolases , transaldolase , transketolase , peroxiredoxins , and pyruvate kinase . Although these proteins were largely not identified at later time points , it is unclear if they are no longer in close proximity to the inclusion , or they represent a much smaller percentage of the inclusion membrane proteome at later times and were simply not detected . In addition , a large number of ER proteins were proximity labeled by IncB-APEX2 , for example protein disulfide isomerase , calreticulin , calnexin , endoplasmin , apolipoprotein B-100 , STIP1 , and EMC10 . Finally , members of the 14-3-3 protein family , serpins , and several cytoskeletal proteins—alpha-actinin-4 , emerin , myosins , MYPT1 , tropomyosins—were found to be in close proximity to inclusion membranes . We elected to perform a high throughput RNAi screen in order to test candidate proteins from the 8 hpi proteomic data . Depletion of many of these early association factors led to alterations in chlamydial growth , as measured by the production of IFU . This approach contained single siRNA oligonucleotides for each target , so follow-up studies would have to use independent sequences to ensure off-target effects did not affect IFU . Similarly , we did not assess the effect of knockdown on chlamydial entry into the cell so that could be another factor that impacted IFU . Among proteins recruited to early inclusions , RNAi knockdown of tropomyosins yielded unexpectedly large and disparate effects on bacteria growth . Knockdown of TPM2 enhanced IFU by over 2-fold , while knockdown of TPM4 resulted in close to 2 fold reduced IFU . Although tropomyosins affect actin filament stability , the different forms are thought to be functionally distinct . Our study provides a third proteomic mapping of the inclusion membrane interactome , with each effort exploiting unique approaches and technological systems . We now have the opportunity to synthesize these proteomic datasets to derive a list of ‘high confidence’ interactions that were identified by all three proteomics studies and develop an understanding of multiprotein interactions that may occur with specific C . trachomatis Incs . Six proteins represented high confidence proteins recruited to early inclusions , three of which were previously shown to be recruited to inclusions: PDIA1 ( PDI ) [55] , ASPH , MYPT1 [56] , LRRF1 , 14-3-3β [57] , and LRC59 . By 16 hpi , seven additional high confidence proteins were found to interact with inclusion membranes across all three proteomic studies: VAPA [49] , VAPB [58] , TMOD3 , SNX1 [15 , 16] , STX7 [16] , BAP31 , and CLINT1 . Finally , 24 hpi C . trachomatis inclusions were consistently enriched with three additional host proteins: EGFR [59] , 14-3-3ζ [60] , and RTN4 [37] . The high frequencies with which these proteins have been independently demonstrated to associate with chlamydial inclusions strongly suggests that their enrichments in proteomic data sets are not merely a byproduct of high protein abundance in cells . Interestingly , BAP31 , ASPH , and LRC59 are normally associated with the ER; LRC59 additionally interacts with FGF , and C . trachomatis EB have been shown to interact with FGFR on the cell surface [61] . Another lens with which to interpret inclusion interactome data is towards resolving protein networks that are intimately associated with a particular Inc protein . For example , our data revealed the recruitment of DYHC1 , PCM1 , MARE1 , MAP1B , and PLK1 , all annotated binding partners of DCTN2 , to the inclusion membrane , and these interactions may be mediated through CT192 , an Inc protein shown by AP-MS to also directly interact with DCTN2 [15] . Finally , our study indicated that chlamydial proteins constitute a small portion of the overall inclusion membrane proteome , as compared to host proteins . Only 11 Inc proteins were identified across all time points , suggesting that additional Incs may be expressed at lower levels; Incs may also be difficult to detect compared to many more abundant host proteins . Endogenous IncB colocalizes with CT101 , CT222 , and CT850 in microdomains on the inclusion membrane , but we did not detect these Incs in this study [26] . Co-immunoprecipitation with the microdomain-localizing Incs suggested that IncB does not bind CT101 , CT222 , or CT850 , and colocalization by microscopy does not necessarily suggest that these Incs should be biotinylated by APEX more than other Incs that we detected . This may be due to mislocalization of IncB-APEX2 or these other microdomain-localized Incs may be low in abundance compared to the other Incs detected . The chlamydial protease CPAF has been shown to degrade certain host and chlamydial proteins post-cell lysis [62–64] . It is possible CPAF degraded some Incs and host proteins post-lysis and the degraded proteins were missed by mass spectrometry . IncB has no known binding partners , but many Incs have been shown to dimerize or bind other Incs [65] . This suggests another possibility that most Incs are sequestered into heteromeric complexes , such that even with induced overexpression , IncB based proximity labeling was unable to access the full repertoire of Incs present on the inclusion membrane . This outcome may also represent a limitation of the APEX2 approach for capturing all proximal proteins on inclusion membranes . It may be helpful in future experiments to do mass spectrometry analysis on a membrane fraction of cells following APEX2 labeling to enrich for membrane-integral proteins of the inclusion membrane . Some growth attenuation was observed for the IncB-APEX2 expressing strain , and IncB-APEX2 overexpression may perturb the distribution of endogenous Incs . Future efforts by the field should therefore focus on determining what extent of the inclusion membrane interactome is Inc-specific , and which Incs control the recruitments of host targets . The goal of this study was to exploit IncB-APEX2 to identify and compare proximal proteins to C . trachomatis inclusions across a temporal dimension , through generating quantitative mass spectrometry data at three discrete stages of Chlamydia and inclusion growth . An important limitation of this approach is that IncB-APEX labeled proteins were not cross-compared spatially against APEX2-tagged probes placed at other regions of host cells . In this study , we also report the novel interaction between C . trachomatis inclusions and ER exit sites . Importantly , functional disruption of ERES cargo loading or specificity using chemical and genetic approaches resulted in major defects on inclusion and chlamydial growth . The ERES associated proteins Sec16 , TANGO1 , peflin , and TFG , were identified by IncB-APEX2 as inclusion membrane associated proteins and follow up investigations confirmed the recruitment of Sec16 and the COPII coat protein Sec31 to the cytosolic surface of inclusion membranes . Inclusion–ERES interactions seem to be distinct from previously described IncD- and IncV-mediated ER–inclusion membrane contact sites ( MCS ) [34 , 36 , 37 , 49 , 58] , since the ER membrane protein CERT , which interacts with IncD at MCS , did not colocalize with ERES proteins . Localization of CERT and ER marker PDI was unaffected by inhibition of ERES using FLI-06 or 4PBA . In this regard , our findings strengthen the theme of inclusion membrane and ER interactions as playing important roles for inclusion biology and function during infection . Atlastin-3 and reticulon-4 , which play roles in regulating ER morphology and structure [66 , 67] , were also identified by our data and inclusion-MS [16] . In addition to ERES and COPII components , inclusion membrane interactions contain ERGIC-53 and VIP36 [16] , two homologous proteins which are primarily localized to the ERGIC and function to regulate COPII vesicle fusion , and ER-ERGIC-Golgi syntaxins 5 and 18 [16] . We propose C . trachomatis inclusions intimately interact with COPII mediated ERES to ERGIC vesicular traffic . Reasons for the importance of Sec12 and cTAGE5 , but not Sec16 or Tango1 , in the interaction between ERES and inclusion membranes are at this time unclear . Recent work has shown that nutrient starvation induced Sec12 and cTAGE5 relocation to the ERGIC followed by generation of autophagosome membrane precursors [68 , 69] . There are likely many other roles of ERES that have not yet been described , and the molecular mechanisms of ERES and ERGIC regulation are incompletely understood . FLI-06 blocks cargo recruitment to ERES , but unfortunately there are no known proteins involved in this step of COPII trafficking . The targets of 4-PBA , the p24 ( also called TMED ) family , would be very interesting to study in the context of Chlamydia infection , especially since five of these proteins were found by AP-MS or inclusion-MS [15 , 16] . These experiments may prove challenging , however , as there are 11 members and they are thought to have functional redundancy . Collectively , the functional and proteomic data show that complete developmental growth of Chlamydia requires efficient COPII vesicle production from ERES throughout the developmental cycle . One attractive nutritive benefit for Chlamydia is the acquisition of lipids , as supported by evidence that chlamydiae acquire phosphatidylcholine and other phospholipids from host cells [70 , 71] , recruit lipid droplets to inclusions [72] , redirect cholesterol to inclusions [73] , and require functional host fatty acid synthesis machinery [72 , 74] . Much of our understanding for ERES secretory mechanisms has come from studying the assembly of large cargo into COPII vesicles; for example procollagen , pre-chylomicrons , and very low-density lipoproteins [75] . For export of these bulky cargo , cTAGE5 and TANGO1 are essential . In our system , cTAGE5 disruption impaired IFU production , whereas TANGO1 knockdown had no discernible effect . An explanation for these findings is elusive , notably because only a preliminary functional characterization of these regulatory proteins exists . Interestingly , cTAGE5 and the TANGO-related gene MIA2 can form a fusion protein ( TALI ) which binds TANGO1 and facilitates the recruitment of apolipoprotein B containing lipid complexes to ERES [76] . Based on our proteomic data , apolipoprotein B is recruited to inclusion membranes throughout the developmental cycle .
All reagents were purchased from Thermo Fisher Scientific ( Rockford , IL ) unless otherwise noted . Primary antibodies used in this study and their catalog numbers: Sec12 ( Western blot , PA5-53125 ) , Sec12 ( Immunofluorescence , gift from Kota Saito ) , cTAGE5 ( PA5-29515 ) , Sec31A ( Cell Signaling , Danvers , MA; 13466S ) , Bet3 ( TRAPPC3; PA5-55841 ) , Sec16A ( Bethyl Laboratories , Montgomery , TX; A300-648A-M ) , TANGO1 ( MIA3; Millipore Sigma , St . Louis , MO; SAB2700012 ) , GM130 ( R&D Systems , AF8199SP ) , IncA ( gift from Daniel Rockey ) , CT223 ( gift from Daniel Rockey ) , MOMP ( Virostat , Westbrook , ME; 1621 ) , Flag ( Millipore Sigma; F1804 ) . Secondary antibodies used: Donkey anti-Rabbit HRP , Donkey anti-Goat Alexa 594 , Goat anti-Mouse DyLight 594 , Goat anti-Rabbit Alexa 488 , Streptavidin Alexa 488 . HeLa 229 cells ( ATCC ) or McCoy cells ( obtained from Walt Stamm ) were grown in Roswell Park Memorial Institute 1640 ( RPMI; Gibco ) medium supplemented with 10% fetal bovine serum ( HyClone ) and 2 mM L-glutamine ( HyClone ) at 37°C , 5% CO2 . Chlamydia trachomatis LGV L2 434/Bu was grown in HeLa 229 cells for 48 hours , then infected cells scraped into sucrose phosphate buffer ( 5 mM glutamine , 0 . 2 M sucrose . 0 . 2 M phosphate ) , lysed using bead bashing , and cell debris cleared by centrifugation at 300 × g for 10 minutes . Supernatant containing Chlamydia aliquoted and stored at -80°C . HeLa cells were infected with Chlamydia diluted in Hank’s buffered salt solution ( HBSS; Gibco ) at an MOI ~1 for 2 hours at room temperature . Cells were washed with HBSS , then incubated at 37°C in RPMI . Plasmids were isolated using the Qiaprep Spin Miniprep kit or the HiSpeed Plasmid Midi kit ( Qiagen , Germantown , MD ) . Chlamydia plasmid expressing IncB-APEX2 fusion was made in a modified version of the pASK-GFP parent vector provided by Scott Hefty [29] , where GFP was replaced with IncB-APEX2 [19] and the mKate2 sequence was removed . The plasmid containing the APEX2 sequence was a gift from Scott Hefty . Plasmid grown in dam- E . coli ( C2925; NEB , Ipswich , MA ) prior to Chlamydia transformation . Plasmid transformed strains of C . trachomatis L2 were generated using established procedures [24] . Three different dilutions ( undiluted , 1:2 , 1:10 ) of C . trachomatis L2 stocks were made in 50 μL calcium chloride buffer ( 20 mM Tris pH 7 . 4 , 100 mM CaCl2 ) . In the same buffer , 3 ug plasmid was diluted to 50 μL and added to diluted Chlamydia . The 100 μL Chlamydia–plasmid mixture was incubated for 30 minutes at room temperature . McCoy cells were trypsinized and diluted to a final concentration of 4 x 107 cells/ml in calcium chloride buffer . After 30-minute incubation , 100 μL diluted McCoy cells were added to Chlamydia and DNA mixture , and incubated for 20 minutes . In a 6 well plate , 100 μL McCoy cells , plasmid , and Chlamydia mixture were added to 2 mL medium . At 12–15 hours post infection , cells were washed and medium added containing 2 . 5 U/mL Penicillin G ( Millipore Sigma ) and 1 ug/mL cycloheximide . Cells were incubated for 48 hours , then Chlamydia passaged onto fresh McCoy cells . After first passage , cells were grown in medium containing 10 U/mL Penicillin G and 1 ug/mL cycloheximide . Transformants were passaged at least 2 more times , every 48 hours , until inclusions were apparent . Stocks of transformed Chlamydia were frozen at -80°C as described above . Transformants were not plaque purified to obtain a clonal population but expression of Inc-APEX2 fusions was assessed by microscopy to check for uniform expression and localization following induction with ATc . Mammalian plasmid pmScarlet-CERT was made in the pEGFP-N1 backbone , with Gibson Assembly used to replace EGFP with mScarlet . mScarlet was amplified from the pmScarlet-C1 plasmid , which was a gift from Dorus Gadella ( Addgene plasmid # 85042 ) [77] . CERT was inserted at the N terminus of mScarlet using the XhoI and AgeI restriction sites . The pmClover3-PDI plasmid was made in the pEGFP-N1 backbone . Gibson Assembly was used to replace EGFP with mClover3 , and PDI was inserted at the N terminus of mClover3 using Gibson Assembly . mClover3 was amplified from pKanCMV-mClover3-18aa-actin , which was a gift from Michael Lin ( Addgene plasmid # 74259 ) [78] . Plasmid pmGFP-Sec16L was a gift from Benjamin Glick ( Addgene plasmid # 15776 ) [79] . Biotin-phenol labeling of Chlamydia infected cells was adapted from described a protocol [80] . HeLa cells were grown in 8 well chamber slides ( for immunofluorescence detection of biotinylation; Nunc Lab-Tek ) , or a T75 flask ( for western blot and mass spectrometry ) , then infected with C . trachomatis L2 expressing IncB-APEX2 at an MOI of approximately 1 . Cells were incubated in RPMI containing 1 ng/mL anhydrotetracycline ( ATc; Acros Organics , New Jersey ) and 1 ug/mL cycloheximide ( Gold Biotechnology , St . Louis , MO ) . At 30 minutes prior to time point , 2 . 5 mM biotin-phenol ( Iris Biotech , Marktredwitz , Germany ) in RPMI was added to cells . At each time point , 30% hydrogen peroxide diluted to 100 mM working stock in Dulbecco’s phosphate-buffered saline ( DPBS; Gibco ) was added to cells at a final concentration of 1 mM and allowed to incubate with gentle rocking for 1 minute at room temperature . Labelling solution was aspirated , and cells were rinsed 3 times in quenching solution ( 10 mM sodium ascorbate , 5 mM Trolox , 10 mM sodium azide in DPBS ) . For immunofluorescence , normal fixation and staining protocols were followed . For western blot and mass spectrometry , cells were scraped into quenching solution and centrifuged for 5 minutes at 3 , 000 x g , 4°C . Cells were lysed by resuspending in ice cold RIPA buffer ( 50 mM Tris , 150 mM NaCl , 0 . 1% SDS , 0 . 5% sodium deoxycholate , 1% triton X-100 , pH 7 . 5 ) with Halt protease inhibitor cocktail ( Pierce ) and quenchers ( 10 mM sodium ascorbate , 5 mM Trolox , 10 mM sodium azide ) . Lysate was incubated for 2 minutes on ice , then sonicated 3 x 1 second at 20% amplitude , and clarified by centrifugation for 10 minutes at 15 , 000 x g , 4°C . Part of the lysate was reserved for western blot , and remaining lysate for each sample was snap frozen until mass spectrometry processing . Protein concentrations for individual samples were measured by BCA . The samples were normalized to 2 mg/mL for enrichment . Labeled protein lysates were enriched with streptavidin agarose resin ( Thermo Fisher Scientific , Rockford , IL ) . The resin was prepped for enrichment by placing the resin in a Bio-Rad chromatography column ( Bio-Rad , Hercules , CA ) on a vacuum manifold . The resin was washed with 0 . 5% SDS in PBS ( 1 mL , repeat 2× ) , 6 M urea in 25 mM ammonium bicarbonate ( NH4HCO3 ) ( 1 mL , repeat 2× ) , and PBS ( 1 mL , repeat 4× ) . The resin was transferred to 4 mL cryovials using two 1 mL aliquots of PBS . An additional 0 . 5 mL of PBS was added to each tube followed by 1000 μg of protein ( in 1 . 2% SDS in PBS ) . The total volume of each tube was set to 3 . 0 mL , giving a final SDS concentration of 0 . 2% . Tubes were rotated end over end for 4 hr at room temperature . Following streptavidin capture of biotinylated proteins , the solution was transferred into the Bio-Rad columns , and the solution was removed . The resin was washed with 0 . 5% SDS in PBS ( 1 mL , repeat 2× ) , 6 M urea in 25 mM NH4HCO3 ( 1 mL , repeat 2× ) , Milli-Q water ( 1 mL , repeat 2× ) , PBS ( 1 mL , repeat 8× ) , and 25 mM NH4HCO3 ( 1 mL , repeat 4× ) . The enriched resin was transferred to sealed 1 . 5 mL tubes using two 0 . 5 mL aliquots of 25 mM NH4HCO3 . Samples were centrifuged at 10 , 500 x g , and the supernatant was discarded . 6M Urea was added to the resin for each sample followed by 100 mM TCEP ( 20uL ) and place on a thermomixer for 30 min ( 1200 rpm at 37°C ) . After the samples were reduced , 200 mM iodoacetamide ( 20uL ) was added to alkylate the proteins . The resin was placed back on the thermomixer for 45 min ( 1200 rpm at 50°C ) and covered in foil . Following alkylation , the samples were returned to the Bio-Rad column and rinsed with PBS ( 1mL , repeat 8× ) and 25 mM NH4HCO3 ( 1 mL , repeat 4× ) . The enriched resin was transferred to sealed 1 . 5 mL tubes using two 0 . 5 mL aliquots of 25 NH4HCO3 . Samples were centrifuged at 10 , 500 x g , and the supernatant was discarded . Enriched biotinylated proteins were prepared for LC−MS/MS analysis . 25mM NH4HCO3 ( 200 μL ) was added to the resin for each sample , along with trypsin solution . Resin solutions were placed on the thermomixer at 37°C set at 1200 rpm set for overnight . Following trypsin digestion , the tryptic peptides were collected , and the resin washed once with 25 mM NH4HCO3 ( 150 μL ) . Volatiles were then removed from the combined tryptic peptide supernatant using a speed vacuum . The dried peptides were reconstituted in 25 mM NH4HCO3 ( 40 μL ) and heated for 10 min at 37°C with mild agitation . To remove any solid particulates , samples were centrifuged at 53 , 000 x g for 20 min at 4°C . From each ultracentrifuge vial was removed 25 μL for MS analysis . Samples were stored at −20°C until analysis . Tryptic peptides from enriched proteins were separated using in-house reverse-phase resin columns by LC and analyzed on a Thermo Fisher Velos Orbitrap MS as described previously [81] . Instrument data was acquired for 100 min , beginning 65 min after sample injection into the LC . Spectra was then collected from 400–2 , 000 m/z at a 100k resolution , following by data-dependent ion trap generation of MS/MS using the top six most abundant ions , a collision energy of 35% , and a dynamic exclusion time of 30 s for discriminating against previously analyzed ions . MS/MS spectra were searched using the MSGF+ algorithm and a tag-free quantitative accurate mass and time ( AMT ) tag approach for subsequent unique peptide to protein mapping using LC-MS peak feature detection , as described previously [82] with the following modifications . MS/MS spectra were searched against the following FASTA files for Chlamydia and Homo sapiens: Chlamydia trachomatis serovar L2 434 Bu pL2Plasmid 2018-01-05 and Homo sapiens Uniprot SPROT 2017-04-12 . Peptide MS/MS features from each MS dataset were filtered on an FDR of less than or equal to 1% . Unique peptides , requiring a minimum of six amino acids in length , were filtered using an MS-GF threshold of ≤ 1 ×10−9 , corresponding to an estimated false-discovery rate ( FDR ) <1% at a peptide level . Resulting relative peptide abundances , in replicate across 48 biological samples including control samples , were log transformed and the data was processed for quality control . Elimination of statistical outliers were confirmed using a standard Pearson correlation at a sample level [83] . Parameters for removing inadequate data for qualitative statistics required a minimum of two observations for a peptide across all groups to be compared quantitatively or identified in at least half the biological replicates for a given condition group using previously described methods [84] . Peptides were normalized using median centering , adjusting for overall differences in abundances across samples . Statistical comparisons were made between control and biotinylated groups at each time point and evaluated for quantitative differences using a standard 2-sample t-test and a qualitative difference ( presence/absence markers ) g-test . Statistical test used for each protein is shown in S2 Table . Additional evaluations were done in a similar manner for comparison of conditions across time . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository [85] with the dataset identifier PXD012494 and 10 . 6019/PXD012494 . For KEGG pathway analysis , UniProt identifiers of proteins were uploaded into InnateDB pathway analysis , then processed using the pathway overrepresentation analysis tool . The recommended settings were used for the analysis: hypergeometric algorithm , Benjamini Hochberg p value correction method . Only pathways with p < 0 . 05 after Benjamini Hochberg correction were listed . To assign subcellular localization ontologies to proteins , the Human Protein Atlas database ( version 18 ) was used [33] . Subcellular localization data for all proteins was downloaded , and data for proteins in IncB-APEX2 data set was extracted . Only subcellular locations with enhanced , supported , or approved reliability scores were used . Proteins with more than one annotated location were counted for each location . Highly similar categories were condensed , for example nuclear speckles , nucleoli , nucleoli fibrillar center , and nucleoplasm were all counted as nuclear lumen . Network analysis of APEX2 proteins was done using annotations from StringDB ( version 10 ) . Only interactions with evidence from experiments or databases were considered , with a confidence score of at least 0 . 700 ( high confidence ) . Proteins with no interaction partners were excluded . Annotated interactions were downloaded for the proteins each time point , and this data was compiled into a list of all interactions at different time points using Microsoft Excel . To make the graph more readable , proteins involved in translation were excluded . This data was imported into RStudio software and plotted using the tidygraph package [86] . Code details are provided in the supplemental methods . Graph layout was arranged using the Fruchterman Reingold algorithm , and subsets of closely interacting protein communities were detected using the Louvain method . General categories of some of the protein communities were assigned based on similarities between the UniProt descriptions of proteins in that particular community . If there were only 2 proteins in a group , or there was no consensus between the UniProt descriptions , no category was assigned . To compare to the AP-MS [15] and inclusion-MS [16] protein lists , full data sets were obtained from the supplemental data of each study . Proteins were matched based on UniProt identifiers , and all three data sets were combined using RStudio and Microsoft Excel ( S1 Table ) . For the AP-MS data , MIST scores are listed ( closer to 1 is better ) . All Inc proteins that were found to interact with the host protein were listed , with the MIST score from the first listed Inc retained . For APEX2 and inclusion-MS , p values are listed . For siRNA transfections , HeLa cells were plated in 24 well or 96 well plates to 60–80% confluence . For 24 well plates , 50 μL Opti-MEM ( Gibco ) medium containing 5 pmol siRNA oligonucleotides and 1 . 5 μL Lipofectamine RNAiMAX was added to each well . Following transfection , cells were incubated for 48 hours at 37°C prior to infection or protein analysis by western blot . For 96 well plate experiments , knockdowns were done in duplicate , with 10 μL Opti-MEM containing 1 pmol siRNA oligonucleotides and 0 . 3 μL Lipofectamine RNAiMAX added per well . Oligonucleotides were purchased from Dharmacon , sequences and catalog numbers listed in supplemental methods . Dharmacon siGenome smartpools of 4 different oligonucleotide sequences were used for Sec16 , Tango1 , and Bet3 knockdowns . Dharmacon siGenome individual oligonucleotides were used for all other knockdowns . For mammalian plasmid transfections , HeLa cells were plated in 4 well chamber slides ( Nunc Lab-Tek ) , and infected with C . trachomatis L2 . Immediately after infection , each well was transfected with 50 μL Opti-MEM containing 500 ng plasmid and 1 . 5 μL Lipofectamine 2000 . For qRT-PCR confirming siRNA knockdown , HeLa cells were plated in 24 well plates , and transfected with siRNA oligos as described above . Cells were incubated for 48 hours , then cell pellets were frozen until RNA extraction . For qRT-PCR measuring ER stress , HeLa cells were grown in 6 well plates and infected with C . trachomatis for 20 hours . At 20 hpi , 10 μM FLI-06 , 5 mM 4PBA , 1 μM thapsigargin or DMSO was added in fresh medium . At 24 hpi , cells were trypsinized and cell pellets frozen until RNA extraction . RNA was extracted using the Qiagen RNeasy mini kit ( Qiagen ) . cDNA was made using the iScript cDNA synthesis kit ( Bio-Rad ) . qRT-PCR reactions were set up with SsoAdvanced Universal SYBR Green Supermix ( Bio-Rad ) and run on the StepOnePlus Real-Time PCR system ( Applied Biosystems , Foster City , CA ) . GAPDH was used as the housekeeping gene , and the ΔΔCT method was used to calculate relative expression . Primer sequences are listed in the supplemental methods . For Chlamydia genome copy number analysis HeLa cells were infected with C . trachomatis for 24 hours , then 10 μM FLI-06 , 5 mM 4PBA , 0 . 5 μg/mL chloramphenicol , or DMSO was added in fresh medium . At 48 hpi , cell pellets were trypsinized , centrifuged at 300 x g for 5 minutes , then Chlamydia genomic DNA was extracted using the DNeasy Blood and Tissue kit ( Qiagen ) . qPCR was done with primers to GroEL2 ( sequences listed in supplemental methods ) . For immunofluorescence microscopy , cells were rinsed in HBSS , then fixed for 5 minutes in ice cold methanol ( Images with anti-Sec12 only , for methanol fixation permeabilization was skipped ) or 15 minutes in 3 . 7% paraformaldehyde in HBSS . Cells were rinsed 2 times in HBSS , then permeabilized with 0 . 5% triton X-100 for 15 minutes , and blocked in 1% bovine serum albumin ( BSA ) in PBS for 20 minutes . Samples were incubated with primary antibodies for 1 hour at room temperature in blocking buffer , then rinsed 3 times in blocking buffer , then incubated 45 minutes at room temperature with secondary antibodies in blocking buffer . DAPI was used to stain DNA , and added during the secondary antibody step . For biotin labeling , Streptavidin-Alexa 488 was incubated for 30 minutes at room temperature . For imaging of live cells , media was replaced with HBSS before imaging . Cells were imaged on a Nikon Ti-E inverted microscope and images were captured on a Hamamatsu camera controller C10600 . Images were processed using Volocity software ( PerkinElmer , Waltham , MA ) . Quantification of Sec16A and Sec31A distribution around the inclusion in infected cells was analyzed using CellProfiler ( Version 3 . 1 . 8 ) . Full pipeline available in S1 File . Representative images from certain steps in pipeline shown in supplemental methods . HeLa cells were plated on glass and infected with C . trachomatis L2 . Cells were fixed and stained as described above , with anti-Sec16A or anti-Sec31 , anti-IncA , and DAPI . 20x images were taken using the Nikon Ti-E inverted microscope , as described above . Separate channels of DNA , Sec31A or Sec16A ( will refer to as ERES channel ) , and IncA were loaded into the pipeline . Color images were converted to grayscale , ERES channel intensity was rescaled to make different image batches more comparable . IdentifyPrimaryObjects was used to identify inclusions , nuclei , and ERES . Inclusions were sometimes identified as nuclei in the DAPI channel , so these inclusions were subtracted from the nuclei objects using IdentifyTertiaryObjects . ERES objects in infected cells were retained using RelateObjects , while other ERES objects were not analyzed further . ERES in infected cells were merged using SplitOrMergeObjects so that all ERES associated with a certain inclusion are counted as one object . Inclusions and their associated ERES were counted using DisplayDataOnImage so any measurements could be associated with a certain inclusion . Inclusion perimeter was isolated by shrinking the inclusion objects by 2 pixels using ExpandOrShrinkObjects , then subtracting the shrunken inclusion from the full-size inclusion using MaskObjects . This left objects that were a 2-pixel wide perimeter around the inclusion . Next , any region of the inclusion perimeter that overlapped the ERES objects was masked . MeasureObjectSizeShape was used to measure the size of the inclusion perimeter , as well as the inclusion perimeter left after subtracting any portions that were overlapping ERES . This data was exported to Microsoft Excel , where the length of the inclusion perimeter that didn’t overlap ERES was subtracted from the full perimeter length . This value represents the length of the inclusion that overlaps ERES . This number was divided by the full length of the inclusion to get the proportion of the inclusion that is closely associated with concentrated ERES . Sec31A punctae overlapping the inclusion were counted manually using Volocity software . Spots that overlapped with IncA , or were touching IncA were counted as on the inclusion membrane . Region where inclusion is adjacent to nucleus was excluded from analysis . Images used for counting were 60x magnification , deconvolved z-series . Planes were examined individually to ensure overlap was in the same z dimension . At least 20 inclusions in control and FLI-06 treated cells were analyzed , for each of two independent experiments . Ceramide trafficking to the inclusion was measured as previously described , with slight modifications [87] . HeLa cells were infected with C . trachomatis at an MOI < 1 for 22 hours . Warm media containing 10 μM FLI-06 , 5 mM 4PBA , 3 μg/mL BFA , or DMSO was added to the cells for 1 hour at 37°C . NBD-ceramide was complexed to BSA by adding 10 μM NBD-ceramide to HBSS with 0 . 7% fatty acid free BSA ( Millipore Sigma ) and vortexing thoroughly . After 1-hour incubation with inhibitors , cells were rinsed 1 time with HBSS , then NBD-ceramide/BSA was added and cells incubated for 30 minutes at 4°C . Cells were rinsed two times in HBSS , then incubated for 1 . 5 hours at 37°C in serum free media with 0 . 7% fatty acid free BSA with same inhibitors as before . After 1 . 5 hours , stained nuclei using Hoechst and took images with fluorescence microscope . Analyzed 5 microscopy fields per condition using Volocity software . For each field , made 30 identical circles that fit inside nuclei to get mean background fluorescence , outlined 20 inclusions ( 100 total per replicate ) by hand to measure mean fluorescence inside inclusion . Outlines were done in Hoechst channel only to avoid bias , measurements were taken from NBD channel only . Background fluorescence was subtracted from inclusion fluorescence to get measurements shown in graph . Data compiled from three independent experiments . For quantification of CERT and PDI association with the inclusion following treatment with ERES inhibitors , HeLa cells were plated in glass chamber slides , transfected with pmScarlet-CERT or pmClover-PDI using Lipofectamine 2000 , following manufacturer’s instructions . Cells were then infected with C . trachomatis L2 at an MOI ~1 . At 20 hpi , warm media containing 10 μM FLI-06 , 5 mM 4PBA , 3 μg/mL BFA , or DMSO was added to the infected cells . At 24 hpi the cells were fixed and stained with anti-IncA and DAPI . 60x z-series images were acquired and deconvolved . Images were cropped to focus on specific inclusions , and all planes with IncA out of focus were removed and not included in image analysis . Images were all processed identically using an ImageJ macro ( available in supplemental methods ) . Images were separated by channel and channels were automatically thresholded using the Moments method ( CERT , IncA ) , or Triangle method ( PDI ) to get a binary image of each channel . These binary images were analyzed using the Coloc 2 plugin to obtain the manders M1 and M2 coefficients . Note that Coloc 2 has its own thresholding algorithm that did not work for our images , so we only looked at the M1 and M2 for imported images since they were thresholded before being run through Coloc 2 . These coefficients were exported to Prism for statistical analysis . For PDI , since the majority of the protein is not associated with the inclusion only the manders M2 corresponding to the percentage of IncA that colocalized with PDI . For CERT , we only reported the manders M1 which represents the percentage of CERT ( within the field ) that colocalizes with IncA . For western blot analysis of biotinylated proteins [80] , 6x protein loading buffer was added to lysates , samples were boiled for 5 minutes , followed by cooling on ice . 15 μl lysate was loaded and run on 10% SDS gel in Tris running buffer by SDS-PAGE . Proteins were transferred from gels onto Immobilon PVDF membrane ( Millipore Sigma ) with a Pierce G2 fast blotter in Pierce 1-Step transfer buffer , then blocked with 3% BSA in TBST overnight at 4°C . Blots were incubated with streptavidin-HRP in 3% BSA in TBST for 30 minutes at room temperature , washed 4 x 5 minutes in TBST , incubated with chemiluminescent substrate ( Li-Cor 92695000 , Lincoln , NE ) for 5 minutes , and imaged using a C-DiGit blot scanner ( Li-Cor ) . For western blot analysis of RNAi knockdowns , cell pellets were lysed in ice cold RIPA buffer ( 50 mM Tris , 150 mM NaCl , 0 . 1% SDS , 0 . 5% sodium deoxycholate , 1% triton X-100 , pH 7 . 5 ) with Halt protease inhibitors ( Pierce ) for 30 minutes on ice , with vortexing every few minutes . Lysates were centrifuged for 20 minutes at 14 , 000 x g , and supernatant added to 4x laemmli buffer ( Bio-Rad , Hercules , CA ) . Lysates were run by SDS-PAGE on 5–15% or 5–20% mini-PROTEAN TGX stain free gels ( Bio-Rad ) , transferred to Immobilon PVDF membrane ( Millipore Sigma ) , blocked for 1 hr in 5% milk-TBST , and labeled with antibody and digitally imaged as described above . Infected HeLa cells were lysed at 48 hpi by incubating in water for 20 minutes followed by pipetting to disrupt cells . Serial dilutions of lysate were plated onto fresh HeLa monolayers in a 96 well plate . At 24 hpi , cells were fixed and stained with DAPI and an anti-MOMP antibody . Using immunofluorescence microscopy , 10–15 fields per well were taken at 20x magnification . Inclusions and nuclei in each field were counted using the Fiji distribution of ImageJ [88] . The macro used to count inclusions and nuclei in images is provided in supplemental methods . The overall percentage of infected cells was used to compare IFU between conditions , and the relative IFU calculated for each experiment by comparing to control . For FLI-06 experiments , HeLa cells in 24 well plate were infected with C . trachomatis L2 at an MOI around 1 . FLI-06 was resuspended in DMSO to a concentration of 20 mM and frozen at -80°C until use . For treating cells , FLI-06 in DMSO was diluted 1:2000 in normal cell medium to make a final concentration of 10 μM FLI-06 and serial dilutions were used to make medium with 5 μM and 1 μM FLI-06 . At 2 . 5 , 18 , 24 , or 40 hours post infection , medium was aspirated from different wells in the plate and replaced with pre-warmed medium containing three different FLI-06 concentrations . Control well medium was aspirated and replace with fresh pre-warmed medium . For the 18-hour time point , two wells per concentration were prepared , one of which was aspirated and washed with HBSS at 24 hpi , then incubated in fresh medium . Since the stability of FLI-06 was unknown , medium in wells treated at 2 . 5 hpi was refreshed at 24 hpi , with identical concentrations of FLI-06 . At 48 hpi , primary infection was imaged on microscope and 10–15 brightfield images were taken per well at 20x magnification and inclusion diameter was measured using Volocity software . After images were taken , cells were lysed for IFU assay as described above . IFU was adjusted based on number of inclusions per field in primary infection due to differences in cell growth with the inhibitor . Statistical analysis was performed using GraphPad Prism software . Comparisons between IFU , inclusion diameter , or genome copy number were analyzed using one-way ANOVA with Dunnett’s test for multiple comparisons . Comparison for number of Sec31 punctae on the inclusion membrane with or without FLI-06 treatment were analyzed using an unpaired t-test with Welch’s correction . Ceramide uptake after treatment with inhibitors was analyzed using a one-way ANOVA with Tukey’s multiple comparisons test . ER stress in infected and uninfected cells after treatment with inhibitors was analyzed using a two-way ANOVA with Dunnett’s multiple comparisons test . Quantification of CERT or PDI association with inclusion was analyzed by one-way ANOVA . CERT was additionally analyzed with Dunnett’s multiple comparisons test . Graphs indicate mean and standard deviation , statistical significance is indicated as follows: * , p < 0 . 05; ** , p < 0 . 01; *** , p < 0 . 001; **** , p < 0 . 0001 . | Chlamydia are obligate intracellular bacteria that require the establishment and maintenance of a host vacuole ( the inclusion ) for their developmental growth . Chlamydiae modify the inclusion membrane through the secretion of type III membrane proteins and recruited host factors . These membrane modifications are predicted to transform the inclusion into a favorable replicating environment through the host protein networks they interact with . Despite the essentiality of the inclusion for Chlamydia , only a preliminary understanding exists concerning its membrane protein composition . In particular , the early mechanisms controlling the biogenesis of the inclusion are poorly understood . To develop a deeper understanding of the inclusion membrane interaction proteome , we capitalized on recent advances in Chlamydia genetics to express and localize a proximity-dependent biotinylation tag onto type III secreted inclusion membrane proteins . We then applied this system to identify by mass spectrometry the major host and chlamydial proteins recruited to early , mid , and late stage inclusion membranes of C . trachomatis infected cells . Interestingly , proteomic data showed enrichments of proteins with characterized roles in regulating endoplasmic reticulum exit sites ( ERES ) . Functional testing through protein knockdown and pharmacological inhibition demonstrated that interactions between early C . trachomatis inclusions and ERES are critical for promoting the developmental growth of Chlamydia . |
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Tumor therapy with replication competent viruses is an exciting approach to cancer eradication where viruses are engineered to specifically infect , replicate , spread and kill tumor cells . The outcome of tumor virotherapy is complex due to the variable interactions between the cancer cell and virus populations as well as the immune response . Oncolytic viruses are highly efficient in killing tumor cells in vitro , especially in a 2D monolayer of tumor cells , their efficiency is significantly lower in a 3D environment , both in vitro and in vivo . This indicates that the spatial dimension may have a major influence on the dynamics of virus spread . We study the dynamic behavior of a spatially explicit computational model of tumor and virus interactions using a combination of in vitro 2D and 3D experimental studies to inform the models . We determine the number of nearest neighbor tumor cells in 2D ( median = 6 ) and 3D tumor spheroids ( median = 16 ) and how this influences virus spread and the outcome of therapy . The parameter range leading to tumor eradication is small and even harder to achieve in 3D . The lower efficiency in 3D exists despite the presence of many more adjacent cells in the 3D environment that results in a shorter time to reach equilibrium . The mean field mathematical models generally used to describe tumor virotherapy appear to provide an overoptimistic view of the outcomes of therapy . Three dimensional space provides a significant barrier to efficient and complete virus spread within tumors and needs to be explicitly taken into account for virus optimization to achieve the desired outcome of therapy .
Tumor therapy with replication competent viruses ( oncolytic virotherapy ) is an exciting new field of therapeutics . In principle , amplification of the virus in target cancer cells could allow ongoing spread of the infection within the tumor and its eventual elimination [1 , 2] . The advantages of recombinant viruses for cancer therapy include ( i ) specific engineering for infection , replication and killing of tumor cells [1] , ( ii ) amplification of the therapy itself by the tumor , ( iii ) stimulation of an anti-tumor immune response by breakdown of tumor immune tolerance [3] , ( iv ) a bystander effect especially if the virus is armed with specific genes such as the sodium iodide symporter ( NIS ) [4] . With the exception of cancer therapy with recombinant chimeric antigen receptor ( CAR-T ) T cells , tumor virotherapy is an exercise in population dynamics in which the interactions between the virus , the tumor and the immune system determine the outcome of therapy [5–13] . Many mathematical models have been developed to describe the outcome of such interactions [5 , 6 , 8–13] . Most models are based on the Lotka-Volterra approach and assume mass action kinetics with well-mixed populations . As a result , the models are helpful in illustrating general principles but lack important features , in particular the spatial geometry of the cells in a tumor , to be of predictive value if applied to in vivo scenarios . This is a critical deficiency especially if we are to attempt optimization of therapy [9] . Durrett and Levin and many others have addressed the problem of spatial constraints on the interactions between populations in ecological systems [14–16 and reference therein] . More recently , Paiva et al described a three-dimensional computational simulator of tumor and virus interactions and concluded that complex dynamics are in place with the spatial arrangements between cells being important determinants of outcome [17] . Reis et al reported on a 3D computational model of cancer therapy that illustrated the important differences when considering dynamics in 2 versus 3 dimensions and how restricted the parameter space may be to achieve tumor eradication [18] . Wodarz and colleagues have reported on their work with agent based modeling of tumor virotherapy where space is explicitly considered [7 , 19] . Using experimental data on the spread of adenovirus in a monolayer ( 2D ) of 293T cells as a guide , they showed that various patterns of virus spread such as ‘hollow ring structure’ , ‘filled ring structure’ and a ‘dispersed pattern’ are possible and how space and virus/tumor cell parameters can interact to determine the outcome of therapy [7] . Ring structure formation is associated with a quadratic growth of the virus population which subsequently becomes linear . The dispersed pattern of spread is invariably associated with therapeutic failure while the ring structures may be associated with a cure especially if the center of the ring is associated with elimination of the target population and the virus continues to expand radially and catch up with all the target population ( since a boundary will be reached ) . Wodarz and colleagues found that the local dynamics on a smaller scale can predict the outcome of the spatially explicit system [7] . Interestingly , the experiments also showed two patterns of infection–limited spread versus robust expansion of the infected cell population . Which pattern the infection followed was established early on . Subsequently , they showed that in part this dichotomy in outcomes was due to interferon induction in infected cells that inhibited virus spread [19] . This suggests that there is a local race between spread of the virus against the development of an interferon response which limits viral replication . Modeling suggests that multiple infections by the virus are also necessary to explain the dynamics , especially when the populations are small . However , many modeling approaches described to date have generally lacked any experimental data to validate them . To address this problem , we have developed an in silico computational model that captures the dynamics between the tumor and virus populations in a spatially explicit manner ( two and three dimensions ) . We use in vitro 2D and 3D data to inform the model parameters and then use the computational model to explore various critical properties of oncolytic viruses . We show that the introduction of a third dimension alters the dynamics significantly and that this has important implications for the outcome of therapy .
To quantitate cell populations based on fluorescent imaging , we initially determined the pixel area that represents a cell . Two independent observers quantitated the number of cells ( n = 375 cells per time point per observer ) in a given area based on phase contrast images and the pixel area of the cells in the corresponding fluorescent images . The median number of pixels was 326 . 9 versus 323 ( p = 0 . 8723 , Mann Whitney ) for each observer . There was excellent agreement between the two observers ( Fig 1A ) . This means that the inter observer variability in cell area was 1 . 1% . The number of cells present in a growing population was measured at 6 different time points . We determined that the cell populations grew exponentially in 2D culture ( Fig 1B ) . The estimated doubling time for the population was 28 hours based on an exponential fit to the data . In contrast , the rate of replication of individual cells based on serial tracking was estimated to be 21 . 9 hours ( Fig 1C ) and varied from 20 hours for the first replication ( n = 127 events ) to 22 . 5 hours for the second ( n = 97 events ) . The difference between these two observations was not statistically significant ( Wilcoxon sign rank test , p = 0 . 6563 ) . There was a strong positive correlation between the two cell cycle times observed ( Spearman’s rho = 0 . 795 , p = 0 . 0072 ) . We measured the growth of tumor cells in 3 dimensions serially by imaging multiple spheroids at specific time points ( Fig 2A ) . Each spheroid was monochromatic , implying that each spheroid arose from one founder cell even though a mixture of HT1080 cells with all 4 colors ( blue , yellow , green and red ) were plated . We found a linear increase in diameter of the spheroids as a function of time although there was considerable variability as the spheroids grew ( Fig 2D ) . The median increase in spheroid diameter was 15μm/day or 0 . 63 μm/hr . In addition , we also determined the radius of gyration of representative spheroids ( n = 12 ) across the 3 axes of growth as a function of time [20] . As can be seen from Fig 2E , the tumor cells growing in 3D generally retained a spherical shape with a median radius of gyration of 97 . 5μm in the XY plane , 114μm in the XZ plane and 101 . 7 μm in the YZ plane . Given that the average diameter of a cell is ≈10μm , our observations suggest that the variability in the radius of gyration was of approximately 1 cell in any axis and therefore growth of the spheroids was generally uniform in all directions . Since oncolytic measles viruses ( as well as other viruses ) generally spread from cell to cell , we hypothesized that the number of cells surrounding any given cell is of critical importance . Therefore , we wanted to determine the number of nearest cell neighbors based on whether cells are growing in the 2D plane versus in 3 dimensions . This data informed the development of the computational model to realistically simulate the in vitro dynamics . We studied cell populations by Voronoi tessellation analysis to determine the distribution of nearest neighbors for cells growing in the 2D ( Fig 3A and 3B ) plane as well as in spheroids ( Fig 3C and 3D ) . As expected , the number of nearest neighbors was significantly different in 2D versus 3D with a median of 6 ( range: 3–10 ) versus 16 ( range: 4–30 ) neighbors respectively . We utilized serial imaging studies to determine the rate of growth of the tumor and virus infected cell populations both in the 2D plane and in 3 dimensions . A total of 14 independent experiments were studied in 2D . Fig 4 presents snapshots of the spread of a single focus of infection ( green ) due to syncytium formation where cells fuse together to form a multicellular object . In Fig 5A–5C , we provide a representative case of data capture , digitalization and then analysis of cell population size by the Voronoi tessellation method ( C ) . Fitting of serial imaging data to the mean field solution ( see methods ) , enabled us to determine the best parameter estimates for cell replication and virus spread ( Fig 5D ) . Although the rates of tumor cell and virus spread varied , the median rate of replication for tumor cells was 4 . 39 per hour while the virus infection was spreading at a median rate of 18 . 94 cells/hour which implies that the virus was spreading 4–5 times as quickly as the tumor cell population was growing . A faster rate of spread of the virus compared to tumor cell growth is a necessary condition for any plausible scenario where the virus can eliminate the tumor cell population leading to a potential cure–something that is consistently observed in vitro [21 , 22] and also predicted by others [7 , 19] . We used the best estimate of the parameter set obtained from the data fitting ( the black dot in Fig 5D ) to determine cell population size and compare that to the actual measurements . As can be seen from Fig 5E , the computational output mirrored the experimental results with a high degree of accuracy . In virtually all of our experiments with cells growing in the 2D plane , the virus consistently eliminated the tumor cell population within 48 hours . The dynamics of virus spread in 3D tumor spheroids were surprisingly different with the virus spreading more slowly in the 3D environment . Although various independent foci of infection occurred in each spheroid ( Fig 6 ) , with the formation of multinucleated syncytia ( red ) , many infected cells remained viable for the duration of the experiment ( ~7 days ) . We also observed that many cells in the spheroids never become infected despite being in close proximity to virus-infected cells . More recently we documented syncytium formation in vivo in a mouse dorsal skin fold chamber model of cancer growth ( Kemler et al–submitted ) where again we observed cells in close proximity to highly infected foci that did not become infected for the duration of the experiment . We utilized these observations to perform in silico simulations of cell dynamics either in the 2D plane or in 3 dimensions each under two scenarios: growth on a regular lattice or growth on a Voronoi lattice . We studied the dynamics across a wide range of parameter estimates ( Figs 7 and 8 ) . All four networks studied had 1 × 106 nodes with the 2D networks having a dimension of 1000 × 1000 while the 3D networks had 100 × 100 × 100 dimensions . At the start of each simulation , 90% of the nodes were occupied by normal cells , 9% were occupied by cancer cells and the initial viral inoculum infects 1% of the tumor cell population . If these simulations were allowed to run on an infinitely large and complete network ( appropriately defined ) , the simulations would be stochastically identical to the mean field equations ( see Methods ) . Starting with simulations in 2D , for the set of parameters chosen , simulations led to equilibria with the three cell populations present . The time to reach an equilibrium in the 2D regular lattice architecture was ~6000 time units ( average number of neighbors: 4 ) , while in the 2D Voronoi lattice ( average number of neighbors: 6 ) , the time to equilibrium was 5000 time units . In the case of 3D simulations , the time to equilibrium on the regular lattice ( average number of neighbors: 6 ) was 250 time units , while in the case of the 3D Voronoi lattice ( average number of neighbors: 16 ) , the average time to equilibrium was 150 time units . Therefore , the main determinant of the speed to reach equilibrium is the dimensionality of the network more than the number of neighbors , although the latter is also important . In Figs 7 and 8 , we illustrate specific examples of such simulations in 2D ( Fig 7 ) and 3D ( Fig 8 ) . In parallel , we also determined the results of the mean field solutions given by the mathematical model . It is clear that the mean field solution overestimates the effect of therapy with a larger population of infected tumor cells at equilibrium both in the 2D and 3D simulations . The mean field solution also overestimates the speed at which equilibrium is reached . Spread of the virus in 3D leads to a larger fraction of tumor cells infected at equilibrium compared to the 2D scenario but overall the tumor cell population is larger at equilibrium in the 3D network and illustrates the difficulty of controlling the 3D tumor compared to the tumor cells growing in vitro . There are also striking differences in the pattern of infection in 2D versus 3D that again illustrates the role of connectivity between cells . There are five outcomes of tumor virotherapy regardless of the model and number of dimensions considered . ( i ) The tumor population will go extinct and the virus infected tumor population will soon follow , leading to permanent cure of the tumor . ( ii ) The virus infected cell population goes extinct and the result will be the eventual takeover of the simulation space by the tumor cells since they grow faster than normal cells . This will mean that therapy has failed . ( iii ) The three populations of cells co-exist and have a ( non zero ) stable size . This will imply partial success of therapy . ( iv ) Normal cells are eliminated and at equilibrium only tumor cells and infected tumor cells coexist . ( v ) All populations die out . We do not consider the last scenario in our simulations . We were mainly interested in the range of virus specific parameters that maximize the chance of tumor elimination . Using data from prior work on in vivo tumor control with the same virus [8–11] , we fixed the replication and natural death rates of normal and cancer cells and varied the parameters for virus replication and virus induced cell death rates across a wide range of values ( λ3: 0 − 100; δ3: 0 − 15 ) . A total of 14 , 000 simulations were performed with each simulation continuing until either the tumor cell population was eliminated or 1000 days had passed , whichever came first . We report the cumulative results of these simulations in Fig 9 . As can be seen , the results are qualitatively different . The mean field solution predicts that most of the time , the 3 population equilibrium is the most likely outcome . In 2 dimensions , the parameter range where cure of the tumor is possible is wider compared both to the mean field estimate and the 3D simulations . Moreover , in 2D there is very little difference in output between the regular grid lattice and the Voronoi lattice likely due to the fact that the number of nearest neighbors is similar ( 4 versus 6 respectively ) . However , the probability of a cure is less for a Voronoi type network in 3D compared to a regular lattice , although the Voronoi lattice increases the chances for the co-existence of all three populations with less chance of the tumor taking ( failed therapy ) over compared to the 3D regular lattice . This is likely due to the higher number of neighbors that each cell possesses which increases the chance for infection . All simulations agree that the ideal virus should replicate rapidly ( high λ ) but kill cells slowly ( low δ ) . Indeed , the model shows that there is a wider tolerance for replication rates and less so for the death rates of infected cells . Tumor eradication is more likely on a 2D surface compared to a 3D object for any set of parameters even though in 3D the number of cell neighbors is higher and equilibrium is reached faster , implying faster dynamics of virus spread . This is compatible with our in vitro observations and illustrates some of the intrinsic barriers to virus spread imposed by a 3D architecture versus a surface .
Tumor therapy with replication-competent viruses is an exciting novel approach to cancer therapy in which the target to be eliminated amplifies the agent responsible for its own death . Perhaps the only other member of this paradigm is cancer immunotherapy with chimeric antigen receptor T cells that are stimulated to replicate by engagement of cell surface antigens expressed by tumor cells . However , for successful tumor control with viruses , the latter have to establish foci of infection within the tumor , replicate to amplify the virus population and spread across the tumor . At the same time , the virus has to evade as much as possible the immune response that can neutralize the virus population or eliminate infected cells which would halt virus propagation . The outcome of such therapy is highly dependent on the dynamic interactions between the various populations of cells [5–7 , 23–26] . However , as our results show , the outcome is also quite sensitive to the architecture of the tumor since there may be several barriers to the spread of the oncolytic virus . These barriers may be physical or chemical in nature [13 , 27] . It has been argued that modeling with differential equations that provide a mean field approximation may be good enough to optimize therapy with these viruses and that such equations can capture well the dynamics without the need to consider space explicitly [26] . We have addressed the problems with this postulate in our work . Initially we provided a detailed analysis of tumor cell growth and virus spread in vitro both in 2D and 3D coupled with an analysis of the rate of replication of cells as well as the number of neighboring cells in a given environment . Our modeling approach differs from other publications [17] since we used this data to generate realistic computational models of tumor cell growth and virus spread . Subsequently , we analyzed through simulations many potential scenarios that consider two critical virus parameters: its rate of replication and the rate at which it kills cells . These two parameters have been repeatedly shown to be important for the outcome of virotherapy [6 , 8–10 , 12 , 13 , 17 , 18 , 28] . The wide spectrum of viruses available for oncolytic therapy have different kinetics of spread or can be engineered to alter their kinetics of replication or cell killing [1] . Our in vitro studies show that the same virus will kill cells more slowly in a 3D environment compared to the 2D setting , despite the fact that in 3D the average number of cells in a neighborhood is higher . As a result , in 3D , for the same set of parameters , the probability of tumor elimination is lower compared to the mean field approximation and also less than in a 2D environment despite the system reaching equilibrium by at least an order of magnitude faster . This fits well with all in vitro studies where the virus is highly efficient in killing tumor cells growing in the 2D plane but less so in a 3D environment whether in vitro ( spheroids ) or in vivo as tumor xenografts [8–12 , 29] . There are several possible explanations for the difference in outcome for tumor control in 2D versus 3D . While in 2D virtually all the tumor population is likely accessible to the virus that spreads at a fast rate , the same cannot be said for the 3D scenario where geometry not only makes some areas of the tumor quite distant from the infected foci but the virus also physically appears to spread at a slower rate even between adjacent cells . Moreover , one can envisage scenarios in 3D where a part of the tumor loses contact with the main tumor that is being infected . This will impose even greater spatial restrictions on the spread of the virus and reduces the probability of tumor control even further . However , in 3D the equilibrium is reached faster due to the higher number of contacts between cells . It is not difficult to see why the mean field model will overestimate the effect of therapy , since this approach assumes the presence of a well-mixed population based on mass action kinetics , thus rendering tumor cells accessible to viruses at all times . However , in a 3D structure such as a tumor , not all cells are at the same risk of being infected due to their spatial proximity , or lack thereof , to infected foci . Any part of the tumor that loses contact with infected foci will result in tumor regrowth unless virus can diffuse and establish a new infection there–something that appears to be unlikely with the current scenarios [27] . Moreover , we have observed from in vivo studies that many cells in proximity to a highly infected focus never become infected and the size of infected foci can be quite variable ( Kemler et al , submitted ) . The biological and physical bases for these observations require further analysis . Our work complements that of Wodarz et al [7 , 19] who studied spread of an adenovirus in a monolayer and showed the importance of local interactions on the spread of the virus . Their work expanded on the importance of initial conditions and the potential impact of an antiviral state due to interferon production . It is important to note that 293T cells used in their experiments are not derived from a tumor and so are likely to respond to interferon production . In contrast , we used cell lines that are derived from tumors that generally do not mount a robust immune response against measles virus . We also extended our in vitro studies and computational modeling to 3D where the number of neighbors and the spatial structures become more complex . Our results show that the number of neighbors surrounding a cell can facilitate spread of the virus and leads to the three population equilibrium more often and more quickly ( compare Voronoi network with grid lattice in Fig 9 ) . However , in the presence of an immune response , we hypothesize that the outcome could be worse for a Voronoi type lattice compared to a regular lattice since the latter has a higher probability of a cure . The simulations also show that the mean field solution generally provides a more optimistic view of the outcome compared to the Voronoi type architecture that seems to exist in spheroids . However , the higher number of neighbors in a Voronoi network is associated with failure of therapy less often compared to a regular grid lattice at least in theory . Our results highlight the need for spatially explicit modeling to accurately capture the dynamics of tumor virotherapy . We also show the problems that arise from the introduction of a third dimension into such a model–the probability of a cure decreases significantly when the virus is used in an attempt to cure a 3D tumor compared to cells in the 2D plane .
The human fibrosarcoma cell line HT1080 was obtained from ATCC and grown in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) and maintained at 37°C with 5% CO2 . 293T cells were maintained in DMEM with 10% FBS . The human cell line 293-3-46 was maintained in DMEM with 10% FBS and geneticin ( 1 . 2mg/ml ) while Vero cells were maintained in DMEM with 5% FBS . PCR products containing the yellow fluorescent protein ( YFP ) , enhanced blue fluorescent protein ( eBFP ) , and the tdTomato genes were generated using Roche Fast-Start High Fidelity PCR kit using pCAG-YFP , CFP , pCSCMV:tdTomato ( Addgene ) as template DNA . The primers ( Forward: GGGATCCACGCCACCATGGTGAGCAAGGGCG and reverse: GAGGCGGCCGCAGTTTACTTGTACAGCTCGTCCATGCC ) had restriction sites for BamHI and NotI to facilitate cloning in the lentiviral vector backbone [30] . PCR products were cloned into pHR`CMV-eGFP-SIN ( a gift of Dr Y . Ikeda , Mayo Clinic , Rochester ) after the excision of GFP via digestion with BamHI and NotI . The resulting constructs were verified by restriction digestion and sequencing . Lentiviral particles containing the respective reporter genes were generated via transfection of 293T cells with pMD . G , pΔCMV . 8 . 91 , and the plasmid encoding the vector genome with the fluorophore as previously described [30] . Vector containing supernatants were harvested after 72 hours , filtered ( 0 . 42μm ) and used to transduce HT1080 cells . Cells expressing the reporter gene were sorted by flow cytometry and plated as single cells into 96 well plates in DMEM with 10% FBS and expanded as clones for further studies . In order to generate recombinant replication competent measles viruses expressing different fluorescent reporter genes , PCR products containing the fluorophores eBFP , YFP , and tdTomato were generated using pCAG template DNA ( Addgene ) . PCR primers had the MluI and AatII restriction sites ( underlined ) in their flanking region to facilitate cloning ( Forward: GACGCGTACGCCACCATG GTGAGCAAGGGCG and reverse: GAGACGTCAGTTTACTTGTACAGCTCGTC CATGCC . The PCR products were gel purified , digested and subsequently ligated into pCR2 . 1Topo , expanded in TOP10 cells ( Invitrogen ) , and inserts excised with MluI and AatII digestion followed by ligation into p ( + ) MVeGFP ( N ) that was digested with the same enzymes to remove the eGFP gene . Viruses were rescued by transfection of 293-3-46 cells together with pEMC-La followed by overlay on Vero cells as previously described [4 , 31] . Rescue of the recombinant viruses was inferred from the presence of syncytia and fluorophore detection under ultraviolet light . The recombinant viruses were expanded by infection of Vero cells . Cell associated viruses were freed by freeze thawing of the cells three times in liquid nitrogen followed by filtration . The viral titers ( 50% infectious virus dose , TCID50/ml ) were determined using the Spearman and Karber method as previously described [4 , 31] . All viruses were stored at -80°C until they were used . HT1080 spheroids were grown on either Matrigel coated glass bottom 6 well plates or poly-HEMA coated round bottom plates to prevent cell attachment . For Matrigel coated wells , media , tips , and glass bottom plates were cooled at 4°C and 200μl Matrigel added . Matrigel was allowed to solidify for 15 minutes . HT1080 cells were washed 3 times with phosphate buffered saline ( PBS ) and dislodged by trypsin , counted and overlaid at various concentrations into 2ml DMEM with 10%FBS and 2% Matrigel . The media were freshly replaced every 3–4 days and cells were imaged with a multiphoton microscope ( Olympus ) . Spheroids were infected using measles viruses encoding various fluorophores in 2 mL Opti-MEM for 2 hours at 37°C at an MOI of 1 . 0 . HT1080-tdTomato cells were plated in 6 well plates and 24 hours later infected with MVeGFP at an MOI of 1 . 0 Tumor spheroids from the same cell line expressing eCFP were produced as above and infected with MV-tdTomato . Starting twenty-four hours post infection , the cells were imaged in Z stacks every 30 minutes over the course of a week using an Olympus multiphoton microscope . Digital images were captured for subsequent analysis . Average pixel area per cell: The average pixel area of a cell was determined by having two independent scientists counting the number of cells in a given field of view with a fixed color pixel threshold and correlated this with the phase contrast images of the cells . The total pixel area was divided by the number of cells to determine the average pixel area per cell . Serial images of in vitro cell growth and virus spread were digitally analyzed using the established cell area parameters and the output was converted back into number of cells . Voronoi tessellation: Digital images from the in vitro experiments with cells growing in the 2D-plane or in 3D spheroids were analyzed using MatLab to generate Voronoi tessellation analysis of nearest neighbors in 2D and 3D . We developed a ‘mean field’ mathematical model for tumor growth and viral infection of tumor cells as follows: duNdt=λNuN ( 1-uN-uC-uV ) -δNuN duCdt=λCuC ( 1-uN-uC-uV ) -δCuC-λVuCuV duVdt=-δVuV+λVuCuV In the model , ui represents the various cellular fractions with N representing normal cells , C cancer cells and V the infected cancer cells . λi represents the proliferation and δi the death rates of the respective populations . The model assumes mass action kinetics . The model was fitted to data from in vitro studies using the purpose built simplex induction hybrid ( SIH ) program [32] and the results of the fits displayed as a heat plot ( see results ) . The goodness of fit was determined using the chi square method . The relative parameters for virus infected cells and tumor cells were used to inform the computer simulations . The model we developed can run simulations of cell populations and infection both in 2 and 3 dimensions and population growth can be either on a lattice structure ( regular ) or a Voronoi network with a variable number of neighboring cells [18] . The input for the network is described by a list of adjacencies for each node obtained by analysis of the imaging obtained from in vitro experiments . The simulator itself has no dimensionality and therefore , it can run simulations in two , three or higher dimensions . The number of neighbors for each node and their location can vary as observed in vitro . Routines embedded in the program enable modification of the state of the network to simulate the addition of the virus that will spread within the cell population . Each node in the network is occupied at most by one cell . Three types of cells are possible: normal cells , cancer cells and infected cancer cells . A node without a cell is empty . When a normal or cancer cell proliferates , the new cell generated has to occupy a neighboring empty node while infected cells can target only nodes occupied by a cancer cell since the virus is tumor cell specific [33] and the virus spreads from cell to cell [18 , 21 , 31] . Each cell type has a node type that it considers as a replication target . Empty nodes are proliferation targets for normal and cancer cells while a cancer cell node is a proliferation target for infected cells . The growth and death rates of normal and cancer cells as well as infected cells can be varied as well as the time when the virus is administered . The virus infection parameters can also be specified . Event arrivals follow a Poisson process with the time to the next event being exponentially distributed . Furthermore , the model assumes that cell proliferation and death are independent events and do not depend on the prior state of the network . To initiate the infection , the program determines the coordinates of all cancer cells and identifies the cancer cell that is closest to the center . The simulator has 4 infection routines that can be used to specific which individual nodes are infected at the start of the simulation . ( i ) Random selects cells at random within the tumor population until the pre-specified fraction of cells are infected . ( ii ) Center selects the cancer cell closest to the center of the tumor and this cell is infected followed by its neighbors and neighbors’ neighbors are infected until the required fraction of infected cells is reached . ( iii ) Multinode selects the tumor cells closest to the center for infection and the infection spreads from this focus by generating a line in a random direction that passes through the infected node . Nodes along the line are visited and if they are cancer cells infected with a determined probability . The process continues until normal cells are reached which cannot be infected . ( iv ) Perimeter determines the center of the tumor and the distance of all nodes from the center . Cancer cells occupying the nodes furthest from the center are infected until the pre-specified fraction of cancer cells are infected . The dynamics of normal and uninfected cancer cells continue with the same parameters once the infection is introduced . A simulation starts by reading a set of network files that specify the network structure , node locations and cell types ( optional ) . If no cell type is included , normal cells are assumed . The program has an optional equalization period until the population stabilizes . Cancer cells are then introduced and the simulation allowed to run until the virus is introduced . The simulation is continued until either the cancer or virus population goes extinct or a pre-specified time is reached . The percentage of infected cells at the time of virus administration is an input variable . Initial infection is assumed to occur very rapidly . In all simulations , time is defined by the rate of replication of cells ( the generation time ) . Fig 10 presents a schematic of the process . A copy of the code is available at http://hdl . handle . net/11299/174710 . A total of 7 , 000 runs were performed for each set of spatially explicit simulations ( e . g . 2D grid , 2D Voronoi ) starting with similar initial conditions but varying the parameters related to the rate of virus spread ( λ3 = 0 − 100 ) and virus induced cell death rate ( δ3 = 0 − 15 ) . | Tumor therapy with replicating oncolytic viruses is based on the premise that if the tumor specific virus infects and is amplified by the tumor population and spreads sufficiently within the tumor , it will lead to eradication of the cancer . The outcome of this approach is an exercise in population dynamics , and , as in ecology , depends on the detailed interactions between the various players involved . Mathematical models have been used to capture these dynamics , but space is often explicitly excluded from these models . We combine in vitro experiments studying tumor growth and virus spread in two and three dimensions to inform the development of a spatially explicit computational model of tumor virotherapy and compare the outcome with non-spatial , mean-field models . Viruses generally spread from cell to cell , and therefore the number of nearest neighbors close to an infected cell is important . Experimental data show that in three dimensions , the median number of nearest neighbors is 16 compared to 6 in the 2D plane . However , while simulations in 3D reach equilibrium faster than in 2D , tumor eradication is much less common in 3D than in 2D . Three dimensional space plays a critical role in the outcome of tumor virotherapy and this additional spatial dimension cannot be ignored in modeling . |
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Stabilization of neurotransmitter receptors at postsynaptic specializations is a key step in the assembly of functional synapses . Drosophila Neto ( Neuropillin and Tolloid-like protein ) is an essential auxiliary subunit of ionotropic glutamate receptor ( iGluR ) complexes required for the iGluRs clustering at the neuromuscular junction ( NMJ ) . Here we show that optimal levels of Neto are crucial for stabilization of iGluRs at synaptic sites and proper NMJ development . Genetic manipulations of Neto levels shifted iGluRs distribution to extrajunctional locations . Perturbations in Neto levels also produced small NMJs with reduced synaptic transmission , but only Neto-depleted NMJs showed diminished postsynaptic components . Drosophila Neto contains an inhibitory prodomain that is processed by Furin1-mediated limited proteolysis . neto null mutants rescued with a Neto variant that cannot be processed have severely impaired NMJs and reduced iGluRs synaptic clusters . Unprocessed Neto retains the ability to engage iGluRs in vivo and to form complexes with normal synaptic transmission . However , Neto prodomain must be removed to enable iGluRs synaptic stabilization and proper postsynaptic differentiation .
Synapse development is a highly orchestrated process that enables proper establishment of neural circuits and development of the nervous system . Crucial to synapse assembly is the recruitment and stabilization of neurotransmitter receptor complexes at synaptic sites [1] . Receptor complexes can be inserted directly into synaptic membranes via vesicular trafficking from ER-Golgi network , or they can move into the synaptic regions by lateral diffusion from extrasynaptic pools ( reviewed in [2 , 3] ) . Clustering of neurotransmitter receptors at new synapses induces expression of synaptic components and assembly of postsynaptic structures , such as postsynaptic densities ( PSDs ) , which in turn help maintain the local density of receptors [4] . Neural activity and trans-synaptic communication between pre- and postsynaptic specializations together with intracellular signals within the synaptic partners themselves ensure the maturation , refinement and plasticity of the synaptic connections and synapse growth [5–9] . The molecular mechanisms that coordinate the recruitment and stabilization of receptors at synaptic sites and assembly of synaptic structures with synaptic growth remain unclear . The Drosophila NMJ provides an ideal genetic system to examine the mechanisms that couple synapse assembly with synapse growth and development . The fly NMJ is a glutamatergic synapse similar in composition and physiology to vertebrate AMPA/kainate central synapses [10 , 11] . The fly NMJ iGluRs are tetrameric complexes composed of three essential subunits , GluRIIC , GluRIID and GluRIIE , absolutely required for assembling functional channels [12–14] . The fourth subunit can be either GluRIIA ( type-A channels ) or GluRIIB ( type-B ) [15–17] . GluRIIA and GluRIIB compete for the essential subunits , which are limiting for the formation of functional receptors . Before a muscle is innervated , low levels of iGluRs are present diffusely in the muscle membrane . Innervation triggers the clustering of iGluRs at synaptic locations and postsynaptic differentiation [18–20] . Type-A channels are the first to arrive at nascent synapses , while type-B , which desensitize ten times faster than type-A , mark more mature synapses [12 , 20 , 21] . The fly NMJ iGluRs , but not other PSD components , show very little turnover suggesting that the iGluR complexes are stably incorporated at synaptic sites [22] . At the Drosophila NMJ , clustering of iGluRs and formation of postsynaptic specializations requires an additional essential protein , Neto [23] . Neto belongs to a family of highly conserved transmembrane proteins sharing an ancestral role in the formation and modulation of glutamatergic synapses [24–26] . Vertebrate Neto proteins ( Neto1 and 2 ) and C . elegans Neto/Sol-2 have emerged as auxiliary subunits that modulate the gating properties of AMPA/kainate-type channels and their synaptic localization without influencing their delivery to the cell surface [24–29] . Likewise , Drosophila Neto associates with iGluRs in vivo and controls their trafficking and clustering at NMJ synapses without affecting their muscle expression levels [23] . Reduced synaptic iGluRs alter the function of NMJs causing locomotor defects and reduced synaptic transmission [12 , 30] . Lack of junctional iGluRs also induces a cascade of defects in the assembly and maintenance of postsynaptic specializations [23 , 30] . For example , Neto- or iGluRs-deprived synapses have reduced accumulation of PSD components , such as p21-activated kinase ( PAK ) , and sparse subsynaptic reticulum ( SSR ) , a structure comprised of stacks of muscle membranes surrounding and stabilizing synaptic boutons [31] . Intriguingly , synapses developing at suboptimal Neto/iGluR levels share a number of morphological and physiological defects with mutants in the BMP signaling , a pathway that controls the NMJ growth and confers synaptic homeostasis [32] . Similar to neto mutants , BMP mutant NMJs have fewer boutons and reduced excitatory junctional potential ( EJP ) ( reviewed in [32] ) . Furthermore , Neto in complex with type-A receptors promote the phosphorylation and accumulation of the BMP pathway effector Mad at synaptic locations [33] . The BMP-type signaling factors are produced as inactive precursors , with inhibitory prodomains that must be removed by proprotein convertases to generate the active ligands [34] . Furin-type proteases control the limited proteolysis of inactive BMP precursors and directly regulate their activities [35–37] . In many tissues , sequential processing of BMP prodomains modulates the range and signaling activities of BMP ligands [38] . At the Drosophila NMJ additional TGF-β factors regulate the expression of Glass bottom boat ( Gbb ) , a BMP7 homolog required for the BMP retrograde signaling [39 , 40] . Furin-type proteases activate all these TGF-β-type factors as well as the BMP-1/Tolloid enzymes that augment TGF-β signaling indicating that Furins provide an important means for controlling cellular signaling at the Drosophila NMJ . Here we report that Neto protein levels are critical for synaptic trafficking and clustering of iGluRs . Excess or reduced Neto protein in the striated muscle induced formation of NMJs with reduced number of synaptic boutons , decreased synaptic iGluRs and diminished neurotransmission . Neto activities are regulated by Furin-mediated proteolysis and removal of an inhibitory prodomain . In the absence of prodomain cleavage , Neto engages the iGluRs but fails to promote their recruitment and stable incorporation at synaptic sites and to initiate postsynaptic differentiation . Since Furins also cleave and activate signaling molecules , such as TGF-β factors , Furins may synchronize the processing of Neto and TGF-β to control synaptic growth .
Similar to neto hypomorphs , RNAi-mediated knockdown of Neto in the striated muscle altered NMJ development ( Fig . 1A , B and [23 , 33] ) . Interestingly , neto overexpression in the muscle also induced abnormal synapse development . We rescued neto null mutants ( neto36 ) with neto transgenes with various expression levels and found that excess Neto accumulated at NMJ synapses and extrajunctional locations in a dose-dependent manner ( Fig . 1A , C ) . Low to moderate levels of Neto clustered at synaptic sites ( i . e . using neto-A9 transgene ) , but excess Neto ( neto-A3 , or neto-A1 for the highest level ) had predominantly diffuse distribution with fewer individual synaptic puncta and abundant extrasynaptic signals . Similar patterns were found in animals with overexpressed neto transgene ( where neto-A1 induced the strongest phenotypes ) . Excess Neto had detrimental effects on the viability of rescued animals at all stages of development ( S1 Fig . ) . Neto levels also affected NMJ growth . In larvae with either reduced or excess Neto , the number of boutons was decreased although the branching patterns differed: longer branches at reduced Neto and shorter branches at excess Neto ( Figs 1A , S1 ) . This suggests that independent signaling pathways control NMJ growth and bouton formation . To examine the effects of Neto levels on synapse function we recorded excitatory junction potentials ( EJPs ) and spontaneous miniature potentials ( mEJPs , or minis ) from muscle 6 of third instar larvae ( Fig . 1D-F ) . In control larvae ( Dicer/+: 24B-Gal4/+ ) minis occurred two times per second on average . This was reduced to 0 . 3 events per second at Neto-depleted synapses ( dicer; 24B>netoRNAi ) similar to that observed in neto hypomorphs [23] . Excess Neto showed a reduction in mini frequency , and to a lesser extent in mini amplitude , but only when Neto was expressed at very high levels; larvae with moderate levels of additional Neto had normal mEJPs . The mini frequency appeared particularly sensitive to Neto levels and was significantly reduced in both Neto-depleted and Neto-excess conditions . The reduction in mini frequency and amplitude occurred in muscles with no change in both resting potential and input resistance . The EJP amplitude was similarly sensitive to Neto levels: mild/moderate increase in Neto levels showed no significant change in EJP amplitude , while strong perturbations of the Neto levels ( depletion or excess ) induced significant reduction in the EJP amplitude . Although GluRIIC muscle levels were constant in larvae with increased Neto expression ( Fig . 1B ) , the similarities between the NMJ physiological properties at reduced or excess Neto suggest that excess Neto could affect the number and density of postsynaptic iGluRs . Indeed , excess Neto produced a significant decrease of GluRIIC synaptic clusters: the number of synaptic contacts per bouton did not change , but the intensity of the GluRIIC synaptic signals was reduced to 58% ± 12% of the control ( Fig . 2A-E ) . The anti-GluRIIC also labeled extrasynaptic puncta that occasionally accompanied small Neto clusters , but did not co-localize with the large extrajunctional Neto-positive puncta , presumably associated with secretory vesicles ( Fig . 2C ) . In contrast , the synaptic distribution of Bruchpilot ( Brp ) , an active zone scaffold [41] , remained unaffected by excess Neto , indicating that Neto specifically regulates the distribution of postsynaptic receptors . The decrease of synaptic iGluRs showed no subtype specificities when Neto was overexpressed in a wild-type background ( G14>neto-A1 ) ; both GluRIIA and GluRIIB synaptic levels were similarly decreased ( to 60% and respectively 54% from control ) ( Fig . 2E-F ) . This is consistent with the normal quantal size ( or mini amplitude ) , observed at these NMJs ( Fig . 1E ) [15 , 16] . However , when excess Neto was introduced in the neto null background ( neto36; G14>neto-A3 ) , the GluRIIA synaptic levels were reduced slightly more than the GluRIIB , to 48% and respectively 62% from control . Loss of synaptic pMad , the BMP pathway effector , correlated with small NMJs with reduced synaptic release in neto and importin-β11 mutants [33 , 42] . We found that Neto overexpression also caused attenuation of the synaptic pMad levels likely by decreasing the levels of synaptic type-A receptors ( Fig . 3 ) . Reduced synaptic iGluRs together with diminished retrograde BMP signaling could explain the small size of NMJs with excess or reduced Neto levels . However , there were several differences between these NMJs . Unlike neto hypomorph larvae , which showed diminished synaptic localization of multiple synaptic components , such as p21-activated kinase ( PAK ) , Discs large ( Dlg ) , and α-Spectrin [23] , excess Neto did not affect the synaptic accumulation of any of these proteins ( S2 Fig . ) . In line with normal Brp , excess Neto did not affect the presynaptic localization of cysteine-string protein ( CSP ) [43] . Thus , the neto gain-of-function NMJ phenotypes cannot result from insufficient trafficking and recruitment of postsynaptic components . Normal recruitment of Dlg at synaptic locations was also observed when V5- or GFP-tagged Neto variants replaced the endogenous Neto protein ( S3 Fig . ) . Similar to untagged Neto , excess Neto-V5 or Neto-GFP induced smaller NMJs with normal synaptic transmission ( Neto-GFP-rescued NMJ shown in S3 Fig . ) , indicating that the addition of tags did not affect Neto activities and gain-of-function phenotypes ( Figs 1 , S3 and [23] ) . How could excess Neto diminish the synaptic iGluR levels without affecting any other synaptic components tested here ? Stable synaptic receptors are thought to be part of large aggregates organized by proteins secreted from the presynaptic compartment [44 , 45] and further stabilized by postsynaptic scaffolds [46] . Neto may interact with neuron-secreted proteins that trigger iGluRs synaptic clustering and/or with intracellular motors and scaffolds that promote iGluRs trafficking and stabilization at synaptic sites . Excess Neto may engage in unproductive interactions and overwhelm the cellular machineries involved in the trafficking and clustering of iGluRs at synaptic locations . Since Neto does not affect the net levels of receptor subunits in the postsynaptic muscle ( Fig . 1B and [23] ) , then iGluRs are predicted to accumulate at extrajunctional locations at suboptimal Neto levels . Indeed , genetic manipulation of Neto levels triggered a redistribution of iGluR-positive signals from junctional to extrajunctional locations ( Fig . 2C and [23] ) . Moreover , Neto proteins appear to have no roles in the surface delivery of the iGluRs in vertebrate and in C . elegans [25 , 26] suggesting that reduced or excess Neto levels should induce accumulation of extrajunctional iGluRs at the muscle surface . We tested this prediction by staining the larval fillets in detergent-free protocols with antibodies raised against the extracellular domain of GluRIIC . Under these conditions , extrajunctional GluRIIC staining was barely visible in control , but was very prominent on the muscle of larvae with reduced or excess Neto ( Fig . 4A-B ) . Surface accumulation at extrajunctional locations of GluRIIA was also observed in neto109 hypomorphs [23] . Similar results were obtained in both rescue and overexpression experiments with either neto-A3 or neto-A1 transgenes even though neto-A1 appears to induce a higher Neto expression level ( Figs 4 , 1 ) . Together , our data indicate that optimal Neto levels are crucial for the recruitment and stabilization of iGluRs at synaptic sites . Similar to vertebrate or C . elegans , perturbations of the Drosophila Neto levels do not appear to affect the surface delivery of iGluRs and instead influence the iGluRs distribution between synaptic and extrasynaptic locations . Unlike vertebrate or C . elegans Neto , Drosophila Neto contains a long sequence preceding the first CUB domain ( CUB1 ) . Full-length Neto is predicted to be a 78 kD protein , yet when expressed in S2 insect cell , Neto runs as two bands: a minor band with relative mobility ~100 kD , and a major band of ~85 kD ( Fig . 5A ) . Truncated Neto variants containing only the extracellular part ( Neto-extra ) showed bands of ~60 and ~45 kD . Similar pattern was also detected in neto36 null embryos rescued with a neto-V5 transgene . To examine whether Neto is cleaved , we generated a binary-tagged CUB1 fragment ( Myc-CUB1-V5/His , Fig . 5B ) . This secreted fragment produced three distinct bands corresponding to full length , N-terminal and C-terminal fragments . The C-terminal fragment was purified and analyzed by Edman degradation and mass spectrometry . Three cleavage sites within a region containing tandem repeats of RXXR dibasic motifs , upstream of the CUB1 domain , were identified . The major cleavage site appears to be the R129-Q bond , but R126-S and R123-A bonds could also be cleaved ( Fig . 5C ) . Interestingly , this region is highly conserved in all Drosophila species but not in vertebrate or C . elegans Neto , suggesting that this processing has functional implications for Neto functions in flies . The cleavage sites match the consensus processing sequence for Furin-like proprotein convertases ( PC ) , also known as PACE ( Paired basic Amino acid Cleaving Enzyme ) , which process latent precursor proteins into their biologically active forms [47] . Drosophila genome codes for three Furin-type enzymes: Furin1 ( Fur1 ) , Furin2 ( Fur2 ) , and Amontillado ( Amon ) . Fur1 and Fur2 were expressed and analyzed in vitro , but their mutants have not been described yet [48 , 49] . Mutants in amon , encoding the Drosophila homolog of the neuropeptide precursor processing protease PC2 , display partial embryonic lethality , defective larval growth , and arrest during the first to second instar larval molt [50 , 51] . To confirm that Furins are responsible for cleaving Neto we used an RNAi approach [36] . We generated double strand RNA ( dsRNA ) for each of the three Furin-like coding genes , co-transfected them with Neto expression constructs in S2 cells , and examined the protein products . The efficiency of RNAi treatments was verified by RT-PCR ( Fig . 5D ) . We found that knockdown of Fur1 activities reduced the production of the small , cleaved bands and increased the level of unprocessed form ( Fig . 5E , lanes 1 and 2 ) . However , we did not find any difference by knocking down Fur2 or Amon ( Fig . 5E , lanes 3 and 4 ) . Combination of all 3 different dsRNAs did not further reduce the proportion of uncleaved Neto forms compared to Fur1 RNAi ( Fig . 5E , lanes 1 and 5 ) , indicating that Fur1 is the primary enzyme for cleaving Drosophila Neto in S2 cells . In flies , fur1 is expressed throughout development in multiple tissues including larval central nervous system and carcass [52] . RNAi-mediated fur1 knockdown in the striated muscle produced NMJs with fewer and smaller boutons , normal Brp synaptic clusters , but significantly diminished levels of synaptic iGluRs ( S4 Fig . ) . While these phenotypes are reminiscent of NMJs with suboptimal Neto , they cannot be solely attributed to reduced Neto activities due to lack of processing . Fur1 , like all Furin-type proteases , cleaves and activates multiple developmentally important substrates , including extracellular matrix components and signaling molecules such as TGFβ-type ligands [53] . In fact , stronger RNAi treatments ( in the presence of Dicer or at higher rearing temperature ) distorted the muscle fibers and induced early larval lethality . A pulse of high temperature ( one day at 30°C ) also disrupted the muscle structures . Fur1 knockdown also induced significant reduction in GluRIIA and pMad synaptic signals , likely because inefficient activation of precursor TGFβ-type factors , including Gbb ( S4 Fig . ) . Interestingly , down-regulation of fur1 in motor neurons elicited similar NMJ phenotypes , underscoring the complexity of Fur1-dependent activities . To study the biological relevance of Neto processing by Furin-type proteases we generated a constitutively active Neto variant ( CA-Neto ) , without the prodomain , and a processing mutant Neto ( PM-Neto ) , with an uncleavable prodomain ( Fig . 6A ) . When expressed in S2 cells , Neto-GFP was detected as double bands of expected sizes , mostly processed form . CA-Neto-GFP was found as a single , processed protein , while PM-Neto-GFP was predominantly unprocessed . We noticed that a small fraction of PM-Neto ( <15% ) was processed presumably by promiscuous proteolysis , which may partly remove the prodomain; however , such cleavage usually occurs at ectopic locations , adding or removing additional residues from the processed product . Further mutations in this conserved region did not completely abolish Neto processing , but could impact the proper function of the adjacent CUB domain . To examine the subcellular distribution of Neto variants we took advantage of the apical localization of Neto in epithelial tissues . G14-Gal4 drives the expression of UAS transgenes in muscles but also in salivary glands . We found that all Neto variants localized to the luminal side of the salivary gland ( apical surface ) , indicating that prodomain processing does not affect membrane targeting and apical localization of Neto proteins ( Fig . 6B ) . Nor did prodomain processing impact the ability of Neto variants to form complexes with iGluRs in the striated muscle . Similar to Neto , CA-Neto and PM-Neto retained the capacity to pull-down iGluRs from muscle extracts ( Fig . 6C ) . However , PM-Neto was severely impaired in its ability to rescue the neto null mutants , while CA-Neto generally resembled the Neto control ( Fig . 6D , E ) . Very few PM-Neto rescued animals reached the adult stages: these flies did not fly and had locomotor defects . Similar to the wild-type neto transgenes , moderate levels of CA-Neto rescued the NMJ morphology and iGluRs clustering defects of neto null mutants , while excess CA-Neto generated smaller NMJs with reduced iGluRs synaptic signals ( Fig . 7A , B ) . In contrast , PM-Neto rescued NMJs developed abnormally irrespective of the expression levels . At moderate levels , PM-Neto distributed diffusely and disrupted the synaptic localization of iGluRs , in particular the type-A receptors ( Fig . 7A-D , quantified in 7E , F ) . Animals rescued with high PM-Neto levels died during the early larval stages; the rare third instar escapers did not move and had severely altered NMJs with sparse boutons decorated by irregular Brp-positive aggregates and almost undetectable synaptic GluRIIC puncta ( Fig . 7A , B , G ) . These data suggest that PM-Neto is inadequate for the proper recruitment and stabilization of iGluRs at postsynaptic locations even though PM-Neto appears to bind to GluRIIC in vivo and to enable embryos to hatch into larval stages ( Fig . 6C , D ) . The severity of phenotypes at PM-Neto rescued synapses indicates that prodomain removal is required for iGluRs synaptic clustering , for development of postsynaptic structures , or both . Perisynaptic Dlg signals flank but do not co-localize with PSD components [54] . At control NMJ , Dlg appeared to surround the Neto-positive puncta ( Fig . 8A ) . The synaptic accumulation of Dlg was severely reduced at PM-Neto rescued NMJs , without any detectable change in the level of Dlg protein in larval muscle . These mutant NMJs were hardly recognizable since both Dlg and Neto synaptic signals were diminished and distributed diffusely among very few boutons . Similar to iGluRs and Dlg , PAK did not accumulate at PM-Neto rescued NMJs ( Fig . 8B ) . In contrast , the assembly of presynaptic components was not affected in PM-Neto rescued synapses: Brp and CSP showed discrete synaptic distributions ( Figs 7 , 8C ) . The severe postsynaptic defects at PM-Neto rescued NMJs were not accompanied by cytoskeletal disruption as indicated by normal α-Spectrin distribution ( Fig . 8D ) . Thus , postsynaptic differentiation and organization of PSD structures appear to be specifically affected by Neto processing . The aberrant postsynaptic differentiation at PM-Neto rescued NMJs was also captured by electron micrographs of larval NMJs . These NMJs had rare boutons with no postsynaptic electron dense structures and no detectable SSR , and surrounded instead by dense ribosome fields or myofibrils ( Figs 9A , B-F , and S5 ) . The T-bar structures were often misshaped , collapsed or floating at PM-Neto boutons , suggesting that lack of Neto/iGluRs clustering affects proper assembly and organization of presynaptic structures . Larger T-Bars and synaptic vesicles at PM-Neto-rescued NMJs may reflect a homeostatic compensatory response to reduced postsynaptic receptors . Similar structures were reported in mutants with enhanced presynaptic release [10] . Physiological recordings indicated that the mini frequency was severely reduced at PM-Neto rescued NMJs consistent with drastically reduced synaptic iGluRs ( Fig . 9G , H ) . The mini amplitude was also decreased , likely due to the preferential loss of type-A receptors at these synapses ( Figs 7D , 9I ) . Consistent with the large vesicle seen in electron micrographs we occasionally observed very large minis at PM-Neto rescued NMJs . However , the evoked potentials were normal suggesting a presynaptic compensatory response ( Fig . 9J-L ) . Thus , Neto processing is required for the normal density of postsynaptic iGluRs , but is not essential for triggering a compensatory increase in presynaptic release . PM-Neto not only failed to cluster and stabilize the iGluRs at postsynaptic locations but it was also unable to support the recruitment of postsynaptic components , formation of PSDs , and stabilization of postsynaptic structures . The postsynaptic differentiation program was simply not initiated at PM-Neto rescued NMJs . Our data are consistent with a model in which Fur1-dependent processing activates Neto and allows it to function to stabilize iGluR complexes at synaptic sites . The prodomain may prevent the formation and/or maintenance of stable Neto/iGluR synaptic aggregates by obstructing Neto-mediated protein interactions . Lack of iGluRs clustering precludes the initiation of postsynaptic differentiation .
The increase as well as the decrease of Neto levels affects the NMJ development , albeit with different consequences . Neto-deprived NMJs have diminished postsynaptic specializations and long branches , spanning over large muscle areas , suggesting that lack of postsynaptic receptors maintains the motor neurons in a growing , exploratory state . By contrast , NMJs with excess Neto are short and have normal accumulation of postsynaptic components . In fact , PAK and Dlg signals are slightly elevated at NMJs with excess Neto compared with control ( S2 Fig . ) . Early accumulation of synaptic Dlg may restrict expansion of these NMJs and produce hypo-innervation . Interestingly , overexpression of Neto in the wild-type background ( G14>neto-A1 ) induced gain-of-function phenotypes slightly milder than when the same transgene replaced the endogenous neto in rescue experiments ( compare the last two columns in Fig . 1A ) . This could be due to the different genetic backgrounds or may indicate additional Neto functions that are missing at neto-A1-rescued NMJs . Physiological studies also captured the differences between postsynaptic iGluR receptor fields at different Neto levels . Neto-deprived NMJs in neto hypomorphs or RNAi experiments have severely reduced mini frequency consistent with their reduced postsynaptic iGluRs density ( Fig . 1C-F and [23 , 33] ) . Strong reduction of postsynaptic Neto levels induced a reduction of EJP amplitudes , suggesting that Neto deprivation interferes with the normal homeostatic mechanisms . Similar to iGluRs-deprived synapses , lack of Neto may render these synapses “beyond repair” [12 , 14] . In contrast , the NMJ physiological parameters appeared more to be resilient to excess Neto since addition of moderate levels of Neto did not affect the mEJP and EJP amplitude . However , high levels of excess Neto ( G14>neto-A1 ) induced a significant decrease of mEJP frequency , consistent with the reduced synaptic and increased extrasynaptic iGluRs observed at these NMJs ( Figs 1 , 2 , 4 ) . At central glutamatergic synapses in vertebrates , synaptic receptors are cycling into and out of the synapses indicating that synapses behave as donors or acceptors for receptors , and the extrasynaptic receptors function as a reserve pool [2] . At the Drosophila NMJ , the iGluRs are recruited to the nascent synapses from extrajunctional receptor pools , but are stably integrated in synaptic aggregates with very low turnover [22] . In the absence of Neto , or any essential iGluR subunit , the iGluRs are not recruited at synaptic locations [55] . Conversely , excess Neto induces accumulation of iGluR-positive puncta at extrajunctional locations ( Figs 2 , 4 ) . This is different than overexpression of any of the essential iGluR subunits , which don’t show gain-of-function phenotypes , presumably because other subunits are limiting [11] . Furthermore , the iGluR complexes appear to be on the muscle membrane at suboptimal Neto levels since they are accessible by antibodies in the absence of detergents ( Fig . 4 and [23] ) . Likewise , Neto proteins from worms and mammals appear to have no roles ( or very modest ones ) in the surface delivery of the iGluRs [25 , 26] . We speculate that Neto binds iGluRs on the cell surface and engages in extracellular and/or intracellular interactions that enable the recruitment and clustering of iGluRs at synaptic sites . In this scenario , reduced Neto levels are inefficient for the iGluRs synaptic trafficking and clustering , whereas excess Neto may engage in protein interactions that sequester iGluRs at ectopic locations . At the Drosophila NMJ , Neto activities are regulated by Fur1-dependent limited proteolysis . The removal of Neto prodomain appears to be essential for the stabilization of iGluRs at PSDs . Lack of iGluRs stabilization precludes postsynaptic differentiation although the receptors are functional ( Figs 8 , 9 ) . Thus , synapse activity does not trigger iGluRs clustering or postsynaptic differentiation; instead , stabilization of iGluRs at synaptic sites initiates the recruitment of PSD components and assembly of postsynaptic structures . It has been proposed that a neuron secreted molecule triggers clustering of iGluRs at Drosophila NMJ [18 , 20 , 56] . Secreted molecule ( s ) may mediate iGluRs clustering by binding and trapping Neto/iGluR complexes at new synapses . Mind the gap ( Mtg ) is a neuronal protein reported to organize the synaptic cleft [57] . In mtg null mutant embryos , Neto and iGluRs form aggregates comparable in size with control clusters , but which fail to concentrate at nascent synapses [55] . Unfortunately , we could not detect in vitro interactions between Neto and Mtg . But while the molecular nature of the “trapping” mechanism remains to be determined , our study demonstrates that this process requires the removal of Neto prodomain . The Neto prodomain does not interfere with targeting and apical localization of Neto , nor does it affect its ability to bind iGluRs and form complexes , but it appears to preclude Neto engagement in protein interactions required for the formation of iGluR synaptic clusters . Neto CUB1 domain interacts with itself , but self-association is not enough to explain the formation of large iGluR aggregates . Prodomains could mediate binding to extracellular factors , such as heparan proteoglycans , fibrillin and perlecan that protect the active molecules and modulate their extracellular distribution [58 , 59] . Our study does not address a role for Neto prodomain in binding to extracellular molecules that modulate Neto distribution . The prodomains could also function as chaperones that allow proper folding of biologically active molecules , such as TGF-β-type ligands [34] . However , Neto prodomain is unlikely to play a role in the folding and secretion of Neto because CA-Neto is functional and induces NMJ gain-of-function phenotypes similar to excess Neto . Alternatively , the prodomain could maintain Neto in an inactive form , thus limiting clustering and stable incorporation of Neto/iGluR complexes at PSDs . Similar regulation has been described for the Tolloid/BMP-1 family of enzymes: their prodomains must be removed before the catalytic domains could assume active conformations [60] . It is tempting to speculate that the prodomain masks Neto extracellular domain ( s ) and prevents interactions required for iGluR clustering at PSDs . Is Neto processing a general step in Neto passage through the secretory pathway or could it actively modulate Neto activity/ availability ? To test if processing plays an active role in regulating Neto function we compared the changes in Neto processing in larvae with hunger-induced increase of locomotion [61] . The proportion of processed Neto increased in starved larvae and decreased in fed animals ( Fig . 10 ) , indicating that Neto processing indeed changes in response to an increase in locomotion and/or due to starvation . While this analysis cannot distinguish between the two possibilities , Neto processing emerges as an active mechanism to control the level of Neto available for effective iGluRs recruitment at PSDs . Neto processing/activation phenomenon appears to be highly conserved in insects . Most insects have glutamatergic NMJs , and their genomes encode for Neto proteins with prodomains and Furin minimal sites ( R-X-X-R ) preceding the first CUB domain . For example , Neto proteins in Apis florea and Apis mellifera share an R-Q-M-R motif at positions equivalent to the Furin site in Drosophila Neto . In all cases , the Furin consensus sites are suboptimal suggesting that processing of insect Neto proteins will be slow and restricted by Furin activities . Furins cleave their substrates mainly in late Golgi , though recent data indicate that Furins also function at the cell surface and in the extracellular space [62] . Interestingly , Fur1 also cleaves and activates TGF-β-type ligands , including Gbb , Maverick and Dawdle , which are secreted from muscle and glia and control NMJ development [37 , 39 , 40] . This raises the possibility that Fur1 synchronizes the activation of Neto and TGF-β factors and may serve as a means to coordinate synapse assembly with NMJ growth . This study does not exclude other mechanisms that may regulate the density of synaptic iGluR , such as local insertion of iGluRs from intracellular vesicles [63] . Nonetheless , our study demonstrates that Neto activation by prodomain processing plays an important role in the regulation of iGluR trafficking and clustering at synapses . Trafficking of Neto itself or Neto/iGluR complexes on the muscle membrane may be further controlled by cellular signals that modulate the intracellular domain of Neto and regulate its coupling with scaffold and motor complexes . In fact , Drosophila neto locus codes for two isoforms generated by alternative splicing that differ in their intracellular domains . Both intracellular domains contain multiple putative phosphorylation sites , raising the possibility of rich modulation of Neto/iGluRs distribution in the striated muscle .
Fly lines were generated by standard germline transformation of pUAST-based plasmids containing various neto constructs ( BestGene , Inc ) . Other stocks used in this study were as follows: neto null and hypomorph alleles , neto36 and respectively neto109 [23] , netoRNAi [33] , G14-Gal4 and MHC-Gal4 ( obtained from C . Goodman , University of California at Berkeley ) , da-Gal4 ( BL-5460 ) , 24B-Gal4 ( BL-1716 ) , and elav-Gal4 ( BL-8760 ) . For RNAi-mediated knockout we used the following TRiP lines generated by the Transgenic RNAi Project: GluRIIC ( P[TRiP . JF01854}attP2 ) , and fur1 ( P[TRiP . GL01340] attP40 ) . The control is y1w1118 unless otherwise specified . For rescue analyses , neto transgenes were introduced into neto36 null mutant background using tissue-specific promoters . Since neto is on the X-chromosome we used only FM7-GFP balanced stocks to eliminate any meiotic non-disjunction event . The F1 progenies were genotyped during late embryogenesis and reared at the indicated temperatures . After 24 hours , crawling larvae were removed , counted , and kept at the same temperatures for further analyses or adult viability testing . Neto variants were generated using QuikChange site-directed mutagenesis kit ( Stratagene ) as described previously [23] . CA-Neto has a deletion that joins A51-Q130 and loops out the Neto prodomain . PM-Neto has two point mutations: R123I and R126I . Double-tagged Neto constructs were generated by QuikChange loop-in of various Neto fragments in a previously described AcPA-SP-Myc-V5/His plasmid [64] . This actin promoter/terminator plasmid contains the sequences coding for the Tolloid-related signal peptide , the 5xMyc cassette , a multiple cloning site , followed by the V5 and RGS-6xHis epitopes . All constructs were verified by DNA sequencing . For RNA interference , PCR primers for Furins that carry the T7 promoter sequence at the 5’ end were designed as previously described [36] . The primers were as follows: dFur1-F 5’-TAATACGACTCACTATAGGGACGCAAAGATCCTCTGTGGCA; dFur1-R 5’- TAATACGACTCACTATAGGGACATTGCTCCCGGAACTGC; dFur2-F 5’- TAATACGACTCACTATAGGGACGCTAGAGGCCAATCCGGAA; dFur2-R 5’- TAATACGACTCACTATAGGGACCCTTCTCGCCCCAAAAGTG; Amon-F 5’- TAATACGACTCACTATAGGGACCCACATGGAGCTGGCTGT; Amon-R 5’- TAATACGACTCACTATAGGGACCCTGACTTTGCCGCCATT . PCR products were amplified from genomic DNA or S2 cells cDNA . In vitro transcribed dsRNA was produced using the MEGAscript kit ( Ambion ) . RNAi treatment was carried out by transfections of 5 mg/ml of dsRNA into S2 cells . S2 cells were transfected with indicated constructs and harvested after five days incubation . Total RNA was extracted using TRIZOL reagent ( Invitrogen ) according to manufacturer's instructions . AccuScript High Fidelity First-Strand cDNA Synthesis Kit ( Agilent ) was used to generate cDNAs from the extracted total RNAs according to manufacturer’s instructions . PCR reaction for each target gene was executed using the cDNAs as templates with specific primer pairs ( above ) and β-Actin as a reaction standard ( Actin-Forward: 5’-CTGGCACCACACCTTCTACAATG-3’ , Actin-Reverse: 5’-GCTTCTCCTTGATGTCACGGAC-3’ ) . Wandering third instar larvae were dissected as described previously in ice-cooled Ca2+-free HL-3 solution [65 , 66] . Dissecting larval tissues were fixed in either 4% formaldehyde or Bouin's fixative ( Polysciences , Inc . ) for 20 min or 5 min respectively . PBS containing 0 . 5% Triton X-100 was used for washing and antibody reaction . For detergent-free staining , 1X PBS was used . Primary antibodies from Developmental Studies Hybridoma Bank were used at the following dilutions: mouse anti-GluRIIA ( MH2B ) , 1:100; mouse anti-Dlg ( 4F3 ) , 1:1000; mouse anti-Brp ( Nc82 ) , 1:100; mouse anti-CSP ( 6D6 ) , 1:100; mouse anti-α-spectrin ( 3A9 ) , 1:100 . Other primary antibodies were as follows: rat anti-Neto , 1:1000 [23] , rabbit anti-GluRIIB , 1:2000 ( a gift from David Featherstone ) [67]; rabbit anti-GluRIIC , 1:2000 [33]; rabbit anti-PAK , 1:2000 ( a gift from Nicholas Harden ) [68]; FITC- , rhodamine- , and Cy5-conjugated goat anti-HRP , 1:1000 ( Jackson ImmunoResearch Laboratories , Inc . ) . Alexa Fluor 488- , Alexa Fluor 568- , and Alexa Fluor 647-conjugated secondary antibodies ( Molecular Probes ) were used at 1:400 . All samples were mounted with ProLong Gold reagent ( Invitrogen ) and incubated for 24 hours at RT . Confocal images were acquired using Carl Zeiss LSM 780 or 510 laser scanning microscopic system with Plan-Apochromat 63X/1 . 4 oil DIC objective using ZEN software . Z-stacked images were collected , processed , and analyzed using Imaris X64 ( 7 . 6 . 0 , Bitplane ) or ImageJ ( NIH ) software . In each experiment , samples of different genotypes were processed simultaneously and imaged under identical confocal settings . To quantify fluorescence intensities , confocal regions of interest ( ROIs ) surrounding anti-HRP immunoreactivities were selected and the signals measured individually at NMJs from ten or more different larvae for each genotype ( number of samples is indicated in the graph bar ) . The signal intensities were calculated relative to HRP volume and subsequently normalized to control . For the extrajunctional , cell surface GluRIIC staining , where the GluRIIC positive signals are predominantly in the form of puncta at both Neto-depleted and Neto-excess NMJs , intensities from several size-matched areas of the muscles were collected and averaged using Image J software . The numbers of muscles analyzed per genotype are indicated inside the bars . Quantification of NMJ morphological features was performed at muscle 4 of abdominal segment 4 using the filament tracing function of Imaris software . Boutons were counted manually , while blind to the genotype , using anti-HRP and anti-Dlg staining . Statistical analyses were performed using the Student’s t-test with a two-tailed distribution and a two-sample unequal variance . All graphs represent mean value of all samples of the given genotype ± SEM . Transiently transfected Drosophila S2 cells were used for producing recombinant proteins as previously described [69] . The S2 cells were maintained in M3 ( Shields and Sang M3 insect medium , Sigma ) with 1x insect medium supplement ( Sigma ) and Penicillin/Streptomycin ( Sigma ) , and sub-cultured every 7 days at 2 X 106 cells/ml . For the transfection , dimethyldioctadecyl-ammonium bromide ( DDAB ) solution ( 250 μg/ml ) was mixed with M3 media at 1:2 ratio and incubated 5 min at RT , then the DNA was added to the DDAB-M3 mixture ( 1μg of plasmid DNA to 100 μl suspension ) . The mixture was incubated for 20 min and transfected into S2 cells ( 100 μl mixture to 2 X 106 cells/ml culture ) . After five days , the secreted proteins were collected for analysis , and membrane proteins were extracted by homogenizing cells in lysis buffer ( 50 mM Hepes-NaOH , 150 mM NaCl , 0 . 2 mM EDTA , 0 . 5% NP-40 , 0 . 1% SDS , 2mM AEBSF [MP BIO] , and protease inhibitor cocktail [Roche] ) for 30 min on ice . The lysates were collected by centrifugation at 13 , 000rpm for 30 min at 4°C , separated by SDS-PAGE on 4%–12% NuPAGE gels ( Invitrogen ) and transferred onto PVDF membranes ( Millipore ) . Primary antibodies were used at the following dilutions: rat anti-Neto , 1:1000; chicken anti-GFP ( Abcam ) , 1:2000; anti-GluRIIC , 1:1000; anti-tubulin ( Sigma ) , 1:1000 . Immune complexes were visualized using secondary antibodies coupled with IR-Dye 700 or IR-Dye 800 followed by scanning with the Odyssey infrared imaging system ( Li-Cor Biosciences ) . To analyze muscle proteins , wandering third instar larvae were dissected , and the body walls were mechanically homogenized in lysis buffer for 30 min on ice . The lysates were analyzed by Western blotting . For co-immunoprecipitation , the lysates were incubated with rabbit anti-GFP antibody ( Invitrogen ) for 1 hr at 4°C . Protein A/G UltraLink Resin ( 50% slurry , Thermo Scientific ) was added and incubated overnight at 4°C . The beads were washed with lysis buffer . Proteins were eluted with 1x SDS sample buffer and analyzed by Western blotting . Secreted and processed Neto fragment ( CUB1-V5/His ) was purified using His-Trap affinity column equipped with AKTA FPLC system ( Pharmacia ) and separated by SDS-PAGE . A specific gel band was isolated and analyzed at Microchemistry and Proteomics Analysis Facility , Harvard University . Wandering third instar larvae were dissected in Jan's saline containing 0 . 1 mM Ca2+ and processed as previously described [70] . Dissected larvae were fixed in EM fixative ( 4% p-formaldehyde , 1% glutaraldehyde , 0 . 1 M sodium cacodylate , and 2 mM MgCl2 , pH 7 . 2 ) for 20 min at room temperature followed by incubation overnight at 4°C , then washed extensively ( 0 . 1 M sodium cacodylate , and 132 mM sucrose , pH 7 . 2 ) . The samples were processed and analyzed at the Microscopy and Imaging Core Facility , NICHD . The standard larval body wall muscle preparation first developed by Jan and Jan ( 1976 ) was used for electrophysiological recordings [71 , 72] . Wandering third instar larvae were dissected in physiological saline HL-3 [65] , washed , and immersed in HL-3 containing 0 . 8 mM Ca2+ using a custom microscope stage system [73] . The nerve roots were cut near the exiting site of the ventral nerve cord so that a suction electrode could pick up the motor nerve later . Intracellular recordings were made from muscle 6 . Data were used when the input resistance of the muscle was >5 MΩ and the resting membrane potential was between −60 mV and −80 mV for the entire duration of the experiment . The input resistance of the recording microelectrode ( backfilled with 3 M KCl ) ranged from 20 to 25 MΩ . Muscle synaptic potentials were recorded using Axon Clamp 2B amplifier ( Axon Instruments ) and pClamp software . Following motor nerve stimulation with a suction electrode ( 100 μsec , 5 V ) , evoked EJPs were recorded . Three to five EJPs evoked by low frequency of stimulation ( 0 . 1 Hz ) were averaged . For mini recordings , TTX ( 1 μM ) was added to prevent evoked release [65] . To calculate mEJP mean amplitudes , 50–100 events from each muscle were measured and averaged using the Mini Analysis program ( Synaptosoft ) . Minis with a slow rise and falling time arising from neighboring electrically coupled muscle cells were excluded from analysis [72 , 74] . In addition , when comparing mini sizes between preparations , the Kolmogorov-Smirnov test was administrated . Quantal content was calculated by dividing the mean EJP by the mean mEJP after correction of EJP amplitude for nonlinear summation according to the methods described [75 , 76] . Corrected EJP amplitude = E[Ln[E/ ( E − recorded EJP ) ]] , where E is the difference between reversal potential and resting potential . The reversal potential used in this correction was 0 mV [75 , 77] . Data are presented as mean ± SEM , unless otherwise specified; EJP amplitudes and quantal contents after the nonlinear correction are shown . A one-way analysis of variance followed by Tukey's HSD test was used to assess statistically significant differences among the genotypes . Differences were considered significant at p < 0 . 05 . | Synapse development is initiated by genetic programs , but is coordinated by neuronal activity , by communication between the pre- and postsynaptic compartments , and by cellular signals that integrate the status of the whole organisms and its developmental progression . The molecular mechanisms underlining these processes are poorly understood . In particular , how neurotransmitter receptors are recruited and stabilized at central synapses remain the subject of intense research . The Drosophila NMJ is a glutamatergic synapse similar in composition and physiology with mammalian central excitatory synapses . Like mammals , Drosophila utilizes auxiliary subunit ( s ) to modulate the formation and function of glutamatergic synapses . We have previously reported that Neto is an auxiliary protein essential for functional glutamate receptors and for organization of postsynaptic specializations . Here we report that synapse assembly and NMJ development are exquisitely sensitive to postsynaptic Neto levels . Furthermore , we show that Neto activity is controlled by Furin-type proteases , which regulate the processing and maturation of many developmentally important proteins , from growth factors and neuropeptides to extracellular matrix components . Such concerted control may serve to coordinate synapse assembly with synapse growth and developmental progression . |
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Among broadly neutralizing antibodies to HIV , 10E8 exhibits greater neutralizing breadth than most . Consequently , this antibody is the focus of prophylactic/therapeutic development . The 10E8 epitope has been identified as the conserved membrane proximal external region ( MPER ) of gp41 subunit of the envelope ( Env ) viral glycoprotein and is a major vaccine target . However , the MPER is proximal to the viral membrane and may be laterally inserted into the membrane in the Env prefusion form . Nevertheless , 10E8 has not been reported to have significant lipid-binding reactivity . Here we report x-ray structures of lipid complexes with 10E8 and a scaffolded MPER construct and mutagenesis studies that provide evidence that the 10E8 epitope is composed of both MPER and lipid . 10E8 engages lipids through a specific lipid head group interaction site and a basic and polar surface on the light chain . In the model that we constructed , the MPER would then be essentially perpendicular to the virion membrane during 10E8 neutralization of HIV-1 . As the viral membrane likely also plays a role in selecting for the germline antibody as well as size and residue composition of MPER antibody complementarity determining regions , the identification of lipid interaction sites and the MPER orientation with regard to the viral membrane surface during 10E8 engagement can be of great utility for immunogen and therapeutic design .
The HIV-1 envelope protein ( Env ) , a hetero-trimer of non-covalently linked gp120 and gp41 subunits , is the target of broadly neutralizing antibodies ( bnAbs ) [1] . BnAbs recognize several sites of vulnerability on Env [2] . The membrane proximal external region ( MPER ) of Env is one of its most conserved regions [3] and , hence , the focus of vaccine and therapeutic design efforts . Previously , several models have been proposed for the MPER orientation with respect to the viral membrane and for the mechanism by which MPER antibodies approach their respective epitopes in vivo [4–7] . Several reports suggested that the MPER is relatively inaccessible to antibodies in the pre-fusion conformation and is exposed only transiently after CD4 binding [4 , 8–10] . Other studies suggested that the MPER is partially laterally inserted into the viral membrane [6] in its pre-fusion form and that neutralization would require antibodies to "extract" the MPER from the membrane [5 , 7] . Early cryo-electron tomography ( cryoET ) of native HIV-1 [11] and SIV [12] Env on virions interpreted the MPER and transmembrane ( TM ) region as three independent helices organized in a tripod-like fashion followed by a turn at Lys683 ( the last residue of the gp41 ectodomain; UNIPROT ID: Q70626 , HIV1-LW123 numbering ) . In other cryoET studies [13 , 14] , the gp41 stem was proposed to adopt a compact stalk organization within the trimer suggesting instead an extended , intertwined helical architecture for the MPER and TM region . An extended helical conformation was observed in a recent NMR structure of a construct spanning the MPER and part of the gp41 TM domain that showed no break in helicity at Lys683 [15] and also in the NMR structure of the gp41 TM , which revealed a triple-helix , quaternary TM organization in bicelles [16] . X-ray crystallography and EM [17–19] studies have described in atomic detail the structure of a soluble , stabilized Env construct ( BG505 SOSIP . 664 gp140 trimer ) , but these SOSIP structures lack the MPER , TM and cytoplasmic domains . In a recent 4 . 2 Å resolution cryo-EM structure of a native HIV-1 Env trimer ( ΔCT ) containing the MPER and TM domains in complex with antibody PGT151 , the micelle-embedded TM domain could not be resolved , but the structure also suggested that the MPER may be inaccessible in the pre-fusion form of Env [20] . Furthermore , in the presence of MPER antibody 10E8 , an 8 . 8 Å resolution cryo-EM structure illustrated that the three MPER epitope regions within the trimer form a triple helix [20] and the 10E8-bound Env appears to be elevated off the micelles . This elevation in comparison to the pre-fusion form suggested how the MPER may be engaged by 10E8 , but the presence of detergent and the lack of a membrane in that study limited the ability to draw definitive conclusions about 10E8 engagement of native Env on virions . Due to the proximity of MPER to the membrane , MPER binding antibodies are thought to interact with the membrane and , indeed , some have been shown to interact with lipids [21–24] . Several bnAbs target the MPER , with 10E8 and 4E10 neutralizing about 98% of all HIV-1 subtypes tested [25] . 10E8 is the most potent of the MPER antibodies and lacks the polyreactivity of 4E10 . 10E8 and 4E10 target the same helical epitope ( C-terminal MPER residues 671–683 ) but differ in their modes of binding [25–27] . Residues 672–674 adopt a 310 helical turn when engaged by 4E10 , but are part of a continuous α helix ( region 671–683 ) when bound by 10E8 . The 2F5 epitope ( residues 656–671 ) [28 , 29] , which is N-terminal to the 10E8 and 4E10 epitopes , adopts an extended conformation with a type 1 β-turn and the Z13e1 epitope ( residue 668–677 ) [30] consists of linked helical turns . Although these antibodies target linear MPER epitopes on gp41 [25 , 26 , 29 , 30] , binding to individual lipids [21] or liposomes [31] also suggests that their complete epitopes include viral membrane components . Indeed , studies of MPER antibodies suggest a role for their complementarity determining region ( CDR ) H3 in binding to viral membrane [22 , 24 , 31–34] , but no clear picture has emerged as to how each antibody is oriented with respect to the MPER and viral membrane during engagement . To determine which regions of 10E8 might interact with the HIV-1 membrane , and motivated by our recent findings regarding 4E10 interaction with lipids that compose the viral membrane [23] , we undertook a crystallographic study of 10E8 in complex with lipids and an epitope scaffold [35] . We complemented our observations of 10E8-lipid binding with binding and neutralization experiments as well as structure determination of several 10E8 mutants . Our combined results have identified which 10E8 regions interact with the viral membrane and indicated that the MPER likely adopts an upright orientation with respect to the viral membrane during antibody engagement , as we also proposed for 4E10 [23] . Our high-resolution structural study increases our understanding of the relative MPER location , orientation and conformation during MPER antibody binding , and provides insights for the design of immunogens and therapeutic antibodies .
Crystal structures of 10E8 in complex with T117v2 epitope scaffold and lipids 06:0 phosphatidylglycerol ( PG ) ( 2 . 37 Å resolution ) or 06:0 phosphatidic acid ( PA ) ( 2 . 62 Å resolution ) led to identification of a lipid-binding site at the proximity of the CDRL1 and CDRH3 loops ( Fig 1A ) . Electron density for the lipid head groups and part of the PG acyl tail were observed ( S2 Fig ) . The orientation of the lipid fragments with respect to 10E8 suggests that the hydrophobic lipid tails do not interact with the Fab or T117v2 and are disordered . The 06:0 PG and 06:0 PA head groups are bound into a crevice delineated by CDRL1 ( Leu28 ( L ) , Arg29 ( L ) , Ser30 ( L ) , His31 ( L ) and Tyr32 ( L ) ) , FRL3 ( Ala66 ( L ) , Ser67 ( L ) and Gly68 ( L ) ) , and by Trp100b ( H ) , Ser100c ( H ) and Gly100d ( H ) at the CDRH3 tip ( Fig 1B and 1C; where ( L ) stand for light and ( H ) for heavy chains ) . Glycerol , the cryo-protection component of the crystals , occupies the lipid-binding site in the 10E8-T117v2 structure when exogenous lipids are not added in the crystallization experiments ( Fig 1D ) . Interestingly , the 10E8 light chain displays basic surface patches arising from somatically mutated ( Arg17 ( L ) , Arg24 ( L ) , Arg70 ( L ) ) , and germline-encoded ( Arg29 ( L ) , Lys51 ( L ) , Arg61 ( L ) ) residues in a plane with the bound lipid head groups observed in our structures , as well as with K/R683 , the last residue of the gp41 ectodomain before the TM region ( Fig 2A ) . Thus , this basic as well as Ser/Thr/Asn rich polar surface ( Fig 2B ) is likely to interact with polar and negatively charged lipid head groups of the viral membrane [36] . To further investigate and validate that the light chain of 10E8 is involved in binding to the lipid head groups of the membrane upper leaflet , we designed several 10E8 light-chain mutants ( Fig 2C ) : mutant 1 reverts the three somatically mutated arginines to 10E8 germline residues ( R17Q ( L ) , R24Q ( L ) , R70T ( L ) ) ; mutant 2 replaces most of the lipid-proximal arginine residues with aspartate or glutamate ( R24E ( L ) , R29E ( L ) , R70E ( L ) , as well as R17D ( L ) ) ; mutant 3 has all the basic residues as well as some of the polar residues on this surface mutated to D or E ( R17D ( L ) , R24E ( L ) , R29E ( L ) , K51D ( L ) , N52D ( L ) , S65D ( L ) , S67D ( L ) , R70E ( L ) , S76D ( L ) ) ; mutant 4 has R29 ( L ) and Y32 ( L ) , which flank the lipid head groups , mutated to glutamate ( R29E ( L ) and Y32E ( L ) ) ; and mutant 5 has R29 ( L ) and Y32 ( L ) mutated to alanine ( R29A ( L ) and Y32A ( L ) ) . Overall , mutants 1 to 3 were constructed to coat the presumed membrane-interacting surface of 10E8 with different amounts of negative charge to investigate binding affinity and specificity of the 10E8 epitope ( protein and lipid ) and to examine the effects of the mutations on neutralization potency . In addition , mutants 4 and 5 were designed to disrupt the lipid-binding site that we observe in the crystal structures . To investigate the effect of the light-chain surface mutations on 10E8 IgG binding to the T117v2 scaffold , we performed surface plasmon resonance ( SPR ) experiments . SPR analysis ( Table 1 and S3 Fig ) shows that , with the exception of mutant 4 for which the residues involved in lipid head group binding were mutated to glutamate ( R29E ( L ) and Y32E ( L ) ) , all other mutants retained picomolar affinity ( KD ) to the T117v2 scaffold ( Table 1 ) . Mutations R29E ( L ) and Y32E ( L ) in mutant 4 ( 1 . 2 nM KD for T117v2 ) resulted in a ~ 40 fold reduction in binding compared to the affinity-matured 10E8 IgG ( 0 . 029 nM for T117v2 ) , while mutation of the same residues to alanine in mutant 5 ( ~0 . 12 nM KD ) resulted in only a 4-fold decrease in binding . As 10E8 interaction with the MPER peptide epitope is only with the heavy chain ( Fig 1A ) , these results indicate that , except for mutant 4 , all other light-chain mutants do not interfere with 10E8 binding to the MPER-scaffold . Structure determination and neutralization potency experiments with the best binding mutants were then performed to determine if changing the light-chain surface charge by mutation leads to conformational changes or compromises neutralization . Abolition or decrease in neutralization would suggest that the 10E8 light chain is oriented toward and interact with the viral membrane . We focused on mutants 1–3 and 5 , as binding to the peptide-scaffolded T117v2 was decreased for mutant 4 . X-ray structures of mutants 1–3 and 5 ( Table 1 ) were determined in complex with the T117v2 scaffold at 1 . 6 Å ( mutant 1 ) and 2 . 0–2 . 2 Å ( mutants 2 , 3 and 5 ) resolution . Superposition of the Cα atoms of the variable domains of mutants and wild-type 10E8 shows nearly identical conformations ( Cα r . m . s . d . of 0 . 40 , 0 . 23 , 0 . 31 and 0 . 39 Å for mutants 1 , 2 , 3 , and 5 , respectively ) . Thus , the mutations mainly result in different charge distributions on the light-chain surface ( Fig 3A–3E ) , with the most negative surface observed for mutant 3 . Although mutant 1 has a slightly larger r . m . s . d . ( 0 . 4 Å ) , the differences in the main chain occur mainly at the Fab elbow region . Only in mutant 5 do the R29A ( L ) and Y32A ( L ) mutations produce a slight shift in the main-chain for FRL2 ( residues 48 , 49 ) , CDRL2 ( 50–56 ) , FRL3 ( 57–72 ) and CDRL1 ( 24–32 ) with maximum Cα differences observed in CDRL2 ( 1 . 3 Å for Lys51 ( L ) ) , FRL3 ( 0 . 9 Å for Gly68 ( L ) ) and CDRL1 ( 0 . 9 Å between Arg29 ( L ) and Ala29 ( L ) ; S4 Fig ) . The shift for CDRL2 in mutant 5 may be the direct result of substituting Tyr32 ( L ) with alanine , which allows the nearby Phe48 ( L ) side chain to rotate and influence the conformation of Lys51 ( L ) , Asn52 ( L ) and Asn53 ( L ) . None of the mutations affect binding to T117v2 ( in agreement with the SPR study , Table 1 ) as the mutant CDRH3 conformations are almost identical to wild-type 10E8 ( S4 Fig ) . The four mutant structures show different bound ligands ( Fig 3B–3E ) depending on the residue mutated and on the components of the crystallization mother liquor and cryoprotectants ( n . b . all mutants were crystallized without lipids and in different conditions ) . Mutant 1 has glycerol from the cryoprotectant in the lipid-binding site ( Fig 3B; S2 Table; S5A–S5C Fig ) , as also observed for the wild-type 10E8-T117v2 complex cryoprotected with glycerol ( Fig 1D; S2E and S2F Fig ) . In mutant 2 for which Arg29 ( L ) was mutated to glutamate , only water molecules seem to occupy this site when ethylene glycol was used for cryoprotection rather than glycerol . However , in mutant 3 , with the same mutation ( R29E ( L ) ) , a phosphate from the crystallization condition occupies the site ( S5E and S5F Fig ) suggesting that replacement of Arg29 ( L ) with a negative charge might still retain binding to at least a phosphate , which is a component of phospholipid head groups ( n . b . glycerol was absent from the buffers used with crystallization of mutant 3 , and thus does not compete for phosphate binding to this site ) . In mutant 5 , where R29 ( L ) and Y32 ( L ) are mutated to alanine ( Fig 3E ) , glycerol ( used for cryoprotection ) is not observed at this location in either of the two Fabs in the asymmetric unit , suggesting that the lipid binding site is severely disrupted by these mutations . In parallel , we probed the neutralization potency of 10E8 mutants 1–3 and 5 against a panel of 109 viruses [37] to determine if these mutations in the light chain interfere with neutralization . A decrease in neutralization potency compared to wild type was observed with most of the strains for mutant 1 in which three arginines ( two are proximal to the lipid-binding site ) were reverted to the germline residues ( Fig 3B and Table 2 ) . A more significant decrease in neutralization was observed for mutant 2 ( Fig 3C and Table 2 ) . Increasing the amount of negatively charged residues on the light-chain surface in mutant 3 results in loss of neutralization ( Fig 3D and Table 2 ) of most of the viruses that we tested with the breadth dropping to 34% . Interestingly , mutation of only R29 ( L ) and Y32 ( L ) to alanine in mutant 5 , which was designed to disrupt the observed lipid-binding site ( Fig 3E ) , also abrogated the neutralization of most of the viruses tested ( breadth of 44% , Table 2 ) , despite retaining picomolar binding affinity to the epitope scaffolds . Thus , mutation of basic and polar residues on the light-chain surface to acidic residues does not interfere with binding to the MPER peptide epitope , but neutralization potency and breadth decreased as the number of negatively charged residues on the surface increased . Substantial loss of neutralization occurred when the lipid-binding site was disrupted ( mutant 5 ) or when the light-chain surface patch contained mainly acidic residues ( mutant 3 ) . We noticed that our 10E8 wild-type IgG shows about ten-fold higher neutralization potency compared with results reported previously [25] . To probe the quality of our 10E8 and mutants IgG preparations , we performed size exclusion chromatography-multi-angle light scattering ( SECMALS ) analysis of the samples . 10E8 and mutant 1 showed the presence of three major peaks in each sample . Each of these peaks had a molecular weight of approximately 150 kDa , consistent with the expected size of monomeric antibody ( S6 Fig ) . We attribute this behavior to a slow conformational isomeration of 10E8 , which results in differential interaction with the size exclusion matrix as previously reported for a solubility-optimized 10E8 mutant ( 10E8v4 ) [38] . Thus , our results suggest conformational isomerization for both 10E8 wild type and mutant 1 . In contrast , mutants 2 , 3 and 5 each eluted as a single peak with a molecular weight of approximately 150 kDa . The SECMAL analysis shows that the IgG samples used for neutralization are all monomeric with only insignificant amounts ( <0 . 5% ) of aggregates observed for wild-type 10E8 and mutant 1 ( S6 Fig ) . The structural , neutralization and binding analyses of wild-type and light-chain 10E8 mutants suggest that the 10E8 variable light-chain residues facilitate approach to and interaction with the viral membrane . To obtain further insights into 10E8 binding to gp41 of HIV Env on the viral surface , and encompassing our recent findings on 4E10 binding to the lipids [23] , we generated a model of 10E8 bound to the viral membrane-gp41 epitope assembly based on the orientation of the lipids and gp41 observed in our crystal structures ( Fig 4A ) using the CHARMM force field membrane builder [39] . The viral membrane head groups of the outer leaflet were roughly placed in a plane containing the basic side chains of the 10E8 light-chain , Arg17 ( L ) , Arg24 ( L ) , Arg29 ( L ) and Arg70 ( L ) , Lys51 ( L ) , the lipid head group , and gp41 Lys/Arg683 ( Fig 4A and 4B ) . Our model suggests that 10E8 epitope includes the gp41 helical peptide ( residues 671–683 ) tilted about 75–80° from the viral membrane surface and lipid head groups of the membrane . The 10E8 light chain would then face the membrane , with which it interacts via CDRH3 , CDRL1 , and FRL3 , and possibly additional residues ( Ser/Thr/Arg/Lys ) of the light-chain surface ( Fig 4B ) . Indeed , the relatively large percentage of short polar residues , serine and threonine , on this light-chain β-sheet surface form a flat polar region that can perhaps also interact with the head groups of the various lipids composing the viral membrane . Fitting of the model into the experimental EM map of the CD4-bound Env ( EMDB-5455 [40] ) shows that 10E8 CDRs H1 and H2 might interact with additional regions of Env ( Fig 4D ) , as also suggested by the cryo-EM structure of 10E8 in complex with the native HIV-1 Env trimer ( ΔCT ) and PGT151 [20] .
The MPER region of gp41 , although highly conserved and targeted by several very broadly neutralizing antibodies ( 10E8 , 4E10 and 2F5 ) , has so far not led to a vaccine immunogen that elicits such antibodies . 4E10 and 2F5 antibodies have been studied in great detail [21 , 26 , 27 , 29] . Their binding to phospholipids prompted the suggestion that such antibodies are rarely produced due to tolerance mechanisms resulting from interaction with ‘self-components’ [41–44] . However , the binding of 4E10 to cardiolipin [44] and its overall reactivity profile appears to differ from those of autoimmune antibodies [22 , 45] . 10E8 , which binds to the same MPER epitope region as 4E10 , does not show cross-reactivity with cardiolipin or other autoantigens [25] , although binding to cholesterol-rich liposomes was recently demonstrated [31] . Our crystallographic study of 10E8 binding to two phospholipids PG and PA reveals a lipid-binding site in a cavity delineated by CDRL1 , CDRH3 and FRL3 ( Figs 1A and 4A ) . In healthy cells , PG and PA are less abundant lipids of the plasma membrane [46] . However , the amount of these lipids increases on the HIV-1 membrane that is acquired from the host cell during budding [36 , 47–49] . A diversity of ligands ( PG , PA , phosphate , glycerol ) is observed in our structures in the lipid-binding site and , therefore , other phospholipid head groups could potentially occupy this cleft . The lipid binding site residues are relatively conserved in the 10E8 germline IGLV3-19*01 sequence with only two differences observed for light-chain residues 31 ( tyrosine in the germline-encoded sequence and histidine in mature 10E8 ) and 66 ( serine in the germline and alanine in mature 10E8 ) . The presence of the tyrosine and serine at the respective positions in germline does not influence the conformation of CDRL1 and FRL3 in this cavity as comparison with the structure of the unbound germline 10E8 ( PDB 5JO5 [50] ) shows that the side chains of residues 31 and 66 point away from this cleft and do not change the topology of the lipid-binding site . However , CDRH3 , which also delineates this cavity , is not visible in the germline structure as its residues appear to be disordered . Thus , it is not clear if the lipid-binding site is fully recapitulated in the 10E8 germline . In addition to the lipid-binding site , we observed that the 10E8 light-chain surface is coated by several basic ( arginine and lysine ) and multiple polar ( serine and threonine ) residues , which together with Lys683 , the last residue of the gp41 ectodomain , lie in a plane that roughly coincides with the lipid head groups observed in our structure . Several light-chain mutants show similar binding affinity to the epitope scaffold but with decreased neutralization potency and breadth suggesting that , although binding to the MPER peptide epitope is not compromised , binding to its composite epitope formed by MPER peptide and viral membrane lipids on the virus surface would be affected . Furthermore , the light-chain mutations do not alter the Fab conformation , consistent with the binding experiments , but result in more acidic surface , strongly suggesting that the 10E8 antibody interacts with the viral membrane via this basic-polar light-chain patch . Our data also suggest an upright orientation of the MPER helical epitope of 10E8 with respect to the viral membrane ( Fig 4B ) , which is tilted about 78°±3° from the bilayer during antibody engagement ( similar to that observed for 4E10 [23] ) . This angle is defined by the epitope’s helical axis intersecting the axis of the plane of the lipid head group in the direction from which 10E8 approaches the MPER in the CHARM model . This MPER orientation with respect to the membrane and the angle of antibody approach is also suggested by the recent cryo-EM structure of native JR-FL EnvΔCT at 8 . 8 Å resolution that contains the MPER and TM domains in complex with 10E8 and PGT151 antibodies [20] . This cryo-EM structure provided fascinating new insights into the 10E8 interaction with Env and on new features outside the MPER that are part of the 10E8 epitope ( e . g . N88 and N625 glycans ) , although no detailed information was possible for the 10E8 interaction with membrane lipids ( or the micelle in the EM study ) or for the TM region . Our study suggests that 10E8 interacts with the helical MPER with an angle of approach of ~43°±3° as measured from the viral membrane to the pseudo dyad axis between the variable light and heavy chains ( defined by atoms Cβ of Ser179 and Cα of Phe100a , with the light-chain variable region interacting with the phospholipid head groups on the membrane surface . In this model , CDRH3 inserts between the MPER epitope and the lipid head groups with the aromatic residues at its tip located within the hydrophilic region of the lipid bilayer . It is likely that CDRH1 , CDRH2 and FRH3 of 10E8 form additional contacts with Env as shown in our MD model fitted into the EM map of the full-length Env bound to CD4 [40] ( Fig 4D ) and as shown in the JR-FL EnvΔCT-10E8-PGT151 EM structure [20] . In the JR-FL EnvΔCT-10E8-PGT151 EM structure , the presence of PGT151 , which binds with a stoichiometry of two Fabs per trimer , leads to a disruption in the symmetry of the spike causing the three 10E8 Fabs to have slightly different orientations compared to each other ( i . e . slightly different angle of tilt toward the micelle position and rotations around the dyad axis between the heavy and light chains ) . In our model , the three 10E8 Fabs bind with the same angle of approach to their respective MPER-viral membrane composite epitope . The 10E8 Fab in the EM structure with the closest orientation to the one that we propose here is tilted only ~8° more towards the predicted position of the membrane surface . Gp120 conformational changes on receptor and co-receptor engagement could also promote 10E8 binding . Comparison of this 10E8 model with a previous model of 4E10 bound to the gp41 epitope-lipid bilayer ( Fig 4B and 4C; [23] ) shows a similar orientation of the MPER with regard to the viral membrane . 10E8 and 4E10 both interact with the residues on the same face of the helical MPER epitope , but the Fab variable regions that contact the gp41 epitope differ . The two antibodies are rotated by about 90° around their pseudo-dyad axes with respect to each other , with 10E8 interacting with the viral membrane via the light chain and CDRH3 , while 4E10 interacts via CDRH1 and CDRH3 . 4E10 may also make more extensive interactions with upstream regions of Env ( Fig 4E ) than 10E8 ( Fig 4D ) or most likely engage its epitope as fusion intermediate forms after receptor engagement when gp120 regions are no longer in the way . Reduction in the 10E8 interaction with other regions of Env compared with 4E10 may possibly explain its increased neutralization potency . Our combined design , structural and functional study has provided an explanation for how two extremely broad MPER antibodies engage their common epitopes at the stem of the gp41 ectodomain . The location and conformation of MPER with regard to gp41 and the membrane at different stages of viral fusion remains unclear , but the information presented here helps to fill in missing pieces of the dynamic viral fusion process . These structural and functional insights are important for design of therapeutic antibodies and immunogens as HIV vaccines . The information here can enable the MPER to be linked to lipids in an appropriate orientation as in liposomes or chimeric viruses for vaccination and provide information for improving the pharmacodynamic and pharmacokinetic properties of 10E8 for therapeutic applications .
Genes for 10E8 IgG and Fabs mutants were synthesized by Genscript , Inc . All antibodies and Fabs were expressed in FreeStyle 293S cells ( Invitrogen ) and purified as described previously [25] . Briefly about 400 μg heavy-chain and 200 μg light-chain vectors were diluted in 25 ml Gibco Opti-MEM I ( Invitrogen ) reduced-serum medium , sterile filtered , and mixed with 25 ml final volume Opti-MEM I pre-incubated with 1 ml of 293fectin ( Invitrogen ) . After 30 minutes incubation , the mixture was added to 1 L of cells ( about 1 . 2x106 cells/ml density ) in FreeStyle 293 expression medium ( Invitrogen ) . The Fabs were purified on a lambda light chain Capture Select affinity column pre-equilibrated with 1x PBS buffer . The unbound material was washed out with the same buffer and the bound Fabs were eluted with 0 . 1 M glycine , pH 3 . 0 , and immediately neutralized using Tris pH 8 . 0 . Fractions were concentrated and buffer exchanged into 20 mM sodium acetate , pH 5 . 5 , then loaded on a Mono S column ( GE Healthcare Life Sciences ) equilibrated with the same buffer . The proteins were eluted with a linear gradient of 0 to 50% , 1M KCl in 20 mM sodium acetate , pH 5 . 6 . The concentrated samples were stored in 1xHBS ( 150 mM NaCl , 10 mM HEPES , pH 7 . 4 ) . T117v2 was expressed and purified as previously described [35] . The purified 10E8 variants were incubated in a 1:1 molar ratio with T117v2 scaffold and purified by size exclusion chromatography using a HiLoad 16/600 SuperDex 200pg column ( GE Healthcare Life Sciences ) in 1xHBS buffer . Kinetics and affinities of antibody-antigen interactions were measured on a ProteOn XPR36 ( Bio-Rad ) using GLC Sensor Chip ( Bio-Rad ) and 1x HBS-EP+ pH 7 . 4 running buffer ( 20x stock from Teknova , Cat . No H8022 ) supplemented with BSA at 1mg/ml . The Human Antibody Capture Kit instructions ( Cat . No BR-1008-39 from GE Healthcare Life Sciences ) were used to prepare chip surfaces for ligand capture . In a typical experiment , about 6000 RU of capture antibody was amine-coupled in all six flow cells of the GLC Chip . Regeneration was accomplished using 3 M magnesium chloride with 180 seconds contact time and injected four times per each cycle . Raw sensograms were analyzed using ProteOn Manager software ( Bio-Rad ) , including interspot and column double referencing , and either Equilibrium or Kinetic fits with Langmuir model , or both , were employed when applicable . Analyte concentrations were measured on a NanoDrop 2000c Spectrophotometer using Absorption at 280 nm . Pseudoviruses were generated by transfection of 293T cells ( ATCC ) with an HIV-1 Env expressing plasmid and an Env-deficient genomic backbone plasmid ( pSG3ΔEnv , ( NIH AIDS Reagent Program 11051 ) ) , as described previously [51] . Pseudoviruses were harvested 72 hours post-transfection for use in neutralization assays . Neutralizing activity was assessed using a single round of replication pseudovirus assay and TZM-bl target cell ( NIH AIDS Reagent Program 8129 ) . TZM-bl cells were seeded at a density of 5 , 000 cells/well in half-volume white luminescent 96 well plates ( Costar 3688 ) , one day prior to assay . Assay and growing medium was Complete DMEM [Dulbecco's Modified Eagle Medium ( Corning Cellgro MT15013CV ) with 200 μM L-glutamine ( Gibco 25030081 ) , 100 U/ml Penicillin-Streptomycin ( Invitrogen 15140–122 ) , and ten percent fetal bovine serum ( Thermo Scientific HyClone SH3091003 ) ] . To this plate was added pseudovirus , which was preincubated with serial dilutions of antibody for 1 hour at 37°C in duplicate with 25 μl per well final volume . Virus-infected ( no serum ) and uninfected cell wells were controls on each cell plate . After 24 hours , 75 μl of Complete DMEM was added to each well , bringing the total volume to 100 μl; the plates were replaced in the incubator another 48 hours . Prior to virus signal determination , the liquid medium was removed from the plates , cells were lysed with 45 μl per well Promega Cell Lysis buffer ( Product number E1531 ) , and the plates were then shaken for 10 min at 1000 RPM on a Jitterbug Microplate Incubator/Shaker . Thirty μl of Promega Flash substrate ( Promega Luciferase 1000 Assay System E4550 ) was added per well , and luminescence was measured via Synergy 2 Multi-Mode Reader ( BioTek ) . SECMALS analysis was performed by separating approximately 50 μg IgG on a Superdex 200 Increase 10/300 GL column ( GE Healthcare Life Sciences ) in phosphate buffered saline ( 140 mM NaCl , 2 . 7 mM KCl , 5 . 6 mM Na2HPO4 , 1 . 8 mM KH2PO4 , pH 7 . 4 ) and measuring UV absorption at 280 nM and multi-angle light scattering on a DAWN HELEOS II system with Optilab T-rEX refractometer ( Wyatt Technology ) . Raw values were background subtracted and normalized to the maximum signal intensity of each injection . Molecular weights were calculated using the ASTRA6 software package ( Wyatt Technology ) . Data were plotted using GraphPad Prism version 7 . 0a for Mac ( GraphPad Software ) . X-ray diffraction data were collected at APS 23ID-B and 23ID-D beam lines and at SSRL on 12–2 and 11–1 beam lines ( S1 and S2 Tables ) and were auto-indexed and processed with HKL-2000 [52] or XDS [53] . Molecular replacement was performed with Phaser [54] using one of the 10E8 Fabs ( PDB 4G6F [25] ) and the T117 scaffold ( PDB 3LF6 [35] ) as search models . Model rebuilding in Coot [55] and refinement with Phenix [56] were performed following an initial rigid body refinement step . The refinement cycles , for structures solved between 2 . 0 and 2 . 6 Å , included refinement of individual atomic coordinates , cartesian simulated annealing , refinement of individual isotropic atomic displacement parameters and optimization of X-ray/stereochemistry and X-ray/ADP weights . For the 1 . 6 Å structure of 10E8 mutant 1 in complex with T117v2 , refinement of individual atomic coordinates , cartesian simulated annealing , occupancy and individual atomic displacement parameters refinement with anisotropic ADP for protein atoms and isotropic ADP for solvent were performed as well as optimization of X-ray/stereochemistry and X-ray/ADP weights . X-ray diffraction and refinement statistics are reported in S1 Table for the wild-type 10E8 mature-T117v2 complexes bound to lipids and in S2 Table for 10E8 mutant-T117v2 complexes . Structure figures were generated with Pymol [57] . The atomic coordinates and structure factors of 10E8-T117v2 structures have been deposited in the Protein Data Bank , with the accession codes: 5T6L ( for 10E8-T117v2 ) and 5T85 , 5T80 for co-crystals with 06:0 PG and 06:0 PA , respectively and those for 10E8 mutants-T117v2 structures with the accession codes: 5SY8 ( for 10E8 mutant 1-T117v2 ) , 5TFW ( for 10E8 mutant 2-T117v2 ) , 5T29 ( for 10E8 mutant 3-T117v2 ) and 5T5B ( for 10E8 mutant 5-T117v2 ) . A model of the trimeric MPER epitope-transmembrane region of the gp41 was constructed using PDB 2MOM as a template as described previously [23] , with the orientation of the MPER epitope modeled base on crystal structures determined in this study . The structural information on the 10E8 Fab and the PG fragment was transferred to the trimeric model by superposing the MPER epitope region of the T117v2 scaffold to the corresponding region in the model . The crystallographic 06:0 PG lipid fragment was extended to the size of a 1 , 2-dihexadecanoyl-sn-glycero-3-phospho- ( 1'-rac-glycerol ) ( DPPG ) molecule by adding the lipid tails in the direction perpendicular to the plane that includes the light-chain surface residues , the head group of the PG fragment , and Lys683 . The putative transmembrane region model constructed solely to anchor the lipid bilayer was then used in membrane building with CHARMM [39] . A rectangular box ( x = y = 157 . 7 Å ) was used to generate a heterogeneous bilayer containing 400 lipids on the upper leaflet and 434 lipids on the lower leaflet of the membrane , by replacement method [58] . The HIV-1 membrane lipid composition [47] was taken into account when choosing the composition of the bilayer . The Monte Carlo method was used to place counter potassium ions and NVT ( constant volume ) ensemble was used during six equilibration steps at a constant temperature of 303 K . The final model has Lys683 of the MPER and the head group of the crystallographically observed lipid embedded into the head group region of the membrane outer leaflet , while the side chains of the residues of the aforementioned light-chain surface are embedded in , or touching , this hydrophilic layer . The model remained stable during equilibration steps . | The trimeric Env glycoprotein located on HIV surface is the target of broadly neutralizing antibodies and is the focus of vaccine and therapeutic approaches to prevent HIV infection . Structural studies with HIV Env trimers have shed light on the complete epitopes of several broadly neutralizing antibodies . However , structural determination of the complete epitopes of the highly cross-reactive MPER antibodies has been technically difficult due to the viral membrane component and that these epitopes are probably only exposed transiently after Env engages CD4 . In this study , we structurally characterize the interaction of the broadest and most potent MPER-targeting antibody , 10E8 , with viral membrane lipids and scaffolded MPER and propose how 10E8 approaches the MPER-viral membrane epitope during neutralization . Our results indicate that 10E8 interacts with the viral membrane via its light chain and engages MPER in an upright orientation with respect to the HIV-1 membrane . These findings are of interest for design of HIV-1 vaccines and therapeutics . |
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Plasmacytoid dendritic cells ( pDC ) are innate immune cells that sense viral nucleic acids through endosomal Toll-like receptor ( TLR ) 7/9 to produce type I interferon ( IFN ) and to differentiate into potent antigen presenting cells ( APC ) . Engagement of TLR7/9 in early endosomes appears to trigger the IRF7 pathway for IFN production whereas engagement in lysosomes seems to trigger the NF-κB pathway for maturation into APC . We showed previously that HIV-1 ( HIV ) localizes predominantly to early endosomes , not lysosomes , and mainly stimulate IRF7 rather than NF-κB signaling pathways in pDC . This divergent signaling may contribute to disease progression through production of pro-apoptotic and pro-inflammatory IFN and inadequate maturation of pDCs . We now demonstrate that HIV virions may be re-directed to lysosomes for NF-κB signaling by either pseudotyping HIV with influenza hemagglutinin envelope or modification of CD4 mediated-intracellular trafficking . These data suggest that HIV envelope-CD4 receptor interactions drive pDC activation toward an immature IFN producing phenotype rather than differentiation into a mature dendritic cell phenotype .
Type I interferon ( IFN ) plays a dichotomous role in chronic viral infections such as Human Immunodeficiency Virus-1 ( HIV ) , contributing to the control of viral replication during the earliest stages of infection , yet fueling disease progression by activating target cells for infection , decreasing antiviral gene expression , enabling infection with increased reservoir size , and accelerating CD4 T-cell loss [1–8] . Plasmacytoid dendritic cells ( pDC ) are thought to play a significant role in IFN responses during HIV infection , arriving rapidly at sites of mucosal transmission [4] and relocating from blood to lymphoid tissues where they produce pro-apoptotic and pro-inflammatory IFN [9–11] . Cellular mechanisms underlying HIV-stimulated IFN production by pDC are only partially understood . We have previously shown that abundant IFN is produced by pDC upon HIV stimulation through endosomal recognition of genomic RNA by TLR7 . This response requires the presence of HIV envelope protein on viral particles , interactions between CD4 and the viral envelope protein , HIV endocytosis and endosomal acidification; however , co-receptor usage , viral fusion and viral replication are not required [12 , 13] . Cell-to-cell infection seems to amplify pDC responses to HIV , however precise mechanisms underlying differences between cell-free and cell-to-cell pDC activation are not clearly defined [14] . We and others have shown that pDC are highly resistant to HIV infection , and this block to replication is IFN-independent [15 , 16] . In addition to IFN production , pDC can act as antigen-presenting cells ( APC ) to activate T-cell–mediated adaptive immune responses [17–21] . Acquisition of an APC phenotype requires specific signals that are distinct from the signals that induce large amounts of IFN . We have previously shown that HIV stimulated pDC express low levels of the co-stimulatory molecule CD86 and express Indoleamine 2 , 3-dioxygenase ( IDO ) , a potent inducer of regulatory T cells , indicating that they do not differentiate into mature APC and fail to stimulate potent T cell responses [22 , 23] . However , pDC can differentiate into APC with influenza virus or the synthetic TLR7 agonist R837 and are able to cross-present antigens from HIV-1-infected apoptotic cells to HIV-specific CD8+ T lymphocytes , demonstrating that pDC do not have an intrinsic defect in presentation of HIV antigens , but rather that sensing of HIV does not provide the signals that are required for efficient differentiation of pDC into APC [17] . pDC sense single stranded RNA or unmethylated DNA containing Cytosine–Guanosine dinucleotides ( CpG ) through Toll-like receptors ( TLR ) 7 and 9 , respectively , located in endosomal compartments . Both TLR7 and TLR9 signal through the adapter protein myeloid differentiation primary response gene 88 ( MyD88 ) . Downstream IFN signaling occurs in response to activation of IFN genes through phosphorylation of interferon regulatory factor 7 ( IRF7 ) , whereas downstream signaling of nuclear factor kappa-light-chain-enhancer of activated B cells ( NF-κB ) leads to the transcriptional activation of proinflammatory kinases and upregulation of MHC and co-stimulatory molecules necessary for maturation into APC . [12 , 24] . The functional response of pDC to pathogens is flexible . As posited by the spatiotemporal model of pDC sensing [25] , differential pDC activation is likely related to the subcellular location where the TLR senses the pathogen . Thus , engagement of TLR 7/9 in the early endosomes of pDC preferentially triggers the IRF7 signal cascade , leading to type I IFN responses; whereas engagement of TLR7/9 in lysosomes preferentially triggers the NF-κB signal cascade , leading to the production of proinflammatory cytokines TNFα and IL6 , upregulation of co-stimulatory molecules , and an APC phenotype [25 , 26] . Differential trafficking and therefore sensing of synthetic TLR9-activating CpGs is attributed to sequence-related secondary and tertiary structural features of the CpGs . CpGs which contain phosphodiester backbones and palindromic motifs ( CpGA ) form multimeric complexes and traffic to early endosomes for IRF7 signaling whereas CpGs which contain phosphorothioate backbones and lack palindromic motifs ( CpGB ) traffic as monomers to lysosomes for NF-κB signaling . Intermediate CpGs ( CpGC ) combine structural elements of both CpGA and CpGB , traffic to both compartments , and stimulate both IRF7 and NF-κB signaling [27–29] . While the spatiotemporal model of pDC sensing has been most clearly evaluated using synthetic TLR9 agonists ( CpG ) , we have shown that the model also applies to HIV and TLR7 , whereby HIV traffics to early endosomes in pDC , activating IRF7 signaling rather than NF-κB signaling [22 , 23] . The upstream events that determine activation of each of these pathways , and in particular , HIV virion trafficking in pDC , are currently unknown , however , prior studies suggest that HIV envelope may play a major role [13 , 30 , 31] . Here we demonstrate that HIV trafficking and pDC phenotype is predominantly determined by envelope-CD4 interaction , such that manipulation of HIV envelope or CD4 intracellular trafficking enables modulation of divergent sensing of HIV .
We hypothesized that HIV envelope protein interactions with cell surface CD4 determine the intracellular trafficking of HIV and the resultant signaling in pDC , based on the spatiotemporal model of TLR signaling [25] . To test this , we replaced HIV envelope protein with envelope protein from a virus that activates pDC to differentiate into mature pDC , namely influenza virus . Influenza virus hemagglutinin envelope glycoprotein ( HA ) binds to sialic acids on the cell surface to trigger clathrin-dependent endocytosis [32] . We pseudotyped HIV virions with influenza hemagglutinin glycoprotein envelope ( HA-HIV ) and first compared the functional response of purified pDC to HIV , influenza , and HA-HIV , in terms of magnitude and kinetics of TNFα ( TNF ) and IFN production . TNF is produced downstream of NF-κB signaling and IFN is produced downstream of IRF7 signaling . TNF and IFN were measured by intracellular cytokine staining ( ICS ) at 30 minutes , 2 hours , 6 hours , and 12 hours and in the culture medium by cytokine bead array ( CBA ) and ELISA , respectively , at 2 hours , 6 hours , and 12–24 hours . As previously demonstrated [22] , the response of pDC to HIV was characterized by delayed IFN and TNF responses with IFN predominating at later time points . In comparison , both HA-HIV and influenza stimulated pDC to rapidly produce TNF within 30 minutes- 2 hours , an effect which plateaued by 12–24 hours . Influenza also stimulated early IFN secretion , within 2 hours . HA-HIV induced IFN secretion , albeit at lower levels than Flu itself ( Fig 1A and 1B ) , possibly due to faster trafficking kinetics and early global cytokine shutdown ( as evidenced by earlier IFN and TNF shutdown compared to Flu in ICS Fig 1A ) . Strikingly , TNF was always produced antecedent to IFN , as evidenced by ICS staining , and as has been observed in murine pDC [33] ( Fig 1A ) . After overnight incubation of pDC , HIV stimulated minimal upregulation of CD86 and HLA-DR while HA-HIV and influenza stimulated strong upregulation of CD86 and HLA-DR expression , providing further evidence that HA-HIV and influenza activate NF-κB signaling/maturation pathways in pDC while HIV does not ( Fig 1C ) . Similar maturation and IFN effects were seen whether pDC were stimulated with X4 lab strain , MN or HIV backbone pNL43-ΔEnv-vpr+-luc+ pseudotyped with X5 HIV envelopes ( JRFL , REJO , JOTO ) as compared to HIV backbone pNL43-ΔEnv-vpr+-luc+ pseudotyped with hemagglutinin envelopes H1 and H5 ( S1A Fig ) . A functional measure of pDC maturation is to test whether cells become refractory to re-stimulation by TLR agonists , known as TLR tolerance . We had previously shown that HIV-activated pDC maintain an immature phenotype and are not refractory to re-stimulation to produce IFN . This effect was not due to activation of pDC that had failed to become activated during the previous overnight incubation [22] . We therefore compared the effects of HIV , influenza , HA-HIV , and CpGB , a potent pDC TLR9 maturation stimulus , to inhibit re-stimulation , as a marker of complete pDC maturation . As compared to HIV , HA-HIV inhibited re-stimulation of pDC similarly to influenza and CpGB , thus signifying that HIV pseudotyped with HA matured pDC fully ( Fig 1D ) . Overall , swapping HIV envelope with influenza envelope induced a mature pDC phenotype similar to that induced by influenza activation . Notably , MN HIV , a CXCR4 lab strain of HIV was used in these experiments . However , HIV ( pNL43-ΔEnv-vpr+-luc+ ) pseudotyped with envelopes JRFL , REJO , or JOTO ( all R5-tropic ) all stimulate pDC to produce IFN , and not to mature , as expected since co-receptor usage is not essential for pDC sensing of HIV ( S1A and S1B Fig ) [12] . Because HA-HIV and influenza similarly stimulated pDC to mature , we sought to investigate whether HA-HIV traffics similarly to influenza in pDC . HIV virions ( pNL43-ΔEnv-vpr+-luc+ ) pseudotyped with HA and packaging green florescent protein ( GFP ) and HIV virions ( pNL43-ΔEnv-vpr+-luc+ ) pseudotyped with JRFL ( R5 envelope ) and packaging GFP were generated , using eGFP-Vpr plasmids , as previously described [22] . Influenza packaging GFP were also generated , as previously described [34 , 35] . We found that HA-HIV , similarly to influenza and unlike HIV itself , rapidly trafficked to lysosomes by 30 minutes as evidenced by co-localization with Lysotracker , a dye that traffics to these organelles ( Fig 2A and 2C ) . Both influenza and HA-HIV extensively co-localized with Lysotracker at 30 minutes and 2–4 hours , whereas HIV was barely visible inside the cell at these early time points . After overnight incubation HIV was well visualized inside the cell , but did not co-localize well with lysotracker . Influenza and HA-HIV still seemed to co-localize with lysotracker even though the florescent signal was faded , likely due to lysosomal degradation of the virions ( S2A–S2D Fig ) . We confirmed that HIV traffics to early endosomal ( EEA1 ) compartments by 18 hours as previously shown [22] , whereas influenza and HA-HIV traffic significantly less to these compartments and the green signal is faded at 18 hours ( Fig 2B and 2D ) . Thus , the nature of the viral envelope seems crucial to determining trafficking of virions in pDC , and for the downstream signaling pathways activated in different intracellular compartments . Our results indicate that viral envelope protein dictates early intracellular trafficking of virions in pDC , suggesting that trafficking is directed by interaction of HIV envelope protein with its cognate receptor . In pDC , sensing of HIV involves CD4-mediated endocytosis [12] . CD4 is a type I integral membrane glycoprotein that can be internalized through clathrin-mediated endocytosis . The intracellular tail of CD4 displays motifs important for its internalization: a dileucine motif that allows interaction with the clathrin adaptor 2 ( AP-2 ) [36] and an adjacent serine , whose phosphorylation augments the affinity of the dileucine motif for AP-2 [36] , thereby regulating CD4 endocytosis . In cells of macrophage-monocyte lineage , CD4 is constitutively endocytosed at low levels through clathrin-coated pits to early and recycling endosomes [37] , as CD4 is serine-phosphorylated to some extent even in unstimulated cells [36] . We first examined whether CD4 itself and CD4-associated targeting motifs are responsible for the predominant localization of HIV in early endosomes . We used HEK 293 T ( HEK ) cells and HEK reporter cells as a model because HEK cells do not express CD4 under native conditions and therefore manipulation of CD4 trafficking and viral-CD4 interactions can be studied more clearly . Moreover , due to technical limitations , it was not possible to transfect or transduce primary pDCs or the Gen2 . 2 pDC cell line to undertake these studies . We engineered hybrid CD4 molecules , mutating its intracytoplasmic tail or swapping it with the intracellular domain of CD205 ( DEC205 ) and Lamp-1 ( Fig 3A ) . CD205 and Lamp-1 contain distinct lysosome-targeting motifs in their intracytoplasmic tail that induce constitutive targeting to late endosomes/lysosomes [38–40] . DEC-205 expresses the coated-pit internalization sequence ( FSSVRY ) and lysosome-targeting motif ( EDE ) , whereas Lamp-1 expresses the lysosomal targeting motif ( YQTI ) . Several mutants were tested , as visualized in Fig 3A: ( 1 ) . CD4-WT ( wild type CD4 ) , ( 2 ) . CD4-STOP ( lacking the cytoplasmic domain ) , ( 3 ) . CD4-DEC ( replacing the CD4 cytoplasmic domain with the DEC-205 cytoplasmic domain to shuttle CD4 to the lysosomes ) , and ( 4 ) . CD4-LAMP ( replacing the CD4 cytoplasmic domain with the Lamp-1 cytoplasmic domain to shuttle CD4 to the lysosomes ) . The CD4 mutant sequences were introduced into lentiviral vectors for stable transduction of HEK cells for microscopy , and HEK-Blue hTLR7-expressing cells to measure NF-κB activation by HIV . HEK-Blue hTLR7 co-express human TLR7 and an NF-κB inducible secreted embryonic alkaline phosphatase ( SEAP ) reporter gene . CD4 expression was maintained in the presence of puromycin , and CD4 expression across cell lines was uniform at 65–75% . For these experiments we used JRFL HIV packaging GFP- HIV Gag-iGFP ( GFP HIV ) to track HIV intracellular trafficking . Following incubation with HIV for 2 to 4 hours , CD4-expressing cells bound and endocytosed HIV efficiently , as shown in Fig 3C . This time point was chosen because HIV was not visualized well before 2 hours , and the fluorescent signal was faded after overnight incubation . The main path of viral entry in CD4-expressing HEK cells is CD4-mediated endocytosis as CD4 blockade completely abrogated HIV uptake ( Fig 3B ) . HIV co-localized extensively with CD4 , whatever CD4 construct the cells expressed , in the range of 65% to 80% co-localization per cell , as measured by single cell Mander’s coefficient ( Fig 3D ) . HEK cells which are not expressing CD4 do not take up HIV as represented by the ( - ) condition in representative images ( Fig 3C ) . Although the intracellular distribution pattern appeared different between the different CD4 constructs , with CD4-WT and CD4-STOP appearing more cell-surface associated and CD4-LAMP and CD4-DEC appearing more internal compartment associated , the overall fluorescence intensities of cell-associated HIV were comparable . To better characterize the intracellular localization of GFP- HIV in HEK cells expressing CD4 hybrids , cells were exposed to GFP-HIV for 2–4 hours , then fixed and stained for the early endosomal marker EEA1 , the recycling endosomal marker transferrin receptor ( TfR ) , or the lysosomal marker LAMP1 . As an additional lysosomal marker , after 2–4 hours of GFP-HIV incubation , lysotracker was added to culture media , and live imaging was performed . Internalized HIV trafficked predominantly to early EEA1+ and TfR+ endosomes in CD4-WT , similar to trafficking of HIV in pDC ( Fig 4 ) . However , HIV trafficked mainly to LAMP-1+ and lysotracker+ lysosomes in CD4-DEC and CD4-LAMP ( Fig 5 ) . Surprisingly HIV trafficked to EEA1+ compartments in CD4-STOP instead of remaining on the cell surface , but did not traffic predominantly to TfR ( Fig 4A–4D ) . As endosomes containing EEA1 ultimately direct trafficking to the degradative machinery of the cell whereas endosomes containing TfR do not [41] , it is likely that some of the mutant CD4-STOP proteins are targeted for degradation to some extent . Overall , these data are consistent with the expected trafficking patterns of each CD4 construct , and indicate that replacing the intracellular tail of CD4 with those of CD205 or LAMP-1 targets CD4 and HIV to the late endosomes/ lysosomes , demonstrating that targeting motifs in CD4 drive the intracellular localization of HIV via CD4-mediated endocytosis . We next examined the functional consequences of CD4 altered trafficking . The lentiviral constructs ( Fig 3A ) were transduced into HEK-Blue cells to measure NF-κB activation . Notably this cell type is not optimized to produce IFN , therefore only the NF-κB signaling arm was tested in this system . R848 , a TLR7/8 agonist , was used as a positive control and strong NF-κB activator that rapidly accumulates in late endosomes [42] . The TLR9 agonist CpGB was used as a negative control in these TLR7-expressing cells and did not induce NF- κB activation in any condition ( Fig 6 ) . Whereas HIV did not induce NF- κB activation in CD4-WT and CD4-STOP expressing cells , it induced NF-κB activation in CD4-DEC and CD4-LAMP expressing cells . These results support the spatiotemporal model of cell signaling , whereby partition into early or late endosomes regulates IFN vs NF-κB signaling [25] . HIV traffics to early endosomes in pDC and in CD4-WT expressing cells , and induces weak NF-κB signaling , whereas retargeting CD4 to lysosomes allows for NF-κB activation by HIV . We also examined more precisely why native CD4 delivers HIV into early endosomes . Endocytosis and intracellular CD4 trafficking is dependent on a dileucine motif in its intracellular domain , and is regulated by phosphorylation of an adjacent Serine ( Ser408 ) . Phosphorylation of Ser408 , as occurs with phorbol ester ( PMA ) , dramatically enhances CD4 delivery from the cell surface to the lysosomes [43] . In contrast to PMA stimulation , HIV activation of human pDCs does not cause marked CD4 internalization at early timepoints . Whether pDC are unstimulated or stimulated with HIV , CD4 internalization is grossly unchanged , whereas PMA stimulation , in the absence or presence of HIV , causes internalization at early timepoints ( Fig 7 ) . To investigate whether CD4 phosphorylation on Ser408 targets CD4 and HIV to late endosomes/ lysosomes , we generated two mutations of the serine residue: one to alanine to abrogate phosphorylation ( CD4-SA ) , and one to the phospho-mimic Glutamic Acid ( CD4-SE ) , and produced HEK cells stably expressing these CD4 mutants . Intracellular localization of HIV and CD4 was monitored by microscopy . As shown in Fig 8 , HIV co-localized with CD4 and appeared less membrane associated in CD4-SE cells , whereas it remained predominantly on the surface of CD4-SA cells . In CD4-SA cells , intracellular HIV accumulated in EEA1+ and TfR+ compartments ( Fig 9 ) , similarly to WT-CD4 cells ( Fig 4 ) , whereas HIV accumulated in Lamp-1+ and lysotracker+ compartments in CD4-SE cells ( Fig 10 ) , similarly to CD4-DEC cells and CD4-Lamp cells ( Fig 5 ) . To test the functional consequences , we transduced HEK-Blue cells with CD4-SA and CD4-SE , as compared to CD4-WT , to measure NF-κB activation , as above . Whereas HIV did not induce NF-κB activation in CD4-WT and CD4-SA expressing cells , it induced NF-κB activation in CD4-SE expressing cells ( Fig 11 ) . Thus , CD4 phosphorylation on Ser408 appears to target CD4 and HIV to late endosomes/ lysosomes , whereas it is routed to early endosomes in its absence ( CD4-SA and CD4-WT ) . Poor specificity of available anti-phospho-Ser408 CD4 antibodies precluded the possibility of studying the phosphorylation state of Ser408 CD4 in primary pDCs after HIV activation . While our data strongly suggest that HIV trafficking and subsequent immune signaling in pDC is driven by HIV envelope/CD4 interactions , we tested an alternative hypothesis of pDC signaling , that the strength of TLR signaling in the early sorting endosomes determines the trafficking and subsequent signaling of TLR agonists . Previous work showed that TLR signaling in other cell types accelerates endosomal maturation through TLR-induced p38 mitogen-activated protein kinase ( p38 ) signaling , as the absence of a TLR/MyD88 signal diminishes phagosome maturation [44] . TLR activation also activates lysosomal function in myeloid DC , which could further influence TLR signaling pathways [45 , 46] . According to this model , strong TLR triggering in the early endosome would accelerate endosomal maturation , and endocytosed viruses or oligonucleotides would rapidly reach late endosomes for NF-κB signaling [25 , 33] . Using murine pDC differentiated from TLR7-/- and TLR9-/- bone marrow , we tested this hypothesis . As HIV cannot be used in murine immune systems , we tested TLR9 agonists , focusing on CpGB . CpGB has been shown to rapidly traffic to late endosomes/ lysosomes in mouse and human pDC [25 , 26] , and it induces strong NF-κB signaling and pDC maturation [22] . According to this model where TLR signaling induces endosomal maturation , CpGB would traffic rapidly to lysosomes in wild type ( WT ) or TLR7-/- pDC , but would fail to traffic to lysosomes in murine TLR9-/- pDC . Because MyD88 knockout DCs lacking TLR signaling have been described as having an altered constitutive rate of endosomal maturation [44 , 47] , we also constructed a control oligonucleotide lacking stimulatory CpG motifs ( due to inversion of the CpG motif into a GpC dinucleotide ) which lacks TLR triggering activity . Wild type , TLR7-/- , and TLR9-/- murine pDC were derived from bone marrow with Flt3 ligand , as previously described [48] and purified >85% ( S3A Fig ) . pDCs were incubated with FAM-labeled CpGB or nonactivating FAM-labeled GpC control and were imaged by flow cytometry and live microscopy . We first confirmed the specificity of the various pDC TLR knock out cells by evaluating the expression of the maturation molecule CD86 after overnight incubation of wild type , TLR9-/- , and TLR7-/- pDC with TLR7 agonist R848 and TLR9 agonist FAM-CpGB . As expected , R848 matured WT and TLR9-/- pDC but did not mature TLR7-/- pDC , whereas FAM-CpGB matured WT and TLR7-/- pDC but did not mature TLR9-/- pDC . pDC stimulated with FAM-GpC did not mature WT pDC due to the lack of CpG immunostimulatory motifs ( S3B Fig ) . We then monitored trafficking of FAM-CpGB to lysosomes in WT , TLR7-/- and TLR9-/- pDC by microscopy , by measuring co-localization with Lysotracker Red . Across a z stack spanning the midplane of the cell , we observed that both FAM-CpGB in WT , TLR7-/- and TLR9-/- pDC ( S4A and S4C Fig ) , and FAM-GpC in WT pDC ( S4B and S4D Fig ) , rapidly and extensively trafficked to lysosomes within 15-20min . Thus , TLR activation did not affect intracellular trafficking of TLR agonists in murine pDC , suggesting that an alternative model also exists for human pDC . Altogether , these data demonstrate that divergent HIV-1 sensing by pDC is mediated by CD4-HIV envelope interactions .
Although many receptors and signaling pathways have been shown to modulate TLR signaling pathways in pDC [49] , it is likely that the functional outcome of TLR signaling is determined upstream and early on . The spatiotemporal model of TLR signaling [25] was proposed to account for the functional flexibility of pDC in response to TLR signaling , and argues that the surface or intracellular localization of TLR signaling initiation determines which downstream signaling pathway is triggered , with differing functional outcomes [50] . This is because each compartment is associated with different adaptors and signaling platforms , specialized in inflammatory cytokine secretion and NF-κB activation , or signaling through IRF7 and IFN production [25 , 33 , 50] . As HIV steadily traffics to early endosomes in pDC , a compartment associated with IFN signaling [25] , our goal was to understand how early HIV trafficking is regulated in pDC and how it affects pDC functional response . We studied whether HIV trafficking in pDC involves envelope-receptor interaction and targeting signals in endocytic receptors . A hybrid virus was constructed , where HIV envelope was replaced by influenza hemagglutinin envelope , while maintaining all other HIV structural components unchanged ( HA-HIV ) . In contrast to HIV , influenza is rapidly endocytosed by pDC and triggers a strong NF- κB activation , secretion of inflammatory cytokines , and maturation of pDC [22 , 51] . Strikingly , HA-HIV was rapidly routed to late endosomes/ lysosomes in pDC , contrary to HIV with its native envelope . Furthermore , it induced early secretion of inflammatory cytokines and strong pDC maturation , in a manner and kinetics similar to influenza virus . This demonstrates that virus envelope directly determines HIV trafficking and pDC phenotype . Despite different structural components and nucleic acids , HA-HIV and influenza induced a similar functional response in pDC , which strengthens the importance of viral envelope in determining pDC phenotype . This latter extended to the unresponsiveness of pDC to further stimulation , whereas HIV stimulated pDC could be re-stimulated to produce IFN . HA-HIV triggers IFN secretion , although at lower levels than Flu itself . This may be due to kinetics differences in trafficking to late endosomes and activation of negative signaling pathways or exhaustion . Trafficking to late endosomes , NF-κB activation and pDC maturation correlates with a state of refractoriness , likely established early during stimulation , and already evidenced by a global cytokine shutdown after a few hours . As shown in Fig 1A , this shutdown occurs earlier for HA-HIV than for Flu , at the time when IFN secretion is starting to be amplified . It is likely HA-HIV traffics significantly faster to late endosomes and triggers early cytokine shutdown . Another example of HIV pseudotyping is VSV-G pseudotyped HIV , where VSV-G from vesicular stomatitis virus is used to allow HIV uptake and infection of many cellular subtypes . The putative receptor for VSV-G has been recently suggested to be LDL receptor [52] , which traffics mainly to recycling endosomes but rarely to lysosomes . In accordance with this trafficking pattern and localization in early endosomes , VSV-G-pseudotyped HIV behaves mostly like HIV with a native envelope to trigger high levels of IFN but little pDC maturation [12] . In addition to demonstrating that viral envelope determines HIV localization and pDC phenotype , HA-pseudotyped HIV may also provide a tool to study HIV antigen presentation and vaccine design [53] , as it enhances expression of MHC and co-stimulatory molecules on pDC , and influenza itself triggers a developmental program suited for antigen presentation in pDC [21 , 54] . If the nature of the viral envelope dictates HIV trafficking in pDC , it may be due to intracellular targeting motifs present in the viral receptor ( s ) [38] . HIV is mainly taken up through CD4 in pDC , and we tested whether altering the intracytoplasmic domain of CD4 would affect HIV trafficking and TLR signaling . Indeed , we observed that swapping CD4 intracytoplasmic domain for DEC205 or Lamp1 intracytoplasmic domain dramatically re-routed HIV into late endosomes/ lysosomes in CD4 expressing cells , whereas intracellular HIV was localized predominantly in early endosomes in cells expressing native CD4 . DEC205 and Lamp1 contain lysosomal targeting motifs which are likely responsible for constitutive CD4 and HIV targeting to late compartments . In addition , redistribution of HIV to late endosomes was accompanied by activation of NF-κB , not observed when HIV accumulates in early endosomes , again consistent with the spatiotemporal model of TLR signaling . CD4 contains a dileucine motif in its intracytoplasmic domain , which is essential for CD4 endocytosis [36] . In addition , two adjacent Serines , Ser408 and Ser415 , can be phosphorylated and impact CD4 endocytosis and distribution . Completely deleting these motifs by removing the whole CD4 intracytoplasmic domain indeed almost completely abrogated CD4-mediated HIV endocytosis . However , phosphorylation of Ser408 not only enhances CD4 endocytosis [36] , but also redirects CD4 to lysosomal compartments [43] . Ser408 phosphorylation enhances its association with clathrin Adaptor protein AP-1 and AP-2 [36] . In our experiments , mutating Ser408 to Glutamic acid ( CD4-SE ) to mimic Ser408 phosphorylation , induced a complete redistribution of CD4 and HIV into lysosomes ( Fig 10 ) suggesting that in pDC , HIV traffics by default to recycling endosomes due to the CD4 dileucine motif , in the absence of Ser408 phosphorylation . These results are supported by our CD4 internalization studies where we found that HIV-activation of pDC does not seem to alter CD4 internalization , as compared to PMA-activation . Similarly , in HIV infected cells , Nef triggers endocytosis and degradation of CD4 through a dileucine based motif [55 , 56] and CD4 endocytosis and targeting to lysosomes are encoded in different regions of the Nef protein [56 , 57] . The relatively stable localization of HIV in early endosomes , observed for as long as 18 to 24h , remains unexplained . Furthermore , we observed strong co-localization of CD4 with HIV throughout the course of the study . Although the interaction between CD4 and HIV envelope may be stable enough to maintain CD4-HIV co-localization for a prolonged period of time , other explanations are possible . A recent study described in detail how endocytosis of HIV is coupled to dynamin-dependent endocytosis and partial fusion with plasma and endosomal membrane [58] , which may tether HIV envelope and CD4 to the endosomal membrane in the absence of fusion . Furthermore , pDC possess specialized large perinuclear intracellular stores of MHC-I molecules , with characteristics of recycling endosomes in immature pDC , that can be used as sites for rapid MHC-I loading and peptide presentation [21] . These intracellular stores may represent the stable compartment in which HIV accumulates in pDC , and prolonged localization in these early recycling endosomes may ultimately have important consequences for HIV antigen presentation . pDC may harbor HIV in these structures until activated by a maturation stimulus . Indeed , pDC are capable of HIV antigen cross-presentation [17] , and cross-presentation is strongly enhanced by maturation-inducing stimuli [59 , 60] . Upon influenza activation , stored MHC-I molecules are translocated to the cell surface for efficient cross-presentation by pDC [21] , indicating that the process of maturation drives antigen presentation . On the other hand , the non-acidic environment and limited access to MHC-II compartments may prevent efficient MHC-II peptide generation and association with MHC-II molecules . In addition , MHC-II clustering and antigen presentation by pDC is dependent on NF-κB [51 , 61] , which HIV weakly induces due to localization in early endosomes . The lack of pDC maturation induced by HIV might prevent effective cross-presentation and MHC-II restricted presentation , due to localization in early endosomes and weak NF-κB activation . Thus , the particular compartmentalization of HIV can affect HIV antigen presentation . As shown here , HA-pseudotyped HIV , which traffics to late endosomes and activates NF- κB , may serve as a tool to enhance cross-presentation of HIV antigens by pDC . We also tested whether TLR signaling itself alters maturation of endocytic compartments in pDC as was previously demonstrated in the case of murine macrophages , where TLR/ MyD88 signaling induced marked phagosome maturation , possibly through p38 MAP kinase activation [44] . We tested this model in murine pDC , and compared trafficking of CpGB in WT , TLR7-/- or TLR9-/- pDC . We did not observe any difference in trafficking of CpGB in these conditions , and internalized CpGB was rapidly found within late endosomes/ lysosomes whether TLR signaling had occurred or not . As MyD88-/- cells have altered endosomal maturation [47] , we also used a non-stimulatory type B oligonucleotide ( lacking immunostimulatory CpG motifs ) in WT pDC . However , its trafficking was identical to the stimulatory CpGB oligonucleotide . Furthermore , as described previously [44 , 62] , the lack of TLR signaling , as with nonstimulatory GpC , decreased the speed of CpG endocytosis ( S4E Fig ) , however all oligonucleotides localized identically . These data argue against the above model , whereby rate of endocytosis and/ or agonist potency drive its intracellular localization , and establish trafficking as independent of TLR signaling in pDC [47 , 63] . Using purified human pDCs and viruses , we demonstrate that HIV trafficking in pDC at early timepoints is determined by the initial envelope-receptor ( CD4 ) interaction , and is regulated by receptor targeting motifs . Whether this is still valid in the case of cell-associated virus , this remains to be determined . Engineering of viral envelope for increased pDC maturation and antigen presentation ( e . g . HA-HIV ) or for increased IFN secretion [64] may prove useful for vaccine design and modulation of chronic immune activation in HIV disease .
PBMCs were separated on Ficoll-Hypaque ( Amersham Biosciences ) from buffy coats ( New York Blood Center ) . pDC were purified by BDCA-4 magnetic bead separation ( Miltenyi Biotec ) as described previously [12] , with a purity ranging from 80 to 95% . Cells were cultured in RPMI 1640 Glutamax ( Invitrogen ) with 5% PHS ( Innovative Research , MI ) , gentamycin , and HEPES . HIV-1MN ( X4-tropic ) were produced at the AIDS Vaccine Program , National Cancer Institute as previously described [12 , 23 , 65] . Plasmids encoding JRFL HIV-1 envelope , JOTO HIV-1 envelope , REJO HIV-1 envelope , pNL43-ΔEnv-vpr+-luc+ and pCAGGS ( human airway trypsin-like protease to cleave HA0 to HA1 and HA2 ) were provided by Carol Weiss ( FDA , Silver Spring , MD ) , plasmids encoding CMV/R Influenza H1 ( A/PR8/8/34 ) ( VRC 7702 ) , CMV/R influenza A/PR/8/1934 NA ( VRC 9776 ) , CMV/8R A/Thailand/1Kan-1/2004 H5 ( VRC 7705 ) , and CMV/8R A/Thailand/1Kan-1/2004 NA ( VRC 7708 ) , and were provided by Gary Nabel and Chih-Jen Wei ( Vaccine Research Center , NIH , Bethesda , MD ) . Plasmids encoding vpr-gfp and MN HIV pNL4-3 Δvpr were obtained from David Ott and Jeffrey Lifson ( AIDS Vaccine Program , Frederick , MD ) . Plasmids encoding HIV Gag-iGFP JRFL were obtained from Benjamin Chen ( Mount Sinai School of Medicine , NY , NY ) . The influenza hemagglutinin viral pseudotypes were generated by calcium phosphate co-transfection of 3 . 0x106 HEK cells in a 10cm2 dish with 10ug HIV core ( pNL43-ΔEnv-vpr+-luc+ ) , 400ng hemagglutinin envelope ( VRC 7702 , VRC 9776 , VRC 7705 , or VRC 7708 ) , 100ng PR8 NA ( VRC9776 ) , and 100ng HAT pCAGGS , with media change after 6 hours , and viral harvest at 48 hours . HIV pseudotypes were generated by calcium phosphate co-transfection of 3 . 0x106 HEK cells in a 10cm2 dish with 10ug HIV core ( pNL43-ΔEnv-vpr+-luc+ ) and 6ug Env in pCDNA3 . 1 ( e . g . REJO , JOTO , JRFL ) . HIV Gag-iGFP is a full-length molecular clone of HIV derived from pNL4-3 that packages GFP inserted into the Gag protein between the MA and CA domains of Gag , with JRFL Env cloned into the place of the NL4-3 Env . To generate Gag-iGFP virions for CD4-expressing HEK experiments , 20ug of plasmid was transfected using the calcium phosphate method , with media change at 6 hours and transfection for 48 hours . For all viruses , transfection supernatants were filtered through a 0 . 45uM membrane , pelleted through a 20% sucrose cushion at 25 , 000g for 2hrs at 4°C . Pelleted viruses were resuspended in PBS , aliquoted , and stored at -80°C until use . HIV virions were quantified using p24 ELISA ( AIDS Vaccine Program ) and HA-HIV viruses were quantified using turkey hemagglutination inhibition assay for hemagglutination unit ( HAU ) as well as p24 ELISA . PR8 influenza was provided by David Levy ( NYU ) and influenza packaging GFP was provided by Jesse Bloom ( Fred Hutchinson Cancer Research Center , Seattle , WA ) as previously described [34 , 35] and were quantified by using turkey hemagglutination inhibition assay for hemagglutination unit . Purified pDC were stimulated at 50 , 000 cells/100uL media at 37° C with 5% CO2 with: MN HIV 300ng ( AIDS Vaccine Program , National Cancer Institute ) , HIV pseudotyped HIV envelopes 300ng , type B CPG oligodeoxyribonucleotide ( CpGB ) 2 ug 5’ T*C*G*T*C*G*T*T*T*T*G*T*C*G*T*T*T*T*G *T*C*G*T*T*-3’ where asterisks indicate a phosphorothioate bond ( IDT ) , Resiquimod ( R848 ) 10μM ( 3M Corporation , St . Paul , MN ) , influenza virus PR8 HAU 10 heat inactivated at 56°C for 30 minutes in a water bath , or HA-HIV HAU 10 . For intracellular staining , brefeldin A was added after 30 minutes , 2 hours , 6 hours , or 12 hours; at 24 hours cells were washed , fixed , and stained with PE-conjugated CD123 PE ( BD Biosciences ) , APC-conjugated TNFα ( eBioscience ) , and FITC-conjugated IFN-α ( BD Biosciences ) in 0 . 05% saponin , and analyzed by FACS . Following incubation of pDCs ( 50 , 000 cells/100uL ) with HIV MN and HIV JRFL , influenza , and HA-HIV , culture supernatants were tested for IFN and TNF by IFNα by ELISA ( PBL Interferon Source ) and human inflammatory kit CBA ( Abcam ) , respectively , following manufacturer instructions . For surface staining after overnight incubation , cells were stained with CD123 PE , CD86 APC , and HLA-DR PerCP ( BD Pharmigen ) , washed , fixed with 4%PFA , and analyzed by FACS . For IFNα restimulation experiment , pDCs were incubated overnight with influenza , HIV , HA-HIV , or CpGB , washed , and then incubated again with the same agonists . Culture supernatants were tested after the first and second overnight incubation for IFNα by ELISA ( PBL Interferon Source ) . CD4 expression plasmid ( pcDNAI ) was provided by Nathaniel Landau ( NYU , NY , NY ) . It was used as a template to mutate CD4 by overlapping PCR . Mutated CD4 were then inserted into pLenti vectors , and lentiviruses were produced using Mirus TransIT co-transfection of 3 . 0x106 HEK cells in a 10cm2 dish for 72 hours with 1 . 45 ug VSVg , 2 . 05 ug RSR-Rev , 2 . 9 ug pMDL plasmids ( provided by Dr Landau ) and 8 . 7 ug pLenti-CD4 constructs ( WT , STOP , DEC , LAMP , SE , SA ) . CD4-STOP was encoded of the first 425 amino-acids , including the transmembrane domain of CD4 . CD4-DEC contained the full extracellular domain of CD4 ( 397 amino acid in the immature form ) and the transmembrane and intracytoplasmic domain of DEC205 ( 56 amino acids ) . CD4-Lamp encoded the full extracellular and transmembrane domain of CD4 , and 12 amino acids of Lamp intracytoplasmic domain . CD4-SE and CD4-SA was identical to CD4 WT , except for a mutation of Serine 408 ( mature form ) for glutamate and alanine , respectively . CD4-expressing HEK cell lines were generated by infecting 3 . 0x106 HEK cells in a 10cm2 dish with lentivirus transfection supernatants and CD4 expression was maintained in the presence of puromycin . C57BL/6 wild type , TLR7-/- and TLR9-/- double-knockout mouse femurs were provided by B . Pulendran , Emory University . Mice were maintained in specific-pathogen-free conditions at the Emory Vaccine Center vivarium in accordance with all animal protocols reviewed and approved by the Institute Animal Care and Use Committee of Emory University . Bone marrow cells were isolated by flushing femurs with PBS supplemented with 2% heat inactivated FBS . BM cells were resuspended in Tris-ammonium chloride at room temperature for 1 minute to lyse RBC , washed , then cultured in RPMI 1640 Glutamax ( Invitrogen ) with 10%FBS , 1nM sodium pyruvate , 10mM HEPES buffer , 100 units/mL penicillin , 100ug/mL streptomycin , 2mM L-glutamine , 1% MEM nonessential amino acids . BM cells were cultured for 8 days at 1 × 106 cells/ml in 24-well plates in culture medium supplemented with 200 ng/ml recombinant murine Flt-3 ligand ( Peprotech ) as previously described [48] . After 8 days pDCs were purified from BM cells using the mouse plasmacytoid dendritic cell isolation kit II ( Miltenyi ) with a purity ranging from 85 to 95% and cells were phenotyped by CD11c PerCP and PDCA1 APC ( BD Pharmigen ) . Purified murine pDC ( WT , TLR7-/- , and TLR9-/- ) were stimulated overnight at 50 , 000 cells/100uL media at 37°C with 5% CO2 with R848 10μM ( 3M ) , 5’FAM-CpGB 5’ TCGTCGTTTTGTCGTTTTGTCGTT-3’ 2 ug ( IDT ) , or 5’FAM-GpC 5’-TGCTGCTTTTGTGCTTTTGTGCTT-3' 2ug ( IDT ) , both with phosphodiester backbones , and were stained with CD11c PerCP , PDCA1 APC , and CD86 PE ( BD Pharmigen ) . Purified murine pDC ( 200 , 000 cells/200uL ) were stimulated in 0 . 01% Poly L-lysine ( Sigma ) coated 8 chamber polystyrene vessel tissue culture treated glass slides ( CultureSlides BD Falcon ) in 10% FBS culture media ( 200uL ) with FAM-CpGB 2ug or FAM-GpC 2ug and stained with 1uM lysotracker ( Invitrogen ) . Cells were imaged by live microscopy for one hour using the Advanced Precision PersonalDV Imaging system , with temperature ( 37°C ) and C02 ( 5% ) humidity control , at a size of 512 X 512 pixels and a bit depth of 16 using a 60X , 1 . 4 N . A . oil objective lens . Images were deconvoluted using the DeltaVision deconvolution system and analyzed using ImageJ . Purified human pDC ( 50 , 000 cells/50uL ) were stimulated in Fibronectin ( Corning ) coated Ibidi imaging chambers μ-Slide VI0 . 4 ( Ibidi , Madison , WI ) in 5% PHS culture media ( 200uL ) with GFP PR8 influenza 10 HAU , GFP HA-HIV ( 10HAU/500ng p24 ) or GFP JRFL pseudotyped HIV 500ng p24 and stained with 1uM lysotracker ( Invitrogen ) . Live imaging was carried out after 30 minutes , 2–4 hours , or after overnight stimulation of cells , using a Yokogawa CSU-X1 spinning disk mounted on a Zeiss AxioObserver Z1 and controlled by MetaMorph under conditions that were reproduced across all experiments . 488nm and 561nm laser lines were generated by a Prairie Technologies Aurora solid state laser and fluorescence and brightfield ( phase ) images were captured with a Hamamatsu EM-CCD C9100 digital camera set at an EM gain of 11 MHz , a size of 512 X 512 pixels and a bit depth of 16 using a 63X , 1 . 4 N . A . oil objective lens . Temperature ( 37°C ) , C02 ( 5% ) , and humidity were controlled using a Tokai Hit incubator . CD4 expressing HEK cells were incubated ( 200 , 000 cells/200 μl ) with purified NA/LE mouse anti-human CD4 ( 10 μg/ml; BD Pharmigen ) or isotype control purified NA/LE mouse IgG1κ for 30 minutes . HIV Gag-iGFP was then added , and cells were placed back in culture for 18 hours . Cells were washed , fixed with 4% PFA , and analyzed by FACS . Purified human pDC ( 100 , 000 ) were incubated for 18 hours with GFP PR8 influenza 10 HAU , GFP HA-HIV ( 10HAU/500ng p24 ) or GFP JRFL pseudotyped HIV ( HIV Gag-iGFP JRFL ) 500ng p24 in 8 chamber polystyrene vessel tissue culture treated glass slides ( CultureSlides BD Falcon ) in 10% FBS ( Gibco ) culture media ( 200uL ) then cells were washed x2 , fixed with 4% paraformaldehyde , permeabilized with 0 . 1% Triton X-100 , blocked with 0 . 5% BSA in PBS , stained with mouse anti-EEA1 ( 0 . 5 ug; BD biosciences ) at 4°C overnight , washed x 2 then stained with donkey anti-mouse TRITC ( Jackson Immunoresearch ) for one hour at RT , then washed , dried , and mounted in DAPI Anti-fade GOLD ( Vector labs ) . Alternatively pDCs were incubated with media , phorbol 12-myristate 13-acetate 100ng/mL ( PMA ) , HIV , or PMA and HIV for 4 hours then cells were washed x2 , fixed with 4% paraformaldehyde , permeabilized with 0 . 1% Triton X-100 , blocked with 0 . 5% BSA in PBS , and then stained with CD4 PE ( BD Pharmigen ) for one hour , washed x2 , dried , and mounted in DAPI Anti-fade GOLD ( Vector labs ) . HEK cells transduced with CD4 mutants ( 25 , 000 ) were incubated overnight in 8 chamber polystyrene vessel tissue culture treated glass slides ( CultureSlides BD Falcon ) in 10% FBS ( Gibco ) culture media ( 200uL ) . iGFP HIV 500ng was added to culture media for 2–4 hours then cells were washed x2 , fixed with 4% paraformaldehyde , permeabilized with 0 . 1% Triton X-100 , blocked with 0 . 5% BSA in PBS , and then stained with CD4 PE ( BD Pharmigen ) for one hour . Alternatively , cells were stained with mouse anti-EEA1 ( 0 . 5 ug; BD biosciences ) , mouse anti-transferrin receptor ( 0 . 5ug; Invitrogen ) , mouse anti LAMP-1 H4A3 ( 0 . 5ug; Developmental Studies Hybridoma Bank University of Iowa ) at 4°C overnight , washed x 2 then stained with donkey anti-mouse TRITC ( Jackson Immunoresearch ) for one hour at RT , then washed , dried , and mounted in DAPI Anti-fade GOLD ( Vector labs ) . Fixed cells were imaged using a Zeiss LSM 880 confocal microscope configured to generate laser lines at 488nm and 561nm , as well as transmitted light . Images were scanned using a 100X , 1 . 46 N . A . oil objective lens at a size of 1024 X 1024 pixels and a bit depth of 12 . Images were captured using sequential ( multitrack ) acquisition to avoid bleedthrough of signal between channels . Colocalization of fluorescently labeled ligands ( green ) with endosomal markers or CD4 receptor ( red ) were analyzed quantitatively using JACoP plugin on ImageJ software or MetaMorph colocalization analysis . Image pair channel files of single image mid-planes were opened in as separate 16-bit ( grey scale ) image files . Individual cell regions were identified using the corresponding brightfield image , images were thresholded , and colocalization analysis was performed using the MetaMorph “measure colocalization” application , which measures the localization correlation between corresponding pixels in two paired images and provides the Manders correlation [66] . 50–100 cells were analyzed to generate the individual coefficients and data was plotted using GraphPad Prism software . CD4-expressing HEK-Blue hTLR cell lines were generated by infecting 3 . 0x106 HEK-Blue hTLR cells in a 10cm2 dish with CD4 lentivirus transfection supernatants . Stable CD4 expression of cell lines ( WT , STOP , DEC , LAMP , SA , and SE ) were cultured in the presence of puromycin and CD4 expression was verified by FACS and microscopy . 200 , 000 cells were placed in HEK-blue culture media overnight ( 200uL ) with CpGB , HIV , or R848 and NF-KB activation was measured using the HEK-blue detection system , with blue color depicting NF-κB activation , as measured by ELISA according to manufacturer specification ( Invivogen ) . Statistical significance was determined by the unpaired Student’s t test and analysis of variance . | Plasmacytoid dendritic cells ( pDC ) are innate immune cells that are specialized to produce type I interferon ( IFN ) and to activate adaptive immune responses . Although IFN is an anti-viral cytokine , it may contribute more to pathogenesis than to protection during chronic viral infections , including chronic HIV infection . pDC sense HIV to produce abundant IFN but minimal NF- κB–dependent production of TNFα and minimal up-regulation of co-stimulatory molecules , suggesting that HIV promotes pDC to become interferon producing cells ( IPC ) rather than antigen presenting cells ( APC ) . Here , we use florescent HIV virions pseudotyped with influenza hemagglutinin ( HA ) envelope and a cell system expressing CD4 molecules with modified intracellular trafficking . We found that HIV virions pseudotyped with HA stimulate pDC to mature , similar to influenza-stimulated pDC , and traffic intracellularly similarly to influenza . We also find that CD4-mediated intracellular trafficking guides HIV trafficking and downstream signaling . Our study presents new and important findings which demonstrate that divergent HIV sensing by pDC to produce IFN , rather than to become mature antigen presenting cells , is mediated specifically by CD4-HIV envelope interactions . |
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Although it is generally believed that CD4+ T cells play important roles in anti-Leishmania immunity , some studies suggest that they may be dispensable , and that MHC II-restricted CD3+CD4−CD8− ( double negative , DN ) T cells may be more important in regulating primary anti-Leishmania immunity . In addition , while there are reports of increased numbers of DN T cells in Leishmania-infected patients , dogs and mice , concrete evidence implicating these cells in secondary anti-Leishmania immunity has not yet been documented . Here , we report that DN T cells extensively proliferate and produce effector cytokines ( IFN-γ , TNF and IL-17 ) and granzyme B ( GrzB ) in the draining lymph nodes and spleens of mice following primary and secondary L . major infections . DN T cells from healed mice display functional characteristics of protective anti-Leishmania memory-like cells: rapid and extensive proliferation and effector cytokines production following L . major challenge in vitro and in vivo . DN T cells express predominantly ( > 95% ) alpha-beta T cell receptor ( αβ TCR ) , are Leishmania-specific , restricted mostly by MHC class II molecules and display transcriptional profile of innate-like genes . Using in vivo depletion and adoptive transfer studies , we show that DN T cells contribute to optimal primary and secondary anti-Leishmania immunity in mice . These results directly identify DN T cells as important players in effective and protective primary and secondary anti-L . major immunity in experimental cutaneous leishmaniasis .
The spectrum of disease collectively called Leishmaniasis is caused by several species of protozoan parasites belonging to the genus Leishmania . The disease is currently endemic in 88 countries , affecting an estimated 12 million people with over 1 . 5–2 million new cases and 70 , 000 deaths each year [1] . Because Leishmania parasites reside mainly within macrophages , a strong cell-mediated immunity is required to control intracellular parasite replication and disease progression [2] , [3] , [4] , [5] , [6] . Experimental L . major infection in mice closely mimics the human cutaneous disease and is an excellent model for understanding the factors that regulate cell-mediated immunity . Resistance to cutaneous leishmaniasis is associated with strong IFN-γ response , which activates infected macrophages leading to nitric oxide and reactive oxygen species production and destruction of the intracellular parasites [4] , [7] , [8] , [9] . Although it is generally believed that CD4+ T cells play a primary role in mediating anti-Leishmania immunity , a study suggests that they may be dispensable and that MHC II-restricted CD3+CD4−CD8− ( double negative , DN ) T cells are critical for regulating primary anti-Leishmania immunity [10] . In addition , several studies have reported increased numbers of DN T cells in blood of Leishmania-infected patients [11] , [12] , dogs [13] , and in spleens of Leishmania-infected mice [14] . These cells have been proposed to contribute to primary and vaccine-induced immunity against Leishmania . However , direct evidence implicating DN T cells in anti-Leishmania immunity has not yet been clearly documented . Here , we report for the first time , that infection with L . major leads to activation and proliferation of DN T cells in the draining lymph nodes ( dLNs ) and spleens of infected mice . These cells produce effector cytokines ( IFN-γ and TNF ) , display functional characteristics of memory-like cells and contribute to optimal primary and secondary protection against L . major infection .
Recovery from natural or experimental L . major infection is associated with strong T cell proliferation and IFN-γ production in spleens and dLNs . To investigate the contribution of CD4+ T cells in this process , we co-cultured CD8+ T cell-depleted splenocytes from healed mice with L . major-infected BMDCs in vitro . Surprisingly , we found in addition to CD4+ T cells , strong proliferative and IFN-γ responses by CD3+CD4−CD8− ( DN ) T cells ( Fig . 1A and Fig . S1A and B ) . Proliferating DN T cells also produced TNF ( Fig 1B ) , IL-17 ( Fig . S2A ) and little IL-2 ( Fig . 1D ) , suggesting they are polyfunctional in cytokine production . Indeed , most of the IFN-γ-producing DN cells also co-produced TNF ( Fig . 1C ) . Interestingly , although DN T cells proliferate significantly more than CD4+ T cells , their quantitative ability to produce IFN-γ and TNF was significantly lower than those of CD4+ T cells ( Fig . S2B–D ) . In addition , DN T cells also produced GrzB ( Fig . 1E ) , suggesting they may perform effector functions in L . major-infected mice . DN T cells from L . major-infected mice did not proliferate or produce IFN-γ following stimulation with OVA-loaded DCs ( Fig . 1F ) , but were activated by DCs pulsed with SLA or freeze-thawed L . major ( Fig . 1F ) . Collectively , these results suggest that the proliferation and cytokine production by DN T cells from healed mice are L . major specific . Our co-culture system showed that Leishmania-specific DN T cells are activated following in vitro recall response . To determine whether DN T cells are activated in vivo , we adoptively transferred CFSE-labeled T cells from healed Thy1 . 2 mice into naive Thy1 . 1 mice that were then challenged with L . major the next day . Both CD4+ and DN T cells from healed donor mice showed extensive proliferation and IFN-γ production compared to those from naive mice ( Fig . 2A–D ) . The in vivo relevance of DN T cell response was further confirmed by BrdU incorporation ( Fig . 2E and F ) . Interestingly and similar to CD4+ T cells , the percentage of proliferating and IFN-γ-producing DN T cells in healed mice were significantly higher than those in naïve mice following L . major challenge , suggesting that DN T cells display functional characteristics of memory T cells ( rapid proliferation and cytokine production ) . Indeed , we found that the percentage of DN T cells in lymph nodes ( Fig 3A ) of healed mice that express CD62LhiCD44hi ( central memory-like ) was significantly higher ( p<0 . 05 ) than those in naive mice ( Fig . 3B ) . Following adoptive transfer of whole T cells from healed mice and subsequent L . major challenge , almost all the proliferating donor CD4+ T cells downregulated their CD62L expression ( i . e . were CD62Llo ) . In contrast , the proliferating DN T cells contained an almost equal proportion of CD62Llo and CD62Lhi populations ( Fig . 3C ) . In addition , more DN T cells were CD62LhiCD44hi compared to CD4+ T cells ( Fig . 3D ) . In addition to αβ T cells , NKT and γδ T cells also do not express CD4 and CD8 molecules . To determine whether Leishmania-reactive DN T cells are NKT and γδ T cells , we assessed the expression of αβ , γδ and NK1 . 1 molecules on DN T cells by flow cytometry . As shown in Fig . 4A , DN T cells predominately ( > 90% ) expressed αβ TCR and not NK1 . 1 and γδ molecules , indicating that they are not NKT or γδ T cells . To further determine whether DN T cells are CD4+ or CD8+ T cells that have down-regulated their surface molecules following activation , we assessed highly enriched ( > 99% purity , Fig . S3 ) DN , CD4+ and CD8+ T cells for CD4 and CD8 transcripts by RT-PCR . DN T cells did not express CD4 and CD8 mRNA ( Fig . 2B ) , suggesting they are not CD4+ or CD8+ T cells that have down-regulated their surface molecules . In addition , highly purified CD4+ , CD8+ and DN T cells maintained their respective phenotypes following in vitro restimulation for 5 days with L . major-infected BMDCs ( Fig . 4C ) . To determine whether Leishmania-reactive DN T cells display regulatory properties as previously reported in other systems [12] , [15] , [16] , we co-cultured CD4+ and DN T cells with L . major-infected BMDCs and assessed CD4+ T cell proliferation and IFN-γ production by flow cytometry . DN T cells did not affect CD4+ cell proliferation and IFN-γ production ( Fig . 4D ) , suggesting that they do not exhibit regulatory/suppressive properties . To determine whether Leishmania-reactive DN T cells are restricted by MHC II molecule , we co-cultured highly enriched T cells from healed mice with infected BMDCs in the presence or absence of anti-MHC II antibodies . Anti-MHC II antibodies blocked proliferation and IFN-γ production by both CD4+ and DN T cells in a dose-dependent manner ( Fig . 5A ) . In addition , L . major-infected BMDCs from MHC II KO mice failed to induce proliferation and IFN-γ production by DN and CD4+ T cells ( Fig . 5B ) . In contrast , proliferation and IFN-γ production by DN T cells were minimally affected following co-culture with infected BMDCs from CD1d KO mice ( Fig . 5C ) , confirming that DN T cells are mostly restricted by MHC II molecules . We found that Leishmania-reactive DN T cells are recalled in healed mice following L . major challenge in vitro and in vivo suggesting that they may be induced following primary infection . To determine this , we assessed CD4+ and DN T cells response in the dLNs and spleens of infected mice C57BL/6 mice at different times after infection corresponding to early , peak and resolution of lesion progression ( Fig . 6A ) . As expected , there was strong CD4+ T cell response ( proliferation and IFN-γ production , Fig . 6B ) at all times ( 3 , 6 and 12 weeks ) post-infection . Similarly , DN T cells from infected mice also strongly proliferated and produced IFN-γ following restimulation with infected BMDCs ( Fig . 6C ) . In contrast , CD4+ and DN T cells from naïve mice did not proliferate or produce IFN-γ upon stimulation with L . major-infected BMDCs ( Fig . 6B and C ) . Collectively , these results show that Leishmania-reactive DN T cells are induced during primary L . major infection and could contribute to anti-Leishmania immunity . To determine if DN T cells contribute to primary immunity against L . major , we selectively depleted CD4+ and CD8+ or all T cells by treatment with anti-CD4/CD8 or anti-Thy1 . 2 mAbs , respectively , during the course of primary L . major infection ( Fig . S4 ) . Mice depleted of both CD4+ and CD8+ T cells still had some IFN-γ-producing CD3+ DN T cells ( Fig . 7A and 7B ) and harbor significantly ( p<0 . 01 ) lower parasite burden ( Fig . 7C ) compared to those depleted of all T cells ( by anti-Thy1 . 2 mAb treatment ) , indicating that DN T cells contribute to optimal control of parasite proliferation during primary L . major infection . Next , we used both in vitro and in vivo approaches to investigate whether DN T cells contribute to secondary anti-Leishmania immunity . Highly purified DN T cells from healed ( but not naïve ) mice significantly ( p<0 . 05 ) inhibited parasite proliferation in infected BMDMs and this effect was comparable to those of CD4+ T cells ( Fig . S5A and B ) . These results provide direct in vitro evidence that DN T cells could control parasite growth in L . major-infected BMDMs . Next , we used two different experimental approaches to determine whether DN T cells contribute to secondary anti-Leishmania immunity in vivo . First , we selectively depleted CD4+ and CD8+ or all CD3+ T cells ( as in Fig . 7A above ) in healed mice and after 24 hr , rechallenged them with L . major . As shown in Fig . 7D , CD4+ and CD8+ T cells-depleted mice , which still had DN T cells , retained some level of infection-induced resistance as evidenced by significantly ( p<0 . 01 ) lower parasite burden compared to naïve mice ( primary infection ) . In contrast , depletion of all CD3+ T cells completely abrogated secondary immunity ( Fig . 7D ) . Second , we assessed the ability of highly enriched ( purity > 96% , Fig . S6 ) DN T cells from healed mice to protect naïve animals against virulent L . major challenge . Adoptively transferred DN T cells from healed mice protected naïve mice against virulent L . major challenge as evidenced by significantly lower parasite burden ( Fig . 7E ) . Collectively , these in vitro and in vivo observations strongly implicate Leishmania-reactive DN T cells in contributing to optimal anti-Leishmania immunity in mice . Apart from lacking CD4 molecules , DN T cells display functional characteristics similar to CD4+ T cells ( MHC-II restriction , proliferation , IFN-γ production and parasite control ) . To further investigate how Leishmania-reactive DN T cells differ from CD4+ T cells , we compared the transcriptional profile of proliferating DN and CD4+ T cells following restimulation with L . major-infected BMDCs . Although most of the 84 mouse innate and adaptive immune genes showed similar pattern and level of expression in both cell types , some genes were preferentially upregulated or downregulated in DN T cells compared to CD4+ T cells ( Fig . 8A ) . The gene transcripts showing ≥ 2 folds difference in DN T cells were further analyzed and validated by quantitatively real-time PCR ( Fig 8B and C ) . Interestingly , most of the upregulated transcripts in DN T cells were genes associated with innate immune responses , including C3 , Mac-1 ( CD11b ) , myeloperoxidase ( Mpo ) , lysozyme , etc . In contrast , the downregulated transcripts ( relative to CD4+ T cells ) included genes associated with adaptive immunity , including CCR4 , Foxp3 , Gata-3 , etc . Collectively , these results suggest that despite mediating anti-Leishmania immunity ( akin to CD4+ T cells ) , Leishmania-reactive DN T cells are phenotypically distinct from conventional CD4+ T cells .
We show here that DN T cells proliferate and produce effector cytokines in secondary lymphoid organs of mice following primary and secondary L . major challenges . DN T cells from healed mice display functional characteristics of anti-Leishmania memory-like cells: they rapidly proliferate and produce effector cytokines ( TNF and IFN-γ ) in response to L . major challenge in vitro and in vivo and mediate infection-induced immunity ( rapid protection ) following adoptive transfer in vivo . Leishmania-reactive DN T cells express predominantly αβ TCR , are restricted by MHC class II molecules , lack immunoregulatory properties and display transcriptional profile distinct from conventional CD4+ T cells . To the best of our knowledge , this is the first extensive characterization and demonstration of the protective ability of Leishmania-reactive DN T cells in vitro and in vivo . It is generally believed that CD4+ T cells play a dominant role in anti-Leishmania immunity . However , the finding that CD4 deficient mice were resistant while MHC class II deficient mice were highly susceptible to L . major challenged this dogma [10] and suggests that MHC II-restricted CD4−CD8− T cells may be more important in regulating primary anti-Leishmania immunity . Indeed , several studies have reported the expansion of CD3+CD4−CD8− ( DN ) T cells in the blood of Leishmania-infected patients and dogs , and in spleens of Leishmania-infected mice [11] , [12] , [13] , [14] . These cells have been proposed to contribute to primary and vaccine-induced immunity although a concrete evidence implicating them in immunity has not yet been demonstrated . Our studies directly show the importance of Leishmania-reactive DN T cells in mediating optimal primary and secondary anti-Leishmania immunity in mice . The precise origin and development of peripheral DN T cells is not clearly understood and is controversial . Some reports suggest that DN T cells originate in the thymus by escaping negative selection [17] , [18] , [19] . In contrast , several reports suggest that DN T cells are generated in the periphery rather than in the thymus [19] , [20] , [21] , [22] . These cells comprise about 1–5% of total T cells in non-transgenic mice and in humans [11] , [23] making them difficult to isolate and subsequently study . TCR transgenic [24] or lpr ( Fas mutation ) mice [19] , [25] , which present increasing accumulation of DN T cells are widely used to investigate the function and developmental origin of DN T cells . DN T cells have been shown to influence long-term allograft survival [24] , [26] , [27] , prevent the development of autoimmune disease [28] , [29] , [30] , and contribute to control of intracellular pathogens [31] , [32] . In addition , DN T cells have been shown to possess immunoregulatory and alloreactive properties , inhibit autoreactive CD4+ T cells and mediate MHC I-restricted killing of allogenic target cells [20] , [24] , [25] . Our studies show that Leishmania-reactive DN T cells are restricted by MHC class II and may not have immunoregulatory properties because they failed to suppress CD4+ T cell proliferation in vitro ( Fig . 4D ) . Rather , a large percentage of proliferating DN T cells produced IFN-γ , TNF , IL-17 and GrzB , which is consistent with their effector functions as seen in other studies [11] , [12] , [29] . Previous studies that have reported the expansion and possible protective role of DN T cells in leishmaniasis focused mainly on primary Leishmania infection [11] , [12] , [13] , [14] . We extend these studies during secondary immunity by showing rapid expansion and effector functions ( cytokine production and parasite control ) by DN T cells following challenge infection . Healed mice had more proliferating and IFN-γ-producing DN T cells compared with naive mice following L . major challenge ( Fig . 2 ) , and adoptive transfer of DN T cells from healed ( but not naïve mice ) rapidly protected naïve mice against virulent L . major change . Moreover , DN T cells from healed mice expressed high levels of CD44 and majority of them were CD62LhiCD44hi , which are characteristics markers expressed by central memory-like cells . Collectively , these results suggest that DN T cells display functional characteristics of memory cells and contribute to optimal secondary immunity against L . major . How do DN T cells mediate their anti-Leishmania immunity ? We speculate that this may be related in part to their ability to produce IFN-γ and TNF , key cytokines that activate infected macrophages leading to intracellular parasite killing . Indeed , we found that Leishmania-reactive DN T cells in the spleens and lymph nodes are highly proliferative and produce IFN-γ , TNF and granzyme B . Importantly , we also found that DN T cells from immune mice were recruited to and proliferate at the infected footpads ( Fig . S7 ) . In addition , our in vitro co-culture experiments with infected BMDMs and highly enriched DN T cells show that suppression of parasite proliferation was associated with increased nitric oxide production , a key effector molecule that mediate destruction of parasites in infected cells . The findings that DN T cells mediate comparable ( or even superior ) protection against L . major in vitro and in vivo may challenge the dogma that CD4+ T cells are the major T cell subset that mediates anti-Leishmania immunity . Indeed , the proliferation of DN T cells was either comparable or sometimes higher than those of CD4+ T cells following in vitro or in vivo L . major challenge ( see Figs . 1–3 ) . Interestingly , although the percentage of IFN-γ-producing DN T cells was sometimes higher than those of CD4+ T cells , their MFI was significantly lower ( Fig . 1C ) , an observation that explain the relatively lower IFN-γ transcripts in DN compared to CD4+ T cells ( Fig . 8 ) . In addition , the numbers of Leishmania-reactive CD4+ T cells were quantitatively ( ∼ 3–4 fold ) higher than those of DN T cells . Thus , despite their superior proliferative response , DN T cells may still play a subordinate role to CD4+ T cells in vivo . Furthermore , it is conceivable that CD4+ T cells may be required for proper activation and effector functions of DN T cells . In line with this , we have observed that proliferation and IFN-γ production by highly enriched DN T cells is impaired in cultures devoid of immune CD4+ T cells in vitro and in vivo ( Fig . S8 ) . It is conceivable that DN T cells may assume increased roles in the absence of CD4+ T cells . For example , SIV infection in nonhuman primates does not result in immune dysfunction and progression to simian AIDS because DN T cells partially compensate for defective CD4+ T cell functions upon SIV-induced CD4+ T cell depletion in these animals [33] , [34] . Similarly , a strong DN T cell-mediated HIV Gag-specific response has been associated with seronegativity in HIV-exposed individuals [35] . It is interesting that the expression of genes associated with innate immune responses including C3 , were significantly higher in Leishmania-reactive DN T cells than in CD4+ T cells . While commonly associated with initiation of inflammation and critical molecule involved in first line of defense against pathogens , the complement proteins , particularly C3 and its degradation fragments are also known to prominently influence the adaptive immunity [36] , [37] . Recent studies have been shown that some subset of T cells express C3 and that its intracellular activation is not only required for homeostatic T cell survival [38] , but also in optimal Th1 induction and differentiation into effector cytokine ( particularly IFN-γ ) production [38] , [39] . It is conceivable that C3-expressing DN T cells in L . major-infected mice might be involved in IFN-γ production leading to effective macrophage activation , nitric oxide production and parasite killing . Collectively , our studies provide direct evidence for DN T cells in mediating anti-Leishmania immunity akin to CD4+ T cells . We propose that DN T cells complement CD4+ T cells to mediate efficient primary and secondary anti-Leishmania immunity in mice . In the absence of DN T cells , the induction of effective anti-Leishmania immunity may be either delayed or impaired . In a recent preliminary study , we observed impaired induction of DN T cells in spleens and draining lymph nodes of L . major-infected highly susceptible BALB/c mice . It would be interesting to determine whether the susceptibility of BALB/c mice to L . major infection is related in part to this impaired expansion of DN T cells . Collectively , our studies clearly identify DN T cells as important subset of T cells that contribute to optimal anti-Leishmania immunity .
All mice were kept at the University of Manitoba Central Animal Care Services ( CACS ) facility in accordance to the Canadian Council for Animal Care guidelines . The University of Manitoba Animal Use Ethics Committee approved all studies involving animals , including infection , humane endpoints , euthanasia and collection of samples . Six to 8 wk-old female C57BL/6 ( Thy1 . 2 ) mice were obtained from Charles River , St Constante PQ , Canada . Thy1 . 1 and MHC class II deficient ( MHC II KO ) C57BL/6 mice were purchased from The Jackson Laboratory ( Bar Harbor , ME ) . Female CD1d deficient C57BL/6 mice were kindly supplied by Dr . Xi Yang from a breeding colony maintained at the University of Manitoba Central Animal Care Services ( CACS ) Facility . Leishmania major parasites ( MHOM/80/Fredlin ) were grown in M199 culture medium ( Sigma , St . Louis , MO ) supplemented with 20% heat inactivated FBS ( HyClone , Logan , UT ) , 2 mM glutamine , 100 U/ml penicillin , and 100 µg/ml streptomycin . For infection , mice were injected with 2×106 ( primary infection ) or 5×106 ( secondary infection ) stationary-phase promastigotes in 50 µl PBS suspension into the right ( primary ) or left ( secondary ) hind footpad . Lesion sizes were monitored weekly by measuring footpad swelling with calipers . Parasite burden in the infected footpads was determined by limiting dilution assay . Parasite titers were determined from the highest dilution at which growth was visible . Bone marrow cells were isolated from the femur and tibia of naïve C57BL/c mice and differentiated into macrophages using complete medium supplemented with 30% L929 cell culture supernatant as previously described [40] . BMDCs were differentiated in petri dishes in the presence of rmGM-CSF ( 20 ng/ml; Peprotech , Rocky Hill , NJ ) . BMDMs and BMDCs were infected at a cell-to-parasite ratio of 1∶5 and after 6 hr , free parasites were washed away and infected BMDCs were used to stimulate purified CD3+ , CD4+ or DN T cells from naïve or healed mice in vitro . To assess the ability of CD4+ or DN T cells to control parasite proliferation , infected BMDMs were co-cultured with CD4+ or DN T cells and parasite proliferation in infected BMDMs was determined at different times by counting Giemsa-stained cytospin preparations under light microscope at ×100 ( oil immersion ) objective . Infected mice were sacrificed and spleens and dLNs were collected and made into single-cell suspensions . Cells were labeled with CFSE dye ( 1 . 5 mM; Molecular Probes , Eugene , OR ) and resuspended at a concentration of 2×106 cells per milliliter in RPMI 1640 supplemented with 10% heat-inactivated FBS , 100 U/ml penicillin , 100 µg/ml streptomycin , and 5×10−5 M 2-mercaptoethanol ( complete medium ) , plated with 100 µl per well in 96-well tissue culture plates , and stimulated with infected BMDCs ( BMDC: T cell = 1∶100 ) or soluble anti-CD3/CD28 mAb ( 1 µg/ml; BioLegend , San Diego , CA ) . After 5 days , proliferation and cytokine production were determined by flow cytometry . In some experiments , CFSE-labeled T cells from spleens and dLNs of infected mice were co-cultured with L . major-infected WT , MHC II KO , or CD1d KO BMDCs for 5 days , stimulated with PMA , BFA and ionomycin for 4–6 hr and proliferation , IFN-γ , TNF , IL-2 , IL-17 and GrzB expression by different T cell subsets were analyzed by flow cytometry . In some experiments , anti-MHC II antibodies were used to block MHC II-TCR interaction in vitro . Healed ( > 12 weeks post-infection ) Thy1 . 2 C57BL/6 mice were sacrificed and single-cell suspensions from the dLNs and spleens were made . T cells ( Thy1 . 2+ ) were enriched by positive selection using mouse CD90 . 2 ( Thy1 . 2 ) selection kit according to the manufacturer's protocols ( StemCell Technologies , Vancouver , BC ) . Enriched T cells ( > 98% purity ) were labeled with CFSE dye , and 107 cells were adoptively transferred into naive congenic ( Thy1 . 1 ) mice by tail vein injection . After 24 hr , the recipient mice were challenged with 5×106 L . major , sacrificed after 7 days and cell proliferation and IFN-γ expression by donor ( Thy1 . 2 ) cells in the dLNs and spleens were determined directly ex vivo . For in vitro co-culture experiments , DN ( CD3+CD4−CD8− ) and CD4+ T cells were purified from pooled spleens and dLNs of healed or naïve mice by cell sorting ( FACSAria III , BD Biosciences ) . For in vivo adoptive transfer studies , DN T cells were enriched using a combination of in vivo depletion and positive selection . Briefly , L . major-infected and healed mice ( > 12 weeks post-infection ) were first injected with 200 µl GK1 . 5 and TIB210 ascites ( i . p ) to deplete CD4+ and CD8+ cells . After 48 hr , DN T cells were purified using mouse CD90 . 2 selection kit ( StemCell Technologies , Vancouver , BC ) . Enriched DN T cells were > 99% negative for CD4 and CD8 expression and > 95% positive for CD3 by flow cytometry . To assess the numbers ( percentages ) and proliferation of DN T cells at the site of infection , CFSE-labeled whole T cells from L . major-infected Thy1 . 2 mice were adoptively transferred into naïve Thy1 . 1 mice that were then challenged with L . major . After 7 days , recipient mice were sacrificed and donor cells were recovered from the footpads as we previously described [41] . Briefly , the footpads were disinfected in 70% ethanol , the skins were peeled off and homogenized gently in PBS with tissue grinders . The crude homogenates were resuspended in 7 ml of cold PBS , carefully layered on top of 5 ml Ficoll and the infiltrating cells were separated by centrifugation according to the manufacturer's suggested protocols . The cells were collected , resuspended in 5 ml complete medium , counted , stained directly for expression of various cell surface markers and analyzed by flow cytometer by gating on Thy1 . 2+ donor cells . For reverse transcription-PCR ( RT-PCR ) , cells from spleens of healed mice were stained with fluorescent-conjugated anti-CD3 , anti-CD4 and anti-CD8 antibodies . CD4 , CD8 and DN T cells were sorted to high purity by gating on CD3+ cells . CD4 , CD8 and GAPDH gene expression in sorted cells were analyzed by RT-PCR . For PCR array , CFSE-labeled whole spleen cells from healed mice were stimulated with L . major-infected BMDCs for 5 days and proliferating ( CFSElo ) CD4+ and DN T cells were purified by cell sorting . Eighty-four innate and adaptive immune genes in CD4+ and DN T cells were analyzed with Mouse Innate & Adaptive Immune Responses PCR Array kit ( Qiagen , Frederick , MD ) . PCR array was performed by a real-time cycler ( Bio-Rad CFX96 ) and analyzed with web-based PCR Array Data Analysis Software ( Qiagen , Frederick , MD ) . To quantify gene expression levels , equal amounts of cDNA were mixed with SYBR Green PCR master mix ( Toyobo , Osaka , Japan ) and primers specific for the gene of interest ( Table S1 ) . 18S rRNA was amplified as an internal control . Naïve and healed mice were injected with 2 mg of BrdU i . p . per mouse and then challenged with 5×106 L . major in the next day . BrdU solution was prepared in sterile water , protected from light exposure , and changed daily . The night before the assay , mice were injected i . p . with 0 . 8 mg of BrdU in PBS . The next day , mice were sacrificed , spleens were harvested and BrdU staining was performed using BrdU Staining Kit according to the manufacturer's suggested protocol ( BD PharMingen ) . Healed mice were depleted of CD4 and/or CD8 T cells by injecting i . p . 200 µl ascites containing anti-CD4 ( GK1 . 5 ) or anti-CD8 ( TIB 210 ) mAb ( or both ) per mouse or depleted of total T cells by injecting i . p . 100 µg anti-Thy1 . 2 mAb ( TIB 107 ) per mouse , once a week , and then challenged with 5×106 L . major . Data are presented as means and standard error of mean ( SEM ) . Two-tailed Student's t-test or ANOVA were used to compare means and SEM between groups using GraphPad Prism software . Differences were considered significant at p<0 . 05 . | Although it is generally believed that CD4+ T cells mediate anti-Leishmania immunity , some studies suggest that CD3+CD4−CD8− ( double negative , DN ) T cells may play a more important role in regulating primary anti-Leishmania immunity . Here , we report that DN T cells extensively proliferate and produce effector cytokines in mice following primary and secondary L . major infections . Leishmania-reactive DN T cells utilize αβ T cell receptor ( TCR ) and are restricted by MHC class II molecules . Strikingly , DN T cells from healed mice display functional characteristics of protective anti-Leishmania memory-like cells: rapid and extensive proliferation , effector cytokine production in vitro and in vivo , and accelerated parasite control following secondary L . major challenge . These results directly identify DN T cells as important players in protective primary and secondary anti-L . major immunity in experimental cutaneous leishmaniasis . |
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Soil-transmitted helminths ( STHs ) are a major health concern in tropical and sub-tropical countries . Oesophagostomum infection is considered endemic to West Africa but has also been identified in Uganda , East Africa , among primates ( including humans ) . However , the taxonomy and ecology of Oesophagostomum in Uganda have not been studied , except for in chimpanzees ( Pan troglodytes ) , which are infected by both O . bifurcum and O . stephanostomum . We studied Oesophagostomum in Uganda in a community of non-human primates that live in close proximity to humans . Prevalence estimates based on microscopy were lower than those based on polymerase chain reaction ( PCR ) , indicating greater sensitivity of PCR . Prevalence varied among host species , with humans and red colobus ( Procolobus rufomitratus ) infected at lowest prevalence ( 25% and 41% by PCR , respectively ) , and chimpanzees , olive baboons ( Papio anubis ) , and l'hoest monkeys ( Cercopithecus lhoesti ) infected at highest prevalence ( 100% by PCR in all three species ) . Phylogenetic regression showed that primates travelling further and in smaller groups are at greatest risk of infection . Molecular phylogenetic analyses revealed three cryptic clades of Oesophagostomum that were not distinguishable based on morphological characteristics of their eggs . Of these , the clade with the greatest host range had not previously been described genetically . This novel clade infects humans , as well as five other species of primates . Multiple cryptic forms of Oesophagostomum circulate in the people and primates of western Uganda , and parasite clades differ in host range and cross-species transmission potential . Our results expand knowledge about human Oesophagostomum infection beyond the West African countries of Togo and Ghana , where the parasite is a known public health concern . Oesophagostomum infection in humans may be common throughout Sub-Saharan Africa , and the transmission of this neglected STH among primates , including zoonotic transmission , may vary among host communities depending on their location and ecology .
Soil-transmitted helminths ( STHs ) are parasitic nematodes that cause infection via eggs and larvae , which are shed in feces and persist in the soils of tropical and sub-tropical countries [1] . STHs infect over one billion people worldwide [2] and may cause a combined disease burden as substantial as that caused by malaria or tuberculosis [3] . Nevertheless , these parasites are largely neglected in research , perhaps in part because the diseases they cause are suffered by the world's most impoverished populations [1] . Although roundworms ( Ascaris lumbricoides ) , hookworms ( Necator americanus and Ancylostoma duodenale ) and whipworms ( Trichuris trichiura ) are of global importance , other “lesser” parasites are localized to specific regions [1] . This includes Oesophagostomum spp . , a genus of nodule-causing worms with L3 larvae that are infective via ingestion after 4–7 days [4]–[7] . The human burden of Oeosphagostostomum infection is considered localized to West Africa , specifically the countries of Togo and Ghana [5] , [8] , [9] . A variety of mammals , including pigs , ruminants [10] , [11] , and non-human primates are frequently parasitized by Oesophagostomum . Infections in wild primates appear to be asymptomatic; clinical signs and mortality due to Oesophagostomum have only been recorded in captive settings [10] , [12] . Eight species of Oesophagostomum have been recorded in wild primates , of which the three most common , O . bifurcum , O . stephanostomum , and O . aculeatum , are able to infect humans [5] , [7] , [11] , [13] . Of these , O . bifurcum appears to be the only species to regularly parasitize humans , with human infections by other species considered incidental [5] , [9] . In Togo and Ghana , the majority of human Oesophagostomum cases occur within endemic foci [5] , [8] and affect 20% and 90% of the population , respectively , with prevalence highest in rural areas [12] , [14] , [15] . The only known species to cause infection within these countries is O . bifurcum , which also infects the region's non-human primates , including patas monkeys ( Erythrocebus patas ) , mona monkeys ( Cercopithecus mona ) , and olive baboons ( Papio anubis ) [4] , [10] , [16] . However , previous research has indicated that O . bifurcum is not commonly transmitted among primate species ( including humans ) because different parasite variants within the species are adapted to specific hosts [4] , [16] , [17] . In Uganda , a number of primate species harbor Oesophagostomum , as evidenced by microscopic detection of eggs in feces . These include members of the primate subfamilies Cercopithecinae and Colobinae , as well as chimpanzees ( Pan trogolodytes ) [18]–[20] . There have also been reports of oesophagostomiasis in human patients in Uganda , although no such reports , to our knowledge , have been published since the 1980s [5] , [21] , perhaps due to under-reporting or improvements in treatment . With the exception of chimpanzees , which are infected with both O . bifurcum and O . stephanostomum [22] , the species of Oesophagostomum infecting Ugandan primates and humans remains unknown . In this study , we examined Oesophagostomum within the primate community of Kibale National Park , Uganda , using a combination of microscopic and molecular methods . Species-specific identification of eggs by microscopy alone is difficult , because eggs are similar morphologically to other STHs , including hookworms , Trichostrongylus spp . , and the “false hookworm” Ternidens deminutus [5] , [9] , [23]–[25] . In other studies , coproculture of L3 larvae or necropsy to isolate adult worms have been used to identify these parasites to species [25] , [26] . Here , we used molecular methods to detect and sequence Oesophagostomum DNA directly from feces; such methods have proven informative for other similar studies [27] , [28] . In addition , we used phylogenetic comparative methods to ascertain whether primate host traits explain variation in prevalence of Oesophagostomum infection among host species . Our sampling and analyses included nearby human populations to assess whether Oesophagostomum is a public health concern in the region , as well the parasite's local propensity for zoonotic transmission .
Prior to data collection , all protocols were reviewed and approved by the Uganda National Council for Science and Technology and the Uganda Wildlife Authority , as well as by the Institutional Review Board and the Animal Care and Use Committees of McGill University and the University of Wisconsin-Madison . Due to low literacy rates , oral informed consent was obtained from all adult subjects and a parent or guardian of all minor participants by trained local field assistants and documented by witnessed notation on IRB-approved enrollment forms . Kibale National Park ( 0°13′–0°41′N , 30°19′–30°32′E ) is a 795 km2 semi-deciduous protected area in Western Uganda . Primate research has occurred in Kibale for over four decades , focusing on chimpanzees and red colobus monkeys ( Procolobus rufomitratus ) [29] , [30] . As a result , a number of primate groups are habituated to human presence , and many individuals are recognizable based on a combination of physical attributes and collars affixed as part of a larger project on primate health and conservation [31] . Samples from monkeys in the Kanyawara area of Kibale National Park were collected from red-tailed guenons ( Cercopithecus ascanius ) , blue monkeys ( Cercopithecus mitis ) , l'hoest monkeys ( Cercopithecus lhoesti ) , grey-cheeked mangabeys ( Lophocebus albigena ) , olive baboons ( Papio anubis ) , red colobus , and black and white colobus ( Colobus guereza ) ( Figure 1 ) . Chimpanzee samples were collected from Kanyanchu , an area that has a habituated chimpanzee community as a result of tourism ( Figure 1 ) . All samples were collected non-invasively immediately after defecation and placed into sterile tubes . Date , location , species , age and sex category , and social group membership were recorded . Human samples were collected after the receipt of Institutional Review Board-approved informed consent following World Health Organisation protocols . Samples collection occurred in three villages: Ibura , Kanyansohera , and Kasojo , which are less than 5 km from the border of the park ( Figure 1 ) . Individuals between the ages of 2 and 70 were suitable participants of this study . Consenting participants were given instructions on how to collect the sample , which was then retrieved for processing within one day . Samples were subjected to a modified ethyl acetate concentration method , recommended in the approved guidelines of the Clinical and Laboratory Standards Institute for the identification of intestinal-tract parasites [32] , [33] . Concentration by sedimentation was performed in the field using one gram of undiluted feces without fixture in formalin , as formalin is a known inhibitor of the polymerase chain reaction [34] . All materials were sterilized prior to use , and care was exercised throughout the procedure to prevent contamination . Sediments were left uncapped for two hours after completion of the procedure to allow ethyl acetate that may inhibit polymerase chain reaction ( PCR ) to volatilize . Sediments were then suspended in 2 mL RNALater nucleotide stabilization solution ( Sigma-Aldrich , St . Louis , MO , USA ) and frozen at −20°C until shipment to North America . Thin smears from sedimented feces were used for microscopy [35] . All eggs of the genus Oesophagostomum were identified at 10× objective magnification on a Leica DM2500 light microscope . Data were recorded on size , shape , color and internal contents of eggs . Images were captured at 40× objective magnification of all specimens using an Infinity1 CMOS digital microscope camera and Infinity Camera v . 6 . 2 . 0 software ( Lumenera Corporation , Ottawa , ON , Canada ) . Samples were considered negative after the entire sediment sample was scanned and no eggs were found . We note that while identification of Oesophagostomum eggs was based on a rigorous set of characteristics , this genus cannot easily be distinguished from hookworm infection by eggs alone . However , hookworms have not been found in previous surveys of the gastrointestinal parasites of this primate community [19] , [20] , suggesting that eggs identified with morphological characteristics of both Oesophagostomum and hookworm were almost certainly Oesophagostomum . DNA was extracted from 200 µL of sedimented feces using a ZR Fecal DNA MiniPrep Kit ( Zymo Research Corporation , Irvine , CA , USA ) , following manufacturer protocols . External PCR was performed targeting the ribosomal internal transcribed spacer 2 gene using primers NC1 ( 5′-ACGTCTGGTTCAGGGTTGTT-3′ ) and NC2 ( 5′-TTAGTTTCTTTTCCTCCGCT-3′ ) , which generated products that ranged in size from 280 to 400 bp , suggesting that , as expected , the primer set detected a number of parasitic helminths present in the samples [27] , [36] . Subsequently , an internal , semi-nested PCR generating amplicons of predicted size 260 bp was performed using primer NC2 and newly designed Oesophagostomum-specific primer , OesophITS2-21 ( 5′-TGTRACACTGTTTGTCGAAC-3′ ) . Primer OesophITS2-21 was generated by aligning publicly available sequences of the Oesophagostomum internal transcribed spacer 2 gene [26] , [36]–[39] , and GenBank accession numbers HQ283349 , HQ844232] . In total , eight species of Oesophagostomum were represented in the alignment . Other species of varying relatedness , including other members of the taxa Chabertiidae ( Chabertia ovina , Accession No . JF680981; Ternidens deminutus , Accession No . HM067975 ) , Strongylidae ( Strongylus vulgaris [40] ) , and Strongylida ( Necator americanus [36] , and Ancylostoma duodenale [41] ) were also included . Priming regions were selected to be identical among all species of Oesophagostomum but divergent from the other genera . Primer ITS2-21 was highly specific as confirmed by sequencing , since all PCR products matched Oesophagostomum despite the fact that a number of other parasites ( including Strongyloides , Necator and Trichuris ) , were identified in the same samples during microscopic examination . External PCR was performed in 25 µL volumes using the FailSafe System ( Epicentre Biotenchnologies , Madison , WI , USA ) with reactions containing 1× FailSafe PCR PreMix with Buffer C , 1 Unit of FailSafe Enyme Mix , 2 . 5 picomoles of each primer ( NC1 and NC2 ) , and 1 µL of template . Reactions were cycled in a Bio-Rad CFX96 platform ( Bio-Rad Laboratories , Hercules , CA , USA ) with the following temperature profile: 94°C for 1 min; 45 cycles of 94°C for 15 sec , 50°C for 30 sec , 72°C for 90 sec; and a final extension at 72°C for 10 min . Internal PCR was performed in 25 µL volumes using the DyNAzyme DNA Polymerase Kit ( Thermo Scientific , Asheville , NC , USA ) with reactions containing 0 . 5 Units of DyNAzyme I DNA Polymerase , 1× Buffer containing 1 . 5 mM MgCl2 , 2 . 5 picomoles of each primer ( OesophITS2-21 and NC2 ) , and 1 µL of template . Reactions were cycled with the following temperature profile: 95°C for 1 min; 45 cycles of 95°C for 15 sec , 55°C for 30 sec , 70°C for 90 sec; and a final extension at 70°C for 5 min . Amplicons were electrophoresed on 1% agarose gels stained with ethidium bromide , and purified from gels using the Zymoclean Gel DNA Recovery Kit ( Zymo Research Corporation , Irvine , CA , USA ) according to the manufacturer's instructions . Products were Sanger sequenced in both directions using primers OesophITS2-21 and NC2 on ABI 3730xl DNA Analyzers ( Applied Biosystems , Grand Island , NY , USA ) at the University of Wisconsin-Madison Biotechnology Center DNA Sequencing Facility . Sequences were hand-edited and assembled using Sequencher v4 . 9 ( Gene Codes Corporation , Ann Arbor , MI , USA ) and all ambiguous bases were resolved by repeat PCR and re-sequencing , as described above . All new sequences were deposited in GenBank , under Accession Numbers KF250585 - KF250660 . Sequences were aligned using the computer program ClustalX [42] with minor manual adjustment . Published reference sequences were included to identify putative species ( AF136575 , Y11733 , AF136576 ) and as outgroups ( HQ844232 , Y11738 , Y11735 , Y10790 , AJ006149 ) , and were trimmed to the length of the newly generated sequences using Mesquite v . 2 . 75 [43] . Trimmed sequences yielded the same tree topology as did untrimmed sequences ( by neighbor-joining method; results not shown ) , suggesting that the amplified region was sufficient for taxonomic discrimination . Phylogenetic trees were reconstructed using maximum likelihood in MEGA v . 5 . 05 [44] and the Hasegawa-Kishino-Yano substitution model [45] . Phylogenetic support was assessed using 1 , 000 bootstrap replicates . To estimate Oesophagostomum genetic diversity , percent nucleotide-level sequence identity among sequences was calculated as the uncorrected pairwise proportion of nucleotide differences ( p-distance ) in MEGA v5 . 05 [44] . Diagnostic performance of microscopy versus PCR was estimated by calculating sensitivity ( i . e . , true positive rate ) and specificity ( i . e . , true negative rate ) using MedCalc v . 12 . 5 . 0 ( MedCalc Software , Ostend , Belgium ) . Prevalence of infection was calculated as the number of samples found to be positive for Oesophagostomum divided by the total number of samples collected , with 95% confidence intervals calculated using the modified Wald method [46] . To determine whether prevalence differed among primate host species , a chi-square test was conducted in Quantitative Parasitology v3 . 0 [47] . To explore variation in prevalence among hosts while controlling for their phylogenetic non-independence , a phylogenetic least squared regression ( PGLS ) was conducted in R [48] using the ape [49] and caper [50] libraries . Prevalence of Oesophagostomum was included as the dependent variable , and various primate life history traits were independent variables: terrestriality ( predominantly terrestrial versus predominantly arboreal ) , maximum home range [51]–[56] , maximum group size [51] , [53] , [55] , [57]–[60] , percentage time spent in polyspecific associations [61] , [62] , average female body mass , and average daily travel distance ( the latter was log transformed since the relationship was close to exponential ) [55] , [56] , [62]–[64] . Humans were omitted from the PGLS analysis because many of these traits vary widely among human populations , making accurate estimations problematic . To determine the degree to which each Oesophagostomum lineage ( i . e . , taxonomic unit ) identified by DNA sequencing was host restricted , we calculated the phylogenetic dispersion of infected hosts using the net relatedness index ( NRI ) in R [48] using the ape [49] and picante libraries . Mean pairwise distance ( MPD ) was weighted by the ratio of occurrence of each Oesophagostomum within each lineage , and compared to null expectation in 1000 randomly assembled communities . Results are reported as standard effects sizes , with values close to 1 indicating phylogenetic evenness ( i . e . , Oesophagostomum lineages infect a greater diversity of hosts than would be expected by chance ) , while values <0 . 05 indicate phlylogenetic clustering ( i . e . , Oesophagostomum lineages are host-specific ) .
A total of 318 fecal samples from primates , including humans , were collected ( Table 1 ) . Of these , 112 were positive for Oesophagostomum by microscopy , for a community-wide prevalence of infection of 35 . 2% ( Table 1 ) . All eggs identified by microscopy were similar in internal and external morphology in samples from all primate species ( Figure 2 ) . Eggs were 65–80 by 35–50 µm in size , which is consistent with previous results from this community [19] , [20] ( Figure 2 ) . PCR generated single , clear amplicons of expected size ( 260 bp ) in 222 samples , indicating positive detection of Oesophagostomum DNA , for an overall prevalence of 69 . 8% . No amplicons were present in remaining samples . Resulting DNA sequences overlapped 100% with published sequences and contained no insertions or deletions , making alignment unequivocal . When PCR results were compared to microscopy , the overall sensitivity of PCR was 100% ( 95% CI 96 . 8%–100 . 0% ) , but specificity was only 47 . 5% ( 95% CI 40 . 5%–54 . 7% ) . Thus , PCR did not classify any microscopy-positive samples as negative but identified 109 microscopy-negative samples as positive . Prevalence of Oesophagostomum infection ( as determined by both microscopy and PCR ) varied significantly among host species ( microscopy: chi-square = 54 . 31 , df = 8 , P<0 . 0001; PCR: chi-square = 112 . 2 , df = 8 , P<0 . 0001 ) . Both microscopy and PCR identified humans as having the lowest prevalence of infection ( 8 . 3% and 25 . 0% , respectively ) , followed by red colobus ( 17 . 2% and 40 . 6% , respectively ) . Chimpanzees , l'hoest monkeys , and olive baboons had the highest prevalence by both methods , with 100% prevalence by PCR in all three species ( although sample sizes were low in some cases; Table 1 ) . PGLS indicated that terrestriality , maximum home range , maximum group size , percent time spent in polyspecific associations , and average female body mass were not significant univariate predictors of Oesophagostomum prevalence ( all P>0 . 05 from PGLS with lambda = ML; Table 2 ) . However , log daily travel explained nearly 55% of the variation in prevalence among host species ( P<0 . 05 , R2 = 0 . 546 , from PGLS with the ML estimate of lambda = 0 ) . In a multivariate model , both group size and log daily travel were significant predictors of prevalence , with group size showing a negative relationship and log daily travel a positive relationship ( Table 2 ) . This two-predictor model including group size and daily travel explained over 75% of the variation in Oesophagostomum prevalence among species ( model P<0 . 01 , R2 = 0 . 7701; Table 3 ) . From 222 positive samples , 76 were randomly selected for sequencing to represent as even a number of positive samples per host species as possible . All 76 sequences most closely matched published Oesophagostomum ITS-2 DNA sequences using the BLASTn tool on the National Centre for Biotechnology Information website . Phylogenetic analysis resolved these sequences into three clades ( Figure 3 ) . Clade 1 contained all 12 sequences from olive baboons , one sequence from l'hoest monkeys , one sequence from grey-cheeked mangabeys , three sequences from red colobus and one sequence from red-tailed guenons . These sequences were identical to published reference sequences for O . bifurcum [26] , [36] . Five additional sequences from l'hoest monkeys sorted into clade 1 and were 97 . 1% similar to this same O . bifurcum reference sequence . Clade 2 contained all eight sequences from chimpanzees , five sequences from blue monkeys , two sequences from black and white colobus , two sequences from grey-cheeked mangabeys , three sequences from red colobus , and twelve sequences from red-tailed guenons . All sequences in clade 2 were identical to an O . stephanostomum reference sequence [26] . Clade 3 was composed of two nearly identical branches ( 99 . 4% identity ) that contained all six sequences from humans , as well as sequences from three blue monkeys , three black and white colobus , five grey-cheeked mangabeys , two red colobus , and two red-tailed guenons . These sequences were 92 . 4–93 . 0% and 93 . 0–93 . 6% similar to O . bifurcum , and O . stephanostomum , respectively , but were not identical to any published reference sequence . Host species were phylogenetically clustered within O . bifurcum clade 1 ( NRI = −1 . 76 , P<0 . 05 ) . Clade 2 ( O . stephanostomum ) did not vary significantly from the null expectation of no host clustering , NRI = 0 . 86 , P = 0 . 75 ) . Clade 3 was marginally phylogenetically over-dispersed with respect to distribution of host species ( NRI = 1 . 24 , P = 0 . 04 ) .
Here we evaluate the prevalence of Oesophagostomum infection in wild primates and humans in Western Uganda using both microscopy and PCR . Our results clearly show that prevalence varied significantly among host species . Humans had the lowest prevalence of infection likely because of avoidance behaviors such as sanitation practices [65] , [66] and because of the common use of antihelminthics in the region . Red colobus and black and white colobus also had comparatively low prevalence of infection , as found in previous studies [16] , [20] , [67] . This observation may reflect colobine gastrointestinal physiology , which is characterized by folivory and foregut fermentation [68] , and the associated regular ingestion of plant secondary compounds that may suppress infection by pathogenic organisms [69] . Conversely , the high prevalence of infection in chimpanzees , olive baboons , and l'hoest monkeys may reflect reduced physiological barriers to infection or increased susceptibility . To explain this interspecific variation in prevalence , we examined correlations between life history variables and prevalence among host species . We found that two variables , daily travel distance and group size , explain over 75% of the variance in Oesophagostomum prevalence among host species . Surprisingly , body mass , the strongest predictor of helminth species richness elsewhere , was not significant here [67] . Previous studies have concluded that group-living animals with small home ranges are likely to suffer high intensities of infection due to frequent environmental re-exposure [70]–[72] . Our results indicate the opposite in the case of Oesophagostomum: smaller primate groups with large daily travel distances had higher prevalence . Animals with larger day ranges may encounter greater habitat variation [73] , which may increase exposure to Oesophagostomum from environmental sources . In addition , previous research has implicated terrestriality as an important factor affecting the prevalence of trematode parasites in primates [74] . In our study , the three host species with highest Oesophagostomum prevalence ( chimpanzees , olive baboons and l'hoest monkeys ) were also the only three predominantly terrestrial species . Although this trend was not statistically significant , it is possible that terrestrial primates contact soil more frequently , and thus the infective stages of STHs . Although group size was not a significant predictor of prevalence in univariate analyses , our multivariate analysis found smaller groups with large daily travel distances to be at greatest risk of infection . This finding contrasts with previous studies showing that increased intragroup contact increases exposure [71] , [75] . In Kibale , positive associations between group size and parasite richness have been documented for protozoan parasites in mangabeys [76] . However parasite richness is not necessarily associated with prevalence . Small primate groups might maintain high intra-group infection rates for certain parasites if transmission within the group is frequent , thus maintaining high prevalence ( as seen here ) without correspondingly high parasite richness . Our study detected substantial cryptic phylogenetic diversity in Oesophagostomum infecting Ugandan primates . Currently , the principal human Oesophagostomum species is considered to be O . bifurcum [5] , while other great apes harbor O . stephanostomum [6] , [12] , [18] , [77] . Recently , however , chimpanzees inhabiting a northern sector of Kibale were identified as positive for O . bifurcum , making this the first discovery of O . bifurcum in non-human apes . The same study identified chimpanzees also infected or co-infected with O . stephanostomum [22] . In our phylogenetic analysis , we identified both O . bifurcum and O . stephanostomum in the Kibale primate community . However , we found only O . stephanostomum in chimpanzees , although the possibility of undetected O . bifurcum infections cannot be ruled out . In addition , we identified a third Oesophagostomum lineage that did not cluster with any published sequence and thus may represent a previously uncharacterized taxon . It is possible that this new taxon has remained undetected in previous molecular investigations . We examined the OB primer that has been used previously to identify O . bifurcum [36] and conclude that it would probably not amplify our newly identified taxon due to mismatched bases at both the 5′ and 3′ ends of the primer . It is therefore possible that the new taxon we identified exists elsewhere ( e . g . in Togo and Ghana ) but has been not been detected or differentiated from other members of the genus . However , we caution that these inferences are based on a short region of a single gene , and that sequencing additional genes as well as morphological characterization of L3 larvae and adults will be necessary to confirm these findings . Nonetheless , our results suggest a heretofore unappreciated degree of hidden genetic diversity within this well-described genus of parasites that are known to infect humans . Interestingly , all Oesophagostomum sequences recovered from humans clustered with the previously undescribed third taxon , and not with published O . bifurcum sequence from humans elsewhere in Africa ( clade 1 ) [36] . In Ghana , geographic separation between humans and non-human primates infected with Oesophagostomum , despite apparently conducive environments for zoonotic transmission , motivated efforts to determine the host range of the parasite using molecular methods [28] . Genome-wide analyses ( amplified fragment length polymorphism , random amplification of polymorphic DNA ) suggested that O . bifurcum clusters into distinct groups by host species , thus suggesting that zoonotic transmission is uncommon [4] , [17] . By contrast , in our study area , no such geographic separation exists between humans and non-human primates . In this setting , we found that both humans and non-human primates were infected with the novel Oesophagostomum clade 3 , which is phylogenetically over-dispersed compared to the other Oesophagostomum clades . While our conclusions await verification from more detailed examination of the Oesophagostomum genome , our results nevertheless suggest that this novel clade may be broadly transmissible among species of distantly related primate hosts , including humans . The Kibale ecosystem is known for its high degree of spatio-temporal overlap between humans and non-human primates and its ensuing high rates of transmission of diverse pathogens across primate species [78]–[81] . Our results provide further evidence for cross-species pathogen transmission between wild primates and humans in this region . Our paired analyses applying both microscopy and PCR to the same samples indicate that traditional methods based on microscopy may significantly underestimate prevalence . Concentration methods followed by microscopic visualisation of eggs in thin smears are considered definitive diagnostic methods for soil-transmitted helminth ( STH ) infections [82] , [83] . Previous studies that have used fecal sedimentation and microscopy have reported Oesophagostomum infection prevalence estimates between approximately 3% and 10% in wild Ugandan primates [19] , [20] . These values are considerably lower than what we report here using molecular methods; however , our results parallel other studies that have estimated prevalence using molecular methods [16] , [22] . Not surprisingly , we find that PCR is more sensitive than microscopy , perhaps because it can detect Oesophagostomum infection even when eggs are not present . For example , tissues or secretions shed by adult worms into the intestinal lumen would be detected by PCR , as would eggs that have hatched into L1 larva prior to fixation during the sedimentation procedure . To our knowledge , ours is the first study in several decades to report human Oesophagostomum infection in Uganda , a country that is over 3 , 000 km from known foci of infection in West Africa [5] . Given that the prevalence of Oesophagostomum was 25% in our sample of people , we suspect that this parasitic infection occurs more commonly across Sub-Saharan Africa than previously thought and may be causing infections that are untreated or misdiagnosed . Our finding of a previously genetically uncharacterized lineage of Oesophagostomum that may be transmitted among primate species underscores that the diversity ( genetic and otherwise ) of this parasite genus may be under-sampled in Africa . Further ecological studies of Oesophagostomum in Uganda and elsewhere are needed to quantify the degree of enzootic versus zoonotic transmission . Regardless of the outcome of such research , our results suggest that Oesophagsotomum should be considered a pathogen of concern beyond its accepted foci of infection in Togo and Ghana , and perhaps across all of equatorial Africa . | Nodule worms infect the gastrointestinal tracts of a number of mammalian species , including humans and other primates . This study sought to identify the species of nodule worms causing infections within and around an East African national park in Uganda where monkeys and apes co-occur and overlap with people . Some primates , particularly those traversing large distances in small groups , were most susceptible to nodule worm infection . Additionally , molecular analyses identified three separate groups of nodule worm that could not be distinguished based on microscopic examination of their eggs . One of these groups was found in humans as well as other primates and had not previously been genetically characterized . These results suggest that certain types of nodule worm may be restricted to particular hosts , while others may be transmitted among primates , including humans . Nodule worms are currently thought to be a human health concern only in some West African countries . This research suggests that nodule worms have a broader geographic impact in humans than previously appreciated . |
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Dengue virus ( DENV ) causes more human infections than any other mosquito-borne virus . The current lack of antiviral strategies has prompted genome-wide screens for host genes that are required for DENV infectivity . Earlier transcriptomic studies that identified DENV host factors in the primary vector Aedes aegypti used inbred laboratory colonies and/or pools of mosquitoes that erase individual variation . Here , we performed transcriptome sequencing on individual midguts in a field-derived Ae . aegypti population to identify new candidate host factors modulating DENV replication . We analyzed the transcriptomic data using an approach that accounts for individual co-variation between viral RNA load and gene expression . This approach generates a prediction about the agonist or antagonist effect of candidate genes on DENV replication based on the sign of the correlation between gene expression and viral RNA load . Using this method , we identified 39 candidate genes that went undetected by conventional pairwise comparison of gene expression levels between DENV-infected midguts and uninfected controls . Only four candidate genes were detected by both methods , emphasizing their complementarity . We demonstrated the value of our approach by functional validation of a candidate agonist gene encoding a sterol regulatory element-binding protein ( SREBP ) , which was identified by correlation analysis but not by pairwise comparison . We confirmed that SREBP promotes DENV infection in the midgut by RNAi-mediated gene knockdown in vivo . We suggest that our approach for transcriptomic analysis can empower genome-wide screens for potential agonist or antagonist factors by leveraging inter-individual variation in gene expression . More generally , this method is applicable to a wide range of phenotypic traits displaying inter-individual variation .
Dengue virus ( DENV ) is a mosquito-borne RNA virus of the Flavivirus genus ( family Flaviviridae ) that causes an estimated 390 million human infections annually [1] . Although the first dengue vaccine was recently approved in a few countries [2 , 3] , its potential impact is still uncertain [4] . In the absence of specific therapeutics , dengue prevention is limited to vector control , which can be effective but is difficult to sustain in the long term [5] . DENV exists as four serotypes ( DENV-1 , -2 , -3 and -4 ) that are phylogenetically related and loosely antigenically distinct [6] . DENV has a positive-sense , single-stranded RNA genome that encodes only three structural proteins and seven non-structural proteins . Due to this minimal genetic material , DENV depends on numerous host cellular factors to complete its lifecycle that represent promising targets for the development of antiviral strategies [7 , 8] . Accordingly , recent genome-wide screens identified multiple human and insect factors required for DENV infectivity [9–12] . For example , several endoplasmic reticulum-associated proteins are necessary for Flavivirus infection in both human and insect cells [9 , 12] . Functional validation in vivo in Aedes mosquitoes is an important step of such genome-wide screens because candidate host factors identified in model systems are not necessarily confirmed in more biologically relevant organisms . For instance , when the orthologues of three candidate host factors identified in Drosophila cells were tested in the main DENV vector Aedes aegypti , only one had a conserved function in mosquitoes in vivo [11] . One difficulty associated with in vivo experiments is that multiple tissues can become infected and may display tissue-specific responses [13 , 14] . In the field , mosquitoes acquire DENV infection after feeding on a viremic host . Following the infectious blood meal , DENV infection is initially established in the mosquito midgut before the virus spreads systemically to infect the salivary glands and is eventually released in the saliva , through which it is transmitted to the next host [15] . Anatomical barriers to DENV propagation in Ae . aegypti have been described , namely a midgut infection barrier and a midgut escape barrier [16] . These tissue barriers are quantitative genetic traits controlled by the mosquito genotype [17–19] and specific interactions between mosquito and virus genotypes [20] . Viral genetic determinants [21] , the mosquito RNA interference ( RNAi ) pathway [22 , 23] , and putative receptors [24] have been suggested to mediate these barriers , but overall their molecular nature is still poorly understood [25] . With a few exceptions [26–28] , the specific mosquito genes that modulate DENV infection in the midgut of Ae . aegypti remain to be identified . Earlier functional genomics studies of DENV infection in the Ae . aegypti midgut focused on mosquito innate antiviral immunity [26 , 29–31] , or documented transcriptome-wide patterns of gene expression upon DENV exposure [13 , 14 , 32 , 33] . It is worth noting that all these studies used either reference laboratory strains of Ae . aegypti , such as the Rockefeller and the Liverpool strains , or mosquito lines artificially selected for DENV resistance or susceptibility . Although transcriptomic responses may substantially vary between different Ae . aegypti strains [13 , 34] , laboratory strains are experimentally powerful because their usually high level of inbreeding minimizes inter-individual variation . To further reduce inter-individual variation , most of these earlier studies examined differential gene expression based on pools of mosquitoes . Here , we used an alternative functional genomics approach that takes advantage of inter-individual variation in a field-derived Ae . aegypti population . Using mosquitoes and a DENV isolate originating from Kamphaeng Phet Province in Thailand , we simultaneously examined the transcriptome of 45 individual midguts by RNA sequencing ( RNA-Seq ) following oral DENV exposure . In addition to a conventional pairwise comparison of DENV-infected versus uninfected control midguts , we examined the correlation between individual midgut viral RNA load and gene expression level among DENV-infected midguts . The aim of the correlation analysis was to identify genes modulating midgut infection without being differentially expressed between DENV-infected and uninfected individuals . For instance , a transcript whose average expression is not significantly different between DENV-infected mosquitoes and uninfected controls would go undetected by pairwise comparison . However , the expression level of this transcript could be significantly correlated with viral RNA load within DENV-infected individuals . Our correlation analysis thus identifies this transcript as a candidate . We demonstrated that this approach has two main advantages . First , it led us to the identification of a set of candidate genes that was not detected by pairwise comparison . Second , the sign of the correlation ( i . e . , positive or negative association with viral RNA load ) was used to make a prediction about the agonist or antagonist effect of the gene product on virus infection . Agonist refers to a gene promoting virus replication whereas antagonist refers to a gene impairing virus replication . We used Pearson’s determination coefficient as a simple measure of the linear co-variation between viral RNA load and gene expression . We confirmed the validity of our approach with a candidate gene encoding a sterol regulatory element-binding protein ( SREBP ) . SREBPs are transcriptional regulatory proteins conserved among metazoans that modulate lipid biosynthesis [35] . SREBP was identified by our correlation analysis but not by conventional pairwise comparison . Positive correlation between SREBP expression and DENV RNA load in the midgut was consistent with an agonist effect of this gene . As predicted , SREBP knockdown in vivo resulted in reduced viral RNA load , revealing a previously unknown agonist role of this mosquito gene during early DENV infection of the Ae . aegypti midgut .
We sampled Ae . aegypti mosquitoes from a natural population in Thailand and conducted our experiments within the first ten generations of laboratory colonization . In order to preserve its genetic diversity , the colony was maintained as an outbreeding population with several hundreds of reproducing adults at each generation . To examine the temporal dynamics of midgut infection in individual mosquitoes , we monitored DENV genomic RNA concentration in individual midguts of Ae . aegypti females following exposure to an infectious blood meal containing 1 . 08 x 107 focus-forming units per mL ( FFU/mL ) of blood . This infectious dose was chosen to maximize midgut infection prevalence . During a 10-day time-course experiment , 137 out of 138 tested midguts were positive for DENV RNA ( Fig 1 ) . Based on the presence of undigested blood observed during midgut dissection , blood digestion took up to 4 days ( Fig 1 ) . Lack of significant variation in the midgut viral RNA load measured immediately after blood feeding indicated that female Ae . aegypti ingested similar amounts of DENV ( Fig 1 , 0 hour post virus exposure ) . Viral RNA load in the midgut dropped during the first 6 hours post exposure , then increased exponentially for 3 days before reaching a plateau from 7 to 10 days post exposure . Statistical significance of differences across time points is shown in Fig 1 . Within each time point , midguts displayed inter-individual variation in viral RNA load as early as 6 hours post exposure . For instance , we observed up to 1 , 000-fold and 10 , 000-fold differences in DENV load among individual mosquito midguts on day 1 and day 4 , respectively ( Fig 1 ) . Viral RNA load can be several orders of magnitude higher than infectious titer [36] but we chose to focus on viral RNA load rather than infectious titers for two reasons . First , we were primarily interested in host factors influencing viral replication and viral RNA load is a better proxy for viral replication efficiency than infectious titer . The latter is a composite phenotype that can be influenced by several other steps than viral replication such as viral particle assembly and maturation . Second , the first few days of mosquito infection by arboviruses are characterized by a so-called eclipse phase during which infectious particles are undetectable [37] . Therefore , our results show that midgut viral RNA load varied significantly not only over the time course but also among individual mosquitoes at a given time point . We next investigated whether this individual variation in viral RNA load could be leveraged to identify novel host factors that modulate midgut infection . To identify mosquito genes contributing to natural inter-individual variation in midgut viral RNA load , we used a non-conventional approach for transcriptome analysis . We reasoned that correlating viral RNA load with gene expression at the inter-individual level among DENV-infected mosquitoes could provide information that would be missed by pairwise comparison between DENV-infected and uninfected individuals . To validate our method , we focused on the exponential growth phase of DENV midgut infection ( Fig 1 ) . Although successful infection of the midgut is essential for subsequent virus dissemination and transmission , DENV host factors during midgut infection remain largely unknown . Viral dissemination from the midgut to other tissues typically begins around 4 days post exposure [38] and it was confirmed in this mosquito population . We focused on day 1 and day 4 post exposure because they displayed the largest inter-individual variation in viral RNA load ( Fig 1 ) . Forty-five individual midguts collected either 1 or 4 days after virus exposure were used for transcriptome analysis by RNA-Seq . They consisted of 16 DENV-infected midguts collected 1 day post exposure , 17 DENV-infected midguts collected 4 days post exposure and 6 control midguts collected at each time point from individuals fed on uninfected blood . The mean number of raw sequencing reads per library that mapped to Ae . aegypti transcripts was significantly higher on day 1 than on day 4 post exposure ( ANOVA: P < 0 . 01 ) , presumably because the digestion process was on-going on day 1 but not on day 4 ( S1 Fig ) . Therefore , we analyzed day 1 and day 4 midgut transcriptomes separately in all subsequent statistical analyses . However , the total number of mapped raw reads per library did not vary significantly between DENV-infected and control midguts ( ANOVA: P = 0 . 9 ) . A total of 13 , 843 unique mosquito transcripts were detected considering both time points together . To correct for multiple testing , we calculated a false discovery rate ( FDR ) according to the Benjamini-Hochberg procedure [39] . Based on an FDR threshold of 0 . 1 , we identified 273 Ae . aegypti candidate transcripts by either pairwise comparison or correlation analysis ( S2–S5 Tables ) . Only four transcripts were detected by both methods across all time points ( Fig 2A ) . By pairwise comparison , 230 transcripts were differentially expressed between DENV-infected and control midguts ( Fig 2A ) . All of these transcripts were identified 1 day post exposure ( Fig 2B , blue and yellow dots ) . The correlation analysis identified 43 candidate transcripts whose expression was correlated with midgut viral RNA load ( Fig 2A ) . The majority of those transcripts were identified 4 days post exposure ( Fig 2B , red dots ) . Among the four transcripts in common between the two methods , two ( AAEL010168 and AAEL010169 ) were both correlated to viral RNA load and differentially expressed at the same time point ( day 1 ) whereas the two others ( AAEL000293 and AAEL017516 ) were detected by the pairwise comparison on day 1 and by the correlation analysis on day 4 ( Fig 2B , yellow dots ) . According to gene ontology ( GO ) classification at the biological process level , most of the candidate transcripts belong to metabolism , transcription/translation , oxidation-reduction and proteolysis categories , irrespective of the time point and analysis strategy . Candidates identified by pairwise comparison include transcripts encoding several zinc-finger proteins and immune-related transcripts previously associated with DENV infection in Ae . aegypti such as the transcription factor REL1A [30] and the Complement-related factor AaMCR [31] ( S2 Table ) . Several candidates identified by pairwise analysis are genes involved in lipid metabolism , such as the 85-kda calcium-independent phospholipase A2 ( AAEL012835 ) , a ceramidase ( AAEL007030 ) , a lipase ( AAEL001837 ) , a Niemann-Pick-type C2 protein ( AAEL009953 ) [27] and a regulator of the Wnt pathway ( AAEL004858 ) ( S2 Table ) . The correlation analysis identified 43 candidate transcripts , of which 39 were not differentially expressed between DENV-infected and uninfected control midguts at any of the time points . The expression level of these genes was linearly associated with midgut viral RNA load , either positively ( n = 18 ) or negatively ( n = 21 ) . Because of the statistical association between viral RNA load and gene expression , we hypothesized that the sign of the correlation ( i . e . , positive or negative ) could predict the effect of the candidate transcript on DENV infection ( i . e . , agonist or antagonist ) . The correlation analysis detected several immune-related genes encoding , for instance , a serine protease inhibitor ( AAEL008364 ) , a thioester-containing protein 3 ( AAEL008607 ) or a leucine-rich immune protein ( AAEL008658 ) . Expression of immune-related genes was most often negatively correlated with viral RNA load 4 days post virus exposure ( S4 Table ) . Conversely , the expression of two genes involved in lipid homeostasis was positively correlated with viral RNA load 4 days post exposure . One encodes a fatty acid synthase ( AAEL001194 ) and the other a sterol regulatory element-binding protein ( SREBP , AAEL010555 ) . To demonstrate the value of our correlation analysis and to provide the proof of concept that the sign of the correlation could be used to make a functional prediction relative to virus infection , we chose gene AAEL010555 ( SREBP ) for functional validation in vivo because of its known role in other viral infections [40–44] . SREBP was not differentially expressed between DENV-infected and control midguts at any of the two time points ( Fig 3A ) . However , midgut viral RNA load and SREBP expression were positively correlated on day 4 based on our FDR significance threshold of 0 . 1 ( Fig 3B ) . The correlation was stronger ( r = 0 . 70; P = 0 . 004 ) when the three individual midguts with viral loads >107 RNA copies were excluded , consistent with a differential relationship at low versus high viral loads . We predicted that the positive correlation observed for this gene 4 days post exposure indicated an agonist role during midgut infection , and therefore that SREBP knockdown during DENV midgut infection would result in reduced viral RNA load . To test the putative agonist role of SREBP during midgut infection by DENV , we used RNAi-mediated gene knockdown assays in vivo ( Fig 4A ) . Double-stranded RNA ( dsRNA ) targeting SREBP ( dsSREBP ) was injected into the thorax of Ae . aegypti females to reduce SREBP expression . Control mosquitoes were injected with the same amount of dsRNA targeting green fluorescent protein ( dsGFP ) . Three days later , we offered mosquitoes a DENV infectious blood meal and quantified viral RNA load in individual midguts by quantitative RT-PCR 1 and 4 days post exposure . SREBP knockdown efficiency was 99 . 7% , 79 . 3% and 47 . 0% on day 0 , day 4 and day 7 post DENV exposure , respectively ( S3A Fig ) . We observed a significant drop in midgut viral RNA load following SREBP knockdown on day 4 post DENV exposure ( Fig 4B ) . There was a 50% reduction in midgut viral RNA load in mosquitoes injected with dsSREBP relative to mosquitoes injected with dsGFP . To further confirm the role of SREBP as a DENV agonist , we performed RNAi-mediated gene knockdown assays in vivo in a different mosquito population . The field-derived Ae . aegypti population used for the transcriptomic analysis was originally collected in Thailand . We repeated the experiment in another field-derived mosquito population from Cambodia and also observed a statistically significant reduction of viral RNA load in the midgut following SREBP silencing ( S2 Fig ) . In the control groups , the mosquito population from Cambodia had significantly higher prevalence ( P = 0 . 0465 ) but lower viral RNA load ( P < 0 . 0001 ) at day 4 than the population from Thailand . This result is consistent with the agonist role of SREBP in DENV replication regardless of the mosquito geographical origin or intrinsic level of susceptibility . In addition to SREBP , RNAi-mediated knockdown was performed against two control genes already known to modulate DENV midgut infection in Ae . aegypti , Argonaute-2 ( AAEL017251 , Ago2 ) , and Cactus ( AAEL000709 ) [29 , 30] . For all target genes ( SREBP , Ago2 , Cactus ) , a significant decrease in gene expression was measured on the day of DENV exposure although knockdown efficiency varied between target genes at later time points ( S3 Fig ) . Knockdown of all target genes did not consistently affect the blood-feeding rate , which varied according to the interaction between treatment and experiment ( S4 Fig ) . As expected , Ago2 knockdown resulted in a statistically significant 42% increase of midgut viral RNA load 4 days post exposure . Note this could be an underestimation , due to the negative feedback loop of using a RNAi-mediated gene silencing assay to knockdown a gene involved in the RNAi pathway . Likewise , Cactus knockdown was associated with a statistically significant 81% decrease of DENV load in the midgut 4 days post exposure ( Fig 4B ) . These results were repeatable in at least two separate experiments , thereby validating our gene knockdown assay . Mosquitoes injected with dsRNA against Ago2 or Cactus died faster than dsGFP-injected controls ( Cox model: P < 0 . 0001 ) , whereas no significant difference was detected in the survival of mosquitoes injected with dsSREBP or dsGFP until day 7 post injection ( Cox model: P = 0 . 3 ) ( Fig 4C ) . Unlike viral RNA load , the proportion of DENV-infected midguts 4 days post exposure was not influenced by the knockdown of any of the three target genes ( Fig 4D ) .
We used a non-conventional analysis of transcriptomic data to identify new DENV host factors during early midgut infection in a field-derived mosquito population . We observed substantial variation in the individual midgut viral RNA load following oral exposure to the same infectious dose of DENV . This variation presumably results primarily from genetic differences because the viral RNA load measured in midguts on day 0 was almost equal among individuals and all environmental conditions were standardized . Instead of erasing this variation by pooling individual samples prior to transcriptomic analysis , we hypothesized that this variation contained valuable information that could be leveraged . Our approach is expected to identify genes whose expression does not necessarily differ between DENV-infected and uninfected mosquitoes ( i . e . , that go undetected by conventional pairwise comparison ) but is linearly correlated with viral RNA load . It is worth noting that we used viral RNA load as a proxy for viral replication efficiency in the midgut , which does not directly translate into the level of vector competence . Indeed , viral RNA load in the midgut may not correlate with the probability of virus transmission ( e . g . , [45] ) . As a consequence , the candidate genes that we identified do not necessarily meet the requirements to be considered as potential effectors in genetic-based vector control strategies but mainly as host factors agonist or antagonist of viral replication at early time points following infection . The 230 candidate genes identified by pairwise comparison between DENV-infected and control midguts were detected 1 day post exposure . This indicates that the strongest modulation of midgut gene expression occurs early upon infection . This reinforces observations from previous transcriptomic studies that detected the highest number of differentially expressed genes from 18 to 24 hours post DENV exposure [14 , 32] . The absence of differentially expressed genes detected 4 days post exposure in our study could result from the use of individual transcriptomes , which reduces the potential bias due to outliers ( i . e . , genes with extreme expression levels ) that may often exist in mosquito pools . The use of individual midgut transcriptomes allowed identification of 39 genes whose expression was correlated with viral RNA load , in the absence of differential expression between DENV-infected and control individuals . Although this is less than the 226 candidate genes only identified by conventional pairwise comparison , the sign of the correlation allows a strong prediction to be made about the effect of these additional candidates on DENV infection . Only four candidates were detected by both methods , indicating limited overlap between the two analyses and emphasizing their complementarity . GO classification did not reveal cellular or molecular functions specific to one type of analysis , but numerous genes in the current annotation of the Ae . aegypti reference genome remain anonymous and lack a predicted function . Improved genome annotations in the future may help to determine whether our correlation analysis and conventional pairwise comparison identify fundamentally different classes of genes . Based on the available annotations , numerous candidate genes were related to lipid metabolism regardless of the analysis . Identification of candidate genes involved in lipid metabolism is consistent with previous studies [13 , 14 , 27 , 46] . To confirm the validity of our correlation approach and to test our hypothesis that the sign of the correlation was predictive of the agonist or antagonist effect of the gene , we focused on a gene encoding a sterol regulatory element-binding protein ( SREBP ) . SREBP was only detected by the correlation analysis and its expression was positively correlated to viral RNA load , suggesting that this gene promotes virus infection . We confirmed this prediction by RNAi-mediated gene knockdown assays in vivo . SREBP genes are conserved among metazoans . Humans harbor three SREBP isoforms whereas only one SREBP homologue was identified in Drosophila melanogaster ( HLH106 ) and in Ae . aegypti [47 , 48] . SREBPs are membrane-bound transcription factors regulating cholesterol and fatty acid synthesis [49] . In our field-derived Ae . aegypti population , SREBP was not differentially expressed between DENV-infected and uninfected control midguts , in contrast with an earlier study that reported down-regulation of this gene after DENV exposure in pools of mosquitoes from a laboratory strain of Ae . aegypti [14] . Whether this discrepancy results from differences between mosquito strains , sampling strategy ( pooling versus individual transcriptomes ) , or other differences in the experimental strategy ( virus strain , midgut versus whole body , etc . ) is unknown . Studies in mice and flies indicate that SREBP is likely an essential gene during early development . SREBP knockout increased embryonic lethality in mice , and Drosophila SREBP mutants died at the larval stage while dietary supplementation with fatty acids rescued mutants to adulthood [35 , 50 , 51] . An earlier transcriptomic study in Ae . aegypti reported that SREBP expression was up regulated following blood uptake [52] , which is in line with the fact that lipids from the blood meal are required for oocyte maturation [53] . In our two Ae . aegypti population , SREBP knockdown did not significantly impact short-term adult survival . Conversely , we observed a 35% reduction in mosquito survival within 24 hours following Ago2 knockdown , and a 15% reduction in mosquito survival associated with Cactus knockdown following blood feeding . The fitness cost observed in both control treatments could have resulted from immune impairment or from disruption of other processes regulated by the RNAi and Toll pathways . Our results demonstrated that SREBP is an agonist factor during early DENV infection of the Ae . aegypti midgut . Although the underlying mechanism remains to be elucidated , SREBP knockdown was associated with a 53 . 8% decrease of DENV RNA load in the midgut of our Ae . aegypti population from Thailand ( and 26 . 9% in the population from Cambodia; S2A Fig ) . Knocking down Ago2 , a critical component of the mosquito antiviral response , resulted in a similar effect size ( a 42% increase ) in midgut viral RNA load . However , the relatively weak correlation between SREBP expression and viral RNA load indicates that other host factors determine the efficiency of viral replication . Our finding is consistent with the central role of lipid homeostasis during viral infections . Lipids are required for efficient replication of numerous viruses in mammalian cells including DENV [54 , 55] . SREBP proteins are transcription factors that regulate a variety of genes involved in lipid synthesis [56] . Hepatitis C virus , a member of the Flaviviridae family , increases the amount of lipid droplets through a DDX3X-IKK-α-SREBP pathway that allows assembly of viral particles in human cells [57] . DENV infection also increases the number of lipid droplets in mammalian cells [58] and recently lipid droplets were suggested to play a role during DENV infection in Ae . aegypti [46] . Human cytomegalovirus , hepatitis B virus and hepatitis C virus have been shown to activate SREBP , which can result in an increase in lipid synthesis to promote viral infection [40–44] . In insects , Drosophila C virus replication is attenuated in SREBP null mutant flies [59] . Thus , our finding that SREBP is a host factor promoting DENV infection in Ae . aegypti adds to the accumulating evidence for a widespread agonist role of this gene during viral infections . Our results illustrate how transcriptomic data obtained at the individual level can enhance functional genomics studies and improve our understanding of host-pathogen interactions . Based on transcriptome sequencing of individual mosquito midguts , we took advantage of inter-individual variation in gene expression and midgut viral RNA load by using their co-variation as an indication of a functional relationship . The candidate genes that we identified by this method should be useful for other investigators in the field . Identification of DENV host factors in vivo paves the way for future mechanistic studies and may ultimately contribute to the development of novel antiviral strategies . More generally , our transcriptomic approach should be of interest in other organisms because it is applicable to virtually any continuous trait with inter-individual variation .
The Institut Pasteur animal facility has received accreditation from the French Ministry of Agriculture to perform experiments on live animals in compliance with the French and European regulations on care and protection of laboratory animals . This study was approved by the Institutional Animal Care and Use Committee at Institut Pasteur under protocol number 2015–0032 . Mosquito cells ( Ae . albopictus C6/36 ) were maintained in Leibovitz's L-15 medium ( Life Technologies ) supplemented with 10% foetal bovine serum ( FBS , Life Technologies ) , 1% non-essential amino acids ( Life Technologies ) and 0 . 1% Penicillin-Streptomycin ( Life Technologies ) at 28°C . DENV-1 isolate KDH0030A was originally derived in 2010 from the serum of a dengue patient attending Kamphaeng Phet Provincial Hospital , Thailand [20] . Informed consent of the patient was not necessary because the virus isolated in laboratory cell culture was no longer considered a human sample . DENV-1 isolate was passaged three times in C6/36 cells prior to its use in this study and full-length consensus genome sequence is available from GenBank under accession number HG316482 . Virus stock was prepared in C6/36 cells as previously described [60] and a mock-inoculated flask was prepared simultaneously as a negative control . DENV-1 infectious titer was measured in C6/36 cells using a standard focus-forming assay ( FFA ) as previously described [60] . Most experiments were carried out with Aedes aegypti mosquitoes derived from a wild population originally sampled in 2013 in Thep Na Korn , Thailand and took place within 10 generations of laboratory colonization . One experiment was carried out with Ae . aegypti mosquitoes derived from a wild population originally sampled in 2015 in Phnom Penh City , Cambodia and took place 8 generations after laboratory colonization . Experimental infections were carried out as previously described [60] . Briefly , four- to seven-day-old females were offered a washed rabbit erythrocyte suspension mixed 2:1 with pre-diluted DENV-1 KDH0030A viral stock and supplemented with 10 mM ATP ( Sigma ) , to reach an expected titer of 107 FFU/mL . A control blood meal was prepared with the supernatant of mock-inoculated C6/36 cells . Mosquitoes were allowed to blood feed for 30 min through a pig-intestine membrane using an artificial feeder ( Hemotek Ltd , Blackburn , UK ) set at 37°C . Samples of the blood meals were saved and stored at -80°C for further titration . Fully engorged females were incubated at 28°C , 70% relative humidity and under a 12-hour light-dark cycle in 1-pint cardboard cups ( 20–30 females per cup , at least 2 cups/condition ) with permanent access to 10% sucrose . Upon harvest , females were freeze-killed at -80°C and transferred on ice . Midguts were dissected in 1X phosphate-buffered saline ( PBS ) under 10X magnification . Forceps were decontaminated between each individual using Surfa’Safe ( Anios ) to prevent cross contamination . Individual midguts were immediately homogenized for 30 sec at 6 , 000 rpm in tubes ( VWR ) containing ~20 1-mm glass beads ( BioSpec ) in 800 μL of TRIzol ( Life Technologies ) and stored at -80°C . Samples were thawed at room temperature ( 20–25°C ) and 150 μL of chloroform ( Sigma-Aldrich ) were added followed by vortexing for 30 sec . After a 5-min incubation at 4°C , samples were centrifuged at 4°C for 15 min at 14 , 000 rpm . The upper aqueous phase was harvested and transferred to a cold tube containing 400 μL of 2-propanol ( Sigma-Aldrich ) supplemented with 1 μL GlycoBLUE ( Ambion , Life Technologies ) . Samples were incubated at -20°C overnight and centrifuged at 4°C for 15 min at 14 , 000 rpm to pellet RNA . The pellet was washed with 800 μL of 70% ice-cold ethanol ( Sigma-Aldrich ) at 4°C for 10 min at 14 , 000 rpm , and allowed to dry for 10 min at 37°C . Total RNA was resuspended in 6 μL , of which 1 μl was diluted into 9 μl of RNase-free water for DENV quantification by RT-qPCR , while the remaining 5 μl were used for transcriptome sequencing . All the samples were stored at -80°C until use . DENV RNA was quantified using NS5-specific primers and TaqMan probe ( S1 Table ) with SuperScript III Platinum One-Step RT-qPCR kit ( Life Technologies ) and serial dilutions of total DENV RNA of known concentration ( from 109 to 101 DENV RNA copies/μL ) as a standard , as previously detailed [60] . Each RT-qPCR plate included negative controls derived from uninfected samples and a no template control . The RT-qPCR results were validated if the slope of the standard curve was between -3 . 33 and -3 . 65 , corresponding to 90–100% efficiency . Individual midgut libraries were prepared from total RNA extracts from individual midguts after quality control with a Bioanalyzer RNA 6000 kit ( Agilent ) . Purification and fragmentation of mRNA , cDNA synthesis , end-repair , A-tailing , Illumina indexes ligation and PCR amplification were performed using TruSeq RNA Sample Prep v2 ( Illumina ) followed by cDNA quality check by Bioanalyzer DNA 1000 kit ( Agilent ) . Libraries were diluted to 10 pM after Qubit quantification ( ThermoFisher ) , loaded onto a flow cell , clustered with cBOT ( Illumina ) . Single-end reads of 51 nucleotides in length were generated on a HiSeq2000 sequencing platform ( Illumina ) . Sequencing reads with a quality score <30 were trimmed using Cutadapt [61] . Passing-filter reads were mapped to Ae . aegypti transcripts ( AaegL3 . 1 , http://vectorbase . org ) using Bowtie2 [59] with the “sensitive” option . They were processed with the Samtools suite [62] to create of a matrix of raw counts used for gene expression analysis . The RNA-Seq data were deposited to SRA under accession number PRJNA386455 ( https://www . ncbi . nlm . nih . gov/bioproject/386455 ) . All analyses of midgut transcript expression were performed in R ( v . 3 . 2 . 3 , http://www . r-project . org/ ) using the DESeq2 package v . 1 . 8 . 0 [63] . Following normalization of raw read counts by the relative log expression method implemented in DESeq2 [64] , normalized read counts were considered separately according to time post DENV exposure . Two complementary analyses were run for each time point . First , a pairwise comparison was used to identify genes differentially expressed between DENV and control conditions . Differential expression was evaluated using the DESeq2 generalized linear model with its default parameters ( activated outlier detection and independent filtering ) . Statistical significance of differential expression was determined based on a 10% false discovery rate ( FDR ) . Second , a correlation analysis in DESeq2 measured the strength of the linear relationship between log2-transformed normalized read counts and the log10-transformed viral RNA load per midgut in DENV-1 samples only . Statistical significance of the linear relationship was determined based on a 10% FDR threshold . Genes with no detectable or very low expression ( i . e . , median < 50 normalized read counts ) were filtered out after the statistical analysis . DNase-treated RNA purified from a pool of Ae . aegypti midguts was used to produce a PCR template for dsRNA synthesis . Briefly , gene-specific PCR primers for dsRNA preparation were designed ( S1 Table ) using E-RNAi web-service v . 3 . 2 [65] with 21-bp length for siRNA specificity prediction and default parameters otherwise . A 500-bp fragment of the GFP gene was amplified with specific primers ( S1 Table ) and cloned into pCRII TOPO vector ( Life Technologies ) . A T7 promoter was incorporated into the PCR amplicon with tagged primers ( S1 Table ) . PCR was conducted in a 25-μL reaction containing 2 μL of template cDNA , 5 μM of each T7 primer , 1 . 5 mM MgCl2 , 200 μM of dNTP mix and 0 . 5 unit of native Taq polymerase ( ThermoFisher ) as follows: 3 min at 95°C , 40 cycles of 1 min at 94°C , 1 min at 58°C , 1 min at 72°C , and a final step of 10 min at 72°C . Synthesis of dsRNA was performed overnight at 37°C using MEGAscript RNAi kit ( Life Technologies ) with 1 μg of PCR product purified by MinElute Kit ( Qiagen ) . After column purification , 1:10 ( vol/vol ) 3M sodium acetate pH 5 . 5 ( Life Technologies ) and 1:2 . 5 ( vol/vol ) 100% ethanol ( Sigma-Aldrich ) were added , followed by overnight precipitation at -80°C . After centrifugation for 30 min at 14 , 000 rpm , the dsRNA pellet was washed with 800 μL of 100% ethanol , followed by 15 min centrifugation at 14 , 000 rpm . The dsRNA pellet was air-dried , resuspended in RNase-free water , adjusted to a concentration of 7 μg/μL with a Nanodrop spectrophotometer , and stored at -20°C until use . Four- to 7-day-old females were ice-chilled and intrathoracically injected with 2 x 69 nL of a 7 μg/μL dsRNA ( ~1 μg dsRNA ) from the gene of interest using a Nanoject-II device ( Drummond ) . Control mosquitoes were injected with dsGFP . Mosquitoes were allowed to recover from injection for 2 days before being offered an artificial DENV-1 blood meal as described above . Both dsCACTUS and dsAGO2 were used as controls for DENV-1 load modulation in the midgut . Total RNA from individual midguts was reverse transcribed into cDNA in a reaction mixture containing 5 nM random hexamers , 0 . 2 mM of dNTP mix , 10 μL of template and RNase-free water up to 14 . 5 μL . After incubation at 65°C for 10 min , samples were chilled on ice for 5 min . For each reaction , 4 μL of 5X First-Strand buffer , 1 μL of 0 . 1 mM Dithiothreitol , 40 units of RNase-OUT and 100 units of MML-V reverse transcriptase ( Life Technologies ) were added to a final volume of 20 μL . After 10 min at 25°C , cDNA synthesis was conducted at 37°C for 50 min and terminated at 70°C for 15 min . cDNA samples were stored at -20°C until use . Gene expression was assayed by relative quantitative PCR ( qPCR ) using a LightCycler96 machine ( Roche ) . The qPCR mix contained 200 nM of each primer , 10 μL of 2X SYBR-green I Master Mix ( Roche ) and PCR grade water to 18 μL , with 2 μL of cDNA template to a final volume of 20 μL . Settings were an initial denaturation step of 5 min at 95°C , followed by 40 cycles of 10 sec at 95°C , 20 sec at 60°C and 10 sec at 72°C . Melting curve were used to confirm the absence of non-specific PCR amplicons using the following program: 5 sec at 95°C , 60 sec at 65°C and continuous fluorescence acquisition up to 97°C with a ramp rate 0 . 2°C/sec . Relative expression was calculated as 2 - ( Cqgene-Cqrp49 ) , using the Ae . aegypti ribosomal protein-coding gene rp49 ( AAEL003396 ) for normalization . Infection prevalence was analyzed as a binary response variable ( 0 = absence , 1 = presence ) using logistic regression . Continuous response variables were analyzed using analysis of variance ( ANOVA ) . Explanatory variables included time point ( ordinal ) , experimental condition ( nominal ) and experiment ( nominal ) . Viral RNA load was log10-transformed and RNA-Seq normalized read counts were log2-transformed prior to analysis . Midgut gene expression normalized by rp49 ( referred to as expression ) was analyzed without log-transformation . Models including interactions were analyzed with type-III ANOVA , whereas models without interactions were analyzed with type-II ANOVA . Interactions terms were removed from the final model if they were not statistically significant ( P > 0 . 05 ) . When the ANOVA assumption of normal error distribution could not be met , a non-parametric Wilcoxon test was performed for pairwise comparisons . Multiple pairwise comparisons were performed with t-tests followed by Holm correction for multiple testing [66] . A Cox regression model including dsRNA injection and DENV exposure as covariates was used to compare mosquito survival across treatments [67] . This model is appropriate to analyze the effect of several variables on the time it takes for an event to happen . Statistical analyses were computed in the R environment and plotted with the R package ggplot2 ( v . 2 . 2 . 0 ) [68] . | Dengue virus ( DENV ) is transmitted among humans by mosquitoes , primarily Aedes aegypti . Despite their potential as targets to interrupt DENV transmission , mosquito genes that modulate infection in Ae . aegypti remain largely unknown . Using a field-derived Ae . aegypti population , we observed substantial variation in DENV load in the mosquito midgut . We hypothesized that this inter-individual variation contained valuable information to identify host factors modulating viral infection . We analyzed single-midgut transcriptomes using an approach that takes advantage of inter-individual variation among infected mosquitoes . We demonstrated the added value of this method by identifying novel host factors during early DENV infection of Ae . aegypti that went undetected by conventional pairwise comparison between DENV-infected and control groups . We confirmed the agonist role of a candidate gene encoding a sterol regulatory element-binding protein , which underlines the importance of lipid metabolism during DENV infection of the mosquito midgut . Our method for transcriptomic analysis can enhance genome-wide screens for host factors by taking advantage of inter-individual variation . It is also applicable to a wide range of phenotypic traits displaying inter-individual variation . |
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Regulated degradation of proteins by the 26S proteasome plays important roles in maintenance and signalling in eukaryotic cells . Proteins are marked for degradation by the action of E3 ligases that site-specifically modify their substrates by adding chains of ubiquitin . Innate immune signalling in plants is deeply reliant on the ubiquitin-26S proteasome system . While progress has been made in understanding substrate ubiquitination during plant immunity , how these substrates are processed upon arrival at the proteasome remains unclear . Here we show that specific members of the HECT domain-containing family of ubiquitin protein ligases ( UPL ) play important roles in proteasomal substrate processing during plant immunity . Mutations in UPL1 , UPL3 and UPL5 significantly diminished immune responses activated by the immune hormone salicylic acid ( SA ) . In depth analyses of upl3 mutants indicated that these plants were impaired in reprogramming of nearly the entire SA-induced transcriptome and failed to establish immunity against a hemi-biotrophic pathogen . UPL3 was found to physically interact with the regulatory particle of the proteasome and with other ubiquitin-26S proteasome pathway components . In agreement , we demonstrate that UPL3 enabled proteasomes to form polyubiquitin chains , thereby regulating total cellular polyubiquitination levels . Taken together , our findings suggest that proteasome-associated ubiquitin ligase activity of UPL3 promotes proteasomal processivity and is indispensable for development of plant immunity .
The ubiquitin-26S proteasome system ( UPS ) plays an essential cellular role in selective degradation of proteins that are short-lived or damaged . Degradation of proteins is mediated by an enzymatic cascade in which a small and highly conserved ubiquitin molecule is covalently attached to the substrate . Typically an ubiquitin-activating E1 enzyme forms a high-energy thioester bond to an ubiquitin adduct , which is then transferred onto the active site of an ubiquitin conjugating E2 enzyme . In partnership with an E3 ligase that recruits a specific substrate , the E2 enzyme facilitates formation of an isopeptide bond between the ε-amino group of a lysine residue within the substrate and the carboxy-terminal group of ubiquitin . Reiterations of this reaction cycle result in subsequent ubiquitin molecules being similarly attached to internal lysines of the preceding ubiquitin moiety , thereby generating a polyubiquitin chain on the substrate [1 , 2] . Lysine 48-linked chains of four or more ubiquitins show high affinity for ubiquitin receptors within the 19S regulatory cap of the proteasome [3] . Substrate degradation involves its unfolding by chaperone activity of the 19S particle , cleavage and release of the polyubiquitin chain for recycling , and subsequent threading of the unfolded substrate into the 20S subunit of the proteasome , a barrel-shaped multi-catalytic proteinase [4] . In comparison to other eukaryotes , plant genomes often encode for a disproportionally large number of genes related to the ubiquitin-26S proteasome system . Particularly E3 ligases are overrepresented , with the Arabidopsis genome , for example , encoding for over 1 , 400 different predicted E3 ligase components [5] . Accordingly , protein ubiquitination plays vital roles in numerous aspects of plant biology . Indeed , genetic analyses have shown that many developmental and environmental response pathways exhibit a high degree of dependency on components of the ubiquitin-mediated proteasomal degradation pathway [5–7] . Over the last decade it has become increasingly clear that plant immune responses are particularly dependent on ubiquitin-mediated protein degradation [8–11] . Basal resistance as well as race-specific pathogen resistance triggered by intracellular NLR ( nucleotide-binding/leucine-rich repeat ) immune receptors was compromised by mutation of UBA1 , one of two ubiquitin-activating E1 enzymes in Arabidopsis [12] . Similarly , a screen for ubiquitin conjugating E2 enzymes in tomato revealed important roles for a subset of these enzymes in both local immunity and pathogen effector-induced suppression of immune responses [13] . Furthermore , various E3 ligases of the RING and Plant U-box ( PUB ) types have been identified to play both positive and negative roles in orchestration of plant immune responses [8–11] . Whereas several PUB ligases regulate signalling by pathogen pattern recognition receptors , RING-type E3 ligases have been shown to regulate the proteins levels of NLR immune receptors . Levels of the NLR receptors SNC1 and RPS2 are regulated by the RING-type modular SCFCPR1 ( i . e . SKP1/Cullin1/F-box ) E3 ligase in which the F-box protein , CPR1 ( constitutive expressor of pathogenesis-related ( PR ) genes 1 ) , functions as the substrate adaptor that recruits these NLR receptors [14 , 15] . Failure to degrade these and other NLR receptors can lead to their excessive accumulation , which is associated with spontaneous cell death in absence of pathogen threat [16–21] , emphasising the importance of E3 ligases in cellular decisions of life and death . Ubiquitination also plays key roles in signalling by the immune hormone salicylic acid ( SA ) . Upon pathogen recognition SA accumulates in both local and systemic tissues where it induces profound changes in gene expression to prioritise immune responses over other cellular functions [22] . SA-induced transcriptional reprogramming is mediated by the transcription coactivator NPR1 ( nonexpressor of PR genes ) , a master regulator of plant immunity [23] . Mutation of NPR1 renders plants completely insensitive to SA and consequently defective in local and systemic immune responses [24–27] . Interestingly , transcription coactivator activity of NPR1 is regulated by its signal-induced degradation in the nucleus . In absence of pathogen threat , NPR1 activity is continuously restricted by proteasome-mediated clearance from the nucleus , thereby preventing untimely immune gene expression [28] . Instead of stabilising NPR1 , unexpectedly SA was found to facilitate recruitment of NPR1 to a modular multi-subunit Cullin-RING-Ligase 3 ( CRL3 ) [28 , 29] . Importantly , CRL3-mediated ubiquitination and turnover of NPR1 was necessary for the SA-induced transcriptional activation of its target genes . Taken together , these findings underline the importance of the ubiquitin-26S proteasome system in regulating diverse aspects of plant immune signalling . Upon arrival at the proteasome , ubiquitinated substrates may be extensively remodelled by various proteasome-associated ubiquitin chain modifying enzymes , including ubiquitin ligases of the HECT-type family [30 , 31] . This family of ligases utilise a conserved cysteine residue in the HECT domain that forms a covalent thioester bond with ubiquitin before it is transferred onto the substrate . The ubiquitin remodelling activities of some HECT-type ligases are thought to increase proteasome processivity [32–35] . Given the indispensable roles protein ubiquitination plays in plant immunity , we investigated if HECT-type ubiquitin ligases are involved in proteasome-mediated degradation during immune signalling . Here we report that specific HECT-type ubiquitin ligases of the Ubiquitin Protein Ligase ( UPL ) family regulate SA-mediated plant immune signalling . In particular we show that UPL3 associated with proteasomal degradation pathway components and provided the proteasome with ubiquitin ligase activity , which was necessary for large scale SA-induced transcriptional reprogramming and immunity . These data suggest that UPL3 plays a vital role in promoting immune-related proteasomal processivity .
The Arabidopsis UPL family consists of 7 members that all contain a C-terminal HECT domain that accepts ubiquitin from an E2 conjugating enzyme and then transfers it to the target substrate . N-terminal to the HECT domain , UPLs contain different interaction motifs , including ubiquitin-associated ( UBA ) , ubiquitin-like ( UBL ) and ubiquitin-interacting motifs ( UIM ) , armadillo repeats ( ARM ) , and IQ calmodulin and C-type lectin binding motifs ( Fig 1 ) [36] . As calcium and calmodulin have been implicated in plant defence[37] , we first explored if IQ calmodulin binding motif-containing UPL6 and UPL7 proteins play a role in plant immune responses . We generated upl6 and upl7 knock-out mutants ( S1 Fig ) and infected these plants with a low dosage of the bacterial leaf pathogen Pseudomonas syringae pv . maculicola ( Psm ) ES4326 . At this dosage wild-type plants were resistant to this pathogen , while the SA-insensitive npr1 mutant displayed enhanced disease susceptibility ( Fig 2A ) . Mutant upl6 and upl7 plants exhibited similar levels of resistance to Psm ES4326 as the wild type . Moreover , upl6 upl7 double mutants also effectively suppressed the growth of this pathogen , indicating that UPL6 and UPL7 do not regulate basal resistance responses . To assess if UPL6 and UPL7 regulate induced resistance responses , plants were treated with SA prior to infection with Psm ES4326 . Whereas SA induced resistance in wild-type plants , it failed to activate defences in mutant npr1 plants which remained susceptible ( Fig 2B ) . Both upl6 and upl7 single mutants as well as upl6 upl7 double mutant plants displayed normal SA-induced resistance to Psm ES4326 ( Fig 2B and 2D ) . This was accompanied by normal levels of SA-induced expression of immune marker genes in single mutants ( Fig 2C ) , while the upl6 upl7 double mutant was moderately compromised in expression of SA-responsive PR genes ( Fig 2E ) . These data suggest that UPL6 and UPL7 ubiquitin ligases play only minor roles in SA-mediated immune responses . Next we investigated if UPL ubiquitin ligases with ubiquitin-related domains were involved in orchestrating immune responses . UPL1 and UPL2 are closely related , containing both UBA and UIM signatures , whereas UPL5 harbours an ubiquitin domain ( Fig 1 ) . We selected knockout mutants for each ( S1 Fig ) and infected these plants with Psm ES4326 . At a low infection dosage all three mutants exhibited resistance responses , whereas control npr1 mutants showed the expected disease susceptible phenotype ( Figs 3A and S2A ) . In some bioassays upl5 allowed slightly lower growth of Psm ES4326 ( Fig 3A ) , but this was inconsistent between assays and did not occur at higher inoculation dosages ( Fig 3B ) . Therefore we conclude that upl1 , upl2 and upl5 display relatively normal basal resistance responses . To examine if these UPL ligases regulate induced resistance as activated by SA , plants were treated with SA prior to infection with Psm ES4326 . While SA treatment induced immunity against this pathogen in wild-type and upl2 plants , it failed to enhance resistance in both upl1 and upl5 mutants , which instead resembled the SA-insensitive npr1 mutant in this respect ( Figs 3B and S2B ) . To assess if UPL1 and UPL5 mediate SA signalling , we investigated SA-responsive immune gene expression . Treatment with SA induced strong , NPR1-dependent expression of PR-1 and several WRKY genes in wild-type plants ( Fig 3C ) . By contrast , activation of these genes was strongly reduced in both upl1 and upl5 mutant plants . Together these data indicate that UPL1 and UPL5 are positive regulators of SA-mediated gene expression and immunity . Similar to UPL1 , the related UPL3 and UPL4 ubiquitin ligases contain domains with armadillo-type folds ( Fig 1 ) . Mutant upl3 plants have previously been reported to exhibit aberrant leaf trichome morphology [36] , but otherwise show normal growth and development ( Figs 4A and S1 ) . Similarly , upl4 knockout mutants also exhibited normal growth ( Fig 4A ) . Infection with a low dosage of Psm ES4326 revealed that compared to wild type , mutant upl4 plants displayed normal disease resistance , while upl3 mutants were more susceptible to this pathogen ( Fig 4B ) . Because UPL3 and UPL4 are closely related , we generated upl3 upl4 double mutants that showed reduced growth , early senescence and produced fewer seeds compared to either parent ( Figs 4A and S3A ) . Infection of upl3 upl4 double mutants resulted in striking leaf chlorosis and enhanced levels of Psm ES4326 growth ( Fig 4B ) . These data indicate that UPL3 and UPL4 function additively in the regulation of plant growth and development , and positively modulate basal resistance . Given the pleiotropic phenotypes of the upl3 upl4 double mutant , we decided to continue with our investigation into the single mutants instead . Treatment with SA of mutant upl4 plants induced resistance to Psm ES4326 to a similar extent as in wild type ( Fig 4C ) . By contrast , upl3 mutants resembled the SA-insensitive npr1 mutant in that SA failed to induce resistance to Psm ES4326 ( Fig 4C ) . This phenotype was observed in multiple mutant upl3 alleles and constitutive expression of a transgene consisting of Yellow Fluorescent Protein fused to UPL3 ( YFP-UPL3 ) rescued SA-induced resistance in the upl3 mutant background ( S3B Fig ) . However , constitutively expressed YFP-UPL3 did not rescue basal resistance to Psm ES4326 , suggesting that dynamic UPL3 expression or 5’ and 3’ untranslated regions , which were not included in our expression construct , may also play important gene regulatory roles . The immune phenotypes observed above agreed with the SA-responsive gene expression patterns we subsequently uncovered in the mutants . While upl4 mutants showed predominantly wild type-like immune gene expression profiles in response to SA , mutant upl3 plants failed to activate several immune marker genes ( Fig 4D ) . Again this phenotype was observed in multiple mutant upl3 alleles and constitutive expression of YFP-UPL3 restored SA-responsive PR-1 gene expression in the upl3 mutant background ( S3C Fig ) . To explore if reduced SA-responsive marker gene expression was a transcriptome-wide effect , we performed an RNA Seq experiment on SA-treated wild-type and mutant upl3 plants . SA treatment resulted in differential expression of 2 , 117 genes ( ≥ 2 fold , p = 0 . 05 ) of which 1 , 177 were up- and 940 downregulated after 24 hours . Although some changes were detected between control-treated wild-type and upl3 plants , much larger differential gene expression changes became apparent after SA treatment ( Fig 5A ) . Differences in gene expression were mostly in amplitude with less dramatic activation or repression observed in upl3 mutants compared to the wild type ( Figs 5A , 5B and S4 and S1 Table ) . Indeed , of the 1 , 177 genes activated by SA in the wild type , 860 were expressed at least 1 . 5-fold lower in upl3 mutants ( Figs 5C and S5A ) . Conversely , 515 of 940 SA-repressed genes were down regulated at least 1 . 5-fold less in upl3 mutants ( Figs 5C and S5B ) . These data suggest that UPL3 acts to amplify SA-responsive gene expression changes . To identify the binding sites of potential transcription factors on which UPL3 may act , we performed promoter motif analyses on differentially expressed SA-responsive genes . Analyses of SA-induced UPL3-dependent promoters revealed they are enriched with variants of the immune-related W-box motif ( Fig 5D ) , while promoters that were suppressed by SA in a UPL3-dependent manner contained variants of the developmental E-box motif ( Fig 5E ) . The W-box motif binds WRKY transcription factors , several of which are indispensable for the full activation of SA-dependent gene expression and immunity [23 , 28] . As the W-box is pervasive in SA-responsive genes [23 , 38] and was highly enriched in UPL3 activated but not in UPL3 repressed genes ( Fig 5F ) , our findings indicate that UPL3 acts as a genome-wide amplifier of SA-responsive transcriptional reprogramming and establishment of immunity . To understand how UPL3 might function as a general transcriptional amplifier for SA-responsive genes , we performed a yeast two-hybrid screen for interactors . Because the N-terminus of UPL3 contains armadillo repeats ( Fig 1 ) that are thought to provide a large surface for protein-protein interactions [39] , we used the N-terminal 670 amino acids as bait . In addition to self-interaction , we identified six components related to the ubiquitin-26S proteasome system ( Fig 6A , S2 Table ) . These included the non-ATPase regulatory subunit RPN7 which forms part of the 19S regulatory particle , as well as the armadillo-repeat superfamily protein At3g15180 that contains a domain ( InterPro:IPR019538 ) found in proteasomal chaperones involved in assembly of the proteasome [40] . Moreover , we identified three E3 ubiquitin ligases: ( i ) the F-box protein EBF2 which is part of an SCFEBF1/2 ubiquitin ligase that targets the ethylene-responsive transcription factor EIN3 for proteasome-mediated degradation [41 , 42]; ( ii ) the U-box type E3 ligase PUB23 that has been implicated in plant immunity , interacts with and ubiquitinates the 19S proteasome regulatory particle subunit RPN6 [43 , 44]; and ( iii ) the U-box type E3 ligase PUB31 that is involved in abiotic stress tolerance [45] . Finally , UPL3 was found to interact with UBP12 , a deubiquitinase of the proteasome pathway that negatively regulates immunity [46] . In agreement with these protein-protein interactions , UPL3 was found previously to co-purify with a pathogen effector that targets proteasomes [47] , suggesting UPL3 may physically associate with proteasomes . Indeed , pull down of the proteasomal subunit S2 revealed that YFP-UPL3 co-immunoprecipitated with proteasomes largely independent of SA treatment ( Fig 6B ) . Next we considered how physical association with the proteasome allows UPL3 to function as an amplifier of the SA-responsive transcriptome . SA-responsive gene expression strongly depends on the function of the 26S proteasome [8 , 28] . Indeed , reduced activation of SA-responsive immune genes in upl3 mutants resembled the effect of pharmacological inhibition of the proteasome with the proteasomal inhibitor MG132 in SA-treated wild-type plants ( Fig 6C ) . Given the interconnection between UPL3 and multiple components of the ubiquitin-26S proteasome system , including the 19S subunit , we considered that UPL3 may regulate gene expression by altering total cellular ubiquitination levels . Therefore we treated wild-type and upl3 plants with SA and/or MG132 and pulled down ubiquitinated proteins . Figs 6D and S6 show that compared to wild type , upl3 mutants exhibited markedly reduced levels of total cellular polyubiquitination . Moreover , ubiquitination of RPN10 , a substrate of many different ubiquitin ligase types [48] , was also reduced . This remarkable phenotype suggests that UPL3 promotes polyubiquitination of either a small group of heavily ubiquitinated proteins or an extraordinary wide range of substrates . Our findings suggest that UPL3 may aid the proteasome to reinforce polyubiquitination of its substrates upon their arrival . To explore if plant proteasomes harbour E3 ligase activity immunopurified proteasomes were incubated with E1 and E2 enzymes , Flag-ubiquitin and ATP . Under these conditions proteasomes readily converted free ubiquitin into conjugates ( Fig 6E ) . To investigate if this proteasome-associated E3 ligase activity was dependent on UPL3 , we repeated the assay by comparing proteasomes from upl3 mutants with or without expression of YFP-UPL3 . Proteasomes from water-treated YFP-UPL3 ( in upl3 ) plants formed polyubiquitin conjugates and this activity was stimulated by prior treatment with SA ( Fig 6F ) . By contrast , proteasomes from both water- and SA-treated upl3 mutants exhibited markedly reduced formation of ubiquitin conjugates , demonstrating that proteasome-associated ubiquitin ligase activity was largely UPL3 dependent . Taken together our findings suggest UPL3-dependent proteasome-associated ubiquitin ligase activity is necessary for SA-responsive transcriptional reprogramming and immunity .
The ubiquitin-26S proteasome system plays indispensable roles in transcriptional regulation of plant immune genes but how substrates are processed upon arrival at the proteasome remained unclear . Here we demonstrated that members of the HECT-domain family of UPL ubiquitin ligases play an important role in SA-dependent transcriptional responses and immunity . In particular we report that proteasomes harbour UPL3-dependent ubiquitin ligase activity that was necessary for total cellular substrate polyubiquitination as well as SA-responsive transcriptional reprogramming and immunity . Our findings show that UPL1 , UPL3 , UPL4 and UPL5 function as important regulators of SA-responsive gene expression and immunity ( Figs 3 , 4 and 5 ) . Previous work has found that UPL members play roles in developmental gene expression programmes . UPL3 has been reported to regulate trichome branching by targeting for proteasomal degradation the transcription factors GLABROUS 3 ( GL3 ) and ENHANCER OF GL3 ( EGL3 ) , which control trichome development and flavonoid metabolism [36 , 49] . UPL5 was identified as an interactor of WRKY53 , a transcription factor that promotes leaf senescence [50] . In vitro and in vivo analyses indicated that UPL5 ubiquitinated WRKY53 and targeted it for degradation . Consequently , mutant upl5 plants displayed enhanced expression of a WRKY53-responsive senescence marker gene and accelerated appearance of senescing leaves [51] . Interestingly , WRKY53 is not only a regulator of developmental responses; it was also identified as a regulator of SA-dependent plant immunity . WRKY53 gene expression is SA inducible and a direct transcriptional target of the master immune coactivator NPR1 . Mutation of WRKY53 together with WRKY70 , whose expression is highly correlated with WRKY53 , resulted in susceptibility to Psm ES4326 [23] . Therefore it is plausible that UPLs also regulate the stability of WRKY transcription factors during activation of plant immunity . Indeed , transcriptomic analyses of upl3 mutants indicated that the W-box to which WRKY transcription factors bind , was highly overrepresented in SA-induced , UPL3-dependent gene promoters ( Fig 5 ) . UPLs could remove repressors such as WRKY58 from immune-responsive promoters or facilitate the turnover of WRKY activators whose transcriptional activity may require instability akin to NPR1 coactivator [8 , 23 , 28 , 52] . In this respect , it is worth noting that the broad impact of UPL3 on the SA-responsive transcriptome resembles that of WRKY18 , which functions as an auxiliary amplifier of SA-responsive gene expression [23] . Mutation of UPL3 had a remarkable impact on total cellular polyubiquitination levels , a phenotype rarely observed for E3 ubiquitin ligase mutants . So how could UPL3 have such a large effect on the cellular accumulation of so many polyubiquitin conjugates ? Yeast two-hybrid assays indicated that UPL3 may associate with the 19S regulatory particle of the proteasome ( Fig 6A , S2 Table ) and in planta YFP-UPL3 co-immunoprecipitated with proteasomes ( Fig 6B ) . We show that this interaction was responsible for proteasome-associated E3 ligase activity ( Fig 6 ) . Several proteasome-associated ubiquitin ligases have been described and consequently it has been proposed that instead of regarding substrate ubiquitination and delivery to the proteasome as separate steps , these two steps may in fact be coupled for some substrates [32] . Coupling of ubiquitination to degradation may enhance substrate affinity for proteasome receptors or prevent substrate deubiquitination . Thus , proteasome-associated ubiquitin ligases could have large substrate repertoires . Indeed , the yeast ubiquitin ligase HUL5 and its mammalian homologue KIAA10 are abundantly associated with the proteasome 19S regulatory subcomplex and show high sequence similarity to Arabidopsis UPL3 [30 , 31 , 53] . Similar to knock-out of UPL3 reported here , deletion of HUL5 led to a total cellular reduction in polyubiquitinated substrates [31] . HUL5 appears to indiscriminately amplify the degradation of substrates by elongating their ubiquitin chains , an activity that is not typical for an E3 ubiquitin ligase . Whereas most E3 enzymes have specific substrate targets , E4 enzymes are thought to extend existing ubiquitin chains without much apparent specificity [54] . Instead , their co-location with protein complexes such as the proteasome may provide substrate specificity [31 , 55] . Thus , we propose that similar to the E4 enzyme activity of HUL5 , UPL3 may also function to elongate ubiquitin chains of proteasome-bound substrates . The importance of this activity was previously demonstrated by substrate stalling and incomplete degradation by proteasomes in hul5Δ mutant yeast and in human cells by knocking down the orthologue UBE3C , indicating that ubiquitin chain elongation is necessary for processive degradation of substrates [34 , 35] . Likewise , proteasomal association of another yeast HECT-type ubiquitin ligase , UFD4 , which also shows high sequence similarity to UPL3 , including Armadillo repeats , was found to be necessary for complete substrate degradation [56] . Proteasomal stalling or incomplete degradation of immune-related transcriptional regulators could explain the immune compromised phenotypes of mutant upl3 plants . To date a number of prototypical E3 ubiquitin ligases have been found to also associate with the proteasome , albeit in lower abundance than for example HUL5 . Remarkably , core and variable subunits of the modular SCF ubiquitin ligase also bind the proteasome [32 , 57] . In Arabidopsis the major developmental SCF ligase substrate adapters UFO , COI1 and TIR1 associate with the proteasome [58] , further supporting the notion that ubiquitination of substrates and their proteasomal delivery may be directly coupled processes . Here we report that UPL3 may associate with E3 ligases , including ethylene-responsive SCFEBF2 as well as PUB23 and PUB31 U-box type E3 ligases ( Fig 6A , S2 Table ) . SCFEBF2 targets for proteasomal degradation the indispensable ethylene-responsive EIN3 transcription factor that cross-regulates SA biosynthesis and SA-responsive genes [59 , 60] , while PUB23 , together with its homologues PUB22 and PUB24 , mediates pattern recognition receptor-mediated immune signalling by targeting exocytosis regulators [43 , 61] . Thus , it is plausible that in addition to regulating proteasomal degradation of substrates from the SA signalling pathway , UPL3 may also control immunity by cooperating with E3 ligases from other immune-associated pathways . Such cooperation between E4 ligase-like activities and E3 ligases has been suggested previously . In yeast the RING-type E3 ligase Ubr1 , which targets N-end rule pathway substrates for proteasomal degradation , physically interacted with UFD4 , resulting in the formation of longer substrate-attached polyubiquitin chains [62] . This and an additional report [62] of interaction between HECT-type and other E3 ligases suggest that ubiquitin ligase pairing at the proteasome facilitates processive ubiquitination and degradation of substrates . In conclusion , our findings implicate proteasome-associated HECT-type ubiquitin ligases in the control of plant immune signalling by facilitating substrate polyubiquitination and proteasomal processivity . We reveal this unexpected E4 ligase-like activity plays important roles in the genome-wide amplification of SA-responsive gene transcription and is indispensable for establishment of immunity .
Arabidopsis thaliana wild-type Col-0 , transgenic and mutant plants were sown on soil and grown under a 16/8 hr light/dark regime . After 10–12 days seedlings were separated and transferred to larger pots and grown for an additional 2 . 5–3 weeks . Mutant upl1-1 ( SALK_063972 ) , upl2-2 ( SALK_008974 ) , upl3-2 ( SAIL_339_F05 ) [36] , upl3-4 ( SALK_035524 ) , upl4-1 ( SALK_091246 ) , upl5-1 ( SALK_116446 ) , upl6-1 ( SALK_055609 ) , upl7-1 ( SALK_119373 ) were isolated from the SALK and SAIL collections [63 , 64] and the npr1-1 mutation has been described previously [24] . Double mutants were created by crossing upl3-4 with a second UPL4 knockout mutant allele , upl4-2 ( SALK_040984 ) , while upl6 was crossed with a second UPL7 knockout mutant allele , upl7-2 ( SAIL_403_A11 ) . According to the manufacturer’s instructions the coding sequence of UPL3 ( At4g38600 ) was cloned into pCR8/GW/TOPO ( Thermo-Fisher Scientific ) and recombined with YFP-containing pEarleyGate 104 ( Earley et al . , 2006 ) using LR clonase ( Life Technologies ) to generate the 35S::YFP-UPL3 transgene . The 35S:: YFP-UPL3 vector was transferred into Agrobacterium tumefaciens strain GV3101 ( pMP90 ) using a freeze-thaw method and subsequently transformed into upl3-4 plants by floral dipping [65] . Transgenic plants were selected on soil by repeatedly spraying glufosinate ammonium . Psm ES4326 was grown overnight in liquid LB medium supplemented with 10 mM MgSO4 . Bacterial cells were collected by centrifugation , diluted to the appropriate concentrations and pressure-infiltrated into leaves . In planta bacterial growth was determined 4–5 days after infection by spreading serial dilutions of leaf extracts on LB plates supplemented with streptomycin ( 100 μg/ml ) , 10 mM MgSO4 and 50 μM cycloheximide . To test induced resistance adult plants were sprayed 24 hours prior to pathogen infiltration with water or 0 . 5 mM SA ( sodium salicylate , Sigma-Aldrich #S3007 ) until the leaves were extensively covered with fine droplets . For induction of immune genes and protein analyses , 4-week old soil-grown plants were sprayed with water or 0 . 5 mM SA until the leaves were extensively covered with fine droplets . Alternatively , 12-day-old MS-grown seedlings were submerged in 6-well plates containing 10 ml ( per well ) of water supplemented with or without 0 . 5 mM SA for 6 hours . For proteasome inhibition experiments , seedlings were submerged in solutions containing vehicle ( DMSO ) , 0 . 5 mM SA and vehicle , or 0 . 5 mM SA and 100 μM MG132 for 6 hours . RNA extractions and cDNA synthesis were performed as described [28] . Quantitative qPCR was carried out on 20-times diluted cDNA using Power SYBR Green ( Life Technologies ) and gene-specific primers on a StepOne Plus Real Time PCR system ( Life Technologies ) . For RNA Seq analyses , RNA was extracted from biological triplicate samples as described [28] and further purified using an RNeasy Mini Kit ( Qiagen ) according to the manufacturer’s instructions . qPCR was carried out to confirm appropriate induction of SA-responsive marker genes . RNA was then quantified and submitted to GATC Biotech ( Constance , Germany ) for RNA sequencing . The RNA Seq reads were aligned to the Arabidopsis thaliana TAIR10 genome using Bowtie . TopHat identified potential exon-exon splice junctions of the initial alignment . Strand NGS software in RNA Seq workflow was used to quantify transcripts . Raw counts were normalised using DESeq with baseline transformation to the median of all samples . Data were then expressed as normalised signal values ( i . e . log2[RPKM] where RPKM is read count per kilobase of exon model per million reads ) for all statistical tests and plotting . RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI ( www . ebi . ac . uk/arrayexpress ) under accession number E-MTAB-7374 . Extraction of overrepresented octamer sequences was performed as previously reported [66] on the top 281 and 292 differentially expressed UPL3-activated and UPL3-repressed gene promoters , respectively . The enriched octamers were aligned according to a conserved pentamer sequence , followed by analysis using Weblogo version 2 . 8 . 2 ( http://weblogo . berkeley . edu/ ) . Additionally , promoters were analysed for statistical over- or underrepresentation of the W-box using POBO [67] . Yeast two-hybrid screening and data analyses were performed by Hybrigenics Services ( Paris , France ) . Amino acids 1–670 of UPL3 were cloned into vector pB29 ( N-UPL3-LexA-C fusion ) and screened against a prey library derived from RNA extracted from Arabidopsis thaliana rosettes infected either with virulent P . syringae pv . tomato DC3000 or with an avirulent strain expressing AvrRpt2 . A total of 65 . 2 million interactions were analysed and 353 positive clones sequenced . Interactions were categorised by confidence scores that are based on a statistical model of the competition for bait-binding between fragments [68 , 69] . For co-immunoprecipitation experiments , tissue was pulverised in liquid nitrogen and protein extracted in 2 volumes of proteasome extraction buffer containing 125 mM Tris-HCl ( pH7 . 7 ) , 0 . 25 mM EDTA , 2 . 5 mM MgCl2 , 5% glycerol , 5 mM ATP , and protease inhibitors ( 50 μg/mL N-p-Tosyl-L-phenylalanine chloromethyl ketone ( TPCK ) , 50 μg/mL Nα-Tosyl-L-lysine chloromethyl ketone hydrochloride ( TLCK ) , 0 . 6 mM phenylmethylsulfonyl fluoride ( PMSF ) ) . Protein extracts were centrifuged ( 17 , 000 g , 20 min . at 4°C ) , supernatants filtered through 0 . 22 μM syringe filters and incubated for 2 hours at 4°C with anti-proteasome S2 antibody ( Abcam , ab98865 at ratio 1:250 ) . Next , protein A-agarose was added ( 20 μl/ml ) and incubated with gentle rocking for another hour . Agarose beads were collected by brief centrifugation and washed 5 times with extraction buffer . Bound proteins were eluted by incubation in SDS sample buffer supplemented with 50 mM dithiothreitol ( DTT ) for 5 min . at 95°C . For analyses of cellular polyubiquitin conjugate levels , twelve-day-old seedlings were placed in solutions containing vehicle ( DMSO ) , 0 . 5 mM SA and vehicle , or 0 . 5 mM SA and 100 μM MG132 for 6 hours . Tissue was then blotted dry and pulverised in liquid nitrogen . Protein was extracted in two volumes of extraction buffer , consisting of phosphate buffered saline supplemented with 1% Triton X-100 , 10 mM N-ethylmaleimide , phosphatase inhibitor cocktail 3 ( Sigma-Aldrich ) , protease inhibitor cocktail [50 μg/mL N-p-Tosyl-L-phenylalanine chloromethyl ketone ( TPCK ) , 50 μg/mL Nα-Tosyl-L-lysine chloromethyl ketone hydrochloride ( TLCK ) , 0 . 6 mM phenylmethylsulfonyl fluoride ( PMSF ) ] , and 0 . 2 mg/ml recombinant GST-tagged tandem ubiquitin binding entities ( TUBE ) [70] . Protein extracts were centrifuged ( 17 , 000 g , 20 min . at 4°C ) , supernatants filtered through 0 . 22 μM syringe filters and incubated overnight at 4°C with 50 μl/ml of packed Protino Glutathione Agarose 4B ( Machery Nagel ) . Agarose was washed 5 times with extraction buffer and bound proteins eluted by incubation in SDS sample buffer supplemented with 50 mM dithiothreitol ( DTT ) for 10 min . at 80°C . Proteasomal E3 ligase activity was assessed by extracting protein from liquid nitrogen pulverised tissue in 2 volumes of proteasome extraction buffer containing 125 mM Tris-HCl ( pH7 . 7 ) , 0 . 25 mM EDTA , 2 . 5 mM MgCl2 , 5% glycerol , 5 mM ATP , and protease inhibitors ( 50 μg/mL N-p-Tosyl-L-phenylalanine chloromethyl ketone ( TPCK ) , 50 μg/mL Nα-Tosyl-L-lysine chloromethyl ketone hydrochloride ( TLCK ) , 0 . 6 mM phenylmethylsulfonyl fluoride ( PMSF ) ) . Protein extracts were centrifuged ( 17 , 000 g , 20 min . at 4°C ) , supernatants filtered through 0 . 22 μM syringe filters and incubated overnight at 4°C with anti-proteasome S2 antibody ( Abcam , ab98865 at ratio 1:250 ) . The next day extracts were centrifuged ( 17 , 000 g , 10 min . at 4°C ) and supernatants collected . Protein A-agarose was then added ( 20 μl/ml ) and incubated with gentle rocking for one hour . Agarose beads were collected by brief centrifugation and washed 3 times with proteasome extraction buffer . Subsequently , agarose beads were incubated for 18 hours with gentle shaking at 30°C in 80 μl reaction buffer ( 125 mM Tris-HCl ( pH7 . 7 ) , 0 . 25 mM EDTA , 2 . 5 mM MgCl2 , 5 mM ATP , 1 mM DTT , 10 μM NSC632836 deubiquitinase inhibitor ) supplemented with , human recombinant E1 enzyme ( 0 . 2 or 0 . 4 μg , BioVision ) , recombinant E2 enzyme UbcH5c ( 0 . 2 μg , Ubiquigent ) , and recombinant human Flag-ubiquitin ( 10 μg , Boston Biochem ) . Agarose beads were eluted by incubation in SDS sample buffer supplemented with 50 mM dithiothreitol ( DTT ) for 10 min . at 80°C . All proteins were analysed by SDS-PAGE followed by western blotting using anti-ubiquitin ( anti-ubiquitinylated proteins clone FK2 , Merck ) , anti-RPN10 ( polyclonal antibody against Arabidopsis RPN10 , Abcam ) , anti-proteasome S2 ( polyclonal antibody against full-length Arabidopsis S2 , Abcam ) , anti-Flag ( monoclonal anti-Flag M2 antibody , Sigma ) and anti-GFP ( mixture of monoclonal antibodies from clones 7 . 1 and 13 . 1 , Roche ) antibodies . | Plants are continuously exposed to different disease agents , including bacteria , fungi , oomycetes and chewing or sucking insects . To protect themselves plants have evolved a sophisticated multi-layered immune system that depends on the reprogramming of large gene repertoires to prioritize the expression of immune genes over normal cellular household genes . Activity of the proteasome , a large proteolytic complex that degrades proteins , is vital to coordinate the expression of immune genes . While it is well understood that proteins marked by a chain of the small polypeptide ubiquitin can be targeted to the proteasome for degradation , it remains unclear how these proteins are processed by proteasomes . Here we identify the enzyme UPL3 that enabled plant proteasomes themselves to add further ubiquitin chains to cellular proteins destined for degradation . This is thought to be an important activity that increases the affinity of substrates for proteasomes while preventing them from stalling during degradation . Importantly , we show that this activity of UPL3 is indispensable for gene expression reprogramming and establishment of disease resistance . Thus , by enabling proteasomes to add ubiquitin marks to its substrates , UPL3 regulates key aspects of plant immunity that could be further exploited in future crop protection strategies . |
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The membrane scission event that separates nascent enveloped virions from host cell membranes often requires the ESCRT pathway , which can be engaged through the action of peptide motifs , termed late ( L- ) domains , in viral proteins . Viral PTAP and YPDL-like L-domains bind directly to the ESCRT-I and ALIX components of the ESCRT pathway , while PPxY motifs bind Nedd4-like , HECT-domain containing , ubiquitin ligases ( e . g . WWP1 ) . It has been unclear precisely how ubiquitin ligase recruitment ultimately leads to particle release . Here , using a lysine-free viral Gag protein derived from the prototypic foamy virus ( PFV ) , where attachment of ubiquitin to Gag can be controlled , we show that several different HECT domains can replace the WWP1 HECT domain in chimeric ubiquitin ligases and drive budding . Moreover , artificial recruitment of isolated HECT domains to Gag is sufficient to stimulate budding . Conversely , the HECT domain becomes dispensable if the other domains of WWP1 are directly fused to an ESCRT-1 protein . In each case where budding is driven by a HECT domain , its catalytic activity is essential , but Gag ubiquitination is dispensable , suggesting that ubiquitin ligation to trans-acting proteins drives budding . Paradoxically , however , we also demonstrate that direct fusion of a ubiquitin moiety to the C-terminus of PFV Gag can also promote budding , suggesting that ubiquitination of Gag can substitute for ubiquitination of trans-acting proteins . Depletion of Tsg101 and ALIX inhibits budding that is dependent on ubiquitin that is fused to Gag , or ligated to trans-acting proteins through the action of a PPxY motif . These studies underscore the flexibility in the ways that the ESCRT pathway can be engaged , and suggest a model in which the identity of the protein to which ubiquitin is attached is not critical for subsequent recruitment of ubiquitin-binding components of the ESCRT pathway and viral budding to proceed .
The membrane scission event that separates the lipid membrane of nascent enveloped virions from host cell membranes is , in many cases , an orchestrated event requiring the participation of the class E vacuolar protein sorting ( VPS ) , or endosomal sorting complex required for transport ( ESCRT ) pathway . Ordinarily , the ESCRT pathway induces topologically equivalent cellular membrane scission events including the biogenesis of multivesicular bodies ( MVBs ) [1] , [2] and the membrane abscission event at the conclusion of cell division [3] , [4] . Components of the pathway can be recruited , either directly or indirectly , through the action of short peptide motifs called late ( L- ) domains in viral structural proteins [5] , [6] . Three classes of viral L-domains and cognate cofactors have been defined thus far: PT/SAP motifs bind Tsg101 [7] , [8] , [9] , [10] , LxxLF or YPXL motifs bind ALIX [11] , [12] , [13] , and PPxY domains bind Nedd4-like HECT ubiquitin ligases [14] , [15] , [16] , [17] , [18] . Disruption of late domain function results in the failure of membrane scission and the accumulation of assembled virions that remain tethered to the surface of the host cell by a continuous membrane . The ESCRT machinery is composed of ∼25 proteins , many of which participate in the formation of several multiprotein complexes , known as ESCRT-0 , -I , -II , -III [19] , [20] , [21] . ESCRT-III components are thought to drive the membrane scission event [22] , [23] , [24] , [25] and appear to be generally required for L-domain-dependent viral budding [7] , [11] , [12] , [13] , [26] . In contrast , other components of the ESCRT-pathway appear to be required in an L-domain specific way . For example , PTAP-dependent budding is especially sensitive to ESCRT-I perturbation , while YPXL-dependent budding is especially sensitive to ALIX depletion . Since ALIX interacts directly with ESCRT-III via its Bro1 domain [11] , [12] , [13] , [27] , [28] and ESCRT-I indirectly interacts with ESCRT-III via ALIX and/or ESCRT-II , [11] , [12] , [13] these observations suggest that YPXL and PTAP motifs access the same core scission machinery via alternative routes . In contrast , it has remained somewhat unclear how PPxY motifs access the scission machinery . Overexpression of certain HECT ubiquitin ligases that bind directly to PPxY or other motifs can markedly stimulate budding , and the catalytic activity of the HECT domain is essential for this activity [17] , [29] , [30] , [31] . Indeed , overexpression of catalytically inactive or truncation mutants of the HECT ligase WWP1 inhibits PPxY-dependent budding [17] , [29] . Some components of the ESCRT pathway are also required for PPxY-induced budding [7] , [31] , [32] . However , the precise means by which HECT ligase recruitment subsequently results in the engagement of the ESCRT machinery is not completely defined . One model invokes direct ubiquitination of Gag as the key event . This notion derives from observations that several components of the ESCRT pathway are thought to recognize ubiquitinated cargo through various low affinity ubiquitin-binding domains [7] , [33] , [34] , [35] , [36] , [37] and that monoubiquitination of cellular cargos can serve as a signal for endosomal trafficking and delivery to the lysosome [21] , [38] , [39] . Indeed , several observations are consistent with the notion that ubiquitination of retroviral Gag promotes virus particle release . For example , studies have noted an enrichment of free ubiquitin in retrovirus particles , and ubiquitinated Gag species have also been detected therein [14] , [40] , [41] , [42] , [43] . Additionally , late budding defects have been observed in cells treated with proteasome inhibitors , perhaps due to the depletion of free ubiquitin [14] , [44] , [45] . Mutation of multiple ubiquitin acceptor lysine residues in Gag has been shown to inhibit particle production by retroviruses [46] , [47] . Finally , direct fusion of ubiquitin to the C-terminus of Gag proteins has been shown to alleviate inhibition of particle release imposed by proteasome inhibitors , or to obviate the requirement for an L-domain in particle release [44] , [48] . Other observations suggest that PPxY and ubiquitin ligase-dependent budding may involve mechanisms other than direct Gag ubiquitination . In particular , overexpression of wild-type WWP1 stimulates PPxY-dependent particle production by a lysine-free Gag protein [29] in the absence of detectable Gag ubiquitination . This finding suggests the possibility that HECT ligases may promote budding by catalyzing the ubiquitination of specific trans-acting host factors , rather than Gag . Additionally , a HECT-truncated WWP1 protein , lacking the entire HECT domain , inhibits murine leukemia virus ( MLV ) budding more potently than the full length WWP1 protein with a disrupted active site [17] , suggesting that HECT domains may possess activities other than ubiquitin conjugation that are important for their function in viral budding . Moreover , HECT domains localize to aberrant endosomal ( so called class E ) compartments induced by overexpression of catalytically inactive ATPase VPS4 [17] , which is required for the disassembly of ESCRT complexes after each round of budding [49] , [50] . Since many VPS factors are trapped on VPS4-induced compartments , HECT domains may be recruited to these compartments by interaction with VPS proteins , either directly or through unidentified bridging factors . It has also been reported that HECT ubiquitin ligases can bind to , and/or catalyze the ligation of ubiquitin to , certain class E VPS factors [31] , [32] , [51] . Thus , the ubiquitin ligases might act as recruitment factors rather than , or in addition to , conjugating ubiquitin to key target proteins . In this study we investigated the role of PPxY motifs , HECT ubiquitin ligase domains and ubiquitin in viral budding , using a lysine-free viral protein from the prototypic foamy virus ( PFV ) , in which the attachment of ubiquitin to Gag can be rather precisely controlled . We show that the catalytic activity of a variety of HECT domains , targeted to a PPxY motif in assembling particles via a common C2/WW domain fragment of WWP1 , is essential for their ability to promote PPxY-dependent VLP release . In each case , however , Gag ubiquitination is dispensable for their activity . Rather , the ability of the chimeric ubiquitin ligases to promote budding correlated broadly , albeit imperfectly , with their ability to catalyze autoubiquitination , Moreover , we show that artificial recruitment of an isolated HECT domain can also stimulate budding , while a HECT domain becomes dispensable for PPxY motif dependent budding if the C2/WW domains of WWP1 are directly linked to the C-terminal domain of Tsg101 , an ESCRT-I subunit . Finally , we demonstrate that direct fusion of a single ubiquitin moiety to the C-terminus of PFV Gag is also capable of promoting budding , in a manner that recapitulates the ESCRT protein requirement for budding induced by PPxY-dependent ubiquitin ligase recruitment in the absence of ubiquitin acceptors in Gag . These results support a model in which PPxY motif-induced HECT ubiquitin ligase recruitment leads to the deposition of ubiquitin at or near the site of viral budding . However , the identity of the protein to which ubiquitin is attached , be it Gag or a bystander protein , perhaps including the HECT ubiquitin ligase itself , does not appear to be critical in order for subsequent recruitment of ubiquitin-binding class E VPS proteins and viral budding to proceed .
To ascertain what properties of HECT domains are important for stimulation of virus particle release , we compared the properties of a panel of HECT domains . Nine members of the Nedd4-like HECT ubiquitin ligase family have been described in humans and these have the same domain organization as a single prototype member of this family in yeast , namely Rsp5 ( reviewed in [52] ) . Specifically , an N-terminal C2 domain directs the protein to membranes , a central cluster of ‘WW” domains binds ligands , such as PPxY motifs , and a C-terminal HECT domain harbors the E3 ubiquitin ligase activity . Some of the intact ubiquitin ligases have been shown to vary in their ability to promote PPxY-dependent MLV virion release , due at least in part to differences in the affinities of their WW domains for the MLV L-domain [17] , but whether the various the C-terminal HECT domains are equivalently able to induce particle release has not been investigated . We reasoned that variation in the ability of HECT domains to stimulate virus budding , correlated with a given property of the HECT domains , might suggest properties that are important for inducing virion release . Since WWP1 has been previously shown to be efficiently recruited by a number of PPxY-type L-domains , including that of MLV [17] , we constructed a panel of chimeric ubiquitin ligases , consisting of membrane targeting and PPxY motif-binding domains ( C2 and WW domains ) of human WWP1 , coupled to various catalytic HECT domains derived from human WWP2 , Nedd4 , Nedd4L , Itch , Smurf1 , Bul2 or yeast Rsp5 HECT ligases ( Fig . 1A ) . To determine whether these chimeric ubiquitin ligases could support viral budding , we co-expressed each of them with a plasmid expressing a modified PFV Gag protein . Importantly , PFV Gag offers the advantage that it is naturally almost devoid of lysine resides . While PFV Gag normally requires a cognate Env protein for particle release , we have previously shown that appending a myristoylated , palmitoylated peptide from Lck at its N-terminus can overcomes this requirement by directing PFV Gag to the plasma membrane and thereby allowing the generation of extracellular particles in the absence of any other viral protein [29] . Throughout this study we used this N-terminally modified Gag protein , termed Lck-Gag , bearing a K396R mutation that renders the PFV Gag completely lysine-free . Examples of engineered variants of this Gag protein are illustrated in Fig . 1B , and include those that is otherwise unmodified and encode the natural PSAP late domain ( Lck-Gag ( PSAP ) ) , a PSAP mutant that contains no known L-domain ( Lck-Gag ( L- ) ) or another variant that has a PPxY late domain derived from MLV Gag appended to its C-terminus ( Lck-Gag-PY , Fig . 1B ) . In addition , we used an Lck-Gag-PY derivative containing three lysine residues adjacent to a PPxY late domain ( Lck-Gag-PY-3K ) to assess HECT ligase-induced Gag ubiquitination ( [29] , illustrated in Fig . 1B ) . Overexpression of ubiquitin ligases encoding a variety of HECT domains ( WWP1 itself , WWP1/Nedd4 , WWP1/Nedd4L , WWP1/Itch , WWP1/Smurf1 , or WWP1/Bul2 ) stimulated PPxY-dependent budding of lysine-free Lck-Gag-PY ( Fig . 2A , B ) . Conversely , WWP1/WWP2 and WWP1/Rsp5 did not stimulate budding or had marginal activity . The strongest stimulation was observed using chimeric ligases containing the Nedd4L and Itch HECT domains . Importantly , overexpression of chimeric ligases in which the catalytic cysteine was mutated to serine , failed to stimulate PPxY-dependent particle release ( Fig . 2A ) , indicating that the catalytic activity of each HECT domains was required , even when the viral structural proteins lack ubiquitin acceptors . To assess the relative catalytic activities of the chimeric HECT ligases , and assess whether this correlated with their differential ability to promote budding , we compared their abilities to carry out autoubiquitination and to ubiquitinate a Gag substrate encoding three lysine residues in close proximity to a PPxY late domain ( Lck-Gag-PY-3K , see Fig . 1B ) . To accomplish this , we immunoprecipitated either Gag or HECT ubiquitin ligases from 293T cell lysates , prepared 36 hours after co-transfection with plasmids expressing Lck-Gag-PY-3K , HA-tagged ubiquitin , and each of the YFP-fused chimeric HECT ligases . Cell lysates were prepared using denaturing , detergent-rich buffer ( containing 0 . 5% SDS ) to ensure dissolution of protein complexes , and ubiquitinated species were detected by immunoprecipitation with either αPFV Gag or αGFP antibodies followed by immunoblot analysis of the precipitates with an αHA antibody ( Fig . 3 ) . Each of the chimeric HECT ubiquitin ligases was able to reasonably efficiently catalyze the addition of 1 to 3 ubiquitin moieties to the Lck-Gag-PY-3K substrate ( Fig . 3A , upper panels ) . There was some variation in the ability of the HECT domains to catalyze the ligation of ubiquitin to Lck-Gag-PY-3K , with WWP1/Rsp5 and WWP1/Bul2 catalyzing the highest and WWP1/Nedd4 the lowest levels of ubiquitin ligation to Lck-Gag-PY-3K ( Fig . 3A ) . However , there was no correlation between the extent to which each HECT domain stimulated Lck-Gag-PY-3K ubiquitination ( Fig . 3A ) and the degree to which it stimulated the release of VLPs assembled using Lck-Gag-PY or Lck-Gag-PY-3K ( Fig . 2A and data not shown ) . For example , WWP1/Bul2 and WWP1/Nedd4 , which induced the highest and lowest levels of Gag ubiquitination , respectively ( Fig . 3A ) , stimulated budding to a similar extent ( about 6-fold , Fig . 2A ) . Moreover , WWP1/Rsp5 , which efficiently catalyzed Gag ubiquitination ( Fig . 3A ) , enhanced particle release only marginally ( Fig . 2A ) , much less efficiently than the WWP1/Nedd4L that induced comparatively modest levels of Gag ubiquitination ( Fig . 3A ) . We observed a better , albeit imperfect , correlation between the ability of the chimeric HECT ligases to catalyze autoubiquitination and to stimulate VLP production ( Fig . 3B , Fig . 2B ) . Chimeric ligases that strongly promoted Lck-Gag-PY VLP release ( e . g . WWP1/Itch and WWP1/Nedd4L ) were more heavily autoubiquitinated , while those that failed or only marginally promoted VLP release ( WWP1/WWP2 and WWP1/Rsp5 , Fig . 2 ) exhibited the lowest levels of autoubiquitination ( Fig . 3B ) . The correlation was imperfect , however , since WWP1/Nedd4 , which moderately enhanced particle release ( Fig . 2 ) , was consistently highly auto-ubiquitinated ( Fig . 3B ) . Notably , there was no correlation between the ability of the HECT ubiquitin ligases to catalyze autoubiquitination , and their ability to catalyze ubiquitin ligation to Lck-Gag-PY-3K ( Fig . 3A , B ) . Overall , these data confirm our previous finding that direct Gag ubiquitination is dispensable for HECT ligase-dependent budding [29] and further indicates that intrinsic catalytic activity of the HECT ubiquitin ligases is critical for their ability to stimulate budding . We next asked whether the need to recruit a HECT domain in the context of PPxY/WWP1 interaction was necessary for particle release , or whether the HECT domain could be bypassed by direct recruitment of putative downstream effectors . Additionally , we asked whether recruitment of a HECT domain in the absence of the other domains ( C2 and WW ) found in the Nedd4-like family of proteins was sufficient to stimulate particle budding . To accomplish this , we constructed hybrid L-domain cofactors in which the essential domains were split and linked to putatively complementing domains in another L-domain cofactor ( Fig . 4A ) . Specifically , Tsg101 is a core component of ESCRT-I and contains two domains that are functionally important with respect to viral budding . The N-terminal ubiquitin E2 variant ( UEV ) domain interacts directly with P ( T/S ) AP peptide motifs and ubiquitin [7] , while the C-terminal portion of the protein is a key structural component of ESCRT-I , interacting with other components , e . g . VPS28 and VPS37 [53] , [54] , [55] and is essential to support Tsg101 dependent budding . We constructed an artificial putative chimeric L-domain cofactor in which the C2/WW domains of WWP1 were linked to the C-terminal portion of Tsg101 ( Tsg-C ) that constitutes the core structural component of ESCRT-I ( residues 157–390 , Fig . 4A ) . Notably , overexpression of this chimeric protein , termed WWP1-Tsg-C , stimulated Lck-Gag-PY particle release in a dose-dependent manner but had no effect on particle production by the L-domain-deficient Lck-Gag ( L- ) protein ( Fig . 4B , left and middle panels ) . This chimeric protein , therefore , appeared capable of recruiting a functional ESCRT-I complex to PPxY L-domains and thereby stimulating particle production . Conversely , WWP1-Tsg-C overexpression inhibited Lck-Gag ( PSAP ) budding in a dose-dependent manner ( Fig . 4B , right panel ) . We surmise that since this chimeric protein lacks the domains required for interaction with PT/SAP motifs , it acts as an inhibitor of PSAP-dependent budding by sequestering endogenous components ( e . g . VPS28 and VPS37 ) into retargeted ESCRT-I complexes that can be recruited to PPxY , but not PT/SAP , L-domains . Thus , these experiments demonstrate that the requirement for a HECT domain ( and , by inference , the requirement for ubiquitin ligation ) in PPxY/ubiquitin ligase dependent viral budding can be bypassed , if an alternative link to the ESCRT machinery is provided . In a reciprocal experiment , we asked whether the PPxY motif and the C2/WW domains of WWP1 could be functionally replaced in the context of HECT domain/ubiquitin dependent budding . In other words , we determined whether recruitment of a HECT domain is sufficient to stimulate particle release , in the absence of the other protein domains ( C2 and WW ) to which it would ordinarily be linked . Specifically , we attempted to redirect P ( T/S ) AP-dependent particle production through a HECT domain-dependent pathway , by constructing chimeric proteins , termed Tsg-WWP1 , Tsg-Itch and Tsg-Nedd4L , that contained the N-terminal UEV domain ( residues 1–157 ) of Tsg101 linked to one of the three respective HECT domains ( Fig . 4A ) . To test the function of these artificial putative L-domain cofactors , we also constructed an attenuated “leaky” mutant of the PT/SAP motif in the Lck-Gag ( PSAP ) protein , namely Lck-Gag ( ASAP ) , by mutating the first proline residue of the PSAP motif to alanine . In the context of the HIV-1 PTAP motif , such a mutation reduces the affinity for , but does not eliminate binding to the Tsg101 UEV domain [7] . Correspondingly , the budding of Lck-Gag ( ASAP ) , was attenuated as compared to Lck-Gag ( PSAP ) , but the ASAP motif clearly retained some weak residual ability to stimulate budding ( Fig . 4C , leftmost three lanes ) , suggesting that it retains some residual ability to recruit the Tsg101 UEV domain . Overexpression of Tsg-WWP1 , Tsg-Itch or Tsg-Nedd4L , respectively ) resulted in clear stimulation of Lck-Gag ( ASAP ) budding ( Fig . 4C ) . Tsg101-Itch was the most potent of the three Tsg101-HECT proteins tested by this approach , and its overexpression resulted in a particle yield that matched or even exceeded that observed in the presence of the intact PSAP motif ( Fig . 4C ) . In contrast , expression of catalytically inactive versions of Tsg-WWP1 , Tsg-Itch or Tsg-Nedd4L inhibited rather than enhanced Lck-Gag ( ASAP ) particle production ( Fig . 4C ) . Because the Tsg101 UEV domain contains ubiquitin-binding activity that might complicate the interpretation of these results , we repeated these experiments using a mutant Tsg101 UEV domain ( N45A ) that is defective for ubiquitin binding , linked to a WWP1 HECT domain . The mutant Tsg ( N45A ) -WWP1 fusion stimulated budding at least as efficiently as did the unmanipulated Tsg-WWP1 protein ( Fig . 4D ) . Overall , the experiments in Fig . 4 demonstrate that the domains of the PTAP and PPxY binding cofactors can be functionally split into modular , interchangeable domains that are ( i ) necessary for binding to the L-domain and ( ii ) interface with downstream effectors that are critical for budding . Most notably , these findings suggest that simple recruitment of a HECT domain to sites of particle budding , irrespective of its mode of recruitment , and in the absence of ubiquitin acceptors on the viral protein , is sufficient to stimulate particle release and that other HECT ubiquitin ligase domains are dispensable for budding . The aforementioned experiments demonstrated that the requirement for a catalytically active HECT domain could be obviated by direct recruitment of ESCRT-I to a viral protein ( Lck-Gag-PY ) whose budding would normally be dependent on such recruitment . We next asked whether the requirement for HECT domain recruitment could similarly be obviated , in the context of a nearly identical viral protein , by simply depositing ubiquitin at the site of particle assembly , in the absence of ubiquitin ligase recruitment . To mimic the deposition of ubiquitin at sites of virion assembly , in the absence of ubiquitin ligase recruitment , we expressed an Lck-Gag protein , lacking L-domains , with a single ubiquitin appended at its C-terminus ( Lck-Gag-Ub , Fig . 5A ) . Ubiquitin is normally conjugated to proteins by an isopeptide bond between the C-terminal glycine residue of ubiquitin and the ε-amino group of a lysine residue within the substrate protein . Therefore , to avoid aberrant conjugation of our Gag-ubiquitin chimeras to other proteins we deleted two glycine residues from the C-terminus of ubiquitin ( Fig . 5A ) . Cells expressing ubiquitin-fused , but L-domain-deficient Gag ( Lck-Gag-Ub ) generated extracellular particles while those expressing the unfused , L-domain deficient counterpart Lck-Gag ( L- ) protein did not ( Fig . 5B ) . Directly fused ubiquitin-dependent particle release was strongly inhibited , in a dose dependent manner , by expression of a catalytically inactive version of the ATPase VPS4 ( Fig . 5C ) , indicating that the ESCRT pathway was required for Lck-Gag-Ub particle release . Thus , in the context of Lck-Gag , direct ubiquitin fusion appeared capable of substituting for a PSAP or PPxY containing L-domain . These results are similar to findings made by Joshi et al . who showed that direct fusion of ubiquitin to EIAV Gag can functionally substitute for the ALIX-binding YPDL L-domain encoded therein [48] . Similarly , we also found that ubiquitin-dependent budding was dependent on the ubiquitin hydrophobic patch residues ( L8 and I44 ) and additionally , marginally dependent on residues ( Q62 and E64 ) that have been implicated in ubiquitin-Tsg101 UEV domain interaction ( Fig . 5D ) . However , lysine residues ( K48 and K63 ) that are often important for the conjugation of further ubiquitin molecules could be mutated without affecting fused ubiquitin-dependent particle release ( Fig . 5D ) . Next we analyzed the effect of combining L-domains and ubiquitin on VLP release . To accomplish this , Lck-Gag proteins containing various combinations of the L-domains and C-terminally fused ubiquitin ( Fig . 6A ) were expressed . Quantitative analyses revealed that directly fused ubiquitin-dependent ( Lck-Gag-Ub ) particle release was at least as efficient as that driven by PSAP ( Lck-Gag ( PSAP ) ) or PPxY ( Lck-Gag-PY ) L-domains ( Fig . 6B ) . Moreover , and in contrast to the previous report with EIAV Gag [48] , we found that the combined presence of fused ubiquitin and a PSAP L-domain ( in Lck-Gag ( PSAP ) -Ub ) resulted in strongly synergistic effects on particle release ( Fig . 6B ) . Specifically , Lck-Gag ( PSAP ) -Ub generated ∼20-fold and ∼6-fold more particles than Lck-Gag ( PSAP ) and Lck-Gag-Ub , respectively ( Fig . 6B ) . No such synergy was observed when a PPxY L-domain and ubiquitin were combined in the same Gag protein . In fact , the Lck-Gag-Ub and the Lck-Gag-PY-Ub generated extracellular particles with approximately the same efficiency ( Fig . 6B ) . Less dramatic , but nonetheless synergistic enhancement of particle release was evident when PPxY and PSAP motifs were both present ( in the absence of ubiquitin fusion , Fig . 6B ) . In this case , the presence of the PPxY motif ( in Lck-Gag ( PSAP ) -PY ) enhanced particle release approximately ∼5-fold as compared to the situation where the PSAP motif was the only L-domain ( in Lck-Gag ( PSAP ) , Fig . 6B ) . Overall these results are consistent with the notion that ubiquitin behaves essentially like an L-domain , and further suggests that it functions synergistically with a PT/SAP motif , and redundantly with a PPxY motif . We next attempted to mimic a situation that is somewhat typical of retroviruses , where only a fraction of Gag expressed in cells carries ubiquitin . This was done by co-expressing ubiquitin-fused and unfused Lck-Gag proteins in varying proportions . When this was done in the context of a Lck-Gag proteins lacking a PSAP motif ( by co-expressing Lck-Gag ( L- ) and Lck-Gag-Ub ) , particle production was most efficient when a large fraction of the total Lck-Gag protein carried ubiquitin , and no stimulation of particle production was detectable when less than 25% of the Gag protein carried fused ubiquitin ( Fig . 6C , left panel ) . When similar experiments were done in the presence of a PSAP late domain , by co-expressing Lck-Gag ( PSAP ) and Lck-Gag ( PSAP ) -Ub , stimulation of particle release was observed when smaller fractions of Gag , as little as a few percent , carried ubiquitin ( Fig . 6C , right panel ) . Nonetheless , larger fractions of ubiquitin fused Gag had larger stimulating effects on particle release . Thus , these experiments suggest that the greater the number of ubiquitin molecules that are present at sites of particle assembly , the more efficient is particle release; however , relatively modest amounts of ubiquitin can significantly enhance particle budding in the presence of a PSAP motif . Several class E vacuolar protein-sorting factors have been reported to possess ubiquitin binding activity ( Table 1 ) . Although the affinity of such domains for monoubiquitin is generally quite weak ( Kd>100µM ) , several class E factors form multiprotein complexes with several ubiquitin-binding surfaces , which could provide sufficient avidity for their retention at sites of virion assembly . Under such a scenario , efficient recruitment of ESCRT complexes might require deposition of relatively large numbers of ubiquitin molecules in the vicinity of the assembling particle , a notion that is consistent with the finding that a large fraction of Gag must carry ubiquitin to compensate for the absence of a late domain ( Fig . 6C ) . To determine which of the mammalian ESCRT complexes and associated proteins might be most important for ubiquitin dependent budding , we performed a directed yeast two-hybrid screen in which ubiquitin binding to a range of human class E VPS factors and associated proteins was surveyed . These included components of ESCRT-0 ( Hrs , HBP/STAM ) , ESCRT-I ( Tsg101 , VPS28 , VPS37A , B , C , Mvb12 ) , ESCRT-II ( Eap20 , Eap30 , Eap45 ) ESCRT-III ( CHMP1A , 1B , 2A , 2B , 3 , 4A , 4B , 4C , 5 , 6 ) , as well as several ESCRT-associated proteins or proteins that are known to bind to components of the class E VPS pathway ( ALIX , LIP5 , VPS4 , UBPY , CMS , CIN85 ) . Most of these proteins , including known ubiquitin binding factors ( Table 1 ) , gave either weak or non-specific signals . Since we were testing ubiquitin binding by each protein individually and outside of its natural context and in the absence of ESCRT complex partners , it was perhaps to be expected that this assay would fail to detect ubiquitin interactions in at least some instances . Nonetheless , HBP/STAM , ALIX , and UBPY binding gave robust signals in WT ubiquitin binding assays , and binding was abolished when the ubiquitin hydrophobic patch was mutated ( I44A ) , ( Fig . 7A ) . We next determined the effect of siRNA mediated disruption of known ubiquitin-binding complexes , as well the additional ESCRT-associated factors that were positive in our yeast 2-hybrid survey ( ALIX and UBPY ) , on PPxY-dependent and fused ubiquitin-dependent Lck-Gag budding . The core components of the known ubiquitin binding ESCRT complexes ( ESCRT-0 , ESCRT-I and ESCRT-II ) were targeted using pools of four siRNAs directed to Hrs , Tsg101 and Eap45 , respectively . The potency of the siRNA pools was estimated by cotransfecting them with plasmids expressing YFP-tagged target proteins , followed by quantitative western blotting . By these criteria the Tsg101 , Eap45 , ALIX and UBPY siRNAs appeared effective ( Fig . 7B ) . However , knockdown of Hrs was inefficient , so its effect on budding could not be reliably assessed . Because antibodies to Tsg101 and ALIX were available , the level of endogenous proteins could also be monitored in these siRNA experiments . Quantitative western blotting analyses ( examples are shown in Fig . 7C ) indicated that Tsg101 and ALIX proteins were reduced to 38±9% and 16±4% of endogenous levels , respectively . Notably , control experiments showed that Lck-Gag ( PSAP ) particle release was specifically inhibited ( ∼5-fold ) by Tsg101 siRNA , but only marginally affected by EAP45 , ALIX and UBPY depletion ( Fig . 7C , D ) , while EIAV Gag particle release was specifically inhibited ( ∼3-fold ) by ALIX depletion , but not by depletion of the other ESCRT-associated proteins ( Fig . 7C , D ) . Ubiquitin-dependent ( Lck-Gag-Ub ) budding was modestly inhibited ( ∼3-fold ) by depletion of either Tsg101 or ALIX but was barely affected by UBPY or Eap45 siRNAs ( Fig . 7C , D ) , suggesting that ubiquitin binding to ESCRT-I and ALIX contributes to its ability to mediate particle release . This finding mirrors a previous report using ubiquitin fused to EIAV Gag [48] . Additionally , however , we further found that Lck-Gag-PY exhibited a similar pattern sensitivity to class E factor-targeting siRNAs , in that it was modestly sensitive to Tsg101 and ALIX but not Eap45 or UBPY siRNAs ( Fig . 7C , D ) . Similarly , the budding of an MLV Gag protein , that carries the same PPxY L-domain was also modestly sensitive to depletion of Tsg101 and ALIX Fig . 7C , D ) . Because ESCRT-I and ALIX perturbation both affected ubiquitin and PPxY-dependent budding , we sought to determine whether their simultaneous depletion would exhibit a stronger inhibitory effect . Unfortunately , cotransfection of the two pools of siRNAs ( or each pool together with normalizing control RNA duplexes , ) rendered each somewhat less effective , perhaps due to dilution of the active siRNAs ( Fig . 8A ) . Specifically , Tsg101 protein levels were reduced to 42±2% and 50±2% of endogenous levels , while ALIX protein levels were reduced to 30±3% and 27±2% of endogenous levels , when the Tsg101 or ALIX targeted siRNAs were cotransfected together or with normalizing control siRNAs , respectively ( Fig . 8A ) . Thus , under these conditions , siRNAs targeting ALIX did not inhibit Lck-Gag-Ub or Lck-Gag-PY particle release ( Fig . 8A , B ) . Nevertheless , simultaneous ( albeit partial ) depletion of Tsg101 and ALIX had a significantly stronger inhibitory effect on Lck-Gag-Ub , Lck-Gag-PY and MLV Gag budding ( Fig . 8A , B ) than did the more effective individual depletion of either Tsg101 or ALIX alone ( Fig . 7C , D ) , suggesting that they both proteins contribute to optimal PPxY and ubiquitin-dependent budding .
The precise role of HECT ubiquitin ligases in promoting PPxY-dependent virion release has , heretofore , been somewhat unclear . Our previous studies suggest that their ubiquitin ligase activity is critical for their ability to stimulate budding [17] , [29] , but the functionally relevant substrate for ubiquitination has been difficult to define . Additionally , there is some evidence suggesting that HECT ubiquitin ligases may also function as adaptors for bridging factors that recruit ESCRT proteins to assembling virions [17] , [32] , [51] . We compared the activities of HECT domains from various Nedd4-like family HECT ubiquitin ligases by fusing them to the C2 and WW domains of WWP1 . While this strategy does not illuminate which ubiquitin ligases are responsible for viral budding in the natural context , it does allow an assessment of HECT domain function in a uniform background . We found that HECT domains varied significantly in their ability to stimulate PPxY-dependent particle release in this context . This variability was evident when there were no ubiquitin acceptors in the Gag protein and correlated better with the ability of the HECT domains to drive autoubiquitination than with their ability to ubiquitinate a modified Gag substrate that contained lysines proximal to a PPxY motif . The correlation between autoubiquitination and budding was imperfect , however , and it is possible that variation among the HECT domains in their ability to catalyze different lengths and types of ubiquitin chains ( e . g . K48 versus K63-linked chains ) , or their ability to ubiquitinate other bystander proteins , could influence their ability to stimulate viral budding . In this regard it was notable that there was no correlation between the ability of the HECT domains to undergo autoubiquitination versus their ability to catalyze ubiquitin ligation to Lck-Gag-PY-3K . It was nonetheless true that the ability of the HECT domains to stimulate budding was , in every case , absolutely dependent on their ability to catalyze the ligation of ubiquitin to a substrate . This suggests that the proposed role of HECT domains as adaptors that bind directly to downstream factors is of secondary importance in stimulating budding , or that this adaptor function requires catalytic activity . This latter scenario could , conceivably , be operative as a result of HECT autoubiquitination . These studies underscore the remarkable flexibility in the ways that the ESCRT pathway can be engaged to achieve viral budding ( Fig . 9 ) Using a single viral Gag protein as a model , particle budding could be achieved by: ( i ) conventional direct recruitment of the ESCRT pathway via PTAP binding to Tsg101 , ( ii ) direct recruitment of the ESCRT pathway via PPxY binding to a hybrid cofactor consisting of the C2/WW domains of WWP-1 linked to the C-terminal domain of Tsg101 , ( iii ) recruitment of a HECT ubiquitin ligase via a PPxY motif , ( iv ) recruitment of an isolated HECT domain to a PTAP motif using a hybrid L-domain cofactor consisting of the UEV domain of Tsg101 linked to a HECT domain or ( v ) direct fusion of ubiquitin to Gag . These results suggest that the cellular factors ( in this case Tsg101 , ubiquitin ligases and ubiquitin ) that are either directly recruited or deposited at the site of viral particle budding behave as modular entities , with domains that are necessary and sufficient for their own recruitment , and distinct domains that are necessary and sufficient for the subsequent recruitment of downstream effectors of particle release ( Fig . 9 ) . When HECT domains were used to promote budding , the requirement for catalytic activity was absolute , irrespective of how they were recruited to Gag and , importantly , in the absence of ubiquitin acceptors on the viral Gag protein . This finding suggests that ligation of ubiquitin to trans-acting factors , perhaps including the HECT domain itself ( i . e . autoubiquitination ) , rather than Gag is important for viral budding . It is superficially paradoxical , therefore , that ubiquitin could promote budding of the very same Gag protein even when ubiquitin was not ligated to a trans-acting factor , but rather was directly fused to Gag . These findings suggest that the identity of the protein ( s ) to which ubiquitin is attached is not of critical importance , and ubiquitination substrates can , in principle , include Gag , the ubiquitin ligase itself , or other trans-acting proteins . The mere presence of ubiquitin at the site of particle assembly appears sufficient to engage the ESCRT pathway and stimulate budding . The intrinsic manipulability of L-domains , the proteins that bind to them ( specifically ESCRT-I and HECT ubiquitin ligases ) and the apparent lack of importance of the identity of ubiquitination substrate suggests that each serve simply as recruitment factors to engage the downstream machinery that mediates membrane fission and particle release . Since ubiquitin binds to the very same factors ( ESCRT-I and ALIX ) that are bound by PT/SAP and YPXL type L-domains , and depends on them to stimulate budding , then ubiquitin itself can be conceptually viewed , in the context of viral budding , as a transferable L-domain that acts in a position-independent manner . In essence , this notion is a simple extension of the concept originally demonstrated by Parent et al , who showed that conventional L-domains function in a position independent , transferable manner [56] . A finding that is consistent with the aforementioned arguments , is that budding that was dependent either on a PPxY motif or a ubiquitin fused directly to Gag exhibited similar dependence on particular components of the ESCRT pathway . Notably , perturbation of individual segments of the pathway ( ESCRT-I and ALIX ) caused partial inhibition of ubiquitin-dependent Lck-Gag-PY , Lck-Gag-Ub and MLV Gag particle release . Previous work has shown that Mason-Pfizer monkey virus particle release , which is dependent on a PPxY motif , is quite strongly inhibited by depletion of Tsg101 [31] and that budding of a EIAV Gag-ubiquitin fusion protein is modestly inhibited by Tsg101 or ALIX depletion [48] . We found that simultaneous perturbation of ESCRT-I and ALIX resulted a stronger suppression of Lck-Gag-PY , Lck-Gag-Ub and MLV Gag particle release than did depletion of either protein alone , suggesting that both ESCRT-I and ALIX can contribute to optimal PPxY- and ubiquitin-dependent budding ( Fig . 9 ) . Indeed , the class E VPS pathway includes multiple ubiquitin-interacting factors , each of which could , in principle , provide parallel mechanisms for engaging the ESCRT machinery . While ESCRT-I and ALIX appeared to be most important for PPxY- and ubiquitin-dependent budding , these experiments do not exclude a contributory role for other ubiquitin binding complexes in the ESCRT pathway . A similar notion was recently demonstrated in yeast , where simultaneous disruption of ubiquitin binding by ESCRT-I , -II and Bro1 ( the yeast homologue of ALIX ) was necessary to block the sorting of ubiquitinated cargo to the lysosome [57] . Thus , ubiquitin has several potential entry points into the ESCRT pathway , and it appears that multiple interactions must be simultaneously inhibited in order to profoundly inhibit ubiquitin- or HECT ligase-dependent budding . Since ubiquitin-binding class E VPS factors generally have a low affinity for individual ubiquitin molecules ( Table 1 ) , the efficiency with which they are recruited to , and retained at , sites of particle assembly is likely related to the number of ubiquitin molecules that are locally present . Indeed , in the context of direct ubiquitin fusion to Lck-Gag , particle release efficiency increased as the proportion of Gag molecules that carried a ubiquitin was increased , and directly fused ubiquitin could effectively bypass the need for a conventional L-domain only when a large fraction ( >50% ) of the Gag molecules were fused to ubiquitin . This approximates to ∼1000 to 2500 ubiquitin molecules per assembling virion . Previous studies have shown that direct ubiquitin fusion to RSV or EIAV Gag can alleviate a late budding defect imposed by proteasome inhibitors or functionally replace a YPDL L-domain [44] , [48] . However , this study is the first to demonstrate that ubiquitin can act synergistically with a PTAP motif , resulting in dramatically enhanced particle release when both are present . Moreover , the ability of fused ubiquitin to stimulate budding became evident at significantly lower Gag-ubiquitin abundance ( 5% to 25% of total Gag ) when a PTAP motif was also present in Gag . Since ubiquitin could serve as an additional docking site for Tsg101 , it might synergize with PTAP motifs by increasing the overall affinity of the assembling Gag lattice for individual ESCRT-I complexes . In fact , this property was predicted by previous binding studies involving Tsg101 UEV domain , PTAP containing peptides and ubiquitin [7] . Ubiquitin might also synergize with PTAP motifs by providing binding sites for distinct class E VPS factors ( e . g . ALIX ) , thereby optimally utilizing all the available components of the ESCRT machinery . Consistent with these ideas , PTAP and PPxY L-domains behaved synergistically in driving particle release , as did PTAP and Gag-fused ubiquitin . However , a PPxY motif and Gag-fused ubiquitin behaved redundantly , consistent with the notion that that they ultimately function through the same mechanism .
pCAGGS-based expression plasmids encoding Lck-Gag ( PSAP ) , Lck-Gag ( L- ) , Lck-Gag-PY , and Lck-Gag-PY-3K plasmids have been described previously [29] . The Lck-Gag ( ASAP ) plasmid was derived from Lck-Gag ( PSAP ) by PCR-based site-directed mutagenesis . The Lck-Gag ( PSAP ) -PY plasmid was generated by replacement of a StuI/XhoI fragment from the Lck-Gag ( PSAP ) plasmid with the corresponding fragment from the Lck-Gag-PY plasmid . cDNAs expressing Lck-Gag-Ub ( ubiquitinΔGG ) fusion proteins were generated by overlap-extension PCR , using pCAGGs-Lck-Gag ( PSAP ) , Lck-Gag ( L- ) , and Lck-Gag ( PSAP ) -PY as templates for the N-terminal portions and pHA-ubiquitin as the template for the C-terminal portion . The K48R , K63R , F4A , L8A , I44A , and QE62 , 64AA point mutations were introduced into the Lck-Gag-Ub construct by PCR-based mutagenesis . Each cDNA was cloned into pCAGGs for expression in mammalian cells . DNAs encoding the HECT domains from WWP1 ( residues 543–922 ) , WWP2 ( 491–870 ) , Nedd4 ( 520–902 ) , Nedd4L ( 593–975 ) , Itch ( 483–862 ) , Smurf1 ( 374–757 ) , and Rsp5 ( 431–809 ) were amplified from plasmids encoding the full-length HECT ligases [17] , [58] . The Bul2 HECT domain ( encoding residues 1189–1572 ) was PCR amplified from 293T cell cDNA . The catalytically inactive WWP1 HECT domain ( C890S ) was amplified from a previously described full-length mutant WWP1 ligase [17] . Catalytic point mutants of the remaining HECT domains were made by PCR-based mutagenesis . Chimeric ubiquitin ligases , comprising the C2 and WW domains ( residues 1–542 ) of WWP1 and each of the HECT domains described above were generated by overlap PCR . Likewise , plasmids expressing Tsg-WWP1 , Tsg-Nedd4L and Tsg-Itch ( residues 1–157 of Tsg101 fused to HECT domains of WWP1 , Nedd4L , or Itch ) as well as WWP1-Tsg-C ( residues 1–542 of WWP1 fused to residues 157–390 of Tsg101 ) were constructed by overlap-extension PCR . All cDNAs encoding chimeric proteins were inserted into pCR3 . 1/YFP , to express proteins fused to the C-terminus of YFP , for in mammalian cells . The class E VPS factor yeast two-hybrid library and plasmids expressing Vps4 E228Q , Tsg101 , Hrs , ALIX , UBPY , and Eap45 fluorescent fusion proteins in mammalian cells have been described previously [8] , [12] , [54] . Yeast two-hybrid plasmids encoding wild type and I44A mutant ubiquitin were constructed by PCR amplification of ubiquitinΔGG from the pHA-ubiquitin plasmid using 5′ and 3′ primers appended with EcoRI restriction sites and cloning into the pGBKT7 ( Clontech ) and pVP16 vectors [8] . For Gag particle release assays , 5×105 293T cells in six-well plates were transfected using polyethylenimine ( Polysciences ) with 1 µg of pCAGGs/Gag-derived plasmids , alone or with 1 µg of pCR3 . 1/YFP , pCR3 . 1/YFP-WWP1/HECT , pCR3 . 1/YFP-Tsg-HECT , or pCR3 . 1/YFP-C2-WW-Tsg-C plasmids , or the indicated amounts of pCR3 . 1/YFP-Vps4 E228Q plasmid . For EIAV and MLV VLP release assays , 293T cells were transfected in the same format with 500ng of , pCR3 . 1/EIAVGag or pCR3 . 1/MLVGag-HA plasmids . VLPs were pelleted by ultracentrifugation of 2 ml of 0 . 22-µm-filtered culture supernatants , collected 48 hours after transfection , over a 2ml 20% sucrose cushion for 90 min at 115 , 000×g . VLP and cell lysates were analyzed by Western blotting . 293T cells ( 5×105 ) in six-well plates were cotransfected with 1 µg of pCAGGs/Lck-Gag-PY-3K , 500 ng of pHA-ubiquitin , and 1 µg of the indicated chimeric pCR3 . 1-WWP1-HECT ligase . At 36h after transfection , cells were thoroughly lysed at room temperature in detergent-rich RIPA buffer ( 50mM Tris pH 7 . 4 , 150mM NaCl , 1mM EDTA , 1 . 0% glycerol , 0 . 5% SDS , supplemented with protease inhibitor tablets ( Roche ) and 5mM N-ethylmaleimide to inhibit deubiquitination ) and cleared of cellular debris by microcentrifugation . The lysates were then diluted 5-fold in the same buffer containing NP-40 rather than SDS , to adjust the concentration of SDS to 0 . 1% and NP-40 to 1 . 0% , and split into two fractions . From one fraction , Gag proteins were immunoprecipitated with αPFV serum and protein G-Sepharose beads . From the other fraction , YFP-HECT ligase proteins were immunoprecipitated with αGFP monoclonal antibody and protein G-Sepharose beads . Immunoprecipitates and unfractionated cell lysates were analyzed by Western blotting . 293T cells ( 3×105 ) in six-well plates were transfected with siGENOME siRNAs targeting Luciferase , Tsg101 , Hrs , Alix , UBPY , or Eap45 ( Dharmacon ) using Lipofectamine 2000 ( Invitrogen ) . After 24h , cells were transfected with the same siRNAs and the indicated Gag expression plasmids . VLP and cell lysates were prepared 48 h after the second transfection . To assess knockdown efficiency , 293T cells were transfected once with YFP-Tsg101 , -Hrs , -ALIX , -UBPY , or -Eap45 expression plasmids and corresponding siRNAs . GFP expression in cell lysates harvested 48 h after transfection was assayed by quantitative Western blotting . Virion and cell lysates and immunoprecipitates were separated on polyacrylamide gels , transferred to nitrocellulose membranes , and probed with various antibodies: anti-PFV human serum , anti-HIV-1 p24CA ( 183-H12-5C ) , anti-EIAV equine serum ( VMRD , Inc . ) , anti-GFP ( Roche ) , and anti-HA ( HA . 11 , Covance ) anti-Tsg101 ( 4A10 , Abcam , Cambridge , MA ) or anti-ALIX rabbit serum ( a gift from Wes Sundquist ) . Subsequently , the blots were probed with species-specific peroxidase-conjugated secondary antibodies and chemiluminescent substrate reagents . Alternatively , for quantitative Western blotting , membranes were probed with species-specific antibodies conjugated to IRDye800CW , and fluorescent signals were detected and quantified using a LICOR Odyssey scanner . Yeast cells ( Y190 ) were transformed with the pGBKT7- and pVP16-derived plasmids described above . Transformants were selected and protein-protein interactions were assayed by β-galactosidase reporter activity as previously described [8] . | The release of an enveloped virus particle from an infected cell requires the separation of the viral and cell membranes . Many enveloped viruses accomplish this by parasitizing a set of cellular proteins , termed the ESCRT pathway , that normally separates cellular membranes from each other . In some cases , viral structural proteins encode peptides motifs that bind directly to , and thereby recruit , the ESCRT machinery . Alternatively , viruses can recruit enzymes , termed ubiquitin ligases , that bind to other proteins , and catalyze the addition of ubiquitin to them . It has , heretofore , been somewhat unclear precisely how the recruitment of ubiquitin ligases leads to the engagement of the ESCRT machinery . We show that the simple recruitment of a fragment of a ubiquitin ligase that is responsible for the addition of ubiquitin to other proteins is sufficient to drive virus particle release , even when it is not possible to attach ubiquitin to viral proteins . Paradoxically , we also found that simple attachment of ubiquitin to the same viral protein can also drive particle release . These results show that there is flexibility in the ways in which the ESCRT machinery can be recruited and how ubiquitin can be co-opted to enable this . |
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Pore-forming toxins ( PFTs ) are by far the most abundant bacterial protein toxins and are important for the virulence of many important pathogens . As such , cellular responses to PFTs critically modulate host-pathogen interactions . Although many cellular responses to PFTs have been recorded , little is understood about their relevance to pathological or defensive outcomes . To shed light on this important question , we have turned to the only genetic system for studying PFT-host interactions—Caenorhabditis elegans intoxication by Crystal ( Cry ) protein PFTs . We mutagenized and screened for C . elegans mutants resistant to a Cry PFT and recovered one mutant . Complementation , sequencing , transgenic rescue , and RNA interference data demonstrate that this mutant eliminates a gene normally involved in repression of the hypoxia ( low oxygen response ) pathway . We find that up-regulation of the C . elegans hypoxia pathway via the inactivation of three different genes that normally repress the pathway results in animals resistant to Cry PFTs . Conversely , mutation in the central activator of the hypoxia response , HIF-1 , suppresses this resistance and can result in animals defective in PFT defenses . These results extend to a PFT that attacks mammals since up-regulation of the hypoxia pathway confers resistance to Vibrio cholerae cytolysin ( VCC ) , whereas down-regulation confers hypersusceptibility . The hypoxia PFT defense pathway acts cell autonomously to protect the cells directly under attack and is different from other hypoxia pathway stress responses . Two of the downstream effectors of this pathway include the nuclear receptor nhr-57 and the unfolded protein response . In addition , the hypoxia pathway itself is induced by PFT , and low oxygen is protective against PFT intoxication . These results demonstrate that hypoxia and induction of the hypoxia response protect cells against PFTs , and that the cellular environment can be modulated via the hypoxia pathway to protect against the most prevalent class of weapons used by pathogenic bacteria .
Pore-forming toxins ( PFTs ) are by far the most abundant and amongst the most important bacterial protein virulence factors [1] . These toxins , secreted by many pathogenic bacteria , function by binding to receptors on host cell plasma membrane , oligomerizing , inserting , and then forming holes [2] . The importance of PFTs in promoting bacterial pathogenesis has been demonstrated by numerous experiments where individual PFTs have been genetically deleted from pathogenic bacteria and the bacteria then tested for reduced virulence [3] . Examples of PFTs that contribute significantly to bacterial virulence include α-toxin by Clostridium septicum , streptolysins by Group A and B Streptococci , α-toxin by Staphylococcus aureus , Vibrio cholerae cytolysin ( VCC ) , α-hemolysin from uropathogenic E . coli , cytolysin from Enterococcus faecalis , and crystal ( Cry ) proteins from Bacillus thuringiensis ( Bt ) . Although they are expressed by many bacterial pathogens and are broadly important as potentiators of infection , the effects of these toxins on host cells have been vastly understudied . There are several reasons for this lack of attention . First , their mechanism of action is deceptively simple . Second , most of the attention has been given to understanding how PFTs can change conformation from secreted , soluble proteins to insoluble proteins embedded in the plasma membrane . Third , because breaching of the plasma membrane is a major insult to a cell , a multitude of cellular responses to PFTs have been reported , including Ca2+ influx , K+ efflux , increased endocytosis/exocytosis , vacuolization , necrosis , and apoptosis [3] , [4] , [5] , [6] . Because the responses are so large and extensive , it has been daunting to determine whether these responses contribute to defense , intoxication , both , or neither . Fourth , most of the studies carried out to date involved cells in isolated culture , which does not always accurately recreate the response of cells to toxins in the context of intact tissue . To address many of these limitations , an excellent genetic system for studying PFT responses in an intact animal has recently emerged: the Bacillus thuringiensis ( Bt ) Crystal ( Cry ) PFT – Caenorhabditis elegans toxin-host interaction system [7] . C . elegans has become an important genetically tractable organism for studying immune responses to bacterial pathogens [8] . Bt is thought to be a natural pathogen of C . elegans [9] , [10] , [11] and is famous for the production of three-domain PFTs that are widely used in insect biocontrol [12] . The interaction of Cry proteins with C . elegans allowed for the first molecular PFT defense pathway identified , p38 mitogen-activated protein kinase ( MAPK ) pathway [13] . Loss of the p38 MAPK pathway was shown to result in loss of protection against Cry PFTs in C . elegans and was subsequently shown to result in loss of protection against PFTs in mammalian cells as well [13] , [14] . This same system was used to discover that the unfolded protein response ( UPR ) is also required for PFT defenses as a downstream target of the p38 MAPK pathway [15] . Both the p38 MAPK pathway and the UPR were demonstrated to be activated by PFTs in C . elegans and mammals [15] , [16] . Apart from these studies , only one other study to date has demonstrated a specific molecular pathway as involved in PFT responses [17] . Since , when studying intracellular PFT response pathways in the past , we have screened for C . elegans mutants hypersensitive to PFTs [13] , [15] , we reasoned that we could learn something different by screening for the opposite phenotype– C . elegans mutants resistant to PFTs . The reason for this assumption is that no intracellular pathway mutants were known that can make cells resistant to PFTs in general . Here we report on the results of a PFT resistance screen and find , unexpectedly , that resistance can be achieved by mutations that up-regulate the C . elegans low oxygen ( hypoxia ) response . Elimination of HIF-1 ( hypoxia inducible factor 1 ) , the main effector of the hypoxia pathway , abrogates this resistance and can lead to PFT hypersensitivity . This protection applies to multiple different PFTs and is clearly distinguished from the role of the HIF-1 pathway in other stress responses and aging . Furthermore , the hypoxia pathway is activated in response to PFTs , and low oxygen is itself protective against PFT attack . Our results indicate that the hypoxia/low oxygen response is likely to be of general importance for cellular responses to small-pore PFTs .
To identify pathways important for cellular responses to PFTs , we screened for mutants resistant to the PFT Cry protein , Cry21A . Cry21A is a three-domain Cry protein that targets nematodes and is in the same family as Cry5B [11] . Like Cry5B [18] , secondary structure programs predict Cry21A contains all the α helical segments that are involved in pore-formation in three-domain Cry proteins [19] . All three-domain Cry proteins , like Cry5B and Cry21A , are believed to act as PFTs , and pore-forming activity has been demonstrated for all Cry proteins so studied to date [12] . In the past , we have screened for mutants resistant to Cry5B , which has given rise to detailed understanding of the Cry5B receptor [20] , [21] , [22] but not to information on intracellular responses to Cry PFTs . Our rationale for screening for Cry21A PFT resistant animals was that it could elucidate new information about how cells respond to PFTs since Cry5B resistant mutants are only weakly resistant to Cry21A . To perform this screen , C . elegans hermaphrodites were mutagenized with EMS and allowed to self-fertilize for two generations . Sixty eight thousand F2 mutagenized hermaphrodites were fed an intoxicating dose Cry21A PFT and then screened for those that resisted intoxication . One mutant line , allele ye49 , was identified that bred true and is resistant to Cry21A PFT produced either from Bt or E . coli ( Figures 1A and 1B ) . To identify the gene mutated in ye49 , we performed standard three-factor and single-nucleotide mapping experiments , which narrowed the search to a region on chromosome V , between markers snp_F15H10 and snp_T21C9 , that includes 43 genes . Mutation in one of the genes in this region , egl-9 , had been previously identified as resistant to cyanide produced by Pseudomonas aeruginosa PA01 [23] . We therefore performed complementation testing between ye49 and the egl-9 null allele egl-9 ( sa307 ) and found that ye49/egl-9 ( sa307 ) animals are resistant to Cry21A PFT , indicating ye49 fails to complement egl-9 ( sa307 ) and most probably mutates the same gene ( Figure 1C ) . Furthermore , injection of an extrachromosomal array bearing the egl-9 promoter and coding region restored wild-type Cry21A susceptibility to ye49 animals ( Figure 1D ) . In addition , sequencing of egl-9 cDNA isolated from the ye49 mutant identified a point mutation ( W508-to-stop ) that upon translation is predicted to truncate the protein in the prolyl hydroxylase domain , thereby eliminating protein hydroxylase function ( Figure 1E ) . These results demonstrate that Cry21A PFT resistance phenotype associated with ye49 is due to loss of egl-9 function mutation . As predicted from this conclusion , feeding of double-stranded RNA to cause RNA interference ( RNAi ) results in animals resistant to Cry21A ( Figure 1F ) . The EGL-9 protein is a prolyl hydroxylase and functions in the C . elegans low oxygen response ( hypoxia ) pathway ( Figure 2A; [24] ) . The ability of cells to respond to hypoxia is mediated by a transcription factor called HIF-1α . Under normal oxygen ( normoxia ) conditions , HIF-1α ( called HIF-1 in C . elegans ) is hydroxylated by a prolyl hydroxylase ( EGL-9 in C . elegans or PHD in mammals ) that then increases HIF-1's affinity for von Hippel-Lindau tumor suppressor protein ( called VHL-1 in C . elegans ) , part of an E3 ubiquitin ligase complex . Association of HIF-1 with VHL-1 eventually leads to HIF proteasomal degradation . When EGL-9 is disabled by mutation , HIF-1 is stabilized at constitutively high levels even under normoxic ( normal oxygen ) conditions [25] . Since loss of EGL-9 function confers resistance to Cry21A PFT , we hypothesized that other elements of the hypoxia pathway might be important as well . We therefore performed quantitative dose-dependent mortality assays using null or putative null alleles of all the above elements of the hypoxia pathway . L4 staged animals from each genotype and wild-type N2 were placed in numerous doses of Cry21A PFT or Cry5B PFT and scored for viability after a few days ( Figures 2B and 2C ) . As predicted from the above studies , we find that animals lacking EGL-9 are quantitatively resistant to Cry PFTs . At doses from 1–16 µg/mL Cry21A and 10–80 µg/mL Cry5B PFTs , egl-9 ( sa307 ) and egl-9 ( ye49 ) animals are resistant to PFT-induced mortality relative to wild-type animals , with resistance strongest at higher PFT doses ( Figures 2B and 2C; Table 1 ) . For example , 7×–10× more egl-9 mutant animals are alive at 8 µg/mL Cry21A or 40 µg/mL Cry5B PFT than wild-type animals at the same doses ( P<0 . 001; Table 1; note , direct dose comparison between Cry21A and Cry5B toxicity is not possible since Cry21A assays are performed with Bt spore-crystal lysates and Cry5B assays with purified protein ) . Based on LC50 values ( concentration at which 50% of the animals are dead ) , loss of EGL-9 results in 3–5 fold resistance over wild-type animals to Cry21A or Cry5B PFTs ( Table 1 ) . Note , since all our mortality assays are carried out in liquid medium , resistance to the PFT cannot be attributed to improved avoidance behaviors . Cry21A PFT resistance was also confirmed using a quantitative brood size assay ( Figure S1 ) . We also found that vhl-1 ( ok161 ) mutant animals are resistant over a similarly wide range of Cry21A and Cry5B PFT doses ( Figures 2B and 2C; Table 1 ) . For example , 6 . 2× and 7 . 4× more vhl-1 mutant animals are alive at 8 µg/mL Cry21A and 40 µg/mL Cry5B , respectively , than wild-type animals . Based on LC50 values , vhl-1 mutant animals are 4× resistant to Cry5B PFT . We also tested rhy-1 ( ok161 ) mutant animals on Cry21A PFT . RHY-1 ( regulator of hypoxia-inducible factor ) antagonizes HIF-1 function by inhibiting expression of some HIF-1 target genes via a VHL-1 independent pathway [26] . Animals lacking RHY-1 are also resistant to Cry21A ( Table 1; Figure S2 ) . Based on LC50 values , animals lacking RHY-1 are 5 . 7× resistant to Cry21A PFT ( Table 1 ) . These data demonstrate that loss of function mutations in genes that normally antagonize HIF-1 function all result in resistance to Cry protein PFTs . In other words , stimulation of HIF-1 function via removal of HIF-1 inhibitory factors results in PFT resistance . To confirm that the resistance associated with egl-9 mutants was going through HIF-1 , we looked at the dose-dependent response of hif-1 ( ia04 ) and egl-9 ( sa307 ) hif-1 ( ia04 ) double mutant animals to Cry21A and Cry5B PFTs . When fed Cry21A , hif-1 ( ia04 ) animals have a response that is indistinguishable from wild-type animals ( Figure 2B; Table 1 ) . egl-9 ( sa307 ) hif-1 ( ia04 ) double mutant animals have a statistically identical response to Cry21A as wild-type animals at all doses except at 8 µg/mL ( Table 1 ) . Furthermore the LC50 values of hif-1 ( ia04 ) and egl-9 ( sa307 ) hif-1 ( ia04 ) animals on Cry21A PFT are statistically identical ( P>0 . 05 ) but both statistically different than that of egl-9 ( sa307 ) ( P<0 . 01; Table 1 ) . These results have been qualitatively confirmed using RNAi—whereas wild-type animals subject to egl-9 RNAi are resistant to Cry21A , hif-1 ( ia04 ) mutant animals subject to egl-9 RNAi are not ( Figure S3 ) . Similarly , whereas egl-9 ( sa307 ) mutants are resistant to Cry21A , this resistance is suppressed by RNAi of hif-1 . Thus , HIF-1 is required for Cry21A resistance mediated by loss of EGL-9 . The results with Cry5B PFT are similar to those of Cry21A ( Figure 2C; Table 1 ) in that HIF-1 is required for Cry5B resistance mediated by loss of EGL-9 ( i . e . , loss of HIF-1 suppresses Cry5B PFT resistance associated with loss of EGL-9 ) . There is , however , one striking difference between the hif-1 results with Cry21A and Cry5B . Both hif-1 ( ia04 ) and egl-9 ( sa307 ) hif-1 ( ia04 ) animals are hypersensitive to Cry5B PFT relative to wild-type animals . That is , animals lacking HIF-1 are more readily killed by Cry5B PFT than wild-type animals , especially at doses ∼5–10 µg/mL ( P<0 . 05; Figure 2C; Table 1 ) . Thus , hif-1 is required for intrinsic cellular defenses ( INCED ) [15] against Cry5B PFT . With regards to the different results with Cry5B and Cry21A , we speculate that perhaps Cry5B PFT intoxicates more potently than Cry21A and that , whereas increased HIF-1 activity is protective against all PFTs , loss of HIF-1 activity is more acutely felt when the stronger PFT is present . In the case of Cry21A , other INCED pathways are sufficient for full protection even in the absence of HIF-1 . Cry proteins are small-pore PFTs . To test whether or not the hypoxia pathway was more generally required for INCED against PFTs , we fed C . elegans two V . cholerae strains that differ primarily in their ability to produce another small-pore PFT , VCC . VCC is a virulence factor of V . cholerae and mutants lacking VCC are attenuated for pathogenesis in vivo , especially for strains lacking cholera toxin [27] , [28] . The strains we use are CVD109 ( VCC+ ) and CVD110 ( VCC− ) that are nearly isogenic ( except for the presence of cholera toxin B subunit in CVD110 , which should not matter since C . elegans lacks sialic acid that the B subunit binds to as part of its GM1 receptor [29] ) . Although both strains are pathogenic when fed to C . elegans , CVD109 ( VCC+ ) is more virulent than CVD110 ( VCC− ) , demonstrating that VCC is a virulence factor for C . elegans ( Figure 3A ) . Our results with hypoxia pathway mutants on CVD109 ( VCC+ ) and CVD110 ( VCC− ) are striking and parallel those with Cry PFTs . When feeding on CVD109 ( VCC+ ) , egl-9 ( sa307 ) animals are resistant relative to wild-type animals ( Figure 3A; Table 2; median survival 4 vs . 3 days respectively; P<0 . 001 ) . This resistance is dependent upon the presence of VCC since when feeding on CVD110 ( VCC− ) , egl-9 ( sa307 ) animals are not resistant ( Figure 3A; Table 2 ) . Similarly , hif-1 ( ia04 ) and egl-9 ( sa307 ) hif-1 ( ia04 ) animals are , as with Cry5B PFT , hypersensitive relative to wild-type animals on CVD109 ( VCC+ ) ( median survival of 2 , 1 , and 3 days respectively; P<0 . 0001; Figure 3B; Table 2 ) . This hypersensitivity is dependent upon the presence of VCC since these mutants are not hypersensitive when feeding on CVD110 ( VCC− ) ( Figure 3B; Table 2 ) . It is interesting to note that egl-9 ( sa307 ) mutant animals are hypersensitive compared to wild-type animals to CVD110 ( VCC− ) strain ( median survival of 4 and 6 days respectively; P<0 . 0001; Figure 3A; Table 2 ) . We speculate that while activation of the hypoxia pathway ( in an egl-9 mutant or otherwise ) protects the animals against VCC and PFTs ( hence egl-9 mutants are resistant to the VCC+ strain ) , activation of the hypoxia pathway may make the animals more susceptible to other V . cholerae virulence factors . The relative contribution to these responses ( protection versus susceptibility ) is dependent upon which virulence factors are present and their relative contribution to virulence . In the VCC+ strain , the PFT has important function . Hence , the protective role of pathway activation can be discerned . In the VCC− strain , the PFT defense is no longer needed . Hence , the susceptible role can be discerned . It is this give-and-take interaction between the host and virulence factors that could partly explain why constitutive mutation in egl-9 is not selected in the wild . In any event , taken together , our Cry PFT and VCC data demonstrate that stabilization of HIF-1 results in resistance to VCC PFT whereas loss of HIF-1 results in hypersensitivity to VCC PFT . Because loss of EGL-9 results in resistance to PFTs ( here ) and cyanide [23] , [30] , we hypothesized that egl-9 mutant animals might show resistance to other stressors as well . We found that , relative to wild-type animals , animals lacking EGL-9 are resistant to killing by 1 ) the pathogen Pseudomonas aeruginosa PA14; 2 ) heat stress; and 3 ) oxidative stress ( Figure 4A , Table 2; Figures 4B and 4C ) . Since correlation between stress response and lifespan had previously been reported , such as in the daf-2 mutant [31] , [32] , we tested whether loss of EGL-9 had an effect on longevity . Indeed , egl-9 ( ye49 ) and egl-9 ( sa307 ) mutant animals live longer than N2 wild-type when feeding on the standard E . coli strain ( Figure 4D , Table 2 ) . To study the relationship between the hypoxia response pathway and resistance to stresses in more detail , we asked if the resistance to these different stresses via loss of EGL-9 was , as for resistance to PFTs , mediated through HIF-1 . Unexpectedly , we found that hif-1 ( ia04 ) loss-of-function mutant animals as well as egl-9 ( sa307 ) hif-1 ( ia04 ) mutant animals are resistant to P . aeruginosa PA14 infection , heat stress , and oxidative stress ( Figure 4E , Table 2; Figures 4F and 4G ) . Both mutant strains are also long lived ( Figure 4H; Table 2 ) . Thus , in the case of these stresses , but unlike that of PFT response , loss of either EGL-9 , HIF-1 , or both results in stress resistance . We speculate that , in the case of these other stresses , hydroxylation of HIF-1 by EGL-9 may result in its activation prior to degradation . Similar results have been previously reported in that mutation of either hif-1 or egl-9 results in C . elegans resistant to pathogenic E . coli [33] . With regards to lifespan , published studies are contradictory but there is at least one published report with egl-9 mutants long lived and two with hif-1 long-lived [34] , [35] , [36] . In any event , our results demonstrate that role of the hypoxia pathway in PFT INCED is separable from that of other stress responses . Bt Cry PFTs attack intestinal cells [21] , [22] , [37] . It is possible that the hypoxia defense pathway functions within the cells targeted by the PFTs or that the hypoxia pathway is functioning cell non-autonomously . To address this question , we expressed egl-9 under the control of various promoters including the intestinal specific cpr-1 promoter [21] , [22] , [38] and the unc-31 promoter , which is expressed in all neurons and in secretory cells of the somatic gonad [39] . We find that expression of wild-type EGL-9 under the cpr-1 promoter in the intestinal cells of egl-9 ( sa307 ) animals ( Figure 5 ) , but not under the unc-31 promoter in the neuronal or secretory cells ( not shown ) , is sufficient to rescue the egl-9 ( sa307 ) Cry21A resistance phenotype . Control animals in which green-fluorescent protein ( GFP ) was expressed from the cpr-1 promoter did not result in rescue . Quantitative mortality assays using two independent lines of cpr-1::egl-9-transformed egl-9 ( sa307 ) mutant animals confirm that intestinal-specific expression of EGL-9 rescues Cry21A PFT resistance to a level statistically indistinguishable from N2 wild-type ( not shown ) . These data are consistent with the hypoxia pathway acting to directly counteract the effects of PFTs and not , for example , providing protection via altered behavior . To address how the hypoxia pathway might function in protection against PFTs , we sought in two ways to find functional downstream effectors of the pathway . First , we compared known functional targets of the hypoxia pathway in C . elegans and asked if any of these are involved in PFT defenses . One pathway immediately surfaced , the unfolded protein response or UPR [40] . It has been recently reported that the hypoxia pathway genetically functions upstream of the XBP-1 arm of the UPR with regards to longevity in C . elegans [35] . Furthermore , we have previously shown that the XBP-1 is required for PFT INCED since loss of XBP-1 leads to animals that are hypersensitive to Cry5B PFT [15] . These data suggest that the XBP-1 arm of the UPR is one downstream target of the hypoxia PFT INCED . To test this suggestion , we examined whether or not the hypoxia pathway regulates activation of the XBP-1 UPR pathway . Activation of the XBP-1 UPR pathway can readily be discerned by examining xbp-1 mRNA , which is spliced upon activation of the pathway [41] . We indeed find that activation of the hypoxia pathway results in activation of the UPR as seen by a 1 . 4 fold increase in spliced xbp-1 levels in egl-9 mutant animals ( P<0 . 001; see Materials and Methods ) . Thus , one functional downstream effector of the hypoxia pathway for PFT defenses is the XBP-1 UPR . We conversely asked if any of the genes known to be involved in PFT INCED are known to be important for the hypoxia pathway . From over 100 PFT INCED genes we have identified in our lab , we found one and only one currently known to be regulated by the hypoxia pathway , nhr-57 . nhr-57 was initially identified as part of the hypoxia pathway by the fact that its expression is positively regulated by hif-1 and negatively regulated by egl-9 and vhl-1 [42] , [43] . In fact , nhr-57 transcriptional activation is considered the most reliable marker for activated HIF-1 function in C . elegans [26] . We confirmed using quantitative PCR that in egl-9 mutant animals , nhr-57 transcripts are induced 15 fold and that this increase is completely dependent upon HIF-1 ( data not shown ) . However , to date no functional role of nhr-57 for any HIF-1-regulated pathway has been shown . We find that knock down of nhr-57 results in animals slightly but statistically hypersensitive to Cry5B PFT ( e . g . , 21% reduction in viability for nhr-57 RNAi at 20 µg/mL Cry5B PFT versus vector-only RNAi control , P = 0 . 02; n = 90; see Materials and Methods ) and therefore defective in PFT INCED . More impressively , we find that knock down of nhr-57 completely suppresses the resistance to Cry21A PFT associated with loss of EGL-9 ( Figure 6 ) . Taken together , these results indicate that the nuclear receptor nhr-57 is a second functional downstream effector of the hypoxia PFT defense pathway . Although the above data demonstrate the hypoxia pathway is important for PFT INCED , they do not directly address whether the defense against PFTs is related to a low oxygen response or to some other function of the HIF-1 pathway . We therefore examined whether the hypoxia pathway itself is activated by PFTs using nhr-57 expression , the canonical marker for HIF-1 pathway activation by low oxygen in C . elegans ( see above ) . We find that 4 and 8 hours of treatment with PFT significantly induces nhr-57 expression 5 . 3 and 3 . 6 fold respectively ( Figure 7A ) . Shorter treatments with PFT do not . Thus , PFT induces the hypoxia pathway . If a low oxygen response is involved in responding to PFTs , then one might predict that exposure to low oxygen might confer protection against PFT attack since the low oxygen environment might strongly and rapidly induce the correct protective response . We therefore exposed C . elegans hermaphrodites to low ( 2% ) oxygen levels minus or plus the presence of E . coli-expressing Cry5B PFT . We find that low oxygen is indeed protective against PFT intoxication in that animals exposed to PFT in a low oxygen environment for 24 hours are significantly healthier than animals exposed to PFT in normoxia ( Figure 7B ) . Similar results were obtained for animals exposed to a low oxygen environment for three days ( Figure S4 ) . In contrast and as expected , hif-1 ( ia04 ) mutant animals exposed to Cry5B PFT do not get any protection when placed in a hypoxic environment ( Figure 7B ) , confirming that the protective effect of hypoxia against PFT is due to activation of the HIF-1 pathway .
Our results demonstrate that the hypoxia pathway protects C . elegans against PFTs , whether Bt Cry protein PFTs or a PFT used by a mammalian pathogen , V . cholerae VCC . We find that activation of HIF-1 pathway by removal of any of EGL-9/PHD , VHL-1 , or RHY-1 , makes C . elegans more resistant to PFTs than they normally are . This resistance is completely abrogated upon loss of HIF-1 , which can additionally result in animals hypersensitive in PFTs . Resistance to PFTs functions in the cells directly targeted by PFTs and is not associated with other hypoxia-mediated stress resistance phenotypes . Furthermore , exposure to PFT induces transcriptional activation of the HIF-1 low oxygen pathway , and exposure of animals to low oxygen protects animals against PFT intoxication , through a HIF-1-dependent mechanism . A schematic summarizing our findings here is in Figure 7C . Consistent with our finding that activation of the HIF-1 pathway is protective against PFTs , it has been shown that expression of the HIF-1α protein is increased in human airway cells by S . aureus supernatants , of which α-toxin is a major constituent [44] . The simplest interpretation of our data is that PFT intoxication is associated with low oxygen in cells , and that the hypoxia pathway is therefore needed to protect the cells against this condition . Alternatively , although less parsimoniously , it is possible that both hypoxia and PFTs trigger the same set of HIF-1 downstream mediators that are protective against both assaults but that are not otherwise linked by the presence of low oxygen . Two downstream effectors of the hypoxia PFT INCED pathway are the UPR and nhr-57 . The fact that nhr-57 is involved in hypoxia PFT INCED suggests that multiple transcriptional responses are key to mounting an effective defense against PFTs . The link between the XBP-1 UPR , hypoxia , and PFTs is intriguing . It has already been shown in mammalian cells that hypoxia induces activation of the XBP-1 UPR as detected by an up-regulation in xbp-1 mRNA splicing by low oxygen [45] . Furthermore , it has been shown that XBP-1 protects cells against hypoxia-induced apoptosis [45] . Therefore , we speculate that one role of the hypoxia pathway in PFT INCED is to induce an XBP-1-linked protective response against PFT/low oxygen-mediated apoptosis . Given that the p38 MAPK pathway is also linked to PFT defenses and the UPR , it will be interesting to explore further links between hypoxia , the UPR , p38 and apoptotic pathways in response to PFTs . Although this report is not the first of hypoxia pathway involvement in immunity , it is the first showing a link between hypoxia and protection of cells that are being directly attacked by a virulence factor . Control of the metabolic shift to glycolysis by HIF-1α has been shown to play an important role in myeloid cell-mediated inflammatory response [46] . Furthermore , it has been shown that bacteria increase HIF-1α protein expression and stimulate HIF-1α transcriptional activity in macrophages , regulating the expression of immune effectors molecules , including antimicrobial peptides , nitric oxide and tumor necrosis factor-α [47] . Our results point to a new and different role of the hypoxia pathway , namely in providing autonomous protection of epithelial cells against PFTs . To our knowledge , these results are the first to demonstrate that an intracellular pathway can be altered to promote general resistance to PFTs . Although a few receptor mutants that confer resistance to PFTs have been previously identified , these do not confer general resistance . A logical extension of our findings is that significant therapeutic benefit against a wide range of bacterial pathogens such as S . aureus , Streptococci , Clostrida , V . cholerae ( all of which use PFTs as virulence factors ) could be achieved by up-regulation of HIF-1 and/or by hypoxia . The identification of the hypoxia pathway as an important PFT INCED pathway thus unexpectedly provides a novel and potentially powerful means of protecting against the single most common mode by which bacterial pathogens attack us .
Strains were maintained at 20°C under standard conditions [48] . The wild-type strain for this study is N2 Bristol [48] . The strains egl-9 ( sa307 ) , hif-1 ( ia04 ) , egl-9 ( sa307 ) hif-1 ( ia04 ) , vhl-1 ( ok161 ) , rhy-1 ( ok1402 ) , the Hawaiian strain CB4856 and HT1593 [unc-119 ( ed3 ) ] were obtained from the Caenorhabditis Genetic Center ( CGC ) . All strains were either previously outcrossed or outcrossed here at least six times ( e . g . , egl-9 ( ye49 ) , rhy-1 ( ok1402 ) ) . egl-9 ( sa307 ) is a null allele of egl-9 that carries an internal 243-bp deletion removing part of exons 5 and 6 [23] . hif-1 ( ia04 ) allele removes exons 2 , 3 and 4 of hif-1 , including the DNA binding domain , and is believed to be a null allele [49] . The vhl-1 ( ok161 ) allele removes exons 1 and 2 of vhl-1 and is believed to be a null allele [25] . The rhy-1 ( ok1402 ) allele deletes exons 2 , 3 and 4 of rhy-1 and is also believed to be null [26] . Images were acquired using an Olympus SZ60 dissecting microscope and a Canon PowerShot A620 digital camera . For production in Bt , the cry21A gene was cloned under 700 bp of the cry6A promoter region and subcloned into the Bt/E . coli shuttle vector pHT3101 . The plasmid was transformed into a nontoxic host Bt strain ( 4D22 ) . Cry21A SCLs were prepared using standard procedures [50] and the concentration was measured relative to BSA standards on protein gels . Mutagenesis and selection of Cry21A resistance mutants was carried out as described for Cry5B [20] except Cry21A SCLs were used to a final concentration of 0 . 25 µg/mL Cry21A . The 68 , 000 F2 animals were taken from a larger population of 1 , 300 , 000 F2 animals that came from 240 , 000 mutagenized F1 animals . Animals were incubated for 72 hours at 20°C and scored for overall health , including color , size , movement and brood size . Clonal lines were established from candidates and retested . Complementation tests were performed by testing F1 progeny from the cross between egl-9 ( sa307 ) males and dpy-17 ( e164 ) ;ye49 hermaphrodites . As a control , cross-progeny from egl-9 ( sa307 ) males into dpy-17 ( e164 ) and from N2 males into dpy-17 ( e164 ) ;ye49 were also tested . ye49 was mapped between dpy-11 ( e224 ) and unc-76 ( e911 ) using standard three-factor mapping . A dpy-11 ( e224 ) ye49 unc-76 ( e911 ) triple mutant was then made in order to perform single nucleotide polymorphism mapping with the Hawaiian strain ( CB4856 ) [51] . Genomic DNA and cDNA prepared from egl-9 ( ye49 ) animals were used to sequence the egl-9 gene . For transformation rescue , a 13 . 4kb-PCR fragment covering from 3kb upstream to 2kb downstream of egl-9 transcript was amplified with primers GAGCAACTCGTGGGTTTGTT and CTTCCAGAGGCGTTGTCTTC using the LongAmp Taq ( Biolabs ) from N2 genomic DNA and injected in egl-9 ( ye49 ) worms as described [52] . For tissue-specific rescue , egl-9 rescuing plasmids were constructed by PCR amplification of unc-31 and cpr-1 promoters and then fused to egl-9 and gfp open reading frames using the Multisite Gateway cloning system ( Invitrogen ) and pCG150 ( containing unc-119 rescuing fragment ) [53] . The constructs were verified by sequencing and integrated into HY0843 [unc-119 ( ed3 ) ;egl-9 ( sa307 ) ] by ballistic bombardment [54] with a PDS-1000/He Biolistic Particle Delivery System ( Bio-Rad , Hercules , CA ) . Two independent lines of each transgenic strain were examined . For Cry21A E . coli toxin assays , we used E . coli JM103 with pQE9 empty vector or a cry21A gene insert under control of the lacZ promoter [11] . Since Cry21A is expressed at very high levels by E . coli [11] and too potent for scoring for resistance , we diluted the toxin-expressing bacteria with non-toxin-expressing bacteria at a ratio of 1∶40 for all tests in this study , similar to that previously described for Cry5B studies [13] , [15] . Dose-dependent mortality assays with purified Cry5B were performed as described [52]; hermaphrodite viability was scored after 6 days at 25°C . Cry21A SCLs were used for quantitative mortality assays as described above except hermaphrodite viability was scored after 72 hours at 20°C . Each assay was set up with triplicate wells for each concentration of Cry toxin , and each experiment was performed in at least three independent trials . Typically 180 worms were scored for each concentration . V . cholerae lifespan assay was performed as described [55] except the overnight culture was spread on Brain Heart Infusion ( BHI ) plates . CVD109 Δ ( ctxAB zot ace ) and CVD110 Δ ( ctxAB zot ace ) hlyA:: ( ctxB mer ) Hgr strains , derived from V . cholerae El Tor E7946 , were used [56] . The experiment was performed three times with approximately 50 worms per strain at room temperature ( 22°C ) . P . aeruginosa lifespan assays were performed on slow-killing plates as described [15] . Heat shock assays were performed as described [57] . For the oxidative stress analysis , synchronized young adults were exposed to 7 . 5 mM t-butyl hydrogen peroxide as described [58] and were observed after 6 hours . Life-span assays were initiated by allowing adult hermaphrodites to lay eggs on NG plates spread with OP50 . When these eggs hatched and the nematodes reached the L4 stage they were transferred to fresh NG plates with OP50 supplemented with 25 µM 5-fluorodeoxyuridine ( FUDR ) to prevent eggs from hatching . The nematodes were scored for live/dead every 48 hours by tapping the nose at least three times ( no movement for all taps was scored as dead ) . For RNAi tests , adult hermaphrodites were allowed to lay eggs on NG plates containing 100 µM Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) and 50 µg/mL ampicillin spread with E . coli strain HT115 expressing double-stranded ( ds ) RNA ( from the Ahringer library [59] ) for 8 hours and then removed . The eggs were allowed to develop into L4 larvae on RNAi plates at 20°C . L4 hermaphrodites ( ten per genotype or line ) were picked onto toxin plates spread with 100 µl of a mixture of E . coli strain HT115 expressing the same dsRNA and HT115 harboring cry21A-expressing vector at the ratio 40∶1 . For no toxin control plates , 100 µl of HT115 with dsRNA was spread . nhr-57 ( RNAi ) testing on Cry5B PFT was performed slightly differently ( Kao et al . , manuscript in preparation ) . Briefly , rrf-3 ( pk1426 ) animals were fed E . coli-expressing dsRNA in liquid media with 1mM IPTG at 25°C for ∼30 h . 20 µg/ml of Cry5B or 20 mM HEPES control were then added , as well as 200 µM FUdR . Hermaphrodites viability was scored after 6 days at 25°C . ( As this assay is set up differently , direct comparison with dose-dependent mortality assays presented in Table 1 and associated Figures is not possible ) . To test worms under hypoxia , L4 wild type and hif-1 ( ia04 ) mutant animals were pipetted onto toxin plates spread with 30 µl of a mixture of E . coli OP50 strains expressing or not Cry5B at the ratio 1∶1 . Plates were placed immediately in a 2% O2 chamber for 24 hours , while control plates were placed in room air . Images were taken with an Olympus BX60 microscope as described [15] . Real time PCR was performed as described [15] . To determine the levels of spliced xbp-1 mRNA , we used primers xbp-1_sqf2 GCATGCATCTACCAGAACGTC and xbp-1_sqr2 GTTCCCACTGCTGATTCAAAG to amplify cDNA from wild-type and egl-9 ( sa307 ) animals . The forward primer xbp-1_sqf2 anneals to exon 1 and the reverse primer xbp-1_sqr2 anneals to the exon1-exon 2 junction sequence produced when intron 1 is spliced out . The experiment was carried out using two independent sets of cDNA and two repeats within each set . Primers TTATCGAGTTTCTCGCATTGG and AAGTCTGCAATCACGCTCTGT were used to quantify expression of nhr-57 . Induction of expression of nhr-57 by Cry5B was tested in glp-4 ( bn2 ) animals treated for 1 , 2 , 4 and 8 hours on E . coli OP50 strains expressing Cry5B or not . The experiment was carried out using three independent sets of cDNA . Normalization in all cases was to eft-2 transcript levels . LC50 values were determined by PROBIT analysis [60] . Mortality assays were plotted using GraphPad Prism 5 . 0 ( San Diego ) . Statistical analysis between two values was compared with a paired t-test . Statistical analysis among three or more values of one independent variable was done with matched one-way ANOVA with Tukey's method and of more than two independent variables by two-way ANOVA with the Bonferroni post test . For lifespan analysis , survival fractions were calculated using the Kaplan-Meier method and survival curves compared using the logrank test . Statistical significance was set at P<0 . 05 . | Bacteria make many different protein toxins to attack our cells and immune system in order to infect . Amongst them , pore-forming toxins ( PFTs ) , which punch holes in the protective plasma membrane that surrounds cells , are by far the most abundant and constitute important virulence factors . Since the integrity of the plasma membrane is fundamental to maintaining the normal intracellular environment , the breaching of the plasma membrane by PFTs results in many and dramatic intracellular responses . However , we know little about the relevance of these responses to cell survival or cell intoxication . Here , using the only genetic system for studying pore-forming toxin effects in a whole animal , we show that the same response that protects cells against low oxygen stress unexpectedly also protects cells against pore-forming toxins . Mutations in the animal that hyper-activate the low oxygen response actually make animals resistant to pore-forming toxin attack , whereas mutations that inactivate the low oxygen response make animals more susceptible . Furthermore , a low oxygen environment itself is protective against pore-forming toxins . These data show a new and powerful connection between low oxygen responses and defense against the single most common mode of bacterial attack . |
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Mayaro virus ( MAYV ) is an emerging , mosquito-borne alphavirus that causes a dengue-like illness in many regions of South America , and which has the potential to urbanize . Because no specific treatment or vaccine is available for MAYV infection , we capitalized on an IRES-based approach to develop a live-attenuated MAYV vaccine candidate . Testing in infant , immunocompetent as well as interferon receptor-deficient mice demonstrated a high degree of attenuation , strong induction of neutralizing antibodies , and efficacy against lethal challenge . This vaccine strain was also unable to infect mosquito cells , a major safety feature for a live vaccine derived from a mosquito-borne virus . Further preclinical development of this vaccine candidate is warranted to protect against this important emerging disease .
Mayaro virus ( MAYV ) is an important and growing human health concern in the neotropics . First isolated in Mayaro county , Trinidad in 1954 , cases of Mayaro fever ( MAY ) have since been reported in 9 different countries in northern South America [1] . In addition , serological surveys suggest that MAYV has expanded into the Central American countries of Costa Rica , Guatemala , and Panama [2] . Typical presentations of MAY consist of an acute febrile illness accompanied by headache , retro-orbital pain , myalgia , vomiting , diarrhea , and rash [3] . However , the hallmark manifestation of MAY is arthralgia [4] , which is often severe and debilitating , and can persist for up to a year , with recurring relapses possible . The high incidence of dengue fever in the same areas in which MAYV circulates , and the similarity of the initial signs and symptoms , leads to the misdiagnosis and underreporting of MAY cases [5] , [6]; therefore , MAYV is typically neglected as an important cause of tropical diseases . For example , in several areas of northern South America approximately 1% of all febrile illness that is clinically similar to dengue is caused by MAYV [7] . MAYV is a zoonotic pathogen that circulates in an enzootic cycle involving Haemagogus spp . mosquitoes and as yet unidentified vertebrate hosts [3] . Although seropositivity has been detected in birds and rodents , non-human primates have consistently demonstrated the highest rates of antibodies , suggesting that they are the principal reservoir hosts . Infection of humans typically occurs in communities near humid tropical forests , and is often associated with logging or other forest activities [1] , [8]–[10] . However , as land use and demographic changes in South America lead to human populations expanding within regions of tropical forest , an increasingly higher percentage of the population may be at risk [11] . In addition , the demonstration that the urban mosquito , Aedes aegypti , can transmit MAYV after exposure to bloodmeals with titers approximating human viremia levels [5] , [12] raises the concern that the virus could emerge into an urban transmission cycle similar to that of its close relative , chikungunya virus ( CHIKV ) . MAYV belongs in the family Togaviridae , genus Alphavirus . Despite circulating exclusively in the New World , MAYV belongs genetically , antigenically [13] , [14] . The genome of MAYV is a single-stranded , positive sense RNA , approximately 11 . 45 Kb in length that encodes 4 nonstructural proteins ( nsP1-4 ) on the 5′ end and 3 structural proteins on the 3′ end , including the capsid and envelope glycoproteins , E1 and E2 ( Fig . 1 ) [13] , [15] . Genomic RNA includes 2 open reading frames ( ORFs ) ; the nonstructural polyprotein ORF is translated in a cap-dependent manner from genomic RNA , while the structural polyprotein ORF is translated from a subgenomic RNA transcript , which is also capped [16] , [17] . There is no licensed vaccine available for MAY , and current control strategies rely on reducing human exposure to potentially infected mosquito vectors . Only one attempt to generate a vaccine for MAYV infection is described in the literature [18] . Formalin inactivation of wild-type ( wt ) MAYV strain TRVL15537 was tested in immunocompetent CD-1 mice using a single vaccination . This vaccine was immunogenic , and some efficacy was demonstrated via passive transfer of immune mouse sera to infant mice , followed by lethal challenge . The ideal MAYV vaccine would produce rapid , long-term immunity after a single dose to rapidly control outbreaks , with a low risk of adverse side effects . The vaccine would also need to be cost effective for use in resource-poor parts of Latin America , and easy to produce . For a live-attenuated vaccine , which typically meets most of these criteria , mosquito-transmission incompetent would also be highly desirable for use in non-endemic locations . To produce such a vaccine , we employed an attenuation strategy involving an encephalomyocarditis virus ( EMCV ) internal ribosome entry site ( IRES ) , which has been successfully used for other alphavirus vaccines [19]–[24] . Replacement of the subgenomic promoter reduces expression of the structural proteins , which are now translated via the IRES from genomic RNA , and the inefficient recognition of the IRES by insect ribosomes results in a phenotype that is also incapable of replicating in mosquito cells [25] . For this study , we tested the efficacy of an IRES-based vaccine candidate for MAYV ( henceforth called MAYV/IRES ) , which was highly attenuated , efficacious , and safe when tested in murine models .
A full-length genomic cDNA clone was generated from MAYV strain CH using RT-PCR and standard cloning methods as described previously [20] . The virus strain , a 2001 human isolate from Iquitos , Peru , was obtained from the World Reference Center for Emerging Viruses and Arboviruses at the University of Texas Medical Branch . It was passaged once on Vero cells before RNA extraction . Details on primers and restriction sites are available upon request . To produce an attenuated MAYV that was capable of replicating in vertebrate cells , but not in invertebrate cells , the translation of viral structural proteins was placed under control of the EMCV IRES , directly downstream from the subgenomic promoter . The subgenomic promoter was also inactivated with 14 synonymous mutations using standard PCR-based mutagenesis methods ( Fig . 1 ) . These mutations were chosen to inactivate the promoter while preserving the amino acid sequence of the nsP4 C-terminus . A single PCR-derived amplicon containing mutated subgenomic promoter and IRES sequence was cloned into wt MAYV plasmid at SanDI – NcoI sites . The complete cDNA clone was sequenced to ensure that no errors occurred during PCR amplifications or cloning . Plasmid DNA was linearized with PacI prior to in vitro transcription , semi-quantified by gel electrophoresis , and recombinant viral RNA was electroporated into Vero cells using conditions described previously [20] . Titers of rescued wt MAYV and MAYV/IRES were both 4 . 0×107 PFU/mL at 28 h post electroporation . Cell culture supernatants were harvested 28 h post electroporation , centrifuged to pellet cell debris , and stored at −80°C . All mice were purchased from Charles River Laboratories ( Wilmington , MA ) . Animal studies were approved by the University of Texas Medical Branch Institutional Animal Care and Use Committee . African green monkey kidney ( Vero ) and human fetal lung fibroblast cells ( MRC-5 ) cells were purchased from the American Type Culture Collection ( ATCC , Manassas , VA ) and maintained in culture with Dulbecco's Modified Eagle's Medium ( DMEM ) supplemented with 5% fetal bovine serum ( FBS ) and gentamicin sulfate and incubated at 37°C with 5% CO2 . Aedes albopictus-derived C6/36 cells were maintained in DMEM supplemented with 10% FBS , 1% tryptose phosphate broth ( TPB ) solution , and an antibiotic mixture of penicillin/streptomycin at 29°C and 5% CO2 . Vero and MRC-5 cells were used to assess the replication kinetics of the MAYV/IRES vaccine candidate and wt MAYV . Cells were grown to 95% confluency in 6-well plates . Virus was added to each well at a multiplicity of infection ( MOI ) of 0 . 1 plaque forming units ( PFU ) /cell in triplicate and incubated with the cells for 1 h . The cells were then washed twice with phosphate buffered saline ( PBS ) to remove residual virus , and 2 mL of medium were added to each well . At designated timepoints ( 6 , 12 , 24 , 36 and 48 hours post infection ( hpi ) for Vero cells , and 24 , 48 , 72 , and 96 hpi for MRC-5 cells ) , the culture supernatant was harvested for virus titration by plaque assay , then fresh medium ( 2 mL ) was added to replace the volume . To assess the stability of the MAYV/IRES vaccine candidate , 5 passages were performed in duplicate on both Vero and C6/36 A . albopictus cells in T25 flasks , with the cells at 95% confluency before infection at a MOI of 0 . 1 PFU/cell . As a control , wt MAYV was also passaged . Vero cells were incubated at 37°C and 5% CO2 for 48 h , while the C6/36 Ae . albopictus cells were incubated at 29°C and 5% CO2 for 72 h . Culture supernatants were then collected and used to infect a new flask at the same MOI . Virus titers from each passage were measured by plaque assay . To evaluate the genetic stability of the MAYV/IRES vaccine candidate , viral genomes from Vero passages 3 and 5 of both MAYV/IRES and wt MAYV were fully sequenced . Viral RNA was extracted using a QIAamp Viral RNA Mini Kit ( Qiagen , Valencia , CA ) . This was followed by RT-PCR which was performed in a two-step reaction process involving SuperScript III One-Step RT-PCR System ( Invitrogen , Grand Island , NY ) in conjunction with Phusion High-Fidelity DNA Polymerase ( New England Biolabs , Ipswich , MA ) . PCR amplicon sizes were confirmed by gel electrophoresis and then purified by a QIAquick PCR Purification Kit ( Qiagen ) . A BigDye kit ( Applied Biosystems , Foster City , CA ) was then used to prepare the samples for Sanger sequencing . Thirty-nine overlapping amplicons were used to cover the entire genome; primer sequences are available from the authors . Infant outbred CD1 mice have been shown to develop disease similar to humans for the arthralgic alphavirus CHIKV [26] , and were therefore chosen as a model to evaluate the MAYV/IRES attenuation . Cohorts of six-day-old outbred CD1 mice were infected over the dorsum subcutaneously ( SC ) with 104 PFU , a dose used previously [26] , and were subsequently monitored daily for 10 days for survival and body weight . To evaluate immunogenicity , cohorts of adult 28-day-old CD1 mice were also infected SC with 105 PFU , and survival and body weights were monitored daily until day 28 post infection . Mice were bled on days 1–3 after infection , and serum was tested for viremia by plaque assay [27] to assess attenuation . On day 28 post infection , the animals were bled and a plaque reduction neutralization test ( PRNT ) was performed on the sera to measure antibodies as described previously [27] . MAYV produces no detectable disease in adult , immunocompetent mice . Therefore , to assess attenuation , cohorts of ca . 5–8-week-old interferon type I receptor-deficient A129 mice were infected intradermally ( ID ) on the left footpad ( FP ) with 104 PFU . The animals were monitored for survival , body weight changes , and viremia . Footpad swelling was also measured using a caliper at the site of inoculation . At day 28 post infection , sera were collected and PRNTs were performed . On day 29 post infection , the mice were challenged SC with 104 PFU of wt MAYV strain CH . The mice were monitored the following 7 days for survival , change in body weight , and viremia . The University of Texas Medical Branch ( UTMB ) Institutional Animal Care and Use Committee approved the animal experiments described in this paper under protocol 02-09-068 . UTMB complies with all applicable regulatory provisions of the U . S . Department of Agriculture ( USDA ) - Animal Welfare Act; the National Institutes of Health ( NIH ) , Office of Laboratory Animal Welfare - Public Health Service ( PHS ) Policy on Humane Care and Use of Laboratory Animals; the U . S Government Principles for the Utilization and Care of Vertebrate Animals Used in Research , Teaching , and Testing developed by the Interagency Research Animal Committee ( IRAC ) , and other federal statutes and state regulations relating to animal research . The animal care and use program at UTMB conducts reviews involving animals in accordance with the Guide for the Care and Use of Laboratory Animals ( 2011 ) published by the National Research Council . Analysis of variance ( ANOVA ) followed by a Tukey's post-hoc test , Kruskall-Wallis with Bonferroni correction for multiple comparisons , Kaplan-Meier , and Mann-Whitney test were performed using Prism 5 ( GraphPad Software , La Jolla , CA ) . P-values<0 . 05 were considered significant .
To assess the replication kinetics , virus derived from electroporated Vero cells was compared to wt MAYV after infection of Vero cells ( Fig . 2A ) . Infections were performed in triplicate ( n = 3 ) at a MOI of 0 . 1 PFU/cell . Both MAYV/IRES and wt MAYV titers peaked 36 hpi , but wt MAYV had a slightly higher titer of 1 . 1×108 PFU/mL while MAYV/IRES had a peak titer of 7 . 8×107 PFU/mL . Significant differences were seen only at the 48 hpi timepoint ( ANOVA , p<0 . 05 ) . Plaque morphology was consistent throughout the experiment , with wt MAYV having a slightly larger ( 0 . 5–3 mm ) and more diffuse plaque morphology than MAYV/IRES ( 0 . 5–2 mm ) under 0 . 4% agarose in MEM ( 48 h incubation ) . MRC-5 cells are well characterized and widely used in cell culture-based vaccine production . Therefore , we also measured the replication kinetics of the MAYV/IRES vaccine candidate , as well as wt MAYV on this cell line in triplicate wells ( n = 3 ) at a MOI of 0 . 1 PFU/cell ( Fig . 2B ) . The MAYV/IRES virus reached a peak titer of 106 . 7 PFU/ml at 72 hpi , which was much later and at a lower titer than wt MAYV . Plaque morphology measured on Vero cells of MAYV/IRES virus derived from MRC-5 or Vero cells was comparable . The stability of MAYV/IRES was tested in vitro by 5 serial passages in Vero cells , in duplicate at an MOI of 0 . 1 PFU/cell . MAYV/IRES maintained a slightly lower titer than wt MAYV throughout the passages , with a range of 4 . 2×107 PFU/mL after passage 2 , to a peak of 1 . 9×108 PFU/mL after passage 3; wt MAYV titers remained between 108 and 109 PFU/mL ( data not shown ) . To evaluate the genetic stability of the MAYV/IRES vaccine candidate , the complete consensus sequences of passages 3 and 5 were determined using overlapping amplicons generated by RT-PCR , and no mutations were detected . MAYV/IRES was also serially , blind passaged 5 times in C6/36 A . albopictus mosquito cells to confirm its lack of mosquito host range . As expected , the virus was not detected during any passage , while wt MAYV replicated to high titers ( data not shown ) . Cohorts of 6-day-old CD1 mice were infected SC with 104 PFU of either MAYV/IRES ( n = 14 ) , wt MAYV ( n = 15 ) , or were sham-infected with PBS ( n = 15 ) . Mice infected with wt MAYV began to die starting 3 dpi and complete mortality was seen by day 8 ( data not shown ) . All MAYV/IRES- and sham-infected mice survived until the study was terminated 10 days after inoculation . As expected , the wt MAYV-infected cohort did not gain weight as quickly as the MAYV/IRES- or sham-infected animals , and the average weight of wt-infected animals declined rapidly beginning 4 days post-infection . There was no significant difference in weight change between MAYV/IRES- and sham-infected animals ( Kruskall-Wallis with Bonferroni correction for multiple comparisons ) . Due to the high mortality in infant CD1 mice infected with wt MAYV , adult CD1 mice ( 28 days-old ) were also tested as a potential virulence model . Mice were infected SC with 105 PFU of either MAYV/IRES ( n = 10 ) or wt MAYV ( n = 10 ) , and negative controls were sham ( PBS ) -infected ( n = 6 ) . Unlike the infant 6-day-old CD1 mice , the 28-day-old mice all survived infection with wt MAYV until the study was terminated 28 days after infection . To assess with greater sensitivity signs of disease , the animals were weighed post-vaccination ( Fig . 3A ) . The MAYV/IRES- and sham-infected cohorts gained weight steadily throughout the experiment , while the wt MAYV-infected mice lost some weight initially , but recovered by day 5 post-infection , then proceeded to gain weight in a manner similar to the other cohorts . However , these differences in weight change were not significant ( p≥0 . 07 , Kruskall-Wallis with Bonferroni correction for multiple comparisons ) . To quantify viral loads of the MAYV/IRES vaccine candidate , viremia was assessed post-vaccination ( Fig . 3B ) . Both MAYV/IRES and wt MAYV produced a peak viremia titer at day 2 post-infection , but MAYV/IRES viremia was of shorter duration and of significantly lower mean peak titer , just over 103 PFU/mL , compared to 107 PFU/mL for wt MAYV . Serum neutralizing antibody titers were measured at 28 days post-infection using an 80% PRNT . MAYV/IRES titers ranged from 160 to ≥640 ( mean = ≥304 ) , and were not significantly different from those of wt MAYV-infected animals ( Kruskall-Wallis with Bonferroni correction for multiple comparisons ) ( Fig . 3C ) . A129 mice lack functional type 1 interferon receptors and are therefore a very sensitive model for human arthritic alphavirus infection [28] . They have been used as a lethal model for alphavirus vaccine safety and challenge studies [20] . Cohorts of adult A129 mice ( n = 8 ) were infected with MAYV/IRES or wt MAYV , or sham-infected with PBS . Injections were performed intradermally on the left footpad with 104 PFU . All MAYV/IRES- and sham-infected mice survived until the experiment was terminated on day 28 , while all wt MAYV-infected mice died by day 5 ( Fig . 4A ) . Both the MAYV/IRES and wt MAYV cohorts lost weight initially , but wt MAYV-induced loss was more dramatic and significantly greater than that of the MAYV/IRES-infected animals ( Fig . 4B ) ( p<0 . 01 , Kruskall-Wallis with Bonferroni correction for multiple comparisons ) . There was no significant difference in footpad swelling among cohorts until 3 days after infection , when wt MAYV-infected mice showed a large increase in footpad diameter , which was significantly greater than mean swelling of both MAYV/IRES- and sham-infected cohorts ( Fig . 4C ) ( p<0 . 01 , Kruskall-Wallis with Bonferroni correction for multiple comparisons ) . Viremia was measured post-vaccination to quantify the viral load ( Fig . 4D ) . Both MAYV/IRES and wt MAYV cohorts reached high titers in the peripheral blood , with MAYV/IRES peaking at day 3 post-infection with a titer of 5 . 5×108 PFU/mL and wt MAYV reaching a slightly higher titer of 1 . 4×109 PFU/mL . Differences were significant only on day one post-infection ( p<0 . 001 , Kruskall-Wallis with Bonferroni correction for multiple comparisons ) . At day 28 post-infection , 7 of the 8 MAYV/IRES-vaccinated A129 mice had neutralizing antibody titers ≥640 , while the remaining mouse had a titer of 320 ( mean = ≥604 ) . The mean PRNT antibody titer for A129 mice was significantly higher than that for CD1 immunocompetent mice ( Student's T-test , p<0 . 01 ) , possibly reflecting greater vaccine replication in the former ( although the ages were not exactly matched ) . The sham-vaccinated A129 mice ( n = 3 ) did not have detectable antibodies ( <20 ) . Mice were then challenged SC with 104 PFU of wt MAYV to assess the efficacy of the MAYV/IRES vaccine . All vaccinated mice survived , while all of the sham-vaccinated mice were dead by day 7 , representing a significant difference in mortality ( p<0 . 01 , Kaplan-Meier; see Fig . 5A ) . To monitor disease in a more sensitive manner , weight was measured post-vaccination ( Fig . 5B ) . The sham-vaccinated , challenged cohort lost weight more quickly and dramatically than the MAYV/IRES-vaccinated group ( p<0 . 01 , Mann-Whitney ) . To assess viral load , viremia post-challenge was also measured ( Fig . 5C ) . The MAYV/IRES-vaccinated group showed a decreased viremic response upon challenge compared to the sham-vaccinated animals , only reaching a mean titer of 2 . 0×102 PFU/mL at day 3 post-challenge , while the control group reached a much higher titer of 4 . 8×108 PFU/mL 3 days post-challenge ( p<0 . 05 , Mann-Whitney ) .
It has been over 60 years since the discovery of MAYV in Trinidad , and there is still no licensed vaccine available despite continued outbreaks , and the potential for urban transmission in a dengue-like cycle [5] , [12] that could expose millions of people . Our MAYV/IRES vaccine was designed to offer single-dose , rapid protection to protect people both in endemic regions and in the event of an urban outbreak . Previous attempts to generate a vaccine to protect against MAY focused on inactivated wt virus [18] . A single vaccination proved immunogenic in adult CD1 mice , and efficacy was demonstrated indirectly via passive transfer of the immune mouse sera to infant mice , followed by lethal challenge . However , no further testing of this vaccine has been reported . To capitalize on the advantages of live-attenuated vaccines , including rapid and long-lasting immunity as well as ease of manufacture , we used the IRES-based attenuation approach that has been demonstrated to offer highly stable and predictable attenuation for alphaviruses [19]–[24] . Unlike traditional alphavirus attenuation derived from cell culture passages that typically relies on unstable point mutations , resulting in reactogenicity and the potential for reversion to wt virulence and transmissibility [29]–[32] the IRES-based rationale approach suppresses structural viral protein expression by elimination of the subgenomic promoter using multiple inactivating mutations . Thus , reversion is highly unlikely because the promoter sequence is very specific and intolerant of change [33] , resulting in superior attenuation stability following serial mouse passages compared to traditional point mutation-dependent attenuation [22] . Further safety is achieved through the use of the encephalomyocarditis virus IRES , which inefficiently mediates translation in insect cells [25] , and thus eliminates the possibility for mosquito transmission . Finally , the titers of nearly 108 PFU/cell of MAYV/IRES produced by vaccine substrate-approved Vero cells should be adequate for large-scale manufacture , and the stability we demonstrated following Vero cell passages will be critical for licensure . Like previous studies using the IRES-based alphavirus attenuation approach , our results showed that MAYV/IRES is stable in cells of mammalian origin ( Vero ) , but incapable of efficient replication in a C6/36 A . albopictus cell line . Previous studies have showed that other IRES-based attenuated alphaviruses are also incapable of replication after intrathoracic inoculation into A . albopictus mosquitos [20] , [22] . In every murine model we tested , MAYV/IRES was highly attenuated , only producing minimal signs of disease in the highly stringent A129 model that cannot mount an effective interferon response . This vaccine candidate was also highly immunogenic , inducing high levels of neutralizing antibody titers in both adult CD1 and A129 mice at 28 days post-vaccination . Challenge of A129 vaccinated mice at 29 days post-infection with a high dose of wt MAYV showed complete protection from detectable disease , despite the high virulence and complete lethality of MAYV in unvaccinated animals . These murine studies indicate that MAYV/IRES is highly attenuated , highly immunogenic , and provides strong protection against MAYV challenge . Further studies in another animal model are needed . Typically , nonhuman primates such as macaques reproduce human-like disease after alphavirus infection [24] , [34]–[39] . These animals should be evaluated as models for human MAYV to determine if they will be useful for the next steps in preclinical evaluation of MAYV/IRES . A variety of alternative vaccine development approaches are available for alphaviruses including inactivated virus , subunit protein , DNA and virus-like particles ( VLP ) as well as traditionally attenuated and chimeric vaccines [40] , [41] . All of these approaches emphasize safety but have significant drawbacks including a multiple dose requirement for efficacy , short-lived immunogenicity necessitating boosters , challenging delivery ( DNA via electroporation ) and complex , expensive manufacture ( VLPs ) and the risk of residual live virus after inactivation , which was shown to result in the death of an eastern equine encephalitis-vaccinated horse in California [42] . Our MAYV/IRES candidate overcomes all of these shortcomings to generate rapid immunity following a single dose , and should have greatly reduced reactogenicity due to its robust , highly stable attenuation design . Although further testing should be done to evaluate the duration of protective immunity , other IRES-based alphavirus vaccines have generated completely protective immunity in macaques for over one year ( C . Roy , S . C . W . , unpublished ) . MAYV/IRES therefore should be ideal for inducing rapid , long-lived immunity after a single dose for use in developing countries where MAYV is endemic , as well as for a traveler's vaccine for persons visiting South America . In summary , our MAYV/IRES vaccine candidate is highly attenuated and immunogenic , unable to infect mosquito cells , and provides protection from lethal challenge in murine models . These results indicate that further preclinical development of MAYV/IRES is justified for its evaluation as a potential human vaccine that could protect people from MAY in South America , but also on other locations if the virus spreads and urbanizes like the closely related CHIKV [5] , [43]–[46] . Furthermore , MAYV/IRES should be evaluated for its ability to protect against CHIKV and Ross River viruses , other closely related alphaviruses that cause epidemics in Africa and Asia , or Australia and Oceania , respectively . CHIKV is of particular concern because in December of 2013 it invaded the Caribbean , representing the first autochthonous transmission in the Western Hemisphere [47]–[49] . This event could portend a major epidemic throughout the Americas if spread to the mainland occurs into dengue-endemic regions where both A . aegypti and A . albopictus mosquito vectors are present along with a nearly naïve human population . The latter vector is highly susceptible to Asian CHIKV strains with adaptive mutations that dramatically enhance its vectorial capacity [50]–[55] , and it is unknown if similar mutations could enhance MAYV urbanization in a similar manner . An effective vaccine could greatly mitigate these risks and have a major impact on public health in South America . | Mayaro virus ( MAYV ) is a mosquito-borne alphavirus that causes severe and sometimes chronic arthralgia in persons in South America , where it circulates in forest habitats . It is widely neglected because it is typically mistaken for dengue due to the overlap in the clinical signs and symptoms , and the lack of laboratory diagnostics in most endemic locations . Furthermore , MAYV has the potential to initiate an urban transmission cycle like that of dengue , which could result in a dramatic increase in human exposure . Because there is no effective vaccine or specific treatment , we developed a candidate vaccine to protect against MAYV infection . We used an attenuation approach based on the elimination of the MAYV subgenomic promoter and insertion of a picornavirus internal ribosome entry site to mediate translation of the structural proteins . This vaccine was well attenuated in mouse models , highly immunogenic , and protected against fatal MAYV infection . Our results indicate that this MAYV strain is promising for further development as a potential human vaccine . |
You are an expert at summarizing long articles. Proceed to summarize the following text:
Deciphering the effects of nonsynonymous mutations on protein structure is central to many areas of biomedical research and is of fundamental importance to the study of molecular evolution . Much of the investigation of protein evolution has focused on mutations that leave a protein’s folded structure essentially unchanged . However , to evolve novel folds of proteins , mutations that lead to large conformational modifications have to be involved . Unraveling the basic biophysics of such mutations is a challenge to theory , especially when only one or two amino acid substitutions cause a large-scale conformational switch . Among the few such mutational switches identified experimentally , the one between the GA all-α and GB α+β folds is extensively characterized; but all-atom simulations using fully transferrable potentials have not been able to account for this striking switching behavior . Here we introduce an explicit-chain model that combines structure-based native biases for multiple alternative structures with a general physical atomic force field , and apply this construct to twelve mutants spanning the sequence variation between GA and GB . In agreement with experiment , we observe conformational switching from GA to GB upon a single L45Y substitution in the GA98 mutant . In line with the latent evolutionary potential concept , our model shows a gradual sequence-dependent change in fold preference in the mutants before this switch . Our analysis also indicates that a sharp GA/GB switch may arise from the orientation dependence of aromatic π-interactions . These findings provide physical insights toward rationalizing , predicting and designing evolutionary conformational switches .
The role of protein biophysics is increasingly recognized in the study of evolution , and the study of protein biophysics has also benefitted from evolutionary information [1–4] . Emerging from a more physical perspective of molecular evolution is the realization that natural selection can act on a nonsynonymous mutation as long as it modifies the conformational distribution , even if it leaves the folded structure of a protein unchanged and maintains the original biological function . For instance , if the mutation stabilizes a nonnative “excited” conformational state which is structurally distinct from native , this state can potentially serve an additional “promiscuous” biological function which is then subject to natural selection [5] . This effect , demonstrated experimentally [6] , is a direct consequence of the ensemble nature of protein conformations and follows simply from the principle of Boltzmann distribution [7 , 8] . Similarly , even if the most stable structure of a protein is robust against a mutation , the protein’s functional structural dynamics can be modulated by the mutation , which should then also be subjected to natural selection [5 , 9] . In this way , positive selection of an excited conformational state favors mutations that gradually increase the stability of the excited state , so that it finally becomes the new dominant native structure or one of two ( or more ) native structures with comparable stabilities in a “bi-stable” ( or “multi-stable” ) protein . Protein sequences interconnected by mutations and encoding for the same folded structure form neutral networks [10] . Bi-stability was predicted to occur at the intersection of neutral networks [8 , 10] . Consistent with theory [7 , 8 , 11–14] , some phylogenetically reconstructed ancestral proteins are bi-stable [15] . Although there is no direct measurement to date of a gradually shifting conformational equilibrium for a set of naturally occurring amino acid sequences traversing two neutral networks , recent advances in NMR spectroscopy allow mutational changes in the stability of nonnative excited states to be detected [16] . A handful of conformational switches and bi-stable sequences have now been designed in the laboratory [17–19] . Among them , the one that is most extensively characterized is the set of designed mutant sequences that span the human serum albumin-binding and IgG-binding domains of Streptococcus protein G [19 , 20] . The wildtype sequences of these proteins , termed GAwt and GBwt respectively , are of equal length ( 56 residues ) in the experimental system . GAwt and GBwt have only 16% sequence identity with very different folded structures . GAwt folds to a three-helix bundle ( 3α ) , whereas folded GBwt is a helix packing against a four-stranded β-sheet ( 4β+α ) . By carefully selecting amino acid substitutions , Alexander et al . created mutant sequence pairs with 30% , 77% , 88% , 95% , and 98% identity while still maintaining the original different folds . A single L45Y substitution separates the pair of mutants GA98 and GB98 with 98% identity . L45Y switches the dominant fold of GA98 from that of GA ( 3α ) to that of GB ( 4β+α ) for GB98 [19 , 20] . As suggested by theory [7 , 8] and by molecular dynamics simulations of the unfolded states of the GA88/GB88 pair [21] , appreciable excited-state populations for the alternative fold should be present in the GA/GB mutants with 95% , 88% , or even 77% identity . Ligand binding data provide evidence that GA98 and another mutant GB98-T25I that also adopts the 3α GA fold have excited-state populations of the alternative GB fold . However , GB98-T25I is the only mutant for which the alternative fold is directly observable by NMR [22] , as nonnative populations lower than ~1% are currently difficult to detect experimentally . By simulating the folding energy landscapes of the mutants , the goal of the present computational analysis is to gain physical insights into the mechanism of the GA/GB conformational switch , including how it might evolve via a gradual increase in stability of the alternate fold as the mutants approach the switch . The most direct method of molecular simulation is to use a completely general physics-based potential . Such an approach has succeeded recently in showing that it is computationally possible for a series of mutants of a 16-residue peptide to undergo an α to β switch [14] . Owing perhaps to the limitations of molecular dynamics forcefields [23 , 24] , folding simulations with fully transferrable potentials have not reproduced much of the switching behavior of the larger GA/GB system [25 , 26] , although complementary theoretical methods have made useful progress . For instance , some of the GA/GB mutants can be assigned to their correct native folds by various threading approaches [8 , 27] or a “confine-and-release” simulation algorithm applied to the GA88/GB88 and GA95/GB95 pairs [28] , suggesting that the forcefields used in these techniques may be quite adequate . But the conformations sampled by these techniques are limited only to those very similar to the GA and GB folded structures [8 , 27] , or at best include also a highly confined set of conformations between them [28] . As such , the rather restricted conformational sampling in these techniques can mask possible shortcomings of the forcefields , e . g . , by missing low-energy conformations that the techniques fail to sample . Therefore , to address fundamental physics of the GA/GB system , as for any protein folding study , it is necessary to employ self-contained explicit-chain models that extensively sample both the folded and unfolded conformations [29] . One class of self-contained models proven useful in biomolecular studies is the Gō-like explicit-chain structure-based models ( SBMs ) . These models are native-centric in that the only contacts favored by the potential are those that exist in the known native structures [29–32] . Most SBMs studied to date are single-basin in that they target a single native structure; but dual- and multi-basin SBMs can be constructed to fold to two or more native structures . The latter approach has been employed to analyze the conformational switches between different functional states of a protein [33–36] . A prime example is the large-scale allosteric conformational transition between the open and close forms of adenylate kinase [34 , 37] . Recent applications of dual-basin all-atom SBMs to the GA/GB system suggest that the conformational preferences of some of the mutants can be rationalized to an extent by their differences in steric packing [38 , 39] . However , the effects of nonnative interactions that are not present in either the GA or GB folds are not considered in these SBMs; but nonnative interactions are important for protein evolution because they may lead to detrimental aggregation [40–42] . In any event , the extent to which these dual-basin SBMs are generalizable is not clear . They have only been applied to a small number of mutants , viz . , GA95/GB95 and GA98/GB98 in ref . [38] and GA98/GB98 in ref . [39] . Moreover , in some cases , it appears necessary to single out contacts involving the mutated residues for ad hoc treatment [38] . To delineate the utility and limitation of common physical notions in accounting for experimental GA/GB observations , we introduce a model that combines a SBM potential with a physics-based transferrable all-atom potential . Going beyond prior efforts that considered only two or four sequences , our model is applied coherently to an extensive set of twelve GA/GB sequence variants covering the 3α and 4β+α folds . Favorable nonnative contacts are possible in our formulation because of the transferrable terms . This “hybrid” modeling approach recognizes that current knowledge of protein energetics is not sufficiently adequate—thus the need for a native-centric bias—yet at the same time posits that physical nonnative effects should manifest at least as a perturbation [43] . Within this conceptual framework , the transferrable component represents what we believe we know physically , whereas the SBM component represents the extent of our ignorance , which we should aim to eliminate in the future . To tackle the GA/GB system , we generalize the well-studied hybrid approach for a single-basin SBM [43–50] to one based upon a dual-basin SBM [33–36 , 51] . The formalism is general , however , and thus should be applicable also to conformational switches other than GA/GB . As detailed below , the GA/GB switching predicted by our model agrees with experiment . Moreover , the robustness and physicality of our predictions are buttressed by control simulations indicating a lack of folding of decoy protein sequences with folded structures very different from that of either GA or GB . Interestingly , refinements of the transferrable component in our potential to better account for the π-interactions of aromatic residues [52] leads to a sharper conformational switch , suggesting that incorporation of more accurate descriptions of the physical interactions can lead to tangible improvement of the model under the present framework .
As noted above , SBMs are valuable conceptual tools; but SBMs and hybrid models are admittedly interim measures . Ultimately , one wishes to simulate biomolecular processes using a completely transferrable physical potential . With this in mind , to maximize the physical content , our hybrid model was constructed with a native-centric , structure-based component as nonspecific and as unimposing as we found technically possible . For example , in contrast to previous all-atom SBMs for GA/GB [38 , 39] that enforce detailed native biases on dihedral angles and inter-atom distances [32] , the SBM component of our hybrid model constrains only the Cα-Cα distances between residues that are at least three sequence positions apart . The rest of the interactions—including local backbone preferences and sidechain excluded volume—are provided entirely by the transferable component . The SBM component of our model is sequence independent , in that the same native-centric potential applies to all GA/GB variants ( Fig 1 ) . In this way , the spatially coarse-grained SBM component serves merely to enable folding to the GA or GB native structures in an unbiased manner , all the while reducing as much as possible any artefactual memory of the sequence-structure relationship of any particular sequence . Accordingly , the differences in population in the two alternate folds for different sequences are determined solely by the physical transferable potential that admits nonnative as well as native interactions . As described in Methods , the present sequence-independent SBM component is based on the consensus Cα-Cα native contact maps for GA and GB . Each consensus map was constructed using the four PDB structures for GA or GB variants for which experimental folded structures are available ( Fig 2a and 2b ) . The consensus map contains only the native contacts common to all four PDB structures . Two residues of a given PDB structure are defined to form a native contact if the closest distance between any two non-hydrogen sidechain atoms , one from each residue , does not exceed 6 Å . Here the SBM energy for each consensus residue-residue native contact is constructed as a multi-Gaussian well potential [53] , wherein the position of the minimum for each of the wells is determined by the four defining PDB structures . In most cases , the individual minima fuse into a single wider well because they are in close proximity ( Fig 2c ) , although in some cases they retain their distinct minima when there are larger variations in contact distances among the PDB structures ( Fig 2d ) . The potentials for all contacts in the two consensus native contact maps ( Fig 2e ) are provided in S1 Fig and S2 Fig . Summing the energy terms for individual consensus native GA contacts gives the overall native-centric potential EA for GA and EB for GB , the strengths of which are given , respectively , by εA and εB ( Methods ) . A bi-stable SBM potential , ESBM , is then obtained by combining EA and EB . The multi-Gaussian contact potentials here ensure that all native conformers used as input for the SBM potential are at an energy minimum of the same depth ( εA or εB ) for a given fold . This approach captures the salient features of the two folds while allowing sufficient flexibility to accommodate variations in backbone and sidechain configurations among different GA/GB sequences . To achieve an unbiased baseline sampling of the GA and GB folds , the SBM energy scales εA and εB are expected to be somewhat different and thus a calibration is necessary . Indeed , it has long been known from the study of single-basin SBMs that imposing a single SBM energy scale for different native structures would result in a spurious correlation between folding temperature and native contact density that is not observed experimentally [54] . For our system , the GA fold was found to be only slightly more dominant in test simulations using min ( EA ) = min ( EB ) and the GB fold was only slightly more dominant for εA = εB . ( Supporting Information S1 Text and S3a and S3b Fig and S3c and S3d Fig , respectively ) , whereas εA = 0 . 96εB allows for unbiased baseline sampling to produce results consistent with experiment . To minimize native-centricity as much as possible , we have examined the effect of different overall SBM interaction strengths and arrived at a workable value of εB = −0 . 37 ( S1 Text , S4 Fig and S5 Fig ) . This strength corresponds to a weak native bias relative to the transferrable component , yet strong enough to guide folding . Under εB = −0 . 37 , on average only less than one third ( 18 . 9/60 . 2 = 0 . 31 ) of the stabilization of GB98 is contributed by the SBM component ESBM ( S6 Fig ) . The rest ( 69% ) is contributed by the transferrable Etrans . Further analyses in S1 Text and S7 Fig–S11 Fig , including Hamiltonian replica exchange simulations ( S9 Fig and S10 Fig ) , indicate that the GA98/GB98 switching behavior is robust over values of εB ranging from −0 . 30 to −0 . 50 ( S7 Fig and S8 Fig ) , and that folding and switching are observed only when neither ESBM nor Etrans vanishes ( S11 Fig ) . We adopt for Etrans the implicit-solvent all-atom potential developed at Lund University ( available as PROFASI ) , which accounts for backbone , non-bonded excluded-volume , hydrogen-bonding , charged and hydrophobic side chain interactions in a physical manner [55 , 56] . With a SBM component providing minimally necessary restriction on the accessible conformational space , the transferable component of our hybrid model modulates the stability of the native and unfolded populations . Using the progress variables QA and QB and the simulation procedure described in Methods , the present modeling setup correctly identifies the native basin of 12 sequence variants of the GA/GB system ( Fig 3a ) . The variables QA≡EA/95εA and QB≡EB/137εB are continuum versions of the discrete native contact fraction Q commonly used in protein folding studies [57 , 58] . For the energy landscapes in Fig 3a , the GA and GB native basins are situated , respectively , at QA≈0 . 9 , QB≈0 . 15 and QA≈0 . 3 , QB≈0 . 85; whereas the basin for the common unfolded state is centered at QA≈0 . 4 , QB≈0 . 15 . The dual native-bias of the SBM notwithstanding , Fig 3a shows that the transferable component is sufficiently strong to capture the physical mutational effects , resulting in significant shifts in populations and , in the case of GAwt , GBwt and GA30/GB30 , virtual depopulation of the entire alternate native basin . We computed a free energy difference ΔF ( GA-GB ) ≡ −ln ( PA/PB ) between the GA and GB folds for all the sequence variants ( Fig 3b ) , where PA and PB are the populations of the two native basins defined , respectively , by QA ≥ 0 . 6 , QB < 0 . 6 and QB ≥ 0 . 6 , QA < 0 . 6 . Thus , a negative ΔF ( GA-GB ) favors GA whereas a positive ΔF ( GA-GB ) favors GB . The replica-exchange simulation results in Fig 3b show that the single L45Y mutation from GA98 to GB98 entails a small yet appreciable shift in favor of GB , a robust finding corroborated by constant-temperature simulations ( S12 Fig ) . The aromatic Y45 partakes in a hydrophobic cluster in GB but apparently contributes little to stability in GA [22] . In the present transferrable potential , the hydrophobicity-based non-bonded energy term is mostly responsible for favoring this Y45-containing hydrophobic GB cluster because the strength of the term scales with the number of contacting nonpolar atoms , and aromatics provide large contact areas [55] . The three mutations separating GA95 and GB95 result in a more notable population shift . In addition to L45Y , the other two amino acid substitutions are I30F leading from GA95 to GA98 and L20A leading from GB98 to GB95 . Notably , the phenylalanine substitution of I30F fits into the hydrophobic core of both GA ( partially buried ) and GB ( almost fully buried ) . As the sequence separation between the pair is further widened ( GA88/GB88 , GA77/GB77 , GA30/GB30 , and GAwt/GBwt differ by 7 , 13 , 39 , and 47 mutations respectively , Fig 1 ) , the GA/GB free energy difference increases . The value of ΔF ( GA-GB ) increases rather smoothly from GAwt to GBwt as expected . The only exception is the step from GA88 to GA95 , for which there is a decrease in GB propensity instead of the expected increase ( Fig 3b ) . As mentioned above , for the GA30/GB30 pair , and the GAwt/GBwt pair that shares only 16% of their amino acids , the preference for the dominant native structure is so strong that only the fringe but not the bottom of the alternate native basin was sampled ( Fig 3a ) . These free energy shifts are echoed by the balance between transition frequencies to and from the native basins along Monte Carlo simulation trajectories . Using a three-state division of the QA/QB energy landscape into unfolded ( U ) , GA , and GB regions , a gradual shift from U↔GA to U↔GB transitions is concomitant with the sequence variation from GAwt to GBwt ( S1 Text and S13 Fig ) . We also compared the experimental and simulated melting temperatures of the GA/GB variants ( Fig 3c and 3d ) . Because the model potential in the present hybrid GA/GB model lacks cooperativity-enhancing desolvation barriers [59 , 60] and neglects temperature dependence in the solvent-mediated interactions [29 , 61 , 62] , simulated and experimental Tms are not directly comparable . For example , as suggested by related kinetic trends in other protein folding models [29] , insufficient folding cooperativity in the present hybrid model likely caused the simulated Tm range to be narrower than that observed experimentally ( the Tm ratios of GB98 over GA77 is 0 . 99 for simulation and 0 . 88 for experiment; see Fig 3d ) . Nonetheless , for the sequence variants from GA77 to GB77 , the correlation between simulated and experimental Tm is reasonably good . The consistency in Tm trend for seven of these eight variants is apparent in the comparison using a normalized non-absolute temperature scale ( Fig 3c ) as well as in the scatter plot for absolute temperatures ( Fig 3d ) . The steady drop in experimental Tm from GA77 to GB98 was captured very well by simulation ( Fig 3c ) . The outlier GB88 is known to be very unstable experimentally ( Tm ≈ 44°C ) . Curiously , this effect is also reflected in our model , albeit to an exaggerated degree . Combined structure-based clustering of the simulated GA98 and GB98 conformations allows for an analysis of likely kinetic events during bi-stable folding ( Methods ) . The centroid positions of 50 conformational clusters on the QA/QB landscapes are shown in Fig 4 together with the outlines of the bi-stable GA98 free energy landscape , which is quite similar to that of GB98 ( Fig 3a ) . The size of a cluster is the number of sampled conformations that are within a certain degree of structural similarity among themselves . Each centroid conformation is a representative of all the conformations in a given cluster . Fig 4 shows that the centroid positions cover most accessible regions of the free energy landscape . Naturally , the unfolded state harbors the majority of clusters because unfolded conformations are structurally most diverse . The most extended conformations are positioned in the bottom-left region with small QA and QB values as expected ( cluster no . 7 ) . Under our model potential , there is a significant bias in favor of helical structures instead of unstructured coils in the unfolded ensemble . As has been demonstrated , kinetic information can be gleaned from features on low-dimensional free energy landscapes determined solely by equilibrium sampling of one or two progress variables [63–65] . In using QA/QB landscapes for kinetic inference , we are following this tradition . It should be noted , however , that not all kinetic properties , especially those related to kinetic trapping , are deducible from low-dimensional landscapes [45 , 50] . For instance , not all structurally similar conformations based on the superposition-map measure and indicated by connecting lines in Fig 4 are readily accessible to one another kinetically . Therefore , here we qualify the “transition state” and “intermediate states” suggested by free energy landscape features as “putative” . With this caveat in view , we identify the conformations around the 0 . 66 < QA < 0 . 74 , 0 . 12 < QB < 0 . 22 bottleneck region as the putative transition state for GA folding . Likewise , we identify the conformations around the two bottleneck regions around 0 . 3 < QA < 0 . 55 , 0 . 35 < QB < 0 . 43 and 0 . 28 < QA < 0 . 4 , 0 . 58 < QB < 0 . 66 as two putative transition states for GB folding ( yellow boxes in Fig 4 ) , and the local-minima region between the latter two transition states as a putative GB intermediate state . Along the QA direction at QB ≈ 0 . 15 , a simple folding transition via a compact transition state TS-GA is apparent in Fig 4 . This putative process starts from an extended , mostly disordered state ( cluster no . 7 ) . Subsequently , more helices form and the chain first collapses into a loose arrangement of three helices around TS-GA and then proceeds to form the ordered native GA structure , with cluster no . 43 and adjacent clusters differing only by their disordered termini . Folding along QB at QA ≈ 0 . 35 is more complex . Fig 4 suggests that the second ( C-terminal ) β-hairpin is formed upon reaching the first GB transition state TS1-GB , but at this stage the rest of the protein chain is still relatively open . The GB intermediate state that follows consists mainly of a variety of conformations with the second β-hairpin aligned with the N-terminal β-strand . TS1-GB encompasses more conformational diversity than the single centroid conformation might convey . When we partition the conformational ensemble in this region into two or more clusters ( S14 Fig ) , alternative pathways across this transition region appear possible . One of the alternate pathways may entail a “mirrored” version of the second β-hairpin collapsing and accumulating as an “off-pathway” intermediate ( see , e . g . , the centroid conformation of cluster no . 12 in S15 Fig ) . As such , conformations with this topology likely constitute a kinetic trap that requires significant unfolding before folding to the GB native state can proceed . Direct transition from an “on-pathway” intermediate to native GB is expected for those conformations with native-like orientation of the terminal secondary structure elements . To reach the second putative GB transition state TS2-GB , excess helical structure needs to be converted into the fourth β-strand . The chain then proceeds to sample different near-native orientations of the central helix relative to the β-sheet , and attempt packing of the hydrophobic core before finally arriving at the GB native state ( cluster no . 4 ) . A detailed analysis of the population shift caused by the L45Y mutation in the conformational clusters in Fig 4 indicates that L45Y can start biasing in favor of the GB structure even when the folding is in its early stage ( S1 Text and S15 Fig ) . In this process , the aromatic-aromatic Y45-F52 interaction , which is more frequent in GB98 than in GA98 , is seen as playing a significant role in the GB-favoring effect of L45Y ( S16 Fig ) . As a test of the robustness of our hybrid model , we challenged it by several other sequences from the PDB that have the same 56-residue chain length as the GA/GB sequences but with native folds different from either GA or GB . The same GA/GB SBM was applied with each sequence’s Lund potential used as the transferrable component . The goal is to ascertain whether these decoy sequences would mistakenly adopt the GA or GB fold . Seven of the decoy sequences tested behaved reassuringly . Despite the GA/GB SBM , they did not populate either of the GA/GB native basin , even though some of their native conformations have secondary structures similar to those of GA or GB ( Fig 5a–5g ) . This result shows that Etrans can override ESBM , underscoring that the transferrable physical potential plays a highly significant , if not dominant , role in our model . Among the decoys tested , serine protease inhibitor infestin 4 is an interesting exception because its native structure is not similar to GA but it populates the GA basin ( Fig 5h ) ; but the bulk of its conformations remain unfolded . In this regard , depopulation of both native basins is remarkable for the double helical Ral binding domain because its helical secondary structures are similar though not identical to that of GA ( Fig 5i ) . Finally , to test whether our model can fold a non-GA/GB sequence if its native fold is essentially identical to either GA or GB , we considered a modified 56-residue version of Protein L ( Methods ) . Protein L has only ~ 16% sequence identity with GBwt but adopts the overall GB fold experimentally . Reassuringly , our simulation shows that the modified Protein L sequence is compatible with the GB basin but not the GA basin ( Fig 5j ) . Apart from decoys , we also challenged our formulation with an alternative structure switch in the GA/GB system discovered more recently . Experiments indicate that the T25I mutant of GB98 reverts back to the helical structure of the GA folds , but with an additional L20A mutation can be restored to the GB fold [22] . Our simulations show a high degree of bi-stability for these two sequences as for GA98 and GB98 . Nonetheless , we also found a small free energy difference that is consistent with the experimentally observed native structures of these two variants ( Fig 6a and 6b ) . Another pair of possible GA/GB switch sequences that came to our attention was proposed recently [66] , but the predicted switching behavior has not been confirmed by experiment or investigated by explicit-chain modeling . Our simulations here are in agreement with the predictions in finding that sequence “S2” prefers the GA fold while “S1” prefers the GB fold ( Fig 6c and 6d ) . The free energy differences for these two alternative switch mutations are provided in S17 Fig . Our results suggest that GB98-T25I , L20A and S1 favor GB via different mechanisms . GB98-T25I , L20A predominantly stabilizes the entire unfolded state and parts of the GB native state yet leaving the native GA basin appreciably populated ( Fig 6b ) , whereas the S2 to S1 mutation P54V clearly destabilizes the GA fold ( Fig 6c ) . The analysis of the L45Y mutation in S15 Fig reveals that a major part of its stabilizing effect on the GB fold is through enabling the aromatic-aromatic Y45-F52 interaction in GB98 . In view of this observation and the general importance of π-related interactions in biomolecular processes [49 , 67] , we constructed a rudimentary π-π interaction potential for F and Y residues ( Methods ) . Our goal here is to explore how an orientation-dependent interaction between aromatics that goes beyond simple hydrophobic effects may affect the behavior of the GA/GB conformational switch , although a comprehensive study of aromatic interactions is beyond the scope of this work . By using three geometric variables for two neighboring aromatic rings ( Fig 7a ) , we derived an empirical π-π potential [68] for F and Y from PDB statistics ( Fig 7b ) . When this π-π potential replaces the simpler hydrophobic interactions among F and Y residues in the original Lund potential , the effect of L45Y is affected appreciably ( Fig 7c ) . We define a difference landscape for the original Lund potential ( Fig 7c , left ) as the difference between the GA98 and GB98 panels in Fig 3a . The difference landscape for the modified transferrable potential ( Fig 7c , right ) is similarly defined using the QA/QB landscapes of GA98 and GB98 in S18 Fig that incorporates our π-π potential . In the Lund potential ( Fig 7c , left ) , L45Y stabilizes the unfolded and GB intermediate states rather homogeneously ( stabilization indicated by blue coloring ) . The GA native basin is destabilized ( red coloring ) , but so are parts of the GB native basin . In contrast , with the π-π potential ( Fig 7c , right ) , L45Y has a stronger impact . It now destabilizes most of the unfolded state and parts of the GA native basin whereas the stabilization focuses more on the intermediate and native basins of GB . Although the present π-π potential is rudimentary , this comparison suggests that orientation-dependent π-π interactions likely play a significant role in the experimental sharpness of the GA/GB conformational switch .
Beside this overall success , two findings from our investigation are of experimental relevance: ( i ) existence of an equilibrium intermediate for GB folding ( Fig 3a , GB panels ) ; and ( ii ) a critical role of the second β-hairpin in the GB folding pathway ( Fig 4 and S15 Fig ) . On both counts , our model results are in general agreement with experimental findings ( see below ) , lending additional credence to our contention that the present hybrid model is capable of capturing essential physics of GA/GB bi-stability and the GA98/GB98 conformational switch . Firstly , our prediction of a GBwt ( also called protein G or GB1 ) intermediate is in line with several [69–72] though not all [73] simulation studies . Experimental evidence for a GB folding intermediate was presented , but there is no clear consensus yet regarding the existence and/or nature of a GB intermediate–unlike the generally recognized two-state nature of GA folding . Two early experiments oncluded that GBwt folding is two-state [74 , 75] . In contrast , another early continuous-flow ultrarapid mixing experiments on GBwt suggested a native-like intermediate [76] , but this conclusion was disputed [77] . A later FRET study also found an intermediate near the urea denaturation midpoint of GBwt [78] . A subsequent equilibrium GBwt unfolding experiment showed two-state behavior; but the kinetic chevron rollover was indicative of an intermediate [79] . The latter finding is in line with a recent experimental and molecular dynamics study showing that GBwt folding is three-state [80] . As for GB variants , one study found that GB88 and GA88 are two-state folders [21] . However , an investigation on a different set of variants GA30/GB30 , GA77/GB77 , and GA88/GB88 supported three- and two-state folding , respectively , for all GB and GA variants [81] . Taken together , recent evidence appears to be somewhat more preponderant on the existence , rather than non-existence , of a GB folding intermediate; and is unequivocally affirmative of the two-state nature of GA folding . This trend is reflected by our simulated free energy landscapes in Fig 3a . Secondly , Fig 4 and S15 Fig suggest that the second β-hairpin is critical and more important than the first β-hairpin in GB folding . Although this finding was deduced from an analysis of GA98 and GB98 clusters , it is likely applicable to other GB variants , including GBwt , because of the similarity among their free energy landscapes ( Fig 3a ) . Indeed , NMR experiments on peptides from GBwt found that , in isolation , the second β-hairpin is much more stable than both the helix and the first β-hairpin . It forms a stable , native-like β-hairpin with its three aromatic residues W43 , Y45 , and F52 forming a cluster stabilized by both hydrophobic and ( probably π-related ) polar interactions [82] . In contrast , the first hairpin was found to be mostly flexible in isolation and not native-like [83] . Hydrogen exchange experiments on the entire GBwt protein also revealed an early folding state with the second β-hairpin having the highest protection factors , whereas the helix has a lower and the first hairpin has the lowest [77 , 84] . Based on Φ-value analysis for a single transition state , another study also pointed to the presence of the second β-hairpin in the GBwt transition state [74] . Taken together , the experimental data summarized above provide support for a critical role of Y45-F52 in favoring early formation of the second β-hairpin and its partial collapse together with the helix , as suggested by our simulation ( compare TS1-GB in Fig 4 and S14 Fig ) . In this regard , some differences between the folding transition states of GB variants and that of GBwt were reported . In particular , Φ-value analysis [85] has found that the first transition state in GB30 is more sensitive to mutations in the second β-hairpin whereas GB88 is more sensitive in the first hairpin [81] . Nonetheless , the same set of data for GB88 is suggestive of native-like transition-state contacts , such as I6-T53 , that are between strands at the two termini because some of their residues have high Φ-values ( e . g . , 0 . 48 for I6 and 0 . 42 for T53 ) . If this is indeed the case , the experimental data is not inconsistent with our simulation result suggesting that the anti-parallel alignment of the termini is an early rate-limiting event for GB folding ( Fig 4 and S15 Fig ) . Taking all the evidence presented together , the performance of our model suggests that the remarkable GA/GB bi-stability phenomenon can be rationalized to a significant extent by specific hydrophobic interactions , though our physical understanding is still far from complete . As discussed above , future improvement in matching theory with experiment should be sought by enhancing folding cooperativity and increasing sharpness of the conformational switch in our model . One possible direction is to incorporate desolvation barriers in the transferrable potential because this is a robust physical feature of solvent-mediated interactions that have a significant impact on folding cooperativity [29] . Another direction , which was initiated with some success here , is to devise a more accurate description of aromatic interactions [67] . In this respect , a natural next step is to extend our model π-π interactions to encompass Trp and to adopt a more comprehensive account of the relative position and orientation of interacting aromatic sidechains that goes beyond the three variables in Fig 7 . Despite the simplicity of the Lund potential , it has succeeded in folding several smaller proteins [55 , 86] and the 92-residue Top7 [87] . However , in long unbiased folding simulations using only the Lund potential with no SBM , we were unable to observe stable native-like conformations of GA/GB variants , indicating that as-yet-unknown energetic contributions , in addition to those in the Lund potential , are needed for a complete physical account . The GA/GB system is a useful benchmark for testing forcefields and simulation techniques . Recent success in using all-atom explicit-water molecular dynamics to simulate folding of a number of small proteins is remarkable [88–90] . However , despite the notable advance and ongoing force-field improvement [23 , 91] , no ab initio forcefield to date has been able to fold the GA/GB variants correctly [25] . In this context , hybrid modeling is a highly useful interim approach to gain physical insight into protein folding energetics , effects of mutations , and to assist in protein design . Owing to its reliance on SBMs , this approach is limited to proteins with known structures . Nonetheless , for many globular proteins , the native structure is either known or can be inferred through homology or sequence-based statistical models [92 , 93] , and are therefore amenable to hybrid modeling . Common approaches to estimate mutational ΔΔG [94] only consider the known native structure with little or no regard to the unfolded state and folding dynamics . Hybrid models can address this shortcoming by providing testable predictions about the mutational effects on the entire free energy landscape . Indeed , because of its computational tractability , hybrid models can facilitate efficient development and testing of physically more accurate transferrable potentials , and thus can contribute to an ultimate elimination of the current necessity for SBMs .
As described above in Results , we derived for the native-centric SBM component of our hybrid model two consensus native contact maps that capture the general features of the GA and GB folds by using PDB structures for four GA sequence variants and four GB sequence variants ( Fig 2a and 2b ) . The sequences and their corresponding structures ( in parentheses ) are GAwt ( 2FS1 ) , GA88 ( 2JWS ) , GA95 ( 2KDL ) , GB98 ( 2LHC ) , GBwt ( 1PGA ) , GB88 ( 2JWU ) , GB95 ( 2KDM ) , and GB98 ( 2LHD ) . All of these PDB structures except the x-ray structure for GBwt were determined using NMR and contain multiple model structures . For simplicity , we used only the first model in each NMR PDB file in our analysis . Assuming that these consensus contact maps provide a reasonable coverage of the structural variations in the GA/GB system , we apply these maps to sequence variants GA30 , GB30 , GA77 , and GB77 as well , since no detailed structural data were available for the latter four sequences [20] . We introduce EA and EB as the individual native-centric potential energy functions for the GA and GB folds , respectively . EA and EB depend on the Cα-Cα distances rij for all residue pairs i , j that belong to the given consensus native contact map via the following Gaussian form [53]: EA=εA∑i , jnA[∏sns ( 1−e− ( rij−dij ( s ) ) 2/2w2 ) −1] , and a similar equation for EB with all instances of “A” replaced by “B” . Here the summation over i , j for EA and EB runs over , respectively , all nA = 95 and nB = 137 contacts in the consensus contact maps for GA and GB . The product over s takes into account the multiple native distances dij ( s ) for residue pair i , j in the ns = 4 PDB structures contributing to the consensus map . The strength of EA or EB is given , respectively , by εA or εB , which corresponds to the well depth for a single native contact . The w parameter that controls well width is set at 0 . 5 Å . In the present study , this formulation leads to a wide potential well for an overwhelming majority of consensus contacts . Because in most cases the native Gaussian wells for individual structures overlap considerably , we observe only minor barriers between individual Gaussian minima among all the consensus native potentials shown in S1 Fig and S2 Fig . In Fig 2c and 2d , examples of the consensus potential Eij=∏sns{1−exp[− ( rij−dij ( s ) ) 2/2w2]} for an individual contact ( black curves ) are provided together with the corresponding energy term 1−exp[− ( rij−dij ( s ) ) 2/2w2] for one of the four contributing PDB structures ( color curves ) . The above Gaussian form of the native-centric energy function is more suitable than the Lennard-Jones ( LJ ) form for our present purpose . As has been noted , it is difficult to produce a viable combined energy function from multiple native-centric LJ functions for multiple structures unless the conformational diversity is approximated by a single centroid structure [95] . LJ potentials are inflexible in their well shape ( width ) . Each inter-residue contact comes with a built-in repulsion term determined by the minimum-energy contact distance in LJ . As a result , multiple instances of the same contact at varying distances can lead to occlusion of the shorter-range contact by the repulsion of the longer-range contact if the LJ form is used instead of the Gaussian form to construct a combined energy function in accordance with the equation above ( S1 Fig and S2 Fig ) . As outlined above , the total potential energy Etotal is the sum of a native-centric component and a transferrable component , viz . , . Etotal = ESBM + Etrans . The dual-basin native-centric SBM component ESBM is constructed simply as ESBM = EA + EB . Aiming to increase the weight of the transferrable component in our model potential , we did not employ the more native-specific prescription of logarithmic mixing in ref . [35] for ESBM . For the transferrable component Etrans , we adopt the Lund potential: Etrans = Elocal + EEV + EHB + ESC + EHP , where the energy terms on the right are for local backbone interactions ( Elocal ) , non-bonded excluded volume ( EEV ) , hydrogen bonds ( EHB ) , charged ( ESC ) and hydrophobic ( EHP ) sidechain interactions . Bond lengths and bond angles are kept constant , as described by the original authors [55] . Dimensionless energy units are used in our simulations with Boltzmann constant kB effectively set to unity . We use a Monte Carlo ( MC ) [96] package [56] from Lund University to conduct parallel tempering ( temperature replica exchange ) MC simulations [97] . MC chain moves included backbone and side chain rotations as well as biased Gaussian steps [98] . All simulations were initialized from random chain conformations and time propagated in units of MC cycles . Each cycle consisted of a number of elementary conformational MC updates scaled to the number of rotational degrees of freedom of the simulated protein chain so that on average all degrees of freedom were perturbed once per cycle . For example , for GA98 and GB98 these numbers of degrees of freedom were 283 and 282 , respectively . Initially , parallel tempering simulations were performed over 32 replicas per simulation over a wide temperature range . This is then followed by a second simulation using a finer temperature grid around the melting ( unfolding ) temperature , Tm , determined as the temperature at which the heat capacity function CV ( T ) =1kBT2 ( ⟨Etotal2⟩T−⟨Etotal⟩T2 ) computed from the first set of simulations attains its maximum . Here T is absolute temperature of the simulation , Etotal is the total energy defined above , and <…>T denotes conformational averaging at T . The refined temperature grid was tuned to ascertain sufficient replica exchange acceptance probability around Tm ( ~99% ) . Replica exchange was attempted every 5 , 000 MC cycles , the first 30% ( 3 . 0×106 MC cycles ) of every trajectory was excluded from analysis . Populations simulated at different temperatures were reweighted to Tm using WHAM [99] . In select instances , 128 constant-T simulations at Tm with increased sampling were conducted to corroborate parallel tempering results ( S5 Fig and S12 Fig ) . In view of the need for a high computational throughput for varying input parameters and sequences , most simulations were terminated after 107 MC cycles ( ~2 . 8×109 elementary MC updates ) . We verified that the resulting simulated ΔF ( GA‒GB ) for GA98 and GB98 is reasonably robust in longer simulations . From the replica exchange simulations around Tm for GA98 and GB98 , for each sequence we randomly sampled 20 , 000 conformations obtained at the two sequences’ respective Tms . These conformations were combined into a single pool of 40 , 000 conformations for clustering analysis . Each conformation in the pool was represented as a ( 4×56 ) -dimensional vector . The first 56 and second 56 components of this vector are the distances between the Cα atoms in the given conformation and the corresponding Cα atoms , respectively , of an optimally superposed GA98 PDB structure ( 2LHC ) and an optimally superposed GB98 PDB structure ( 2LHD ) . Similarly , the third 56 and fourth 56 components of the ( 4×56 ) -dimensional vector are the distances between the Cβ atoms in the given conformations and the corresponding Cβ atoms in the optimally superposed PDB structures , respectively , for GA98 and GB98 . Structural superpositions were optimized using the MDtraj [100] implementation of Theobald’s algorithm for RMSD calculations [101] . The ( 4×56 ) -dimensional distance vectors were then clustered by the k-means algorithm [102] with k = 50 chosen as the number of clusters . Cluster centroids are defined as actual conformations situated closest to the cluster centers in the ( 4×56 ) -dimensional space . We define a distance measure between the centroids of two conformational clusters as the Cartesian distance between the centroids’ ( 4×56 ) -dimensional vectors normalized by ( 4×56 ) 1/2 . We refer to this distance measure as RMSDsm because it is the root mean square difference of the centroids’ superposition maps , RMSDsm . The latter is defined for any pair of conformations Cμ and Cν as RMSDsm ( Cμ , Cν ) =14×56∑i=14×56 ( di ( μ ) −di ( ν ) ) 2 where di ( μ ) and di ( ν ) are the components of the ( 4×56 ) -dimensional vectors representing , respectively , conformations Cμ and Cν . RMSDsm was first used in the general clustering for all conformations . For Fig 4 , the general definition was applied to pairs of cluster centroids , wherein only pairs with RMSDsm ≤ 5 . 75 Å are shown by connecting lines . This threshold was chosen solely for the presentational purpose of not obstructing the visualization in Fig 4 yet providing as much information as possible about the structural relationships between clusters that share a reasonable degree of geometric similarity . The sequence of the modified version of protein L in Fig 5 was obtained by first structurally aligning its PDB structure ( 2PTL ) with that of GB1 ( 1PGA ) and then removing the unaligned N- and C-terminal tails . Internal loop residues 12 , 40 , 41 , and 42 were also removed and a glycine was inserted between residues 23 and 24 . This procedure led to the following sequence used in Fig 5: VTIKANLIFANSTQTAEFKGTFAEKATSEAYAYADTLKKEYTVDVADKGYTLNIKF . Interactions between aromatic residues in the Lund potential are treated only by its hydrophobic side chain potential [55] . To explore possible π-interactions that are not hydrophobic in nature but are nonetheless known to play significant structural roles in biomolecules [49 , 67 , 103 , 104] , we modified the Lund potential for Phe and Tyr , replacing their contact-area-dependent hydrophobic interactions by an orientation-dependent potential . This rudimentary π-π potential is parametrized by three geometric variables r , θ , φ characterizing the relative position and orientation of two aromatic rings ( Fig 7a ) . There is one Trp in the GA/GB sequences ( W43 ) ; but for simplicity we restrict our exploration to Phe and Tyr , leaving the treatment of the geometrically more complex Trp to future studies . The present π-π interaction is derived as a statistical potential from a PDB data set obtained through the PDB-SELECT [105] repository at http://swift . cmbi . ru . nl/gv/select/index . html . The sequence similarity cut-off was 30% , R-factor cutoff was 0 . 21 , and resolution cut-off was 2 . 0 Å . The dataset contained 9 , 796 protein crystal structures ( created on January 26 , 2013 ) . For all the observed F-F , Y-Y , and F-Y contact pairs in this data set , the number of occurrences P ( r , θ , φ ) of r , θ , φ were distributed into bins of size 0 . 3 Å for r between r = 3 Å and 12 Å and bins of size 3° for θ , φ between θ , φ = 0° and 90° . Based on this statistics and following Procacci and coworkers [68] , we define a rudimentary π-π interaction energy Eππ ( r , θ , φ ) = −εππ{1+ln[P ( r , θ , φ ) /Pmax]/|ln ( Pmin/Pmax ) |} for each of the three residue type pair F-F , Y-Y , or F-Y , where Pmax and Pmin are , respectively , the maximum and minimum non-zero values of P ( r , θ , φ ) among all the bins for a given pair . We further set Eππ = 0 for all r , θ , φ bins that received zero entry from the PDB data set . In this way , for εππ > 0 , the present π-π potential is an attractive interaction that varies between Eππ = −εππ and 0 ( Fig 7b ) . Here we use εππ = 1 . 5 for all three residue type pairs . | The biological functions of globular proteins are intimately related to their folded structures and their associated conformational fluctuations . Evolution of new structures is an important avenue to new functions . Although many mutations do not change the folded state , experiments indicate that a single amino acid substitution can lead to a drastic change in the folded structure . The physics of this switch-like behavior remains to be elucidated . Here we develop a computational model for the relevant physical forces , showing that mutations can lead to new folds by passing through intermediate sequences where the old and new folds occur with varying probabilities . Our approach helps provide a general physical account of conformational switching in evolution and mutational effects on conformational dynamics . |
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Taenia spp . infections , particularly cysticercosis , cause considerable health impacts in endemic countries . Despite previous evidence of spatial clustering in cysticercosis and the role of environmental factors ( e . g . temperature and humidity ) in the survival of eggs , little research has explored these aspects of Taenia spp . epidemiology . In addition , there are significant gaps in our understanding of risk factors for infection in humans and pigs . This study aimed to assess the influence of socio-economic , behavioural and environmental variables on human and porcine cysticercosis . A cross-sectional survey for human taeniasis ( T . solium and T . saginata ) , human cysticercosis ( T . solium ) and pig cysticercosis ( T . solium ) in 416 households in western Kenya was carried out . These data were linked to questionnaire responses and environmental datasets . Multi-level regression was used to examine the relationships between covariates and human and porcine cysticercosis . The HP10 Ag-ELISA sero-prevalence ( suggestive of cysticercosis ) was 6 . 6% for humans ( 95% CI 5 . 6%–7 . 7% ) , and 17 . 2% for pigs ( 95% CI 10 . 2%–26 . 4% ) . Human taeniasis prevalence , based on direct microscopic observation of Taenia spp . eggs ( i . e . via microscopy results only ) was 0 . 2% ( 95% CI 0 . 05%–0 . 5% ) . Presence of Taenia spp . antigen in both humans and pigs was significantly associated with a range of factors , including positive correlations with land cover . The presence of HP10 antigen in humans was correlated ( non-linearly ) with the proportion of land within a 1 km buffer that was flooding agricultural land and grassland ( odds ratio [OR] = 1 . 09 and 0 . 998; p = 0 . 03 and 0 . 03 for the linear and quadratic terms respectively ) , gender ( OR = 0 . 58 for males compared to females , p = 0 . 02 ) , level of education ( OR = 0 . 62 for primary level education versus no formal education , p = 0 . 09 ) , use of well water for drinking ( OR = 2 . 76 for those who use well water versus those who do not , p = 0 . 02 ) and precipitation ( OR = 0 . 998 , p = 0 . 02 ) . Presence of Taenia spp . antigen in pigs was significantly correlated with gender and breeding status of the pig ( OR = 10 . 35 for breeding sows compared to boars , p = 0 . 01 ) , and the proportion of land within a 1 km buffer that was flooding agricultural land and grassland ( OR = 1 . 04 , p = 0 . 004 ) . These results highlight the role of multiple socio-economic , behavioural and environmental factors in Taenia spp . transmission patterns . Environmental contamination with Taenia spp . eggs is a key issue , with landscape factors influencing presence of Taenia spp . antigens in both pigs and humans .
Taeniasis and cysticercosis are two human disease outcomes caused by parasites in the genus Taenia: taeniasis is infection with an adult tapeworm , while cysticercosis is infection with larval stages ( of Taenia solium ) in body tissues . Taeniasis , acquired via ingestion of undercooked meat containing the larval stage of the parasite , is not a significant health problem , generally producing asymptomatic infections or mild symptoms . However , carriers of T . solium tapeworms are a source of infection for human cysticercosis , which can produce long-term health problems . The transmission of Taenia spp . from a tapeworm carrier occurs via the shedding of eggs in faeces , followed by their ingestion by animal hosts ( e . g . pigs for T . solium and cattle for Taenia saginata ) and subsequent development into cysticerci [1] . Humans can also act as a ‘dead-end’ host for the larval stage of T . solium: accidental ingestion of tapeworm eggs results in the development of cysticerci in various tissues . The development of cysticerci in the central nervous system causes the most serious form of the disease , neurocysticercosis , which can produce neurological symptoms including seizures and is thought to be the leading cause of adult-onset epilepsy , responsible for up to one third of acquired epilepsy in T . solium endemic areas [2] . Due to the role of faecal contamination in transmission , Taenia spp . infections are common in developing countries with inadequate sanitation [3] . Recent pig population growth in some regions , including parts of sub-Saharan Africa , has led to concerns over increasing incidence of taeniasis , cysticercosis , and neurocysticercosis [3] . Despite the recognition of T . solium as a significant health problem , there are still few data available regarding its incidence and spatial distribution; substantial gaps in our epidemiological understanding; and a lack of reliable diagnostic tools for field use [1 , 4] . Thus , taeniasis and cysticercosis are considered to be neglected tropical diseases [4] . Increased risk of cysticercosis in pigs and humans has been associated to a lack of latrine availability or use [3 , 5 , 6] , and free-ranging pig husbandry practices [7–9] , highlighting the importance of environmental contamination . A single tapeworm can release up to 300 , 000 eggs per day , but the influences of environmental factors on egg survival have not been well studied [10] . Egg survival is influenced by temperature and humidity , with tropical regions being particularly suitable for transmission [10] . Surface moisture and humidity are thought to be the main constraining factors for Taenia spp . eggs in the environment: the eggs are vulnerable to desiccation and survival is greatly reduced under dry conditions , regardless of temperature [11] . Mechanical spatial spread of eggs can also occur via movement in streams , rivers or flood waters and via the activity of dung beetles . Epidemiological analysis of several helminth species ( e . g . hookworm , roundworm Ascaris lumbricoides and whipworm Trichuris trichiura ) whose transmission cycles involve environmental contamination and subsequent egg maturation in the soil , has highlighted the role of environmental factors , including rainfall , temperature and vegetation cover , in the spatial distribution of these infections [12 , 13] . Soil-related factors ( e . g . soil type ) and land cover are also associated with helminth distributions , due to effects on soil humidity and egg maturation [14] . Although the lifecycle of Taenia spp . does not require egg maturation in the soil , egg survival and on-going transmission patterns are likely to exhibit correlations with environmental and climatic variables . Spatial analyses have demonstrated significant clustering of taeniasis , porcine cysticercosis and human cysticercosis , with evidence of aggregation of human and porcine cysticercosis cases within close proximity to human tapeworm carriers [15–18] . However , the extension of these analyses to encompass environmental covariates has not been carried out , despite the potential value . This research aimed to assess the hypothesis that spatial clustering of cysticercosis is the result of a combination of ( a ) localised transmission cycles giving rise to spatial aggregations and ( b ) the impact of environmental conditions on egg survival and , thus , onward transmission . The influence of socio-economic , behavioural and environmental variables was assessed for human and porcine cysticercosis . Evidence for the influence of environmental factors on the distribution of Taenia spp . infections may provide the basis for further epidemiological research , to support the development and targeting of control programmes .
Ethical approval was granted by the Kenya Medical Research Institute Ethical Review Board ( SC1701; human sample collection ) , the Animal Welfare and Ethical Review Body ( AWERB ) at The Roslin Institute , University of Edinburgh ( approval number AWA004; pig sample collection ) and the University of Southampton ethics review committee ( ID 1986; secondary data analysis ) . Written informed consent was obtained for all study participants and individual data was stored without identifiable information for the purposes of the analysis presented in this manuscript , to ensure anonymity . The research focused on an area of western Kenya , as illustrated in Fig 1 , which was selected as representative of areas at high risk of zoonotic diseases in the Lake Victoria crescent area of East Africa . The population density is approximately 500 per km2 and subsistence agriculture ( mixed crop-livestock ) is the predominant occupation , with the cattle population outnumbering the pig population . The study population includes several ethnicities , with the majority from the Luhya , Luo , Teso and Samia ethnic groups . The climate is bimodal , with rainy seasons from March to May and August to November , and an annual average temperature of approximately 22°C ( range 14°C to 30°C ) [19] . Data from a cross-sectional survey examining a range of zoonotic and non-zoonotic diseases ( including Taenia spp . ) in 416 households , carried out between July 2010 and July 2012 , were used . Taeniasis detection was carried out for human participants using microscopy ( sensitivity 28 . 6% to 52 . 5%; specificity 85 . 7% to 99 . 9% [20 , 21] ) and copro-antigen ELISA ( sensitivity and specificity of 98% and 99 . 1% , respectively [20] ) , which identify current Taenia spp . infections . It should be noted that these methods detect both T . solium and T . saginata infections , but cannot differentiate them . Detection of Taenia spp . HP10 antigen ( which is suggestive of cysticercosis ) was carried out for human participants and pigs utilising the HP10 antigen ELISA ( sensitivity 44 . 4% to 84% for porcine sera and 75% to 84 . 8% for human sera; specificity 45% to 100% for porcine sera and 94% to 96 . 5% for human sera [22–25] ) . A detailed description of the survey protocol and diagnostic methods is provided in S1 File . Individual infection status data was linked with covariates at the individual ( e . g . age ) and household ( e . g . presence of a latrine ) levels , including questionnaire responses and geographically linked datasets , as listed in Table 1 . Further information regarding the covariate datasets used is provided in S2 File . Based on a suspected overestimation of human taeniasis ( see results and discussion for further information ) , this outcome was not included in further statistical analyses . Due to the clustered nature of the data , the between-household variability in the odds of occurrence for each outcome was assessed . For each outcome , a single-level and a multilevel logistic regression model ( including household level random effects ) were fit to the data with no covariates . Likelihood ratio tests were used to assess the null hypothesis of no difference in the outcome between households . Where the null hypothesis was rejected ( presence of Taenia spp . antigen in human sera ) , a multilevel model was used; where the null hypothesis was not rejected ( presence of Taenia spp . antigen in porcine sera ) a single-level model was applied . For the land cover and precipitation covariates , the functional forms of associations with each of the outcome variables were assessed using univariable logistic regression analysis . Models including the covariates as linear , quadratic , square root and log terms were fitted to the data . Model comparison , based on AIC values and Chi-squared tests , was carried out to assess whether the non-linear terms improved the fit of the model . Where a non-linear term resulted in a statistically significant ( p-value of 0 . 05 or less ) improvement in model fit ( reduction in AIC ) , this term was used rather than the linear term in further analysis . Following the assessment of the functional form of associations , univariable logistic regression models were used to assess the significance of each of the covariates indicated in Table 1 . This was followed by multivariable logistic regression including all the individual level covariates with a p-value of 0 . 1 or less in the univariable analysis . Next , household level covariates with a ( univariable analysis ) p-value of 0 . 1 or less were included , one at a time . At all steps , covariates no longer significantly associated with the outcome were removed . Where covariates were correlated with one another , covariate selection was performed based on understanding of the transmission cycle and comparison of AIC values . A receiver operating characteristic ( ROC ) curve was created using observed outcomes and fitted values for each multivariable model , and the area under the ROC curve ( AUC ) calculated as a measure of model fit ( AUC = 1 indicates perfect prediction; AUC = 0 . 5 indicates a prediction which performs no better than random ) . All statistical analyses were carried out in the R statistical software with lme4 ( multilevel models ) and stats ( single level models ) packages . The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication . See S1 Checklist for the STROBE checklist for this cross-sectional study .
416 households were recruited into the study with a minimum of 1 occupant and a maximum of 21 , giving a mean household population of 5 . 1 . Of the 416 households , 56 . 2% kept cattle and 16 . 9% kept pigs . Over half of pig keeping households ( 65% ) kept only one pig and the mean herd size was 2 . 6 . In total , 2113 humans ( approximately 0 . 15% of the human population within the overall study area ) and 93 pigs were included in the study , with stool samples obtained from 2057 humans ( for taeniasis detection ) and serum from 2092 humans and 93 pigs ( for cysticercosis detection ) . Females accounted for 53 . 6% of the human study population . The correlation between the two diagnostic methods for taeniasis ( microscopy and copro-antigen ELISA ) was zero: four participants were positive for taeniasis via microscopy only , 397 were positive for taeniasis via copro-antigen detection only and none were positive using both methods . The prevalence of taeniasis , based on direct observation of Taenia spp . eggs ( i . e . using only the microscopy results ) , was 0 . 2% ( 95% confidence interval [CI]: 0 . 05%–0 . 5% , note that this includes both T . saginata and T . solium and the methods used cannot differentiate between them ) . Based on the lack of correlation between the two diagnostic methods , and unexpectedly high number of positive results , the taeniasis results were not used in further statistical analyses as a precautionary measure . The prevalence of Taenia spp . antigen ( suggestive of cysticercosis ) was 6 . 6% in humans ( 95% CI: 5 . 6%–7 . 7% ) and 17 . 2% in pigs ( 95% CI: 10 . 2%–26 . 4% ) . Taenia spp . antigen was detected in at least one householder in 74 of the 416 households . Of the 55 pig-keeping households , 13 had at least one pig with detected Taenia spp . antigen . See Fig 2 for the observed spatial distribution of the disease outcomes and Tables 2 and 3 for descriptive data for prevalence of Taenia spp . antigen detection in human and porcine sera respectively . Significant between-household variability was observed in the prevalence of Taenia spp . antigen ( suggestive of cysticercosis ) in humans , ( likelihood ratio test p<0 . 005 ) , but not in pigs ( likelihood ratio test p>0 . 05 ) . Therefore , a multilevel model was applied for human data , and single-level model for porcine data . The majority of environmental covariates , when included in a univariable model as non-linear terms , did not significantly improve model fit in comparison to inclusion as linear terms ( Tables A and B in S3 File ) . A non-linear relationship ( quadratic ) was indicated between the percentage of land that was flooding agricultural and grassland , and presence of Taenia spp . antigen in humans ( p = 0 . 04 ) . Thus , subsequent analysis of human data included the percentage of land that was flooding agricultural and grassland as a quadratic term . Univariable regression results are presented in Tables C to E in S3 File . Sanitation related covariates ( frequency an individual uses a latrine; presence of latrine within household; type of latrine; evidence of latrine use; or evidence of scavenging around the latrine by pigs ) , which have previously been shown to be associated with cysticercosis occurrence , were not significantly correlated with the presence of Taenia spp . antigen in pigs or humans . The results from the multivariable human model for presence of Taenia spp . antigen in humans ( Table 4 ) indicate a smaller odds of antigen presence in males compared to females ( OR = 0 . 58 , p = 0 . 02 ) and in those with primary education or higher compared to those with no education ( OR for primary level = 0 . 62 , p = 0 . 09 ) . The use of wells as a water source was associated with a higher odds of antigen presence ( OR = 2 . 76 , p = 0 . 02 ) . The percentage of land that was flooding agricultural or grassland had a quadratic relationship with presence of antigen in humans: lower odds of antigen presence were associated with very low or very high percentages of this land cover class , while the odds of antigen presence were highest in areas with intermediate percentages of the land cover class ( OR = 1 . 09 and 0 . 998; p = 0 . 03 and 0 . 03 for the linear and squared terms respectively ) . Precipitation was negatively associated with the outcome ( OR = 0 . 998 , p = 0 . 02 ) . The final model for presence of Taenia spp . antigen in pigs ( Table 5 ) indicated that breeding sows had significantly higher odds of antigen presence compared to male pigs ( OR = 10 . 35 , p = 0 . 01 ) , and flooding agricultural land and grassland demonstrated a positive association with the outcome ( OR = 1 . 04 , p = 0 . 004 ) . The AUC value was 0 . 96 for the human model , indicating excellent model fit , and 0 . 77 for the porcine model , indicating fair model fit .
Previous evidence has indicated spatial clustering of taeniasis and cysticercosis . This may be the result of localised transmission cycles; the impact of environmental conditions on egg survival; or a combination of these [15–18] . The results presented here indicate endemic transmission of Taenia spp . in the study area , and demonstrate significant associations between assumed cysticercosis in both pigs and humans ( based on presence of Taenia spp . HP10 antigen in sera ) , and land cover , after accounting for other known risk factors . This supports the hypothesis that spatial heterogeneity in the distribution of infections may be influenced by environmental conditions , highlighting the interplay between socio-economic , behavioural and environmental factors in Taenia spp . infection risk in humans and pigs . A recent review of previously published studies across Africa demonstrated taeniasis prevalence ranging from 0% to 8 . 7% ( although these studies do not use a standardised diagnostic protocol ) [1] . A higher prevalence of 13 . 15% has been reported from Ghana , based on detection by microscopy [26] . The detected prevalence of taeniasis based on direct observation of Taenia spp . eggs in this study was 0 . 02% , which lies within the previously reported range . However , the results from the copro-Ag ELISA suggested a far larger number of tapeworm carriers within the study population . The methods used for taeniasis detection do not allow differentiation of Taenia species , and so this prevalence estimate includes both T . saginata and T . solium . Previous research has highlighted that hyper-endemic transmission of T . solium ( characterised by human cysticercosis prevalence of up to 27% and porcine prevalence up to 75% ) can be associated with a taeniasis prevalence of less than 7% , and , in general , cysticercosis prevalence is higher than prevalence of taeniasis [27–29] . Based on our detected prevalence of Taenia spp . antigen ( suggestive of cysticercosis ) of 6 . 6% in humans and 17 . 2% in pigs , along with a lack of correlation between microscopy and copro-Ag ELISA results in this study , we suspect that the copro-antigen ELISA results were inaccurate , and thus , these data were not included in further analyses . More accurate assessment of taeniasis prevalence in this setting is warranted to ensure an accurate picture of the epidemiology of Taenia spp . is available . This could be achieved by providing antihelminitic treatment to those with a positive copro-antigen test , followed by assessment of stools for expelled tapeworms . However , this was not feasible to achieve within the described study given the broad range of infectious diseases targeted ( i . e . the study was not focussed solely on detection of Taenia spp . ) and the fact that copro-Ag ELISA was carried out after the completion of the field work in our Nairobi laboratory on anonymised samples . Detected prevalence of cysticercosis ( or Taenia spp . antigen ) varied from 0% to 21 . 6% in humans and from 0% to 56 . 7% in pigs from previous research across Africa ( and in general , porcine prevalence was higher than human prevalence in individual countries ) , although again , these did not use standardised diagnostic protocols [1] . Prevalence of Taenia spp . antigens in this study population was 6 . 6% for humans and 17 . 2% for pigs , which fall within the ranges previously reported . In a nearby study site ( also in western Kenya ) , a substantially higher prevalence of porcine cysticercosis ( 32 . 8% ) was detected: no data on human prevalence was available from this study site [5] . The results from this study , in combination with previously published data , highlight substantial variation in the prevalence of cysticercosis across different regions . However , a scarcity of data and a lack of understanding of the spatial heterogeneity of Taenia spp . transmission remain . Odds of Taenia spp . antigen presence in humans were smaller in those with any level of formal education when compared with those with no education . Lack of education is commonly associated with higher prevalence of infectious diseases , particularly those related to sanitation [30] . Females had larger odds of antigen presence compared to males , which may be related to the daily activities of females within the study population: women provide up to 75% of agricultural labour in smallholder farming in Kenya , which may result in increased exposure to faecal contamination in the environment [31] . A similar gender difference has been identified elsewhere [32] , although this finding cannot be generalised to all settings [33] . The use of well water was positively correlated with antigen presence in humans , indicating that contamination of well water with faecal pollutants is common . Previous research in Tanzania has also demonstrated larger odds of cysticercosis in those consuming “unsafe” water [34] . Protected ( or improved ) water sources , such as well constructed boreholes , can prevent faecal contamination: the sides of the borehole can be lined and the top covered to prevent direct entry of surface water and other contaminants . Wells , although they may be improved ( e . g . covered to prevent surface water influx ) , are generally at higher risk of contamination as they are often left uncovered or inadequately covered , allowing potentially contaminated surface water to enter . Wells are also shallow in comparison to boreholes , meaning that even when covered , surface water has a shorter duration of soil filtration before entering the well , increasing further the risk of contamination [35 , 36] . Within the study area , well water is more common in areas with frequent flooding , presumably due to the requirement of a high water table . This combination of flooding and vulnerable water supplies may enhance contamination , thus increasing the risk of infection . Flooding agricultural land and grassland was also ( non-linearly ) associated with presence of antigens in humans , indicating a role for landscape factors in cysticercosis . The eggs of Taenia spp . are highly susceptible to desiccation , suggesting this association may be related to varying soil humidity in different landscapes ( e . g . soil humidity will be highest under vegetation and in areas that flood periodically ) [11] . In addition , human activities vary in different types of landscape , thus , altering contamination and exposure: agricultural land and grassland are accessed more frequently by humans than , for example , woodland , enhancing the possibility of environmental contamination ( those working in the field do not always use a latrine ) and subsequent exposure to eggs . Flooding may also be related to the movement of eggs , with flood waters potentially resulting in contamination of land with eggs from elsewhere . Precipitation was also significantly , and negatively , associated with presence of Taenia spp . antigen in humans . Based on the previous discussion regarding flooding and access to ground water , this relationship is not as expected . The southern part of the study area ( which is at the lowest elevation ) experiences the least rainfall , but includes the largest proportion of flooding land and has a larger proportion of the population using groundwater sources , particularly well water . A possible explanation for the observed relationship is the action of overland water flow following precipitation leading to eggs being washed away , whereas flooding events may be associated with egg deposition . In terms of presence of Taenia spp . antigen in pigs , breeding female pigs had significantly higher odds of antigen presence compared to male or non-breeding females . This may be due to a longer period of exposure in the household ( breeding females will be retained for a longer period than pigs raised for sale or slaughter ) , although age group alone was not significantly associated with porcine cysticercosis . In addition , flooding agricultural land and grassland was positively associated with the outcome , indicating that this land cover class may act to promote survival of eggs in the environment or enhance the exposure of pigs to faecal material . As discussed previously , this land cover class represents areas which are likely to have high soil moisture contents , may experience contamination via the movement of pathogens during periods of flooding and are likely to have high levels of human activity , thus increasing the possibility of faecal contamination . It is important to recognise that a positive HP10-antigen ELISA result is suggestive of the presence of a viable cyst in the host ( i . e . cysticercosis ) , which may not have been recently acquired . Due to the short lifespan of pigs , a positive HP10-antigen ELISA result will relate to a relatively recent infection . However , a positive result in humans may relate to a historical infection since cysts can remain in a host for several years . In addition , a positive result does not necessarily indicate neurocysticercosis: this may relate to muscular , neuro- or ocular-cysticercosis . The results should also be interpreted with consideration of the performance of the diagnostic methods used . The sensitivity of HP10 antigen-ELISA has been estimated at between 44 . 4% and 84% for porcine sera and between 75% and 84 . 8% for human sera . The specificity has been reported as between 45% and 100% for porcine sera and between 94% and 96 . 5% for human sera [22–25] . This assay was found to have low cross-reactivity with other helminth infections , except for cross-reactivity with other Taenia species , including Taenia hydatigena [37] . There is a lack of empirical data regarding the prevalence of T . hydatigena in pigs in East Africa: its presence has been documented , but its prevalence is thought to be low , with a recent study in Tanzania indicating a prevalence of 6 . 6% in pigs [38–40] . Further validation of the HP10 antigen-ELISA for detection of porcine cysticercosis using pig necropsy as the gold-standard , within the study area , is currently being planned . It was not feasible to conduct this within the described study . The results of statistical analysis relating to the presence of antigen in pigs are less certain than those relating to the presence of antigen in humans , based on the smaller sample size ( 93 pigs ) , which limits our ability to draw firm conclusions . Movements of pigs and humans have not been considered in this analysis , although these movements are of clear importance for exposure to infection: pigs within this study area have been found to scavenge for food within a mean home range area of 10 , 343 m2 [41] . The inclusion of land cover within 1 km of the household should partially deal with this limitation . The proximity to tapeworm carriers has also been identified as an important factor determining the occurrence of cysticercosis [15 , 16] . As this study was based on a sample of individuals from the study area , information on the locations of all tapeworm carriers was not available and , thus , it was not possible to include this aspect in our analysis . Overall , these results provide a useful insight into the epidemiology of Taenia spp . infections in a rural community and highlight key areas where interventions should be targeted . The World Health Organization lists the interventions for control of taeniasis and cysticercosis as: preventive chemotherapy; diagnosis and treatment of taeniasis cases; improved health education; improved sanitation; improved pig husbandry; treatment of pigs; vaccination of pigs; and improved meat inspection and processing [42] . This research , in combination with an understanding of the transmission cycle of Taenia spp . , indicates that environmental contamination by eggs is a key issue , with environmental factors influencing the potential for cysticercosis in pigs and humans; large-scale interventions to address the control of cysticercosis should thus consider ways of reducing contamination in the environment as a means of reducing transmission . This research provides an initial view of the complex interplay between individual level factors , household level factors and environmental factors in the spatial distribution of Taenia spp . infections in humans and pigs , indicating roles for both ( a ) localised transmission and ( b ) the influence of environmental factors . In reaching these conclusions we acknowledge the limitations of the assay procedures employed: further investigations , particularly the direct verification of parasitic infection and species identification by a combination of antihelmintic treatment , tapeworm identification , PCR and the inspection of pig carcasses are warranted . Further research describing the environmental determinants of tapeworm and cysticercosis over larger areas and in different ecological systems would deliver the potential to provide national or regional level spatial predictions of infection risk . Such outputs would significantly improve our understanding of geographical heterogeneity in taeniasis and cysticercosis and allow the implementation of geographically targeted interventions . | Taenia spp . can cause tapeworm infections in the human gut , and infection with the larval stage of Taenia solium ( cysticercosis ) can lead to serious outcomes , such as epilepsy . Transmission occurs in areas with poor sanitation and a lack of adequate meat inspection practices , although there are still gaps in our understanding of how socio-economic , behavioural and environmental factors influence the occurrence of these parasites . Humans and pigs residing in 416 households in an area of western Kenya were tested for larval stage infections ( cysticercosis ) . Statistical methods were applied to examine the relationships between a range of socio-economic , behavioural and environmental factors and disease occurrence . The results indicate that several factors , including land cover , influence the distribution of cysticercosis infections in humans and pigs . Further research in this area may provide significant understanding regarding the influence of environmental drivers on Taenia spp . infections , delivering evidence to support the targeting of disease control activities . |
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While the vast majority of genome size variation in plants is due to differences in repetitive sequence , we know little about how selection acts on repeat content in natural populations . Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize ( Zea mays ) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America . We combine genotyping , low coverage whole-genome sequence data , and flow cytometry to test for evidence of selection on genome size and individual repeat abundance . We find that population structure alone cannot explain the observed variation , implying that clinal patterns of genome size are maintained by natural selection . Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats , likely due to their large individual contribution to genome size . To better understand the phenotypes driving selection on genome size , we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size . We find weak support for a positive correlation between genome size and cell size , but stronger support for a negative correlation between genome size and the rate of cell production . Reanalyzing published data of cell counts in maize shoot apical meristems , we then identify a negative correlation between cell production rate and flowering time . Together , our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines , connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes .
Genome size varies many orders of magnitude across species , due to both changes in ploidy as well as haploid DNA content [1 , 2] . Early hypotheses for this variation proposed that genome size was linked to organismal complexity , as more complex organisms should require a larger number of genes . Empirical analyses , however , revealed instead that most variation in genome size is due to noncoding repetitive sequence and that genic content is relatively constant [3 , 4] . While this discovery resolved the lack of correlation between genome size and complexity , we still know relatively little about the makeup of many eukaryote genomes , the impact of genome size on phenotype , or the processes that govern variation in repetitive DNA and genome size among taxa [5] . A number of hypotheses have been offered to explain variation in genome size among taxa . Across deep evolutionary time , genome size appears to correlate with estimates of effective population size , leading to suggestions that genetic drift permits maladaptive expansion [6] or contraction [7] of genomes across species . Other models propose that variation may be due to differences in the rates of insertions and deletions [8] or a consequence of changes in modes of reproduction [9 , 10] . While each of these models find limited empirical support [11 , 12] , counterexamples are common [9 , 10 , 13 , 14] . In addition to these neutral models , many authors have proposed adaptive explanations for genome size variation . Numerous correlations between genome size and physiologically or ecologically relevant phenotypes have been observed , including nucleus size [15] , plant cell size [16] , seed size [17] , body size [18] , and growth rate [19] . Adaptive models of genome size evolution suggest that positive selection drives genome size towards an optimum due to selection on these or other traits , and that stabilizing selection prevents expansions and contractions away from the optimum [20] . In most of these models , however , the mechanistic link between genome size and phenotype remains unclear [21] . Much of the discussion about genome size variation has focused on variation among species , and intraspecific variation has often been downplayed as the result of experimental artifact [22] or argued to be too small to have much evolutionary relevance [23] . Nonetheless , intraspecific variation in genome size has been documented in hundreds of plant species [23] , including multiple examples of large-scale variation [24–26] . Correlations between intraspecific variation in genome size and other phenotypes or environmental factors have also been observed [24 , 25 , 27] , suggesting the possibility that some of the observed variation may be adaptive . Here we present an analysis of intraspecific genome size variation in the model system maize ( Zea mays ssp . mays ) and its wild relative highland teosinte ( Zea mays ssp . mexicana ) . Genome size in Zea varies dramatically both within [28] and between [29] subsepecies , and previous work has also found substantial intraspecific variation in transposable element ( TE ) abundance [30 , 31] , the number of auxiliary B chromosomes [32] , and the number and location of heterochromatic knobs [32] . Several authors have observed negative correlations between genome size and altitude [25 , 28] and some repeats show similar clinal variation [33] . It remains unclear , however , whether these patterns can be explained by natural selection or how genome size might impact plant fitness . We take advantage of parallel altitudinal clines in maize landraces from Mesoamerica and South America to investigate the evolutionary processes and sequence differences underlying genome size variation . Leveraging the intraspecific genome size variation in Zea taxa , we model genome size as a quantitative trait , using flow cytometry and genotyping to show that natural selection has reduced genome size in high elevation populations . In a similar analysis of repeat content from low coverage shotgun sequencing , we also identify evidence of selection directly on knob variants . We then perform growth chamber experiments to measure the effect of genome size variation on the developmental traits of cell production and leaf elongation in the related wild highland teosinte Z . mays ssp . mexicana . These experiments find modest support for slower cell production in larger genomes , but weaker support for a correlation between genome size and cell size . Based on these results and reanalysis of published data , we propose a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines , connecting repetitive sequence variation to important differences in adaptive phenotypes .
We sampled 77 diverse maize landraces from across a range of altitudes in Meso- and South America ( S2 Table ) . Flow cytometry of these samples revealed a negative correlation with altitude on both continents ( Fig 1A , r = -0 . 51 and -0 . 8 , respectively , p-value <0 . 001 ) . We used low-coverage whole-genome sequencing mapped to reference repeat libraries to estimate the abundance of repetitive sequences in each individual with estimated genome size , and validated this approach by comparing sequence-based estimates of heterochromatic knob abundance to fluorescence in situ hybridization ( FISH ) data from mexicana populations ( Fig 2 and S4 Fig; see Methods for details ) . We observed substantial variation among landraces in the abundance of individual transposable element families ( S5 Fig ) , and both transposable elements as a whole and heterochromatic knobs showed clear decreases in abundance with increasing altitude in Meso- and South America ( TE r = -0 . 57 , -0 . 72; 180bp knob r = -0 . 48 , -0 . 83; TR1 knob r = -0 . 66 , -0 . 81; p-value <0 . 001 ) , mirroring the pattern seen for overall genome size ( Fig 1 ) . In contrast , we found only a weak positive correlation between B chromosome abundance and altitude ( p-value >0 . 05 ) ( S6 Fig ) . We next sought to evaluate whether the observed clines in genome size and repeat abundance simply reflected underlying genetic differences due to population structure or could be better explained by natural selection . We adopted an approach similar to Berg and Coop [34] , modeling genome size as a quantitative trait that is a linear function of relatedness and altitude ( see Methods , Eq 1 ) . Across maize landraces , we rejected a neutral model in which genome size is unrelated to altitude , estimating a decrease of 108Kb and 154Kb in mean genome size per meter gain of altitude in Meso- and South America , respectively ( S8 Table ) . We then evaluated whether selection has acted on individual repeats , treating abundance of each repeat class as a quantitative trait in a comparable model that includes genome size as a covariate ( Methods , Eq 2 ) . In both Meso- and South America , TR1 knobs showed evidence of selection , while 180bp knobs also showed evidence of selection in South American landrace germplasm ( S8 Table ) . Finally , our models for total transposable element content were not significant in either continent , and the number of individual TE families showing significant correlations with altitude was no greater than expected by chance ( 46/1156 , binomial test p-value >0 . 05 ) . The wild ancestor of maize , Zea mays ssp . parviglumis ( hereafter parviglumis ) , grows on the lower slopes of the Sierra Madre in Mexico . A related wild teosinte , Zea mays ssp . mexicana ( herafter , mexicana ) , diverged from parviglumis ≈60 , 000 years ago [35] and has adapted to the higher altitudes of the Mexican central plateau [36] . We sampled leaves and measured genome size of two individuals each from previously collected populations of both subspecies ( 6 parviglumis populations and 10 mexicana populations ) [37 , 38] . Though both subspecies exhibit considerable variation , parviglumis samples have larger genomes than mexicana ( S7 Fig; one tailed t-test p-value<0 . 05 ) but do not differ from lowland maize in Mexico , consistent with our observations of decreasing genome size along altitudinal clines in Mesoamerican and South American maize . To evaluate clinal patterns across populations of highland teosinte in more detail , we sampled multiple individuals from each of an additional 11 populations of mexicana across its altitudinal range in Mexico ( S4 Table ) . Genome size variation across these populations revealed no clear relationship with altitude ( S8 Fig ) , but genotyping data [39] revealed consistent evidence of genetic separation ( S9 Fig ) and higher inbreeding coefficients ( two-sided t-test p-value <0 . 001 ) in the three lowest altitude populations ( see Methods ) . These three populations are also phenotypically distinct and relatively isolated from the rest of the distribution ( A . O’Brien , pers . communication ) . We thus excluded these three populations , applying our linear model of altitude and relatedness to 70 individuals from the remaining 8 populations . After doing so , we find a negative relationship between genome size and altitude in mexicana ( Fig 1E , p-value <0 . 001 ) of similar magnitude to that seen in maize ( loss of 270Kb/m ) , suggesting parallel patterns of selection across Zea . In agreement with our results in maize , TR1 knob repeats showed evidence of selection after controlling for their contribution to genome size ( S8 Table ) , though 180bp knob repeats did not . We found no evidence for selection on TE abundance after controlling for genome size , and none of the sequence from mexicana mapped to our B-repeat library . To test whether genome size might be related to flowering time through its potential effect on the rate of cell production , we performed a growth chamber experiment to measure leaf elongation rate , cell size , and genome size using 201 mexicana individuals from 51 maternal families sampled from a single natural population ( see Methods ) . Individual plants varied by as much as 1 . 13Gb in 2C genome size , with observed leaf elongation rate ( LER ) varying from 1 to 8 cm/day ( mean 4 . 56cm/day; S9 Table ) . Without correcting for any relatedness , we see that genome size and leaf elongation rate have a negative correlation of -0 . 134 ( p-value = 0 . 0576 ) , while genome size and cell size are not correlated ( p-value = 0 . 4452 ) . To incorporate the family structure in our sample and directly connect genome size with cell division rate in a parametric fashion , we designed a Bayesian model of leaf elongation as a function of cell size , cell production rate , and genome size ( see Methods ) . Our posterior parameter estimates suggest a weak but positive relationship between genome size and cell size ( γGS; Fig 3A ) and a negative relationship between genome size and cell production rate ( βGS; Fig 3B ) . We found that our inferences were sensitive to prior specifications for leaf elongation rate and cell size ( S3 Fig ) , but prior means ≥ 4cm/day for leaf elongation rate combined with prior means ≤ 0 . 003cm for CS , returned reliably negative relationships between genome size and cell production rate ( see Methods ) . Recent work exploring shoot apical meristem ( SAM ) phenotypes across 14 maize inbred lines [41] allowed further exploration of our hypothesized connection between cell production and flowering time . Because Leiboff et al . sampled SAM at equivalent growth stages , we interpreted variation in cell number as representative of differences in cell production rate among lines . We re-analyzed these data to investigate whether the cell number reported in each SAM was correlated with flowering time ( Fig 3C ) . After estimating genetic values for each inbred line used and correcting for population structure and the effects of two candidate genes ( see Methods ) , we find a negative correlation between flowering time and cell production across all three developmental stages sampled ( slopes of -0 . 11 , -0 . 08 , and -0 . 08 and p-values <0 . 01 , <0 . 001 , and 0 . 170 , respectively ) .
We report evidence of a negative correlation between genome size and altitude across clines in Meso- and South America in both maize and its wild relative highland teosinte ( Fig 1 ) . Maize was domesticated from parviglumis , suggesting that large genome size was likely ancestral , and we observed no difference in genome size between lowland Meso-American maize landraces and parviglumis . The subsequent colonization of highland environments occurred independently in Mesoamerica and South America [42] , and while the populations share a number of adaptive phenotypes , they exhibit little evidence of convergent evolution at individual loci [43] . The teosinte subspecies mexicana is also found in the highlands of Mesoamerica [36] , likely after its split from the lowland teosinte parviglumis ≈60 , 000 years ago [35] . Previous investigations of genome size have also identified negative altitudinal clines in maize and teosinte [25 , 28] ( but see Rayburn et al . [44] for a positive cline in the U . S . Southwest ) , suggesting that this observation is general and not an artifact of our sampling . Although we find altitudinal trends in genome size across all three clines , our initial evaluation of genome size in highland teosinte found no significant correlation with altitude , due primarily to the small genomes observed in the three lowest altitude populations ( S8 Fig ) . We excluded these three mexicana populations because they showed higher levels of inbreeding than other mexicana populations as well as evidence of shared ancestry with parviglumis ( S9 Fig ) . We speculate that the relationship between genome size and altitude may be more complex for low altitude mexicana due to the confounding effects of admixture impacting both adaptation and repeat evolution . These populations are nonetheless interesting and worthy of future investigation , as their genome size is smaller than either parviglumis or high altitude mexicana but their knob content does not differ from other mexicana populations , suggesting perhaps that inbreeding or admixture may have affected transposable element or other repeat abundance . Our results suggest the best explanation for the observed clines in intraspecifc genome size variation is natural selection . Several authors have identified ecological correlates of variation in plant genome size and argued for adaptive explanations of such clines [25 , 28 , 45] , but relatively few have corrected for relatedness among individuals or populations [27] . We employ a modeling approach that considers genome size as a quantitative trait and uses SNP data to generate a null expectation of variation among populations , allowing us to rule out stochastic processes and instead pointing to the action of selection in patterning clinal differences in genome size . Alternative explanations for our observations , including mutational biases and TE expansion , are unlikely . For example , plants grown at high altitudes are exposed to increased UV radiation and UV-mediated DNA damage may lead to higher rates of small deletions [46] . But because UV damage causes small DNA deletions , it is unlikely to generate the gigabase-scale difference we see across altitudinal clines in the short time since maize arrived in the highlands [47] . And while expansion or replication of TEs in lowland populations could lead to increased rates of insertion and larger genome size , our analysis of reads mapping to individual TE families finds no evidence that this has occurred in a widespread manner , and genome size estimates from the direct wild ancestor of domesticated maize ( the lowland teosinte parviglumis ) suggest that smaller highland genomes are the derived state . Having concluded that natural selection is the most plausible explanation for decreasing genome size at higher altitudes , we then asked whether these observations were the result of selection on genome size itself or merely a consequence of selection on specific repeat classes . We find no evidence of selection on B repeats , consistent with the relatively mixed signals found in previous literature [48] . We also find little evidence of selection on TEs after controlling for genome size . Because individual TEs are relatively small , however , models of polygenic adaptation lead us to expect that such loci are unlikely to show a strong signal [49] . Nonetheless , TEs show the strongest overall correlation with genome size , suggesting that frequent small deletions of individual elements are likely a major contributor to genome size change across populations . In contrast to TEs , in both maize and teosinte the 350bp TR1 knob repeat shows greater differentiation in abundance across altitude than can be explained by population structure alone , even after accounting for changes in total genome size . The 180bp knob shows a similar strong decline in abundance in maize landraces , but is only statistically significant in the analysis of landraces in South America . Selection on genome size might be expected to act especially strongly on knobs , as each locus may contain many megabases of repeats and knob abundance is a large contributor to intraspecific genome size variation [50 , 51] . These results are surprising , however , given the selfish nature of knobs and their ability to distort segregation ratios in female meiosis in the presence of a driving element known as abnormal chromosome 10 ( Ab10 ) [52] . While our genotyping data do not include markers diagnostic of Ab10 , previous analyses show that selection along altitudinal gradients has been sufficient to decrease the frequency of at least one allele of the drive locus itself [53] . It is not entirely clear why we see more evidence of selection on the TR1 knob variant , which contributes nearly an order of magnitude fewer base pairs to the genome . The TR1 variant generally shows weaker drive , but has been shown to compete successfully against the 180bp variant [54] . It is thus possible that the weaker drive of TR1 makes it more susceptible to selection on overall genome size , and that the subsequent decrease in TR1 abundance may increase drive of the 180bp knob variant , potentially ameliorating the effects of selection against 180bp knobs at higher altitude . Finally , while we see decreasing abundance of both knob variants with increasing altitude , we note that knobs alone are not driving the overall signal: rerunning our model for genome size after removing base pairs attributable to both knob repeats still finds evidence of selection on genome size in all three clines ( Mesoamerica p-value = 0 . 029; South America p-value = 0 . 04; mexicana p-value = 0 . 02 ) . Several authors have hypothesized that genome size could be related to rates of cell production and thus developmental timing [52 , 55] . We tested this hypothesis in a growth chamber experiment in which we measured leaf elongation rates across individuals from a single population of highland teosinte that exhibited wide variation in genome size . Our approach to characterizing the effect of genome size on the rate of cell production is consistent with scaling laws proposed in a recent study of the relationships between genome size , cell size , and cell production rate [56] ( see Methods ) . We found only weak evidence for a positive correlation between genome size and cell size , a result that contrasts with the findings of many authors who have reported more definitive positive correlations between genome size and cell size across species [57 , 58] . One potential explanation for this result may be found in recent work in Drosophila where larger repeat arrays were shown to lead to more compact heterochromatin despite the physical presence of more DNA [59] . We speculate that such an effect may ameliorate some of the physical increase in chromosome size due to the expansion of certain repeats , especially tandem arrays such as those found in dense heterochromatic knobs . In support of the hypothesis that smaller genomes may enable more rapid development , our leaf elongation model indicates a negative correlation between genome size and cell production rate in our highland teosinte population . Though these results showed strong prior sensitivity , the sign of the relationship between genome size and cell production rate did not change for prior mean values of leaf elongation rate within the range of those published for maize ( from 4 . 6 cm/day [60] to 12 cm/day [61] ) , all equal to or larger than the rates observed in our experiment . Tenaillon et al . [62] also find a negative correlation between the rate of leaf elongation and genome size among inbred lines , albeit one that does not survive statistical correction for population structure . We hypothesize that selection on flowering time is the driving force behind our observed differences in genome size . Common garden experiments show that highland populations of both maize and teosinte flower earlier than their lowland counterparts [63 , 64] , and an artificial selection experiment in maize found the traits to be genetically correlated ( r ≈ 0 . 14; data from Rayburn et al . [65] assuming heritabilities of h2 = 0 . 8 for flowering time and h2 = 1 for genome size ) . Larger genomes require more time to replicate [45] , and slower rates of cell production in turn may lead to slower overall development or longer generation times [55] , though our data cannot tease apart an S-phase effect from a general cell cycle effect . Slower cell production is unlikely to be directly limiting to the cells that eventually become the inflorescence , as only relatively few cell divisions are required [66] . However , signals for flowering derive from plant leaves [67 , 68] , and slower cell production will result in a longer time until full maturity of all the organs necessary for the plant to flower . Consistent with our hypothesis , reanalysis of published data from SAM of maize inbred lines suggests that plants with more cells in their SAM at a given developmental stage ( and thus faster rates of cell production ) also exhibit earlier flowering [41] . Further evidence supporting this idea comes from Jain et al . [69] , who observe the predicted negative correlation between genome size and flowering time among a diverse panel of maize inbreds ( although the relationship is not significant after correcting for kinship ) . Finally , though additional environmental factors have been hypothesized to elicit adaptive changes in genome size e . g . [27 , 70] , we are unaware of alternative selective explanations for the genome size correlations seen in our Mesoamerican or South American altitudinal clines , and we suggest that future efforts should focus on experimental validation of the mechanistic connection between genome size and both cell production and flowering time suggested by our results . The causes of genome size variation have been debated for decades , but these discussions have often ignored intraspecific variation . Our results suggest that differences in optimal flowering times across altitudes are likely indirectly effecting clines in genome size due to a mechanistic relationship between genome size and cell production and developmental rate . We also show that selection on genome size has driven changes in repeat abundance across the genome , including significant reductions in individual repeats such as knobs that contribute substantially to intraspecific variation in genome size . We speculate that our observations on genome size and cell production may apply broadly across plant taxa . Intraspecific variation in genome size appears a common feature of many plant species , as is the need to adapt to a range of abiotic environments . Cell production is a fundamental process that retains similar characteristics across plants , and genome size is likely to impact cell production due to the constraints on replication kinetics that result from having a larger genome . Together , these considerations suggest that genome size itself may be a more important adaptive trait than has been previously believed , and that the phenotypic effects of genome size may have consequences for the evolution of individual repeats .
We quantified genome size in 77 maize landraces ( S2 Table; [43] ) and two samples from previously collected populations of parviglumis ( n = 6 ) and mexicana ( n = 10 ) ( S3 Table; [38] ) . For our growth chamber experiment , we sampled 201 total seeds from 51 maternal plants collected from 11 populations of mexicana ( S4 and S5 Tables ) . To assess the error associated with flow cytometry measures of genome size , we used 2 technical replicates of each of 35 maize inbred lines ( S6 Table ) . We germinated seeds and grew plants in standard greenhouse conditions and sent leaf tissue from each individual to Plant Cytometry Services ( JG Schijndel , NL ) for genome size analysis . Vinca major , with a genome size of 2 . 1pg/1C , was used as an internal standard for flow cytometric measures , and standard and unknowns were co-prepared and co-stained . Replicated maize lines showed highly repeatable estimates ( corr = 0 . 92 ) , with an average difference of 0 . 0346pg/1C between estimates . We used genotyping-by-sequencing ( GBS ) [71] data from Takuno et al . [43] for maize accessions along altitudinal clines in Mesoamerica and South America . For the 11 mexicana populations used in our linear model , we used GBS SNP data from O’Brien et al . [39] . All samples were filtered with TASSEL ( V5 . 2 . 37 ) [72] to remove sites with >40% missing data and individuals with >90% missing data , resulting in 170 total individuals with genotyping data for 223 , 657 sites . We elected to use this per-site cut off as it did not qualitatively change the site frequency spectrum ( S1 Fig ) . Kinship matrix calculation was performed using centered identity-by-state ( IBS ) as implemented in the software TASSEL [73] . We elected to use random imputation in our kinship calculations , as mean imputation biases the estimate of inbreeding within individual [73] . However , we tested both mean and KNN [74] imputation , and our results were robust to both methods . Inbreeding statistics for individual mexicana plants were calculated from the diagonal of the randomly imputed kinship matrix . Admixture analyses were performed using Admixture v1 . 23 [75] . For admixture analyses we also included additional GBS data from diverse maize inbred lines [76] , landraces and teosintes [77] ( S2 and S4 Tables ) , for a total of 611 individuals before filtering . We filtered individuals and sites as above , but additionally removed one individual ( the sample with lowest sequencing depth ) of each pair with an IBS distance closer than 0 . 07 . A Hardy-Weinberg filter was then applied using only outbred genotypes with a read depth between 9-300 using a chi-squared goodness of fit test , p-value <0 . 05 . We then thinned sites by linkage disequilibrium , removing lower coverage sites within physical distance less than 1000bp and sites with r2 >0 . 8 and significant at p-value <0 . 05 . Only sites with at least 12 high depth genotypes were tested . After filtering , 526 individuals and 18 , 716 sites remained . We used whole genome shotgun sequencing to estimate repeat abundance in the same 77 maize landrace accessions and 93 mexicana individuals for which we estimated genome size , as well as an additional set of mexicana individuals used to validate the approach cytologically ( see below , data available on Figshare at DOI 10 . 6084/m9 . figshare . 5117827 ) . DNA was isolated from leaf tissue using the DNeasy plant extraction kit ( Qiagen ) according to the manufacturer’s instructions . Samples were multiplexed and sequenced in 3 lanes of a Miseq ( UC Davis Genome Center Sequencing Facility ) for 150 paired-end base reads with an insert size of approximately 350 bases to a depth of <0 . 5X coverage per sample . The first lane included all maize landraces used for selection studies , the second had the mexicana populations used for FISH correlations , and the third included all mexicana samples used for analysis of clinal variation . We gathered reference sequences for 180bp knob , TR1 knob , B chromosome , and rDNA repeats from NCBI . CentC repeats were taken from Bilinski et al . [78] , and chloroplast DNA and mitochondrial DNA were taken from the maize reference genome ( v2 , www . maizesequence . org ) . B chromosomes repeats [79] were matched against the maize genome ( v2 , www . maizesequence . org ) using BLAST , and any regions within the repeats that had alignments of greater than 30bp with 80% homology were masked . The remaining unmasked regions with length greater than 70bp were used as a mapping reference for B-repeat abundance . For the transposable element database , we began with the TE database consensus sequences [80 , 81] . Using BLAST , we masked shared regions and retained unique regions of 70bp or greater as our reference , repeating this process to additionally mask tandem repeats . We mapped sequence reads to our repeat library using bwa-mem [82] with parameters -B 2 -k 11 -a to store all hit locations with an identity threshold of approximately 80% . We filtered out plastid sequence and calculated Mb of repeat by multiplying our estimated abundance by genome size . The correlation between the abundance of each repeat and genome size were as follows: TE = 0 . 95; 180bp knob = 0 . 81; TR1 = 0 . 86 . Previous simulations suggest that this estimate has good precision and accuracy in capturing relative differences across individuals [78] . We selected two individuals each from 10 previously collected populations of mexicana [37] for fluorescence in situ hybridization counts of knob content ( FISH; S3 Table ) . FISH probe and procedures closely followed Albert et al . [40] . We model genome size as a phenotype whose value is a linear function of altitude and kinship ( Eq 1 ) . We assume genome size has a narrow sense heritability h2 = 1 , as it is simply the sum of the base pairs inherited from both parents . In our model P is our vector of phenotypes , μ is a grand mean , A is a vector of altitudes included as a fixed effect , g represents an additive genetic component modeled as a random effect with covariance structure given by the kinship matrix ( K ) , and ε captures an uncorrelated error term . The coefficient βalt of altitude then represents selection along altitude , while the additive genetic ( VA ) and error ( Vϵ ) variances are nuisance parameters . P = μ + β a l t * A + g + ε g ∼ M V N ( 0 , V A K ) ε ∼ N ( 0 , V ϵ ) ( 1 ) We implemented our linear model in EMMA [83] to test for selection on genome size . In a second model , we then include genome size ( GS ) as a fixed effect in order to test for correlations between specific repeat classes and altitude conditional on genome size ( Eq 2 ) . Controlling for genome size allows us to test whether we see evidence of selection on a repeat beyond its contribution to the total base pairs it contributes toward genome size . Because TEs make up 85% of the genome , for example , without such a correction any selection on genome size will appear to be selection on TEs . P = μ + β a l t * A + β G S * G S + g + ε g ∼ M V N ( 0 , V A K ) ε ∼ N ( 0 , V ϵ ) ( 2 ) We sampled 202 seeds from multiple maternal plants collected in a single high altitude ( 2408m ) mexicana population at Tenango Del Aire , chosen because it exhibited the most variation in genome size in our altitudinal transect of mexicana . Germinated seedlings were transferred to soil pots and into a growth chamber ( 23°C , 16h L/8h D ) . We measured leaf length daily for 3 days after the first visible emergence of the third leaf . We clipped the first 8cm of leaf material from the tip of the measured leaf , then extracted a 1cm section which was dipped in propidium iodide ( . 01mg/ul ) for fluorescent imaging ( 10x magnification , emission laser 600-650 , excitation 635 at laser power 6 ) . Cell length was measured for multiple features , including stomatal aperture size and rows adjacent to stomata . Lengths across different features were highly correlated , so stomatal aperture size was used as the repeated measure of cell lengths in the growth model . We model leaf elongation rate ( LER ) as the product of cell size ( CS ) and the rate of cell production ( CP ) : L E R = C S * C P . ( 3 ) The general approach is described as a model with two “mediators” in Mackinnon [84] and a full explanation can be seen in S1 Appendix . The multiplicative expression in Eq 3 is linearized by taking the natural logarithm on both sides of the equation , and model-fitting is performed on the log scale . We hypothesize that genome size affects LER only through its effects on CS and CP . The strategy for estimation of genome-size effects is illustrated by path diagrams shown in S2 Fig , where additional details are given . We adopt a computational Bayesian approach for parameter estimation , incorporating seedling and maternal random effects in models that make use of the hierarchical dataset structure ( cells and days of growth within seedlings , seedlings within maternal parents ) . The signs and magnitudes of our estimated effects , and therefore our conclusions , are sensitive to different specifications of prior information . We identified previous averages for maize stomatal cell size and daily leaf elongation rate ( CS = 0 . 003 cm , LER = 4 . 0-4 . 8cm/day or 2mm/hr ) [60 , 85–87] , and incorporated these into informative priors for the random effects . Because our model shows prior sensitivity , we also identify prior means for which the sign of the relationship between genome size and cell production rate ( βGS ) , or cell size ( γGS ) changes ( S3 Fig ) . We generated posterior samples of model parameters using JAGS , a general-purpose Gibbs sampler invoked from the R statistical language using the library rjags [88] . We allowed for a burn in of 200 , 000 iterations and recorded 1 , 000 posterior estimates by thinning 500 , 000 iterations at an interval of 500 . To evaluate evidence for a relationship between cell production rate and flowering time , we used flowering time and meristem cell number data for 14 maize inbred lines from Leiboff et al . [41] . Because meristems were sampled at an identical growth stage and time point , differences in cell number should reflect differences in the rate of cell production . We fitted a mixed linear model to estimate the best linear unbiased estimates ( BLUEs ) of the cell counts for each growth period separately: Y i j = μ + α i + β j + ε ε ∼ N ( 0 , V E ) ( 4 ) In this model , Yij is the cell count value of the ith genotype evaluated in the jth replicate; μ , the overall mean; αi , the fixed effect of the ith genotype; βj , the random effect of the jth block; and ε , the model residuals . Each line’s genotype at trait-associated SNPs for the candidate genes BAK1 and SDA1 [41] was considered as a fixed effect and replication as a random effect . We then fit mixed linear models to study the relationship of flowering time and cell counts by controlling for population structure and known trait-associated SNPs: Y = μ + α G + β B A K 1 + β S D A 1 + g + ε g ∼ M V N ( 0 , V A K ) ε ∼ N ( 0 , V E ) ( 5 ) Here Y is the flowering time ( days to anthesis ) ; μ , the overall mean; α , the fixed effect; βBAK1 and βSDA1 the fixed effects of the BAK1 and SDA1 loci; g a random effect modeled with a covariance structure given by the kinship matrix K; and ε an uncorrelated error . The additive genetic ( VA ) and environmental ( VE ) variances are nuisance parameters . Cell counts were included as fixed effects and the standardized genetic relatedness matrix was fitted as a random effect to control for the population structure [89] . The genetic relatedness matrix was calculated using GEMMA [90] from publicly available GBS genotyping for these lines ( AllZeaGBSv2 . 7 at www . panzea . org , [72] ) . In the calculation , we used 349 , 167 biallelic SNPs after removing SNPs with minor allele frequency <0 . 01 and missing rate >0 . 6 using PLINK [91] . | Genome size in plants can vary by orders of magnitude , but this variation has long been considered to be of little functional consequence . Studying three independent adaptations to high altitude in Zea mays , we find that genome size experiences parallel pressures from natural selection , causing a reduction in genome size with increasing altitude . Though reductions in overall repetitive content are responsible for the genome size change , we find that only those individual loci contributing most to the variation in genome size are individually targeted by selection . To identify the phenotype influenced by genome size , we study how variation in genome size within a single wild population impacts leaf growth and cell division . We find that genome size variation correlates negatively with the rate of cell division , suggesting that individuals with larger genomes require longer to complete a mitotic cycle . Finally , we reanalyze data from maize inbreds to show that faster cell division is correlated with earlier flowering , connecting observed variation in genome size to an important adaptive phenotype . |
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Many eukaryotic genes play essential roles in multiple biological processes in several different tissues . Conditional mutants are needed to analyze genes with such pleiotropic functions . In vertebrates , conditional gene inactivation has only been feasible in the mouse , leaving other model systems to rely on surrogate experimental approaches such as overexpression of dominant negative proteins and antisense-based tools . Here , we have developed a simple and straightforward method to integrate loxP sequences at specific sites in the zebrafish genome using the CRISPR/Cas9 technology and oligonucleotide templates for homology directed repair . We engineered conditional ( floxed ) mutants of tbx20 and fleer , and demonstrate excision of exons flanked by loxP sites using tamoxifen-inducible CreERT2 recombinase . To demonstrate broad applicability of our method , we also integrated loxP sites into two additional genes , aldh1a2 and tcf21 . The ease of this approach will further expand the use of zebrafish to study various aspects of vertebrate biology , especially post-embryonic processes such as regeneration .
Model system genetics is undergoing a major shift from forward to reverse genetics , driven by a combination of two key factors . The first is the increasing robustness of genomic and proteomic tools , which enable rapid identification of candidate genes with potential roles in biological processes of interest . The second factor is the proliferation of precise , efficient , and easy to use genome editing tools , from zinc finger nucleases to TALE-guided nucleases and CRISPR/Cas9 ( reviewed in [1] ) . CRISPR/Cas9 in particular has made introduction of mutations into genomes of various organisms very straightforward and cost-effective . Zebrafish first rose to prominence as a vertebrate model system that made development and organogenesis amenable to forward genetic analyses [2 , 3] . Over the past twenty years , use of the zebrafish has expanded to include biological processes which occur much later in ontogenesis . Examples of such biological processes include regeneration [4] , various aspects of behavior from habituation [5] to sleep-wake cycle [6] , and carcinogenesis [7] . Genetic analyses of post-embryonic processes in zebrafish have had to rely on overexpression of dominant negatives , knockdown using morpholino oligonucleotides , modulation using small molecules , and similar approaches . The inability to generate true conditional mutants has led scientists to try alternatives , such as mutant rescue using floxed Bacterial Artificial Chromosomes [8] , random gene trapping using conditional vectors [9–11] , or targeted integration of a Cre-inducible gene trap cassette [12] . Only recently has generation of the first floxed zebrafish mutant been reported [13] . In contrast , mouse geneticists have been refining conditional mutagenesis since the introduction of the term “floxed” in 1994 [14] . We have recently developed a method for integration of epitope-coding sequences into the zebrafish genome using oligonucleotide-directed repair of CRISPR/Cas9-induced double strand breaks [15] . High germline transmission rates suggested that sequential integration of two loxP sites might be feasible . Single-stranded oligonucleotide templates have been successfully used to integrate loxP sites , either sequentially or two at a time , into the mouse genome [16–19] . However , since only non-palindromic mlox sites have so far been successfully integrated into the zebrafish genome using oligonucleotide templates [20 , 21] , we were concerned that the palindromic nature of the loxP site may interfere with homology-directed repair in zebrafish embryos . To test the feasibility of oligonucleotide-mediated integration of loxP sites , we selected four genes which play essential roles in development and may also be required for regeneration: tbx20 , fleer , aldh1a2 and tcf21 . tbx20 is required for heart development in mouse and zebrafish , but is also required for mouse cardiac homeostasis [22–26] . tbx20 is upregulated during zebrafish heart regeneration , and overexpression of Tbx20 improves the regenerative capacity of the mouse heart [27 , 28] . fleer is essential for ciliogenesis , and zebrafish embryos lacking fleer display pleiotropic phenotypes including laterality defects , kidney cysts , and failure to inflate brain ventricles [29] . While it is not known if cilia are required for regeneration , their roles in mechanosensing as well as shh signal transduction suggest that they may be needed for regeneration to occur . aldh1a2 mutants display pleiotropic phenotypes in neural , heart , and pectoral fin development [30 , 31] . Upregulation of aldh1a2 mRNA is the signature of endocardial and epicardial activation after cardiac injury [32] . Finally , tcf21 mutants display defects in morphogenesis of branchial arch-derived structures [15 , 33] . tcf21 is also constitutively expressed in the epicardium and epicardial-derived cells of adult zebrafish , and tcf21 expressing cells are required for cardiac regeneration [34] . Here we found that single stranded oligonucleotide templates can efficiently direct integration of loxP sites into CRISPR/Cas9-induced double strand breaks in the zebrafish genome . We performed sequential integration of two loxP sites to generate a conditional ( floxed ) allele of tbx20 . We have used this conditional allele to demonstrate that early expression tbx20 is essential for zebrafish heart development . Using fleer as a model , we demonstrate that Cre-revertible Gene Breaking Transposon ( GBT ) mutants can be readily converted into fully conditional alleles by the addition of a single loxP site . Using aldh1a2 , we demonstrate that prior to -or in parallel with- generation of a floxed allele , the target exon can be readily removed to test if the deletion produces a phenotype . Finally , using tcf21 as a model for small genes , we demonstrate that a loxP site can be integrated into the 5’ untranslated region without significantly impairing gene function .
Conditional loss-of-function mutagenesis requires two loxP sites flanking an exon ( or several exons ) of a gene . We have recently developed a methodology for oligonucleotide-mediated integration of epitope-coding sequences into the zebrafish genome [15] . The relatively high efficiency of our method prompted us to speculate that conditional mutagenesis could be achieved by sequential integration of loxP sites . However , since oligonucleotide-mediated integration of wild type loxP sites into the zebrafish genome has not yet been demonstrated , we first needed to test if it was at all feasible . We selected tbx20 for our initial experiments ( Fig 1 ) and chose to flox its second exon because it encodes the first few amino acids of the T-box DNA binding domain , and because removal of the second exon would put exons 3–7 out of reading frame . Two highly active sgRNAs , tbx20 sgRNA9 and tbx20 sgRNA10 ( Fig 1a and 1b ) , flanking the second exon , were identified by loss of a restriction enzyme site and/or T7 endonuclease assay ( assessment of tbx20sgRNA9 activity is shown in S1 Fig ) . To integrate a loxP site into intron 2 of tbx20 , we designed an oligonucleotide template for homology directed repair of the double strand break induced by tbx20 sgRNA10 . ( Fig 1c ) . Since we have frequently observed small indels at the homology arm junction in past experiments , we decided to flank the loxP sequence with three nucleotides of “spacer” sequence . We injected tbx20 sgRNA10 along with nCas9n mRNA into embryos at the one cell stage , followed by injection of the HDR oligonucleotide with 21 nucleotide-long homology arms as previously described [15] . Indels were readily detected using the T7 endonuclease assay , and integration of the loxP site was detected by PCR in pooled injected embryos . Siblings of tested embryos were raised , incrossed , and screened for germline transmission of integration of the loxP site by nested PCR . One out of twelve incrosses produced embryos positive by both 5’ and 3’ nested PCR . Notably , nested PCR for the 3’ end worked poorly , with only one of three batches of embryos appearing barely positive ( Fig 1d top vs bottom panels ) . Poor amplification with loxP-specific primers turned out to be a recurring issue . Several different loxP primers were designed over the course of this work; for some loci , several loxP-specific primers were tested on injected embryos . Nevertheless , sequencing of the 5’ and 3’ PCR fragments indicated integration of the full-length loxP site , with a 10-nucleotide partial target site duplication at the 5’ end of the HDR oligonucleotide template . Integration of the loxP site was further confirmed by performing a short flanking PCR on individual embryos and sequencing the larger PCR fragment . Siblings were raised to adulthood and 16 were genotyped by tail clip . We identified four adults heterozygous for a loxP site ( Fig 1e , red arrows , sequenced in Fig 1f ) . Two additional fish had a single band corresponding to a loxP-containing allele , but no wild type band ( yellow arrows ) . Since both parents were mutagenized , we interpreted this as the presence of a deletion removing at least one of the primer binding sites on the homologous chromosome . In parallel , we performed experiments to integrate a loxP site into the first intron of tbx20 . Sequence analysis revealed the presence of a polymorphic poly-T stretch a few nucleotides downstream of tbx20 sgRNA9 target site , with wild type AB fish having either 9 or 10 thymidines . The shorter stretch ( 9 thymidines ) seemed more prevalent among the fish we were injecting . In this experiment , we tested an HDR oligonucleotide template with asymmetric-length homology arms , antisense to the PAM-containing strand [35] . We designed a 120 nt-long oligonucleotide template for HDR ( Fig 1g ) , and injected it into 1-cell zebrafish embryos along with nCas9n mRNA and tbx20sgRNA9 RNA . Integration of the loxP site was detected in pools as well as in individual injected embryos as described in Materials and Methods . Siblings of tested embryos were raised to adulthood and screened for germline transmission of a loxP site by nested PCR as described in Materials and Methods . One out of three incrosses gave embryos that were positive for the loxP site both at the 5’ and 3’end by nested PCR . Integration of the loxP site was confirmed by sequencing individual embryos . Siblings of genotyped embryos were raised to adulthood and 16 fish were genotyped for presence of the loxP site by short flanking PCR . Three fish were found to be heterozygous for the loxP-containing allele , leading to establishment of the tbx20tpl136 loxP integration line ( Fig 1h ) . To engineer a floxed allele , we incrossed F1 fish heterozygous for the intron 2 loxP site and injected the embryos with tbx20 sgRNA9 and nCas9n mRNA , followed by injection of the HDR template oligonucleotide targeting intron 1 ( Fig 2a ) . Injected embryos were raised to adulthood and genotyped for the presence of the intron 2 loxP site . Among 61 adults , 28 were heterozygous and 14 were homozygous for the loxP site ( Fig 2b ) . All 42 were outcrossed and screened by nested PCR for integration of the loxP site into intron 1 ( Fig 2c ) . Nine were positive by 5’ end PCR ( 3’ with regard to the HDR oligonucleotide ) , and 12 were positive by both 5’ and 3’ end PCRs . In many cases the 3’ end nested PCR fragment was larger than would be predicted , sometimes by a few hundred bases . For six germline transmitters ( candidate founders ) , we extracted DNA from individual embryos and performed flanking PCR to assess germline mosaicism and to confirm complete loxP site integration ( S2 Fig ) . The larger PCR bands observed in two candidate founders yielded poor quality sequences , and these founders were not followed up further . Sequencing the larger PCR bands from the four other founders revealed a high prevalence of insertions and deletions , most remarkably centered around the oligo-T stretch immediately 3’ to loxP integration ( corresponding to the 5’ end of the HDR oligonucleotide ) . Subsequent re-sequencing of the locus revealed that the chromosome containing the loxP site in intron 2 had the longer 10-nucleotide oligo-T , while our HDR template oligonucleotide contained 9 . Nonetheless , one of the candidate founders ( NP#39 ) had transmitted integration of a full-length loxP site , albeit with an insertion of an additional 62 nucleotides immediately 3’ to it ( Fig 2d ) . Siblings of genotyped embryos from founder NP#39 were raised to adulthood . Eight adult fish that were raised from an outcross of candidate founder NP#39 were tail clipped and screened by flanking PCR ( Fig 2e ) . In four of the fish , a larger PCR band corresponding to a floxed allele was observed ( red arrows in Fig 2e ) . Two of the four purified PCR bands were sequenced directly and found to contain identical full-size loxP integrations with a partial target site duplication . For further confirmation , these fragments were cloned into pJet PCR cloning vector and sequenced , and the resulting floxed allele was designated tbx20tpl145 ( Fig 2d ) . We then proceeded to incross these “F1” fish heterozygous for tbx20tpl145 and to inject some of the embryos with Cre mRNA as described previously [36] . All uninjected embryos were phenotypically normal ( n = 52 ) . This observation , together with the fact that tbx20 deficiency leads to severe cardiac defects [22 , 25 , 26] , enabled us to conclude that the integration of two loxP sites does not significantly impair tbx20 expression . Among embryos injected with Cre mRNA , approximately 25% displayed severe cardiac defects at 3 dpf in two independent experiments . We genotyped three embryos with severe cardiac defects and five siblings ( Fig 2g ) , corresponding to images in Fig 2f , and found that the three abnormal embryos appeared to have undergone biallelic Cre-mediated excision of the second exon of tbx20 . This observation was confirmed by sequence analysis of PCR bands ( Fig 2k ) . In contrast , phenotypically wild type embryos were either homozygous wild type or heterozygous for the ( excised ) floxed allele . Some Cre-injected embryos were raised to adulthood and genotyped . Adult fish heterozygous for the deletion were incrossed , and one quarter of embryos were found to display severe heart defects indistinguishable from that seen in Cre-injected embryos of the previous generation ( Fig 2j ) . We next tested if Cre-mediated deletion of the second exon of tbx20 can be achieved in a temporally-regulated manner . We crossed fish heterozygous for the floxed tbx20tpl145 allele to fish expressing the tamoxifen-inducible CreERT2 under the control of the ubiquitin promoter [37] . Obtained embryos were selected for GFP fluorescence ( the marker of Tg ( ubb:CreERT2 ) ) , incubated in 4-hydroxytamoxifen for 24 hours between 2 dpf and 3 dpf , and individual embryos were genotyped . Deletion of exon 2 of tbx20 was readily observed in embryos incubated with 4-hydroxytamoxifen , but not in control embryos ( Fig 2h and 2i ) . As expected , all embryos were phenotypically normal because all had a wild type allele of tbx20 . Together , these experiments led us to conclude that we have successfully engineered a fully conditional ( floxed ) allele of tbx20 . Many experimental approaches in genomics , proteomics and metabolomics require a large amount of starting tissue . Due to their small size , hundreds to thousands of zebrafish embryos or larvae may be needed for each experiment . Genotyping such large numbers of embryos is impractical , leaving two approaches: morpholino knockdown or germline replacement [38 , 39] . We hypothesized that if embryos obtained from incross of parents homozygous for the floxed allele were injected with Cre mRNA , we would be able to obtain clutches of all-mutant embryos . To test this hypothesis , we genotyped adults obtained from tbx20tpl145 incross and identified homozygotes . Homozygous fish were incrossed , and embryos were injected with Cre mRNA at 1-cell stage . As expected , Cre-injected embryos displayed full tbx20 mutant phenotype , while non-injected embryos were all phenotypically wild type ( S3 Fig ) . Thus , large clutches of all-mutant embryos , along with wild type controls , could be generated using this approach . In zebrafish , tbx20 is expressed in bilateral cardiac precursors prior to their migration and coalescence . Expression appears to persist through larval development , and is upregulated in response to injury in adult hearts [22 , 25–27 , 40] . In mouse , cardiomyocyte-specific deletion of Tbx20 in adults leads to lethal cardiomyopathy and arrhythmia , indicating that TBX20 is required not only for cardiac development , but also for homeostasis [24] . To test when it is required for heart development in zebrafish , we sought to induce ubiquitous loss of tbx20 function at different time points . While performing the experiments described in the paragraph above , we noted that the ubb:CreERT2 line is multi-copy , e . g . carries multiple unlinked integrations of the transgene . We established two lines , sub-designated ubb:CreERT2 . C and ubb:CreERT2 . F which were single-copy genetically ( 50% of progeny positive for the cardiac GFP ( myl7:eGFP ) transgenesis marker [37] ) , and crossed them a tbx20tpl122 partial deletion mutant obtained during epitope tagging experiments to be described elsewhere ( Burg et al . , unpublished , and S4 Fig ) . We then crossed ubb:CreERT2 . C , tbx20tpl122 double heterozygotes to tbx20tpl145 floxed homozygotes , and exposed embryos to 4-OHT at different time points . We found that exposure to 0 . 5 μM 4-OHT at 6 hpf reliably induced a full tbx20 loss-of-function phenotype . In contrast , only a small subset of embryos exposed to 4-HT at 10 hpf displayed a milder cardiac phenotype , while all embryos exposed at 14 or 24 hpf were phenotypically normal . In order to eliminate the possibility that 4-HT was less effective at 10 hpf compared to 6 hpf , we performed qPCR to assess the completeness of Cre recombination at the DNA level . We found that excision was 99% complete in embryos exposed to 4-HT at 6 hpf , and 94% complete in embryos exposed at 10 hpf ( S1 Table ) . Thus , 4-HT is somewhat less efficient at inducing ubb:CreERT2-mediated recombination at 6 hpf compare to 10 hpf . Consequently , absence of phenotype in embryos exposed at 10 hpf may not be due to timing loss of Tbx20 function , but instead due to slightly lower efficiency of recombination . To explore the possibility that 4-HT is taken up less well by older embryos , we next compared the toxicity of 4-HT at 6 vs 10 hpf ( S2 Table ) . We treated pools of embryos from the same clutch with 4-HT ranging in concentration from 5 μM to 60 μM and found that embryos treated with 25 μM 4-HT at 6 hpf had a 73% survival rate , while embryos given the same dose given at 10 hpf had a 100% survival rate . Survival rate at higher doses dropped off significantly faster in embryos treated at 6 hpf compared to 10 hpf; however , even the 10 hpf embryos displayed toxicity effects at doses above 25 μM . We then repeated our loss-of-function experiment by exposing 10 hpf embryos to 20 μM 4-HT , and 6 hpf embryos to either 5 μM or 0 . 3 μM 4-HT ( Fig 2l , S1 Table ) , using two different single-copy ubb:CreERT2 drivers . We found the Cre-mediated loss of function was at least 95% efficient in embryos exposed to 20 μM 4-HT at 10 hpf , but the embryos displayed very mild , if any , cardiac abnormalities . Exposure to very low doses of 4-HT at 6 hpf induced near-complete excision of the second exon when ubb:CreERT2 . C driver was used . In contrast , excision was significantly less efficient with ubb:CreERT2 . F driver . However , we observed that 90%-efficient excision of the second exon was sufficient to induce a severe cardiac defect ( Fig 2l and 2m , S1 Table ) . We also performed a semi-quantitative test for the time lag of CreERT2-mediated recombination after exposure to 4-HT . Pools of 20 embryos exposed to 5 μM 4-HT at 6 hpf and 10 hpf were collected 30 minutes , 1 hr , 2 hrs and 4 hours after exposure . The excision band was readily detectable after 2 hours in 6 hpf treated embryos , and 1 hour after 4-HT exposure in 10 hpf embryos ( S5 Fig ) , indicating that a significant subset of cells may have lost tbx20 function by then . Increased intensity of the excision band at 4 hours after exposure indicates that recombination is likely not complete yet at 2 hours after addition of 4-HT . Gene Breaking Transposons ( GBTs ) have been used to generate a large collection of gene trap mutants in zebrafish [36 , 41] [42 , 43] In a typical scenario , a GBT integrates into an intron of a gene and terminates the expression of the mutated gene , leading to complete loss of function . In most widely used GBT’s , the gene trap cassette is flanked by direct loxP sites . Expression of Cre recombinase leads to very efficient excision of the gene trap cassette , reverting the mutant phenotype and leaving a single loxP site flanked by terminal repeats of the Tol2 transposon . Thus , the addition of a single loxP site should convert such reverted gene traps to fully conditional ( floxed ) alleles . In the fleertpl19 mutants selected for these experiments , the gene breaking transposon was integrated into the first intron of fleer , eliminating expression of downstream exons [36] ( Fig 3b ) . We first injected embryos with Cre mRNA and established a reverted line fleertpl19R ( Fig 3c ) , which was homozygous viable . To engineer a conditional allele , we reviewed the exons of fleer for reading frame phase and the presence of conserved protein domains . Exons 2 , 3 , and 4 all begin and end in the same reading frame , and mRNAs lacking one or more of them may be translated into functional protein . Furthermore , the first 12 exons all begin and end in either phase 0 or phase 1 ( Fig 3a ) . We therefore decided to attempt to generate a deletion removing all three N-terminal tetratricopeptide repeats ( likely important for protein-protein interactions ) of fleer by engineering a loxP site into intron 7 ( Fig 3c and 3d ) . We sequenced the seventh intron from fish homozygous for the reverted gene trap , designed sgRNAs targeting it and identified flr sgRNA3 as being sufficiently active by Surveyor assay as described in ( 15 ) . To integrate a loxP site , we designed a 110-base HDR template oligonucleotide with asymmetric homology arms , antisense to the PAM . Embryos were injected with flr sgRNA3 , nCas9n mRNA , followed by injection of the HDR template oligonucleotide . Fish were raised to adulthood and screened for germline transmission of the loxP site by nested PCR . After screening 14 F0 fish , we identified one that transmitted a perfect integration of the loxP site . Siblings were raised to adulthood , and one out of twenty-one adult F1 fish was positive for the loxP integration ( sequenced in Fig 3e ) and was assigned allele name flrtpl141 . We then proceeded to test the conditionality of this allele by incrossing flrtpl141 F2 heterozygous fish and injecting embryos with Cre mRNA ( Fig 3f ) . Twenty-five percent of the progeny were expected to be homozygous for the floxed allele , and approximately one quarter of the injected embryos displayed a phenotype consistent with previously published flrm477 chemical mutant , as well as flrtpl19 gene trap mutant [29 , 36] . Embryos were first genotyped using primers spanning the intron 7 loxP site , which amplified the WT allele as well as any non-excised flrtpl141 . The same embryos were also genotyped for the loxP site in intron 1 , and amplicons were detected in embryos carrying flrtpl141 . Finally , embryos were genotyped by PCR across both loxP sites , with the excision product being readily detected in all flrtpl141-positive embryos embryos injected with Cre mRNA ( Fig 3f , bottom panel ) . In this last experiment , any non-excised floxed allele was undetectable due to large amplicon size . All genotyped embryos with the fleer mutant phenotype were homozygous for flrtpl141 , while all heterozygous and WT siblings were phenotypically wild-type . Pharmacological perturbation of retinoic acid ( RA ) signaling leads to severe defects in regeneration across various model systems and tissues ( reviewed in [44] ) . Conditional mutants capable of inactivating RA signaling in specific cell types are needed to better understand the requirement of RA signaling for regeneration . Zebrafish have at least three genes coding for retinal dehydrogenases: aldh1a2 , aldh1a3 and aldh8a1 . Upregulation of aldh1a2 expression serves as the hallmark of endocardial and epicardial activation , making it an attractive target for conditional mutagenesis . In order to test which retinal dehydrogenases are likely to make an important contribution to retinoic acid production during regeneration , we analyzed RNA sequencing data from zebrafish hearts at 1 , 3 and 5 days after cryoinjury and confirmed that aldh1a2 is highly expressed and upregulated in response to cryoinjury [45] . In contrast , aldha8a1 was expressed at a much lower level , and expression of aldh1a3 was undetectable ( S6 Fig ) . The observation that other RA-synthesizing enzymes are poorly , if at all , expressed in the regenerating heart suggests that loss of aldh1a2 function is likely to lead to severe reduction in RA signaling in the regenerating heart . This hypothesis cannot currently be directly tested , as aldh1a2 mutants display pleiotropic embryonic phenotypes and do not survive [30 , 31] . For aldh1a2 , we decided to target exon 8 because it encodes a part of the highly conserved alcohol dehydrogenase domain ( Fig 4 ) . In addition , it begins and ends in different phases of the reading frame . With exon 8 deleted , in-frame transcripts can be generated by either skipping exons 5–7 or by skipping exon 9 . Both of these scenarios would remove additional parts of the dehydrogenase domain with highly conserved amino acids . We sequenced the region flanking exon 8 from four wild type TLF fish ( two males and two females ) , and designed four sgRNAs ( S7 Fig ) . We tested their activity by direct sequencing of PCR fragments generated on pools of injected embryos [46] and found that aldh1a2 sgRNA1 and aldh1a2 sgRNA4 were highly active , while aldh1a2 sgRNA2 and aldh1a2 sgRNA3 produced very few indels . To test if deletion of the eighth exon would lead to loss of function phenotypes comparable to those of previously published aldh1a2 mutants , we co-injected both aldh1a2 sgRNA1 and aldh1a2 sgRNA4 along with nCas9n mRNA , and observed the appearance of a PCR band corresponding to the expected deletion product . Injected embryos were raised to adulthood , and two incrosses of F0 fish were performed . In pools of embryos from both incrosses , a PCR band corresponding to the expected deletion size was readily observed ( S7 Fig ) . Sibling F1s were raised to adulthood and genotyped . Two out of seventeen analyzed F1 fish from incross A were found to be heterozygous for a deletion of 484 base pairs ( Fig 4c , S7 Fig ) . One out of fifteen analyzed F1 fish from incross B was found to be heterozygous for a deletion of 466 base pairs ( Fig 4c , S7 Fig ) . We crossed the F1 fish heterozygous for aldh1a2tpl137 allele to the F1 fish heterozygous for the aldh1a2tpl138 allele . Approximately 25 percent of 3 dpf embryos lacked pectoral fins and had moderate to severe pericardial edema . In addition , the majority of embryos displaying these phenotypes also displayed curved tail consistent uncoordinated left-right somitogenesis as previously reported [47] ( Fig 4d ) . The absence of pectoral fin buds at 30 hpf was confirmed by in situ hybridization using a probe against tbx18 . Since both tcf21 and aldh1a2 are expressed in the branchial arches and both mutants display a defect in the development of branchial arch-derived structures , we also performed in situ hybridization using a probe against tcf21 . We found that while tcf21 expression in the posterior branchial arches is absent in aldh1a2 mutants , expression of tcf21 in the first and second branchial arches of aldh1a2 mutants persists ( Fig 4f ) . We therefore concluded that aldh1a2 is not required for tcf21 expression in the first and second branchial arches . Linkage of these phenotypes to aldh1a2 exon 8 deletion was confirmed by 3-primer genotyping PCR ( S8 Fig ) . We designed a 110 base long HDR template oligonucleotide ( Fig 4g ) and injected it into embryos which were also injected with aldh1a2 sgRNA1 and nCas9n mRNA . Integration of the loxP site was readily detected in pooled DNA of injected embryos as described in Materials and Methods . Embryos were raised to adulthood and seven incrosses ( 14 F0 fish ) were screened for germline transmission of integration of the loxP site by nested PCR . Five pairs were positive by nested 5’ and 3’ PCR . In three cases , loxP integrations transmitted through the germline also contained an insertion of additional sequences within the 5’ homology arm ( S9 Fig ) . One founder pair ( R9x10 ) transmitted a precise integration of the loxP site , and 3/8 individual embryos analyzed were found to be heterozygous for the loxP-containing allele . Founder pair R13x14 transmitted integration of the loxP site with a single nucleotide substitution within the 5’ homology arm . Adult F1s raised from these crosses were genotyped by PCR ( S9 Fig ) . Three out of fourteen adults from pair R9x10 proved to be heterozygous for an allele containing a precise integration of the loxP site , designated aldh1a2tpl139 ( Fig 4h ) . One out of twelve adults from the F1 family R13x14 was heterozygous for the loxP integration with a single nucleotide substitution , and we established a backup loxP-containing allele , designated aldh1a2tpl140 . Two F1 fish heterozygous for aldh1a2tpl139 were incrossed , and all embryos appeared phenotypically normal at 5 days post fertilization . Sixteen were genotyped for the presence of the loxP site , and four were found to be homozygous ( Fig 4i ) . We concluded that integration of the loxP site into intron 7 does not significantly impair expression of aldh1a2 . In contrast to the first four large , multiple exon genes that we targeted , tcf21 is a small , two-exon gene , making it impossible to integrate both loxP sites into introns . We speculated that the 5’ UTR might be a suitable target for loxP integration . First , the loxP site does not contain ATG codons that would cause premature translation initiation . Second , most regulatory transcription factor binding sites are expected to be upstream of the transcription initiation site ( not immediately downstream ) , making it less likely that the integration of a loxP site would disrupt a transcription factor binding site . Finally , 3’ UTRs may contain regulatory elements such as microRNA binding sites which are rather difficult to predict . We designed two short guides targeting the 5’ UTR of tcf21 , and both were reasonably efficient at inducing DSBs ( S10 Fig ) . We decided to first perform loxP integration into the tcf21 sgRNA5 target site and designed a HDR repair oligonucleotide with asymmetric arms ( S11 Fig ) . Embryos injected with tcf21 sgRNA5 , nCas9n mRNA and the HDR oligonucleotide were raised to adulthood and screened for germline transmission of the loxP site . Out of nine incrosses , three were positive for germline transmission of a loxP site by nested PCR . One had an insertion of approximately 100 nucleotides and was not analyzed further . Single embryos from the other two transmitter pairs were analyzed . An incomplete loxP site was identified in one of the pairs , and integration of a full loxP site without indels was found in the second pair ( tcfinxB ) . Siblings of analyzed embryos from tcfinxB were raised to adulthood and genotyped . Of twenty-four siblings that were genotyped , four were positive by PCR for integration of the loxP site . All four were sequenced and were found to have the same precise integration , designated tcf21tpl144 ( S11 Fig ) . To test for the possibility that integration of loxP site impairs expression of tcf21 , we crossed tcf21tpl144 F1 fish to a previously established tcf21 frameshift mutant tcf21tp119 [15] . All embryos were phenotypically normal , demonstrating that the loxP-containing allele tcf21tpl144 is functionally wild type .
We have used oligonucleotide-mediated homology directed repair to integrate loxP sites into five different locations in the zebrafish genome: intron 1 of tbx20 , intron 2 of tbx20 , intron 7 of fleer , intron 7 of aldh1a2 , and the 5’ UTR of tcf21 . Across all five loci , we have observed remarkably high rates germline transmission and germline mosaicism: the median rate of germline transmission of a complete loxP site was 6 . 3% ( 1 out of 16 F0 fish screened ) , while the median rate of germline mosaicism was 20% ( 1 out of 5 F1 fish screened from germline-transmitting founders ) ( S3 Table ) . A conditional mutagenesis workflow based on our experience is provided in Fig 5 . Our data clearly demonstrates the feasibility of conditional mutagenesis by oligonucleotide-mediated sequential integration of loxP sites into the zebrafish genome . Two aspects of our method are likely to have contributed to its success . First , based on the previous observation of a high rate of indels at the junctions between the integrated sequences and homology arms [15] , we flanked the loxP site by 3-nucleotide spacer sequences . A loxP site consists of an 8-base core flanked by 13-base inverted repeats . The melting temperature of the inverted repeats is calculated at 32°C , meaning that a loxP site may form a hairpin in zebrafish embryos . The 3-nucleotide spacers may have facilitated homology-directed repair by providing spacing between the hairpin and homology arms . Indeed , successful oligonucleotide-mediated integration of the mlox site , which has 3 substitutions in one of the inverted repeats , has been reported by two laboratories [20 , 21] . We have not directly compared the efficacy of oligonucleotide templates with short or long homology arms , but 110-base long oligonucleotides with a longer 5’ arm , antisense to the PAM , have been successfully used in the majority of our experiments , leading us to recommend this particular design . Further gains in HDR efficiency may be achieved by using phosphorothioate-modified oligonucleotides [48 , 49] . The second aspect of our method is screening for germline transmission by nested PCR . Over the course of our work , we have used several different loxP-specific primers and find that even in nested PCR , different loxP-specific primers need to be tested with different genomic primers to identify optimal pairs . The palindromic nature of the loxP site likely contributes to poor performance of loxP-specific primers . Given that it takes a considerable amount of time to engineer a conditional mutant , it makes sense to ensure that the mutant will display a phenotype as early as possible . This is a two-step process . To determine the likelihood that the selected gene will be required for biological function of interest , it may be important to examine the expression of homologs which may provide identical or similar biochemical activity , as we have done for retinal dehydrogenases in regenerating hearts . Second , it is important to ensure that loss of the exon selected for floxing will lead to a severe or null phenotype . Using aldh1a2 as the model locus , we demonstrate that a pair of sgRNAs can be used to direct very efficient excision of the target exon . While our sgRNA target sites were less than 500 base pairs apart , larger deletions can also be readily induced using sgRNAs spaced over 15 kilobases apart [50] . An additional benefit of a deletion allele is that it can be crossed to Cre drivers of interest , eliminating the need to back-cross the floxed allele to obtain homozygotes ( Fig 5 ) . An alternative way to ensure that a conditional mutant will display a phenotype of interest is to start from a Cre-revertible Gene Breaking Transposon allele [36 , 41–43] . The very high efficiency of Cre/lox recombination makes it possible to establish a line homozygous for Cre-reverted GBT allele with minimal effort . Only one loxP site then needs to be integrated to convert such Cre-reverted gene traps into fully conditional alleles , as demonstrated with fleer . One of the challenges of using CRISPR/Cas9 to integrate loxP sites into the introns is that intronic sequences are often of low complexity , have low G/C content , and are repetitive . These features may limit targeting possibilities by making it difficult to design highly efficient sgRNAs . Requirement for GC-rich 5’-NGG-3’ PAM sequence poses an additional complication . Nonetheless , we were able to find suitable targets in each gene and intron of interest . We have used several methods to assess guide RNA activity: T7 endonuclease assay , loss of restriction enzyme site , and direct sequencing of PCR fragments [15 , 46] . Activity assessment is always performed on DNA extracted from a pool of at least 10 embryos , thus providing a quantitative estimate of average editing efficiency . In our experience , about half of the guides meet the minimum requirement of at least 50% editing efficiency . CRISPR/Cas9 systems recognizing A/T-rich PAMs , such as Cpf1 [51 , 52] , will make editing of intronic sites even more straightforward . Conditional mutagenesis relies on the availability of high quality , tissue-specific drivers of tamoxifen-inducible Cre recombinase . In the mouse , the majority of drivers have been engineered by integrating CreERT2 into the specific loci , thus likely fully recapitulating the expression of the “driver” gene . In zebrafish , drivers are typically engineered by Tol2-mediated transgenesis of an expression cassette . High activity of Tol2 transposase [53–55] means that many lines , at least initially , are multicopy , with each copy subject to position effects ( for extreme examples , see [56–58] ) . As we have shown for two different copies of ubb:CreERT2 , it is not sufficient to validate a given transgenic construct; the specific transgene integration , as a single-copy transgenic line , has to be validated for use in loss-of-function experiments . Our genome editing experiments were performed in three different genetic backgrounds: AB ( tbx20 ) , TLF ( aldh1a2 and tcf21 ) , and undefined pet store-derived ( fleer ) . The majority of our experiments were performed in the United States , but initial integrations of loxP sites into tbx20 were recovered in Germany . We therefore believe that our conditional mutagenesis methodology can be readily adapted by any laboratory and performed in any genetic background . Additionally , the oligonucleotide templates we used for homology directed repair are both inexpensive and widely available . The ability to efficiently generate conditional mutants in zebrafish offers new possibilities for understanding the roles of pleiotropic genes , and essential for studies of post-embryonic processes such as regeneration .
All animal experiments described in this manuscript have been approved by Temple University IACUC committee under protocol numbers ACUP 4354 , ACUP 4709 and ACUP 4164 . nCas9n was prepared as previously described [15] . pT3TS-nCas9n [59] was linearized with XbaI and transcribed using the T3 mMessage mMachine in vitro transcription kit ( ThermoFisher Scientific AM1348 ) . Transcribed mRNA was purified using the Qiagen RNeasy MinElute kit ( 74204 ) , diluted to 150 ng/μL in RNAse free water ( ThermoFisher Scientific AM9937 ) and 2 μL aliquots were made . Aliquots were stored at -80°C . sgRNA was prepared as previously described [15] . We used cloning-free PCR method similar to the ones described previously [60 , 61] to produce sgRNA synthesis template . We first performed PCR using guide-specific and M13F primers on DR274 [62] template . Guide-specific and all other primers used in this study are listed in S4 Table . We then performed agarose gel electrophoresis and purified the bands using GeneJet Gel Extraction kit ( ThermoFisher Scientific K0692 ) . Purified band was used as the template for the second PCR reaction with sgT7 and sgRNA-R primers . The obtained PCR fragment was purified using GeneJet PCR purification kit ( ThermoFisher Scientific K0702 ) and used as the template for in vitro transcription using MEGAshortScript T7 transcription kit ( AM1354 ) . After RNA synthesis , concentration of sgRNA was assessed by agarose gel electrophoresis . Concentration was estimated by comparing to RiboRuler RNA ladder ( SM1833 ) . Unpurified transcription reaction mix was diluted to approximately 60 ng/μL and 8 μL aliquots were made . Aliquots were stored at -80°C . An 8 μL aliquot of sgRNA was thawed and mixed with 2 μL aliquot of nCas9n mRNA . 3 nL of mix were injected into the yolks of 1-cell zebrafish embryos as described previously [15 , 63] . Appropriate loxP HDR oligonucleotide was diluted to 50 ug/μL in RNAse free water , and 1 nL was injected into the yolks of zebrafish embryos immediately after the RNA injection . At 3–5 dpf , 20 embryos were pooled into a standard microcentrifuge tube and lysed . 0 . 8–1 . 0 μL of lysate was used as the template for PCR reactions in 20–25 μL volume . All PCR reactions were performed using either NEB 2X Taq Master Mix ( M0270L ) , Thermo Scientific Taq ( EP0402 and 2x PCR Master Mix Cat# AB-0575/DC ) , or Kapa 2G Fast ReadyMix +dye ( Kapa Biosystems-KM5101 ) . CRISPR activity was assessed by either direct sequencing of PCR amplicons , T7 assay ( NEB M0302S ) , Surveyor assay ( IDT 706025 ) , or RFLP . To test for loxP integration , PCR reactions were performed using different combinations of flanking and loxP-specific primers , either on pools of 10–20 injected embryos or on 8 individual embryos . Embryos from F0 incross or outcross were pooled into batches of 20 and DNA prepped . Three batches from each pair were tested for loxP integrations by nested PCR ( gene specific primers described below ) . The first flanking PCR was run and amplification was confirmed by gel electrophoresis . The PCR was purified , diluted 1:100 with water , and 1 μL was used as a template for the 5’ and 3’ nested reactions . Siblings from positive crosses were raised to adulthood . All PCR reactions were performed using either NEB 2X Taq Master Mix , Thermo Scientific Taq or Kapa 2G Fast ReadyMix . DNA ladders used were Thermo Scientific GeneRuler DNA ladder ( SM0331 ) and ThermoFisher Scientific 100 bp DNA ladder ( #SM0241 ) ( Fig 1d ) . Adult F1 fish were tail clipped , DNA prepped , and genotyped by running a short flanking PCR ( gene specific primers described below ) designed to amplify approximately 450 bp or less . PCRs were run on 2% agarose gels , and amplicons of appropriate size were purified and sequenced to confirm loxP integration . All PCR reactions were performed using either NEB 2X Taq Master Mix , Thermofischer Scientific Taq or Kapa 2G Fast ReadyMix . DNA ladders used were Thermo Scientific GeneRuler DNA ladder . Fish containing the fleer GBT were incrossed and embryos were injected with 25 pg Cre mRNA . After being raised to adulthood , the injected fish were tail clipped and genotyped for the reverted gene trap using the flrUTR-F1/Tol2-F8 primer pair . tbx20tpl145: Embryos from the incross of tbx20tpl145 heterozygotes were injected with 25 pg Cre mRNA . Individual embryos were genotyped by PCR using the tbx20In1-F4/tbx20In2-R2 primer pair . fleertpl141: Embryos from the outcross of the heterozygous floxed fleertpl141 to wt were injected with 25 pg Cre mRNA . Individual embryos were genotyped by PCR for the presence of the intron 7 loxP site using the flrEx7-F1/flrIn7-R4 primer pair , for the presence of the intron 1 loxP site using the flrUTR-F1/loxP-R1 primer pair , and for the excision product using the tol2-R7/flrIn7-R4 primer pair . Fish heterozygous for either floxed tbx20tpl145 or fleertpl141 were crossed to the transgenic line Tg ( ubi:CreERT2 ) . Embryos were screened for GFP ( transgene marker ) at 2dpf , and GFP positive embryos were split and incubated with either 5 μM 4-HT or ethanol ( control ) for 24 hours . Individual embryos were DNA prepped and genotyped using the primer pair tbx20In1-F4/tbx20In2-R1 for tbx20 , or as described for Cre injection for fleer . Expression values of RA-synthesizing genes ( aldh1a2 and aldh8a1 ) in zebrafish hearts at various time points post sham operation or cardiac injury were extracted from the whole-genome RNAseq datasets previously published [45] Fragments per kilobase of transcript per million mapped reads ( FPKM values ) were summarized and presented in S3 Fig . aldh1a2sgRNA1 and aldh1a2sgRNA4 were co-injected along with nCas9n mRNA as previously described . Injected fish were raised to adulthood , crossed , and screened for germline transmission of deletion alleles by PCR using the aldh1a2-F2/aldh1a2-R6 primer pair . Siblings were raised to adulthood , tail-clipped , and screened for the presence of the deletion by 3 primer PCR using the primers aldh1a2-F4/aldh1a2-R1/aldh1a2-R6 . In situ hybridization was performed on embryos obtained from the incross of aldh1a2tpl137 and aldh1a2tpl138 heterozygotes as described [64] . tcf21 cDNA was amplified using tcf21-5′ UTR-F1 and tcf21-3′ UTR-R2 . tbx18 cDNA was amplified using tbx18-5′ UTR-F1 and tbx18-R10 . Each fragment was cloned into pGEM-T vector for transcription using the Ambion T7 Megascript kit ( AM1334 ) and labeling using Roche-DIG labeling mix ( Roche 11277073910 ) . Fish homozygous for the floxed tbx20tpl145 allele were crossed to fish heterozygous for tg ( ubb:CreERT2 ) and the tbx20 deletion allele , tbx20tpl122 . Embryos were treated with the stated concentrations of 4-HT or ethanol at 6 or 10 hpf for 24 hours . Embryos were scored for phenotype , lysed at 3dpf , and genotyped using the primer pair tbx20In1-F1/tbx20In2-R2 . All embryos used for qPCR were genotyped for tbx20tpl145/tpl122 or tbx20tpl145Δ/tpl122 . qPCR was performed using a Roche LightCycler 480 and LightCycler 480 SYBR Green I Master Mix ( 04707516001 ) . Primer pairs used for qPCR were tbx20In1-F1/tbx20In2-R2 and aldh1a2-F5/aldh1a2-R7 . Mean Cp values for tbx20 were normalized to aldh1a2 in Microsoft Excel . | Some genes are expressed and function in a single tissue , and the effect of their loss on that tissue can be readily determined . Frequently , however , genes that are necessary for the development or maintenance of one tissue are also important for other tissues or cell types . Genes of the latter type are difficult to analyze because of the complications resulting from an organism having multiple defects in different tissues . The solution pioneered by mouse geneticists is to inactivate the gene of interest in only one tissue at a time . This elegant approach requires the ability to make specific edits to the genome , a technology that was not readily available to zebrafish researchers until recently . Using the CRISPR/Cas9 genome editing tool , we have developed a simple , reliable , and efficient method to insert DNA sequences into the zebrafish genome that enable conditional gene inactivation . Our methodology will be useful not only for the study of genes that play important roles in multiple tissues , but also for the genetic analysis of biological processes which occur in adult animals . |
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As in other eukaryotes , protein kinases play major regulatory roles in filamentous fungi . Although the genomes of many plant pathogenic fungi have been sequenced , systematic characterization of their kinomes has not been reported . The wheat scab fungus Fusarium graminearum has 116 protein kinases ( PK ) genes . Although twenty of them appeared to be essential , we generated deletion mutants for the other 96 PK genes , including 12 orthologs of essential genes in yeast . All of the PK mutants were assayed for changes in 17 phenotypes , including growth , conidiation , pathogenesis , stress responses , and sexual reproduction . Overall , deletion of 64 PK genes resulted in at least one of the phenotypes examined , including three mutants blocked in conidiation and five mutants with increased tolerance to hyperosmotic stress . In total , 42 PK mutants were significantly reduced in virulence or non-pathogenic , including mutants deleted of key components of the cAMP signaling and three MAPK pathways . A number of these PK genes , including Fg03146 and Fg04770 that are unique to filamentous fungi , are dispensable for hyphal growth and likely encode novel fungal virulence factors . Ascospores play a critical role in the initiation of wheat scab . Twenty-six PK mutants were blocked in perithecia formation or aborted in ascosporogenesis . Additional 19 mutants were defective in ascospore release or morphology . Interestingly , F . graminearum contains two aurora kinase genes with distinct functions , which has not been reported in fungi . In addition , we used the interlog approach to predict the PK-PK and PK-protein interaction networks of F . graminearum . Several predicted interactions were verified with yeast two-hybrid or co-immunoprecipitation assays . To our knowledge , this is the first functional characterization of the kinome in plant pathogenic fungi . Protein kinase genes important for various aspects of growth , developmental , and infection processes in F . graminearum were identified in this study .
In eukaryotic organisms , reversible protein phosphorylation by protein kinase ( PK ) is involved in the regulation of various growth and developmental processes and responses to environmental stimuli . Approximately 30% of cellular proteins are phosphorylated [1] . The eukaryotic PK superfamily consists of conventional and atypical protein kinases . Conventional PKs ( ePKs ) have been classified into eight groups , AGC , CAMK , CK1 , CMGC , RGC , STE , TK , and TKL , based on their similarities in amino acid sequences , domain structures , and modes of regulation [2] , [3] . Protein kinases with a conserved kinase domain ( PF00069 ) but not classified into these eight groups are categorized as the ‘other’ group of ePKs . Atypical PKs ( aPKs ) lack significant sequence similarity with ePKs . Four groups of aPKs , Alpha , PIKK , PDHK , and RIO , are known to possess protein kinase activity [2] , [3] . In general , approximately 1% of predicted genes encode protein kinases in higher eukaryotes , such as human , mouse , rice , and Arabidopsis [4]–[6] . In the budding yeast Saccharomyces cerevisiae , 127 PK genes have been identified , which is approximately 2% of its genome . Many of them play critical roles in signal transduction , cell division , sexual reproduction , and stress responses . The genome of Schizosaccharomyces pombe contains 117 PK genes . Approximately 85% of its kinome is shared with S . cerevisiae , indicating that these two yeasts have a high degree of homology in their PK genes [7] . To date , genomes of over 40 filamentous fungi have been sequenced . Besides the model filamentous fungi Neurospora crassa and Aspergillus nidulans , genome sequences are available for a number of plant pathogenic fungi , including Magnaporthe oryzae , Ustilago maydis , and four Fusarium species . In general , less than 1% of the predicted genes in filamentous fungi encode protein kinases [8] , [9] . In addition to the well conserved cell-cycle related genes , several PK genes are known by classical genetic studies to be important for hyphal growth in N . crassa and A . nidulans [10] , [11] . In plant pathogenic fungi , a number of PK genes are known to be important for pathogenesis , including the key components of well-conserved MAP kinase ( MAPK ) , calcium , and cAMP signaling pathways [12]–[14] . However , a systematic functional characterization of the kinomes of filamentous fungi or fungal pathogens has not been reported . Fusarium head blight ( FHB ) or scab , caused by Fusarium graminearum , is one of the most important diseases on wheat and barley [15] , [16] . In addition to causing severe yield losses under favorable environmental conditions , this pathogen produces harmful mycotoxins , such as deoxynivalenol ( DON ) and zearalenone . DON is an important virulence factor in the wheat scab fungus [17] , [18] . In addition to its economic importance , F . graminearum is a tractable genetic system amenable to molecular and genomic studies . Gene replacement with the split-marker approach is highly efficient [19] . To date , three PK genes , GPMK1 , MGV1 , and SNF1 , have been shown by targeted deletion to be important for various developmental and plant infection processes [20]–[24] . In this study , we identified 116 putative PK genes in F . graminearum . Although 20 of them appear to be essential , mutants were generated for the other 96 PK genes and characterized for defects in growth , conidiation , colony and conidium morphology , germination , stress responses , plant infection , DON production , and sexual reproduction . In total , 42 PK mutants were significantly reduced in virulence or non-pathogenic , and 45 mutant were defective in sexual reproduction . A number of these protein kinase genes , including two that are unique to filamentous fungi , are dispensable for hyphal growth and likely encode novel fungal virulence factors . We used the interlog method [25] to predict the PK-PK and PK-protein interaction networks of F . graminearum , which has two Cdc2 kinase and two aurora kinase genes . Results from this study indicate that PK genes are important for various developmental and plant infection processes in F . graminearum . The functions of some well conserved PK genes , such as IME2 and BUB1 , differ significantly between F . graminearum and S . cerevisiae .
Among the 13 , 321 predicted genes of F . graminearum , 116 encode putative protein kinases ( Table S1; Figure 1 ) . Eight of them are atypical PKs . All of the PK genes were manually annotated . Problems with the open reading frame prediction were identified and corrected for 22 of them ( Table S1 ) . In comparison with S . cerevisiae , F . graminearum has fewer PK genes ( Table S1 ) . It lacks distinct orthologs of 19 yeast PK genes , including DBF4 , SMK1 , MEK1 , NNK1 , ELM1 , ALK1 , YGK3 , NPR1 , ISR1 , and HAL5 . None of them are essential in yeast but some , such as ALK1 and ELM1 , are involved in mitosis or cytokinesis . For 22 single copy PK genes in F . graminearum , S . cerevisiae has two or more paralogs , including TOR1/TOR2 , CKA1/CKA2 , DBF2/DBF20 , YPK1/YPK2 , PKH1/PKH2 , and RIO1/RIO2 ( Table S1 ) . In contrast , F . graminearum has two orthologs of IPL1 ( Fg06959 and Fg02399 ) and CDC28 ( Fg08468 and Fg03132 ) , which are single copy genes in yeast . It also contains 28 putative PK genes , including Fg01058 , Fg02488 , Fg00792 , and Fg01559 , that have no distinct orthologs in S . cerevisiae ( Table S1 ) . Many of them are unique to filamentous fungi . Interestingly , several PK genes are closely linked in F . graminearum ( Table S2 ) . For examples , Fg04053 and Fg04054 are only 7626-bp apart . Their chromosomal positions are conserved in F . verticillioides , F . oxysporum , and A . nidulans but not in M . oryzae and N . crassa . Fg06939 and Fg06940 encode kinases orthologous to yeast Sat4 ( Hal4 ) and Tos3 , respectively . Their orthologs also are closely linked in F . verticillioides , F . oxysporum , M . oryzae , A . nidulans , and N . crassa ( Table S2 ) . We searched the PlexDB database ( www . plexdb . org ) that contains published microarray data of F . graminearum [26] , [27] to compare expression levels of different PK genes . During barley infection , the expression of 12 PK genes , including Fg06385 ( Gpmk1 ) , Fg07295 ( Mmk2 ) , Fg07329 ( Gsk3 ) , Fg08691 ( Pbs2 ) , Fg09660 ( Pkc1 ) , and Fg10228 ( Swe1 ) was increased 48 hpi ( Table S3 ) . By 144 hpi , their expression levels were up-regulated 5-fold or more . In contrast , no PK genes were reduced over 5-fold during barley infection , although the expression of Fg01559 , Fg07745 , Fg09150 , and Fg07344 was reduced approximately 2-fold at 144 hpi ( Table S3 ) . During spore germination , Fg03132 is highly expressed at early stages . In comparison with ungerminated conidia , it was up-regulated over 11- and 8-fold at 2 and 8 h , respectively . The other CDC28 ortholog , Fg08468 , also was up-regulated but to a lesser extent . Its expression was increased 2 . 9- and 2 . 0-fold at 2 and 8 h , respectively ( Table S3 ) . The expression of Fg06502 ( Rio1 ) , Fg01347 ( Bub1 ) , and Fg00472 ( Sch9 ) also peaked at 2 h . These genes may play a role in the establishment of polarized growth . Fg01271 ( Cdc5 ) , Fg13318 ( Mec1 ) , Fg09408 ( Kin3 ) , and Fg08635 ( Dbf2 ) were up-regulated at 2 and 8 h , when cell division and cytokinesis are activated during germination . The gpmk1 and mgv1 mutants were generated in previous studies [20] , [22] , [24] . For all of the other PK genes , gene replacement constructs were generated by the split-marker approach [19] and transformed into protoplasts of the wild-type strain PH-1 . The resulting hygromycin-resistant transformants were screened by PCR with primers F5 and R6 ( Figure S1 ) located in the deleted region . All putative knockout mutants were further confirmed by PCR with primer pairs F7/H855R and R8/H856F [28] . Primers F7 and R8 were located outside the flanking sequences of the gene replacement constructs ( Figure S1 ) . Only transformants that underwent homologous recombination in the flanking sequences contain PCR products of expected sizes . A total of 20 PK knockout mutants ( Table S1 ) were selected for verification by Southern blot hybridizations . All of them , including the Fg00362 , Fg04053 , Fg08701 , Fg08906 , Fg10228 , and Fg10381 mutants ( Table S1 ) , were confirmed to be true deletion mutants . For 96 PK genes , we were able to identify at least two or more knockout mutants with similar phenotypes described below . Twelve of them are orthologous to essential genes in S . cerevisiae or S . pombe ( Table 1 ) , indicating that these protein kinases are not required for hyphal growth in F . graminearum . However , many of these mutants grew poorly . Of the 20 PK genes for which we failed to identify knockout mutants , at least 35 transformants from three or more independent transformations were screened ( Table 1 ) , indicating that knockout mutants may be nonviable . Sixteen of them are orthologous to essential genes in S . cerevisiae , including FgPKC1 ( Fg09660 ) , FgTRA1 ( Fg06089 ) , FgKIN28 ( Fg07423 ) , and FgHRR25 ( Fg08731 ) . For the Fg05306 , Fg05775 , Fg06637 , and Fg05393 genes , their orthologs in yeast are not essential but we failed to identify knockout mutants after screening over 60 transformants from at least three independent transformations ( Table 1 ) . Deletion of these genes may be lethal because of the gene replacement efficiency of the split-marker approach in F . graminearum . All of the knockout mutants were characterized for defects in vegetative growth , colony morphology , pigmentation , conidiation , conidium morphology , conidium germination and germ tube growth , hyphal tip growth and branching , perithecium formation , ascospore production , ascospore dispersal , DON production , wheat and corn infection , and responses to treatments with 0 . 05% H2O2 , and 0 . 7 M NaCl . The resulting phenotypic data were deposited in a searchable database available at fgkinome . nwsuaf . edu . cn . Overall , deletion of 64 non-essential PK genes ( 66 . 7% ) resulted in at least one of the 17 phenotypes examined . Because of the importance of protein kinases in fungal growth and differentiation , many of these mutants have pleiotropic defects , and we were able to isolate at least one PK mutant defective in each phenotype examined in this study . When analyzed for the association between different phenotypes by Pearson correlation efficient , defects in plant infection and vegetative growth had the highest correlation efficient followed by the correlation between sexual reproduction and growth rate or virulence ( Table S4 ) . Although phenotypes of at least two knockout mutants were examined for each gene , we have selected nine genes ( Fg04053 , Fg05734 , Fg07251 , Fg02795 , Fg07329 , Fg07344 , Fg08906 , and Fg10381 ) for complementation assays . For all of them , the reintroduction of the wild-type allele rescued the defects observed in the corresponding mutants . Among the 96 non-essential PK genes , 32 of them ( 66 . 7% ) were found to play critical roles in vegetative growth . Deletion of any one of these genes , including GzSNF1 and MGV1 [20] , [29] , resulted in over 30% reduction in growth rate ( Table 2 ) . Many of these mutants had abnormal colony morphology , growth , or branching patterns ( Figure 2A; fgkinome . nwsuaf . edu . cn ) . The Fg00362 , Fg01188 ( Cbk1 ) , and Fg04053 mutants had the most significant reduction in growth ( >90% ) and formed compact colonies with limited hyphal growth ( Figure S2 ) . The Fg00362 and Fg01188 mutants had similar morphological defects and produced densely aggregated vegetative hyphae that were wider and had shorter compartments and fewer branches . Their orthologs in N . crassa are the POD-6 ( polarity-defective 6 ) and COT-1 genes that are functionally related in regulating hyphal growth [10] . The Fg04053 mutant appeared to have less severe defects in hyphal morphology and branching ( Figure S2 ) . It formed non-pigmented colonies with rare aerial hyphae . Like Fg00362 , Fg04053 lacks a distinct ortholog in S . cerevisiae . Several protein kinase genes , including FgCAK1 ( Fg04947 ) and Fg10066 , were found to be important for normal hyphal morphology . The Fgcak1 deletion mutant produced wavy hyphae with reduced branching ( Figure 2B ) . In the Fg10066 mutant , hyphae became narrower and often had swollen tips ( Figure 2B ) . Fg10066 is the only ortholog of three yeast paralogous CK1 ( casein kinase 1 ) genes YCK1 , YCK2 , and YCK3 . In S . cerevisiae , the yck1 yck2 yck3 triple deletion mutant is nonviable . F . graminearum has only two CK1 genes , Fg10066 and Fg08731 . Fg08731 , like its yeast ortholog HRR25 , is an essential gene in F . graminearum ( Table 1 ) . The Fgbud32 ( Fg10037 ) deletion mutant was reduced in aerial hyphal growth and produced whitish colonies ( Figure 2 ) . In yeast , BUD32 regulates bud site selection . Although deletion of BUD32 is not lethal in S . cerevisiae , its ortholog is an essential gene in S . pombe . In F . graminearum , hyphal branching was reduced in the Fgbud32 mutant . However , it often had bifurcated hyphal tips and displayed clustered branching ( Figure 2 ) , suggesting that FgBUD32 plays an important role in hyphal branching . The Fgcdc15 ( Fg10381 ) and Fgsky1 ( Fg02795 ) mutants also were reduced in hyphal branching and produced less aerial hyphae that the wild type , but they grew faster than the Fgbud32 mutant ( Figure 2 ) . In F . graminearum , conidia are formed either directly on short hyphal branches or on phialides that are often formed in clusters in liquid cultures . Three PK genes , Fg00362 , Fg01188 , and Fg10066 , are found to be essential for conidiation . In addition , 33 PK mutants were reduced in conidiation by over 50% in comparison with PH-1 ( Table 2 ) . Conidia were rarely formed by the Fg01312 , Fg04053 , Fg007329 , and Fg10037 mutants ( Table 2 ) . For most of these conidiation mutants , their growth rate also was significantly reduced . In fact , among the mutants with over 80% reduction in growth rate , only the Fgtpk2 ( Fg07251 ) mutant was reduced less than 80% in conidiation . However , the Fgrim15 ( Fg01312 ) and Fg08631 ( Ypk2-like ) deletion mutants were reduced over 90% in conidiation but had no obvious defects in vegetative growth . These two PK genes may be important for conidiophore development or conidiogenesis . The Fgrim15 and Fg08631 mutants often formed conidia directly on short hyphal branches ( Figure 3A ) . Clusters of phialides were rarely observed , which may be responsible for reduced conidiation . The Fgcdc15 ( Fg10381 ) mutant also was significantly reduced in conidiation . It often formed conidia directly at the hyphal tips ( Figure 3A ) , indicating that septation is important for conidiophore or phialide development . The Fgswe1 , Fgbud32 , Fg06939 , Fg04053 , Fgfpk1 ( Fg04382 ) , Fggsk3 ( Fg07329 ) , and Fgcla4 ( Fg06957 ) mutants formed smaller or shorter conidia with abnormal morphology ( Figure 3B ) . All the conidium morphology mutants also were significantly reduced in conidiation , indicating that these six PK genes may be involved in the regulation of conidium development and maturation processes . Whereas conidia of the Fg06939 , Fgpfk1 , and Fg04053 mutants tended to have four compartments , most of conidia produced by the Fgswe1 mutant were single- or two- celled ( Figure 3B ) , similar to microconidia produced by other Fusarium species . Conidia of the Fggsk3 mutant were highly vacuolated and had curved apical compartments and less septation ( Figure 3C ) . Interestingly , although the Fgcdc15 mutant produced conidia with normal size and morphology , it also had less septation in conidia ( Figure 3C ) . It often had only one or two septa towards the ends of conidia . Among 93 PK mutants that produced conidia , none of them was blocked in conidium germination . Interestingly , in the Fg04053 mutant , approximately 5% of freshly harvested conidia had germinated in sporulating cultures . This PK gene may play a role in self-inhibition of conidium germination in the spore-producing cultures . After germinating for 12 h , many PK mutants with growth defects produced shorter germ tubes than the wild type . Nine of them , including the Fg10228 , Fg00479 , Fg00472 , Fg09897 , Fg10228 , Fg07251 , and Fg07329 mutants , had the most significant defects in germ tube morphology , growth , or branching ( Table S5 ) . In the Fg01641 and Fg09897 mutants , some conidium compartments produced more than one germ tube , resulting in an increase in the number of germ tubes produced by individual conidia . F . graminearum is a homothallic fungus and ascospores play a critical role in its infection cycle as the primary inoculum . When assayed for sexual reproduction on carrot agar plates , most of the PK mutants ( 51 ) were normal in the production of perithecia and cirrhi . A total of 20 PK mutants failed to produce perithecia ( Table 3 ) . Six of them were mutants in genes of the Mgv1 and Gpmk1 MAPK pathways , which are known to be required for sexual reproduction in F . graminearum [20] , [22] , [24] . Interestingly , mutants deleted of the Fg00408 , Fg08691 , and Fg09612 genes that are orthologous to yeast SSK22 , PBS2 , and HOG1 [30] were also blocked in perithecium formation ( Table 3 ) . These results indicate that all three MAPK pathways are important for sexual reproduction in F . graminearum . The 26 PK mutants that formed perithecia but failed to produce cirrhi could be divided into two types . Type I mutants were defective in the development of ascogenous hyphae , asci , or ascospores even after prolonged incubation ( Figure 4A; Table 3 ) . The Fg04947 , Fg05734 , Fg06793 , and Fg08701 mutants produced a few small perithecia that were blocked in the development of ascogenous hyphae . In contrast , the Fgdbf2 ( Fg08635 ) and Fgswe1 ( Fg10228 ) mutants produced morphologically normal perithecia that contained aborted ascogenous tissues ( Figure 4A ) . Type II mutants were blocked in ascospore release . These mutants formed ascospores inside perithecia ( Figure 4A ) but failed to produce cirrhi after incubation for one month or longer . Among them , the Fg08468 , Fg07344 , Fg06878 ( Cmk1/2 ) and Fg10095 mutants were significantly reduced in ascospore formation . They produced only a few ascospores per perithecium . In the Fg08468 mutant , fascicles of aborted asci with no mature ascospores were observed , indicating that Fg08468 is important for ascosporogenesis . In addition , we found that ascospores formed by the Fg07251 ( Tpk2 ) , Fg01641 ( Sak1 ) , and Fg01058 ( Cbk1-like ) mutants had morphology defects . While ascospores of the Fg07251 mutant were often fragmented in the middle , the Fg01641 mutant produced highly vacuolated ascospores ( Figure 4B ) . For the Fg01058 mutant , some ascospores appeared be single-celled and spherical ( Figure 4B ) . Normal mature ascospores are four-celled in F . graminearum . Interestingly , most of the ascospores formed by the Fgkin1 ( Fg09274 ) mutant had germinated or were germinating inside perithecia ( Figure 4B ) . FgKIN1 and vesicle trafficking must play a critical role in preventing ascospores from germination before being released . The Fg00408 , Fg08691 , and Fg09612 mutants had no obvious growth after incubation for 3 days on CM with 0 . 7 M NaCl ( Figure 5A ) . When examined microscopically , germ tubes of these mutants were significantly stunted with NaCl treatment ( Figure 5B ) . These mutants also were hypersensitive to 1 M sorbitol and 0 . 7 KCl ( Figure 5C ) , indicating that the MAPK cascade orthologous to the yeast Hog1 pathway is conserved in F . graminearum for regulating responses to hyperosmotic stress . Like osmoregulation MAPK pathway mutants , the Fgfpk1 ( Fg04382 ) mutant was hypersensitive to 0 . 7 M NaCl ( Figure 5A ) , indicating that proper regulation of phospholipid translocation also plays a role in normal response to hyperosmotic stress . The Fgsat4 ( Fg06939 ) and Fgkin1 ( Fg09274 ) mutants also had increased sensitivity to 0 . 7 M NaCl ( Figure 5A ) but they were normal in response to 1 M sorbitol ( Figure 5C ) . However , the Fgsat4 mutant was more tolerant to 0 . 7 M KCl than the wild type ( Figure 5C ) . In yeast , Sat4 kinase is involved in salt tolerance by regulating the Trk1-Trk2 potassium transporters [31] . FgSAT4 may be specifically involved in the regulation of K+/Na+ transporter genes in F . graminearum . In contrast , the Fgkin1 mutant was hypersensitive to 0 . 7 M KCl . The presence of 0 . 7 M KCl but not 1 M sorbitol inhibits its growth ( Figure 5C ) . In S . pombe , the kin1 mutant expresses increased sensitivity to excess chloride ion [32] . These results indicate that FgSAT4 and FgKIN1 are not directly involved in osmoregulation , but they play key roles in avoiding K+ and Cl- toxicity in F . graminearum . Interestingly , several PK gene deletion mutants had increased tolerance to hyperosmotic stress ( Figure 5A; Table S6 ) . Two of them , the Fgsrb10 ( Fg04484 ) and Fgprr2 ( Fg08906 ) mutants , grew faster than PH-1 under hyperosmotic conditions . The Fgprr2 mutant was normal in growth , but the Fgsrb10 , Fgcak1 ( Fg04947 ) , and Fgsnf1 ( Fg09897 ) mutants were reduced in growth rate on regular medium , but addition of 0 . 7 M NaCl suppressed their growth defects . In S . cerevisiae , SNF1 is required for the expression of glucose-repressed genes , thermotolerance , and peroxisome biogenesis . Like its orthologs in other plant pathogenic fungi , GzSNF1 is important for vegetative growth , sexual reproduction , and pathogenesis [29] . However , it is not known whether SNF1 orthologs are involved in tolerance to hyperosmotic stress in fungi . Fg01641 is orthologous to yeast Sak1 , which is an upstream kinase for the Snf1 complex . In yeast , the Cak1 kinase is responsible for the activation of Srb10 , which is a kinase converges with Snf1 on the Sip4 transcriptional activator [33] . Therefore , it is possible that some of these genes are functionally related in F . graminearum to negatively regulate subsets of genes involved in response to hyperosmotic stress . In comparison with the wild type , the Fg00472 , Fg04382 , Fg05418 ( Yak1 ) , and Fg13318 mutants were hypersensitive to oxidative stress ( Table S6 ) . Their growth was more severely reduced by 0 . 05% H2O2 than that of the wild type ( Figure 6 ) . Although to a less extent , the Fgssk2 , Fgpbs2 , and Fghog1 mutants also were more sensitive to H2O2 than the wild type ( Table S6 ) , indicating that the osmoregulation pathway is also involved in regulating responses to oxidative stress . The Fggsk3 ( Fg07329 ) and Fgbud32 ( Fg10037 ) mutants had no visible hyphal growth in the presence of 0 . 05% H2O2 . However , growth of these two mutants was severely reduced on regular PDA . Interestingly , H2O2 treatment inhibited conidium germination in the Fggsk3 but not the Fgbud32 mutant . After incubation for 24 h with as low as 0 . 01% H2O2 , conidium germination was not observed in the Fggsk3 mutant . Therefore , FgGSK3 may play a role in response to oxidative stress during conidium germination . In contrast , the Fgkic1 ( Fg05734 ) and Fg08701 mutants were more tolerant to oxidative stress than the wild type ( Table S6 ) . The Fg08701 mutant became almost insensitive to hydrogen peroxide . In the presence of 0 . 05% H2O2 , it grew faster than the wild type ( Figure 6 ) . Fg08701 encodes a Gin4-like kinase but it has no distinct ortholog in the fission or budding yeast . Deletion of Fg08701 may result in enhanced expression of genes involved in ROS scavenging . In infection assays with flowering wheat heads , 42 PK deletion mutants were found to have a disease index less than 5 ( Table 2; Figure 7A ) . Under the same conditions , the wild type had a disease index of approximately 14 . Among them , 22 PK mutants were found to be non-pathogenic or caused symptoms only on the inoculated kernels , indicating defects in colonization or spreading . In F . graminearum , the Gpmk1 and Mgv1 MAPK genes are known to be important for plant infection . Thus , it is not surprising that other components of these two MAPK pathways are required for plant infection ( Figure 7B ) . Interestingly , the Fgssk22 , Fgpbs2 , and Fghog1 mutants also were defective in plant infection , indicating that the osmoregulation pathway may play a critical role in overcoming plant defense responses and infectious growth in F . graminearum . Like many other filamentous ascomycetes , F . graminearum has two genes encoding the catalytic subunits of protein kinase A ( Fg07251 and Fg08729 ) . Fg07251 is orthologous to CpkA of M . oryzae and its orthologs in other fungal pathogens that are known to be essential for plant infection [13] , [34] . The Fg07251 mutant was non-pathogenic but it , unlike the cpkA mutant , was significantly reduced in growth . In contrast , the Fg08729 mutant had no detectable phenotype , suggesting that it plays a minor role in PKA activities . Besides genes related to the signaling pathways , 16 PK genes , including Fg10381 , Fg10066 , Fg07344 , Fg00362 , Fg04053 , Fg01188 , Fg02795 , Fg07329 , Fg08635 , Fg04484 , Fg09897 , Fg11812 , Fg10037 , Fg01641 , Fg13318 , and Fg05418 , were essential for spreading from inoculated kernels to nearby spikelets . They had a disease index less than 1 . 5 . Many of them , such as the Fg00362 and Fg01188 deletion mutants , were significantly reduced in growth rate , which may contribute to their reduced virulence . When analyzed for the association between reduced virulence and other phenotypes , it is not surprising that defects in plant infection and vegetative growth were found to have the highest correlation efficient ( Table S4 ) . Among all of the 32 mutants with over 30% reduction in growth rate , only the Fgkin82 ( Fg04382 ) and Fg08701mutants had a disease index greater than 7 ( Table 2 ) . These two genes may have different functions during vegetative growth and infectious growth . However , among the mutants with a disease index less than 5 , four PK mutants ( Fg05418 , Fg01312 , Fg04770 , and Fg08906 ) were not significantly affected vegetative growth . In addition , the Fg09274 , Fg07344 , Fg00472 , Fg10095 , Fg06793 , Fg03284 , and Fg11812 deletion mutants were reduced less than 30% in growth rate ( Table 2 ) , indicating that defects other than growth rate may be responsible for reduced virulence in these mutants . For mutants with a disease index larger than 1 . 5 , infected wheat kernels were harvested and assayed for DON production . Except for the Fg04947 mutant that produced barely detectable amounts of DON , all other mutants assayed produced significant amounts of DON ( >400 ppm ) in infested kernels ( Table S7 ) . However , DON production was reduced in many of these PK mutants . In 22 mutants , the level of DON in infested wheat kernels was less than 900 ppm . Among them , eight mutants had a disease index less than 5 ( Table S7 ) . These results indicate that reduction in DON production was positively correlated with changes in virulence in most of these PK mutants , which is consistent with the importance of DON in plant infection [17] , [18] . However , a few PK mutants had no significant changes in DON production , such as the Fg06957 and Fg05547 mutants , but were drastically reduced in virulence ( Table S7 ) . Factors other than DON production , such as defects in growth and stress responses , may be responsible for reduced virulence in these mutants . For the protein kinase genes with distinct orthologs in S . cerevisiae , we used the interlog approach to predict their interaction networks in F . graminearum . A total of 231 interactions were identified based on their yeast interlogs ( Figure 8 ) . Among them are three MAPK cascades , Fg05484-Fg09903- Fg06385 , Fg00408-Fg08691-Fg09612 , and Fg06326-Fg07295-Fg10313 . Mutants of each MAPK pathway expressed similar phenotypes . Other predicted PK-PK interactions include the Fg04484-Fg09897 , Fg10228-Fg08468 , and Fg11812-Fg10313 interactions ( Figure 9A ) . Both the Fg04484 and Fg09897 mutants grew faster in the presence of 0 . 7 M NaCl ( Figure 5 ) . The same approach was used to predict the interactions of protein kinases with other proteins of F . graminearum . The predicted PK-protein interactome consists of 763 pairs of interactions ( Figure S3 ) . The main hubs of predicted networks include Fg08468 ( Cdc28 ) , Fg08731 ( Hrr25 ) , Fg07855 ( Cdc7 ) , Fg01271 ( Cdc5 ) , Fg10313 ( Mgv1 ) , Fg09897 ( Snf1 ) , Fg05393 ( Pho85 ) , and Fg10037 ( Bud32 ) ( Figure S3 ) . For the two putative Cdc2/Cdc28 orthologs , only Fg08468 was included in this analysis as the representative . It was predicted to interact with 22 protein kinases and 85 other proteins . In S . cerevisiae , HRR25 is involved in regulating diverse events , including vesicular trafficking , DNA repair , and chromosome segregation . FgHrr25 also was predicted to interact with 59 proteins . To verify the predicted interactions , components of the Gpmk1 and Mgv1 MAPK pathway were selected for yeast two-hybrid and co-immunoprecipitation ( co-IP ) assays . In yeast two-hybrid assays , the FgSte50-Fst7 , FgSte50-Fst11 , Fst11-Fst7 , Fst7-Gpmk1 , and FgMmk2 ( Fg07295 ) -Mgv1 interactions were confirmed by growth on SD-His ( Figure 9A ) and LacZ activities ( Figure 9B ) . FgSte50 was included in this experiment because its ortholog interacts with Ste7 and Ste11 in other fungi [35] , [36] . As predicted , the interaction of Fst11 with Gpmk1 , Pbs2 , or Hog1 was not detected . The FgMmk2-Mgv1 and FgSte7-Gpmk1 interactions were further verified by co-immunoprecipitation ( co-IP ) assays ( Figure 9C ) . We also used co-IP assays to confirm the predicted interaction between FgKIN4 ( Fg11812 ) and Mgv1 ( Figure 9C ) .
The FgBck1 ( Fg06326 ) -FgMmk2 ( Fg07295 ) -Mgv1 ( Fg10313 ) MAPK cascade is orthologous to the cell integrity pathway in yeast . Similar to the mgv1 mutant , the Fgbck1 and Fgmmk2 mutants formed small , whitish colonies and were defective in plant infection ( Figure 7 ) as well as production of perithecia ( Table 3 ) . They also were defective in hyphal fusion and tended to produce wavy hyphae . In S . cerevisiae , the Pkc1 protein kinase C functions upstream from the yeast cell wall integrity pathway . Like Pkc1 in yeast , FgPKC1 ( Fg06268 ) is an essential gene in F . graminearum . According to the predicted PK-protein interaction networks , Mgv1 have more interacting proteins than the other two MAPKs ( Figure S3 ) . Mgv1 may regulate a number of downstream targets , such as the Mig1 and Swi4 orthologs [40] , [41] in F . graminearum . The interactions of Mgv1 with FgMmk2 and FgKin4 were confirmed by yeast two-hybrid or co-IP assays . The MAPK cascade orthologous to the yeast HOG pathway [42] also is well conserved in F . graminearum . As expected , the Fgssk2 ( Fg00408 ) , Fgpbs2 ( Fg08691 ) , and Fghog1 ( Fg09612 ) mutants were hypersensitive to hyperosmotic stresses ( Figure 5 ) . These mutants also were significantly reduced in virulence and blocked in sexual reproduction . This MAPK pathway is dispensable for plant infection in M . oryzae but is essential for pathogenesis in Botrytis cinerea and Alternaria alternata [43] , [44] . The Fgssk2 , Fgpbs2 , and Fghog1 mutants had increased sensitivities to H2O2 and reduced growth rate , which may contribute to its defects in plant infection . Interestingly , the osmoregulation pathway appears to regulate the development of aerial hyphae and fruiting bodies in F . graminearum . The Fgssk2 , Fgpbs2 , and Fghog1 mutants were sterile and rarely produced aerial hyphae on agar pates . Hyphae of these mutants had smaller branching angles and tended to grow in parallel on the surface ( Figure S4 ) . These phenotypic effects have not been reported in other fungi . Therefore , it will be important to determine the role of this MAPK pathway in aerial hyphal growth , sexual reproduction , and pathogenesis . Besides PK genes related to the MAPK and cAMP signaling pathways , over 30 PK mutants were significantly reduced in virulence ( Table 2 ) . However , many of them had severe growth defects , which may be directly responsible for reduced virulence . Among the PK mutants with a disease index less than 5 , only the Fgyak1 ( Fg05418 ) , Fgrim15 ( Fg01312 ) , Fgprr2 ( Fg08906 ) , and Fg04770 mutants had no significant changes in growth rate . Although these genes are conserved in filamentous ascomycetes , none of them has been reported to be important for pathogenesis in plant pathogens . The Fgrim15 and Fg04770 mutants were reduced in the production of DON ( Table S7 ) , which is a critical virulence factor in Fusarium head blight [17] . The Fgyak1 and Fgprr2 mutants rarely spread from the inoculated kernels to nearby spikelets on wheat heads ( disease index<1 . 5 ) . The Fgyak1 mutant had increased sensitivity to H2O2 ( Table S6 ) , 0 . 01% SDS , and 200 µg/ml Congo Red . In S . cerevisiae , Yak1 is known to regulate the stress-responsive transcription factors Hsf1 and Msn2 . Fg05418 may have similar functions in F . graminearum . Interestingly , the Fgprr2 mutant had increased tolerance to hyperosmotic and oxidative stresses but was reduced in virulence ( Figure 5; 6 ) . The Fgkin4 ( Fg11812 ) , Fgsch9 ( Fg00472 ) , Fg10095 , Fgctk1 ( Fg06793 ) , Fgcka1 ( Fg03284 ) , Fg07344 , Fgsrb10 ( Fg04484 ) , and Fgapg1 ( Fg05547 ) mutants had approximately 30% reduction in growth rate but a disease index less than 2 . 6 ( Table 2 ) . None of their orthologs except APG1 is known to be important for plant infection in fungal pathogens . In A . nidulans , the Kin4-related kinase KfsA is implicated in regulating septum formation [45] . The Fgkin4 mutant , similar to the Fgcdc15 mutant , was defective in septum formation ( Figure 10 ) , conidiation , and sexual reproduction , further indicating that septation plays a critical role in pathogenesis and development in F . graminearum . In M . oryzae , the ortholog of CDC15 is an important virulence factor [46] . The Fgsch9 mutant was reduced in DON production ( Table S7 ) and had increased sensitivity to oxidative stress ( Table S6 ) , which may be related to its reduced virulence . In yeast , Sch9 is functionally related to the PKA and TORC pathways . The role of TOR pathway is not clear in plant pathogenic fungi . The only TOR kinase gene in F . graminearum , Fg08133 , is essential ( Table 1 ) . Orthologs of APG1 and autophagy are known to be important for pathogenesis in M . oryzae and U . maydis [47] , [48] . It is likely that the Fgapg1 mutant had similar defects in autophagy and infectious growth . Orthologs of CTK1 and SRB10 are involved in cell division in S . cerevisiae but have not been characterized in plant pathogenic fungi . The Fgctk1 and Fgsrb10 mutants all had pleiotropic defects in growth , conidiation , sexual reproduction , and plant infection ( Tables 3 ) . These two genes are likely involved in basic cellular processes , such as cell cycle or cytokinesis in F . graminearum . Because ascospores are the primary inoculum , sexual reproduction is a critical stage of wheat scab disease cycle . A total of 45 PK genes , including all 12 members of the STE group , were found to be important for sexual reproduction . Many of these mutants also were defective in vegetative growth and plant infection . However , several PK genes , including Fg01058 , Fg01347 , and Fg06970 appear to play more specific or important roles in sexual reproduction . Deletion of these genes had no other significant phenotypic changes . Although deletion of Fg01058 or Fg01347 only resulted in defects in ascospore release , the Fg06970 mutant failed to produce perithecia ( Table 3 ) . Whereas Fg01058 is unique to filamentous fungi , Fg01347 is orthologous to yeast Bub1 , a protein kinase phosphorylated by Cdc28 and involved in cell cycle checkpoint . Fg06970 is orthologous to yeast Psk1 and Psk2 , two PAS domain-containing protein kinases that regulate protein synthesis and carbohydrate metabolism and storage [49] . Besides the Fg06970 mutant , 19 additional mutants that were blocked in perithecium formation . These mutants may be defective in female fertility or in the switch from vegetative growth to sexual reproduction . In the budding yeast , the Smk1 , Mek1 , Sak1 , and Ime2 kinases are required for sporulation . F . graminearum and other filamentous ascomyctes lack Smk1 and Mek1 orthologs . MEK1 is a meiosis-specific protein kinase , and Smk1 MAPK regulates late stages of ascospore formation . Whereas the yeast ime2 and sak1 mutants are defective in sporulation , the Fgime2 ( Fg04418 ) and Fgsak1 ( Fg01641 ) mutants still produced ascospores , although the latter two mutants had pleiotropic defects . These observations indicate that ascospore formation is regulated by different mechanisms in F . graminearum than in S . cerevisiae . However , the Fgctk1 ( Fg06973 ) , Fgcak1 ( Fg04947 ) , Fgkic1 ( Fg05734 ) , Fgswe1 ( Fg10228 ) , and Fgdbf1 ( Fg08635 ) mutants were aborted in ascus or ascospore development ( Table 3 ) . Their orthologs also are involved in sexual reproduction in yeast . Therefore , some genetic elements are conserved between yeast and filamentous fungi for sexual reproduction . Among the 20 PK genes for which we failed to isolate knockout mutants in F . graminearum , most of their orthologs are essential genes in S . cerevisiae , S . pombe , or A . nidulans ( Table 1 ) . The CBK1 , KIC1 , and other RAM complex genes are essential in the wild type but not in the ssd1 mutant of S . cerevisiae [50] . F . graminearum has an ortholog of SSD1 ( Fg07009 ) , but deletion of FgCBK1 or FgKIC1 is not lethal ( Table 1 ) . Although deletion of individual genes is not lethal , the phk1 phk2 and ypk1 ypk2 double mutants are not viable . Fg10725 and Fg05845 are orthologs of the yeast Pkh1/Pkh2 and Ypk1/Ypk2 kinases , respectively . The downstream targets of Pkh1 and Pkh2 include Pkc1 , Ypk1 , and Ypk2 . Ypk1 phosphorylates and down-regulates the Fpk1 kinase , a known flippase activator [51] . In F . graminearum , the Fgfpk1 ( Fg04382 ) deletion mutant was reduced in growth and had increased sensitivities to hyperosmotic and oxidative stresses . Deletion of PHO85 , IRE1 , KNS1 , or VPS1 is not lethal in S . cerevisiae , but we failed to identify knockout mutants of their orthologs in F . graminearum ( Fg05393 , Fg05775 , Fg06637 , and Fg05306 ) . The ortholog of VPS1 in A . nidulans , VPSA , is involved in vacuole biogenesis . The vpsA mutant is viable but has poor vegetative growth [52] . In yeast , PHO85 encodes a cyclin-dependent kinase ( CDK ) involved in the regulation of cellular responses to nutrient levels and environmental conditions . In A . nidulans , phoA and phoB are two CDKs homologous to PHO85 . Although deletion of phoA or phoB is not lethal , the phoA phoB double mutant is not viable , suggesting an essential role for PhoA and PhoB in cell cycle control and morphogenesis [53] . F . graminearum and many other filamentous fungi have only one PHO85 ortholog . In U . maydis and C . neoformans , deletion of the PHO85 ortholog is lethal [54] , [55] . Aurora kinases regulate chromosome condensation and segregation during cellular division . In S . cerevisiae , deletion of the IPL1 gene is lethal . The aurora kinase gene ark1 also is essential in S . pombe . Similar to the yeasts , all of the sequenced filamentous fungi , including F . verticillioides and F . oxysporum , have a single aurora kinase gene . In contrast , F . graminearum has two aurora kinase genes , Fg06959 and Fg02399 . Whereas Fg06959 appears to be an essential gene , deletion of Fg02399 had no significant phenotypic effects other than reduced conidiation ( Table 2 ) . Similar to their orthologs in yeast , the FgCDC5 ( Fg01271 ) , FgCDC7 ( Fg07855 ) , FgTEL1 ( Fg06089 ) , FgMPS1 ( Fg01137 ) , FgSGV1 ( Fg07409 ) , and FgNIMA ( Fg09408 ) genes are essential in F . graminearum . Most likely , they have conserved functions in F . graminearum . While Kin3 is not essential in S . cerevisiae , its ortholog , NimA , is required for the regulation of mitosis in A . nidulans [56] . In contrast , CDC15 is essential in yeast but deletion of its ortholog in A . nidulans ( SEPH ) is not lethal [57] . In F . graminearum , the Fgcdc15 ( Fg10381 ) deletion mutant was reduced in vegetative growth and conidiation . It was significantly reduced by not blocked in septation in conidia ( Figure 3C ) and hyphae ( Figure 10 ) . Both Fg00677 and Fg03284 are orthologous to CKA1 , which encodes the alpha catalytic subunit of casein kinase 2 ( CK2 ) that is essential for cell cycle progression and proliferation in yeast . Although Fg00677 is essential in F . graminearum , the Fg03284 deletion mutant had no obvious defects in growth and conidiation but was significantly reduced in virulence . In C . albicans , the homozygous cka2 but not cka1 mutant has attenuated virulence in the mouse model of oropharyngeal candidiasis [58] . However , the phenotype of the cka2 mutant can be suppressed by overexpression of CKA1 . It is possible that Fg00677 and Fg03284 have similar functional relationship in F . graminearum . In S . cerevisiae , SWE1 is not essential but plays important roles in the cell cycle . The ANKA kinase gene , an ortholog of SWE1 , also is involved in the regulation of septation and cell cycle checkpoint responses [59] . In F . graminearum , the Fgswe1 ( Fg10228 ) mutant was reduced in septation ( Figure 10 ) . However , it had pleiotropic defects hyphal growth , conidiation , and plant infection . In yeast , Swe1-mediated inhibition of Cdc28 is important for its checkpoint functions and pseudohyphal growth . FgSWE1 may be important for infectious growth in planta in F . graminearum . The SWE1 ortholog is essential in U . maydis [60] . In S . cerevisiae , Rad53 is required for cell-cycle arrest in response to DNA damage . Two of the downstream targets of Rad53 are Dun1 and Dbf4 . In F . graminearum , the Fgrad53 ( Fg00433 ) mutant had no obvious defects other than reduced conidiation . Whereas the Fgdun1 ( Fg07121 ) mutant had no detectable phenotypes , F . graminearum , like many other filamentous fungi , lacks a distinct ortholog of Dbf4 , an essential gene required for the initiation of DNA replication . Chk1 is the other kinase functional as a DNA damage checkpoint effector in yeast and other eukaryotes [61] . Similar to the Fgrad53 mutant , the Fgchk1 ( Fg01506 ) deletion mutant were normal in growth and plant infection but had increased sensitivity to UV irradiation . It appears that FgRad53 and FgChk1 kinases are important for DNA damage repair but dispensable for pathogenesis in F . graminearum . In yeast , both Rad53 and Chk1 are phosphorylated by Mec1 , an essential gene involved in the cell cycle checkpoint control in response to DNA damage . In F . graminearum , deletion of FgMEC1 ( Fg13318 ) was not lethal but the Fgmec1 mutant was significantly reduced in virulence . It also was reduced in growth rate , conidiation , had increased sensitivity to H2O2 . In A . nidulans , the AtmA and UvsB kinases , orthologs of Tel1 and Mec1 , also are functionally related in regulating DNA damage responses and act upstream from the ChkA and ChkB check point kinases [62] . Among the 28 F . graminearum PK genes that lack distinct orthologs in S . cerevisiae , four of them have orthologs in S . pombe ( Table S8 ) . Whereas the function of ppk23 is not clear , the intracellular gradient of Pom1 is used as the sensor for cell length in S . pombe [63] . In F . graminearum , the Fgppk23 ( Fg05406 ) mutant had no phenotype other than reduced conidiation . The Fgpom1 ( Fg10095 ) mutant was defective in plant infection , DON synthesis , and sexual reproduction although it was only slightly reduced in conidiation and vegetative growth . Although prp4 and sid1 are essential genes in S . pombe , the Fgsid1 ( Fg07344 ) and Fgprp4 ( Fg04053 ) mutants were viable but displayed pleiotrophic defects . The Fgprp4 mutant has severe growth defects . Prp4 is involved in spliceosome functions in S . pombe [64] . Interestingly , the Fgprp4 mutant was unstable . We had identified over a dozen spontaneous suppressor mutants with faster growth rate . Further characterization of the Fgprp4 mutant and suppressor mutations will be useful to determine the role this protein kinase in RNA splicing and fungal pathogenesis . For 15 of the other 24 PK genes that appear to be specific for filamentous fungi , their knockout mutants had no obvious phenotypic changes ( Table S7 ) . Some of them may be not true PK genes . Among the rest 9 genes , deletion of Fg09150 resulted in approximately 80% reduction in conidiation but had no other detectable phenotypic effect . In contrast , the Fg01058 mutant was defective only in ascospore morphology and release , suggesting that these two PK genes have specific functions during asexual and sexual reproduction , respectively . The Fg00792 , Fg01559 , Fg02488 , and Fg06420 mutants were slightly reduced in DON production but had no significant defects in plant infection . Therefore , Fg00362 , Fg03146 , and Fg04770 are the only three PK genes that are absent in the yeasts but important for plant infection in F . graminearum ( Table S8 ) . The Fg00362 mutant grew poorly ( Table 2 ) . POD-6 , an ortholog of Fg00362 , has been functionally characterized in N . crassa [10] . It interacts with COT-1 and plays a critical role in hyphal growth . Their orthologs likely have conserved functions in F . graminearum because the Fgpod6 and Fgcot1 mutants had the same growth defects ( Figure S2 ) . In contrast , the Fg03146 and Fg04770 mutants had no obvious defects in growth . Orthologs of Fg03146 and Fg04770 have not been characterized in filamentous fungi . It will be important to further characterize these two novel fungal virulence factors .
Protein sequences of F . graminearum were searched against the Kinomer v . 1 . 0 HMM library using the HMMSCAN program from the HMM software suite HMMer ( version 3 . 0 for windows ) to identify and classify protein kinases as described [3] , [65] . The cut off value was set to 20 . We also searched for additional putative PK genes that are predicted by the Broad Institute ( www . broadinstitute . org/annotation/genome/fusarium_graminearum ) or MIPS ( mips . helmholtz-muenchen . de/genre/proj/FGDB ) to contain the protein kinase domain ( Pkinase , PF00069 ) . Phylogenetic analysis was conducted with MEGA version 5 [66] . The catalytic domain sequences were aligned with COBALT [67] and trimmed with trimAl [68] . The maximum likelihood phylogeny tree was visualized using Interactive Tree Of Life Version 1 . 9 ( http://itol . embl . de/# ) . The split-marker approach [19] was used to generate the gene replacement constructs for the PK genes . The primers used to amplify the flanking sequences for each gene are available at fgkinome . nwsfau . edu . cn . The resulting PCR products were transformed into protoplasts of the wild-type strain PH-1 [69] as described [17] , [20] . Hygromycin B ( Calbiochem , La Jolla , CA ) was added to a final concentration of 250 µg/ml for transformant selection . Putative knockout mutants identified by screening with primers F5 and R6 were further analyzed by PCR with primers F7 and H856R or primers H855F and R8 to confirm the gene replacement events ( Figure S1 ) . All of the mutants generated in this study were preserved in 15% glycerol at −80°C . Colony morphology and growth rate were assayed with potato dextrose agar ( PDA ) cultures grown at 25°C for three days . Conidiation was assayed with 5-day-old CMC cultures as described [20] , [70] . Conidium morphology was examined and photographed with an Olympus BX-51 microscope . For assaying conidium germination and germ tube growth , freshly harvested conidia were cultured in liquid YEPD medium for 12 h . Slab cultures grown on a thin layer of complete medium ( CM ) for 36 h were examined for defects in hyphal tip growth and branching [20] , [70] . Aerial hyphae of 7-day-old carrot agar cultures were pressed down with 300 µl of sterile 0 . 1% Tween 20 . Perithecium formation and cirrhi production were assayed after incubation at 25°C for 2 weeks . For mutants that formed perithecia but failed to produce cirrhi 3-4 weeks after fertilization , at least 10 perithecia were examined for ascospores and ascogenous hyphae . For assaying sensitivities to various stresses , vegetative growth was assayed on PDA plates with 0 . 7 M NaCl , 0 . 05% H2O2 , 0 . 01% SDS , or 200 µg/ml Congo Red [71] . Conidia harvested from 5-day-old CMC cultures were resuspended to 106 spores/ml . Flowering wheat heads of cultivar Xiaoyan 22 were drop-inoculated with 10 µl of conidium suspensions at the fifth spikelet from the base of the inflorescence [72] , [73] . After the inoculation , wheat heads were capped with a plastic bag for 48 h to maintain the moisture . Spikelets with typical symptoms were examined 14 days post-inoculation ( dpi ) . Diseased wheat kernels were pooled to assay for DON production as described [20] . For stalk rot assays , 8-week-old corn plants of cultivar Pioneer 2375 were inoculated as described [70] , [74] and assayed for symptoms 14 dpi . Infection assays with corn silks were conducted as described [20] . The protein-protein interaction ( PPI ) networks of S . cerevisiae were downloaded from the Database of Interacting Proteins ( DIP , dip . doe-mbi . ucla . edu/dip ) and SGD ( www . yeastgenome . org ) . Orthologous pairs of F . graminearum and S . cerevisiae genes were obtained from the Inparanoid database [75] and by BlastP searches . To strengthen the reliability of predicted interactions , the bit score cut off value was set to 200 . The predicted PPI interaction map was generated with the Cytoscape program [76] . Protein-protein interactions were assayed with the Matchmaker yeast two-hybrid system ( Clontech , Mountain View , CA ) . ORFs of the GPMK1 , FgSTE50 ( Fg04101 ) , and FgSTE7 ( Fg09903 ) were amplified from first-strand cDNA of PH-1 and cloned into pGBK7 ( Clontech ) as the bait constructs . For the FgSTE11 ( Fg05484 ) , FgHOG1 ( Fg09612 ) , and FgPBS2 ( Fg08691 ) genes , their ORFs were amplified and cloned into pGADT7 as the prey constructs . Prey constructs also were generated for the GPMK1 and FgSTE50 genes . The resulting bait and prey vectors were co-transformed in pairs into yeast strain AH109 ( Clontech ) . The Leu+ and Trp+ transformants were isolated and assayed for growth on SD-Trp-Leu-His medium and galactosidase activities with filter lift assays as described [77] . The positive and negative controls were provided in the Matchmaker Library Construction & Screening Kits ( Clontech ) . The GPMK1 and MGV1 genes were amplified and cloned into pDL2 by the yeast gap repair approach [78] , [79] to generate the 3xFLAG fusion constructs . Similar approaches were used to generate the GFP fusion constructs for the FgMMK2 ( Fg07295 ) , FgKIN4 ( Fg11812 ) , and FgSTE7 ( Fg09903 ) genes with the pFL3 vector [80] . The resulting fusion constructs were verified by DNA sequencing and transformed in pairs into PH-1 . Transformants expressing pairs of fusion constructs were confirmed by western blot analysis . For co-IP assays , total proteins were isolated and incubated with the anti-FLAG M2 beads as described [81] . Proteins eluted from beads were analyzed by western blot detection with a monoclonal anti-GFP ( Roche , Indianapolis , IN ) antibody . | Fusarium head blight caused by Fusarium graminearum is one of the most important diseases on wheat and barley . Although protein kinases are known to play major regulatory roles in fungi , systematic characterization of fungal kinomes has not been reported in plant pathogens . In this study we generated deletion mutants for 96 protein kinase genes . All of the resulting knockout mutants were assayed for changes in 17 phenotypes , including growth , reproduction , stress responses , and plant infection . Overall , deletion of 64 kinase genes resulted in at least one of the phenotypes examined . In total , 42 kinase mutants were significantly reduced in virulence or non-pathogenic . A number of these protein kinase genes , including two that are unique to filamentous fungi , are dispensable for hyphal growth and likely encode novel fungal virulence factors . Ascospores are the primary inoculum for wheat scab . We identified 26 mutants blocked in ascospore . We also used the in silico approach to predict the kinase-kinase interactions and verified some of them by yeast two-hybrid or co-IP assays . Overall , in this study we functionally characterize the kinome of F . graminearum . Protein kinase genes that are important for various aspects of growth , developmental , and plant infection processes were identified . |
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Helminth infections have proven recalcitrant to control by chemotherapy in many parts of Southeast Asia and indeed farther afield . This study isolates and examines the influence of different aspects of the physical and social environment , and uneven intervention effort contributing to the pathogenic landscape of human Opisthorchis viverrini infections . A cross-sectional survey , involving 632 participants , was conducted in four villages in northeast Thailand to examine the impact on prevalence and parasite burden of the reservoir dam environment , socio-economic , demographic , and behavioral factors , and health center intervention efforts . Formalin-ether concentration technique was used for diagnoses , and multivariate models were used for analyses . The importance attributed to O . viverrini infections varied among health centers in the four study villages . Villages where O . viverrini infections were not prioritized by the health centers as the healthcare focus were at a higher risk of infection ( prevalence ) with odds ratio ( risk factor ) of 5 . 73 ( 3 . 32–10 . 27 ) and p-value < 0 . 01 . Priority of healthcare focus , however , did not appear to influence behavior , as the consumption of raw fish , the main source of O . viverrini infections in the study area , was 11 . 4% higher in villages that prioritized O . viverrini infections than those that did not ( p-value = 0 . 01 ) . Landscape variation , notably proximity to reservoir , affects vulnerability of local population to infection . Infection intensity was higher in population located closer to the reservoir with risk ratio of 2 . 09 ( 1 . 12–4 . 02 ) and p-value < 0 . 01 . Patterns of infection intensities among humans were found to match fish infection intensity , where higher infection intensities were associated with fish obtained from the reservoir waterbody type ( p-value = 0 . 023 ) . This study demonstrated the importance of environmental influence and healthcare focus as risk factors of infections in addition to the socio-economic , demographic , and behavioral factors commonly explored in existing studies . The reservoir was identified as a crucial source to target for opisthorchiasis intervention efforts and the need to consider infection intensity in disease control efforts was highlighted . The holistic approach in this study , which underscores the close relationship between the environment , animals , and humans in development of human infections or diseases , is an important contribution to the framework of One Health approach , where consideration of helminth diseases has largely been overlooked .
Helminthiases , which include foodborne trematodiases , lymphatic filariasis , schistosomiasis , and soil-transmitted helminthiases , are the most common neglected tropical diseases ( NTDs ) in southeast Asia [1] . They disproportionally affect the poor or marginalized population in developing countries , trapping the afflicted in a vicious cycle of poor health outcomes and poverty , and costing billions of dollars in treatment each year [2] . The increasing recognition of the burden caused by helminth infections has brought about large-scale control programs by the World Health Organization and other nationwide control programs in countries in Asia [3 , 4] , Latin America [5] , and sub-Saharan Africa [6] , where helminthiases are prevalent . These programs have primarily relied upon chemotherapy for helminthiases control [7] . Many chemotherapy programs have relatively limited objectives , resulting in reduced infection levels in the short-term [8] . Re-emergence of the disease , and possibly even development of resistant strains of parasites , is common once a program has been terminated , however [7] . Evidence already exists of the reduced efficacy of drugs used to combat lymphatic filariasis [9] and schistosomiasis [10] , with frequent treatment involving anthelmintic drugs appearing to hasten the development of drug resistance in some animals [11] . While chemotherapy has reduced levels of infection in the short-term , ensuring that positive health benefits extend beyond the cessation of chemotherapy programs has been challenging without improvements in the other factors that predispose populations to helminthiases [12 , 13] . Helminth infections , and indeed many infectious diseases , are strongly influenced by environmental and socio-economic conditions , and by human behavior and the effectiveness of health service provision [13] , or what Lambin et al [14] term the pathogenic landscape for disease . A major increase in schistosomiasis following the construction of dams and irrigation infrastructure has been well-documented [15] , as has the eradication of schistosomiasis in Japan through modernization of agricultural practices [16] and reduced hookworm infections as a result of improvements in sanitation and housing [17] . A One Health approach permits consideration of vulnerabilities at the environment-animals-humans interface [18 , 19] , accounting for the complex and highly dynamic process of infection , where a change in one underlying factor can drastically alter the situation for the other conditions , leading to the uneven distribution of diseases even in places with seemingly similar conditions . Such unevenness is observed in opisthorchiasis , an infection caused by the foodborne trematode Opisthorchis viverrini , where large variations in the disease burden may be observed in a relatively small geographic area [20 , 21] . Despite the close association of helminth parasite life cycle and life strategies with the physical environment and animal hosts , the One Health approach has rarely been applied to study of helminthiasis [22] . Yet , understanding such factors that underpin infections with a high focality can provide important contributions to the framework of One Health approach to a broader range of diseases , enabling intervention efforts that are tailored to local pathogenic landscapes , and in particular finely resolved vulnerabilities to the disease , to better accommodate future variations [23] . Moreover , the influence of , for example , environmental conditions can be easily masked by other factors that contribute to the extent and severity of a disease outbreak , such as health intervention efforts [24] . For example , intensive chemotherapy efforts have mitigated schistosomiasis burdens associated with recent hydro-infrastructure developments [25 , 26] , but such effects are palliative and temporary if the underlying factors causing infections , including infection of animal hosts and environmental conditions that promote and maintain pathogenesis , remain [27] . Opisthorchiasis is a major NTD in southeast Asia , and in the Mekong River basin in particular . The parasite involved , O . viverrini , is one of only three metazoan pathogens classified as a group 1 carcinogen , with sufficient evidence to establish a link between O . viverrini and cancer in humans [28] . Carcinogenicity of opisthorchiasis stems not only from prolonged infection and re-infection but also from the repeated treatment involving praziquantel anthelminthic , which can induce DNA damage leading to the development of hepatobiliary abnormalities , including cholangiocarcinoma ( CCA ) [29 , 30] . CCA is among the leading causes of cancer-associated mortality in the Mekong River basin [31] . O . viverrini is closely associated with wetland ( rice ) -based agriculture where drainage canals can facilitate infection of fish hosts by snail-shed cercariae [32] . The trematode has a three-host life cycle with freshwater Bithynia spp . snails and cyprinid fish as , respectively , the first and second intermediate hosts , and humans as the definitive host [33] . Human infection occurs through the consumption of raw or undercooked cyprinid fish , which is a common practice in the Mekong River basin . Small-scale freshwater fishing activities provide a major source of protein and additional income for local communities [21] , while raw fish consumption has led to the persistence of opisthorchiasis in many parts of the region despite decades of control efforts [34 , 35] . The control efforts have , to date , largely been restricted to chemotherapy and education campaigns , where the measure of success of control programs is limited to prevalence reduction instead of reinfection rate and long-term sustainability [36] . Despite the close relationship of opisthorchiasis with the physical and social environment , research on the range of factors that underpin the cycle of infection and reinfection has largely been neglected [18 , 37] . Particularly , there is little information on the association between human infection intensity and fish infection variation in different waterbody types . In fact , infection intensity is much less frequently reported than infection prevalence in O . viverrini studies [eg . 38–40] . The same is the case for other helminthiases , including soil-transmitted helminthiases [41] . This is problematic because infection intensity enables a very different understanding of the disease transmission and life strategies as compared with infection prevalence , in addition to being a factor in the most severe forms of infectious disease , including the risk of developing CCA in the case of O . viverrini infections . The focus of this study is the pathogenic landscape for opisthorchiasis , in particular the epidemiological role of dam construction and subsequent reservoir creation , socio-economic conditions , demographic factors and behavior , and variations in the efficacy of the provision of health services . This study illustrates the causes of an uneven distribution of disease burden , identifying contributing factors of infection while controlling for existing chemotherapy control efforts . Infection intensity is determined in addition to infection prevalence , and the variations in factors shaping intensity and prevalence examined . This study has the potential to facilitate improved health intervention efforts that take into account the high focality of opisthorchiasis . The approach and results have wider applicability , to the study of other NTDs , especially those with complex , environmentally sensitive life cycles .
Ethical approval for this study was obtained from the institutional review board of National University of Singapore , Singapore ( Reference code: A-14-122 , approved on 20 August 2014 ) and Khon Kaen University , Thailand ( Reference code: HE571229 , approved on 22 July 2014 ) . Permission for fieldwork was obtained from the subdistrict health centers . Meetings were held with heads of the health centers and health center workers to explain the purpose , procedures , risks , and benefits of the study . Health center workers were briefed , using Thai language , on the participant information sheet and the need to obtain written consent from the participants , and on how to administer the questionnaire , and to obtain fecal samples . All adult subjects were informed about the study design and objectives , and all study subjects gave written consent . No children were involved in this study . Identifiable information collected including names were anonymized using code numbers . After fecal examination , for participants tested positive with parasitic infection , personal information and corresponding infection results were made available only to the health center in the village so that treatment could be administered . Deworming medication was provided to the health centers for treatment of participants who were tested positive with infection . Those infected with O . viverrini were treated with praziquantel at an oral dose of 40 mg/kg . All medications were administered by certified nurses from the health centers . After the survey , only code numbers were retained by the principle investigator with the infection results and survey responses . No identifiable information was kept nor published . This study was conducted in four villages in the catchment for the Ubolratana reservoir ( 16°43’40°N , 102°34’45°E ) , northeast Thailand ( Fig 1 ) . Two of the villages , Sai Mun and Huay Bong , are located in the province of Nong Bua Lamphu , to the north of the reservoir . The other two , Fa Luem and Pho Tak , are in the province of Khon Kaen , to the south of the reservoir ( Fig 1 ) . According to Ong et al [42] , levels of O . viverrini infection of intermediate fish hosts were greater in fish caught in the main body of the reservoir when compared with those captured in rivers draining into the reservoir . In order to examine the influence of the physical environment on human O . viverrini infection , villages of varied levels of exposure to fish infected with O . viverrini were sampled . Two of the villages sampled , one in the north and one in the south of the reservoir , are located along the river inlets in the study area , and two , one in the north and one in the south of the reservoir , are located along the shore of the main body of the reservoir . Hereinafter , the villages are referred to as north ( N ) -river , N-reservoir and south ( S ) -river , and S-reservoir . O . viverrini infection prevalence and intensity were compared between villages located along the river inlets and reservoir to highlight and examine possible environmental influences . Samples in the north and south of the reservoir were compared to determine the association of infection with inter-provincial health jurisdiction . For reference , infection prevalence and intensity for each village were also presented , but no analyses were performed on them . A cross-sectional study was conducted between August and December , 2014 . Fecal samples and questionnaire-based surveys on socio-economic , demographic , and behavioral factors of participants ( S1 File and S2 File ) were collected from August 2014 , and any infected individuals identified from the results were treated during the months of November and December 2014 . Participating households were selected from information provided by the local health center using a random number generator . All members from the selected households who were 21 years or older at the time of the survey were invited to participate . Using StatCalc in Epi Info 7 . 1 . 5 software at confidence interval level of 95% and margin of error at 5% , a sample size of 125 was needed for each village . A total of 756 participants from the four study villages were eventually invited . Current infection status involving O . viverrini , other foodborne parasites , and soil-transmitted helminths were determined from the analysis of fecal samples . A single fecal sample was provided by each participant . The samples were returned to the health center on the same day and kept on ice . Samples were transported to the laboratory the following morning where they were stored at -20°C until analyzed for their parasite content . To increase the number of fecal samples returned , each village was visited on two consecutive mornings for the transportation of samples . Samples were processed using the formalin-ether concentration technique [43] and examined under the microscope by experienced laboratory technologists . The formalin-ether concentration technique is the current gold standard diagnostic for O . viverrini infection [44] , although immunological and molecular techniques to increase the sensitivity and specificity of diagnoses are being developed [34 , 44] . O . viverrini eggs were counted and recorded , and evidence of other intestinal parasites noted . Infection prevalence was tabulated by dividing the number of infected people with the total number of people sampled , while infection intensity was determined as the number of O . viverrini eggs per gram ( epg ) of fecal sample . Infection statuses of participants were provided to the head of the health centers along with medications for the treatment of O . viverrini and other intestinal parasites . Information on past treatment of O . viverrini was obtained from both health center records and completed questionnaires ( the latter were used to identify participants who received O . viverrini treatment from institutions other than health centers , including hospitals ) . A questionnaire-based survey was conducted to determine the association of socio-economic , demographic , behavioral factors with O . viverrini infection prevalence and intensity . Variables used in this study were selected based upon existing studies on O . viverrini risk factors [45 , 46] , while the set of possible responses in the multiple-choice questionnaire were formulated based on preliminary semi-structured interviews conducted with 251 respondents in the catchment of the Ubolratana reservoir . Demographic information , such as age and gender , of participants were provided by the health centers . Age was tabulated based on the year of birth of the participant and was expressed as a continuous variable . Other data , including level of education and occupation , were obtained through the questionnaires . As each participant may have more than one occupation , the various occupation types were each presented as an explanatory variable . Per capita income was calculated by dividing household income by the number of household members . Participants were considered as living “Below poverty line” or “Above poverty line” by comparing their household’s per capita income to average 2014 poverty line values from the National Economic and Social Development Board of Thailand for the provinces of Nong Bua Lamphu ( 2357 baht ) and Khon Kaen ( 2514 baht ) [47] . Participants were given the option of whether they wished to disclose information on their income . Levels of awareness of the hazard of O . viverrini infection and patterns of consumption of the raw fish dishes Koi pla ( freshly prepared raw fish salad ) , Mum pla , and Pla som ( both of which are lightly fermented raw fish dishes ) , which are commonly eaten in the study area , were determined through the questionnaire survey . Participants were also asked for the reasons behind their consumption/non-consumption of raw fish . Variables examined in this study were summarized in S1 Table . The Isarn Agenda , a program aimed at CCA prevention and control in northeast Thailand , was introduced in 2012 . The program involves fecal examination , ultra-sound scan for CCA above 40 years of age , exhibiting risky behavior , notably the consumption of raw fish . People found with opisthorchiasis are treated . Education programs are also created for primary school children . The Isarn agenda is not equally applied throughout northeast Thailand , however , as each province has the autonomy to decide on health priorities locally . In Khon Kaen province , in the southern part of the study area , only two districts , which are not included in this study , adopted the Isarn agenda , while other districts opted to focus on non-communicable diseases , such as cardiovascular diseases and diabetes . All districts in Nong Bua Lamphu , in the northern part of the study area , adopted the Isarn agenda . Thus , of the four villages examined in this study , the N-river and N-reservoir villages adopted the Isarn agenda , and the S-river and S-reservoir village did not . The prevalence and intensity of infections in the four study villages were compared with past O . viverrini infection diagnostic tests conducted by the health centers . Unlike mass drug administration efforts for other helminth parasites , praziquantel anthelmintic were given as a treatment for O . viverrini only for patients who were tested positive for infection by the parasite . As such , past attempts in O . viverrini diagnostic tests can also be used to determine past chemotherapy efforts by the health centers . Furthermore , information on local health priorities and perceptions of opisthorchiasis was also obtained from the heads of the health centers . Criteria for inclusion in the analyses included providing consent , not having withdrawn from the study , provision of suitable stool sample , and having a completed questionnaire . The prevalence and intensity of O . viverrini infections , and the reasons for/for not consuming raw fish , were analyzed for their association with environmental factors . The most notable environmental factor included in analysis was the type of waterbody from which the fish used in raw fish dishes originated from ( river or reservoir ) . Examined social factors included O . viverrini awareness , age , gender , and occupation . The possible influence of interventions by health centers , and variations in their level of implementation , was also investigated ( S1 Table ) . Bivariate analyses were first performed on each explanatory variable; variables with p-values below 0 . 2 were next entered into multivariate models . To examine prevalence , data from all participants were used in analyses . To examine intensity , only participants who tested positive for infection were included . To examine reasons for consumption , only participants who consumed raw fish were used for the analyses; conversely , in examining reasons for non-consumption , only participants who do not consume raw fish were used . Logistic regression was employed for analyzing infection prevalence , reasons for consumption , and reasons for non-consumption . The models were simplified with backward elimination and variable deletion determined using a chi-squared test for non-significant difference in deviance . Quasi-Poisson regression was used for analyzing infection intensity in the case of overdispersion . The model was simplified with backward elimination and variable deletion determined using F-test for non-significant difference in deviance . In addition , chi-squared test was used to test for variation in proportion of raw fish consumption by location , gender , and O . viverrini awareness . Variations in level of infections of fish by waterbody type ( reservoir or river inlet ) were also analyzed , using data from Ong et al [42] . A t-test with unequal variances was used on log ( x+1 ) transformed data , as data were not normally distributed . Differences in levels of infections in fish according to waterbody type [42] are compared with results from this study . Results from these analyses were used as a basis for examining the role of factors that have contributed to the pathogenic landscape for opisthorchiasis in the study area .
Of the 756 participants invited , 632 suitable samples were obtained ( 83 . 60% ) . The mean age of participants is 52 . 6 years . Among the participants , 54 . 2% were females and 45 . 8% were males . Comparison of the O . viverrini infection prevalence and intensity of the four villages showed that the S-river village had the highest prevalence at 40 . 21% , while the N-reservoir village had the highest infection intensity at 99 . 41 epg ( Fig 2A ) . When the villages were grouped according to their provincial health jurisdiction , infection prevalence in the north villages in Nong Bua Lamphu province ( 5 . 45% ) was statistically significantly lower than that of the south villages in Khon Kaen province ( 26 . 42% ) ( Fig 2B ) . When villages were grouped according to their proximity to waterbody types , infection prevalence did not vary much between villages located close to the reservoir and to the river inlets , but infection intensity was significantly higher for the reservoir villages at 93 . 72 epg than for the river villages at 38 . 54 epg . Bivariate analyses of the infection status of other foodborne parasites and soil-transmitted helminths revealed that O . viverrini infection prevalence was higher in participants who were also infected with other parasites , particularly those infected with soil-transmitted helminths ( Fig 3A ) . Participants who had raw fish consumption behavior were found to be significantly associated with higher O . viverrini prevalence ( Fig 3A ) , while the intensity of infection was significantly higher for participants who had not been dewormed ( 121 . 19 epg ) than for those who had been ( 56 . 7 epg ) ( Fig 3B ) . However , such associations were not observed in the multivariate models . Past O . viverrini deworming history did not greatly influence O . viverrini infection prevalence ( Fig 3A ) , and no statistically significant association was observed between O . viverrini awareness and both the O . viverrini infection prevalence and intensity . The mean age of participants found infected with O . viverrini was 56 . 1 years while the mean age that of the uninfected was 52 . 0 . Age was positively associated with infection prevalence in the multivariate model . Among social factors ( Fig 4 ) , both the bivariate and multivariate analyses suggested that gender was significantly associated with infection prevalence , while farming as an occupation and poverty line were significantly associated with infection intensity . Bivariate analyses indicated that the explanatory variables of the presence of soil-transmitted helminths , location , age , gender , education , farming as an occupation , above or below the poverty line , and raw fish consumption were significantly associated with O . viverrini infection prevalence ( p < 0 . 05 ) . Other foodborne parasitic infection was associated with O . viverrini infection prevalence at p < 0 . 20 . These variables were hence entered into a multivariate regression model . Results of the multivariate logistic regression model showed that the likelihood of infection was higher among villagers in the south , increased with age , and was the greatest in males and in those who consumed raw fish ( Table 1 ) . However , the consumption of raw fish is higher ( 63 . 6% ) in the two villages located in the province of Nong Bua Lamphu ( N-river and N-reservoir ) when compared with the two villages studied in the province of Khon Kaen ( S-river and S-reservoir ) ( 52 . 2% ) ( χ2 = 7 . 31 , df = 1 , p-value = 0 . 01 ) . Males were also more likely to consume raw fish ( 63 . 5% ) than females ( 54 . 2% ) ( χ2 = 4 . 83 , df = 1 , p-value = 0 . 03 ) . O . viverrini awareness was found to be negatively associated with raw fish consumption , with 28 . 6% of participants who were unaware of O . viverrini reported not eating raw fish as compared to 48 . 7% of participants who were aware of O . viverrini ( χ2 = 7 . 86 , df = 1 , p-value = 0 . 01 ) . The variables of past O . viverrini deworming , waterbody type , farming as an occupation , and income relative to the poverty line were significantly associated with infection intensity ( p < 0 . 05 ) . Other foodborne parasitic infection and soil-transmitted helminth infection were associated with O . viverrini infection intensity at p < 0 . 20 . Consequently , these variables were examined together in a multivariate Quasi-Poisson regression model . Results of the multivariate Quasi-Poisson regression model revealed that increased infection intensity was found in participants from villages located closest to the reservoir , participants who were not farmers , and participants who chose not to disclose their income information ( Table 2 ) . Comparison of spatial variation in human and fish infection indicated that , similar to human infection intensity , fish infection density was significantly higher in the reservoir waterbody type than the river waterbody type ( t = 2 . 66 , df = 10 . 22 , p-value = 0 . 023 ) , but not significantly different between the north and south ( t = -0 . 04 , df = 9 . 39 , p-value = 0 . 97 ) . Participants who were unaware of O . viverrini were more than twice as likely to state that they ate raw fish because it tasted delicious , while those living in the S-river and S-reservoir villages were more likely to state that they ate it out of habit . The odds of eating raw fish because of friends decreased with every one-year increase in age . Males were also more than twice as likely to eat raw fish because of friends as females ( Table 3 ) . The likelihood of not eating raw fish in order to avoid being infected by O . viverrini was at least eight times higher among participants who were aware of the risks of infection . When asked about the reason for selecting the option of avoiding O . viverrini despite having responded “No” in the question regarding O . viverrini awareness , some of the participants explained that they had been encouraged by health volunteer workers or nurses to avoid eating raw fish because of the parasite , even though they were unsure about what the parasite was . Participants who knew about O . viverrini were also about seven times more likely to avoid eating raw fish due to other health reasons . Participants who did not know about O . viverrini were more likely to avoid eating raw fish because they dislike it . In addition , participants who said that they disliked raw fish were more likely not to have received treatment in the past , are younger , or live in either N-river or N-reservoir village ( Table 4 ) . In the S-river village , there have been no attempts to determine O . viverrini infections , including fecal examination , for at least 10 years . The priorities of the health center of the S-river village focused on the health effects of pesticide use and respiratory tract infections ( Table 5 ) . By comparison , in the S-reservoir village , fecal tests of O . viverrini infection were performed in 2007 and 2008 , with infection prevalence estimated at 0% and 2% , respectively ( Table 5 ) . Because of funding constraints , the direct smear technique was employed and only relatively few people were tested in the village . A recent fecal examination done in 2014 , in villages belonging to the same health jurisdiction as the S-reservoir village , yielded infection prevalence similar to that of the S-reservoir village in 2007 and 2008 . Similar to the S-river village , the S-reservoir health center staff did not view opisthorchiasis as a top priority; instead , diabetes and hypertension were the main concerns . In the N-river village , fecal examination was performed in 2011 and 2012 with infection prevalence estimated at 5 . 25% and 2 . 26% , respectively . The local health center prioritized teenage pregnancy and parasitic diseases as the top health concerns , with campaigns aimed at reducing rates of teenage pregnancy organized by health center staff . In the N-reservoir village , fecal examination was performed in 2012 and 2013 , and O . viverrini prevalence was estimated at 8 . 24% and 0 . 27% , respectively . Different from the south villages , the local health centers of both N-reservoir and N-river villages used the single Kato-Katz thick smear technique for O . viverrini infection test . Hypertension and diabetes , the top health concerns of the S-reservoir village , were also prioritized by the local health center staff of the N-reservoir village as the major health concerns , among work related injuries and gastrointestinal diseases ( Table 5 ) .
The results show that examining prevalence alone risks ignoring important parasitic infection trends . Although there was not a significant difference in O . viverrini infection prevalence between villages located near river inlets as compared with villages near the Ubolratana reservoir , infected villagers from near the reservoir had more than double the parasite intensity as compared with villagers from near the river . The pattern of infection intensities among humans thus matched the infection density of fish collected from these locations , with higher overall fish infection associated with the reservoir when compared with river inlets [42] , while there was no difference in infection density in the fish from the south or north reservoir . Distance to waterbody had an impact on where villagers tended to source the fish used in raw fish dishes; villagers who lived close to the river tended to procure fish from the river , while those living close to the reservoir tended to procure fish from the reservoir . As fish in the reservoir is more plentiful , people who lived farther away from both river inlets and reservoir also tended to rely upon fish caught from the reservoir [42] . Differences in fish infection levels depending on waterbody can affect the level of exposure of humans to the risk of infection , as is evident in the results; the average intensity of infection in the S-river village was low despite the lack of chemotherapy effort , and lower than both N-reservoir and S-reservoir villages , despite the recent chemotherapy treatment efforts in the N-reservoir village in particular . Using only infection prevalence as the measure of success for intervention effort can problematically lead to individuals with high infection intensities in low prevalence areas being overlooked . Even in individuals with low infection intensity , it is possible to develop CCA , as observed in this study and other biomedical studies . During the course of the survey in this study , a participant who was tested negative for infection was diagnosed with CCA and passed away shortly after diagnosis . The participant had a history of raw fish consumption and no record of past O . viverrini treatment . The apparent absence of O . viverrini eggs in the fecal sample could have been due to a low intensity of infection or bile duct obstruction [48] . Biomedical studies show that opisthorchiasis-induced inflammation can lead to the development of O . viverrini-induced advanced periductal fibrosis ( APF ) and CCA , which are driven by common cellular mechanisms , marked by elevated level of plasma interleukin-6 [49] . Participants with the most elevated level of plasma interleukin-6 were found to have an increased risk of 19 and 150 times of developing APF and CCA , respectively , as compared with other O . viverrini infected individuals with no detectable plasma interleukin-6 [49] . The risk of developing APF was found to increase with increased infection intensity [50 , 51] and duration of infection [50] . The findings in this study are of relevance to the concept of One Health , as they highlight the close relationship between the health of humans and that of the health/infection status of the animal hosts and physical environment . The findings identify the reservoir as an important target for opisthorchiasis intervention efforts and also underscore the importance of considering infection intensity in the understanding of the pathways through which the parasite is transmitted . Comparative multilocality studies are necessary to gain useful insights into the similarity or difference in relationships between opisthorchiasis and the environment in such reservoir systems . Far higher infection prevalence in males than in females accords with findings from some previous studies [52 , 53] . Little difference in prevalence between genders has also been reported [38 , 54] , although Hasewell-Elkin et al [54] notes that the frequency of high infection intensities may be higher among males . Males are also more likely to die from opisthorchiasis . As males are often the main income earners in families in Thailand , opisthorchiasis can exert a disproportionate economic toll on those affected [49] . One reason for a higher infection prevalence and intensity among males is likely to be their socializing behavior: raw fish dishes are often available for consumption at social gatherings of males . Infection prevalence also tended to increase with age from 21 years in this study . This finding is at odds with existing results , which indicate a plateauing of infection prevalence in the late teens followed by a decline in later life [34 , 35] . In some studies , fishermen and/or farmers were found to have higher infection prevalence [38 , 45] . This is because local fishermen often make a dish of Koi pla from their catch to celebrate that day’s fishing [23] . Farmers may also harvest fish from their rice paddies and prepare and consume the catch on the spot . Conversely , in this study , infection in fishermen and farmers was not significantly higher than for other occupations . Higher infection intensities were found only in participants who were not farmers . The participants who were not farmers have other occupations including contract worker , craftsman , fisherman , foodseller , office worker , stay at home , and others . There was however no significant difference in intensity among people who belonged to those occupation types and those who do not ( Fig 4 ) , suggesting that the observed higher infection intensity in people who are not farmers is not determined by a single occupation type . Higher infection intensity was also found only in participants who chose not to disclose their income information . No clear pattern was observed between occupation types and the disclosure of income ( S2 Table ) . Consequently , the socio-economic and demographic factors selected in this study could not identify the specific groups of people at risk of higher infection intensity . Recent chemotherapy efforts in three of the four villages may have weakened links with the range of factors that result in infections . While there was no significant difference in infection prevalence and intensity with O . viverrini awareness , O . viverrini awareness appeared to reduce the proportion of people who reported consuming raw fish . Participants who were aware of O . viverrini were also more likely to avoid raw fish consumption in order to avoid opisthorchiasis and other health issues , while participants who were unaware of O . viverrini were more likely to avoid consumption due to personal dietary preferences . Awareness campaigns may be able to affect personal health decisions to a certain extent , although more holistic effort is needed to tackle this long-standing issue . The pattern of villagers residing in the south of the reservoir being much more likely to be infected than villagers in the north may reflect inter-provincial differences in health priorities and treatment efforts . Use of praziquantel to treat infections can result in a sharp decline in prevalence [55] . For example , praziquantel administration brought about an immediate decline of O . viverrini prevalence from approximately 60% to 14% , while infections among the control , untreated group increased from 65% to 71% within the same time frame [56] . Likewise , a similar decline in prevalence ( 67% to 16% ) during three years of praziquantel administration is reported in Sripa et al [37] . Favorable results following chemotherapy-based treatment efforts do not necessarily imply long-term success of a campaign , however . Resurgence of infection has been observed soon after the cessation of a campaign [57] . This study revealed disparate healthcare concerns and opisthorchiasis control efforts . While the particular focus of the health center can be tailored to the needs of the villages within the sub-district [58] , funding allocation for healthcare is decided at provincial level . As the S-river and S-reservoir villages are part of districts in Khon Kaen province , where the Isarn Agenda was not implemented , limited funding was made available for opisthorchiasis control efforts . The lack of fecal examination for O . viverrini infection for the past decade may account for the high infection prevalence recorded in the S-river village . In the S-reservoir village , where a relatively limited treatment program was in place , infection prevalence was second only to the S-river village . Direct smear was used in both villages to test for infections as it is the most affordable , despite it being the least sensitive method [59] . The low sensitivity of the test may have led to erroneous results in the form of low prevalence data . Due to an apparent low prevalence of O . viverrini and increasing prevalence in chronic diseases , particularly diabetes and hypertension , it is not unexpected that the local health center staff increasingly prioritize such chronic diseases as their top health concerns . Coupled with the affordability and simplicity of testing for diabetes and hypertension , regular blood sugar tests and blood pressure tests are offered by the health centers , which may in turn shift the health focus of the villagers to such chronic diseases . Indeed , during the course of this study , the villagers and health center staff of the south villages have expressed that O . viverrini infection is not an issue of concern in the village . Coincidentally , fecal examinations were carried out by the health center staff in 2014 , the same year of this present study , to survey O . viverrini infections in villages within the sub-district of the S-reservoir village . As the health center knew about our intent of sampling in the S-reservoir village , the health center sampling was conducted in all villages of the sub-district except the S-reservoir village . Their survey reported an overall infection prevalence of 0 . 25% for those villages , which was close to the prevalence observed for the S-reservoir village in 2007 and 2008 ( Table 5 ) . Nevertheless , the prevalence was in stark contrast to the much higher levels obtained in this study ( i . e . , 18 . 45% for the S-reservoir and 40 . 21% for the S-river , Fig 3A ) , with the disparity likely due to the difference in sensitivity of fecal examination methods employed . Unfortunately , disparate healthcare focus , coupled with limited funding and a less sensitive opisthorchiasis screening method may have given villagers–and health center staff–a false impression of the importance of opisthorchiasis . Villagers who consume raw fish may be lulled into a false sense of security when any tests for O . viverrini infection generate negative results , despite the frequent consumption of raw fish , as mentioned by several villagers interviewed . Fecal examinations were carried out by the health centers concerned on a greater number of individuals in the N-river and N-reservoir villages . The diagnostic tests for O . viverrini infection also relied upon the more sensitive single Kato-Katz thick smear . Infection prevalence in the N-river village , at 5 . 3% and 2 . 3% in , respectively , 2011 and 2012 , was close to the 4 . 6% prevalence obtained in this study . Similarly , close results were found for the N-reservoir village ( 8 . 2% and 0 . 3% in , respectively , 2012 and 2013 , compared with 6 . 4% in this study ) . Despite the increased focus on opisthorchiasis and CCA after the implementation of Isarn Agenda , the lower infection prevalence , and no significant differences in awareness of the risks of O . viverrini infection , the proportion of participants who reported eating raw fish remained high in these two north villages . This study emphasizes the influence of health center focus on O . viverrini infection prevalence . While most of the prior work has emphasized on human behavior and social risk factors for helminth diseases including opisthorchiasis , healthcare focus and provision can greatly affect the risk of infection and the vulnerability of local populations [60] . Healthcare focus and provision can also sheds light on the varying stakeholders’ values determining the pathogenic landscape of diseases . Stakeholders’ values can influence the outcome and direction of healthcare provision as illustrated in the variation in provincial funding and health center focus in this study . In the cases of other disease intervention efforts that substantially rely on external donor funding , there can be potential conflicting interests between local population , funding donors , or even pharmaceutical companies [61] . The influences of healthcare focus and interests of other stakeholders thus need to be considered when deciphering the factors contributing to disease risks . The holistic approach in this study has identified important features of helminth parasitism , specifically , opisthorchiasis , which include the connectivity of animal hosts and humans facilitated by waterbodies and human behavior; human behavioral and physical environmental conditions that facilitated reinfection; and the influence of healthcare interventions on infection prevalence . Identification of such features of parasitism is an important contribution to the framework of One Health approach [23] , where consideration of helminth diseases has largely been overlooked [22] .
While the role of socio-economic , demographic , and behavioral risk factors on O . viverrini infection have been investigated in previous studies , this study identified other additional influential environmental and healthcare implementation risk factors in O . viverrini infection . Humans interact with the environment reciprocally , thereby influencing their risks of disease infection . Human modifications of the environment , particularly in the form of dam construction and reservoir creation , have changed the aquatic habitats for the O . viverrini intermediate fish hosts . As O . viverrini infection intensities in the fish vary across different waterbody types , humans affect their risks of consuming O . viverrini infected raw fish through fish procurement location preferences . In opisthorchiasis studies and that of other helminthiasis , infection intensity is still much less frequently reported . The importance of considering infection intensity in a cross-sectional infection study is exemplified in this study , owing to the critical role of intensity in the most serious forms of many infectious diseases , including opisthorchiasis , and in providing insights into parasite transmission risks . Healthcare focus can directly affect human infection prevalence through chemotherapy and indirectly guide villagers’ risk perceptions through the choices of health campaigns or monitoring programs . Chemotherapy in the case of helminthiases such as opisthorchiasis is only palliative , with re-infections quickly occurring if the underlying factors that expose humans to infection are not dealt with . There is thus a need for a holistic approach to integrate the factors accounting for the broader pathogenic landscape within which diseases such as opisthorchiasis persist . | Many of the large-scale helminth control programs around the world have primarily relied upon drug treatment . Reliance on drug treatment alone does not deal with the ultimate causes of infection , resulting in reduced infection levels only in the short-term . Re-emergence of infections and possibly even development of drug resistance in parasites are common once the programs have been terminated . There is thus a need for consideration of a broader context , including environmental influence and healthcare focus , within which infections thrive . This study examines the roles of a reservoir dam environment , inter-provincial healthcare focus variation , and socio-economic , demographic , and behavioral factors to highlight the varying roles of such factors contributing to this disease landscape . The findings underscore the importance of a holistic approach in infection studies in order to provide more sustainable disease treatment and elimination outcomes . |
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Biological systems are known to be both robust and evolvable to internal and external perturbations , but what causes these apparently contradictory properties ? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network . Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space , and a robust neighbor where perturbations have no effect on the attractor landscape . Using chemical inhibition and overexpression of nodes , we validated that perturbations affect the evolvable core more strongly than the robust neighbor . We also found that the evolvable core has a distinct network structure , which is enriched in feedback loops , and features a higher degree of scale-freeness and longer path lengths connecting the nodes . In addition , the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability , low species broadness , and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability , high species broadness , and oncogenes . Intriguingly , US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores .
Organisms have evolved so that their networks are robust against the effects of mutations , but evolvable in response to environmental changes [1]–[4] . Genetic mutations can profoundly change network structures , so mutational robustness of a network indicates how well the network can preserve its own dynamic behavior upon changes to its structure . In a similar way , evolvability of a network represents how well a network can produce appropriate dynamic behavior in response to environmental changes . Although robustness and evolvability are apparently opposite notions , they are simultaneously implicit in biological organisms . There are three main research results on mutational robustness and evolvability . First , mutational robustness facilitates evolvability as high mutational robustness increases the diversity of genotypes that can evolve [5]–[7] . Second , biological networks have evolved to have scale-free structures [8] and highly optimized tolerance ( HOT ) structures [9] so as to increase mutational robustness . Third , biological systems have evolved to possess modular structures [10]–[12] , critical regime [2] , [13] , hub nodes [14] , [15] , and hierarchical structures [15] so as to simultaneously increase mutational robustness and evolvability . These investigations mainly focused on either revealing the relationship between mutational robustness and evolvability or unraveling the structural characteristics of biomolecular regulatory networks which have evolved to increase robustness and evolvability . Although a number of studies have been done on mutational robustness and evolvability of the biomolecular regulatory networks [2] , [5]–[15] , many questions still remain unsolved . For instance , the evolutionary design principles by which the mutational robustness and evolvability are implemented in biomolecular regulatory networks are poorly understood . For this purpose , we need to identify not only the network components and their molecular interactions but also the dynamic properties of the network . Previous studies have shown that signaling networks can effectively be analyzed by considering the cellular phenotype as a high-dimensional state attractor [16]–[21] . An attractor is a mathematical concept representing a stable steady state or limit cycle ( a repeating sequence of states ) adopted by a dynamic system , in this case a signaling network [16]–[21] . Based on this concept a signaling network is mapped into an attractor landscape , where each point in this landscape represents one state of the network defined by a set of state values containing the activity states of all signaling proteins in the network [16]–[21] . Although an attractor landscape of a signaling network is composed of various attractors , cellular behavior typically reaches a dominant stable state known as “primary attractor” , which represents the normal cellular state or phenotype [16]–[21] . The set of states , which converge to an attractor , is called the “basin of attraction” and the primary attractor has the biggest basin of attraction [16]–[21] . In this paper , we show that the human signaling network consists of a subgroup of interactions for mutational robustness and the other subgroup of interactions for evolvability . For this purpose we used an integrated human signaling network constructed by Helikar et al . [22] , where the connections between nodes ( edges ) are described by well-characterized Boolean logics derived from mechanistic data in the biochemical literature , which based on a set of logical rules specify whether a connection exists or not . This network model is composed of representative signal transduction pathways regulated by three major receptor families including receptor tyrosine kinases , G protein-coupled receptors , and integrins . It was shown that the Boolean dynamic model of this network has the ability to replicate known qualitative behaviors of the actual human signaling network . Based on the Boolean network model , we first identified an attractor landscape of the model , and then we decomposed the network into two subgroups of interactions: the evolvable core , which preserves the basin of the primary attractor in state space , and the robust neighbor , which has no influence on the basin of the primary attractor . Decomposition of the network elucidated that the evolvable core has more scale-freeness than the robust neighbor and that the robust neighbor contributes to reducing the characteristic path length of the evolvable core , thereby constituting the HOT structure . We validated the theoretical predictions related to the different effects of perturbations in the evolvable core compared to the robust neighbor through biochemical experiments . Our network decomposition analysis further indicates that the genes with high evolvability score are associated with evolvability-related properties whereas those with high robustness score are associated with robustness-related properties . Intriguingly , US Food and Drug Administration ( FDA ) -approved drug targets have high evolvability score whereas experimental drug targets ( targets of drugs in the pipeline or not yet approved by the FDA ) [23] have high robustness score . Thus , the decomposition of a biomolecular regulatory network into an evolvable core and a robust neighbor can not only reveal the evolutionary design principle of the network , but also help identifying potential drug targets .
The attractor landscape is a useful representation of phenotypes of biological systems [2] . Hence we defined the two subgroups of interactions ( the evolvable core and robust neighbor ) of a biomolecular regulatory network based on the attractor landscape ( see Materials and Methods for the definition of these subgroups of interactions ) . In order to decompose the human signaling network ( Fig . 1A ) into the evolvable core and robust neighbor , we first identified the attractor landscape of the network through Boolean simulation . Since the human signaling network consists of 139 nodes , we would have to calculate transitions between 2139 states to obtain its attractor landscape , which is unfeasible . Therefore , we used 10 , 000 randomly selected initial states to identify the approximated attractor landscape ( see Materials and Methods ) . The reason why we used the sampling size 10 , 000 is because it is feasible , and because we could show that for sample sizes 10–100 fold bigger than 10 , 000 the distributions of estimated ( relative ) basin sizes are very similar ( Fig . S1 ) . From this random sampling approach , we obtained an approximated landscape of the human signaling network . From the 10 , 000 initial states , we obtained 135 attractors and found one primary attractor whose basin contained approximately 56% out of the 10 , 000 initial states ( Fig . S1A ) . This primary attractor was a limit cycle composed of a repeating sequence of six states ( Table S1 ) . In these six states , 123 nodes were ‘OFF’ and the remaining 16 nodes were ‘ON’ at least once in their cyclic state transitions . The sub-network composed of these 16 nodes and their interactions consists of three separate modules: a module for phosphatidylinositol signaling , a module for Raf activation ( composed of three inactivated forms of Raf and PP2A ( Protein serine/threonine Phosphatase 2A ) ) , and a module for PKC ( Protein Kinase C ) activation ( composed of PKC_primed which is an inactivated form of PKC ) ( Fig . S2 ) . The ‘ON’ nodes in the primary attractor are related to precursors of second messengers or inactive forms of kinases . In other words , the primary attractor can be considered as a ‘ready’ state of the signaling network , which might be the nominal condition of cell signaling [24]–[28] . Next , we developed an algorithm for the decomposition of the human signaling network ( see Materials and Methods ) , which allowed us to identify the evolvable core with 408 edges ( Fig . 1B ) and the robust neighbor with 167 edges ( Fig . 1C ) . The lists of links in the evolvable core and robust neighbor are provided in Table S2 and Table S3 , respectively . We obtained similar results when using different random seeds of initial states ( Fig . S3A ) and deletion order ( Fig . S3B ) . In order to compare the attractor landscape of the original human signaling network and its evolvable core , we projected all the obtained attractor states , which correspond to 139-dimensional vectors , onto a 2-dimensional plane using principle component analysis ( PCA ) . Fig . 1D shows the projected landscape of the 135 attractors of the original network . Using the same 10 , 000 initial states as used to find the attractors of the original network , we obtained 106 attractors for the evolvable core . Then we projected all the obtained attractor states onto the same plane in Fig . 1D , after applying the same linear transformation as in the PCA analysis of the original network . Fig . 1E shows this projected landscape of attractors of the evolvable core . The attractor landscape of the original network and that of the evolvable core are very similar despite the fact that the evolvable core was obtained by removing edges whose deletion did not change the landscape of the primary attractor only . Furthermore , the approximated relative basin sizes of each attractor were also similar ( Figs . 1D and E ) . These results imply that the landscape of the evolvable core largely represents the landscape of the original human signaling network . We wanted to experimentally validate the theoretical prediction that perturbations in the evolvable core have a stronger effect on the network than perturbations in the robust neighbor . Therefore , we carried out a series of biochemical experiments where we induced perturbations through chemical inhibition or overexpression of the nodes with high evolvability score and those with high robustness score ( see Materials and Methods and Table S4 for the definition of these scores ) , and compared the phosphorylation of four network outputs: ERK , Akt , p38 and JNK ( Figs . 2 and S4 ) . To perturb the nodes with high evolvability score , we overexpressed the constitutive active HRasV12 mutant ( Ras perturbation ) , the HRasV12C40 mutant ( PI3K perturbation ) , Raf1 ( Raf perturbation ) , Src , and ASK1; and we carried out chemical inhibition with blebbistatin ( Actin perturbation ) . To perturb the nodes with high robustness score , we overexpressed the HRasV12G37 mutant ( RalGDS perturbation ) , MLK2 , MLK3 , and MKK6; and we carried out chemical inhibition with the drug ML7 ( Myosin perturbation ) . All kinases were GFP tagged , and each experiment was carried out in triplicate . A representative western blot is shown in Fig . S4 . To facilitate comparison between the different types of experiments all measurements were quantified and normalized to the value of the respective controls ( untreated cells or cells expressing a control plasmid ) . The results show that the overall normalized perturbation effect of the evolvable core is higher than that of the robust neighbor ( Figs . 2A , 2B and S5 ) . The average of all the perturbation effects for the evolvable core was significantly higher ( p-value = 1 . 09E-2 ) than that for the robust neighbor ( Fig . 2C ) . In the previous subsections , we showed that the human signaling network could be decomposed into an evolvable core and a robust neighbor . The attractor landscape of the evolvable core is very similar to that of the original network , and the edges of the robust neighbor do not affect the landscape of the original network . How do the two sub-networks differ in terms of structure ? The interlinked structure of feedback loops in a network is an important factor determining the characteristics of the attractor landscape , such as the number of attractors and the distribution of basin sizes [29] , [30] . Hence , we first compared the numbers of self-feedback loops ( Fig . 3A ) , two-node feedback loops ( Fig . 3B ) , and three-node feedback loops ( Fig . 3C ) in the evolvable core sub-network to the numbers of these loops in similar random edge-deleted sub-networks; and also compared the numbers of these loops in the robust neighbor sub-network to the numbers of these loops in similar random edge-selected sub-networks ( see Materials and Methods for the definition of these sub-networks ) . For this purpose we constructed 100 random-deletion sub-networks by taking the human signaling network and randomly deleting 167 edges; and 100 random-selection sub-networks by taking sub-networks composed of 167 edges randomly selected from the human signaling network . Subsequently , we calculated the average number of feedback loops in the random-deletion sub-networks and compared them to the numbers in the evolvable core sub-network; and calculated the average number of feedback loops in the random-selection sub-networks and compared them to the numbers in the robust neighbor sub-network . We found that the evolvable core sub-network contains significantly more feedback loops compared to the random-deletion sub-networks , whereas the robust neighbor sub-network contains significantly less feedback loops compared to the random-selection sub-networks ( Figs . 3A , 3B , and 3C ) . We obtained similar results when using different random seeds of initial states ( Figs . S5A , S5C , and S5E ) and deletion order ( Figs . S5B , S5D and S5F ) . Scale-freeness is one of the representative characteristics of biological networks . We calculated the degree heterogeneity and the degree distribution [31] , [32] as a measure of this scale-freeness . As a result , we found that the degree heterogeneity of the evolvable core sub-network is significantly higher than that of random-deletion sub-networks ( Fig . 3D ) . The degree distribution of the original network is similar to that of an Erdös random network [33] which has many middle-degree nodes , whereas the degree distribution of the evolvable core sub-network is similar to that of a scale-free network [33] which has much more low-degree nodes ( Fig . 3E ) . This implies that many middle-degree nodes were deleted during the link-deletion process that identified the evolvable core . In fact , we verified that the ratio of robust neighbor links to the whole links for the middle-degree ( from 6 to 9 ) nodes is higher than those for the low-degree ( from 2 to 5 ) and high-degree ( from 10 to 42 ) nodes ( Fig . 3F ) . We obtained similar results using different random seeds of initial states ( Figs . S6A and S6C ) and deletion order ( Figs . S6B and S6D ) . It is well-known that biological networks which transfer information such as cell signaling pathways satisfy the small-world property as well as scale-freeness [34] . In order to explore the small-world property of the evolvable core sub-network , we compared the characteristic path length [32] of the evolvable core sub-network to 100 random-deletion sub-networks . We found that the characteristic path length of the evolvable core sub-network is larger than those of random-deletion sub-networks ( Fig . 3G ) . This implies that appending the robust neighbor to the evolvable core increases the small-world property . We obtained similar results using different random seeds of initial states ( Fig . S6E ) and deletion order ( Fig . S6F ) . In order to verify this , we also compared the network structures of the robust neighbor sub-network to the 100 previously mentioned random-selection sub-networks . The result shows that the number of connected components ( Fig . 3H ) and the characteristic path length ( Fig . 3I ) of the robust neighbor sub-network are significantly smaller than those of random-selection sub-networks . We obtained similar results using different random seeds of initial states ( Figs . S7A and S7C ) and deletion order ( ) . In summary , the robust neighbor sub-network contains many middle-degree nodes that are closely connected to each other . Hence we conclude the structure of the robust neighbor sub-network is similar to the HOT structure [9] , [35] , and has been evolutionarily designed to be robust to changes or a targeted attack . The evolvable core is defined by the minimal subgroup of interactions that preserves the attractor landscape and the robust neighbor is defined by the subgroup of interactions satisfying that deletion of any link in the subgroup of interactions does not affect the attractor landscape . From this definition , we speculated that a link perturbation on the evolvable core could induce a new phenotype with higher probability than that on the robust neighbor . In order to confirm this conjecture , we investigated the relationship between the evolutionary rate and the evolvability score for each node , and found a significant positive correlation between them ( Fig . 4A ) . Furthermore , we found that the species broadness is significantly negatively correlated with the evolvability score ( Fig . 4B ) . This result implies that mutations in the evolvable core can induce new phenotypes more frequently , since the mutation of genes with high evolutionary rates can facilitate positive selection [36] and genes with low species broadness result from rapid evolution [37] . We again obtained similar results using different random seeds of initial states ( Fig . S8A and S8C ) and deletion order ( Fig . S8B and S8D ) . Furthermore , we looked at genes related to the immune system and oncogenes . The immune system is known to rapidly evolve in order to cope with rapidly evolving pathogens [38] , [39] . Oncogenes denote genes that promote cancer when mutated or overexpressed . Cancer is a system which utilizes some of the robustness mechanisms of normal tissues and further evolves them to become more robust due to the greatly enhanced ability of generating genetically heterogeneous cells that increase the population fitness under selection [40] . Therefore , the genes related to the immune system might have higher evolvability score than other genes whereas the oncogenes might have higher robustness score than the other genes . As expected , the genes related to immune system have a high normalized average evolvability score ( Fig . 4C ) , whereas oncogenes have a high normalized average robustness score ( Fig . 4D ) . These findings support the notion that the evolvable core is related to evolvability , and the robust neighbor is related to robustness in terms of biological data . We obtained similar results using different random seeds of initial states ( Figs . S9A and S9C ) and deletion order ( Figs . S9B and S9D ) . Because a link perturbation on the evolvable core could be more effective in changing the cellular phenotype than a link perturbation on the robust neighbor , we can speculate that drug targets might have higher evolvability scores than non-drug targets . We found that the FDA-approved drug targets have a high normalized average evolvability score ( Fig . 5A ) . Similarly , we can expect that the experimental drug targets might have a high normalized average evolvability score . Surprisingly , we found that the experimental drug targets have a high normalized average robustness score ( Fig . 5B ) . Since many drugs have multiple target proteins , we considered combination of targets for all the FDA-approved drugs and the union of targets of all the experimental drugs . The overlap between 1330 targets of FDA-approved drugs and 765 targets of experimental drugs is only 297 . To show that this overlap does not influence our result on the relationship between evolvability score and drug target , we further analyzed the FDA-approved drug targets that are not experimental drug targets and the experimental drug targets that are not FDA-approved drug targets , and obtained the same results ( Fig . S10 ) . Moreover , we have analyzed the evolvability scores of all the targets of each multi-target drug and calculated the standard deviation of the scores . We found that the average of the standard deviations for all multi-target drugs ( 0 . 0557 , see Table S5 ) is much smaller than the standard deviation of the evolvability scores of all the nodes in the network ( 0 . 298 ) . This indicates that most of the targets are still included either in the evolvable core ( for the FDA-approved drugs ) or in the robust neighbor ( for the experimental drugs ) and mixed inclusion is uncommon even for the multi-target drugs . Why do FDA-approved drug targets and experimental drug targets have such contrasting scores ? To answer this question , we investigated the distribution of receptors and kinases since most FDA-approved drug targets are membrane proteins such as receptors whereas the experimental drug targets also include proteins localized in various cellular compartments [23] . Interestingly , we found that the receptors have a high normalized average evolvability score ( Fig . 5C ) whereas the kinases have a high normalized average robustness score ( Fig . 5D ) . This implies that the deletion of a link connected to a receptor is more likely to significantly change the cellular phenotype than the deletion of a link connected to a kinase . It also explains why FDA-approved drug targets and experimental drug targets have such contrasting scores and why they have different cellular compartmental distributions [23] . We obtained similar results using different random seeds of initial states ( Figs . S11A , S11C , S11E , and S11G ) and deletion order ( Figs . S11B , S11D , S11F , and S11H ) .
Here we show that the human signaling network can be decomposed into an evolvable core and robust neighbor by analyzing the attractor landscape . We also show that the two subgroups of interactions are different in terms of structure and biological meaning . We further validated salient properties of and predicted associations with the evolvable core and robust neighbor experimentally through specific chemical inhibition or overexpression of wild-type and mutant proteins . Like any model our model is not a one-to-one description of the real biological network but a simplified abstraction that can explain general network properties . Thus , we would not expect that every detail can be experimentally confirmed; this even is rarely possible in classic biochemical experiments which test only one or few components of a network . Thus , the experimental work has to be taken as a validation of the general properties of the network , and viewed in the context of the overall results . The experimental results simply add another piece of information to the usefulness of the approach to elaborate network structures with different properties through modeling . One remarkable point in Fig . 2A is that the perturbation effect of ASK1 was most significant . This is particularly meaningful if we consider the following facts: ( i ) Only ASK1 among the six perturbed evolvable core nodes in the experiments is connected to all of the four output nodes through evolvable core links; ( ii ) The normalized proportion of the paths in the evolvable core from ASK1 to each output node over such paths in the original network is higher than those of the other five perturbed evolvable core nodes , which means that most of the paths from ASK1 to output nodes remain invariant during the network reduction to evolvable core ( Table S6 ) . Hence , the experimental result in Fig . 2A is meaningful even though it cannot be a full experimental validation of the simulation results . The proposed concept and analysis can be applied to any other biomolecular regulatory network that was shaped by evolution . In the conceptual framework of the attractor landscape , deletion of a robust neighbor link causes cryptic genetic variation [6] , whereas deletion of an evolvable core link changes the phenotype of the biological system represented by the attractor landscape ( Fig . 6 ) . Hence , a molecule which has many robust neighbor links would have robustness-related properties , whereas a molecule which has many evolvable core links would have evolvability-related properties as we found in the human signaling network . Wagner [41] showed that genotypic robustness and genotypic evolvability share an antagonistic relationship , whereas phenotypic robustness promotes phenotypic evolvability . In this regard , the coexistence of evolvable core and robust neighbor in the human signaling network implies that both phenotypic robustness and phenotypic evolvability are reflected on the human signaling network , since the concepts of evolvable core and robust neighbor of the human signaling network are related to phenotypic evolvability and phenotypic robustness , respectively . One might consider that the concept of ‘robust neighbor link’ is similar to that of ‘redundant link’ in the context of canalizing function , which is a function of multiple input variables with the property that one of its inputs can solely determine the output value regardless of other inputs [20] , [42] , [43] ( see Fig . S12A ) . However , they are different because the robust neighbor links can be identified by considering global dynamics whereas the redundant links can only be identified by examining the local relationship between the regulatory inputs and the resulting output of a particular node . To further clarify this , we determined and compared both robust neighbor links and redundant links in our example network . We identified 325 redundant links and found that 192 links out of these 325 redundant links are evolvable core links , which are not redundant when considering the attractor landscape ( i . e . not robust neighbor links ) ( Fig . S12B and Table S7 ) . Aldana et al . [2] defined that a network is evolvable if , as a result of perturbations , new attractors emerge . On the other hand , we defined that a network is evolvable if , as a result of perturbations , the attractor landscape is significantly changed in terms of the primary attractor . To investigate the relationship between these two concepts , we considered 408 sub-networks obtained by deleting the 408 evolvable core links one by one . By simulating each of the 408 sub-networks , 3 , 500 new attractors were obtained from 10 , 000 initial states that were included within the basin of the primary attractor of the original network . We found that about 88% of the 3 , 500 new attractors are not the attractors of the original network . This implies that our concept of evolvability is closely related to the concept of evolvability suggested by Aldana et al [2] . However , our concept of evolvability is broader and more inclusive in that a network is evolvable if , as a result of perturbations , an initial state which was included in the basin of attraction of the primary attractor of the original network converges to any of other attractors of the original network or a new one . In the literature , multiple definitions of evolvability were suggested [41] , [44]–[51] . In general , a system is said to be evolvable if the genotypic variation in the system can produce heritable phenotypic variation [41] . We think that the difference among the multiple definitions is caused by the different definition of the phenotypic variation . Aldana et al . considered the emergence of a new attractor as phenotypic variation . On the other hand , we considered the variation of attractor landscape as phenotypic variation since phenotypic variation includes not only the emergence of new attractors but also the transition between attractors [16] , [17] , [52] , [53] . In this study , we identified the evolvable core and robust neighbor of the human signaling network on the basis of its inherent network dynamics with all the state values of input nodes ( i . e . nodes without any incoming link ) set to ‘OFF’ and synchronously updating the Boolean functions . To examine whether this result might depend on the input conditions or asynchronous update of Boolean functions , we further carried out extensive simulations for various input conditions and asynchronous update of Boolean functions . This also links the biochemical data better with the simulations , as to see activation , and subsequent differences in activation , of the measured output nodes stimulation with growth factors , such as serum , is necessary . It turns out that the decomposition into the evolvable core and robust neighbor does not depend on the input conditions or synchronous/asynchronous update of Boolean functions , and that the evolvable core and robust neighbor are mostly invariant and do not much depend on such conditions ( Figs . S13 and S14 ) even though the primary attractor might change ( Tables S8-S18 ) . This change in primary attractor upon different input conditions makes biological sense , as specific parts of the network are switched ON , in addition to the nodes that are already switched ON with all input nodes ‘OFF’ . In other words , the network switches from a ‘ready’ to an ‘active’ state . Helikar et al . [22] showed that there is an emergent function of information processing in the human signaling network . We have further investigated whether such an emergent function is preserved in the evolvable core and found that it is well-preserved ( see Fig . S15 and Table S19 ) . This suggests that the evolvable core might be the minimal structure with the complexity that can create such an emergent function . Previous research suggested that biological networks have evolved to have scale-free [8] and HOT [9] structures so as to increase mutational robustness . However , these studies could not unravel the dynamic characteristics underlying the mutational robustness of the biological networks since they only focused on topological characteristics . Our results about the topological difference such as degree heterogeneity of the evolvable core and low characteristic path length of the robust neighbor , shed light on the previous results in terms of network dynamics that explain the eventual state transition of molecular components in the network in a collective way since we used attractor landscape analysis to decompose the network into the two subgroups of interactions . Furthermore , we show that the two subgroups of interactions are different in terms of biological meaning as well as topological characteristics . Even though we divided the network into the two subgroups of interactions based on the Boolean simulation only , the two subgroups of interactions are distinguished from each other in terms of many biological properties such as evolutionary rate , species broadness , and relationships to the immune system , oncogenes , FDA-approved drug targets , experimental drug targets , receptors , and kinases . To examine the potential generality of our result , we first have analyzed another large-scale signaling network example ( JAK/STAT signaling network ) obtained from the curated signaling pathway database SignaLink [54] . For this example , we have also identified and analyzed the evolvable core and robust neighbor on the basis of Boolean modeling with the logical rules adopted from Li et al . [55] . Secondly , we have analyzed another curated logic model ( keratinocyte signaling network ) [56] . As a result , we found that the evolvable cores and robust neighbors of the JAK/STAT signaling network and the keratinocyte signaling network show consistent results in terms of genetic properties ( evolutionary rate , species broadness , relationship with immune system , and relationship with oncogene , see Fig . S16 and S17 ) . This segregation will be useful for understanding large-scale genomic data , which are now being generated , by predicting which mutations or gene deletions are likely to affect the phenotype . Moreover , we could validate the existence of the evolvable core and robust neighbor through biological experiments . In our previous research about network kernels [37] , we showed that a signaling network can be divided into a kernel and non-kernel . The kernel represents a part that preserves transient dynamics , whereas the evolvable core here represents a part that preserves steady state dynamics . Although these two concepts seem to be similar in terms of preserving some dynamic behavior , they are very different in terms of evolutionary rates and drug targets . Further studies will be needed to unravel the relationship between the kernel and the evolvable core of various biological networks .
We adapted the Boolean network model of the human signaling network [22] composed of 139 nodes and 575 links ( Fig . 1A ) . In the Boolean network model , each node is associated with a logic table that determines the state of the node for a given input node set [16]–[21] , except the nodes without any incoming link [22] . Network dynamics were simulated by updating all the Boolean functions simultaneously from the initial state to the corresponding final attractor state , where a network state is a collective binary representation of all node variables [16]–[21] . The nodes without any incoming link can be considered as input nodes of the network such as ligands of the signaling network . We fixed the state values of those nodes as ‘OFF’ at each time step since we wanted to analyze nominal dynamics of the system without any external input signal . The evolvable core of a network is defined by the subgroup of interactions satisfying the condition that deletion of any edge in this subgroup of interactions causes a significant change of the attractor landscape of the original network by changing its primary attractor . The robust neighbor is defined by the subgroup of interactions satisfying that deletion of any edge in the subgroup of interactions does not affect the attractor landscape of the original network much , by preserving its primary attractor . The evolvable core ( robust neighbor ) sub-network is defined by a sub-network composed of the evolvable core ( robust neighbor , respectively ) links and all the nodes of the original network . The evolvability ( robustness ) score of a node is defined by the proportion of evolvable core ( robust neighbor , respectively ) links connected to the node over all links associated with the node . We identified the evolvable core and the robust neighbor of a Boolean network through the following processes ( see Fig . S17 ) : ( i ) A Boolean network can be represented by a directed graph , where V is a set of nodes , E is a set of edges , and L is a set of logic tables . Each edge can be represented by where vi is a start node and vj is an end node . The logic table of node vj can be represented by l ( vj ) and the reduced logic table of node vj when the state value of vi is x ( 0 or 1 ) can be represented by . The logic table of each node is a set of output node states for each combination of input node states . ( ii ) We randomly sample 10 , 000 initial states which converge to the primary attractor in the original network . ( iii ) We then consider a copy , termed ‘Reduced Network’ , of the original network . For each edge we remove the insignificant edges in which , and update the Reduced Network with the edge-removed network . ( iv ) We then define a set of edges for the reduction which is empty initially . ( v ) For each edge in the Reduced Network we test if the 10 , 000 initial states are attracted to the primary attractor in the Reduced Network with the selected edge removed . If the primary attractor is preserved , we add the selected edge to . ( vi ) We then randomly select an edge from and test if the 10 , 000 initial states are attracted to the primary attractor in Reduced Network with the selected edge removed . If the primary attractor is preserved , we update the Reduced Network with the edge-removed network . ( vii ) We repeat the above process ( vi ) until the Reduced Network cannot be reduced any more . After all , the Reduced Network becomes the evolvable core and the sub-network obtained by subtracting the evolvable core links from the original network becomes the robust neighbor . The experiments with chemical inhibitions and overexpression of HRas and HRas mutants were performed in HeLa cells ( ATCC CCL-2 ) , the overexpression of GFP-tagged kinases was performed in HEK293 ( ATCC CRL-11268 ) . ML7 ( I2764 ) and blebbistatin ( B0560 ) were purchased at Sigma . Antibodies against ppERK ( M8159 ) and total ERK ( M5670 ) were from Sigma . Antibodies against pJNK ( 9251 ) , total JNK ( 9252 ) , pP38 ( 9211 ) , total P38 ( 9212 ) , pAKT ( 9275 ) and total Akt ( 9272 ) were from Cell Signaling . The antibody against Ras ( OP40 ) was from Calbiochem . All GFP-tagged kinase plasmids were generated through Gateway® cloning ( Invitrogen ) , using the pDONR-223 gateway entry vectors from the ‘Human Kinase Open Reading Frame collection’ ( Addgene 1000000014 ) , and the destination vector 221 pCS-EGFP . As a control plasmid we used the same destination vector , inserted with EGFP ( pDONR-EGFP as entry vector ) . HRas mutants in the pDCR vector were a kind gift from Dr . Pierro Crespo . In these experiments expression of the empty vector was used as a control . For the chemical inhibitions , HeLa cells were treated with either 2 µM ML7 for MLCK inhibitions or 1 µM blebbistatin for myosin inhibition , 3 hours before lysis . For all overexpression experiments , cells were transfected 24 hours after seeding and 48 hours before lysis . In the experiments with chemical inhibitions and HRas mutant overexpression in HeLa cells , the cells were starved for 3 hours , and stimulated with 10% fetal bovine serum for 1 hour for JNK activation . Cells were starved for 16 hours , and stimulated with 10% fetal bovine serum for 10 minutes for ERK , Akt and P38 activation . All measurements upon overexpression of the GFP-tagged kinases in HEK293 were performed in growing conditions , 48 hours after transfection . All cells were lysed in 20 mM HEPES , 150 mM NaCl , 1% NP40 . SDS-PAGE was performed , followed by Western blotting using the antibodies against pJNK , pP38 , total ERK or pAkt or pan-Ras . The membranes were then stripped with 1%SDS , 0 . 2 M glycin at pH 2 . 5 , re-blocked in 4%BSA in TBS-T and incubated with antibodies against total JNK , total P38 , ppERK or total AKT . We chose our outputs according to the following criteria , i . e . that ( i ) they are linked to nodes in the evolvable core and robust neighbor enabling a comparative assessment of perturbation experiments; and ( ii ) they are experimentally tractable . This is how we selected to measure ERK , Akt , p38 , and JNK activation . In addition , all of the outputs are linked to the primary attractor: ERK activation is linked closely to Raf ( which is the main upstream activator of ERK ) as well as PKC; Akt activation is closely linked to phosphatidylinositol signaling; and the stress activated MAP kinases JNK and p38 are closely linked to PKC and also phosphatidylinositol signaling . The degree heterogeneity was defined by the variance of the degree distribution divided by the average of the degree distribution [31] . The characteristic path length was defined by the average of the shortest path lengths over all pairs of nodes [32] . The evolutionary rates were defined by the ratios of the non-synonymous substitution rates ( dN ) and the synonymous substitution rates ( dS ) for homologous gene pairs in human and mouse , and we obtained the evolutionary rates of 13815 genes from the Human PAML Browser [57] . We defined the species broadness of a gene as the number of species in which homologs of the gene exist . The homolog information of 20 species was extracted from the HomoloGene database [58] in the NCBI and the species broadness of 19571 genes was obtained from the database . In order to investigate the correlation between the evolutionary rates or species broadness and the evolvability score , we mapped each node of the network into the corresponding genes based on EntrezGene IDs ( see Table S4 ) . Some nodes such as PIP_4 do not have corresponding EntrezGene ID , some nodes such as MKK3 correspond to one EntrezGene ID , and some nodes such as MLCP correspond to multiple EntrezGene ID . Based on the transformation , we obtained 631 genes which have EntrezGene ID . Among 631 genes , 549 genes have evolutionary rate values and 629 genes have species broadness values . The list of genes related to immune system was selected as the genes classified into the gene ontology ( GO ) term ‘immune system process ( GO:0002376 ) ’ [59] . This list contains 944 genes related to immune system , 109 of which are included in the human signaling network ( Table S20 ) . The list of oncogenes was obtained from the OMIM database [60] in the NCBI . This list contains 51 oncogenes , 12 of which are included in the human signaling network ( Table S21 ) . The drug target list was obtained from the DrugBank database [61] . This list contains 1330 FDA-approved drug targets , 168 of which are included in the human signaling network ( Table S22 ) and 765 experimental drug targets , 52 of which are included in the human signaling network ( Table S23 ) . The list of genes related to receptor or kinase was obtained on the basis of GO terms , ‘receptor activity ( GO:0004872 ) ’ or ‘kinase activity ( GO:0016301 ) . This list contains 1688 genes related to receptors , 177 of which are included in the human signaling network ( Table S24 ) and 770 genes related to kinases , 107 of which are included in the human signaling network ( Table S25 ) . In order to calculate the normalized average evolvability or robustness score , we mapped each node of the network into the corresponding genes based on EntrezGene IDs and calculated the average of the proportions of evolvable core ( or robust neighbor ) links of the resulting 631 genes . The normalized average evolvability ( robustness ) score is defined as the average of the proportions of evolvable core ( robust neighbor , respectively ) links for a particular gene group ( genes related to immune system , oncogenes , FDA-approved drug targets , experimental drug targets , receptors , or kinases ) divided by the average of the proportions of evolvable core ( robust neighbor , respectively ) links for the total 631 genes . We performed one-sided two sample t-test to compare the number of feedback loops ( Figs . 3A–C ) , degree heterogeneity ( Fig . 3D ) , and the characteristic path length ( Fig . 3G ) for the evolvable core and random-deletion sub-networks; the number of connected components ( Fig . 3H ) and characteristic path length ( Fig . 3I ) for the robust neighbor and random-selection sub-networks; the average perturbation effect ( Fig . 2C ) . We performed Pearson's correlation test to analyze the significance of the correlation between the evolutionary rates ( Fig . 4A ) or species broadness ( Fig . 4B ) and the evolvability score . In order to compare the normalized average evolvability ( robustness ) score of a particular gene group ( the genes related to the immune system process , oncogenes , FDA-approved drug targets , experimental drug targets , receptors , or kinases ) with that of the random control group , the permutation test with 100 , 000 permutations was performed . The random control group was obtained by randomly selecting genes out of 631 genes where the sample size was fixed as the size of the given particular gene group . We have implemented the proposed decomposition algorithm as software . It is available from the http://sbie . kaist . ac . kr/software and as part of the Supplementary Materials . | Biological systems are known to be robust and evolvable to internal mutations and external environmental changes . What causes these apparently contradictory properties ? This study shows that the human signaling network can be decomposed into two structurally distinct subgroups of links that provide both evolvability to environmental changes and robustness against internal mutations . The decomposition of the human signaling network reveals an evolutionary design principle of the network , and also facilitates the identification of potential drug targets . |
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MicroRNAs ( miRNAs ) are stable , small non-coding RNAs that modulate many downstream target genes . Recently , circulating miRNAs have been detected in various body fluids and within exosomes , prompting their evaluation as candidate biomarkers of diseases , especially cancer . Kaposi's sarcoma ( KS ) is the most common AIDS-associated cancer and remains prevalent despite Highly Active Anti-Retroviral Therapy ( HAART ) . KS is caused by KS-associated herpesvirus ( KSHV ) , a gamma herpesvirus also associated with Primary Effusion Lymphoma ( PEL ) . We sought to determine the host and viral circulating miRNAs in plasma , pleural fluid or serum from patients with the KSHV-associated malignancies KS and PEL and from two mouse models of KS . Both KSHV-encoded miRNAs and host miRNAs , including members of the miR-17–92 cluster , were detectable within patient exosomes and circulating miRNA profiles from KSHV mouse models . Further characterization revealed a subset of miRNAs that seemed to be preferentially incorporated into exosomes . Gene ontology analysis of signature exosomal miRNA targets revealed several signaling pathways that are known to be important in KSHV pathogenesis . Functional analysis of endothelial cells exposed to patient-derived exosomes demonstrated enhanced cell migration and IL-6 secretion . This suggests that exosomes derived from KSHV-associated malignancies are functional and contain a distinct subset of miRNAs . These could represent candidate biomarkers of disease and may contribute to the paracrine phenotypes that are a characteristic of KS .
MicroRNAs ( miRNAs ) are small , non-coding RNAs that are capable of fine-tuning gene expression through translational repression and/or mRNA degradation . In the past , miRNAs have emerged as important regulators in nearly every cellular process , but perhaps the largest biological consequence of miRNA dysregulation is in cancer [1] , [2] , [3] , [4] , [5] , [6] . The relationship between intra-tumor miRNA signatures and cancer progression has been well established , leading to the discovery of specific miRNAs or miRNA clusters that modulate gene expression in cancer [7] , [8] , [9] . We and others have shown that miRNA signatures can classify tumors into distinct classes and are predictive of disease outcome [3] , [4] , [6] , [10] , [11] . In our prior study , we found that the host miRNA profile differed depending on the degree of transformation among cells , even though all samples were infected by the same virus and thus expressed similar levels of viral miRNAs [6] . This suggests that host miRNA profiles impart information about viral infection above that provided by detecting the presence of the infectious agent . MiRNA regulation is complex in malignancies associated with viral infection such as herpesvirus-associated cancers [2] , [6] , [12] , [13] . Viral infection can trigger changes in the miRNA profile through the expression of viral genes that modulate the host miRNA repertoire . Some viruses such as Kaposi's sarcoma-associated herpesvirus ( KSHV ) and Epstein-Barr Virus ( EBV ) in addition encode their own miRNAs , which fine-tune host gene expression to promote latent viral persistence , immune evasion , and tumor progression [8] , [9] , [14] , [15] , [16] , [17] . These viral miRNAs are often expressed within the tumor and can reveal important information regarding viral latency and disease progression [18] . Furthermore , recent studies have highlighted important functions of the viral miRNAs in regulation of the viral life cycle , immune evasion and angiogenesis through validated mRNA targets [7] , [14] , [19] , [20] , [21] , [22] , [23] . In KSHV-associated cancers , the KSHV miRNAs can account for as much as 20% of all mature miRNA species within a cell and are highly conserved among isolates ( Figure S1 and [12] , [14] , [17] ) . KSHV is the etiological agent of Kaposi's sarcoma ( KS ) , the most common AIDS-defining cancer worldwide [24] . KSHV is also associated with the B cell lymphoma Primary Effusion Lymphoma ( PEL ) and with the plasmablastic variant of Multicentric Castleman's Disease ( MCD ) . Despite the availability of Highly Active Anti-Retroviral Therapy ( HAART ) , KS continues to occur in the US and worldwide . Treatment of KS remains a challenge and stable , minimally invasive biomarkers for diagnosis are lacking [25] , [26] . Therefore , the discovery of plasma miRNA biomarkers for KSHV-associated malignancies could improve diagnostics through early detection and could influence treatment through non-invasive monitoring of tumor responses . MiRNA biomarkers can be sampled from blood , saliva , or other bodily fluids , offering a feasible diagnostic test even in resource-poor regions such as the “KS belt” in sub-Saharan Africa [24] , [27] . Viral microRNAs are the most attractive candidate biomarker because of their specificity for KSHV . However , a combination of viral microRNAs with cellular microRNA biomarkers is even more useful , as it may help differentiate among stages of KS progression or response to therapy and as it can identify cellular microRNAs that are common among KS and other cancers . We previously determined the cellular and viral miRNA profile in KS tumor biopsies as well as in PEL and found that the expression of viral miRNAs varies with disease state [3] , [4] , [6] . In addition to the viral miRNAs , key cellular miRNAs are involved in KSHV transformation and KS progression [8] , [9] , [28] , [29] . The detection of circulating miRNAs in plasma , serum and other bodily fluids suggests their utility as minimally invasive biomarkers for cancer diagnostics [11] , [30] , [31] , [32] , [33] , [34] . These circulating miRNAs are unusually stable ( i ) due to their packaging in microvesicles or exosomes , ( ii ) due to their RNA folding and size and/or ( iii ) due to their presence in Ago-containing ribonucleic acid:protein ( RNP ) complexes [32] , [34] , [35] , [36] . At this point it is unclear which of these mechanisms is the most efficient . Evidence suggests that all three mechanisms contribute to diagnostic utility by increasing miRNA stability . There are a variety of vesicles that are secreted from cells , each with slightly varying content and surface marker composition . Microvesicles can range in size from 30 nm–1000 nm and each follow different pathways of biogenesis ( reviewed in [37] , [38] , [39] ) . Recent studies have additionally shown that microvesicles from tumor cells may have altered morphology , size and surface markers , including the expression of tumor antigens compared to microvesicles that are released from non-tumor cells [40] , [41] , [42] , [43] . MiRNAs have been detected in microvesicles , exosomes and/or nanovesicles . This study refers to these vesicles collectively as exosomes based on common surface marker expression and morphological characteristics . Transfer of exosomes and their contents from tumor cells to surrounding , uninfected cells may be an important form of cellular communication and has been demonstrated in cell culture models , for instance in EBV-associated cancers [44] , [45] . Additionally , exosomes may provide a means of paracrine signaling from virally infected cells to adjacent , non-permissive cells [46] . This study attempts to bridge the gap between clinical samples and cell culture models . To do so we compared the detailed , circulating miRNome of KS in clinical human samples and in KS mouse models [47] , [48] , [49] . This confirms the presence of circulating KS and KSHV-specific miRNAs in vivo in the context of KSHV infection . Multiple KSHV miRNAs and members of the miR-17-92 cluster of cellular miRNAs were detected within patient exosomes . These circulating miRNA signatures may serve as a new mechanism of paracrine signaling for mediating KSHV pathogenesis and may represent a reservoir for novel biomarkers .
To date , most studies on viral exosomes have used tissue culture models of infection . To expand on these studies , we utilized a series of clinical samples and two novel robust mouse models of KSHV pathogenesis [47] , [48] , [49] . The sample groups and number of samples included in each group are outlined in Table S1 . Briefly , human plasma from healthy , KSHV-negative controls or from AIDS patients with either KS or a non-KS malignancy was used to isolate exosomes . The HIV viral load and CD4+ T cell counts were similar in both KS and non-KS malignancy groups ( data not shown ) . KS tumor biopsies and primary PEL pleural fluid were also included and served as positive controls for the presence of KSHV compared to control human plasma . We also used two mouse models previously characterized in our lab [47] , [48] , [49]: the 801 latency locus transgenic mouse model which expresses all viral miRNAs in B cells [50]; and a xenograft model using TIVE L1 tumor cells , which maintain KSHV [48] . These cells are xenografted into SCID mice , which results in robust and reproducible tumor formation [48] . H&E staining revealed similar phenotypes of KS and our TIVE xenograft mouse model while both of these differed from the staining observed in PEL ( Figure S2 ) . The KSHV-TIVE model [48] represents another instance of extended yet incomplete KSHV lytic transcription , as recently demonstrated in KSHV-infected lymphatic endothelial cell cultures under puromycin selection [51] and previously in KSHV-infected mouse endothelial cells [52] . Similarly , a KSHV cell line model of transformed rat mesenchymal precursors yields some lytic gene expression but with minimal amounts of virions produced [53] . The KSHV-TIVE endothelial cell model maintains KSHV in the absence of selection and like other long-term KSHV-infected endothelial cell cultures they remain tightly latent . Neither sodium butyrate nor exogenously provided RTA/Orf50 are able to induce infectious virus production ( R . Renne , personal communication ) or complete , genome-wide lytic transcription in TIVE L1 cells [48] . Subcutaneous implantation into mice can activate many viral genes , although these represent only approximately half of the genes turned on during lytic reactivation in PEL cells and are insufficient to produce infectious virions . A similar , abortive lytic expression profile has been observed in KSHV-infected human TIVE-L1 cells [48] as well as in KSHV-infected mouse and rat endothelial cells [52] , [53] . This incomplete transcription program is incompatible with virion production and in the case of KSHV-infected LEC has been termed a novel latency program [51] . For this reason , we refer to the TIVE xenograft mouse model as a latent KSHV model due to the lack of virions produced . Exosomes and circulating miRNAs were purified as shown in Figure 1A and detailed in methods . Following purification , total RNA was isolated from each sample group and used for Taqman-based qPCR profiling of the cellular miRNA repertoire ( 754 human miRNAs ) as described [3] , [4] , [54] . Agilent RNA analysis showed that exosomes expressed small RNAs but lacked both 18S and 28S ribosomal RNA ( Figure S3 ) . Figure 1 shows the distribution of miRNAs in different sample subsets ( Figure 1B–E ) . Each boxplot shows the expression levels for the different sample groups . The expression of individual microRNAs are denoted by solid circles . As demonstrated in Figure 1B , the majority of miRNAs present in control human plasma ( KSHV− ) in the supernatant fraction are susceptible to RNase , representing free , circulating miRNAs . These miRNAs are likely not encapsulated in Ago-RNP complexes nor microvesicles [35] . The exceptions were miRNAs miR-16 , miR-195 and miR-197 , which could be detected despite RNase treatment . This RNase resistance of these particular miRNAs is consistent with prior observations [35] . Levels of the C . elegans cel-mir-39 spike-in were abolished ∼16 , 000-fold after RNase treatment and were decreased when incubated with pleural fluid prior to RNA isolation ( Figure S4 ) . This verifies the activity of our RNase treatment and confirms that pleural fluid , like other body fluids , has some intrinsic RNase activity [33] , [34] , [35] . Therefore , the majority of RNAs that are stable in plasma and pleural fluid are likely RNase-resistant and protected within exosomes . In samples enriched for exosomes derived from either control human plasma or mouse serum , we were able to readily detect both human and mouse miRNAs ( Figure 1C ) . Figure 1D denotes the relative expression levels of miRNAs in cells , exosomes and the free , circulating fractions of control human plasma . As expected , exosomal and other circulating miRNAs are detectable but are present at lower levels compared with intracellular miRNAs . MiRNAs are readily detected in all sample types tested including tumor biopsies and exosomes from control plasma or serum and malignant effusions such as pleural fluid ( Figure 1E ) , though the miRNA yield was highest in tumor tissue . Although we used human plasma and serum from mice in the majority of experiments , we also performed miRNA profiling with control mouse plasma . Importantly , we did not observe significant differences in the levels of miRNAs found in plasma versus serum in this study . The comparison of this small subset of miRNAs across the different variables shown in Figure 1B–E did not afford us the statistical power to identify differences among individual miRNAs expressed in these samples . However , these data establish the framework for further analysis and confirms qPCR as a reliable platform for the profiling of miRNAs in a diverse group of clinical samples [54] , [55] . Furthermore , we validate the presence of exosomal miRNAs in cell-free patient plasma and mouse serum . Isolation of exosomes using the Exoquick method has previously been validated to yield similar electron microscopy ( EM ) structures and miRNA array populations as other techniques [56] . Nonetheless , we sought to confirm the presence of exosomes in our patient samples using two independent isolation techniques . Enriched exosomes from the Exoquick protocol revealed similar structures via electron microscopy compared to exosomes enriched by differential ultracentrifugation ( Figure 2A ) . However , while Exoquick samples did contain exosomes ( determined by size and morphological characteristics ) , they yielded images with high background by electron microscopy due to the crowding agent present in the ExoQuick solution . This background was not due to contaminating cellular debris , as high-speed centrifugation and elimination of cellular debris using a sucrose cushion failed to eliminate background in the EM images ( Figure S5 ) . By comparison , differential ultracentrifugation yielded exosomes of similar size and morphology with minimal background ( Figure 2A ) . Patient pleural fluid and BCBL1 cell supernatant yielded exosomes that appeared similar by EM . We therefore pursued the Exoquick method for further study , as these samples required much less sample input , a key benefit when working with clinical samples and mouse models . To further establish the purity of our exosomes , we performed Western blots for previously established exosomal markers including the tetraspanin CD9 , Hsp90 alpha/beta and flotillin [57] , [58] . We first analyzed the expression of flotillin , which is enriched in exosomes [58] , [59] , [60] . Flotillin was expressed in all human and mouse exosome samples ( Figure 2B , C ) but was not present in the supernatant fractions containing freely circulating miRNAs ( Figure 2D , S ) . Hsp90 alpha and beta , which are also highly enriched in exosomes , were detected in PEL cells and pleural fluid-derived exosomes but , as expected , were absent in the supernatant fraction ( Figure 2E–G ) . Finally , we assessed the expression of the tetraspanin CD9 , another exosomal marker . The KS exosomal subgroup ( KS-E ) expressed detectable levels of CD9 whereas the supernatant fraction ( S ) did not express the exosome marker ( Figure 2H ) . As a negative control for exosomes , we used the exosome-depleted supernatant fraction from BJAB cells ( - ) . The mouse exosome samples isolated from serum of control and transgenic ( Tg ) mice also showed robust expression of the CD9 exosome marker , indicating that these samples are enriched for exosomes ( Figure 2H ) . The increased expression observed in the mouse samples most likely reflects the ratio of input used to the total fluid volume present in human and mouse . A mouse has a total blood volume of 1 . 5 mls of which we use 250 ul ( ∼17% ) whereas human blood volume is approximately ∼5 L of which we used 250 ul ( 0 . 005% ) . We also tested the presence of exosomal markers in samples purified by ultracentrifugation and received similar results , validating the use of the Exoquick method for our study ( Figure S6 ) . Furthermore , the ExoQuick method yields more exosomes than other methods tested and uses approximately 100-fold less starting material . Increased expression of KSHV miRNAs correlates with disease state and tumor progression in endothelial cells [3] , [6] . EBV viral miRNAs have been detected in exosomes isolated from cultured lymphoma cell lines , NPC patients and xenografted mice [44] , [45] , but thus far it has not been shown that KSHV miRNAs are also loaded into exosomes . To address this question , we examined patient-derived exosomes for the presence of KSHV-encoded miRNAs . Figure 2I shows the qPCR products separated by size on a Caliper nanofluidics platform . Exosomes derived from serum of three independent KSHV-positive TIVE L1 xenograft tumor mice and PEL fluid contained KSHV miR-K2 ( Figure 2I ) . Total RNA from KSHV-positive latently-infected BCP-1 PEL cells were used as a positive control . KSHV miRs K12-4-5p , K12-4-3p , K12-5 , K12-6-5p , K12-10a and K12-11 were also detected in pleural fluid and xenograft tumor mice ( data not shown ) . KSHV miRNAs were undetectable in the KSHV-negative BJAB cell line ( Figure 2I ) . This shows that systemically circulating exosomes contain appreciable levels of mature KSHV miRNAs and therefore exosomes containing KSHV miRNAs can travel from the subcutaneous tumor graft into the bloodstream and are stable enough to circulate systemically . Since we harvested the blood at day 10–15 after tumor cell injection , this result is likely to reflect steady-state levels of exosomal miRNAs . Notably , the L1 TIVE xenograft model does not generate infectious virus [48] . Hence , exosome encapsulated KSHV miRNAs show promise as a highly sensitive marker for latent KSHV tumor cells . In order to study exosome-associated viral miRNAs in more detail , we used the BCBL1 PEL cell line to assess KSHV miRNA expression . We found that 14 out of 14 KSHV microRNAs tested were expressed at detectable levels in exosomes from latent BCBL1 cells ( Figure S7 ) . Methods for purifying virions and exosomes can lead to co-precipitating of both exosomes and virions , therefore making it difficult to physically separate them for analysis . To distinguish the source of these viral miRNAs as exosomal or virion-associated , we purified exosomes using three different techniques . In addition to using the ExoQuick method of purification , we also utilized differential ultracentrifugation and a new bead affinity purification technique that positively selects for CD63+ exosomes , an exosomal marker not present on KSHV virions . Expression of KSHV microRNAs was then assessed following enrichment of exosomes using either method ( Figure 3 , Figure S7 ) . In addition , we passed the samples through a 0 . 2 µm filter prior to exosome isolation but after the removal of cellular debris . Although KSHV virions are approximately 180 nm in size , they tend to aggregate , a phenomenon well-recognized in earlier studies studying infectivity of cell-free virus [61] , [62] , [63] . This aggregation of virions makes it difficult to clear even a 0 . 2 µm filter . This was experimentally confirmed by filtering concentrated KSHV stocks , which resulted in a decrease in titer of approximately 4 logs ( data not shown ) . Exosomes , however , which range in size from 30–100 nm , can easily pass through a 0 . 2 µm filter , as is evident from EM imaging of filtered patient-derived exosomes ( Figure 2A ) . Consistent with this , the expression levels of KSHV miRNAs were only slightly affected by filtering ( Figure 3A , Figure S7 ) . By contrast , filtering of exosome samples resulted in a dramatic decrease in viral load ( Figure 3B ) . We observed similar expression patterns of viral miRNAs in exosomes isolated and filtered following both ExoQuick and ultracentrifugation methods . This was confirmed by Caliper gel electrophoresis , which showed the presence of KSHV miRNA products in both the exosome and filtered exosome fractions ( Figure S7 ) . A lower shifted band corresponding to primer dimers was detected in the no template control reactions . Note also that the Caliper images represent non-quantitative accumulation of product after 55 cycles , whereas quantification was based on the exponential phase of the PCR reaction . Using the CD63+ exosome isolation method , we consistently observed expression of KSHV miRNAs regardless of filtration ( Figure 3A ) . The levels of viral miRNAs were not significantly different in exosome preparations from latent or lytically induced PEL cells ( Figure 3A ) . If these miRNAs were predominantly present within virions , we would expect a robust increase in viral miRNA levels concomitantly with increased virion production following reactivation as we observed for KSHV load ( Figure 3B ) . Furthermore , RNase treatment of samples slightly decreased viral miRNA levels in the CD63− supernatant but did not affect KSHV miRNA expression in CD63+ fractions , suggesting that these miRNAs are primarily protected within exosomes ( data not shown ) . Analysis of exosomes isolated by CD63 affinity capture confirmed the presence of CD9 , another well established exosomal marker ( Figure 3F ) . CD9 levels were unaffected by filtering samples and RNase treatment ( Figure 3F ) . Taken together , this demonstrates that the KSHV miRNAs are predominantly contained within exosomes released from latently-infected tumor cells . Having determined that viral miRNAs were present in exosomes , we next sought to analyze the distribution of KSHV DNA among our samples and biochemical fractions . There are two mechanisms that lead to KSHV viral DNA being detectable in body fluids: ( i ) virions [64] , ( ii ) tumor cell-released free viral DNA , as has been demonstrated for EBV [65] , [66] , [67] , [68] . To eliminate the contribution of cell-free viral DNA , we treated all samples with DNase prior to DNA isolation . We evaluated BCBL1-derived exosomes purified using different techniques for the presence of KSHV DNA ( Figure 3B , C ) . Exosome-enriched samples were passed through a 0 . 2 µm filter , which led to a drastic decrease in KSHV load using both purified virus stock and exosomes ( Figure 3B , data not shown ) . Although the viral load increased following reactivation , filtering of exosomes from lytic BCBL1 cells abolished viral load to approximately the limit of detection . DNase treatment of samples , which effectively eliminated freely circulating tumor-associated DNA , further decreased KSHV load in filtered fractions ( data not shown ) . We also compared the presence of viral DNA in exosome and supernatant fractions of samples enriched by CD63 bead affinity purification or differential centrifugation ( Figure 3C ) . Viral DNA was detected in the exosome-depleted supernatant fraction ( CD63− ) after bead affinity purification but was undetectable in the CD63+ exosome fraction . Conversely , viral DNA was enriched in the exosome pellet following differential ultracentrifugation , as both virions and exosomes sediment at similar densities during centrifugation . This establishes CD63-based affinity capture as an efficient way to separate exosomes and virions . Since we detected KSHV miRNAs , but not KSHV DNA in the CD63-affinity purified exosomes , this suggests that the primary source of the viral miRNAs we observe is exosomes rather than virions . We also evaluated viral load in exosomes purified using the ExoQuick method . The advantage of the ExoQuick method compared to CD63 capture is greater efficiency ( using only 250 µl as input ) , which is essential when profiling large numbers of clinical samples . We found KSHV DNA in both exosomal and free supernatant fractions of plasma and pleural fluid ( Figure 3D , E ) . The highest viral load was found in exosomes derived from PEL pleural fluid . No viral DNA was detected in our negative control samples or in exosomes purified from non-KS , HIV+ patients ( Figure 3D , 3E lanes CHP and AMT respectively ) . Both ExoQuick and ultracentrifugation methods yielded KSHV DNA in the KS patient plasma and PEL fluid exosome fraction ( Figure 3 ) . Thus , neither differential centrifugation not ExoQuick can with certainty be used to separate exosomes from virion particles . However , CD63+ exosomes contain little KSHV DNA , especially following filtering of exosome samples . This novel method confirms that the majority of the signal detected in the viral load assay was due to free DNA and KSHV virion DNA . To further address the possibility that virions may also be present in our exosome fraction and may contribute to our results , we looked for KSHV virions and viral proteins in our exosome-enriched samples . We did not detect any virions by EM following ExoQuick or ultracentrifugation isolation of exosomes ( Figure 2A ) . We analyzed more than 20 grids for the presence of virions in our exosome-enriched samples . Quantitative analysis of exosome-enriched pellets by differential centrifugation revealed 2 , 319 exosomes and no virions on three sample grids . The supernatant fraction was also imaged by EM for the presence of exosomes and only 13 exosomes were detected , validating ultracentrifugation as an efficient method for exosome isolation . We also compared the number of exosomes from latent and lytic BCBL1 cell supernatants by EM . Both latent and lytic samples had similar numbers of exosomes detected on representative grids , averaging 135 and 126 , respectively ( while the DNase resistant viral load differed by >10-fold ) . This suggests that the presence of exosomes in our samples may be static and independent of virus production . Finally , we probed exosome-enriched samples for KSHV structural proteins . KSHV K8 . 1 was readily detected in BCBL1 PEL cells following lytic reactivation ( Figure S8 ) . However , PF-derived exosomes , which contained the highest viral load of our samples , did not express K8 . 1 . These data confirm that our exosome-enriched samples do not contain appreciable levels of KSHV virions ( Figures S7 , S8 ) . Although KSHV protein and DNA are not found in exosomes , we find systemically circulating KSHV miRNAs in exosomes derived from patients , tissue culture models and mouse models of KS ( Figure 2I , Figures S7 , S8 ) . This establishes exosome-associated viral miRNAs as new biomarkers for KSHV-associated cancers . It also suggests that detecting viral miRNAs may offer greater sensitivity of diagnosing viral infection than viral load measurements . To obtain a more complete picture , we profiled the host miRNA repertoire in each of our sample groups using both exosomal and exosome-depleted supernatant preparations . The C . elegans cel-mir-39 spike-in was used as an internal normalizing control . Unsupervised clustering analysis revealed two distinct clusters , which are shown as projected onto the first three principal components ( Figure 4A ) . Unsupervised clustering groups samples and the different miRNAs based on similar expression levels . The result is typically shown as a heatmap . Principal component analysis is used to reduce the complexity of the data further without loss of statistical power . It combines the multiple measurements of each sample ( or each miRNA ) to such that the data can be represented in three dimensions ( the principal component axes ) . Individual analysis of the human and mouse profiling samples ( Figures 4B , C ) illustrates the more divergent clusters representing miRNAs elevated in tumor versus control samples in the mouse model . We expected to see more defined clusters in our mouse models since the xenografts represent biological replicates with limited variability compared with human clinical samples . The miRNA profile in the human samples alone clearly separated samples into KSHV-associated and control groups . When we further narrowed the miRNAs to known oncomiRs and tumor suppressor miRNAs , the classification improved ( Figure 4D ) . A list of these ∼150 oncomirs and tumor suppressor miRNAs is shown in Table S2 . As a negative control for our analysis we clustered an unrelated sample . We performed unsupervised clustering of miRNAs in HEK293 cells following infection with West Nile Virus ( WNV ) ( Chugh and Dittmer , unpublished data ) . This comparison yielded very different clusters of miRNAs compared with the KSHV exosome data as noted by further predicted target analysis ( Table 1 ) . This establishes a unique oncomir signature of KS- and PEL-associated exosomes . We further examined the expression of individual oncomirs and tumor suppressor miRNAs in the mouse exosome subset by heatmap analysis ( Figure 4E ) . Oncomirs were defined as host miRNAs readily studied for their role in tumorigenesis and related cancer signaling pathways while tumor suppressor miRNAs have been demonstrated to functionally inhibit these processes ( Table S2 ) . We identified this subset of oncogenic miRNAs because ( a ) we previously extensively validated these assays [3] , [4] , [55] and ( b ) they represent miRNAs with experimentally verified expression and function . The most distinct expression pattern was the apparent separation between TIVE xenograft and control mouse serum ( Figure 4E , Panel i ) . The majority of oncomiRs in this cluster were increased in exosomes from KS xenograft tumor models and were only minimally or not detectable in the control mice ( ctrl versus xeno ) . We next compared miRNA expression profiles of control and xenograft mice to our latency locus transgenic mouse model ( Figure 4E , Panel ii ) . In this novel model , only the KSHV latent genes and miRNAs are expressed in B cells [50] . However , none of the viral structural genes are present . We found that exosomes derived from the transgenic model differed from that of control mice and shared some oncogenic miRNA expression with the xenograft mice . As this transgenic mouse model phenotype represents B cell hyperplasia , these highly expressed miRNAs may be reflective of change in the miRNome regulated by the KSHV latency locus prior to tumor formation ( Figure 4E ) . We further compared the exosomal miRNA profile of transgenic mice to that of PEL-associated exosomes from primary pleural fluid ( Figure 4E , panels iii , iv ) . This yielded similarities in induced exosomal oncogenic miRNA expression between the 801 transgenic mouse model and PEL patient fluid ( Panel iii , Cluster 1 ) . Interestingly , we also identified a subset of microRNAs that was solely induced in the KSHV latency locus transgenic mouse model ( Figure 4E , Panel iv , Cluster 2 ) . Figure S9 further compares the exosomal miRNA profile in an independent set of transgenic and control mice and indicates elevated levels of oncogenic miRNA expression in the transgenic mouse model . Analysis of miRNA profiles in both Clusters 1 and 2 also revealed a subset of oncogenic miRNAs that were exclusively expressed in exosomes , suggesting that these miRNAs may be preferentially incorporated from the tumor site into exosomes for intercellular communication ( Figure 4E , Panels iii , iv , Figure S10 ) . Taken together , we find that the most elevated oncomiR levels in exosomes were observed in the TIVE xenograft tumor group , as these mice were bearing large , well-vascularized tumors , which facilitates expression and release of miRNAs . This demonstrates for the first time that exosomal miRNAs , including KSHV miRNAs , can be detected in mouse models of KS . Our human clinical samples of AIDS-KS recapitulated the trends in oncogenic miRNA expression observed in our mouse models ( Figure 4F , Figure S11 ) . Cluster 1 represents a subset of oncogenic miRNAs that are most highly expressed in exosomes derived from PEL pleural fluid ( Figure 4F ) . Several miRNAs in this cluster were also elevated in KS patient-derived exosomes . This pattern of miRNA expression may reflect a signature of KSHV-associated malignancies . A subset of miRNAs within this cluster could also represent miRNAs overexpressed in KS and other cancers since we observed oncogenic miRNA induction in other AIDS malignancies as well as KS-associated exosomes ( Figure 4F ) . Cluster 2 shows another subset of miRNAs with elevated expression in exosomes from KS or AIDS malignancy patients . This cluster also includes several miRNAs that seem to be preferentially expressed within exosomes compared to the supernatant fraction . We noticed little difference in the miRNA profile from control plasma exosomes versus RNase-treated control plasma exosomes , indicating that exosomes are indeed resistant to RNase treatment [35] ( Figure 4F , lanes CHP exo and RNase-CHP exo ) . We also compared the miRNA profile in pleural fluid-derived exosomes exposed to RNase to determine if they responded similarly to our exosomes from control human plasma . Exosomes from PEL patient pleural fluid exhibited higher levels of miRNA expression . RNase treatment only slightly changed the miRNA profile , similar to that observed in control exosomes ( Figure S12 ) . This demonstrates that different patient samples respond similarly to RNase treatment and further validates that the majority of our signal was derived from exosome-contained miRNAs . Since patient samples may display a high degree of genetic variability and therefore miRNA signatures could differ , we sought to address the issue of individual variance of patient miRNA profiles using three PEL patients . Pleural fluid-derived exosomes were independently isolated and the miRNA expression profile was compared to that of control human exosomes . Many of the “PEL signature” miRNAs were expressed in all three patients , suggesting that these could be used as novel biomarkers of PEL present in pleural fluid ( Figure S13 ) . Another subset of miRNAs was expressed in 2 out of 3 patients . Since PEL is a rare malignancy , we obtained only three patients , each of varying disease states . Different factors such as disease state , co-infection with EBV or HIV status could contribute to absence of these biomarkers in one of the patients . However , despite the inherent genetic variability among patients , we could identify multiple miRNAs that were expressed at high levels in all three PEL patients compared to controls ( Figure S13 ) . We further analyzed the expression of the oncomiR cluster in the exosome sample subsets . For this analysis , we defined a relative expression score based on the CT where a higher expression score corresponds to a lower CT . Specifically , we calculate the expression class by binning CTs such that a CT of 20–25 corresponds to an expression value of 3 . MicroRNAs expressed with CTs of 25–30 are assigned an expression value of 2 . 5 . Scores are assigned in 0 . 5 increments until CT of 45+ equals zero , or not detected . In addition to patient-derived exosomes , we determined the miRNA profile for KS biopsies and PBMCs derived from control human plasma . The full profiling data is shown in Figure S14 . Figure 5A demonstrates that , as expected , KS biopsies ( KS , left ) displayed the highest expression of oncomirs . By comparison , a large number of oncomirs were undetectable ( expression score = 0 ) in biofluids . KS-associated exosomes also contained oncomirs in moderate ( expression score = 1 ) and some at very high levels ( expression score = 2 ) . While oncomiRs are readily expressed in both control and malignant samples , we found that the number of highly expressed oncogenic miRNAs was lower in control exosomes ( Neg ) . Note that members of the miR-17–92 cluster are denoted by blue dots and are highly expressed in KS biopsies and KS-associated exosomes compared with controls ( expression score>1 . 5 ) . The levels of oncogenic miRNAs were abolished in exosome-depleted supernatant fractions treated with RNase ( Figure 5A ) . The exceptions were miRNAs including miR-16 , miR-195 and miR-197 , which were previously shown to be RNase-resistant ( Figure 1B , [35] ) . This demonstrates that most oncogenic miRNAs were present in exosomes . Oncomirs specifically expressed in tumor samples at the highest expression level included miR-106a , miR-17 , miR-454 , let-7e , miR-451 , miR-886-5p , miR-601 and miR-625 ( expression score of ≥2 , Figure 5A ) . Note , that a high expression score is the result of both the underlying high level of expression of the specific miRNA species and the sensitivity of the particular qPCR assay . One of the most well-studied oncogenic miRNA clusters is the miR-17-92 cluster . The 6 mature miRNA species in this cluster tend to be co-regulated [69] and we previously found this miR cluster upregulated in KS [3] , [4] . Members of the paralog cluster miR-106b/25 are also well-known for their role in tumorigenesis and share target genes with the miR-17-92 cluster [70] , [71] . We therefore investigated whether KS-associated exosomes contained members of these two miRNA clusters . In our clinical and mouse model samples , levels of the miR-17-92 and miR-106b/25 clusters were induced in exosomes derived from KSHV-associated mouse serum , primary human pleural fluid and KS biopsies compared with control exosomes ( Figure 5B , C ) . Since we did not observe a similar enrichment of all tumor-associated miRNAs within the exosomes , these miR-17-92 members are likely to be preferentially incorporated into exosomes . Members of these oncogenic clusters were slightly elevated in exosomes from KS patient plasma , although this was not statistically significant ( Figure 5B ) . However , exosomes derived from pleural fluid expressed much higher levels of the miR-17-92 and miR-106b-25 cluster members , with the exception of miR-25 and miR-92a ( Figure 5B ) . The increased expression of oncogenic miRNAs within PF-derived exosomes may be because of direct contact of the pleural fluid to PEL cells , suggesting that malignant effusions may be a very effective source for obtaining exosomes ( Figure 5B ) . Induction of the miR-17-92 cluster member miRNAs was most pronounced when we compared exosomes derived from the xenograft mouse model to control mouse exosomes ( Figure 5C , p≤ . 000059 ) . Therefore , we find that exosome-associated oncomirs are uniquely upregulated in samples from KS tumor-bearing animals and primary PEL patients . Interestingly , even the B cell hyperplasia latency locus transgenic mouse model showed increased levels of these miRNAs in systemically circulating exosomes ( Figure 5C , p≤0 . 05 ) . Several miRNAs seemed to be preferentially incorporated into exosomes ( Figure 4E , F ) . Therefore , we analyzed these in detail . As shown in Figure 5D , miRNAs miR-19a , miR-21 , miR-27a , miR-130 and miR-146a were enriched within exosomes and virtually undetectable as free , circulating miRNAs in the supernatant . Their relative expression levels were significantly elevated in mouse models of KS ( p≤4×10−5 , Figure 5D ) . To confirm these results , we performed Caliper gel electrophoresis analysis on the qPCR products , which confirmed that these miRNAs were overexpressed in exosomes from our transgenic and TIVE xenograft mouse models ( Figure S15 ) . Taken together , these data reveal that members of the miR-17-92 cluster are exclusively incorporated into exosomes and may exhibit diagnostic potential and contribute to tumor development and pathogenesis of malignancies such as KS . We profiled the circulating miRNAs in a second , independent pair of KS patients ( n = 2 ) and compared them to TIVE xenograft mice along with the appropriate controls ( Figure S16 ) . One of the KS patients profiled had an unusually high KSHV load , multiple internal lesions and cytokine dysregulation [72] . Unsupervised clustering analysis confirmed that the TIVE L1 xenograft mice had a distinct circulating miRNA profile from control mice , but also revealed that this mouse model shared similarities to the human miRNome detected in pleural fluid ( Figure S16 ) . Like the xenograft mice , the two KS case study patients expressed distinct circulating miRNA signatures when compared with control human plasma ( Figure S16 ) . The KS patient with more advanced disease ( DG1 , cytokine dysregulation and high KSHV load ) displayed a miRNA profile more similar to TIVE xenograft exosomes . These independent biological replicates and multiple clinical cases share a common , robust signature ( Figure S16 , Figure 4E , F ) . To gauge the importance of the KS exosome signatures , we analyzed the oncogenic miRNAs upregulated in tumor-derived exosomes using Gene Ontology pathway analysis and found that many of the miRNAs targeted pathways previously shown to be central to KSHV pathogenesis ( Table 1; asterisks ) . PI3K/Akt signaling is central to “Pathways in Cancer” , which had the highest correlation to upregulated miRNAs . It is known to be dysregulated following KSHV infection [73] , [74] , [75] . Many of the other pathways listed in Table 1 contribute to both the KEGG Pathways in Cancer and the Pancreatic Cancer pathway . For instance , MAPK is important in the control of replication and KSHV reactivation from latency while KSHV inhibits TGF-beta signaling through mechanisms including miRNA-targeted silencing [76] , [77] . KSHV LANA has been shown to bind GSK3-beta , leading to an upregulation of beta catenin in KS and PEL through the regulation of Wnt signaling [78] . TLR signaling has previously been shown to play a role in both primary infection of monocytes and reactivation from latency [79] , [80] . Finally , two of the pathway hits—focal adhesion and adherens junctions—are known to be important in viral entry , cytoskeletal remodeling and cell adhesion during KSHV infection and KS tumorigenesis including in adjacent KSHV-negative spindle cells within the KS lesion [81] . As control , we also analyzed the GO pathways associated with the miRNA signature of an unrelated virus ( WNV ) . This confirmed that the roles of the signaling pathways were unique to our exosome profiling of KSHV-associated malignancies ( Table 1 ) . We also performed GO pathway analysis using two additional , independent analysis databases: Panther and Ingenuity Pathway Analysis ( Table S4 and S5 ) . These revealed highly significant pathways targeted by oncomirs including angiogenesis , integrin signaling , transformation , migration and invasion ( Tables S4 , 5 ) . Previous studies of the oncogenic miR-17-92 cluster have also revealed roles in similar pathways such as NFkB signaling , angiogenesis , TLR , MAPK , STAT and TGF-beta signaling [69] , [82] , [83] , [84] , [85] , [86] , [87] , [88] , [89] , [90] , [91] . Since many of the GO analysis pathway hits have been previously functionally validated , it is likely that some of the exosomal miRNAs found overexpressed in this study contribute to KSHV signaling . One function that many of these pathways shared is the involvement in cell migration , which is important for tumorigenesis and noted in Table 1 . We therefore used cell migration as a bioassay to show that our exosome-enriched samples yielded intact , functional exosomes . Since cell migration was a shared functional outcome of several of the gene ontology pathway hits , we sought to test the effect of KS and PEL-derived exosomes on the migration of endothelial cells . hTERT-immortalized HUVECs [92] were treated with exosomes isolated using the ExoQuick kit . Exosomes derived from patient PEL pleural fluid were added to cells for 24 hours and the wound healing scratch assay was performed to test the migration capability of these cells . Figure 6A demonstrates that hTERT-HUVECs treated with patient-derived exosomes displayed enhanced cell migration by 8 hours post-initiation of the scratch assay . Cells treated with exosomes derived from control human plasma ( CHP ) showed delayed migration compared with cells receiving the exosomes derived from pleural fluid ( Figure 6A ) . This confirms that this effect was not due to ExoQuick itself , since control exosomes isolated using this protocol did not increase migration . Since we also detected KSHV DNA in the supernatant fraction of pleural fluid and the presence of virions can also affect migration , we analyzed the migration capability of cells treated with exosome-depleted supernatant ( PF sup ) . hTERT-HUVECs exposed to PF supernatant also displayed enhanced migration compared to control exosomes but cells treated with pleural fluid-derived exosomes still migrated more rapidly . As a positive control , we also treated hTERT-HUVECs with IL-6 , which resulted in increased migration similar to that observed with the pleural fluid supernatant fraction ( IL-6 versus PF sup ) . Of note , exosomes are known to carry proteins as well as miRNAs [44] . At this point , we cannot assign this exosome phenotype to either moiety . The data also suggests that while cytokines and virus present in the supernatant can affect cell migration , patient-derived exosomes further accelerate this process . Exosomes isolated from cell culture models and patients have been shown to express phosphatidylserine ( PS ) on their surface [39] , [93] . Since Annexin V can bind PS on the surface , annexin blocking of exosomes has been previously used as a means of inhibiting exosome fusion and transfer of exosomal contents [44] , [45] , [93] . Therefore , we also performed the scratch assay in the presence of annexin blocking ( Figure 6B ) . Exosomes and supernatants were incubated with Annexin prior to initiation of the scratch assay . Annexin blocking did not seem to affect the migration of hTERT-HUVECs treated with control ( CHP ) exosomes . However , the enhanced migration potential of cells treated with pleural fluid-derived exosomes was reversed with annexin blocking , demonstrating that this phenotype is due to exosomal transfer . Cells treated with exosome-depleted supernatants from pleural fluid were not affected by annexin blocking . Similarly , IL-6 enhanced cell migration regardless of annexin blocking . Therefore , any virus or cytokines present in this supernatant enhanced migration via a different mechanism independent of exosomes . Figure 6C provides a boxplot representation of the scratch assay data . This confirms that cells treated with pleural fluid-derived exosomes exhibit increased migration , which is reversed by treatment with annexin . This is also observed following treatment of cells with exosomes derived from the PEL cell line BCBL1 . We formally tested the individual contributions of each factor to the increased migration phenotype using a Dunnett confidence interval test which evaluates the significance of different treatments compared to a common control and adjusts for potential bias due to multiple comparisons being performed ( Figure 6D ) . As represented by the black circles ( with brackets representing the 95% confidence interval ( CI ) ) , treatment with IL-6 or exosomes from either pleural fluid or PEL cells independently led to significantly increased closure of the wound compared to exosomes isolated from KSHV-negative control human plasma ( CHP ) . By contrast exosome-free , mock treated cells behaved similarly to cells treated with exosomes from KSHV-negative CHP . All scratch assays were performed in triplicate for three independent biological replicates over a span of two weeks . In each biological replicate , we observed the same phenotype . Table 2 shows the linear , multivariate analysis of the data , which measures the difference between two experimental conditions after adjusting for all other factors . Exosomes derived from pleural fluid of a PEL patient ( p≤10−11 ) or from the BCBL1 PEL cell line ( p≤10−7 ) significantly enhanced migration of hTERT-HUVECs at 8 hours post-infection compared to CHP ( Table 2b–d ) . When comparing the supernatant and exosome fractions of pleural fluid and PEL cell supernatants , exosomes were more potent ( p≤0 . 031 ) , but we still observed a significant effect on HUVEC migration for the supernatant ( Table 2j ) . This is not entirely unexpected , since supernatants from PEL patients and PEL cells have large amounts of soluble IL-6 , IL-10 and VEGF [49] . Still exosomes independently confer an enhanced migration phenotype to hTERT-HUVECs . Annexin blocking of exosome fusion supports this ( p≤10−7 ) and resulted in reversal of the enhanced migration effect of PEL-derived exosomes ( Table 2k , columns b–d ) . This demonstrates that our purified exosomes have biological activity , and second that the KS and PEL patient-derived exosomes confer a phenotype of enhanced migration to endothelial cells , which is likely to contribute to KS-associated angiogenesis . We next analyzed migration of hTERT-HUVECs treated with exosomes using the xCelligence system , which allows for highly accurate , quantitative measurements of cell migration in real-time . The xCelligence Cell Invasion and Migration ( CIM ) Plate 16 consists of an upper and lower chamber separated by a microporous membrane coated with gold microelectrode sensors on the bottom side . As cells migrate toward the chemoattractant in the bottom chamber , the impedance signal increases and results in a corresponding increase in Cell Index ( proprietary readout , Roche application note ) . hTERT-HUVECs were treated with patient- , cell line- or mouse model-derived exosomes . Cells were then serum starved and plated into the upper chamber of the CIM Plate . Migration towards the chemoattractant FBS was continuously monitored every two minutes for a period of 24 hours . Figure 6E shows that hTERT-HUVECs treated with KSHV-associated exosomes exhibited increased migration compared with cells treated with exosomes from control human plasma ( red ) . This assay independently demonstrates that exosomes from patient PEL fluid , the BCBL1 PEL cell line , and a xenograft mouse model of KS confer an enhanced migration phenotype to hTERT-HUVEC cells . Since IL-6 plays a significant role in KSHV pathogenesis , we analyzed the levels of IL-6 present in the scratch assay supernatants by ELISA ( Figure 6F ) . hTERT-HUVECs treated with patient-derived exosomes secreted high levels of IL-6 . IL6 secretion in response to exosome treatment was decreased when the exosome fraction was incubated with annexin V ( p≤0 . 003 ) . These experiments suggest that efficient exosome transfer drives enhanced cell migration , possibly through the increased induction of cytokines such as IL-6 . Note , though , that these experiments did not distinguish between miRNA and protein components of the exosomes . In sum , the exosomal signature associated with KSHV-related malignancies could not only be a reservoir of clinically important diagnostic biomarkers but may also be a novel mechanism of paracrine signaling that mediates KSHV-associated pathogenesis and tumorigenesis .
Circulating miRNAs , especially those within exosomes , have emerged as novel biomarkers [31] , [32] , [33] , [94] , [95] . Their main advantage is stability and ease of detection as all miRNAs can be profiled with a common platform . We previously established and validated such a miRNA profiling platform [54] . Bodily fluids such as plasma can be obtained using minimally invasive techniques and lend themselves to repeat sampling , for instance to follow therapy . In the case of PEL , periodic ( in extreme cases every few days ) draining of pleural cavities is medically indicated . Although the exosomal miRNA profile of malignancies associated with EBV have been previously reported [44] , [45] , this is the first study to examine the circulating miRNA profile of KSHV-associated cancers . This is also one of a few studies to compare patient tumors to xenograft mouse models [96] . We extend previous findings on exosomal miRNAs , which were largely based on cell culture models . KSHV-encoded miRNAs were detectable in systemically circulating exosomes ( Figure 2I and Figure 3 ) , including in xenograft mouse models of KS . This suggests that viral miRNAs can have effects far from the site of the infected cell . Furthermore , viral microRNAs could potentially serve as highly specific biomarkers of KSHV-associated malignancies , particularly if the lesions are internal and comprised of mostly latently infected cells . We found similar levels of viral miRNAs in exosomes derived from latently infected PEL cells compared to PEL cells undergoing lytic reactivation ( Figure 3A ) . Most KS tumor cells and most PEL are latently infected and even if lytic gene expression is observed in a subset of cells , virions are seldom produced [97] , [98] . A significant complication of characterizing exosomal miRNAs in virally associated diseases is that miRNAs may be incorporated into virions . Previous studies have shown that viral RNAs can be detected within herpesvirus virions , including KSHV and EBV [99] , [100] . Recently , Lin et al . demonstrated the presence of viral , as well as cellular miRNAs in purified KSHV virions [64] . Exosomes are difficult to physically separate from virions due to their similar sedimentation velocities , buoyant densities , biogenesis and heterogeneous nature of exosomes [44] , [46] . Others have circumvented this issue using cell culture models that are incapable of virus production , such as HCV subgenomic replicon ( SGR ) cells [46] . Analogous to this model , we employed several latent models of KSHV infection , including the latently infected TIVE xenograft mice , the latency locus transgenic mice and the BCBL1 latent PEL cell line [48] , [50] . We believe that the majority of miRNAs we detect here are exosomal , rather than virion-associated . To support this interpretation , we offer three lines of evidence . First , we were able to detect all viral miRNAs in latent BCBL1 exosomes and filtering samples led to decreased viral load but did not significantly affect levels of KSHV miRNAs ( Figure 3 , Figure S7 ) . We detected similar amounts of KSHV miRNAs in exosomes isolated from latent PEL supernatant as in exosomes from supernatant of induced PEL ( Figure 3 ) . In the same samples , we observed a greater than 10-fold increase in viral DNA . This suggests that KSHV miRNAs are released into exosomes from latently infected PEL , analogous to exosomal EBV miRNAs which are released from latently infected cells [44] , [96] . Note , that we are able to detect KSHV miRNAs in exosomes from 250 µl of latently infected cell supernatant , whereas at least 500 mls were previously used to enrich for virion-associated miRNAs [64] . We could also detect KSHV miRNAs in the bloodstream of mice , which carry KSHV latently-infected TIVE-E1/L1 xenografts . These cells do not generate infectious virions [48] and ( R . Renne , personal communication ) . Second , we were able to isolate exosomes by CD63-mediated affinity purification ( Figure 3 ) . Herpesvirus virions and exosomes co-purify in almost all centrifugation schemas designed to enrich for exosomal fractions ( i . e . differential ultracentrifugation , sucrose gradients , ExoQuick solution ) . By contrast , anti-CD63 Dynabeads positively select exosomes which carry CD63 as one of their surface markers [57] , [101] while CD63 ( − ) virions are eliminated . This resulted in an enrichment of KSHV miRNAs and concomitant depletion of viral DNA ( Figure 3 ) , demonstrating that indeed viral miRNAs are present in exosomes . We were also unable to detect any contaminating virions in our samples enriched for exosomes by electron microscopy and structural viral proteins were absent in our exosome-enriched samples ( Figure 2A and Figure S8 ) . Thirdly , KSHV miRNAs could be detected in exosomes isolated from the serum of our xenograft mouse model . These xenograft mice harbor latently infected cells , which do not generate infectious virus . This suggests that viral miRNAs are constantly released and circulate systemically in exosomes in mice ( and patients ) who harbor KSHV latently infected cells . Taken together , these data suggest that KSHV latently infected cells can release viral miRNAs and further demonstrates that exosomes are the source of these circulating miRNAs . Human oncogenic miRNAs were easily detected in tumor-derived exosomes isolated from patient plasma and pleural fluid ( Figure 4 ) . Further analysis confirmed increased levels of the well-studied miR-17-92 cluster miRNAs . Our data also show potentially important similarities and differences in the miRNA profile from AIDS patients with KS compared to patients with other non-viral AIDS-associated malignancies ( Figure 4F ) . This subset of exosomal miRNAs could reflect differences between the varying progression of different malignancies in AIDS patients or similarities among AIDS-associated cancers and merits further study . Exosomal miRNAs are readily detected in pleural fluid samples , representing an alternate sample source with potentially higher correlation to disease state for patients with malignant effusions . Since pleural fluid is more proximal to the tumor site than plasma , which circulates throughout the body , we reason that the circulating miRNome from malignant effusions may be more reflective of the tumor itself . However , further studies comparing the miRNA signatures of pleural fluid-derived exosomes from PEL and other non-KSHV-associated malignancies such as lung cancer are necessary to reveal diagnostic biomarkers unique to PEL . We also demonstrated that human and viral miRNAs are present in circulating exosomes in xenografted mice ( Figures 2 , 4 ) . We used the TIVE L1 [48] xenograft model , which has been shown to be predictive of anti-KS therapies [98] , [102] . The KSHV miRNAs that we consistently detected in these mouse models could only stem from the human graft . Due to the high conservation of cellular miRNAs within the oncogenic clusters , the cross-species detection of miRNAs using the human assays makes it difficult to distinguish miRNAs of human versus mouse origin in these models ( Table S3 , ABI product information , miRBase ) . In some cases , the mature miRNAs share 100% sequence homology across the entire length , not just the seed region ( miRBase , [103] ) and in many cases the targets have co-evolved as well [69] . We observed greater levels of miRNAs in the mouse exosomes compared to human exosomes , which may be due to the fixed 250 µl sample size with respect to the overall amount of blood circulating within a human ( approximately 5 L ) or mouse ( approximately 0 . 0015 L ) . Specific host miRNA markers of tumorigenesis also emerged in our mouse models . We showed previously that host miRNAs are distinct for different stages of KS tumor progression [6] . Therefore , tumorigenic miRNAs combined with viral miRNAs would offer a very specific biomarker signature and may also identify biomarkers for other related cancers . Therefore , we analyzed expression of the oncogenic miR-17-92 and 106b/25 clusters and found that they were significantly enriched in exosomes from TIVE tumor-bearing mice compared with controls ( Figure 5 ) . Several of the oncogenic miRNAs expressed in exosomes were previously found at highly expressed levels in the TIVE cell line independently ( R . Renne , personal communication ) . The mir-17-92 cluster was previously shown to be upregulated in KS tumor biopsies [3] , [4] . This is the first demonstration that the miR-17-92 cluster miRNAs are incorporated into exosomes from KSHV-associated malignancies . These oncogenic miRNAs have also been detected in exosomes derived from leukemia cells and those derived from breast milk [56] , [104] , suggesting that their function is at least in part to mediate paracrine phenotypes . Viral and cellular miRNAs originating from the tumor enter the mouse circulatory system and are readily detected in serum . Since our mouse model exosome signatures recapitulate the clinical KS signatures , this supports the validity of xenograft mice as a reliable model system for KS . We also observed a subset of miRNAs that were highly induced in exosomes and were virtually undetectable in the free , circulating miRNA fraction ( Figure 5D ) . The miRNAs detected exclusively in the exosome fractions are either known to be oncogenic or shown to be upregulated by KSHV infection [105] , [106] . This suggests that certain miRNAs are preferentially incorporated into exosomes and that many proliferative and tumor-associated miRNAs fall into this class . Recently , Palma et al [107] found that selectively exported miRNAs from malignantly transformed cells may be incorporated into customized exosomal particles distinct from the microvesicles that originate from untransformed cells . It is conceivable that these have different systemic stability and thus become enriched in a blood sample . This may be the case with KSHV-induced miRNAs as well since we found miRNAs originating from our transgene model also to be enriched in this fraction . Exosomes serve as a means of intercellular communication with surrounding cells and the contents of exosomes can be shared between cells through the mechanism of exosomal transfer [44] , [45] . Exosomes can deliver functional miRNAs to recipient cells and consequently downregulate expression of target genes [44] , [45] , [108] . Leukemia cell-derived exosomes have recently been shown to affect endothelial cell function through microRNA transfer [56] . Moreover , tumor-derived microRNAs were recently reported to play a functional role through binding to Toll-like receptors , thereby inducing an inflammatory response and influencing tumor growth and metastasis [109] . This in vivo relevance was further demonstrated by inhibiting tumor-secreted miRNAs , which altered tumor formation in mice [109] . Dendritic cell-derived exosomes can be used to prime the immune response as cancer immunotherapy to suppress tumor burden [110] , [111] , [112] . Exosomes have also been recently tested in clinical trials to reduce tumor size [110] , [111] , [112] , [113] . Collectively , these studies further demonstrate the in vivo relevance of exosomes and their potential as mediators of disease phenotypes . In this study , we find stable , systemic KSHV miRNAs and oncomiRs . GO pathway analysis of predicted targets of the oncogenic miRNAs expressed in exosomes revealed a variety of pathways targeted by KSHV during pathogenesis ( Table 1 ) . Since several of these pathways shared a role in cell migration , we further tested the effects of patient-derived exosomes on migration of hTERT-HUVECs . Treatment of cells with exosomes from pleural fluid led to earlier , enhanced migration of endothelial cells , giving these patient-derived exosomes a functional biological role ( Figure 6 ) . Therefore , it is possible that miRNAs specifically expressed within exosomes play a role in disease progression and mediate paracrine effects , which are a hallmark of KSHV tumorigenesis .
De-identified human plasma samples were obtained from healthy controls , patients enrolled in the UNC AIDS Malignancy Trial IRB#09-1201 ( diagnosed with KS or other , non-KS malignancies ) and patients with Kaposi's sarcoma . Primary pleural effusion fluid from three patients was also obtained . For mouse controls , pooled plasma from C57/BL6 mice was obtained from Innovative Research ( Novi , Michigan ) . Blood was collected from C57/BL6 control mice and serum was isolated using a serum-gel tube ( Sarstedt ) . Blood sera were also purified from KSHV latency locus ( 801 ) transgenic mice [50] and Balb/c mice injected with TIVE , latently infected KSHV+ endothelial cells [48] . Purified serum was collected for each group . Samples from each group were pooled as shown in Table S1 to control for individual genetic variation among sample groups and to increase the material available for exosome isolation . Exosome isolations for each sample group were performed in duplicate . The mice were held in UNC animal facilities . Veterinary care was provided by the University veterinarians and support animal care staff . The animal facility is an American Association of Accreditation of Laboratory Animal Care ( AAALAC ) accredited facility . The mice were maintained according to AAALAC guidelines and approved by institutional animal care and use committee ( IACUC ) under protocol #10-247/“KSHV latency mice” . The UNC Chapel Hill animal welfare assurance number is: A-3410-01 . Human plasma , pleural fluid , mouse plasma or serum was centrifuged at 300×g for 10 minutes to pellet any cells . 250 µl of supernatant was transferred to a fresh tube and incubated with 63 µl Exoquick precipitation solution as per the manufacturers' instructions ( System Biosciences , Mountain View , California ) . After incubation for 16 hours at 4°C , contents of each tube were centrifuged for 30 minutes at 1 , 500×g to pellet exosomes . The supernatant containing free , circulating miRNAs was transferred to a fresh tube and the exosomal pellet was resuspended in 100 µl of nuclease-free , PCR-grade water ( Life Technologies , Carlsbad , California ) . Other studies also have validated the Exoquick protocol and have not detected any significant differences in exosome populations compared with ultracentrifugation methods [56] . Patient pleural fluid and tissue culture supernatants ( 35 mls ) were centrifuged for 30 minutes at 2 , 000×g to pellet cells . The supernatant was transferred to a fresh tube and centrifuged at 12 , 000×g for 30 minutes at 4°C . Filtering was performed after clearance of cellular debris and prior to ultracentrifugation where noted . Supernatants were transferred to ultracentrifuge tubes and spun in a SW32Ti swinging bucket rotor for 70 minutes at 110 , 000×g . The supernatant was discarded and the pellet was resuspended in 35 mls of sterile PBS and passed through a 0 . 2-micron filter . Exosomes were centrifuged at 110 , 000×g for an additional 70 minutes to wash . The supernatant was again discarded and the pellet was resuspended in 1 ml of sterile PBS . Samples were transferred to 1 . 5 ml ultracentrifuge tubes and concentrated by ultracentrifugation at 110 , 000×g for 70 minutes using a TLA-100 . 3 rotor . The resulting pellet was resuspended in a small volume and used for subsequent experiments . Samples ( 35 ml starting material ) were ultracentrifuged as previously described to obtain exosome-enriched samples ( ∼500 µl ) . These samples were further enriched for CD63+ exosomes using the CD63+ Dynabead exosome isolation kit according to manufacturer's instructions ( Invitrogen , Life Technologies #10606D ) . Briefly , 500 µl of sample was incubated with 100 µl CD63+ Dynabeads overnight at 4°C . Exosomes were positively selected using a Dynabeads magnet and samples were washed to eliminate non-specific binding . Bead-bound exosomes were resuspended in 300 µl PCR-grade water and approximately 100 µl was used as input for further RNA , DNA and protein analysis by Western blot and qPCR ( Figure 3 ) . Prior to DNA isolation using the Magnapure automated system ( Roche ) , beads were treated with Proteinase K ( 200 µg/ml ) for 2 hours at 55°C to dissociate beads and exosomes . To obtain filtered samples , cell supernatants or patient fluids were first cleared of cellular debris . The resulting supernatant was passed through either a ( 1 ) Nalgene 250 ml Rapid-flow filter unit , 0 . 2 µm CN membrane , 50 mm diameter ( Thermo Scientific , #126-0020 ) for ultracentrifugation and Dynabead methods or ( 2 ) Whatman Puradisc 25AS 0 . 2 µm polyethersulfone membrane filter ( #6780-2502 ) for the ExoQuick methods . The flow-through was then used as input for downstream exosome enrichment protocols ( ExoQuick , ultracentrifugation and CD63+ Dynabeads ) . Flow-through did not seem to affect exosome yield or loss of exosomal markers ( Figures 2 , Figure S6 and data not shown ) . Filtration of samples resulted in a decrease in KSHV load as determined by qPCR for LANA DNA ( Figure 3 ) . Select samples were treated with RNase prior to exosome isolation . RNase treatment was performed as described previously [34] . Briefly , samples were incubated with RNase ( Roche – product # 11119915001 , includes both RNase A and T ) at 37°C for 30 minutes to destroy any freely circulating RNAs ( Figure S4 ) . Exosomes were then isolated using the ExoQuick precipitation solution . Aliquots of purified exosome samples were absorbed directly onto glow-charged thin carbon foils on 400-mesh copper grids without fixation and stained with 2% ( w/v ) uranyl acetate in water . The grids were examined in an FEI Tecnai 12 ( Hillsboro , OR ) electron microscope at 80 kV . Images were captured on a Gatan Orius CCD Camera ( Gatan , Pleasanton , CA ) using Digital Micrograph software . Images for publication were arranged and contrast optimized using Adobe Photoshop CS4 . The supernatant and exosome fractions from each pooled group were used in full as input for RNA isolations . Total RNA was isolated using TRI reagent ( Molecular Research Center , Cincinnati , Ohio ) followed by a phenol/chloroform extraction and ethanol precipitation of RNA as previously described [3] , [4] , [6] . Prior to RNA isolation , 25 fmol of C . elegans cel-mir-39 RNA was added to each sample as a spike-in control [33] , [35] . Total RNA was resuspended in nuclease-free , PCR-grade water and the RNA concentration was determined using the NanoDrop spectrophotometer ( Thermo Scientific , Waltham , Massachusetts ) . Exosomes were isolated using the Exoquick kit as described above . Both exosomal fractions and supernatants were lysed in 100 µl NP40 lysis buffer ( 50 mM Tris , 150 mM NaCl , 1% NP-40 with 50 mM NaF , 1 mM sodium vanadate , 30 mM beta-glycerophosphate , 1 mM PMSF and protease inhibitor cocktail ( Sigma , St . Louis , Missouri ) . Lysates ( 10 µl ) were run on a 10% SDS-PAGE gel , transferred to a nitrocellulose membrane ( Hybond , GE Healthcare , Pittsburgh , Pennsylvania ) and blocked in 5% dry milk in Tris-buffered saline with 0 . 1% Tween 20 overnight at 4°C . CD9 was detected using the CD9 EXOAB antibody kit as per the manufacturer's instructions ( System Biosciences , Mountain View , California ) . Anti-flotillin-2 ( BD #610383 ) was used at 1∶5000 and anti-beta actin ( Sigma #A2228 ) , anti-Hsp90 alpha ( Assay Designs #SPS-771 ) and anti-Hsp90 beta ( Assay Designs #SPA-842 ) were used at 1∶2000 . Secondary HRP antibodies ( Vector Labs Cat# PI-1000 – rabbit , Cat#PI-2000 – mouse , Burlingame , California ) were used at 1∶10 , 000 and blots were developed using Pierce ECL Western blotting substrate ( Pierce , Rockford , Illinois ) . Approximately 1 µg of total RNA in 75 µl PCR-grade water was DNase-treated using the Turbo-DNase kit ( Life Technologies , Carlsbad , California ) . The RNA was run on an Agilent RNA Nano 6000 chip to assess RNA quality and the presence of small RNA populations . Next , samples ( 200 ng ) were used as input for cDNA synthesis using the Megaplex RT kit version 3 . 0 , Human Pools A and B ( Life Technologies , Carlsbad , California ) . Following cDNA synthesis , samples were further amplified using the Megaplex PreAmp kit version 3 . 0 , Human Pools A and B ( Life Technologies , Carlsbad , California ) . The PreAmp product was diluted 5-fold and the amplified cDNA samples were used as previously described [54] using a library of 754 Taqman cellular miRNA primers ( Life Technologies , Carlsbad , California ) and a robotic pipetting system for automated plate setup [54] ( Tecan , Männedorf , Switzerland ) . qPCR reactions were run on a Lightcycler 480 ( Roche , Indianapolis , Indiana ) . Automated plate setup and replicates correlated well , with little standard deviation between replicate CTs and no significant quadrant errors ( Figure S17 , average standard deviation among 4 replicates = 0 . 35 CT ) . Reactions were also performed to detect levels of the spike-in control cel-mir-39 and the KSHV miRNAs using individual Taqman RT and qPCR miRNA assays ( data not shown ) . PCR products of KSHV miR-K2 were run on an HTDNA 1K chip on the Caliper LabChip GX ( Caliper Life Sciences , Hopkinton , Massachusetts ) to confirm the results via gel electrophoresis . In-depth statistical analysis of technical replicates was performed in R and revealed little variation in CTs below 45 . CT variation among the same sample in each of 4 quadrants was also assessed and no significant deviation was observed ( Figure S17 ) . Cycle threshold ( CT ) values for each sample were averaged across two technical replicates ( one replicate from each exosome isolation ) and those with a CT greater than 45 were excluded and recorded as negative . The remaining data were assigned expression scores based on a specific range of CT values . The CT range of expression was 20–45 , with CT = 20 as the highest expression score ( expression score = 3 ) and CT = 45+ yielding the lowest score of 0 . Expression scores were assigned in increments of 0 . 5 , with one expression class including a range of 4 CTs . Therefore , any significant difference reported was confirmed as a difference greater than 4 CTs or approximately 16-fold . The expression scores were then subjected to unsupervised classical clustering with Pearson coefficient using Array Miner™ ( Optimal Design , Brussels , Belgium ) . PCA three-dimensional clustering figures and heatmaps of miRNA expression are shown . Exosomes were isolated using the Exoquick kit as described above ( System Biosciences , Mountain View , California ) . Exosome pellets and supernatants containing free , circulating miRNAs and proteins were resuspended in 200 µl PCR-grade water . Exosome-enriched samples were then treated with DNase for 30 minutes at 37°C according to manufacturer's instructions for the Turbo DNA-free kit ( Ambion , Life Technologies ) . DNase-treated samples were then adjusted to 500 µl volume and used as input for DNA extraction on the Magnapure ( Roche , Indianapolis , Indiana ) using the large volume kit and program settings for total nucleic acid from plasma samples . DNA was eluted in 100 µl total volume . Extracted DNA ( 5 µl ) from each sample was used to determine the presence of KSHV using primers: F primer 5′-GGAAGAGCCCATAATCTTGC-3′; R primer 5′- GCCTCATACGAACTCGAGGT-3′ . Ten-fold dilutions of a KSHV oligonucleotide target with the following sequence were used to generate a standard curve: 5′-GGAAGAGCCCATAATCTTGCACGACTCAGACCTGGAGTTCGTATGAGGC-3′ . PCR products were then loaded on an HTDNA 1K chip on the Caliper LabChip GX ( Caliper Life Sciences , Hopkinton , Massachusetts ) to confirm the presence of KSHV DNA . Oncomirs that were upregulated in the KSHV-associated sample groups were input into the microRNA target prediction database ( MetA MicroRNA target Interference ( MAMI ) , http://mami . med . harvard . edu/ ) . The settings used for target prediction were highest stringency and included only 3′UTR target sites . The Entrez IDs of the predicted targets were used as input for the GO pathway database DAVID . The KEGG pathway terms of highest correlation were determined along with statistical significance ( P value ) and the number of predicted targets in each pathway . Specific pathways involved in migration were determined by searching peer-reviewed literature that included mechanistic data for migration and each specific pathway . Pathway analysis was performed for microRNAs induced in KSHV-associated malignancies from our exosome study and for microRNAs induced by WNV infection in hTERT-HUVEC cells . hTERT-HUVEC cells [92] were seeded at 80% confluence in a 24-well plate and allowed to equilibrate overnight before treating with exosomes for a period of 24 hours . Annexin blocking was performed as previously described [44] . Briefly , exosomes or supernatant were incubated with Annexin V-FITC for 1 hour at room temperature prior to adding to cells . Cells were grown in EGM-2 media with all supplements ( Lonza , EGM-2 Bulletkit ) . Each well was scratched using a standard 200 µl pipette tip and the location of the scratch was marked to locate the initial scratch at subsequent time points . Cells were washed with media to eliminate floating cells and replaced with fresh media immediately after the wound initiation . Images were captured at 0 h , 8 h and 16 h after the initial scratch . Images are shown at 100× magnification and were obtained on a Leica DMIL microscope using a HI Plan 10×/0 . 25 PHI objective and QImaging camera ( Cooled color , RTV 10 bit ) paired with QCapture imaging software 3 . 0 . hTERT-HUVEC cells were treated with exosomes isolated using the ExoQuick method . After 24 hours of incubating with exosomes , cells were serum-starved for 6 hours and then lightly trypsinized for 3 minutes to detach cells . Trypsin was inactivated with media containing FBS and cells were centrifuged at 300 g for 5 minutes . Cells were washed with PBS and the remaining pellet was resuspended to a concentration of 300 , 000 cells/ml in serum-free EBM-2 media ( Lonza ) . 30 , 000 cells were plated per well of the upper chamber of an xCelligence CIM Plate 16 ( Acea Biosciences ) . Prior to CIM plate assembly , both sides of the membrane were coated with 20 µg/ml fibronectin . Media containing FBS was placed in the lower chamber as the chemoattractant . The upper and lower chambers of the CIM plate were assembled and reads were taken every 2 minutes for a period of 24 hours using the RTCA DP xCelligence instrument ( Acea Biosciences ) . Supernatants from the scratch assay ( hTERT-HUVECs treated with exosomes ) were analyzed for levels of IL-6 using ELISA according to manufacturer's protocol ( eBioscience , #88-7066-88 ) . Briefly , supernatants were collected at 16 hours post-scratch and were diluted 1∶10 for ELISA . A standard curve of IL-6 positive control was generated and levels of IL-6 ( pg/ml ) were calculated . The average of three technical replicates of two independent experiments was calculated . | Circulating microRNAs ( miRNAs ) , such as those found in exosomes , have emerged as diagnostic tools and hold promise as minimally invasive , stable biomarkers . Transfer of tumor-derived exosomal miRNAs to surrounding cells may be an important form of cellular communication . Kaposi's sarcoma-associated herpesvirus ( KSHV ) is the etiological agent of Kaposi's sarcoma ( KS ) , the most common AIDS-defining cancer worldwide . Here , we survey systemically circulating miRNAs and reveal potential biomarkers for KS and Primary Effusion Lymphoma ( PEL ) . This expands previous tissue culture studies by profiling clinical samples and by using two new mouse models of KSHV tumorigenesis . Profiling of circulating miRNAs revealed that oncogenic and viral miRNAs were present in exosomes from KS patient plasma , pleural effusions and mouse models of KS . Analysis of human oncogenic miRNAs , including the well-known miR-17-92 cluster , revealed that several miRNAs were preferentially incorporated into exosomes in our KS mouse model . Gene ontology analysis of upregulated miRNAs showed that the majority of pathways affected were known targets of KSHV signaling pathways . Transfer of these oncogenic exosomes to immortalized hTERT-HUVEC cells enhanced cell migration and IL-6 secretion . These circulating miRNAs and KS derived exosomes may therefore be part of the paracrine signaling mechanism that mediates KSHV pathogenesis . |
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Informed consent is one of the principal ethical requirements of conducting clinical research , regardless of the study setting . Breaches in the quality of the informed consent process are frequently described in reference to clinical trials conducted in developing countries , due to low levels of formal education , a lack of familiarity with biomedical research , and limited access to health services in these countries . However , few studies have directly compared the quality of the informed consent process in developed and developing countries using the same tool and in similar clinical trials . This study was conducted to compare the quality of the informed consent process of a series of clinical trials of an investigational hookworm vaccine that were performed in Brazil and the United States . A standardized questionnaire was used to assess the ethical quality of the informed consent process in a series of Phase 1 clinical trials of the Na-GST-1/Alhydrogel hookworm vaccine that were conducted in healthy adults in Brazil and the United States . In Brazil , the trial was conducted at two sites , one in the hookworm non-endemic urban area of Belo Horizonte , Minas , and one in the rural , resource-limited town of Americaninhas , both in the state of Minas Gerais; the American trial was conducted in Washington , DC . A 32-question survey was administered after the informed consent document was signed at each of the three trial sites; it assessed participants’ understanding of information about the study presented in the document as well as the voluntariness of their decision to participate . 105 participants completed the questionnaire: 63 in Americaninhas , 18 in Belo Horizonte , and 24 in Washington , DC . Overall knowledge about the trial was suboptimal: the mean number of correct answers to questions about study objectives , methods , duration , rights , and potential risks and benefits , was 45 . 6% in Americaninhas , 65 . 2% in Belo Horizonte , and 59 . 1% in Washington , DC . Although there was no difference in the rate of correct answers between participants in Belo Horizonte and Washington , DC , there was a significant gap between participants at these two locations compared to Americaninhas ( p = 0 . 0002 and p = 0 . 0001 , respectively ) , which had a lower percentage of correct answers . Attitudes towards participating in the clinical trial also differed by site: while approximately 40% had doubts about participating in Washington , DC and Belo Horizonte , only 1 . 5% had concerns in Americaninhas . Finally , in Belo Horizonte and Washington , high percentages cited a desire to help others as motivation for participating , whereas in Americaninhas , the most common reason for participating was personal interest ( p = 0 . 001 ) . Understanding of information about a Phase 1 clinical trial of an experimental hookworm vaccine following informed consent was suboptimal , regardless of study site . Although overall there were no differences in knowledge between Brazil and the US , a lower level of understanding about the trial was seen in participants at the rural , resource-limited Brazilian site . These findings demonstrate the need for educational interventions directed at potential clinical trial participants , both in developing and developed countries , in order to improve understanding of the informed consent document .
The principle of informed consent is internationally recognized as one of the essential elements of the ethical conduct of research involving human subjects [1 , 2] . Within its ethical and legal foundations , obtaining informed consent has two specific objectives: to respect and promote the autonomy of research participants , and to protect the research subjects from possible harm or exploitation [3] . The informed consent process depends upon five criteria: the willingness to participate , the capacity to make a decision , disclosure of information , comprehension , and the decision to participate [4] . The quality of the informed consent process is determined primarily by the level of the study volunteers’ understanding and by the absence of coercion from the decision-making process [5 , 6] . The findings of research indicate that , in general , there are gaps in individuals’ knowledge of various aspects of the clinical trials for which they are being consented , which may potentially impact their decision to participate [7–12] . In this sense , critics fear that for many clinical trials , the informed consent process may not be fully meeting its intended objectives [13] . Breaches in the informed consent process are frequently described in reference to clinical trials conducted in developing countries [14] . Low levels of formal education , a lack of familiarity with biomedical research , and limited access to health services in these countries have been associated with an inadequate informed consent [15–20] . A meta-analysis of the subject , however , demonstrates that this problem is not limited to developing countries , as several aspects of the informed consent process are poorly understood by participants in clinical trials both in developing and in developed countries [3] . In fact , one of the few studies dedicated to an empirical comparison of consent obtained in developed and developing countries revealed that there were no substantial differences in the participants’ knowledge between the two settings [21] . Furthermore , Mandava et al observed that the understanding of information about studies varies within both groups of volunteers and that to assume that clinical trials conducted in developing countries are less ethical than those conducted in developed countries is an oversimplification of an undoubtedly complex situation [11] . Most of the studies published to date on the informed consent process , however , have acknowledged limitations in their methodologies . Critically , the use of different measurement instruments has hindered comparison of results between different clinical trials . In this sense , these investigations have provided limited contributions to the discussion on the comparative quality of the consent process in developed and developing countries . Given the methodological limitations of studies reported in the literature , we conducted an investigation to compare the quality of the informed consent process of a series of clinical trials performed in Brazil and the United States , using a standardized questionnaire . Our research sought to answer the following question: can the ethical quality of the informed consent of participants in clinical trials carried out in developed countries be considered superior to that obtained in developing countries ? The authors hypothesize that there is no substantial difference in the quality of informed consent of research subjects living in developed and developing countries . The justification for this study resides in the need to assess whether there is cause for concern regarding the protection of research participants in countries in which clinical trials are being conducted . The identification of differences and similarities between informed consent processes in developed and developing countries may aid in the implementation of specific strategies to protect participants in each research setting . Among these strategies , we can cite the need to support and inform institutional ethical review committees in their evaluations of the informed consent process of proposed clinical research .
A descriptive quantitative study with a cross-sectional design was conducted to compare the quality of the informed consent process of two Phase 1 clinical trials performed in Brazil and the United States , respectively , of the Na-GST-1/Alhydrogel hookworm vaccine that is being developed by the Sabin Vaccine Development Partnership [22] . The trials were carried out in the cities of Belo Horizonte and Americaninhas ( Brazil ) and in Washington , D . C . ( United States of America ) . In Brazil , a Phase 1 clinical trial was conducted between 2011 and 2014 of the safety and immunogenicity of Na-GST-1/Alhydrogel administered with or without the GLA-AF immunostimulant in healthy adults ( protocol SVI-10-01 , NCT01261130 ) . The principal objective of this trial was to estimate the frequency of adverse events to the candidate hookworm vaccine . Vaccinations were conducted first in the hookworm non-endemic site of Belo Horizonte and then in the hookworm-endemic area of Americaninhas , to establish the vaccine’s safety in a hookworm-unexposed population before testing it in endemic areas . A Phase 1 clinical trial of similar design was conducted of Na-GST-1/Alhydrogel administered with or without a different immunostimulant ( a CPG oligodeoxynucleotide ) in Washington , DC , starting in 2014 ( protocol SVI-GST-03 , NCT02143518 ) . In both clinical trials , healthy adults ( aged 18–45 years in Brazil and 18–50 years in the USA ) were enrolled and vaccinated by intramuscular injection according to a 0 , 2 , and 4-month schedule . In Brazil , the SVI-10-01 clinical trial was carried out in two separate centers: in Belo Horizonte and in Americaninhas , 556 km from Belo Horizonte . Americaninhas is a town of approximately 1500 residents located in the mostly rural municipality of Novo Oriente de Minas Gerais , in the Mucuri Valley , in the northeast part of the state of Minas Gerais . Belo Horizonte is the capital of Minas Gerais , with a population of 2 , 479 , 175 inhabitants and a human development index ( HDI ) of 0 . 81 , which is considered very high [23] . On the other hand , Americaninhas is a region with low social indicators: it has an HDI of 0 . 60 , the 6th-worst amongst Minas Gerais municipalities [24] . Low levels of formal education are a concern: 57 . 1% of its inhabitants are illiterate [24] . In the United States , the SVI-GST-03 clinical trial was conducted at the George Washington ( GW ) Medical Faculty Associates , a high-volume outpatient clinic affiliated with the GW hospital in the urban center of Washington , District of Columbia . For both clinical trials , all participants underwent the informed consent interview and , if they decided to participate , signed an informed consent form ( ICF ) that had been approved by the ethical review committees of the Centro de Pesquisas René Rachou and the Brazilian federal Ministry of Health ( for SVI-10-01 ) , as well as the George Washington University ( for both trials ) . The approved ICFs that were used for the trial in Brazil in Belo Horizonte ( S1 ICF ) and Americaninhas ( S2 ICF ) , as well as the approved ICF that was used for the trial in Washington , DC ( S3 ICF ) are included as supporting information . The ICFs for the two trials differed primarily in the description of the different adjuvants that were used in the vaccine formulations ( GLA-AF in Brazil vs . CPG in the United States ) and in country-specific requirements such as the inclusion of language related to the Health Insurance Portability and Accountability Act in the United States . However , the ICFs were the same regarding the description of the study rationale and the nature of the hookworm vaccine , the risks and benefits of the Na-GST-1/Alhydrogel hookworm vaccine , the number of vaccinations to be administered , the duration of the study ( per participant ) , the type of procedures to be conducted , the fact that participation was voluntary , and that consent could be withdrawn at any time with no negative consequences to participants . Completion of the informed consent questionnaire was optional and was not required for participation in the rest of the respective study . Data were collected through the use of a semi-structured questionnaire consisting of 32 questions that assessed the participants’ understanding of the information about the study presented in the informed consent document for the respective clinical trial as well as the voluntariness of their decision to participate . The questions sought to evaluate their knowledge of the purpose of the clinical trial , the study methods , the duration of the trial , the participants’ rights , and the potential risks and benefits of participation . The participants’ socio-demographic and economic information were also collected . Given the lack of appropriate existing questionnaires for this type of clinical trial , especially in two languages , questions , although not pre-tested , were based on published questionnaires used for clinical studies for other disease areas [25–27]; the International Ethical Guidelines for Biomedical Research [1]; and , on the experience the researchers have gained from working in this field for over 13 years . In order to improve understanding of the questions , the authors followed the recommendations of Vieira , which included using plain and easily understandable language ( assessed by the Flesch reading-ease score ) ; using general language rather than technical terminology; and , avoiding negative phrases and words with double meaning [28] . The questionnaire was originally formulated in Brazilian Portuguese and later translated into English by experts on the research subject . With the agreement of the researchers involved , this process favored an interpretation of concepts rather than a literal translation of terms . Questions were also tailored to the specific goals , risks and benefits of each clinical trial . Given the need for reliability of the measuring instrument , the researchers opted for using open-ended questions since pre-determined answers to close-ended questions might influence the participants’ responses [28] . The preference for this type of question arises from a study by Lindegger et al , which revealed that the participants’ understanding of the informed consent information was overestimated when evaluated by instruments using close-ended questions compared with those using open-ended questions [26] . Data were collected after all volunteers participated in the informed consent process and signed the informed consent form . In Brazil , the questionnaire was administered by interviewers who had received training in how to standardize data collection and improve reliability , in order to minimize the risk of information bias . The interviewers were undergraduate and graduate students in nursing , education , psychology and medicine , had no relationship with the clinical research staff , and were specially trained to comprehensively transcribe the participants' responses . In the USA , the questionnaires were self-administered at the study site clinic . Different methods of applying the questionnaire in the USA and Brazil were chosen due to differences in the level of education of the volunteers , as had been observed in previous studies carried out in the same areas . The administration of the questionnaire lasted on average 10 minutes and it was carried out at the same setting in which the informed consent process was conducted . Most of the questionnaires were completed on the day of first vaccination or , in a small number of cases at all sites , immediately after signing the informed consent form . After collection , data were coded and entered into an SPSS database ( version 14 . 0 ) and Microsoft Excel . In order to ensure reliability , data were independently entered twice . In cases of discrepancy between the two entries , the lead researchers referred to the original questionnaire and determined the actual response by consensus . Analysis of the open questions followed the categorization of the responses , based on the criterion of appearance frequency . To avoid bias in the process of categorization , this step was performed independently by two different professionals . The end results of this stage were compared; in cases of disagreement between the categorizations , the professionals debated , each justifying their choice . After an agreement had been reached by consensus , the participant’s response was classified into the appropriate category . Data were initially analyzed using descriptive statistics including frequency calculations ( simple and relative ) , as well as mean and standard deviation . Subsequent analyses compared the percentages of correct answers ( categorical variables ) using the chi-square test . A Knowledge Index ( KI ) was created to measure participants' knowledge on all issues evaluated . This index consists of the sum of the participants’ correct responses divided by the total number of questions ( 11 ) and is expressed as a percentage ranging from 0% ( the participant answered all questions incorrectly ) to 100% ( the participant answered all questions correctly ) . The analysis variable was analysed by calculating the mean , median and interquartile ranges . The KI was compared between study sites using the one-way ANOVA and Tukey-HSD tests . A significance level ( p value ) of 0 . 05 was used for all analyses . The normality of continuous variables was assessed by the Kolmogorov-Smirnov test .
A total of 105 study participants completed the informed consent questionnaire and were included in the analysis: 63 ( 60% ) from Americaninhas , 18 ( 17% ) from Belo Horizonte and 24 ( 23% ) from Washington , DC . In Americaninhas , 3 of 66 ( 4 . 5% ) participants enrolled in the clinical trial declined to complete the questionnaire , whereas 12 of 36 ( 33% ) and 0 of 24 ( 0% ) declined in Belo Horizonte and Washington , DC , respectively . Significantly more participants in Belo Horizonte declined to complete the questionnaire than in either Americaninhas or Washington , DC ( p = 0 . 016 ) , although the reasons for refusal were not recorded . The average age of those completing the questionnaire was 29 . 3 years ( SD 8 . 9 , range 18 to 50 ) , which varied significantly by study site ( Belo Horizonte , 23 . 7 years , Americaninhas , 29 . 6 years , Washington DC , 32 . 8 years; p = 0 . 021 , Kruskal-Wallis test ) . The proportion of study participants who were female ( 46 . 7% ) , on the other hand , did not vary significantly between the study sites . Regarding the maximum level of education achieved by participants , 37 ( 35 . 2% ) had primary education , 33 ( 31 . 4% ) had secondary education , 25 ( 23 . 8% ) had post-secondary education and 6 ( 5 . 7% ) had post-graduate education; 4 ( 3 . 8% ) participants were deemed illiterate ( all in Americaninhas ) . Levels of education were not uniform across the study sites: the chi-square test revealed a statistically significant difference between the site of the clinical trial and the participants’ maximum level of education , with the lowest levels of education observed in Americaninhas ( p<0 . 001 ) . The distribution of the participants according to their level of education is shown in Table 1 . Most participants did not have a health insurance plan ( n = 77; 73 . 3% ) , had never participated in a clinical trial ( n = 78; 74 . 3% ) , and had no formal employment contract ( n = 72 , 68 . 6% ) . The chi-square and Kruskal-Wallis tests revealed statistically significant differences between the location of the clinical trial and having a health insurance plan ( p<0 . 001 ) , or having previously participated in a clinical trial ( p = 0 . 030 ) . Regarding health insurance , it appears that only three participants had formal insurance in Americaninhas ( 4 . 0% ) , with higher proportions being found in Belo Horizonte ( 16 . 6% ) and Washington , DC ( 79 . 1% ) . In terms of formal employment , most participants in Americaninhas ( 71 . 3% ) and in Washington , DC ( 79 . 1% ) had jobs , with a lower proportion in Belo Horizonte ( 44 . 4% ) . While 55 . 5% of participants in Washington , DC had previously participated in a clinical trial , much lower rates were seen among the participants in either Belo Horizonte ( 0 . 5% ) or in Americaninhas ( 25 . 5% ) . Table 2 shows the absolute and relative frequencies of correct answers to questions that evaluated participants’ knowledge regarding information about the clinical trial that was contained in the informed consent form . A majority of participants knew the correct answers to at least seven out of the eleven questions . The research subjects from Belo Horizonte had the highest percentage of correct answers , with an average of five questions ( 45% ) answered correctly , followed by four in the United States ( 36% ) , and one in Americaninhas ( 19% ) . The analysis of each question demonstrated that the majority of participants understood that deciding not to participate in the clinical trial for which they were being asked to volunteer would not result in any negative consequences . However , in Americaninhas only 51% recognized that declining to participate was not associated with of any negative consequences of not participating , compared to 83% and 87% in USA and Belo Horizonte , respectively . In addition , high proportions were aware of what they should do in case of illness during the trial , the possibility that they might experience anticipated or unanticipated adverse effects after being vaccinated , and that they could contact members of the study team if they had any doubts or questions about the trial . As shown in Table 2 , there were statistically significant differences between the study sites in the comprehension of the following items: the study objectives ( p = 0 . 001 ) ; the consequences of choosing not to participate in the study ( p = 0 . 002 ) ; the potential risks of the investigational vaccine ( p = 0 . 037 ) ; and the possibility of unanticipated adverse effects ( p = 0 . 001 ) . Table 3 summarizes the participants’ knowledge about information contained in the informed consent form by calculating a “knowledge index” ( KI ) consisting of the mean number of correct answers to these questions on the questionnaire , by study site . In Americaninhas , participants had an average of 45 . 9% of correct answers; in Belo Horizonte , 65 . 2%; while in Washington , DC , the percentage was 59 . 1% . Table 4 provides comparisons between the average number of correct answers in the KI according to the one-way ANOVA and Tukey-HSD tests . The overall association between the study site and the average knowledge about the clinical trial was statistically significant ( one-way ANOVA: F = 13 . 931 , p = 0 . 0001 ) . Although there was no difference in the rate of correct answers between participants in Belo Horizonte and those in Washington , DC ( p = 0 . 437 ) , there was a significant gap between the KI of participants at these two locations compared to Americaninhas ( p = 0 . 0002 and p = 0 . 0001 , respectively ) , where a lower percentage of correct answers was recorded . Table 5 provides the absolute and relative frequencies of responses concerning the participants’ attitudes towards clinical research and the voluntariness of their decision to participate in the clinical trial . In all three locations , the majority of participants reported not being afraid of participating in the research and trusting the investigators responsible for the trial . However , in Belo Horizonte , 72 . 2% of participants declared having enrolled in the study only for the benefits , a situation that was not observed at the two other sites . Regarding the participants’ doubts about participating in the clinical trial , whereas in Washington , DC , and Belo Horizonte approximately 40% reported having doubts , in Americaninhas only 1 . 5% admitted having some concerns about participating . Virtually all participants ( 90 . 5% ) resident at that site believed that participation in the clinical trial could lead to improvements in their health . In Washington , DC , only 4 . 2% of participants admitted to being afraid to participate , while the comparable values in Americaninhas and in Belo Horizonte were higher , at 38 . 1% and 27 . 7% , respectively ( p = 0 . 01 ) . It appears that at the Brazilian study sites , participation in informational meetings about the study with study team members was a significant factor that influenced their decision to participate ( 76 . 2% and 77 . 8% in Americaninhas and Belo Horizonte , respectively ) , while in Washington only 41 . 7% cited this influence ( p = 0 . 001 ) ( Table 5 ) . Table 6 details participants’ responses regarding their attitudes to the clinical trial using the Likert scale . In both Brazil and the United States , most ( 90 . 4% and 100% , respectively ) respondents agreed or strongly agreed when asked if they agreed or disagreed with the statement , “You want to participate in the study . ” When asked if they only “tolerated” participation in the clinical trial , most subjects in the United States and in Belo Horizonte disagreed or strongly disagreed . On the other hand , in Americaninhas , most participants agreed or strongly agreed with the same question ( p = 0 . 02 ) . Table 7 shows the absolute and relative frequencies of the study subjects’ motivations for participating in the clinical trial . When asked about their main motivations for participating , those in Belo Horizonte and Americaninhas reported personal or social advantages that would benefit them . In contrast , of the study participants in Washington , DC , 17 . 4% said that participating in the trial would bring more benefits mainly to society through development of a new vaccine for hookworm . In Americaninhas , the most common reason for participating was motivated by personal interest; in Belo Horizonte , half of participants reported that their main reason for participating was a desire to help others . In the United States , high percentages cited the possibility of helping others and receiving monetary compensation as reasons for participating . The reasons for participating in the clinical trial varied significantly by study site ( p = 0 . 001 , chi-square test ) .
The results of the study reported herein indicate that there were no substantial differences between the overall quality of the informed consent obtained from participants in similar clinical trials conducted in the United States , a developed country , and in Brazil , a developing one . Such a conclusion is supported by the absence of any statistically significant differences between participants in Belo Horizonte and the United States in their knowledge of information about the clinical trial contained in the informed consent form . However , our research nevertheless showed a significant association between the particular site where the trial was conducted and the quality of the informed consent process: statistically significant differences were observed in the study participants’ knowledge about the trial between Americaninhas , Belo Horizonte and Washington , DC , with residents of Americaninhas having the lowest percentage of correct answers on the informed consent questionnaire . The inequality of living conditions within the Brazilian population is a widespread reality . In many regions of Brazil , substantial disparities exist between urban and rural areas with regard to household income , basic infrastructure , access to healthcare , and quality of education . For example , the municipality of Padre Paraíso , the seat of the Americaninhas district where one of the clinical trials of this study took place , has an HDI of 0 . 60 that is classified as low , whereas in Belo Horizonte , the capital of the state of Minas Gerais , the HDI is considered “very high” at 0 . 81 [24] . Socio-demographic and economic characteristics such as advanced age , low level of education , female gender , and low socioeconomic status have been associated with a reduced quality of the informed consent process [29–33] . Therefore , it is essential that the characteristics of the potential research participants being recruited into a clinical trial be adequately analyzed in order to identify factors that may negatively influence the quality of the informed consent obtained from them . These characteristics must also be analyzed to support researchers in developing strategies to encourage the dissemination and understanding of information about clinical research , such as the use of appropriate language in the informed consent form and the development of more relevant educational interventions that match the context of study participants . There are suggestions in the literature that efforts to establish greater links between researchers and participants , such as creating an atmosphere of openness to dialogue and giving opportunities for asking questions , can facilitate the consent process [34] . Regarding the lower percentage of success on the informed consent questionnaire observed in Americaninhas , the relative lack of understanding at this site about the scientific purpose of the study suggests that the study subjects in Americaninhas did not consider themselves to be participants in clinical research . Instead , they may have conflated the scientific purpose of the clinical trial with the provision of medical care , a phenomenon that has been termed the “therapeutic misconception” [33] . Although the study volunteers were participating in a Phase 1 trial that by definition may not provide any direct benefit to participants , this phenomenon may have resulted from the fact that they either received the hepatitis B vaccine as the comparator vaccine or were offered it at the end of the study and , if necessary , received treatment for hookworm and anemia , as well as being referred for further investigations or management in cases of other illnesses or medical conditions . Commonly observed amongst clinical trial participants who are socially and economically disadvantaged , the therapeutic misconception results from confusion between routine medical care provided to study participants in the context of a trial and the objectives of the study in which an experimental product is being tested . Instead of understanding that they are participating in an experiment , these individuals believe that the research protocols are tailor-made for them and their health-related issues [35 , 36] . This phenomenon was observed in a previous study conducted in Americaninhas , in which many participants in a clinical trial of the Na-ASP-2 vaccine against hookworm believed that the purpose of the investigation was the medical treatment of its participants rather than the testing of an experimental preventative vaccine [7] . The manifestation of the therapeutic misconception is in conflict with the doctrine of informed consent since the participant experiencing such a misconception may not adequately weigh the risks and benefits of their participation in the clinical trial and instead base their decision to participate on incorrect criteria and false expectations [37] . The limited knowledge of the consequences of not participating in a clinical trial that was observed in Americaninhas is similar to the findings of Tam et al [3] , which demonstrated that participants in clinical trials conducted in developing countries are less acquainted with issues related to participation or refusal of participation in a study . Many studies have affirmed that information about the right to refuse participation and to withdraw consent at any time , without affecting their rights or access to medical care is one of the most important items to be conveyed to prospective research participants , particularly to those in developing countries [38 , 39] . Despite the above differences , it should be noted that most participants—at all study sites—had an incomplete knowledge of the information contained in the informed consent form . The highest average knowledge as assessed by a structured questionnaire amongst the three locations was in Belo Horizonte ( 65 . 2% ) , which is lower than the average reported in other studies of the informed consent process in developing countries [40–42] , in developed countries [43] , and in both types of countries [21] . Regardless of the setting in which the research is conducted , a lack of understanding of the information about the research impairs the quality of the individuals’ decision to participate [44] . The inability to describe the risks and possible adverse effects of participation in a particular clinical trial , observed mostly in Americaninhas and in Washington , DC , has been seen in other studies of the informed consent process , such as in a breast cancer clinical trial conducted in Japan [45] . In that study , the scores achieved by participants were acceptable in terms of a broad understanding of the informed consent document , but were low for particular items such as the experimental nature of the study , potential risks , benefits , and compensation . The limited understanding of information contained in the informed consent form may influence the voluntariness of an individual’s decision to participate [8] . In this sense , the results of the questionnaire related to knowledge about the clinical trial discussed above suggest that the informed consent obtained in Americaninhas may have been relatively compromised in relation to the voluntary decision to participate , in comparison to other places . Other aspects related to the willingness of volunteers from Brazil to participate in the study consist of potential indirect benefits associated with participation in the clinical research , such as learning about the disease and receiving a medical examination as part of screening . While such care is provided free of charge by the universal Brazilian public system of health in Americaninhas , access to medical care is hampered due to a shortage of medical professionals in the region and to excess demand , conditions that may have had a direct influence on willingness to participate in the study . Aspects related to the participants’ attitudes also influence the voluntariness and the quality of the informed consent , such as fear of participation , trust in the study team , the expression of doubt , and the underlying intentions and motivations for participating . Regarding the voluntariness of the decision to participate in the clinical trials that were the subject of the current research , most participants at all three sites reported not being afraid of participating and having trust in the investigators who were responsible for the clinical trials , an important finding that contributed to the quality of the informed consent in both countries . Jenkins and Fallowfield remark that fear and dissatisfaction with certain research procedures were cited as reasons for people declining participation in cancer research studies [46] . Despite this , a study aimed at analyzing attitudes toward participation in clinical research in a developing country demonstrated that the majority of respondents reported that they would not like to entrust decisions about their health to physicians . Only a small proportion of participants , particularly those without any formal education , would leave the process of making health decisions in the hands of physicians [47] . The study participants from Americaninhas , unlike those from Belo Horizonte or Washington , DC , had very few doubts or concerns about the clinical research in which they were participating . This might be explained by a reduced capacity to ask questions during the informed consent process due to lower levels of education [48] . In addition , we found that the popular image of the physician overlapped with the image of the researcher in this community , something previously observed in this region [49] . This situation might intimidate study volunteers , making them less likely to ask critical questions of the researchers [40] . It has previously been shown that clinical trial participants with higher levels of education are more inclined to discuss their potential participation in the study with the research team [35] . In contrast , in Americaninhas the belief that participation in a clinical trial may improve their health status , may lead them to be less critical of the research aspects of the study [50] . Regarding the desire to enroll in the clinical trial , we found that most participants actively wished to participate; however , paradoxically , in Americaninhas , half merely “tolerated” participation . This contradiction might be attributed to two factors: a difficulty in understanding the question , despite the care taken during questionnaire preparation and implementation; or , a limited voluntariness in the decision-making process , resulting from a lack of understanding of the information about the trial , third-party influences , or the level of trust in the researchers [8] . Another aspect that may be associated with limited voluntariness and that may help to explain this contradiction is the notion that participation in the clinical trial might improve one’s health . Especially in developing countries , the level of trust in the investigators conducting a clinical trial might influence a reluctant volunteer during the consent process due to their degree of authority in such an environment; a potential research participant might feel that participation cannot be refused to such an individual [51] . For example , a review by Mandava et al revealed that participants from developing countries are less likely to refuse participation in research and are more likely to worry about the consequences of their refusal to participate [11] . Regarding the motivation for participating in the clinical trial conducted in Americaninhas , most individuals attributed it to a personal decision taken of their own free will . Such motivation may be due to the endemic level of hookworm infection in the study area; that is , proximity to the disease being studied might lead the volunteer to associate participation in the clinical trial with the prospect of improving their health . In contrast , in both Belo Horizonte and in Washington , DC , the participants reported that their participation was motivated by the possibility of helping other people , perhaps driven by their reduced exposure to the disease for which the vaccine is being developed . Specifically in the case of the United States , volunteers named the financial benefit deriving from their participation as a major factor in enrolling in the research . Receiving a financial incentive for participation in clinical trials was reported as a reason for enrolling in another healthy volunteer study conducted in New Haven , Connecticut , in which 58% of the respondents reported it as the primary motivation for participating [52] . The same study identified a positive correlation between financial interest and a greater understanding of the informed consent document , an aspect that could not be evaluated in the current study given its dissimilar objectives and methodology and the fact that monetary compensation for clinical trial participation is not permitted in Brazil . The findings of our study are supported by the fact that we utilized a set of strategies employed to ensure method reliability , internal data validity , and minimization of bias . First , the questionnaires used open-ended questions to assess the participants’ knowledge of information contained in the informed consent document . This type of tool is able to measure more accurately the actual knowledge of a topic , and avoids overestimation of responses or influencing responses by presenting pre-determined options [26 , 28] . We also sought to ensure that knowledge of the information conveyed in the informed consent form derived merely from the reading of the document and was not influenced by the experience of participating in the trial . This was achieved by administering the questionnaires after the consent procedure but before the first study vaccination . In most cases the questionnaire was administered on the first day of vaccination although in a handful of cases it was administered on the same day as consent was obtained; it is possible that differences in time between signing the consent form and completing the questionnaire may have affected the observed results , however the number of participants who completed the questionnaire immediately was too small to make valid comparisons . This is a potential limitation of our research , as is the fact that the informed consent forms were not identical between the studies conducted in Brazil and in the United States due to slight differences in study design , primarily the use of different adjuvants in the two trials . However , this is unlikely to have significantly impacted our results since the major differences observed were between participants in Americaninhas and those in Belo Horizonte and Washington , DC , rather than between the Brazilian and American studies . The validity of the data was also ensured by standardizing the questions and how the responses were recorded , thereby increasing reliability and permitting more robust statistical analysis of the data [53] . Standardization was essential to compare the participants’ knowledge , especially since the questionnaires were administered in three diverse study locations . In order to ensure standardization of the process , interviewers received training prior to the application of the questionnaire . The questionnaire used in this research addressed all of the necessary aspects of an ethically sound informed consent , in contrast to the study by Ellis et al in which only seven themes were evaluated [21] . This same study pioneered the comparison between levels of quality of informed consent in developed ( USA ) and developing ( Mali ) countries . However , the investigators used a questionnaire with a primarily educational purpose that did not take into consideration the rigor of scientific research . In order to build upon the findings of this study , the questionnaire design used in our research was based on other tools validated for this purpose and on the researchers’ experience developing and administering similar surveys [7–8 , 27 , 54] . Despite the fact that international ethical guidelines and literature on the subject of informed consent call for additional measures to protect the rights of research participants in developing countries , the findings of this study suggest that the characteristics of participants at each specific study site need to be considered , regardless of the country in which they are located [1 , 26] . This observation was emphasized by Lobato [55] , who demonstrated that certain characteristics of study participants may be negatively associated with the quality of the informed consent that is obtained , particularly those associated with extrinsic or intrinsic vulnerability [16] .
The study described herein provides the first empirical comparative analysis of the quality of the informed consent process of participants in a clinical trial in a developed country with participants from Brazil . In addition , it compared the quality of the informed consent process in different Brazilian contexts . Regarding the methodology , this is the first study to investigate the theme using clinical trials of similar design and testing the same investigational products , and that measured results through a standardized questionnaire designed specifically for that purpose . From this perspective , it may contribute especially to building a body of knowledge about the quality of informed consent worldwide . As described above , potential limitations of this study include the lack of validation of the questionnaire prior to its use , as well as the different methods of its implementation in both countries . Furthermore , it is understood that a larger sample of participants could give a different result is that the same sample used could produce other results in different population groups and ages or in other countries , or in different locations within Brazil and the US given local differences . Despite this , based on our results , we conclude that the use of the terms “developed” and “developing” to describe countries is a reductionist exercise to define participants as vulnerable , whereas a rigorous consideration of the specific characteristics of each group of individuals recruited as participants in a clinical trial is necessary . These findings demonstrate also the need for educational interventions directed at clinical trial participants , both in developing and developed countries , in order to improve understanding of the informed consent document . | Informed consent is an essential element of the ethical conduct of clinical trials of new vaccines , regardless of the study setting . However , the quality of informed consent is often suboptimal . Some research has suggested that the quality of the informed consent process may be reduced in resource-limited areas compared to developed country settings . To test this , we conducted a study of the quality of the informed consent process in two similar Phase 1 clinical trials of the Na-GST-1/Alhydrogel hookworm vaccine that were conducted in healthy adult volunteers in Brazil and in the United States . In Brazil , the trial was conducted at two sites , one a large urban area ( Belo Horizonte ) , and the other a rural , resource-limited region of the state of Minas Gerais; in the United States , the trial was conducted in Washington , DC . A structured questionnaire was administered after the informed consent document was signed at each of the three clinical trial sites , which tested understanding about the information contained in the document and attitudes toward the volunteers’ participation in the clinical trial . The results indicate that there were no substantial differences between the overall quality of the informed consent obtained from participants in the United States and in Brazil . However , a significant association was found between the particular site where the trial was conducted and the quality of the informed consent process , with residents of the site in rural Brazil having the lowest percentage of correct answers on the informed consent questionnaire . The informed consent process should therefore take into account the specific characteristics of the population in which the trial is being conducted . |
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Plant innate immunity is mediated by Resistance ( R ) proteins , which bear a striking resemblance to animal molecules of similar function . Tobacco N is a TIR-NB-LRR R gene that confers resistance to Tobacco mosaic virus , specifically the p50 helicase domain . An intriguing question is how plant R proteins recognize the presence of pathogen-derived Avirulence ( Avr ) elicitor proteins . We have used biochemical cell fraction and immunoprecipitation in addition to confocal fluorescence microscopy of living tissue to examine the association between N and p50 . Surprisingly , both N and p50 are cytoplasmic and nuclear proteins , and N's nuclear localization is required for its function . We also demonstrate an in planta association between N and p50 . Further , we show that N's TIR domain is critical for this association , and indeed , it alone can associate with p50 . Our results differ from current models for plant innate immunity that propose detection is mediated solely through the LRR domains of these molecules . The data we present support an intricate process of pathogen elicitor recognition by R proteins involving multiple subcellular compartments and the formation of multiple protein complexes .
Plants , like animals , are able to launch successful defense responses against invading microorganisms . For this purpose , plants have developed a variety of strategies that include molecular , chemical , and physical barriers to infection . One of the most important of these defense systems relies on germline-encoded molecules that can specifically recognize a particular pathogen or strain of a given pathogen . These molecules are encoded by Resistance ( R ) genes , and each R protein typically initiates a defense response in the presence of one pathogen-derived elicitor protein that is termed the Avirulence ( Avr ) determinant [1] . The genetic relationship between R and Avr proteins was elegantly stated in the gene-for-gene hypothesis [2] , and this type of plant defense is now described as the plant innate immunity . Over the last several years , approximately 40 R genes have been cloned [1] . These genes confer resistance to several classes of pathogens , including viruses , bacteria , fungi , oomycetes , insects , and even nematodes . Surprisingly , the protein products of these R genes are structurally similar to each other and contain a few , conserved domains . The leucine-rich repeat ( LRR ) domain is the most common domain among R proteins , and it is also found in animal innate immunity molecules , including Toll from Drosophila and Toll-like receptors ( TLRs ) , and nucleotide binding-oligomerization domain proteins ( NODs ) from mammals [3 , 4] . Members of the largest class of R proteins possess , in addition to the LRR , a central nucleotide binding site ( NB ) domain that is similar to the NB of the NODs and the animal cell death effector proteins Apaf1 and CED4 [1 , 5] . The NB-LRR class of R proteins is further subdivided according to the N-terminal domain of these proteins . Some proteins contain a Toll-interleukin 1 receptor homology region ( TIR ) domain , whereas others possess a coiled-coil ( CC ) domain . Like the LRR and NB domains , the TIR domain is found in animal innate immunity proteins , specifically Toll and the TLRs [6] . In recent years , extensive molecular and genetic analyses have been performed in a number of R-Avr systems . One interesting aspect of R protein function is its localization . These proteins have been found in a variety of cellular locations , depending on the localization of the eliciting pathogen or its Avr determinant . For example , the tomato Cf proteins , which recognize extra-cellular Cladosporium fulvum Avr proteins , are localized to the plasma membrane [7] . Interestingly , Arabidopsis RPM1 and RPS2 are associated with cellular membranes although they do not possess any canonical membrane-targeting domains [8 , 9] . This subcellular localization is consistent with the membrane localization of their corresponding Avr elicitors , AvrRpm1 and AvrRpt2 , respectively [9 , 10] . Apart from the plasma membrane , R proteins may also be found in the nucleus of plant cells , as is the case with RRS1-R in the presence of its bacterial Avr elicitor , PopP2 [11] . However , many NB-LRR R proteins do not carry recognizable subcellular targeting signatures and so are believed to be cytoplasmic . However , a cytoplasmic localization has only been demonstrated for the Solanaceae R protein Bs2 [12] and for barley Mla1 [13] . One caveat to most of these studies is that they involved the generation of biochemical extracts or artificial systems like protoplasts . A more nearly ideal approach for this analysis should use nondisruptive techniques to examine localization in intact , living tissue . In addition to studies on localization , researchers have also identified some of the host proteins that are involved in R protein activation and signaling downstream of the activation event . Recent work from several groups has attempted to address a central issue in plant innate immunity: how R proteins recognize pathogen-derived Avr proteins and initiate a defense response . Early models of the R-Avr relationship proposed a direct interaction between the host and pathogen proteins based on the gene-for-gene hypothesis . However , a direct interaction has been demonstrated for few R-Avr pairs: [11 , 14–18] . It should be noted that these interactions have been demonstrated in heterologous systems like yeast and in vitro binding assays . The paucity of detectable R-Avr interactions has led to the hypothesis that other host proteins may facilitate the association of R and Avr proteins and that these accessory host proteins are critical for the activation of the resistance protein . This idea is articulated in the guard hypothesis , which proposes that the Avr protein induces a change in a host protein that is normally recruited by the pathogen via its Avr protein to establish a successful infection , and it is this change that is sensed by the R protein ( guard ) , leading to the activation of the R protein and subsequent defense signaling [19] . This model for R protein activation is supported by evidence from several R-Avr systems . In Arabidopsis , both RPM1 and RPS2 and their cognate Avr elicitors interact with a host protein RIN4 [9 , 20] . RIN4 is modified in the presence of these Avr proteins , and it is believed that this modification is a key step in the activation of the R proteins . Additional support for the guard hypothesis comes from tomato Cf2 and the host protease Rcr3 [21] , as well as from Arabidopsis RPS5 and PBS1 [22] . Thus , data suggest that some R proteins may indirectly recognize Avr proteins through other host proteins . This mode of activation is in contrast to mammalian TLR function in which TLRs directly recognize pathogen-associated molecular patterns ( PAMPs ) through their extracellular LRR domains [23] . Interestingly , genetic analyses of plant R proteins have identified a crucial role for the LRR in conferring the specificity of R-Avr systems [14 , 24] , To date , an in vivo association between an R protein and its corresponding Avr protein has not been demonstrated . This is true even for cases in which a direct interaction has been demonstrated in yeast two-hybrid assays or in vitro . There is no obvious biological explanation for this seeming anomaly . One possibility is that detection has been technically challenging , presumably because R proteins are present at relatively low levels in plant cells [25] . Attempts to increase R protein levels using strong viral promoters like the Cauliflower mosaic virus ( CaMV ) 35S promoter have failed to yield a wild-type resistance response [26] , suggesting that R protein levels are fine-tuned within a cell and that an effective resistance response is dependent on these levels . We have optimized the expression and detection of several R proteins by standard microbiology and molecular biology techniques in an effort to directly address the issues of pathogen recognition and R protein activation . One of the classic model systems for studying plant-virus interactions involves Tobacco mosaic virus ( TMV ) infection of Nicotiana tabacum ( tobacco ) plants . Tobacco and other Nicotiana species carrying the N gene are resistant to infection by all strains of TMV , except the Ob strain [27] . The N gene has been cloned , and it encodes a TIR-NB-LRR R protein [28] . The first visible outcome of infection of N-containing plants by TMV is the formation of necrotic lesions at the infection sites [28 , 29] . These necrotic lesions are part of the stereotypical R gene–dependent defense response that is called the hypersensitive response ( HR ) . The helicase domain of the TMV replicase proteins , termed the p50 protein , is necessary and sufficient to elicit an HR in N-containing Nicotiana plants [30 , 31] . The HR resulting from N–p50 interaction is generally assumed to indicate N function [31] . In this work , we have used the N-TMV system to investigate how an R protein recognizes its pathogen-derived elicitor protein . We set out to determine the subcellular localization of N and its Avr elicitor , p50 . Using biochemical approaches and fluorescence microscopy , we show that N and p50 are both cytoplasmic and nuclear proteins . We also investigated whether nuclear localization was important for a defense response and found that N's presence in the nucleus was indeed required for its function . However , p50 could elicit N-mediated responses even when expressed exclusively in the cytoplasm . Once we determined that N and its elicitor are present in the same subcellular compartments , we then tested the association between N and p50 . We show by co-immunoprecipitation and fluorescence microscopy-based assays in intact , living tissue that N and p50 associate in planta . This is the first report of R-Avr association occurring in living tissue undergoing a defense response . Another interesting finding is that the TIR domain of N is critical for this association to occur . These results propose additional functions for the TIR domain in addition to its known role as an adaptor for signaling in animal innate immunity . We propose that the N TIR domain acts as an adaptor between the pathogen Avr protein and the signaling function of the R protein .
In order to localize the N protein , we fused a tandem affinity purification ( TAP ) tag containing nine copies of the MYC epitope ( 9xMYC ) to the C terminus of N [32] . The full genomic clone of N including its endogenous 5′ and 3′ regulatory sequences and introns was used for this purpose , and the tagged N construct was called gN-TAP . This genomic construct was used to drive expression of N and to facilitate the alternative splicing that is required for N function [33] . To investigate the localization of the TMV elicitor , p50 , we used the p50 sequence from the U1 strain of the virus . Two tandem copies of the HA epitope were fused to the C-terminus of p50 . Expression of p50-U1-HA was driven by the strong CaMV 35S promoter in an effort to mimic the high levels of viral replicase that accumulate during TMV infection . To determine whether gN-TAP and p50-U1-HA were functional , we co-infiltrated Nicotiana benthamiana leaves with Agrobacterium cultures expressing gN-TAP and/or p50-U1-HA . Tissue co-expressing the two proteins exhibited cell death typical of the HR 2 d after co-infiltration ( Figure 1A ) . Tissue expressing either gN-TAP or p50-U1-HA alone did not show the HR cell death response ( Figure 1A ) . The expression of gN-TAP ( Figure 1B , lane 1 ) and p50-U1-HA ( Figure 1C , lane 1 ) was confirmed by Western blot analysis . The subcellular localization of N and p50 was first examined by cell fractionation . Tissue transiently expressing gN-TAP was collected , and protein extracts were ultra-centrifuged to produce crude soluble ( S100 ) and membrane ( P100 ) fractions . The proteins in the fractions were separated by SDS-PAGE and analyzed by Western blot with anti-MYC antibodies . gN-TAP was found in the S100 soluble fraction of protein extracts in the absence of TMV or p50-U1 ( Figure 1D , panel 1 ) , and its localization did not change when co-expressed with its Avr elicitor , p50-U1-HA ( unpublished data ) or in the presence of TMV itself ( Figure 1D , panel 2 ) . Similar analysis for p50-U1-HA showed it to be associated primarily with the S100 fraction ( Figure 1E , panel 1 ) . These results suggest that both N and p50-U1 are soluble proteins . We then used the noninvasive technique of fluorescence microscopy on intact , living leaf tissue as an independent approach to confirm the localization of N and p50-U1 . This method avoids the tissue disruption and possible introduction of artifacts that could have occurred during preparation of protein extracts used in our biochemical cell fractionation . The advent of newer forms of fluorescent molecules that give stronger emissions allows the imaging of fusion proteins expressed at fairly low levels [34] . For fluorescence detection , the Citrine variant of enhanced yellow fluorescent protein ( EYFP; [35] ) was fused to the C-terminus of N . Again , N was in its full genomic context including its endogenous 5′ and 3′ regulatory sequences and introns , and Citrine-tagged N was called gN-Citrine . p50-U1 was tagged at its C-terminus with the Cerulean variant of enhanced cyan fluorescent protein ( ECFP; [36] ) to generate p50-U1-Cerulean . Again , the 35S promoter was used to drive expression of p50-U1-Cerulean . We confirmed that gN-Citrine and p50-U1-Cerulean were functional by assessing their ability to produce HR-associated cell death when co-expressed ( Figure 2A ) . Expression of either construct alone did not result in HR-associated cell death ( Figure 2A ) . gN-Citrine ( Figure 2B , lane 1 ) and p50-U1-Cerulean ( Figure 2C , lane 1 ) were readily detected by Western blot analysis . Citrine alone or gN-Citrine was transiently expressed in N . benthamiana leaves by agroinfiltration . Sections were cut from the infiltrated leaves and observed under the confocal microscope . As expected , Citrine alone , driven by the strong 35S promoter , was localized to the cytoplasm and nuclei of cells ( Figure 2D , column 1 ) . Interestingly , gN-Citrine ( Figure 2D , column 2 ) produced a similar pattern of fluorescence to that of Citrine alone ( Figure 2D , column 1 ) . These data are consistent with the biochemical analysis of gN-TAP , and show that N is a cytoplasmic protein . However , unexpectedly , gN-Citrine was also detected in the nuclei of most cells examined ( Figure 2D , column 2 ) . For p50-U1 localization , a similar pattern of fluorescence was obtained for Cerulean alone ( Figure 2E , column 1 ) and p50-U1-Cerulean ( Figure 2E , column 2 ) . Together , these biochemical and cell biological analyses indicate that N and p50-U1 are cytoplasmic and nuclear proteins . As a control , we were interested in examining a p50 from a TMV strain that could not elicit N-mediated defense . The Ob strain of TMV does not elicit N-mediated responses at temperatures above 20 °C [27] . As expected , the p50 from the Ob strain of TMV ( p50-Ob ) does not cause HR-associated cell death when expressed in N-containing plants [37] . The amino acid sequences of p50-Ob and p50-U1 are 64% identical and 80% similar [38] . We therefore attempted to characterize p50-Ob . For this purpose , two copies of the HA epitope tag were fused to the C-terminus of p50-Ob to generate p50-Ob-HA . Interestingly , p50-Ob-HA was not detected with antibodies in Western blot analyses ( unpublished data ) . Following the approach used for p50-U1 , p50-Ob was tagged with Cerulean to produce p50-Ob-Cerulean . Like p50-Ob-HA , p50-Ob-Cerulean was not detected by Western blot ( unpublished data ) . Surprisingly , we detected low levels of p50-Ob-Cerulean in chloroplasts ( Figure 2E , column 3 ) . The fluorescence signal aligns with the stroma of the chloroplasts , and in some cases , stromules ( stroma-filled tubules that connect plastids ) were identified . It must also be noted that the signal generated by p50-Ob-Cerulean was very weak , even though the 35S promoter was used to drive its expression . It is possible that the failure of p50-Ob to elicit N-mediated defense is due to its localization to chloroplasts and exclusion from the cytoplasm or nucleus where N is found . Analyses of the N-terminus of p50-Ob indicated that it contains a chloroplast localization signal ( unpublished data ) . Therefore , we decided to use a chimeric p50 containing the first 192 amino acids from p50-U1 and the remaining sequence from p50-Ob ( p50-U1-Ob ) [38] . The p50-U1-Ob chimera fails to elicit N-mediated resistance [38] . To investigate the localization pattern of p50-U1-Ob , two tandem copies of the HA epitope tag were fused to the C-terminus of p50-U1-Ob . p50-U1-Ob-HA expression was driven by the 35S promoter , and the protein was detected by Western blot analysis ( Figure 1C , lane 2 ) . We examined the localization of the p50-U1-Ob chimera by cell fractionation . p50-U1-Ob-HA was found in the S100 fraction of cell extracts ( Figure 1E , panel 2 ) . To determine p50-U1-Ob localization in intact , living leaf tissue , a C-terminal Cerulean tag was attached to this fusion to generate p50-U1-Ob-Cerulean , and it was transiently expressed in N . benthamiana leaves . Unlike p50-Ob-Cerulean , the p50-U1-Ob-Cerulean chimera was detectable by Western blot analysis ( Figure 2C , lane 2 ) . p50-U1-Ob-Cerulean was detected in the cytoplasm and nucleus of transfected cells by fluorescence microscopy ( Figure 2D , column 4 ) . Thus , although it does not elicit N-mediated resistance , the p50-U1-Ob chimera has an identical subcellular localization pattern to p50-U1 that elicits N-mediated defense . Moreover , the p50-U1-Ob chimera provides us with a suitable control for our experimental system . Since N's nuclear localization was unexpected , we investigated whether it was important for a defense response . For this , we prevented N's nuclear accumulation by fusing a nuclear export signal ( NES ) to the C-terminus of gN-Citrine . The NES sequence was derived from the human immunodeficiency virus-1 ( HIV-1 ) Rev protein [39] . As expected , gN-Citrine-NES was excluded from nuclei and found only in the cytoplasm of plant cells when examined by fluorescence microscopy ( Figure 3A , column 2 ) . A mutant NES ( NESmut ) in which critical leucine residues have been substituted with alanine , fails to prevent N's nuclear localization ( Figure 3A , column 3 ) , and in these instances , gN-Citrines-NESmut's localization is identical to gN-Citrine ( Figure 3A , column 1 , and Figure 2D , column 2 ) . We then co-expressed gN-Citrine , gN-Citrine-NES , or gN-Citrine-NESmut and p50-U1-HA and examined whether an HR occurred . As expected , gN-Citrine and p50-U1-HA produced an HR ( Figure 3B , column 1 ) . Interestingly , gN-Citrine-NES and p50-U1-HA co-expression did not result in HR ( Figure 3B , column 2 ) , whereas the ability to produce an HR was restored in gN-Citrine-NESmut and p50-U1-HA ( Figure 3B , column 3 ) . This suggests that N's nuclear localization is required for a defense response . To identify which domain of N directed its intracellular distribution , we used previously described mutants of N that carry deletions of the TIR , NB , or LRR domains ( [26]; see below ) . None of these mutants produces an HR cell death in the presence of TMV [26] , indicating that all three domains are necessary for mounting a successful defense response . Each of these N deletion mutants was tagged at its C-terminus with Citrine for localization by fluorescence microscopy . Again , N mutants were created in their full genomic context including N's endogenous 5′ and 3′ regulatory sequences and introns . Surprisingly , all three N mutants retained their nuclear and cytoplasmic localization ( Figure 3C ) , although lower levels of the LRR deletion mutants appeared to accumulate in nuclei ( Figure 3C , column 3 ) . It should be noted that the TIR , NB , and LRR domains do not together constitute the entire N protein , and that there are regions outside these domains that are unaffected in each of the three deletions . Our data suggest that subcellular distribution of N is determined by amino acid sequences outside of the TIR , NB , and LRR domains , because the distribution of the deletion mutants is similar to that of gN-Citrine . Similarly , we wanted to determine whether the nuclear localization of p50 was necessary for a defense response . Using the same strategy we had employed to investigate the function of nuclear N , we attached a C-terminal NES to p50-U1-Cerulean to determine its intra-cellular distribution and examine whether an HR still occurred in the absence of nuclear p50-U1 . As expected , the NES prevented the nuclear accumulation of p50-U1-Cerulean-NES ( Figure 3D , column 2 ) , and the fusion protein was able to enter the nucleus when the NES was mutated ( Figure 3D , column 3 ) . When p50-U1-Cerulean , p50-U1-Cerulean-NES , or p50-U1-Cerulean-NESmut was infiltrated into N-containing N . bethamiana plants , HR was observed ( unpublished data ) . These results suggest that the nuclear localization of p50-U1 is not required for recognition by N and a subsequent defense response . Taken together , our data indicate that recognition may occur in the cytoplasm of plant cells , supporting the hypothesis that nuclear N has another function in addition to pathogen recognition . Given that N and p50 were found in the same subcellular compartments , we decided to investigate their association . For this we attempted to co-immunoprecipitate gN-TAP and p50-U1-Cerulean . N . benthamiana plants were infiltrated with a mixture of Agrobacterium cultures expressing gN-TAP and p50-U1-Cerulean or Cerulean . We were unsure when these proteins would associate , but we assumed that it would be before HR lesions became visible at 48 h post-infiltration ( hpi ) . Therefore , we allowed sufficient time for Agrobacterium to establish a successful infection and T-DNA integration , and then collected samples over a time course from 16 to 48 hpi . Extracts were tumbled with anti–green fluorescent protein ( GFP ) antibodies to immunoprecipitate p50-U1-Cerulean or Cerulean . Isolated immunocomplexes were analyzed by Western blot , and gN-TAP was detected with anti-MYC antibodies . We found that at 46 hpi , we were able to detect gN-TAP in immunoprecipitated p50-U1-Cerulean complexes ( Figure 4 , lane 2 ) but not with those that contained only Cerulean ( Figure 4 , lane 1 ) . Interestingly , p50-U1-Ob-Cerulean , which does not produce HR when co-expressed with N , was unable to pull down gN-TAP ( Figure 4 , lane 3 ) . These data demonstrate that N and p50 associate with each other prior to the observation of a visible defense response . As a second , independent method for assessing the association detected by co-immunoprecipitation , a bimolecular fluorescence complementation ( BiFC ) assay was used . BiFC , as originally described , splits a fluorescent molecule into two parts that are then individually fused to two proteins whose association is being investigated [40] . If the proteins associate , then the portions of the fluorescent molecule are brought into close proximity with each other , and the fluorescent molecule is reconstituted . It should be noted that BiFC does not require that the associating proteins make direct contact with each other and therefore cannot distinguish between direct and indirect associations [40] . However , BiFC has the distinct advantage of using intact , living tissue to study associations . It does not involve the chemical fixation or physical disruption of tissues and is therefore less likely to produce artifacts . We used Citrine for the BiFC analysis of N and p50-U1′s association . One tag contained the amino-terminal 155 amino acids ( YN155 ) , and the other contained the remaining Citrine sequence ( YC155 ) . As a control to show that the two portions of Citrine could reconstitute fluorescence , published interactions using the 14-3-3 protein , T14-3c , which is known to homodimerize , were repeated [41] ( Figure S1A ) . N , in its full genomic context ( endogenous 5′ and 3′ regulatory sequences and introns ) , was then tagged with YN155 to produce gN-YN . p50-U1 was tagged with YC155 to give p50-U1-YC , and its expression was driven by the 35S promoter . The tags did not interfere with the activity of N and p50-U1 , and they were able to produce HR cell death when transiently co-expressed in N . benthamiana leaves ( unpublished data ) . Samples were collected from plants expressing gN-YC and/or p50-U1-YC at 46 hpi , and observed under the confocal microscope . As expected , gN-YC and p50-U1-YC did not produce any fluorescence when expressed individually ( Figure 5A , columns 1 and 2 ) . However , when gN-YN and p50-U1-YC were co-expressed , fluorescence was detected in both cytoplasm and nuclei of cells ( Figure 5A , column 3 ) . When we co-expressed gN-YN and a widely used reporter gene , β-glucoronidase ( GUS ) , tagged with YC155 ( GUS-YC ) , we were not able to detect any fluorescence ( Figure 5A , column 4 ) . Our GUS-YC fusion protein is functional ( Figure S1B and S1C ) , and as expected , GUS-YC alone did not generate fluorescence ( Figure S1D ) . This indicates that the fluorescence we detected with gN-YN and p50-U1-YC was due to a specific association of these proteins . We also examined the association between N and the p50-U1-Ob chimera . YC155 was fused to p50-U1-Ob to produce p50-U1-Ob-YC , and as expected , it did not produce fluorescence when expressed alone ( Figure 5B , column 1 ) . Consistent with our co-immunoprecipitation findings , p50-U1-Ob-YC was not able to complement gN-YN , and fluorescence was not observed when they were co-expressed ( Figure 5B , column 2 ) . Thus , the failure of this p50 chimera to elicit N-mediated responses may be due to its inability to associate with N . Taken together with the co-immunoprecipitation assays , these BiFC results demonstrate that N and p50 associate in plant cells . Having determined that N and p50 associate , we wanted to examine whether the interaction was direct or indirect . For this , we transcribed and translated N in vitro and performed a co-immunoprecipitation assay with recombinant ( HIS ) 6-p50-HA purified from Escherichia coli . We failed to pull down N with p50 in this direct binding assay ( Figure S2 ) . A recent publication has shown that full-length N and p50 interact directly in yeast two-hybrid assays , but interestingly , this interaction was not demonstrated by in vitro pull down [18] . Since N and p50 associate in vivo , we wanted to determine which domain of N was responsible for association . For this , we used previously described mutants of N that carry deletions of the TIR , NB , or LRR domain ( Figure 6A ) . To investigate the ability of the mutants to associate with p50-U1 , we fused the TAP tag to their C-termini as described for tagging with Citrine . gN-mutant-TAP constructs were co-expressed with p50-U1-Cerulean in N . benthamiana leaves . At 46 hpi , tissue was collected and protein extracts prepared . Anti-GFP antibodies were used to immunoprecipitate p50-U1-Cerulean from extracts , and the precipitate was probed with anti-MYC antibodies after separation by SDS-PAGE . Surprisingly , mutants missing the P-loop of the NB , the entire NB , or the LRR retained the ability to co-immunoprecipitate with p50-U1-Cerulean ( Figure 6B , lanes 4 , 5 , and 6 ) . Unexpectedly , the mutant lacking the TIR domain did not co-immunoprecipitate with p50-U1-Cerulean ( Figure 6B , lane 3 ) . These findings were corroborated by the BiFC assay . N mutants lacking the TIR , NB , or LRR domain were tagged with YN155 , and expression was confirmed by Western blot analysis ( unpublished data ) . Each tagged mutant was then co-expressed with p50-U1-YC or GUS-YC as a control , and tissue was monitored for fluorescence at 46 hpi . The loss of the NB or LRR domain did not disrupt the ability of N and p50-U1 to associate , and fluorescence was detected in those samples ( Figure 7A , columns 1 and 3 ) . Again , as in the co-immunoprecipitation assays , the TIR-deletion mutant failed to complement p50-U1 , and fluorescence was not observed ( Figure 7B , column 1 ) . As expected , none of the mutants gave BiFC with GUS-YC ( Figure 7A , columns 2 and 4; Figure 7B , column 2 ) . The TIR domain of N is therefore necessary for association with the Avr elicitor , p50-U1 . It was possible that removing the TIR domain had disturbed the remaining portion of N and this was responsible for the loss of association , not the loss of the TIR domain per se . To examine this possibility , mutants carrying point mutations in their TIR domains were assessed for their ability to associate with p50-U1 . Both mutants used , N ( D46H ) and N ( W141S ) , disrupt N-mediated resistance [26] . TAP-tagged point mutants could not be co-immunoprecipitated with p50-U1-Cerulean ( Figure 6B , lanes 7 and 8 ) . Similarly , when they were tagged with YN155 , they failed to complement p50-U1-YC by BiFC ( Figure 7C , columns 1 and 3 ) . We had confirmed expression of these mutants by Western blot ( unpublished data ) . These results suggest that the point mutants disturb the function of N by interfering with the ability to associate with its Avr elicitor . Thus , the wild-type TIR domain of N is necessary for association with p50-U1 . The TIR domain of N was then directly tested for its ability to associate with p50-U1 . Using the same strategy applied to full-length N , the TIR domain was TAP-tagged in N's genomic context including its endogenous 5′ and 3′ regulatory sequences to give N ( TIR ) -TAP . N ( TIR ) -TAP and p50-U1-Cerulean or p50-U1-Ob-Cerulean were co-transfected into N . benthamiana plants . Tissue was collected 46 hpi , and protein extracts were prepared . Co-immunoprecipitation using anti-GFP antibodies was performed , and the precipitate probed with anti-MYC antibodies . N ( TIR ) -TAP was found in complexes containing p50-U1-Cerulean ( Figure 8A , lane 1 ) , but not in those isolated using p50-U1-Ob-Cerulean ( Figure 8A , lane 2 ) . For the BiFC assay , N-TIR was tagged with YN155 to produce N ( TIR ) -YN , which was then co-expressed with p50-U1-YC or p50-U1-Ob-YC . Consistent with the co-immunoprecipitation results , fluorescence was detected when N ( TIR ) -YN complemented p50-U1-YC ( Figure 8B , column 1 ) . No fluorescence was observed between N ( TIR ) -YN and p50-U1-Ob-YC ( Figure 8B , column 2 ) . As an additional control , we checked for complementation between N ( TIR ) -YN and GUS-YN , and observed none ( Figure S3 , column 1 ) . The TIR domain of N is therefore both necessary and sufficient for association with p50-U1 . We then investigated the specificity of the N ( TIR ) -p50-U1 association . To do this , we chose the TIR domains from two R proteins closely related to N , tomato BS4 and Arabidopsis RPP5 , and examined their association with p50-U1 . N and BS4 share 54% identity and are most similar at the TIR domain [42] , whereas the TIR domains of N and RPP5 share 52% identity [43] . The TIR domains of BS4 and RPP5 were each placed under the control of N's 5′ and 3′ endogenous regulatory regions . Thus , the only difference between the N ( TIR ) and BS4 ( TIR ) and RPP5 ( TIR ) constructs used in our analysis are the coding sequences . We examined the association between BS4 ( TIR ) or RPP5 ( TIR ) and p50-U1 by the BiFC assay . For this , BS4-TIR and RPP5 ( TIR ) were tagged with YN155 to produce BS4 ( TIR ) -YN and RPP5 ( TIR ) , respectively . The expression of these constructs was confirmed by Western blot ( unpublished data ) . No Citrine fluorescence was observed in tissue co-expressing BS4 ( TIR ) -YN and p50-U1-YC ( Figure 8B , column 3 ) or RPP5 ( TIR ) -YN and p50-U1-YC ( Figure 8B , column 4 ) . As expected , BS4 ( TIR ) -YC and RPP5 ( TIR ) -YC expressed with p50-U1-Ob-YC or GUS-YC do not produce fluorescence ( Figure S3 , columns 2–5 ) . Thus , TIR domains of BS4 and RPP5 do not associate with p50-U1 . Thus , the observed association between N-TIR and p50 is specific and depends on the sequence of the TIR domain . We examined whether the association we observed between N ( TIR ) and p50-U1 was a direct interaction by pull-down assays , using in vitro transcribed and translated N ( TIR ) and ( HIS ) 6-p50-HA purified from E . coli . N ( TIR ) did not precipitate p50-U1 in this assay ( Figure S2 ) , and this is consistent with recent findings [18] . This suggests that the association between N ( TIR ) and p50-U1 is indirect and may involve other proteins .
Here we show that the tobacco R protein N and its cognate Avr determinant from TMV , p50 , are cytoplasmic and nuclear proteins . N's nuclear localization is required for a defense response . Further , N and p50 associate in living plant cells as determined by both biochemical and non-destructive microscopic analysis . We have also identified the domain of the R protein that mediates this association . Based on the results of genetic analyses of alleles of R genes and several studies of direct interactions by yeast two-hybrid assays , we expected the LRR domain of N to mediate the association with p50 . Surprisingly , we found that the TIR domain was not only necessary for association with p50 , but also sufficient . We propose a new model for how N may recognize the presence of its elicitor in a plant cell ( Figure 9 ) . We have examined the localization of N and p50 by both biochemistry and fluorescence microscopy . Biochemical assays have been successfully used to localize multiple R proteins [8 , 9 , 12 , 13] . In addition , we utilized confocal fluorescence microscopy to observe the proteins in their native state and subcellular location with minimal disruption to the tissue . For this , we used improved EYFP and ECFP variants , Citrine and Cerulean , respectively , which produce greater fluorescence signal and hence allow easier detection of our fusion proteins [35 , 36] . Both N and p50 from the U1 strain of TMV are cytoplasmic proteins . This finding was largely expected for N , given that it possesses no obvious subcellular targeting signatures in its sequence and shares sequence similarity to other predicted cytoplasmic R proteins , including BS4 [42] and RPP5 [43] . Surprisingly , in addition to being cytoplasmic , both N and p50-U1 show localization to nuclei . Another R protein , barley MLA1 , also shows apparent localization to two subcellular compartments [13] . N's nuclear localization is required for a defense response since preventing its nuclear accumulation of N disrupts the production of an HR . What is the possible significance of N's nuclear localization in mediating a defense response ? Interestingly , we had previously identified plant-specific transcription factors as proteins that interact with N [32] . Further , nuclear-localized Arabidopsis RRS1 possesses a WRKY DNA-binding domain as a C-terminal extension to its TIR-NB-LRR core structure [11 , 44] . Taken together , these findings hint at a previously undescribed role for R proteins regulating nuclear events , possibly gene transcription . It will be interesting to determine whether this is indeed the function of N and other nuclear R proteins . Although p50-U1 is also nuclear , we determined that its nuclear localization was not required for a defense response . In the context of TMV replication , the helicase domain of the viral replicases , p50 , is not likely to have access to the nucleus , suggesting that recognition of p50 may occur in the cytoplasm . Thus , our results may indicate that the first phase of defense , recognition of p50 , occurs in the cytoplasm while a second , important phase responsible for the actual signaling response occurs in the nucleus . However , we do not yet know how cytoplasmic recognition is communicated to the nucleus . Despite the availability of cloned R and Avr gene sequences for the past decade or so , and the demonstration that some R and Avr proteins interact directly with each other in vitro , an association between an R protein and its cognate Avr elicitor had not been previously demonstrated in intact plant tissue . Here we used co-immunoprecipitation and BiFC to show that N and p50 associate in N . benthamiana . It should be noted that co-immunoprecipitation and BiFC do not conclusively distinguish between a direct or indirect association . Therefore , we cannot rule out the possibility that the N-p50 association may be mediated by other host factor ( s ) . Consistent with this idea , host proteins like Arabidopsis RIN4 that interact with both an R protein and its corresponding Avr elicitor have been identified [9 , 20] . Indeed , we failed to demonstrate a direct interaction between N and p50-U1 by in vitro pull-down assay . Genetic analyses of flax L alleles , which encode TIR-NB-LRR R proteins , have pointed to the LRR as being critical for determining the specificity of L-Avr recognition [24] . L alleles that differ solely in the sequence of their LRRs confer resistance to different Avr determinants . This suggests that the specificity is derived from the ability of the LRR to associate with , and hence recognize , an Avr protein . This is supported by a recent report of direct L–AvrL interactions in yeast [14] . The LRR domain of the rice R protein Pi-ta has also been shown to interact with its corresponding Avr protein [17] and , interestingly , the NB-LRR region of N interacts with p50 in yeast and in vitro [18] . It is important to note , however , that further analysis of the flax alleles also found that other regions of the L proteins in addition to the LRR domain , particularly the TIR and NB domains , were involved in conferring specificity to L-Avr recognition [45] . Our studies have determined that the TIR domain of N is necessary and sufficient for association with the p50 Avr elicitor ( Figure 7A and 7B ) . Also , the association we observed between N ( TIR ) and p50 was specific since BS4 ( TIR ) and RPP5 ( TIR ) could not associate with p50 despite their similarity . Our data therefore support a critical role for the TIR domain in mediating the R–Avr interaction . Interestingly , results from other R proteins support a possible role for domains other than the LRR in association with pathogen-derived elicitors . For example , Arabidopsis RPM1 interacts with the host protein RIN4 through its amino-terminus [20]; RIN4 in turn also interacts with RPM1′s Avr elicitor AvrRpm1 . Further , tomato Pto , a kinase that acts as an R protein , interacts with the N-terminus of Prf , a CC-NB-LRR protein required for Pto function [46] . Pto and Prf act closely to regulate not only recognition of pathogen elicitor molecules , but also subsequent defense signaling , and their coordination function depends on their interaction in the plant cell [46] . Thus , it appears that multiple regions of R proteins are involved in interacting with pathogen-derived elicitor molecules , suggesting a complex mode of R protein activation prior to initiating a defense response . At first glance , our findings appear to contradict two recent papers that investigate the N-p50 association . The first suggests that N and p50 do not associate in plant tissue [47] , whereas the other found that N and p50 directly interact in yeast two-hybrid assays , and further , that this interaction occurred through N's NB-LRR region [18] . However , a closer examination reveals that these findings can be assimilated into a coherent model for N's function . In the initial phase of recognition , N and p50 associate through N's TIR domain , most likely through the involvement of other host proteins , because we found this association is indirect ( Figure 9A ) . This is the association that we have detected by co-immunoprecipitation and BiFC in living tissue . The absence of other host proteins from the yeast two-hybrid system may explain why Ueda and co-workers failed to observe the association between TIR and p50 [18] . This N ( TIR ) –p50 association is possibly the event that leads to the observed oligomerization of N that is proposed to be mediated by N's TIR domain [47] . Next , there is a direct interaction between N and p50 that occurs through N's NB and LRR domains ( Figure 9B ) . This interaction is facilitated through conformational changes of N that result from the disruption of the interaction between the TIR-NB and LRR domains by p50 [18] . This interaction may be weaker than the first , accounting for our failure to observe it in plant tissue . Both interactions likely occur in the cytoplasm because p50 is not needed in the nucleus to initiate a defense response . Subsequently , recognition is communicated to the nucleus , and signaling leading to a defense response follows ( Figure 9C ) . Nuclear-localized N is critical to this process by an as-yet-unknown mechanism , but it may involve changes to the conformation of N , re-distribution of N between the nucleus and cytoplasm , or biochemical modification such as phosphorylation . We have proposed a complex model for p50 recognition by the N protein that involves different multiple compartments and different regions of N interacting with p50 at different times during the recognition event . Our model also explains the findings from flax ( discussed above ) that both the LRR and the TIR domains contribute to the specificity of R–Avr interactions . Although most of these findings were unexpected , they are consistent with the emerging view that pathogen recognition is a complex process involving players other than the R and elicitor proteins . An intricate recognition system allows for the fine control of the output of this initial event in defense , an important consideration when the outcome for most cells that detect the presence of a pathogen-derived elicitor is death . We expect that our model for N , although differing in the small details , will hold true for other R proteins and their elicitors . It will be interesting to see whether other R proteins are also nuclear-targeted and if so , to determine what function these proteins perform in the nucleus . Also , the investigation of the formation of multiple complexes by different R proteins with their cognate elicitors in addition to high-resolution structures for R proteins will be most useful in explaining these interactions and how they culminate in defense . Finally , it will be exciting to investigate the nuclear–cytoplasmic partitioning of N and other R proteins , and determine the role of these proteins in the nucleus .
To generate N-TAP and N deletion mutants-TAP constructs , the TAP tag consisting of 9xMYC-3xHis-3C protease cleavage site-2xIgG binding domain from the vector pYL436 [32] was cloned into the unique SacI site at the 3′ end of the N , NΔTIR , NΔP-loop , NΔNB , NΔLRR2–14 , N ( D46H ) , and N ( W141 ) S constructs described in [26] . The Citrine sequence was amplified by polymerase chain reaction ( PCR ) and was cloned in place of the TAP tag to generate gN-Citrine . The HIV Rev NES sequence ( LQLPPLERLTL ) and NES mutant sequence ( LQAPPAERATL ) [39] were included in the downstream PCR primer to amplify Citrine , and cloned into gN to create gN-Citrine-NES and gN-Citrine-NESmut . The N-terminal 465 nucleotides of Citrine sequence were amplified by PCR , and cloned into N and N deletion mutants in place of the TAP tag to generate N-YN and N deletion mutants-YN constructs . To generate the TIR domain fused to the TAP and YN155 tags , the TIR sequence was PCR amplified and cloned between the NcoI and SacI sites of the gN-TAP and gN-YN constructs . The TIR region of BS4 and RPP5 was PCR amplified from tomato and Arabidopsis cDNA , respectively , and cloned between NcoI and SacI sites of gN-TAP . The p50 region of TMV-U1 and Ob replicases was amplified using a primer containing 2xHA sequence , and cloned into pYL400 , a T-DNA vector containing the 35S promoter and the NOS terminator cassette . Cerulean and the C-terminal 255 bases of Citrine were cloned into the 3′ end of p50 to generate the p50-Cerulean and p50-YC constructs , respectively . The HIV Rev NES sequence and NES mutant sequence [39] were included in the downstream PCR primer to amplify Cerulean , and cloned into 35S-p50-U1 to create p50-Cerulean-NES and p50-Cerulean-NESmut . To produce p50-U1-Ob constructs , p50-U1 sequence ( nucleotides 1–576 ) and p50-Ob sequence ( nucleotides 577–1 , 338 ) were amplified and inserted in place of p50-U1 sequence in p50-U1-HA , p50-U1-Cerulean , and p50-U1-YC plasmids . PCR-amplified p50-U1-HA was recombined into DEST17 vector ( Invitrogen , Carlsbad , California , United States ) to generate ( HIS ) 6-p50-HA . To generate GUS-YC , the GUS sequence was amplified from pCAMBIA3301 and was inserted in place of p50-U1 in the p50-U1-YC construct . All constructs were confirmed by DNA sequencing . Agrobacterium cultures were grown overnight in LB medium containing appropriate antibiotic selections . Cells were pelleted at 3 , 000 rpm and resuspended in infiltration medium containing 10 mM MgCl2 , 10 mM 2-morpholinoethanesulfonic acid ( MES ) , and 150 μM acetosyringone , and incubated at room temperature for 2–3 h . Strains containing N-derived constructs were infiltrated into N . benthamiana leaves at an optical density ( OD600 ) = 1 . 8 , and those containing p50-derived constructs were infiltrated at OD600 = 1 . 0 . For co-infiltration , equal volumes of Agrobacterium were mixed . Cultures were infiltrated into leaves with a 1-ml needleless syringe . N . benthamiana plants were grown on light carts under 24 h of light , and 4–5-wk-old seedlings were used for all assays . Protein was extracted from ground tissue with buffer containing 150 mM NaCl , 20 mM Tris/HCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% β-mercaptoethanol , 1 mM PMSF , and complete protease inhibitors ( Roche , Indianapolis , Indiana , United States ) . Protein concentrations were determined by Bradford assay ( Bio-Rad , Hercules , California , United States ) , and equal amounts were loaded onto polyacrylamide gels . Proteins were transferred to PVDF membrane ( Millipore , Billerica , Massachusetts , United States ) for Western blot analysis . Antibodies used were as follows unless otherwise stated: mouse anti-MYC ( Santa Cruz Biotechnology , Santa Cruz , California , United States ) , rat anti-HA ( Roche or Covance , Berkeley , California , United States ) , mouse anti-GFP ( Covance ) , anti-mouse horseradish peroxidase conjugate ( Sigma , St . Louis , Missouri , United States ) , and anti-rat IgG peroxidase ( Roche ) . Samples were ground in liquid nitrogen , and protein was extracted in buffer containing 50 mM HEPES ( pH 7 . 5 ) , 150 mM NaCl , 500 mM sucrose , 10 mM EDTA , 1 mM DTT , 1 mM PMSF , and complete protease inhibitors ( Roche ) . Cell debris was spun down at 10 , 000×g at 4 °C . Extracts were ultra-centrifuged at 100 , 000×g at 4 °C for 1 h . The supernatant or soluble fraction ( S100 ) was collected , and the pellet ( P100 ) was washed with extraction buffer before resuspension in an equal volume of buffer as the S100 . Western blot analysis was carried out as described above . Plant tissue expressing proteins of interest was collected and ground in liquid nitrogen . Protein was extracted with IP buffer containing 100 mM NaCl , 20 mM Tris/HCl ( pH 7 . 5 ) , 1 mM EDTA , 0 . 1 % Triton X-100 , 10 % glycerol , 1 mM DTT , 2 mM NaF , 1 mM PMSF , and complete protease inhibitors ( Roche ) . Cell debris was pelleted at 20 , 000×g , and extracts were incubated with 50-μl Protein A bead slurry ( GE Healthcare , Piscataway , New Jersey , United States ) equilibrated in IP buffer . Samples were tumbled at 4 °C for at least 1 h . Protein A beads were spun down at 600×g and the supernatant collected . A total of 1-μl rabbit anti-GFP antibodies ( Abcam , Cambridge , Massachusetts , United States ) were added to each 1-ml sample , and the mixture was tumbled at 4 °C for 2 h . A total of 50 μl Protein A bead slurry equilibrated in IP buffer was added to each sample , and the antibodies were allowed to couple to the beads with rotation for 1 h at 4 °C . Beads were spun down at 600×g and the supernatant discarded . Beads were washed three times with IP buffer . A total of 25-μl 4xSDS loading buffer was added to each sample and boiled for 4 min . Immunoblotting was carried out as described previously . ( HIS ) 6-p50-HA protein was produced in C43 ( DE3 ) cells ( Lucigen , Middleton , Wisconsin , United States ) and affinity purified using nickel-NTA resin ( Qiagen , Valencia , California , United States ) . Approximately 1 μg of purified protein was used to pull down 35S-Met-labeled in vitro–translated ( TNT; Promega , Madison , Wisconsin , United States ) N and N ( TIR ) as described in [32] . TNT mixture was supplemented with 1 . 5 mM magnesium chloride and 0 . 2 mM potassium acetate . Live plant imaging was performed on a Zeiss Axiovert 200M light microscope equipped with a LSM 510 NLO confocal microscope ( Carl Zeiss , Thornwood , New York , United States ) using either a 40× or 63× C-Apochromat water immersion objective lens ( numerical aperture [NA] 1 . 2 ) . Tissue samples were cut from N . benthamiana leaves at approximately 46 hpi and infiltrated with water . The 458-nm and 514-nm laser lines of a 25-mW argon laser ( Coherent , Santa Clara , California , United States ) and the 543-nm laser line of a 1-mW helium neon laser ( LASOS Lasertechnik , Jena , Germany ) with appropriate emission filters were used to image Cerulean , Citrine , and chloroplast autofluorescence , respectively . In some instances , 488-nm and 568-nm laser lines of a 15-mW argon:krypton laser ( Coherent ) were used for Citrine and chloroplast autofluorescence . All images were acquired in fastline switch mode and processed with the Zeiss LSM 510 ( Ver . 3 . 2 ) channel unmixing algorithm to eliminate crosstalk . | Each year , up to 10% of world agricultural production is lost to pests and diseases caused by a variety of pathogens including bacteria , fungi , nematodes , and viruses . Scientists have understood for nearly a century that plants carry their own immune system that actively engages pathogens and prevents many infections . One aspect of the plant immune system is defined by the gene-for-gene hypothesis: a plant Resistance ( R ) gene encodes a protein that specifically recognizes and protects against one pathogen or strain of a pathogen carrying a corresponding Avirulence ( Avr ) gene . In tobacco and its relatives , the N resistance protein confers resistance to infection by the Tobacco mosaic virus ( TMV ) . We have used N , and the TMV elicitor , p50 , to investigate the mechanism of gene-for-gene resistance . We show that N and p50 associate in the cytoplasm and nucleus of plant cells and that this association is mediated by N's TIR domain , which is structurally similar to animal innate immunity molecules . Our findings provide novel insight into how R proteins recognize pathogen Avr proteins , and should help in long-term efforts to enhance crop yield . |
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Tubercidin ( TUB ) is a toxic adenosine analog with potential antiparasitic activity against Leishmania , with mechanism of action and resistance that are not completely understood . For understanding the mechanisms of action and identifying the potential metabolic pathways affected by this drug , we employed in this study an overexpression/selection approach using TUB for the identification of potential targets , as well as , drug resistance genes in L . major . Although , TUB is toxic to the mammalian host , these findings can provide evidences for a rational drug design based on purine pathway against leishmaniasis . After transfection of a cosmid genomic library into L . major Friedlin ( LmjF ) parasites and application of the overexpression/selection method , we identified two cosmids ( cosTUB1 and cosTU2 ) containing two different loci capable of conferring significant levels of TUB resistance . In the cosTUB1 contained a gene encoding NUPM1-like protein , which has been previously described as associated with TUB resistance in L . amazonensis . In the cosTUB2 we identified and characterized a gene encoding a 63 kDa protein that we denoted as tubercidin-resistance protein ( TRP ) . Functional analysis revealed that the transfectants were less susceptible to TUB than LmjF parasites or those transfected with the control vector . In addition , the trp mRNA and protein levels in cosTUB2 transfectants were higher than LmjF . TRP immunolocalization revealed that it was co-localized to the endoplasmic reticulum ( ER ) , a cellular compartment with many functions . In silico predictions indicated that TRP contains only a hypothetical transmembrane domain . Thus , it is likely that TRP is a lumen protein involved in multidrug efflux transport that may be involved in the purine metabolic pathway . This study demonstrated for the first time that TRP is associated with TUB resistance in Leishmania . The next challenge is to determine how TRP mediates TUB resistance and whether purine metabolism is affected by this protein in the parasite . Finally , these findings may be helpful for the development of alternative anti-leishmanial drugs that target purine pathway .
Leishmania spp . are the causative agents of leishmaniasis , a parasitic protozoan disease that affects 12 million people worldwide with an estimated annual incidence of approximately 1 million , including both visceral and cutaneous cases [1] . The leishmaniasis chemotherapy is complicated because most of drugs used are expensive , toxic , and require long periods of supervised therapy [2] . Pentavalent antimonial is the WHO-recommended drug for the treatment of leishmaniasis; however , it has several side effects and reports of parasite resistance have been described worldwide [3] . Cases that are unresponsive to antimonial treatment are usually treated with amphotericin or pentamidine , although these drugs also have several side effects [3] . Miltefosine is the first effective oral drug developed to treat visceral leishmaniasis . It has been used in India for more than a decade [4] and an increase in the failure rate has been reported [5 , 6] . Considering the limitations of the currently used chemotherapy and the lack of effective vaccines for the leishmaniasis , the identification of new drugs and vaccine approaches for the treatment of leishmaniasis is required . A rational strategy for chemotherapeutic exploitation in parasitic diseases can be developed , based on the identification of fundamental metabolic differences between parasite and host . New potential drug targets have been identified in molecular and biochemical studies to identify potential targets of the parasite that can be used in future therapies [7 , 8] . An interesting pathway for exploration in the parasite is the purine metabolism . Purine nucleotides and their derivatives are precursors of a variety of cellular and metabolic processes , including energy production , cell signaling , synthesis of nucleic acids , modulation of enzymatic activities and synthesis of co-enzymes [9–11] . Leishmania and other protozoan parasites are unable to synthesize purine nucleotides de novo and must salvage them from the host [10] . This unique characteristic may be the basis for the susceptibility of Leishmania to purine analogs [12 , 13] . Purine uptake in Leishmania is required for parasite viability during all life cycle stages [14] . Parasite nucleoside transporters , located on the plasma membrane , perform an essential function in uptake of purine nucleosides from the host into the parasite , which is the first step in the salvage process [15] . TUB is a toxic adenosine analog that is incorporated into nucleic acids in microorganisms and in mammalian cells . TUB has been previously described as a potential antiparasitic agent due to its inhibition of purine uptake in Schistosoma mansoni , S . japonicum [16 , 17] , Trypanosoma gambiensi [18] and Leishmania spp . [19 , 20] . Considering the potential antiparasitic activity of TUB , in this study we aimed to identify the potential loci involved in TUB resistance in L . major . This knowledge is essential to understand the mechanism of action and resistance of this compound , as well as for identifying potential drug targets in the parasite . Accordingly , an overexpression/selection method with cosmid genomic libraries of LmjF was used to isolate two loci involved in TUB resistance [21] . One of the isolated cosmids ( cosTUB1 ) contains a locus encoding NUPM1 , a putative transcription-factor-like protein , previously described as toxic nucleoside resistance ( TOR ) [22] . TOR was described in L . amazonensis promastigote mutants resistant to TUB after drug selection in vitro , as an atypical multidrug resistance protein [22–24] . The other isolated cosmid ( cosTUB2 ) contains a locus involved in TUB resistance following overexpression . This locus is not related to the tor gene or to other previously described locus involved in drug resistance in Leishmania [21] . Interestingly , these two loci are associated with two different resistance profiles: while tor also confers resistance to both inosine dialdehyde and allopurinol , the other locus confers resistance to inosine dialdehyde and hypersensitivity to allopurinol [21] . Based on these previous findings , in this study , we mapped , sequenced the genomic regions of two cosmids ( cosTUB1 and cosTUB2 ) and identified the two genes related to TUB resistance in LmjF . TUB resistance gene in cosTUB1 encodes the previously described TOR protein , while the locus in cosTUB2 encodes a hypothetical protein . Of the 8 , 272 protein-coding genes predicted and annotated in LmjF genome , approximately 50% are annotated as hypothetical proteins; most of them are likely involved in essential cellular processes [25 , 26] . Thus , the identification and characterization of TRP , may contribute for increasing the understanding of the purine pathway in Leishmania and the role of this protein in drug resistance mechanisms .
Leishmania major Friedlin ( LmjF ) strain ( MHOM/IL/1980/Friedlin ) promastigotes were grown at 25°C in M199 medium supplemented with L-glutamine , 10% heat-inactivated fetal calf serum , 0 . 25% hemin , 12 mM NaHCO3 , 100 μM adenine , 40 mM HEPES , 50 U/mL penicillin and 50 μg/mL streptomycin . TUB , allopurinol , pentamidine , hygromycin B ( HYG ) and G418 were obtained from Sigma-Aldrich ( St . Louis , MO , USA ) . Transfected parasites were cultured in M199 medium supplemented with increasing concentrations of HYG ( 125 to 500 μg/mL ) or G418 ( 32 to 500 μg/mL ) , depending on the drug resistance marker . Cosmids cosTUB1 and cosTUB2 associated with TUB resistance were previously isolated by an overexpression/selection strategy in LmjF as described by Cotrim et al . [21] . Briefly , two genomic libraries containing 30–40 kb inserts of genomic DNA from LmjF strain constructed in the shuttle vector cLHYG [27] were prepared by shearing or Sau3A partial digestion [21] . After transfection of these two cosmid libraries , parasites were plated on semisolid media in the presence of two independent concentrations of TUB ( 0 . 9 and 1 . 8 μM ) . A total of 39 colonies obtained after 10–15 days of incubation were then transferred to M199 liquid medium containing increasing concentrations of HYG ( from 125 to 500 μg/mL ) to increase the cosmid copy number [21] . Cosmid DNA was recovered from these primary TUB-resistant transfectants , used to transform into Escherichia coli DH5α strain and analyzed by restriction enzyme digestion [21] . Southern blot analysis confirmed the presence of two independent loci involved in TUB resistance , one containing tor gene and the other corresponding to a new locus . To confirm the role in TUB resistance , cosTUB1 and cosTUB2 were transfected back into LmjF and after increasing cosmid copy number , tested for TUB resistance . Deletions of cosTUB1 and cosTUB2 were generated by partial digestion with KpnI and ApaI , respectively , followed by self-ligation , to identify the gene ( s ) involved in TUB resistance . A pTRP construct was generated by total digestion of cosTUB2 with ClaI and EcoRV restriction enzymes , followed by ligation of the 3 kb fragment into the pSNBR vector [28] , previously digested with the same enzymes . Transfections were performed as described by Coburn et al . [29] . Promastigotes from the late logarithmic phase were harvested , washed and resuspended in electroporation buffer . A total of 20–40 μg of cosmid and plasmid DNA was mixed on ice with 4x107 cells in a 2-mm cuvette and subjected to electroporation ( 500 μF , 2 . 25 kV/cm ) using a Bio-Rad Gene Pulser apparatus . Mock transfection was performed in absence of cosmid or plasmid DNA for the negative control while the transfection with cosmid or plasmid DNA of empty vector was performed as control . Transfected parasites were kept on ice for 10 minutes and then transferred to 10 mL M199 medium . The antibiotics HYG or G418 were added after 24 hours , depending on the drug resistance marker . For drug susceptibility analysis , promastigotes ( 106 promastigotes/mL ) were incubated at 25°C in the presence of increasing TUB concentrations for 72 hours , and then the number of parasites was determined using a Coulter T890 ( Beckman , CA , USA ) . The 50% inhibitory concentration ( IC50 ) was measured at the time when control cultures lacking the drug had reached the late logarithmic phase of growth [30 , 31] . The results are expressed as the means ± standard error . Statistical analysis was performed using the non-parametric Kruskal-Wallis test and p < 0 . 05 was considered significant . To identify the genomic region of interest in L . major genome databases , a 1 . 0 kb EcoRI fragment from cosTUB1 and a 1 . 0 kb EcoRV fragment from cosTUB2 were subcloned into a pUC-π vector . The subcloned fragments were sequenced using a MegaBACE 1000 automated sequencer ( GE Healthcare , UK ) with DYEnamic Dye Terminator kit ( GE Healthcare , UK ) , according to the manufacturer’s instructions . Analyses of the nucleotide sequences were performed using Lasergene Software ( DNASTAR , Inc . ) and Clone Manager 9 Software . Sequence data for the remaining regions were obtained from LmjF GeneDB [25] . Nucleotide sequences obtained were used to map the genomic region corresponding to the two different loci using LmjF database . In silico analyses were also conducted to estimate the insert sizes of both cosmids after digestion with different restriction enzymes . In silico analysis of the genomic regions involved in TUB resistance was performed using DNASTAR and Clone Manager 9 Software . BLAST searches of LmjF GeneDB [25] and TriTrypDB [32] were performed using the standard settings . Multiple alignments were performed using the Constraint-based Multiple Protein Alignment Tool ( Cobalt ) [33] . Prediction of transmembrane domains was performed with the TMHMM Server [34 , 35] while the predictions of protein function , sub-cellular localization and the tridimensional TRP structure were performed using ProtFun [36 , 37] , TargetP Server [38 , 39] and Phyre2 [40] , respectively . The sequence data described in this paper are available under the following accession numbers: XM_001685128 . 1 for gene ID LmjF . 31 . 1940 ( nupm1 ) , and XM_001685135 . 1 for gene ID LmjF . 31 . 2010 ( trp ) . All sequence data are also available at www . tritrypdb . org . Total RNA from promastigotes in stationary growth phase and during the growth curve on days 3 , 5 , 7 and 9 , were isolated using TRIzol reagent ( Life Technologies , Carlsbad , CA , USA ) , according to the manufacturer’s instructions . RNA samples were treated with DNase I ( Thermo Scientific , Lithuania , EU ) and RNA concentration and purity were determined using a spectrophotometer at A260/A280 ( Nanodrop ND1000 , Thermo Scientific , USA ) . Reverse transcription was performed using 2 μg of total RNA as template , reverse transcriptase and random primers ( cDNA synthesis kit , Thermo-Scientific , Canada ) , according to the manufacturer’s instructions . Equal amounts of cDNA were run in triplicate in a total volume of 25 μL containing Power SYBR Green Master Mix ( Life Technologies , Warrington , UK ) and the following primers ( 10 μM ) : TRP_F 5´-CGGTGTAGATGAACCAGCAGTAG-3´ , TRP_R 5´-CTCACAGAGGGATTTCGAGAGTG-3´ , GAPDH_F 5´-AACGAGAAGTTCGGCATAGTCGAG-3´ and GAPDH_R: 5´-ACTATCCACCGTCTTCTGCTTTGC-3´ . The mixture was incubated at 94°C for 5 minutes , followed by 40 cycles at 94°C for 30 sec , 64°C for 30 sec and 72°C for 30 sec . A negative control in the absence of reverse transcriptase was included in RT-qPCR assays for check DNA contamination in RNA samples . Reactions were carried out using an Exicycler 96 ( Bioneer , Daejeon , Korea ) . The copy number of the target gene ( trp ) and housekeeping gene ( gapdh ) were quantified in three biological replicate samples , considering the molar mass concentration , according to a standard curve generated from a ten-fold serial dilution of a quantified and linearized plasmid containing the target fragment for each quantification test . The normalized trp/gapdh ratio of the absolute number of molecules of each target was used as parameter of the relative expression of trp in the cosTUB2 and pTRP transfectants relative to LmjF or the line transfected with the empty vector ( pSNBR or cLHYG ) . Analyses were performed using Analysis Exicycler3 Software ( Bioneer , Daejeon , Korea ) . The open reading frame ( ORF ) of gene trp ( LmjF . 31 . 2010 ) was amplified by PCR using the following primers containing the restriction enzyme sites for BamHI and NotI ( underlined ) : TRP_F_BamHI 5´-GGATCCATGGAGTGCATCAACCAAGAGAGC-3´ and TRP__R_NotI 5´-GCGGCCGCTCACATGGCACAGATAAACACC-3´ . The amplified fragment was cloned into the pET28a ( Novagen , USA ) expression vector and sequenced to confirm the insertion direction . The pET-TRP plasmid obtained was then used to transform into E . coli ( BL21 ( DE3 ) CodonPlus-RIL ) . Selected clones were grown aerobically at 37°C in LB medium containing kanamycin ( 30 μg/mL ) and chloramphenicol ( 35 μg/mL ) to a culture OD600 0 . 6–0 . 8 . pET-TRP expression was induced by 1 mM of isopropyl-β-D-thiogalactopyranoside . After induction , the culture was lysed by sonication ( Sonics–VCX500 ) with 20 mM sodium phosphate , 500 mM sodium chloride and 5 mM imidazole . Lysed samples were clarified by centrifugation at 10 , 000 x g , for 15 minutes at 4°C , and inclusion bodies were solubilized with 20 mM sodium phosphate , 500 mM sodium chloride , 5 mM imidazole and 8 M urea . Recombinant TRP was obtained by affinity chromatography with a 1 mL HisTrap HP column ( GE Healthcare , Uppsala , Sweden ) . The purified recombinant TRP was analyzed by SDS-PAGE and then used to produce a rabbit polyclonal anti-TRP antibody by Proteimax Biotechnology ( Sao Paulo , Brazil ) . Approximately 107 promastigotes in the stationary growth phase and during the growth curve on days 3 , 5 , 7 and 9 were washed with PBS and then lysed with lysis buffer ( 100 mM Tris-HCl pH 7 . 5 , 2% Nonidet P40 , 1 mM PMSF and protease inhibitor cocktail ( Sigma-Aldrich , St Louis , MO , USA ) ) . Cells were disrupted by ten freeze/thaw cycles in liquid nitrogen and 42°C , and were then cleared of cellular debris by centrifugation at 12 , 000 x g for 15 minutes at 4°C . Equal amounts of total protein ( 25 μg ) were solved using SDS-PAGE and transferred to a nitrocellulose membrane ( Hybond-C , Amersham Biosciences , Buckinghamshire , England ) using a Trans-Blot SD apparatus ( Bio-Rad , USA ) . The membrane was incubated with Blocking Buffer ( LI-COR Bioscience , Lincoln , NE , USA ) and then with anti-TRP serum ( 1:2000 dilution ) , overnight , at 4°C . After incubation with primary antibody , the membrane was incubated with biotin anti-rabbit antibody ( Santa Cruz Biotechnology , CA , USA ) ( 1:1000 dilution ) for 1 hour at room temperature and then with streptavidin ( Santa Cruz Biotechnology , CA , USA ) ( 1:2000 dilution ) for 30 minutes at room temperature for biotin-streptavidin binding . Anti-α-tubulin ( Sigma-Aldrich , St . Louis , MO , USA ) ( 1:1000 dilution ) was used to normalize the amount of protein in the blot . All steps were followed by washing 3 times with PBS . The membranes were scanned using an Odyssey CLx apparatus ( Li-COR , Lincoln , NE , USA ) in both 700 and 800 nm channels using an Odyssey System . Odyssey Imaging CLx instrument was used at the 5/5 intensity setting ( 700/800 nm ) . Quantification of the protein level was performed with Image Studio2 . 1 Software ( Li-COR , Lincoln , NE , USA ) . TRP target band densities were normalized against α-tubulin for blotting comparisons in LmjF , cosTUB2 and pTRP transfectants . Statistical analysis was conducted using the Mann-Whitney U-test , and p < 0 . 05 was considered significant for three independent experiments . Approximately 106 promastigotes of LmjF and pTRP transfectants in the stationary growth phase were washed with PBS and adhered to cover slips treated with poly-L-lysine ( Sigma-Aldrich , St . Louis , MO , USA ) for 15 minutes . The cells were then fixed with 3% paraformaldehyde for 10 minutes and treated with 50 mM ammonium chloride for 10 minutes . The fixed cells were permeabilized and blocked with 0 . 1% Triton X-100 and 0 . 1% BSA in PBS for 10 minutes at room temperature . To analyze sub-cellular TRP localization , anti-TRP polyclonal antibody ( 1:100 dilution ) was visualized using an anti-rabbit secondary antibody conjugated to Alexa488 ( Life Technologies , Carlsbad , CA , USA ) ( 1:500 dilution ) . Anti-BiP/GRP78 ( BD Bioscience , Iowa , USA ) ( 1:500 dilution ) was visualized using an anti-mouse secondary antibody conjugated to Alexa594 ( Life Technologies , Carlsbad , CA , USA ) ( 1:500 dilution ) . Nuclear and kinetoplast DNA were labeled using DAPI . Each step was followed by washing with PBS 10 times . The coverslips were mounted in ProLong media ( Life Technologies , Carlsbad , CA , USA ) . All imaging was performed at the Molecular Imaging Center ( MIC ) of the University of Bergen , using a Zeiss LSM 510 Meta confocal microscopy . Co-localization images were edited using Photoshop 6 .
Using the overexpression/selection strategy described by Cotrim et al . [21] , a library of 17 , 900 independent genomic cosmid transfectants in LmjF were recovered from semisolid plates in two TUB concentrations . Thirty-nine colonies showing differential survival were recovered and then analyzed by restriction enzyme digestion . cosTUB1a and cosTUB1b were recovered each one from a single colony , while cosTUB2 was recovered from several colonies [21] . Southern blot analysis demonstrated that cosTUB1a and cosTUB1b were involved in TUB resistance conferred by the TOR protein [21] . This protein has been previously described as related to TUB resistance in selected L . amazonensis promastigote mutants [22 , 24] . Since cosTUB1a and cosTUB1b were referred to the same TUB resistance gene , we decided to examine only cosTUB1a followed of mapping , functional and sequencing analysis . Parasites transfected with the cosmid cosTUB1 showed moderate TUB resistance ( 1 . 95-fold resistance ) compared with the LmjF ( S1 Table ) . To map the gene likely involved in the resistance phenotype , a set of deletions was generated using the restriction enzyme KpnI . Four independent deletions were generated , transfected back into LmjF and amplified by HYG selection . Transfected parasites with the deletions cosTUB1-ΔKpnI-III and cosTUB1-ΔKpnI-IV exhibited 2 . 04- and 2 . 82-fold resistance , respectively , compared with LmjF parasites ( S1 Fig and S1 Table ) . No significant differences in resistance were observed among the other deletions compared with LmjF or with transfected parasites carrying the empty vector cLHYG ( S1 Table ) . To identify the gene of cosTUB1 involved in TUB resistance , a 1 . 0 kb EcoRI fragment from this cosmid ( S1 Fig ) was sub-cloned into the pUC-π vector and sequenced . In silico analysis revealed that the cosTUB1 insert corresponds to a genomic DNA region from chromosome 31 of LmjF containing five ORFs ( S1 Fig ) . Two of which encode hypothetical proteins ( LmjF . 31 . 1910 and LmjF . 31 . 1920 ) , and the other three encode peptidase m20/m25/m40 family-like protein ( LmjF . 31 . 1890 ) , dihydrouridine synthase ( LmjF . 31 . 1930 ) and transcription-factor-like NUPM1 protein ( LmjF . 31 . 1940 ) ( S1 Fig ) . According to the map and the functional analysis , the 3 . 0 kb fragment represented in the deletion cosTUB1-ΔKpnI-IV , contained the locus likely involved in TUB resistance ( S1 Fig ) . In this region , we identified the gene that encodes the transcription-factor-like NUPM1 protein ( LmjF . 31 . 1940 ) ( accession number XP_001685180 . 1 ) ( S1 Fig ) . This protein has 77% similarity to NUPM1 of L . amazonensis , which has been previously described as TOR protein and it is related to TUB resistance in selected L . amazonensis promastigote mutants [22 , 24] . According to TriTrypDB , nupm1 encodes a 53 . 1 kDa protein with only one predicted transmembrane domain and no other predicted domain ( S2 Fig ) . The other cosmid , cosTUB2 , was recovered 37 times and presented a different resistance profile compared with cosTUB1 . It conferred a 3 . 78-fold increase in resistance compared with LmjF ( Table 1 ) . Using the same strategy as that described for cosTUB1 , we mapped the likely gene involved in the resistance phenotype through the generation of a set of deletions with the restriction enzyme ApaI ( Fig 1 ) . Four independent deletions were generated , transfected back into LmjF and then selected using HYG selection . Parasites transfected with the cosTUB2-ΔApaI-III deletion exhibited 2 . 0-fold resistance compared with LmjF ( Fig 1 and Table 1 ) . No significant difference was observed among the transfectants containing the other three deletions compared with LmjF or with transfected parasites carrying the empty vector cLHYG ( Table 1 ) . To map the genomic region corresponding to the locus involved in TUB resistance , a 1 . 0 kb EcoRV fragment from cosTUB2 ( Fig 1 ) was sub-cloned into the pUC-π vector and sequenced . Sequence analysis indicated that this DNA fragment corresponded to a second region of chromosome 31 of L . major . In silico analysis indicated the presence of eight ORFs in the 30 kb genomic region of cosTUB2 . Four of these genes were annotated as encoding hypothetical proteins ( LmjF . 31 . 2010 , LmjF . 31 . 2040 , LmjF . 31 . 2050 and LmjF . 31 . 2060 ) , while the other four genes encoded a non-coding RNA ( LmjF . 31 . ncRNA ) , a glycoprotein-like ( GP63-like ) ( LmjF . 31 . 2000 ) , a succinyl-diaminopimelate-desuccinylase-like ( SDD-like ) protein ( LmjF . 31 . 2020 ) and an ubiquitin-fusion protein ( LmjF . 31 . 2030 ) ( Fig 1 ) . In silico data associated with genomic mapping and functional analysis indicated that the gene LmjF . 31 . 2010 , located in the 9 . 6 kb fragment of cosTUB2 and also in cosTUB2-ΔApaI-III ( Fig 1 ) , encodes a hypothetical protein that could be involved in TUB resistance . To confirm LmjF . 31 . 2010 as the gene involved with TUB resistance , we sub-cloned the region encompassing the LmjF . 31 . 2010 gene ( a 3 kb fragment from cosTUB2 digested with ClaI-EcoRI ) into pSNBR vector , previously digested with the same enzymes ( Fig 1 ) . This construct ( pTRP ) was transfected back into LmjF and the amplification and overexpression of this gene was obtained by increasing the concentration of G418 . As expected , the pTRP transfectants exhibited 1 . 91-fold resistance compared with LmjF ( Table 1 ) , confirming the role of LmjF . 31 . 2010 gene in TUB resistance . The trp mRNA expression in LmjF promastigotes was measured by RT-qPCR . Quantification of trp transcripts in the cosTUB2 and pTRP transfectants relative to LmjF or to the transfected line with the empty vector ( cLHYG or pSNBR ) was performed using trp gene as target . The data were normalized by the amount of gapdh transcript . Both sequences corresponded to single copy genes . As shown in Fig 2 , the cosTUB2 transfectant exhibited 19 . 4- and 16 . 8-fold increase in trp mRNA expression compared with LmjF and with cLHYG transfectant , respectively . In contrast , no significant change in trp mRNA expression was observed between the pTRP transfectant and LmjF or pSNBR transfectant ( Fig 2 ) . Additional data obtained during the time-course of growth curve of LmjF promastigotes demonstrated that trp transcript expression was increased in the day 3 , in the logarithimic growth phase . As shown in S3 Fig , trp mRNA transcript expression was 2-fold increase on day 3 compared with days 5 , 7 and 9 . The same profile was observed at protein levels , with an increase of 1 . 5-fold on day 3 compared with days 5 , 7 and 9 ( S3 Fig ) . Western blot analysis of cell lysates from LmjF and parasites transfected with cosTUB2 and pTRP were performed using an anti-TRP polyclonal antibody . As shown in Fig 3 , the signal intensity of TRP expression was significantly increased in the cosTUB2 and pTRP lysates compared with LmjF lysate . Sequence analysis of the trp gene ( LmjF . 31 . 2010 ) ( accession number XP_001685187 . 1 ) indicated that it encodes a 63 . 4 kDa protein . It is conserved in the genus Leishmania , and it is not present in other trypanosomatids , such as Trypanosoma brucei and T . cruzi ( Fig 4 ) . This gene encodes for a protein that contains just one hypothetical transmembrane domain and no other putative conserved domain or peptide signal ( S4 Fig ) . Interestingly , multiple sequence alignments revealed the presence of 11 amino acids specific to L . braziliensis ( LbrM ) ( S4 Fig ) . Indeed , TRP of L . major contains approximately 85% of similarity with its orthologs of the subgenus Leishmania and 54% of similarity with its ortholog of L . ( Viannia ) braziliensis ( S4 Fig ) . Additional in silico data based on the Protein Functional Category and Enzyme Class Database ( ProtFun Server ) , which predicts cellular role and enzyme class based on gene ontology , indicated that TRP is an enzyme involved in the purine and pyrimidine pathway . In silico analysis of TRP using the TargetP 1 . 1 Server , which predicts the sub-cellular localization of eukaryotic proteins revealed the absence of any sequence corresponding to a mitochondrial targeting peptide . 3D protein prediction was performed by submitting the amino acid sequence to the Phyre2 web portal for protein modeling , prediction and analysis . The predicted model of TRP based on heuristics to maximize confidence , percent identity and alignment coverage is shown in Fig 5 . Some disordered regions were observed , but the prediction showed 90% confidence . Some regions have interesting folding patterns with similarities to transmembrane helices , multidrug efflux transporter , hydrolase/transport protein and transferase ( Fig 5 ) . For cellular immunolocalization of TRP , we used antibodies against the recombinant TRP . Considering our hypothesis that TRP is an ER protein , we used an ER marker ( anti-BiP/GRP78 ) . Confocal microscopy showed that TRP is co-localized in ER in stationary phase promastigotes of LmjF and of pTRP transfected line ( Fig 6 ) . We next analyzed whether the transfected lines overexpressing the trp gene were resistant to other drugs . Interestingly , the cosTUB2 and pTRP transfectants showed cross-resistance to pentamidine , with 2 . 0- and 5 . 0-fold resistance , respectively , compared with LmjF ( Table 2 ) . No cross-resistance to allopurinol was observed . Indeed , the cosTUB2 transfectants were more sensitive to allopurinol .
The molecular mechanism of action of compounds used in leishmaniasis treatment is not well known . Overexpression/selection methods have been used for identification of drug targets and potential drug resistant genes [21 , 30 , 31 , 41] . Because of the limited knowledge about the purine metabolism in Leishmania , we proposed in this study to elucidate the purine pathway in Leishmania examining the resistance phenotype after transfection of a cosmid genomic library , followed by drug pressure using TUB . This drug has been demonstrated to have potent activity against promastigotes forms of L . amazonensis , L . braziliensis , L . infantum chagasi and L . major [19 , 20] . The same antiparasitic efficacy has also been reported in vitro against intracellular amastigotes and in vivo infection against L . amazonensis when the drug was associated with the specific inhibitor of nucleoside transport for mammalian cells , the nitrobenzylthioinosine ( NBMPR ) [20] . Two cosmids conferring TUB resistance , cosTUB1 and cosTUB2 , were isolated from transfected parasites with a genomic library constructed in the cLHYG vector [21] . The gene related to TUB resistance in cosTUB1 provided 2 . 82-fold resistance compared with LmjF . In silico analysis of the genomic region in L . major GeneDB revealed that cosTUB1 is located on chromosome 31 and that 1 , 455 base pair gene present in the 3 . 0 kb fragment encodes NUPM1-like protein . A previous study first referred to this protein as TOR because it was involved in TUB resistance in selected L . amazonensis mutants [23] . According to these authors , L . amazonensis becomes resistant to TUB by decreasing the capacity to accumulate this exogenous purine . Later , it was suggested that the decrease was related to the reduction in the activity of purine transporters [24] . The TOR protein mediates resistance by redirecting the adenosine permease from the plasma membrane to the multi-vesicular tubule lysosome [24] and TUB resistant parasites overexpressing tor are unable to uptake the toxic purine and become resistant to the drug [24] . Moreover , TOR could act at the protein level and affect the activity and/or the amount of transporters and other proteins [24] . The protein has similarities to Oct-6 , a mammalian transcription factor of the Pou family [24 , 42] . Members of the Oct family bind to the octamer motif , a cis-acting regulatory element enhancer and stimulate transcription via this octamer motif [42] . In contrast to TOR identification , we could not find any previously described gene in cosTUB2 related to TUB resistance . Similar to cosTUB1 , the cosTUB2 insert contains a genomic region of chromosome 31 , but the region is distinct from that one in cosTUB1 . According to the restriction map and functional analysis , the 9 . 6 kb fragment present in both cosTUB2 and the cosTUB2-ΔApaI-III deletion was suggested to be the region related to TUB resistance . In silico analysis showed that this region coded for a hypothetical protein ( LmjF . 31 . 2010 ) , a succinyl-diaminopimelate-desuccinylase-like ( SDD-like gene ) and part of the glycoprotein 63 ( GP63-like ) . GP63-like is the major surface glycoprotein in Leishmania promastigotes , beside lipophosphoglycan ( LPG ) and GIPLs [43] . Several functions of GP63 have been described in the vertebrate host , including cell adhesion to mammalian cells . It is predominantly expressed in the form present in the insect host and unknown relationship with drug resistance or purine metabolism has been described . Further , SDD-like is not either associated with resistance to toxic nucleosides . Its function is related to synthesis and metabolism of amino acids [44] . Thus , we focused our study on the hypothetical protein LmjF . 31 . 2010 . The coding region of this gene was cloned for generating the construct pTRP , which was transfected into LmjF . The protocol for overexpression/selection of transfectants conferred moderate level of resistance to TUB ( 1 . 91-fold resistance ) compared with that conferred by cosTUB2 ( 3 . 78-fold resistance ) . Despite the low level of resistance mediated by pTRP , there are several indications that trp is involved in TUB resistance . First , the signals for protein translation were intact . Second , the levels of resistance were significant for cosTUB2 ( with a 30 kb insert ) , cosTUB2-ΔApaI-III ( with a 26 kb insert ) and pTRP ( with a 3 kb insert ) . Third , genes that conferred resistance by transfection to other drugs , such as terbinafine and itraconazol [21] , vinblastina [45] , primaquine [46] , pentamidine [30] , antimony , miltefosine and amphotericin [41] presented a moderate resistance profile as observed for trp gene . As an example , the squalene synthetase gene ( sqs1 ) has been identified in a cosmid that conferred resistance to terbinafine ( 1 . 47-fold resistance ) in L . major [21] . The analysis of trp transcripts revealed that the RNA level was increased in cosTUB2 compared with LmjF . In contrast , we observed similar RNA expression levels when comparing pTRP transcripts . Interesting , the increased resistance level conferred by cosTUB2 ( 3 . 78-fold resistance ) versus pTRP ( 1 . 91-fold resistance ) was not correlated with the relative trp mRNA abundance . These results indicate that trp expression could be subjected to some negative regulation . The increased transcription of trp in cosTUB2 transfectants compared with pTRP transfectants can be explained by the action of cis and trans elements as regulatory factors of mRNA generated by cosTUB2 and pTRP . Moreover , cosmids may contain regulatory sequences that are missing in the plasmid . Indeed , the length of the cosmid DNA can provide a chromatin-like structure that allows to enhance the transcription and consequently promote an increase in protein translation [47] . Western blot analysis revealed that the protein levels differed between LmjF and the transfected lines ( cosTUB2 and pTRP ) , with an increase of TRP expression in the transfected lines . The sub-cellular immunolocalization of TRP in the ER suggests that it is associated with the secretory pathway [48] . Proteins located in the ER may be related to the stress response , the downregulation of translation , spliced leader silencing and protein misfolding [49] . In addition , GFP-tagged TOR has been previously demonstrated to be present at multiple locations in Leishmania ( i . e . , mitochondria and Golgi/trans Golgi regions ) except the nucleus [24] . TOR appears to act at the protein level and affect the activities and/or concentration of a class of transporters and other proteins [24] . TUB is transported by adenosine permease , but this purine analog , which is toxic to the parasite , must first enter the cell . According to Detke et al . ( 2007 ) , parasites become resistant to this toxic purine nucleoside due to the functional loss of the appropriate transporter by mutation or amplification of the tor gene , which leads to a decrease of TUB entry into the cell [24] . Nucleoside transport in Leishmania is mediate by transporters located in the plasma membrane of the parasite . There are five different members that have different and selective substrate specificities [14] . The regulation of these transporters occurs via salvage pathway because Leishmania does not synthesize purine de novo [10] . The nucleoside transporters of L . donovani , LdNT1 . 1 and LdNT1 . 2 , were previously described . Both transporters mediate adenosine and pyrimidine uptake [15 , 50] , they are members of the equilibrative nucleoside transporter ( ENT ) family [51 , 52] and exhibit approximately 30% amino acid identity with mammalian ENTs [10] . This low identity is due to differences in the nucleoside transporter members between Leishmania and the mammalian host . It has been previously demonstrated that the use of TUB in association with the nucleoside transport inhibitors can result in highly selective toxicity against the parasite , thereby protecting the host against TUB toxicity [20] . LmaNT3 transporter from L . major was reported with a homology of LdNT1 . 1 with 33% amino acid sequence identity [53] . It mediates the hypoxanthine , xanthine , adenine and guanine uptake . Interestingly , its functions are optimal at neutral pH in the promastigote form [54] . In contrast , LmaNT4 has very low transport activity at neutral pH , but it is functional in the acid pH that is found within acidified phagolysomal vesicles of host macrophages during the intracellular amastigote stage of the parasite [54] . It was also demonstrated that Leishmania overexpressing adenosine permease exhibits an increased sensitivity to TUB [24] . In contrast , resistance to TUB occurs due to functional loss of adenosine and guanosine permease through mutation or amplification of the tor gene [22–24] . The reduction in adenosine permease is due to reduction in the amount of transporter per se and to the re-routing of the normal trafficking of this transporter from the plasma membrane to the multi-vesicular tubule lysosome [24] . Even in relation to the nucleoside transporters , we hypothesize that the cross-resistance to pentamidine exhibited by cosTUB2 and pTRP transfectants is TRP-dependent . It has been reported that several of the 12 ENTs family identified in T . brucei with involvement in the salvage pathway can also transport pentamidine [54] . Pentamidine is a second-line drug used as an alternative to the leishmaniasis treatment with pentavalent antimony . The drug enters into the Leishmania promastigote or amastigote via a high affinity pentamidine transporter [55] . The mitochondrion is an important target and the drug is involved in the binding and disintegration of kinetoplast DNA [56–58] . Integration of the pathways involved in TUB and pentamidine resistance can be reinforced by the immunolocalization of TRP close to the perinuclear network , although additional studies are required to elucidate this relationship . In contrast , no cross-resistance was observed for allopurinol . Allopurinol is known to inhibit enzymes of the purine salvage pathway in Leishmania [59] . The mechanism of action of allopurinol is involved in the conversion to ribonucleoside triphosphate analogs and incorporation into RNA , thereby disrupting macromolecular biosynthesis [60] . Interestingly , TRP overexpression led to a 2-fold increase in allopurinol susceptibility [21] . Three other hypothetical proteins ( LmjF . 31 . 2040 , LmjF . 31 . 2050 and LmjF . 31 . 2060 ) , non-coding-RNA ( LmjF . 31 . ncRNA ) and ubiquitin-fusion protein ( LmjF . 31 . 2030 ) were identified in cosTUB2 . Although the term ncRNA is commonly used for RNA that does not encode a protein , it does not mean that the RNA has no genetic information and function . It has been described studies of the involvement of ncRNAs in RNA splicing , editing , translation and turnover [61] . In contrast , ubiquitin is a conserved protein , with a difference of only 3 amino acids between Saccharomyces cerevisiae and humans [62] . Protein ubiquitylation is a recognized signal for protein degradation that can control post-translational modifications [55] . It is also known that internalization and retargeting of membrane proteins is frequently initiated by ubiquitination [63 , 64] . Although LmjF . 31 . ncRNA and LmjF . 31 . 2030 were represented in cosTUB2-ΔApaI-I and cosTUB2-ΔApaI-IV , respectively , no TUB resistance per se was observed . We also verified that TRP contains only one hypothetical transmembrane domain and no putative conserved domain . The identification of a single hypothetical transmembrane domain confirms our co-localization , indicating that it is not a membrane transporter , in contrast with the ENTs described in Leishmania that contain 11 hypothetical transmembrane domains [52 , 65] . The location of the transmembrane domain of TRP in the C-terminal region suggests that this protein can be anchored . Comparative analysis based in TriTrypDB demonstrated that trp is specific in the genus Leishmania , with no ortholog identified in T . brucei or T . cruzi . In silico analysis revealed that trp gene is located in the same genomic region of chromosome 31 of L . infantum ( LinJ . 31 . 2050 ) , L . tarantolae ( LtaP . 31 . 2440 ) and L . braziliensis ( LbrM . 31 . 2270 ) . According to TriTrypDB , trp gene is constitutively expressed , however our findings demonstrated that trp is more expressed in logarithmic phase . This result can emphasize the likely relation with purine pathway and the potential role of this protein during the replication of the parasite . All these results indicate the importance of characterizing a hypothetical protein not only by functional genomics , but also according to its general biological features , allowing the acquisition of new knowledge about signaling pathways , metabolism , stress response , drug resistance and in the identification of new therapeutic targets . Purine transport can be considered a potential target , since the mechanism of action is different in Leishmania and its host . Aoki et al . ( 2009 ) demonstrated that the association with a specific inhibitor of the nucleoside transport for mammalian cells , NBMPR , protects infected mammalian host from the toxic effects of TUB . NBMPR inhibits only the mammalian nucleoside transport , thus protecting the host and not the parasite from the TUB toxicity , similarly as proposed in Schistosoma model [16 , 17] . In conclusion , the TRP , initially annotated as a hypothetical protein was described in this work as involved with TUB resistance . | The identification of genes associated with drug resistance has contributed for understanding of the mechanisms of action of compounds against Leishmania , as well as , in the identification of the resistance mechanisms mediated by the proteins encoded by these genes . Differently from the mammalian host , Leishmania is unable to synthetize purine nucleotides de novo and must rescue preformed purines from its host . Due to this metabolic difference between host and parasite , the purine metabolism can be considered as a potential target for drug targeting . TUB is a toxic adenosine analog that was already demonstrated as effective against Leishmania . Using a strategy of gene overexpression after cosmid genomic library transfection , we isolated , mapped , sequenced and identified two genes involved in TUB resistance in L . major . In one of the cosmids , we identified NUPM1-like protein , an atypical multidrug resistance protein previously described in L . amazonensis involved in TUB resistance . The other cosmid contains a novel resistance marker involved in TUB resistance , described here as the TRP . Co-localization of TRP in the ER of LmjF and in silico structural predictions indicated that TRP might be an ER lumen protein . Our findings may be useful to elucidate the purine pathway in the parasite and to understand the role of TRP in the mechanism of TUB resistance . |
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The mechanism for cortical folding pattern formation is not fully understood . Current models represent scenarios that describe pattern formation through local interactions , and one recent model is the intermediate progenitor model . The intermediate progenitor ( IP ) model describes a local chemically driven scenario , where an increase in intermediate progenitor cells in the subventricular zone correlates to gyral formation . Here we present a mathematical model that uses features of the IP model and further captures global characteristics of cortical pattern formation . A prolate spheroidal surface is used to approximate the ventricular zone . Prolate spheroidal harmonics are applied to a Turing reaction-diffusion system , providing a chemically based framework for cortical folding . Our model reveals a direct correlation between pattern formation and the size and shape of the lateral ventricle . Additionally , placement and directionality of sulci and the relationship between domain scaling and cortical pattern elaboration are explained . The significance of this model is that it elucidates the consistency of cortical patterns among individuals within a species and addresses inter-species variability based on global characteristics and provides a critical piece to the puzzle of cortical pattern formation .
Cerebral cortical patterns have fascinated scientists for centuries with their beauty and complexity . Numerous groups relate malformations in sulcal patterns to different diseases in humans , such as autism [1] and attention deficit/hyperactivity disorder ( ADHD ) [2] . Though many advances have occurred in cortical development and sulcogenesis , the understanding of how sulci form and what factors determine the placement of sulci is still limited . The cerebral cortex across species displays a variety of shapes and sizes and also wide array of sulcal patterning . Studying the evolutionary development of sulcal patterns might provide clues about the cortical development taking place in humans . A major advance in determining how these sulcal patterns form was the introduction of the axonal tension hypothesis [3] . This hypothesis describes a mechanically-based scenario where axonal tension , created by developing corticocortical connections in strongly interconnected regions , pulls together gyral walls and creates a folding pattern . This hypothesis furthered the concept that variability between folding patterns among individuals is genetically driven , not just the consequence of random mechanical buckling from a confined cortex . Other mechanochemical models have also been proposed to explain morphogenesis in the central nervous system [4] . Recently , it has been suggested that a cortical pattern can arise based on regional patterns of intermediate progenitor ( IP ) cells in the subventricular zone ( SVZ ) [5] . The intermediate progenitor model , which builds upon the intermediate progenitor cell hypothesis [6] , states that during the development of the cortex certain radial glial cells in the ventricular zone ( VZ ) are activated to create IP cells that travel to the SVZ . These IP cells amplify the amount of neurons created in a given radial column . Furthermore , a subset of IP cells creates a local amplification of neurons in upper cortical layers surrounded by areas of non-amplification , resulting in a wedge shape in the cortex . This wedge shape is representative of a gyrus . This new hypothesis is still being debated [7] , [8] and , if correct , could be a scenario for chemically-based pattern formation in the cortex . Here , a relatively simple and , we believe , elegant chemically-driven mathematical model is proposed to explain how IP cell subsets are distributed spatially and temporally in the developing cortex . Our model , which we call the Global Intermediate Progenitor ( GIP ) model , uses a Turing reaction-diffusion system [9] containing an activator and inhibitor on a prolate spheroidal surface to determine regional areas of activation of the production of IP cells . The GIP model allows determination of the placement of the initial sulci underlying observed complex cortical patterns . It also demonstrates that the initial folds of the arising sulcal pattern are governed by the global shape of the lateral ventricle . The dependency on the global shape provides a critical piece to the puzzle of cortical development .
The patterns created by Turing reaction-diffusion systems have been used to describe pattern formation in numerous biological systems [12] . Though biological Turing patterns have not been proven as rigorously as chemical Turing patterns [13] , recent results [14] give supporting evidence of Turing patterns formation in a biological setting . A Turing system is a reaction-diffusion system , given by ( 1a ) ( 1b ) containing an activator ( U ) and an inhibitor ( V ) that are diffusing throughout their domain and interacting with each other as described by the reaction kinetics ( F and G ) . The reaction kinetics chosen for the GIP model are from the Barrio-Varea-Maini ( BVM ) [15] system given by ( 2a ) ( 2b ) where ( u , v ) = ( U−U0 , V−V0 ) and ( U0 , V0 ) is the steady state . Not much is known about the possible interactions between u and v that regulate the production of IP cells . Hence the BVM system is ideal because the kinetic equations do not assume any prior knowledge of how the reactants ( activator and inhibitor ) interact and instead takes a phenomenological approach . These kinetics ( Equations 2a , b ) also provide control over the amount of linear ( α and γ for u; and β for v ) , quadratic ( r2 ) , and cubic ( r1 ) interactions . The diffusivity ratio and domain scaling are given by d and δ , respectively . Key aspects of the system that determine what pattern will arise include the ratio of the diffusivities of the activator and inhibitor , domain scale and shape , and quadratic versus cubic terms in the kinetic reactions [12] , [16] . In order to analyze the nonlinear BVM system , the kinetics are approximated linearly by expanding them in a Taylor series around the steady state and neglecting higher order terms resulting in ( 3 ) Solutions of Equation 3 are of the form ( u , v ) = T ( t ) X ( x ) . The temporal solution is T ( t ) = eλ ( k2 ) t , where λ ( k2 ) is the temporal eigenvalue . The spatial solution solves the Helmholtz Equation ( ∇2X+k2X = 0 ) in the given domain where k2 is the spatial eigenvalue . Turing patterns have been studied in depth in 1D [12] , 2D [17] , and spherical domains [18] . In all these domains , the solution to the domain's associated eigenvalue problem can predict which pattern will form . For a spherical domain , the eigenvalue solution yields k2 = n ( n+1 ) /r2 , where n is the spherical harmonic index and r is the radius . An increase in k2 , which depends on domain scaling when diffusion coefficients are held constant , results in an increase in n and changes the predicted Turing pattern . Here , we derive a formula that predicts the Turing pattern observed on a prolate spheroidal surface which represents the SVZ . A prolate spheroid is created by rotating an ellipse about its major axis . It has a focal distance , where a and b are the major and minor axes , respectively . Spheroidal coordinates are expressed as ( ξ , η , φ ) where ξ is the radial term; η = cos θ , where θ is the asymptotic angle with respect to the major axis; and φ is the rotation term . To predict which pattern will emerge , the Helmholtz equation is expanded with respect to the prolate spheroidal coordinate system [19] resulting in ( 4 ) where . Because the Helmholtz equation ( ∇2X+k2X = 0 ) is separable in prolate spheroidal coordinates , we rewrite X in terms of X = R ( c , ξ ) S ( c , η ) Φ ( φ ) , such that S ( c , η ) , R ( c , ξ ) , and Φ ( φ ) satisfy ( 5 ) ( 6 ) ( 7 ) where m and ρ are separation constants . Since multiple , discrete values of ρ are possible for a given m and ρ is also dependant on c , the notation will be ρmn ( c ) . Because our domain is a prolate spheroidal surface , the radially-invariant solution is needed ( i . e . ) . In order for Equation 6 to hold , must equal zero for a nontrivial solution . This results in ( 8 ) where ξ0 is the spheroidal radius of the shell that conserves a surface area of 4π ( comparable to the surface area of a unit sphere ) . The significance of the formula in Equation 8 is that it relates a given domain size ( controlled by k2 ) and domain shape ( the eccentricity of the prolate spheroid controlled by f ) to the arising pattern . To demonstrate this formula's ability to predict pattern formation , the system ( Equations 2a , b ) is discretized similar to that of a sphere [18] . A forward-Euler finite difference scheme is used and u and v are discretized such that u ( η , φ ) = ( −1+h1dη , h2dφ ) where h1 = 0 , ‥ , 34 , and h2 = 0 , ‥ , 68 . The continuity with respect to η around the north and south poles is maintained as described in [18] and periodic boundary conditions are used for φ . In Figure 2A , Amn is plotted for n = 0 , ‥ , 7 and m = 0 , ‥ , n . Numerous simulations were executed and two are shown here . The first simulation ( Figures 2B and 2C ) corresponds to k2 = 30 . When k2 = 30 ( asterisk in middle ) is plotted on the Amn vs . k graph ( Figure 2A ) , k2 corresponds with A35 and predicts a ( 3 , 5 ) pattern that agrees with the numerical simulation ( Figures 2B and 2C ) . The second simulation corresponds to k2 = 60 ( Figure 2D and 2E ) and when plotted ( top right asterisk ) on the Amn vs . k graph , predicts a ( 7 , 7 ) pattern that is observed in the numerical simulation .
The model presented here addresses the directionality of the initial sulci formed . In order to use the predictive power of the proposed prolate spheroidal harmonic system , sulci need to be formulated in terms of prolate spheroidal harmonics . Since the initial sulcal formations mimic stripes , only the prolate spheroidal harmonics resulting in striped patterns were studied . In order to form a sulcus , the gyral banks on either side of the sulcus need to be created . In terms of the production of IP cells , the areas on either side of the sulcus will need to be ‘activated’ while the area of the sulcus is ‘not activated’ ( see Figure 3B and 3E ) . The two sulcal directions considered are sectorial and transverse . Sectorial sulci extend in the direction from the frontal lobe around the Sylvian fissure to the temporal lobe . This represents the direction of the major axis of the prolate spheroid approximating the lateral ventricle as shown in Figure 3A . The alignment of sulcal pits ( deepest part of sulcus ) along the major axis of the lateral ventricle in the human has been shown [21] . In terms of spheroidal harmonics , the pattern of IP cells needed to create sectorial sulci is ( m , n ) = ( 1 , 1 ) for 1 sulcus ( Figure 3C ) , ( 2 , 2 ) for 2 sulci , and so forth . Transverse sulci form in the direction of rings around the VZ as shown in Figure 3D . This direction corresponds to ( 0 , 2 ) for 1 sulcus ( Figure 3F ) , ( 0 , 4 ) for 2 sulci , and so forth . In each species displaying a cortical pattern , a number of sectorial sulci ( or sulcal pits ) are observed . The exact number of sectorial sulci is not the focus here . Of interest , rather , are the occurrence of a transverse sulcus , the transition from transverse to sectorial sulci , and the role of lateral ventricular eccentricity . For f = 3 ( Figure 4A ) , as the domain scaling ( k2 ) increases , A11 is reached first , followed sequentially by A02 and A04 . This sequence corresponds to a sectorial sulcus forming first . If the focal distance is increased , e . g . if f = 4 ( Figure 4B ) , there is a shift in the Amn curves and , as k2 increases , A02 will now occur before A11 . This results in a transverse sulcus forming before the first sectorial sulcus . A further increase in focal distance to f = 6 ( Figure 4C ) again shifts the Amn curves , so that A04 now occurs before A11 . Two transverse sulci will now form before a sectorial sulcus is created . These scenarios illustrate how focal distance plays a role in determining the order of pattern formation .
The GIP model illustrates how sulcal placement and directionality is related to changes in focal distance . In order to determine the effect of changes in focal distance on cortical pattern formation , the evolutionary development of cortical patterns was examined . The lateral ventricle is a c-shaped cavity with an anterior horn that extends into the frontal lobe of the hemisphere and an inferior pole that enters the temporal lobe [11] . During the critical stages of brain development the volume of the lateral ventricle increases [22] which also increases the surface area of the lateral ventricle ( i . e . k2 increases ) . Also , as species have evolved the neocortex has expanded , resulting in major evolutionary advances [23] . As the frontal and temporal lobes expand , the lateral ventricle extends into the lobes increasing the lateral ventricular eccentricity resulting in changes in the cortical pattern obtained . For example , overlaying an evolutionary ladder on the scenarios described in Figure 3 implies that the cortices of species on the lower rungs of the evolutionary ladder , such as the cat , do not display transverse sulci before the formation of sectorial sulci ( Figures 4A and 5A ) . Following this evolutionary ladder , at some point the first transverse sulcus appears , as shown in Figure 4B . This second stage is representative of the formation of the calcarine sulcus in species such as the lemur ( Figure 5B ) . Further along the evolutionary ladder , the second transverse sulcus appears ( Figure 4C ) . This is representative of the central sulcus found in higher order primates such as the human ( Figure 5C ) . For humans , this predicted ordering of sulcal formation correlates well with what has been observed during development through the examination of naturally aborted fetuses [24] and MRI study on preterm infants [25] . The first sulci to appear are the anterior calcarine and central sulcus which are in the transverse direction ( blue lines in Figure 5C ) . This is followed by the formation of the superior and inferior frontal sulci , superior and inferior temporal sulci , the intraparietal sulcus and the cingulated sulcus; all which form in the sectorial direction ( red lines in Figure 5C ) . The GIP model also provides a plausible explanation for the development of the central sulcus . Lemurs and humans are both members of the primate order . The lemur is of the suborder prosimian , which is the most primitive of the primates [26] . Most prosimians can be distinguished from anthropoids , the higher primates , by the absence of the central sulcus [26] . Therefore , this model links evolutionary development , through the lateral ventricular eccentricity , to the development of the central sulcus . The GIP model is a theoretical model that builds upon the ideas of the IP model . One argument that has been presented against the intermediate progenitor model is that an “elaborately choreographed set of developmental instructions [regulating the production of IP cells] would be required to account for the tremendous complexity of human cortical convolutions” [7] . The beauty of the GIP model is that it provides an uncomplicated approach that relates to a biologically plausible mechanism of pattern formation . It uses chemical morphogens that may be governed by specific genes to control IP cell production , resulting in the ability to predict the placement and directionality of sulcal pattern formation . The GIP model reveals the role that the global shape of the lateral ventricle has on the positioning of the initial sulci during cortical development . This model explains the development of the initial folds , particularly how two transerve sulci can form before any sectorial sulci in the human . There are many sulci , such as the precentral and postcentral sulcus , that form after this event which are not in the scope of this present work . Also , we believe the Sylvian fissure is formed by the c-shape of the lateral ventricle , which is not applicable to the model . Lateral ventricular shape , or shape of any nontrivial object , is not easy to quantify . The GIP model approximates the lateral ventricle with a prolate spheroid allowing the capture of key shape characteristics in one parameter , the focal distance ( f ) . This approximation also gives the resulting patterns in terms of prolate spheroidal harmonics which contain an order based on the prolate spheroidal indices , m and n . The Helmholtz equation could also be solved on a given triangulated mesh representing the lateral ventricle resulting in a set of eigenvalues and eigenfunctions . The eigenfunction whose associated eigenvalue produces diffusion-driven instability would be the predicted pattern formed . A drawback of this latter approach occurs when comparing predicted patterns from different triangulated meshes . Since each mesh has its own parameterization there is no way of knowing which shape characteristic is responsible for the change in pattern formation . Although changes in the volume of the lateral ventricle in humans during the developmental stages are documented [22] , quantified data on the size and shape of the lateral ventricle during these critical stages is lacking . Further investigations into the size and shape of the lateral ventricle during developmental stages across species are needed . Such parameters could then be incorporated into the GIP model to test its cortical patterning predictions for specific species . Also , further investigations into how the production of IP cells is regulated ( i . e . how the activator and inhibitor interact ) would enhance this model . Several genes ( Pax6 , Ngn2 , and Id4 ) have been shown to modulate the production of IP cells in mice [10] . Further studies into how this modulation occurs , and if this modulation changes evolutionarily , could be incorporated into the reaction kinetics in the GIP model enhancing the cortical patterning predictions . In conclusion , this chemically-based mathematical model ( the global intermediate progenitor ( GIP ) model ) extends the intermediate progenitor model [5] , which describes local phenomena , to encapsulate global characteristics . In doing so , the GIP model shows how the global shape of the lateral ventricle , which drives the shape of the VZ , plays a key role in cortical pattern development . This model is able to capture changes in VZ shape along with the complementary role of domain scaling in only two parameters: 1 ) the focal distance of the prolate spheroid approximating the lateral ventricle , and 2 ) k2 , which is dependent on domain scaling , as given by the formula in Equation 8 . The model also has the ability to predict why the cortex of certain species may have little or no folding , and it accounts for the order and directionality of the sulci formed in different species . We consider this model a first step toward a chemically driven and mathematically predictive explanation of cortical folding development across species . | The size and shape of the cerebral cortex varies across species . The cortical folding pattern also varies from a smooth surface where no pattern is visible , as observed in the common treeshrew ( Tupaia glis ) and Eastern mole ( Scalopus aquaticus ) , to an intricate labyrinthine pattern , as observed in humans . One current model , the intermediate progenitor model , describes the creation of a fold through local interactions in the ventricular zone which surrounds the lateral ventricle . Here we extend the local scenario described in the intermediate progenitor model to include global characteristics that differ between species . We approximate the lateral ventricle with a prolate spheroid and examine how patterns on a spheroidal surface change based on size and eccentricity . Our model reveals a direct correlation between pattern formation and lateral ventricular size and shape . This model's significance is that it elucidates the consistency of cortical patterns among individuals within a species and addresses inter-species variability based on global characteristics , such as size and shape of the lateral ventricle , and provides a critical piece to the puzzle of cortical pattern formation . |
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Environmental factors such as temperature can alter mosquito vector competence for arboviruses . Results from recent studies indicate that daily fluctuations around an intermediate mean temperature ( 26°C ) reduce vector competence of Aedes aeygpti for dengue viruses ( DENV ) . Theoretical predictions suggest that the mean temperature in combination with the magnitude of the diurnal temperature range ( DTR ) mediate the direction of these effects . We tested the effect of temperature fluctuations on Ae . aegypti vector competence for DENV serotype-1 at high and low mean temperatures , and confirmed this theoretical prediction . A small DTR had no effect on vector competence around a high ( 30°C ) mean , but a large DTR at low temperature ( 20°C ) increased the proportion of infected mosquitoes with a disseminated infection by 60% at 21 and 28 days post-exposure compared to a constant 20°C . This effect resulted from a marked shortening of DENV extrinsic incubation period ( EIP ) in its mosquito vector; i . e . , a decrease from 29 . 6 to 18 . 9 days under the fluctuating vs . constant temperature treatment . Our results indicate that Ae . aegypti exposed to large fluctuations at low temperatures have a significantly shorter virus EIP than under constant temperature conditions at the same mean , leading to a considerably greater potential for DENV transmission . These results emphasize the value of accounting for daily temperature variation in an effort to more accurately understand and predict the risk of mosquito-borne pathogen transmission , provide a mechanism for sustained DENV transmission in endemic areas during cooler times of the year , and indicate that DENV transmission could be more efficient in temperate regions than previously anticipated .
The ability of Aedes aegypti to transmit viruses , in particular dengue viruses ( DENV ) , has long been known to be influenced by temperature [1]–[6] . It is generally assumed that higher mean temperatures facilitate DENV transmission due to faster virus propagation and dissemination within the vector . Vector competence , the probability of a mosquito becoming infected and subsequently transmitting virus after ingestion of an infectious blood meal [7] , is generally positively associated with temperature , whereas the duration of the virus extrinsic incubation period ( EIP ) associates negatively with temperature [6] . The norms of reaction ( i . e . , phenotypic variation across environmental variation ) of vector competence and EIP have been well documented for a large range of temperatures for Ae . aegypti . At high temperatures ( 26°C and above ) , DENV dissemination and transmission can be observed in one week or less [5] , [6] , [8] , [9] . Lower temperatures generally extend the duration of EIP [5] , [6] , [8]; at 21°C and below , the EIP for DENV can be in the order of several weeks [3] , [4] . Despite these prolonged incubation periods , DENV-infected Ae . aegypti are capable of transmitting virus under laboratory conditions after incubation at temperatures as low as 13°C [4] , and can become infective after incubation under temperature as low as 10°C [8] . Evidence to support an upper thermal threshold for DENV transmission is more limited . There is a well-established link between temperature and many of the life-history traits of Ae . aegypti , with a ( population dependent ) thermal optimum for development , reproduction and survival [10] . Beyond this , subsequent increases in temperature become detrimental for the mosquito; i . e . , immature development rate slows as mortality increases , adult reproductive function is impaired in the high 30°'s , and adult survival declines as temperature continues to rise [11] , [12] . Ae . aegypti vector competence for DENV has been detected up to a maximum of 35°C [6] , but at temperatures in excess of this , accurately measuring vector competence indices before the mosquito dies is difficult . What is much less well-documented is the influence of fluctuations in daily temperature on the norm of reaction of vector competence and EIP . Indeed , environmental temperature under natural conditions does not remain constant , but oscillates between a minimum at night and a maximum during daytime . Results from studies using realistic fluctuating temperature profiles support the notion that fluctuating temperatures may alter estimates of both life history traits and vector competence of mosquitoes [9] , [12]–[16] , with the magnitude of the diurnal temperature range ( DTR ) associated with the degree of response observed . Vector competence of Ae . aegypti for DENV examined under fluctuating temperatures , indicated that a large DTR of ∼20°C around an intermediate mean of 26°C ( i . e . , ∼16°C to 36°C; temperatures representative of conditions mosquitoes in central west Thailand would be exposed to in the low DENV transmission season ) reduced the proportion of Ae . aegypti females with a midgut infection and reduced female survival . At a mean of 26°C , EIP did not vary if temperature fluctuations were symmetric whereas EIP tended to last longer under more natural asymmetric fluctuations [9] , [13] . While the effects of realistic temperature fluctuations on Ae . aegypti vector competence and EIP for DENV at an intermediate mean temperature ( 26°C ) have recently been described [9] , [13] , [14] , the impact of fluctuations at the upper and lower thermal limits are unknown . Short periods of the day spent at extreme temperatures may affect key steps of the mosquito infection process . Evidence suggests that DENV transmission may be more limited by lower daily temperatures [17] , as opposed to average daily temperatures . In this study we investigated whether fluctuations at high and low mean temperatures alter adult survival , vector competence and/or EIP of Ae . aegypti for DENV serotype-1 ( DENV-1 ) , compared to constant temperatures . We then explored how this might affect the geographical range of DENV in light of our understanding of the thermal limits of DENV transmission . Based on theoretical predictions [9] , we expect that large fluctuations at low temperatures will enhance transmission ( increase infection/dissemination probability and reduce the EIP of the virus ) because of time spent under warmer ( more optimal ) conditions , whereas fluctuations at high temperatures will have a negative effect because of time spent at elevated temperatures detrimental to the vector and/or the virus . To test this hypothesis , we exposed mosquitoes to high and low temperatures with and without fluctuations across two experiments , and assayed mosquitoes for virus infection .
We determined the effect of constant and fluctuating temperature regimes at both high and low mean temperatures , on the survival and vector competence of Ae . aegypti for DENV-1 . Over the course of two experiments we tested seven temperature regimes . At the low temperatures , we exposed mosquitoes to three constant temperatures ( 16°C , 20°C and 26°C ) and one fluctuating temperature regime ( a DTR of 18 . 6°C around a mean of 20°C ) . The minimum programmed temperature for the fluctuations was 11 . 7°C and the maximum was 30 . 3°C . Given the low temperatures in this experiment and the associated uncertainty of whether we would identify any infection , we included the 26°C treatment as a control temperature , knowing we could detect DENV infected females at this temperature . At the upper end of the temperature scale , we tested two constant temperature regimes ( 30°C and 35°C ) , and one cyclic temperature regime with a DTR of 7 . 6°C . Temperatures fluctuated between 27 . 1°C and 34 . 7°C , around a mean of 30°C . We included the 35°C constant temperature treatment to ensure that the peak temperature was not a limiting factor of infection potential . The magnitude and asymmetrical shape of the temperature profiles were based on temperature recordings from Central West Thailand where DENV is endemic [14] . Fluctuating temperature regimes followed a sinusoidal progression during the day , and a negative exponential decrease at night , with minimum and maximum temperatures reached at 06:00 and 14:00 respectively . A 12∶12 hr light∶dark cycle was used , with the light schedule changing at 08:00 and 20:00 . Experimental mosquitoes were housed in KBF115 incubators ( Binder , Tuttlingen , Germany ) that maintained climatic conditions . HOBO data loggers ( Onset , Cape Cod , MA ) recorded temperatures on an hourly basis in the two incubators with fluctuations . Actual air temperatures within the incubators followed the programmed temperature profile closely . There was an average of <0 . 3°C difference between the daily programmed temperature and the actual air temperature inside the incubators across treatments . Relative humidity was maintained between 70% and 80% across all treatments , and was also recorded by data loggers . Ae . aegypti used in our experiments were collected from Kamphaeng Phet Province , Thailand as pupae during January 2011 and sent to UC Davis as F1 eggs . After eggs were received , they were hatched and reared at a low density ( 1 . 3 larvae/10 mL ) in 24 cm×29 cm×5 cm containers with 1 . 5 L of deionized water . Colony maintenance was conducted under standard insectary conditions ( constant 28°C±2°C , 70–80% RH ) and a 12∶12 hr light∶dark cycle , with >500 females per generation . Larvae were fed a 1∶1 mix of bovine liver powder and puppy chow , with 0 . 1 g per 200 larvae each day for the first four days , 0 . 2 g on the fifth day , 0 . 3 g on the sixth , and then 0 . 2 g on the remaining two days , at which time most larvae had pupated . Generation F4 mosquitoes used in experiments . When females were 4–5 days old , access to sucrose was removed for 24–36 hr , after which time females were fed defibrinated sheep blood ( QuadFive , Ryegate , MT ) , mixed with DENV-1 freshly grown in cell culture prior to mosquito exposure , using an artificial feeding system . Virus supernatant was harvested after scraping and then separating all cells by centrifugation . Mosquitoes were fed through a desalted porcine intestinal membrane stretched over the bottom of a warm water-filled jar to maintain a temperature of 37°C . The viral isolate used , SV2951 obtained from Ratchaburi , Thailand , had been passaged at 28°C seven times in Ae . albopictus C6/36 cells prior to use in this study . While this is potentially sufficient time for adaptation to cell culture temperatures , we do not consider it likely that this would influence our results as 28°C is not deemed as a stressful temperature for DENV . Confluent cultures of C6/36 cells grown in 25-cm2 flasks were inoculated at a virus multiplicity of infection of 0 . 01 and left to grow for 10 days at 28°C in 5% CO2 . The infectious blood meal consisted of 50% defibrinated sheep blood ( Quadfive , MT ) , 45% viral supernatant harvested at Day 10 , and 2 . 5% sucrose solution ( diluted 1∶4 in water ) and 2 . 5% adenosine triphosphate disodium salt ( Sigma-Aldrich , MO ) at a final concentration of 5×10−3 M . We prepared one blood meal for each experiment . The blood meal for the low temperature experiment was calculated to contain 5 . 86×105 focus forming units ( FFU ) /ml of DENV-1 . The calculated titer for the high temperature experiment was 7 . 89×105 FFU/ml . Mosquitoes in both experiments were limited to 35 min feeding , to minimize the effect of virus degradation in the infectious blood meal . Mosquitoes were allowed 2–3 hr to begin digestion after the blood meal . We subsequently sedated them using CO2 and retained only fully engorged females to set up experimental groups . For the low temperature experiment , forty-four replicate 1-pint paper cartons ( Science Supplies WLE , NJ ) with mesh tops , each containing 20 engorged females were set up . Twelve cartons were placed into each of the experimental temperature regimes , and eight cartons into the control 26°C incubator . For the high temperature experiment , we tested 28 replicate cartons each containing 16 females . Nine cartons were placed into the constant temperature incubators , and 10 into the 30°C plus fluctuation incubator . We assessed vector competence at 7 , 14 , 21 and 28 days post exposure ( DPE ) to the infectious DENV-1 blood meal ( i . e . , days of EIP ) for mosquitoes in the low temperature experiment . At each time point , we sampled three replicate cartons of mosquitoes from each experimental temperature , and two from the control 26°C treatment . At the high temperatures , mosquitoes were sampled at 3 , 6 and 9 DPE . Three cartons were randomly removed from each incubator at each time point . The additional carton in the 30°C fluctuation treatment was also tested at 9 DPE . Because the course of DENV infection in the mosquito is faster at higher temperatures than at lower ones [4] , [6] , we sampled mosquitoes more frequently in the high temperature experiment to improve our statistical power of identifying differences among treatments . For all surviving mosquitoes in each carton , we measured two components of vector competence , midgut infection and virus dissemination from the midgut in infected females , using a qualitative indirect fluorescence assay ( Q-IFA ) . Virus EIP measurements were based on detection of a disseminated DENV infection in the mosquito . We separated and tested bodies ( comprising of the thorax and abdomen ) for midgut infection and heads for disseminated infection , independently . Samples were placed into 1 mL viral transport medium ( VTM; 77 . 2% low glucose DMEM , 18 . 5% heat-inactivated fetal bovine serum , 3 . 8% penicillin/streptomycin , and 0 . 15% gentamycin and nystatin ) with approximately ten 2 mm glass beads ( Fisher Scientific , Pittsburg , PA ) in a screw-top plastic vial . Following collection , all samples were frozen at −80°C for later analysis by Q-IFA . We also collected the whole bodies ( without separation of heads ) of dead females daily and tested them for infection status . Results from analysis of dead mosquitoes were included in our survival analyses . All data was analyzed using JMP software , version 10 ( SAS Institute Inc . , NC ) . Vector competence was analyzed by nominal logistic regression of the infection or dissemination status as a full-factorial function of temperature and DPE , and carton nested within temperature and DPE . Records of survival for individual females exposed to the infectious blood meals were kept throughout the duration of the both experiments . Female survival was analyzed using a Kaplan-Meier ( log-rank ) analysis , with females that were sacrificed on scoring days right-censored . We tested for differences in survival curves between different temperature regimes and infection status of recently dead mosquitoes . We corrected for multiple comparisons between treatment groups for our logistic regression and Kaplan-Meier analyses using a Bonferroni correction . We used an infectious fluorescent focus assay [18] to titrate virus in blood meals offered to the mosquitoes . One-day old confluent monolayers of Vero ( green monkey kidney ) cells in 8-well chamber slides ( Nunc , Rochester , NY ) were inoculated with serial 10-fold dilutions of virus and blood meal samples . Dilutions were prepared in 2% FBS media in duplicate and inoculum was allowed to infect the cells for 1 hr at 37°C . A negative control ( the media used for the dilutions ) was included in all titrations . The overlay applied to the cells after the incubation was made of a 1∶1 mix of 2% FBS media∶carboxymethyl cellulose ( CMC; 2% in PBS ) . We allowed 2 days for virus to replicate in the monolayer , then the media was removed and the cells washed carefully . In each washing step , PBS was added to cells three times , allowed to rest for 3–5 min , before PBS was again removed . The cells were fixed with 3 . 7% formaldehyde for 30 min , then washed and stained with 75 µL 1∶250 dilution of primary mouse anti-DENV monoclonal antibody ( MAB8705; Millipore , MA ) at 37°C for 1 hr . The cells were again washed to minimize background fluorescence , and then stained with 75 µL 1∶85 dilution FITC-conjugated secondary goat anti-mouse antibody ( AP124F; Millipore , MA ) for 30 min , which was used to detect and count the number of fluorescent foci under an FITC-fitted fluorescent microscope at 20× magnification . To test mosquito samples for the presence of infectious DENV , we used a qualitative fluorescence assay . We homogenized the tissue samples for 4 min in a Retsch Mixer Mill 400 , at 30 Hz . We filtered 300 µL of the sample through 0 . 22 µm cellulose acetate centrifuge filters ( Costar Spin-X , Corning , Japan ) and 50 µL of the filtered supernatant was inoculated in duplicate onto a 1-day old confluent monolayer of Vero cells , seeded at a density of 2 . 5×105 cells/well in a 96-well culture plate . The inoculum was allowed to infect the cells for 1 hr at 37°C , before a standard maintenance media containing 2% FBS overlay was applied to the cells in each well , and the plate was incubated 37°C for 4 days . Positive and negative controls were used in each plate . We then removed the overlay , washed and fixed the cells in 3 . 7% formaldehyde for 20 min . The washing and staining steps that followed were exactly the same as for the FFA , except that the volumes used for antibody staining were 50 µL for each of the primary and secondary antibodies . We viewed cells under FITC-fitted fluorescence microscope at 10× to screen for the presence or absence of green fluorescence , which was indicative of a sample being either infected or uninfected by DENV , respectively .
Compared to a constant temperature , large diurnal temperature fluctuations at a mean of 20°C reduced the EIP50 for Ae . aegypti with a disseminated DENV-1 infection by approximately 36% , from 29 . 6 to 18 . 9 days . These results indicate a greater potential for DENV transmission at cool temperatures with natural fluctuations , and at an accelerated rate compared to what would be predicted by analysis of a 20°C constant temperature regime . Nevertheless , low intrinsic mortality under each of the low temperatures ( those below 26°C ) supports the potential for a mosquito to complete virus EIP at low temperatures , allowing for subsequent transmission following a protracted incubation period . Females exposed to a large DTR around a 20°C mean were more likely to have detectable disseminated DENV-1 after 28 days compared to those reared under a constant , control temperature ( 100% vs . 41 . 7% dissemination ) . Whether fluctuations at 20°C also increased the maximum proportion of infected females with a disseminated infection compared to 20°C constant cannot be ascertained from our data . We did not see dissemination at the constant 20°C temperature plateau or reach maximal levels in our 28 day experiment . It is possible that dissemination levels could have reached 100% if we had held mosquitoes for a longer time . Regardless , the accelerated EIP under the cyclic temperature compared to the equivalent constant temperature indicates the potential for laboratory experiments using constant temperatures to significantly underestimate the duration of EIP in nature . Relatively low mortality rates under the three cooler temperatures ( <20% after 28 days ) compared to 26°C constant ( ∼30% ) suggest that lifespan will not be a limiting factor in transmission potential during cooler times of the year or in more temperate environments . Epidemiologically , this substantial reduction in the EIP of DENV at low temperatures with fluctuations , in combination with low mortality rates , would be expected to increase vectorial capacity , and thus virus transmission potential , compared to constant temperatures . It would be useful in future experiments to improve temporal resolution by increasing sampling between the intervals we used , and allow mosquitoes at cooler constant temperatures longer to complete the EIP to identify maximum dissemination levels . We observed a very low proportion of DENV-1 infected females held at 16°C constant . The youngest of the three infected females identified was found dead at 4 DPE , while the remaining two females were collected at 7 and 21 DPE during our weekly sampling . Due to slow digestion at such a low temperature , it is possible that the 4 and 7 DPE mosquitoes retained some infectious blood from the blood meal several days earlier . Although it is possible for a mosquito to become infected with DENV at 16°C , as shown by a single individual with a body infection at 21 DPE , this low temperature sharply reduced vector competence for DENV in Ae . aegypti . While we did not observe any mosquito with dissemination at 16°C , Ae . aegypti exposed to DENV and held at temperatures as low as 13°C for 32 days have previously been demonstrated to be capable of transmission [4] . We did not examine mosquitoes after 28 DPE and thus it is possible we did not allow enough time to observe transmission ( as estimated by dissemination ) under the 16°C treatment , and/or the mosquitoes used differed in their susceptibility to DENV infection [19] , [20] . There was no detectable effect of the small fluctuations around a high mean of 30°C in the proportion of females with a midgut infection or disseminated virus , or in the duration of the EIP compared to the constant temperature control . The entire temperature profile ( ∼27°C to 35°C ) falls within limits known to be highly conducive to DENV transmission , therefore , the lack of observable change is possibly due to the magnitude of the DTR not being large enough to produce a detectable response given our sample size . We did not test the large DTR around a mean of 30°C because there are few locations that we are aware of that have such large amplitude fluctuations at high temperatures . We therefore restricted our use of the large DTR to lower temperatures . Our cyclic low temperature treatment was derived from ambient conditions in dengue-endemic northern Thailand between December and January [21] . Results from previous studies indicate that midgut infection levels were lower under fluctuating temperature regimes with a mean of 26°C compared to constant temperatures , leading to reduced transmission potential [9] . Conversely , in the present study we observe that fluctuating temperatures at a lower mean lead to positive changes in the probability of virus dissemination from the midgut , consequently increasing transmission potential . Lambrechts et al . [9] predicted infection and dissemination probabilities of females infected with DENV and the duration of the EIP under various magnitudes of DTR . Their theoretical model predicted ∼50% of Ae . aegypti would become infected at both a constant 20°C and 20°C with large fluctuations . Although observed infection levels in our experiments under both temperature profiles were lower than that predicted we did not detect a statistical difference between these two temperature regimes , in agreement with the model . A mean of 18°C was predicted to be a pivotal mean temperature , above which fluctuations would decrease dissemination probability and below which they would enhance dissemination . Our results on dissemination rates imply that this predicted pivotal temperature rather lies between 20°C and 26°C . We hypothesize that the opposite effects of these two temperatures is due to differences in rates of viral growth/replication at different temperatures experienced by the mosquitoes . At a mean of 26°C , viral replication rates at the lower extreme of the temperature profile ( ∼18°C ) might slow the virus more than it accelerates it at the upper end of the scale ( ∼36°C ) , resulting in a net deceleration compared to the rate at a constant 26°C . Conversely at a mean of 20°C , where replication is already slow , the low temperatures experienced by mosquitoes at the bottom of the fluctuating temperature profile lower the rate of replication to zero , but the relative increase in replication as the temperature rises to ∼30°C at the peak of the profile during the day will increase replication far more than it is decreased overnight , leading to a net acceleration . Lambrechts et al . [9] did not model the effect of DTR above a mean of 28°C , although according to their predictions , small fluctuations are expected to result in close to a 100% midgut infection , and 80% dissemination , with a virus EIP shorter than 10 days . Observed dissemination results and estimates of EIP in our study are not in disagreement with this prediction , although again infection levels were lower . Midgut infection , dissemination and EIP estimates to produce the model were obtained from multiple experimental mosquito-flavivirus infections ( not including DENV ) , and as a result , this discrepancy between the predictions and observed results may be a result of differences between vector-virus systems . The low infectious titers used in these two experiments are likely responsible for the low proportion of infected individuals obtained . Despite this , such titers fall within the reported range of viremia observed in humans [22] , [23] . Although we used only a single serotype ( DENV-1 ) to test the hypothesis that fluctuations at high and low mean temperatures would alter mosquito vector competence , the EIP of the virus , and adult survival , cumulative results from our group [9] , [13] demonstrate consistency between results from similar experimental temperature regimes , despite using two mosquito populations , two serotypes ( DENV-1 and DENV-2 ) , two virus strains within one of these serotypes , and different infectious titers of the blood meals . We are therefore confident that the present study reveals another level of complexity in the interaction between the vector , viral pathogen and temperature . Our results indicate that the effect of fluctuations around a low mean temperature markedly reduce EIP , which has important implications for determining DENV transmission risk at the northern and southern edges of DENV's geographic range , areas with a mean temperature that would normally be considered too low for DENV transmission to occur . Additionally , seasonal variation in DENV transmission , which is a common feature of DENV transmission dynamics [6] , [24] , can be associated with changes in mean temperature and DTR [9] , [25] , [26] . Conditions similar to the low temperature fluctuating profile used in this study ( e . g . , a mean below 22°C and DTR greater than 15°C ) , are observed in the low DENV transmission season throughout many parts of South East Asia , including areas in northern Thailand , Myanmar and central/northern India [21] . Each of these countries lie within the top 20 countries reporting the largest number of dengue cases annually [27] , and despite low mean temperatures due to northern latitudes and often altitude , according to the World Health Organization , seasonal DENV transmission still occurs annually in such areas . Studies in Anopheles stephensi indicate a similar response to cyclic temperatures . A DTR at low temperatures enhances malaria transmission , while at higher temperatures equivalent DTRs reduced transmission potential [16] . Another recent study on arboviruses examined the interplay between temperature and EIP in Culex pipiens infected with West Nile virus [28] , demonstrating that environmental conditions could enhance transmission of one variant over another . In this study however , realistic temperature fluctuations were not considered . An improved understanding of pathogen transmission across more realistic environmental conditions will allow for greater accuracy in modeling efforts to aid vector control and disease prevention in the future . It is , therefore , important that in future studies when researchers test mosquitoes at lower temperatures , realistic conditions are considered . Similar responses to temperature changes have been reported for life-history trait estimates of Ae . albopictus and Ae . aegypti [10] , [29]–[32] . It is likely that their responses to fluctuations in temperature would be comparable . Ae . albopictus often display a generalist blood feeding behavior [33] , and is a competent vector of DENV [34]–[36] . Importantly , the species inhabits both tropical and temperate climates [37] . It is significantly more tolerant to cooler conditions than Ae . aegypti [38] and , therefore , poses a risk for arbovirus transmission in more temperate regions ( e . g . , Europe ) [39] . As such , similar experiments on Ae . albopictus are warranted to better understand virus transmission potential in more temperate environments . We observed limited mortality throughout the duration of both experiments , and identified females with a disseminated infection in six of the seven temperature treatments tested ( all but 16°C ) . Mosquitoes were raised under conditions with optimal nutrition and were maintained in an environment with limited risk of death other than intrinsic factors and temperature . We do not know the maximum potential lifespan of mosquitoes exposed to each of these temperature regimes . We planned the experimental duration to be long enough for mosquitoes of each temperature to discern the duration of the EIP under each treatment , but did not attempt to estimate longevity . Epidemiologically , although these estimates represent a conservative estimate of the number of mosquitoes that might survive to such a time in order to transmit DENV , the high survival estimates compared to the duration of the EIP indicate that a relatively large proportion of infected mosquitoes in both experiments were capable under laboratory conditions of surviving to an age where they could transmit DENV to a susceptible host . Similar to previous studies [40] , [41] , we observed reduced mortality in virus-infected females as opposed to those that were exposed , but uninfected . We observed this result , however , only at mean temperatures of 30°C or above . One hypothesis for this result is that mounting an immune response against the virus is more energetically costly than allowing the virus to establish infection [41] . Contrary to the results previously reported , we did not observe a significant difference between the survival of uninfected and infected females at low temperatures [40] , [41] . This apparent interaction between temperature and infection could be due to the rapid proliferation of the virus at higher temperatures inducing a stronger immune response , where as at low temperatures , virus replication is slower and the immune response is , therefore , milder . Had we assessed survival for longer than 28 days , we may have detected a response when survival rates started to decline . Our results indicate that the use of constant temperature experiments to assess Ae . aegypti vector competence for DENV at low temperatures underestimate the potential rate at which transmission may occur under more natural , fluctuating temperature profiles . Low intrinsic mortality at low temperatures with fluctuations similarly favors increased potential for virus transmission . Our results , therefore , provide a mechanism for sustained DENV transmission in endemic areas during cooler times of the year and indicate that transmission could be more efficient in temperate regions than previously anticipated . | Mosquitoes in the wild are exposed to daily fluctuations in temperature , but in the laboratory , the effect of temperature on vector competence is generally assessed using constant temperatures . Recent studies demonstrate that realistic fluctuations in temperature around an intermediate mean ( 26°C ) can alter life-history traits , population dynamics , and the ability of a mosquito to become infected with and transmit dengue virus ( DENV ) . Here we tested how fluctuations around high and low mean temperatures influence vector competence and the extrinsic incubation period . Small fluctuations around a high mean temperature ( ∼8°C swings around 30°C ) had no detectable effect on vector competence . Large fluctuations around a low mean ( ∼18°C swings around 20°C ) demonstrate that only 18 . 9 days were required for 50% of DENV-exposed mosquitoes to develop a disseminated infection , compared to 29 . 6 days at constant 20°C . Twenty-eight days post-exposure to the infectious blood meal , 100% of mosquitoes tested had a disseminated infection under fluctuating temperatures , but under a constant temperature this proportion was only 42% . Reduced duration of extrinsic incubation increases the potential for pathogen transmission . Results indicate that the rate of dengue transmission by mosquitoes in temperate regions with natural fluctuations may be underestimated by experiments conducted under constant temperatures . |
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Both cholera and food insecurity tend to occur in impoverished communities where poor access to food , inadequate sanitation , and an unsafe water supply often coexist . The relationship between the two , however , has not been well-characterized . We performed a secondary analysis of household-level data from the 2012 Demographic and Health Survey in Haiti , a nationally and sub-nationally representative cross-sectional household survey conducted every five years . We used multivariable logistic regression to evaluate the relationship between household food security ( as measured by the Household Hunger Scale ) and ( 1 ) reported history of cholera since 2010 by any person in the household and ( 2 ) reported death by any person in the household from cholera ( among households reporting at least one case ) . We performed a complete case analysis because there were <1% missing data for all variables . There were 13 , 181 households in the survey , 2 , 104 of which reported at least one household member with history of cholera . After adjustment for potential confounders , both moderate hunger in the household [Adjusted Odds Ratio ( AOR ) 1 . 51 , 95% Confidence Interval ( CI ) 1 . 30–1 . 76; p < . 0001] and severe hunger in the household ( AOR 1 . 73 , 95% CI 1 . 45–2 . 08; p < . 0001 ) were significantly associated with reported history of cholera in the household . Severe hunger in the household ( AOR 1 . 85 , 95% CI 1 . 05–3 . 26; p = 0 . 03 ) , but not moderate hunger in the household , was independently associated with reported death from cholera in households with at least one case of cholera . In this study we identified an independent relationship between household food insecurity and both reported history of cholera and death from cholera in a general population . The directionality of this relationship is uncertain and should be further explored in future prospective research .
Food insecurity is defined as a persistent lack of access to food in adequate quantity or quality [1] . Food insecurity is linked to , and often results from , poverty [2] , which is associated with poor health outcomes including mortality [3–6] . Food insecurity itself is also associated with an increased risk of death , even after controlling for neighborhood or household wealth and other social determinants of health [7] . Food insecurity has been specifically linked to poor health outcomes for patients with a variety of conditions including Human Immunodeficiency Virus ( HIV ) infection and cardiovascular disease [1] . While the effect of food insecurity on health is often thought of as synonymous with malnutrition , which has profound short and long-term health consequences [8] , it has also been implicated in a number of other pathways which impact health . These include behavioral pathways , like clinical follow up , medication adherence , and substance use; and mental health pathways , like depression and anxiety [1 , 9–14] . Food insecurity is also associated with chronic inflammation [15–17] , which in turn is related to worse health outcomes in a number of conditions including infectious diseases like HIV and tuberculosis ( TB ) [18 , 19] . The relationship between health and food insecurity has in many cases been found to be bi-directional , with poor health outcomes increasing subsequent risk of food insecurity [1 , 9] . This creates the potential for a vicious cycle and may result from the impoverishing effects of healthcare utilization and illness . Cholera remains a major cause of morbidity and mortality worldwide and is now endemic in Haiti since being inadvertently introduced in 2010 [20 , 21] . A wide variety of potential risk factors for cholera , including poverty , have been identified and inform institutional guidance during the response to a cholera outbreak [22 , 23] . The relationship between food insecurity and cholera , however , has not been well studied despite the fact that both cholera outbreaks and food insecurity tend to occur in impoverished communities where poor access to food , inadequate sanitation , and an unsafe water supply often co-exist [1 , 20] . The most recent example of this confluence is in Yemen , where 15 million people are estimated to lack access to safe water and sanitation , at least 17 million people are food insecure , and more than 700 , 000 cases of cholera along with 2 , 000 deaths from cholera have been reported since 2016 [24] . As in other health-related settings , it is plausible that food insecurity may increase risk of cholera , or the severity of cholera , through multiple pathways–including malnutrition , by impairing immune or gut barrier function [25 , 26]; behavioral pressures , by increasing the likelihood of drinking unsafe water or eating unsafe food; by impacting mental health; or through other mechanisms . An episode of cholera has also been found to cause significant financial strain on a household [27–29] , and may increase the downstream risk of food insecurity . In a recent analysis of HIV-affected households in rural Haiti we found that household food insecurity was independently associated with reported history of cholera [30] . However , food insecurity and HIV-related outcomes are closely linked [9 , 13 , 14 , 17 , 31] , and thus it is unknown whether this relationship exists among HIV-unaffected households . In this study we sought to answer this question by exploring the relationship between risk of cholera and food insecurity in a general population , using data from the 2012 Demographic and Health Survey ( DHS ) in Haiti [32] .
This study is a secondary analysis of household-level data from the 2012 DHS in Haiti , a nationally and sub-nationally representative cross-sectional household survey conducted every five years by the Ministry of Public Health and Population with support from Independent Consulting Firm International [32] . The DHS survey sampling methods have been previously described [33] . The 2012 DHS in Haiti used a two-stage cluster sampling design to produce indicator estimates for the ten administrative departments of Haiti , the capital region , and for internally-displaced person ( IDP ) camps resulting from the earthquake in 2010 [32] . In the first stage , 445 clusters ( 144 urban , 256 rural , 45 IDP camps ) were systematically selected with probability proportional to population size . This was done by selecting clusters at a fixed interval ( with likelihood of selection of each cluster proportional to its population size ) from a randomly determined starting point on a list of all the Enumeration Areas established by the Fourth General Census of Housing and Population in 2003 . In the second stage , a systematic sample of households was drawn from each of these clusters by selecting them at a fixed interval from a randomly determined starting point on a list of all households within a given cluster for a total of 13 , 388 households , of which 13 , 181 were successfully surveyed . The primary survey respondents were female heads of household . Because of the ongoing cholera epidemic in Haiti at the time , the DHS assessed history of cholera by asking the primary survey respondent how many household members had cholera since October 2010 , and whether those with a reported history died as a result of the illness ( Supporting Information Table 1 ) . Food security was assessed using the Household Hunger Scale , a validated subset of three items from the Household Food Insecurity Access Scale ( HFIAS ) which has been shown to be culturally invariant ( Table 1 ) [34] . The Household Hunger Scale classifies food security into one of three categories: little to no hunger in the household ( score of 0–1 ) , moderate hunger in the household ( score of 2–3 ) , or severe hunger in the household ( score of 4–6 ) . Procedures and questionnaires for DHS surveys have been reviewed and approved by the Independent Consulting Firm Institutional Review Board ( IRB ) , and all analyzed data were anonymized . We used multivariable logistic regression to evaluate the association between food security in the household and ( 1 ) reported history of cholera by any person in the household and ( 2 ) reported death from cholera by any person in the household ( among households with at least one reported case of cholera ) . Based on published literature , we identified twelve potential confounders measured by the DHS: rural/urban setting; number of household members; number of children under five years old; possession of land usable for agriculture; possession of livestock , herds , or farm animals; primary household roof material; primary household floor material; improved source of drinking water ( piped household water , protected wells or springs , collected rainwater ) ; time required to reach a water source; access to a latrine; number of rooms for sleeping; and wealth index . The wealth index is a composite measure of a household’s cumulative living standard calculated by the DHS using ownership of certain assets , materials used for housing construction , and types of water access and sanitation facilities . We conducted a complete case analysis because there were 2 ( 0 . 02% ) households missing the Household Hunger Scale , none missing cholera outcomes , and <1% missing all other covariates of interest . We first calculated unadjusted odds ratios ( OR ) with 95% confidence intervals ( CI ) between the Household Hunger Scale and reported history of cholera and reported death from cholera using bivariate logistic regression models . We then used bivariate logistic regression models to identify which of the previously listed covariates were correlated with both the relevant outcome and the Household Hunger Scale with p<0 . 2 and included these variables in multivariable logistic regression analyses . We did not use backwards or stepwise elimination . If both the wealth index and a component of the wealth index met criteria for inclusion in the model , only the wealth index was included . The Household Hunger Scale was modeled as moderate hunger in the household compared to no hunger in the household and severe hunger in the household compared to no hunger in the household . We calculated the variance inflation factor to assess for multicollinearity among model covariates . An inflation factor greater than 2 . 50 was considered indicative of multicollinearity . We performed statistical analysis using SAS version 9 . 4 ( SAS Institute , Cary , North Carolina ) , using survey commands to apply sampling probability weights and account for clustering and stratification in the sample design .
The DHS surveyed 13 , 181 households which contained a median of 4 . 4 ( IQR 2 . 8–6 . 2 ) household members . Of these households , 2 , 104 reported at least one household member with a history of cholera since 2010 ( Table 2 ) . There was a mean of 1 . 35 ( SD 1 . 02 ) people with reported history of cholera per household among households reporting at least one , and 151 ( 1 . 1% ) households reporting history of cholera also reported at least one member who had died as a result . Households reporting at least one person with history of cholera were significantly more likely to be located in a rural zone , own land usable for agriculture , and own animals . These households were significantly less likely to have access to an improved water source or latrine . They also had more household members , were more likely to have an earth floor , and had less wealth . Table 3 shows the unadjusted and adjusted relationship between food security and ( 1 ) reported history of cholera in the household and ( 2 ) reported history of death from cholera in the household ( among households with at least one reported case of cholera ) . In unadjusted analysis , relative to households with little to no hunger , both moderate hunger in the household ( OR 1 . 90 , 95% CI 1 . 66–2 . 19; p < . 0001 ) and severe hunger in the household ( OR 2 . 21 , 95% CI 1 . 85–2 . 63; p < . 0001 ) were significantly associated with reported history of cholera in the household . Among households reporting at least one cholera case , severe hunger in the household ( OR 1 . 87 , 95% CI 1 . 05–3 . 34; p = 0 . 03 ) was associated with at least one death from cholera in the household relative to households with little to no hunger . The multivariable model for the relationship between food security and reported history of cholera in the household included the Household Hunger Scale , urban or rural setting , number of children age five or less in the household , wealth index , and number of rooms for sleeping . Compared to households with little to no hunger , both moderate hunger in the household [Adjusted Odds Ratio ( AOR ) 1 . 51 , 95% Confidence Interval ( CI ) 1 . 30–1 . 76; p < . 0001] and severe hunger in the household ( AOR 1 . 73 , 95% CI 1 . 45–2 . 08; p < . 0001 ) were significantly associated with reported history of cholera in the household . The multivariable model for the relationship between food security and reported history of death from cholera in the household included the wealth index . Severe hunger in the household ( AOR 1 . 85 , 95% CI 1 . 05–3 . 26; p = 0 . 03 ) , but not moderate hunger in the household ( AOR 1 . 02 , 95% CI 0 . 60–1 . 71; p = 0 . 95 ) , was significantly associated with reported death from cholera compared to little or no hunger in the household . There was no evidence of multicollinearity in either multivariable model .
In this analysis of 13 , 181 households surveyed in the 2012 DHS in Haiti , we found moderate and severe household food insecurity to be independently associated with a reported history of cholera , and severe household food insecurity to be independently associated with a reported history of death from cholera among households with at least one reported case of cholera . The DHS was conducted during the peak of the cholera epidemic , with 453 , 536 suspected cases and 3 , 835 deaths in Haiti from 2011–2012 [35] . We previously found that food insecurity was associated with a reported history of cholera in a multivariable analysis of 352 HIV-affected households in Haiti [30] . However , there is a well-documented relationship between HIV-related morbidity and mortality and food insecurity [9 , 14 , 31] , and the generalizability of these findings to HIV-unaffected populations was uncertain . Another case-control study in Haiti found that a diverse diet , which is one element of food security [36] , was independently associated with a decreased risk of cholera [37] . In this study we have now identified an independent relationship between household food insecurity and both reported history of cholera and death from cholera in a general population in Haiti . Because of the cross-sectional nature of the DHS it was not possible to determine the temporal relationship between food insecurity and reported history of cholera and thus the directionality of this association is unknown . In other health settings food insecurity has been found to exist both upstream and downstream of disease morbidity and mortality , in a vicious cycle [1 , 9] , and a similar framework may be true in the setting of cholera . Food insecurity may increase risk of cholera in several ways . Malnutrition as a result of food insecurity could directly increase the risk of cholera infection by impairing immune and gut barrier function [25 , 26] . Food insecurity might also increase risk of cholera indirectly , by impacting behavior . Just as food insecurity increases high-risk practices in the setting of HIV [12] , it is plausible that people living in food insecure households are more likely to engage in behaviors that increase their risk of cholera , including drinking from unsafe water sources or consuming unsafe food . On the other hand , an episode of cholera in a household may also increase subsequent risk of food insecurity , in part as a result of the direct and indirect costs of illness and healthcare utilization . Despite the typically short duration of illness , cholera has been found to have substantial household costs in both the epidemic and endemic setting [27–29] . In addition to the link between risk of cholera and food insecurity , our findings also suggest an independent relationship between food insecurity and cholera-related mortality . Again , the temporality and direction of this relationship is uncertain and may plausibly be bidirectional . People with cholera in severely food insecure households may have increased risk of death compared to those in food secure households because of more severe illness in the setting of malnutrition , decreased ability to seek or access care , worse mental health status at baseline that may impact response to acute illness , or other unknown mechanisms . On the other hand , households in which a member dies as a result of cholera are likely to incur magnified costs because of funeral expenses and a permanent loss in contribution to household income which may decrease household economic resilience and increase risk of subsequent food insecurity . The strengths of this study include a rigorous sampling methodology , a large and representative sample , the availability of multiple potential cofounding covariates , little missingness of data , and a cross-culturally validated measure of food insecurity . This study also has some limitations . As mentioned , we were unable to determine the directionality of the relationship between reported history of cholera and household food insecurity . Poverty and food insecurity are closely related , and there may be residual confounding by the impact of poverty on cholera risk which is not accounted for by the DHS wealth index . However , we believe this is likely to be minimal as the wealth index is a well-validated and reasonably comprehensive measure of socioeconomic status , Additionally , there may be other confounders of the relationship between food insecurity and cholera risk which were not measured in the DHS and thus not adjusted for in our analysis . Potential unmeasured confounders should be addressed in future research and include diet composition , hygiene practices , and household water treatment . The DHS assessed food insecurity and history of cholera through self-report which is subject to recall bias . If some survey respondents over-reported both food insecurity and cholera , this would have inflated the estimated odds ratio relative to the true odds ratio . However , the survey was conducted shortly after the cholera epidemic peaked in Haiti , and less than two years after it began . We believe that significant episodes of diarrheal illness are likely to be remembered in a household within this time frame . Microbiologic confirmation of cholera was not available and thus some cases may have been a result of other causes of acute watery diarrhea . In any case , cholera was generally diagnosed using a standard clinical definition in Haiti at the time , in keeping with the World Health Organization case-definition of cholera during an active outbreak [38] . While this study suggests that food insecurity and cholera risk are independently related at the household level , we also note the confluence of regional food insecurity and massive cholera epidemics in Haiti and Yemen [24 , 35 , 39] . Future research should address whether there is also an association between regional food insecurity and risk of cholera epidemics . In conclusion , we found a significant relationship between household food insecurity and both reported history of cholera and reported death from cholera in a large representative sample of Haitian households after adjusting for potential confounders . The underlying mechanisms and directionality of this association are uncertain and should be explored in future prospective research . A better understanding of the relationship between food insecurity and cholera could inform both future cholera outbreak prediction and response , particularly in settings where poor food access and cholera risk factors are known to co-exist [24] . | In this study , we identified an independent relationship between household food insecurity , defined as a persistent lack of access to food in adequate quantity or quality and measured using the Household Hunger Scale , and reported history of cholera and death from cholera in a general population . We performed a secondary analysis of household-level data from the 2012 Demographic and Health Survey ( DHS ) in Haiti , a nationally and sub-nationally representative cross-sectional household survey conducted every five years . The 2012 survey was conducted during the height of the cholera epidemic , with 453 , 536 suspected cases and 3 , 835 deaths in Haiti from 2011–2012 . We used multivariable logistic regression to control for measured confounders . The underlying mechanisms and directionality of the association between food insecurity and reported history of cholera are uncertain and should be explored in future prospective research . A better understanding of the relationship between food insecurity and cholera could inform both future cholera outbreak prediction and response , particularly in settings where poor food access and cholera risk factors are known to co-exist . |
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Chronic intestinal parasite infection is a major global health problem , but mechanisms that promote chronicity are poorly understood . Here we describe a novel cellular and molecular pathway involved in the development of chronic intestinal parasite infection . We show that , early during development of chronic infection with the murine intestinal parasite Trichuris muris , TGFβ signalling in CD4+ T-cells is induced and that antibody-mediated inhibition of TGFβ function results in protection from infection . Mechanistically , we find that enhanced TGFβ signalling in CD4+ T-cells during infection involves expression of the TGFβ-activating integrin αvβ8 by dendritic cells ( DCs ) , which we have previously shown is highly expressed by a subset of DCs in the intestine . Importantly , mice lacking integrin αvβ8 on DCs were completely resistant to chronic infection with T . muris , indicating an important functional role for integrin αvβ8-mediated TGFβ activation in promoting chronic infection . Protection from infection was dependent on CD4+ T-cells , but appeared independent of Foxp3+ Tregs . Instead , mice lacking integrin αvβ8 expression on DCs displayed an early increase in production of the protective type 2 cytokine IL-13 by CD4+ T-cells , and inhibition of this increase by crossing mice to IL-4 knockout mice restored parasite infection . Our results therefore provide novel insights into how type 2 immunity is controlled in the intestine , and may help contribute to development of new therapies aimed at promoting expulsion of gut helminths .
Gastrointestinal parasitic helminth infections are extremely prevalent , affecting nearly one quarter of the world population . Development of chronic infection , defined as the presence of adult worms in the host , results in severe morbidity and health problems and has been heavily linked with promotion of poverty in affected regions [1] . Current treatments involve the use of anti-helminthic drugs to kill the parasite , but this does not prevent rapid re-infection with worms and encounters problems with drug resistance . As infections with these intestinal parasites are usually chronic , it is likely that helminths are able to influence the immune system to prevent their expulsion . Therefore , understanding the cellular and molecular pathways that regulate the immune response during helminth infection will be crucial in identifying novel therapeutic targets for these poorly managed infections . A key cytokine that plays a multi-functional role in controlling immune responses is transforming growth factor beta ( TGFβ ) [2] . TGFβ can affect many different cell types , with data highlighting a crucial role for TGFβ in regulation of CD4+ T-cells , both dampening and promoting effector responses depending on the context of the immune response [3] , [4] . Importantly , although many cells can produce TGFβ , it is always made as an inactive complex that must be activated to produce biological function [5] . Thus , activation of TGFβ is a key regulatory step in controlling the function of TGFβ in the immune system . Given its importance in regulating diverse T-cell responses , it is not surprising that TGFβ plays a crucial role in the maintenance of immune homeostasis and prevention of autoimmunity . Thus , mice lacking TGFβ receptors in T-cells develop multi-organ inflammatory disease [6] , [7] and lack of TGFβ production by T-cells results in autoimmunity and colitis [8] . Interestingly , recent data has implicated TGFβ-like molecules produced by helminths in regulating immune responses during parasite infection [9] . However , the function of TGFβ during helminth infection and how it is regulated to control immune responses to intestinal parasites is poorly understood . Here we show that mice infected with the intestinal parasite Trichuris muris , a homologue of the human pathogen Trichuris trichuria [10] , display enhanced TGFβ signalling in CD4+ T-cells early during infection and that antibody-mediated blockade of TGFβ significantly reduces worm burden during the development of a chronic infection . . We find that integrin αvβ8 expressed by dendritic cells ( DCs ) , which we have previously shown to be a key pathway in activating TGFβ during intestinal homeostasis [11] , [12] , is required for early induction of TGFβ signalling in CD4+ T-cells during development of chronic helminth infection . Importantly , mice lacking integrin αvβ8 expression on DCs are completely protected from chronic infection , with this protection resulting from a specific early upregulation of a Th2-type immune response . Our results therefore provide novel insights into regulatory mechanisms of importance during chronic gastrointestinal parasite infection , and may help contribute to the development of new therapies aimed at promoting expulsion of helminth infection .
Development of a chronic parasite infection is believed to result from an inappropriate suppression of host immunity , although the exact molecular mechanisms governing these pathways remain unclear . Given the fundamental importance of CD4+ T-cells in regulating parasite infection and the key role for TGFβ in regulating many aspects of T-cell biology , we analysed TGFβ signalling in T-cells during development of a chronic infection with the helminth Trichuris muris . In C57BL/6 mice receiving 30 T . muris eggs , a dose shown previously to induce a chronic infection [13] , we observed a specific increase in phosphorylation of Smad 2/3 ( pSmad2/3 ) in mLN CD4+ T-cells , which is the initial signalling event triggered by engagement of TGFβ with its receptor [14] . This increase in TGFβ signalling was observed as early as day 3 post-infection , and was still evident at day 7 post-infection ( Figure 1A and B ) , before returning to levels seen in uninfected mice by day 14 post-infection ( Figure 1B ) . Similar early increases in CD4+ T-cell pSmad2/3 were also observed in cells taken from the lamina propria of the parasite's niche , the caecum and proximal colon ( Figure S1 in Text S1 ) . These data indicate that TGFβ signalling in CD4+ T-cells is an early hallmark of chronic T . muris infection . To directly examine the functional importance of TGFβ in the development of a chronic T . muris infection , we injected C57BL/6 mice with a TGFβ function-blocking antibody before and during infection . Interestingly , mice receiving TGFβ function-blocking antibody were significantly protected from worm infection ( Figure 1C ) . Thus , our data indicate that , during development of chronic infection , TGFβ plays an important role in promoting infection by the intestinal parasite T . muris . We next sought to determine the mechanisms responsible for enhanced TGFβ signalling and function during T . muris infection . One potential explanation for enhanced TGFβ signalling observed in CD4+ T-cells is enhanced activation of host latent TGFβ during infection . We have recently identified integrin αvβ8 , expressed by DCs , as a key activator of latent TGFβ in the intestine during immune homeostasis [11] , [12] . Thus , to determine the importance of this pathway in promoting TGFβ signalling in CD4+ T-cells during T . muris infection , we analysed T-cell responses in C57BL/6 control mice and mice lacking integrin αvβ8 on DCs ( Itgb8 ( CD11c-Cre ) mice ) [11] during infection . Interestingly , the increase in TGFβ signalling observed in CD4+ T-cells early during T . muris infection was significantly reduced in Itgb8 ( CD11c-Cre ) mice , with pSmad2/3 levels remaining similar to those observed in uninfected mice during the first week of infection ( Figure 2A and B ) . This integrin αvβ8-dependent induction of Smad2/3 phosphorylation was confirmed by Western blot analysis for pSmad2/3 in purified CD4+ T-cells from infected mice ( Figure 2C ) . In contrast , we did not observe any differences in pSmad2/3 induction in dendritic cells between control and Itgb8 ( CD11c-Cre ) mice ( Figure S2A and B in Text S1 ) , indicating that the integrin αvβ8-mediated TGFβ activation does not trigger autocrine TGFβ signalling in DCs during early infection . To directly test whether DCs produced enhanced levels of active TGFβ via expression of integrin αvβ8 during T . muris infection , we isolated DCs from control and Itgb8 ( CD11c-Cre ) mice and measured their ability to activate TGFβ using an established active TGFβ reporter cell line [15] . Indeed , we observed an enhanced ability of intestinal DC activation to produce active TGFβ early during the development of chronic T . muris infection , which was completely absent in DCs lacking expression of integrin αvβ8 ( Figure 2D ) . Thus , during development of chronic T . muris infection , enhanced TGFβ activation by integrin αvβ8 on DCs is important in triggering TGFβ signalling pathways in CD4+ T-cells . To determine whether TGFβ activation by integrin αvβ8 on DCs was functionally important during development of chronic infection with T . muris , we analysed worm numbers in control and Itgb8 ( CD11c-Cre ) mice infected with a chronic dose of T . muris eggs . Strikingly , Itgb8 ( CD11c-Cre ) mice were completely protected from chronic infection by T . muris at day 35 post-infection , with mice showing protection as early as day 14 post-infection ( Figure 2E ) . Indeed , protection from infection observed in Itgb8 ( CD11c-Cre ) mice was even more pronounced than that observed using antibody-mediated blockade of TGFβ function ( Figure 1C ) . It has been reported that expression of CD11c-Cre may drive recombination in a subset of CD4+ CD11clo activated T-cells [16] , and we have previously reported that integrin αvβ8 is expressed by CD4+ T-cells [11] . Thus , to test whether protection from infection in Itgb8 ( CD11c-Cre ) mice could be due to deletion of the integrin in T-cell subsets , we infected mice lacking integrin αvβ8 on T-cells via expression of CD4-Cre ( Itgb8 ( CD4-Cre ) mice ) [11] . In contrast to Itgb8 ( CD11c-Cre ) mice , Itgb8 ( CD4-Cre ) mice showed no protection from infection with T . muris ( Figure S3A in Text S1 ) and showed an identical parasite-specific IgG2a/IgG1antibody bias which is associated with development of a chronic infection ( Figure S3B in Text S1 ) . Taken together , these data suggest that integrin αvβ8-mediated TGFβ activation by DCs is essential in the promotion of chronic T . muris infection . We next sought to determine the mechanisms responsible for protection from infection in mice lacking the TGFβ-activating integrin αvβ8 on DCs . CD4+ T-cells are key in promoting expulsion of intestinal parasite infection , including T . muris [17] , and TGFβ signalling is triggered in these cells early during infection ( Figure 1A and B ) . However , recent evidence has proposed that novel innate lymphoid cells can play crucial roles in the expulsion of several parasite infections [18] , [19] , [20] , [21] . Thus , to determine the function of a CD4+ T-cell response in the expulsion of T . muris observed in Itgb8 ( CD11c-Cre ) mice , we first bred mice onto a C57BL/6 SCID background lacking all lymphocytes . In the absence of total lymphocytes , protection from infection was completely absent , with Itgb8 ( CD11c-Cre ) SCID−/− mice showing similar susceptibility to infection as control mice ( Figure 3A ) . To specifically test the role of CD4+ T-cells in protection from infection observed in Itgb8 ( CD11c-Cre ) mice , we depleted CD4+ T-cells using an anti-CD4 antibody ( Figure S4A in Text S1 ) . Absence of CD4+ T-cells restored susceptibility to infection in Itgb8 ( CD11c-Cre ) mice ( Figure 3B ) . Taken together , these results indicate that protection from infection in the absence of integrin αvβ8 expression on DCs is not via a direct effect of innate lymphoid cells , but driven by a classical CD4+ T-cell response , although a role for innate cells in initial priming cannot be ruled out . CD4+ Foxp3+ regulatory T-cells ( Tregs ) have been implicated in inhibiting immune responses to helminths [22] including some strains of T . muris [23] . Additionally , we have previously shown that integrin αvβ8-mediated TGFβ activation by specialised intestinal DCs is a crucial mechanism by which Foxp3+ Tregs are induced in the gut [12] , and that Itgb8 ( CD11c-Cre ) mice have reduced Foxp3+ Treg levels in their intestine [11] . Thus , one potential explanation for protection from infection in Itgb8 ( CD11c-Cre ) mice is that there is a reduced Treg response induced during infection in these mice . To address this possibility , we first directly assessed the role of Foxp3+ Tregs during development of chronic T . muris infection by using the DEREG mouse model on a C57BL/6 background , which allows specific ablation of Foxp3+ Tregs by injection of diphtheria toxin [24] . Despite robust depletion of Foxp3+ Tregs ( Figure S4B in Text S1 ) we did not see any enhanced ability of Foxp3+ Treg-depleted mice to expel worms ( Figure 3C ) . In agreement with a lack of role for Foxp3+ Tregs in the development of chronic T . muris infection , we did not see any enhancement of Foxp3+ Treg levels during the course of infection ( Figure 3D ) . Additionally , to directly assess whether reduced Foxp3+ Treg numbers Itgb8 ( CD11c-Cre ) mice was responsible for protection from infection , we rescued Treg numbers by adoptively transferred Foxp3+ Tregs from GFP-Foxp3 mice [25] prior to infection . Despite enhancement of Treg numbers in Itgb8 ( CD11c-Cre ) mice after adoptive transfer of GFP-Foxp3+ Tregs ( Figure S5 in Text S1 ) , Itgb8 ( CD11c-Cre ) mice were still highly protected from development of a chronic infection ( Figure 3E ) . Taken together , these data indicate that the protection from infection observed in Itgb8 ( CD11c-Cre ) mice is driven by CD4+ T-cells , but independently of Foxp3+ Treg cells . During development of a chronic infection with T . muris , mice develop a Th1-type immune response at the expense of a protective Th2-type response [13] . Thus , an alternative explanation for the expulsion of a normally chronic infection of T . muris by Itgb8 ( CD11c-Cre ) mice is that , in the absence of early CD4+ T-cell TGFβ signalling , mice produce a Th2-type response instead of the usual non-protective Th1 response . To test this possibility , we analysed the production of Th1 and Th2 cytokines during infection in control and Itgb8 ( CD11c-Cre ) mice . Strikingly , as early as 3 days post-infection , we observed a significant increase in production of the Th2 cytokine IL-13 , which was still elevated at 7 days post-infection ( Figure 4A ) . In contrast , although there was a slight enhancement of the Th1 cytokine IFNγ 3 days post-infection , this was not significantly different between control and Itgb8 ( CD11c-Cre ) mice ( Figure 4B ) . Control mice developed a marked enhancement in IFNγ production by day 18 post-infection , as expected during development of a chronic infection , and this was not observed in Itgb8 ( CD11c-Cre ) mice ( Figure 4B ) . Neither control nor Itgb8 ( CD11c-Cre ) mice produced any detectable IL-4 at any tested timepoint post-infection , a cytokine previously shown to be involved in protection from T . muris infection ( Figure S6 in Text S1 and data not shown ) . We next investigated the cellular source of the early IL-13 production in Itgb8 ( CD11c-Cre ) mice using flow cytometry . We observed a significant population of IL-13+ CD4+ T-cells within the intestinal lamina propria early during infection in Itgb8 ( CD11c-Cre ) which was not apparent in control mice ( Figure 4C ) . We also observed a slight increase in IFNγ+ lamina propria CD4+ T-cells in Itgb8 ( CD11c-Cre ) mice early post-infection; however , these levels were not significantly different from those seen in control mice ( Figure 4C ) . Interestingly , in mice treated with a TGFβ function-blocking antibody which resulted in protection from infection ( Figure 1C ) , we observed a similar increase in CD4+ T-cell IL-13 production , with no difference in IFNγ production observed ( Figure 4D ) . Furthermore , mice treated with TGFβ blocking antibody developed a skewed parasite-specific IgG1 response during infection , indicative of an enhanced type2 immune response ( Figure 4E ) . Taken together , these data indicate that TGFβ activation by DC-expressed integrin αvβ8 is important in controlling IL-13 production in CD4+ T-cells early during development of chronic infection . To test whether the enhanced production of IL-13 early during infection was responsible for expulsion of a chronic T . muris infective dose , we crossed the Itgb8 ( CD11c-Cre ) mice with C57BL/6 IL-4 knockout mice , which have previously been shown to lack the ability to generate an IL-4/13-mediated Th2 response during T . muris infection [26] . As both control mice and Itgb8 ( CD11c-Cre ) mice did not display production of IL-4 early during T . muris infection ( Figure S6 in Text S1 ) , these mice allowed us to test the role of the enhanced IL-13 response seen early during infection in Itgb8 ( CD11c-Cre ) mice . As expected , Itgb8 ( CD11c-Cre ) ×IL-4−/− mice no longer demonstrated an early IL-13 production in the intestinal CD4+ T-cells ( Figure 5A ) . Strikingly , Itgb8 ( CD11c-Cre ) ×IL-4−/− mice were completely susceptible to infection , with parasite burdens comparable to those seen in control mice ( Figure 5B ) . Taken together , these data indicate that lack of the TGFβ-activating integrin αvβ8 on DCs results in a heightened CD4+ T-cell Th2 immune response during T . muris infection which is responsible for rapid parasite expulsion .
Infection with intestinal helminths can result in either expulsion or development of chronic infection , often depending on the type of CD4+ T-cell response generated . Generally , a chronic infection results when inappropriate Th1 cytokine production occurs , as opposed to an inability of CD4+ T-cells to mount a response . Expulsion of the parasite relies on the production of Th2 cytokines , in particular IL-13 which drives a combination of cytokine-mediated expulsion mechanisms such as increased epithelial cell turnover in the intestine [27] , enhanced mucus production [28] and increased production of RELM-β [29] . Our data now demonstrate an essential role for TGFβ and the TGFβ-activating integrin αvβ8 expressed by DCs in promoting chronic intestinal parasite infection , using T . muris , a mouse model of the prevalent human parasite Trichuris trichuria . We observed that TGFβ signalling in CD4+ T-cells is triggered early during T . muris infection , and antibody-mediated blockade of TGFβ function significantly protects mice from infection . Mechanistically , we find that enhanced TGFβ signalling in T-cells during infection occurs via expression of the TGFβ-activating integrin αvβ8 on DCs and that lack of this integrin on DCs completely protects mice from infection due to an enhanced protective Th2 response . We have therefore identified a novel pathway that regulates Th2 immune responses in the gut that could potentially be targeted to upregulate host protective immune responses during gut parasite infection . Recent data suggest that in certain chronic parasite infections , induction of Foxp3+ Tregs is important in suppression of protective immunity and development of chronic infection [30] , [31] , [32] . Given the fundamental role of TGFβ in induction of Foxp3+ Tregs from naive CD4+ T-cells , and the fact that Itgb8 ( CD11c-Cre ) mice have previously been shown to have impaired induction of intestinal Foxp3+ Tregs [12] , we hypothesised that protection from T . muris infection observed in Itgb8 ( CD11c-Cre ) mice was due to reduced induction of Foxp3+ Tregs . However , when Foxp3+ Tregs were depleted before and during the course of infection no protection from infection was observed . Indeed , in contrast to Itgb8 ( CD11c-Cre ) mice , no enhancement of CD4+ T-cell IL-13 production was observed early during infection in Foxp3+ Treg-depleted mice ( Figure S7 in Text S1 ) . Additionally , in agreement with previous reports [23] we did not see a significant increase in Foxp3+ Tregs during T . muris infection . Instead , TGFβ activation by DC-expressed integrin αvβ8 appears important in suppression of IL-13 production by CD4+ T-cells early during T . muris infection . This is in agreement with previous data from in vitro studies , suggesting that TGFβ can downregulate expression of GATA-3 in T-cells ( a key transcription factor in promoting Th2 cell differentiation ) [33] , [34] . Indeed , recent data suggest that TGFβ-mediated induction of the transcription factor Sox4 is important in preventing GATA-3 transcription to drive Th2 development [35] . Furthermore , we only observed an early increase in CD4+ T-cell specific pSmad2/3 signalling during a chronic Th1-promoting low dose infection and not during an acute Th2 promoting high dose infection in C57BL/6 mice ( Figure S8 in Text S1 ) Thus , our data suggests that activation of TGFβ by integrin αvβ8 early during T . muris infection is important in suppression of protective Th2 cell development , which leads instead to production of an inappropriate Th1 response and development of chronic infection . Although we did not detect any IL-4 production in Itgb8 ( CD11c-Cre ) mice during infection , given that we crossed these mice to IL4 KO mice to eliminate enhanced IL-13 production by T-cells , we cannot rule out a potential role for low level production of IL-4 ( below our limits of detection ) in protection from infection . In addition to effects on T-cells , TGFβ has wide-ranging effects on multiple other immune cell types [36] . Recent reports have highlighted an important role for novel innate lymphoid cells in promoting protective type 2 immunity during certain parasite infections [18] , [19] , [20] , [21] . Hence , it could be postulated that protection from infection seen in the absence of integrin αvβ8 results from an enhanced innate lymphoid cell response . However , protection from chronic T . muris infection observed in Itgb8 ( CD11c-Cre ) mice did not correlate with enhanced type 2 cytokine production from any cell types apart from CD4+ T-cells ( data not shown ) , and protection from infection was completely dependent on CD4+ T-cells . Thus , although innate lymphoid cell depletion would be required to definitively rule out their role in this enhanced Th2 response , it appears unlikely that lack of integrin-mediated TGFβ activation by DCs is promoting expulsion of the parasite via effects on non-CD4+ T-cells . Given the crucial importance of TGFβ in regulating CD4+ T-cell responses , our current model is that TGFβ activated by DCs acts directly in CD4+ T-cells to regulate type 2 responses during T . muris infection . A recent study by Heitmann et al . ( 2012 ) suggests that CD4+ T-cell type 2 responses can be regulated via TGFβ signalling in DCs [36] . Thus , mice expressing a dominant negative construct of the TGFβ receptor II in myeloid cells ( hence are refractory to TGFβ signalling ) display enhanced Th2 responses during infection with the helminth Nippostrongylus brasiliensis [36] . However , we observed no difference in pSmad2/3 induction in DCs from control versus Itgb8 ( CD11c-Cre ) mice early during infection ( Figure S2 in Text S1 ) . Thus , our data indicate that activation of TGFβ by integrin αvβ8 on DCs does not regulate Th2 cells indirectly via autocrine TGFβ signalling during T . muris infection . Velhoden et al 2008 [37] have demonstrated that mice expressing a dominant negative TGFβ receptor specifically on CD4+ T-cells ( CD4-DN-TGFβRII mice , thus T-cells are refractory to TGFβ ) are more susceptible to infection with T . muris using an acute model of infection . This finding initially appears to conflict with our data , as we demonstrate that both antibody-mediated blockade of TGFβ and lack of the TGFβ activating integrin αvβ8 on DCs promotes expulsion of the parasite . However , recent data suggests that CD4-DN-TGFβRII mice display high levels of IFNγ level during intestinal helminth infection [38] , [39] which , given the known role of IFNγ in promoting chronic T . muris infection [13] , may explain the enhanced levels of infection observed in CD4-DN-TGFβRII mice . An important question that remains are which specific subset of intestinal DCs are involved in modulating CD4+ T-cells to suppress Th2 responses ? Although a functionally important gut population of CD11c+ T-cells does exist [16] , which may be targeted in our CD11c-cre knock-out system , mice lacking integrin αvβ8 on T-cells ( Itgb8 ( CD4-Cre ) mice ) were completely susceptible to T . muris infection ( Figure S3 in Text S1 ) . These data indicate that it is indeed an αvβ8-expressing DC population ( or a related CD11c+ mononuclear phagocyte population ) that is important in inhibiting Th2 responses in this infection . An important DC subset likely involved during infection are the migratory CD103+ DC [40] , as we have previously demonstrated that this cell subset expresses high levels of integrin αvβ8 [12] . We have observed some integrin αvβ8 expression on the CD11c+ CD103- DC subset in the colon [12] , which has been suggested to include both DCs and macrophage-like cell populations [41] . However , although some subsets of CD11c+ CD103- intestinal cells have been shown to migrate to lymph nodes to modulate T-cell responses [42] , a large population do not normally migrate . Of note , we did not observe any alteration in the levels of αvβ8 expression on either CD103+ or CD103- LILP subset during the development of a chronic infection ( Figure S9 in Text S1 ) . Therefore , the exact DC population involved in downregulation of Th2 responses via integrin αvβ8 remains to be determined . Nevertheless , this key role for DC-expressed integrin αvβ8 in modulating Th2 responses , in addition to its previous essential roles in the induction of Foxp3+ Tregs [12] and Th17 cells [43] , places DC-expressed integrin αvβ8 as a key orchestrator of CD4+ T-cell immunity . In summary , we have highlighted an important cellular and molecular pathway by which the TGFβ-activating integrin αvβ8 expressed by DCs represses protective Th2 immunity during intestinal parasite infection with T . muris . Thus , given the poor treatments currently available for chronic parasite infection , further work should focus on the potential for targeting integrin αvβ8 therapeutically to enhance protective immunity during Trichuris infection . Additionally , whether the pathway is involved in the development of other chronic infections and Th2-associated disease is the focus of current studies .
C57 BL/6 mice were purchased from Harlan Laboratories . Mice lacking integrin αvβ8 on DCs via expression of a conditional floxed allele of β8 integrin in combination with CD11c-Cre ( Itgb8 ( CD11c-Cre ) mice ) [11] , DEREG mice [24] , GFP-Foxp3 mice [25] and IL-4−/− mice [44] , all on a C57BL/6 background , have been previously described . All mice were maintained in specific pathogen-free conditions at the University of Manchester and used at 6 to 8 weeks of age . All animal experiments were performed under the regulations of the Home Office Scientific Procedures Act ( 1986 ) , specifically under the project licence PPL 40/3633 . The project licence was approved by both the Home Office and the local ethics committee of the University of Manchester . The techniques used for maintenance and infection of T . muris were as previously described [45] Mice were orally infected with 20–30 eggs for a low-dose infection and 150 for an acute infection . Worm burdens were assessed by counting the number of worms present in the caecum as described previously [45] . To block TGFβ , mice were injected i . p with 0 . 5 mg of anti-TGFβ blocking antibody ( clone 1d11 . 16 . 8 ) ( BioXCell , NH , USA ) or control IgG1 every 2 days from 4 days prior to infection . CD4+ cells were depleted via i . p . injection of 2 mg anti-CD4 antibody ( YTS 191 ) 47 every 2 days from 6 days prior to infection . Foxp3+ Tregs were depleted in DEREG mice as described [24] , via i . p . injection of 200 ng diphtheria toxin ( Merck ) every 2 days from 2 days prior to infection . Spleens were removed from Foxp3GFP mice , disaggregated and red blood cells lysed . Cells were blocked with anti-FcγR antibody and labelled with anti-CD4 antibody ( clone GK1 . 5; eBioscience ) before sorting for CD4+ , GFP+ populations using a FACS Aria . Cell purity in all experiments was >99 . 8% . Mice were injected i . p . with 0 . 5×106 cells 3 days prior to infection . mLNs were excised from mice and incubated with shaking for 20 min at 37°C in RPMI with 0 . 08 U/ml liberase blendzyme 3 ( Roche ) or 1 mg/ml collagenase VIII and 50 U/ml DNAseI , respectively . Cells were blocked with anti-FcγR antibody ( clone 24G2; eBioscience ) before enrichment using a CD11c enrichment kit and LS MACS column ( Miltenyi Biotec ) . Enriched DCs were labelled with anti-CD11c antibody ( clone N418; eBioscience ) and sorted using a FACS Aria . In all experiments , subset purity was >95% . DCs were incubated with mink lung epithelial cells transfected with a plasmid containing firefly luciferase cDNA downstream of a TGFβ-sensitive promoter [15] in the presence of 1 µg/ml lipopolysaccharide . Co-cultures were incubated overnight in the presence of 80 µg/ml control mIgG or anti-TGFβ antibody ( clone 1d11 ) and luciferase activity detected via the Luciferase Assay System ( Promega ) according to manufacturer's protocol . TGFβ activity was determined as the difference in luciferase activity between control mIgG-treated samples and samples treated with anti-TGFβ antibody . Mesenteric lymph nodes ( mLNs ) were removed from mice and disaggregated through a 100 µm sieve . Caecum and proximal colon were excised and lamina propria lymphocytes were prepared essentially as described [46] with slight modification in the tissue digestion step ( digestion medium used was RPMI with 10% Foetal calf serum , 0 . 1% w/v collagenase type I and Dispase II ( both Invitrogen ) , and tissue was digested for 30 min at 37°C ) . Cell suspensions were blocked with anti-FcγR antibody ( clone 24G2; eBioscience ) before labelling with antibodies specific for CD4 ( clone GK1 . 5; eBioscience ) , Foxp3 ( clone FJK-16s; eBioscience ) , IL-13 ( clone eBiol13A; eBioscience ) , IFNγ ( clone XMG1 . 2; eBioscience ) or p-Smad 2/3 ( Santa Cruz ) . For pSmad2/3 staining , an Alexa Fluor 594-labelled donkey anti-goat secondary antibody was used ( Invitrogen ) . All samples were analysed on a FACS LSRII . mLN and LILP cells were prepared as described above before incubating with 50 ug/ml of concavelin A or T . muris excretory/secretory ( E/S ) antigen for 48 hours . Cell-free supernatants were analysed for cytokine production via cytometric bead array ( BD ) or paired ELISA antibodies ( anti- IFNγ , clone XMG1 . 2 and biotin anti- IFNγ , clone R4-6A2; anti-IL-13 , clone eBio13A and biotin anti-IL-13 , clone eBio1316H and anti-IL-4 , clone 11B1and biotin anti-IL-4 , clone BVD6-2462 ( eBioscience ) . For intracellular cytokine analysis cells were incubated for 12 hours with 50 ug/ml T . muris E/S antigen followed by PMA/ionomycin stimulation for 4 hour with addition of monensin for the final 3 hours . Cells were then stained with antibodies against IL-4 , IL-13 and IFNγ using the eBioscience Foxp3 permibilization kit according to the manufacturer's instructions . CD4+ T-cells were isolated from mLN via negative selection using a CD4+ T-cell isolation kit ( Miltenyi Biotec ) during the course of a chronic T . muris infection and lysed using 1% Triton-X100 in Tris buffer ( 50 mM Tris-HCl , 150 mM NaCl pH 7 . 4 ) plus 5 mM EDTA , 20 µg/ml leupeptin and aprotinin , 0 . 5 mM AESF and 2 mM NaVO3 . Lysates were analysed by Western blot with antibodies to detect p-Smad 2/3 ( Millipore ) and β-actin ( Sigma Aldrich ) , using the Invitrogen Nupage gel system according to manufacturer's instructions . Total RNA was purified from sorted DC subsets using an RNAeasy minikit according to manufacturer's protocol ( Qiagen ) . RNA was reverse transcribed using Oligo dT primers , and cDNA for specific genes detected using a SYBR green qPCR kit ( Finnzymes ) Gene expression normalised to HPRT expression . HPRT Forward: GCGTCGTGATTAGCGATGATGAAC , HPRT Reverse: GAGCAAGTCTTTCAGTCCTGTCCA , Integrin β8 Forward: GGGTGTGGAAACGTGACAAGCAAT , Integrin β8 Reverse: TCTGTGGTTCTCACACTGGCAACT . Results are expressed as mean ± SEM . Where statistics are quoted , 2 experimental groups were compared using the Student's t-test for non-parametric data . Three or more groups were compared using the Kruskal–Wallis test , with Dunn's multiple comparison post-test . P≤0 . 05 was considered statistically significant . | Infection with intestinal parasitic worms is a major global health problem , with billions of people infected world-wide . Often these worms ( known as helminths ) develop a long-lasting chronic infection , due to failure of the host to mount the correct type of immune response that would normally expel the parasite . However , how the immune system is controlled leading to chronic helminth infection is not well understood . Here we identify a novel pathway of importance in the development of chronic helminth infection . Using a model parasite which infects mice , we find that a protein called transforming growth factor beta ( TGFβ signals to T-cells early during the development of chronic infection and that blocking this signal protects mice from infection . We have also uncovered a key pathway and cell type that controls TGFβ function during development of chronic infection . When a protein called integrin αvβ8 is absent from dendritic cells of the immune system , TGFβ is no longer activated to signal to T-cells and mice are able to mount a protective ( type 2 ) immune response resulting in worm expulsion . Our findings therefore provide new insights into how chronic infections develop and identify potential molecular targets for the prevention of chronic helminth infection . |
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CD8+ T cells are essential for host defense to intracellular bacterial pathogens such as Mycobacterium tuberculosis ( Mtb ) , Salmonella species , and Listeria monocytogenes , yet the repertoire and dominance pattern of human CD8 antigens for these pathogens remains poorly characterized . Tuberculosis ( TB ) , the disease caused by Mtb infection , remains one of the leading causes of infectious morbidity and mortality worldwide and is the most frequent opportunistic infection in individuals with HIV/AIDS . Therefore , we undertook this study to define immunodominant CD8 Mtb antigens . First , using IFN-γ ELISPOT and synthetic peptide arrays as a source of antigen , we measured ex vivo frequencies of CD8+ T cells recognizing known immunodominant CD4+ T cell antigens in persons with latent tuberculosis infection . In addition , limiting dilution was used to generate panels of Mtb-specific T cell clones . Using the peptide arrays , we identified the antigenic specificity of the majority of T cell clones , defining several new epitopes . In all cases , peptide representing the minimal epitope bound to the major histocompatibility complex ( MHC ) -restricting allele with high affinity , and in all but one case the restricting allele was an HLA-B allele . Furthermore , individuals from whom the T cell clone was isolated harbored high ex vivo frequency CD8+ T cell responses specific for the epitope , and in individuals tested , the epitope represented the single immunodominant response within the CD8 antigen . We conclude that Mtb-specific CD8+ T cells are found in high frequency in infected individuals and are restricted predominantly by HLA-B alleles , and that synthetic peptide arrays can be used to define epitope specificities without prior bias as to MHC binding affinity . These findings provide an improved understanding of immunodominance in humans and may contribute to a development of an effective TB vaccine and improved immunodiagnostics .
Infection with Mycobacterium tuberculosis ( Mtb ) remains an important cause of infectious disease , morbidity , and mortality worldwide [1] and has emerged as a major opportunistic infection in individuals with HIV/AIDS [2] . Control of infection with Mtb relies heavily on the cellular immune system , that is , the interaction of lymphocytes and Mtb-infected macrophages and dendritic cells ( DCs ) [3 , 4] . CD8+ T cells are associated with strong CD4+ TH1 cell responses , and are not only essential for effective immunity to viral pathogens , but also for immunity to some intracellular bacteria , such as Listeria monocytogenes and Salmonella species [5] . Increasing experimental evidence in the mouse tuberculosis ( TB ) model has suggested a protective role for CD8+ T cells in the host response . For example , adoptive transfer or in vivo depletion of CD8+ cells showed that this subset could confer protection against subsequent challenge [6–8] . β2-microglobulin–deficient mice , deficient in expression of major histocompatibility complex ( MHC ) class I , are more susceptible to Mtb [9] and to large doses of Bacille Calmette Guérin [10] infection than their wild-type littermates . This finding has been corroborated in CD8-deficient mice [11] and other mice deficient in class I processing and presentation [11–13] . However , mice lacking class Ia–restricted CD8+ T cells demonstrate more moderate susceptibility to Mtb infection [14 , 15] . In humans , Mtb-specific CD8+ T cells have been identified in Mtb-infected individuals and include CD8+ T cells that are classically , MHC-Ia , restricted [16–22] , and non-classically , MHC-Ib , restricted by HLA-E [18 , 23] , and by CD1 [24–26] . Taken together , studies of mice and humans support an important role for CD8+ T cells in TB immunity . For most infections , the repertoire of the CD8 response is shaped by the entry of antigen into the MHC-I processing pathway , binding of peptides and/or non-peptide antigens to MHC-I molecules , and recognition of these structures by T cells . Ultimately , a relatively limited subset of pathogen-specific T cells emerge , a process that has been termed immunodominance [27] . While substantial effort has focused on defining immunodominant CD8 antigens for important human viral pathogens such as HIV and cytomegalovirus ( CMV ) , little is known about the antigens recognized by human CD8+ T cell in response to intracellular bacterial infections . Furthermore , although a number of commonly recognized CD4 Mtb antigens have been described [28 , 29] ( ESAT-6 , CFP10 , Ag85 , etc . ) , surprisingly little is known about common Mtb antigens recognized by human CD8+ T cells . The majority of CD8+ epitopes that have been identified were defined by testing of Mtb peptides selected for high-affinity binding to MHC class Ia molecules ( HLA-A2 in most cases; [19 , 20 , 30–34] ) . In almost all of these examples , however , the ex vivo frequency of these T cells in Mtb-infected individuals is low or undetectable , suggesting that these specificities may not represent immunodominant responses . In contrast , in the limited cases in which T cells have been used to define epitopes contained in selected Mtb antigens , high ex vivo frequencies have been demonstrated [17 , 35] , suggesting that a T cell–centered approach can identify immunodominant epitopes . Moreover , CD8+ T cell responses to some Mtb antigens that represent good CD4 antigens ( CFP10 , ESAT-6 , Ag85 , and Mtb39 ) have been detected at high frequency in persons infected with Mtb [17–19 , 34] . Therefore , we used a limited library of overlapping synthetic peptides representing several known CD4 Mtb antigens to determine the magnitude of the CD8 response to these antigens in persons with active TB and latent TB infection ( LTBI ) , as well as uninfected individuals . Furthermore , we utilized a panel of Mtb-specific CD8+ T cell clones to define minimal epitopes recognized within these antigens and determined the contribution of these novel epitopes to the ex vivo Mtb-specific CD8 response .
To define immunodominant Mtb-specific CD8 antigens , and to determine whether or not these responses result from infection with Mtb , we have used CD8+ T cells from uninfected donors , those with LTBI , or those actively infected with Mtb . Responses were determined either directly ex vivo , or using CD8+ T cell clones obtained by limiting dilution cloning on Mtb-infected autologous DCs [36] . As much is known about dominant CD4 Mtb antigens , a panel of these commonly recognized antigens was selected for further evaluation . These were Mtb39 , CFP10 , Mtb8 . 4 , Mtb9 . 9A , ESAT-6 , Ag85b , 19kDa , and EsxG . To avoid bias introduced by using peptides of predicted HLA binding specificity , we synthesized overlapping peptides ( 15 aa , overlap 11 aa ) to represent the proteins of interest [17] . To accurately determine the ex vivo effector cell frequencies of CD8+ T cells , linear regression analysis was used . As shown in Figure 1 , using D466 as an example , magnetic bead–purified CD8+ T cells were tested against peptide-pulsed DCs over a range of CD8+ T cell numbers in an IFN-γ ELISPOT assay . A positive assay was determined as described below and if positive , the antigen-specific frequency was determined using linear regression . Uninfected individuals ( n = 14 ) , those with LTBI ( n = 20 ) , and those with active TB ( n = 12 ) were evaluated for CD8 responses to a panel of Mtb CD4+ T cell antigens , as well as to Mtb-infected DCs . All individuals tested had robust CD8+ T cell responses to Mtb-infected DCs and were of greater magnitude in individuals with active TB than in those with LTBI ( p = 0 . 01; Figure 2; Table 1 ) . However , CD8+ T cell responses to the panel of Mtb antigens were found almost exclusively in those infected with Mtb in that statistically significant differences between uninfected and Mtb-infected individuals were noted for seven of ten antigens for both the magnitude of the response ( Figure 2 ) and the proportion of positive assays ( Table 1 ) . However , differences in CD8+ T cell responses between individuals with active TB and LTBI were not statistically different . While strong CD8+ T cell responses were observed against many of the antigens tested , it is equally notable that several individuals with strong Mtb-directed CD8+ T cell responses did not have demonstrable responses to many of the antigens tested ( unpublished data ) . These ex vivo frequency data demonstrated the presence of high-frequency responses to a number of known Mtb antigens , but did not shed light on the restricting allele , minimal epitope , or dominance hierarchy within the gene of interest . To address this question , we performed limiting dilution cloning of human CD8+ T cells using Mtb-infected DCs [36] , and generated panels of both classically and non-classically HLA-restricted CD8+ T cell clones . Using peptide pools representing known CD4 antigens , the antigenic specificity of the HLA-Ia-restricted clones was defined in more than half of the clones ( Table 2 ) . This approach is demonstrated in detail for a single representative clone , D466 D6 , derived from an individual with active TB . As shown in Figure 3A , testing the clone against autologous DCs pulsed with a panel of peptide pools unambiguously defined the antigenic specificity as CFP10 . The clone was then tested against each of the 15-mer peptides that comprise the CFP10 pool , revealing that the epitope was contained within CFP101–15 ( Figure 3B ) . Each possible 8-aa , 9-aa , 10-aa , and 11-aa peptide was then synthesized and tested for reactivity , revealing antigenic activity between aa 2–11 ( Figure 3C ) . Similarly , each clone was tested against lymphoblastoid cell lines ( LCLs ) sharing at least one HLA type with the donor ( Figure 3D ) . Autologous LCL and IHW 9058 LCL , which share B4501 and C1601 , presented the epitope to the clone , identifying both B4501 and C1601 as possible restricting alleles . However , C1601+ D433 LCL did not present the epitope , eliminating C1601 as a candidate-restricting allele . Therefore , D466 D6 was restricted by HLA-B4501 . As demonstrated in Figure 4 , by testing each plausible epitope over a broad range of concentrations , the minimal epitope was defined as CFP102–12 for D466 D6 . Experimental data supporting the assignment of the minimal epitope is provided for each clone in Figure 5 , and a summary of the antigenic specificity , minimal epitope , and HLA-restricting allele is presented in Table 3 . Unexpectedly , all but one of the T cell clones were restricted by HLA-B alleles . Furthermore , a minority of those observed were 9 aa in length . Because each of the individual CD8+ T cell clones was derived based on growth in the presence of Mtb- infected DCs , we sought to determine whether or not the antigen and epitopes identified reflected immunodominant epitopes ex vivo . Two independent approaches were pursued , the first to determine if the response was present at high frequency , and the second to determine what proportion of the total response to the antigen was constituted by the epitope . To determine the ex vivo effector cell frequency , as described in Figure 1 , each epitope was tested using autologous DCs and magnetic bead–purified CD8+ T cells derived from the donor from whom the T cell clones was isolated . A summary of the ex vivo epitope-specific effector cell frequencies is presented in Table 3 . For comparison , effector cell frequencies using autologous DCs infected with Mtb as the antigen-presenting cells ( APCs ) are shown as well . For 11 CD8+ T cell clones recognizing distinct epitopes , the epitope-specific frequency exceeded 50% of the total Mtb-specific CD8+ T cell response . For six of these clones , the epitope-specific frequency actually exceeded the total frequency of Mtb-specific CD8+ T cells . Conversely , for two clones , the epitope-specific T cell frequency constituted the minority of the total Mtb-specific CD8+ T cell response . Thus , overall , the epitopes reflected high-frequency responses , and could be considered a response that has been primed by exposure to Mtb . Notably , T cell clones isolated from four donors recognized CFP10 . To determine if the epitopes defined reflected a substantial proportion of the total response to the antigen of interest , magnetic bead–purified CD8+ T cells from three donors with sufficient available peripheral blood mononuclear cells ( PBMCs ) were tested for reactivity to each individual 15-mer peptide , the peptide pool , and peptide representing the minimal epitope . As is demonstrated in Figure 6 , the ex vivo frequencies to the minimal epitope , 15-mer peptide ( s ) containing the minimal epitope , and peptide pool were remarkably concordant . These data , then , suggest that for each donor a dominance hierarchy has been clearly established , and was reflected in the original clones . Finally , as is noted in Table 3 , daughter clones of identical specificity were frequently identified , a result that would be predicted based on an immundominance hierarchy . TCR V beta staining was used to confirm the clonal relationship between daughter clones . Interestingly , in two cases , the identical minimal epitope and HLA restriction was represented by two distinct clones ( Table 3 ) . Because much work on human CD8+ T cell responses to Mtb has relied upon the use of HLA prediction algorithms , as each epitope was defined we asked whether or not the epitopes would have been predicted by these approaches . Given the prevalence of HLA-B alleles and 10-mer and 11-mer epitopes , it is perhaps not surprising that many of these epitopes were not ranked strongly ( unpublished data ) . This left open the possibility that either HLA binding was indeed not predictive of antigenicity , or simply highlighted the limitations of those algorithms at the time they were used . To address this question experimentally , the IC50 for each peptide that had been synthesized in the course of definition of the minimal epitope was determined against a panel of human HLA molecules ( Table S1 ) . Shown in Table 3 is the IC50 for the minimal epitope with the cognate restricting allele . These data demonstrate that the T cell epitopes bound avidly to HLA , and show a high degree of concordance between the T cell epitope data and HLA binding data ( Figure 5; Table S1 ) .
Although the complete repertoire of CD8+ T cell responses in Mtb remains incompletely characterized , the following conclusions can be drawn . First , CD8+ T cell responses are present in persons infected with Mtb at frequencies that are comparable to that seen following many common viral infections such as vaccinia , influenza , and CMV [37 , 38] . This conclusion is based both on the pooled peptide experiments described above , and on the observation that when defined , dominant epitopes are present at high ex vivo frequencies . Conversely , we have not observed high-frequency responses to CD4+ T cell antigens in those without evidence of infection with Mtb . This observation strongly supports the hypothesis that these responses reflect an adaptively acquired response to infection with Mtb , rather than an innate response . By contrast , CD8+ T cell responses to Mtb-infected DCs were equivalent in infected and uninfected individuals . Using limiting dilution analysis , we have studied the proportions of Ia- versus Ib- restricted CD8+ T cells in Mtb-infected compared to uninfected individuals and have noted that Ib-restricted CD8+ T cell responses predominate in uninfected individuals ( D . Lewinsohn , unpublished data ) . Therefore , we speculate that the response detected to Mtb-infected DCs in uninfected individuals may reflect chiefly a Ib-restricted response . While we did not observe an association of responses to specific antigens with disease status , a larger study might reveal more subtle differences . All but one of the epitopes that have been mapped to date are restricted by HLA-B molecules . The reasons for this possible skewing are not yet clear . It is a formal possibility that the cloning methods we used biased isolation of HLA-B- over HLA-A-restricted T cell clones . However , using identical cloning methodology , we have isolated T cell clones specific for vaccinia , CMV , and influenza that did not display a preference for HLA-B restriction ( D . Lewinsohn , unpublished data ) . Furthermore , in all three cases where we tested ex vivo the entire set of peptides representing CFP10 , the entire response mapped to the HLA-B-restricted epitope ( Figure 6 ) . Therefore , we speculate that Mtb antigens may preferentially bind to HLA-B molecules , that Mtb preferentially interferes with HLA-A processing and presentation , that infection with Mtb leads to selective upregulation of HLA-B , or that HLA-B is preferentially delivered to the Mtb phagosome . Nonetheless , these data are consistent with those reported by Kiepiela et al . [39] , in which HIV-specific T cell responses were found to be 2 . 5-fold more likely to be HLA-B- than HLA-A-restricted , and observed that viral load was more closely linked to HLA-B than HLA-A alleles . Human infection with mycobacteria has been demonstrated in pre-urban Egypt [40] and in pre-Columbian Peru [41] , observations consistent with the hypothesis that mycobacterial infection has driven the diversity and peptide-binding repertoire of the HLA-B locus . Furthermore , our data is consistent with the hypothesis that the diversity of HLA-B alleles is related to the containment of both viral and bacterial pathogens . Hence , delineation of immunodominant epitopes and antigens within the context of important human pathogens will likely require careful evaluation of those epitopes presented by HLA-B . While the immune response within an individual to a given antigen is narrowly focused , dominant epitopes that would be useful for population-based analysis have yet to be defined . This conclusion is based on the fact that few of the HLA-A2 epitopes described to date have proved to be widely recognized , and , most importantly , on the observation that a wide variety of HLA alleles appear to be used in the recognition of Mtb antigens . The antigen CFP10 is an excellent case in point . As is demonstrated in Table 3 , T cell clones have been used to define high-frequency epitopes restricted by a variety of HLA alleles . While the N-terminal 15 aa could reasonably be considered immunogenic , in all but one case ( CFP102–11; HLA-B44 ) the minimal epitope defined has been unique to the individual from whom the T cells were derived . By using a T cell–driven approach to epitope identification , it is possible to define dominant epitopes in humans infected with Mtb . While it is striking that for several of the T cell clones the ex vivo frequency of epitope-specific T cells was equal to or exceeded the total ex vivo frequency of Mtb-specific T cells , we acknowledge that the use of peptide-loaded DCs to determine T cell frequency likely overrepresents the proportion of the total Mtb-specific CD8+ T cell responses . Possible reasons are that Mtb infection is likely to produce relatively less cognate peptide compared to peptide loading , and that Mtb infection could possibly interfere with class I processing and presentation . Although the current observations are limited to a small panel of known CD4 antigens , current work is underway to perform a genome-wide survey of dominant CD8 antigens in Mtb . Definition of this panel will likely prove useful in the further study of the natural history of infection with Mtb and in the design of novel vaccines and diagnostics . In TB , evaluation of epitopes based strictly on HLA binding algorithms has focused on HLA-A2 , and has often failed to define dominant epitopes . Our observation that many of the epitopes are HLA-B restricted , and often longer than 9 aa , for which these algorithms are less robust , may help explain these findings . However , when evaluated experimentally , all of the minimal epitopes exhibit high-affinity binding to the cognate-restricting allele . In this regard , substantial progress in HLA prediction algorithms is evident [42 , 43] , and could facilitate more efficient identification of dominant epitopes . Here , more information with regard to longer peptides will be of benefit . Finally , we speculate that these caveats are likely to apply to other intracellular pathogens as well . One limitation to current knowledge is that the responses in humans have been made at a single time point . In this regard , a feature of Mtb is the chronic exposure to antigen that may persist for many years . How this chronic infection influences the shaping of the immune response and dominance repertoire is an important question that remains unresolved . The advent of new reagents for immunologic studies in the mouse model [44] will likely be useful in this regard . For example , chronic antigenic exposure seems likely to alter the affinity of the T cell response over time . Furthermore , it is possible that such long-term infection might lead to clonal exhaustion or dysfunction as has been described for chronic viral infection [38] . Finally , given the very high-frequency responses that we and others have observed to Mtb-infected DCs and to single antigens , it appears that the immune response to Mtb occupies a sizable fraction of the host's immunological activity , similar to that previously observed for infection with CMV [45] . If so , then this may have important implications for the aging immune system , and potentially for the requirements for the long-term containment of intracellular infection . For example , it is possible that threshold Mtb-specific CD8 frequencies are necessary for the maintenance of a state of chronic persistence . Conversely , the substantial immunological effort directed at Mtb may limit the host's ability to effectively combat novel infections . As a result , this static picture leaves open important questions as to the evolution of the Mtb-specific responses , and its relationship with chronic infection with Mtb . It seems likely that Mtb has evolved potent mechanisms to modulate the immune response . At present , specific mechanisms for MHC-I immune modulation have not been described . However , it appears that TLR II stimulation via the Mtb-derived 19-kDa lipoprotein can modulate both MHC-I and MHC-II antigen processing , and can interfere with IFN-γ signaling [46–49] . Further work on the T cell subsets important in Mtb , including the immunodominant epitopes , will extend our understanding of the immunology of TB and potentially contribute to the development of a vaccine against this major killer . How the Mtb-specific CD8+ T cell response fits into the natural history of infection with Mtb remains poorly characterized . For example , the ontogeny of the CD8+ T cell response relative to infection with Mtb remains unknown , as does the relationship of CD8+ T cell frequencies with regard to bacterial burden . However , by defining commonly recognized CD8+ T cell antigens and epitopes , it will become increasingly possible to employ direct ex vivo analysis to more precisely define Mtb-specific T cell responses in various subject groups of particular interest .
Study participants , protocols , and consent forms were approved by the Oregon Health and Science University institutional review board . Informed consent was obtained from all participants . Uninfected individuals and individuals with LTBI were recruited from employees at Oregon Health and Science University as previously described [36] . Uninfected individuals were defined as healthy individuals with a negative tuberculin skin test and no known risk factors for infection with Mtb . Individuals with LTBI were defined as healthy persons with a positive tuberculin skin test and no symptoms and signs of active TB . Individuals with active TB were recruited via institutional review board–approved advertisement and were self-referred from the Multnomah County TB Clinic , Portland , Oregon , United States , or from the Washington County TB Clinic , Hillsboro , Oregon , United States . In all active TB cases , pulmonary TB was diagnosed by the TB Controller of these counties and confirmed by positive sputum culture for Mtb . PBMCs were isolated from whole blood obtained by venipuncture or apheresis . Culture medium consisted of RPMI 1640 supplemented with 10% fetal bovine sera ( BioWhittaker , http://www . cambrex . com/ ) , 5 × 10−5 M 2 ME ( Sigma-Aldrich , http://www . sigmaaldrich . com/ ) , and 2 mM glutamine ( GIBCO BRL , http://www . invitrogen . com/ ) . For the growth and assay of Mtb-reactive T cell clones , RPMI 1640 was supplemented with 10% human serum . Mtb strain H37Rv was obtained from the American Type Culture Collection ( http://www . atcc . org/ ) and prepared as previously described [36] . Peptides were synthesized by Genemed Synthesis ( http://www . genemedsyn . com/ ) . Synthetic peptide pools consisted of 15 mers overlapping by 11 aa , representing Mtb proteins demonstrated to be potent CD4 antigens . Peptide pools representing CFP10 [50 , 51] , ESAT-6 [52] , Mtb39a ( two pools , A & B ) [53] , Mtb8 . 4 [54] , Mtb 9 . 9A [16] , EsxG [55 , 56] , 19kDa antigen [57] , and antigen 85b ( two pools , A & B ) [58] were synthesized . Peptides were resuspended in DMSO , and up to 50 peptides were combined into one pool such that each peptide in the pool was at a concentration of 1 mg/ml . Peptide pools were stored at −80 °C . EBV-transformed B cell lines , LCLs , were either generated in our laboratory using supernatants from the cell line 9B5–8 ( American Type Culture Collection ) or obtained from the National Marrow Donor Program ( http://www . marrow . org/ ) . LCLs were maintained by continuous passage as previously described [18] . Mtb-specific T cell clones were isolated from individuals with LTBI or active TB , using Mtb-infected DCs as APCs and limiting dilution cloning methodology as previously described [36] . Briefly , CD8+ T cells were isolated from PBMCs using negative selection using CD4 antibody-coated beads and then positive selection using CD8 antibody-coated magnetic beads per the manufacturer's instructions ( Miltenyi Biotec , http://www . miltenyibiotec . com/ ) or via flow cytometry . In this case , CD4-PE ( BD Biosciences , catalog #555347; http://www . bdbiosciences . com/ ) negative and CD8-APC ( BD Biosciences , catalog #555369 ) positive cells ( purity >99% ) were sorted on a Becton Dickinson LSR II ( http://www . bd . com/ ) . T cells were seeded at various concentrations in the presence of a 1 × 105–irradiated autologous Mtb-infected DC , generated as described below , and rIL-2 ( 5 ng/ml ) in cell culture media consisting of 200 μl of RPMI 1640 supplemented with 10% human sera . Wells exhibiting growth between 10–14 d were assessed for Mtb specificity using ELISPOT and Mtb-infected DCs as a source of APCs . T cells retaining Mtb specificity were further phenotyped for αβ T cell receptor expression and CD8 expression by FACS and expanded as described below . V beta usage was determined using the IOTest Beta Mark Kit from Beckman Coulter , catalog #IM3497 ( http://www . beckmancoulter . com/ ) . To expand the CD8+ T cell clones , a rapid expansion protocol using anti-CD3 mAb stimulation was used as described previously [18] . Monocyte-derived DCs were prepared according to a modified method of Romani et al . [18 , 59] . To generate Mtb-infected DCs , cells ( 1 × 106 ) were cultured overnight in the presence of Mtb at a multiplicity of infection = 50:1 . We have determined that this multiplicity of infection is optimal for detection of Mtb-specific CD8+ T cells , as heavy infection is required to optimize entry of antigen into the class I processing pathway [60] . After 18 h , the cells were harvested and resuspended in RPMI/10% human serum . The MHC peptide binding assay utilized measures the ability of peptide ligands to inhibit the binding of a radiolabeled peptide to purified MHC molecules , and has been described in detail elsewhere [61] . Briefly , purified MHC molecules , test peptides , and a radiolabeled probe peptide are incubated at room temperature in the presence of human β2-microglobulin and a cocktail of protease inhibitors . After a 2-d incubation , binding of the radiolabeled peptide to the corresponding MHC class I molecule is determined by capturing MHC/peptide complexes on W6/32 antibody ( anti-HLA A , B , and C ) or B123 . 2 ( anti-HLA B , C , and some A ) coated plates , and measuring bound cpm using a microscintillation counter . For competition assays , the concentration of peptide yielding 50% inhibition of the binding of the radiolabeled peptide is calculated . Peptides are typically tested at six different concentrations covering a 100 , 000-fold dose range , and in three or more independent assays . Under the conditions utilized , where [label] < [MHC] and IC50 ≥ [MHC] , the measured IC50 values are reasonable approximations of the true Kd values . The IFN-γ ELISPOT assay was performed as described previously [18] . For determination of ex vivo frequencies of CD8+ T cells responding to Mtb infection or Mtb antigens , CD8+ T cells were positively selected from PBMCs using magnetic beads ( Miltenyi Biotec ) such that >97% of the cell population were CD8+ T cells . These CD8+ T cells were used as a source of responder T cells and tested in duplicate at four different cell concentrations ( 5 × 105 , 2 . 5 × 105 , 1 . 2 × 105 , and 6 × 104 cells/well ) . Autologous DCs ( 20 , 000 cells/well ) were used as APCs , and DCs were either infected with Mtb or pulsed with peptide pools ( 5 μg/ml , final concentration of each peptide ) and then added to the assay . For assays using T cell clones , T cells ( 1 , 000 or 5 , 000 cells/well ) were incubated with autologous LCL ( 20 , 000 cells/well ) in the presence or absence of antigen . Negative and positive controls were included in each assay and consisted of wells containing T cells and DCs either without antigen or without antigen but with inclusion of phytohemagglutanin ( PHA , 10 μg/ml; EMD Biosciences , http://www . emdbiosciences . com/ ) , respectively . For all assays , responding T cells were incubated with APCs overnight . To determine the ex vivo frequency of antigen-specific T cells , the average number of spots per well for each duplicate was plotted against the number of responder cells per well . Linear regression analysis was used to determine the slope of the line , which represents the frequency of antigen-specific T cells . The assay is considered positive , i . e . , reflecting the presence of a primed T cell response , if the binomial probability [62] for the number of spots is significantly different by experimental and control assays , i . e . , if the experimental line is statistically significantly different from the control line . To determine differences in ex vivo T cell frequencies between groups , Wilcoxon/Kruskal–Wallis analysis was used . Ex vivo T cell frequencies and MHC binding assays were performed exactly as described above .
The accession numbers from TubercuList ( http://genolist . pasteur . fr/TubercuList/ ) for Mtb proteins discussed in the manuscript are as follows: 19kd ( Rv3763 ) ; Ag85B ( Rv1886c ) , CFP10 ( Rv3874 ) ; ESAT 6 ( Rv3875 ) ; EsxG ( Rv0287 ) , Mtb8 . 4 ( Rv1174c ) ; Mtb9 . 9A ( Rv1793 ) ; Mtb39 ( Rv1196 ) . | CD8+ T cells are essential for host defense to intracellular bacterial pathogens such as Mycobacterium tuberculosis ( Mtb ) , Salmonella species , and Listeria monocytogenes , yet little is known about the antigens recognized by human CD8+ T cells in response to tuberculosis ( TB ) . TB , the disease caused by Mtb infection , remains one of the leading causes of illness and death worldwide and is a frequent complicating infection in individuals with HIV/AIDS . Therefore , we undertook this study to define commonly recognized CD8 Mtb antigens . First , we measured the frequencies of CD8+ T cells recognizing Mtb antigens known to be recognized by CD4+ T cell antigens in persons infected with Mtb . In addition , we identified the Mtb antigen and epitope recognized by several CD8+ T cell clones isolated from infected individuals . The epitope was presented to the T cell clones by an HLA-B allele in all but one case . We conclude that Mtb-specific CD8+ T cells are found in high frequency in infected individuals and are restricted predominantly by HLA-B alleles . These findings provide an improved understanding of how the human immune system recognizes intracellular pathogens and may contribute to the development of an effective TB vaccine and improved immunodiagnostics . |
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Eradication of HIV infection will require the identification of all cellular reservoirs that harbor latent infection . Despite low or lack of CD4 receptor expression on Vδ2 T cells , infection of these cells has previously been reported . We found that upregulation of the CD4 receptor may render primary Vδ2 cells target for HIV infection in vitro and we propose that HIV-induced immune activation may allow infection of γδ T cells in vivo . We assessed the presence of latent HIV infection by measurements of DNA and outgrowth assays within Vδ2 cells in 18 aviremic patients on long-standing antiretroviral therapy . In 14 patients we recovered latent but replication-competent HIV from highly purified Vδ2 cells demonstrating that peripheral Vδ2 T cells are a previously unrecognized reservoir in which latent HIV infection is unexpectedly frequent .
The infecting HIV genome integrates into host chromatin where , transcriptionally silent and unaffected by antiretroviral therapy ( ART ) , it represents a major challenge towards efforts to eradicate infection [1] . Virologic latency is defined as durable , quiescent infection from which replication-competent HIV can emerge after cell activation . To date , resting memory CD4+ T lymphocytes are the major cell type in which latency has been documented in vivo [2–5] . However , efforts to eradicate HIV infection require the identification of all potential cellular reservoirs and therefore , while conventional αβ T cells , which include resting memory CD4+ T cells , constitute the major subpopulation of T lymphocytes , the γδ T cell population merits study as a potentially important site of latent infection . In the absence of pathological conditions such as infection , γδ T cells represent between 2 and 10% of total circulating CD3+ T lymphocytes [6] . Among peripheral CD3+ γδ T cells , those expressing a TCR formed by the Vγ9 and Vδ2 variable regions ( hereafter referred to as Vδ2 cells ) constitute up to 90% of γδ T cells [7] . These Vδ2 cells specifically recognize non-peptidic phosphorylated metabolites of isoprenoid biosynthesis , such as the potent activator ( E ) -4-hydroxy-3-methyl-but-2-enyl pyrophosphate ( HMBPP ) , present in most pathogenic bacteria [8 , 9] , or isopentenyl pyrophosphate ( IPP ) , produced also by the human mevalonate biosynthesis pathway [10] , but are not recognized by conventional αβ T cells . In vitro , both compounds have the same effect on γδ T cells , activating them . In adults , most Vδ2 cells are memory cells that can be further classified according to expression of CD45RO and CD27 surface markers [11–13] . During HIV-1 infection , the peripheral blood Vδ2 cell subset is depleted while Vδ1 cells are expanded , leading to an inversion of the Vδ2/Vδ1 ratio [14 , 15] . An indirect mechanism involving CCR5/α4β7 signalling was hypothesized to explain the specific depletion of Vδ2 cells [16] . However , an additional mechanism may be the direct infection of these cells as productive HIV infection of γδ T cells within peripheral blood mononuclear cells ( PBMC ) [17] as well as infection of γδ T cell clones by the CXCR4-tropic laboratory clone HIVLAI [18] has been reported . Similarly , SIV can infect both Vδ1 and Vδ2 cells , albeit infrequently [19] . Despite the documented capacity of HIV to infect γδ T cells , the potential of γδ T cells to serve as a persistent reservoir of infection has not been studied . Moreover , the memory phenotype of Vδ2 cells suggests that these cells could play a role as durable in vivo reservoirs of HIV infection . Using a viral outgrowth assay to detect latent but replication-competent HIV [20 , 21] , complemented by measures of HIV DNA , we demonstrate for the first time that peripheral Vδ2 cells in ART-treated patients with complete suppression of HIV plasma viremia harbour latent HIV that can replicate following ex vivo induction . We report the discovery of a new reservoir of HIV within peripheral Vδ2 cells , and suggest that infection in this population may be founded by immune activation that transiently upregulates the CD4 receptor on Vδ2 cells .
To study the role of Vδ2 cells as reservoirs of persistent , latent HIV infection , 18 HIV-infected male volunteers , who initiated ART in acute HIV infection ( AHI; n = 9 ) or in chronic HIV infection ( CHI; n = 9 ) and received stable ART for a median of 3 . 4 years [range 1 . 9–9 . 5] were studied . A comparison between AHI and CHI-treated patients’ characteristics at the time of study showed that CHI patients had , as expected , a statistically significant lower nadir CD4 count ( p = 0 . 017 ) and a significantly longer time on ART ( p = 0 . 004 ) . Median CD8+ T cell count was lower and pre-therapy plasma HIV RNA was higher in the AHI patients although these differences did not achieve statistical significance ( Table 1 ) . To ensure that other contaminating cells did not contribute to the recovery of HIV from isolated Vδ2 cells , we incubated freshly isolated patients’ PBMC with raltegravir and abacavir for 24 hours to avoid the possibility that de novo integration events could occur ex vivo after cell donation . γδ T cells were then enriched from PBMC using magnetic immunoaffinity beads , and non-activated ( HLA-DR- ) Vδ2 cells were further purified by FACS-sorting ( Fig 1A and 1B ) . This process excluded αβTCR+ cells ( classical CD4+ T cells ) from pre-sort samples ( Fig 1C ) , as detailed in Materials and Methods . To further confirm that Vδ2 cells were not already activated , aliquots of isolated Vδ2 cells were cultured in 5U/mL IL-2 prior to the addition of target cells in the viral outgrowth assay . HIV p24 measurements from these cultures were uniformly negative . Total HIV DNA levels were then quantified in isolated Vδ2 cells , unfractionated PBMC and total resting CD4+ T ( r-CD4 ) cells , when available ( Fig 2A ) in patients treated in AHI and CHI . As previously published in studies of other cell populations [22] , DNA levels varied widely but interestingly , Vδ2 cells showed the highest level of pol HIV DNA copies per 106 Vδ2 cells ( mean of 873 . 6 HIV copies/106 cells ) . Due to the low number of Vδ2 cells available for analysis the limit of quantitation of Vδ2 cells was 50 . 6 copies/106 cells , and 5 . 1 copies/106 cells for the other cell populations , where more cells could be analyzed . HIV DNA levels within Vδ2 cells were not statistically different between AHI and CHI-treated patients ( p = 0 . 37 ) . Within PBMC and r-CD4 cells HIV DNA levels were higher in CHI patients than in AHI patients , although this difference did not achieve statistical significance ( p = 0 . 06 for PBMC and p = 0 . 65 for resting CD4+ T cells ) . We recovered an average of 638 . 6 HIV DNA copies/million γδ T cells from CHI patients , and an average of 1108 . 7 copies/ million from AHI patient . Conversely , we recovered an average of 30 . 3 HIV DNA copies/ million rCD4 cells from CHI patients and an average of 21 . 4 copies/ 106 rCD4 cells from AHI patients . We calculated the contribution of HIV DNA in Vδ2 cells and r-CD4 cells to the total HIV-DNA+ PBMC ( Fig 2B ) as follows: First , we calculated the total HIV DNA copies in each cell population by multiplying the average HIV copy number per million cells to the percentage of Vδ2 or r-CD4 cells present in total PBMC . Then , this total HIV copy number was divided by the total HIV copy number in PBMC to obtain the proportion of HIV corresponding to Vδ2or r-CD4 populations . Vδ2 cells contributed 1 . 6% and 8 . 1% to the total HIV DNA copy numbers in PBMC of CHI and AHI patients , respectively . Resting CD4 T cells contributed 4 . 9% to the total HIV DNA copy number in PBMC of CHI patients and 1 . 9% in AHI patients . None of the comparisons between cell types or type of patients were statistically different . We performed viral outgrowth assays using highly purified Vδ2 cells to demonstrate the recovery of replication-competent HIV after , but not prior to , activation of Vδ2 cells from HIV-1 infected patients on fully suppressive ART . As expected , percentages of CD3+Vδ2 cells were lower in patients treated in CHI compared to patients treated in AHI ( mean 0 . 25% vs . 0 . 77%; Table 2 ) . Replication-competent HIV was detected in at least one culture replicate in 14 out of 18 patients ( 78% ) , with no virus recovered in two AHI and two CHI patients . Overall , for AHI patients we assayed 94 cultures of which 21 were positive for HIV p24 , compared to 53 total cultures for CHI patients with virus recovered in 20 ( 22 . 3% vs . 37 . 7%; Table 2 ) . Therefore , replication-competent virus was more frequently recovered from patients treated in CHI than from AHI-treated patients . In parallel to these assays and as part of other projects in the lab , viral outgrowth assays with isolated r-CD4 cells were performed as previously described [21 , 23 , 24] . A summary of the outgrowth assays for r-CD4 cells is included in Table 2 . Our results show that Vδ2 cells constitute a novel reservoir for HIV infection that may persist for years as latent Vδ2 cell infection was detected in CHI patients despite long-term suppressive ART and the lack of intermittent low-level viremia ( “blips” ) during the prior two years of clinical follow-up ( Table 1 ) . In addition , we analyzed two patients ( C . 2 and C . 3 ) one year after their first evaluation , and replication-competent HIV was again recovered from their Vδ2 cells ( S1 Fig ) . An expanded longitudinal analysis is underway to assess the stability of this reservoir . In ten of the 14 patients in whom virus was recovered ( A . 2 , A . 3 , A . 5 , A . 6 , A . 7 , C . 2 , C . 3 , C . 6 , C . 7 and C . 9 ) HIV was detected in cultures of only 5000 cells , suggesting a high frequency of infection within Vδ2 cells ( Fig 3 ) . Next , we calculated the frequency of infection expressed as infectious units per million ( IUPM ) isolated Vδ2 cells in 14 patients with available cell dilutions ( seven AHI and seven CHI ) ( Fig 4A ) . In addition , IUPM r-CD4 cells from the same patients were calculated to compare both cell populations . Percentages of r-CD4 cells , number of cells cultured and total number of positive and total cells assayed are shown in Table 2 . As expected , the confidence interval for Vδ2 cells was much greater than for r-CD4 cells due to the lower number of Vδ2 cells assayed and therefore the estimation of the frequency of infection is less accurate ( Fig 4B ) . However , we could not detect statistical differences when IUPM Vδ2 cells were compared to IUPM r-CD4 cells , suggesting that despite the inaccuracy of the co-culture system , Vδ2 cells may be frequently latently infected . Interestingly , while quantifying the frequency of infection within Vδ2 cells by limiting dilution assay , in some patients we found an unusual pattern of recovery of HIV with more positive wells at lower dilutions and no virus recovered when more cells were cultured ( Fig 3 ) . As γδ T cells possess innate , nonspecific antiviral function [25 , 26] , we hypothesized that an antiviral activity of the uninfected γδ T cells might reduce the recovery of HIV in cultures with high cell inputs , yielding the unusual pattern of viral outgrowth seen in some patients . To test this hypothesis , we performed viral inhibition assays , co-culturing Vδ2 cells from a healthy , uninfected donor , with autologous CD4+ T cells that had been HIV-infected ex vivo at ratios of 1 CD4 cell and 0 . 1 or 0 . 01 γδ cells . Vδ2 T cells inhibited HIV production from infected CD4+ T cells , with increased inhibition seen at higher cell inputs in the co-culture system ( Fig 5 ) . In addition , in some wells we blocked the cytotoxic activity of γδ T cells by pre-incubating the isolated Vδ2 cells with a cocktail of antibodies against CD8 , NKG2D and CD16 ( Fig 5 ) . HIV p24 production was 74 . 6% inhibited in the 1:0 . 1 ratio conditions and 41 . 8% in the 1:0 . 01 conditions . When the cocktail of antibodies was used , these percentages decreased to 26 . 9% and 15 . 5% in the 1:0 . 1 and 1:0 . 01 ratios , respectively . Isolated Vδ2 cells can be infected in vitro , [17 , 18] ( S2 Fig ) despite low or absent surface CD4 receptor expression prior to activation and HIV infection of these cells is inhibited by CD4 blockade [17] , ( S2 Fig ) . We further investigated the CD4-dependence of HIV infection in Vδ2 cells . Total PBMC from uninfected donors were activated with IL-2 alone or IPP and IL-2 , and surface marker expression was analyzed by flow cytometry ( Fig 6A and S3 Fig ) . As expected , CD4 receptor expression was detected on <0 . 3% of Vδ2 cells at day 0 , but became detectable on up to 25% of cells after six days of culture in the presence of IL-2 alone , or IPP and IL-2 . All increases were statistically significant ( p <0 . 01; Fig 6A ) . Interestingly , treatment with exogenous IL-2 alone induced CD4 expression to similar levels as did IPP and IL-2 ( mean 15 . 3% and 15 . 9% , respectively ) . In contrast , none of these conditions significantly increased the surface expression of CCR5 after six days in culture ( Fig 6A ) . In addition , the activation status of Vδ2 cells was also assessed in the same cells by analyzing the expression of the MHC Class II HLA-DR receptor , the IL2 receptor alpha chain CD25 , and the activation marker CD38 ( Fig 6B ) . After six days in culture with IL-2 alone or IPP and IL-2 the expression of these markers was significantly increased ( p <0 . 05 in all cases ) although treatment with IL-2 alone induced activation in no more than 20% of Vδ2 cells ( Fig 6B ) . Based on these results we hypothesized that immune activation , driven in this instance by HIV infection , might upregulate CD4 expression on Vδ2 cells in vivo . To test this hypothesis we measured surface expression of CD4 and CCR5 expression in Vδ2 cells donated by three viremic patients diagnosed during the acute phase of HIV infection , prior to the initiation of ART ( Fig 7 ) . Based on history and diagnostic testing , the estimated date of infection in these patients was less than 23 days prior to sampling . Likely related to the pathological immune activation of acute HIV infection , 9 . 5% , 15 . 6% and 15 . 9% of Vδ2 cells expressed CD4 ( Fig 7A ) , as compared to <0 . 3% of Vδ2 cells in healthy donors . In addition , we also analyzed the percentage of Vδ2 cells that coexpressed CD4 and CCR5 ( Fig 7B ) . This observation in these unique patients supports our hypothesis that pathological immune activation in early HIV infection promotes the upregulation of CD4 expression in Vδ2 cells , making them targets for HIV infection in vivo .
We have discovered that peripheral resting Vγ9Vδ2 cells can act as a cellular reservoir of persistent , latent HIV infection . Using a gold-standard coculture assay that defines the presence of latent infection , we find that the frequency of replication-competent virus in these cells is substantial , with virus recovered in as few as 5000 Vδ2 T cells that lack activation markers . This unexpected finding is supported by the detection of HIV DNA within this cell population , implying that at least a fraction of the HIV DNA detected within Vδ2 cells represents replication-competent virus . We propose a mechanism to explain the infection of Vδ2 cells despite the absence of CD4 expression in their surface . As isolated Vδ2 cells can be infected in vitro we hypothesize that the activated immune environment during untreated HIV infection induces transient CD4 upregulation , rendering Vδ2 cells permissive for HIV-1 infection , and founding a substantial population of latently infected γδ cells . This hypothesis is supported in vivo by the detection of expression of CD4 and CCR5 in Vδ2 cells in untreated patients in early acute HIV infection . Latent infection of γδ cells could occur at other times as well , when cellular activation results in upregulation of CD4 expression . Recovery of purified Vδ2 cells was reduced in patients treated in CHI , compared to patients treated in AHI , as there is a dramatic loss of Vδ2 cells early after infection with HIV [14 , 15 , 18] . Such depletion is only partially reversed by ART [27] . Remarkably , replication-competent HIV was quantified in purified Vδ2 cells in 77% of patients who had been treated and suppressed for a median of nearly four years . Although γδ T cells represent only a small fraction of the total CD3+ T lymphocytes , frequency of infection within isolated Vδ2 cells was not statistically different from that in r-CD4 cells . However , it is important to highlight that this estimation is not as accurate as the estimation for r-CD4 cells , as reflected by the 95% confidence intervals , because a significantly lower number of cells were assayed . It is also important to note that maximum activation conditions were differently assayed for both cell populations . Our results may overestimate the frequency of infection in γδ cells compared to r-CD4 cell calculations , as viral outgrowth starts during the first 24 hours of incubation , when γδ cells can spread the virus to the γδ-CD8-depleted-PBMC , required for Vδ2 cell activation . In addition , our data suggest the possibility that the ratio of defective to replication-competent proviruses might be lower in Vδ2 cells although future comprehensive sequence analysis will be required to fully address this possibility . A relative deficiency of restriction factors such as APOBEC3G/3F [28 , 29] in γδ T cells might explain these findings . In addition , a recent study has shown that HIV DNA might be present in cells other than conventional CD4+ T cells and myeloid cells suggesting that HIV may persist in other cell types [30] . As γδ cells can be phenotypically classified using CD45RA and CD27 markers , it is possible that in some prior studies these cells may have been included in the evaluation of HIV infection within the latently infected resting memory cell population . Interestingly , our results and others [22] have shown that in some patients , HIV DNA levels within r-CD4 cells are lower than in total PBMC , suggesting that in those patients , other cell types might have a high contribution to total HIV DNA measurements . In addition , activated T cells have also been reported to have a greater HIV DNA contribution than r-CD4 cells [23 , 24] . Although we found higher levels of HIV DNA within isolated γδ T cells , the rarity of these cells within PBMC makes them to less frequent contributors to the total reservoir than r-CD4 cells . Of note , the custom antibody cocktail used to isolate r-CD4 cells does not completely deplete the γδ T cells and therefore comparisons between r-CD4 cells and γδ T cells are not totally accurate . We have begun studies to evaluate the exact contribution of γδ T cells in assays using total r-CD4 cells . Although we recovered HIV from low numbers of γδ T cells in 14 of 18 patients , in several patients the recovery of replication-competent virus was less than expected in cultures with higher Vδ2 cell input . In these cases , estimates of the frequency of infection cannot be made with these data as such estimates depend on the assumption of a normal distribution of infection . We speculate that in these cultures , sufficient Vδ2 cells were present to exert potent antiviral activity [25 , 26] , leading to inhibition of spread of the virus during the outgrowth phase of the assay . In support of this , we demonstrate a dose-dependent inhibition of the p24 production in culture when isolated in vitro-HIV-infected CD4 cells were cocultured with Vδ2 cells . We also show that blocking CD16 , NKG2D and CD8 receptors , Vδ2 cell cytotoxic capacity is partially inhibited suggesting that other receptors are also involved in exerting this function . HIV can infect isolated γδ T cells in vitro [17 , 18] . In this study , we have shown that this occurs through a mechanism that involves the transient upregulation of the CD4 receptor after activation . CD4 expression is upregulated in Vδ2 cells in vitro following activation with IPP and IL-2 . This transient upregulation of CD4 has also been reported after infection of γδ T cells with human herpesvirus [31] . Treatment with HMBPP and IL-2 also induced the expression of CD4 . Although the vast majority of Vδ2 lymphocytes do not express CD4 , in the setting of lymphopenia , rapid T-cell turnover , or heightened immune activation , increased IL-2 levels could lead to CD4 upregulation in Vδ2 cells , making them susceptible to HIV infection in vivo . We directly observed this phenomenon prior to ART in viremic , newly infected patients , suggesting that this mechanism is plausible in vivo . Therefore , in addition to a previously reported indirect mechanism to explain peripheral Vδ2 cell depletion [16] , we describe a potential direct effect of HIV infection on Vδ2 cell depletion . Direct infection of Vδ2 cells by HIV could lead to depletion of most Vδ2 , while others might survive and establish latent infection . Infection of Vδ2 cells can have significant consequences , as these cells constitute an important bridge between the innate and adaptive immune response [32] . Therefore , due to reduced Vδ2 cell signaling , dendritic cell function [33–36] , follicular B cell help , or CD4+ T cell responses [37] might be impaired . In summary , our results demonstrate that Vδ2 cells are a novel latent reservoir for replication-competent HIV . Although these T lymphocytes are generally rare , the frequency of latent infection in these cells makes it likely that they contribute measurably to the total burden of latent , quiescent HIV infection . Moreover , we demonstrate that isolated Vδ2 cells can be infected in vitro , and illustrate a mechanism that could allow γδ T cell infection despite constitutive low expression of the CD4 receptor . We found that activation of Vδ2 cells upregulates CD4 expression , enabling HIV infection . The durability of latent infection within this novel cell population must still be established in longitudinal studies . However , given the broad efforts to discover reagents that disrupt latency in resting CD4 central memory cells as a first step towards viral eradication therapies , it may be necessary to also address the responsiveness of proviral HIV genomes within resting γδ T cells to such strategies .
All patients provided written informed consent , and studies were approved by the UNC Institutional Review Board . Our criteria to define and select patients treated in acute HIV infection ( AHI ) have previously been reported [38 , 39] . Briefly , patients identified in AHI ( plasma HIV RNA positive and HIV Western blot negative ) were enrolled and initiated ART within 45 days of the estimated date of infection . Serial measurements of plasma viremia and CD4+ T cell count were performed , and when patients were aviremic ( <50 HIV RNA copies/ml ) on ART for >6 months , cells were obtained by continuous-flow leukapheresis . Patients studied after initiation of ART in chronic HIV infection ( CHI ) had a history of stable , successful treatment , and plasma HIV-1 RNA levels <50 copies/mL for >2 years without blips . Buffy coats from uninfected donor volunteers were obtained from the New York Blood Center ( Long Island City , NY , USA ) . As part of the routine preparation for the outgrowth assay , prior to γδ T cell isolation , freshly isolated patients’ PBMC were incubated with raltegravir and abacavir for 24 hours to avoid any potential de novo infection due to HIV reactivation . For infectivity assays , isolated γδ T cells from fresh PBMC from healthy non-HIV infected were used . γδ T cells were first enriched by negative selection using a commercially available cocktail containing monoclonal antibodies ( mAb ) directed against non-γδ cells , including antibodies against granulocytes , red blood cells , dendritic cells , pan-αβ T cells , NK cells , stem cells , monocytes ( StemCell Technologies , Vancouver , Canada ) and afterwards the cells were isolated by fluorescent activated cell sorting ( FACS ) using a Reflection sorter ( iCyt , Champagne , IL , USA ) or a FACSAria II ( BD ) . Each sorting experiment was validated performing instrument quality controls , and running isotype controls and fluorescence minus one control . In addition , gating strategy for each specific experiment was based on the selection of the target cell by CD3 ( clone SK7 , BD ) and Vδ2 ( clone B6 , Biolegend , San Diego , CA ) mAbs , and exclusion of potential contamination of CD4+ T cells in a third channel using the CD4 mAb ( clone RPA-T4 , BD ) , and in some experiments αβTCR mAb ( clone MOPC-21 , Biolegend ) . To identify singlets we performed a 2-step doublet discrimination using HxW of the pulse; first in the SSC direction then in the FSC direction . In all our preparations the singlets were always well separated from the doublets and no contamination with αβ T cells or CD4+ T cells was detected in the presort sample ( Fig 1 ) . In addition , HLA-DR mAb ( clone TU36 , BD ) was used to exclude potential pre-activated γδ T cells , and fixable aqua ( Life Technologies , Grand Island , NY ) was used to exclude dead cells . Cells were collected in RPMI-1640 containing stable Glutamine and Hepes ( Gibco , Life Technologies , Grand Island , NY ) , 10% pooled human AB serum ( Sigma-Aldrich , St Louis , MO ) and 10% PenStrep ( Sigma-Aldrich ) ( hereafter referred to as γδ medium ) . After isolation , an aliquot was used to analyze the purity of the sorted population ( > 99% γδ T cells , < 1% all other cells ) ( Fig 1 ) . After sorting , Vδ2 T cells were centrifuged and resuspended in the suitable volume of γδ medium to perform the standardized co-culture assay [20 , 21] . As part of the standard method , cells were plated in limiting dilution , when possible , and activated with 100nM HMBPP ( kindly provided by Dr . H . Jomaa , Justus-Liebig University , Giessen , Germany ) or 1μM IPP ( Sigma ) and 100U/mL IL-2 ( Peprotech , Rocky Hill , CT ) for 24 hours . In addition , as γδ T cell activation requires the presence of APC [40] but γδ T cells and CD8+ T cells possess anti-HIV activity [26] , allogeneic non-HIV infected PBMC used as APC were first depleted of γδ T cells and CD8+ T cells , added to cultures at a 1:4 ratio ( isolated γδ T cells: PBMC depleted ) , and incubated for 24 hours at 37°C and 5% CO2 . Vδ2 cells were then washed and resuspended in γδ medium containing 10U/mL IL-2 and cocultured with allogeneic PHA-activated PBMC depleted of CD8+ T cells as target cells . CD8+ T cells were depleted by negative magnetic isolation following manufacturer’s instructions ( StemCell Technologies ) . Complete medium containing 10U/mL IL-2 was replaced and refreshed every three or four days . Supernatants were stored at -80°C for analysis of viral p24 production by ELISA ( ABLinc , Rockville , MA , USA ) at days 15 , and 19 . As a control Vδ2 cells were cultured without activation prior to the addition of target cells in 5U/mL IL-2 , following the same coculture protocol , which were uniformly negative . Quantitative viral outgrowth assay for r-CD4 cells was performed in parallel using the standard procedure previously reported in our lab [21 , 23 , 24] and described above also for γδ cells . However , activation procedures were different between both cell populations . Briefly , magnetically isolated r-CD4 cells were activated with 2μg/mL PHA , 60U/mL IL-2 and irradiated PBMC from a non-infected donor , and target cells were added as previously reported and as explained above . Infectious units per million Vδ2 and r-CD4 cells ( IUPM ) were calculated using the R software developed at the University of North Carolina that allows calculating the point estimate of the frequency of infection and the 95% confidence interval . Total HIV pol DNA copies within isolated Vδ2 cells , unfractionated PBMC and resting CD4+ T cells were quantified by droplet digital PCR ( ddPCR ) using primers , probes and conditions previously reported [41] . Briefly , DNA was extracted from frozen cell pellets of 1x105 Vδ2 cells and 1x106 PBMC and r-CD4 cells on average , using the Qiagen DNeasy Blood and Tissue kit ( Qiagen , Maryland , USA ) and concentration of DNA was measured using the Nanodrop ( Thermo Scientific ) . PCR reactions were loaded into the Bio-Rad QX-100 droplet generator . Each reaction consisted of a 20μL mix containing 10 μL ddPCR Probe Supermix , 900 nM primers , 250 nM probe , and template DNA . Following amplification in a standard thermo cycler ( 10 min . at 95°C , 40 cycles of 30 sec . at 94°C , 60 sec . at 58°C and final 10 min . at 98°C ) droplets were immediately analyzed as positive or negative using the Bio-Rad QX-100 droplet reader . The no template controls were used to set the threshold , and copy number was calculated using the manufacturer’s software and normalized to the total number of cells ( Bio-Rad QuantaSoft v . 1 . 2 ) . As a control , dilutions of DNA from J89 cells , which contain a single copy of HIV , was used in all runs . In addition , to control for the different number of cells assayed , we run independent assays to compare HIV DNA levels in PBMC samples of 1x105 cells and 1x106 cells , which showed similar copy number . Samples were run in triplicate and the limit of quantitation ( LOQ ) was calculated based upon the frequency of false positives in the no template control . We calculated the contribution of HIV DNA in Vδ2 cells and r-CD4 cells to the total HIV-DNA+ PBMC as follows: First , we calculated the total HIV DNA copies in each cell population by multiplying the average HIV copy number per million cells to the percentage of Vδ2 or r-CD4 cells present in total PBMC . Then , this total HIV copy number was divided by the total HIV copy number in PBMC to obtain the proportion of HIV corresponding to Vδ2or r-CD4 populations . Vδ2 cells contributed 1 . 6% and 8 . 1% to the total HIV DNA copy numbers in PBMC of CHI and AHI patients , respectively . Resting CD4 T cells contributed 4 . 9% to the total HIV DNA copy number in PBMC of CHI patients and 1 . 9% in AHI patients . None of the comparisons between cell types or type of patients were statistically different . Freshly isolated Vδ2 cells from HIV-negative volunteers were activated using 100nM HMBPP and 100U/mL IL-2 for 24 hours and spinoculated for 2 hours with 1ng/mL of the CCR5 JR-CSF strain . Cells were then washed twice to remove the excess of virus , cultured in triplicate in γδ medium containing 20U/mL of IL-2 for 7 days and supernatants were analyzed for HIV p24 production on days 4 and 7 . As a control for the infection conditions , isolated CD4+ T cells from the same uninfected blood donor volunteer were stimulated in parallel using 2μg/mL PHA and 60U/mL IL-2 . Supernatants from day 0 ( basal levels after virus exposure ) were also stored and measured as a negative control . In some wells , Vδ2 cells and CD4 cells were incubated with 50μg/mL of an anti-CD4 mAb ( clone RPA-T4 , BD ) before exposure to HIV . Experiment was repeated using three different donors . Vδ2 and CD4+ T cells were FACS-sorted from PBMC of healthy donor volunteers . CD4 cells were activated with 2μg/mL PHA and 60U/mL IL-2 for 24h washed and infected by spinoculation with the viral strain JR-CSF following the same protocol described above , and co-cultured in triplicate at different ratios of autologous Vδ2 cells ( 1:0 . 1 and 1:0 . 01 CD4:Vδ2 ) . In some wells cytotoxic activity of γδ cells was blocked using a mixture of purified mAb against CD8 and CD16 from Biolegend , and NKG2D from Miltenyi Biotec . Media was refreshed at days 4 and 7 and supernatants stored until p24 ELISA quantification ( ABLinc , Rockville , MA , USA ) . Experiments were performed in three different donors . PBMC from healthy donor volunteers isolated from fresh buffy coats were incubated with 1μM IPP and 100U/mL IL-2 or 100U/mL IL-2 alone . Expression of CD4 and CCR5 receptors along with expression of activation markers HLA-DR , CD25 and CD38 , was controlled by flow cytometry at days 0 and six of culture . mAb used were; CD4-FITC ( clone RPTA-4 , BD ) , Vδ2-PE ( clone B6 , Biolegend ) , CCR5-V450 ( clone 2D7/CCR5 , BD ) , HLA-DR-FITC ( clone TU36 , BD ) , CD25-V450 ( clone M-A251 , BD ) and CD38-PerCPCy5 . 5 ( clone HIT2 , BD ) . Briefly , cells were blocked with FBS ( Sigma ) for 10 minutes on ice , resuspended in staining buffer ( PBS-2% FBS ) , incubated for 20 minutes on ice in the dark with combinations of the mAb , or suitable isotype controls , and washed twice . Cells were then fixed with 2% paraformaldehyde solution and analyzed in the Attune acoustic cytometer ( Applied Biosystems ) . Differences between patients treated in AHI and patients treated in CHI were compared using the two-tailed Mann-Whitney U-test and comparisons between mean values were performed by the two-tailed Student t-test . Statistical Analyses were performed using the IBM-SPSS version 21 . 0 ( Chicago , Illinois , USA ) and p values <0 . 05 were considered statistically significant . | Antiretroviral therapy ( ART ) has led to a decreased HIV-related morbidity and mortality across the world . While successful ART restores health , it does not cure infection as latent HIV-1 remains integrated within different cell populations , unaffected by ART . To date resting memory CD4+ T cells are the best-characterized cellular reservoir . However , eradication of HIV-1 infection requires the description of all latent cellular reservoirs harboring replication-competent HIV-1 . We describe the discovery of an unexpected cellular reservoir within γδ T lymphocytes . This novel reservoir must be considered as strategies to clear latent HIV are developed and tested . |
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In most species , crossovers ( COs ) are essential for the accurate segregation of homologous chromosomes at the first meiotic division . Their number and location are tightly regulated . Here , we report a detailed , genome-wide characterization of the rate and localization of COs in Arabidopsis thaliana , in male and female meiosis . We observed dramatic differences between male and female meiosis which included: ( i ) genetic map length; 575 cM versus 332 cM respectively; ( ii ) CO distribution patterns: male CO rates were very high at both ends of each chromosome , whereas female CO rates were very low; ( iii ) correlations between CO rates and various chromosome features: female CO rates correlated strongly and negatively with GC content and gene density but positively with transposable elements ( TEs ) density , whereas male CO rates correlated positively with the CpG ratio . However , except for CpG , the correlations could be explained by the unequal repartition of these sequences along the Arabidopsis chromosome . For both male and female meiosis , the number of COs per chromosome correlates with chromosome size expressed either in base pairs or as synaptonemal complex length . Finally , we show that interference modulates the CO distribution both in male and female meiosis .
Crossovers ( COs ) are recombination events characterized by a reciprocal exchange of genetic material . In most eukaryotes , they are essential for the segregation of homologous chromosomes at the first meiotic division . When CO formation or localization is impaired , aneuploid gametes are formed [1] leading to sterility , embryo-lethality or developmental problems . The number of COs per chromosome and per meiosis is tightly controlled . Firstly , in most species , there is a need for one obligatory CO per pair of homologous chromosomes . Secondly , interference ( a lower frequency of close-by COs than expected if they were to occur independently [2] ) has been shown to play a role in controlling the number of COs . The mechanism that mediates interference is still poorly understood . However , in the past few years , powerful approaches to quantify interference have been developed and applied to a number of organisms like Arabidopsis thaliana [3] , Human [4] , mouse [5] or maize [6] . The most used approaches involve the “counting” [7] and the “gamma” [8] models which parametrize the distribution of distances between successive crossovers on the bivalent . These models also give predictions for crossover patterns in gametes , and thus have been used to measure interference strength from genetic segregation data [8] . Thirdly , the distribution of COs along chromosomes is not homogeneous . In all species , the CO rate drops in centromeric regions with estimates between 5 to more than 200 fold depending on the organism [9] . COs are also rare in heterochromatic regions but the centromeric effect has been decoupled from the heterochromatic effect [10] . GC content was shown to positively correlate with the CO rate in many species such as rat , mice , human , zebra finch , honeybee and maize , even at a broad scale [11]–[14] . The underlying mechanisms responsible for this correlation are still under discussion ( see [15] , [16] , discussion ) . In contrast , we reported that in A thaliana , the variation in CO rate of a male-female averaged map was negatively correlated to GC content [17] . Variations in CO rates also correlate with several other genomic features such as transposable elements ( TE ) density , the CpG ratio , gene density , nucleotide polymorphisms or chromosomal architecture properties like distance to telomeres or centromeres [11] , [18]–[21] . Nevertheless , none of these other characteristics are systematically correlated with CO rate variation across every species . Thus , what causes variation in CO rates along chromosomes is still poorly understood . The various features that correlate with this non-homogeneity in CO rates may have causal relationships or may be only incidentally related . CO rates and distribution can vary between male and female meiosis in the same species ( reviewed in [22] ) . Haldane suggested that the heterogametic sex has a lower CO rate as a consequence of selection against recombination between the sex chromosomes [23] . However , this hypothesis , referred to as the Haldane and Huxley rule has been since called into question: less recombination in the homogametic sex than the heterogametic sex has been observed in some species and heterochiasmy ( different crossover rates in male and female meiosis ) has been found without the presence of sex chromosomes in plants such as Allium [24] , Brassica oleracea [25] and A . thaliana [26] , [27] or animals like the saltwater crocodile [28] . Other hypotheses have been proposed ( reviewed in [29] ) but none satisfactorily explain the variations in heterochiasmy in all species . Strikingly , a correlation was reported between CO number per chromosome and the total length of synaptonemal complex ( SC ) ( a proteinaceous structure that links homologous chromosomes at the pachytene stage of meiosis I [30] ) . Several studies have shown that CO number and SC length vary coordinately , even in situations where DNA length is constant [31] . For example , in human meiosis , males have about half the CO number and total SC length compared to females [32] . This correlation was also reported in male and female meiocytes in Dendrocoelum lacteum [33] and zebrafish [34] , and in many other species with various individuals of the same population [35]–[37] . The reasons for this correlation are still poorly understood . A . thaliana has a comparatively small genome estimated to be between 125 and 157 Mb at the haploid stage [38] , [39] . DNA is distributed on 5 pairs of chromosomes . Chromosomes 2 and 4 are acrocentric and carry on the telomeric half of their short arm several hundreds of copies of rDNA 18S , 5 . 8S and 25S constituting the Nucleolar Organizer Regions ( NORs ) [38] . Thus , including the NOR , their size is approximately between 22 and 25 Mb . Chromosomes 1 , 3 and 5 , are metacentric . Their sizes vary from 19 . 7 to 30 . 4 Mb ( http://www . ncbi . nlm . nih . gov/mapview/ ) . A few years ago , we published detailed genetic maps of A . thaliana chromosome 4 [17] , [27] . The first map was built by genotyping progeny obtained after self-fertilization of an F1 between the two accessions Columbia and Landsberg erecta . It determined the sex-averaged distribution of COs along chromosome 4 . This sex-averaged distribution pattern was found to be highly non-homogeneous with successive regions of high and low CO rates . Regions with significantly higher CO rates had a high CpG ratio and low GC content . For the second map , the same F1 Columbia x Landsberg erecta was used either as male or a female in a backcross with Columbia . By genotyping the progeny of these two crosses on chromosome 4 , we demonstrated that male and female CO rates were dramatically different , with a male/female ratio of 1 . 64 . Positive interference was also found both in male and female meiosis . The CO distribution contrasted too between male and female meiosis , with very high male CO rates at both ends of the maps while at the same locations female CO rates were either average or below average . A similar ratio between male and female meiotic CO rates was reported recently using the same parental accessions but the limited number of meioses studied ( 137 female and 92 male ) did not allow precise comparison of the CO distribution between male and female meiosis [40] . To determine if the CO landscape found in male and female meiosis was peculiar to chromosome 4 , we decided to perform the analysis of CO rates and distributions along all five chromosomes of A . thaliana . Moreover , we strengthened the analysis by investigating the correlations between CO rates and several genomic features genome wide in both male and female meiosis separately . Finally , we performed a quantitative analysis of the interference strength using the gamma model .
For the five chromosomes , the average frequencies of the parental alleles at each marker locus were examined . In the male population we found regions of the genome with a significant segregation distortion at p-values less than 0 . 01 , i . e , regions where the observed genotypic frequencies departed from the 1∶1 ratio predicted if no selection bias occurred during the generation of populations ( Figure 1 ) . No significant departure from normal ratios was detected in the female population . Thus all the observed cases of segregation bias are likely to be linked to a problem in the male gametophyte . The strongest segregation distortions were detected on chromosome 1 with values up to 1 . 49∶1 ( Ler:Col ) and 2 . 70∶1 ( Col:Ler ) at position 7 , 267 , 270 and 26 , 188 , 466 respectively ( Figure 1 ) . This is consistent with the hypothesis of two genes under selection , with a preference for the Ler allele at the first position and Col at the second position . The segregation bias affects the estimates of recombination rates , in particular for markers located between the two selected genes . To calculate this effect , we estimated the relative fitness of the Col:Ler alleles , 0 . 67∶1 at position 7 , 267 , 270 and 1∶0 . 37 at position 26 , 188 , 466 ( see Materials and Methods ) . We then determined the CO rates for the 60 intervals between the two markers , correcting for the bias produced by the selection . We found that instead of the 78 cM estimated with our genetic map between the two selected markers , the corrected distance was 95 cM . Thus for the whole chromosome 1 male , instead of 142 cM , the corrected length is 159 cM . On the other chromosomes , the segregation distortion was too small to have a significant impact on CO rates , distributions or correlation analyses . We obtained 13 , 535 COs in our two populations , 8 , 532 and 5 , 003 in male and female meiosis respectively . This difference was highly significant ( chi2 test , p = 1 . 2 e-202 ) . On average , there were 11 . 15 and 6 . 6 COs per male and female meiocyte respectively ( Table 1 ) . Thus , the genetic map length for male meiosis was 575 cM ( 1 cM per 209 kb on average ) and 332 cM ( 1 cM per 361 kb on average ) for female meiosis . The global male to female CO ratio was 1 . 73 . This ratio was similar to the ratio of male to female total SC length ( 1 . 69 ) obtained in the same genetic background [27] . We then compared male and female CO rates at the level of the bivalent ( pair of homologous chromosomes ) ( Table 1 ) . For male meiosis , the mean number of COs per cell varied between 1 . 7 for chromosome 4 bivalent ( the smallest ) to 3 . 2 COs for chromosome 1 bivalent ( the longest ) . In female meiosis , fewer COs were found per chromosome with 1 . 1 on chromosome 4 bivalent up to 1 . 6 on chromosome 1 bivalent . For both male and female meiosis , a linear correlation was observed between the size of chromosomes in Mb and the average number of COs per chromosome ( Figure 2 ) ( R2 = 0 . 98 in M and in F ) but with a different slope . For male meiosis , we also analyzed the correlation between the number of COs per chromosome and the size of the SC in µM for each chromosome obtained in two different studies [41] , [42] . We again obtained a linear correlation ( Figure 2 ) . This is expected given the close proportionality between SC length in µM and physical length in Mb for male meiosis ( R2>0 . 999 , data not shown ) . In conclusion , the five chromosomes undergo more COs in male meiosis than in female meiosis and this difference becomes more substantial when the physical length of the chromosome is greater . We looked at the distributions of CO numbers per chromosomes in male and female populations ( Figure 3 , Table S4 ) . In the hypothesis of non-interfering COs , their numbers per chromosome are distributed according to a Poisson law of mean given by the genetic length . We thus calculated the theoretical distribution for each chromosome using the measured average number of COs . As readily seen in Figure 3 , for all five chromosomes , both in male and female meiosis , the observed and the expected ( Poisson ) distributions show clear differences ( all p-values <10−19 ) . In all cases , there is a deficit in plants with no CO and an excess of plants with one CO compared to the Poisson distribution . As an illustration , in the female population , only 8 . 6% of plants had more than one CO whereas 14 . 4 were expected in the absence of interference . This effect was particularly obvious on the small chromosomes 2 and 4 where only 4 . 4% and 3 . 9% of plants had multiple COs while 12% and 10 . 6% were expected respectively . Thus we observed a decrease of events with no or many COs , and excess of events with one CO which reduces the variance of CO number per chromosome , as predicted as a consequence of interference ( see Discussion ) . Interference reduces the variance in the number of crossovers but also the variance in the distance between adjacent crossovers . Thus we measured the interference intensity by fitting the gamma model to estimate its parameter nu ( 95% confidence intervals indicated in brackets ) . In male meiosis , for the successive five chromosomes , we have ( starting from chromosome 1 to 5 ) 2 . 6 [2 . 4–2 . 9] , 2 . 5 [2 . 2–2 . 8] , 2 . 5 [2 . 2–2 . 7] , 3 . 5 [3 . 1–4 . 0] , and 3 . 0 [2 . 7–3 . 3] . Similarly , in female meiosis , we have 2 . 7 [2 . 4–3 . 1] , 2 . 8 [2 . 4–3 . 3] , 2 . 6 [2 . 2–3 . 0] , 4 . 1 [3 . 3–4 . 9] , and 3 . 5 [3 . 0–4 . 0] . In all cases , the hypothesis of no interference , corresponding to nu = 1 , is excluded . In the male population , the CO rate per interval varied from 0 to 30 cM/Mb and in the female population from 0 to 12 cM/Mb . Strikingly , visual examination of the graphs suggested that the regions with the most contrast between CO distribution in male and female meiosis were the terminal regions of the chromosomes ( Figure 4 ) . To analyze these differences in more detail , in the male and female populations , we compared the CO rate of each interval for the five chromosomes to the "mean" CO rate of each chromosome arm ( excluding the centromeric heterochromatic regions , see Materials and Methods , Table S2 , Figure 4 ) . For each chromosome , both in male and female meiosis , we observed a number of "hot" ( 40 and 32 in male and female populations respectively ) and "cold" ( 80 and 73 in male and female populations respectively ) intervals ( Table S2 ) . ( An interval was considered to be "hot" or "cold" when the 95% confidence of its CO rate did not contain the mean CO rate of the considered arm ( Table S2; see Materials and Methods ) ) . Indeed , in the male population , 27/40 of the "hot" intervals were located in the telomeric third of the arms of the chromosomes and the remaining ones were mainly localized in the pericentromeric area . Conversely in the female population , only three out of the 32 "hot" intervals were located in the distal third of the chromosomes while most of the others were pericentromeric . For the "cold" intervals , the proportions were inversed with 15/65 in male meiosis located in the distal area and 58/73 in female meiosis . Interestingly , only two "hot" intervals were shared while 27 "cold" were common between male and female populations . In a pairwise comparison , 46 intervals were significantly different between male and female populations ( see Materials and Methods , p<0 . 05 ) . Not surprisingly , the vast majority of these intervals ( 43/46 ) were located at the ends of the chromosomes ( Figure 4 ) . This led us to ask if the observed differences in global CO rates per chromosome between male and female meiosis were only due to the intervals at chromosomal ends . We thus compared the male and female genetic length of each chromosome when removing intervals belonging to the ends . Explicitly , we considered two cases , the first where 30% of the physical length was removed ( thus 15% of the total length on each end ) , hereafter referred to as “−30%” , and the second where 50% was removed , hereafter referred to as “−50%” . Genetic intervals overlapping these truncated regions were entirely removed . Map lengths were computed by counting recombination events using all markers rather than only adjacent markers , to overcome the limitations coming from missing data . We did not include the two small chromosomes 2 and 4 in this analysis because of their peculiar structure: the terminal end of their short arm consists of several megabases ( 3 to 6 ) of the sequences of the nucleolar region ( NOR ) for which we do not have markers . Thus we could not look at the effect of the chromosomal end on CO rates on these two chromosomes . By taking away the genetic intervals corresponding to 30% of the physical length , we eliminated 30 of the 32 intervals with significant different CO rates , whereas taking away 50% of the physical length kept only 1 significant interval , on chromosome 3 . We analyzed the effect of the truncations on the distribution of chromosomes with 0 , 1 , and 2 or more COs . We found that the observed fraction lost is higher than expected for individuals with 2 or more COs and less for those with one CO ( Table S5 ) . Thus , the truncation indeed penalizes more severely the individuals with many rather than few COs . The deviations from the expectation are modest , and they are also unsurprising since there is positive interference , so for instance individuals with 2 COs have these COs more frequently in the extremities . On all three chromosomes 1 , 3 , and 5 , and for both truncations ( “−30%” or “−50%” ) , the male genetic map remained longer than the female one . The male to female ratio decreased as the number of intervals kept in the analyses was reduced , for instance in the case of chromosome 1 from 1 . 75 ( all intervals , ) to 1 . 38 ( “−30%” ) and to 1 . 33 ( “−50%” ) . The other chromosomes showed the same trend ( Table 2 ) . In spite of this trend , the male/female differences remained highly significant ( p-value <10−8 ) . Thus , even though an important part of the differences between male and female genetic maps is due to the intervals at the sub-telomeric ends , chromosomes recombine more in the central part in male meiosis than in female meiosis ( Table 2 ) . Seven out of eight of the intervals found to be significantly different between male and female on chromosome 4 in our previous study [27] were also retrieved in this study . The eighth interval was the least significantly different in the previous study and was borderline in this study . Our CO map thus looks robust in this genetic background . In conclusion , during both male and female meiosis the CO distribution is not homogeneous along the chromosomes and these distributions exhibit very contrasting patterns between the male and female populations . Moreover , even if the telomeric regions , which showed the greatest contrast , are removed , the lengths of the remaining genetic maps are still significantly different between male and female meiosis . In a previous study , we reported that high CO rates in a sex-averaged F2 population correlated positively with the CpG ratio but negatively with the GC content [17] . Simple repeats only gave a weak positive correlation and all the other parameters tested ( TE density , gene density , pseudogene density ) did not show a correlation . We repeated similar analyses with our separate male and female CO maps here . Strong correlations ( p-value <10−3 ) were found in the female population . CO rates for all chromosomes correlated negatively ( chromosomes 1 , 2 , 3 , and 5 strongly and chromosome 4 weakly [10−2< p-value <10−3] ) with GC content and gene density ( Figure 5 , Figure S1 ) . For TEs , the correlation was the other way around: positive and strong for chromosomes 1 , 2 and 3 , weak for chromosomes 4 and 5 . It has to be noted that the R values found were in the same range than those published in sex averaged studies for human , honey bee or zebra finch [11]–[13] , [43] . No significant correlations were found for any of the five chromosomes in the male population of Arabidopsis for these three parameters . On the other hand , in the male population , for chromosomes 1 and 5 , recombination rates correlated strongly ( weakly for chromosome 3 ) and positively with the CpG ratio , while in the female population , only chromosome 1 correlated weakly ( Figure 5 ) . We tested if these differences in the strength of the correlation were mainly due to the telomeric intervals . We reanalyzed the correlations on chromosome 1 , 3 and 5 in the “−30%” and “−50%” cases , as was done in the comparison of the size of the genetic maps in male and female meiosis ( see above ) . In the “−30%” case , clearly , in female meiosis , the strength of all the correlations between CO rates and GC% , gene density and TEs were weakened . Moreover , they all disappeared in the “−50%” case ( Figure 5; Figure S1 ) . A contrario , no change was observed on the male side . These results prompted us to look at the GC content , the genes and the TE density along the arms of chromosomes . In fact , these three features exhibit a significant gradient , negative for TEs and positive for genes and GC% from the centromeric to the telomeric end for all chromosome arms except the short arms of chromosomes 2 and 4 ( correlation p-values <10−4; Figure S1 ) . Thus , the correlations between GC% , gene and TE densities and the recombination rate in female meiosis , and the fact that this correlation disappeared when the telomeric intervals were removed from the analysis , could be mainly due to the distribution of these features along the chromosomes . A similar analysis was conducted with CpG ratio . Surprisingly , the weak correlation found in female meiosis on chromosome 1 strengthened in the “−30%” and “−50%” cases but , in male meiosis , all the correlations disappeared ( Figure 5; Figure S1 ) . There is no significant variation in CpG distribution along the chromosomes and thus the weakening of the correlation cannot be attributed to the architecture of this feature along the chromosomes ( Figure S1 ) . Finally , no correlation was found between recombination and either coding GC , GC1 , GC2 , or GC3 ( G or C in position 1 , 2 or 3 of a codon ) in both male and female meiosis ( Figure S1; Table S3 ) .
In male meiosis , the mean number of COs per chromosome varies linearly with the length of the SC . Moreover , we found that the ratio of the male vs female genetic map length is comparable to the ratio of the total length of the SC in the same genetic background in male and female meiosis ( 1 . 69; [27] ) . CO rates and SC length have been shown to co-vary in several species including human , mice , Drosophila , and zebrafish ( reviewed in [31] ) . The exact nature of this relationship remains unknown but recent data gave new insight into our understanding of this observation . In C . elegans , a mutation in a gene coding for a subunit of condensin modifies both the length of the SC and the CO rate [44] . Note that the length of the axes is modified even in the absence of the DNA double strand breaks that initiate meiotic recombination . Hence , it is tempting to suggest that the length of the SC determines the number of COs . However , in various species , it has been reported that , proportionality between genetic and SC length was generally observed for long but not for short chromosomes . In various species such as in yeast , dog , mouse , or pigeon , small chromosomes often have a higher density of COs [45]–[48] . It has been hypothesized that this observation reflects the rule of the "obligatory" CO where one CO must occur per pair of homologous chromosomes to ensure their proper segregation at the first meiotic division [5] , [49] , [50] . In mammals , it has been found that the number of chromosome arms is a better predictor of CO numbers suggesting that , especially for metacentric chromosomes , one CO per chromosome may not be sufficient for the correct segregation of homologous chromosomes [51] , [52] . However , two different studies suggest that the model “at least 1 CO per chromosome” rather than per arm has a better fit with human data [53] , [54] . We did not observe a higher density of COs on short compared to long chromosomes in A . thaliana . However , there is not much size difference between Arabidopsis chromosomes ( 30 . 4 Mb for the longest and 18 . 6 Mb for the smallest ) compared to other organisms where large differences have been observed such as mice ( 197 Mb and 61 Mb ) or S . cerevisiae ( 1 , 5 Mb and 320 kb ) ( http://www . ncbi . nlm . nih . gov/mapview/ ) . In male meiosis , the linear fit between CO number per chromosome and chromosome size is equally good in Mb or µM of SC . This is expected since we found a very clear proportionality between SC length in µM and physical length in Mb . [38] . We observed that the male/female CO ratio differs significantly between long and short chromosomes . Long chromosomes have a higher ratio than short chromosomes . Once again , it could be an effect of the obligatory CO . All chromosomes under a certain threshold size , estimated to be 17 . 3 Mb in female and 13 . 9 Mb in male , would undergo only the obligatory CO , giving a M/F ratio of one . Above this minimal size , there would be an increase in CO number proportional to the chromosome length but in a different way in male versus female meiosis . Such a hypothesis corresponds to the following formula: LG = 0 . 5+a ( LMb−Lthr ) where LG is the genetic size in Morgans ( half the average number of COs per bivalent ) , LMb is the physical size in Mb , and Lthr is a threshold physical size . This is similar in spirit to the model proposed by Li and Freudenberg [55] , in which Lthr = 0 . For completeness , we have fitted that particular model to our male and female set of data , obtaining p-values below 10−13 in female meiosis and 10−39 in male meiosis . Thus our data do not support that model at all . This was not unexpected because we know that there is positive CO interference in Arabidopsis , so the relationship proposed by Li and Freudenberg [55] should become non-linear as the genetic size approaches 50 cM . We have also confirmed that both in male and female meiosis , the distributions of CO number per chromosomes are not random . Similar results were also found by Toyota et al [40] . However , in their study , neither chromosome 4 during pollen formation in early flowers nor chromosome 5 during pollen formation in late flowers exhibit an observed CO distribution per chromosome significantly different from a Poisson distribution . This discrepancy could probably be explained by the limited number of meioses studied ( 92 and 93 respectively ) . We found that the variance of the number of COs is smaller than would be expected under the hypothesis of no interference . Further analyses using the gamma interference model confirmed that the estimated interference parameter nu is always significantly higher than 1 ( expected value without interference ) for all chromosomes in male and female meiosis . This is not surprising since positive interference has been previously reported in A . thaliana ( reviewed in [56] , [57] ) . The parameter nu is a measurement of interference strength which does not depend on interval sizes ( as opposed to coincidence coefficients ) , and may be easily related to the parameter m of the counting model [7] by the relation: nu = m+1 when nu is an integer . The values of nu estimated in the present paper range between 2 and 5 , and we do not observe any significant difference in interference strength between chromosomes , or between male and female meiosis , based on the 95% confidence intervals . Our values for nu are similar to previous results on A . thaliana [3] obtained with comparable methods , but in the latter case , sample sizes were smaller and no confidence intervals were given . nu has been found to vary between species . For tomato chromosomes 1 and 2 , Lhuissier et al . [58] found nu = 7 . 9 and nu = 6 . 9 based on MLH1 immunolocalization along the synaptonemal complex . In mouse , similar methods indicated nu = 7 . 5 and nu = 10 . 1 for chromosomes 1 and 2 [59] . Estimates of nu were also obtained in dog ( 6 . 5 [46] ) , cat ( 3 . 7 , [60] ) , and shrew ( 11 to 16 [60] ) . However , the mechanisms underlying the variations of nu are not understood . We confirmed that the sex-related difference in CO distribution previously identified on chromosome 4 is a characteristic of all five chromosomes [27] . In male meiosis , CO rates are very high at both ends of the chromosomes and high on proximal parts of chromosome arms . On the other hand , female CO rates are high on proximal regions but very low at the telomeric ends of the chromosomes . This pattern is very similar to the male-female CO distribution observed in humans with the noticeable difference that in human the CO number ratio is the opposite: 1 . 8 more COs in female than male [61] . In humans , it was suggested that COs arise in regions that initiate synapsis in prophase I of meiosis [62] , [63] . However , during Arabidopsis male meiosis , synapsis initiates at many sites along the chromosomes including those in the terminal part [64] . Some of these sites coincide with the future localization of COs but synapsis initiates also at loci that will not be involved in reciprocal exchange . A similar situation has also been reported in other plants ( discussed in [64] , [65] ) . Our results would suggest that there is an additional level of control of CO distribution other than the constraints imposed on synapsis initiation . We confirmed that the difference in the size of the genetic maps between male and female meiosis first observed on chromosome 4 holds true for the 5 chromosomes . The average male/female ratio is 1 . 73 . A similar ratio was reported in a recent study [40] . When the most contrasting intervals for recombination located at the telomeric intervals were removed , the sizes of the genetic maps were still significantly different between male and female meiosis . Thus these telomeric intervals are not sufficient to explain the differences in CO rates per chromosome . It suggests that all along the chromosomes , COs are more prone to occur on a male chromosome than on a female chromosome . However , the biological reasons of these differences are still unknown . We previously reported that CO rates correlated negatively with GC content and positively with the CpG ratio on chromosome 4 but no correlation was found with genes or TE densities . However , that analysis was done only for chromosome 4 and only with a sex-averaged genetic map . In this present study , we readdressed this issue using our male and female CO maps on all five chromosomes . We found correlations mainly in female meiosis . Female CO rates correlated strongly and negatively with GC content and genes density but positively with TEs density . All these correlations weakened and/or disappeared when telomeric intervals were removed from the analysis . We observed that TEs , genes and GC% have a specific location along the chromosome arms ( Figure S1 ) . They all exhibit a significant gradient from centromeres to telomeres , positive for genes and GC% and negative for TEs . Therefore , it is tempting to suggest that the observed correlations in female meiosis could be indirect due to the specific distributions of these features along the arms of the chromosomes . Under the hypothesis of a positional effect between CO rates and chromosomes features , our data suggest that meiotic CO rates and GC% are not correlated in Arabidopsis , in either male or in female meiosis . However , when previously studied in several species , CO rates and GC% were always reported to be positively correlated [11]–[14] , [18] , [43] , [66] , [67] . On the other hand , in human , Kong et al [43] noticed that the correlation became negative when the CpG ratio was included in a multiple regression model . Moreover , when the strength of this correlation was studied at different scales , such as in S . cerevisiae and humans , it was shown to be very strong at a fine scale ( 5 kb in yeast , 15 to 128 kb in human ) and to weaken dramatically at a broad scale ( 30 kb in yeast or 1 Mb in humans ) [16] , [66] , [68] suggesting that the relationship could be complex . The cause of these correlations is still under debate . It has been suggested that recombination could shape genome evolution through a process called biased gene conversion ( BGC ) [67] , [69] . BGC refers to two possible mechanisms: mismatches created during the recombination process could be more frequently repaired towards GC leading to an increased probability of fixing GC alleles [70]; alternatively , the allele containing the least GC may initiate DSBs more frequently and be thus repaired by the GC-rich allele [71] . The former hypothesis is well supported by recent analysis in human [15] but at contrario , in S . cerevisiae where GC content is not driven by recombination [16] . The high level of inbreeding in A . thaliana populations , ( outcrossing has been estimated at around 1% but could reach 14 . 5% in some populations ( [72] ) has been suggested to attenuate the effect of BGC [18] and could explain why no correlations were observed in our analysis . In conclusion , our study provides a detailed survey of the CO landscape in male and female meiosis in Arabidopsis thaliana . We detected very specific sex-related patterns along the five chromosomes that highlight new differences between male and female meiosis .
The Arabidopsis thaliana accessions “Columbia-0” ( Col ) ( 186AV ) , “Landsberg erecta” ( Ler ) ( 213AV ) , were obtained from the “Centre de Ressources Biologiques” at the “Institut Jean Pierre Bourgin” , Versailles , France ( http://dbsgap . versailles . inra . fr/vnat/ ) . The Col accession was crossed to Ler to obtain F1 hybrids . Col plants were then crossed with an F1 hybrid used either as the male ( Col× ( Col×Ler ) ) or as the female ( ( Col×Ler ) ×Col ) . Seeds from these crosses were sown in vitro , and then , after two weeks seedlings were grown in a greenhouse under standard conditions for three weeks . After three weeks , whole plants were collected in 96 well plates and freeze-dried . For the ( Col× ( Col×Ler ) ) and ( ( Col×Ler ) ×Col ) populations , plant material was lyophilized then ground in 96 well plates with wells hermetically closed with plastic caps . 1 ml of Extraction Buffer ( Tris pH 8 0 . 1 M , EDTA 50 mM , NaCl 0 . 5 M , SDS 1 . 25% , PVP 40 000 1% , Sodium Bisulfite 1% , pre-warmed at 65°C ) was then added to each well and the plates were incubated at 65°C for 30 min . 300 µl of cold 60% K Ac 3 M , 11 . 5% glacial acetic acid was added to each well . The plate was sealed with a Thermowell film ( Corning ) , shaken gently and placed on ice for 5 min . After centrifugation in a A-4-62 rotor ( Eppendorf ) at 3 , 220 g and 4°C for 10 min , 800 µl of the supernatant was transferred to a clean DeepWell plate and 1 mL of CGE buffer ( 1/3 Guanidine hydrochloride 7 . 8 M , 2/3 ethanol 96% ) was added per well . 600 µL of the mixture was filtered with a Whatman Unifilter 800 GF/B plate placed on Deep Well plate ( Greiner bio-one ) and centrifuged for 2 min at 5 , 806 g in a Nr 09100F rotor ( Sigma ) at room temperature . The flow-through was discarded . This step was repeated twice . The membrane was washed twice by adding 500 µl of Washing buffer ( 37% Aqueous solution , 63% ethanol 96% ) ( Aqueous solution: K Ac 160 mM , Tris HCl pH 8 22 . 5 mM , EDTA 0 . 1 mM ) and then centrifuged for 2 min at 5 , 806 g at room temperature . The DNA was eluted with 70 µl of H2O by centrifugation for 2 min at 363 g at room temperature . This step was repeated once . RNAse A was added to 0 . 5 µg/ml and the DNA concentration was determined using the Quant-iT dsDNA BR assay Kit ( Invitrogen ) with an ABI 7900HT real-time PCR system ( Applied Biosystems , Framingham , MA , USA ) . For the ( Col× ( Col×Ler ) ) and ( ( Col×Ler ) ×Col ) populations , a set of 384 SNPs markers ( Table S1 ) were chosen from Monsanto database and the Salk Institute data-base on the basis of an even physical spacing along the chromosomes . Markers were validated according to Illumina with their Assay Design Tool http://www . illumina . com/ . Support and genotyping was carried out at the Plateforme Génomique de Toulouse using BeadXpress technology http://www . illumina . com . BeadXpress raw data were processed using Illumina's BeadStudio Genotyping Module V3 . 2 software and report files produced containing normalized intensity data and SNP genotypes were transferred to a Microsoft file for analysis . Genotypes were checked using a genotyping cluster file automatically generated by BeadStudio . Nine additional markers ( Table S1 ) were genotyped at the CNG using TaqMan probes ( assay-by-design Service Overview , Applied Biosystems ) according to the manufacturer's recommendations and end point fluorescence was detected using an ABI7900HT reader ( Applied Biosystems , Framingham , MA , USA ) . Scatter plots for each SNP locus were obtained using the SDS Software Workspace ( Applied Biosystems ) . Fluorescence data were transferred to a Microsoft Excel file for analysis . Markers and plants with too many undetermined genotypes were removed from the final dataset . The resulting populations comprised on average 1 , 505 and 1 , 507 plants with genotype data from 380 and 386 markers for the male and female populations , respectively ( 380 markers in common ) . We used PCR and DNA sequencing to verify 222 and 163 singletons in the male and female populations , respectively . For a given population , we call NC ( respectively NL ) the number of plants with the Col ( respectively Ler ) allele at a particular locus . To see the statistical significance of the segregation distortion at that locus , we tested whether the hypothesis of no distortion ( a fraction 0 . 5 for each allele ) resulted in a p-value smaller than 1% . This defined a region outside of an interval centered on the value 0 . 5; the half-width of this interval is 2 . 33 s where s is the standard error satisfying s2 = 1/ ( 4 ( NC+NL ) ) . The associated bands for all chromosomes ( cf . Figure 1 ) were slightly irregular; this is because the number of valid data varied at each locus . Two methods can be used to determine the genetic length ( LG ) of a chromosome: ( 1 ) the lengths of all the intervals are added , using Haldane's formula to go from recombination rate to genetic distance; ( 2 ) the number of COs for each plant is averaged , assuming that one never has more than one CO at a time in the same interval . Both approaches are excellent approximations given the small interval sizes in this study . These two methods are in fact very similar , but when data are missing , the second method is more precise as it can detect recombination events that are missed by the first approach because it uses more than two loci at a time . Given the number of COs for each plant , extracting the 95% confidence interval on LG is straightforward; it is 1 . 96 times the standard error . A slight generalization is necessary for Table 1 in which we display confidence intervals for the fractions f = LG ( male ) /LG ( female ) . Noting that one is in the limit where both the numerator and denominator are well estimated ( each has a small relative variance ) , we can use the approximation whereby the relative variance of the ratio is replaced by the sum of the relative variances: . Furthermore , in this same limit , f has a Gaussian distribution so from the variance of f we extract in the usual way the desired 95% confidence interval . To test the hypothesis that male and female genetic lengths were the same ( Table 2 ) , we applied the t-test using the "t . test" of the software package R . We did this for whole chromosomes and also for chromosomal regions obtained by removing telomeric parts . To estimate the intensity of crossover interference , we have fitted the gamma interference model to our crossover data for each chromosome in male and female meiosis separately , following the procedure described by McPeek and Speed [8] , and Broman and Weber , [74] . Such models parametrize the distribution of distances between successive crossovers . They may be fitted to experimental data by using a classical maximum-likelihood approach , taking advantage of the fact that the gamma model in particular makes it possible to compute the likelihood of a set of experimental crossover positions as a function of the parameter nu . The estimate of this parameter is thus a measurement of the interference strength . It can also be thought of as a generalization allowing a continuous interference parameter satisfying nu = m+1 , where m is an integer associated with counting discrete events in the counting model [7] . To fit the model to our data , we used what is referred to as “thinning” [8] , [74]: the gamma model describes the crossovers at the level of the bivalents , so to get a model for crossovers at the gametic level , it is necessary to thin , i . e , remove with probability 0 . 5 crossovers on the bivalent . Then using such thinning makes it possible to fit the gamma model to marker segregation data , and we used this procedure here . The recombination rate between two adjacent markers is estimated from the number of recombinants , using only plants that have no missing data at those two markers . If Nr ( respectively N ) is the number of recombinant ( respectively all ) plants , the recombination rate r is estimated as Nr/N . The corresponding 95% confidence interval is given by 1 . 96 s where s is the standard error satisfying s2 = r ( 1−r ) /N . The recombination rate per base pair ( and the associated confidence interval ) is obtained by dividing by the number of base pairs . Finally , the mean recombination rate per base pair on a chromosome arm is calculated using a weight for each interval , which is simply its length in base pairs . Intervals belonging to heterochromatic regions ( see below ) are excluded from the calculation . Intervals are defined as hot or cold ( cf . Figure 4 ) if their 95% confidence intervals do not contain the mean calculated for that arm . Male and female recombination rates are considered as significantly different when the statistical test of equality gives a p-value of less than 5% ( Benjamini correction included ) [75] . The same method as above was used to compare male and female recombination rate from [27] to those obtained in this study ( Benjamini correction included ) . We use epigenetic features to infer the heterochromatic regions . We first take the levels of H3K4me3 and H3K27me3 modifications as measured in plantlets for each gene , one at a time ( data provided by [76] ) . Both are markers of euchromatin , so we first test for the presence of either of these . Then 2 kb sized windows are used to obtain average levels of presence . Finally , we consider an interval to be heterochromatic if the average in that interval is below the threshold 0 . 2 . As expected , in all chromosomes , the centromeric region is then labeled as heterochromatic as well as the pericentromeric regions . Furthermore , following this procedure , chromosome 4 has a large heterochromatic region on its short arm , again in agreement with inferences in previous works . To test for possible associations between recombination rates ( per base pair ) and genomic features , one must first remove the centromeric regions ( which have low recombination and have unusual genomic content ) , otherwise they would dominate the analysis . We thus exclude all the heterochromatic intervals ( defined as explained previously ) . Then for each remaining interval , we use the TAIR9 data files to determine the following contents , measured per base pair: GC , coding GC , GC1 , GC2 , GC3 , CpG , and gene density . The potential linear association between these quantities is examined via the R2 of the fit and the p-value for the hypothesis of no association using the implementation provided by "lm" in the R software package . Consider the segregation distortion along chromosome 1 for male meiosis , the profile indicates strong distortion around two loci with a relatively smooth behavior between the two , making it plausible that only those two loci are under selection . Clearly , such a segregation distortion can bias our estimate of genetic lengths; we present here a simple model for correcting for such a bias . As a first simpler case , suppose that only one locus is under selection . We parameterize the selection process by having the meiosis happen normally ( no segregation distortion ) but follow it by keeping only a fraction s of the gametes that carry the less favored allele at the locus under selection . The gametes carrying the favored allele are all kept . Say we want to examine the recombination rate between two markers; let r be this rate before selection . The four possible genotypes of a gamete at these two markers are AB , Ab , aB , ab and before selection their frequencies are ( 1−r ) /2 , r/2 , r/2 and ( 1−r ) /2 . Among both the recombinant and non-recombinant genotypes , exactly half of the gametes carry the favored allele and half carry the unfavored allele . The selection process changes the number of recombinants by a factor ( 1+s ) /2 , but the same is true of the non-recombinants . Thus the naive estimation of the recombination rate , given by the fraction of observed recombinants , is an unbiased estimator for the true recombination rate r . Now to deal with the case where two loci L1 and L2 are under selection , we generalize the previous parametrization by having two selection coefficients , s1 and s2 . In our context , the less frequent allele is Col for the locus L1 and Ler for L2 . If a gamete has both favored alleles , it is kept; if it has one unfavored allele , selection keeps it with probability s1 or s2 depending on the locus with that allele; and finally if the gamete carries both unfavored alleles , selection keeps it with probability s1s2 ( no epistasis ) . In contrast to the single locus case , the selection here does change the ratio of recombinant and non recombinant gametes . It is thus necessary to use a more sophisticated estimate of the recombination rate between two markers than the naive estimate ( the fraction of measured recombinants ) . We do so as follows . For each interval delimited by adjacent markers Mi and Mi+1 , we enumerate all possible genotypes for those markers and for the two loci under selection . If these markers are distinct from the two loci – which we assume here for simplicity of presentation – , there are 16 possible genotypes . When we order L1 , L2 , Mi , and Mi+1 along the map , we define three consecutive intervals . Within the standard Haldane model of CO formation , the frequencies of the 16 genotypes are simply determined by the three recombination rates r12 , r23 , r34 of these intervals . To go from these frequencies to the ones after gametic selection is a simple affair and of course involves selection coefficients . Our computation is decomposed into the following steps . Assuming s1 and s2 given , we first use the 16 measured frequencies to fit the three unknown parameters r12 , r23 , r34 . Minimizing the weighted chi squared between the 16 observed and theoretical frequencies performs this fit . Then , we add the chi squared for all the intervals , defining a total chi squared for the pair ( s1 , s2 ) . This total chi squared is then minimized , leading to the inferred values ( s*1 , s*2 ) of the selection coefficients . Finally , using ( s*1 , s*2 ) , the recombination rates for all the intervals ( Mi , Mi+1 ) are recomputed and from that we extract the corrected total genetic length . In practice , when the two loci under selection are far away as in the case of chromosome 1 , the correction vanishes outside of ( L1 , L2 ) because effectively one then has only one relevant marker under selection . We thus used the procedure just described only for those intervals ( Mi , Mi+1 ) between ( L1 , L2 ) . This approach to correct for segregation bias in the genetic length LG was only necessary for chromosome 1 ( male meiosis ) , slightly increasing the naïve estimate of that chromosome's genetic length ( see Table 1 ) . As a consequence , the difference between male and female on chromosome 1 is slightly enhanced by the correction , and so omitting this correction in such tests is conservative . In particular for Table 2 , where the test is performed on whole and truncated chromosomes , we see that even without this correction , the male/female ratio is significantly statistically different from one . This model [55] stipulates that the genetic length rises linearly with physical length but has an offset associated with the obligatory CO . This corresponds to the relationship where α is a proportionality constant . We have fitted this formula for the 5 chromosomes of Arabidopsis , treating separately the M and F cases ( there is thus one value of α for M and one for F ) . In the case of chromosome 1 M , the genetic length has been corrected to take into account the segregation distortion ( see previous explanations ) . The fits have been implemented by linear regression minimizing the weighted chi-sqared where is the variance of the estimator of the genetic length of chromosome i . Explicitly , is calculated as the variance of the number of COs on chromosome i divided by the number of plants used in this experiment . The test of the model of Li and Freudenberg is obtained by using the value of after the fit taking into account the number of degrees of freedom . | Reciprocal exchanges of genetic material ( crossovers ) between homologous chromosomes ensure their proper segregation to generate gametes . Their number and location along chromosomes are tightly regulated . We localized precisely the position of 13 , 535 crossovers in more than 3 , 000 plants of Arabidopsis thaliana . While A . thaliana is a hermaphrodite plant with male and female meiosis occurring in the same flower and thus with the same genome , we observed dramatic differences in the distribution and the rate of crossovers along chromosomes in male and female meiosis . On average , chromosomes recombine 1 . 7 times more in male than in female meiosis . Moreover , male CO rates are very high at both ends of each chromosome , whereas female CO rates are very low . Finally , for the first time in a eukaryote , we show that the correlations between CO rates and various chromosome features differ in male and female meiosis . Female CO rates correlated strongly and negatively with GC content and gene density but positively with transposable elements density , whereas male CO rates correlated positively with the CpG ratio . However , most of the correlations could be explained by the structure of the Arabidopsis genome . |
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Epilepsy is one of the most common neurological disorders affecting about 1% of the world population . For patients with focal seizures that cannot be treated with antiepileptic drugs , the common treatment is a surgical procedure for removal of the seizure onset zone ( SOZ ) . In this work we introduce an algorithm for automatic localization of the seizure onset zone ( SOZ ) in epileptic patients based on electrocorticography ( ECoG ) recordings . The proposed algorithm builds upon the hypothesis that the abnormal excessive ( or synchronous ) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain . Thus , when this abnormal activity starts , signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals . The SOZ localization is executed in two steps . First , the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges’ weights quantify the pair-wise causal influence between the recorded signals . Then , the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase . Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists ( success rate of 17 out of 19 ) .
Epilepsy is one of the most common neurological disorders affecting about 70 million people worldwide . It is characterized by recurrent episodes of abnormal neural activity in the central nervous system [1] . This activity leads to transient occurrence of signs and/or symptoms , also known as epileptic seizures . The clinical symptoms of epileptic seizures range from auras , to spasmodic muscular contractions , up to loss of consciousness [2 , 3] . Epileptic seizures can be roughly divided into two groups , based on the location in the brain from which the abnormal neural activity originates and how it propagates . In partial , or focal , seizures the abnormal neural activity originates from a limited area in the brain , commonly referred to as the seizure onset zone ( SOZ ) . On the other hand , primary generalized seizures begin with a widespread electrical discharge that involves most of the brain . In this work we consider focal epilepsy and present an algorithm for SOZ localization , that is , determining the area in the brain where the abnormal neural activity leading to a focal seizure originates ( a guiding hypothesis throughout our work is that in focal seizures there is a singular focal point , from which this activity originates ) . The common and simplest approach to treat epilepsy is using antiepileptic drugs . Yet , in about 25–33% of the patients this approach is not effective [4] , and a patient is diagnosed with refractory epilepsy . A possible treatment approach for refractory epilepsy is a resective surgery procedure to remove the areas in the brain that are necessary and sufficient to generate the abnormal neural activity that leads to epileptic seizures . Currently , it is not known how these areas , also referred to as the epileptogenic zone ( EZ ) , can be mapped . Therefore , in clinical practice , the SOZ is used as an approximation for the EZ [5] , and in the resective surgery the estimated SOZ is removed ( assuming this region is not responsible for indispensable brain functions ) . Recent longitudinal trials indicate that long-term seizure freedom can be achieved in up to two thirds of the patients who undergo surgery [6] . The main tool for SOZ identification ( localization ) , in cases where the SOZ is not evident in a non-invasive electrocorticography ( EEG ) or in an MRI , is invasive EEG ( also known as electrocorticography ( ECoG ) ) . In ECoG grids or strips of electrodes are placed on the cortex [2] , allowing a direct measurement and recording of the brain’s electrical activity ( local field potentials ) . These recordings , together with video monitoring , are used by expert neurologists to approximate the electrodes associated with the area within which the SOZ lies . In this paper we describe an algorithm that localizes the SOZ based on the ECoG recordings . Such an automated solution will provide a valuable tool for neurologists to assist in SOZ localization and perhaps increase localization accuracy over current methods . The algorithm proposed in this paper builds upon a fundamental property of focal seizures reported in [7]: the abnormal neural activity associated with focal seizures starts in the SOZ and spreads to other areas in the brain . Therefore , at the beginning of such activity , signals recorded at electrodes located in vicinity of the SOZ should have a relatively large causal influence on the rest of the recorded signals . This calls for an algorithm that estimates and incorporates the causal influence between the different recorded signals into its SOZ localization . Since the electrodes in an ECoG grid are relatively close together [8] , the signals recorded in the different electrodes are statistically dependent . In such a case , to fully quantify the statistical causal influence between two electrodes , one must evaluate this influence when conditioning on the rest of the electrodes [9 , 10] . Unfortunately , even for moderate-size grids with 16 electrodes , this task is too computationally demanding and requires a huge amount of data per each seizure . Therefore , in this paper we take a different path and approximate the underlying causal influence structure . Instead of ( statistically ) conditioning on the rest of the recordings , the proposed algorithm applies a practical approximation by considering the electrodes as nodes in a directed graph , where the edges’ weights are estimations of the pair-wise causal influences . In this work we focus on the following question: how should the SOZ be inferred from the estimated graph ? The procedure for estimating the graph is discussed in S1 Text . The method of representing causal influences among a set of random variables using directed graphs is not new . In [11] this approach was used while adding the constraint that the graph should be acyclic . In this case it is assumed that the joint density follows a causal Markov condition [11] . However , with ECoG recordings the Markovian structure of the underlying density is not known , and it must be estimated from the recordings . A possible approach for estimating this structure is via minimizing the KL divergence [12 , Sec . 8 . 5] between the true density and an approximated density induced by a spanning tree . As shown in [13] , the best tree ( in terms of minimizing the KL divergence ) can be found using the maximum-weight spanning tree algorithm ( Edmonds’ algorithm ) [14] . Moreover , the underlying hypothesis for localization based on this approach is that the root of the spanning tree should correspond to the origin of the causal activity . This localization approach was taken in [15] , which our work improves upon in multiple dimensions . In particular , [15] assumes that the underlying density follows a specific structure in order to apply Edmonds’ algorithm . However , it is not clear if this structural assumption accurately describes the observed signals . In addition , the algorithm of [15] localizes the SOZ using only the outgoing weights , whereas other works [16 , 17] use both incoming and outgoing node weights . Hence , our work contrasts with [15] by using the PageRank algorithm to account for the structure of the estimated graph rather than assuming a specific structure , and basing our localization on both the incoming and outgoing weights to each node . Another approach for inferring the SOZ from the estimated graph was proposed in [16 , 17]: based on the findings of [7] , the nodes in the SOZ should have properties of “sources of causal influence” with large outgoing flow and small incoming flow ( the total incoming flow subtracted from the total outgoing flow is referred to as the net-flow ) . The two main drawbacks of this approach is that it ignores the structure of the estimated graph , and ranks the nodes ( electrodes ) based only on their one-step neighbors . Our study shows that for some of the patients this approach works well , while for others the results can be improved by a more sophisticated inference approach . To account for the structure of the graph ( and for multi-step neighbors ) , we propose to use a variant of Google’s famous PageRank algorithm [18 , 19] . The PageRank algorithm , initially designed for ranking web pages , is based on the following thesis [20]: A web page is important if it is pointed to by other important pages . Motivated by this thesis , the PageRank algorithm views the web as a directed graph with pages as nodes and hyperlinks as edges , and ranks the web pages based on the steady-state probability of a random surfer visiting each page . Using terminology taken from another web ranking algorithm , the hyperlink-induced topic search ( HITS ) algorithm [21] , the PageRank algorithm can also be viewed as assigning authority scores to the nodes . A high authority score is given to a page that is linked by many other pages with high authority scores . Thus , we use PageRank to calculate an in-flow ( authority ) score for each node in the graph . To calculate an out-flow score we use the Reverse PageRank algorithm [22] . As PageRank ranks based on the dominant right eigenvector of the directed graph , it accounts for its structure . We emphasize that in our algorithm the PageRank does not model the propagation of the abnormal neural activity . Instead , it is used to evaluate the importance of a node in terms of its causal influence on the rest of the network . It should further be noted that the PageRank algorithm was already used in the context of neuroscience problems . For example , [23] studied the network architecture of functional connectivity within the human brain connectum , and used four centrality measures , of which PageRank was one , to provide insights on this connectivity . Numerous works have studied the problem of localizing the SOZ using ECoG recordings . We refer the reader to [2 , 16] and references therein for background on this topic . Many of the algorithms proposed in previous studies are based on some form of a ( causal ) connectivity graph and use the following three main steps: i ) Pre-processing the ECoG recorded signals; ii ) Estimating the connectivity graph from the processed ECoG signals; and iii ) Inferring the SOZ from the estimated connectivity graph . While the algorithm proposed in the current paper follows a similar approach , it uses an improved method to estimate the connectivity graph and applies a novel method to infer the SOZ from it . Specifically , the proposed algorithm analyzes two types of 10 seconds blocks: at the beginning of a seizure ( an ictal block ) as well as blocks randomly sampled when the patient is resting ( rest blocks ) . By using information from both types of blocks , the proposed algorithm accounts for the structure of the estimated network when no seizure is evolving . The value of 10 seconds was chosen to provide a good tradeoff between the number of samples in a block and the stationarity of the observed signals over a block . When the block is much longer than 10 seconds the data may not be stationary , while when the block is much shorter than 10 seconds the number of samples available for estimating the pair-wise causal influences is too small ( the estimation may not be accurate enough ) . In contrast to the proposed algorithm , previous studies used significantly longer blocks [16 , 17] . To quantify the pair-wise causal influences between the recordings , the proposed algorithm uses a combination of a parametric causality measure , Granger causality [24] , and a non-parametric measure , directed information [25] . Our results show that this combined procedure improves upon using each of the above approaches ( parametric or non-parametric ) separately . Finally , the proposed algorithm uses a novel approach to infer the SOZ from the estimated graph . In particular , by using a variation of the PageRank algorithm , a score is assigned to each node . The algorithm then selects the SOZ nodes as the nodes that have high scores compared to other nodes , as well as compared to scores calculated based on the rest blocks . We emphasize that previous studies [16 , 17] did not account for the rest blocks as part of the localization procedure . Our analysis , on the other hand , suggests that rest block should be taken into account when localizing the SOZ in order to avoid biased results .
The proposed algorithm was tested on 19 data-sets , taken from patients undergoing surgical treatment for medically refractory epilepsy . These data-sets are listed on the online iEEG portal [26] ( http://www . ieeg . org ) . The patient-specific information is detailed in Table 1 . The ECoG signals were sampled at rates between 500 Hz and 5 KHz: data-set I001_P034_D01 was sampled at 5 KHz ( Mayo Clinic , Rochester , MN ) . Data-sets Study_004-2—Study_037 were sampled at 500 Hz ( Mayo Clinic , Rochester , MN ) , and data-sets HUP64_phaseII—HUP87_phaseII ( Hospital of the University of Pennsylvania , Philadelphia , PA ) were sampled at 512 Hz . Each of the data-sets contains ECoG recordings , as well as annotations indicating which time intervals in the recordings correspond to seizures . The video recordings are used to generate these annotations . The data-sets also include reports describing the spatial locations , on the cortex , of the electrodes , and comments by expert neurologists as to where the seizures originate from . We refer to an electrode that is highlighted in these comments as an electrode of interest ( EOI ) . Some of these data-sets contain recordings from several strips and grids . In these cases , the proposed algorithm analyzed the largest grid of electrodes ( in all considered cases the largest grid was located over the suspected SOZ ) . The name of the analyzed grid and its size are specified in Table 1 . Table 1 also specifies the surgical outcome ( class ) for each patient . Note that three patients were not resected , and there is no follow-up for three other patients . We summarize the localization results using the following two metrics: Next , we provide a detailed description of our localization results . The localization results for the patients detailed in Table 1 are presented in Figs 1–5 . As a ground truth we use the EOIs indicated by the neurologists and detailed in the data-sets reports . In Figs 1–5 , the EOI electrodes ( nodes ) are marked by a bold annulus , whereas the nodes detected by our proposed algorithm are marked by solid brown circles . The reports in the iEEG portal contain a unique numbering for each electrode in each of the grids . This numbering is also included in the grids presented in Figs 1–5 . For instance , in Fig 1- ( a ) , node 1F is marked by 1 which corresponds to the numbering used in the report . This , together with the fact that node 2F is marked by 7 , implies that node 3A corresponds to node 18 in the report . The proposed algorithm applies a variant of PageRank on the estimated causal influence graph to calculate a rank ( score ) for each of the nodes . Then , natural candidates for the SOZ are the nodes with the top p0 percentile of scores . In order to verify that the calculated ranks are not due to chance and indeed capture an evolving abnormal neural activity that leads to a seizure , the proposed algorithm also calculates similar scores for an ensemble of recordings taken while the patient is resting . From this ensemble the algorithm creates an empirical distribution of the scores for each electrode , and requires electrodes in the SOZ to have a score in the top p1 percentile of the calculated empirical distribution . A detailed description of the inference procedure is provided in the Methods section . The results in Figs 1–5 were obtained using p0 = 10 and p1 = 5 . The values of p0 and p1 control the tradeoff between the false positives ( identifying electrodes not in the SOZ ) and the false negatives ( SOZ electrodes not identified ) . Note that the number of indicated EOIs can be relatively large , for instance , in Fig 1- ( b ) , 10 nodes out of 36 are indicated as EOIs . This number also differs between data-sets . The value of p0 was selected to provide a good balance between the success rate and the FPR , namely , inferring at most 10% of nodes in the grid as SOZ candidates . The value of p1 controls the significance level ( enables the algorithm avoiding the possible bias caused by an inherent property of the patients’ brain ) . We discuss the implications of this parameter in the Discussion section ( see the The structure of the estimated causal influence graph subsection and the A comparison with different inference approaches subsection ) . Closely examining the localization maps in Figs 1–5 , it can be observed that our algorithm successfully localized the SOZ ( using the terminology defined above ) in 17 out of the 19 data-sets . The two exceptions are data-set study_023 in Fig 3- ( a ) and data-set HUP64_phaseII in Fig 3- ( d ) . Regarding data-set study_023 ( Fig 3- ( a ) ) , it can be observed that the localization concentrates in the lower left corner of the grid . While the reports for this data-set clearly indicate that the SOZ is nodes 2H–3H ( electrodes 58–59 ) , they also state the following: “The EEG showed fast activity at LTG #59 at 01:20:59 , which then evolves into spike activity in LTG #58 and 59 . At 01:21:10 , there was spread of spike and wave activity to LTG #2 , 3 , 10 , 11 , 18 , and 19” . Thus , our algorithm accurately inferred the area to which the activity spread . By analyzing a time interval that significantly precedes the seizure start point marked in the reports ( see the Methods section for a discussion regarding the analyzed time intervals ) , the inference can be significantly improved . Regarding data-set HUP64_phaseII ( Fig 3- ( d ) ) , it can be observed that four of the inferred nodes are concentrated around the EOI while the other four are spread over the grid . The reason for marking this inference as non-successful is the fact that exactly 50% of the electrodes overlap with the EOI , or with the nodes strictly adjacent to the EOI . Thus , this localization can be viewed as a partial success .
As mentioned above ( see also the Methods section ) , the ( statistical ) significance of the calculated scores is evaluated in order to preclude scores which were obtained by chance or which are not a result of the evolving seizure activity . In other words , the objective of the post-processing is to verify that the high scores are due to the evolving activity of an epileptic seizure and not an inherent property of the patients’ brain . To test this hypothesis , the algorithm generates an empirical distribution of the scores calculated over random blocks recorded while the patient is resting , see the Methods section for a detailed description of this procedure . Our study shows that , for a specific patient , the estimated causal influence graph has patterns that are common between a rest state and the beginning of a seizure , namely , the beginning of the ictal state . This implies that one must account for rest blocks in order to avoid having the localization results biased by the inherent structure of the causal influence graph . Figs 6 and 7 demonstrate that the causal influence graph estimated in rest blocks and in a block at the beginning of a seizure indeed have a common structure . Each of the sub-figures in Figs 6 and 7 is a heat map of an estimated graph ( the entries are the estimations of the pair-wise causal influences ) . The procedure for creating ( estimating ) this graph is briefly discussed in the Methods section , while a detailed description is provided in S1 Text . The left column corresponds to the ictal blocks ( beginning of a seizure ) , while the middle and right columns correspond to random blocks used as part of the post-processing procedure . Each row corresponds to a different data-set: HUP65_phaseII , HUP70_phaseII , HUP78_phaseII , and HUP87_phaseII . In each sub-figure , a yellow in the ( i , j ) location implies high estimated causal influence from node i to node j in the respective graph . The main dark blue diagonal in each of the heat maps corresponds to the causal influence between an electrode to itself that is set to zero . It can be observed that , per data-set , i . e . , in the same row in Fig 6 or in Fig 7 , the heat maps follow a similar structure . On the other hand , this structure is different from one data-set to another ( between different rows ) . In the first row of Fig 6 , corresponding to data-set HUP65_phaseII , one can observe hot super and sub diagonals . In the second row of Fig 6 , corresponding to HUP70_phaseII , one can observe a small hot region in the bottom right of the map . In the first row of Fig 7 , corresponding to data-set HUP78_phaseII , one can also observe a hot region in the bottom right of the map , yet , this region is significantly smaller than the one in the second row of Fig 6 . Finally , in the second row of Fig 7 , that corresponds to HUP87_phaseII , one can observe small hot squares at the upper-left part of the map . These findings indicate that an inference procedure that ignores the structure during rest times , e . g . , [16 , 17] , may not be aware of the structure that is present when there is no neural activity leading to a seizure . This may result in a biased inference . One may conjecture that the structural resemblance demonstrated in Figs 6 and 7 is due to epileptic activity in a rest state , commonly referred to as interictal discharges [27 , 28] . Yet , we note here that the starting point of the evaluated rest block is randomly selected ( see the Methods section for details ) . Moreover , the patterns depicted in Figs 6 and 7 appear in all analyzed rest blocks . Thus , as interictal discharges are relatively sparse , we conjecture that this structure is not due to the interictal discharges . At the same time , we note that interictal discharges can be used to assist in localizing the SOZ [29] . Designing a robust method to incorporate the interictal discharges in our algorithm is part of our future research plans . A natural question is how good are the results reported in the Results section compared to the performance of other inference algorithms . To answer this question we tested two alternative inference approaches as well as two methods for estimating the pair-wise causal influences . Before discussing the alternative inference approaches we first provide some background on the problem of estimating the causal influence graph . The proposed algorithm uses ECoG signals from two types of intervals ( blocks ) : 10 seconds at the beginning of a seizure ( an ictal block ) and 10 seconds randomly selected from a period in which the patient is resting . Intuitively , in ictal blocks the seizure activity has not spread out across the brain yet , and therefore these blocks should give clear insights as to the SOZ location . The length of the analyzed blocks is chosen to be 10 seconds . This follows as these blocks are used to estimate the weights in the causal-influence graph , and this places two contradicting constraints on their length . On the one hand , the analyzed ECoG signals should be approximately stationary . According to [35] , ECoG signals are approximately stationary only for a few seconds . On the other hand , the considered blocks should be long enough to facilitate non-parametric accurate estimation of the causal influence . Our study shows that blocks of 10 seconds provide a good tradeoff between the above two constraints ( see the detailed discussion in the Description of the setup subsection ) . The heat maps in Figs 6 and 7 indicate that the causal influences in rest blocks ( the middle and right column ) are lower compared to those in the ictal blocks ( depicted on the left column ) , namely , the graphs are more blue and less yellow . Extending this observation , our study shows that the seizure evolution process can be examined in terms of the causal influence graph , as depicted in Fig 8 for data-set HUP65 phaseII . Similarly to Figs 6 and 7 , each of the sub-figures in Fig 8 is a heat map of an estimated graph ( the entries are the estimations of the pair-wise causal influences ) , where in each sub-figure the graph was estimated from a different time window . The procedure for estimating these graphs is briefly discussed in the Methods section , while a detailed description is provided in S1 Text . It can be observed that in Fig 8- ( a ) ( which corresponds to a rest state ) , the causal influence is relatively low ( yet the pattern of hot super and sub diagonals is apparent ) . The causal influence is higher in Fig 8- ( b ) that shows the graph estimated from the recordings of pre-ictal state ( 10 seconds before the seizure starting point ) . The causal influence increases in Fig 8- ( c ) –8- ( e ) , corresponding to the first 10 seconds ( ictal block ) , 10 to 20 seconds after the seizure starts , and 20 to 30 seconds after the seizure starts , respectively . Finally , in Fig 8- ( f ) , that corresponds to 30 to 40 seconds after the seizure starts , there is a decrease in the causal influence compared to Fig 8- ( e ) . A possible explanation for this decrease is that after 30 seconds from the seizure starting point it already spread throughout the grid . Indeed , the reports corresponding to data-set HUP65_phaseII indicate that after about 30 seconds from the seizure starting point the activity was apparent in the whole grid: “rhythmic sharps of variable amplitudes are recorded throughout the grid diffusely ( generalized seizure electrographically ) ” . High frequency oscillations were recently suggested as good bio-markers for the epileptogenic zone [36–38] . Yet , as stated in [36] , to record high frequency oscillations , the ECoG recordings must be sampled at a minimum rate of 2 KHz . The oscillatory events can then be visualized by applying a high-pass filter and increasing the time and amplitude scales . As 18 of the 19 data-sets were sampled at approximately 500 Hz , analysis of high frequency oscillations cannot be applied . Moreover , as discussed in S1 Text , to efficiently estimate the pair-wise causal influence graph we down-sample the recorded signals , see the discussion about the impact of the sampling rate on the signals memory order and the resulting number of samples required for accurate estimation . While the analyzed signals can represent any limited frequency band ( not necessarily the low frequencies ) , the results presented in this work were obtained by analyzing the activity in frequencies below 100 Hz . We note that filtering out the high frequencies was also applied in [39] . On top of the sampling frequency limitations described in the previous section , it must be noted that the ECoG recordings in general , and the estimated causal-influence graph in particular , do not provide a complete representation of the epileptic network . Since it is not possible to record the electrical activity from the whole brain ( the grids’ size is limited ) , the true SOZ may not be covered by the recording grid . In this work , we assume that the preceding analysis was executed , e . g . , using EEG or MRI imaging , and that the grid was located based on a good ( yet rough ) estimation of the SOZ location . Another source of inaccuracy is the fact that the recorded signals might be influenced by ( or correlated with ) a strong signal originating from a location out of the grid . This may call for analysis of causal influence graphs in the presence of latent variables , see , for example , [40–42] and references therein . However , these works either assume a linear model , or derive estimation methods which require a very large number of samples . As discussed in S1 Text , the number of available samples for estimating the causal influences is inherently small , and thus these techniques cannot be used . Finally , as discussed above , since the electrodes in an ECoG grid are closely located , the recorded signals might be statistically dependent . In this case , to fully quantify the statistical causal influence between two electrodes , one must evaluate this influence when statistically conditioning on the rest of the electrodes . However , even for small grids , this task is too computationally demanding and requires a huge number of samples . Despite the incomplete representation of the causal-influence network via the pair-wise causal influence graph , the inference results presented above suggest that the used approximation is accurate enough for the purpose of localizing the SOZ . A major concern regarding any automatic localization algorithm is the computational aspects [16 , 17] . In particular , for large grids , the computational complexity of estimating the causal influence graph is high since N ( N − 1 ) values must be estimated . This leads to the question: can the proposed algorithm be executed in real-time to yield results within minutes from the time that the recording session ends ? We assert that it can , given that the main computational load of our algorithm is the estimation of the causal-influence graphs of the random rest blocks , see the Methods section . This follows as the number of seizures per patient is relatively small ( see Table 1 where data-set Study_033 is the largest with 17 seizures ) , while in order to create the empirical distributions we estimate the causal influence graph for 200 rest blocks . Note that there is no need to wait until the end of the recording session to execute this estimation task . In fact , estimation of the causal influence graphs for the rest blocks can be executed in parallel to the recording procedure , thus , significantly reducing the computational load at the end of the procedure . We further note that estimating the graph can be performed using a dedicated hardware ( Graphics Processing Unit ) and in parallel over several processors [43] , reducing the required time even further . Finally , we emphasize that from the perspective of graph theory , the estimated graphs are very small ( compared to graphs with thousands or even millions of nodes ) . Therefore , the computational complexity of the inference procedure based on the PageRank algorithm is negligible . The impact of an automatic localization algorithm could be significant , in particular in view of the improved inference performance reported in Tables 2 and 3 . First , it can provide an objective point of view regarding the SOZ location . Second , the proposed algorithm can save analysis time for the neurologists by providing a pointer to a set of electrodes suspected to be located over the SOZ . Third , while the proposed algorithm focuses on inferring the SOZ , the techniques developed in this work can be used to learn other mechanisms and dynamics of the brain . For instance , understanding modifications in the neural network due to learning [44] , or extending the above discussion on seizure evolution . Finally , we note that the proposed PageRank-based analysis of the graph can be applied after any procedure for developing a weighted directed graph indicative of causal influences . While due to computational complexity constraints , and the limited number of available samples , the proposed algorithm uses the pair-wise DI , in principle , any procedure that estimates a weighted directed graph ( for instance , estimating the causally conditioned DI ) can be used before applying the PageRank algorithm . In terms of future research , we currently have three main directions: First , the proposed algorithm uses the rest periods to create the empirical distributions used in the post-processing stage . An interesting question is how to use these blocks to learn about the epileptic activity of the patient , thus improving the inference accuracy . We believe that by identifying rest blocks with interictal discharges , it will be possible to further take advantage of the recorded rest blocks . Second , the similarity between the structure of the graphs estimated in rest and pre-ictal blocks motivates analyzing these structures also during the seizure itself . Such an analysis can shed light on the transition from rest to seizure and on the propagation of the seizure activity over the network . Third , an important aspect in estimating the pair-wise causal influences ( or any statistical functional that involves memory ) is estimating the length of the auto-time-dependence of the ECoG recordings ( for Gaussian signals this reduces to the actual length of the auto-correlation function ) . The parameter can also be interpreted as the Markov order of the sequence . Using tools from the theory of machine learning , namely , a data-driven estimator of the Markov order , in [45] we are studying the empirical distribution of the estimated Markov order over different states ( rest and ictal ) and different patients .
The patients included in this study ( listed in the iEEG portal [26] ) provided a written and informed consent in accordance with the University of Pennsylvania Institutional Review Board and Mayo Clinic Institutional Review Board for inclusion in the current study . We begin the Methods section with a formal description of the setup . In particular , we specify what parts of the ECoG recordings are analyzed by the algorithm , and formally define its output . In the subsequent subsections we discuss the application of the PageRank algorithm , and the method used to infer the SOZ . The input to our algorithm are ECoG recordings from an epileptic patient ( diagnosed to have a refractory epilepsy ) , as well as annotations information that indicates about the state of the patient in a given time interval ( resting , pre-ictal , ictal , etc . ) . The labeling of these time intervals is done based on video recording of the patients . The annotations information also includes a report , composed by the expert neurologists , that specify the EOIs . The recordings and annotations information for all patients are listed in the International Epilepsy Electrophysiology ( iEEG ) portal [26] , see Table 1 for the specific patient information . The objective of the algorithm is to localize the SOZ , namely , to find a ( small ) subset of electrodes that are located close to ( above ) the SOZ . We emphasize that the proposed algorithm takes as input the time intervals of the seizures , it does not detect them . Fig 9 provides a high-level block diagram of the proposed algorithm . As discussed in the A comparison with different inference approaches subsection , the algorithm combines estimation of the causal influence graph quantified using the DI metric ( DI-Graph ) , with estimation of the causal influence graph quantified using the GC measure ( GC-Graph ) . Specifically , as the DI measure does not assume any parametric model for the data , the algorithm first infers the SOZ from the DI-Graph . While this works in most cases , it is possible that this inference will not lead to any candidate ( electrode ) to be part of the SOZ ( this is discussed in the sequel ) . In this case , the algorithm infers the SOZ from the GC-Graph . As stated above , a detailed description of the procedure for estimating the DI-Graph and the GC-Graph is provided in S1 Text . The procedure for inferring the SOZ from the estimated graph is discussed below . As indicated in Fig 9 , the inputs to the proposed algorithm are the ECoG recordings . This leads to the following natural question: which parts of the ECoG recordings are analyzed ? In contrast to [16] and [17] that focused only on the ECoG recordings corresponding to seizures , the proposed algorithm uses the annotations’ information and analyzes two types of time intervals ( blocks ) : The length of the analyzed blocks is chosen to be 10 seconds . An example of the recorded signals ( ictal as well as rest blocks ) , for data-set Study_016 , is depicted in Fig 10 . The block length is chosen to be 10 seconds as these blocks are used to estimate the pair-wise causal influences , which places two contradicting constraints on the length of the analyzed blocks . On the one hand , as a statistical measure is estimated , the analyzed ECoG signals should be approximately stationary . According to [35] , ECoG signals are approximately stationary only for a few seconds . On the other hand , the considered blocks should be long enough ( contain enough samples ) to facilitate accurate estimation of the statistical measure . Our study shows that blocks of 10 seconds provide a good tradeoff between the above two constraints . In particular , executing the proposed algorithm for several block lengths ( 5 , 10 , and 20 seconds ) revealed that 10 seconds provides the best localization performance , as indicated in Table 4 . A detailed description of the electrodes inferred to be part of the SOZ is given in S2 Text . Fig 11 depicts a block diagram of the processing applied to estimate S DI ( or S GC ) . This processing consists of two main parts: estimation of the causal influence graph , and inference of S DI ( S GC ) from the estimated graph . In this section we focus on the right part of Fig 11 ( emphasized in dark red ) . Let the sampling rate in recording the ECoG signals be Fs Hz , and let the number of recorded electrodes be N ( recall that typical values are Fs = 500 Hz and N = 64 ) . The input to the first block in Fig 11 is a 10 ⋅ Fs × N matrix denoted by V ictal . This matrix contains the recordings from the first 10 seconds of a seizure . The ith column in V ictal corresponds to recordings from the ith electrode . The output of this block is an N × N matrix G , representing a complete directed graph with N nodes , where the ith node corresponds to the ith recording electrode . The graph G does not contain self loops . The element in the ith row and jth column of ( the matrix representation ) G , [ G ] i , j , is the weight of the edge between nodes i and j; it quantifies ( via DI or GC ) the causal influence of the signal recorded in the ith electrode on the signal recorded in the jth electrode . The values of G i , i are set to zero . A detailed description on how these quantities are estimated is provided in S1 Text . To infer the nodes corresponding to the SOZ from the graph , we first note that a common problem in network analysis is to identify the most important nodes in the network . As the exact interpretation of importance is often application dependent , it can be quantified using many different measures [46] . In the current work we evaluate the importance of a node by quantifying the amount that this node serves as a source of information flow , i . e . , causal influence , in the graph . The objective of the dark red part in Fig 11 ( denoted by “SOZ Inference” ) is to find these important nodes in G . A related problem is to rank the nodes in a network , and similarly to the case of importance , there are many definitions and algorithms for computing rankings [47] . One of these algorithms is the famous PageRank algorithm . Next , we discuss how the PageRank algorithm can be used to infer the SOZ from the graph G . Recall our underlying hypothesis that the abnormal neural activity starts at the SOZ and then spread to the other electrodes . Hence , in terms of causal information flow , nodes in the SOZ should have high outgoing flow and low incoming flow . This reasoning led to the net-flow metric . The PageRank algorithm quantifies the importance of a node based on its incoming links ( while accounting for the structure of the whole graph ) . A detailed description of the “vanilla” version of the PageRank algorithm ( in the context of ranking web pages ) is available in [48] . To quantify the importance of a node based on its outgoing links we propose to use the Reverse PageRank algorithm [22] . Using terminology taken from the hyperlink-induced topic search algorithm [21] , PageRank assigns authority scores to the nodes . In a recursive manner , a high authority score is given to a node that is linked by many other nodes with high authority scores . Thus , the authority score can be seen as an in-flow score that accounts for the structure in the graph . To obtain an equivalent to the out-flow score , Reverse PageRank is used to calculate hub scores . Again , in a recursive manner , a high hub score is given to a node that is linked to many other nodes with high hub scores . Motivated by the arguments that led to the net-flow metric , we propose to use the difference between the hub and authority scores as the metric for ranking the nodes . Before formally describing how to calculate these metrics , we emphasize that in our algorithm the PageRank does not model the propagation of the seizure . Instead , it is used to evaluate the importance of a node in terms of its causal influence on the rest of the network . To calculate the authority scores we apply a ( modified ) PageRank on the graph G ( G can be either the DI-Graph or the GC-Graph ) . Let the matrix P ¯ j , i be defined as: P ¯ j , i = G i , j ∑ k = 1 N G i , k . ( 1 ) Note that the elements of P ¯ are positive , while its columns sum to one . Therefore , the column vectors in P ¯ are in fact probability vectors . Further note that , in contrast to the PageRank described in [48] where the column vectors correspond to the uniform distribution , in ( 1 ) the elements in a given column of P ¯ can be different from each other . Next , the matrix P is generated from the matrix P ¯ by replacing any zero column in P ¯ with a vector containing entries that are all equal to 1 N . Finally , the authorities ( column ) vector a is calculated as the solution of: ( α P + ( 1 - α ) v a e T ) a = a , ( 2 ) where e denotes the unity column vector , 0 < α < 1 , and va is a column probability vector , i . e . , a vector with positive elements that sum to unity . The vector va is commonly referred to as the teleportation distribution . The addition of this vector ensures that there is always a unique a that solves ( 2 ) . Generally speaking , there are two main approaches for choosing va . When va is close to uniform , it is common that PageRank is used to calculate a network centrality measure , thus calculating the importance of each node based on the structure of the entire graph . On the other hand , va can be a fixed personalization vector that is exploited to bias the result of the towards certain parts of the graph . This can be viewed as a localized measure of importance . The parameter α , commonly referred to as the dumping factor , controls the teleporation probability . Finally , the importance of a node is its corresponding probability in the vector a , which leads to the following two comments: As indicated above , to calculate the hub scores ( that quantify the importance of a node based on its outgoing links ) we propose to use the Reverse PageRank . This can be easily done by applying ( 1 ) – ( 4 ) with G replaced by its transpose G T and va replaced by vh , where the elements of vh are calculated via: v h , i = ∑ k = 1 N G i , k ∑ i = 1 N ∑ j = 1 N G i , j . ( 5 ) The resulting vector of hub scores is denoted by h . Finally , the score of node i is given by: s i = h i - a i . ( 6 ) Thus , si quantifies the amount of total flow ( of causal influence ) for node i , while accounting for the graph structure . Before discussing how to infer the SOZ , we note that it is common that neuronal activities associated with several seizures are recorded in each recording session ( most data sets contain data associated with multiple seizures , see Table 1 ) . Based on the assumption that there is a single focus , we combine these instances by averaging the estimated graphs ( the DI-Graph or the GC-Graph ) . Slightly abusing the notation , this results in the graph G that is then used as the input to the PageRank algorithm . This approach was also taken in [16] . We now describe the method for selecting the set of nodes corresponding to the SOZ . Recall that the algorithms calculates both the DI-Graph and the GC-Graph . As there is no known statistical model for ECoG recordings , the algorithm first uses the DI-Graph for inferring the SOZ . As will be clear shortly , for some data-sets the inference based on the DI-Graph results in no candidate electrodes to be declared as part of the SOZ . In such a case the algorithm uses the GC-Graph to infer the SOZ . Let s = { s i } i = 1 N be the vector of scores calculated from the estimated DI-Graph , and let S 0 ( DI ) ∈ { 1 , 2 … , N } be the set of nodes that constitute the top p0 percentile of s . Thus , S 0 ( DI ) is a natural candidate to be declared as the SOZ . Yet , one better first verify that: A possible method to verify the above two points is via a comparison of the estimated scores si to their null-distribution . This null-distribution ( specific for each score ) should reflect the distribution of si when there is no abnormal neural activity leading to a seizure , such that a high si value will reflect a strong total flow due to the abnormal activity ( that leads to a seizure ) . As the true null-distributions are not known , we calculate an empirical distribution based on the recorded rest blocks . The rest blocks used to generate these empirical distributions are selected at random , and therefore , with high probability , satisfy the assumption that they do no include an evolving epileptic activity ( see the discussion preceding Figs 6 and 7 ) . The procedure for creating the empirical distributions is illustrated in Fig 12 . Let NS ≥ 1 denote the number of analyzed seizures ( number of seizures in the data set ) . To create the empirical distributions we randomly choose NS blocks ( 10 seconds time intervals ) recorded while the patient is in a rest state . We emphasize that the starting point of these blocks is random , and the only constraint is that these blocks contain valid recordings ( non-corrupted voltage traces ) . We apply the presented inference procedure ( estimating the graphs , averaging , and calculating the sources scores ) on the NS blocks to obtain the scores { s i ˜ } i = 1 N that correspond to the currently sampled random rest blocks . By repeating this procedure 200 independent times we create an empirical null-distribution for each s ˜ i . Note that an empirical distribution is generated for each si , separately . The algorithm now uses the generated empirical distributions as part of the SOZ inference . Let S 1 ( DI ) ∈ { 1 , 2 … , N } denote the set of nodes for which si is in the top p1 percentile of the generated empirical distribution of s ˜ i . Therefore , for a small value of p1 , the scores of the nodes in the set S 1 ( DI ) are significant . The algorithm now calculates the set S DI = S 0 ( DI ) ∩ S 1 ( DI ) , i . e . , the set of nodes that have high scores for being sources in the graph , and simultaneously are statistically significant compared to their calculated empirical distributions . If this set is not empty , it is declared to be the SOZ . In case the set S DI is empty , the above procedure is repeated using the GC-Graph instead of the DI-Graph . The empirical distributions are generated by estimating the GC-Graph from the rest blocks , and the corresponding sets S 0 ( GC ) and S 1 ( GC ) are obtained . The SOZ is now declared to be S GC = S 0 ( GC ) ∩ S 1 ( GC ) . The algorithm terminates at this point even if the set S GC is empty . The localization results detailed in the Results section were obtained using p0 = 10 and p1 = 5 . In three out of the 19 data-sets detailed in Table 1 the set S DI was empty: Study_006 , Study_021 , and Study_033 . In these data-sets , using the GC-Graph lead to a successful localization . Interestingly , in the two data-sets with non-successful localization ( study_023 and HUP64_pahseII ) the inference results based on the DI-Graph and the GC-Graph are very similar . Finally , we note that the results in Table 3 , the “Top 5%” row , were obtained using p0 = 5 and p1 = 100 ( no comparison to the empirical distributions ) . | Epilepsy is a common neurological disorder characterized by abnormal electrical disturbances in the brain that result in transient occurrence of signs and/or symptoms , also known as seizures . In focal epilepsy , this electrical activity originates from a limited area in the brain , commonly referred to as the seizure onset zone ( SOZ ) . For patients with focal epilepsy that cannot be treated with medications , the common treatment is a resective surgery to remove the SOZ . This work presents an algorithm for SOZ localization based on electrocorticography recordings . Such an automatic solution has the potential to increase the localization accuracy , to provide a validation of the neurologist’s SOZ region , and to ultimately reduce or eliminate the analysis time of the neurologist . Inference results for 19 patients show a close match between the SOZ inferred by the proposed algorithm and the SOZ estimated by expert neurologists . |
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According to World Health Organization ( WHO ) prevalence estimates , 1 . 1 million people in Mexico are infected with Trypanosoma cruzi , the etiologic agent of Chagas disease ( CD ) . However , limited information is available about access to antitrypanosomal treatment . This study assesses the extent of access in Mexico , analyzes the barriers to access , and suggests strategies to overcome them . Semi-structured in-depth interviews were conducted with 18 key informants and policymakers at the national level in Mexico . Data on CD cases , relevant policy documents and interview data were analyzed using the Flagship Framework for Pharmaceutical Policy Reform policy interventions: regulation , financing , payment , organization , and persuasion . Data showed that 3 , 013 cases were registered nationally from 2007–2011 , representing 0 . 41% of total expected cases based on Mexico's national prevalence estimate . In four of five years , new registered cases were below national targets by 11–36% . Of 1 , 329 cases registered nationally in 2010–2011 , 834 received treatment , 120 were pending treatment as of January 2012 , and the treatment status of 375 was unknown . The analysis revealed that the national program mainly coordinated donation of nifurtimox and that important obstacles to access include the exclusion of antitrypanosomal medicines from the national formulary ( regulation ) , historical exclusion of CD from the social insurance package ( organization ) , absence of national clinical guidelines ( organization ) , and limited provider awareness ( persuasion ) . Efforts to treat CD in Mexico indicate an increased commitment to addressing this disease . Access to treatment could be advanced by improving the importation process for antitrypanosomal medicines and adding them to the national formulary , increasing education for healthcare providers , and strengthening clinical guidelines . These recommendations have important implications for other countries in the region with similar problems in access to treatment for CD .
Chagas disease is clinically manifested in two stages – an acute stage and a chronic stage . The acute stage lasts for approximately 4–8 weeks and is characterized by flu-like symptoms or a characteristic local swelling at the site of parasite entry [8] , [9] , following which an infected person enters the indeterminate form of the chronic phase of infection . Among those with the indeterminate chronic form , about 20–30% of patients progress to the chronic cardiac or digestive forms of Chagas disease [10] . The most common course of Chagasic cardiomyopathy includes conduction system abnormalities early in the disease , resulting in heart failure . In all phases , serological tests such as the enzyme-linked immunosorbent assay ( ELISA ) test , the indirect haemagglutination assay ( IHA ) , and the indirect immunofluorescent antibody test ( IIF ) are used for diagnosis [4] , [9] , [11] . Because these tests can be difficult to interpret , the WHO recommends the use of two concomitantly positive tests to make a confirmed diagnosis [11] , [12] . Currently , benznidazole and nifurtimox are the only antitrypanosomal medicines available to treat T . cruzi infection . Antitrypanosomal therapy is strongly recommended by WHO for acute , congenital or reactivated infections , and for chronic infection in children under the age of 18 [13] , [14] , [15] . Recent scientific evidence about the clinical effectiveness of these medications has led to the expansion of treatment indications to include adults in the chronic phase of the disease without advanced cardiomyopathy [1] , [11] , [16] , [17] , [18] , [19] . Though no randomized controlled trial has directly compared the two medications [11] , WHO guidance and the clinical literature place greater emphasis on the use of benznidazole [4] as a first-line therapy because there is more clinical evidence for its efficacy , and it has a more favorable side-effect profile and is better tolerated by adult patients [9] , [15] , [16] , [17] , [18] , [20] . A randomized clinical trial of benznidazole is underway to determine its efficacy in slowing progression of disease among patients with early to moderate stage Chagasic cardiomyopathy [21] , [22] . Both benznidazole and nifurtimox have undergone changes to their global supply chains over the past decade . Benznidazole was manufactured by Roche until 2003 , at which time the rights and manufacturing technology were transferred to the Pernambuco state pharmaceutical laboratory in Brazil , Laboratorio Farmaceutico do Estado Pernambuco ( LaFepe ) [23] , [24] . Between 2004 and 2006 , LaFepe produced several batches of benznidazole using active pharmaceutical ingredient that was donated by Roche [24] . Then , after a period of no production , LaFepe resumed production of benznidazole in late 2011 and the medicine is now distributed by several entities including LaFepe , WHO , and Masters Pharmaceuticals . Nifurtimox is manufactured by Bayer HealthCare in El Salvador . In 2007 Bayer reached an agreement with WHO for Bayer to donate nifurtimox to WHO and for WHO to distribute the medicine through the WHO-Bayer Nifurtimox Donation Program [25] . Access to treatment for Chagas disease in Mexico must be considered in the context of the Mexican health system and its recent reforms . Mexico has three major national insurance schemes , the Instituto Mexicano del Seguro Social ( IMSS ) , Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado ( ISSSTE ) , and Seguro Popular ( SP ) [26] . IMSS and ISSSTE together offered coverage to approximately 42 . 6 million private sector ( IMSS ) and public sector ( ISSSTE ) employees in 2010 [26] . As of 2011 , SP , a social health insurance program started in 2003 , offers a package of 284 essential services to approximately 51 . 8 million Mexicans , according to the Mexican government [10] , [27] , [28] , [29] . Affiliation with SP requires a fixed family contribution that is based on a progressive scale by income , though individuals and families who fall in the lowest two income deciles are exempt from payment of a premium [26] , [27] . The national Program on Onchocerciasis , Leishmaniasis and Chagas Disease within the Mexican Secretary of Health's National Center for the Prevention and Control of Diseases ( CENAPRECE ) is the unit responsible for establishing guidelines and coordinating national activities for Chagas disease control . The State Secretaries of Health report patients who are diagnosed by ISSSTE , IMSS and SP systems to the national Program , which then turn provides medicines to treat confirmed cases . Figure 1 shows the process of case registration for a patient with Chagas disease .
IRB exemption was obtained from Harvard School of Public Health ( Protocol# 21514-101 ) and the National Institute for Public Health ( INSP ) located in Cuernavaca , Mexico . Oral informed consent was obtained from all interviewees .
Table 2 provides a list of national level obstacles to treatment access for Chagas disease , based on our analysis of data collected in this study . The list includes all obstacles that were mentioned during interviews and could be triangulated using a second data source .
This study provides evidence regarding the extent of treatment access for Chagas disease in Mexico and the barriers that influence the level of access . In particular , the study demonstrates that the number of Chagas disease cases registered at the national level in Mexico since 2007 is approximately 0 . 41% of expected cases and that 120 registered , eligible cases were awaiting treatment at the time of the study . These findings also indicate that Mexico has made an effort to register new cases and provide treatment at both the state and national level and thus show an increased commitment to addressing this disease in Mexico . Our findings also demonstrate that epidemiologic surveillance for Chagas disease remains a challenge in Mexico and that the complexity of the case registration system may delay or limit registration . Evidence from national data shows that problems in the supply chain of medicines make it difficult to ensure timely access to treatment as cases are registered and further , that the medicine provided by the national program since 2009 has exclusively been nifurtimox , a medicine that has been identified in the clinical literature and international guidelines as second-line therapy [20] . The lack of awareness and understanding of the disease and its treatment among both physicians and populations at risk was another important challenge related to the persuasion policy intervention area [46] . Patient and provider awareness of the disease has implications for efforts to strengthen epidemiologic surveillance and the willingness of physicians to treat infected patients when medicines are available . Additionally , access to treatment for Chagas disease has until 2012 been further weakened by its exclusion from the package of health interventions that are covered under SP [27] , [28] . While its addition to the CAUSES in 2012 represents an important step ( organization ) toward increasing access to treatment , clinical information about the disease is still lacking in this document and neither benznidazole nor nifurtimox is listed as a treatment for the diseases in this category . In addition to these barriers , it is important to acknowledge the role of international actors and policies as barriers to access to treatment for Chagas disease in Mexico and potentially in other countries as well . The global shortage of benznidazole in 2011 and the challenges in obtaining nifurtimox through WHO exist outside the Mexican context but directly affect efforts by the Mexican national and state control programs to increase access to treatment [23] , [24] . These findings provide new information on the state of treatment for Chagas disease in Mexico and the barriers that prevent more widespread access . Previous work on this subject has suggested that efforts to control and treat Chagas disease in Mexico are insufficient [36] , [53] but no study has previously measured the gap in access to treatment or analyzed related obstacles . In addition , a recent study estimated the economic burden associated with Chagas disease to exceed seven billion dollars globally and several studies have described the need for increased treatment globally [3] , [5] , [33] , [53] , [54] . This study is one of the first to examine the multiple complex factors within the health system that prevent more widespread treatment access in a particular country setting . It is important to note , however , that the state of Morelos did successfully procure benznidazole and offers an important case for showing how a state can take significant initiative in improving access to treatment for Chagas disease . Some of the findings from the Mexican experience may be relevant to treatment access for Chagas disease in other countries in the region . For instance , reliance on nifurtimox as a first-line therapy in both the 2010 Mexican guidelines for vector-borne diseases and in procurement of medicines at the national level raises questions about the reasons for this choice and whether other countries may also choose to procure nifurtimox through the donation program now or in the future instead of purchasing benznidazole through the private market . In the case of Mexico , the regulatory status of the drugs , especially the lack of commercial permits for them , and the exclusion of antitrypanosomal therapies for Chagas disease from the Mexican national formulary have severely limited sources of financing to buy benznidazole , causing the national program to instead rely on the free nifurtimox . However , little information exists about whether other countries also rely on nifurtimox as a first-line therapy and if so , why . Though clinical guidelines overwhelmingly suggest that benznidazole is better tolerated and that the clinical evidence of its efficacy is more robust , clear international consensus guidelines for the treatment of Chagas disease have not been published and relatively limited data are available about the use and clinical outcomes for the two drugs by different countries around the world . There are several limitations to this study . First , data on the prevalence of Chagas disease are limited both in Mexico and globally . This constitutes an important challenge to efforts to address this disease in Mexico . In this analysis , we use the official 2010 prevalence estimate from the Mexican Secretary of Health because it is more conservative than the most recent WHO estimate and because the WHO estimate does not have a clear evidence base . This choice may result in our analysis showing greater access to treatment ( as a proportion of total infected cases ) than may actually exist in Mexico . Some actors within the Mexican Secretary of Health have argued that the epidemiology of Chagas disease in Mexico is focal and that states with a high burden of disease should undertake activities to address this disease at a state level , while others have maintained that the prevalence of Chagas disease is substantial across much of the country and that the disease should be a national priority , especially given the migration of populations from endemic areas both within Mexico and from neighboring countries to Mexico [36] , [50] . To provide a more reliable estimate of national prevalence , a nationally representative epidemiologic survey could be conducted , both nationally and by state . This would advance efforts by both the state and national programs to make more informed decisions about the priority and resources that are warranted for Chagas disease treatment . A second limitation is that we consider benznidazole as the first line antitrypanosomal medicine , despite the lack of definitive international consensus on this issue . We made this decision because benznidazole is being used exclusively as the reference treatment regimen in clinical trials of new drugs , is named as the first line therapy in the treatment guidelines of several non-governmental organizations [20] , and is cited as such in the vast majority of the clinical literature [9] , [17] , [18] . It is worth noting , however , that there is some diversity on treatment regimens within Mexico . Although the national program has used nifurtimox from the WHO donation program , the state of Morelos in Mexico has purchased benznidazole for its treatment program . Morelos registered 263 cases between 2007 and 2011 , and treated 148 cases with benznidazole and 4 with nifurtimox . This study was also limited by lack of data availability at the national and global levels . At the national level in Mexico , this included a lack of national treatment guidelines or data prior to 2010 , a dearth of information about treatment eligibility or patient refusal of treatment , and a lack of data on treatment dose , completion or clinical outcomes . In particular , it was difficult to determine what proportion of patients would be treatment eligible according to the guidelines given that no data were available on co-morbidities or patient clinical history that would allow a more thorough analysis of patients in whom treatment may be contraindicated . Furthermore , there is limited evidence about access to treatment in other countries to provide a comparison for assessing Mexico's achievement in this area . Of note , however , a recent study estimated that less than 1% of those infected with T . cruzi receive treatment globally , suggesting that the extent of access in Mexico is likely to be similar in other countries [5] . Based on these findings , there are three important strategies that could be undertaken to increase access to treatment for Chagas disease in Mexico . First , under regulation , an effort could be made to ease the importation process for these drugs . Ideally , this could be accomplished by securing COFEPRIS approval for both medicines and adding them to the national formulary , which could require actions by the relevant producers of benznidazole and nifurtimox . However , as noted above , benznidazole and nifurtimox are not approved by the United States Federal Drug Administration or the European Medicines Agency , in part because full clinical trials have not been completed for either drug . This lack of approval from two leading regulatory bodies may affect the willingness of other national regulatory bodies to approve the medicines . That said , both medications are included on the WHO Essential Medicine List [55] . In addition , clinical evidence continues to accumulate in favor of these drugs and efforts by institutions such as the Drugs for Neglected Diseases Initiative are being made to register the drugs in countries such as Colombia , Paraguay and Bolivia . In other contexts , alternative regulatory approaches such as investigational protocols are being utilized to make the drugs available [18] . Also with respect to regulation , countries with a high burden of Chagas disease may consider instituting laws that mandate rigorous epidemiologic surveillance and health education as well as prevention , diagnosis and treatment of the disease . For instance , Argentina offers a model for such legislation in National Law No . 26281 . This law requires , among other things mandatory diagnostic testing and reporting for Chagas disease in all pregnant women and in newborns in the first year of life born to infected mothers . Second , under persuasion , efforts could be expanded to provide disease-specific health education programs on Chagas disease for physicians , healthcare providers and populations at risk . Increased awareness of the disease and a better understanding of appropriate treatment methods is a critical aspect of strengthening case registration and access to treatment . In addition , health education activities have been emphasized in other national control programs such as those in Guatemala [56] and the Southern Cone initiative and have been used alongside vector control to increase awareness of the disease in high risk communities and among physicians and health workers . Increased awareness of the disease and of treatment methods is a critical aspect of strengthening case registration and access to treatment . Given the importance of this programming , the WHO and PAHO also play a potentially important role in terms of encouraging these programs and providing guidance on their design and implementation . Third , under organization , it is important to strengthen existing guidelines in Mexico for the diagnosis and treatment of Chagas disease and information availability about the supply chains for these two medicines . This includes the addition of a clinical description of Chagas disease and the two medicines to its entry in the CAUSES and the creation of a clinical guide for diagnosis and treatment as this information is critically important to strengthen awareness of treatment for Chagas disease and information for practitioners about how to diagnose and treat the disease . In addition , better public reporting of medicines released and used at the state , national and global levels is needed . In conclusion , this study found that access to treatment for Chagas disease in one high burden country ( Mexico ) is limited in important ways and identified three critical obstacles to treatment access: regulatory barriers to importation , a lack of understanding of the disease and its treatment , and a dearth of clinical guidelines [5] . Several of these barriers are likely to affect access in other countries as well , especially the lack of regulatory approval and registration of benznidazole and nifurtimox and the lack of publically available information on their supply chains . Finally , the study proposed a series of actions that could be taken in Mexico , based on a general analytical framework , to improve access to treatment for Chagas disease . These recommendations have important implications for other countries in the region with similar problems in access to treatment for Chagas disease . | Chagas disease is a vector-borne disease caused by the parasite Trypanosoma cruzi . The disease is most frequently transmitted by triatomine insects but can also be passed through blood donation or from mother to child at birth . Experts estimate that 8 million people are infected with Chagas disease globally and that 1 . 1 million of these infections are found in Mexico . Most public health programs for Chagas disease focus on preventing new infections through vector control and screening the blood supply . However , in recent years there has been a greater focus on treating the disease with one of two available medications , benznidazole or nifurtimox . This study explores access to these two drugs in Mexico . The study shows that less than 0 . 5% of those who are infected with the disease received treatment in Mexico in years . The study also identified important factors that limit access in Mexico , including the exclusion of both drugs from the national health insurance program and problems importing these medications . Finally , the paper suggests ways that these problems can be overcome in Mexico , while providing helpful insight for other countries that struggle with similar problems in treating this disease . |
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Schistosomiasis is a parasitic disease infecting hundreds of millions of people worldwide . Treatment depends on a single drug , praziquantel , which kills the Schistosoma spp . parasite only at the adult stage . HDAC inhibitors ( HDACi ) such as Trichostatin A ( TSA ) induce parasite mortality in vitro ( schistosomula and adult worms ) , however the downstream effects of histone hyperacetylation on the parasite are not known . TSA treatment of adult worms in vitro increased histone acetylation at H3K9ac and H3K14ac , which are transcription activation marks , not affecting the unrelated transcription repression mark H3K27me3 . We investigated the effect of TSA HDACi on schistosomula gene expression at three different time points , finding a marked genome-wide change in the transcriptome profile . Gene transcription activity was correlated with changes on the chromatin acetylation mark at gene promoter regions . Moreover , combining expression data with ChIP-Seq public data for schistosomula , we found that differentially expressed genes having the H3K4me3 mark at their promoter region in general showed transcription activation upon HDACi treatment , compared with those without the mark , which showed transcription down-regulation . Affected genes are enriched for DNA replication processes , most of them being up-regulated . Twenty out of 22 genes encoding proteins involved in reducing reactive oxygen species accumulation were down-regulated . Dozens of genes encoding proteins with histone reader motifs were changed , including SmEED from the PRC2 complex . We targeted SmEZH2 methyltransferase PRC2 component with a new EZH2 inhibitor ( GSK343 ) and showed a synergistic effect with TSA , significantly increasing schistosomula mortality . Genome-wide gene expression analyses have identified important pathways and cellular functions that were affected and may explain the schistosomicidal effect of TSA HDACi . The change in expression of dozens of histone reader genes involved in regulation of the epigenetic program in S . mansoni can be used as a starting point to look for possible novel schistosomicidal targets .
It has been widely recognized in recent years that epigenetic effectors of chromatin remodeling are promising targets for therapeutic intervention , because they play a key role in epigenetic regulation of gene expression in all eukaryotes [1 , 2] . For schistosomiasis , new therapeutic interventions are highly desirable [3] because it is a parasitic disease that affects over 250 million individuals worldwide [4 , 5] , praziquantel is the only approved drug available for treatment [6] and resistant isolates of the Schistosoma mansoni parasite have been identified [7 , 8] . Chromatin is a complex structure of DNA packed into strings of nucleosomes , which are comprised of histone proteins that compact the eukaryotic genome and also regulate DNA accessibility to transcription , recombination , DNA repair and replication [9] . A range of modifications on the amino-terminal tail of histones , such as acetylation , methylation , ubiquitination , phosphorylation and sumoylation , are involved in chromatin remodeling and transcription regulation . These histone modifications are dynamically laid down and removed by histone modifying enzymes ( HMEs ) [10] . Two antagonistic enzyme families act to control the dynamics of histone acetylation , namely histone acetyltransferases ( HATs ) and histone deacetylases ( HDACs ) [11] , thus regulating many cellular processes such as nucleosome assembly , folding of chromatin and gene transcription [12] . In the past decade , HDACs have emerged as promising targets for epigenetic-based therapies intended to reverse aberrant epigenetic states associated with cancer; similar to the large majority of anticancer drugs , HDAC inhibitors ( HDACi ) induce tumor cell death [13 , 14] . Schistosome HDACs were characterized and studied in recent years as potential new drug targets , with the strategy of testing known HDAC-inhibiting anti-cancer drugs to kill schistosomes [15–17] . The rationale of the approach is based on the fact that the parasite shares some of the characteristics of malignant cells , such as high levels of metabolic activity and of cell division , an effective host immune evasion , and an intense oxidative metabolism [18] . In fact , it is already known that all HDAC classes can be inhibited by Trichostatin A ( TSA ) in human cells [1] , and that the parasite treatment with TSA leads to epigenetic changes in the chromatin and guides the parasite to apoptosis [15] . Also , in vitro assays have identified new compounds that inhibit SmHDAC8 ( class I ) [17] and SmSirtuins ( class III ) [19] deacetylases . In addition , in silico analyses [20] have pointed to a large number of S . mansoni histone binding partners potentially involved in the regulation of gene expression , DNA replication , cell death , cellular growth and proliferation [20] , thus suggesting that drug-induced histone modifications could affect these cellular processes in the parasite . In the present study , we report the histone acetylation status and the large-scale gene expression transition promoted by TSA HDACi , and confirm the chromatin acetylation changes in some of the gene loci with altered levels of transcription . Further , our gene expression analyses have pointed to the Polycomb Repressive Complex 2 ( PRC2 ) as being significantly affected by TSA , and this led us to test GSK343 [21] , an inhibitor of EZH2 , the histone methyltransferase component of PRC2 , as a possible schistosomicidal compound . Indeed , we found that the GSK343 EZH2 inhibitor was active in vitro against S . mansoni and acted synergistically with TSA , significantly increasing parasite death .
Animal experimentation was conducted in accordance with the Ethical Principles in Animal Research adopted by the Brazilian College of Animal Experimentation ( COBEA ) , and the protocol/experiments have been approved by the Ethics Committee for Animal Experimentation of Instituto Butantan ( CEUAIB n° 4704040515 ) . S . mansoni is maintained in the laboratory using the intermediate snail host Biomphalaria glabrata and as definitive host the golden hamster ( Mesocricetus auratus ) . Cercariae were released from infected snails and mechanically transformed to obtain schistosomula in vitro [22] . Newly transformed schistosomula were maintained for 12 h in M169 ( Vitrocell ) medium supplemented with 2% fetal bovine serum ( FBS ) ( Vitrocell ) , 1 μM serotonin , 0 . 5 μM hypoxanthine , 1 μM hydrocortisone , 0 . 2 μM triiodothyronine , penicillin/streptomycin , amphotericin , gentamicin ( Vitrocell ) at 37°C and 5% CO2 [23] , after which time the drug treatment was initiated , as described below . Adult worms were obtained from 7-week infected hamsters by left ventricular perfusion , and release of worms from the hepatic portal vein . Paired worms were maintained in RPMI medium ( Gibco ) supplemented with 10% fetal bovine serum ( FBS ) ( Vitrocell ) , penicillin/streptomycin , amphotericin ( Vitrocell ) at 37°C and 5% CO2 . The parasites were treated with 1 μM Trichostatin A ( Cayman Chemical ) , a concentration that has been shown by Dubois et al . [15] to be sub-lethal , and the negative controls with an equivalent amount of ethanol ( vehicle of TSA ) , for 12 , 24 and 48 h for microarray experiments , and for 12 h for ChIP-qPCR and western-blotting experiments . The microarray platform 4x180k was designed by our group and printed by Agilent , and it contains probes to the S . mansoni predicted genes ( "Smp genes" ) that were annotated by the genome-sequencing project in the ASM23792v2 version of the genome [24]; the probes covered Smp genes as follows: 12 mitochondrial Smp genes , 9255 sense predicted Smp genes , in addition to probes to the opposite strand of 9079 out of the Smp 9255 predicted genes . A total of 1517 additional Smp genes predicted in the genome could not be represented by unique probes , given the recommended parameters for probe design of the Agilent microarray platform . Positive and negative control probes as well as probes for spike-in RNAs were included as recommended by the Agilent expression array design instructions ( Agilent eArray ) . The microarray platform design along with gene annotation names was deposited at NCBI gene expression omnibus ( GEO ) under accession number GPL22001 , and the dataset series under accession numbers GSE83208 , GSE83209 , GSE83210 , GSE83211 . For each time point , total RNA from three biological replicates of schistosomula ( treated or control ) was extracted and purified using RNeasy Mini Kit ( QIAGEN ) according to the manufacturer’s instructions . 100 ng of each RNA sample were labeled with Cy3 or Cy5 using Low Input Quick Amp Labeling Kit ( Agilent ) ; the amplification method that is part of the protocol used to generate labeled cRNA is strand-specific and does preserve the strandedness of the labeled transcripts . Hybridizations were performed according to the Agilent protocols for two-color microarrays with dye-swap for technical replicates . After hybridizations and washings , microarrays were scanned with the SureScan Microarray Scanner ( Agilent ) . For quantitative RT-PCR , complementary DNAs were obtained by reverse transcription of 100 ng schistosomula total RNA using 6-mer random primers and SuperScriptIII Reverse Transcriptase ( Invitrogen ) and the qPCR amplification was done with SYBR Green Master Mix ( Life Technologies ) and specific primer pairs with the Applied 7500 PCR System ( Applied Biosystems ) . Primer pairs were designed for specific S . mansoni genes by Primer3 online software ( S1 Table ) . The results were analyzed by comparative Ct method and the statistical significance was calculated with the t-test . House-keeping gene PSMD Smp_000740 was chosen according to [25] . Feature Extraction Software ( Agilent ) was used to calculate the intensity of each spot from scanned microarray images . Raw intensity data was deposited at GEO under accession number GSE83211 . The low intensity spots were filtered out from the data by applying the IsPosAndSignif flag from the Feature Extraction Software , a Boolean flag , established via a 2-sided t-test , indicating if the mean signal of a spot is greater than the corresponding background . Total intensity data were normalized by Trimmed Mean method ( 40% ) , excluding positive and negative external controls present in the platform . The log2 ratio between treated and control sample intensities was calculated for each spot in the array . For genes that were represented in the array by multiple probes mapping along the gene , the mean intensity signal was calculated . Pearson correlation among biological replicates and time points were calculated revealing correlation coefficients in the range 0 . 75 to 0 . 88 ( Fig A in S1 Text ) . Significance Analysis of Microarray ( SAM ) [26] was used as the statistical test , applied individually for each time point using one-class approach [26] . Genes were considered as differentially expressed with q-value ≤ 0 . 05 . Gene Ontology terms for S . mansoni genes were downloaded from the Metazoa Mart database ( http://metazoa . ensembl . org/biomart/martview/ ) with a total of 6165 genes , and GO enrichment was calculated using Ontologizer tool [27] applying Parent-Child test with the Benjamini-Hochberg correction method [28] . To identify enriched gene networks among differentially expressed genes , QIAGEN’s Ingenuity Pathway Analysis software ( IPA , QIAGEN Redwood City , www . qiagen . com/ingenuity ) was used , considering 4758 S . mansoni genes encoding putative homologs to human proteins , as determined with BLASTP [29] ( coverage > 20% and amino acids similarity > 40% ) ( S2 Table ) . We assessed the H3K4me3 ChIP-Seq data by Roquis et al . [30] that was generated from schistosomula obtained 21 h after transformation of cercariae; we downloaded their raw data SRX1113460 , mapped them to the S . mansoni genome version ASM23792v2 using the HOMER pipeline [31] , which employs Bowtie2 to perform reads mapping and calculates significantly enriched peaks by requiring that each significant peak read density should be at least 4-fold higher than the peaks density in the surrounding 10 kb region [31] . The genomic coordinates of significant H3K4me3 peaks were associated with the genomic coordinates of the transcription start site ( TSS ) for known Smp genes using BedTools [32] within a window of ± 500 bp , as we have previously described [33]; in this way we were able to associate the presence of significant H3K4me3 marks to 4525 Smp genes in their promoter regions ( ± 500 bp of Smp gene 5´-end ) ( S2 Table ) . We tested whether the genes showing differential expression in the presence of TSA and having the H3K4me3 transcription start site mark at their TSS region had a higher mean fold-change in expression compared with the genes without the presence of this mark . For this purpose , we compared the mean fold-change ( treated/control ) between the two groups , namely differentially expressed genes that had a significantly enriched H3K4me3 mark at their TSS and differentially expressed genes that had no H3K4me3 mark , and applied the statistical t-test ( p-value < 0 . 05 ) . We used reader histone motifs from the Conserved Domains Database ( CDD ) ( https://www . ncbi . nlm . nih . gov/cdd ) to identify all S . mansoni proteins that would be predicted to recognize lysine and arginine modified by methylation and acetylation , and serine modified by phosphorylation . For this approach , we used Blastp ( https://www . ncbi . nlm . nih . gov/blast ) with parasite proteins as query and CDD files as subject , applying a 1e-10 cutoff of significance of alignment . S2 Table exhibits Smps with histone reader motif found in this analysis . Adult worms ( treated or control ) were used to prepare histone acid extracts . 50 worm pairs were soft lysed with 500 μl lysis buffer ( PBS containing 0 . 5% Triton X-10 , 0 . 02% NaN3 and Mini Protease Inhibitor Cocktail—Complete from Roche ) in a glass Potter homogenizer . The samples were centrifuged ( 10 min , 2000 g at 4°C ) and pellets containing the nuclear material were washed once in 200 μl lysis buffer then centrifuged again [15] . Histones were extracted from the nuclear fraction by suspending the pellet in 400 μl 0 . 25 M HCl with protease inhibitor and the solution was incubated overnight at 4°C in order to precipitate acid proteins [34] . The samples were centrifuged ( 60 min , 16000 g at 4°C ) and the supernatants ( with histone proteins ) were concentrated with trichloroacetic acid 33% [35] . The final pellet with histones was eluted in MilliQ water with protease inhibitors and protein concentration was determined with the Micro BCA Protein Assay kit ( Pierce Biotechnology ) . Of each sample , 10 μg of histone enriched extract was loaded on 15% SDS-Polyacrylamide gels , and after protein separation , transferred to a nitrocellulose membrane ( Amersham ) . Briefly , membranes were blocked with Tris-buffered saline ( TBS ) containing 0 . 1% Tween 20 and 5% skimmed milk ( TBST/5% milk ) , and then probed overnight with primary antibodies in TBS/2% BSA . Membranes were washed with TBST and incubated for 1 h in TBST/5% milk with secondary antibody conjugated with IRDye ( IRDye 800CW goat anti-rabbit and IRDye 700CW goat anti-mouse from Licor Biosciences ) . After washing the membranes in TBST , the bands were visualized and quantified with the Odyssey Infrared Imaging System ( Licor Biosciences ) . Acetylation of histones was measured with specific monoclonal antibodies to the following lysine modifications: Histone H3 acetyl K9 C5B11 ( Cell Signaling ) ( 1:1000 ) , Histone H3 acetyl K14 ab52946 ( Abcam ) ( 1:1000 ) , Histone H3 tri methyl K27 ab6002 ( Abcam ) ( 1:1000 ) and to normalize the samples anti-Histone H3 ab24834 ( Abcam ) ( 1:1000 ) was used . The ChIP protocol for crosslinking and sonication of schistosomula was based on a protocol described elsewhere [36] . The parasite suspension was sonicated using Epishear ( Active Motif ) with a 3 mm microprobe with 20% amplitude , 10 pulses of 30 s each , shearing the DNA into 100–1000 bp . The immunoprecipitation was performed with EZ-Magna ChIP Chromatin Immunoprecipitation kit ( Millipore ) with the following antibodies: Anti-Histone H3 ( Abcam ) , Histone H3 acetyl K9 C5B11 ( Cell Signaling ) , Histone H3 acetyl K14 ab52946 ( Abcam ) , Histone H3 tri methyl K27 ab6002 ( Abcam ) , Normal mouse IgG 12-371B ( Millipore ) and Normal Rabbit IgG PP64B ( Millipore ) . The recovered DNA in the precipitates was detected by qPCR with SYBR Advantage qPCR Premix ( Clontech ) and primers designed to specific gene promoter regions of interest ( S1 Table ) . We targeted these primers to approximately 500 bp upstream of the coding sequence , based on the fact that the H3K4me3 ChIP-Seq data for schistosomula from Roquis et al . [30] , when mapped to the S . mansoni genome as previously described [33] , falls within 500 bp of the transcription start site ( TSS ) of transcripts detected by RNA-Seq [33] . Primers were designed to non-repetitive regions within the promoter region of the selected set of genes indicated in the figure , with only one exception , Smp_174840 ( SmCBX5 ) , a gene for which the genomic upstream TSS region is highly repetitive; in this case , we designed primers at the first exon of the SmCBX5 gene . As a qPCR normalizer control we used the gene promoter region for the SmVal19 gene ( Smp_123090 ) , which was not expressed either in the HDACi- or the vehicle-treated schistosomula assays , and has no histone acetylation and methylation marks at its promoter region , as seen in the public ChIP-Seq datasets from [37] available at the Schistosoma genome browser ( http://schistosoma . usp . br ) . Schistosomula were equally distributed in 96-well microtiter plates ( 300 larvae per well ) , and the drugs ( TSA , GSK343 or a combination of the two ) or the corresponding vehicle ( control ) were added , as indicated in the legends to the figures . At each time point indicated in the figures , the parasites ( from a given set of wells in the plate ) were stained with 2 μg/mL propidium iodide ( PI ) and visualized at 10 x magnification using a Nikon Eclipse fluorescent inverted microscope . Dead parasites become stained with PI and were detected with a rhodamine filter ( 536 nm ) , and total parasites inside the well were counted using light optical microscopy [38] . For each time point a new set of wells was used , because the staining procedure was lethal to the parasites . The number of biological replicates that were assayed , as well as the number of parasites that were counted per replicate , is stated in the legends to the figures . For the LD50 assay , incubation with GSK343 was maintained for 96 h before counting . For the assay of synergy between TSA and GSK343 , parasites viability was measured each day along 4 days . Data were analyzed with Origin software ( OriginLab , Northampton , MA ) . The amino acid sequence of SmEZH2 ( Smp_078900 ) was used for the identification of template structures of SET domain using Blast algorithm at RCSB Protein Data Bank ( PDB ) [39] . Two PDB structures of human EZH2 SET domain , 4MI0 and 4MI5 , showed 63 . 8% and 64 . 9% amino acid sequence identity and 90 . 9% and 91 . 8% coverage , respectively , when compared with SmEZH2 SET domain ( from amino acids 746 to 978 ) using the EMBOSS Needle tool ( http://www . ebi . ac . uk/Tools/psa/emboss_needle/ ) . UCSF Chimera [40] was used to generate a superimposed model from the two PDB structures with the MatchMaker tool and Needleman-Wunsch algorithm . The sequence of SmEZH2 SET domain was aligned with the model using Clustal Omega . This sequence alignment was used to obtain twenty virtual structural models with Modeller 9v10 [41] , from which we selected the one with the lowest normalized DOPE-score ( zDOPE , Z-score of Discrete Optimized Protein Energy ) . The software SCWRL4 . 0 [42] was applied to the selected virtual model to improve protein side-chain conformations and KobaMIM [43] was used to refine the structure . Finally the virtual model of SmEZH2 SET domain was analyzed with Molprobity [44] and ERRAT [45] . To perform molecular docking we used the previous knowledge of amino acids of hEZH2 that interact with SAM cofactor [46] to set a grid box of 30x30x30 Å around this region , in the virtual model of SmEHZ2 , using the AutoDock Vina software [47] . We used the 3D ligand structures of GSK343 ( CID: 71268957 ) , GSK926 ( CID: 67466175 ) and SAM cofactor ( CID: 34756 ) from PubChem ( https://pubchem . ncbi . nlm . nih . gov ) to simulate the protein-ligand complex and obtain binding energies . This process consisted of 10 docking simulations using the following parameters: number of binding modes equal to 20 ( to maximize binding free energy calculations ) , search exhaustiveness of 50 and 3 kcal/mol of maximum energy difference , also receptors were considered as rigid and ligands as flexible . Binding energies are shown as mean ± S . D . calculated from the 10 docking simulations . Pymol ( PyMOL Molecular Graphics System , Version 1 . 8 Schrödinger , LLC ) was used for visualization of the three dimensional virtual model of SmEZH2 SET domain . Visualization of the two-dimensional diagram summarizing the molecular interactions between ligands and EZH2 was prepared using LigPlot program [48] .
The extent of histone acetylation in S . mansoni adult worms under the effect of the HDACi TSA was investigated after 24 h of parasite exposure to the drug . Histone marks H3K9ac and H3K14ac , associated with transcriptional activation , were studied by western blotting with monoclonal antibodies against the specific acetylated lysine 9 ( K9 ) and lysine 14 ( K14 ) residues of histone H3 . Histone hyperacetylation was detected both at H3K9ac and H3K14ac ( Fig 1A and 1B ) ; three independent biological replicates showed a statistically significant ( p-value ≤ 0 . 05 ) increase in acetylation . In parallel , the H3K27me3 histone mark , a non-related mark of transcription repression , was assayed as a control and found not to be affected by the TSA treatment ( Fig 1C ) ; this also suggests that no overall changes in histone modification had been triggered as a consequence of histone hyperacetylation . To explore the effect of HDACi on gene expression , three independent biological replicates of schistosomula were exposed in vitro to TSA or drug vehicle and large-scale gene expression changes were accessed by microarrays . Three different time points after drug exposure were analyzed ( 12 , 24 and 48 h ) . Our custom designed strand-specific Agilent microarray platform has probes for 9255 S . mansoni protein-coding gene transcripts; in addition , probes for the opposite complementary strand are present on the microarray , to detect an eventual antisense transcription for 9079 out of the 9255 gene loci . The number of genes affected by exposure to the HDACi , compared with vehicle at each time point , is shown in Table 1 . Note that the fraction of affected genes increased along the time of drug exposure and reached 54% within 48 h of treatment ( Fig 2A , Table 1 ) . It is interesting to note that at 24 h there was a predominant up-regulation of 2719 genes in the presence of the drug compared to vehicle , and only 1129 genes were down-regulated ( Table 1 , Fig 2A ) , while at 48 h of treatment just one quarter of the affected genes were up-regulated , greatly increasing the fraction of down-regulated genes . Venn diagrams for the subsets of up-regulated and down-regulated genes ( Fig B in S1 Text ) show that a large set of genes are affected exclusively at just one of the three time points analyzed . It is noteworthy that 1781 genes were affected in common at the three time points analyzed ( Fig 2B and Fig C in S1 Text ) . Overall , the data indicate a modification of the parasite’s gene transcription program along the time course of drug exposure . The strand-specific cRNA labeling protocol that was used here allowed the detection of transcriptional activity antisense to protein-coding genes , and indeed we detected that TSA treatment did affect antisense RNA ( asRNA ) transcription . Similar to the mRNAs , the fraction of affected asRNAs increased along the time of drug exposure and reached 45% of all expressed asRNAs within 48 h of treatment ( Table 1 ) ; the majority of them were up-regulated in the presence of TSA ( Table 1 ) . Considering the mRNA and the asRNA from the same locus , we found that at each time point there were over 700 loci where the pattern of expression change with drug was the same for both strands , namely both mRNA and asRNA were simultaneously up-regulated or both were down-regulated ( Table A in S1 Text ) upon TSA exposure . In addition , at each time point we detected over 400 loci where only the asRNA was differentially expressed by exposure of schistosomula to TSA ( most of them up-regulated ) , and the sense protein-coding mRNA of the same locus was not affected by the drug treatment ( Table A in S1 Text ) . Knowing that the H3K4me3 histone mark is related to transcriptionally active chromatin , we collected the H3K4me3 ChIP-Seq data for schistosomula from Roquis et al . [30] , mapped them to the S . mansoni genome as previously described [33] , and asked if the genomic positions having significantly enriched H3K4me3 marks would correspond to the positions of genes that would be more susceptible to changes in expression due to HDAC inhibition by TSA . For this analysis , we categorized the differentially expressed genes according to the presence or the absence of a significantly enriched H3K4me3 mark at their promoters ( see Methods ) , and computed the distribution of gene expression fold-change for each group at 12 , 24 and 48 h after treatment of schistosomula with TSA ( Fig 3A ) . Interestingly , we found that at 12 h after TSA treatment ( Fig 3A ) , genes that have the H3K4me3 histone mark at their promoters showed a median log2 fold-change ( treated/control ) of 0 . 55 , i . e . , on average they showed a median 1 . 5-fold activation in the presence of TSA relative to control . On the contrary , genes without the H3K4me3 mark at their promoters showed a median log2 fold-change of -0 . 61 , i . e . , on average they showed a median 1 . 5-fold inhibition in the presence of TSA relative to control ( Fig 3A ) , a significantly different pattern from that of genes with the H3K4me3 mark ( p-value < 1 x 10−7 ) . The differences in the pattern of expression change between the two groups were still observed after 24 h of TSA ( p-value < 1 x 10−7 ) and vanished at 48 h ( Fig 3A ) ; such a persistent difference at 24 h in the pattern of expression change between the genes with and without the H3K4me3 mark can be further appreciated with the cumulative distribution plot of Fig D in S1 Text , where the cumulative curve for genes with the mark is clearly shifted to the right , indicating a larger change in gene expression for the genes with the mark compared with the ones without . Also , the Kolmogorov-Smirnov test showed a significant difference ( p-value ≤ 0 . 0001 ) in the distribution profiles of log2 fold-change ( treated/control ) between the two groups of genes with or without the H3K4me3 mark , at each of the two early time points . Fig 3B shows an example of genomic distribution pattern of H3K4me3 marks along chromosome 4 for all significant differentially expressed genes at 12 h . It can be seen that a great number of differentially expressed genes are associated with the presence of the H3K4me3 mark at their promoters ( Fig 3B ) . Fig 3B also illustrates how the pattern of activation/inhibition of the genes in chromosome 4 changed at 24 h compared with the pattern at 12 h , nevertheless most of the expression changes persisted at 24 h . Gene Ontology analysis pointed to distinct enriched categories for up- and down- regulated genes in schistosomula at each of the three analyzed time points after HDACi treatment ( Tables 2–4 ) . Categories associated with ATP metabolism , such as ATP catabolic process ( GO:0006200 ) , ATP binding ( GO:0005524 ) and Purine nucleotide binding ( GO: 0017076 ) were enriched among the up-regulated genes after 12 , 24 and 48 h treatment respectively . The category of phosphorus metabolic process ( GO:0006793 ) was enriched among the up-regulated genes at 12 and 24 h . Many interesting categories are involved with regulation of DNA and chromatin , such as DNA replication ( GO:0006260 ) among the up-regulated genes at 24 and 48 h , and minichromosome maintenance ( MCM ) complex ( GO:0042555 ) among the up-regulated genes after 48 h treatment . Interestingly , the nucleosome category ( GO:0000786 ) was enriched among down-regulated genes after 48 h treatment . Genes affected in common at the three time points of TSA treatment ( 1781 genes ) were separated into two subsets of up-regulated or down-regulated genes ( Fig C in S1 Text ) and were classified into a number of enriched GO categories ( Table 5 ) , including the GO associated with DNA replication processes , which was also detected as enriched in the GO analyses of individual time points . A set of 48 genes ( out of the 1781 genes ) were affected in common at all three time points , but not with a consistent direction of expression change at all time points ( Fig C in S1 Text ) , thus not being included in the GO enrichment analysis . The vast majority of the 1781 genes exhibited a sustained gene expression change all along the HDACi treatment period , being either sustainably up- or down-regulated across the three time points ( 12 , 24 and 48 h ) ( Fig C in S1 Text ) . S3 Table gives the list of genes in each enriched GO category present in this analysis . Consistent with the GO analysis , Ingenuity Pathway Analysis ( IPA ) also pointed to an enriched network of genes from the DNA replication mechanism , with most of them detected as up-regulated by the HDACi treatment after 24 h ( Fig 4 ) . It is interesting to note the presence of a set of genes encoding DNA polymerases , pre-replication complex organization , GINS complex and minichromosome maintenance ( MCM ) proteins; all these proteins are closely involved in the initiation , regulation and progression of DNA replication . Upon longer exposure to HDACi ( 48 h treatment ) , two different enriched gene networks were detected , with most of the genes being down regulated , and being involved in cell movement of smooth muscle cells ( Fig 5A ) and in the production of reactive oxygen species ( Fig 5B ) . A set of differentially expressed genes was selected for RT-qPCR validation of the microarray results ( six up-regulated genes , four down-regulated genes ) ; selection was based on the following criteria: genes involved in signaling pathways such as SmChk1 , SmHistK , SmTyrK and SmSGPL , and also genes that encode proteins participating in chromatin remodeling such as SmCBX5 , SmEED , SmSET , SmSirt2 , SmWD40 and SmWD-repeat . A statistically significant change of expression was detected by RT-qPCR for all selected genes at all three time-points of TSA treatment under analysis ( Fig 6 ) . The same fold-change pattern was detected both by qPCR and microarray , corresponding to a Pearson correlation greater than 0 . 95 for 12 , 24 and 48 h . As a control , SmEZH2 histone methyl-transferase , not differentially expressed in the microarray , was also included in the RT-qPCR ( Fig 6 , rightmost bars ) . The overall increased acetylation of histones and the genome-wide gene expression regulation that were observed , led us to investigate the possible increased occupation by acetylated histone of the promoter region upstream of genes that were detected as up-regulated upon HDACi exposure . For this , we performed chromatin immunoprecipitation ( ChIP ) with antibodies against the histone marks related to transcription activation , namely H3K9ac , H3K14ac and H3K4me3 followed by qPCR using primers targeting the specific genomic DNA sequences of promoter regions of a set of selected genes that were up-regulated by TSA after 12 h treatment . We detected a significant increase of the H3K9ac mark at the promoter region of four out of eight chosen genes in schistosomula treated with TSA ( Fig 7A ) ; this result is corroborated by the fact that the total histone H3 occupancy at the promoter regions for all eight tested genes is not affected by TSA treatment ( Fig 7B ) . H3K14ac did not show an increased occupancy at any of the promoter regions tested ( Fig E in S1 Text ) . Also , the unrelated transcription repression histone mark H3K27me3 ( Fig E in S1 Text ) was not found enriched in any of the tested genes as expected , corroborating with western blotting assay where no change in this histone mark was detected . Using the Blastp tool we searched for S . mansoni genes encoding proteins with histone reader motifs , and we identified 195 histone readers among the parasite expressed genes . Interestingly , many of them were detected as differentially expressed ( q-value ≤ 0 . 05 ) after 12 h treatment ( 73 genes ) , 24 h ( 89 genes ) and 48 h treatment ( 85 genes ) ( Table 1 ) , although the number of affected genes was not sufficient to cause the class of histone readers to be significantly enriched . Most of these differentially expressed histone reader genes have the histone reader domain recognizing histone methyl-lysine , such as Ankyrin , WD40 and PHD domains ( S2 Table ) . These genes encode key proteins in the regulation of chromatin remodeling complexes recruiting proteins with the ability of writing or erasing histone modifications . Notably , among the differentially down-regulated genes we found SmEED ( Smp_165220 ) , which encodes a component of the Polycomb Repressor Complex 2 ( PRC2 ) and contains 7 repeat-units of WD40 motifs that are necessary for EED to recognize histone H3K27me3 [49] . EED is responsible for the regulation of EZH2 methyl-transferase activity of PRC2 , which inserts the H3K27me3 histone mark that determines transcription inhibition [49] . Having found that SmEED , an activator of EZH2 , was down-regulated in the presence of TSA , we hypothesized that a small molecule inhibitor of SmEZH2 methyltransferase might increase parasite mortality when given simultaneously with TSA . To evaluate SmEZH2 as a possible new anti-parasite epigenetic target , we tested GSK343 , a compound identified in human cancer cells as a highly potent , selective EZH2 inhibitor [21] . We assayed the in vitro effect of GSK343 on the viability of schistosomula after 4 days exposure and found that LD50 was 24 . 5 μM ( Fig F in S1 Text ) . Next , we followed schistosomula viability along 4 days of treatment with GSK343 ( Fig 8 ) and found zero viability of parasites at 50 μM GSK343 already after two days of treatment ( Fig 8 ) . At 20 μM GSK343 , a concentration below LD50 , 85% of schistosomula remained viable on day 4 ( Fig 8 ) . To test for the possible synergistic effect of both histone modifying enzyme inhibitors , we first exposed the parasites to a low dose of 1 μM TSA alone , a drug concentration which has previously been shown to cause very low mortality of schistosomula [15] . Indeed , we found that essentially 100% of schistosomula remained viable at day 4 of treatment with 1 μM TSA ( Fig 8 ) . Simultaneous exposure of schistosomula to 1 μM TSA plus 20 μM GSK343 caused a significant decrease in schistosomula viability to 70% after four days treatment , compared with 85% viability of schistosomula with 20 μM GSK343 alone ( p-value ≤ 0 . 001 two-way ANOVA test ) ( Fig 8 ) . The enhanced mortality of schistosomula caused by GSK343 in the presence of TSA compared with GSK343 alone is a clear indication of the synergistic effect of the two drugs . Noting that GSK343 appears as an interesting compound with a schistosomicidal effect , we performed the homology modeling of the potential drug target SmEZH2 and computed the predicted binding energy between the compound and the enzyme . As a template we used hEZH2 which has 746 amino acids; just the methyltransferase SET domain ( comprised of 233 amino acids ) has the crystal structure solved [50] . The SmEZH2 gene ( Smp_078900 ) , in turn , encodes a protein with 1026 amino acids with a conserved SET domain , with 64% identity and 91% coverage to the hEZH2 SET domain . Alignment of the sequences comprising the SET domain from human EZH2 , for which the structures with atomic resolution of 2 Å are available ( 4MI0 and 4MI5 ) with the sequence of S . mansoni EZH2 SET domain ( Fig G in S1 Text ) showed an identity higher than 60% , allowing the homology modeling of the SmEZH2 SET domain ( Fig 9A and 9B ) . The obtained refined homology model evaluated by Molprobity [44] exhibited in Ramachandran plots 95 . 7% of its residues in favored regions and 99 . 1% in allowed regions with two outliers ( Fig H in S1 Text ) , and ERRAT plots showed an overall quality factor of 93% for the SmEZH2 model structure . SmEZH2 presents an insertion of 19 amino acids at the SET domain compared to the hEZH2 ( Fig G in S1 Text ) , which could not be modeled and possibly this fact has reduced the overall resolution of the achieved model; this insertion appears in the SmEZH2 model as a loop external to the region involved in catalysis ( Fig 9A ) . In fact , SmHDAC8 insertions in the catalytic domain correspond to external solvent exposed loops that are not involved in catalysis as shown by X-ray crystallography [17] and the same is probably true of SmEZH2 . Using previous information of which amino acids interact with the cofactor SAM at the hEZH2 SET domain [46] , we defined the region at SmEZH2 to be used in the predictions of docking of SAM to SmEZH2 , that were computed with AutoDock Vina [47] , also taking into consideration the competitive mode of inhibition of cofactor SAM and compound GSK343 in hEZH2 , as shown in the literature [21] . This region is highly conserved between SmEZH2 and hEZH2 sequences , diverging only at V904I and Y908T . Compound GSK343 and SAM were predicted by the docking analyses to bind to the same protein region of SmEZH2 ( Fig 9C and 9D ) , sharing common amino acids ( Arg901 , Asp906 , Met909 , Ser911 , Leu913 , Asp923 and Thr925 ) at a binding distance of 3 . 5 Å as indicated in Fig 9C and 9D , thus suggesting that GSK343 could act as a competitive inhibitor of SAM in SmEZH2 . The interaction between cofactor SAM and hEZH2 ( Fig I in S1 Text ) occurs at the same protein region as that predicted for SAM interaction with SmEZH2 ( Fig 9D ) , and despite the conservation of sequence between hEZH2 and SmEZH2 in this region , just two amino acids predicted to be in close proximity to the SAM cofactor are in common , comparing SmEZH2 ( Ser911 and Leu913 , Fig 9D ) and hEZH2 ( Ser669 and Leu671 , Fig I in S1 Text ) . The predicted binding energies of cofactor SAM to hEZH2 and SmEZH2 were similar ( -6 . 3 ± 0 . 05 and -6 . 8 ± 0 . 27 kcal/mol ) . Notably , the predicted binding energies of inhibitor GSK343 to the models were more negative compared to the cofactor; GSK343 had a predicted binding energy of -7 . 83 ± 0 . 09 kcal/mol with SmEZH2 , and for hEZH2 the binding energy was -8 . 1 ± 0 . 22 kcal/mol .
We explored the effect of HDACi TSA on schistosomula gene expression , showing a large number of affected genes specifically related to different cellular functions . Remarkably , genes encoding proteins with activity at the DNA replication fork were up regulated , mainly after 24 h treatment , such as the genes responsible for Replication Complex organization ( ORC1 , ORC2 , ORC3 , CDC6 , CDT1 , MCM3 , MCM4 , MCM5 , MCM6 , MCM7 , HBO1 , FACT and RFC1-5 ) ( Fig 10A ) . Acetylation of histones is closely associated with DNA replication , stimulating the replication activity at the origin , which is recognized by the origin recognition complex ( ORC ) –heterohexamer with DNA-dependent ATPase activity [51] . After ORC binds to the origin , factors CDC6 ( cell division cycle 6 ) and CDT1 ( DNA replication factor ) are recruited and facilitate the loading of the MCM complex ( minichromosome maintenance protein complex MCM2-7 ) with helicase activity , forming a ring around the replication origin , encircling the pre-replication complex ( Pre-RC ) [52] . CDT1 recruits a histone acetyltransferase ( HBO1 ) to Pre-RC , which preferentially targets the histone H4 residues K5 , K8 and K12 , enhancing MCM2-7 loading through a mechanism requiring its acetyltransferase activity [53] . Further to this known mechanism involving a HAT , the replication fork also commits the histone chaperone FACT ( facilitates chromatin transcription ) that interacts with histones H2A-H2B and H3-H4 dimers promoting nucleosome disassembly and assembly [54] . We suggest that all this balanced mechanism of chromatin replication is being activated by HDAC inhibition , due to the increased gene expression of all the genes involved in the replication machinery . Also , the increased expression of genes encoding proteins from the replisome component , responsible for the replication initiation origin , fork progression and histone dynamics , as well the hyperacetylation of histones , suggests a genome-wide open chromatin status in the parasite due to the treatment with HDACi . The mechanism of chromatin deacetylation is important for the maturation of nascent chromatin , and is required for fork progression and stability . In line with this , disruption of HDAC functions by HDACi directly affects replication and generates a reduction in the rate of replication fork progression [55] . We observed important genes with increased expression related to this process such as replication factor c ( RFC1-5 ) and its downstream partner , PCNA ( Fig 10B ) . As a central fork component , the heterotrimeric clamp PCNA , besides orchestrating DNA synthesis and stimulating DNA polymerases activity and nucleosome assembly , also recruits HDACs to chromatin maturation , as well as other maturation factors [52] . The up-regulation of PCNA , Pol alpha , and Pol delta with TSA treatment possibly affects the cohesin rings upon fork passage . We suggest that the increased expression of components of the replication machinery may cause a replication stress as a result of HDAC inhibition , creating the possibility for DNA damage , a process that has been shown to occur upon hyperacetylation of histones [55] . An important cellular function , namely control of the quantity of reactive oxygen species ( ROS ) appears to be decreased by TSA HDACi , because 20 out of 22 genes that encode proteins responsible for reducing free radicals generation are down-regulated in schistosomula upon TSA treatment , and 2 out of 4 genes that increase ROS are up-regulated . In fact , in human cancer cells HDACi are thought to cause apoptosis through the induction of DNA damage and genomic instability as a result of the generation of ROS [56] . Recently , it was shown that depletion of SmCBP1 ( Smp_105910 ) , an HAT , resulted in an increase of neoblast proliferation [57] , and in our data this gene is down regulated after 12 and 48 h treatment . In line with this finding , neoblast genes such as FGF receptor gene ( Smp_175590 ) and Ago2 ( Smp_179320 ) [58] are up-regulated after 24 h treatment , so we suggest that under the stress of a sub-lethal dose of TSA the parasite is promoting the proliferation of its somatic stem cells . In fact , stem cell proliferation for tissue regeneration is induced by apoptosis after tissue injury [59] , and HDAC activity is an essential regulator of tissue regeneration in model organisms under the effect of HDACi [60 , 61] . We found that up to about half of the gene loci in S . mansoni showed antisense transcription and that 33 to 45% of total expressed antisense RNAs were differentially expressed upon TSA treatment ( Table 1 ) . Recently , a report on S . japonicum genes differentially expressed between genders has shown that a total of 685 and 430 genes were detected in males and females , respectively , as having significant fold-change values ≥ 2 simultaneously in the forward and the reverse strand [62] . Our results confirm that also in S . mansoni , a large number of genes exhibit antisense transcription , and that frequently the antisense transcripts show significant differential expression . Further experiments are needed to understand the role of these antisense messages in the parasite biology . The epigenome of S . mansoni has been recently explored , associating the gene expression levels with chromatin modifications [30 , 37] , or identifying chromatin epigenetic marks at the transcript start sites ( TSSs ) of genes [33] . Here we observed that on average the genes with a significantly enriched H3K4me3 mark at their TSSs showed an activation of transcription upon HDAC inhibition , whereas those genes without the mark showed on average an inhibition of transcription in the presence of TSA . In humans , it has already been described that the presence of the H3K4me3 mark at a gene TSS is often coupled with histone acetylation marks in the promoter region and gene body , allowing chromatin opening and transcription elongation [63] . Our results suggest that there is also a coupling between these two marks in S . mansoni . However , it should be noted that changes in transcription might not always be directly associated with hyperacetylation , for example when hyperacetylation affects the expression of a transcription factor ( TF ) that will in turn modify the expression of the TF target genes , regardless of whether the promoters of the latter are hyperacetylated or not . TSA treatment affected the expression of dozens of histone reader genes . Histone readers are important components of the chromatin remodeling complexes , and are able to precisely recognize histone post-translational modifications and recruit components responsible for regulating transcription , DNA replication , DNA damage and chromatin remodeling [64] . Our observed change in expression of SmEED , a histone reader from the Polycomb Repressor Complex 2 ( PRC2 ) , caused by a sub-lethal dose of TSA , suggested that another component of PRC2 , the EZH2 methyltransferase could be tested as target for inhibition . We therefore tested GSK343 , an EZH2 inhibitor [21] , and found that GSK343 was active in vitro against S . mansoni and acted synergistically with TSA , significantly increasing parasite death . This approach opens the perspective of using the information gathered here about the change in expression of the dozens of histone reader genes involved in regulation of the epigenetic program in S . mansoni as a starting point to look for possible novel schistosomicidal targets .
We have shown that the TSA HDAC inhibitor , a known schistosomicidal drug , causes a wide range of gene expression changes in S . mansoni , and we were able to point to a number of cellular functions that were affected in the parasite , such as DNA replication and control of reactive oxygen species . A new epigenetic enzyme SmEZH2 emerged as a novel potential drug target to be studied with schistosomicidal activity , with its inhibition having a synergistic anti-parasitic effect along with HDAC inhibition . | Human schistosomiasis is a disease caused by the parasite Schistosoma spp . that affects over 230 million people worldwide . Treatment depends on a single drug , praziquantel , and the search for new drugs calls for exploiting strategies that are successful for other pathologies such as cancer , including the test of inhibitors targeting chromatin enzymes responsible for modifying histone proteins associated with DNA . Histone modifications regulate cellular gene expression . Inhibitors targeting an important class of these histone-modifying enzymes , namely Histone Deacetylases ( HDACs ) , are known to induce in vitro mortality of the parasite ( at the schistosomula and adult worm stages ) , however the molecular changes caused in the parasite were not known . In this scenario , we studied the effect of the HDAC inhibitor Trichostatin A on the parasite genome-wide gene expression , on histone modifications at gene promoter regions and on the chromatin acetylation status , and found important affected gene pathways . In addition , this approach showed affected genes associated with other histone modifications , which led us to test and identify a synergistic schistosomicidal agent , GSK343 , an EZH2 histone methyltransferase inhibitor . Our work points to the class of histone methyltransferase modifying enzyme as a novel drug target to be explored in the future for parasitosis treatment . |
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Gene flow between populations that are adapting to distinct environments may be restricted if hybrids inherit maladaptive , intermediate phenotypes . This phenomenon , called extrinsic postzygotic isolation ( EPI ) , is thought to play a critical role in the early stages of speciation . However , despite its intuitive appeal , we know surprisingly little about the strength and prevalence of EPI in nature , and even less about the specific phenotypes that tend to cause problems for hybrids . In this study , we searched for EPI among allopatric populations of the butterfly Euphydryas editha that have specialized on alternative host plants . These populations recall a situation thought typical of the very early stages of speciation . They lack consistent host-associated genetic differentiation at random nuclear loci and show no signs of reproductive incompatibility in the laboratory . However , they do differ consistently in diverse host-related traits . For each of these traits , we first asked whether hybrids between populations that use different hosts ( different-host hybrids ) were intermediate to parental populations and to hybrids between populations that use the same host ( same-host hybrids ) . We then conducted field experiments to estimate the effects of intermediacy on fitness in nature . Our results revealed strong EPI under field conditions . Different-host hybrids exhibited an array of intermediate traits that were significantly maladaptive , including four behaviors . Intermediate foraging height slowed the growth of larvae , while intermediate oviposition preference , oviposition site height , and clutch size severely reduced the growth and survival of the offspring of adult females . We used our empirical data to construct a fitness surface on which different-host hybrids can be seen to fall in an adaptive valley between two peaks occupied by same-host hybrids . These findings demonstrate how ecological selection against hybrids can create a strong barrier to gene flow at the early stages of adaptive divergence .
The idea that ecological divergence can drive speciation has been discussed , studied , and widely accepted since the time of Darwin [1]– . It is thus surprising that we know so little about one of the most important mechanisms by which ecological divergence may contribute to species formation . Imagine two populations adapting to different ecological niches . Such adaptation will reduce gene flow between the populations if hybrids have intermediate phenotypes that fare poorly in both parental habitats—in essence , if hybrids fall in an adaptive valley between the fitness peaks associated with the niches of their parents . This phenomenon is called extrinsic postzygotic isolation ( EPI ) because it obstructs gene flow after hybrid zygotes have formed ( hence postzygotic ) and arises from an interaction between hybrids and their environment rather than from inherent developmental defects ( hence extrinsic ) . Verbal theory and intuition suggest that EPI may be a widespread and important component of speciation . For one , it is expected to develop early , at the onset of phenotypic divergence . Barriers that develop early are critical to speciation because they can facilitate subsequent divergence and speed the accumulation of additional forms of isolation . Early barriers that are postzygotic , in particular , can lead to direct selection for assortative mating via reinforcement [2] , [10] . Second , EPI has the potential to act in any system involving ecological divergence , regardless of its biological particulars . This sets it apart from several forms of prezygotic isolation , which can only contribute to speciation when the alternative niches are directly linked to mate choice ( e . g . , habitat isolation , temporal isolation , pollinator isolation , and sexual isolation based on ecological traits that double as mating cues [6] , [8] ) . Given the potential importance and generality of extrinsic postzygotic isolation , many have lamented how little we know about its prevalence , strength , and character in nature ( e . g . , [6]–[8] , [11] ) . It has received much less empirical attention than any other major form of reproductive isolation , perhaps because its ephemeral nature makes it difficult to study . The clearest examples in which ecological selection against hybrids has been established and quantified include benthic and limnetic morphs of the threespine stickleback [12]–[15] , large and small billed Geospiza ground finches [16] , [17] , basin and mountain subspecies of big sagebrush [18] , mimetic races of Heliconius butterflies [19] , and Neochlamisus leaf beetle host races [20] ( see also [21]–[30] ) . In host races of Rhagoletis fruit flies [31] , [32] hybrids possess intermediate traits that are almost certain to be maladaptive , though the effect has not been directly quantified ( see also [33] ) . While these studies confirm the existence of extrinsic postzygotic isolation in natural systems , major gaps in understanding remain . One gap involves the relative importance of EPI to other ecological forms of isolation known to contribute to speciation . As noted above , prezygotic isolation can provide a potent barrier to gene flow when divergent selection acts directly on characters that influence mate choice [6]–[8] . Such mating barriers are better documented than EPI and appear to be stronger than EPI in the systems where EPI has been shown to exist ( e . g . , sexual isolation in sticklebacks , Darwin's finches , and Heliconius butterflies [19] , [34]–[36]; habitat isolation in Neochlamisus leaf beetles and other insect host races [20] , [23] , [37] ) . We are aware of no cases where ecological selection against hybrids is the primary isolating factor between diverging populations and it remains possible that it is often incidental to speciation . Or perhaps its most important contribution comes at an early stage of divergence that has rarely been examined—among allopatric populations at or before the time they come into contact in sympatry . We also know relatively little about the mechanistic bases of EPI . The most common empirical approach to studying the phenomenon focuses on quantifying it and distinguishing it from intrinsic postzygotic isolation , where hybrids perform poorly due to inherent developmental defects . Postzygotic isolation is detected by showing that hybrids are less fit than their parents in the specific environments to which the parents are adapted . Confirmation that the isolation is extrinsic is then achieved by showing that the fitness deficit disappears in a “neutral” environment [12] or , more rigorously , by showing that the rank order of fitness of the two backcross types switches between parental environments [14] , [38] . This approach has the advantage of assaying fitness directly in hybrid individuals , but it does not reveal which interactions between hybrids and their environment are dysfunctional . Are certain types of traits ( e . g . , morphological , physiological , or behavioral ) more likely to lead to hybrid dysfunction than others ? A third gap in current knowledge of EPI is that , with a few notable exceptions ( e . g . , [15]–[17] ) , existing estimates of its strength come from laboratory environments [20] , [23] , [25] , [27] , [29] , [30] or from modified field environments ( e . g . , enclosures that exclude predators/grazers [12]–[14] , [18] , [21] ) . There are obvious reasons for this practice . Tracking individual organisms and measuring their fitness in nature is difficult , if not impossible , in most systems . Yet fitness estimates that come from artificial environments will only be accurate to the extent that key aspects of the ecological niches in question have been replicated . For example , bringing the alternative host plants of diverging insect populations into the lab or greenhouse allows researchers to evaluate the costs hybrids face due to intermediate digestive physiology but will likely miss those associated with factors such as host-specific predators , pathogens , and microclimates . The scarcity of field studies raises the possibility that we have systematically underestimated the true strength of extrinsic postzygotic isolation in nature . The checkerspot butterfly Euphydryas editha provides a tractable system in which to begin addressing these gaps in understanding . The species is made up of multiple , allopatric populations in various stages of adaptation to distinct host plants [39] . Those populations adapted to Collinsia torreyi and Pedicularis semibarbata ( Figure 1A ) are distributed along the western slope of the Sierra Nevada in California ( Figure 1B ) in patches of coniferous woodland habitat where the two host plants are intermingled at the scale of inches to feet [40] . Despite host sympatry , the butterflies at any given site have evolved to lay eggs on just one of the two plant species and ignore the other , with the identity of the used host flip-flopping back and forth between sites ( Figure 1B ) . The populations that use different hosts have diverged in several important host-related traits , ranging from larval performance and foraging height to adult female host preference , oviposition site height , and clutch size ( Figure 2 ) [40] . Consistent host-associated differences in morphology and random genetic markers , on the other hand , remain elusive . A previous examination of >400 AFLPs revealed significant genetic differentiation among populations [39] , [41] , but this differentiation was not associated with the use of the two host plants examined here ( subset of analyses from [41] summarized in Text S1 ) . Here , we obtain field-based estimates of the strength of extrinsic postzygotic isolation in this system and describe its underlying mechanisms . For one trait known to have diverged among populations , we directly measure the fitness of hybrids expressing that trait in the field . For the five remaining traits , we first determine the extent to which hybrids are intermediate and then manipulate a control group of organisms to quantify the effects of intermediacy on fitness in the field . This approach mirrors the way we think about EPI intuitively . In particular , it allows us to empirically estimate the shape of a fitness surface on which hybrids can be seen to fall in a valley between two peaks corresponding to the phenotypes of “pure” parental populations . Our results provide compelling evidence that extrinsic postzygotic isolation can provide a strong and primary barrier to gene flow at the early stages of ecological divergence .
We examined the performance of young hybrid larvae by placing hybrid eggs on naturally growing host plants in the field one day before hatching and then monitoring the growth and survival of the resulting larvae for 10 d . Hybrids were only examined on the host to which their mothers were adapted , and thus on which their mothers would have laid their eggs: CP and CC on Ctor , and PC and PP on Psem . Pure larvae were also included on their traditional hosts for reference: C on Ctor and P on Psem . Same-host hybrids did not differ from the corresponding pure larvae in either growth or survival ( CC = C on Ctor , PP = P on Psem; Figure S1 ) . Different-host hybrids performed well on Ctor ( CP = CC/C for both growth and survival; Figure S1A , Table S1 ) . However , on Psem they grew 15%–30% more slowly ( PC<PP/P for growth; PC = PP/P for survival; Figure S1B , Table S2 ) . The trend for reduced growth on Psem was significant only when predators were excluded ( ANOVA p = 0 . 0001 and 0 . 18 in the absence and presence of predators , respectively; Table S2 ) , probably because our predator exclusion technique allowed us to control for variation in the quality of individual Psem plants ( see Methods ) . In summary , hybrids between populations adapted to Psem and Ctor were at a slight disadvantage on one of the two host plants . This weak , asymmetric effect is likely to be extrinsic since it disappeared on a third host ( see section entitled “Different-Host Hybrids Are Not Intrinsically Unfit” ) . The nature of the behavioral phenotypes addressed in this article makes it clear that their effects on fitness are extrinsic rather than intrinsic . This distinction is not as clear , however , with regard to the reduced growth rates of young PC larvae on Psem ( see first section of Results ) . We therefore examined larval growth rate on a third host plant , Castilleja applegatei . If the original reduction in growth on Psem is extrinsic , it may dissipate on this “neutral” host [12] . Indeed , PC larvae grew just as quickly as P and PP larvae when reared on C . applegatei ( p = 0 . 11; Figure S2A ) . The neutral environment test is not fail-safe since intrinsic fitness problems may also fail to penetrate in certain environments ( [6]: p . 250 ) . We therefore searched for other signs of inherent incompatibilities in different-host hybrids by examining egg viability , female teneral weight , sex ratios , and fertility . We found none ( Figure S2 ) . Extrinsic postzygotic isolation is often conceptualized using the metaphor of an adaptive surface; it occurs when hybrids are intermediate and fall in an adaptive valley between two fitness peaks occupied by the parental populations . We transformed our empirical data on hybrid host preferences and clutch sizes into such a surface in order to help visualize the extent to which intermediate values of these traits generate isolation . Other phenotypes were not included since doing so would require more than three dimensions . The shape of the surface was estimated by combining our field data on the survival of various sized clutches ( Figure 5A and 5B , right panels ) with a logical translation of host preference values into probabilities of laying on each host ( see Methods ) . The surface has two peaks corresponding to the optimal clutch size on each host plant ( Figure 6 ) . The position of same and different-host hybrids on the surface was simulated based on the results of our host preference and clutch size assays ( Figure 4A and 4D; see Methods ) . The two types of same-host hybrid females resemble pure insects and sit upon the two peaks; CC females strongly prefer Ctor and lay small clutches ( Figure 6 , blue dots ) , while PP females prefer Psem and lay large clutches ( Figure 6 , yellow dots ) . Different-host hybrids , on the other hand , fall in the adaptive valley between the peaks; they readily accept both host species and lay their eggs in medium-sized clutches ( Figure 6 , green dots ) .
Among the traits we examined in different-host hybrid insects , oviposition behaviors were the most maladaptive . First , medium clutch sizes lowered offspring survival on both hosts—with small clutches being optimal on Ctor and large clutches being optimal on Psem . This effect is distilled in Figure 6 . Interestingly , predator pressure is substantially higher on Psem than on Ctor ( compare mean survival of all larvae on two hosts in Figure S1; [46] ) and unpublished experiments indicate that the advantage enjoyed by large clutches on Psem disappears when predators are excluded . Host-specific predator regimes may therefore help explain why the relationship between clutch size and offspring survival was opposite on the two hosts . Different-host hybrid females accrue further fitness losses due to additional maladaptive oviposition behaviors . When laying on Ctor , they preferred phenologically older plants on which offspring fitness was severely compromised ( 30% growth deficit , 70% survival deficit ) . It makes sense that a preference for older plants would be maladaptive on an annual plant like Ctor . Coping with early host senescence is a major challenge for E . editha populations that use annual hosts [44] , [45] , [47] , [48] . Larvae cannot enter diapause until mid to late 3rd instar , yet many find themselves on senescing plants as 1st or 2nd instars ( 28% of 53 clutches censused in 1995 and 32% of 25 clutches censused in 2009 at TR ) . When laying on Psem , different-host hybrids differed from Psem-adapted insects in failing to lay their eggs near the ground . Instead , they laid at the place where they first contacted and tasted the plant ( compare Videos S1 and S2 ) . Field experiments again revealed that this behavior is maladaptive , reducing offspring development time by 30%–50% . Preliminary thermal data suggest that eggs and larvae found near the ground develop more quickly than those found on middle to upper Psem leaves because they are closer to the hot , sandy soil and therefore experience a warmer microenvironment . Although the behavior of hybrid larvae was less deleterious than that of adult females , it affected both sexes and is also expected to contribute to isolation . Different-host hybrid larvae foraged at lower levels on Ctor than insects with two Ctor-adapted parents , which foraged at the top . This behavior did not have a detectable fitness effect when larvae hatched onto young , still budding Ctor plants but probably contributed to the 15% growth deficit observed when larvae were reared on mature plants . Ctor senesces from the bottom up , and plant material found at the base and middle of the plant was less nutritious than that found at the top . Psem , on the other hand , is a perennial herb that retains moisture throughout the summer . Host senescence is not a problem for larvae on this host . Moreover , the youngest leaves appear in the center of the plant , being neither high nor low ( Figure 1A ) . These differences , in combination with the potential temperature effect mentioned above , help explain why the relationship between foraging height and growth rate on Psem was opposite to that on Ctor . The last trait we show to contribute to isolation among E . editha populations adapted to Ctor and Psem is early larval performance . However , the effect was asymmetric and relatively weak , consisting of a 15%–30% growth deficit on Psem only . We suspect that different-host hybrids have problems digesting or detoxifying Psem , but could not test this directly due to the difficulty of manipulating an insect's physiology independently from other traits . We can at least rule out the contribution of foraging height since the larvae in our experiments on Psem did not stray from their natal site . It is also unlikely that intrinsic incompatibilities contributed , since different-host hybrids showed no signs of any developmental defects and grew just as quickly as PP insects on both Castilleja applegatei and young Ctor . Although we could attempt to synthesize the various growth and survival deficits described above into a single measure of overall hybrid fitness , this exercise would give the false impression that our understanding of the system is complete . For one , although we expect E . editha growth rates to correlate with lifetime fitness , the exact relationship between growth , survival , and reproduction is poorly defined . Second , there may be other relevant trait differences between populations of which we are unaware—for example , in males or post-diapause larvae . Lastly , our estimates come from a single year and do not incorporate annual environmental fluctuations likely to influence the overall strength of EPI ( e . g . , [16] , [51] ) . Despite these caveats , the diversity of traits that we show to affect hybrids and the surprising strength of their effects in at least one place and time make it clear that extrinsic postzygotic isolation provides a substantial overall barrier to gene flow in this system . We found not a single positive effect of hybridization on fitness . Neither do we expect that effects associated with one trait might mitigate those stemming from another . Instead , the deleterious effects are likely to accumulate over the life cycle . As an example of how this might occur , consider the hybrid offspring of an adult male E . editha that immigrated from a Psem-adapted population to a Ctor-adapted population and mated with a local female . Eggs would be laid on Ctor , the plant preferred by the female . Hatching larvae would forage lower on the host than local larvae and , in most years , would grow more slowly due to their failure to locate the best food at the host meristems . This reduction in growth rate would increase larval mortality due to host senescence and extend the period of exposure to parasitoid attack . Those hybrids that survived to adulthood would be smaller and/or eclose later than local Ctor-adapted butterflies—with a penalty paid in fecundity and/or time . Adult female hybrids would have weak host preferences , sometimes choosing to lay eggs on Psem and sometimes choosing Ctor . When laying on Psem they would lay clutches too small for optimal survival on that host and oviposit high enough on the plant to slow the development of their eggs and young larvae . When laying on Ctor , they would tend to choose the least suitable , most phenologically advanced individuals and then lay clutches too large for optimal survival on that host . Note that the majority of these fitness costs stem from behaviors specific to females . EPI should therefore be most effective at blocking the flow of maternally inherited genes , including those found on the W chromosome and in the mitochondrial genome . We cannot rule out the possibility that different-host hybrids would perform well on a host plant that we have not investigated , for example , one of the host plants used by E . editha in other parts of its range . However , no other known E . editha host plants are associated with butterflies showing the unique combination of phenotypes that characterize the different-host hybrids studied here . Moreover , work from other systems suggests that colonization of a novel environment by hybrid organisms sometimes generates a third species , rather than impeding or reversing divergence between the two parent species [52]–[55] . Although related , Ctor and Psem differ remarkably in chemistry [40] , growth form ( Figure 1A ) , life history ( annual versus perennial ) , and visitation rate by E . editha predators [46] . As discussed above , each of these is likely to play a role in mediating the fitness costs of the traits addressed in this study . Several of them , however , are difficult or impossible to replicate indoors , and we would have underestimated the strength of EPI had we conducted our experiments in the laboratory or greenhouse . For example , the predators that put smaller clutches at a disadvantage on Psem would not have been present in the laboratory . Even field cages designed to prevent the movement of larvae on naturally growing plants would have excluded predators and thus been problematic . Likewise , the temperature gradient we suspect of slowing the development of eggs and larvae found on the upper leaves of Psem plants would have been difficult to duplicate in the laboratory . This study thus highlights the importance of estimating EPI in the field when possible . Most previous studies of extrinsic postzygotic isolation in insects that specialize on distinct host plants were conducted in the laboratory ( [20] , [23] , [25] , [27] , [30] , but see [21] ) . These studies revealed growth and survival deficits in F1 individuals ranging from 10%–50% ( averaged across both host plants; Table S9 ) . We suspect , however , that EPI may be significantly stronger and more prevalent than the lab estimates suggest . Indeed , most existing estimates of EPI , including our own , are best interpreted as minima , either because they come from the laboratory or because they incorporate only a subset of life stages and traits . By focusing on specific traits , we have shown that behavioral divergence is a potent source of ecological selection against hybrids in this system . There are a few additional examples of this kind . One example involves apple and hawthorn host-races of the apple maggot-fly Rhagoletis pomonella . These insects orient towards the odor of their own host and avoid the odor of the alternative host . F1 hybrids avoid the odors of both parental hosts and are likely to have problems finding suitable oviposition sites in nature [32] , [56] . Other examples come from bird populations that use different wintering grounds , or that migrate along different routes in order to avoid geographic barriers on their way to the same wintering grounds [33] , [57] , [58] . In at least one case , hybrids between SE- and SW-migrating European blackcap populations inherited a genetic tendency to migrate in an intermediate southerly direction—a behavior expected to take them directly over formidable geographic barriers including the Alps , the Mediterranean Sea , and the Sahara desert [33] . Both of these examples involve the inheritance of intermediate niche preferences—with the niches taking the form of host plants in the first example and wintering grounds in the second . One of the most deleterious behavioral incompatibilities described in the current study also involved niche preference ( for phenologically unsuitable Ctor individuals ) . The fact that adaptation to a new niche is often accompanied by the evolution of a new preference for that niche suggests that maladaptive behavioral preferences may be widespread drivers of EPI in animals . As outlined in the introduction , the best-documented ways in which ecological selection leads to reproductive isolation involve mating barriers . These barriers arise when niche-adaptation is directly linked to mate choice . For example , many insects choose mates from among those they meet on their preferred host plant , causing habitat isolation ( e . g . , [37] , [59]–[62] ) . Many plants can only exchange pollen with those whose flowers attract the same suite of pollinators , causing pollinator isolation ( e . g . , [63] , [64] ) . In some organisms , ecological traits are used as , or affect , mating cues , causing sexual isolation ( e . g . , [35] , [65] , [66] ) . Interestingly , ecological mating barriers appear to be absent in this system . E . editha does not mate on its host on a micro-scale , and the habitats occupied by populations adapted to Psem and Ctor are essentially the same on a macro-scale . This means that neither immigrant inviability nor habitat isolation should prevent local insects from mating with differently adapted migrants . Furthermore , the growing seasons of the two host plants both begin at snow melt in early spring , leaving little opportunity for temporal isolation between the two types of populations . At first glance , the absence of such host-associated mating barriers may explain why E . editha host shifts are not generally associated with speciation events [67]–[69] . Nevertheless , consideration of the particular host plants and populations addressed here tells a complementary story . Psem and Ctor ( and their respective genera ) are unique among hosts of E . editha in the degree and dimensionality of divergent adaptation that is associated with feeding on them [40] . Perhaps as a result , the two plants are never used jointly by a single generalized population . Instead the populations that use them remain stubbornly allopatric , despite widespread host sympatry . Our finding of substantial EPI in the absence of host-associated mating barriers provides an explanation for this curious geographic scenario and suggests a potential route to complete speciation . Imagine a mated female migrant from a Psem-adapted population arriving at a site where local butterflies use Ctor ( or vice versa ) . She will likely lay eggs on her preferred host plant , Psem . The apparent absence of premating barriers , however , will make it difficult for her offspring to establish a stable , sympatric , host race . Instead , they are expected to mate with local insects and produce hybrids . Strong EPI , in turn , should stem the flow of genes from those hybrids back into the local population . The combination of little to no prezygotic isolation and strong EPI may thus explain the existence of a set of allopatric populations that are significantly isolated yet unable to move into sympatry . If this situation persists , non-ecological forms of prezygotic isolation may have time to accumulate , or increased migration rates may trigger reinforcement [70] . Whatever the ultimate fate of these populations , this study illustrates how extrinsic postzygotic isolation can accumulate over the life cycle of an insect and reduce gene flow between populations that are adapting to distinct ecological niches .
In the montane populations studied here , E . editha completes a single generation per year . Adults fly anytime between May and July . Females lay eggs in clutches , which hatch after approximately 2 wk . Young larvae must feed for a further 2 wk , until mid-third instar , before they are able to enter diapause , which lasts through winter to snowmelt the following spring . Post-diapause larvae then resume feeding for 2–3 wk , pupate in the ground litter , and eventually eclose as adults . E . editha is most specialized on particular hosts during the adult female and pre-diapause larval stages . Adult females are adapted to recognize their host species and lay a particular-sized clutch at a particular height upon it ( Figure 2 ) [40] . Pre-diapause larvae are adapted to digest/detoxify the host that their mother chose for them ( Figure 2 ) [40] . They tend to remain with their siblings and commence feeding in the exact spot where they hatched until they run out of edible leaf material in the immediate vicinity . This means that larval position is determined by the mother's oviposition site choice for the first 2–4 d on Ctor ( which has small , quickly defoliated leaves ) and 10–14 d on Psem ( which has larger leaves ) . After this time , pre-diapause larvae make their own decisions about where to feed and rest on the natal plant and are adapted to do so at particular heights ( Figure 2 ) [40] . They generally stay on the natal plant until it is completely defoliated ( usually at least 10 d ) . Post-diapause larvae are less specialized . For example , in populations where eggs and pre-diapause larvae are found only on Psem , wandering post-diapause larvae may feed extensively on newly germinated Ctor seedlings in early spring [71] . Pupae and adult males have no particular relationship with host plants . We mated butterflies from the four populations highlighted in Figure 1B according to the bold arrow combinations illustrated in Figure 1C . The females used in these matings were collected as pupae or final/penultimate instar larvae in the early spring of 2004 and 2005 , reared to pupation on Ctor leaves in small cups , allowed to eclose in small cages , and kept in sealed plastic containers on ice for 0–7 d prior to mating . The males were caught as adults in their native habitats . All mating configurations were equally easy to achieve . To obtain immature stages for our experiments , we solicited eggs from mated females in captivity . Hybrid eggs ( CC , CP , PC , PP ) were obtained from females mated as described above , while “pure” eggs ( C and P ) were obtained from wild-caught females that had mated with males from their own populations prior to capture . We incubated all eggs in the presence of leafy material from both host plants to control for the possibility that host-adaptive phenotypes may be induced by exposure of eggs to volatile plant chemicals . To obtain adults for our oviposition behavior assays , we set aside 20–30 eggs from each of the females mated as described above and reared them to adulthood on Ctor . Ctor was used for all larvae , regardless of hybrid type , for three reasons: ( 1 ) Ctor is abundant and easy to gather/transplant , ( 2 ) all larvae develop faster on Ctor than on Psem , and ( 3 ) feeding all larvae the same host controls for larval host effects . Upon hatching , young larvae were reared to diapause on live or freshly gathered Ctor , and kept at 0–4°C through the winter in the basement room of an unheated cabin at Sagehen Creek Field Station ( 6 , 380 ft elevation in the Sierra Nevada of California ) . Buffered from above-ground fluctuations , the temperature conditions in this room mimic those experienced by naturally occurring larvae that spend the winter at similar elevations in the ground litter buried under many feet of insulating snow . In the subsequent spring ( 2006 ) , the larvae were reared on live , potted Ctor under a shade cloth in the field . Those individuals that developed through to pupation were allowed to eclose in small cages and were weighed on a microbalance . Adult males were then housed in cages hanging in the shade and fed artificial nectar ( honey , raw sugar , salt , and amino acids ) once a day and offered mud puddles every 2–4 d . Adult females were placed in sealed plastic containers on ice for 0–21 d until we were ready to mate them and test their behavior . We mated most same-host hybrid females to different-host hybrid males and vice versa . More than 1–2 wk on ice prior to mating caused some females ( regardless of cross type ) to have difficulty laying eggs and/or to lay unhealthy , shriveled eggs . We excluded such females from our assays . | When two populations adapt to different ecological environments , they may become reproductively incompatible with each other and eventually form distinct species . One form of incompatibility thought to contribute to this process occurs when hybrids between diverging populations are ecologically maladapted . They suffer reduced survival and reproduction because they possess intermediate traits that are ill-suited to both parental environments . Although this phenomenon is potentially important at the early stages of speciation , it is difficult to study in the field and is often invisible in the laboratory—leaving us with few empirical examples . We use a series of behavioral assays and manipulative field experiments to examine hybrids between populations of a butterfly that have adapted to use distinct host plants . We show that the hybrids are perfectly healthy in the laboratory . However , when taken into the field , they interact with their host plants in intermediate and anomalous ways that lower the growth and survival of both themselves and their offspring . Our findings confirm that ecological selection against hybrids has great potential to block gene flow at the early stages of adaptive divergence . |
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The intraerythrocytic parasite Plasmodium—the causative agent of malaria—produces an inorganic crystal called hemozoin ( Hz ) during the heme detoxification process , which is released into the circulation during erythrocyte lysis . Hz is rapidly ingested by phagocytes and induces the production of several pro-inflammatory mediators such as interleukin-1β ( IL-1β ) . However , the mechanism regulating Hz recognition and IL-1β maturation has not been identified . Here , we show that Hz induces IL-1β production . Using knockout mice , we showed that Hz-induced IL-1β and inflammation are dependent on NOD-like receptor containing pyrin domain 3 ( NLRP3 ) , ASC and caspase-1 , but not NLRC4 ( NLR containing CARD domain ) . Furthermore , the absence of NLRP3 or IL-1β augmented survival to malaria caused by P . chabaudi adami DS . Although much has been discovered regarding the NLRP3 inflammasome induction , the mechanism whereby this intracellular multimolecular complex is activated remains unclear . We further demonstrate , using pharmacological and genetic intervention , that the tyrosine kinases Syk and Lyn play a critical role in activation of this inflammasome . These findings not only identify one way by which the immune system is alerted to malarial infection but also are one of the first to suggest a role for tyrosine kinase signaling pathways in regulation of the NLRP3 inflammasome .
Malaria is a widespread infectious disease that affect up to 300 million individuals in the tropical and sub-tropical regions of the world , and is responsible for 2–3 million deaths annually [1] . Malaria is caused by parasites of the Plasmodium genus and is characterized by episodic fevers , anemia , headache and organ failure . Plasmodium parasites feed on erythrocyte hemoglobin and uses a heme detoxification mechanism that results in the formation of an insoluble , inert , dark-brown crystalline metabolic waste called hemozoin ( Hz ) [1] , [2] . Hz is involved in the fever observed during the malaria process as intravenous injection of Hz caused thermal deregulation and was associated with the induction of pyrogenic cytokines [3] . In addition , the release of both Plasmodium-derived Hz and merozoites during the erythrocyte burst phase of the disease coincides with the massive induction of pro-inflammatory cytokines , such as IL-1β and TNF , and with the periodic fevers characteristic of malaria [3] , [4] . IL-1β secretion is controlled by the recently described inflammasome , a signaling platform scaffold composed of NLR family members such as NLRC4 ( NOD-like receptor containing CARD domain or IPAF ) and members of the NLRP ( NOD-like receptor containing pyrin domain ) family including NLRP1 and NLRP3 ( also known as NALP3 and cryopyrin ) . In addition , the NLRP3 inflammasome is composed of the adaptor molecule ASC ( Apoptosis-Associated Speck-Like Protein ) and the effector molecule caspase-1 , the latter which is responsible for the cleavage of pro-IL-1β into its active form [5] , [6] . TNF is induced by a wide variety of innate receptors but in particular by many members of the Toll-like receptors ( TLR ) . It was previously reported that Hz can induce IL-1β secretion in vitro and in vivo [7] , [8] , however , TLRs are not required for the Hz-induced inflammatory response [9] . Given the clear association of IL-1β with the induction of fever and recent studies demonstrating that the NLRP3 inflammasome senses inorganic materials , such as monosodium urate ( MSU , a gout-associated uric-acid crystals ) , silica , asbestos and aluminum hydroxide by producing IL-1β [6] , we tested whether Hz can activate the NLRP3 inflammasome . In addition , while NLRP3 ligands have been well identified , little is known about the upstream mechanisms that regulate its activation . Some mechanisms that have been proposed include efflux of potassium , increased intracellular calcium , reactive oxygen species ( ROS ) generation and lysosome disruption [6] , [10] . However , having previously reported that both MSU and Hz can trigger production of inflammatory mediators via the activation of signaling cascades involving MAP kinase family members and various transcription factors , we have herein addressed the role of upstream signaling in the activation of the inflammasome that results in IL-1β production in response to the malarial pigment Hz .
In these studies we utilized a chemically synthesized Hz to prevent contamination that could result from native Hz purification; the synthetic Hz is morphologically and chemically similar to native Plasmodium-isolated Hz ( Fig . S1 ) . Previously , we reported that both synthetic and native Hz induce similar expression profiles of chemokines and pro-inflammatory cytokines [7] . In addition , the synthetic Hz was subjected to elemental analysis to assess its purity . Theoretical calculated values of the molecular formula of Hz ( C68H62N8O8Fe2 ) give 66 . 35% of carbon ( C ) , 5 . 08% of hydrogen ( H ) and 9 . 10% of nitrogen ( N ) . We have obtained elemental values from our synthetic Hz preparation very close with the theoretical one ( C: 66 . 5%; H: 5 . 3%; N: 8 . 9% ) . To further show the purity of Hz , we performed an agarose gel with 200 µg of Hz and we did not detect any trace DNA or RNA contamination ( Fig . S2A ) and treatment with DNase or RNase did not interfere with Hz-induced IL-1β production ( Fig . S2B ) . These data indicate that our synthetic Hz preparation is high purity and free of contaminant . To evaluate whether Hz activates the inflammasome , we measured IL-1β secretion by PMA-differentiated human monocytic cell line ( THP-1 ) stimulated with increasing concentrations of Hz or MSU . Hz- and MSU-induced IL-1β production was found to be comparable ( Fig . 1A ) . In accordance with previous studies showing that HSP-90 stability [11] modulates inflammasome assembly , we found that Hz-induced IL-1β secretion was reduced in the presence of the HSP-90 inhibitor geldanamycin D ( Fig . 1B ) . Inhibition of caspase-1 activity using a specific competitor ( Y-VAD-FMK ) [12] or a broad caspase inhibitor ( Z-VAD-CHO ) also blocked Hz-induced IL-1β ( Fig . 1C ) . To confirm the activation of caspase-1 we used the bone-marrow-derived macrophages ( BMDM ) , since detection of the active form of caspase-1 in THP-1 cells is difficult as reported by others [13] , [14] . Here , we show that Hz induced cleavage of caspase-1 to its enzymatically active ( p10 subunit ) form . BMDM were pre-stimulated with LPS in order to prime the induction of pro-IL-1β . As shown in Figure 1D , Hz and MSU , but not the pre-treatment with LPS , induced cleavage of caspase-1 and mature IL-1β production , which was completely abolished in BMDM from caspase-1 deficient mice . These results suggest a role for the inflammasome in Hz-induced IL-1β production . To further establish which intracellular receptors and/or adaptor proteins are activated by Hz , we used BMDM from mice deficient in NLRP3 , ASC or another NLR , NLRC4 ( NLR containing CARD domain , also known as IPAF ) . We found that Hz- and MSU-induced caspase-1 activation and IL-1β maturation were dependent on NLRP3 and ASC but not NLRC4 ( Fig . 2A ) . On the other hand , macrophages from NLRC4 mice failed to respond to Salmonella typhimurium infection ( Fig . S3 ) . To evaluate whether activation of the NLRP3 inflammasome is involved in Hz-induced inflammatory responses in vivo , mice were injected intraperitoneally with Hz and then neutrophil recruitment to the site of injection was examined . Hz induced significant recruitment of neutrophils to the peritoneal cavity in wild type , but not in ASC-deficient ( Fig . 2B ) or in NLRP3-deficient mice ( Fig . 2C ) . As expected , NLRC4 was not involved in the inflammatory response induced by Hz ( Fig . 2C ) . We further investigated whether IL-1β directly contributed to the recruitment of neutrophils . As expected , IL-1β deficient mice showed a significant decrease in the number of neutrophils elicited by Hz stimulation ( Fig . 2D ) . However , we did not observe a complete abrogation of neutrophil influx as previously seen with IL-1 receptor-deficient mice stimulated with other inflammasome ligands [15] . These results suggest that a portion of the Hz-induced inflammatory response in vivo may results from other ligands of the IL-1 receptors and/or other cytokines and chemokines known to be induced by Hz [3] , [7] , [8] . Thus far , we have shown that Hz-induced IL-1β production is dependent on the NLRP3 inflammasome , in addition , it is known that IL-1β is involved in malarial fever [4] . To evaluate the role of IL-1β and the NLRP3 inflammasome during malarial disease we infected IL-1β- and NLRP3-deficient mice with Plasmodium chabaudi adami DS , which is a mouse virulent strain . Of interest , both IL-1β- and NLRP3 mice presented a slight but significant lower body temperature ( Fig . 3A and 3B ) and parasitemia ( Fig . 3C and 3D ) in the early phase of infection . These knockout mice also showed a significantly prolonged survival compared with wild type mice , but ultimately succumbed to the infection ( Fig . 3E and 3F ) . Finally , in the late phase of infection , the level of IL-1β was significantly lower in NLRP3-deficient mouse in comparison with wild type mice ( Fig . 3G ) and was not detectable in IL-1β-deficient mouse ( data not shown ) . These results indicate that IL-1β is an important factor in the pathophysiology during malaria infection . Hz is rapidly engulfed by phagocytes , both in infectious and experimental conditions [2] . Therefore , to test the importance of phagocytosis on Hz-induced IL-1β production , cells were treated with cytochalasin D - a powerful actin polymerization inhibitor - prior to the addition of the crystals . Consistent with other crystals that induce inflammasome activation [15] , [16] , [17] , we found that Hz-induced IL-1β seems to be dependent on its internalization ( Fig . 4A ) . Furthermore , under certain conditions phagocytosis requires cholesterol-rich lipid domains [18] and as expected , cholesterol depletion by MβCD inhibited HZ-induced IL-1β ( Fig . S5A ) , which was due to the disruption of lipid rafts ( Fig . S5C ) . Further characterization of Hz phagocytosis by confocal immunofluorescence microscopy revealed that Hz was internalized in a vacuole that acquired lysosomal features , as shown by the presence of Lamp-1 surrounding the engulfed Hz phagosomes ( Fig . 4B ) . Phagocytosis is generally accompanied by the generation of reactive oxygen species ( ROS ) , which modulates inflammasome activation by crystals such as silica [19] , MSU [15] and asbestos [20] . Since Hz induces ROS production [7] its requirement in Hz-induced IL-1β production was evaluated . The ROS scavenger , N-acetylcysteine ( NAC ) inhibited both Hz- and MSU-induced IL-1β production ( Fig . 4C ) , which suggests a potential upstream role for ROS in inflammasome activation by Hz . Cellular potassium efflux is another critical step in inflammasome activation induced by all known NLRP3 activators [21] , [22] . As shown in the Figure 4D , inhibition of potassium efflux by high concentrations of extracellular potassium decreased IL-1β production induced by Hz . The above results suggest that Hz shares a common mechanistic pathway in the activation of the NRLP3 inflammasome with classical triggers such as ATP and others insoluble crystals [21] , [23] . Recently , lysosomal destabilization has been proposed as one mechanism whereby inorganic materials such as silica and aluminum hydroxide activate the inflammasome [17] . To assess lysosomal morphology in the context of Hz stimulation , we performed a confocal analysis of PMA-matured THP-1 cells loaded with a self-quenched conjugate of ovalbumin ( DQ-OVA ) that fluoresces only upon proteolytic degradation . We found that Hz did not affect the shape of lysosomes in comparison to untreated cells . In contrast , silica-treated cells contained swollen lysosomes ( Fig . 4E ) , suggesting that Hz may activate the inflammasome through distinct , but related pathway . Indeed , inhibition of the lysosomal cysteine protease ( cathepsin B ) by the specific inhibitor CA-074 abrogated IL-1β induced by Hz and silica ( Fig . 4F ) [17] . However , it is still unclear how this enzyme is involved in inflammasome activation and indeed , many of the proximal signaling events in NLRP3 and NLR activation remain unknown . Whereas we obtained clear evidence that Hz can induce IL-1β production in an inflammasome-dependent manner that required active cathepsin B , we did not find evidence of Hz-induced lysosomal rupture as previously reported with silica [17] . Release of cathepsin B without lysosomal rupture has been observed in monocytes treated with the potassium ionophore nigericin [24] . In addition , the widely expressed Spleen Tyrosine Kinase ( Syk ) was shown to be required for cathepsin B release into the cytosol in a model of B cell receptor-mediated apoptosis [25] . We therefore screened Hz-activated macrophages for changes in their tyrosine phosphorylation profiles . Consistent with the possible involvement of Syk , we observed a band with an apparent molecular weight of 72 kDa that was phosphorylated in response to Hz , but not MSU ( Fig . 5A ) . We then carried out anti-Syk immunoprecipitation , followed by anti phospho-tyrosine analysis and found that Syk was phosphorylated in response to Hz , but not MSU stimulation ( Fig . 5B ) . Even by extending the time-course of stimulation , MSU did not induce Syk phosphorylation ( Fig S4A ) . Syk is typically activated via receptors or adaptor proteins containing immunoreceptor tyrosine-based activation motifs ( ITAMs ) or ITAM-like domains phosphorylated by Scr family kinases following receptor clustering [26] , [27] . The Src kinase inhibitor PP2 decreased the Hz-induced Syk phosphorylation in a dose dependent manner ( Fig . 5C ) . Syk activation can be mediated by the Scr family kinase member Lyn [28] . Lyn is typically found in lipid raft signaling platforms and disruption of these rafts by MβCD ( Fig . S5C ) indeed blocked , in dose-dependent manner , Syk phosphorylation in Hz-stimulated monocytes ( Fig . S5B ) . Using BMDM from Lyn-deficient mice , we found that Hz-induced Syk phosphorylation required Lyn , and further confirmed that MSU does not utilize this signaling pathway in either murine or human macrophages ( Fig . 5 ) . Next we evaluated the role of Lyn and Syk in Hz-induced IL-1β production . IL-1β secretion stimulated by Hz was inhibited in macrophages treated with the Syk inhibitor piceatannol ( Fig . 6A ) , the Scr kinase inhibitor PP2 ( Fig . 6B ) , and more specifically using Lyn-deficient BMDM ( Fig . 6C ) . Importantly , in this last experiment , Hz-induced IL-1β production was only partially inhibited , which suggest that another member of the Src kinase family could play the same role of Lyn , since these kinases are known to be functionally redundant [28] . Of note , MSU-induced IL-1β production was not affected in Lyn-deficient BMDM pre-treated with LPS . To evaluate the relative roles of LPS and Hz in the induction of this signaling pathway , we treated BMDM with LPS and we observed that LPS by itself did not induce phospho-Syk , and indeed pre-treatment with LPS reduced Hz-induced Syk phosphorylation ( Fig . S4B ) . Furthermore , Hz-induced Syk activation is not affected by the absence of the MyD88 adaptor protein ( Fig . S4C ) . However , MyD88-deficient cells show a delay in the phosphorylation of c-jun N-terminal kinase ( JNK ) stimulated by LPS ( Fig . S4C ) , similar as previously reported [29] . These results rule out a possible effect of LPS on Syk phosphorylation . Consistent with the involvement of this kinase in a pathway upstream of the inflammasome , NLRP3- , ASC- and NLRC4-deficient macrophages exhibited normal Syk phosphorylation upon Hz stimulation ( Fig . 6D ) . Syk activates various downstream signaling pathways , including phosphoinositide 3-kinase ( PI3K ) [30] and extracellular signal-regulated kinase ( ERK ) . To test whether the PI3K pathway is required for propagation of the Syk signaling pathway following Hz exposure , the PI3K inhibitor wortmannin was used prior to Hz stimulation . Inhibition of PI3K indeed abrogated IL-1β maturation ( Fig . 7A ) . We have previously identified MAPK activation upon Hz stimulation of macrophages [31] . We therefore attempted to isolate which pathways might be required for Hz-induced IL-1β production using known p38 and ERK kinase inhibitors . Whereas p38 phosphorylation can be observed following Hz stimulation , inhibition of p38 with SB203580 failed to block Hz-induced IL-1β production ( Fig . 7B–D ) . On the other hand , inhibition of ERK with Apigenin abrogated Hz-induced IL-1β secretion ( Fig . 7E ) . Altogether , these results reveal that Lyn/Syk activation following Hz exposure initiates the PI3K and ERK signaling pathways and these pathways appear to regulate the production of mature IL-1β . While a number of stimuli are known to activate the NLRP3 inflammasome , there is no evidence that NLRP3 directly recognizes these ligands . Therefore an indirect pathway of NLRP3 activation is likely , however the identity of the direct molecular switch of NLRP3 has not been identified . Our studies provide the first evidence for a role of tyrosine kinase signaling molecules in NLRP3 activation . To examine whether Syk can modulate the inflammasome by directly interacting with its components , we immunoprecipitated Syk and then immunoblotted for potential partners associated with Syk by silver staining and western blotting ( Fig . 8A ) . Selected differential bands were analyzed by LC-tandem mass spectrometry . Interestingly , two to three different peptides covering 11–23% of the Pyrin domain ( Pyd ) [32] were identified . Pyrin domains are known to mediate protein-protein interactions and are crucial in many of the NLR inflammasome complexes , and in particular , mediate the NLRP3 and ASC interaction [6] . We therefore confirmed by western blotting whether NLRP3 or ASC can be co-immunoprecipitated ( co-IP ) with Syk . Whereas NLRP3 was shown to weakly interact with Syk , ASC was found to strongly associate with this kinase upon Hz stimulation ( Fig . 8B ) . These findings suggest that Syk , and possibly other unidentified signaling kinases , can associated with the ASC/NRLP3 inflammasome . Another possible mechanism is that Syk could be controlling the NLRP3 inflammasome by regulating cathepsin B activation . First , we tested if Hz can induce release of the active form of cathepsin B in the supernatant and as showed in the Figure 9A , Hz did not induce cathepsin B release into supernatant as has been observed with MSU and silica . However , using a cathepsin B substrate that emits red fluorescence upon cleavage we demonstrated that Hz induces rapid ( 30 min ) and transient ( maximum 1 . 5 h ) intra-compartmental cathepsin B activation that was dependent on Syk activation ( Fig . 9B ) . These results indicate that Syk not only can associate with the inflammasome component but it can also modulate cathepsin B activation .
It has been described that NLRP3 senses many crystalline materials that are involved in inflammatory diseases , such as MSU [15] , silica [19] , and asbestos [20] . Here we provide the first demonstration that the malaria pigment hemozoin ( Hz ) can also activate the NLRP3 inflammasome . Importantly , the Hz concentration shown to activate the NLRP3 inflammasome in vitro is similar in range to the concentration of Hz in the blood of patients with moderate parasitemia [8] , [33] . Moreover , it was never shown in the previous studies that direct contact between a crystal and NLRP3 is necessary to induce activation . Similarly , we found that Hz does not translocate from the phagosome/lysosome compartment to the cytoplasm , as it is located within LAMP-1-positive compartments , suggesting that Hz activated the NLRP3 inflammasome in an indirect manner . It has been proposed that the NLRP3 inflammasome senses not only pathogen-associated molecular patterns but also danger signals such as stress-related molecules [5] . In agreement , here we show that Hz-induced IL-1β production was dependent on ROS generation and potassium efflux into the cytoplasm . In addition to previous studies on the inflammasome , we further identified an upstream signaling pathway involving the Src kinase Lyn , the tyrosine kinase Syk and Syk-downstream kinases such as PI3K and ERK that collectively appear to be involved in the regulation of Hz-induced IL-1β production . Simultaneously to us , it has been recently reported that Syk kinase is involved in upstream signaling of NLR inflammasome triggered by fungi [34] . Whether these findings represent a general regulatory mechanism of this intracellular innate immune response will need further investigation . The Lyn/Syk pathway appears to be uniquely activated in the innate response to Hz crystals , as opposed to other NLRP3-activating crystals such as MSU . In our hands , MSU did not induce Syk or Lyn phosphorylation in PMA-differentiated THP-1 cells nor in BMDM . However , MSU was previously reported to trigger Syk phosphorylation in dendritic cells [35] and human neutrophils [36] , as well as Lyn phosphorylation in neutrophils [37] . An intriguing question is how this signaling cascade may modulate the inflammasome/IL-1β production . For instance , we found some indication that Syk can interact with ASC , but not NLRP3 . ASC , as it is well known , interacts with NLRP3 . These results suggest that Syk may modify ASC . In support of this finding , there is evidence that the ASC pyrin domain can be phosphorylated [38] . Moreover , hyperphosphorylated PSTPIP1 ( proline serine threonine phosphatase-interacting protein ) was shown to interact with the pyrin protein [39] , resulting in its conformational change and further its interaction with ASC [40] . Another possible mechanism whereby kinases can modulate IL-1β production is by modulating intracellular calcium concentration or cathepsin B activation . Syk is involved in the activation of intracellular calcium mobilization in other models [41] . In fact , increased calcium concentrations have been found to modulate inflammasome activation by different stimuli such as MSU and UV radiation [22] , [42] . Finally , Syk was found to control the activation of cathepsin B and Hz-induced IL-1β production was dependent on cathepsin B activation , similar to other inflammasome activators such as silica , MSU [17] or nigericin [24] . We showed that specific inhibition of Syk blocked the Hz-induced cathepsin B activation . Collectively , it is clear that different steps in the Hz-induced IL-1β production can be regulated by intracellular signaling . However , further study will be necessary to better characterize these regulatory events in regards to the different inorganic crystals that can trigger NLRP3 inflammasome activation . Another interesting observation is that Hz-activated cathepsin B occurred in the intracellular compartment and is rapidly quenched ( 1–3 hours ) , suggesting either a transient activation or cathepsin B release into the cytosol . The idea of transient activation of cathepsin B by Hz is supported by the absence of cathepsin B in the supernatant of cells stimulated with Hz and the absence of lysosomal damage upon Hz treatment . The mechanism utilized by Hz-activated cathepsin B to modulate the inflammasome remains unclear . However , a possible mechanism is that cathepsin B can activate directly caspase-1 as it has been shown in previous works [17] , [24] . Of interest , both caspase-1 and cathepsin B , in addition to inflammasome components and IL-1β are found in multivesicular bodies surrounded by LAMP-1 [43] . It is known that Syk and Syk-activated downstream kinases such as PI3K regulate the trafficking of intracellular vesicles [44] . In this way , Hz-induced Syk might be controlling not only the inflammasome cascade but also the trafficking of multivesicles . The Lyn/Syk activation finding raises the intriguing possibility that an as yet unidentified receptor or adaptor protein containing an ITAM or ITAM-like domain , such as Dectin-1 , TREM family members , Siglec or DAP12 [26] , [27] , might be activated upon Hz stimulation to trigger the signaling cascade involved in inflammasome activation . However , a recent work with dendritic cells demonstrated that MSU did not require a surface receptor - instead the crystals interact with surface lipid rafts and this was enough to trigger Syk/PI3K pathway [35] . In our study , we have demonstrated that lipid rafts are involved in the Hz-induced signaling pathway and IL-1β production . Other potential receptors that could mediate Hz-triggered signaling are the Toll-like receptors ( TLR ) . However , we have recently demonstrated in collaboration with Parroche and colleagues [9] that Hz alone fails to activate TLRs except when Hz is coated with parasitic DNA and consequently activating TLR9 . Similarly , we also observed that HEK293 cells transfected with different TLRs were not activated by Hz although these cells were able to induce NF-κB activation following specific ligand stimulations ( Jaramillo and Olivier , unpublished data ) . We also showed that the MyD88 signaling pathway is not involved in the Hz-induced Syk phosphorylation . Experiments to identify surface receptors or lipids that recognize Hz are currently underway . In the present work we further supported the role of NLRP3-mediated IL-1β production in Hz-mediated inflammatory cell recruitment using IL-1β deficient mice . Apart from its inflammatory role , IL-1β is a pyrogenic cytokine that in small concentrations induces the production of other cytokines such as IL-6 and can cause hypertension and fever [45] . In fact , we showed that NLRP3- and IL-1β-deficient mice exhibited lower body temperature during the early phase of P . chabaudi Adami infection . Hz-induced IL-1β can be the mediator of the up-regulation of chemokines and cytokines during malaria infection , which is independent of TLRs but dependent on MyD88 [46] . This suggests that another MyD88 dependent receptor such as IL-1R is involved and supports a role for IL-1β in malaria-related pathology . Corroborating this hypothesis , we showed that IL-1β- and NLRP3- deficient mice showed a better survival than wild type mice in murine experimental model of malaria . Not surprisingly , it was not sufficient to provide full protection likely due to the complexity of malarial disease , which is under the regulation of many different receptors , cytokines , signaling events and physiological features . Collectively , our study provides the first demonstration that a malarial-derived metabolic product , namely hemozoin , can induce NLRP3 inflammasome activation and IL-1β production though the involvement of the Src kinase Lyn and the tyrosine kinase Syk . However , excessive IL-1β secretion can be deleterious to the host; in fact , we observed that higher production of IL-1β correlates with early death in murine experimental malaria . Therefore these findings strongly support the fact that Hz is critical in malaria pathology . A better understanding of the molecular and cellular events regulating malaria inflammatory-related pathologies may provide new insights into the design of treatments aimed at reducing the exaggerated inflammatory disorders and debilitating sequelae .
With the subheading Ethics Statement , all protocols used in this study were approved by the Institutional Animal Care and Use Committees at the McGill University or Yale University . IL-1β- and Lyn-deficient mice were provided by Dr . G . Sébire and Dr . K . W . Harder ( University of Sherbrooke , Quebec and University of British Columbia , Vancouver , Canada ) , respectively . The generation of IL-1β- , Lyn- , NLRP3- , ASC- , caspase-1- , and NLRC4-deficient mice has been described previously [47] , [48] , [49] , [50] , [51] . Caspase-1- , ASC- , and NLRP3-deficient mice were backcrossed onto the C57BL/6 genetic background for at least nine generations . NLRC4-deficient mice were backcrossed onto the C57BL/6 genetic background for at least six generations . Age- and sex-matched C57BL/6 mice purchased from the National Cancer Institute or Charles River were used as WT controls . Hemin ( >99% of purity ) was purchased from Fluka; RPMI-1640 medium , Penicillin-Streptomycin-Glutamine ( PSG ) from Wisent , fetal bovine serum ( FBS ) , Alpha MEM medium from Gibco; CV-Cathepsin B detection kit , PP2 , piceatannol , geldanamycin , cytochalasin D , Y-VAD-FMK and Z-VAD-CHO from Biomol; MSU , anti-human NLRP3 and ASC from Alexis Biochemical; inhibitor protease cocktail from Roche; CHAPs from Fisher; A/G-coupled agarose beads , anti-human pro-IL-1β , anti-human or murine caspase-1 and anti-Syk from Santa Cruz; True Blot anti-rabbit Ig , anti-phosphoY/HRP from eBioscience; PVDF from Bio-rad; anti-LAMP-1 Ab from Developmental Studies Hybridoma Bank at the University of Iowa; anti-human mature IL-1β , anti-pp38 and anti-p38 from Cell signal; anti-pSyk and anti-pY ( 4G10 ) from Upstate; rat or goat anti-murine IL-1β and recombinant IL-1β from R&D system; DQ-OVA from Invitrogen; anti-rat AlexaFluor 568 , cholera toxin B-AlexaFluor 568 from Molecular Probes; DRAQ5 from Biostatus; Fluoromount-G from Southern Biotechnology; all others unlisted or not indicated reagents were purchased from Sigma . L929 and THP-1 cell line from ATCC . MyD88 KO BMDM was generated from MyD88-deficient mice and kindly supplied by Dr . Danuta Radzioch ( McGill University , Montreal , Canada ) . Native and Synthetic Hz have been obtained as previously described [8] , [31] . We have modified synthetic Hz preparation , using high purity chemical reagents ( >99% of purity ) , as follows: 0 . 8 mmol Hemin was dissolved in degassed NaOH ( 0 . 1 M ) for 30 minutes with mild stirring . pH 4 . 0 was adjusted adding drop-wise propionic acid . The mixture was allowed to anneal at 70°C for 18 hours . Then washed three times with NaHCO3 ( 0 . 1 M ) for three hours and the last wash with MeOH . All washes were alternated with distilled H2O . Finally , the sample was then dried in a vacuum oven overnight over phosphorous pentoxyde . All synthetic hemozoin samples were analyzed by X-ray powder diffraction , field emission gun scanning electron microscopy , and infra-red spectroscopy to characterize the crystalline state of Hz . Hz purity was assessed by elemental analysis [52] . THP-1 cells ( ATCC ) were cultured with RPMI-1640 medium supplemented with 10% FBS , 1% PSG , 50 µM of 2-β-mercaptoetanol , Glucose 4 . 5 g/L and 1 mM sodium pyruvate . THP-1 differentiation: ( 1 . 5×106 cells/mL ) were incubated with 0 . 5 µM of PMA , after three hours cells were washed and plated at 0 . 75×106 cells/mL or 0 . 2×106 cell/0 . 5 mL in 12 well plates ( IL-1β ) or 24 well plates containing coverslips ( confocal ) and incubated for 20–24 hours . This treatment increases the phagocytic properties of the cells and induces a constitutive production of pro-IL-1β . Prior to stimulation , cells were washed and 500 µL of Alpha MEM medium without FBS was replaced . Cells were pre-treated with different drugs for 1 hour and stimulate with Hz , MSU or silica as indicated in figure legends . Gender and age matched wild type ( WT ) , NLRP3- or IL-1β-deficient mice were injected i . p . with 5×104 Plasmodium chabaudi adami DS infected red blood cells obtained from syngeneic infected mice . Parasitemia was assessed at day 5 , 7 and then every day by examination of Giemsa stained blood smears and was expressed as mean parasitemia . Body temperature was measured using an infrared thermometer ( La Crosse Technology ) . Survival of mice was monitored and blood serum was collected when the temperature dropped down to 26°C . IL-1β was measured by ELISA with rat monoclonal and goat anti-mouse IL-1β . The detection limit was 6 . 25 pg/mL of IL-1β . Bone marrow cells were obtained by flushing the femurs and tibias from mice . Cells were used from fresh or from frozen marrows . Erythrocytes were lysed with 2 mL of NH4Cl ( 155 mM ) in Tris/HCl ( 10 mM ) , pH 7 . 2 ( 9∶1 solution ) /mouse . Bone marrow cells were adjusted to 7×106 cells/10 mL and plated in 100 mm dishes with RPMI-1640 medium supplemented with 1% of PSG , 10% FBS and 30% ( v/v ) L929 cell culture supernatant . The supernatants of bone marrow cells were changed every two days in order to renew the cytokines and nutrients . After 7 days , the culture dishes were washed with PBS and replaced by ice cold PBS , incubated on ice for 15 min and cells were vigorously detached . BMDM were adjusted to 1 . 5×106/2 mL or 0 . 2×106 cells/0 . 5 mL in RPMI medium supplemented with 5% FBS ( Gibco ) and 1% of PSG and plated in 6 well plates ( IL-1β ) or 24 wells plate ( confocal ) . The next day , cells were washed with warm PBS ( 37°C ) and replaced by 500 µL of Alpha MEM medium without FBS . Cells were , as indicated in figure legends , stimulated with Hz , MSU or infected with Salmonella typhimurium as described by Franchi et al . [53] . Supernatant and cell extract analysis: After designated incubation time , supernatants were collected and protein was precipitated with trichloroacetic acid at 10% final concentration . Precipitates were then dissolved in Tris/HCl 0 . 1 mM pH 8 . 0 and Laemmli sample load buffer . Cell extracts were obtained by lysing cells with Igepal 1% ( for signaling , in 1× PBS , 20% Glycerol , 1× inhibitor protease cocktail , 2 mM Na3VO4 and 1 mM NaF ) or triton 1% ( for caspase-1 , in TNE buffer: 10 mM Tris/HCl pH 7 . 5 , 150 mM NaCl , 5 mM EDTA and 1 . 5× inhibitor protease cocktail ) . Whole supernatant protein and equal amount of protein or cell lysate were subjected to SDS-PAGE and immunoblot analysis . IP: Cells lysates were extracted with lysis buffer ( 1% CHAPs detergent in TNE buffer , 1× inhibitor cocktail , 2 mM Na3VO4 and 1 mM NaF ) . Cells lysates were pre-incubated for two hours at 4°C with protein A/G-coupled agarose beads and 1 µg of unspecific matched isotype control antibody ( Ab ) . Equal amount of protein were immunoprecipitated with protein A/G-coupled agarose beads or True Blot anti-rabbit Ig and 2 µg of specific or unspecific matched isotype control Ab overnight . Beads were spun down 3 times with lysis buffer and proteins were denatured in Laemmli load buffer . SDS-PAGE/Immunoblot: Samples from supernatants , cell extracts or IP were subjected to 10% ( signaling ) or 15% ( IL-1β and caspase-1 ) acrylamide gel ( all reagents from Laboratoire Mat . Inc . , Montreal , Qc , Canada ) or 4–12% NuPAGE® gel ( for p10 caspase-1 and IP , Invitrogen ) . After transfer onto PVDF membranes , they were subjected to immunoblot analysis with the indicated Ab and matched secondary HRP-conjugated Ab . In some experiments , optical density was determined using AlphaDigiDoc 1000 v3 . 2 software ( Alpha Innotech corporation ) . OVA uptake: THP-1 cells ( 0 . 2×106 cells/coverslip 12 mm from Fisher ) were treated with 10 µg of DQ-OVA in the absence or presence of Hz ( 200 µg/mL ) or Silica ( 400 µg/mL ) for 30 min , washed and incubated up to three hours . Laser settings were adjusted on DQ-OVA fluorescence emission that is stronger than hemozoin or silica . Phagosome: BMDM were fixed , permeabilized using 0 . 1% Triton X-100 , and non-specific surface Fcγ-receptor binding were blocked as described [54] . For immunofluorescence experiments , cells were labelled with the rat anti-LAMP-1 Ab and an anti-rat AlexaFluor 568 . DRAQ5 was used to visualize DNA . Cathepsin B activity: THP-1 cells ( 0 . 2×106 cells/coverslip 12 mm from Fisher ) were pre-treated for 30 min with 5 µM of piceatannol and stimulated or not with Hz ( 200 µg/mL ) . A cathepsin B substract ( Arg-Arg ) 2 linked with cresyl violet were given 30 min before the end of incubation time and cleaved substract generated a red fluorescence . All coverslips ( THP-1/OVA or BMDM ) were mounted on slides with Fluoromount-G . Detailed analysis of protein localization on the phagosome was performed by using an oil immersion Nikon Plan Apo 100 ( N . A . 1 . 4 ) objective mounted on a Nikon Eclipse E800 microscope equipped with a Bio-Rad Radiance 2000 confocal imaging system ( Bio-Rad Laboratories , Hercules , CA ) . WT , IL-1β- , NLRP3- , ASC- , caspase-1- and NLRC4-deficient mice were injected intraperitoneally with 800 µg of hemozoin in 1 ml of endotoxin-free PBS . Control groups were injected with 1 mL of PBS . After six hours , the mice were euthanized and the peritoneal cavity was washed with 10 mL of PBS . Cells recovered from the peritoneum were counted and the percentage of neutrophils was determined from an H&E stain ( DiffQuick; Dade Behring , Inc . ) of a cytospun sample . Unpaired Student's t-test was used when comparing two groups and ANOVA/Bonferroni test when comparing more than two groups . The differences were considered significant when p<0 . 05 . Survival curves for infected and control mice were compared using the Mantel-Haenszel test . Statistical analysis was performed using Prism 5 . 00 software ( GraphPad , San Diego , Calif . ) . | Malaria is widespread in the tropical and sub-tropical regions of the world , and is responsible for 2–3 million deaths annually . This disease is caused by parasites of the Plasmodium genus . The parasite feeds on the hemoglobin of red blood cells and generates a metabolic waste called hemozoin ( Hz ) . Hz is released into the blood circulation during the rupture of red blood cells , which coincides with the production of many cytokines such as interleukin-1β ( IL-1β ) , responsible in part for the periodic fever that is characteristic of the malaria disease . Here , we investigated how Hz activates macrophages ( cells that engulf foreign material ) to produce IL-1β . We found that Hz is taken up by macrophages initiating signals such as the tyrosine kinases Syk and Lyn that communicate to intracellular receptors . We also showed that Hz-induced IL-1β production is dependent on activation of the intracellular receptor NLRP3 , the adaptor protein ASC and a protease called caspase-1 that cleaves IL-1β , therefore allowing it to be released from the cells . These findings not only identify one way in which the immune system is alerted to malarial infection but also dissect some of the signaling events triggered by Hz in the NLRP3 inflammasome pathway . |
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